Title and relating text | 1

Final Report Critical Control Point Assessment to Quantify Robustness and Reliability of Multiple Treatment Barriers of a DPR Scheme Co-published by

Reuse-13-03 Critical Control Point Assessment to Quantify Robustness and Reliability of Multiple Treatment Barriers of a DPR Scheme Troy Walker, MIE(Aust) Benjamin D. Stanford, Ph.D. Hazen and Sawyer Stuart Khan, Ph.D. University of New South Wales Cedric Robillot, Ph.D. Headstart Development, PTY Shane Snyder, Ph.D. Ricardo Valerdi, Ph.D. Sudhee Dwivedi, Ph.D. University of Arizona Jim Vickers, PE Separation Processes Inc.

Water Environment & Reuse Foundation Alexandria, VA

Co-sponsors Metropolitan Water District of Southern California West Basin Municipal Water District, CA Orange County Water District, CA City of Scottsdale, AZ Veolia Water Australian Water Recycling Center of Excellence Windhoek Goreangab Operating Company Additional Anonymous Utilities in the United States and Australia

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The Water Environment & Reuse Foundation (WE&RF) is a 501c3 charitable corporation seeking to identify, support, and disseminate research that enhances the quality and reliability of water for natural systems and communities with an integrated approach to resource recovery and reuse; while facilitating interaction among practitioners, educators, researchers, decision makers, and the public. WE&RF subscribers include municipal and regional water and water resource recovery facilities, industrial corporations, environmental engineering firms, and others that share a commitment to cost-effective water quality solutions. WE&RF is dedicated to advancing science and technology addressing water quality issues as they impact water resources, the atmosphere, the lands, and quality of life. For more information, contact: Water Environment & Reuse Foundation 1199 North Fairfax Street, 9th Floor Alexandria, VA 22314 Tel: (571) 384-2100 www.werf.org werf@werf.org This report was co-published by the following organization. IWA Publishing Alliance House, 12 Caxton Street London SW1H 0QS, United Kingdom Tel: +44 (0) 20 7654 5500 Fax: +44 (0) 20 7654 5555 www.iwapublishing.com publications@iwap.co.uk © Copyright 2016 by the Water Environment & Reuse Foundation. All rights reserved. Permission to copy must be obtained from the Water Environment & Reuse Foundation. Library of Congress Catalog Card Number: 2016947136 WE&RF ISBN: 978-1-94124-243-8 IWAP ISBN: 978-1-78040-850-7 This report was prepared by the organization(s) named below as an account of work sponsored by the Water Environment & Reuse Foundation (WE&RF). Neither WE&RF, members of WE&RF, the organization(s) named below, nor any person acting on their behalf: (a) makes any warranty, express or implied, with respect to the use of any information, apparatus, method, or process disclosed in this report or that such use may not infringe on privately owned rights; or (b) assumes any liabilities with respect to the use of, or for damages resulting from the use of, any information, apparatus, method, or process disclosed in this report. Hazen and Sawyer; University of New South Wales; Headstart Development, PTY; University of Arizona; Separation Processes, Inc. This document was reviewed by a panel of independent experts selected by WE&RF. Mention of trade names or commercial products or services does not constitute endorsement or recommendations for use. Similarly, omission of products or trade names indicates nothing concerning WE&RF's positions regarding product effectiveness or applicability.

Water Environment & Reuse Foundation iii

About WE&RF The Water Environment & Reuse Foundation (WE&RF) is a 501c3 charitable corporation seeking to identify, support, and disseminate research that enhances the quality and reliability of water for natural systems and communities with an integrated approach to resource recovery and reuse; while facilitating interaction among practitioners, educators, researchers, decision makers, and the public. Our research represents a portfolio of more than $200 million in water quality research. WE&RF operates with funding from subscribers, donors, state agencies, and the federal government. Our supporters include wastewater treatment facilities, stormwater utilities, and regulatory agencies. Equipment companies, engineers, and environmental consultants also lend their support and expertise. WE&RF takes a progressive approach to research, stressing collaboration among teams of supporters, environmental professionals, scientists, and staff. All research is peer reviewed by leading experts. For the most current updates on WE&RF research, sign up to receive Laterals, our bi-weekly electronic newsletter. Learn more about the benefits of becoming a WE&RF supporter by visiting www.werf.org.

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Abstract & Benefits

Abstract:

As direct potable reuse (DPR) is becoming an option for many utilities in the U.S., a need to assess the full-scale reliability and robustness of critical control points (CCPs) in potable reuse systems has come to light. Beyond simply needing data and validation of the processes, there was also a need to provide guidance regarding how to identify and validate CCPs, how to determine the monitoring requirements around those CCPs, what response procedures are needed (i.e., what to do when systems begin to fail), and how to proceed with assessing and documenting water quality risks in potable reuse systems. This project was tasked with applying the hazard analysis and critical control point (HACCP) methodology to identify CCPs and assess the reliability of those CCPs to manage acute and chronic health risks in DPR applications. The objective was to identify CCPs and then use full scale operating data from facilities around the world to quantify the ability of those CCPs alone and in series to remove chemical and biological contaminants in potable reuse. An evaluation of process monitors and operational response was also included. The primary approach used for this study included the following work objectives: 1. Conduct a hazard assessment to identify health risks, identify water quality objectives, and identify

CCPs for multiple DPR process configurations.

2. Collect chemical and microbial data and conduct challenge studies using full-scale operating facilities.

3. Use Monte Carlo analysis to develop a probabilistic risk assessment to characterize, quantify and support communication about the risk of failing to meet treated water quality targets.

4. Develop recommendations regarding critical limits and critical alarms that can be used in future studies to develop the appropriate response procedures for critical processes and alarms.

The overall conclusions of this research indicate that both membrane- and non-membrane-based potable reuse systems are capable of managing microbial and chemical contaminants of concern and that the current monitoring and removal credit is highly conservative relative to actual process performance. Through full-scale testing the reseachers were able to validate that when membrane integrity is breached in reverse osmosis and microfiltration membranes, current monitoring systems are able to detect breaches before log removal goals are compromised. As a result of this project, a series of process-specific response procedures were developed to manage alerts (indicating a need for potential corrective action) and alarms (indicating a need for immediate shutdown of a unit process within a facility), providing guidance to design teams and operations teams looking at implementing potable reuse. An additional aspect of this study demonstrated a process for evaluating the reliability of process monitors and provided a means to determine where double or triple redundancy may be required for the process monitors.

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Benefits:

Provides an overview of the HACCP process as it applies to a DPR setting. Provides information on the principles of HACCP, the selection of CCPs to manage risks in DPR, a

quantification of the performance of CCPs to remove pathogens and chemicals from recycled water systems using Monte Carlo analysis, an evaluation of process monitoring reliability, and a series of response procedures for handling alerts and critical alarms during treatment.

Discusses full-scale design considerations, as is an evaluation of induced failure events at full scale. Provides evidence of the full-scale reliability and robustness of multiple processes for chemical and

microbial contaminant control and demonstrates their resilience to perturbations and their combined redundancy when used together in a DPR scheme to achieve water quality goals and public health protection.

Keywords: Direct potable reuse, pathogens, chemical contaminants, treatment, risk reduction, treatment, operations, critical control points, HACCP.

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Table of Contents Abstract and Benefits ................................................................................................................iv List of Figures ........................................................................................................................ viii List of Tables ........................................................................................................................ xvii Acronyms and Abbreviations .................................................................................................. xx Acknowledgments ................................................................................................................. xxii Executive Summary ............................................................................................................. ES-1

Chapter 1. Introduction and Background ............................................................................. 1

1.1 Understanding of the Problem ...................................................................................... 1 1.2 Control Point Framework ............................................................................................. 2 1.3 The Use of HACCP Framework at Water Recycling Facilities .................................... 5 1.4 Project Overview and Approach ................................................................................... 6 1.4.1 Understanding Project Constraints and Working Definitions .......................... 6 1.4.2 Project Objectives .......................................................................................... 10 1.4.3 Project Tasks and Approach .......................................................................... 11 1.4.4. Organization of This Report .......................................................................... 13 1.5 References ................................................................................................................... 14

Chapter 2. Hazard Assessment and CCP Selection ............................................................ 15

2.1 Introduction ................................................................................................................. 15 2.2 Source Water Quality Data – Hazards ........................................................................ 17 2.3 Review of Credited Log Removal Values .................................................................. 18 2.4 Risk Assessment and HACCP Workshop ................................................................... 20 2.4.1 Source Water Characterization and Limitations ............................................ 20 2.4.2 Risk Assessment Methodology ...................................................................... 20 2.4.3 Risk Descriptors ............................................................................................. 23 2.4.4 Inherent Risk and Initial Barrier Assessment ................................................ 25

2.4.5 Hazardous Events and Control Measures ...................................................... 28 2.5 Critical Control Point Selection and Monitoring Points ............................................. 29

2.5.1 Critical Control Points Selected – RO Membrane-Based Treatment Train ... 31 2.5.2 Critical Control Points Selected – Ozone–BAC–GAC–UV–Chlorine .......... 33

2.6 Summary ..................................................................................................................... 34 2.7 References ................................................................................................................... 36

Chapter 3. Description of Data Sources and Preparation for Monto Carlo Analysis ..... 37

3.1 Description of Data Sources ....................................................................................... 37 3.1.1 Orange County Water District ....................................................................... 40 3.1.2 Anonymous Australian Utility ....................................................................... 41 3.1.3 Scottsdale Water Campus .............................................................................. 42 3.1.4 Goreangab DPR Facility, Windhoek, Namibia .............................................. 43 3.1.5 Anonymous IPR Pilot, Southeastern United States. ...................................... 44

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3.2 Introduction to Stochastic Variables ........................................................................... 45 3.2.1 Normal Distribution ....................................................................................... 46 3.2.2 Lognormal Distribution ................................................................................. 48 3.2.3 Beta and BetaGeneral Distributions ............................................................... 50 3.2.4 Probability Plots ............................................................................................. 51 3.2.5 Fitting Data to PDFs ...................................................................................... 55 3.2.6 Goodness of Fit .............................................................................................. 57 3.2.7 Censored Data ................................................................................................ 58 3.2.8 Step Changes and Gradual Changes .............................................................. 61 3.3 Probabilistic Assessment of Water Treatment Processes ........................................... 64 3.3.1 PDFs for Single Water Treatment Process Removal ..................................... 64 3.3.2 Probabilistic Assessment of Multiple-Barrier Water Treatment Processes ... 65 3.3.3 Monte Carlo and Latin Hypercube Sampling ................................................ 67 3.3.4 Dependencies and Correlations between Data Sets ....................................... 68 3.4 Summary ..................................................................................................................... 73 3.5 References ................................................................................................................... 73

Chapter 4. Monte Carlo Statistical Analysis of Process Performance .............................. 75

4.1 Probability Density Functions for Pathogen Inactivation Processes ........................... 75 4.1.1 AWTP Pretreatment ....................................................................................... 75 4.1.2 Microfiltration ................................................................................................ 78 4.1.3 Reverse Osmosis ............................................................................................ 84 4.1.4 UV Disinfection and UV–Advanced Oxidation ............................................ 88 4.1.5 Chlorine Disinfection ..................................................................................... 98 4.1.6 Sand Filtration .............................................................................................. 109 4.1.7 Ozonation ..................................................................................................... 112 4.2 Probability Density Functions for Chemical Removal Processes ............................. 118 4.2.1 Reverse Osmosis .......................................................................................... 119 4.2.2 UV–AOP ...................................................................................................... 129 4.2.3 Granular Activated Carbon .......................................................................... 134 4.2.4 Ozonation ..................................................................................................... 142 4.3 Multiple Barrier Monte Carlo Simulations ............................................................... 143 4.3.1 Virus Removal by RO Membrane-Based Process Train ............................. 144 4.3.2 Giardia Removal by RO Membrane-Based Process Train .......................... 148 4.3.3 Cryptosporidium Removal by RO Membrane-Based Process Train ........... 152 4.3.4 Virus Removal by Ozone–BAC-Based Process Train ................................. 154 4.3.5 Giardia Removal by Ozone–BAC-Based Process Train ............................. 158 4.3.6 Cryptosporidium Removal by Ozone–BAC-Based Process Train .............. 162 4.3.7 Chemical Removal across Multiple Barriers ............................................... 176 4.4 Chapter Summary and Conclusions .......................................................................... 172 4.5 References ................................................................................................................. 174

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Chapter 5. Reliability and Availability of Water Reuse Monitoring Systems for Process Performance Verification ................................................................... 175

5.1 Introduction ............................................................................................................... 175 5.1.1 Problem Statement ....................................................................................... 175 5.1.2 Project Background ...................................................................................... 175

5.2 Modeling Methodology ............................................................................................ 187 5.2.1 Introduction to Simulation Modeling ........................................................... 187 5.2.2 Approach ...................................................................................................... 187 5.2.3 Methodology for Developing Computer Simulation of Process Train

in Arena........................................................................................................ 189 5.2.4 Assumptions................................................................................................. 190 5.3 Arena Simulation Model ........................................................................................... 191 5.3.1 Model ........................................................................................................... 191 5.3.2 Performance Data ........................................................................................ 193 5.4 Results ..................................................................................................................... 197 5.4.1 Simulation Results ....................................................................................... 197 5.4.2 Risk Priority Number Tool .......................................................................... 201 5.5 Recommendations ..................................................................................................... 219 5.6 Conclusions ............................................................................................................... 220 5.7 References ................................................................................................................. 220

Chapter 6. Full-Scale Challenge Testing at Scottsdale Water Campus .......................... 221

6.1 Overview ................................................................................................................... 221 6.2 Failure Analysis Methodology .................................................................................. 223 6.2.1 Reverse Osmosis System Testing: 8-Inch Membranes ................................... 224 6.2.2 Reverse Osmosis System Testing: 16-Inch Membranes ................................. 227 6.2.3 Ultrafiltration Membrane Filtration System Testing ....................................... 229 6.3 Results ....................................................................................................................... 231 6.3.1 Reverse Osmosis .......................................................................................... 231 6.3.2 UF Membrane Filtration .............................................................................. 238 6.4 Conclusions ............................................................................................................... 239 6.5 References ................................................................................................................. 240

Chapter 7. Response Procedures and Design Guidance ................................................... 241

7.1 Background Information ........................................................................................... 241 7.2 Control Systems in Water Treatment ........................................................................ 242 7.3 Water Quality Alarms ............................................................................................... 243 7.3.1 Alarm Types ................................................................................................ 245 7.3.2 Alarm Management Considerations ............................................................ 247 7.4 Response Procedures and Implementation of Alarm Strategies ............................... 248 7.4.1 Specific Alert and Alarm Procedures for Identified CCPs .......................... 252 7.5 Equipment Design Standards and Guidance for DPR Systems ................................ 299

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7.5.1 Water Quality Instrumentation .................................................................... 299 7.5.2 Design Issues – Basic Questions to Validate Design ................................... 302 7.6 References ................................................................................................................. 304

Appendix A ............................................................................................................................ 306 Appendix B ............................................................................................................................ 319

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List of Figures

ES.1 Process flow diagram for RO membrane-based treatment option .......................... ES-1 ES.2 Process flow diagram for ozone–BAC-based treatment option .............................. ES-2 ES.3 Critical control points (outlined) – RO membrane-based treatment train ............... ES-5 ES.4 Critical control points (outlined) – ozone–BAC-based treatment train as

shown with optional preozonation step. .................................................................. ES-5

1.1 HACCP system decision tree for defining critical control points in DPR facilities ..... 4 1.2 Process flow diagram for RO membrane-based treatment option ................................ 7 1.3 Process flow diagram for ozone–BAC-based treatment train with optional

preozonation step .......................................................................................................... 7 1.4 General schematic of urban infrastructure with DPR .................................................. 8 1.5 Diagram of a CCP and associated monitor for an RO system ...................................... 9 1.6 Diagram of COPs supporting the RO CCP and plant production ............................... 10

2.1 Overview of first component of risk assessment ........................................................ 21 2.2 Overview of second component of risk assessment .................................................... 22 2.3 Extract of the inherent and residual risk assessment ................................................... 27 2.4 CCP selection table ..................................................................................................... 29 2.5 Critical control points (outlined) – RO membrane-based treatment train ................... 33 2.6 Critical control points (outlined) – ozone–BAC-based treatment train as

shown with optional preozonation step. ...................................................................... 36

3.1 Process flow diagram for OCWD ............................................................................... 40 3.2 Process flow diagram for anonymous Australian utility ............................................. 41 3.3 Process flow diagram for Scottsdale Water Campus .................................................. 42 3.4 Goreangab DPR facility process flow diagram ........................................................... 43 3.5 Process flow diagram for anonymous IPR pilot facility, Southeastern

United States ............................................................................................................... 44 3.6 Normal distribution with mean=25 and standard deviation=2 .................................... 46 3.7 Normal distribution with mean=25 (μ) and standard deviation=2 (σ),

presented as a cumulative density function ................................................................. 47 3.8 An example of a lognormal distribution (mean=10 [μ], standard

deviation=10 [σ]) ........................................................................................................ 48 3.9 An example of a beta distribution (α1, α2, min, max) ................................................ 50 3.0 Scatter plot for 100 sample measurements of hypothetical chemical

contaminant (lognormal distribution) ......................................................................... 51 3.11 Cumulative frequency plot for hypothetical chemical contaminant

(lognormal distribution, linear scale) .......................................................................... 52 3.12 Probability plot for hypothetical chemical contaminant

(lognormal distribution, probability scale).................................................................. 53 3.13 Lognormal probability plot for hypothetical chemical contaminant

(lognormal distribution, probability scale).................................................................. 54 3.14 Cumulative density function fitted to lognormally distributed data ........................... 57

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3.15 Scatter plot for 100 sample measurements of hypothetical chemical contaminant

(lognormal distribution); LOR=200 mg/L .................................................................. 59 3.16 Censored probability plot for hypothetical chemical contaminant;

LOR=200 mg/L ........................................................................................................... 60 3.17 Time series data for sodium concentrations in the feed and permeate solutions

of a reverse osmosis process. ...................................................................................... 61 3.18 Sudden change in ammonia concentrations at the GWRS .......................................... 62 3.19 Sudden change in nitrate concentrations at the GWRS .............................................. 62 3.20 Time series data for TOC concentrations in the feed and permeate solutions

of a reverse osmosis process. ...................................................................................... 63 3.21 Conceptual diagram of multiple barrier process train and treatability

distributions. ............................................................................................................... 66 3.22 Time series for boron concentrations in paired RO feed and RO permeate

samples ........................................................................................................................ 68 3.23 Linear regression curve for boron concentrations in RO feed and RO permeate

samples (r2=0.4) .......................................................................................................... 69 3.24 Reduction in boron from RO feed to permeate with no correlation (r2=0) ................. 70 3.25 Reduction in boron from RO feed to permeate with moderate correlation (r2=0.4) ... 70 3.26 Reduction in boron from RO feed to permeate with strong correlation (r2=0.9) ........ 71 3.27 Linear regression curve for NDMA concentrations in UV feed and UV

permeate samples (r2=0.8) .......................................................................................... 72

4.1 Total coliforms (by MTF) in plant feed and microfiltration feed ............................... 76 4.2 Fecal coliforms (by MTF) in plant feed and microfiltration feed ............................... 76 4.3 Normal fit comparison for simulated total coliform LRV during pretreatment

(MTF).......................................................................................................................... 77 4.4 Normal fit comparison for simulated fecal coliform LRV during pretreatment

(MTF).......................................................................................................................... 77 4.5 Lognormal probability plot for total coliform concentrations in MF feed solutions .. 78 4.6 Normal fit comparison for simulated total coliform LRV during microfiltration

(MTF).......................................................................................................................... 79 4.7 Normal fit comparison for simulated total coliform LRV during pretreatment

(membrane filtration) .................................................................................................. 79 4.8 Assumed operational configuration for the microfiltration process. .......................... 80 4.9 Lognormal probability plot for particle removal by MF Modules A01–A08. ............ 81 4.10 Lognormal probability plot for particle removal by MF Modules B01–B08 ............. 81 4.11 Lognormal probability plot for particle removal by MF Modules D01–D08 ............. 82 4.12 Lognormal probability plot for particle removal by MF Modules E01 and E02 ........ 82 4.13 Fitted lognormal PDF for particle removal by MF Module A01 ................................ 83 4.14 Normal fit comparison for simulated MF particle LRV by Train A ........................... 83 4.15 Normal fit comparison for simulated MF particle LRV by Trains A+B+D

combined ..................................................................................................................... 84 4.16 Fitted Weibull PDF for MS2 bacteriophage LRV by Fluid Systems

HR RO membrane....................................................................................................... 85 4.17 Fitted Weibull PDF for MS2 bacteriophage LRV by Dow Film Tec

RO membrane ............................................................................................................. 85

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4.18 Fitted gamma PDF for MS2 bacteriophage LRV by Hydranautics

ULP RO membrane..................................................................................................... 86 4.19 Fitted gamma PDF for MS2 bacteriophage LRV by Fluid Systems

ULP RO membrane..................................................................................................... 86 4.20 Lognormal probability plot for sulfate concentrations in RO feed and

permeate at GWRS (2008–2014). ............................................................................... 87 4.21 Simulated sulfate LRV for RO treatment and fit comparison to a normal PDF ......... 87 4.22 Fit comparison for UV dose data to a BetaGeneral PDF ............................................ 88 4.23 Fitted BetaGeneral PDF to UV dose data ................................................................... 89 4.24 Fit comparison for UV transmittance data to a BetaGeneral PDF .............................. 89 4.25 Fitted BetaGeneral PDF for UV transmittance data ................................................... 90 4.26 Fit comparison for flow data to a BetaGeneral PDF ................................................... 90 4.27 Fitted BetaGeneral PDF for flow data ........................................................................ 91 4.28 Fit comparison for simulated UV–AOP dose ............................................................. 92 4.29 UV dose–LRV relationship for virus inactivation (r2=0.9997) ................................... 94 4.30 Log UV dose–LRV relationship for Cryptosporidium (r2=0.993) and Giardia

lamblia (r2=0.996) inactivation ................................................................................... 94 4.31 BetaGeneral fit comparison for simulated UV–AOP Giardia LRV ........................... 95 4.32 BetaGeneral fit comparison for simulated UV–AOP Cryptosporidium LRV ............ 96 4.33 BetaGeneral fit comparison for simulated UV–AOP virus LRV ................................ 96 4.34 BetaGeneral fit comparison for simulated UV disinfection Giardia LRV ................. 97 4.35 BetaGeneral fit comparison for simulated UV disinfection Cryptosporidium LRV .. 97 4.36 BetaGeneral fit comparison for simulated UV disinfection virus LRV ...................... 98 4.37 Required CT values for 4 log inactivation of viruses by free chlorine at

pH 6.0–9.0. ................................................................................................................ 100 4.38 Lognormal probability plot for clearwell CT ............................................................ 101 4.39 Normal probability plot for clearwell pH ................................................................. 101 4.40 Lognormal probability plot for clearwell free chlorine residual ............................... 102 4.41 Fit comparison for temperature to a BetaGeneral PDF ............................................. 102 4.42 Fitted lognormal PDF for clearwell CT .................................................................... 103 4.43 Fitted normal PDF for clearwell pH ......................................................................... 103 4.44 Fitted lognormal PDF for clearwell chlorine residual .............................................. 104 4.45 Fitted BetaGeneral PDF for clearwell temperature ................................................... 104 4.46 Simulated PDF for CT3 log, Giardia ................................................................................ 105 4.47 Simulated PDF for CT4 log, viruses ................................................................................. 105 4.48 Lognormal fit comparison for simulated chlorination Giardia LRV ........................ 106 4.49 Lognormal fit comparison for simulated chlorination virus LRV ............................ 107 4.50 Cumulative lognormal fit comparison for simulated chlorination Giardia LRV ..... 107 4.51 Cumulative lognormal fit comparison for simulated chlorination virus LRV .......... 108 4.52 Cryptosporidium, microsphere, and PRD1 log removal vs. filter turbidity

relationship ................................................................................................................ 109 4.53 Sand filtration effluent data from the Goreangab Water Reclamation Plant in

Windhoek, Namibia. ................................................................................................. 110 4.54 Fit comparison for Cryptosporidium LRV by sand filtration ................................... 111

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4.55 Fit comparison for bacteria LRV by sand filtration .................................................. 111 4.56 Fit comparison for virus LRV by sand filtration....................................................... 112 4.57 CT3 log, Giardia vs. temperature based on standard CT tables ...................................... 113 4.58 CT4 log, virus vs. temperature based on standard CT tables .......................................... 113 4.59 Lognormal probability plot for ozonation CTactual. ................................................... 114 4.60 Fit comparison for temperature data to a BetaGeneral PDF ..................................... 115 4.61 Fitted lognormal PDF to CTactual data........................................................................ 115 4.62 Fitted BetaGeneral PDF to temperature data. ........................................................... 116 4.63 Lognormal fit comparison for simulated ozonation Cryptosporidium LRV ............ 117 4.64 Lognormal fit comparison for simulated ozonation Giardia LRV ........................... 117 4.65 Lognormal fit comparison for simulated ozonation virus LRV ................................ 118 4.66 Normal fit comparison for simulated potassium LRV during RO treatment ............ 119 4.67 Rejection diagram for organic micropollutants during membrane treatment

based on solute and membrane properties ................................................................ 123 4.68 Normal fit comparison for simulated chloroform LRV during RO treatment .......... 124 4.69 Lognormal probability plot showing negligible removal of meprobamate

during UV–AOP ....................................................................................................... 129 4.70 Lognormal probability plot showing negligible removal of sucralose during

UV–AOP ................................................................................................................... 129 4.71 Lognormal probability plot showing approximately 1 LRV for NDMA during

UV irradiation ........................................................................................................... 130 4.72 Long-term monitoring data for NDMA in feed and effluent samples of a

UV–AOP process (GWRS). ...................................................................................... 131 4.73 Lognormal probability plots for NDMA concentrations in UV–AOP feed and

effluent samples (GWRS) ......................................................................................... 131 4.74 Normal fit comparison for simulated diuron LRV during UV–AOP treatment ....... 132 4.75 Normal fit comparison for simulated monochloramine LRV during

UV–AOP treatment ................................................................................................... 132 4.76 Normal fit comparison for simulated NDMA LRV during UV–AOP treatment

(Scottsdale) ............................................................................................................... 133 4.77 Normal fit comparison for simulated NDMA LRV during UV–AOP treatment

(GWRS) .................................................................................................................... 133 4.78 Simulated average GAC bed age based on a single bed, refreshed once per year

(365 days) ................................................................................................................. 138 4.79 Simulated average GAC bed age based on two independently operated beds,

refreshed once per year ............................................................................................. 138 4.80 Simulated average GAC bed age based on three independently operated beds,

refreshed once per year ............................................................................................. 139 4.81 Simulated average GAC bed age based on four independently operated beds,

refreshed once per year ............................................................................................. 139 4.82 BetaGeneral fit comparison for acetochlor removal by GAC ................................... 140 4.83 Normal fit comparison for removal of bisphenol A during ozonation. ..................... 142 4.84 Virus removal by Multiple Barrier Simulation No. 1 ............................................... 144 4.85 Virus removal by Multiple Barrier Simulation No. 2 ............................................... 145 4.86 Virus removal by Multiple Barrier Simulation No. 3 ............................................... 146 4.87 Virus removal by Multiple Barrier Simulation No. 4 ............................................... 147

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4.88 Giardia removal by Multiple Barrier Simulation No. 5 ........................................... 148 4.89 Giardia removal by Multiple Barrier Simulation No. 6 ........................................... 149 4.90 Giardia removal by Multiple Barrier Simulation No. 7 ........................................... 150 4.91 Cryptosporidium removal by Multiple Barrier Simulation No. 8 ............................. 151 4.92 Cryptosporidium removal by Multiple Barrier Simulation No. 9 ............................. 152 4.93 Cryptosporidium removal by Multiple Barrier Simulation No. 10 ........................... 153 4.94 Virus removal by Multiple Barrier Simulation No. 11 ............................................. 154 4.95 Virus removal by Multiple Barrier Simulation No. 12 ............................................. 155 4.96 Virus removal by Multiple Barrier Simulation No. 13 ............................................. 156 4.97 Virus removal by Multiple Barrier Simulation No. 14 ............................................. 157 4.98 Giardia removal by Multiple Barrier Simulation No. 15 ......................................... 158 4.99 Giardia removal by Multiple Barrier Simulation No. 16 ......................................... 159 4.100 Giardia removal by Multiple Barrier Simulation No. 17 ......................................... 160 4.101 Giardia removal by Multiple Barrier Simulation No. 18 ......................................... 161 4.102 Cryptosporidium removal by Multiple Barrier Simulation No. 19 ........................... 162 4.103 Cryptosporidium removal by Multiple Barrier Simulation No. 20 ........................... 163 4.104 Cryptosporidium removal by Multiple Barrier Simulation No. 21 ........................... 164 4.105 Cryptosporidium removal by Multiple Barrier Simulation No. 22 ........................... 165 4.106 NDMA removal by Multiple Barrier Simulation No. 23 .......................................... 166 4.107 NDMA removal by Multiple Barrier Simulation No. 24 .......................................... 167 4.108 Diuron removal by Multiple Barrier Simulation No. 25 ........................................... 168 4.109 Atrazine removal by Multiple Barrier Simulation No. 26 ........................................ 169 4.110 Caffeine removal by Multiple Barrier Simulation No. 27 ........................................ 170 4.111 Carbamazepine removal by Multiple Barrier Simulation No. 28 ............................. 171

5.1 RO membrane-based treatment – CCP and associated failure triggers .................... 177 5.2 Ozone-biofiltration-based treatment process – CCP and associated failure

triggers ...................................................................................................................... 183 5.3 Arena simulation model RO membrane-based treatment process train .................... 191 5.4 Arena simulation model ozone–biofiltration-based treatment process train ............. 192 5.5 Standard percentage (fp) for RO membrane-based treatment process train

monitors .................................................................................................................... 199 5.6 Standard percentage (fp) for ozone-biofiltration-based treatment process

train monitors ............................................................................................................ 200

6.1 8 inch RO staging diagram, viewed from end cap .................................................... 224 6.2 16 inch RO staging diagram, viewed from end cap .................................................. 225 6.3 8 inch RO Unit 16 at Scottsdale Water Campus ....................................................... 228 6.4 16 inch RO Unit 21 at Scottsdale Water Campus ..................................................... 228 6.5 RO module cutaway (8 inch diameter) ..................................................................... 228 6.6 End cap (16 inch diameter) ....................................................................................... 228 6.7 Modified interconnector............................................................................................ 228 6.8 Ultrafiltration Unit 6 at Scottsdale Water Campus ................................................... 229 6.9 Module removed and ready for the fibers to be cut .................................................. 229 6.10 MF–UF module ......................................................................................................... 230

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6.11 RO permeate conductivity as measured by the conductivity analyzer on the array .................................................................................................................... 231

6.12 Normalized salt passage from the array .................................................................... 232 6.13 Lab results and log removal values for calcium (8 and 16 inch) .............................. 233 6.14 Lab results and log removal values for sulfate (8 and 16 inch) ................................ 234 6.15 Lab results and log removal values for caffeine (8 and 16 inch) .............................. 234 6.16 Lab results and log removal values for sucralose (8 and 16 inch) ............................ 235 6.17 Conductivity and TOC .............................................................................................. 236 6.18 Example excitation–emission matrix with regions outlined ..................................... 236 6.19 Regional fluorescence volumes for 8 inch RO membrane tests ............................... 237 6.20 Regional fluorescence volumes for 16 inch RO membrane tests ............................. 237 6.21 Membrane filtration pressure decay test results ........................................................ 239

7.1 Control system elements ........................................................................................... 242 7.2 Graphical depiction of a time delay alarm trigger (true) and a false event

resulting in no alarm ................................................................................................. 245 7.3 Graphical depiction of a moving average alarm (true) and a false event

resulting in no alarm ................................................................................................. 246 7.4 Graphical depiction of a block average alarm trigger (true) and a false event

resulting in no alarm ................................................................................................. 246 7.5 Graphical depiction of a point-to-point alarm trigger (true) and a false event

resulting in no alarm ................................................................................................. 247 7.6 Generic alert level response procedure ..................................................................... 250 7.7 Generic critical alarm response procedure ................................................................ 251 7.8 Chloramine dosing alerts (warning) response ........................................................... 263 7.9 Chloramine dosing critical alarm (failure) response ................................................. 264 7.10 MF–UF alerts (warning) response ............................................................................ 265 7.11 MF–UF critical alarm (failure) response .................................................................. 266 7.12 RO alert (warning) response ..................................................................................... 267 7.13 RO critical alarm (failure) response .......................................................................... 268 7.14 UV–AOP alert (warning) response ........................................................................... 269 7.15 UV–AOP critical alarm (failure) response................................................................ 270 7.16 Disinfection system (chlorine) alert (warning) response .......................................... 271 7.17 Chemical disinfection (chlorine) alarm (failure) response ........................................ 272 7.18 Chemical stabilization alert (warning) response ....................................................... 273 7.19 Chemical stabilization alarm (failure) response ........................................................ 274 7.20 Ozone low CT and low-flow alert procedures .......................................................... 286 7.21 Ozone low CT and low-ozone residual alert procedures .......................................... 287 7.22 Ozone low CT and low-UVT alert procedures ......................................................... 288 7.23 Ozone low-CT critical failure response .................................................................... 289 7.24 Ozone–BAC alert response procedures .................................................................... 290 7.25 Ozone–BAC critical failure response procedures ..................................................... 291 7.26 Coagulant–BAC alert response procedures .............................................................. 292 7.27 Coagulant–BAC critical failure response procedures ............................................... 293 7.28 GAC alert response procedures ................................................................................ 294

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7.29 GAC critical failure response procedures ................................................................. 295 7.30 UV disinfection alert response procedures ............................................................... 296 7.31 UV disinfection critical failure response procedures ................................................ 297

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List of Tables

ES.1 Log Removal Summary across Multiple Barriers by RO Membrane-Based Process Train ........................................................................................................... ES-7

ES.2. Log Removal Summary across Multiple Barriers by Non-RO Process .................. ES-7

2.1 Categories of Typical Hazards and Sources from Sewer Collection Systems ............ 17 2.2 Virus Creditable Log Removal by Treatment Process ................................................ 18 2.3 Cryptosporidium Creditable Log Removal by Treatment Process ............................. 19 2.4 Giardia Creditable Log Removal by Treatment Process ............................................ 19 2.5 Likelihood Descriptors................................................................................................ 23 2.6 Consequence Descriptors ............................................................................................ 23 2.7 Risk Matrix ................................................................................................................. 24 2.8 Uncertainty Factors Description ................................................................................. 25 2.9 CCP Selection Process and Indicators – RO Membrane-Based Treatment ................ 31 2.10 CCP Selection Process – Non-Membrane Treatment Process .................................... 34

3.1 Characteristics and Their Determination for Normal Distributions ............................ 47 3.2 Characteristics and Their Determination for Lognormal Distributions ...................... 49

4.1 UV Dose Table for Cryptosporidium, Giardia lamblia, and Virus Inactivation Credit. ......................................................................................................................... 93

4.2 RO Rejection Categories for Inorganic Chemical Substances and Potential Surrogate Chemicals for which RO Rejection PDFs Have Been Developed. .......... 120

4.3 RO Rejection Categories for Organic Chemical Substances and Potential Indicator Chemicals for which RO Rejection PDFs Have Been Developed (assuming MWCO=180, MSC=high) ....................................................................... 125

4.4 RO Rejection Categories for Organic Chemical Substances and Potential Indicator Chemicals for which RO Rejection PDFs Have Been Developed (assuming MWCO=210, MSC=low) ........................................................................ 127

4.5 Model Input Parameters for Predicting Full-Scale MP Breakthrough from Pilot-Scale Results .................................................................................................... 135

4.6 Model Input Parameters for Predicting Full-Scale Organic Contaminant Breakthrough from CD-RSSCT Results ................................................................... 136

4.7 Fitted PDFs for Other Trace Organic Chemical Removal by GAC .......................... 141 4.8 PDFs for the Removal of Trace Organic Chemical Contaminants during

Ozonation. ................................................................................................................. 142 4.9 Multiple Barrier Monte Carlo Simulation No. 1 ....................................................... 144 4.10 Multiple Barrier Monte Carlo Simulation No. 2 ....................................................... 145 4.11 Multiple Barrier Monte Carlo Simulation No. 3 ....................................................... 146 4.12 Multiple Barrier Monte Carlo Simulation No. 4 ....................................................... 147 4.13 Multiple Barrier Monte Carlo Simulation No. 5 ....................................................... 148 4.14 Multiple Barrier Monte Carlo Simulation No. 6 ....................................................... 149 4.15 Multiple Barrier Monte Carlo Simulation No. 7 ....................................................... 150 4.16 Multiple Barrier Monte Carlo Simulation No. 8 ....................................................... 151 4.17 Multiple Barrier Monte Carlo Simulation No. 9 ....................................................... 152 4.18 Multiple Barrier Monte Carlo Simulation No. 10 ..................................................... 153

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4.19 Multiple Barrier Monte Carlo Simulation No. 11 ..................................................... 154 4.20 Multiple Barrier Monte Carlo Simulation No. 12 ..................................................... 155 4.21 Multiple Barrier Monte Carlo Simulation No. 13 ..................................................... 156 4.22 Multiple Barrier Monte Carlo Simulation No. 14 ..................................................... 157 4.23 Multiple Barrier Monte Carlo Simulation No. 15 ..................................................... 158 4.24 Multiple Barrier Monte Carlo Simulation No. 16 ..................................................... 159 4.25 Multiple Barrier Monte Carlo Simulation No. 17 ..................................................... 160 4.26 Multiple Barrier Monte Carlo Simulation No. 18 ..................................................... 161 4.27 Multiple Barrier Monte Carlo Simulation No. 19 ..................................................... 162 4.28 Multiple Barrier Monte Carlo Simulation No. 20 ..................................................... 163 4.29 Multiple Barrier Monte Carlo Simulation No. 21 ..................................................... 164 4.30 Multiple Barrier Monte Carlo Simulation No. 22 ..................................................... 165 4.31 Multiple Barrier Monte Carlo Simulation No. 23 ..................................................... 166 4.32 Multiple Barrier Monte Carlo Simulation No. 24 ..................................................... 167 4.33 Multiple Barrier Monte Carlo Simulation No. 25 ..................................................... 168 4.34 Multiple Barrier Monte Carlo Simulation No. 26 ..................................................... 169 4.35 Multiple Barrier Monte Carlo Simulation No. 27 ..................................................... 170 4.36 Multiple Barrier Monte Carlo Simulation No. 28 ..................................................... 171

5.1 CCPs, Analyzers, Triggers, and Failures for RO-Based Process Train .................... 178 5.2 Ozone–Biofiltration-Based Treatment Process Train ............................................... 184 5.3 Process and Expression Used for Each Analyzer for RO Membrane-Based

Treatment Process ..................................................................................................... 194 5.4 Failures Associated with Each Analyzer in RO Membrane-Based Treatment

Process Train ............................................................................................................. 196 5.5 Scheduled Utilization (Us) for single replication for (a) RO Membrane-Based

Treatment Process Train and (b) Ozone-Biofiltration-Based Treatment Process Train ............................................................................................................. 198

5.6 Occurrence Ranking Index ....................................................................................... 201 5.7 Severity Ranking Index............................................................................................. 203 5.8 Detection Ranking Index .......................................................................................... 209 5.9 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and Detection – Pre-Chloramination Step ................................................. 205 5.10 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and DetectionvMicrofiltration–Ultrafiltration ........................................... 207 5.11 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and DetectionvReverse Osmosis ............................................................... 208 5.12 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and DetectionvUV–H2O2 ........................................................................... 209 5.13 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and DetectionvStabilization ....................................................................... 210 5.14 RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence,

Severity, and DetectionvChlorine Disinfection ........................................................ 211 5.15 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detectionv – Ozone .................................................. 212 5.16 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detection – Ozone–BAC .......................................... 213

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5.17 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detectionv – Coagulant–BAC .................................. 214 5.18 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detection – GAC ...................................................... 216 5.19 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detection – UV ......................................................... 217 5.20 Ozone-Biofiltration-Based Treatment Process System Risk Priority Numbers

for Occurrence, Severity, and Detection – Chlorine ................................................. 218

6.1 Hazard Controlled and Associated Monitoring Parameters ...................................... 221 6.2 Scottsdale Water Campus Membrane Details ........................................................... 223 6-3 Water Quality Test Methods ..................................................................................... 226 6.4 Summary of Conductivity and TOC Removal Data ................................................. 233 6.5 Membrane Filtration Test Results ............................................................................. 238

7.1 Chloramine Alert and Alarm Example Set Points .................................................... 254 7.2 Microfiltration–Ultrafiltration Alert and Alarm (Summary) .................................... 255 7.3 Reverse Osmosis Membrane Alert and Alarm Summary ......................................... 257 7.4 UV-Advanced Oxidation Alert and Alarm Level Summary ..................................... 259 7.5 Chlorine Disinfection Alert and Alarm Level Summary .......................................... 260 7.6 Chemical Stabilization Alert and Alarm Summary .................................................. 262 7.7 Ozone Low CT Alert and Alarm Summary .............................................................. 278 7.8 Ozone-BAC Alert and Alarm Summary ................................................................... 279 7.9 Coagulation-BAC Alert and Alarm Summary .......................................................... 281 7.10 GAC Alert and Alarm Summary .............................................................................. 283 7.11 UV Disinfection Alert and Alarm Summary ............................................................ 285

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Acronyms and Abbreviations

AOP advanced oxidation process

AWTP advanced water treatment plant

BAC biological activated carbon

CCP critical control point

CCPP calcium carbonate precipitation potential

CD-RSSCT constant diffusivity rapid small-scale column test

CDPH California Department of Public Health

CT concentration × time as mg/min/L

DAF dissolved air flotation

DBP disinfection byproduct

DOC dissolved organic carbon

DPR direct potable reuse

EBCT empty bed contact time

EEM (3D fluorescence) excitation–emission matrix

GAC granular activated carbon

GWRS groundwater replenishment system

HACCP hazard analysis and critical control point

IPR indirect potable reuse

KOW octanol–water partitioning coefficient

LFER linear free energy relationship

LRV log removal value

LSI Langelier Saturation Index

LT2ESWTR Long-Term 2 Enhanced Surface Water Treatment Rule

MF microfiltration

MTBE methyl-tert-butyl ether

MTF multiple tube fermentation

MTTF mean time to failure

MTTR mean time to repair

MW molecular weight

MWCO molecular weight cutoff

MWd molecular width

NDMA n-Nitrosodimethylamine

PD-RSSCT proportional diffusivity rapid small-scale column test

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PDF probability distribution function

PLC programmable logic controller

RO reverse osmosis

RPN risk priority number

RSSCT rapid small-scale column test

SCADA supervisory control and data acquisition

SOC synthetic organic contaminant

TOC total organic carbon

UF ultrafiltration

U.S. EPA U.S. Environmental Protection Agency

UV ultraviolet (light)

UVT ultraviolet light transmittance

VOC volatile organic contaminant

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Acknowledgments

This project was funded by the Water Environment & Reuse Foundation in cooperation with Metropolitan Water District of Southern California.

The project team would like to thank all of the utilities, consultants, and universities that provided data and access to facilities throughout this project. The research team especially appreciates the filtration data provided by Ian Douglas and colleagues at the city of Ottawa and the ultraviolet process performance data provided by John Wesely, Kim Ervin, and Eric Sampson at CH2M. The team also thanks Justin Mattingly for his role as project manager from the Water Environment & Reuse Foundation and the PAC for their review provided throughout the project.

Principal Investigators Troy Walker, MIEAus, Hazen and Sawyer Ben Stanford, Ph.D., Hazen and Sawyer Project Team Stuart Khan, Ph.D., University of New South Wales Shane Snyder, Ph.D., University of Arizona Ricardo Valerdi, Ph.D., University of Arizona Sudhee Dwivedi, Ph.D., University of Arizona James C. Vickers, P.E., Separation Processes, Inc. Allison Reinert, EIT, Hazen and Sawyer Aaron Duke, P.E. Hazen and Sawyer Participating Agencies Rich Nagel, Shivaji Deshmukh, and Eric Owens, West Basin Municipal Water District, CA Mike Wehner and Jason Dadakis, Orange County Water District, CA Art Nunez and Binga Talabi, City of Scottsdale, AZ Ben Bowen, Veolia Water Cedric Robillot, Australian Water Recycling Center of Excellence John Esterhuizen, Windhoek Goreangab Operating Company Project Advisory Committee Larry Schimmoller, CH2M Mehul Patel, Orange County Water District Evelyn Cortez-Davis, Los Angeles Department of Water and Power Joseph Barksdale, Gresham Smith & Partners John Dyksen, United Water Amanda Hering, Colorado School of Mines

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Executive Summary

This project was tasked with applying the hazard analysis and critical control point (HACCP) methodology to identify and assess the reliability of critical control points (CCPs) as both processes and monitors to manage acute and chronic health risks in direct potable reuse (DPR) process trains. The HACCP system was originally developed as an engineering means of controlling microbial hazards in processed foods. HACCP is a logical, scientific process control system designed to identify, evaluate, and control hazards, which are significant for food safety. The purpose of a HACCP system is to put in place process controls that will detect and correct deviations in quality processes at the earliest possible opportunity. HACCP focuses on monitoring and maintaining the barriers of treatment rather than on end-of-pipe sampling and testing. This provides the dual advantage of ensuring poor quality is prevented in the first place and allowing for a reduction in end-of-pipe monitoring and associated costs. In this project the HACCP methodology was adapted from the food industry to a DPR framework to evaluate two distinct process trains.

The first process train evaluated (Figure ES.1) employs reverse osmosis (RO) membranes in a configuration of microfiltration (MF)–RO–ultraviolet (UV) light with advanced oxidation process (UV–AOP)–chlorine (Cl2, as free chlorine). The second was an alternative treatment train that does not employ RO membranes (Figure ES.2). This process train was pre-defined at the project outset as ozone (O3)–biological activated carbon (BAC)–granular activated carbon (GAC)–disinfection dose UV–Cl2 and will be referred to as the “ozone–BAC-based treatment” throughout this report. Note that the ozone–BAC-based treatment process was later modified to include a coagulation step, as shown in Figure ES.2, but not described in detail until Chapter 2. It should also be noted that any reference to “UV” is meant to imply disinfection dose, typically near 40 mJ/cm2, whereas any reference to “UV–AOP” is meant to imply a UV dose that is an order of magnitude higher (at least), around 400 mJ/cm2.

Figure ES.1. Process flow diagram for RO-membrane-based treatment option.

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Figure ES.2. Process flow diagram for ozone–BAC-based treatment option.

It is also important to clearly define the term “direct potable reuse” within the framework of this project. Here, DPR is meant to include facilities that produce purified water that can either be further treated by a drinking water treatment plant (i.e., purified raw water production) or those that produce purified, finished water that is directly blended into a distribution system. In both cases there is no environmental buffer included, and therefore the water is assumed to be directly connected to the supply from the water reclamation and resource recovery facility (i.e., wastewater treatment plant). However, it was beyond the scope of this project to differentiate the CCPs (and processes) that might be used in the event of direct connection to the distribution system (i.e., production of purified, finished water) from those that might be used in the production of purified water that would be supplied to a drinking water treatment plant.

Therefore, for this study it was assumed in all cases that the advanced purified water produced by either process train would need to meet drinking water quality standards not only at end of pipe but also in the drinking water distribution system (i.e., production of purified water for direct blending to a distribution system) and that no further treatment beyond the end of the process trains identified would be incorporated. This perhaps imposed an artificially high standard of treatment in this study, but the decision was made early on to apply that standard for this study with the full understanding that a site-specific HACCP approach would have to be applied when the type of DPR and drinking water treatment was defined for a given community.

Another concern and critical factor for the successful operation of any system is the reliability of the asset: Any HACCP or similar system is only as reliable as the equipment, instrumentation, and controls for that plant. In maintaining multiple barriers, it is important that the modes of failure of those barriers are well understood, the criticality to the operation is well defined, and maintenance to manage the assets is targeted correctly.

With the combination of risk assessment and operations support, this report serves as an instrumental step in advancing the acceptance of DPR by demonstrating the robustness and reliability of multiple barriers, both individually and as a combined process, and identifying the CCPs and response process that will be necessary to ensure the safe and continuous operation of DPR systems.

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The HACCP framework can be an integral part of planning and operating DPR facilities without being a burden from a regulatory or certification standpoint. Here, the focus is on developing a risk assessment team (or HACCP team) whose job is to collaboratively determine the water quality goals; assess the inherent risk from the source water and production inputs (i.e., chemicals added during treatment); challenge the assumptions around selected treatment processes to determine if a given process train is likely to produce water quality that meets the goals; assess (test) the ability of the single and combined processes to meet the water quality goals; and determine the monitoring needs, operational plans, and response procedures required to produce water suitable for potable reuse. When applied properly, HACCP provides a framework for ensuring that the water being produced from a DPR facility is safe and ready for further drinking water treatment or direct blending into a distribution system rather than waiting for end-of-pipe testing to provide a moment-in-time snapshot of the quality of a given sample of water.

HACCP, or at a minimum the use of CCPs, has been applied at a number of water recycling projects in order to demonstrate the management of microbiological and chemical risk via multiple barrier processes. CCPs are points in the treatment process that are specifically designed to reduce, prevent, or eliminate a human health hazard and for which controls exist to ensure the proper performance of that process. Given the complementary nature of HACCP principles to existing management plans, this project focused on the practical elements of HACCP as a means of demonstrating the identification and validation of multiple barriers.

Several key outcomes from this project included demonstration of the HACCP methodology and how it can be applied to DPR planning and operations; using full-scale data to quantify the ability of combined processes to remove microbial and chemical contaminants; identification and evaluation of process monitors that are used to inform operations teams of critical alerts and alarms; development of response procedures for how to handle events and process anomalies that may occur; and general recommendations on design guidelines for developing site-specific DPR plans and projects. Several of the key summaries and outcomes are listed in the following pages. HACCP Methodology and Risk Assessment

The review of source water hazards is an important first step in the development of a water quality risk assessment, as it allows the HACCP team to:

identify risks to be managed, from collection (including industrial pretreatment) to distribution

establish the level of removal required over the entire treatment train to meet water quality objectives

identify which barriers (processes) will be required for the removal of specific contaminants or categories of contaminants

A typical risk assessment process should include a significant site-specific sampling campaign over an extended period of time to understand the likely concentration of contaminants and any seasonal or temporal (even diurnal) variation in the source water. For example, if it is known that a given sewer collection system has industry with specific chemical inputs (synthetic organic contaminants [SOCs], volatile organic contaminants [VOCs], metals, pharmaceuticals), then those risks would need to be characterized on the

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basis of normal operation and then again during failures of pretreatment programs or spill events. Likewise, the risks of contamination events from the collection system may need to be managed via additional CCPs that can easily be integrated into the HACCP framework for DPR systems. In short, a proper HACCP risk assessment for a given facility should include a characterization of risks from industrial pretreatment and sewer collection systems all the way through drinking water distribution systems.

For this study, the two treatment trains were treated independently from any upstream risks (e.g., specific industries or wastewater treatment processes) because this exercise was meant to demonstrate the approach for assessing risk and identifying CCPs. Figures ES.3 and ES.4 show the identified process trains with their associated CCPs. Chapter 2 details the results of the risk assessment, and Appendix B contains a complete risk register that was used for the source water characterization and identification of treatment barriers and CCPs. It should be noted that for actual facilities, the risk register (illustrated in Appendix B) is meant to be a “living document” that is updated as needed when either (a) new water quality hazards are identified or (b) new source water characterization (monitoring) reveals elevated risk from hazards that had previously been present at less than risk thresholds. As such, the HACCP framework can be used to evaluate risks and treatment options at the onset of a project and then used to further refine treatment and process modifications in the future.

It is noteworthy that in evaluating the proposed process train of ozone–BAC–GAC–UV–chlorine, the project team encountered initial difficulty in meeting the required log removal of microorgansims via the process identified at the outset of the project. Specifically, the BAC process could not be considered a CCP as a stand-alone process because there was no control mechanism to adjust its ability to achieve pathogen reduction or contaminant removal. Instead, by modifying the process to incorporate a coagulation step ahead of filtration, the BAC process could be incorporated, as it would be effective at reduction of turbidity (and hence a level of microorganism removal) if operated like a biological filter. Therefore, the revised non-membrane treatment process became ozone–flocculation–sedimentation–BAC–GAC–UV–chlorine. This decision, although straightforward from a process design and selection viewpoint, was nonetheless facilitated by CCP selection process.

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Figure ES.3. Critical control points (outlined) – RO membrane-based treatment train.

Figure ES.4. Critical control points (outlined) – ozone–BAC-based treatment train as shown with optional preozonation step.

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Process Performance for Contaminant Removal (Monte Carlo Simulation)

A strength of this study and approach was to use full-scale operational data from unit processes being used or considered for use in DPR systems. By using full-scale data, the range of operational conditions, process upsets, and real equipment and monitor performance data were captured over years of observation. Using the full-scale data, a large database of probability distribution functions (PDFs) was developed for chemical and microbial contaminants, and their removal was modelled across multiple barriers for the RO membrane- and ozone–BAC-based treatment trains. Although significant full-scale data, with some supplemental pilot-scale data, were used to develop the PDFs and resulting analysis, surrogate parameters (e.g., pressure decay testing, sulphate rejection, chlorine or ozone concentration x time [CT]) were used to determine the log removal values (LRVs) for chemical and microbial contaminants. The results of the data collection, analysis, and Monte Carlo simulations include:

1. When full-scale treatment data were used for pathogen removal, the two treatment trains demonstrated LRVs of viruses, Cryptosporidium, and Giardia that exceeded the current California “12-10-10” rule for groundwater injection (California Code of Regulations §60320.208, requiring 12 log removal of viruses, 10 log removal of Cryptosporidium, and 10 log removal of Giardia, through the water recycling scheme, inclusive of processes from wastewater treatment through retention time in the aquifer) in indirect potable reuse (IPR) settings, with the exception of Cryptosporidium.

a. In all cases, data were evaluated in two ways: First, by using published extrapolations and known dose–response relationships, process performance was evaluated mathematically and statistically for disinfection efficacy and chemical removal. Second, regulatory caps, limit of validation caps, or both (i.e., the use of conservative surrogates) were used to evaluate performance within the current regulatory and monitoring construct. In this way, any value that was observed greater than the cap was reduced to be equal to the maximum creditable or validated value. Extra conservatism was added by limited UV–AOP caps to those associated with disinfection credit (i.e., 4 log removal).

b. For the RO membrane-based treatment (Table ES.1), the minimum LRVs observed across the barriers using the capped data were 10 log for virus, 12 log for Giardia, and 10 log for Cryptosporidium.

c. For the ozone–BAC-based treatment, the minimum LRVs observed across the barriers using the capped data were 11 log for virus, 13 log for Giardia, and 7.1 log for Cryptosporidium.

d. However, when UV–AOP was used instead of UV disinfection for the ozone–BAC-based train (Table ES.2), the minimum LRVs observed across the barriers were 14 log for virus, 13 log for Giardia, but still 7.1 log for Cryptosporidium due to restricting the extrapolation not to exceed the 4-log cap on UV/AOP.

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2. A note on the observation of high LRVs: Extrapolation of disinfection efficacy was based on accepted mechanistic equations relating dose (e.g., UV, ozone, or chlorine) to pathogen inactivation but has not been validated beyond the ranges stated in the U.S. Environmental Protection Agency disinfection guidance manuals for various regulatory compliance end points. Although it is likely that actual disinfection performance was greater than the imposed caps, validation of higher disinfection is beyond the limits of quantification and the known validation ranges of surrogates to determine “true” performance. Therefore, both extrapolated values and capped values are presented in Tables ES.1 and ES.2 to provide a balanced assessment of process performance.

Table ES.1. Log Removal Summary across Multiple Barriers by RO Membrane-Based Process Train

Contaminant CINH2 MF RO UV–AOP Cl2

Combined Mean

Combined Min

Viruses N/A N/A 2.7 9.4 120 130 46

Viruses capped N/A N/A 2 4 4 10 10

Giardia N/A 4.6 5.4 7.7 3.9 22 16

Giardia capped N/A 4 2 4 3 12 11

Cryptosporidium N/A 4.6 5.4 7.8 N/A 18 15

Cryptosporidium capped

N/A 4 2 4 0 10 10

Notes: AOP=advanced oxidation process; MF=microfiltration; RO=reverse osmosis; UV=ultraviolet; “Mean” is meant to describe central tendency of the distribution of log removal values, not a true “average” of log-numbers

Table ES.2. Log Removal Summary across Multiple Barriers by Non-RO Process

Contaminant Sediment/Filtration Ozone GAC

UV–AOP Cl2

Combined Mean

Combined Min

Viruses 1.5 35 N/A 9.4 119 160 48

Viruses capped 1.5 4 N/A 4 4 14 13

Giardia 2.2 17 N/A 7.7 3.9 31 15

Giardia capped 2.2 4 N/A 4 3 13 11

Cryptosporidium 2.2 0.9 N/A 7.8 N/A 11 9.8

Cryptosporidium capped

2.2 0.9 N/A 4 0 7.1 6.1

Notes: AOP=advanced oxidation process; GAC=granular activated carbon; UV =ultraviolet; “Mean” is meant to describe central tendency of the distribution of log removal values, not a true “average” of log-numbers

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3. The observations regarding Cryptosporidium must not be taken out of context, however.

a. It is important to note that the filters used in this study were not pretreated with a coagulation–sedimentation step nor operated with the intention of maintaining 0.1 NTU combined filter effluent quality; therefore, higher, LRVs were not observed.

b. In addition, processes such as ozone and UV disinfection could be further optimized within a specific facility to deliver higher doses to target Cryptosporidium. Likewise, a preozonation step could be used to achieve additional Cryptosporidium removal but was not counted in this assessment.

c. Finally, the power of this simulation is that it can be used to determine what set points can be used as critical limits for alarms and help inform the user if additional treatment or process modifications may be needed.

d. It is critical that the reader does not interpret the information provided here to assume that the non-RO membrane-based treatment process is inadequate for Cryptosporidium removal.

4. Inorganic chemical contaminants were modelled by grouping within surrogate classes and found to be removed across RO membranes.

5. Inorganic chemicals had relatively few control mechanisms to improve their removal across the ozone–BAC-based train, but the vast majority of them were present in the model wastewater effluents at concentrations that were already less than drinking water maximum contaminant levels. Therefore, site-specific evaluation regarding efficacy of upstream wastewater effluent concentrations of specific cationic and anionic contaminants is needed to assess whether process steps are necessary to control those hazards (especially acute hazards such as nitrate). If it is determined that additional treatment is needed, the engineering team would need to evaluate the efficacy of coagulation and filtration processes (or other unit processes) to remove those contaminants and determine whether other treatment processes or blending are required to manage those inorganic contaminants.

6. Very few regulated organic chemical contaminants were found in the water of the two process trains at sufficient concentration to measure both influent and effluent concentrations. This made it difficult to model removal, as the contaminants were already removed to less than detection limits. Some theoretical removal was modelled using GAC breakthrough curves, whereas surrogates were used to demonstrate possible removal rates and mechanisms through RO membranes. Most measurable organic chemical removal was generally completed with one process step.

Several gaps were also identified throughout the modeling exercise that point to a need for future monitoring and data collection at IPR and DPR facilities:

Facilities with UV–AOP don’t typically collect information on UV dose via ongoing actinometry measurements or other means that can be used to back calculate achieved chemical removal (and disinfection). Such monitoring would be helpful in future characterization and modeling exercises.

The pathogen removal data across the sedimentation and filtration process is overly conservative in the current approach and could use other surrogates to validate and observe actual log removal over time during full-scale operation. However, better surrogates for pathogen removal by flocculation–sedimentation–filtration are needed.

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More sensitive measures for RO removal of pathogens would allow greater ability to model true removal rather than via a chemical surrogate that may not reflect particle removal mechanisms.

As a general observation, the Monte Carlo simulation provides the reader with a great deal of confidence that combined unit processes provide highly effective and reliable (based on years of full-scale data) barriers to microbial and chemical contaminants. It is also important to keep in mind the difference in excursion of water quality parameters from an acute health risk (e.g., pathogens) versus chronic health risks (e.g., SOCs, VOCs) and use that information in designing operational parameters, acceptable ranges of operation, critical limits, and alarms.

Reliability of Process Monitors

The project team explored the reliability of the monitoring systems of advanced water treatment processes using manufacturer data for possible CCP analyzers or monitors. From a DPR perspective, when there is a risk that a critical process may fail, proper monitoring must be in place to inform operators about the functioning of that process and when changes may need to be made to that asset. However, there is also a risk that a monitor, and subsequently the operator, will fail to notice the failure or improper operation of a specific control point. In that sense, the risk of “failure to notice failure” needs to be quantified to understand the likelihood of such an outcome. The approach used here is based on the reliability of engineering principles and computer simulation tools to provide a methodology for simulating the water treatment process and process monitoring, though the results are limited by the use of manufacturer-provided data instead of actual instrument performance data.

Two methods were used to examine reliability of the monitors of water treatment processes. First, the effectiveness and performance of the monitoring system for RO membrane-based treatment and ozone–biofiltration-based process trains were determined using computer simulations. Second, the risk priority number (RPN) methodology was applied to consider the product of three indices – occurrence, severity, and detection – for each individual monitor in the two distinct process trains. Reliability data and expert-based assessment were then used to determine the value of the three indices to identify the least reliable monitors and possible “pinch points” of concern in the treatment trains.

The simulation model results indicate that, in general, the TOC analyzers are the highest risk (in terms of failure to measure the true value) monitors in ozone–biofiltration-based treatment and RO membrane-based treatment process trains. These results are consistent with the input parameters used in the simulation model. For the RPN tool, highest RPNs should be considered as the riskiest components, and in this case, the severity of failure resulted in the disinfection processes (and their associated monitors) having the highest RPNs. It is notable that the TOC analyzers seem to have the highest risk of failure to provide an accurate measurement, but the disinfection processes and associated monitors had the greatest impact on potential risk. This is important, as it can help focus where calibration and verification need to be high-priority functions, and it may indicate where redundant monitors may provide additional security.

However, it should be noted that this approach has a number of limitations. The first is that development of the rating indices is a subjective process. The narrative definitions of occurrence, severity, and detection will differ based on the nature of the process under evaluation. Second, application of the rating scores has reliability challenges of its own. One subject matter expert might interpret the likelihood of occurrence of failure differently than

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another based on past experiences and technical knowledge about the context under evaluation. Thus, it is important that process monitors be carefully evaluated at each site to help determine where multiple types of monitors, redundant monitors, or increased frequency of calibration and maintenance procedures are warranted.

Response Procedures

In Chapter 6, information was developed around the concept of alerts and critical alarms to illustrate how alarms are addressed for the CCPs identified in the two process trains. Although examples are provided in the chapter for the reader to see how response procedures can be developed for specific unit processes, it is important to realize that each facility will need to develop or modify its own procedures that are specific to the equipment, monitors, and water quality goals. However, a few key considerations were identified that can be carried between systems and are included here based on professional judgment and experience with operating full-scale potable reuse facilities:

Control system events should not be identified or included in the alarm historian, though they should be documented in the overall system historian.

Alerts (warning) should be used to address the normal issues that are associated with maintenance or repair of equipment.

Alerts (warning) should not shut down or disable the operation of the equipment.

Critical alarms (failure) should be used to trigger immediate and automatic shutdown and disabling of equipment until corrective action is successful.

Critical alarms should include parameters for method of measurement (time delay, moving average), including sampling frequency (min, sec) and time basis (min, hr) if deemed appropriate by the regulatory authority.

Critical alarms (failure) should be structured to reflect an unusual or catastrophic occurrence such as an equipment failure or change in water quality monitoring parameters. Ideally, the occurrence can be captured as an alert (warning) before the critical condition is obtained.

Critical alarms (failure) may or may not result in the loss of water quality. Notification of regulatory authorities shall only occur when the water quality from the system is compromised.

Critical alarms (failure) should have restricted (supervisor) access at the human–machine interface level or be programmed at the programmable logic controller level to limit inadvertent changes to the alarm set points.

Alarms should be redundant at the unit and system (common) level, if possible.

A single analyzer located on the influent or effluent to a CCP should not be capable of triggering a critical system failure alone. Redundant instruments should be used around any and all CCPs, with either double or triple validation. The monitors may be redundant for the same parameter (e.g., two turbidimeters) or between parameters (e.g., one TOC analyzer, one conductivity analyzer).

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Chapter 1

Introduction and Background

1.1 Understanding of the Problem

As continued population growth, increasing urban density, and varying climate place heavy burdens on our nation’s water supplies, water agencies and policy makers are examining innovative ways to stretch water supplies, sustain population growth rates, and provide reliability and redundancy in their supply portfolio. As such, many water agencies around the United States and the world have been turning to the potable reuse of municipal wastewater, either directly or indirectly, to help meet growing demand.

However, it is important for any water treatment facility to have a high level of reliability to ensure the quality of delivered water meets or surpasses acceptable standards and the health risks and aesthetic impacts to the public are minimized. The importance is underlined in the case of potable reuse, where the real risks of higher contamination levels in feed water (e.g., during epidemics or after industrial accidents), along with perceived risks associated with public opinion of reuse, require a high level of operational surety. Consistent and assured levels of reliability can be met only with a holistic asset management framework including a robust design, robust and transparent operational management, a carefully managed maintenance strategy, and robust response procedures. The plant must be designed correctly, it must be operated well with realistic and practical demands on operations staff, and the assets and infrastructure must be maintained in a highly reliable condition.

Indirect potable reuse (IPR) is already practiced in many areas of the country, both as part of intentional IPR projects and as part of de facto environmental processes whereby one community’s effluent becomes the next community’s drinking water supply (Riceet al., 2013). In all IPR projects, be they intentional or unintentional, the reclaimed water spends time in an environmental buffer such as a river, lake, reservoir, or aquifer prior to being recovered, further treated, and then distributed to drinking water customers. Environmental buffers in IPR projects have had a number of important functions attributed to them, including additional treatment of waterborne pathogens and chemical contaminants, the provision of time to respond to potential water treatment incidents, and improvement of the public’s perception of potable water reuse (Schroeder et al., 2012; Trussell et al., 2012). In contrast to IPR, the supply of highly treated reclaimed water directly to a drinking water treatment plant or distribution system is known internationally as direct potable reuse (DPR). DPR differs from more established approaches to potable water recycling by the absence of a so-called “environmental buffer.” Many utilities and practitioners within the water community are finding an increasing number of potential benefits of DPR relative to IPR, including reduced energy requirements, construction costs, and operational costs and the ability to better control and maintain water within engineered buffer systems (Schroeder et al., 2012; Trussell et al., 2012; Trussell et al., 2013). DPR may even provide an opportunity to allow potable reuse in situations where a suitable environmental buffer is not available for IPR. However, potential obstacles or disadvantages for DPR, relative to IPR, are primarily related to public perception and acceptance rather than science or engineering.

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1.2 Control Point Framework

As a part of this overall framework, HACCP methodology has been adopted by a number of countries to manage microbiological and chemical contaminants in water treatment systems, including recycled water systems (Halliwell et al., 2015). An excellent review of the history of HACCP and its applications worldwide is provided in the WateReuse 09-03 report by Halliwell et al., (2015).

In brief, the HACCP system was originally developed as an engineering means of controlling microbial hazards in consumed food. HACCP is a logical, scientific process control system designed to identify, evaluate, and control hazards that are significant for food safety. The purpose of a HACCP system is to put in place process controls that will detect and correct deviations in quality processes at the earliest possible opportunity. HACCP focuses on monitoring and maintaining the barriers of treatment rather than on end-of-pipe sampling and testing. This provides the dual advantage of ensuring poor quality is prevented in the first place and allowing for a reduction in end-of-pipe monitoring and associated costs.

In its essence the HACCP process is categorized into seven principles that are used to assess risk and determine a well-defined path forward for managing those risks and operation of the facility. The principles, whether part of the true HACCP/ISO 22000 accredited system or one that is using the principles as a guide through DPR assessment and operation, can be used to assist the process of developing CCPs for potable reuse:

1. Conduct a hazard analysis.

2. Establish critical limits.

4. Establish a system to monitor control of a CCP.

5. Establish the corrective action to be taken when monitoring a CCP is not under control.

6. Establish procedures for verification to confirm that the HACCP system is working effectively.

7. Establish documentation concerning all procedures and records appropriate to these principles and their application.

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It is important to note that the HACCP system identifies CCPs as points in the treatment process that are specifically designed to protect against a human health threat. These should not be mistaken for critical operating point (COPs), which are control points focused on other important operational issues such as production capacity and asset management, but which are not directly related to a human health threat. The HACCP methodology provides a strict definition that allows the HACCP team to focus on risks that are relevant to human health and therefore identify the processes and response procedures necessary to protect public health. Halliwell et al. (2015) provided a dichotomous key of five questions to determine whether a process or point in a process train qualifies as a CCP. The original five questions were as follows:

1. Is there a hazard at this process step? (And what is it?)

2. Do control measures exist for the identified hazard?

3. Is the step specifically designed to eliminate or reduce the likely occurrence of the hazard to an acceptable level?

4. Could contamination occur at or increase to unacceptable levels?

5. Will a subsequent step or action eliminate or reduce the hazard to an acceptable level?

The complete diagram is show in Figure 1.1; however, it was modified slightly from Halliwell et al. (2015) via Question 3 to account for this project’s DPR focus in order to tailor the methodology more precisely for reuse applications. Question 3 was modified to provide a focus on whether the identified barrier would be counted on to provide either a specific, measurable microorganism log removal or a measurable reduction in another contaminant or contaminants that will assist in achieving final water quality targets. This assists in filtering out marginal removal capabilities or those that may not be accepted by regulators.

Original Question 3: Is the step specifically designed to eliminate or reduce the likely occurrence of the hazard to an acceptable level?

Modified Question 3: Is the step required to achieve a log removal of microorganisms, meet water quality targets, or both?

A classic example of applying this methodology to a complete process configuration from water resource recovery to drinking water production is the bar screens at the head of the wastewater treatment plant:

Is there a hazard at this step? Yes, microbial and chemical hazards exist with near certainty in raw sewage.

Do control measures exist for the identified hazard? No, this step in the process is not designed to disinfect or remove dissolved constituents; therefore it is not a CCP.

However, from a facility operations perspective, having the bar screens operating properly is critical to the overall facility operation and production; therefore, it would fall into the COP category. By questioning each step in the complete process train in this manner, operators can to focus on aspects of the facility that may need immediate attention for public health protection versus aspects of the facility that need attention from a plan production and asset maintenance perspective.

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Figure 1.1. HACCP system decision tree for defining critical control points in DPR facilities. Note: #3 modified from Halliwell et al. (2015).

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1.3 The Use of HACCP Framework at Water Recycling Facilities

The HACCP framework can be an integral part of planning and operating DPR facilities without being a burden from a regulatory or certification standpoint. Here, the focus is on developing a risk assessment team (or HACCP team) whose job it is to collaboratively determine the water quality goals; assess the inherent risk from the source water and production inputs (i.e., chemicals added during treatment); challenge the assumptions around selected treatment processes to determine if a given process train is likely to produce water quality that meets the goals; assess (test) the ability of the single and combined processes to meet the water quality goals; and determine the monitoring needs, operational plans, and response procedures required to produce water suitable for potable reuse. When applied properly, HACCP provides a framework for ensuring that the water being produced from a DPR facility is safe and ready for further drinking water treatment or direct blending into a distribution system rather than waiting for end-of-pipe testing to provide a moment-in-time snapshot of the quality of a given sample of water.

HACCP, or at a minimum the use of CCPs, has been applied at a number of water recycling projects in order to demonstrate the management of microbiological and chemical risk via multiple barrier processes. CCPs are points in the treatment process that are specifically designed to reduce, prevent, or eliminate a human health hazard and for which controls exist to ensure the proper performance of that process. It is notable that the ISO 22000 standard was adopted for the implementation of HACCP for the Western Corridor Recycled Water Scheme, a large-scale indirect water recycling scheme in Brisbane, Australia.

This scheme maintains certification to this standard and is regularly audited externally for compliance. Recently, WateReuse 09-03, “Utilization of HACCP Approach for Evaluating Integrity of Treatment Barriers for Reuse” (Halliwell et al.. 2015), investigated the suitability and applicability of HACCP for the U.S. regulatory context, whereas WateReuse 11-02 developed guidance on CCPs, surrogates, and water quality goals for potable reuse (Trussell et al., 2013), though COPs were included in the same listings at CCPs. In Australia a risk-based approach to water treatment was adopted whereby utilities must demonstrate to regulators that they have adequately considered and addressed the risks associated with compliance to drinking and recycled water guidelines. This provides flexibility in treatment options, which facilitates the adoption of alternative approaches such as HACCP.

In contrast, the more prescriptive approach of the U.S. regulatory context requires extensive end-of-pipe testing, negating many of the benefits of the HACCP approach. A key potential issue of HACCP implementation in this context is an additional burden (perceived or real) and cost for utilities that already have well-established operational systems and not wanting to impose another regulatory or auditing burden. However, the HACCP principles can be highly complementary to existing management plans and may be useful in evaluating current procedures or new facilities that have not yet been built or are not yet online.

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1.4 Project Overview and Approach

Given the complementary nature of HACCP principles toward existing management plans, this project focused on the practical elements of HACCP as a means of demonstrating the identification and validation of multiple barriers. However, the project team also leveraged the experience of full ISO 22000 implementation (but without making this an ISO 22000 accreditation effort) and other published work to better inform the design and operation considerations of DPR systems to ensure their reliability within the framework of a U.S., and specifically California, context.

Another concern and critical factor for the successful operation of any system is the reliability of the asset: Any HACCP or similar system is only as reliable as the equipment, instrumentation, and controls for that plant. In maintaining multiple barriers, it is important that the modes of failure of those barriers are well understood, the criticality to the operation is well defined, and maintenance to manage the assets is targeted correctly. Therefore, it is a necessity that individuals defining CCPs, monitoring strategies, and design standards have experience with full-scale operations and asset management.

With the combinations of risk assessment and operations support, this project and report will serve as an instrumental step in advancing the acceptance of DPR by demonstrating the robustness and reliability of multiple barriers, both individually and as a combined process, and identifying the CCPs and response process that will be necessary to ensure the safe and continuous operation of DPR systems. The remaining sections and chapters of this report outline the approach, findings, and key recommendations from the application of the HACCP methodology to DPR facilities.

1.4.1 Understanding Project Constraints and Working Definitions

At the outset, this project was tasked with applying the HACCP methodology to identify and assess the reliability of CCPs (as both processes and monitors) to manage acute and chronic health risks in two prescribed DPR process trains. The first (Figure 1.2) employs reverse osmosis (RO) membranes in a configuration of microfiltration (MF)–RO–ultraviolet light with advanced oxidation process (UV–AOP)–chlorine (Cl2,as free chlorine). The second was an alternative treatment train that does not employ RO membranes (Figure 1.3). This process train was predefined at the project outset as ozone (O3)–biological activated carbon (BAC)–granular activated carbon (GAC)–disinfection dose UV–Cl2 and will be referred to as the “ozone–BAC-based treatment” throughout this report. Note that the ozone–BAC-based treatment process was later modified to include a coagulation step, as shown in Figure ES.2, but not described in detail until Chapter 2. It should also be noted that any reference to UV is meant to imply disinfection dose, typically around 40 mJ/cm2, whereas any reference to UV–AOP is meant to imply a UV dose that is an order of magnitude higher (at least), near 400 mJ/cm2.

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Figure 1.2. Process flow diagram for RO membrane-based treatment option.

Figure 1.3. Process flow diagram for ozone–BAC-based treatment train with optional preozonation step.

It is also important to clearly define the term “direct potable reuse” within the framework of this project. Here, DPR is meant to include facilities that produce purified water that can either be further treated by a drinking water treatment plant (i.e., purified raw water production) or those that produce purified, finished water that is directly blended into a distribution system. In both cases there is no environmental buffer included, and therefore, the water is assumed to be directly connected to the supply from the water reclamation and resource recovery facility (i.e., wastewater treatment plant). However, it was beyond the scope of this project to differentiate the CCPs (and processes) that might be used in the event of direct connection to the distribution system (i.e., production of purified, finished water) from those that might be used in the production of a purified water that would be supplied to a drinking water treatment plant.

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Therefore, for this study it was assumed in all cases that the advanced purified water produced by either process train would need to meet drinking water quality standards not only at the end of pipe but also in the drinking water distribution system (i.e., production of purified water for direct blending to a distribution system) and no further treatment beyond the end of the process trains identified would be incorporated. This perhaps imposed an artificially high standard of treatment on this study, but the decision was made early on to apply that standard for this study with the full understanding that a site-specific HACCP approach would have to be applied when the type of DPR and drinking water treatment were defined for a given community.

Likewise, because of the enormous variability in types of processes used in wastewater treatment and the variable quality of water produced from those facilities, it was not possible to assess in a generic manner the types of controls, CCPs, and monitoring strategies that might be available with a well-defined interface between wastewater treatment and the advanced water recycling facility. An artificial box was drawn around the advanced water treatment facilities that assumed all water quality risks and all CCPs to manage those risks would necessarily be present within the DPR water purification process train (Figure 1.4). It is important to note that, for a HACCP analysis of an existing or planned facility, the entire system from sewer collection to drinking water distribution (including operational interfaces between agencies and entities) should be considered during the risk analysis and when identifying the CCPs; however, this may be difficult because of the various entities that may be involved.

Figure 1.4. General schematic of urban infrastructure with DPR. Note: For this project, the water reuse facility was considered “in a box,” but in reality all points from collection through each stage of treatment and on through distribution must be considered for adequate risk assessment and designation of CCPs.

Another important distinction is related to the use of HACCP. By definition, the HACCP approach focuses on any and all hazards to human health and does not discriminate between long-term or chronic risks (i.e., those from many chemicals) and acute risks (i.e., those from pathogens and chemicals such as nitrate). HACCP is also a process of hazard analysis and therefore implies an assessment of those hazards and the definition of the critical points within a process train that are designed to control those hazards. As such, this project is really the definition and demonstration of a framework that qualified teams of individuals can use to examine the risks for a given source water, determine what processes are needed to manage those risks, determine the appropriate monitoring and process control strategies, and develop the

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operations and response procedures required to manage those risks appropriately. Again, to reiterate the definition provided early in this chapter, CCPs are points in the treatment process that are specifically designed to reduce, prevent, or eliminate a human health hazard and for which controls exist to ensure the proper performance of that process.

From an operations and asset management standpoint, there are other points in the treatment train that are important to ensure the production capacity of the facility and the effective maintenance of equipment. These points are called COPs and are important in the overall operation and maintenance of the facility but do not by themselves directly influence water quality and public health. As such, COPs are points in the treatment process that are specifically designed to maintain the production capacity of the facility and protect working assets. An example of a COP in an RO membrane system would be the introduction of an antiscalant that helps prevent inorganic scaling on the membrane surface but does not provide a means to prevent or eliminate public health threats.

The following is an example to demonstrate the difference between CCPs and COPs: The RO process step is defined as a CCP for microorganism removal as well as other chemicals of concern (Figure 1.5). It operates as a CCP, with a critical monitor of electrical conductivity (EC). If the EC increases to greater than an alert limit (set at some margin less than what would be considered a breach), an alarm is raised on the plant supervisory control and data acquisition (SCADA) system, and corrective action is taken by the plant operations staff. If EC increases to greater than a critical limit (set close to or at what would be considered a breach), the control system will automatically shut the unit down, an alarm condition will be raised on SCADA, and operators will take corrective action.

Figure 1.5. Diagram of a CCP and associated monitor for an RO system.

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For the RO unit, there are a number of other very important monitoring parameters, control actions, and operating responses that are critical to the success of that process but are not in direct relation to public health protection. For example, antiscalant dosing and pH correction are important to manage scaling, and RO recovery is also important in the management of scaling, and other flow settings for the RO must be maintained for correct operation. In these cases, control response and operational responses can be articulated as COPs (Figure 1.6), using the same format for operations for consistency and yet different from CCPs.

Figure 1.6. Diagram of COPs supporting the RO CCP and plant production.

1.4.2 Project Objectives Given the constraints and working definitions described in the previous section, the primary approach used for this study included the following work objectives: 5. Conduct a hazard assessment to identify health risks, identify water quality objectives, and

identify CCPs for both the RO membrane-based treatment train of MF–RO–UV–H2O2–Cl2 and the ozone–BAC treatment train of O3–BAC–GAC–UV–Cl2. Because the objective of this project was to demonstrate the HACCP approach rather than develop a single site case study, the hazard analysis and risk assessment were based on a thorough literature review of previous research on source water quality risks (chemical and microbial) as well as full-scale operating plant experience and operating data.

6. Collect chemical and microbial data and conduct challenge studies using full-scale operating facilities. This study incorporated the extensive amount of existing full-scale and pilot-scale data to develop the range of contaminant concentrations under normal and failure modes of operation.

7. Use Monte Carlo analysis to develop a probabilistic risk assessment to characterize, quantify and support communication about the risk of failing to meet treated water quality targets. This risk assessment also included an evaluation of the likelihood of the failure of monitors used at each CCP.

8. Develop recommendations regarding critical limits and critical alarms that can be used in future studies to develop the appropriate response procedures for critical processes and alarms.

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Details of specific procedures, analyses, computational techniques, and related technical aspects of this project are provided within the chapters where those techniques are associated with specific project data or outputs. Therefore, this report does not have a “Methods” chapter as such. However, the general flow of the approach used in this study is provided on the following pages to serve as a road map for individuals wishing to incorporate the HACCP methodology in assessing their own DPR (or even IPR) projects.

1.4.3 Project Tasks and Approach Task 1: Conduct Hazard Assessment for Key Unit Operations and Determine CCPs In keeping with HACCP principles, a multidisciplinary HACCP team was assembled to assist in the completion of an initial risk assessment and CCP workshop. This team was composed of all the members of the project team, which included experience of IPR plant management and design as well as experts in the field of recycled water quality research for public health, and Marlo Berg, a state regulator with the Texas Commission on Environmental Quality. Review of Source Water Hazards

The first principle of HACCP is to conduct a hazard assessment. In the case of potable reuse, this requires the identification of water quality health hazards presented in the source water as well as other chemicals that may be generated as a result of the treatment process itself (e.g., disinfection byproducts [DBPs]). In cases where HACCP is being conducted at a specific installation, a comprehensive risk assessment is usually conducted for that specific feed water and associated with an intensive monitoring campaign for chemical or biological constituents of concern along with a review of industrial pretreatment programs and potential chemical inventories at those industrial facilities. In the case of this study, generic process trains were considered without any specific anchor to a given wastewater source, thereby making a broad-reaching analysis more challenging. More information on the sources of data is provided in Chapter 3. Review of Hazardous Events and Modes of Failure

In addition to the hazards presented in source water, the team conducted a study of literature and operational data from full-scale facilities to determine likely hazardous events and modes of operational failure at the treatment barriers. A review of operating history of participating water recycling facilities was also included, as was a detailed review of operational maintenance records, which were used to identify particular points of failure for each type of process or combination of processes. The data review included a focus on both planned maintenance, in order to identify equipment or instrumentation that may have a high maintenance requirement, and unplanned maintenance, where equipment may not have a high reliability. Risk Assessment and CCP Selection Workshop

After gathering data from the literature and operational facilities, the HACCP team convened at a workshop to complete a formal hazard risk assessment and determine CCPs for both the RO membrane and ozone–BAC process trains. A key element in the development of a HACCP-based process is to construct a process flow diagram and then verify the process flow diagram using a logical decision tree (Figure 1.1). For a typical (site-specific) HACCP study, this would involve developing a concept diagram, which is then verified by either a plant walk-through or a review of detailed plant design. In the case of this project, the process flow diagrams were developed with detailed knowledge gathered from

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full-scale plants where either the entire process train or parts of the process for RO membrane-based or ozone–BAC-based facilities are used. At the workshop the HACCP team conducted a hazard identification by working through each process step of both the process trains. Hazards were categorized as microbial, chemical, and physical, with some subcategorization of chemicals into relevant groups based on families of chemicals or analytical groups. The risk, treatment objective, and water quality goal associated with each identified hazard were assessed using a scoring approach that rates both the likelihood and consequence of the hazardous event relative to defined water quality objectives or treatment objectives (e.g., those described in California Department of Public Health [CDPH], 2013; EPHC, NHMRC, and NRMMC, 2008; Trussell et al., 2013). As a part of the hazard analysis, the HACCP team also identified control measures that would eliminate or reduce the hazard to an acceptable level. Following the risk assessment, the HACCP team developed a set of CCPs, steps in the process where a control measure is critical to the minimization of a health risk. For recycled water systems, these are most often process steps (e.g., MF, RO, UV–AOP) but can also be operations or procedures (e.g., managing quality of chemical inputs). For each CCP a measurable parameter was identified for process monitoring (e.g., surrogates such as conductivity, TOC, transmembrane pressure, turbidity, and UV transmittance [UVT]). Typically, for a recycled water treatment system, critical limits (i.e., action limits and shutdown limits such as those defined by Singapore PUB) are also identified for the monitoring of a CCP. Given the general, broadly applicable scope of this project, specific critical limits were not developed, but instead ranges from literature and existing facilities were used. Task 2: Conduct Bench/Pilot-Level Challenge Test Studies The purpose of Task 2 was to fill data gaps identified in Task 1, provide a verification step in the selection of CCPs, and provide the data necessary to conduct a Monte Carlo risk analysis in Task 3. This task was largely accomplished by using existing full-scale and pilot-scale data. The most effective demonstration of the effectiveness of CCPs is at full scale; therefore, limited full-scale testing at the Scottsdale Water Campus was used to determine the impact of O-ring failure (e.g., rolled, missing, or broken O-rings) and cut membrane fibers on the sensitivity of the pressure decay test and turbidity as a CCP for membrane filtration. Full-scale testing of RO membrane processes was also conducted to determine the sensitivity of O-ring and interconnector failures. An additional benefit of the test at Scottsdale was the ability to compare the different impacts of O-ring failure from standard 8 inch diameter membrane systems compared to newer generation 16 inch diameter membranes.

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Task 3: Conduct Monte Carlo Risk Analysis and Develop Standard Design Approaches, Operational Procedures, and Response Strategies

Monte Carlo Risk Analysis

A statistical risk analysis was conducted to characterize the risk of failing to meet satisfactory final water quality objectives as determined in the HACCP team workshop. Because all engineered processes operate with a degree of variability and all process control measurements involve a degree of uncertainty, probabilistic techniques, including Monte Carlo simulations, are well suited for this analysis (Khan, 2010).

In order to develop an appropriate model for Monte Carlo simulation, both identified treatment trains required extensive data sets from full-scale facilities, either operating combined process trains or individual process components that could be assessed for long-term operation and reliability. Although it would be ideal to have years of data for every parameter included in the risk assessment and at each process step from full-scale facilities operating the exact RO membrane and ozone–BAC treatment trains, such data were not fully available (though many contaminants had been studied across multiple processes at several full-scale facilities). Therefore, facilities were identified that had extensive data from unit processes or, where possible, combinations of unit processes to feed the Monte Carlo analysis. More information on the assumptions and tests used can be found in Chapter 4. Operational Procedures

It is critical to develop a robust response plan for when something goes wrong in water treatment. Having the right processes in place to control the treatment process is essential, but equally important is the ability to transparently and effectively deal with incidents of failure in a timely and effective manner in order to safeguard public health and maintain public and regulatory confidence. In support of this, standard operating responses were developed in an incident–response framework (i.e., flow diagrams) that incorporates immediate responses to correct process or equipment failures, management of out-of-specification water, monitoring and investigation of the incident, and, an important factor, pathways of communication. The CCP responses follow a logical progression of “if–then” scenarios designed to ensure the safety and reliability of the treated water. Finally, using the information collected in all previous tasks, a set of general design guidelines and considerations for each CCP was prepared as a part of the outcome of this study. These guidelines focus specifically on outcomes to enhance the robustness, reliability, and operability of each CCP from the standpoint of protection of public health. The guidelines focus on key process barriers that are identified as CCPs, with specific details of monitoring equipment and instrumentation. In addition, maintenance and calibration recommendations are provided where applicable.

1.4.4 Organization of This Report

The remainder of this report is organized into chapters that are meant to be stand-alone sections that will provide the reader with information about various aspects of this study without requiring a cover-to-cover read to digest the information. Chapter 2 provides a description of the HACCP methodology for risk assessment and carries the reader through the generic risk assessment that was conducted by the HACCP team for this project. Chapters 3 and 4 inform the reader about the water quality data sources and statistical analysis (Monte Carlo) that was used to evaluate contaminant removal across multiple barriers of the two DPR process trains identified for this study. Chapter 5 provides details on the types of analyzers used in DPR systems to support each of the CCPs and uses an Arena-based reliability model and the calculation of risk priority numbers (RPN) to identify the potential for “failure to observe process

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failure.” Chapter 6 provides the results of the full-scale challenge/failure testing at Scottsdale Water Campus. Finally, Chapter 7 provides a primer on water quality alarms, alarm management, and recommended formats for response procedures and design considerations for each of the CCPs.

1.5 References

California Department of Public Health (CDPH). Draft Title 22 California Code of Regulations, Division 4. Environmental Health, Chapter 3. Recycling Criteria, 2013. http://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/RecycledWater.shtml (Last Accessed 5/31/2016)

Environment Protection and Heritage Council (EPHC), National Health and Medical Research Council (NHMRC), and Natural Resource Management Ministerial Council (NRMMC). Australian Guidelines for Water Recycling: Managing Health and Environmental Risks (Phase 2): Augmentation of Drinking Water Supplies. Biotext Pty Ltd,, Canberra, 2008, p 174. http://www.environment.gov.au/system/files/resources/9e4c2a10-fcee-48ab-a655-c4c045a615d0/files/water-recycling-guidelines-augmentation-drinking-22.pdf (Last Accessed 5/31/2016)

Halliwell, D.; Burris, D.; Deere, D.; Leslie, G.; Rose, J.; Blackbeard, J. Utilization of HACCP Approach for Evaluating Integrity of Treatment Barriers for Reuse. WateReuse Research Foundation, Alexandria, VA, 2015.

ISO (2005). ISO 22000: Food Safety Management. International Standards Organization, Geneva, Switzerland.

Khan, S. J. Chapter 6: Safe Management of Chemical Contaminants for Planned Potable Water Recycling. In Issues in Environmental Science & Technology; Hester, R. E.; Harrison, R. M.; Cambridge, UK: Royal Society of Chemistry RSC Publishing, 2010; Vol 31; pp 114–137.

Rice, J.; Wutich, A.; Westerhoff, P. Assessment of De Facto Wastewater Reuse across the U.S.: Trends between 1980 and 2008. Environ. Sci. Technol. 2013, 47 (19), 11099–11105.

Schroeder, E.; Tchobanoglous, G.; Levernz, H. L.; Asano, T. Direct Potable Reuse: Benefits for Public Water Supplies, Agriculture, the Environment, and Energy Conservation. National Water Research Institute (NWRI), Fountain Valley, CA, 2012, p 20.

Trussell, R. R.; Anderson, H. A.; Archuleta, E. G.; Crook, J.; Drewes, J. E.; Fort, D. D.; Haas, C. N.; Haddad, B. M.; Huggett, D. B.; Jiang, S.; Sedlak, D. L.; Snyder, S. A.; Whittaker, M. H.; Whittington, D. Water Reuse: Potential for Expanding the Nation's Water Supply through Reuse of Municipal Wastewater. Committee on the Assessment of Water Reuse as an Approach to Meeting Future Water Supply Needs, National Research Council. National Academies Press, 2012. http://www.nap.edu/openbook.php?record_id=13303 (Last Accessed 5/31/2016)

Trussell, R. R., Salveson, A.; Snyder, S. A.; Trussell, R. S.; Gerrity, D.; Pecson, B. M. Potable Reuse: State of the Science Report and Equivalency Criteria for Treatment Trains. WateReuse Research Foundation, Alexandria, VA, 2013, p 276.

Water Environment & Reuse Foundation 15

Chapter 2

Hazard Assessment and CCP Selection

2.1 Introduction

The review of source water hazards is an important first step in the development of a water quality risk assessment, as it allows the HACCP team to:

identify risks to be managed, from collection (including industrial pretreatment) to distribution

establish the level of removal required over the entire treatment train to meet water quality objectives

identify which barrier (process) will be required for the removal of specific contaminants or categories of contaminants

A typical risk assessment process should include a significant site-specific sampling campaign over an extended period of time to understand the likely concentration of contaminants and any seasonal or temporal (even diurnal) variation in the source water. For example, if it is known that a given wastewater collection system (i.e., “sewershed”) has industry with specific chemical inputs (synthetic organic contaminants [SOCs], volatile organic contaminants [VOCs], metals), then those risks would need to be characterized on the basis of normal operation and then again during failures of pretreatment programs or spill events. Likewise, the risks of contamination events from the collection system may need to be managed via additional CCPs that can easily be integrated into the HACCP framework for DPR systems. However, given the more general nature of this project and the absence of a specific treatment facility or water source to review, the approach was to review and incorporate U.S. peer-reviewed literature and published data to generate a conservative “model” water source containing the vast majority of known water quality hazards. In order not to lead to overly conservative outcomes, when references were describing hazards at concentrations that would be considered outliers relative to the majority of other situations, the specific context was reviewed in detail to assess the applicability or relevance of the data set. For example, if a watershed was exposed to a very specific industrial contamination leading to unusually high concentration of chemicals of concern, the data were excluded from the overall set as it would be reasonable to expect that a specific source management strategy would be required at this particular site before DPR could be considered. In addition to reviewing the type and concentration of typical source water hazards, the team also collected literature and industry data on the effectiveness of treatment processes to remove these hazards, focusing on the processes that underpin the two treatment trains that are the subject of this study.

16 Water Environment & Reuse Foundation

The team relied on the following sources of information to develop the list and magnitude of source water hazards as well as typical treatment effectiveness:

Guidance from the draft California Reuse Regulations (CDPH, 2013), the Australian Guidelines for Water Recycling (EPHC, NHMRC, and NRMMC, 2008), and the approach incorporated in the recent state of the science report on potable reuse (Trussell et al., 2013) was used to demonstrate the process of risk assessment and selection or prioritization of contaminants of concern or their appropriate indicators and surrogates.

Because of the wealth of existing knowledge from full-scale facilities currently practicing potable reuse, this assessment also included investigating data from a range of operating recycled water facilities in the United States, Australia, and Namibia to identify key microbial and chemical risks and the failures that may lead to their presence in finished water. Operational sites that provided data to support the efforts in this study include:

o Orange County Water District Groundwater Replenishment Scheme, California, USA (MF–RO–UV–AOP)

o West Basin Municipal District Edward C. Little Recycling Facility, California, USA (MF–RO–UV–AOP)

o Scottsdale Water Campus, Arizona, USA (MF–RO–UV–AOP) o Anonymous Water Recycling Scheme, Australia (name withheld), Plants 1A (MF–RO–UV–

AOP) and 1B (MF–RO–Cl2) o Anonymous Water Recycling Plant 2, Australia (ozone–BAC) o Goreangab Water Recycling Plant, Windhoek, Namibia (ozone–dissolved air flotation [DAF]–

BAC–GAC–ultrafiltration [UF]–Cl2) o Anonymous U.S. drinking water treatment facility with full-scale UV disinfection data

In addition to the full-scale operating data, the team reviewed current literature and emerging trends to identify any additional parameters of interest that may not have been considered in the example plants. The literature review of source water hazards (Appendix A) includes 54 separate cited references for water quality sources.

There was also a significant amount of water quality data available for the water recycling treatment trains considered within this project, including from the following WateReuse Research Foundation Projects, of which the team members have been an integral part or on the PAC: 08-05, 08-08, 10-11, 11-01, 11-02, and 13-10. Project 13-10, an ongoing study with a Southeastern U.S. city, included 12 months of operational data with an ozone–BAC potable water reuse pilot system including data from over 200 regulated and unregulated contaminants monitored throughout the study. This project provided a comprehensive data set to support the Monte Carlo analysis.

Another aspect of the literature review was defining exactly what log removal credit would be allowable according to United States Environmental Protection Agency (U.S. EPA) drinking water regulations or California recycled water and drinking water regulations. This has been a source of confusion on past projects in that it has not always been clear where stated log removal values (LRVs) were obtained. In this case, the CDPH and various U.S. EPA documents were referenced to determine the maximum creditable log removal (not necessarily the attainable log removal).

Water Environment & Reuse Foundation 17

2.2 Source Water Quality Data – Hazards As would be expected based on the nature of the sewershed or collection system, the conservative model source water (established through the review process described previously) contained a typical range of hazards, which were grouped under the categories described in Table 2.1.

The assessment of the inherent risk associated with these source water hazards is discussed in subsequent sections. Typically, as a conservative strategy, the maximum recorded concentration of each hazard was compared to the lower of the U.S. EPA or CDPH drinking water guidelines to assess the potential inherent risk (if no treatment was applied before use).

Table 2.1. Categories of Typical Hazards and Sources from Sewer Collection Systems Category Typical Hazards Typical Source

Biologicals includes protozoa (e.g., Cryptosporidium and Giardia), bacteria (e.g., Escherichia coli) and viruses (enteric)

fecal matter found in domestic waste as well as animal contamination of storage reservoirs

Inorganics and metals

nitrate, nitrite, and perchlorate arsenic, lead, manganese, nickel,

antimony, cadmium, chromium

trade waste, domestic waste, and illegal discharge to sewer

Radionuclides uranium, radium, strontium, tritium, iodine

environmental input and domestic waste (hospital discharge patients)

Volatile organic contaminants

industrial compounds such as carbon tetrachloride, dichlorobenzene, dichloromethane, MTBE, tetrachloroethylene, vinyl chloride

trade waste, domestic waste, and illegal discharge to sewer

catchment-specific as linked to specific industries

Synthetic organic contaminants

pesticides1 such as bentazon, carbofuran1, diquat, heptachlor1, molinate, and simazine

runoff/infiltration and illegal discharge

Notes: 1=The reader should check the current regulatory status of any pesticides as some (including carbofuran and heptachlor) may be currently banned or in the process of being phased out of use. MTBE=methyl-tert-butyl ether.

18 Water Environment & Reuse Foundation

2.3 Review of Credited Log Removal Values

According to Title 22, CCR, Division 4, Chapter 3, Article 1 in DPH-14-003E, GW Replenishment Using RW, May 30, 2014, the law states that

for each pathogen (i.e., virus, Giardia cyst, or Cryptosporidium oocyst), a separate treatment process may be credited with no more than 6-log reduction, with at least three processes each being credited with no less than 1.0-log reduction.

In addition, a report prepared by a certified engineer may be used to propose a given log reduction credit for a given process if the report provides “evidence of the treatment process’s ability to reliably and consistently achieve the log reduction” and a “microbial, chemical, or physical surrogate parameter(s) that verifies the performance of each treatment process’s ability to achieve its credited log reduction.” However, as shown in Tables 2.2, 2.3, and 2.4, summarizing the currently accepted log removal credits for viruses, Cryptosporidium, and Giardia, respectively, only known credited log removal credits were used to populate the various treatment options that are later provided in the risk assessment.

Table 2.2. Virus Creditable Log Removal by Treatment Process Treatment California Maximum

Creditable U.S. EPA Maximum Creditable*

Free chlorine 4.0 log(1)

Ozone 4.0 log(1)

UV disinfection 4.0 log (2) 3.0 log(3)

UV–AOP 6 log(7) Membrane filtration Ultrafiltration

0.5 log(4) [4 log](4)

as determined by the state(4)

Reverse osmosis 2.0 log(5, 7) as determined by the state(4) Coagulation/filtration conventional

sedimentation–filtration direct filtration

2.0 log(1)

1.0 log(1)

Notes: Numbered footnotes follow Table 2.4. *=CT calculations depend on temperature and pH; AOP=advanced oxidation process; UV=ultraviolet.

Water Environment & Reuse Foundation 19

Table 2.3. Cryptosporidium Creditable Log Removal by Treatment Process Treatment California Maximum

Creditable U.S. EPA Maximum Creditable*

Free chlorine 0 log(6)

Ozone 3 log(2)

log credit variable(6)

UV disinfection 4 log log credit variable (6)

UV–AOP 6 log(7)

MF–UF (membrane filtration)

4.0 log(4) as determined by the state(4)

Reverse osmosis 2.0 log(5, 7) as determined by the state(4)

Coagulation–filtration conventional

sedimentation–filtration direct filtration

2.5 log (up to 3.5 log)(4)

3 log (up to 4 log)(4)

Lime softening 0.5 log(2)

Notes: Numbered footnotes follow Table 2.4. *=CT calculations depend on temperature and pH; AOP=advanced oxidation process; MF=microfiltration; UF=ultrafiltration; UV=ultraviolet.

Table 2.4. Giardia Creditable Log Removal by Treatment Process Treatment California Maximum

Creditable U.S. EPA Maximum Creditable*

Free chlorine 3 log(1)

Ozone 3 log(1)

UV disinfection 4 log(6)

UV–AOP 6 log(7) MF–UF (membrane filtration)

4.0 log(4) as determined by the state(4)

Reverse osmosis 2.0 log(5) as determined by the state(4) Coagulation–filtration conventional

sedimentation–filtration direct filtration

2.5 log(1)

2.0 log(1)

Notes: *=CT calculations depend on temperature and pH; AOP=advanced oxidation process; MF=microfiltration; UF=ultrafiltration; UV=ultraviolet; 1=USEPA (1999). Alternative Disinfectants and Oxidants Guidance Manual, US EPA Office of Water. EPA/815/R-99/014. 2=USEPA (2007). Simultaneous Compliance Guidance Manual for the Long Term 2 and Stage 2DBP Rules, USEPA Office of Water. EPA 815-R-07-017 3=USEPA (2004). Comprehensive Surface Water Treatment Rules Quick Reference Guide: Systems Using Conventional or Direct Filtration, USEPA Office of Water. EPA 816-F-04-003 4=USEPA (2005). Membrane Filtration Guidance Manual, USEPA Office of Water. EPA 815-R-06-009 5=P.36 “Desalination of Seawater: Manual of Water Supply Practices”. AWWA. Copyright 2011 6=USEPA (2007). Determining Virus and Giardia Inactivation with Chlorine, USEPA Region 8. EPA Region 8 PowerPoint (https://www.epa.gov/dwreginfo/surface-water-treatment-rules), Accessed 6/27/16 7=CA Title 22, CCR, Division 4, Chapter 3, Article 1 in document “DPH-14-003E, GW Replenishment Using RW, May 30, 2014”

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2.4 Risk Assessment and HACCP Workshop

Consistent with the HACCP approach, a risk assessment team was established at the initial CCP workshop, which included a range of water quality, public health, and advanced water treatment experts. The team undertook a semi-quantitative water quality risk assessment process for each of the two proposed treatment trains, applying a systematic methodology to: Identify the hazards and hazardous events that may affect the quality of the final treated water.

Assess the risks posed by the relevant hazards and hazardous events.

Describe how the risks posed by the relevant hazards and hazardous events are to be managed and which control measures need to be implemented.

2.4.1 Source Water Characterization and Limitations

The risk assessment process relies on two aspects: the characterization of the inherent risk in the source water to be treated and the expected performance of the treatment train. The latter can usually be predicted at a design stage by combining existing performance data and specific design parameters. For example, the typical removal of human viruses by UV treatment can be estimated conservatively using existing guidelines so long as the UV reactor is designed and operated according to an agreed set of principles. A treatment train can therefore be assessed for various risks without the actual infrastructure being built.

The source water component, however, is very specific to the actual project being assessed and typically considered on a case-by-case basis, as each catchment may be subjected to a different range of chemical and microbiological inputs. The actual water to be treated needs to be thoroughly characterized to establish the range of contaminant concentrations that might be expected.

In the absence of a specific site or project to assess, as discussed previously, the team established a model source water composition based on the review of existing water quality data from secondary effluent in both California facilities and also incorporating other participating utilities. This provided a conservative water quality envelope that could be subjected to the semi-quantitative risk assessment.

2.4.2 Risk Assessment Methodology

The risk assessment process involved two separate components. The first component is described in Figure 2.1 and consists of identifying source water hazards and assessing the suitability of the treatment trains to reduce any inherent risk to an acceptable level. To achieve this, the following steps were implemented: List and categorize all potential health hazards in the source water.

Assess the inherent public health impact associated with each of these hazards without any form of treatment (i.e., if the water was consumed directly without treatment) by assessing the potential consequence and the likelihood of this consequence occurring.

Review the treatment barriers and the predicted residual risk after treatment if all barriers operate within their design parameters.

At this stage the design of the treatment train should be sufficient to reasonably ensure that no hazards present an unacceptable risk after treatment when all barriers are operating as per design. If this is not the

Water Environment & Reuse Foundation 21

case, then additional barriers, modifications to existing treatment processes, or source control mechanisms may be required, followed by a review of the risk assessment.

Figure 2.1. Overview of first component of risk assessment.

22 Water Environment & Reuse Foundation

The second component of the risk assessment is summarized in Figure 2.2 and consisted of evaluating the impact associated with hazardous events (such as a failure of a specific treatment step) and how such risk would have to be mitigated by implementing critical and other quality control measures. To achieve this, the following steps were implemented: Systematically list hazardous events that could occur on a treatment facility based on the treatment

trains proposed.

Assess the public health impact associated with these hazardous events by identifying the hazards such events introduce and assessing their consequences and likelihood.

Establish additional control (mitigation) measures for hazardous events leading to a significant, unacceptable risk. These control measures form the basis of CCP identification.

Figure 2.2. Overview of second component of risk assessment.

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2.4.3 Risk Descriptors

The team agreed upon a set of standard definitions to use for the likelihood and consequence descriptors as described in Tables 2.5 and 2.6.

Table 2.5. Likelihood Descriptors Likelihood Description

Almost certain Is expected to occur with a probability of multiple occurrences within a year.

Likely Will probably occur within a 1 to 5 year period.

Possible Might occur or should be expected to occur within a 5 to 10 year period.

Unlikely Could occur within 20 years or in unusual circumstances.

Rare May occur only in exceptional circumstances. May occur once in 100 years.

Table 2.6. Consequence Descriptors

Consequence Description Detailed Example

Catastrophic major impact for a large population

Widespread acute health impact expected, resulting in hospitalization, decreased life expectancy, or both.

Major major impact for a small population

Potential acute health impact affecting a limited number of the community.

Moderate minor impact for a large population

Repeated breach of a chronic health parameter, long-term or lifetime exposure required, or potential widespread aesthetic impact.

Minor minor impact for small population

Elevated levels of a chronic health parameter, no health impact expected, or potential local aesthetic impact.

Insignificant insignificant impact or not detectable

No expected health impacts or an isolated exceedance of an aesthetic parameter.

24 Water Environment & Reuse Foundation

A risk matrix was established to systematically categorize the impact and define the risk levels that would be considered significant and unacceptable (Table 2.7). At this preliminary stage of the process, a level of low to moderate would be considered acceptable. Any high or very high risk would require specific treatment or additional mitigation. Letter codes (A1:E5) are provided to index the risk matrix outcomes (low, moderate, high, and very high) to specific combinations of consequence and likelihood. For example, “high (A5)” would be the result of a rare occurrence with catastrophic outcomes whereas “high (E3)” would be an almost certain occurrence with moderate consequences. The letter coding makes it easier to review the full risk register and determine what combination of likelihood and consequence lead to a given risk notation.

Table 2.7. Risk Matrix

Likelihood Consequence Insignificant Minor Moderate Major Catastrophic

Almost Certain

low (E1) moderate (E2) high(E3) very high (E4) very high (E5)

Likely low (D1) moderate (D2) high (D3) very high (D4)

very high (D5)

Possible low (C1) moderate (C2) high (C3) very high (C4) very high (C5)

Unlikely low (B1) low (B2) moderate (B3) high (B4) very high

(B5) Rare low (A1) low (A2) low (A3) high (A4) high (A5)

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Finally, the team considered the level of certainty associated with the assessment process, reflecting that the amount of scientific evidence and data available can vary depending on the type of contaminants or treatment process. The ratings are described in Table 2.8, though in reality these are site specific and should be updated for each potential DPR location. When applied to a specific project location, data uncertainty factors can be used to define where additional monitoring may be required to assess the range of source water contamination. For example, data flagged as “uncertain” for contaminants that have a high to very high risk would need to be part of an extended monitoring campaign, whereas data flagged as “uncertain” but with a low consequence may not need to be investigated further. As monitoring data are obtained, the risk register (Appendix B) would then be updated accordingly in terms of likelihood, consequence, and confidence in the data. Likewise, as new contaminants are identified, they can be added to the risk register and sequentially updated with monitoring and risk assessment information.

Table 2.8. Uncertainty Factors Description Uncertainty Description

Certain There are 5 years of continuous monitoring data, trended and assessed with at least daily monitoring, or

The processes involved are thoroughly understood.

Confident There are 5 years of continuous monitoring data, collated and assessed with at least weekly monitoring or for the duration of seasonal events, or

There is a good understanding of the processes involved.

Reliable At least a year of continuous monitoring data are available and have been assessed, or

There is a reasonable understanding of the processes involved.

Estimate There are limited monitoring data available, or There is a limited understanding of the processes involved.

Uncertain There are limited or no monitoring data available, or The processes are not well understood.

2.4.4 Inherent Risk and Initial Barrier Assessment

The list of hazards in source water was established based on U.S. EPA and California drinking water quality guidelines or targets, which cover a wide range of biological contaminants, inorganics and metals, radionuclides, VOCs, SOCs, DBPs, and disinfectants.

The inherent risk was assessed by assuming that treated secondary effluent was consumed directly without any further treatment. Although this is an unrealistic scenario, it must be considered to define the maximum level of risk to be treated. Some preliminary observations are listed below:

As expected, all biological contaminants were assessed as presenting a very high risk based on their prevalence, concentration, and public health impact. The log removal requirements for these

26 Water Environment & Reuse Foundation

contaminants are well defined (12 log virus, 10 log Giardia, and 10 log Cryptosporidium), and both DPR treatment trains will need to demonstrate that these requirements can be met at all times1.

Depending on the nature of the catchment, in particular the impact of heavy industries, it was found that heavy metals and some inorganics could present a high risk in source water.

Similarly for VOCs, a literature review has shown that industrial catchments could present some significant levels of contaminants such as carbon tetrachloride, dichloromethane, methyl-tert-butyl ether (MTBE), and trichloroethylene). Based on drinking water guidelines, the risk associated with these compounds would be considered high.

The risk associated with SOCs will be very dependent on the impact of runoff from agricultural land on the quality of the catchment and, therefore, the source water to be treated. Initial review showed that the key risk associated with SOCs relates to the presence of pesticides in source water and could be high to very high.

DBPs are expected to present a significant risk because of the presence of precursors in secondary effluent, though the actual risk would be strongly related to specific process parameters. A conservative approach is proposed when assessing the risk associated with DBPs.

On the basis of the literature review and industry knowledge, the presumed or expected ability of each treatment barrier to remove a particular hazard or class of hazards was assessed on a semi-quantitative/qualitative basis and expressed using the following simple color-coded/shaded graphic system:

○ Poor=no significant expected removal (<20%)

◑ Fair=up to 60% removal

◕ Good=up to 90% removal

● Excellent=greater than 90% removal

No data available

The overall ability of the train to remove a particular hazard was expressed using the same system, and this was applied to all hazards for which the inherent risk was assessed conservatively as high or very high. Figure 2.3 provides an extract of the risk register showing how this was applied in the case of the RO membrane-based train. The complete risk register for both treatment trains is attached in Appendix B.

1 In an actual facility, the log removal targets may be met not only by the DPR facility but also as a combination of

the upstream wastewater treatment processes and any downstream drinking water treatment processes. For the purposes of this study, the project team artificially set boundary conditions to exclude consideration of upstream wastewater treatment configurations and downstream drinking water treatment configurations because of the high degree of variability in process selection, design, and control from facility to facility. Therefore, actual LRVs at the DPR facility may not need to be as high as “12-10-10.”

Water Environment & Reuse Foundation 27

Figure 2.3. Extract of the inherent and residual risk assessment.

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Cryptosporidium 0Acute

Health

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Contamination of storage reservoirsCatastrophic

Almost

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(E5)

Giardia lamblia 0Acute

Health

Domestic waste - human and animal faecal matter

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Almost

Certain

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Barium 2 0.006 mg/L 0.003Chronic

HealthTrade waste, Domestic waste, Illegal discharge Minor Possible

Moderate

(C2)

Beryllium 0.004 0.005 mg/L 1.25Chronic

HealthTrade waste, Domestic waste, Illegal discharge Moderate Possible High (C3)

Nitrate (as N) 10 32 mg/L 3.2Acute

HealthTrade waste, Domestic waste, Illegal discharge Major Possible

Very High

(C4)

Inherent Risk(drinking feedwater directly at 2L

per day)

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Cryptosporidium ◕ ● ● ○ ● 10 log UF, RO, UV, Chlorine Insignificant Rare Low (A1)

Giardia lamblia ◕ ● ● ● ● 10 log UF, RO, UV, Chlorine Insignificant Rare Low (A1)

Barium ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Beryllium ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Nitrate (as N) ○ ◕ ○ ○ ◕ N/A RO Minor Unlikely Low (B2)

Barrier Assessment(drinking product water assuming all barriers worked as designed)

Treatment Effectiveness

28 Water Environment & Reuse Foundation

2.4.5 Hazardous Events and Control Measures

The team considered the typical hazardous events that could affect either of the two treatment trains and assessed their likelihood as well as the hazards such events would introduce. It should be noted that the hazardous events risk assessment would be quite specific to the particular design of the treatment plant, the catchment as well as the downstream distribution system; thus, the analysis provided in this chapter is generic and would need to be modified for a specific facility. Once again, the team took a conservative approach, assuming a standard design for the process trains.

The team identified a number of events that could have an impact on the quality of the product water, and these can be separated into the following categories:

Upstream of the advanced treatment

o Accidental contamination of the catchment (such as the one-off discharge of large quantities of industrial chemicals)

o Outbreaks of infectious diseases in the community leading to unusually high levels of pathogens in the source water

o Failure of biological processes that form part of the sewage treatment process

o High rainfall events leading to bypass of the sewage treatment process

Within the advanced treatment

o Catastrophic integrity breaches of MF–UF or RO membrane filtration system

o Catastrophic failures of filtration processes

o Overloading of filters

o Failure of dosing or control systems for UV-, ozone- or chlorine-based disinfection

o Formation of regulated DBPs

o Failure of dosing systems in relation to product water stabilization (increased corrosivity)

o Overdosing, underdosing, or contamination of chemicals added as part of the treatment process

Downstream of the advanced treatment

o Formation of DBPs after the treatment process within the distribution systems

o Downstream impact of water quality on distribution system, including corrosion and release of contaminants (metals) into the system

This list is not exhaustive, and more details specific to a given facility should be considered as part of the actual risk assessment. For each of the hazardous events, control measures were identified that became CCPs when it was found that they were critical to guarantee an acceptable risk to public health. The detailed treatment risk assessment is provided in Appendix B.

Water Environment & Reuse Foundation 29

2.5 Critical Control Point Selection and Monitoring Points

The selection of CCPs for each treatment train was initially conducted during the project team workshop. Typically, in the HACCP process, the CCP selection follows risk assessment; however, the project team elected to determine an initial set of CCPs and review this in light of the subsequently conducted water quality risk assessments. This is an acceptable modification to the HACCP methodology as long as general classes of contaminants and their associated risks are provided to the HACCP team prior to the workshop.

The CCP selection methodology incorporated a modified version of the method outlined in Halliwell et al. (2015) and is shown in Figure 2.4.

Figure 2.4. CCP selection table. Source: Modified from Halliwell (2015).

30 Water Environment & Reuse Foundation

The methodology provides a logical sequence of questions to ensure rigor in the selection of the CCPs. As the team worked through this methodology, a subtle change to Question 3 was proposed in order to tailor the methodology more precisely for reuse applications. The view was to provide a focus on whether the identified barrier would be counted on to provide either a specific, measurable microorganism log removal or a measurable reduction in another contaminant or contaminants that would assist in achieving final water quality targets. This assists in filtering out marginal removal capabilities or those that may not be accepted by regulators. Original Question 3

Is the step specifically designed to eliminate or reduce the likely occurrence of the hazard to an acceptable level?

Modified Question 3 Is the step required to achieve a log removal of microorganisms or meet water quality targets?

Water Environment & Reuse Foundation 31

2.5.1 Critical Control Points Selected – RO Membrane-Based Treatment Train

Table 2.9 outlines the CCP selection that was determined during and then subsequent to the workshop. The team’s response to each particular question is included to demonstrate clearly the rationale for decisions taken. CCP monitoring parameters are also described. The CCPs are indicated by solid outlines in Figure 2.5.

For each of the selected CCPs, the project team also identified monitoring parameters to ensure that the CCP is meeting its required removal performance. These, in turn, will inform the development of operational response procedures, as described in Chapter 6.

Table 2.9. CCP Selection Process and Indicators – RO Membrane-Based Treatment Process Step CCP Decision Monitoring Parameters

Chloramine dosing

Q1 yes Q2 yes Q2a N/A

Q3 no Q4 yes Q5 no CCP

In relation to Q5, chloramine formation and dosing strategies can impact DBP formation.

total (combined) chlorine

Inlet strainer Q1 yes Q2 yes Q2a N/A

Q3 no Q4 no Q5 yes NOT A CCP

In relation to Q1, strainer can potentially remove some contaminants, either biological or chemical.

N/A

MF–UF Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q3, filtration process removes microbiological hazards.

pressure decay integrity test and individual (or combined) filter effluent turbidity Pressure decay integrity testing provides superior resolution; however, it is a discrete test. Turbidity can provide an effective, continuous, backup measure.

Reverse osmosis

Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q3, process removes microbiological and chemical hazards.

electrical conductivity online TOC Electrical conductivity and online TOC are currently the most sensitive analyzers for this task. More sensitive online analytical techniques are emerging that will improve resolution of monitoring point.

32 Water Environment & Reuse Foundation

Process Step CCP Decision Monitoring Parameters

UV–H2O2 Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q3, process removes microbiological and chemical hazards (specifically NDMA).

UV present power ratio (ratio of electrical energy delivered to electrical energy required) confirmed dose of hydrogen peroxide; UVT of feed water UV dose provided for advanced oxidation is significantly higher than that required for disinfection. Operational energy utilized to meet NDMA removal will provide a sufficient UV dose for disinfection targets.

Stabilization Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, hazardous event is downstream mobilization of lead and copper if stabilization process is not well controlled. May not be as significant if final product is delivered to head of a water treatment plant or effectively blended prior to introduction to distribution system.

pH, applied chemical dose, TDS, periodic alkalinity checks, CCPP (calculation) LSI (calculation) breaks down as hardness, alkalinity, pH, and TDS

Chlorination Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, hazards are both microorganisms and potential addition of perchlorate. May not be as significant if additional disinfection credit can be achieved by introduction to inlet of a drinking water treatment plant.

free chlorine residual chlorine dose CT (calculated)

Notes: CCPP=calcium carbonate precipitation potential; CT=concentration x time; DBP=disinfection byproducts; LSI=Langelier Saturation Index; N/A=not applicable; NDMA= n-Nitrosodimethylamine; TDS=total dissolved solids; UV=ultraviolet; UVT=ultraviolet transmittance.

Water Environment & Reuse Foundation 33

Figure 2.5. Critical control points (outlined) – RO membrane-based treatment train.

2.5.2 Critical Control Points Selected – Ozone–BAC–GAC–UV–Chlorine

The non-membrane treatment train selection decisions are shown in Table 2.10. For this process train of ozone–BAC–GAC–UV–chlorine, the team encountered initial difficulty in meeting the required log removal of microorgansims via the process identified at the outset of the project. The BAC process could not be considered as a CCP as a stand-alone process because there was no control mechanism to adjust its ability to achieve pathogen reduction or contaminant removal.

Instead, by modifying the process to incorporate a coagulation step ahead of filtration, the BAC process could be incorporated as it would be effective at turbidity (and hence a level of microorganism) removal if operated like a biological filter. In addition, by considerating ozone–BAC as a single process barrier (similar to including UV–H2O2 as a single barrier), it could also be incorporated as a CCP. Therefore, the revised non-membrane treatment process became ozone–flocculation–sedimentation–BAC–GAC–UV–chlorine. This process is more consistent with the actual process train at the Goreangab plant in Windhoek, Namibia. This decision making, although straightforward from a process design and selection viewpoint, was nonetheless facilitated by CCP selection process. The workings of this process are outlined in Table 2.10, and a summary figure depicting the CCPs is shown in Figure 2.6. It should be noted that in Figure 2.6, the preozonation step is shown as an optional configuration and not listed as a CCP, though in many DPR settings this may be selected as a CCP.

34 Water Environment & Reuse Foundation

2.6 Summary

In applying this methodology to full-scale DPR systems, both in the planning phase and in the evaluation of existing processes, it is easy for a HACCP team to become sidetracked from the decision-making required to evaluate CCPs from a public health standpoint. It is very important that at least one member of each HACCP team be tasked with maintaining that focus for the group and ensuring that COPs are not inadvertently identified as CCPs and likewise that CCPs are not missed if the team becomes overly focused on certain types of risks (contaminants) or perceived risks.

Another key message is, when conducting a CCP analysis, a full evaluation of all steps from industrial pretreatment through collection, wastewater treatment, advanced water purification, drinking water treatment, and distribution needs to be included. This analysis will allow the HACCP team and engineers to determine what additional processes (if any) need to be included in DPR system design and considered as CCPs. It may also shed light on processes at the wastewater treatment plant that could negatively impact the quality of the water. A classic example of this is when polyacrylamide is used at the wastewater plant: The acrylamide monomer is present in polyacrylamide formulations and regulated in drinking water via a treatment technique approach (i.e., limiting the amount of polyacrylamide used, requiring a specified purity of product, or both). Therefore, the introduction of this and other similar types of hazards at the wastewater treatment plant or anywhere in the processes and facilities should be carefully considered and addressed as a CCP.

Table 2.10. CCP Selection Process – Non-Membrane Treatment Process Process Step CCP Decision Monitoring

Parameters

Ozone Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, this process step is concerned with the treatment of microorganisms and chemicals but also DBP formation and control.

ozone dose ozone residual CT (calculated) change in UVT

BAC Q1 yes Q2 no Q2a yes

Q3 N/A Q4 N/A Q5 N/A NOT A CCP

In relation to Q2a, BAC alone cannot be considered a CCP; the process must be considered in combination with ozone for chemical removal and have modifications to provide removal for microorganisms.

Finally, it should be noted that the intent of the HACCP process is to provide a framework through which one evaluates hazards initially and then continues to update the risk register (illustrated in Appendix B) as new information becomes available or new hazards are identified. In this way, “living documents” provide the basis for transparent decision-making for utility managers, engineers, and operations staff.

Water Environment & Reuse Foundation 35

Process Step CCP Decision Monitoring Parameters

Ozone–BAC (combined step)

Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q3, this process step is concerned with the treatment of chemical hazards. The process has been combined to provide a CCP for chemicals.

ozone dose EBCT

Coagulation–BAC (combined step)

Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q3, this process removes microbiological hazards. The process has been combined to provide a CCP for microorganisms.

filtered water turbidity coagulant dose ratio TOC post-BAC to feed TOC

Granular activated carbon

Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, a range of chemical hazards are managed by this process step, including TOC, DBPs and precursors, and other chemicals.

carbon life TOC or UVT

UV disinfection Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, the hazards are microorganisms. UV dose UVT

Chlorine CT Q1 yes Q2 yes Q2a N/A

Q3 yes Q4 N/A Q5 N/A CCP

In relation to Q1, the hazards are both microorganisms and the potential addition of perchlorate. This may not be as significant if additional disinfection credit can be achieved by introduction to the inlet of a drinking water treatment plant.

free chlorine residual chlorine dose CT (calculated)

Notes: BAC=biological activated carbon; CCP=critical control point; CT=concentration x time; DBP=disinfection byproducts; EBCT=empty bed contact time; N/A=not applicable; TOC=total organic carbon; UV=ultraviolet; UVT=ultraviolet transmittance.

36 Water Environment & Reuse Foundation

Figure 2.6. Critical control points (outlined) – ozone–BAC-based treatment train as shown with optional

preozonation step.

2.7 References

CDPH. Draft Title 22 California Code of Regulations, Division 4. Environmental Health, Chapter 3. Recycling Criteria, 2013. http://www.cdph.ca.gov/HealthInfo/environhealth/water/Pages/Waterrecycling.aspx

EPHC, NHMRC, and NRMMC. Australian Guidelines for Water Recycling: Managing Health and Environmental Risks (Phase 2): Augmentation of Drinking Water Supplies, 2008. http://www.ephc.gov.au/sites/default/files/WQ_AGWR_GL__ADWS_Corrected_Final_%20200809.pdf

Halliwell et al. Water Reuse Research Foundation Project #09-03. Utilization of Hazard Analysis and

Critical Control Points Approach for Evaluating Integrity of Treatment Barriers for Reuse. WRRF Final Report 2014.

Trussell, R. R.; Salveson, A.; Snyder, S. A.; Trussell, R. S.; Gerrity, D.; Pecson, B. M. Potable Reuse: State of the Science Report and Equivalency Criteria for Treatment Trains. WateReuse Research Foundation, Alexandria, VA, 2013. p 276.

Water Environment & Reuse Foundation 37

Chapter 3

Description of Data Sources and Preparation for

Monte Carlo Analysis

3.1 Description of Data Sources

In order to develop quantitative measures of the efficacy of multiple CCP barriers in DPR systems, data were required to capture the full range of operational conditions and process upsets that may occur over extended time periods during full-scale operation. Therefore, multiple full-scale facilities were queried for operational and water quality data. For this project, the objective was to observe removal of chemical and microbial contaminants across each barrier in a DPR treatment train. However, most water utilities do not collect such specific data directly; they rely on surrogate monitoring or are more focused on the operational aspects of the barriers (rather than specific log removal or percent removal across each barrier) and the final water quality outcomes. In order to overcome this challenge, several key methods were used to relate the available data to this project:

Where possible, full-scale chemical and pathogen removal data across each barrier were collected for analysis. Most data in this form consisted of weekly or monthly monitoring over the period of several years.

When chemical- or pathogen-specific data were not available, surrogate data were collected (as described later in this chapter), for example:

o For pathogen disinfection, surrogates such as UV dose, chlorine CT, and ozone CT were used.

o For pathogen removal by MF and RO, surrogates such as pressure decay tests (PDT) and salt rejection were used; for pathogen removal by media filtration, turbidity end points were used.

o For chemical removal by RO or UV–AOP, surrogate measures or contaminant relationship by class were used.

o For chemical removal by GAC, breakthrough profiles of TOC (including relevant GAC age) were used along with specific contaminant profiles.

In addition, this project was tasked with quantifying the removal of chemical and microbial pathogens across the RO membrane-based treatment scheme of MF–RO–UV–AOP–chlorine, for which no full-scale applications exist in entirety (MF–RO–UV–AOP is used in several IPR applications, but the addition of chlorine as a final disinfectant with CT is rarely used), and the flocculation–sedimentation–ozone–BAC–GAC–UV–chlorine system, which also does not exist at full scale. Therefore, multiple facilities that had aspects of each treatment train with available full-scale data were used to populate the Monte Carlo simulations for this report.

It is important to point out that not all of the facilities queried were in fact DPR or IPR plants. For UV and chlorine data, the project team relied upon drinking water facilities that were operating with specific drinking water disinfection end points in mind. Several possible arguments could be raised against such an approach, including:

Not having a site with all processes functioning together could cause an observer to miss the interdependencies between the processes.

38 Water Environment & Reuse Foundation

Drinking water disinfection may be different from DPR or IPR disinfection scenarios.

Different upstream water treatment processes may not provide identical influent water quality conditions to the barrier, as would be experienced in DPR or IPR settings.

Although these are all valid arguments for discussion, the project team felt justified in using the approach for the following reasons:

Disinfection by UV or chlorine is a function of water quality parameters that are measured regardless of the water source (e.g., UV transmittance, turbidity, flow, lamp power, UV intensity, temperature, pH, oxidant dose, flow, baffling factor, contact time, residual) and therefore should be independent of water source. This is the basis of U.S. EPA’s Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) and associated benchmarking guidelines.

That said, correlation analysis was conducted and applied where possible, as described in Section 3.3.4.

By the time water from the proposed DPR processes reaches the final disinfection steps, it will have been highly treated through dual membrane processes (MF–RO) or ozone, biofiltration, and GAC. All of these processes significantly reduce turbidity, UV transmittance, and oxidant demand.

Upstream processes in full–scale operation are designed to shut down when certain water quality end points are not met (e.g., too much turbidity breakthrough, insufficient salt rejection), and therefore true domino effects are unlikely to be encountered. Therefore, unit process operation is really a function of feed water quality and operational set points that capture the upstream variability in water quality and respond to excursions from acceptable feed water ranges by initiating alerts or critical alarms with process shutdowns.

Current regulatory paradigms for crediting LRVs across unit processes are already independent of water source. In other words, whether an operating facility is using surface water strongly affected by wastewater, groundwater under the influence of surface water, a pristine source water, or recycled water for potable reuse (and any variation thereof), the maximum LRV for a process is a function of direct measurements of the process and water quality, not the water source. As an example, MF membranes in California may be credited with up to 4.0 log removal of Cryptosporidium and Giardia, regardless of the water source.

Without this approach, only limited pilot-scale data would be available and would not therefore represent full-scale operations, including typical shutdown and startup conditions, and cleaning cycles.

Water Environment & Reuse Foundation 39

With this reasoning in mind, the following utilities provided data throughout this project, some of which were used in the Monte Carlo simulations:

Utility/Facility Water Source/

Treatment Type Treatment Processes

RO Membrane-Based Treatment

Orange County Water District IPR MF–RO–UV–AOP–stabilization

West Basin Municipal Water District IPR ozone–MF–RO–UV–AOP

Scottsdale Water Campus IPR MF–RO–UV–AOP

Anonymous Australian Facility 1A and 1B IPR MF–RO–UV–AOP

Ozone–BAC-Based Treatment

Goreangab Water Treatment Plant DPR ozone–floc–DAF–sand filtration–ozone–BAC–GAC–UF–chlorine

Anonymous IPR pilot, Southeast IPR, pilot data only, 1 year operation

ammonia ion exchange (IX)–TOC IX–ozone–BAC and ammonia IX–TOC IX–UV–AOP–BAC

Anonymous drinking water treatment plant, Southeast

surface water/drinking water treatment

conventional and chlorine disinfection

Anonymous drinking water utility, Pacific Northwest

surface water/drinking water treatment

conventional and UV disinfection and chlorine

40 Water Environment & Reuse Foundation

3.1.1 Orange County Water District

The OCWD groundwater replenishment scheme is an RO membrane-based treatment process that consists of MF–RO–UV–AOP–stabilization, but it does not contain a final dose of chlorine.

Figure 3.1. Process flow diagram for OCWD. Source: www.gwrsystem.com.

This plant provided a very large data set consisting of all grab sample data generated from the inception of the project in 2008 through February 2014. It provided data for the following sample locations: Water entering the recycling facility (Q-1) Microfiltration feed (MFF) Microfiltration concentrate (backwash waste; MFC) Microfiltration effluent (MFE) Reverse osmosis feed (ROF) Reverse osmosis concentrate (ROC) Reverse osmosis product (ROP) Ultraviolet feed (UVF) Ultraviolet product (UVP) Decarbonated product water (DPW) Finished product water (FPW)

Water Environment & Reuse Foundation 41

Of the 335 parameters available, a screening assessment identified 95 that were considered suitable for Monte Carlo analysis. These data were reviewed for a goodness of fit on lognormal probability density functions. All parameters for which a straight line of best fit could reasonably be drawn approximate a lognormal distribution over the sampling period and can be fitted to such a distribution in preparation for Monte Carlo simulations.

The following observations were drawn from the data:

Many of the parameters can be fit to lognormal probability density functions (PDFs) and have good data for advanced water treatment plant (AWTP) source water and RO feed.

A groundwater replenishment system (GWRS) performs integrity testing on a daily basis and has operational procedures in place to correct membranes when high pressure decay test (PDT) values are obtained. LRV is calculated and monitored, but not as a compliance parameter.

Numerous parameters (particularly the inorganic substances) also have excellent data for RO concentrates. These can be used as a sensitive (and conservative) estimate for RO rejection based on mass–balance.

A few chemicals have pre‐ and post‐UV data, but generally little or no removal is evident (as expected for most chemicals), with the exception of n-Nitrosodimethylamine (NDMA) data, which were sufficient to characterize the UV removal by Monte Carlo simulation.

3.1.2 Anonymous Australian Utility

The anonymous Australian utility includes three plants with an RO membrane-based treatment process that consists of MF–RO–UV–AOP–stabilization and a final dose of chlorine.

Figure 3.2. Process flow diagram for anonymous Australian utility.

42 Water Environment & Reuse Foundation

Water quality data were provided for three AWTPs:

AWTP 1: Variable periods from 2009 to September 2011

AWTP 2: Variable periods from 2009 to October 2011

AWTP 3: Variable periods from 2009 to October 2011

The only data used were for inorganic and organic chemical substances in RO feeds and PR permeates (combined).This is because negligible removal was observed for chemical contaminants in AWTP processes prior to RO. In most cases very little data were available for observing removal of chemical contaminants by subsequent treatment processes because most chemicals were at concentrations less than analytical detection limits in subsequent process influents or effluents.

3.1.3 Scottsdale Water Campus

Figure 3.3. Process flow diagram rfo Scottsdale Water Campus.

CIP Neutralization

Reservoir AReservoir B

MC

AP

RO

To WRP

ConcentrateTo Residuals Sewer

Auto Strainers

MA

WT

Auto Strainers

MF Filtrate PS

CartFilters

Product PSTo Wells

Dec

arb

Antiscalant

Sulfuric Acid Lim

e

MF

MF

From WRP

CAP Water

Ammonia

Chlorine

Ammonia Boost

Dechlor Vault

Chlorination Vault

Typ. of 17

Ozone

CIP Waste

Water Environment & Reuse Foundation 43

A large database of chemical contaminant monitoring covering 2009 through June 2014 was provided. This included inorganic and organic chemical parameters measured at locations throughout the plant, including influent and effluent samples across each CCP barrier.

3.1.4 Goreangab DPR Facility, Windhoek, Namibia The Goreangab plant in Windhoek, Namibia, provides a similar process train to that being investigated in this project for the non-membrane treatment process (ozone-BAC-GAC-based treatment). The process flow diagram for the plant is shown in Figure 3.4. Monthly data values from January 2008 through August 2014 were provided. For most analytes, this amounted to 78 samples spanning almost seven years.

Data from the following sampling locations were provided:

raw DAF sand filtration ozone BAC GAC UF final treated water

Figure 3.4. Goreangab DPR facility process flow diagram.

44 Water Environment & Reuse Foundation

In summary, the following observations were made regarding the raw data:

Inorganic parameters (e.g., TDS, total hardness, SO4, Mg, K, Ca, and conductivity) were not at all removed by this treatment train. In fact, some (e.g., Na, Cl) were slightly increased, presumably by chemical addition during treatment.

TOC and dissolved organic carbon (DOC) were each removed by around 1 log over the treatment train.

The DOC removal occurred during DAF, BAC, and GAC. Other processes, including sedimentation–flocculation, ozone, and UF, appeared insignificant for DOC removal.

UV254 removal was similar to DOC, but that removal occurs mostly after ozonation, rather than after BAC. Further removal is achieved by subsequent GAC.

Sand filtration is effective for removing turbidity (~1 log). Most microbial parameters are also observed to be removed (>1 log) after sand filtration, but a lack in intermediate data after DAF makes it impossible to distinguish the role of these two processes in most cases. However, the sand filters here were not operated with the same 0.1 (or 0.5) NTU turbidity goal because the primary filtration mechanism is the UF system at the end of the process train.

3.1.5 Anonymous IPR Pilot, Southeastern United States

The anonymous IPR pilot test was conducted by Hazen and Sawyer to review alternative non-desalination approaches for IPR. This has provided good data for the non-membrane process train. This pilot project has provided data across three alternative process train configurations:

Concept 1: including UV–AOP prior to BAC Concept 2: including O3 prior to BAC Concept 3: including O3 prior to BAC, followed by high intensity UV but without peroxide

Figure 3.5. Process flow diagram for anonymous IPR pilot facility, Southeastern United States.

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Monitoring data were available for approximately 250 parameters. Of these, approximately 110 were deemed to have sufficient data points greater than analytical detection limits for further assessment.

Similar to Orange County’s GWRS, the lognormal probability plots served as a test for goodness of fit for lognormal PDFs. All parameters for which a straight line of best fit could reasonably be drawn approximate a lognormal distribution over the sampling period and can be fitted to such a distribution in preparation for Monte Carlo simulations.

The following observations were drawn for the IPR data:

Many of the parameters can be fit to lognormal PDFs and have good data for before and after most of the unit treatment processes.

There was generally negligible removal for inorganic substances (and TDS) for all treatment processes, which is to be expected given that there was no desalination step (the impact of ion exchange was minimal).

There are some usable data for organics removal by BAC and ozone. Examples include trimethoprim, total organic halogens (TOX), TOC, total Kjeldahl nitrogen (TKN), sulfamethoxazole, some perfluorinated organics (including perfluorooctanesulfonic acid and perfluorooctane sulfonate), some nitrosamines, estrone, caffeine, and atenolol.

The various measures for nitrogen (e.g., total nitrogen, TKN, NO3, NO2) are generally better fit to lognormal PDFs than they were for the GWRS.

3.2 Introduction to Stochastic Variables

The performances of water treatment processes vary depending upon a range of factors including hydraulic flow rate, water composition, and temperature. As a consequence, water treatment performance is a stochastic (rather than deterministic) variable. Stochastic variables are those for which the value or outcome may not be precisely determined or predicted because it involves a degree of variability or randomness.

The stochastic nature of water treatment performance arises from both inherent variability (for example, chlorine residuals or pH may vary over time) and uncertainty (for example, uncertainty introduced by limitations in analytical precision).

A useful way of dealing with a stochastic variable in mathematical models is by describing it, not as a point value, but as a distribution of values. A PDF is a mathematical function that represents a distribution in terms of the probability or frequency of occurrence of specific values within the distribution. The PDF can be conceptualized as a smoothed-out version of a histogram of occurrences of a range of values. If sufficient values of a continuous random variable are sampled, producing a histogram depicting relative frequencies of output ranges, then this histogram will resemble the random variable's probability density.

A brief introduction to some important distributional forms is given here; these are used throughout the following sections of this chapter.

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3.2.1 Normal Distribution

Among the best known and widely used distributional forms is the normal distribution, which is used to describe symmetric continuous data. The normal distribution is also known as the Gaussian distribution. It is defined in terms of two parameters, the mean () and variance (standard deviation squared, σ2). An example of a normal distribution (mean=25, standard deviation=2) showing a histogram of sampled values overlaid with a fitted PDF is presented in Figure 3.6.

Figure 3.6. Normal distribution with mean (μ )=25 and standard deviation (σ)=2. .

5.0% 90.0% 5.0%5.0% 89.9% 5.1%

21.70 28.27

18 20 22 24 26 28 30 32

Concentration (mg/L)

0.00

0.05

0.10

0.15

0.20

0.25

Freq

uenc

y

Normal DistributionComparison with Normal(25,2)

Normal Distribution

Minimum 19.05Maximum 31.52Mean 25.00Std Dev 2.00Values 500

Theoretical

Minimum −∞Maximum +∞Mean 25.00Std Dev 2.00

Water Environment & Reuse Foundation 47

Many distributional forms may be described mathematically in terms of a central tendency and the occurrence of variances from that central tendency. This is the case for the normal distribution, characterized by the familiar bell-shaped curve. The general characteristics of the normal distribution and the functions required to determine summary statistics are presented in Table 3.1.

An alternative form of any PDF is the cumulative density function. The cumulative density function, evaluated at a number x, is the probability of the event that a random variable X with that distribution is less than or equal to x. Figure 3.7 shows the cumulative density function corresponding to the same normal distribution.

Table 3.1. Characteristics and Their Determination for Normal Distributions

Characteristic Determination

Parameters (continuous location parameter)

continuous scale parameter), >0

Domain (continuous)

PDF

Mean

Standard deviation

Variance 2

Skewness 0

Kurtosis 3

Mode

Note: PDF=probability density function.

Figure 3.7. Normal distribution with mean=25 (μ) and standard deviation=2 (σ), presented as a cumulative density function.

x

2)(2

1

2

1)(

x

exf

18 20 22 24 26 28 30 32

Freq

uenc

y

48 Water Environment & Reuse Foundation

The normal distribution is widely applied in modeling quantitative phenomena in natural sciences. Although the mechanisms underlying these phenomena are often unknown, the use of the normal distribution can often be theoretically justified by assuming that many small, independent effects are additively contributing to each observation.

However, for describing the variability of water quality contaminants, there are a number of important limitations to the use of normal distributions. One is that all normal distributions extended through values less than zero to negative infinity (-). A consequence is that significant fractions of some fitted normal distributions may cross into negative territory, thus producing physically meaningless values. A second limitation is that normal distributions are, by their definition, symmetrical. This is a significant limitation for describing water quality characteristics because these are often highly skewed, with long positive tails.

In addition to the normal distribution, there are many alternative distributions that can suitably describe frequency data for various phenomena. Some of these are used to characterize water quality variables and make estimates and inferences.

3.2.2 Lognormal Distribution

The lognormal distribution is used for right-skewed continuous data, as is common with many water quality variables – including both microorganisms and some chemical contaminants. Like the normal distribution, the asymmetrical lognormal distribution may be characterized by just two parameters: in this case, the mean and standard deviation of the logarithms of the transformed variable (y=ln(x)). It can also be described by a third parameter, known as the shift, to accommodate a shift along the x-axis without changing the distribution’s shape. An example of the lognormal distribution is presented in Figure 3.8.

Figure 3.8. An example of a lognormal distribution (mean=10 [μ], standard deviation=10 [σ]).

0 20 40 60 80 100

120

Freq

uenc

y

Water Environment & Reuse Foundation 49

Table 3.2. Characteristics and Their Determination for Lognormal Distributions

Characteristic Determination

Parameters (continuous parameter), >0 continuous parameter), >0

Domain (continuous)

PDF with and

Mean

Variance 2

Skewness

Kurtosis with

Mode

Note: PDF=probability density function.

The general characteristics of the lognormal distribution and the functions required to determine summary statistics are presented in Table 3.2.

Lognormal distributions overcome the two key limitations (described previously) for normal distributions when used for describing water quality data. First, lognormal distributions do not allow negative values and thus can be naturally constrained by a minimum value of zero, but still allow unconstrained upper values (i.e., a maximum of +). Second, lognormal distributions may be highly positively skewed, which is a feature commonly observed in water quality data.

x02)

''ln(

21

'21)(

x

ex

xf

22

2

ln'

2

1ln'

33

332 234

2

1

2322

4

50 Water Environment & Reuse Foundation

3.2.3 Beta and BetaGeneral Distributions

A wide variety of other distributional forms are available and used in various statistical applications. Two that are applied in some aspects of risk assessment include the Beta distribution and the BetaGeneral distribution.

The Beta distribution can take a variety of shapes and thus is relatively flexible for fitting many types of data. The standard Beta distribution has a defined minimum value of zero and a maximum of 1, with the shape within this range specified by two shape parameters, alpha1 and alpha2.

The BetaGeneral distribution (sometimes referred to as a four-parameter Beta distribution) is directly derived from the Beta distribution by scaling the [0,1] range of the Beta distribution with the use of a minimum and maximum value to define the range. As such, it has the same shape flexibility as the Beta distribution but can be spread across any specified data range.

The BetaGeneral distribution has been found to be an effective descriptor of distributions of data other than concentration-derived data. Examples include water temperature (oC), flow rates (m3/hour), and disinfection contact times (minutes).

Figure 3.9. An example of a beta distribution (α1, α2, min, max).

10 15 20 25 30 35 40 45

Freq

uenc

y

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3.2.4 Probability Plots

Probability plots may be used to accurately present measured distributions of stochastic variables. Probability plots display the quantiles, or percentiles (which are equivalent to the quantiles multiplied by 100), of the distribution of sample data.

In order to illustrate the use of probability plots, first consider the scatter plot presented in Figure 3.10. This shows a hypothetical distribution of a water quality contaminant obtained by 100 individual samples. The vertical axis shows the contaminant concentration (mg/L), and the horizontal axis shows the sample number (1–100). It can be observed that the data range is approximately 80 to 630 mg/L.

Figure 3.10. Scatter plot for 100 sample measurements of hypothetical chemical contaminant (lognormal distribution).

Sample number (1-100)

0 20 40 60 80 100

Con

cent

ratio

n (m

g/L)

0

100

200

300

400

500

600

700

52 Water Environment & Reuse Foundation

A cumulative frequency plot for the same sampled water quality data is presented in Figure 3.11. The vertical axis is unchanged, showing the values of the contaminant concentration for each of the data points. The horizontal axis shows the cumulative frequency in percentiles, plotted on a linear scale. The value corresponding to any data point can be interpreted as the percentage of data points that are less than or equal to the corresponding concentration. In this case it can be observed that the 20th percentile is around 170 mg/L, the median (50th percentile) is around 240 mg/L, and the 80th percentile is around 330 mg/L.

Figure 3.11. Cumulative frequency plot for hypothetical chemical contaminant (lognormal distribution, linear scale).

Cumulative frequency (percentile)

0 20 40 60 80 100

Con

cent

ratio

n (m

g/L)

0

100

200

300

400

500

600

700

Water Environment & Reuse Foundation 53

To test whether these data are in fact normally distributed, a simple approach is to change the scale of the horizontal axis from a linear scale to what is commonly known as a probability scale. The probability scale realigns the data in terms of evenly spaced quantiles of a standard normal distribution. This is related to the number of standard deviations that any data point is from the mean value.

The probability-scaled chart is commonly referred to as a probability plot, as shown in Figure 3.12. If the data are normally distributed, then the probability plot will present the data in a straight line (in this case, it does not). Outliers and deviations from normality would be observed as deviations from the straight line.

Figure 3.12. Probability plot for hypothetical chemical contaminant (lognormal distribution, probability scale).

Cumulative frequency (percentile)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Con

cent

ratio

n (m

g/L)

0

100

200

300

400

500

600

700

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Because these data do not plot as a straight line in this probability plot, it can be inferred that the data do not fit a normal distribution. However, it may now be tested whether the logarithms of the data fit a normal distribution by changing the vertical axis from a linear scale to a log scale. This change is presented in Figure 3.13, and indeed, it can be observed that the data can be reasonably well fitted to a straight line. This indicates that the data are approximately lognormally distributed.

Values corresponding to the 5th and 95th percentile may be read with relative accuracy from Figure 3.13 (approximately 130 and 450 mg/L, respectively).

Figure 3.13. Lognormal probability plot for hypothetical chemical contaminant (lognormal distribution, probability scale).

The probability scale shown in Figure 3.13 specifically applies to a normal distribution. However, the general concept of scaling to evenly spaced quantiles can be applied to other distributional forms. This may be achieved by plotting data on specially scaled probability paper or with the assistance of specialized statistical software packages.

Probability plots can be developed by following a relatively simple general procedure. After acquiring the data, the first step is to sort it from the lowest to the highest value. The sorted data values are then ranked from 1 to n, where n is the sample size of the data set. The smallest value is then assigned a rank i=1, and the largest receives a rank i=n.

The data themselves are plotted along one axis (in the preceding examples, this is the vertical axis). The other axis is the plotting position (p), which is a function of the rank i and sample size n. A number of different formulae for assigning the plotting position have been proposed and used (Cunnane, 1978; Helsel and Hirsch, 2002). The most applicable formula for normal distributions (and transformed lognormal distributions) is known as the Blom formula, as presented in Equation 3.1 (Blom, 1958).

Cumulative frequency (percentile)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Con

cent

ratio

n (m

g/L)

20

30

4050607080

200

300

400500600700800

10

100

1000

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Equation 3.1. Blom formula for assigning plotting positions.

To plot the data in terms of percentiles (as opposed to fractiles), it is necessary to multiply the value of the plotting position by 100.

Normal quantiles for a given plotting position may be obtained from most statistics textbooks. As an alternative, probability plotting paper may be used as described previously to rescale the linear scale for quantiles of the standard distribution into the non-linear scale of plotting positions. However, the simplest approach is to use a statistical computer software package to rescale the linear cumulative frequency axis to probability scale.

For lognormally distributed data, the vertical axis must either be plotted as the log values of the data points or rescaled from linear to log scale. This again may be achieved by plotting on specialized log paper or with the aid of statistical or chart-drawing software.

3.2.5 Fitting Data to PDFs

The selection of appropriate PDFs for all stochastic variables is a key step for many applications of quantitative risk assessment (U.S. EPA, 1997). In a probabilistic risk analysis, the selection of PDFs for the most uncertain contributing parameters will strongly influence the distribution of resulting risk determination (Seiler and Alvarez, 1996).

In most cases, only a limited number of model input assumptions and parameters will have a significant impact on the final determined variability or uncertainty. For some assumptions and parameters, equivalent final predicted PDFs may be derived even when some stochastic variables are treated as single point values (deterministic variables). Recognizing this, it is considered good practice to undertake preliminary sensitivity analyses or numerical experiments to identify model inputs, parameters, and exposure pathways that make significant contributions to the assessment end point and its overall variability or uncertainty (Burmaster and Anderson, 1994; U.S. EPA, 1997).

Identifying important pathways and parameters where assumptions about distributional form contribute significantly to overall uncertainty may aid in focusing data gathering efforts. On the other hand, it is important to avoid premature or unjustified elimination of pathways or parameters from full probabilistic treatment. Any pathway or parameter that is eliminated from full probabilistic treatment should be identified and the reasons for its elimination clearly justified.

To aid in the judicious selection and parameterization of a PDF, an evaluation must be made regarding what data are available and whether this information is suitable for this purpose. Knowledge regarding the nature of the parameter being modeled should be used to inform the choice of input distribution. For example, in counting nuclear radiation, a Poisson distribution is usually selected because the nuclear decay process is known to be governed by events following Poisson statistics. In selecting a distributional form, the U.S. EPA (1997) has recommended a series of questions including (but not limited to) the following:

Is there any mechanistic basis for choosing a distributional family? Is the shape of the distribution likely to be dictated by physical or biological properties or other

mechanisms? Is the variable discrete or continuous?

25.0375.0

nipi

56 Water Environment & Reuse Foundation

What are the bounds of the variable? Is the distribution skewed or symmetric? If the distribution is thought to be skewed, in which direction? What other aspects of the shape of the distribution are known?

According to the central limit theorem, normal and lognormal distributions may often be inferred by the structure of the variations in a stochastic variable. If the variability of a quantity may be assumed to be derived from a sum of contributions with many variations but each with a defined mean and variance, the distribution of the sum is asymptotically normal. However, if the variability arises from the product of many factors, each with a variability defined by a mean and variance, the resulting distribution is asymptotically lognormal.

The exponential distribution may be appropriate if the stochastic variable represents a process akin to interarrival time of events that occur at a constant rate. A gamma distribution may be a reasonable candidate if the random variable of interest is the sum of independent exponential random variables (U.S. EPA, 1997).

In situations where the underlying nature of a distribution is unknown or only approximately known, there are a number of considerations and techniques available for assessing and selecting appropriate PDFs and associated parameters (Seiler and Alvarez, 1996). However, caution should be exercised because it has been shown that the PDF shape may significantly influence model outputs for some situations (Lessmann et al., 2005). Where uncertain distributions have been applied for significant parameters, it is important to test the sensitivity of the model findings and conclusions to changes in the modeled distributional shape.

Numerous commercial software packages are available for fitting sampled data to distributions and testing the veracity of the fit. Examples presented here were fitted using @Risk (Palisade Corporation, 2013).

There are a number of ways in which distribution-fitting software can be used to fit sampled data. However, a reliable technique that will later be useful for its application to censored data sets (Section 6.9) is to fit the data as a cumulative distribution. This requires, initially, the calculation of plotting positions (p), as described in Section 6.6.1. Once the data values are paired with their corresponding plotting positions (pi), they may be used to describe the sampled distribution in order for this to be fitted to a standardized cumulative density function.

The lognormally distributed data presented in Figures 3.10 to 3.13 have been fitted to a cumulative density function using @Risk, with the final fit shown by the curved line in Figure 3.14. The suitability of the fit can be visually inferred by the close match between the sampled data points and the curved fit line. Once the data have been fitted to a suitable curve, summary statistics of the fitted curve, including the mean (253.96 mg/L) and standard deviation (99.75 mg/L), can be obtained.

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Figure 3.14. Cumulative density function fitted to lognormally distributed data.

3.2.6 Goodness of Fit

Once a PDF has been selected, it is appropriate to test the goodness of fit of the distribution with known data. Goodness-of-fit tests are formal statistical tests of the hypothesis that the set of sampled observations is an independent sample from the assumed distribution.

To undertake goodness-of-fit tests, graphical tests such as the plotting of histograms, the Probit, or the Logit plot for normal or lognormal distributions may be used. As an alternative, numerical methods such as the chi-squared test, the Kolmogorov–Smirnov test, and the Anderson–Darling test can be used. For each of these, a numerical value is calculated relating the data to the fit statistics, and in each case, the smaller the value, the better the fit. Each of these numerical tests offers variable advantages and disadvantages, and a careful decision should be made when considering which of them (or other available tests) to use.

The chi-squared test is the best known goodness-of-fit statistic. It is calculated by partitioning the x-axis domain into several discrete bins. The chi-squared statistic is then given by Equation 3.2.

Where: K=the number of bins Ni=the observed number of samples in the ith bin Ei=the expected number of samples in the ith bin.

Equation 3.2: The chi-squared test for goodness of fit. A weakness of the chi-squared test is that there are no universally accepted guidelines for selecting the number and location of bins. In some situations it is possible to reach different conclusions for the same data depending on the selection of bins. Some

Lognorm(253.96, 99.750) Shift=+0.98618X <= 441.7

95.0%X <= 127.8

5.0%

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600 700

Contaminant concentration (mg/L)

Cum

ulat

ive

frequ

ency

K

i i

ii

EEN

1

22 )(

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software packages are able to reduce this arbitrariness by adjusting bin sizes based on the fitted distribution with an aim to making each bin contain an equal amount of probability (for example, smaller bins around the mean and larger bins around the tails).

The Kolmogorov–Smirnov and Anderson–Darling tests do not require binning and thus are less arbitrary than the chi-squared test. However, the Kolmogorov–Smirnov focuses on the middle of the distribution and thus does not detect tail discrepancies very well. The Anderson–Darling test more effectively highlights differences between the tails of the fitted distribution and the input data. Decisions regarding the most appropriate tests to use to assess goodness of fit should be made with consideration of the relative significance of the strength and limitations of the particular test.

When testing for normality or lognormality, more powerful tests include Lilliefor’s test, the Shapiro–Wilks test (for sample size <50), and the D’Agostino test (for sample size >50). The latter two are considered to be the tests of choice for normality and lognormality (U.S. EPA, 1997).

Despite the power of complex distribution-fitting techniques, and even despite apparent excellent results of goodness-of-fit tests, caution should always prevail where data are limited or incomplete. Depending on the proportion of available data and the patterns of missingness, significant bias may arise from some common approaches to handling missing data (Gorelick, 2006). Goodness-of-fit tests are considered to have low discriminatory power and generally best used for rejecting poor distribution fits rather than identifying good fits (U.S. EPA, 1997).

Graphical methods can provide clear and intuitive indications of goodness of fit. Among the most common of graphical methods is the frequency comparison, which compares a histogram of the experimental data with the PDF of the fitted data (see Figure 3.6). Probability plots compare the observed cumulative density function with the fitted cumulative density function.

Probability–probability (P–P) plots and quantile–quantile (Q–Q) plots are also commonly used and easily accessible by many statistical software packages. P–P plots compare the observed probability with the fitted probability, and Q–Q plots compare the ith quantile of the data against the ith quantile of the fitted distribution. P–P plots tend to emphasize differences in the middle of the predicted and observed cumulative distributions, whereas Q–Q plots tend to emphasize differences at the tails.

3.2.7 Censored Data

A common difficulty encountered in investigations of many water contaminants is that a substantial portion of the analyzed samples are less than limits of detection (LODs) or limits of reporting (LORs) established by analytical laboratories.

The LOD refers to the lowest concentration of a contaminant that can be reliably detected compared to a blank sample. LORs are typically slightly higher than LODs and used as a means of incorporating a safety factor to account for variable sample matrices, variable detection performance, increased confidence in contaminant identification (reduced false positives), and increased confidence in quantitation.

Although data returned as “less than limit of detection” (<LOD) or “less than limit of reporting” (<LOR) are clearly known to be less than a particular value (as defined by the LOD or LOR), their precise values remain unknown. As such, they are examples of what is commonly called censored data. Censored data can present considerable difficulties when there is a need to fit PDFs, graphically present data, or estimate summary statistics (Helsel, 1990).

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Simply ignoring censored data and using only the fraction of the data that is greater than the LOR distorts the relationship between the sampled data and the underlying true distribution of values. This practice inevitably leads to biases in fitting a PDF and summary statistics such as means and standard deviations.

The most common method in environmental studies for dealing with data greater than LOR is to substitute the value for one-half of the LOR (Helsel, 2005). However, this approach is not considered to be a reasonable method for interpreting censored data as it too can provide a poor representation of the true data distribution, leading to inaccurate summary statistics that may have significant regulatory consequences (Helsel, 2005). This approach can introduce a signal that was not actually present in the original data or obscure a signal that was actually there.

The most statistically appropriate procedures for managing censored data combine the values greater than the LOR with the information contained in the proportion of data less than the LOR. A number of such suitable techniques, of varying complexity and suitability for various data sets, are available for managing censored data (Helsel, 1990, 2005; Travis and Land, 1990). Rigorous techniques are available for estimating distributional parameters for censored trace-level water quality data (Gilliom and Helsel, 1986) and estimation of descriptive summary statistics for censored water quality data (Helsel and Cohn, 1988). However, the problem can be approached quite simply using modern statistical software packages.

As described, there are several steps and important considerations that should be followed when fitting any type of data to a PDF or a cumulative density function. After carefully considering the basis for selecting any particular distributional form, it is appropriate to visually inspect the linearity (or lack thereof) of a cumulative frequency plot for anticipated normal or lognormal data (see Figure 3.13). This can be achieved for censored data sets by following the same procedure as for non-censored sets. Consider the lognormally distributed data from Figure 3.10, but in this case, assume a LOR of 200 mg/L, as shown in Figure 3.15. In this case, all of the samples with a value of less than 200 mg/L would be reported as less than LOR or less than 200 mg/L, and the precise values would remain unknown. This would leave two key pieces of information. First, there would be the remaining data greater than 200 mg/L, for which the distribution could be examined. Second, the proportion of the samples that are less than 200 mg/L would be known. From these two pieces of information, a reasonable estimation of the overall distribution, as shown in the following examples, could be made.

Figure 3.15. Scatter plot for 100 sample measurements of hypothetical chemical contaminant (lognormal distribution); LOR=200 mg/L.

Sample number (1-100)

0 20 40 60 80 100

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cent

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g/L)

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LOR = 200 mg/L

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In this example33 of 100 sample measurements would have been returned with the result less than 200 mg/L, and the remaining 67 points would have been reported with values greater than 200 mg/L. Therefore, values may be assigned to the 34th, 35th, and 36th percentiles, and so on. The plotting positions of these percentiles may be calculated and plotted as described. The result in this case is presented in Figure 3.16.

Figure 3.16. Censored probability plot for hypothetical chemical contaminant; LOR=200 mg/L.

Once the validity of a distributional form has been established, it is possible to use the available data values, paired with their corresponding plotting positions (pi), to describe the sampled distribution in order for this to be fitted to a standardized cumulative density function (as described previously for non-censored data sets).

Cumulative frequency (percentile)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Con

cent

ratio

n (m

g/L)

20

3040506080

200

300400500600800

10

100

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LOR = 200 mg/L

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3.2.8 Step Changes and Gradual Changes

Before fitting PDFs to time series data, it is helpful to initially plot the data in time series form. Doing so will help to ascertain whether the data were composed under consistent conditions or whether gradual or sudden changes may have occurred over time. Failure to detect such changes can lead to inaccurate fits of data to standard PDF forms.

A number of examples of time series data are presented in this section. Figure 3.17 shows time series data for sodium concentrations in the feed and permeate solutions of an RO process. Whereas the permeate concentration is stable over time, a change is apparent in the feed concentration data. The mean sodium concentration in RO feed has remained constant from 2008 to 2014, but the variability decreased during 2012. PDFs fitted to data prior to 2012 would show the same mean as PDFs fitted to data after 2012, but the PDFs for the earlier data would reveal the greater variability. If the intention of the analysis is to make predictions about future treatment performance and reliability, it may be appropriate to select only those data from 2012 onward to construct a PDF.

Figure 3.17. Time series data for sodium concentrations in the feed and permeate solutions of a reverse osmosis process.

Sodium

Year

2008 2009 2010 2011 2012 2013 2014

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cent

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n (m

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RO PermeateRO Feed

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Figure 3.18 shows a time series for ammonia in the feed water to an RO process. A step change in ammonia concentration from an average greater than 20 mg/L to an average less than 5 mg/L is apparent toward the end of 2009. This sudden reduction in ammonia concentration was accompanied by a step increase in nitrate concentration from around 10 to around 40 mg/L (Figure 3.19). A likely explanation for these two observed step changes is that the feed water to the AWTP had changed in quality. This may be explained by an improvement in the biological nitrification process (which would convert ammonia to nitrite and nitrate) at the biological wastewater treatment plant that provides the source water to the AWTP. Again, if the intention of the analysis is to project future water quality and plant performance, it may be appropriate to use only data from 2010 onward, reflecting the ongoing source water quality.

Figure 3.18. Sudden change in ammonia concentrations at the GWRS. Note: Probably represents improvement to nitrification at source plant.

Figure 3.19. Sudden change in nitrate concentrations at the GWRS. Note: Probably represents improvement to nitrification at source plant.

Ammonia

Year

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Although these changes appear to have occurred at a specific time, consequences of process changes, some changes may be much more gradual in nature. Figure 3.20 shows time series data for total organic carbon (TOC) concentrations in RO feed and permeate solutions. In this case a gradual reduction in average TOC concentration can be observed in RO feed from 2008 to 2010. This may reflect numerous possible factors, including gradual process improvement at the conventional biological wastewater treatment plant that provides source water to the AWTP. Understanding the cause of such gradual changes would assist greatly in determining whether the change should be assumed to be reversible or irreversible in future circumstances.

Figure 3.20. Time series data for TOC concentrations in the feed and permeate solutions of a reverse osmosis process.

TOC

Year

2008 2009 2010 2011 2012 2013 2014

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RO PermeateRO Feed

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3.3 Probabilistic Assessment of Water Treatment Processes

Probabilistic techniques have been used for many decades for such diverse applications as nuclear physics and future stock option valuations (Boyle, 1977). Monte Carlo simulation is currently the most widely used method for probabilistic health risk assessment (Lester et al., 2007; Williams and Paustenbach, 2002).

Conventional deterministic approaches to risk assessment tend to rely on multiple conservative assumptions, which are adopted to compensate for the lack of knowledge regarding uncertainty. When such conservative assumptions are compounded over a multi-step calculation, the effect is often that the calculated risk outcomes are comparable with maximum values resulting from a probabilistic approach (Bruce et al., 2007; Jensen et al., 2008; Lonati et al., 2007). This leads to a risk focus on situations with extreme and very low probabilities of occurrence.

Probabilistic risk assessment relies on the incorporation of numerous stochastic variables to compute a final distributional outcome or prediction. In light of their increasing application, the U.S. EPA (1997) has developed guidelines for probabilistic environmental risk assessments. Probabilistic models may be constructed from most deterministic risk calculations simply by replacing single-value variables with PDFs. The existing mathematical transformations (such as multiplications or additions) will still apply.

The underlying calculations for probabilistic models are commonly constructed using spreadsheet software such as Microsoft Excel. Commercial software packages are then available to facilitate the assignment of PDFs and Monte Carlo sampling. Two common probabilistic software packages are @Risk (Palisade Corporation, 2013) and Crystal Ball (Oracle, 2008).

3.3.1 PDFs for Single Water Treatment Process Removal

A number of approaches were adopted in this work for deriving PDFs to describe the variability in performance of individual water treatment processes for the removal or inactivation of chemical and microbial contaminants. The most straightforward approach has been to use historical concentration data for process influent and effluent streams. By fitting each of these to PDF, it is then possible to use a Monte Carlo simulation to derive PDFs for the removal of the contaminant during passage through the treatment process. All such Monte Carlo simulations in this work were performed with 10,000 sample iterations and Latin Hypercube sampling.

For later stage water treatment processes (those preceded by effective pretreatment processes), most contaminants of interest are commonly less than available analytical detection limits. This may be the case in the influents to some processes as well as the effluents. Therefore, it is not possible to rely upon analytical data to derive process performance PDFs in such cases. Alternative approaches have been adopted and are described in detail in the applicable sections of this report. Such processes have commonly relied upon established relationships describing removal or inactivation of contaminants based on observable (and measurable) process parameters. A most important example is the use of CT monitoring for disinfection processes. Where relationships are established for CT and levels of disinfection for specific organisms, it is then possible to use long-term CT records to derive PDFs for disinfection performance.

In this work all contaminant removal PDFs have been derived and presented as LRVs. This has proved convenient because contaminant removal data in this form have generally been very well fit to standard distributional forms including normal, lognormal, and BetaGeneral distributions.

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Having simulated a PDF for contaminant removal, it is then possible to fit the simulated PDF to a standard distributional form. Doing so provides simple numerical descriptors for the PDF and thus a convenient means to communicate, transfer, and reuse these PDFs in subsequent Monte Carlo simulations.

3.3.2 Probabilistic Assessment of Multiple Barrier Water Treatment Processes

In some cases exposure may be determined in terms of sequential factors such as exposure to a chemical from a raw drinking water source after multiple treatment processes, each with variable capability to reduce the concentration of the chemical. A probability density function can be used to describe the variability in source water concentration of the chemical; subsequent PDFs then describe the variable percentage (or fractional) removal of the chemical by various treatment processes such as RO and advanced oxidation. These PDFs can then be used to derive a simulated PDF for final effluent concentration.

The idea of assessing multiple barrier water treatment process performance as a series of unit process performance PDFs has been promoted since the late 1990s (Haas and Trussell, 1998; Olivieri et al., 1999; Sakaji et al., 1998; Tanaka et al., 1998). The concept may be generally depicted as in Figure 3.21 (Haas and Trussell, 1998).

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Figure 3.21. Conceptual diagram of multiple barrier process train and treatability distributions. Source: Haas and Trussell (1998).

Freq

uenc

y

Concentration

Freq

uenc

y

Concentration

Freq

uenc

y

Concentration

Freq

uenc

y

Concentration

Freq

uenc

y

Concentration

Freq

uenc

y

Concentration

C0

C1

C2

C3

C4

C5

Coagulation/flocculation/

sedimentation

Filtration

Ozonation

In-ground storage

Chloramination

To distribution

F0(C0)

F1(C1|C0)

F2(C2|C1)

F3(C3|C2)

F4(C4|C3)

F5(C5|C4)

F5(C5)

Influent PDF

Transformation PDFs

Product PDF

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Concentrations of a particular contaminant at each stage of the multiple barrier treatment process are represented as C0…C5 (Haas and Trussell, 1998). The connection between subsequent concentrations is described as a conditional PDF. For example, F3(C3|C2) is the probability density of the contaminant concentration following ozonation (C3), given the concentration in the influent to ozonation reactor (C2). In the simplest of circumstances, the processes may be assumed to behave in a linear (first-order) fashion, and the distribution may then be determined simply in terms of a ratio of effluent:influent. However, the conditional framework provides a more general approach allowing for more complex treatment process removal functions. Individual conditional PDFs may even be dependent on other descriptors of the system, such as hydraulic flow rates.

The PDF of the product concentrations may be formally evaluated as a multiple integral, which can be expressed as follows (Haas and Trussell, 1998):

f5(C5) = ∫∫∫∫∫f0(C0)F1F2F3F4dC0dC1dC2dC3dC4

Analytical evaluation of this integral may not be possible in many cases and would most generally be determined by probabilistic (Monte Carlo) simulation.

The approach of assessing sequential unit water treatment operations and combining the PDFs by probabilistic techniques was used very effectively in the assessment of a pilot-scale AWTP in San Diego (Olivieri et al., 1999). In that study PDFs were generated for plant influent water quality and sequential treatment performance of four types of treatment processes (MF, UF, RO, and chlorine disinfection). These PDFs were generated by a combination of challenge tests and theoretical considerations. A key advantage of this process was that it allowed for the mathematical estimation of the entire treatment train performance. This estimation would not have been possible simply by end point sampling because final effluents consistently yielded nondetectable results. 3.3.3 Monte Carlo and Latin Hypercube sampling

There are two main sampling types that may be used for randomly sampling distributions for probabilistic analysis. These are known as Monte Carlo sampling and Latin Hypercube sampling. These sampling types differ in how they draw samples from across the range of a PDF.

Monte Carlo sampling refers to the traditional technique for undertaking random sampling from a PDF. Sampling techniques are entirely random, and any given sample may be drawn from anywhere within the range of the input PDF. Samples, of course, are more likely to be drawn from areas of the PDF that have higher probabilities of occurrence. With sufficient iterations, Monte Carlo sampling recreates the input PDF through sampling it. However, for a small number of iterations, significant clustering can occur, and large areas of the PDF may be missed. This clustering can be problematic when a PDF includes low probability outcomes, and this could have a major effect on the results. This problem has led to the development of stratified sampling techniques such as Latin Hypercube sampling (McKay et al., 1979).

Latin Hypercube sampling was designed to accurately recreate the input PDF through sampling in fewer iterations compared with Monte Carlo sampling. To achieve this, it applies stratification to the input PDF. This stratification divides the cumulative distribution curve into equal intervals on the cumulative probability scale (0 to 1). A sample is then randomly taken from each interval, or stratification, of the input distribution. Thus, sampling is forced to recreate the input PDF. Because of the ability to more adequately represent a PDF with fewer iterations, Latin Hypercube sampling is generally preferred over random Monte Carlo simulation (Cullen and Frey, 1999; McKayet al., 1979). In some cases, correlated sampling techniques are also of value (Wu, 2008).

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3.3.4 Dependencies and Correlations between Data Sets

Some data sets are not fully independent from each other. That is, dependencies such as correlations, may exist between data sets. For some water treatment processes, it may be anticipated that correlations may occur between concentrations of contaminants in the influent of the process and concentrations in the effluent. That is, high influent concentrations may commonly lead to relatively frequent occurrence of high effluent concentrations, and low influent concentrations may commonly lead to relatively frequent occurrences of low effluent concentrations.

When very high levels of contaminant removal (e.g., >1 LRV) are achieved, it is usually difficult to detect correlations between influent and effluent water quality data. This may be because with high removal efficiency, high absolute variability (e.g., high standard deviation) in influents leads to relatively absolute low variability (e.g., low standard deviation) in effluent concentrations (the relative measure coefficient of variability may remain high in effluents). The relatively low absolute variability in effluents may lead to masking of the variability introduced by influent variability by other sources of variability. Other sources may include variability introduced during treatment (treatment performance variability) or monitoring (analytical variability). In such circumstances the variability observed in effluent concentrations does not reflect the variability in influent concentrations, and correlations between the two cannot be detected. For example, the time series previously given for sodium (Figure 3.17), nitrate (Figure 3.19), and TOC (Figure 3.20) feature no detectable correlations between RO feed and permeate concentrations.

On the other hand, when only low levels of contaminant removal (e.g., <1 LRV) are achieved, influent concentration variability may be more closely reflected in effluent concentration variability. An example is given for boron concentrations in RO feed and permeate solutions at a U.S. AWTP (Figure 3.22). This figure shows 329 paired data sets (with feed and permeate samples collected at the same time on the same days) from 2008 to 2014. The mean RO feed concentration is 0.39 mg/L, with a standard deviation of 0.035 mg/L, whereas the mean RO permeate concentration is 0.25 mg/L, with a standard deviation of 0.028 mg/L.

Figure 3.22. Time series for boron concentrations in paired RO feed and RO permeate samples.

By plotting RO feed concentrations versus RO permeate concentrations, a moderate correlation between the two data sets can be observed. By fitting a linear regression to the data, it is possible to determine the correlation coefficient (r2), which in this case was determined to be 0.4.

Boron

Year

2008 2009 2010 2011 2012 2013 2014

Con

cent

ratio

n (m

g/L)

0.1

0.2

0.3

0.4

0.5

0.6

RO Feed

RO Permeate

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Figure 3.23. Linear regression curve for boron concentrations in RO feed and RO permeate samples (r2=0.4).

The moderate correlation detected between these two boron concentration data sets implies that feed concentration data samples should not be randomly paired with permeate concentration data samples for the purpose of calculating boron rejection by RO. Instead, this correlation (r2=0.4) should be accounted for in the pairing of feed and permeate samples for this calculation. One approach for doing that would be to use the paired samples to calculate LRVs and then fit the calculated LRV data to a PDF. However, this is not always possible because feed and permeate data are often not as well paired as the current example. An alternative approach is to fit each of the feed and permeate data samples to concentration PDFs (as previously described) and impose a correlation coefficient on the Monte Carlo sampling process. Examples of the impact of imposing a correlation factor are presented in the following figures.

Figure 3.24 shows simulated LRVs for boron rejection by RO produced by a Monte Carlo simulation with no correlation (r2=0) applied in the sampling of RO feed and permeate concentration PDFs. Figure 3.25 shows the same simulated LRVs, but with a moderate correlation (r2=0.4) applied in the sampling. Figure 3.26 shows a more extreme version of these simulated LRVs with a strong correlation (r2=0.9) applied. Note that the simulated mean value (LRV=0.193) is identical in each case. The difference between these results is that the standard deviation has reduced from 0.0587 LRV for no correlation to 0.0462 LRV for a moderate correlation and 0.0235 for a strong correlation.

It is also notable that a small number of samples simulated with no correlation were calculated with negative LRVs (minimum simulated value of -0.0386 LRV). Given that boron can’t be produced during RO filtration, this is further evidence that some correlation must be inherent in the data in order to avoid this physically meaningless result.

Boron

RO feed concentration (mg/L)

0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

RO

per

mea

te c

once

ntra

tion

(mg/

L)

0.15

0.20

0.25

0.30

0.35

0.40

0.45

r2=0.4

70 Water Environment & Reuse Foundation

Figure 3.24. Reduction in boron from RO feed to permeate with no correlation (r2=0).

Figure 3.25. Reduction in boron from RO feed to permeate with moderate correlation (r2=0.4).

-0.0

5

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Freq

uenc

y

-0.0

5

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Freq

uenc

y

Water Environment & Reuse Foundation 71

Figure 3.26. Reduction in boron from RO feed to permeate with strong correlation (r2=0.9).

Following this assessment for boron, a correlation coefficient of 0.4 was assumed for RO feed and permeate data during Monte Carlo simulations of contaminant removal during RO treatment. This has an important impact of reducing the variance, and therefore reducing the likely prediction of negative LRVs, for some poorly rejected (<1 LRV) contaminants. However, the impact to predictions for contaminants with higher rejections (>1 LRV) is negligible. The reason for this is that the variability among the effluent samples (highest values to lowest values) was generally negligible compared to the difference between average feed and effluent concentrations (mean LRV). In such cases the variability observed in simulated LRVs is predominantly a reflection of the variability observed in RO feed concentrations.

-0.0

5

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Freq

uenc

y

72 Water Environment & Reuse Foundation

Similar to the correlation analysis undertaken with boron for the removal of chemicals by RO, correlation analysis was undertaken for feed and effluent samples of a UV–AOP process. UV performance correlation was based in NDMA concentrations as this was one of only very few chemicals that could be measured in both UV feed and UV effluent and for which some removal was observed. Selected paired data for which measurable NDMA concentrations were available in both UV influent and UV effluent were used to prepare the linear regression curve presented in Figure 3.27. Regression analysis revealed a correlation between UV feed and UV permeate samples of r2=0.8. As a consequence, this correlation coefficient was applied in all Monte Carlo simulations of chemical removal during UV–AOP treatment.

Figure 3.27. Linear regression curve for NDMA concentrations in UV feed and UV permeate samples (r2=0.8).

NDMA

UV feed concentration (ng/L)

0 50 100 150 200 250 300

UV

effl

uent

con

cent

ratio

n (n

g/L)

0

2

4

6

8

10

12

14

16

18

r2 = 0.8

Water Environment & Reuse Foundation 73

3.4 Summary

This chapter was meant to provide the reader with a detailed description of the water quality data sources used in this project and how those data were organized for analysis in multi-barrier process scenarios. The data handling techniques, correlation factors, and simulation techniques described in this chapter are applied to quantify the multi-barrier impacts on pathogenic and chemical contaminants in the following chapter.

The key steps in processing data and running Monte Carlo simulations were as follows:

1. Water quality data were sorted and tested for goodness of fit to normal and lognormal PDFs.

2. The data were then fit accordingly to normal or lognormal PDFs.

3. Censored data were managed by fitting data as cumulative distribution functions, accounting for the proportion of data reported as less than analytical detection limits.

4. Where determined to be appropriate (see Section 3.3.4), correlation coefficients were assumed to apply between treatment process influents and effluents.

5. Removal across a treatment barrier was calculated in LRVs for fitted distributions of influent and effluent concentrations using Monte Carlo simulation, applied with correlations where appropriate.

6. All Monte Carlo simulations were performed with 10,000 iterations using Latin Hypercube sampling.

7. Final simulated results were subsequently fit to standard distributional forms to describe LRV distributions, and these were tested for goodness of fit.

3.5 References

Blom, G. Statistical Estimates and Transformed Beta-Variables. New York: Wiley, 1958.

Boyle, P. P. Options: A Monte Carlo Approach. J. Financ. Econ. 1977, 4(3), 323–338.

Bruce, E. D.; Abusalih, A. A.; McDonald, T. J.; Autenrieth, R. L. Comparing Deterministic and Probabilistic Risk Assessments for Sites Contaminated by Polycyclic Aromatic Hydrocarbons (Pahs). J. Environ. Sci. Heal. A 2007, 42(6), 697–706.

Burmaster, D. E.; Anderson, P. D. Principles of Good Practice for the Use of Monte Carlo Techniques in Human Health and Ecological Risk Assessment. Risk Anal. 1994, 14(4), 477–481.

Cullen, A. C.; Frey, H. C. Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. New York and London: Plenum Press, 1999.

Cunnane, C. Unbiased Plotting Positions – a Review. J. Hydrol. 1978, 37(3–4), 205–222.

Gilliom, R. J.; Helsel, D. R. Estimation of Distributional Parameters for Censored Trace Level Water-Quality Data. 1. Estimation Techniques. Water Resour. Res. 1986, 22(2), 135–146.

Gorelick, M. H. Bias Arising from Missing Data in Predictive Models. J. Clin. Epidemiol. 2006, 59(10), 1115–1123.

Haas, C. N.; Trussell, R. R. Frameworks for Assessing Reliability of Multiple, Independent Barriers in Potable Water Reuse. Water Sci. Technol. 1998, 38(6), 1–8.

Helsel, D. R. Less Than Obvious – Statistical Treatment of Data Below the Detection Limit. Environ. Sci. Technol. 1990, 24(12), 1766–1774.

74 Water Environment & Reuse Foundation

Helsel, D. R. Nondetects and Data Analysis: Statistics for Censored Environmental Data. Hoboken, NJ: John Wiley & Sons, Inc., 2005.

Helsel, D. R.; Cohn, T. A. Estimation of Descriptive Statistics for Multiply Censored Water-Quality Data. Water Resour. Res. 1988, 24(12), 1997–2004.

Helsel, D. R.; Hirsch, R. M. Statistical Methods in Water Resources, U.S. Geological Survey, 2002.

Jensen, B. H.; Andersen, J. H.; Petersen, A.; Christensen, T. Dietary Exposure Assessment of Danish Consumers to Dithiocarbamate Residues in Food: A Comparison of the Deterministic and Probabilistic Approach. Food Addit. Contam. 2008, 25(6), 714–721.

Lessmann, K., Beyer, A.; Klasmeier, J.; Matthies, M. Influence of Distributional Shape of Substance Parameters on Exposure Model Output. Risk Anal. 2005, 25(5), 1137–1145.

Lester, R. R.; Green, L. C.; Linkov, I. Site-Specific Applications of Probabilistic Health Risk Assessment: Review of the Literature since 2000. Risk Anal. 2007, 27(3), 635–658.

Lonati, G.; Cernuschi, S.; Giugliano, M.; Grosso, M. Health Risk Analysis of Pcdd/F Emissions from Msw Incineration: Comparison of Probabilistic and Deterministic Approaches. Chemosphere 2007, 67(9), S334–S343.

McKay, M. D.; Beckman, R. J.; Conover, W. J. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics 1979, 21(2), 239–245.

Olivieri, A.; Eisenberg, D.; Soller, J.; Eisenberg, J.; Cooper, R.; Tchobanoglous, G.; Trussell, R.; Gagliardo, P. Estimation of Pathogen Removal in an Advanced Water Treatment Facility Using Monte Carlo Simulation. Water Sci. Technol. 1999, 40(4–5), 223–234.

Oracle. Crystal Ball 11.1.1, 2008.

Palisade Corporation. @Risk Advanced Risk Analysis for Spreadsheets. Ithaca, NY, 2013.

Sakaji, R. H.; Hultquist, R.; Olivieri, A.; Soller, J.; Trussell, R.; Crook, J. Whither the Multiple Treatment Barrier? Water Reuse 98, Lake Buena Vista, FL, American Water Works Association and the Water Environment Federation, 1998.

Seiler, F. A.; Alvarez, J. L. On the Selection of Distributions for Stochastic Variables. Risk Anal. 1996, 16(1), 5–18.

Tanaka, H.; Asano, T.; Schroeder, E. D.; Tchobanoglous, G. Estimating the Safety of Wastewater Reclamation and Reuse Using Enteric Virus Monitoring Data. Water Environ. Res. 1998, 70(1), 39–51.

Travis, C. C.; Land, M. L. Estimating the Mean of Data Sets with Nondetectable Values. Environ. Sci. Technol. 1990, 24(7), 961–962.

U.S. EPA. Guiding Principles for Monte Carlo Analysis, Washington, D.C., 1997.

Williams, P. R. D.; Paustenbach, D. J. Risk Characterization: Principles and Practice. J. Toxicol. Env. Heal. B 2002, 5(4), 337–406.

Wu, Y. F. Correlated Sampling Techniques Used in Monte Carlo Simulation for Risk Assessment. Int. J. Pres. Ves. Pip. 2008, 85(9), 662–669.

Water Environment & Reuse Foundation 75

Chapter 4

Monte Carlo Statistical Analysis of Process

Performance

This chapter provides the results of the statistical assessment of the monitoring data across individual component processes for each of the two treatment trains and the simulation results for the combined treatment processes. Section 4.1 provides the pathogen inactivation data (as LRV) across the CCP processes designated for pathogen control, and Section 4.2 provides chemical removal data across the CCPs for chemical contaminant control. Section 4.3 provides the simulation results of the combined barrier performance for the model DPR systems.

4.1 Probability Density Functions for Pathogen Inactivation Processes

4.1.1 AWTP Pretreatment

Long-term monitoring data were obtained from the Groundwater Replenishment System (Orange County, California). These data ranged from the inception of the project (2008) through February 2014. Some bacterial monitoring was undertaken during this time, including in the feed water to the AWTP (“plant feed”) and the feed water to the MF process (“MF feed”). A distinct difference in bacterial quality of the two sampling locations is observable and assumed to be a consequence of pretreatment by chlorine dosing for chloramine formation prior to MF. Chloramine is formed to minimize biological fouling of the MF system and downstream RO system and therefore not designed to be a disinfection step. However, it is noteworthy that even with this addition of chloramine, 1 to 2 log removal of bacteria may be occurring (though this removal was not accounted for in later full-train Monte Carlo simulations). The unintended consequence from this step is potential DBP formation, which is why chloramination and associated dosing control becomes a CCP. No DBP data were available across this process step; therefore, no additional modeling of the chloramination process was incorporated in the combined treatment train Monte Carlo simulation.

The available data included monitoring of fecal coliform and total coliform by the multiple tube fermentation (MTF) method, with multiple samples taken per week generally during 2008 through February 2014. This comprised a total of 621 samples for fecal coliform and total coliform in each of the two sampling locations.

Data was also provided for Escherichia coli and total coliforms by the Colilert method, but collection of these data began only in November 2013 and so were significantly fewer in number (16 samples for each measure in each of the two sampling locations).

76 Water Environment & Reuse Foundation

These data were plotted to lognormal probability plots for total coliforms by MTF (Figure 4.1) and fecal coliforms by MTF (Figure 4.2). It is apparent that these data are generally well fit to lognormal PDFs, with the exception of some outlying low values for fecal coliform by MTF in the plant feed less than the second percentile of the data set.

Figure 4.1. Total coliforms (by MTF) in plant feed and microfiltration feed.

Figure 4.2. Fecal coliforms (by MTF) in plant feed and microfiltration feed.

Total coliform - MTF

Percentile (%)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

MP

N/1

00

mL

100

101

102

103

104

105

106

107

108

Plant feed

Microfiltration feed

Fecal coliform - MTF

Percentile (%)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

MP

N/1

00

mL

100

101

102

103

104

105

106

107

108

Plant feed

Microfiltration feed

Water Environment & Reuse Foundation 77

An effort was made to identify correlations in plant feed and MF feed data for each of the parameters listed. This was possible because samples were collected on the same days. However, no significant correlation could be found for any of the four paired data sets. Therefore, no correlation was assumed when undertaking probabilistic analysis.

Probabilistic analysis was used to derive PDFs for removal of total coliforms (by MTF, Figures 4.3 and 4.4). The fact that no correlations were assumed in the Monte Carlo sampling led to minor excursions into negative LRV territory in some cases, but this was limited to less than 2% probability in each case. The simulated LRV distributions subsequently fit to normal PDFs in each case.

Figure 4.3. Normal fit comparison for simulated total coliform LRV during pretreatment (MTF).

Figure 4.4. Normal fit comparison for simulated fecal coliform LRV during pretreatment (MTF).

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

0.43 3.55

-2 -1 0 1 2 3 4 5 6

LRV

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Freq

uenc

y

Fit Comparison for Total Coliform - MTFRiskNormal(1.98668,0.94601)

Input

Minimum -1.71Maximum 5.67Mean 1.99Std Dev 0.946Values 10000

Normal

Minimum −∞Maximum +∞Mean 1.99Std Dev 0.946

5.0% 90.0% 5.0%4.8% 90.3% 4.9%

0.83 4.11

-2 -1 0 1 2 3 4 5 6

LRV

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Freq

uenc

y

Fit Comparison for Fecal Coliform - MTFRiskNormal(2.47117,0.98952)

Input

Minimum -1.19Maximum 5.80Mean 2.47Std Dev 0.990Values 10000

Normal

Minimum −∞Maximum +∞Mean 2.47Std Dev 0.990

78 Water Environment & Reuse Foundation

4.1.2 Microfiltration

Bacterial organisms could be regularly detected in MF feed samples, but concentration in MF filtrates were consistently less than analytical detection limits (<2 MPN/100mL for the MTF method, <1 CFU/100 mL for membrane filtration method). Nonetheless, by considering the concentrations of total coliforms detected in MF feeds and considering that they were removed to the precise values of the analytical detection limits in MF permeates, it was possible to develop conservative PDFs for total coliforms.

MF–UF systems are capable of virus removal, and this has been demonstrated in a number of applications (Hudman et al., 1992) provided they are attached to particles such as would occur with upstream coagulation. However, unlike Cryptosporidium and Giardia, virus removal is difficult to correlate to direct integrity measurement methods. As a result, virus removal by MF–UF was not considered in this study.

Two examples are given in Figure 4.5. The first is for total coliforms by the MTF method (621 samples during 2008–2014; Figure 4.6). The second is for total coliforms by the membrane filtration method (21 samples during May–September 2009; Figure 4.7). In both cases, the LRVs shown are conservative minimum values based on analytical detection limits. In reality, significantly greater LRVs may have been achieved than are represented by these figures.

Figure 4.5. Lognormal probability plot for total coliform concentrations in MF feed solutions.

Total coliform in MF feed

Percentile (%)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

MP

N /

CF

U p

er 1

00

mL

10-1

100

101

102

103

104

105

106

107

MTF Method

Membrane Filtration Method

LOD = 2 MPN/100 mL (MTF Method)

LOD = 1 CFU/100 mL (Membrane Filtration Method)

Water Environment & Reuse Foundation 79

Figure 4.6. Normal fit comparison for simulated total coliform LRV during microfiltration (MTF).

Figure 4.7. Normal fit comparison for simulated total coliform LRV during pretreatment (membrane filtration).

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

1.93 4.58

0 1 2 3 4 5 6 7

LRV (based on detection limit in MF filtrate)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Freq

uenc

y

Fit Comparison for Total Coliform - MTFRiskNormal(3.25881,0.80558)

Input

Minimum 0.0904Maximum 6.31Mean 3.26Std Dev 0.806Values 10000

Normal

Minimum −∞Maximum +∞Mean 3.26Std Dev 0.806

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

2.56 5.08

0 1 2 3 4 5 6 7

LRV (based on detection limit in MF filtrate)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Freq

uenc

y

Fit Comparison for Total Coliform - Membrane FiltrationRiskNormal(3.82007,0.76567)

Input

Minimum 0.818Maximum 6.76Mean 3.82Std Dev 0.766Values 10000

Normal

Minimum −∞Maximum +∞Mean 3.82Std Dev 0.766

80

LRVs for pressure (calculatedthrough 2

The data hexpert advnot produ

The most D (ModulModules Eonly the la

The LRVin the U.SCryptospo

It should bhowever, targeted), rigorous (

Figure 4.8

particle remo(TMP) measud LRVs were 015. Only the

had numerousvice from exp

ucing water un

comprehensiles D01–D08)E03 through Eatter three we

s determined S. EPA Memboridium and G

be noted that the LRV requbut rather the

(frequent) test

8. Assumed op

oval can be caurements, whi

obtained frome records for 2

s records for wperienced IPRnder such con

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are for 3 μm brane FiltratioGiardia but n

MF–UF systuirement maye test is used ting to achiev

perational conf

alculated dailch provide a

m the GWRS 2013 and 201

which either R operations mnditions, and t

for Train A (E (Modules Eer, because Trodeling. Note

particles per on Guidance Mot viruses.

ems can all uy not meet thefor diagnosis

ve higher LRV

figuration for

Water

y based on rereasonable suin California

14 were closel

TMP, PDT, omanagers, it wthese data wer

(Modules A01E01–E02). In 2rain E has mue that there wa

direct integriManual. This

tilize the diree full potentia of membrane

V targets coul

the microfiltr

r Environment

esults from PDurrogate for pa. The data incly analyzed.

or LRV was rwas assumed tre filtered fro

1–A08), Train2014 Train E uch fewer modas no indicati

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t & Reuse Foun

DT and transmparticle removcluded daily r

recorded as zethat the particom the data se

n B (Moduleswas expande

dules than Trion of a Train

per the methofor use as a va

est. For currenem (i.e., <4.0 e. In a DPR ap

mended.

s.

ndation

membrane val. Daily records from

ero. Followincular MF trainets.

s B01–B08), Ted to include rains A, B, ann C in the data

odology descralid surrogate

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2008

g n was

Train

nd D, a.

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Water Environment & Reuse Foundation 81

Figure 4.9. Lognormal probability plot for particle removal by MF Modules A01–A08.

Figure 4.10. Lognormal probability plot for particle removal by MF Modules B01–B08.

Train A

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

LRV

2

3

4

5

6

7

89

1

10

Train B

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

LR

V

2

3

4

5

6

7

89

1

10

82 Water Environment & Reuse Foundation

Figure 4.11. Lognormal probability plot for particle removal by MF Modules D01–D08.

Figure 4.12. Lognormal probability plot for particle removal by MF Modules E01 and E02.

Train D

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

LR

V

2

3

4

5

6

7

89

1

10

Train E

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

LR

V

2

3

4

5

6

7

89

1

10

Water Environment & Reuse Foundation 83

Figure 4.13. Fitted lognormal PDF for particle removal by MF Module A01.

Figure 4.14. Normal fit comparison for simulated MF particle LRV by Train A.

4.5

4.6

4.7

4.8

4.9

5.0

5.1

Freq

uenc

y

5.0% 90.0% 5.0%5.0% 90.3% 4.7%

4.6888 4.8064

4.60

4.65

4.70

4.75

4.80

4.85

4.90

LRV

0

2

4

6

8

10

12

Freq

uenc

y

Fit Comparison for Train ARiskNormal(4.747148,0.035488)

Input

Minimum 4.608Maximum 4.887Mean 4.747Std Dev 0.0355Values 10000

Normal

Minimum −∞Maximum +∞Mean 4.747Std Dev 0.0355

84 Water Environment & Reuse Foundation

Figure 4.15. Normal fit comparison for simulated MF particle LRV by Trains A+B+D combined.

Only the PDT PDFs were used in subsequent multiple barrier process simulations. This is because those simulations were undertaken for viruses and protozoa but not bacteria. The coliform PDFs are presented here as additional information only.

4.1.3 Reverse Osmosis

Direct monitoring data for pathogen (Cryptosporidium, Giardia, and virus) removal by RO are very difficult to obtain, primarily because pathogens are often removed to less than available analytical detection limits by pretreatment processes such as MF, and it is rare to directly monitor them across the processes at full scale (performance standards are used instead). As a consequence, pathogens are generally not detectable in feed solutions to RO processes, let alone in permeate solutions.

One approach to characterizing pathogen removal by RO treatment is by challenge testing, which is undertaken by dosing pathogens or suitable surrogate organisms to RO feed solutions at high concentrations. MS2 bacteriophage has been widely used as a surrogate organism for testing the removal of viruses by RO membranes. In one such study, two spiral-wound RO membrane elements were found to provide an effective barrier capable of rejecting MS2 bacteriophage with LRVs equal to or greater than 5.4 (Mi et al., 2004). However, few studies have reported removal variability for RO membranes in the form of PDFs.

One study that did report removal variabilities for MS2 bacteriophage was undertaken to support the assessment of a potential potable reuse project for the city of San Diego (Olivieri et al., 1999). Although the challenge test data from that study were limited (<20 observations per membrane) and conducted using RO membranes that had been specifically vacuum tested for integrity to obtain maximum virus removal, they did produce PDFs for MS2 bacteriophage rejection by four different RO membranes. The fitted PDFs produced in that study are reproduced here for a Fluid Systems HR membrane (Figure 4.16), a Dow Film Tec membrane (Figure 4.17), a Hydranautics Ultra Low Pressure membrane (Figure 4.18), and a Fluid Systems ultra-low-pressure membrane (Figure 4.19). These data appear to be generally

5.0% 90.0% 5.0%5.2% 90.0% 4.8%

4.6013 4.6751

4.54

4.56

4.58

4.60

4.62

4.64

4.66

4.68

4.70

4.72

4.74

LRV

0

2

4

6

8

10

12

14

16

18

20

Freq

uenc

y

Fit Comparison for Trains A+B+D CombinedRiskNormal(4.637796,0.022429)

Input

Minimum 4.558Maximum 4.721Mean 4.638Std Dev 0.0224Values 10000

Normal

Minimum −∞Maximum +∞Mean 4.638Std Dev 0.0224

Water Environment & Reuse Foundation 85

conservative compared to the LRVs reported by Mi et al. (2004), though these data were not used in the full process Monte Carlo evaluation described later in this report.

Figure 4.16. Fitted Weibull PDF for MS2 bacteriophage LRV by Fluid Systems HR RO membrane.

Source: Olivieri et al. (1999).

Figure 4.17. Fitted Weibull PDF for MS2 bacteriophage LRV by Dow Film Tec RO membrane.

Source: Olivieri et al. (1999).

5.0% 90.0% 5.0%

2.348 3.5261.

5

2.0

2.5

3.0

3.5

4.0

MS2 Bacteriophage LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Freq

uenc

yFluid Systems HR - RO Membrane

Weibull(10,3.16)

Minimum 0.00Maximum +∞Mean 3.01Std Dev 0.362

5.0% 90.0% 5.0%

4.105 6.357

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

MS2 Bacteriophage LRV

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Freq

uenc

y

Dow Film Tec - RO Membrane

Weibull(9.3,5.65)

Minimum 0.00Maximum +∞Mean 5.36Std Dev 0.690

86 Water Environment & Reuse Foundation

Figure 4.18. Fitted gamma PDF for MS2 bacteriophage LRV by Hydranautics ULP RO membrane.

Source: Olivieri et al. (1999).

Figure 4.19. Fitted gamma PDF for MS2 bacteriophage LRV by Fluid Systems ULP RO membrane.

Source: Olivieri et al. (1999).

Because of the limited challenge testing data underpinning the PDFs reported by Olivieri et al. (1999), an additional, more conservative approach was also adopted for the current analysis. This approach involved the use of sulfate ion measurements as a surrogate for pathogen removal by RO. The data provided from the

5.0% 90.0% 5.0%

3.792 5.738

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

MS2 Bacteriophage LRV

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Freq

uenc

y

Hydranautics ULP RO unitGamma(63.48,0.0744)

Hydranautics ULP RO unitGamma(63.48,0.0744)

Minimum 0.00Maximum +∞Mean 4.72Std Dev 0.593

5.0% 90.0% 5.0%

2.728 4.352

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Fluid Systems ULP RO unitGamma(50,0.07)

Fluid Systems ULP RO unitGamma(50,0.07)

Minimum 0.00Maximum +∞Mean 3.50Std Dev 0.495

Water Environment & Reuse Foundation 87

GWRS spanned February 2008 through January 2014 and included 98 sulfate measurements in the RO feed (all > analytical detection limit) and 71 measurements in the RO permeate (with about 44% of those > analytical detection limit of 0.5 mg/L; Figure 4.20). These data made it possible to fit lognormal PDFs to sulfate concentrations in RO feed and RO permeate, representing long-term variability. A Monte Carlo simulation based on these feed and permeate PDFs was then used to derive a normal PDF to describe variability of sulfate LRV by RO treatment (Figure 4.21). Although this PDF may then be used as a surrogate for RO removal of pathogens, it should be remembered that pathogens are expected to be removed considerably more effectively than sulfate ions, and therefore, this is a highly conservative surrogate model.

Figure 4.20. Lognormal probability plot for sulfate concentrations in RO feed and permeate at GWRS (2008–2014).

Figure 4.21. Simulated sulfate LRV for RO treatment and fit comparison to a normal PDF.

Sulfate (as RO surrogate)

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Co

nce

ntra

tion

(mg

/L)

0.1

1

10

100

1000

LOR = 0.5 mg/L

RO Feed

RO Permeate

1.5

2.0

2.5

3.0

3.5

4.0

Freq

uenc

y

88 Water Environment & Reuse Foundation

4.1.4 UV Disinfection and UV–Advanced Oxidation

UV data were obtained from a 180 mgd drinking water facility located in the Pacific Northwest United States. The plant is a surface water facility with ozone, chlorination, and UV disinfection using 13 reactors at disinfection doses. The UV data for this analysis were specifically obtained from just one reactor, which was operating continuously during the sample period. Other reactors are turned on or off based on system demand, with flow diverted only to the operational reactor vessels.

The data included hourly values for dose (mJ/cm2), flow (mgd), transmittance (%), and power (no units provided). The data spanned January 1 to December 23, 2014, with a few data gaps, presumably corresponding to periods of nonoperation of this reactor. In all, there were 6562 valid data values for each of the four parameters.

The data sets for dose, transmittance, and flow were each fitted to BetaGeneral PDFs. The BetaGeneral PDFs and fit comparisons are presented in Figures 4.22 through 4.27. Transmittance was fitted with a maximum value constraint of 100%, and all other minimum and maximum values were not externally defined (i.e., optimum BetaGeneral PDFs were fit). Fits were tested for a range of other potential PDF types including normal, lognormal, and gamma distributions, but BetaGeneral provided the best fit results in each of these cases.

Figure 4.22. Fit comparison for UV dose data to a BetaGeneral PDF.

5.0% 90.0% 5.0%2.8% 89.3% 7.9%

53.8 80.8

40 50 60 70 80 90 100

110

Dose (mJ/cm)

0.0

0.2

0.4

0.6

0.8

1.0

Cum

ulat

ive

freq

uenc

y

Fit Comparison for DoseRiskBetaGeneral(2.3381,2.2914,49.002,88.139)

Input

Minimum 40.87Maximum 100.00Mean 68.74Std Dev 8.94

BetaGeneral

Minimum 49.00Maximum 88.14Mean 68.77Std Dev 8.25

Water Environment & Reuse Foundation 89

Figure 4.23. Fitted BetaGeneral PDF to UV dose data.

Figure 4.24. Fit comparison for UV transmittance data to a BetaGeneral PDF.

5.0% 90.0% 5.0%

55.21 82.22

45 50 55 60 65 70 75 80 85 90

Dose (mJ/cm)

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

Freq

uenc

y

DoseBetaGeneral(2.3381,2.2914,49.002,88.139)

DoseBetaGeneral(2.3381,2.2914,49.002,88.139)

Minimum 49.00Maximum 88.14Mean 68.77Std Dev 8.25

5.0% 5.0%11.0% 13.6%

96.10 98.60

70 75 80 85 90 95 100

105

Transmittance (%)

0.0

0.2

0.4

0.6

0.8

1.0

Cum

ulat

ive

Freq

uenc

y

Fit Comparison for TransmittanceRiskBetaGeneral(1059587.4,5.4063,-495068,100)

Input

Minimum 74.53Maximum 99.90Mean 97.53Std Dev 0.853

BetaGeneral

Minimum -495,068.00Maximum 100.00Mean 97.47Std Dev 1.09

90 Water Environment & Reuse Foundation

Figure 4.25. Fitted BetaGeneral PDF for UV transmittance data.

Figure 4.26. Fit comparison for flow data to a BetaGeneral PDF.

5.0% 90.0% 5.0%

95.46 98.96

93 94 95 96 97 98 99 100

101

Transmittance (%)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Freq

uenc

y

TransmittanceBetaGeneral(1059587.4,5.4063,-495068,100)

TransmittanceBetaGeneral(1059587.4,5.4063,-495068,100)

Minimum -495,068.00Maximum 100.00Mean 97.47Std Dev 1.09

5.0% 90.0% 5.0%5.9% 85.1% 9.0%

14.69 16.41

11 12 13 14 15 16 17 18 19

Flow (MGD)

0.0

0.2

0.4

0.6

0.8

1.0

Cum

ulat

ive

Freq

uenc

y

Fit Comparison for FlowRiskBetaGeneral(1.3630,2.1078,14.5248,17.0816)

Input

Minimum 11.70Maximum 18.28Mean 15.48Std Dev 0.642

BetaGeneral

Minimum 14.52Maximum 17.08Mean 15.53Std Dev 0.591

Water Environment & Reuse Foundation 91

Figure 4.27. Fitted BetaGeneral PDF for flow data.

5.0% 90.0% 5.0%

14.673 16.580

14.5

15.0

15.5

16.0

16.5

17.0

17.5

Flow (MGD)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Freq

uenc

y

FlowBetaGeneral(1.363,2.1078,14.5248,17.0816)

FlowBetaGeneral(1.363,2.1078,14.5248,17.0816)

Minimum 14.52Maximum 17.08Mean 15.53Std Dev 0.591

92 Water Environment & Reuse Foundation

The UV system for which these data were acquired applies a dose of around 70 mJ/cm2 (see Figure 4.23). This is a typical UV dose for plants practicing UV disinfection targeting Cryptosporidium, Giardia, and viruses. However, the treatment trains evaluated in RO membrane-based DPR scenarios need to incorporate UV–AOP processes. For advanced oxidation, significantly higher UV doses are applied (400–1000 mJ/cm2). In this study, LRCs were modeled for both disinfection doses and UV–AOP doses.

An increased UV dose can be achieved in practice by the incorporation of additional UV lamps or the arrangement of numerous UV reactors in series. Therefore, an acceptable means of simulating a higher UV dose is by summing numerous independent UV dose PDFs (Lawryshyn and Hofmann, 2015). In order to achieve a UV dose within a typical UV–AOP range, a Monte Carlo simulation was used to sum six independently sampled UV disinfection dose PDFs. A PDF for the combined dose is presented in Figure 4.28. This PDF was used as a simulated UV–AOP dose.

Figure 4.28. Fit comparison for simulated UV–AOP dose.

320

340

360

380

400

420

440

460

480

500

Freq

uenc

y

Water Environment & Reuse Foundation 93

The U.S. EPA has developed UV dose requirements for potable water systems to receive credit for inactivation of Cryptosporidium, Giardia, and viruses (Table 4.1; U.S. EPA, 2015). The UV dose values in Table 4.1 are applicable only to post-filter applications of UV disinfection in filtered systems and unfiltered systems.

The UV dose depends on the UV intensity (measured by UV sensors), the flow rate, and UVT. A relationship between the required UV dose and these parameters must be established and then monitored at the water treatment plant to ensure sufficient disinfection of microbial pathogens.

The UV dose requirements in Table 4.1 account for uncertainty in the UV dose–response relationships of the target pathogens (U.S. EPA, 2006), and for viruses this dose–response relationship is based on adenovirus, one of the more difficult viruses to inactivate by UV. However, they do not address other significant sources of uncertainty in full-scale UV disinfection applications. These other sources of uncertainty are due to the hydraulic effects of the UV installation, the UV reactor equipment (e.g., UV sensors), and the monitoring approach.

Because of these factors, the LT2ESWTR requires potable water systems to use UV reactors that have undergone validation testing. This validation testing must determine the operating conditions under which the reactor delivers the required UV dose for treatment credit (U.S. EPA, 2015). These operating conditions must include flow rate, UV intensity as measured by a UV sensor, and UV lamp status.

The treatment credits listed in Table 4.1 are for UV light at a wavelength of 254 nm as produced by a low-pressure mercury vapor lamp. To receive treatment credit for other lamp types, systems must demonstrate an equivalent germicidal dose through reactor validation testing.

Table 4.1. UV Dose Table for Cryptosporidium, Giardia lamblia, and Virus Inactivation Credit

Log Credit Cryptosporidium UV dose (mJ/cm2)

Giardia lamblia UV dose (mJ/cm2)

Virus UV dose (mJ/cm2)

0.5 1.6 1.5 39

1.0 2.5 2.1 58

1.5 3.9 3.0 79

2.0 5.8 5.2 100

2.5 8.5 7.7 121

3.0 12 11 143

3.5 15 15 163

4.0 22 22 186

Note: UV=ultraviolet.

Source: U.S. EPA (2015).

The derivations of the UV dose–inactivation relationships presented in Table 4.1 were included in Appendix B of the draft U.S. EPA UV Disinfection Guidance Manual, which was released for public review and comment in 2003 (U.S. EPA, 2003b). However, the details were not subsequently included in the final UV Disinfection Guidance Manual (U.S. EPA, 2006). The draft document includes a rigorous statistical analysis of data from a range of published studies for UV disinfection for Cryptosporidium, Giardia, and viruses. In each case, an “80% credible interval” was determined and used for the derivation of a required UV dose to achieve nominal LRVs.

94 Water Environment & Reuse Foundation

No parameters (e.g., linear regression parameters) were provided for the 80% credible interval lines. Therefore, no numerical relationship was provided for direct calculation of LRV from UV dose. However, the 80% credible interval was used to derive the UV doses presented in Table 4.1. These data may therefore be used to back fit an approximate linear regression for the 80% credible interval lines. Therefore, it was possible to approximate the underlying dose–inactivation relationships by plotting LRV versus UV dose for viruses (Figure 4.29) and LRV versus log UV dose for Cryptosporidium and Giardia (Figure 4.30).

Figure 4.29. UV dose–LRV relationship for virus inactivation (r2=0.9997).

Figure 4.30. Log UV dose–LRV relationship for Cryptosporidium (r2=0.993) and Giardia lamblia (r2=0.996) inactivation.

Virus Inactivation

UV Dose (mJ/cm)

50 100 150 200 250

Lo

g in

act

iva

tion

0

1

2

3

4

5

6

Cryptosporidium & Giardia lamblia Inactivation

Log UV Dose (mJ/cm)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Lo

g in

act

iva

tion

0

1

2

3

4

5

6

CryptosporidiumGiardia lamblia

Water Environment & Reuse Foundation 95

First-order regressions fitted to the data in these plots were used to derive approximate relationships between UV dose and LRVs for the three pathogen types:

Cryptosporidium LRV=3.09Log10(UV Dose)–0.255

Giardia lamblia LRV=2.923Log10(UV Dose)+0.005

Virus LRV=0.024UV Dose-0.391

The U.S. EPA UV dose table presents doses for up to 4 LRV only, as this is the maximum performance that the agency will credit for a UV system. However, it is clear from the derivation of these data (presented in Appendix B of the draft U.S. EPA UV Disinfection Guidance Manual, U.S. EPA 2003b) that these relationships hold for much higher UV doses and associated LRVs and that UV dose is additive when multiple reactors are placed in series (Lawryshyn and Hofmann, 2015).

In accordance with this, these relationships were used to derive PDFs for virus, Giardia, and Cryptosporidium disinfection LRVs and UV–AOP LRVs by means of Monte Carlo simulations. The PDFs produced from this process were subsequently fit to BetaGeneral PDFs with good results, as presented for Giardia LRV (Figure 4.31), Cryptosporidium LRV (Figure 4.32), and virus LRV (Figure 4.33) at UV–AOP doses and for Giardia LRV (Figure 4.34), Cryptosporidium LRV (Figure 4.35), and virus LRV (Figure 4.36) at disinfection doses.

Figure 4.31. BetaGeneral fit comparison for simulated UV–AOP Giardia LRV.

96 Water Environment & Reuse Foundation

Figure 4.32. BetaGeneral fit comparison for simulated UV–AOP Cryptosporidium LRV.

Figure 4.33. BetaGeneral fit comparison for simulated UV–AOP virus LRV.

Water Environment & Reuse Foundation 97

Figure 4.34. BetaGeneral fit comparison for simulated UV disinfection Giardia LRV.

Figure 4.35. BetaGeneral fit comparison for simulated UV disinfection Cryptosporidium LRV.

5.0% 90.0% 5.0%4.8% 90.0% 5.2%

5.102 5.608

4.9

5.0

5.1

5.2

5.3

5.4

5.5

5.6

5.7

LRV

0.0

0.5

1.0

1.5

2.0

2.5

Freq

uenc

y

Fit Comparison for Giardia LRVRiskBetaGeneral(2.7912,2.0739,4.93775,5.69326)

Input

Minimum 4.960Maximum 5.692Mean 5.372Std Dev 0.155Values 10000

BetaGeneral

Minimum 4.938Maximum 5.693Mean 5.371Std Dev 0.154

5.0% 90.0% 5.0%4.8% 90.0% 5.2%

5.129 5.664

4.9

5.0

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

LRV

0.0

0.5

1.0

1.5

2.0

2.5

Freq

uenc

y

Fit Comparison for Cryptosporidium LRVRiskBetaGeneral(2.7912,2.0739,4.95570,5.75379)

Input

Minimum 4.979Maximum 5.752Mean 5.414Std Dev 0.163Values 10000

BetaGeneral

Minimum 4.956Maximum 5.754Mean 5.414Std Dev 0.163

98 Water Environment & Reuse Foundation

Figure 4.36. BetaGeneral fit comparison for simulated UV disinfection virus LRV.

4.1.5 Chlorine Disinfection

U.S. EPA Disinfection Profiling and Benchmarking Technical Guidance Manual (2003a) requires drinking water disinfection systems to create a disinfection profile. In order to create a disinfection profile, systems must identify disinfection segments, collect required data for each segment, calculate CT, and calculate inactivation. Necessary data include peak hourly flow, residual disinfection concentration, temperature, and pH (if chlorine is used).

The guidance manual provides approaches for deriving log inactivation values for Giardia and viruses for free chlorine disinfection. The approaches involve calculating a value for CTactual based on measurements of chlorine residual concentration and exposure time. One approach is to collect data, including disinfection residual concentrations and peak hourly flow rates, through vessels. To determine the contact time (T10), the volume (V) of a vessel is divided by the peak hourly flow rate (Q) and then multiplied by the baffling factor assigned to the vessel:

The value for CTactual is then determined by multiplying T10 by residual disinfectant concentration for the disinfection segment:

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

0.924 1.566

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Freq

uenc

y

Fit Comparison for Viruses LRVRiskBetaGeneral(2.3177,2.2717,0.77722,1.70581)

Input

Minimum 0.785Maximum 1.700Mean 1.246Std Dev 0.196Values 10000

BetaGeneral

Minimum 0.777Maximum 1.706Mean 1.246Std Dev 0.196

Water Environment & Reuse Foundation 99

A standard CT value relating to a specific value of log inactivation is then usually obtained from a table for such CT values for various chlorine concentrations, temperature, and (for free chlorine) pH. For Giardia, the standard CT is CT3 log, Giardia, and for viruses, the standard CT is CT4 log, Virus:

3.0 ,

4.0 ,

The most common standard CT values are obtained via an approximation method based on selecting conservative values of pH, temperature, and residual disinfectant concentration from established CT tables to estimate the CTs required for 3 log inactivation of Giardia and 4 log inactivation of viruses.

In order to maintain a regular continuous PDF format, the study did not select discrete values from CT tables but instead adopted methods to interpolate values via regression techniques. An appropriate regression method for Giardia has previously been reported by Smith et al. (1995). This method is used to estimate the CT required to inactivate 3 log Giardia with chlorine:

12.5 ; , 0.353 12.006 . . . .

12.5 ; , 0.361 2.261 . . . . Where: I=3 log removal of Giardia C=free chlorine residual concentration (mg/L) Temp=temperature in oC.

For viruses, CT tables from the U.S. EPA Disinfection Benchmarking Manual were used (2003a). These provide required CT values (mg/min/L) for 4 log inactivation of viruses by free chlorine at pH 6.0 to 9.0 for a range of temperatures between 0.5 and 25o C. These CT values are based on data by Sobsey et al. (1988) for inactivation of Hepatitis A virus Strain HM175. The CTs from that work were multiplied by a safety factor of three to obtain the more conservative CT values for viruses, as used by the U.S. EPA. Unlike for Giardia, no previously reported regression method could be identified. However, one was developed for this work.

The full table of temperature versus required CT values was plotted as shown in Figure 4.37. Visual inspection of Figure 4.37 reveals two inflection points at approximately 5oC and 15oC but relatively linear relationships for the three temperature segments 0.5 to 5oC, 5 to 15oC, and 15 to 25oC. Therefore, linear regressions were fit to each of these three segments to obtain the following regression equations for CT4

log, Virus:

5 ; , 0.8932 12.474

5 15 ; , 0.4 10

15 ; , 0.2 7

100 Water Environment & Reuse Foundation

Figure 4.37. Required CT values for 4 log inactivation of viruses by free chlorine at pH 6.0–9.0.

Data for this analysis were obtained from a U.S. drinking water treatment plant. It included daily records for chlorine residual, flow, temperature, and pH from July 1, 2008, through June 30, 2009. These records were used to calculate 365 daily values for CTactual for the chlorination clearwell, and a lognormal probability plot revealed that these data could be well fit to a lognormal PDF (Figure 4.38). The pH data were similarly well fit to a normal probability plot (Figure 4.39), and free chlorine residual data were well fit to a lognormal probability plot (Figure 4.40). The data for temperature were not well fit to normal or lognormal distributions, but a fit comparison for the temperature data to a BetaGeneral PDF showed good results (Figure 4.41). Corresponding PDFs were then fit for CTactual (Figure 4.42), pH (Figure 4.43), chlorine residual (Figure 4.44), and temperature (Figure 4.45).

4-Log Virus CTrequired

Temperature (oC)

0 5 10 15 20 25 30

CT

requ

ired

(mg

-min

/L)

0

2

4

6

8

10

12

14

Water Environment & Reuse Foundation 101

Figure 4.38. Lognormal probability plot for clearwell CT.

Figure 4.39. Normal probability plot for clearwell pH.

Clearwell CT

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

CT

(m

g-m

in/L

)

10

100

1000

Clearwell pH

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

pH

6.4

6.6

6.8

7.0

7.2

7.4

7.6

102 Water Environment & Reuse Foundation

Figure 4.40. Lognormal probability plot for clearwell free chlorine residual.

Figure 4.41. Fit comparison for temperature to a BetaGeneral PDF.

Clearwell free chlorine residual

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Fre

e c

hlo

rine

(m

g/L

)

0.1

1

10

5.0% 90.0% 5.0%2.4% 93.2% 4.4%

2.00 28.00

0 5 10 15 20 25 30 35

Temperature (oC)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

Input

Minimum 1.00Maximum 33.25Mean 15.16Std Dev 8.97

BetaGeneral

Minimum 1.90Maximum 28.26Mean 15.14Std Dev 8.89

Water Environment & Reuse Foundation 103

Figure 4.42. Fitted lognormal PDF for clearwell CT.

Figure 4.43. Fitted normal PDF for clearwell pH.

5.0% 90.0% 5.0%

71.1 115.7

40 50 60 70 80 90 100

110

120

130

mg-min/L

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Freq

uenc

y

CTLognorm(91.699,13.653)

CTLognorm(91.699,13.653)

Minimum 0.00Maximum +∞Mean 91.70Std Dev 13.65

5.0% 90.0% 5.0%

6.761 7.218

6.6

6.7

6.8

6.9

7.0

7.1

7.2

7.3

7.4

pH

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Freq

uenc

y

pHNormal(6.98989,0.13888)

pHNormal(6.98989,0.13888)

Minimum −∞Maximum +∞Mean 6.990Std Dev 0.139

104 Water Environment & Reuse Foundation

Figure 4.44. Fitted lognormal PDF for clearwell chlorine residual.

Figure 4.45. Fitted BetaGeneral PDF for clearwell temperature.

5.0% 90.0% 5.0%

1.162 2.306

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Chlorine concentration (mg/L)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Freq

uenc

y

ChlorineLognorm(1.6724,0.35237)

ChlorineLognorm(1.6724,0.35237)

Minimum 0.00Maximum +∞Mean 1.67Std Dev 0.352

5.0% 90.0% 5.0%

2.24 27.94

0 5 10 15 20 25 30

Temperature (oC)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Freq

uenc

y

TemperatureBetaGeneral(0.82533,0.76515,0,29.1)

TemperatureBetaGeneral(0.82533,0.76515,0,29.1)

Minimum 1.90Maximum 28.26Mean 15.14Std Dev 8.89

Water Environment & Reuse Foundation 105

In order to establish daily LRV values for Giardia and LRV, it was first necessary to determine the daily values for CT3 log, Giardia and CT4 log, Virus (i.e., the denominator of the estimated log inactivation equation shown previously). Distributions for these values were derived by Monte Carlo simulations from the PDFs for temperature and (for Giardia only) chlorine residual and pH as recorded in daily operating logs. The resulting PDFs are presented for CT3 log, Giardia (Figure 4.46) and CT4 log, Virus.(Figure 4.47). These PDFs are irregular looking, with clustering and overall shapes reflecting the regression relationships that were used to derive them.

Figure 4.46. Simulated PDF for CT3 log, Giardia.

Note: Required CT for 3 log removal.

Figure 4.47. Simulated PDF for CT4-log, viruses.

Note: Required CT for 4 log removal.

0 50 100

150

200

250

300

Freq

uenc

y

1 2 3 4 5 6 7 8 9 10 11

Freq

uenc

y

106 Water Environment & Reuse Foundation

Estimated log inactivations of Giardia and viruses (Giardia LRV and virus LRV) were derived using the relationships described previously (U.S. EPA, 2003a). This was achieved by Monte Carlo simulation based on the CTactual and CTrequired PDFs presented in this section. The resulting PDF and a lognormal fit comparison are presented for Giardia (Figure 4.48) and viruses (Figure 4.49). Cumulative lognormal PDF fit comparisons are also presented for Giardia (Figure 4.50) and viruses (Figure 4.51). However, similar to the UV data, it should be noted that the disinfection performance was extrapolated from U.S. EPA guidelines via linear regression and does not necessarily capture actual disinfection performance at higher chlorine CT values.

An alternative approach would be to calculate daily LRV values for Giardia and viruses and fit PDFs to these directly. However, the approach adopted here is considered to be more realistic because the variables used to derive them are considered to be relatively independent. Therefore, a Monte Carlo simulation with 10,000 iterations will include more combinations of CTactual and temperature than the original data set of 365 days.

Furthermore, the actual fitted PDFs for Giardia LRV (Figure 4.48) and virus LRV (Figure 4.49) may provide a more accurate representation of LRV variability than the less regular simulated results in these figures. This is because the simulated results reflect the irregularity of the CTactual and CTrequired PDFs, which in turn was derived from the irregularity of the way these data were calculated by regression techniques. The irregularity of these regression techniques reflects irregularity in the data used to derive U.S. EPA CT tables.

Figure 4.48. Lognormal fit comparison for simulated chlorination Giardia LRV.

5.0% 90.0% 5.0%5.1% 86.4% 8.5%

1.23 7.90

0 2 4 6 8 10 12 14

LRV

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Freq

uenc

y

Fit Comparison for Giardia LRVRiskLognorm(3.2325,3.3212,RiskShift(0.66481))

Input

Minimum 0.788Maximum 12.37Mean 3.76Std Dev 2.25Values 10000

Lognorm

Minimum 0.665Maximum +∞Mean 3.90Std Dev 3.32

Water Environment & Reuse Foundation 107

Figure 4.49. Lognormal fit comparison for simulated chlorination virus LRV.

Figure 4.50. Cumulative lognormal fit comparison for simulated chlorination Giardia LRV.

5.0% 90.0% 5.0%4.8% 87.7% 7.5%

33.2 265.5

0 50 100

150

200

250

300

350

400

450

LRV

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

Freq

uenc

y

Fit Comparison for Virus LRVRiskLognorm(98.223,119.21,RiskShift(20.362))

Input

Minimum 22.27Maximum 417.59Mean 112.88Std Dev 75.96Values 10000

Lognorm

Minimum 20.36Maximum +∞Mean 118.59Std Dev 119.21

5.0% 90.0% 5.0%5.1% 86.4% 8.5%

1.23 7.90

0 2 4 6 8 10 12 14

LRV

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

Fit Comparison for Giardia LRVRiskLognorm(3.2325,3.3212,RiskShift(0.66481))

Input

Minimum 0.788Maximum 12.37Mean 3.76Std Dev 2.25Values 10000

Lognorm

Minimum 0.665Maximum +∞Mean 3.90Std Dev 3.32

108 Water Environment & Reuse Foundation

Figure 4.51. Cumulative lognormal fit comparison for simulated chlorination virus LRV.

5.0% 90.0% 5.0%4.8% 87.7% 7.5%

33.2 265.5

0 50 100

150

200

250

300

350

400

450

LRV

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

Fit Comparison for Virus LRVRiskLognorm(98.223,119.21,RiskShift(20.362))

Input

Minimum 22.27Maximum 417.59Mean 112.88Std Dev 75.96Values 10000

Lognorm

Minimum 20.36Maximum +∞Mean 118.59Std Dev 119.21

Water Environment & Reuse Foundation 109

4.1.6 Sand Filtration

The city of Ottawa has investigated relationships between sand filtration performance and pathogen LRV. Experiments conducted at a Britannia Pilot Plant in 2014 were used to develop exponential relationships between sand filter effluent turbidity (NTU) and LRVs for Cryptosporidium, 4.5 m polystyrene microspheres (as a surrogate for bacterial cells), and bacteriophage PRD1 (as a surrogate for viruses).

These experimental results were collected from winter challenge trials with elevated surface water turbidity and filter breakthrough (Campbell et al., 2014; Douglas et al., 2015). The data collected from these trials and the exponential functions fitted to them are presented in Figure 4.52.

Figure 4.52. Cryptosporidium, microsphere, and PRD1 log removal vs. filter turbidity relationship. Source: Reprinted with permission from Douglas et al. (2015).

y = 1.1622x‐0.58

y = 0.7141x‐0.738

y = 0.5895x‐0.521

0

1

2

3

4

5

6

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Crypto Log‐removal

4.5 M/S log removal

PRD1 (phage) log removal

Power (Crypto Log‐removal)

Power (4.5 M/S log removal)

Power (PRD1 (phage) log removal)

Cryptosporidium, microsphere, & PRD1 log-removal vs. filter turbidity relationship Britannia Pilot Plant - 2014 trials

log removal (filtration only)

filter effluent turbidity (NTU)

110 Water Environment & Reuse Foundation

Sand filtration effluent data were obtained from the Goreangab Water Reclamation Plant in Windhoek, Namibia. Although no direct pathogen removal data were available, turbidity data in the feed and filtrate of the sand filters were available. Sand filtration effluent data from the Goreangab Water Reclamation Plant are presented in Figure 4.53.

Figure 4.53. Sand filtration effluent data from the Goreangab Water Reclamation Plant in Windhoek, Namibia.

In the United States, drinking water filtration processes may be credited with 2.5 log removal for Cryptosporidium and Giardia based on a combined filter effluent turbidity of 0.1. In the Goreangab facility, UF membranes are used to provide the final physical barrier to Cryptosporidium and Giardia (followed by chlorine); therefore, maintaining a filter performance of 0.1 was not required. Instead, effluent turbidity at this plant ranged from 0.1 to 0.3. As such, the use of turbidity as a surrogate from this location is conservative and not necessarily representative of achievable filter performance.

It is arguable that sand filtration effluent turbidity is a direct function of influent turbidity and, therefore, that relationships between effluent turbidity and filter performance should also account for influent turbidity ranges. However, this assumption does not appear to underpin contemporary filtration performance monitoring for U.S. drinking water systems. Instead, it is considered more likely that effluent turbidity is a function of filter media morphology and capacity to store particles during operation. Thus, influent turbidity doesn’t impact pathogen removal but would impact the amount of time the filter could run before observing turbidity breakthrough.

The relationships developed by the city of Ottawa were used to derive PDFs for Cryptosporidium LRV (Figure 4.54), bacteria LRV (Figure 4.55) and virus LRV (Figure 4.56). In subsequent Monte Carlo simulations, the filter performance for Cryptosporidium LRV was also assumed to apply to Giardia LRV.

Sand filtration effluent turbidity

Percentile (%)

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Tur

bid

ity N

TU

0.01

0.1

1

10

Water Environment & Reuse Foundation 111

Figure 4.54. Fit comparison for Cryptosporidium LRV by sand filtration.

Figure 4.55. Fit comparison for bacteria LRV by sand filtration.

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

Freq

uenc

y

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

2.224 3.295

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fit Comparison for Bacteria LRV - Sand FiltrationRiskLognorm(2.7118,0.32696,RiskShift(0.015108))

Input

Minimum 1.72Maximum 4.42Mean 2.73Std Dev 0.327Values 10000

Lognorm

Minimum 0.0151Maximum +∞Mean 2.73Std Dev 0.327

112 Water Environment & Reuse Foundation

Figure 4.56. Fit comparison for virus LRV by sand filtration.

4.1.7 Ozonation

The derivation of PDFs for ozonation LRVs was undertaken using a procedure analogous to that already described for chlorination LRVs. The relationships given for the standard CT values for Giardia (CT3 log, Giardia) and viruses (CT4 log, Virus) apply for ozonation (U.S. EPA, 2003a):

3.0 ,

4.0 ,

In addition, the U.S. EPA Code of Federal Regulations provides a numerical relationship to derive log credits for Cryptosporidium inactivation by ozone based on ozone CTactual and temperature (U.S. EPA, 2015):

Log credit (ozonation) for Cryptosporidium = (0.0397 × (1.09757)Temp) × CTactual

Values to be used for ozonation CT3 log, Giardia and CT4 log, Virus are available from standard CT tables (U.S. EPA, 2003a). Those tables were used to derive the charts presented for CT3 log, Giardia versus temperature (Figure 4.57) and CT4 log, Virus versus temperature (Figure 4.58). The data presented in each of these figures were then used to develop regression relationships for CT3 log, Giardia versus temperature and CT4 log, Virus versus temperature as presented following each figure.

Water Environment & Reuse Foundation 113

Figure 4.57. CT3 log, Giardia vs. temperature based on standard CT tables. Source: U.S. EPA (2003a).

Regression relationships were developed for CT3 log, Giardia versus temperature (Figure 4.57) as shown:

5 ; , 0.25 3.15

5 10 ; , 0.0946 2.3743

10 15 ; , 0.0954 2.3829

15 20 ; , 0.0454 1.63

20 ; , 0.0474 1.6671

Figure 4.58. CT4 log, virus vs. temperature based on standard CT tables.

Source: U.S. EPA (2003a).

3-Log Giardia CTrequired

Temperature (oC)

0 5 10 15 20 25 30

CT

requ

ired

(mg

-min

/L)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4-Log Virus CTrequired

Temperature (oC)

0 5 10 15 20 25 30

CT

requ

ired

(mg-

min

/L)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

114 Water Environment & Reuse Foundation

Regression relationships were developed for CT4 log, Virus versus temperature (Figure 4.58) as shown:

5 ; , 0.15 1.95

5 10 ; , 0.04 1.4

10 15 ; , 0.08 1.8

15 20 ; , 0.02 0.9

20 ; , 0.04 1.3

Ozonation data were obtained from a water treatment plant in the western United States. The ozonation system is relatively complex, with 12 individual ozone contact chambers. Dissolved ozone residual is measured in three of these chambers (3, 5, and 7), and the ozone residual concentration is based on the determination of an ozone decay rate between these chambers. The measured ozone concentration in Chamber 3 and the determined ozone decay rate are used to derive an overall ozone residual concentration. This overall ozone residual concentration is used with flow data and a baffling factor to derive an overall value for ozone CT.

Long-term hourly data were provided, but only 12 months of data from July 1, 2014 to June 30, 2015, were used in this analysis. This comprised around 8600 hourly data entries. However, it was apparent that the system was not running continuously during this period, and residual ozone concentrations of 0 mg/L were logged for some extended periods. In some cases, even when the ozone analyzer in Chambers 5 and 7 showed 0 mg/L, the analyzer in Chamber 3 recorded readings of up to 0.07 mg/L. These values indicated that the system was not operational at these times, but that the analyzer reported these low values as a consequence of some error (e.g., calibration error). It was further assumed that if the analyzer did read such low values during operation, a CCP would be triggered, and the ozonation system would be shut down. Therefore, all calculated CT values corresponding to Chamber 3’s analyzer reading of less than 0.07 mg/L were filtered from the data set prior to further analysis. This left 7432 data entries for probabilistic analysis.

A lognormal probability plot for ozonation CTactual (Figure 4.59) indicated that the calculated CT data could be well fit to a lognormal PDF. However, the temperature data was found to be a better fit to a BetaGeneral PDF; the results of a fit comparison are presented in Figure 4.60. Figures 4.61 and 4.62 show the fitted PDFs for CTactual and temperature.

Figure 4.59. Lognormal probability plot for ozonation CTactual.

Ozonation CTactual

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

CT

(m

g-m

in/L

)

0.1

1

10

100

Water Environment & Reuse Foundation 115

Figure 4.60. Fit comparison for temperature data to a BetaGeneral PDF.

Figure 4.61. Fitted lognormal PDF to CTactual data.

5.0% 90.0% 5.0%7.9% 82.3% 9.9%

13.86 20.25

5 10 15 20 25 30 35

Temperature (oC)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

Fit Comparison for TemperatureRiskBetaGeneral(0.93253,2.5439,13.564,25.044)

Input

Minimum 8.75Maximum 31.62Mean 16.55Std Dev 2.18

BetaGeneral

Minimum 13.56Maximum 25.04Mean 16.64Std Dev 2.40

-2 0 2 4 6 8 10 12 14

Freq

uenc

y

116 Water Environment & Reuse Foundation

Figure 4.62. Fitted BetaGeneral PDF to temperature data.

12 14 16 18 20 22 24 26

Freq

ucen

cy

Water Environment & Reuse Foundation 117

Monte Carlo simulations based on the fitted PDFs for CTactual and temperature were used to derive PDF for Cryptosporidium LRV (Figure 4.63), Giardia LRV (Figure 4.64), and virus LRV (Figure 4.65). In each case, these ozonation data were subsequently well fit to lognormal PDFs, as shown by the fit comparisons presented in these three figures.

Figure 4.63. Lognormal fit comparison for simulated ozonation Cryptosporidium LRV.

Figure 4.64. Lognormal fit comparison for simulated ozonation Giardia LRV.

5.0% 90.0% 5.0%5.1% 89.9% 5.0%

0.386 1.878

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Freq

uenc

y

Fit Comparison for Cryptosporidium LRVRiskLognorm(0.90823,0.49324,RiskShift(0.038190))

Input

Minimum 0.151Maximum 4.67Mean 0.946Std Dev 0.494Values 10000

Lognorm

Minimum 0.0382Maximum +∞Mean 0.946Std Dev 0.493

5.0% 90.0% 5.0%5.0% 89.9% 5.0%

7.4 32.4

0 10 20 30 40 50 60 70 80

LRV

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Freq

uenc

y

Fit Comparison for Giardia LRVRiskLognorm(16.771,8.1651,RiskShift(0.27890))

Input

Minimum 2.96Maximum 77.84Mean 17.05Std Dev 8.17Values 10000

Lognorm

Minimum 0.279Maximum +∞Mean 17.05Std Dev 8.17

118 Water Environment & Reuse Foundation

Figure 4.65. Lognormal fit comparison for simulated ozonation virus LRV.

4.2 Probability Density Functions for Chemical Removal Processes

Full-scale data appropriate for developing PDFs for chemical removal were available only for two treatment processes: RO and GAC. These represent key organic chemical removal processes for the two principal treatment trains considered in this work. However, only RO is effective for consistent removal of water-soluble inorganic contaminants such as the major anions and cations. For the ozone–BAC-based treatment train, flocculation, sedimentation, and filtration do have potential to remove some of the cations and, when a preozonation step is included before flocculation and sedimentation, additional removal may be achieved. However, each specific cation and anion of concern identified in the site-specific risk assessment would need to be evaluated for removal across the flocculation and sedimentation process. or for subsequent treatment by blending (to achieve concentrations less than drinking water primary or secondary MCLs), or by additional processes such as ion exchange (not evaluated in this study).

In many circumstances it is common to report chemical removal performance as either a fractional removal or by percentage. However, in this work, all chemical removals are reported as LRVs for consistency with the reporting of microbial contaminants. Working in LRVs was also found to be convenient because the removal of many chemical contaminants was found to be well fit to standard PDFs for LRVs. Because RO feed and permeate concentration values were fit to lognormal PDFs, it follows that their simulated LRVs were very well fit to normal PDFs. On the other hand, chemical LRVs from GAC treatment were generally well fit to BetaGeneral PDFs.

The number of chemical contaminants that may potentially be present in source water to AWTPs is effectively limitless. As such, it is not possible to assess the removal performance of all potentially relevant chemicals. Instead, the approach taken for chemical contaminants in this work has been to develop PDFs for specific chemicals that may act as effective surrogate chemicals for a much wider range of chemical contaminants. To extend the concept of surrogate chemicals slightly, chemicals that may be

5.0% 90.0% 5.0%5.0% 89.9% 5.1%

15.1 65.2

0 20 40 60 80 100

120

140

160

180

LRV

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Freq

uenc

y

Fit Comparison for Virus LRVRiskLognorm(34.243,16.350,RiskShift(0.43268))

Input

Minimum 6.05Maximum 163.26Mean 34.68Std Dev 16.37Values 10000

Lognorm

Minimum 0.433Maximum +∞Mean 34.68Std Dev 16.35

Water Environment & Reuse Foundation 119

identified as suitable surrogates for other chemicals may provide satisfactory models for removal performance in a probabilistic water treatment performance assessment.

4.2.1 Reverse Osmosis

As described previously in this chapter, PDFs for all chemical rejections by RO were developed by Monte Carlo simulation with a correlation coefficient of R2=0.4 for sampling of PDFs for RO feed and permeate concentrations. This figure was based on the correlation observed from long-term monitoring data for boron rejection at a full-scale RO plant.

An example of a normal fit comparison for a simulated PDF for an inorganic chemical LRV is provided for potassium in Figure 4.66. PDF parameters for the remaining inorganic contaminants are presented in Table 4.2 and can be easily reproduced for future Monte Carlo simulations.

Figure 4.66. Normal fit comparison for simulated potassium LRV during RO treatment.

The inorganic chemicals for which sufficient concentration data were available to construct PDFs for both RO feeds and permeates are presented in Table 4.2. In this case, rejection categories were developed based on the expected valance of each ionic substance in conventionally treated wastewater at approximately neutral pH. It is anticipated that chemicals could be selected from each of these categories as model indicator chemicals for future Monte Carlo simulations.

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

1.307 1.828

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

LRV

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Freq

uenc

y

Fit Comparison for PotassiumRiskNormal(1.56705,0.15832)

Input

Minimum 0.919Maximum 2.14Mean 1.57Std Dev 0.158Values 10000

Normal

Minimum −∞Maximum +∞Mean 1.567Std Dev 0.158

120 Water Environment & Reuse Foundation

Table 4.2. RO Rejection Categories for Inorganic Chemical Substances and Potential Surrogate Chemicals for which RO Rejection PDFs Have Been Developed

Category Surrogate Chemicals

PDF for LRV (mean, SD) AWTP

Valence +1 potassium RiskNormal(1.56705,0.15832) GWRS

RiskNormal(1.38927,0.10696) Anon. Aus. 1A

RiskNormal(1.25350,0.13977) Anon. Aus. 1B

RiskNormal(1.473440,0.072574) Anon. Aus. 2

RiskNormal(1.59925,0.18843) Scottsdale

sodium RiskNormal(1.457138,0.073175) GWRS

RiskNormal(1.48631,0.11976) Anon. Aus. 1A

RiskNormal(1.45906,0.11711) Anon. Aus. 1B

RiskNormal(1.48915,0.13507) Anon. Aus. 2

RiskNormal(1.47207,0.11522) Scottsdale

Valence +2 copper RiskNormal(1.33076,0.36034) GWRS

zinc RiskNormal(1.73097,0.49033) GWRS

RiskNormal(0.56987,0.24932) Scottsdale

calcium RiskNormal(2.79722,0.23608) Anon. Aus. 1A

RiskNormal(2.72748,0.20063) Anon. Aus. 1B

RiskNormal(3.46352,0.31268) Anon. Aus. 2

RiskNormal(2.88694,0.44914) Scottsdale

magnesium RiskNormal(2.69338,0.11412) Anon. Aus. 1A

RiskNormal(2.65079,0.12063) Anon. Aus. 1B

RiskNormal(3.42970,0.28914) Anon. Aus. 2

RiskNormal(2.82879,0.43763) Scottsdale

Valence +3 iron RiskNormal(2.52515,0.83487) GWRS

Valence -1 bicarbonate RiskNormal(1.23238,0.11321) GWRS

RiskNormal(1.115882,0.072064) Anon. Aus. 1A

RiskNormal(1.009751,0.077259) Anon. Aus. 1B

RiskNormal(1.218394,0.085809) Anon. Aus. 2

chloride RiskNormal(1.69955,0.14101) GWRS

RiskNormal(1.538983,0.084294) Anon. Aus. 1A

RiskNormal(1.50163,0.10051) Anon. Aus. 1B

RiskNormal(1.51838,0.14797) Anon. Aus. 2

RiskNormal(1.592493,0.054285) Scottsdale

nitrate RiskNormal(0.99868,0.14002) GWRS

RiskNormal(0.82925,0.14920) Anon. Aus. 1A

RiskNormal(0.78548,0.14757) Anon. Aus. 1B

Water Environment & Reuse Foundation 121

Category Surrogate Chemicals

PDF for LRV (mean, SD) AWTP

RiskNormal(0.80006,0.30513) Anon. Aus. 2

RiskNormal(1.51886,0.55652) Scottsdale

fluoride RiskNormal(1.16046,0.15759) Anon. Aus. 1A

RiskNormal(1.00910,0.15401) Anon. Aus. 1B

Valence -2 sulfate RiskNormal(2.73832,0.28819) GWRS

RiskNormal(2.56067,0.11881) Anon. Aus. 1A

RiskNormal(2.527705,0.086842) Anon. Aus. 1B

RiskNormal(3.14809,0.19379) Anon. Aus. 2

Others ammonia RiskNormal(0.85298,0.49412) GWRS

RiskNormal(0.62729,0.13194) Anon. Aus. 1A

RiskNormal(0.59308,0.12126) Anon. Aus. 1B

RiskNormal(0.71609,0.13689) Anon. Aus. 2

boron RiskNormal(0.192849,0.046442) GWRS

RiskNormal(0.031349,0.17389) Anon. Aus. 1A

RiskNormal(0.085765,0.15270) Anon. Aus. 1B

RiskNormal(0.099209,0.079008) Anon. Aus. 2

RiskNormal(0.235652,0.076193) Scottsdale

ortho phosphorus RiskNormal(1.14451,0.56161) Anon. Aus. 1A

RiskNormal(1.24108,0.61980) Anon. Aus. 1B

RiskNormal(2.00390,0.29995) Anon. Aus. 2

silica RiskNormal(1.327946,0.026841) GWRS

RiskNormal(1.32004,0.10125) Anon. Aus. 1A

RiskNormal(1.261756,0.070045) Anon. Aus. 1B

RiskNormal(1.67117,0.24769) Anon. Aus. 2

RiskNormal(1.49189,0.18711) Scottsdale

aluminum RiskNormal(0.70089,0.41035) GWRS

Notes: AWTP=advanced water treatment plant; GWRS=groundwater replenishment system; LRV=log removal value; PDF=probability density function; RO=reverse osmosis; SD=standard deviation.

Membrane rejection of chemical contaminants is ultimately determined by complex interactions of electrostatic and other physical forces acting between a specific solute (chemical contaminant), the solution (water and other solutes present), and the membrane itself. The nature of these forces is dependent on numerous physical properties of the solute, solution, and membrane.

A useful guide for the classification of organic contaminants for removal estimation has been proposed by Bellona et al. (2004). This system was derived as the result of a comprehensive review of published studies reporting variable rejection behavior of a wide range of organic solutes by various commercially available membranes. The important molecular factors determining rejection include:

122 Water Environment & Reuse Foundation

Molecular size: The size of a molecule is often approximated by reference to its molecular weight (MW) but can be more accurately described in terms of its molecular diameter and molecular width (MWd).

Electrostatic properties: The electrical charge of a molecule is related to how acidic it is. This is commonly described by an acid dissociation constant (pKa) and its relationship to the overall acidity of the water (pH).

Polarity or hydrophobicity: The polarity of a molecule determines whether it is generally very soluble in water or would prefer to partition to non-water phases. Molecules that tend to partition away from water are said to be hydrophobic. The degree of hydrophobicity is commonly described by an octanol–water partitioning coefficient (log Kow).

The three mechanisms by which a molecule may be rejected by the RO membrane are size exclusion (or sieving), electrostatic repulsion, and hydrophobic adsorption. The most fundamental of the rejection mechanisms is size exclusion. This is a sieving process for which molecular size or geometry prevents large molecules from passing through the dense molecular structure presented by the active surface of the membrane. Size exclusion is believed to be the dominant retention mechanism for relatively large organic molecules such as surfactants, hormones, most pharmaceuticals, proteins, and other molecules with MW greater than 200 atomic mass units (or g/mol) by RO membranes (Drewes et al., 2006; Schäfer et al., 2003). However, commercial membranes vary in terms of their ability to reject molecules by size exclusion. Their ability to do so is often described by the membrane’s molecular weight cutoff (MWCO). This is the manufacturer’s rating of the ability of the membrane to reject an uncharged dextran (sugar) based on molecular weight. Membranes with a low MWCO are commonly referred to as “tight” membranes compared to those with a higher MWCO, referred to as “loose” membranes.

Experiments with looser membranes (nanofiltration, UF, and MF) have revealed that under some conditions some chemicals are prevented from permeating the membrane, largely because of adsorption onto the membrane surface (Schäfer et al., 2003; Yoon et al., 2006). This adsorption is believed to be due to hydrophobic interactions between relatively non-polar solutes and membranes. Such adsorptive removal may be less reliable than removal based purely on size exclusion as variations in solution pH lead to variations in hydrophobicity, and possible saturation of adsorption sites may limit total adsorption capacity if the membranes are not routinely cleaned (Nghiem and Schafer, 2006).

Water Environment & Reuse Foundation 123

The rejection diagram for trace organic chemicals during membrane treatment based on solute and membrane properties is presented in Figure 4.67 (Bellona et al., 2004). Chemicals for which RO rejection PDFs can be generated could serve as surrogates for a much wider range of chemicals. A range of RO rejection categories have been established for inorganic chemicals (Table 4.2) and organic chemicals according to the membrane rejection diagram. The organic surrogate chemicals have been classified according to the membrane rejection diagram in Figure 4.67.

During this process, variable performances were observed for some chemicals at different AWTPs. The range of chemical removals observed were found to roughly fit the predicted categories from the membrane rejection diagram if different assumptions regarding the operational parameters of the RO systems were made for different plants. The key operational parameters incorporated in the membrane diagram are pH, MWCO, and whether the membranes are assumed to have high or low membrane surface charge (MSC).

Figure 4.67. Rejection diagram for organic micropollutants during membrane treatment based on solute and membrane properties.

Source: Bellona et al. (2004).

Notes: MW=molecular weight, pKa=acid dissociation constant, Log Kow=logarithm of octanol–water partitioning coefficient.

MW < MWCO MW > MWCO

pH < pKa pH > pKa pH > pKa

Log Kow > 2 Log Kow < 2 Log Kow > 2 Log Kow < 2

Consider MWidth

Consider MWidth

Fraction dissociated

Consider MWidth

MWd > 0.6 nm

MWd < 0.6 nm

MWd < 0.6 nm

MWd > 0.6 nm MWd >

0.6 nmMWd < 0.6 nm

Low membrane

surface charge

High membrane

surface charge

1. Initial rejection due to adsorption decreased slightly; Moderately rejected

but depends on diffusion and

partition

2. Initital rejection from adsorption decreases; Compound poorly rejected but depends on diffusion and partition

3. Compound poorly rejected

Organic chemical

pH < pKa

< 50% > 50%

4. Compound moderately

rejected5. Electrostatic

repulsuion not as high. Moderate

rejection

6. Rejection is high due to electrostatic repulsion

7. Rejection is moderate to high but depends on partitioning and diffusion

8. Moderate rejection

9. Moderate to high

rejection

10. Rejection very high from

steric and electrostatic exclusion

124 Water Environment & Reuse Foundation

Rejection PDFs from three AWTPs appeared to fit the predictions of the membrane rejection diagram if it was assumed that the MWCO was 180 and the MSC was high. On the other hand, rejection PDFs for one AWTP were found to fit the predictions of the membrane rejection diagram better if it was assumed that the MWCO was 210 and the MSC was low. It is not known (or implied) whether these assumptions truly reflect such physical differences in the membrane characteristics, but they do appear to reflect the different performances of the removal of trace organic chemical contaminants.

Long-term monitoring data from four individual RO trains at three AWTPs were used in the development of Table 4.3. These are the GWRS in California, the Anon. Aus. 2 AWTP (QLD, Australia), and the two separate trains 1A and 1B at the Anon. Aus. AWTP (QLD, Australia). In most cases, chemicals were selected where there were sufficient numbers of detections in both RO feed and RO permeate samples to enable development of PDFs for the chemical concentrations in these matrices. For some very highly rejected contaminants (Rejection Categories 9 and 10), no chemicals could be identified with sufficient detections in RO permeates to enable PDF fitting. In these cases, PDFs were developed as conservative (lower-bound) calculations based on the RO permeate analytical detection limit. Categorization was based on the assumptions of pH=7, MWCO=180, MSC=high.

Long-term monitoring from one other AWTP RO process is presented in Figure 4.68. In this case, categorization was based on the assumptions of pH=7, MWCO=210, MSC=low. These differences reflect the somewhat different performance of this RO plant for trace organic chemical removal compared to the other three AWTPs. An example of an organic chemical contaminant rejection PDF is presented for chloroform in Figure 4.68.

Figure 4.68. Normal fit comparison for simulated chloroform LRV during RO treatment.

Graphical PDFs for LRVs of all other organic contaminants are not presented. However, the @Risk function for the normal PDFs is presented in Tables 4.3 and 4.4. These functions may be used to reproduce these PDFs for future Monte Carlo simulations. Because they are normal PDFs, they can also be easily understood, as the two figures given in the function are the familiar mean and standard deviation, respectively.

5.0% 90.0% 5.0%5.0% 90.2% 4.8%

0.000 0.691

-0.6

-0.4

-0.2 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Freq

uenc

y

Fit Comparison for ChloroformRiskNormal(0.34430,0.20870)

Input

Minimum -0.428Maximum 1.26Mean 0.344Std Dev 0.209Values 10000

Normal

Minimum −∞Maximum +∞Mean 0.344Std Dev 0.209

Water Environment & Reuse Foundation 125

Table 4.3. RO Rejection Categories for Organic Chemical Substances and Potential Indicator Chemicals for Which RO Rejection PDFs Have Been Developed (assuming MWCO=180, MSC=high)

Category Rejection Description Indicator Chemicals (assuming pH=7, MWCO=180, MSC=high)

PDF for LRV (mean, SD) AWTP

1 Initial rejection from adsorption decreases slightly; moderately rejected depending on diffusion and partition

3,4-dichloroaniline, RiskNormal(0.57797,0.48772) Anon. Aus. 1A

bromodichloromethane RiskNormal(0.59554,0.69213) GWRS

2 Initial rejection from adsorption decreases; compound poorly rejected but depends on diffusion and partition

(none identified)

3 Compound poorly rejected chloroform RiskNormal(0.34430,0.20870) GWRS

RiskNormal(0.33174,0.22615) Anon. Aus. 1A

RiskNormal(0.27937,0.18390) Anon. Aus. 1B

methyl isocyanate RiskNormal(-0.13598,0.42510) GWRS

NDMA RiskNormal(0.34931,0.38873) GWRS

RiskNormal(0.21395,0.62522) Anon. Aus. 1A

RiskNormal(-0.010281,0.20973) Anon. Aus. 1B

4 Compound moderately rejected (none identified)

5 Electrostatic repulsion not as high: moderate rejection (none identified)

6 Rejection is high because of electrostatic repulsion acesulfame RiskNormal(2.19212,0.21631) Anon. Aus. 1A

RiskNormal(2.19633,0.15554) Anon. Aus. 1B

RiskNormal(2.844280,0.091800) Anon. Aus. 2

7 Rejection moderate to high but depends on partitioning and diffusion

atrazine RiskNormal(2.3912,1.0500) Anon. Aus. 1A

RiskNormal(1.75403,0.80994) Anon. Aus. 1B

diuron RiskNormal(0.98092,0.37164) Anon. Aus. 1A

RiskNormal(1.02690,0.39058) Anon. Aus. 1B

RiskNormal(1.46814,0.21508) Anon. Aus. 2

fluometuron RiskNormal(1.17991,0.81709) Anon. Aus. 1A

126 Water Environment & Reuse Foundation

Category Rejection Description Indicator Chemicals (assuming pH=7, MWCO=180, MSC=high)

PDF for LRV (mean, SD) AWTP

metolachlor RiskNormal(2.8579,1.1273) Anon. Aus. 1A

simazine RiskNormal(1.8948,1.2658) Anon. Aus. 1A

8 Moderate rejection (none identified) 19A Moderate to high rejection caffeine 2RiskNormal(2.33009,0.23447) GWRS

carbamazepine 2RiskNormal(2.327544,0.058074) GWRS

hydrochlorthiazide 2RiskNormal(2.078766,0.064380) Anon. Aus. 1B

iohexol 2RiskNormal(2.63001,0.14232) GWRS

meprobamate 2RiskNormal(2.065282,0.054608) GWRS

sucralose 2RiskNormal(2.451027,0.064727) GWRS 19B Moderate to high rejection atenolol 2RiskNormal(1.974004,0.057751) GWRS

azithromycin 2RiskNormal(2.67030,0.22496) GWRS

tramadol 2RiskNormal(2.11171,0.13862) Anon. Aus. 1B

venlafaxine 2RiskNormal(2.22379,0.11230) Anon. Aus. 1B

10 Rejection very high from steric and electrostatic exclusion

gemfibrozil 2RiskNormal(2.82660,0.23660) GWRS

ibuprofen 2RiskNormal(2.19495,0.25929) GWRS

naproxen 2RiskNormal(1.96336,0.11074) GWRS

sulfamethoxazole 2RiskNormal(2.11264,0.10032) GWRS

Notes: 1=Category 9 has been split into 9A and 9B. Chemicals in 9B have a positive charge at pH 7, which is not accounted for by this model. Therefore, these chemicals may be better rejected than predicted by this model. 2=These chemicals were consistently less than the analytical detection limit in RO permeate. Therefore, these PDFs are conservative (lower-bound) calculations based on the permeate analytical detection limit. AWTP=advanced water treatment plant; GWRS=groundwater replenishment system; LRV=log removal value; MWCO=molecular weight cutoff; MSC=membrane surface charge; NDMA=n-Nitrosodimethylamine; PDF=probability density function; RO=reverse osmosis; SD=standard deviation.

Water Environment & Reuse Foundation 127

Table 4.4. RO Rejection Categories for Organic Chemical Substances and Potential Indicator Chemicals for Which RO Rejection PDFs Have Been Developed (assuming MWCO=210, MSC=low)

Category Rejection Description Indicator Chemicals (assuming pH=7, MWCO=210, MSC=low)

PDF for LRV (mean, SD) AWTP

1 Initial rejection from adsorption decreases slightly; moderately rejected depending on diffusion and partition

bromodichloromethane RiskNormal(0.082515,0.30569) Scottsdale

dibromochloromethane RiskNormal(0.0010166,0.43972) Scottsdale

2 Initial rejection from adsorption decreases; compound poorly rejected but depends on diffusion and partition

3 Compound poorly rejected n-Nitrosodimethylamine RiskNormal(0.20158,0.47639) Scottsdale

chloroform RiskNormal(0.15176,0.23681) Scottsdale

4 Compound moderately rejected cotinine RiskNormal(1.53912,0.31335) Scottsdale

caffeine RiskNormal(0.88737,0.64661) Scottsdale

n-Nitrosomorpholine RiskNormal(0.83134,0.34479) Scottsdale

n-Nitrosopyrrolidine RiskNormal(0.7438,0.53569) Scottsdale

5 Electrostatic repulsion not as high: moderate rejection

6 Rejection is high because of electrostatic repulsion

7 Rejection moderate to high but depends on partitioning and diffusion

diuron RiskNormal(0.68091,0.39759) Scottsdale

oxybenzone RiskNormal(0.73212,0.66568) Scottsdale

8 Moderate rejection 19A Moderate to high rejection dilantin RiskNormal(1.62729,0.41612) Scottsdale

meprobamate RiskNormal(1.70897,0.33258) Scottsdale

primidone RiskNormal(1.86259,0.60493) Scottsdale

sucralose RiskNormal(1.56139,0.29315) Scottsdale

128 Water Environment & Reuse Foundation

Category Rejection Description Indicator Chemicals (assuming pH=7, MWCO=210, MSC=low)

PDF for LRV (mean, SD) AWTP

19B Moderate to high rejection fluoxetine RiskNormal(1.12302,0.47415) Scottsdale

trimethoprim RiskNormal(0.93413,1.0125) Scottsdale

10 Rejection very high from steric and electrostatic exclusion

gemfibrozil RiskNormal(1.2231,1.0945) Scottsdale

naproxen RiskNormal(1.5152,1.2492) Scottsdale

sulfamethoxazole RiskNormal(1.79879,0.88578) Scottsdale

Notes: 1=Category 9 has been split into 9A and 9B. Chemicals in 9B have a positive charge at pH 7, which is not accounted for by this model. Therefore, these chemicals may be better rejected than predicted by this model. AWTP=advanced water treatment plant; LRV=log removal value; MWCO=molecular weight cutoff; MSC=membrane surface charge; PDF=probability density function; RO=reverse osmosis; SD=standard deviation.

.

Water Environment & Reuse Foundation 129

4.2.2 UV–AOP

Assessment of removal of organic chemical contaminants by UV–AOP was based on long-term UV–AOP feed and effluent monitoring data from two AWTPs (GWRS, with peroxide addition, and Scottsdale, without peroxide addition). Very few organic chemicals were generally measurable in UV–AOP feed data, and even fewer were measurable in UV–AOP effluent data. However, the few chemicals that were measured are described here.

Many chemicals showed negligible removal during UV–AOP irradiation. Examples included acetaminophen, ammonia, caffeine, cotinine, gemfibrozil, meprobamate, oxybenzone, primidone, sucralose, and trimethoprim. Lognormal probability plots, showing negligible removal by UV–AOP irradiation, are provided for meprobamate (Figure 4.69) and sucralose (Figure 4.70) as examples.

Figure 4.69. Lognormal probability plot showing negligible removal of meprobamate during UV–AOP.

Figure 4.70. Lognormal probability plot showing negligible removal of sucralose during UV–AOP.

Meprobamate

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Co

nce

ntra

tion

(ng

/L)

0.1

1

10

100

UV Feed

UV Effluent

Sucralose

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Co

nce

ntra

tion

(ng

/L)

100

1000

10000

UV Feed

UV Effluent

130 Water Environment & Reuse Foundation

Lognormal probability plots for NDMA concentrations in the UV–AOP feed and effluent from long-term monitoring of an AWTP are presented in Figure 4.71. Approximately 1 LRV is evident from these plots, and these data were used to derive an estimated correlation coefficient of R2=0.8. This correlation coefficient was applied to all subsequent Monte Carlo simulations for chemical removal across a UV–AOP treatment process.

Figure 4.71. Lognormal probability plot showing approximately 1 LRV for NDMA during UV irradiation.

NDMA

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Co

nce

ntra

tion

(ng

/L)

1

10

100

1000

UV Feed

UV Effluent

Water Environment & Reuse Foundation 131

A time series of long-term monitoring data for NDMA in UV–AOP feed and permeate from a second AWTP is presented in Figure 4.72. In this case, NDMA was detected greater than the analytical detection limit (2 ng/L) in UV–AOP effluent on only 12 occasions in over six years. Nonetheless, reasonable lognormal probability plots could be fitted, as shown in Figure 4.73 for this highly censored data set. Monte Carlo simulations for removal of NDMA during this UV–AOP process were undertaken assuming the same correlation coefficient (R2=0.8).

Figure 4.72. Long-term monitoring data for NDMA in feed and effluent samples of a UV–AOP process (GWRS).

Figure 4.73. Lognormal probability plots for NDMA concentrations in UV–AOP feed and effluent samples (GWRS).

NDMA

Year

2008 2009 2010 2011 2012 2013 2014

Co

nce

ntra

tion

(ng

/L)

1

10

100

1000

10000

UV Feed UV Effluent

(LOD = 2 ng/L)

NDMA

Percentile

0.2 0.5 1 2 5 10 20 30 50 70 80 90 95 98 99 99.8

Co

nce

ntra

tion

(ng

/L)

1

10

100

1000

10000

UVUV Feed

UV Effluent

132 Water Environment & Reuse Foundation

Sufficient data were available to derive PDFs for LRVs across the UV–AOP treatment process for only three organic chemicals. These were diuron, monochloramine, and NDMA. Normal fit comparisons are presented for diuron (Figure 4.74) and monochloramine (Figure 4.75). Suitable data for NDMA were available for two plants, thus normal fit comparisons are provided for NDMA removal at the Scottsdale AWTP (Figure 4.76) and the GWRS AWTP (Figure 4.77).

Figure 4.74. Normal fit comparison for simulated diuron LRV during UV–AOP treatment.

Figure 4.75. Normal fit comparison for simulated monochloramine LRV during UV–AOP treatment.

5.0% 90.0% 5.0%4.9% 90.0% 5.1%

0.254 0.945

-0.2 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Freq

uenc

y

Fit Comparison for DiuronRiskNormal(0.60111,0.21025)

Input

Minimum -0.157Maximum 1.39Mean 0.601Std Dev 0.210Values 10000

Normal

Minimum −∞Maximum +∞Mean 0.601Std Dev 0.210

5.0% 90.0% 5.0%5.2% 89.9% 4.9%

0.116 1.448

-1.0

-0.5 0.0

0.5

1.0

1.5

2.0

2.5

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Freq

uenc

y

Fit Comparison for MonochloramineRiskNormal(0.77606,0.40567)

Input

Minimum -0.860Maximum 2.28Mean 0.776Std Dev 0.406Values 10000

Normal

Minimum −∞Maximum +∞Mean 0.776Std Dev 0.406

Water Environment & Reuse Foundation 133

Figure 4.76. Normal fit comparison for simulated NDMA LRV during UV–AOP treatment (Scottsdale).

Figure 4.77. Normal fit comparison for simulated NDMA LRV during UV–AOP treatment (GWRS).

5.0% 90.0% 5.0%4.9% 90.1% 5.0%

0.612 1.572

-0.5 0.0

0.5

1.0

1.5

2.0

2.5

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Freq

uenc

y

Fit Comparison for N-NitrosodimethylamineRiskNormal(1.09389,0.29112)

Input

Minimum -0.0119Maximum 2.19Mean 1.09Std Dev 0.291Values 10000

Normal

Minimum −∞Maximum +∞Mean 1.09Std Dev 0.291

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

1.264 1.981

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

LRV

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Freq

uenc

y

Fit Comparison for N-NitrosodimethylamineRiskNormal(1.62255,0.21802)

Input

Minimum 0.813Maximum 2.42Mean 1.62Std Dev 0.218Values 10000

Normal

Minimum −∞Maximum +∞Mean 1.62Std Dev 0.218

134 Water Environment & Reuse Foundation

4.2.3 Granular Activated Carbon

4.2.3.1 Description of Methods for Determining Organic Contaminant Breakthrough

GAC breakthrough data were obtained through the master’s thesis work completed by Allison Reinert (currently at Hazen and Sawyer and working on this project). The research work was completed in order to develop a comprehensive scale-up approach for predicting field-scale organic contaminant removal from rapid small-scale column test (RSSCT) data. A complete description of the modeling and scale-up approach is described in Summers et al. (2014), but a brief description is provided here.

One pilot study and two different designs of RSSCTs were completed to assess the rate and predictability of organic chemical breakthrough from GAC contactors. In all of the studies, DOC was the first monitored constituent to break through. At the point of DOC breakthrough, no other organic contaminants had reached measureable levels. As such, the results suggested that GAC adsorption is an effective tool for removal of organic contaminants if it is primarily used for natural organic matter control. Following DOC breakthrough, only 11 out of the 34 organic contaminants broke through to measurable levels in the pilot, 26 out of 34 broke through to measureable levels in the proportional diffusivity RSSCT (PD-RSSCT), and 31 out of 34 broke through to measureable levels in the constant diffusivity RSSCT (CD-RSSCT). The relatively small number of organic contaminants that experienced breakthrough in the pilot can be attributed in part to biodegradation. Following DOC breakthrough, the first organic chemical to break through in all of the tests was iopromide.

For all of the organic contaminants (with the exception of iopromide in the CD-RSSCT) the RSSCTs over-predicted the organic chemical adsorption capacity of the GAC relative to the full-scale pilot. This difference in organic chemical adsorption capacity is likely caused by particle size–dependent GAC fouling that results from the adsorption of natural organic matter. As such, a fouling factor (Y) is used to address the particle size–dependent fouling of the GAC for the 11 organic contaminants that broke through to measurable detection in the pilot. For the CD-RSSCT, a linear free energy relationship (LFER) was developed to relate Y to Abraham descriptors for the individual organic contaminants using principal component analysis. The Abraham descriptors consist of excess molar fraction (E), dipolarity (s), hydrogen bond acidity (A), hydrogen bond basicity (B), and the McGowan volume (V) and are utilized in the following two equations:

Y=(0.165±0.0406)(PC1)+(0.883±0.143)

PC1=-0.612S–0.247A–0.370B–0.341V–0.558E

Although no LFER was found to describe Y for the PD-RSSCT, two separate dependences on the octanol–water partition coefficient (log D) were found for the PD-RSSCT data and are described by the two following equations:

Y=0.0597(Log D)+0.491

Y=0.0829(Log D)+0.248

However, no organic chemical characteristic could be found to help anticipate which Y equation would be appropriate for a given organic chemical for the PD-RSSCT. Therefore, the PD-RSSCT was not used or recommended for scale-up predictions. The PD-RSSCT is, however, recommended for use when only TOC or DOC breakthrough is of interest.

The fouling factor, Y, was not the only aspect needed to scale up bench-scale breakthrough curves to full-scale GAC breakthrough, however. Particle size–dependent differences in adsorption kinetics also need to

Water Environment & Reuse Foundation 135

be addressed. As such, a pore surface diffusion model was used to scale up RSSCT data to estimate full-scale data (Summers et al., 2014).

In order to model expected (estimated) full-scale breakthrough times from organic chemical pilot results, the input parameters listed in Table 4.5 were used for all organic chemical models.

Table 4.5. Model Input Parameters for Predicting Full-Scale Contaminant Breakthrough from Pilot-Scale Results

Input Type Parameter Value

Fixed bed properties bed length 58.3 cm

bed diameter 2.54 cm

dry GAC mass 136 g

flow rate 42.2 mL/min

EBCT 7.00 min

Adsorbent properties apparent particle density 0.730 g/mL

particle radius 4.60E-4 m

particle porosity 0.500

particle shape factor 1.00

Simulation parameter total run time 600 days

first point displayed 6.48E-8 days

time step 5 days

number of axial elements 10

Number of collocation points axial direction 8

radial direction 5

Notes: EBCT=empty bed contact time; GAC=granular activated carbon.

136 Water Environment & Reuse Foundation

In order to model expected (estimated) full-scale results from CD-RSSCT data for organic contaminant breakthrough, the input parameters listed in Table 4.6 were used for all organic contaminant models.

Table 4.6. Model Input Parameters for Predicting Full-Scale Organic Contaminant Breakthrough from CD-RSSCT Results

Input Type Parameter CD-RSSCT Values

Fixed bed properties bed length 13.5 cm

bed diameter 0.74 cm

dry GAC mass 2.78 g

flow rate 16.1 mL/min

EBCT 0.374 min

Adsorbent properties apparent particle density 0.730 g/mL

particle radius 1.07E-4 m

particle porosity 0.500

particle shape factor 1.00

Simulation parameter total run time 100 days

first point displayed 6.48E-8 days

time step 1 day

number of axial elements 10

Number of collocation points axial direction 8

radial direction 5

Notes: CD-RSSCT=constant diffusivity rapid small-scale column test; EBCT=empty bed contact time; GAC=granular activated carbon.

Additional parameters were also individually put into the pore surface diffusion model depending on organic chemical and existing bench-scale data. Additional individual parameters for organic contaminants included:

Freundlich K (used only for pilot modeling)

RSSCT flux (used only for RSSCT modeling)

Surface-to-pore diffusion flux ratio

Tortuosity

Normalized total flux

An Excel-based macro was used to assimilate all of the variables and provide predicted breakthrough at full scale. The Excel data that were subsequently used for Monte Carlo analysis (as bed volumes and associated breakthrough concentration [C/C0]) were a result of the previous modeling and have been used to estimate GAC effectiveness for contaminant removal.

Water Environment & Reuse Foundation 137

4.2.3.2 Application of Organic Contaminant Breakthrough Methods to Monte Carlo Analysis

PDFs for chemical contaminant LRVs by GAC were derived from the predicted contaminant breakthrough profiles developed by Reinert. Each of these breakthrough profiles was described by a three-parameter sigmoidal curve equation, calculated using SigmaPlot 12.0:

/1

‐ "bedvolumes"

Where a, b, and X0 are all constants specific to a particular chemical (or parameter such as DOC). These constants were previously derived for a series of organic chemical contaminants (Summers et al., 2014).

Once the constants were determined, this relationship could then be used to determine C/C0 (and thus LRV) for any assumed value for the number of GAC bed volumes that have passed. However, in most operating plants, a number of parallel beds would be operated, each with a different bed age (and level of breakthrough occurring for any particular chemical). The number of parallel beds operating has significance for the likely breakthrough concentration for any particular chemical (at any particular time) because the effluents from parallel beds would ultimately be combined, leading to a (weighted) average concentration.

Monte Carlo simulations were used to derive probability density functions for average LRVs, assuming one, two, three, or four parallel (independently operated) GAC beds. The number of bed volumes to have passed through a GAC bed at any particular time was calculated from empty bed contact time (EBCT) as follows:

"Bedvolumes" 24 / 60 /

In this case, the GAC beds were assumed to be operated with EBCT of 10 to 15 minutes, which is typical for a pressure vessel (seven minutes is typical for a filter adsorber). This EBCT was modeled using a uniform PDF between 10 and 15 minutes.

The run time or bed age at any particular time was based on the assumption that the GAC in each bed was replaced or regenerated once per year2. The PDFs for average GAC age are dependent upon the assumed number of independently operated GAC beds. For a single bed, the bed age is a uniform distribution from 1 to 365 days (Figure 4.78). For two independently operated GAC beds, the average bed age at any time is an approximately triangular distribution (Figure 4.79). However, with three (Figure 4.80) or four (Figure 4.81) independently operated beds, the average bed age can be increasingly well fit to a BetaGeneral PDF.

2 In reality, GAC replacement is often much more complicated than a simple annual replacement. The timing of GAC regeneration or replacement may be based on specific contaminant breakthrough maxima, seasonal treatment goals, or other system-specific drivers. Here, annual replacement was selected for simplicity in modeling.

138 Water Environment & Reuse Foundation

Figure 4.78. Simulated average GAC bed age based on a single bed, refreshed once per year (365 days).

Figure 4.79. Simulated average GAC bed age based on two independently operated beds, each refreshed once per year.

5.0% 90.0% 5.0%5.0% 90.0% 5.0%

18.2 346.7

-50 0 50 100

150

200

250

300

350

400

Average bed age (days)

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

Freq

uenc

y

Fit Comparison for 1 bedRiskUniform(-0.0084677,365.02)

Input

Minimum 0.0280Maximum 364.98Mean 182.50Std Dev 105.37Values 10000

Uniform

Minimum -0.00847Maximum 365.02Mean 182.51Std Dev 105.37

5.0% 90.0% 5.0%5.0% 89.9% 5.1%

58.2 307.1

0 50 100

150

200

250

300

350

400

Average bed age (days)

0.000

0.001

0.002

0.003

0.004

0.005

0.006

Freq

uenc

y

Fit Comparison for Av 2 beds / 4RiskTriang(0.37327,184.17,365.02)

Input

Minimum 3.42Maximum 362.33Mean 182.50Std Dev 74.49Values 10000

Triang

Minimum 0.373Maximum 365.02Mean 183.19Std Dev 74.43

Water Environment & Reuse Foundation 139

Figure 4.80. Simulated average GAC bed age based on three independently operated beds, each refreshed once per year.

Figure 4.81. Simulated average GAC bed age based on four independently operated beds, each refreshed once per year.

5.0% 90.0% 5.0%4.7% 90.3% 4.9%

81.1 282.7

-50 0 50 100

150

200

250

300

350

400

Average bed age (days)

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

Freq

uenc

y

Fit Comparison for Average age 3 bedsRiskBetaGeneral(5.6817,5.6669,-30.633,395.05)

Input

Minimum 7.97Maximum 359.42Mean 182.50Std Dev 60.52Values 10000

BetaGeneral

Minimum -30.63Maximum 395.05Mean 182.49Std Dev 60.57

5.0% 90.0% 5.0%5.3% 89.6% 5.1%

98.0 268.2

-50 0 50 100

150

200

250

300

350

400

Average bed age (days)

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

Freq

uenc

y

Fit Comparison for Av 4 beds / 4RiskBetaGeneral(8.5254,8.5793,-38.781,405.19)

Input

Minimum 9.90Maximum 355.59Mean 182.50Std Dev 52.17Values 10000

BetaGeneral

Minimum -38.78Maximum 405.19Mean 182.50Std Dev 52.17

140 Water Environment & Reuse Foundation

Monte Carlo simulations were used to produce simulated PDFs of LRVs for 12 trace organic chemical contaminants. An example of a simulated PDF is presented for acetochlor (Figure 4.82). The fitted PDF details for all 12 trace organics are presented in Table 4.7.

These were established by determining individual values of C/C0 for the range of bed volumes. Multiple independent C/C0 values were determined for four independent beds, and then these were averaged to give the final value. This process simulates each of the beds operating independently, followed by blending of equal volumes of filtrate from each bed.

Figure 4.82. BetaGeneral fit comparison for acetochlor removal by GAC.

5.0% 90.0% 5.0%5.4% 89.8% 4.9%

1.197 1.436

1.0

1.1

1.2

1.3

1.4

1.5

1.6

LRV

0

1

2

3

4

5

6

Freq

uenc

y

Fit Comparison for AcetochlorRiskBetaGeneral(8.7070,10.909,1.01626,1.68410)

Input

Minimum 1.070Maximum 1.528Mean 1.313Std Dev 0.0731Values 10000

BetaGeneral

Minimum 1.016Maximum 1.684Mean 1.313Std Dev 0.0731

Water Environment & Reuse Foundation 141

Table 4.7. Fitted PDFs for Other Trace Organic Chemical Removal by GAC

Chemical PDF

Acetochlor RiskBetaGeneral(8.707,10.909,1.01626,1.6841)

Atrazine RiskBetaGeneral(7.1172,10.074,1.35689,2.18566)

Caffeine RiskBetaGeneral(5.1077,13.83,0.9969,4.3022)

Carbamazepine RiskBetaGeneral(7.4228,8.9628,1.21016,1.6843)

Cotinine RiskBetaGeneral(3.3618,31.736,0.29915,13.409)

Iopromide RiskLognorm(0.77911,0.29926,RiskShift(-0.019434))

Methomyl RiskBetaGeneral(4.1776,12.352,0.68668,3.9721)

Metolachlor RiskBetaGeneral(7.2836,10.011,1.10249,2.00463)

Prometon RiskBetaGeneral(3.7124,9.9529,0.84967,5.3139)

Simazine RiskBetaGeneral(6.1394,13.845,1.7157,4.0917)

Tributyl Phosphate RiskBetaGeneral(5.5512,12.915,0.77431,2.4361)

Warfarin RiskBetaGeneral(8.1844,8.6371,1.21611,1.50997)

Notes: GAC=granular activated carbon; PDF=probability density function.

142 Water Environment & Reuse Foundation

4.2.4 Ozonation

Removal of trace chemicals during ozonation was examined during the pilot plant trials undertaken in the southeastern United States, as described in Chapter 3. A number of plant configurations were tested in this work, with the configuration recorded as IPR Concept 2 used to derive PDFs for the removals (LRVs) for nine trace organic chemical contaminants, as presented in Table 4.8. One example of these PDFs (for bisphenol A) is presented in Figure 4.83. This PDF was derived by means of a Monte Carlo simulation and subsequently fit to a normal distribution.

Figure 4.83. Normal fit comparison for removal of bisphenol A during ozonation.

Table 4.8. PDFs for the Removal of Trace Organic Chemical Contaminants during Ozonation

Organic Chemical PDF

Atenolol RiskNormal(0.146033,0.087338)

Atrazine RiskNormal(0.116588,0.061683)

Bisphenol A RiskNormal(0.90086,0.22211)

Caffeine RiskNormal(0.116312,0.067767)

Carbamazepine RiskNormal(0.52887,0.34336)

Gemfibrozil RiskNormal(0.28577,0.24874)

Ibuprofen RiskNormal(0.095794,0.052292)

Sulfamethoxazole RiskNormal(0.26385,0.29857)

Trimethoprim RiskNormal(0.31193,0.2502)

5.0% 90.0% 5.0%4.9% 90.1% 5.0%

0.534 1.267

-0.5 0.0

0.5

1.0

1.5

2.0

LRV

0.0

0.2

0.4

0.6

0.8

1.0

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4.3 Multiple Barrier Monte Carlo Simulations

Monte Carlo simulations were run to derive the multiple barrier removal of key microbial contaminants across combined treatment trains. A range of potential scenarios, surrogates, and operational requirements were simulated for investigation. Key results are presented here to show the multiple barrier removals of Giardia, Cryptosporidium, and viruses during treatment by key process trains.

All of these Monte Carlo simulations were performed using @Risk with 10,000 iterations and Latin Hypercube sampling. No correlations were assumed in the performances of subsequent treatment processes. Only processes for which PDFs for LRV of the particular pathogens could be established are presented here.

Multiple barrier Monte Carlo simulations are presented for viruses, Giardia, and Cryptosporidium by both the RO membrane-based treatment train and the ozone–BAC-based treatment train. These simulations are inherently conservative, and the reader must keep in mind the following when interpreting outcomes:

In both of those trains, the PDFs used for UV disinfection were simulated UV–AOP doses (~400 mJ/cm2) derived from data obtained from a UV disinfection process (~40–70 mJ/cm2).

LRVs for virus, Cryptosporidium, and Giardia from the sand filters were based on sand filter effluent turbidity. This approach did not apply filter operation goals of 0.1 NTU combined filter effluent, typical in drinking water plants. Instead, it was based on data with higher effluent turbidity, between 0.1 and 0.3 NTU, leading to lower simulated LRVs. The U.S. EPA provides a 2.5 to 3.0 log credit for virus, Cryptosporidium, and Giardia removal across a filter when a flocculation and sedimentation process is applied in front of the filters.

In some cases, particle removal by RO membranes was based on a sulfate surrogate, which is inherently conservative as sulfate molecules are much smaller in size and have different surface and charge characteristics than particles such as viruses and spores.

A small number of multiple barrier Monte Carlo simulations are also presented for trace organic chemical contaminants. Two chemicals (NDMA and diuron) are presented for multiple barrier removal by the RO membrane-based treatment train. The PDFs for the UV processes in these simulations were based on data from UV systems applying advanced oxidation doses (i.e., >400 mJ/cm2), but without the addition of hydrogen peroxide to produce true advanced oxidation conditions by enhanced hydroxyl radicle production. Three chemicals (atrazine, caffeine, and carbamazepine) are presented as examples only for multiple barrier removal by the non-membrane-based treatment train. In all cases, mean chemical removals across two combined barriers are between 1 and 2 LRV. For most other chemicals, removal by single processes was sufficient to result in effluent concentrations less than the method reporting limit. As such, very few chemicals were detected across multiple barriers at a high enough frequency and measurable concentration to populate a multiple process Monte Carlo simulation.

Twenty-two multiple barrier Monte Carlo simulations are presented in the following sections, labeled in order as Multiple Barrier Monte Carlo Simulation No. 1 to Multiple Barrier Monte Carlo Simulation No. 22. It should be noted that throughout the disinfection Monte Carlo simulations, some of the results are censored and modeled assuming a “maximum credit LRV imposed” as allowable by the U.S. EPA or state regulations, whereas others are conducted using the complete PDF of LRVs actually observed.

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4.3.1 Virus Removal by RO Membrane-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 1 is summarized in Table 4.9, and the outcome is presented in Figure 4.84. In this case, the removal of viruses by RO, UV disinfection, and chlorination was simulated. No credit for virus removal by MF was given. Removal by RO was based on that previously reported by Olivieri et al. (1999) for a Hydranautics membrane. UV–AOP was based on data from a water treatment plant applying UV irradiation at disinfection doses. These data were used to simulate AOP doses by assuming an arrangement of six UV disinfection reactors in sequence. Chlorine disinfection was based on CT data from a conventional drinking water treatment plant. As can be observed in Figure 4.84, this plant configuration was simulated to achieve a mean virus removal of 133 LRV, a fifth percentile of 48 LRV, and the lowest of 10,000 sampling iterations resulting in 36 LRV.

Table 4.9. Multiple Barrier Monte Carlo Simulation No. 1

Contaminant(s): Viruses Processes

MF reverse osmosis

UV–AOP chlorination

PDF nil Hydranautics

viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV) imposed

N/A no maximum no maximum no maximum

Observed mean process performance

N/A 4.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.84. Virus removal by Multiple Barrier Simulation No. 1.

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Multiple Barrier Monte Carlo Simulation No. 2 is summarized in Table 4.10, and the outcome is presented in Figure 4.85. This simulation is the same as that for Simulation No 1, but a more conservative surrogate (sulfate rejection) was used for RO removal performance. The results are similar to Simulation No. 1, with mean virus removal of 33 LRV, a fifth percentile of 46 LRV, and a minimum from 10,000 sampling iterations of 33 LRV.

Table 4.10. Multiple Barrier Monte Carlo Simulation No. 2

Contaminant(s): Viruses

Processes MF

reverse osmosis

UV–AOP chlorination

PDF nil sulfate

viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV) imposed

N/A no maximum no maximum no maximum

Observed mean process performance

N/A 2.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.85. Virus removal by Multiple Barrier Simulation No. 2.

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Multiple Barrier Monte Carlo Simulation No. 3 is summarized in Table 4.11, and the outcome is presented in Figure 4.86. In this simulation, maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In the California IPR context of a desired 12 log removal for viruses, each process barrier can receive only a process-specific maximum LRV credit. In this case, the maximum creditable 10 LRV (assuming 4 log credit for UV–AOP based on EPA rather than 6 log credit allowable in California) was predicted to be achieved 100% of the time.

Table 4.11. Multiple Barrier Monte Carlo Simulation No. 3

Contaminant(s): Viruses

Processes MF reverse osmosis UV–AOP chlorination PDF

nil Hydranautics viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV) imposed

N/A 2 4 4

Observed mean process performance

N/A 4.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.86. Virus removal by Multiple Barrier Simulation No. 3.

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Multiple Barrier Monte Carlo Simulation No. 4 is summarized in Table 4.12, and the outcome is presented in Figure 4.87. This simulation is the same as presented for Simulation No. 3, but with the more conservative RO performance surrogate (sulfate). The differences between these two PDFs are negligible, with only a very small number of simulation results (out of 10,000) being visible at values slightly less than the full available credit of 10 LRV. Clearly, UV–AOP could attain beyond 6 log, with a fifth percentile LRV at 8.6, thereby achieving the California 12 log virus removal requirement.

Table 4.12. Multiple Barrier Monte Carlo Simulation No. 4

Contaminant(s): Viruses

Processes MF reverse osmosis UV–AOP chlorination PDF

nil sulfate viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV) imposed

N/A 2 4 4

Observed mean process performance

N/A 2.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.87. Virus removal by Multiple Barrier Simulation No. 4.

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4.3.2 Giardia Removal by RO Membrane-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 5 is summarized in Table 4.13, and the outcome is presented in Figure 4.88. This simulation shows multiple barrier removal of Giardia by MF, RO, UV, and chlorination. The least conservative PDF for RO performance (Dow Film Tec membrane, from Olivieri et al. [1999]) was used because this is based on experiments with MS2 bacteriophage, which is considered to be a conservative surrogate for Giardia because of its much smaller physical size. The results of this simulation show a mean multiple barrier performance of 22 LRV for Giardia, a fifth percentile of 19 LRV, and a minimum value from 10,000 sampling iterations of 16 LRV.

Table 4.13. Multiple Barrier Monte Carlo Simulation No. 5

Contaminant(s): Giardia

Processes MF reverse osmosis UV–AOP chlorination

PDF Giardia by MF Dow Film Tec

Giardia by UV–AOP

Giardia by chlorine

Maximum credit (LRV) imposed

no maximum no maximum no maximum no maximum

Observed mean process performance

4.6 5.4 7.7 3.9

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; PDF=probability density function; UV=ultraviolet.

Figure 4.88. Giardia removal by Multiple Barrier Simulation No. 5.

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Multiple Barrier Monte Carlo Simulation No. 6 is summarized in Table 4.14, and the outcome is presented in Figure 4.89. This simulation is the same as that described for Simulation No. 5, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In the case of California IPR rules, Giardia treatment must achieve 10 log removal, with limits on the maximum removal credit for each process, which includes a 3 log maximum creditable performance from chlorination. The results of the Monte Carlo simulation indicate that this IPR target of 10 LRV could be achieved effectively 100% of the plant operation time even with these individual process limits. The results show a mean multiple barrier performance of 12 LRV for Giardia, a fifth percentile of 11 LRV, and a minimum value from 10,000 sampling iterations of 11 LRV.

Table 4.14. Multiple Barrier Monte Carlo Simulation No. 6

Contaminant(s): Giardia

Processes MF

reverse osmosis

UV–AOP chlorination

PDF Giardia by MF Dow Film Tec

Giardia by UV–AOP

Giardia by chlorine

Maximum credit (LRV) imposed

4 2 4 3

Observed mean process performance

4.6 5.4 7.7 3.9

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; PDF=probability density function; UV=ultraviolet.

Figure 4.89. Giardia removal by Multiple Barrier Simulation No. 6.

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Multiple Barrier Monte Carlo Simulation No. 7 is summarized in Table 4.15, and the outcome is presented in Figure 4.90. This simulation is the same as that described for Simulation No. 6, except that the more conservative surrogate for RO performance (sulfate removal) is applied. When a maximum credit of 2 LRV is applied to RO performance, the final result is almost identical, regardless of which measure is used for RO performance.

Table 4.15. Multiple Barrier Monte Carlo Simulation No. 7

Contaminant(s): Giardia

Processes MF

reverse osmosis

UV–AOP chlorination

PDF Giardia by MF sulfate

Giardia by UV–AOP

Giardia by chlorine

Maximum credit (LRV) imposed

4 2 4 3

Observed mean process performance

4.6 2.7 7.7 3.9

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; PDF=probability density function; UV=ultraviolet.

Figure 4.90. Giardia removal by Multiple Barrier Simulation No. 7.

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4.3.3 Cryptosporidium Removal by RO Membrane-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 8 is summarized in Table 4.16, and the outcome is presented in Figure 4.91. This simulation describes the removal of Cryptosporidium by MF, RO, and UV disinfection. No credit is given for disinfection of Cryptosporidium by chlorination. The results reveal a mean multiple barrier performance of 18 LRV, a fifth percentile value of 17 LRV, and a minimum value from 10,000 sampling iterations of 14 LRV.

Table 4.16. Multiple Barrier Monte Carlo Simulation No. 8

Contaminant(s): Cryptosporidium

Processes MF

reverse osmosis

UV–AOP chlorination

PDF Cryptosporidium by MF

Dow Film Tec Cryptosporidium

by UV–AOP nil

Maximum credit (LRV) imposed

no maximum no maximum no maximum N/A

Observed mean process performance

4.6 5.4 7.8 N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.91. Cryptosporidium removal by Multiple Barrier Simulation No. 8.

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Multiple Barrier Monte Carlo Simulation No. 9 is summarized in Table 4.17, and the outcome is presented in Figure 4.92. This simulation is the same as that presented in Simulation No. 8, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In the case of California IPR rules, Cryptosporidium treatment must achieve 10 log removal, with limits on the maximum removal credit for each process, which includes a zero creditable performance from chlorination. The full 10 LRV was consistently achieved, as shown in Figure 4.92.

Table 4.17. Multiple Barrier Monte Carlo Simulation No. 9

Contaminant(s): Cryptosporidium

Processes MF

reverse osmosis

UV–AOP chlorination

PDF Cryptosporidium by MF

Dow Film Tec

Cryptosporidium by UV–AOP

nil

Maximum credit (LRV) imposed

4 2 4 0

Observed mean process performance

4.6 5.4 7.8 N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.92. Cryptosporidium removal by Multiple Barrier Simulation No. 9.

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Multiple Barrier Monte Carlo Simulation No. 10 is summarized in Table 4.18, and the outcome is presented in Figure 4.93. This simulation is the same as that described for Simulation No. 9, except that the more conservative surrogate for RO performance (sulfate removal) is applied. When a maximum credit of 2 LRV is applied to RO performance, the final result is almost identical, regardless of which measure is used for RO performance.

Table 4.18. Multiple Barrier Monte Carlo Simulation No. 10

Contaminant(s): Cryptosporidium

Processes MF reverse osmosis UV–AOP chlorination

PDF Cryptosporidium by MF

sulfate Cryptosporidium by

UV–AOP nil

Maximum credit (LRV) imposed

4 2 4 0

Observed mean process performance

4.6 2.7 7.8 N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; MF=microfiltration; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.93. Cryptosporidium removal by Multiple Barrier Simulation No. 10.

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4.3.4 Virus Removal by Ozone–BAC-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 11 is summarized in Table 4.19, and the outcome is presented in Figure 4.94. This simulation was undertaken for virus removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration), ozonation, UV disinfection, and chlorination. Removal/inactivation by these four processes produced a PDF for viruses with a mean of 156 LRV, fifth percentile of 63 LRV, and a minimum value from 10,000 sampling iterations of 39 LRV.

Table 4.19. Multiple Barrier Monte Carlo Simulation No. 11

Contaminant(s): Viruses

Processes SF ozonation UV disinfection chlorination

PDF viruses by SF viruses by ozone

viruses by UV disinfection

viruses by chlorine

Maximum credit (LRV) imposed

no maximum no maximum no maximum no maximum

Observed mean process performance

1.5 34.7 1.2 119

Notes: LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.94. Virus removal by Multiple Barrier Simulation No. 11.

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Multiple Barrier Monte Carlo Simulation No. 12 is summarized in Table 4.20, and the outcome is presented in Figure 4.95. This simulation was the same as that described for Simulation No. 11, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for virus removal is 14 LRV. The full 14 LRV was not reached because of the lower performing sand filtration, which did not achieve the maximum 2 LRV as would be expected during normal filter operation. The multiple barrier PDF revealed a mean value of 10.8 LRV, a fifth percentile of 10.4 LRV, and a minimum value from 10,000 sampling iterations of 10.1 LRV.

Table 4.20. Multiple Barrier Monte Carlo Simulation No. 12

Contaminant(s): Viruses

Processes SF ozonation UV disinfection chlorination

PDF viruses by SF viruses by ozone

viruses by UV disinfection

viruses by chlorine

Maximum credit (LRV)

2 4 4 4

Observed mean process performance

1.5 34.7 1.2 119

Notes: LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.95. Virus removal by Multiple Barrier Simulation No. 12.

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A process modification of adding UV–AOP (in terms of UV dose, not including peroxide addition) instead of UV disinfection was tested via Multiple Barrier Monte Carlo Simulation No. 13 and is summarized in Table 4.21, with the outcome presented in Figure 4.96. This simulation was undertaken for virus removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration), ozonation, UV–AOP, and chlorination. Removal and inactivation by these four processes produced a PDF for viruses with a mean of 164 LRV, fifth percentile of 72 LRV, and a minimum value from 10,000 sampling iterations of 48 LRV.

Table 4.21. Multiple Barrier Monte Carlo Simulation No. 13

Contaminant(s): Viruses

Processes SF ozonation UV–AOP chlorination

PDF viruses by SF viruses by ozone

viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV) imposed

no maximum no maximum no maximum no maximum

Observed mean process performance

1.5 34.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.96. Virus removal by Multiple Barrier Simulation No. 13.

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Multiple Barrier Monte Carlo Simulation No. 14 also applied the maximum LRV credit to the UV–AOP modified process train and is summarized in Table 4.22, with the outcome presented in Figure 4.97. This simulation was the same as that described for Simulation No. 13, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for virus removal is 14 LRV. The full 14 LRV was not reached (though the California requirement of 12 log removal was achieved) because of the lower performing sand filtration, which did not achieve the maximum 2 LRV, as would be expected during normal filter operation. The multiple barrier PDF using maximum creditable LRV-censored data revealed a mean value of 13.5 LRV, a fifth percentile of 13.3 LRV, and a minimum value from 10,000 sampling iterations of 13.1 LRV.

Table 4.22. Multiple Barrier Monte Carlo Simulation No. 14

Contaminant(s): Viruses

Processes SF ozonation UV–AOP chlorination

PDF viruses by SF viruses by ozone

viruses by UV–AOP

viruses by chlorine

Maximum credit (LRV)

2 4 4 4

Observed mean process performance

1.5 34.7 9.4 119

Notes: AOP=advanced oxidation process; LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.97. Virus removal by Multiple Barrier Simulation No. 14.

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4.3.5 Giardia Removal by Ozone–BAC-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 15 is summarized in Table 4.23, and the outcome is presented in Figure 4.98. This simulation was undertaken for Giardia removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration based on Cryptosporidium performance), ozonation, UV disinfection, and chlorination. Removal and inactivation by these four processes produced a PDF for Giardia with a mean of 30 LRV, fifth percentile of 18 LRV, and a minimum value from 10,000 sampling iterations of 12 LRV.

Table 4.23. Multiple Barrier Monte Carlo Simulation No. 15

Contaminant(s): Giardia

Processes SF ozonation UV disinfection chlorination

PDF Cryptosporidium by SF

Giardia by ozone Giardia by UV

disinfection Giardia by chlorine

Maximum credit (LRV)

no maximum no maximum no maximum no maximum

Observed mean process performance

2.2 17 5.4 3.9

Notes: LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.98. Giardia removal by Multiple Barrier Simulation No. 15.

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Multiple Barrier Monte Carlo Simulation No. 16 is summarized in Table 4.24, and the outcome is presented in Figure 4.99. This simulation was the same as that described for Simulation No. 15, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for Giardia removal is 13.5 LRV, as a sum of the LRVs of the combined process train. The full 13.5 LRV was generally not reached, primarily because of the sand filtration PDF, which did not achieve the maximum 2.5 LRV, as would be expected during normal filter operation. The multiple barrier PDF revealed a mean value of 12.7 LRV, a fifth percentile of 11.4 LRV, and a minimum value from 10,000 sampling iterations of 10.7 LRV.

Table 4.24. Multiple Barrier Monte Carlo Simulation No. 16

Contaminant(s): Giardia

Processes SF ozonation UV disinfection chlorination

PDF Cryptosporidium by SF

Giardia by ozone

Giardia by UV disinfection

Giardia by chlorine

Maximum credit (LRV) imposed

2.5 4 4 3

Observed mean process performance

2.2 17 5.4 3.9

Notes: LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.99. Giardia removal by Multiple Barrier Simulation No. 16.

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Although the ozone–BAC-based process train with disinfection dose UV demonstrated ample log removal of Giardia, a second simulation was completed using UV–AOP in order to supplement the virus UV–AOP simulation data demonstrated previously. Multiple Barrier Monte Carlo Simulation No. 17 is summarized in Table 4.25, and the outcome is presented in Figure 4.100. This simulation was undertaken for Giardia removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration based on Cryptosporidium performance), ozonation, UV–AOP, and chlorination. Removal and inactivation by these four processes produced a PDF for Giardia with a mean of 31 LRV, fifth percentile of 20 LRV, and a minimum value from 10,000 sampling iterations of 15 LRV.

Table 4.25. Multiple Barrier Monte Carlo Simulation No. 17

Contaminant(s): Giardia

Processes SF ozonation UV–AOP chlorination

PDF Cryptosporidium by SF

Giardia by ozone Giardia by UV–

AOP Giardia by chlorine

Maximum credit (LRV)

no maximum no maximum no maximum no maximum

Observed mean process performance

2.2 17 7.7 3.9

Notes: AOP=advanced oxidation process; LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.100. Giardia removal by Multiple Barrier Simulation No. 17.

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Multiple Barrier Monte Carlo Simulation No. 18 is summarized in Table 4.26, and the outcome is presented in Figure 4.101. This simulation was the same as that described for Simulation No. 17, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for Giardia removal is 13.5 LRV, as a sum of the LRVs of the combined process train. The fully creditable 13.5 LRV was generally not reached, primarily because of the sand filtration PDF, which did not achieve the maximum 2.5 LRV, as would be expected during normal filter operation. However, the California 10 log removal requirement was surpassed even with the censored data. The multiple barrier PDF revealed a mean value of 12.7 LRV, a fifth percentile of 11.4 LRV, and a minimum value from 10,000 sampling iterations of 10.7 LRV.

Table 4.26. Multiple Barrier Monte Carlo Simulation No. 18

Contaminant(s): Giardia

Processes SF ozonation UV–AOP chlorination

PDF Cryptosporidium by SF

Giardia by ozone

Giardia by UV–AOP

Giardia by chlorine

Maximum credit (LRV) imposed

2.5 4 4 3

Observed mean process performance

2.2 17 7.7 3.9

Notes: AOP=advanced oxidation process; LRV=log removal value; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.101. Giardia removal by Multiple Barrier Simulation No. 18.

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4.3.6 Cryptosporidium Removal by Ozone–BAC-Based Process Train

Multiple Barrier Monte Carlo Simulation No. 19 is summarized in Table 4.27, and the outcome is presented in Figure 4.102. This simulation was undertaken for Cryptosporidium removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration), ozonation, and UV disinfection. Removal and inactivation by these three processes produced a PDF for Cryptosporidium with a mean of 8.6 LRV, fifth percentile of 7.9 LRV, and a minimum value from 10,000 sampling iterations of 7.4 LRV. These LRVs fall below the California target of 10, though this is in part because of the fact that the ozone system was not optimized for a full 3 log Cryptosporidium removal. Achieving the full 3 log credit with ozone would bump the mean Cryptosporidium inactivation to over 10 log, and optimization of filter performance to achieve 2.5 log credit would further improve credit. Another limiting factor is that the full-scale filter performance data were collected from facilities where media filtration is not the primary barrier for pathogen reduction (MF–UF is later used to further control pathogens); therefore, the filters were not operated to achieve combined filter effluents of less than 0.1 NTU and could therefore not achieve the 4 to 5 log removal based on the regression equations provided by Douglas et al. (2015).

Table 4.27. Multiple Barrier Monte Carlo Simulation No. 19

Contaminant(s): Cryptosporidium

Processes SF ozonation UV disinfection chlorination

PDF Cryptosporidium by SF

Cryptosporidium by ozone

Cryptosporidium by UV disinfection

nil

Maximum credit (LRV) imposed

no maximum no maximum no maximum 0

Observed mean process performance

2.2 0.9 5.4 N/A

Notes: LRV=log removal value; N/A= not applicable; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.102. Cryptosporidium removal by Multiple Barrier Simulation No. 19.

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Multiple Barrier Monte Carlo Simulation No. 20 is summarized in Table 4.28, and the outcome is presented in Figure 4.103. This simulation was the same as that described for Simulation No. 19, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for Cryptosporidium removal is 9.5 LRV. The full 9.5 LRV was not reached because of the ozonation PDF, which did not consistently achieve the maximum 3 LRV credits, and the sand filtration PDF, which did not consistently reach the maximum 2.5 credits. The multiple barrier PDF revealed a mean value of 7.2 LRV, a fifth percentile of 6.5 LRV, and a minimum value from 10,000 sampling iterations of 6.1 LRV.

Table 4.28. Multiple Barrier Monte Carlo Simulation No. 20

Contaminant(s): Cryptosporidium

Processes SF ozonation UV disinfection chlorination

PDF Cryptosporidium by SF

Cryptosporidium by ozone

Cryptosporidium by UV disinfection

nil

Maximum credit (LRV) imposed

2.5 3 4 0

Observed mean process performance

2.2 0.9 5.4 N/A

Notes: LRV=log removal value; N/A= not applicable; PDF=probability density function; SF=sedimentation and filtration; UV=ultraviolet.

Figure 4.103. Cryptosporidium removal by Multiple Barrier Simulation No. 20.

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To determine whether improving the UV disinfection to UV–AOP in the ozone–BAC-based treatment train, Multiple Barrier Monte Carlo Simulation No. 21 was completed and is summarized in Table 4.29, with the outcome presented in Figure 4.104. This simulation was undertaken for Cryptosporidium removal by the non-membrane treatment train consisting of sedimentation and filtration (in this case, sand filtration), ozonation, and UV–AOP. Removal and inactivation by these three processes produced a PDF for Cryptosporidium with a mean of 11 LRV, fifth percentile of 10 LRV, and a minimum value from 10,000 sampling iterations of 9.8 LRV. These results indicate that a modification to the process train such as increasing UV disinfection to UV–AOP could be used to improve overall LRVs for Cryptosporidium. Other process modifications could include moving to MF membrane filtration or optimizing ozone dose for Cryptosporidium inactivation.

Table 4.29. Multiple Barrier Monte Carlo Simulation No. 21

Contaminant(s): Cryptosporidium

Processes SF ozonation UV–AOP chlorination

PDF Cryptosporidium by SF

Cryptosporidium by ozone

Cryptosporidium by UV–AOP

nil

Maximum credit (LRV) imposed

no maximum no maximum no maximum 0

Observed mean process performance

2.2 0.9 7.8 N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; N/A= not applicable; PDF=probability density function; SF=sedimentation/filtration; UV=ultraviolet.

Figure 4.104. Cryptosporidium removal by Multiple Barrier Simulation No. 21.

0 3 6 9 12 15

Freq

uenc

y

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Multiple Barrier Monte Carlo Simulation No. 22 is summarized in Table 4.30, and the outcome is presented in Figure 4.105. This simulation was the same as that described for Simulation No. 21, except that maximum LRV credits were applied to individual treatment processes at levels normally imposed by U.S. water regulators. In this case, a maximum creditable performance for Cryptosporidium removal is 9.5 LRV. The full 9.5 LRV was not reached because of the ozonation PDF, which did not consistently achieve the maximum 3 LRV credits, and the sand filtration PDF, which did not consistently reach the maximum 2.5 credits. The multiple barrier PDF revealed a mean value of 7.2 LRV, a fifth percentile of 6.5 LRV, and a minimum value from 10,000 sampling iterations of 6.1 LRV.

Table 4.30. Multiple Barrier Monte Carlo Simulation No. 22

Contaminant(s): Cryptosporidium

Processes SF ozonation UV–AOP chlorination

PDF Cryptosporidium by SF

Cryptosporidium by ozone

Cryptosporidium by UV–AOP

nil

Maximum credit (LRV) imposed

2.5 3 4 0

Observed mean process performance

2.2 0.9 7.8 N/A

Notes: AOP=advanced oxidation processes; LRV=log removal value; N/A= not applicable; PDF=probability density function; SF=sedimentation/filtration; UV=ultraviolet.

Figure 4.105. Cryptosporidium removal by Multiple Barrier Simulation No. 22.

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4.3.7 Chemical Removal across Multiple Barriers Multiple Barrier Monte Carlo Simulation No. 23 is summarized in Table 4.31, and the outcome is presented in Figure 4.106. This simulation was undertaken for the removal of NDMA by the membrane-based treatment process installed at the GWRS. It is based on PDFs developed from long-term monitoring data from this plant around the RO and UV–AOP processes. The simulation produced a multiple barrier PDF with a mean NDMA removal of 2.0 LRV, a fifth percentile of 1.2 LRV, and a minimum value from 10,000 sampling iterations of 0.4 LRV.

Table 4.31. Multiple Barrier Monte Carlo Simulation No. 23

Contaminant(s): NDMA

Processes microfiltration reverse osmosis UV–AOP chlorination

PDF nil GWRS GWRS nil

Maximum credit (LRV) imposed

N/A no maximum no maximum N/A

Notes: AOP=advanced oxidation process; GWRS=groundwater replenishment system; LRV=log removal value; N/A= not applicable; NDMA=n-Nitrosodimethylamine; PDF=probability density function; UV=ultraviolet.

Figure 4.106. NDMA removal by Multiple Barrier Simulation No. 23.

5.0% 90.0% 5.0%

1.239 2.707

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

LRV

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Freq

uenc

y

NDMA

NDMA

Minimum 0.391Maximum 3.62Mean 1.97Std Dev 0.445Values 10000

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Multiple Barrier Monte Carlo Simulation No. 24 is summarized in Table 4.32, and the outcome is presented in Figure 4.107. This simulation was undertaken for the removal of NDMA by the membrane-based treatment process installed at the Scottsdale AWTP. It is based on PDFs developed from long-term monitoring data from this plant around the RO and UV–AOP processes. The simulation produced a multiple barrier PDF with a mean NDMA removal of 1.3 LRV, a fifth percentile of 0.4 LRV, and a minimum value from 10,000 sampling iterations of -0.7 LRV (suggesting additional net production of NDMA rather than removal).

Table 4.32. Multiple Barrier Monte Carlo Simulation No. 24

Contaminant(s): NDMA

Processes microfiltration reverse osmosis UV–AOP chlorination

PDF nil Scottsdale Scottsdale nil

Maximum credit (LRV) imposed

N/A no maximum no maximum N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; N/A= not applicable; NDMA=n-Nitrosodimethylamine; PDF=probability density function; UV=ultraviolet.

Figure 4.107. NDMA removal by Multiple Barrier Simulation No. 24.

5.0% 90.0% 5.0%

0.382 2.211

-1.0

-0.5 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

LRV

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Freq

uenc

y

NDMA

NDMA

Minimum -0.717Maximum 3.55Mean 1.30Std Dev 0.557Values 10000

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Multiple Barrier Monte Carlo Simulation No. 25 is summarized in Table 4.33, and the outcome is presented in Figure 4.108. This simulation was undertaken for the removal of diuron by the membrane-based treatment process installed at the Scottsdale AWTP. It is based on PDFs developed from long-term monitoring data from this plant around the RO and UV–AOP processes. The simulation produced a multiple barrier PDF with a mean diuron removal of 1.3 LRV, a fifth percentile of 0.5 LRV, and a minimum value from 10,000 sampling iterations of -0.3 LRV (unlikely to be due to production of diuron but a consequence of imperfect correspondence between influent and effluent data, as well as imperfect assumptions around correlations between these data sets).

Table 4.33. Multiple Barrier Monte Carlo Simulation No. 25

Contaminant(s): Diuron

Processes microfiltration reverse osmosis UV–AOP chlorination

PDF nil Scottsdale Scottsdale nil

Maximum credit (LRV) imposed

N/A no maximum no maximum N/A

Notes: AOP=advanced oxidation process; LRV=log removal value; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.108. Diuron removal by Multiple Barrier Simulation No. 25.

-0.5 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Freq

uenc

y

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Multiple Barrier Monte Carlo Simulation No. 26 is summarized in Table 4.34, and the outcome is presented in Figure 4.109. This simulation was undertaken for the removal of atrazine by non-membrane-based treatment processes. It is based on PDFs developed for atrazine removal by ozonation and GAC. The simulation produced a multiple barrier PDF with a mean atrazine removal of 1.8 LRV, a fifth percentile of 1.6 LRV, and a minimum value from 10,000 sampling iterations of 1.4 LRV.

Table 4.34. Multiple Barrier Monte Carlo Simulation No. 26

Contaminant(s): Atrazine

Processes ozonation GAC UV disinfection chlorination

PDF atrazine by ozone atrazine by GAC nil nil

Maximum credit (LRV) no maximum no maximum N/A N/A

Notes: GAC=granular activated carbon; LRV=log removal value; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.109. Atrazine removal by Multiple Barrier Simulation No. 26.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Freq

uenc

y

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Multiple Barrier Monte Carlo Simulation No. 27 is summarized in Table 4.35 and the outcome is presented in Figure 4.110. This simulation was undertaken for the removal of caffeine by non-membrane-based treatment processes. It is based on PDFs developed for caffeine removal by ozonation and GAC. The simulation produced a multiple barrier PDF with a mean caffeine removal of 2.0 LRV, a fifth percentile of 1.5 LRV, and a minimum value from 10,000 sampling iterations of 1.1 LRV.

Table 4.35. Multiple Barrier Monte Carlo Simulation No. 27

Contaminant(s): Caffeine

Processes ozonation GAC UV disinfection chlorination

PDF caffeine by ozone caffeine by GAC nil nil

Maximum credit (LRV) no maximum no maximum N/A N/A

Notes: GAC=granular activated carbon; LRV=log removal value; N/A=not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.110. Caffeine removal by Multiple Barrier Simulation No. 27.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Freq

uenc

y

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Multiple Barrier Monte Carlo Simulation No. 28 is summarized in Table 4.36, and the outcome is presented in Figure 4.111. This simulation was undertaken for the removal of carbamazepine by non-membrane-based treatment processes. It is based on PDFs developed for carbamazepine removal by ozonation and GAC. The simulation produced a multiple barrier PDF with a mean carbamazepine removal of 2.0 LRV, a fifth percentile of 1.4 LRV, and a minimum value from 10,000 sampling iterations of 0.5 LRV.

Table 4.36. Multiple Barrier Monte Carlo Simulation No. 28

Contaminant(s): Carbamazepine

Processes ozonation GAC UV disinfection chlorination

PDF carbamazepine by ozone

carbamazepine by GAC

nil nil

Maximum credit (LRV) no maximum no maximum N/A N/A

Notes: GAC=granular activated carbon; LRV=log removal value; N/A= not applicable; PDF=probability density function; UV=ultraviolet.

Figure 4.111. Carbamazepine removal by Multiple Barrier Simulation No. 28.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Freq

uenc

y

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4.4 Chapter Summary and Conclusions

This chapter presents a large database of PDFs for chemical and microbial contaminants and modelled their removal across multiple barriers for the RO membrane- and ozone–BAC-based treatment trains. Although significant full-scale data with some supplemental pilot-scale data were used to develop the PDFs and resulting analysis, surrogate parameters (e.g., PDT, sulfate rejection, chlorine or ozone CT) were used to determine the LRVs for chemical and microbial contaminants. Again, it is important to recall that a major assumption in the data interpretation is that linear extrapolation of UV and chlorine disinfection is a valid measure of disinfection performance when extrapolated beyond the boundaries provided in the disinfection manuals. The results of the data collection, analysis, and Monte Carlo simulations include:

When full-scale treatment data are used for pathogen removal, the two treatment trains had demonstrated LRVs of viruses, Cryptosporidium, and Giardia that exceeded the current California “12-10-10” rule for groundwater injection (i.e., CCR 60320.208, requiring 12 log removal of viruses, 10 log removal of Cryptosporidium, and 10 log removal of viruses through the water recycling scheme, inclusive of processes from wastewater treatment through retention time in the aquifer) in IPR settings, with the exception of Cryptosporidium.

o For the RO membrane-based treatment, the minimum LRVs observed across the barriers were 33 log for virus (130 log mean), 16 log for Giardia (22 log mean), and 14 log for Cryptosporidium (18 log mean).

o For the ozone–BAC-based treatment, the minimum LRVs observed across the barriers were 39 log for virus (156 log mean), 12 log for Giardia (28 log mean), and 7.4 log for Cryptosporidium (8.6 log mean).

o However, when UV–AOP was used instead of UV disinfection for the ozone–BAC-based train, the minimum LRVs observed across the barriers were 48 log for virus (164 log mean), 15 log for Giardia (31 log mean), and 9.8 log for Cryptosporidium (11 log mean).

A note on the observation of high LRVs: Extrapolation of disinfection efficacy was based on accepted mechanistic equations relating dose (e.g., UV, ozone, or chlorine) to pathogen inactivation, but has not been validated beyond the ranges stated in the U.S. EPA disinfection guidance manuals for various regulatory compliance end points. However, even the U.S. EPA uses its calculations to show that, for typical Giardia disinfection credit with chlorine, virus inactivation calculates out to as much as 168 log removal (U.S. EPA, 1999). Although this is in effect impossible (there are only approximately 1023 L of water on the Earth and not much more than 106 pathogens of a particular variety/L of raw wastewater), it is clear that the disinfection efficacy is greater than 4 log removal. In effect, “true” performance is likely much greater than 4 log inactivation and approaching complete inactivation (i.e., sterilization) for given pathogen classes. Although more research could be used to validate the limits of disinfection, analytical methods, and ability to culture sufficient quantities of virus to demonstrate greater than 6 log disinfection may prove to be the limiting factor that prevents further progress. Thus, the research is at a point where the evidence demonstrates process performance that far surpasses health protection goals but is beyond the limits of quantification to determine “true” performance.

The observations regarding Cryptosporidium must not be taken out of context, however.

o It is important to note that the filters used in this study were not being operated with the intention of maintaining 0.1 NTU combined filter effluent quality; therefore, higher LRVs were not observed (1.6–3.1 log removal was calculated based on turbidity of 0.1–0.5 NTU).

o In addition, processes such as ozone could be further optimized within a specific facility to deliver higher doses to target Cryptosporidium. Likewise, a preozonation step could be used to achieve additional Cryptosporidium removal but was not counted in this assessment.

Water Environment & Reuse Foundation 173

o Finally, the power of this simulation is that it can be used to determine what set points can be used as critical limits for alarms or can help inform the user if additional treatment or process modifications may be needed.

o It is critical that the reader does not interpret the information provided here to assume that the ozone–BAC-based treatment process is inadequate for Cryptosporidium removal, but it is important to use the information to make informed decisions about what site-specific process needs and modifications may be required for a facility. Alternative process considerations, including the replacement of conventional filtration with MF–UF, could be a method to achieve greater creditable log removal of Cryptosporidium.

Inorganic chemical contaminants were modelled by grouping within surrogate classes and found to be highly removed across RO membranes.

Inorganic chemicals had relatively few control mechanisms to improve their removal across the ozone–BAC-based train, but the vast majority of them were present in the model wastewater effluents at concentrations that were already less than drinking water maximum contaminant levels. Therefore, site-specific evaluation regarding efficacy of upstream wastewater effluent concentrations of specific cationic and anionic contaminants is needed to assess whether process steps are needed to control those hazards (especially acute hazards such as nitrate). If it is determined that additional treatment is needed, the engineering team will need to evaluate the efficacy of coagulation and filtration processes (or other unit processes) to remove those contaminants and determine whether other treatment processes or blending is needed to manage those inorganic contaminants.

Very few regulated organic chemical contaminants were found in the water of the two process trains at sufficient levels to measure both influent and effluent concentrations. This made it difficult to model removal as the contaminants were already removed to less than detection limits. Some theoretical removal was modelled using GAC breakthrough curves, whereas surrogates through RO membranes were used to demonstrate possible removal rates and mechanisms. Most measurable organic chemical removal was generally completed with one process step.

Several gaps were also identified throughout the modeling exercise that point to needs for future monitoring and data collection at IPR and DPR facilities:

Facilities with UV–AOP don’t typically collect information on UV dose via on-going actinometry measurements or other means that can be used to back calculate achieved chemical removal (and disinfection). Such monitoring would be helpful in future characterization and modeling exercises.

The pathogen removal data across the sedimentation and filtration process are overly conservative in the current approach and could use other surrogates to validate and observe actual log removal over time during full-scale operation. However, better surrogates for pathogen removal by flocculation–sedimentation–filtration are needed.

More sensitive measures for RO removal of pathogens would allow greater ability to model true removal rather than via a chemical surrogate, which may not reflect particle removal mechanisms.

The work presented in this report represents MF membranes; therefore, additional work on UF rejection of viruses and the impacts on model results is warranted.

As a general observation, the Monte Carlo simulation provides the reader with a great deal of confidence that combined unit processes provide highly effective and reliable (based on years of full-scale data) barriers to microbial and chemical contaminants. It is also important to keep in mind the difference in excursion of water quality parameters from an acute health risk (e.g., pathogens) versus chronic health risks (e.g., SOCs, VOCs) and use that information in designing operational parameters, acceptable ranges of operation, critical limits, and alarms.

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4.5 References Bellona, C.; Drewes, J. E.; Xu, P.; Amy, G. Factors Affecting the Rejection of Organic Solutes During

Nf/Ro Treatment--a Literature Review. Water Res. 2004, 38(12), 2795–2809. Campbell, A.; Douglas, I.; Emelko, M.; McLellan, N.; Banihashemi, A. Evaluating Pathogen Log

Performance through Pilot-Plant Challenge Studies. Canadian National Conference on Drinking Water, Canadian Water and Wastewater Association, Health Canada. Gatineau, Quebec, October 26–29, 2014.

Drewes, J. E.; Xu, P.; Bellona, C.; Oedekoven, M.; Macalady, D.; Amy, G.; Kim, T. U. Rejection of Wastewater-Derived Micropollutants in High-Pressure Membrane Applications Leading to Indirect Potable Reuse: Effects of Membrane and Micropollutant Properties. WateReuse Foundation, Alexandria, VA, 2006.

Douglas, I.; Campbell, A.; Elliott, J.; Downey, K.; McFadyen, S. Application of Microbial Risk Modelling (Qmra) to Develop Public Health Triggers for Drinking Water Treatment. AWWA International Symposium on Waterborne Pathogens. Savannah, GA, April 13–14, 2015.

Hudman, F.; MacInante, P.; Day, T.; Johnson, W. Demonstration of Memtec Microfiltration for Disinfection of Secondary Treated Sewage, Blackheath, NSW. The Water Board Sydeny-Illawarra-Blue Mountains, Blackheath, NSW. 1992, Vol. 1, 141 pages.

Lawryshyn, Y.; Hofmann, R. Theoretical Evaluation of UV Reactors in Series. J. Environ. Eng. 2015, 141(10), 04015023.

Mi, B.; Eaton, C. L.; Kim, J.-H.; Colvin, C. K.; Lozier, J. C.; Mariñas, B. J. Removal of Biological and Non-Biological Viral Surrogates by Spiral-Wound Reverse Osmosis Membrane Elements with Intact and Compromised Integrity. Water Res. 2004, 38(18), 3821–3832.

Nghiem, L. D.; Schäfer, A. I. Critical Risk Points of Nanofiltration and Reverse Osmosis Processes in Water Recycling Applications. Desalination 2006, 187(1–3), 303–312.

Olivieri, A.; Eisenberg, D.; Soller, J.; Eisenberg, J.; Cooper, R.; Tchobanoglous, G.; Trussell, R.; Gagliardo, P. Estimation of Pathogen Removal in an Advanced Water Treatment Facility Using Monte Carlo Simulation. Water Sci. Technol. 1999, 40(4–5), 223–234.

Schäfer, A. I.; Nghiem, L. D.; Waite, T. D. Removal of the Natural Hormone Estrone from Aqueous Solutions Using Nanofiltration and Reverse Osmosis. Environ. Sci. Technol. 2003, 37(1), 182–188.

Smith, D. B.; Clark, R. M.; Pierce, B. K.; Regli, S. An Empirical Model for Interpolating C*T Values for Chlorine Inactivation of Giardia Lamblia. J. Water Suppy Res. T. 1995, 44(5), 203–211.

Sobsey, M. D.; Fuji, T.; Shields, P. A. Inactivation of Hepatitis A Virus and Model Viruses in Water by Free Chlorine and Monochloramine. Water Sci. Technol. 1988, 20(11/12), 385–391.

Summers, R. S.; Kennedy, A. M.; Knappe, D. R. U.; Reinert, A. M.; Fotta, M. E.; Mastropole, A. J.; Corwin, C. J.; Roccaro, J. Evaluation of Available Scale-up Approaches for the Design of GAC Contactors. Water Research Foundation, Denver, CO, 2014, 199 pages.

U.S. EPA. LT1ESWTR Disinfection Profiling and Benchmarking Technical Guidance Manual. Office of Water (4606m). EPA 816-R-03-004, 2003a.

U.S. EPA. Ultraviolet Disinfection Guidance Manual (Draft). EPA 815-D-03-007, June 2003b. U.S. EPA. Ultraviolet Disinfection Guidance Manual for the Final Long Term 2 Enhanced Surface Water

Treatment Rule, Washington, D.C., 2006. U.S. EPA. Code of Federal Regulations. Title 40, Chapter 1, Subchapter D, Part 141, Subpart W, Section

141.720, Inactivation Toolbox Components, 2015. Yoon, Y.; Westerhoff, P.; Snyder, S. A.; Wert, E. C. Nanofiltration and Ultrafiltration of Endocrine

Disrupting Compounds, Pharmaceuticals and Personal Care Products. J. Membrane Sci. 2006, 270(1–2), 88–100.

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Chapter 5

Reliability and Availability of Water Reuse

Monitoring Systems for Process Performance

Verification

5.1 Introduction

This chapter explores the reliability of the monitoring systems of advanced water treatment processes. From a DPR perspective, when there is a risk that a critical process may fail, proper monitoring must be in place to inform operators about the functioning of that process and when changes may need to be made to that asset. However, there is also a risk that a monitor, and subsequently the operator, will fail to notice the failure or improper operation of a specific control point. In that sense, the risk of “failure to notice failure” needs to be quantified to understand how likely such an outcome would be. The approach used here is based on reliability engineering principles and computer simulation tools that provide a methodology for simulating the water treatment process and process monitoring.

In terms of reliability analysis, most research studies focus on the water treatment processes with minimal consideration of the monitoring systems that determine whether the processes are performing as intended. If the monitoring systems are unreliable, then there is a risk of being unaware of performance degradations or failures that can pose serious health risks. The approach used here helps to identify the monitors that are the least reliable so that they can be improved in order to increase the overall reliability of the monitoring of the water treatment process.

Two methods are used to examine reliability of the monitors of water treatment processes. First, the effectiveness and performance of the monitoring system for RO membrane-based treatment and ozone–biofiltration-based process trains are determined using computer simulations. Second, the RPN methodology is applied to consider the product of three indices, occurrence, severity, and detection, for each individual monitor in the two distinct process trains. Reliability data and expert-based assessment are then used to determine the value of three indices to identify the least reliable monitors and possible “pinch points” of concern in the treatment trains. This analysis would be ideally conducted with actual data from the analyzer, but for this exercise, manufacturer-provided data were used instead.

5.1.1 Problem Statement

The aim of this analysis was to develop a simulation model of the monitoring systems of different process trains to analyze their theoretical reliability. This can help identify operational boundaries and trigger points for each process that would cause failures and inaccurate or skewed readings from the monitors.

5.1.2 Project Background

Secondary treated effluent that has been already treated at a biological wastewater treatment plant comes to the advanced process. The advanced water treatment process is composed of two types of process trains: RO membrane-based treatment and ozone–biofiltration-based process trains. Both have CCPs that have been identified in earlier chapters and are designed to purify the water and reduce the health risk of consumption.

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As illustrated throughout this report, at each stage in the advanced water treatment process, the water flows through multiple treatment steps, including CCPs, which provide a barrier to water quality hazards. At each CCP certain contaminants are removed, prevented from being introduced, or managed to within acceptable levels. The operation of the CCPs is monitored by different analyzers to ensure they are operating within specified boundaries. For example, in Figure 2.5, the water flows through a chloramination stage where the identified hazards include minimizing the formation of DBPs (trihalomethanes, haloacetic acids, and NDMA) while minimizing the introduction of perchlorate and chlorate from the chlorine solution. Here, the monitors would include chlorine analyzers and ammonia analyzers for dose control.

Figure 5.1 provides the sequential flow of different CCPs for the RO-based train, analyzers associated with each CCP, and trigger events that could result in skewed reading or no reading in the analyzer during the monitoring process (also discussed in Chapter 2).

Table 5.1 describes the RO-based process train configuration. It defines the different CCPs throughout the process train, type of analyzer used at each stage to monitor the barrier, and possible reasons for failure of analyzer. Table 5.1 also defines example model equipment types for the analyzers.

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Figure 5.1. RO membrane-based treatment – CCP and associated failure triggers.

Pre-Chloramination

MF/UF RO UV/H2O2 Stabilization Chlorine

Incorrect of chlorine or ammonia

Membrane/O-Ring breach

Membrane breach/break

Insufficient dose of UV of

H2O2

Incorrect chemical

dose/insuffic-ient hardness

addition

Insufficient dose

Chlorine Analyzer + Ammonia Analyzer

Pressure Transducer + Temperature Transmitter

Electrical Conductivity +

TOC

Magnetic Flow Meter + UV

Transmittance

pH Analyzer + Conductivity

Analyzer

Chlorine Analyzer +

Flow Meter

Analyzer Failure

Pressure Transmitter/ Flow Meter/ Temperature Transmitter

Failure

Analyzer Failure

Incorrect Power/ Flow

Meter Failure

Analyzer Failure

Pump Failure

Critical Control Point

Trigger Point

Monitor/Analyzer

Reason for Failure

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Table 5.1. CCPs, Analyzers, Triggers, and Failures for RO-Based Process Train Critical Control Point

Analyzer/ Monitor Trigger Point/ Failure Mode CCP

Process Reason for Failure

Test Analyzer Make and Model

Prechloramination chlorine analyzer; ammonia analyzer

incorrect dose overdose of chlorine

Chloramine is used primarily for MF and RO biofouling control. It’s a mild oxidant that the RO membranes can tolerate. Free chlorine (like chlorinated water supplies or swimming pools) is not tolerated by the membranes. Operators worry about making sure that they have chloramine, not chlorine, and they do this by monitoring the chlorine level, reduction–oxidation (redox) potential, and sometimes ammonia in the water. However, the function of a CCP is about direct impact to health. In this case, chloramine formation can have direct health impacts by creating disinfection byproducts and therefore may be used as a CCP. The goal is to control the right amount of chlorine and ammonia to minimize formation of unwanted byproducts. Therefore, the reliability of the chlorine and ammonia analyzers is essential.

failure of chlorine analyzer failure of ammonia analyzer (if used)

chlorine (Wallace and Tiernan Micro 2000) ammonia (Rosemount model 1056)

MF–UF pressure transmitter flow transmitter temperature transmitter

membrane damage, breach O-ring or other seal breach

Pressure decay integrity testing provides superior resolution; however, it is a discrete test that is performed periodically. Turbidity can provide an effective continuous backup measure or supplemental measurement.

failure of pressure transmitter failure of flow meter failure of

pressure transmitter (Rosemount model 3051TG1F2B21AB4M5Q4)

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Critical Control Point

Analyzer/ Monitor Trigger Point/ Failure Mode CCP

Process Reason for Failure

Test Analyzer Make and Model

The pressure decay test is a discrete sequence that occurs on an operating MF or UF unit at least once a day. Very simply, it pressurizes one side of the hollow-fiber membranes with air, and the operator observes the decay of pressure across the fiber. The air leak rate (measured in drop of pressure over time) can then be correlated to a log removal of microorganisms. The primary monitoring device is a pressure transmitter. Other important devices are a flow meter (magnetic flow meter) and temperature transmitter. As the pressure decay test is not continuous, turbidity can be used as a backup.

temperature transmitter failure of correct operating sequence

flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4) temperature transmitter (Rosemount 3144PD1F1E5M5Q4XA with 0068N21N00A045T20X3)

RO electrical conductivity analyzer online TOC analyzer

membrane breach or breakage O-ring breach

Electrical conductivity and online TOC are currently the most sensitive analyzers for this task. A useful TOC analyzer is the GE Sievers.

conductivity analyzer failure TOC analyzer failure

RO permeate conductivity (Rosemount XMT-C-FF-10) RO permeate TOC (GE/Sievers Model 900 and 5310C)

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Critical Control Point

Analyzer/ Monitor Trigger Point/ Failure Mode CCP

Process Reason for Failure

Test Analyzer Make and Model

UV–H2O2 UV flow meter UV transmittance

insufficient dose of UV insufficient dose of peroxide

The present power ratio is calculated internally by the UV system controls. It is a measure of power supplied to UV lamps, compared with the power that is required. Power that is required is calculated from flow rate (magnetic flow meter) and UV transmittance (UVT meter). To achieve AOP, peroxide is required. The dose of hydrogen peroxide is confirmed with either a small magnetic flow meter or a flow switch on the chemical dose.

incorrect power output delivered magnetic flow meter or flows switch failure

UV transmittance (Trojan OptiView) flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4)

Stabilization pH analyzer; conductivity analyzer

incorrect chemical dose (insufficient hardness addition)

Correct stabilization prevents lead or copper leaching from existing distribution systems. Stabilization may depend on where the recycled water is reintroduced (either water plant or directly to the distribution system). The analyzers used here are as follows:

pH analyzer conductivity analyzer

pH analyzer failure conductivity analyzer failure

pH analyzer (Rosemount XMT-P-FF-10)

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Critical Control Point

Analyzer/ Monitor Trigger Point/ Failure Mode CCP

Process Reason for Failure

Test Analyzer Make and Model

Free chlorine chlorine analyzer flow meter

insufficient dose (dosing pump failure)

This CCP requires a sufficient dose of chlorine for a sufficient time to achieve disinfection targets. Both chlorine dose and contact time or a combined CT value (concentration x time) are monitored. A chlorine analyzer and flow rate are used to determine contact time.

chlorine analyzer flow meter (magnetic) to calculate

detention time.

chlorine analyzer failure flow meter failure presence of ammonia

flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4) chlorine (Wallace and Tiernan Micro 2000)

Notes: AOP=advanced oxidation process; CCP=critical control point; CT=concentration x time; MF=microfiltration; RO=reverse osmosis; TOC=total organic carbon; UF=ultrafiltration; UV=ultraviolet; UVT-ultraviolet transmittance.

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For each of the aforementioned CCPs there is a set of analyzers to monitor their performance.

The ozone–biofiltration-based process treatment system is an alternative approach to produce water for DPR. Unlike the RO membrane-based treatment, it does not use membrane treatment and in particular does not have a salinity reduction step.

As shown in Figure 2.5, the CCPs within this process train are slightly more complex. The multiple concentric circles denote where combinations of processes must be treated as synergistic processes to form CCPs.

Figure 5.2 provides the sequential flow of different CCPs for the ozone–biofiltration-based treatment process, analyzers associated at each stage, and trigger events that could result in skewed reading or no reading in the monitoring process. As illustrated, there are two analyzers (UV transmittance analyzer and ozone dose analyzer) at the ozone CCP. This diagram also defines possible failure events that could decrease the reliability of the analyzer.

Table 5.2 defines different CCPs in the ozone–biofiltration-based treatment process. It describes the processes used by the analyzers to monitor the barrier and trigger events that could result in a failure. It also includes example analyzers used to monitor the barrier.

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Figure 5.2. Ozone–biofiltration-based treatment process – CCP and associated failure triggers.

Analyzer Failure Analyzer Failure Analyzer Failure Analyzer Failure Analyzer Failure Analyzer Failure

OzoneOzone

BACCoagulant

BACGAC UV Chlorine

Incorrect ozone dose

Incorrect ozone dose or

insufficient BAC contact

time

Insufficient coagulant

dose or filter breakthrough

Carbon Exhausted

Insufficient UV dose or

poor transmissivity

Insufficient dose

Ozone Dose Analyzer + UV Transmittance

Ozone Dose Analyzer +

Magnetic Flow Meter

TOC+ Turbidity +Flow Meter

TOC+ UV Transmittance

UV Transmittance

Chlorine Analyzer +

Flow Meter

Critical Control Point

Trigger Point

Monitor/Analyzer

Reason for Failure

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Table 5.2. Ozone-Biofiltration-Based Treatment Process Train Critical Control Point

Analyzer/ Monitor

Trigger Point/ Failure Mode CCP

Process Reason for Failure Analyzer Make and Model

Ozone UV transmittance analyzer ozone residual analyzer

insufficient dose overdose

Ozone, as a disinfectant, provides a microorganism kill and contaminant oxidation. At the same time, too much ozone will form unwanted byproducts.

UV transmittance analyzer failure ozone dose monitoring failure

RO permeate TOC (GE/Sievers Model 900 and 5310C) Electrochemical Devices-FC80 free chlorine analyzer

Ozone–BAC ozone–BAC residual analyzer magnetic flow analyzer

insufficient dose insufficient contact time with BAC

The aforementioned ozone dose is combined with a downstream BAC filter. The ozone oxidizes and breaks up larger organic compounds into smaller ones, which are then removed by microorganisms in the BAC. This CCP is therefore the ozone and BAC combined. Analyzers required are:

ozone dose (as above) empty bed contact time is

calculated with a flow meter (magnetic flow meter).

ozone dose monitoring failure magnetic flow meter failure

Badger Meter: M-Series, electromagnetic flow meter

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Critical Control Point

Analyzer/ Monitor

Trigger Point/ Failure Mode CCP

Process Reason for Failure Analyzer Make and Model

Coagulant–BAC TOC analyzer flow meter analyzer

insufficient coagulant dose filter breakthrough

For the BAC to be able to remove microorganisms effectively, upstream coagulation is required. This CCP is the BAC filter and coagulant dose combined. Analyzers required are:

online TOC Analyzer (as above)

online turbidimeter flow meter on dosing line

or flow switch.

TOC analyzer failure Online turbidity analyzer failure flow meter or flow switch failure

Endress + Hauser: Turbimax CUE21

GAC TOC analyzer UV analyzer

carbon too old. filter bypass

The GAC filter adsorbs organic chemicals. Analyzers required are:

UV transmittance analyzer. TOC analyzer

failure of TOC analyzer failure of UV transmittance analyzer

Endress + Hauser: Turbimax CUE21 RO permeate TOC (GE/Sievers Model 900 and 5310C)

UV UV transmissivity analyzer

insufficient UV dose poor transmissivity

Ultraviolet light provides disinfection by inactivating microorganisms. Analyzers required are:

UV transmittance UV dose (calculated by

transmittance, UV system power output and flow rate meter)

RO permeate TOC (GE/Sievers Model 900 and 5310C) UVT Trojan OptiView

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Critical Control Point

Analyzer/ Monitor

Trigger Point/ Failure Mode CCP

Process Reason for Failure Analyzer Make and Model

Chlorine chlorine analyzer

insufficient dose (dosing pump failure)

This CCP requires a sufficient dose of chlorine for a sufficient time to achieve disinfection. Both chlorine dose and contact time or a combined CT (concentration x time) are monitored. A chlorine analyzer and flowrate are used to determine contact time.

chlorine analyzer flow meter (magnetic) to

calculate detention time.

chlorine analyzer failure flow meter failure

Endress + Hauser: CCM 223/253 free chlorine analyzers

Notes: AOP=advanced oxidation process; BAC=biological activated carbon; CCP=critical control point; CT=concentration x time; GAC=granular activated carbon; TOC=total organic carbon; UF=ultrafiltration; UV=ultraviolet.

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5.2 Modeling Methodology

5.2.1 Introduction to Simulation Modeling

Building upon the RO membrane-based treatment process and ozone–biofiltration-based treatment process configurations, models of these process trains can be developed to evaluate the reliability of their monitoring systems by using computer simulations. Computer simulation modeling is a methodology that involves creating, designing, and analyzing the operational behavior of a system or process via mathematical function, approximations, and assumptions. Complex system behaviors can be modeled by use of computer software, which enables a probabilistic approach to evaluating system performance under different conditions. Simulation models provide more flexible and economical ways to analyze system performance compared to a physical model or a real-time system, though the simulation is based upon real data regarding the reliability of process monitors. Simulation can be used to model hypothetical scenarios and event failures to identify bottlenecks, test operational changes, and do capacity planning. The fidelity of each simulation model determines its ability to represent the actual performance of a system. The closer the simulation model is to actual performance, the more realistic the results.

5.2.2 Approach

To determine the reliability of the monitors throughout the CCP–RO membrane-based process train, resiliency must be defined, and a method to quantify it must be provided. The reliability of a system is defined as “ability of an item to perform a required function, under given environmental and operational conditions and for a stated period” (ISO 8402, 1994).

The availability of a system is defined as “the ability of an item (under combined aspects of its reliability, maintainability, and maintenance support) to perform its required function at a stated instant of time or over stated period of time” (BS 4778, 1991). The average availability Aav of a system is defined as:

Aav = MTTF

MTTF+MTTR (5.1)

Where MTTF is Mean Time to Failure, which defines the mean functioning time of the system. Mean Time to Repair (MTTR) is mean downtime after a failure event, before the monitor is returned to normal service. In reliability theory there are various ways to measure and define the reliability of a system. For the RO membrane-based process train, reliability can be measured as:

MTTF = ∫ 𝑡 𝑓 (𝑡) 𝑑𝑡∞

0 (5.2)

For the simulation model, the time to failure, t, is assumed to be continuously distributed with PDF f(t). This allows randomizing of the failure events of the CCP monitors during the execution cycle.

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Failure Rate is the total number of failure time per unit time, denoted as z(t). Time to failure of a system is defined as the time elapsed from operational beginning at t=0 until its first failure event. The probability of the system to fail is denoted by F(t) for interval [0, t] for u process as defined by:

F (t) = ∫ 𝑓 (𝑢) 𝑑𝑢𝑡

0 𝑓𝑜𝑟 𝑡 > 0 (5.3)

Reliability function rate¸ denoted as R(t), is defined as the probability that the system does not fail during the interval [0, t]. Equation 5.3.3 is integrated with regard to the variable u as follows:

R (t) = ∫ 𝑓 (𝑢) 𝑑𝑢∞

1 (5.4)

Determining the reliability would allow identifying the potential causes of event failure and would facilitate causal analysis (Rausand and Hoyland, 2004).

When modeling reliability, it is important to use actual performance data of the system because it captures the stochastic behavior of the CCP monitors. There are various sources of data that can be used to define the distribution function of the performance of each monitor. The most reliable data are obtained from the manufactures of the monitors. However, this is not always feasible because the reliability performance might be considered proprietary information. In such cases, generic reliability databases used for production could be used to define a baseline PDF. Another alternative is to use expert opinion to define the statistical distribution of the performance of each monitor.

In this report, information from each monitor’s technical data sheet was utilized. Figures of merit such as “accuracy” and “reliability” were converted to probabilities necessary for the computer simulation. For purposes of this analysis, a normal distribution was used to demonstrate the behavior of the monitor over the simulation execution duration (unless otherwise stated). This is because it is equally likely for the performance of a monitor to overperform its design specifications as it is to underperform them.

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5.2.3 Methodology for Developing Computer Simulation of Process Train in Arena

The Arena Simulation Software (Rockwell Automation, USA) is a platform for modeling large, complex processes by depicting the behavior of the systems as a series of well-defined and ordered events. In the context of DPR and CCP monitors, it can be used to model all of the monitors working simultaneously within a process train and then predict the reliability of the individual component monitors and that of the overall combined process monitors. In this way, the practitioner can identify areas of high risk (i.e., bottlenecks that may need more preventative maintenance and verification) and the overall likelihood of being able to detect process monitor failures. The methodology used to develop the computer simulation is described in the following six steps.

Step 1: Document process train configuration. The first step was to understand and prepare the flow chart of the water reuse treatment process. The process of documenting the RO membrane-based treatment and ozone–biofiltration-based treatment process train configuration allowed a focus on different components within the system based on CCPs.

Step 2: Define CCPs, failure modes, monitors, analyzers, and programmable logic controllers (PLCs). The second stage was to isolate each component within the system. This helps in identifying various processes, resources, and components in a simulation model.

Step 3: Identify operational boundaries and trigger points for each process that would set off an alarm. Once different subsystems and components were identified in the process train, determining processing behavior, operational conditions under which various components work, was essential. For example, a chlorine analyzer takes 10 seconds to read and display the condition of a CCP, and this may normally be distributed over a defined period of time.

Step 4: Develop model of process train in Arena environment. Once the different components of the water reuse treatment facility were identified, simulation models were developed using different modules to illustrate real-time performance.

Step 5: Populate process steps with statistical performance data from product specification documentation or field performance data. In the simulation model, statistical performance data from different sources were used to define the behavior of analyzers, including failure events.

Step 6: Run simulation and validate results. The final step was to run the discrete event-based simulation that triggers unique outcomes and replicates real-world scenarios of the water treatment facility based on the input data and predetermined operation run-time settings.

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5.2.4 Assumptions

There are various assumptions and dependencies to be considered in order to develop the simulation model and conduct the sensitivity analysis of the water reuse monitor performance. The input data for the simulation model included processing time and the failure modes of all analyzer equipment. The failure modes are broadly classified into two categories: power failure and equipment failure. The variability in equipment leads to variability in system performance. This randomness is captured in the simulation model through the use of PDFs. The type of PDF used depends on the performance behavior of the equipment. The justification for using a Gaussian distribution for defining the likelihood of failure is based on the central limit theorem, which assumes that samples have equal probability from both sides of the mean (Pham et al., 2006; Morgenstern et al., 2012).

The input for equipment failure was based on the functional specification information available in each product’s specification sheet, which is not ideal as the figure of merit can vary by manufacturer. To produce even more realistic results, it is recommended that performance data from various process trains be collected and utilized for the simulation. The performance data would help determine actual distribution functions that best represent a monitor’s performance over a long time horizon.

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5.3 Arena Simulation Model

5.3.1 Model

A screenshot of the simulation model that aims to replicate the monitoring process of RO membrane-based treatment process train is shown in Figure 5.3. The RO membrane-based treatment process train consists of 12 analyzers that monitor aspects of the six identified CCPs (prechloramination, MF, RO, UV–AOP, stabilization, and final chlorination). Rockwell Arena software, a discrete event simulation environment, was used to develop the simulation model. All of the analyzers are defined as process modules in Arena because this allows them to be associated with a resource such as processing time and failure modes.

Figure 5.3. Arena simulation model RO membrane-based treatment process train.

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In this event-based discrete model, the “Create Water Supply” module is the surrogate for incoming water flow, defined as an exponential distribution. All 12 analyzers used in the RO membrane-based treatment process train receive the continuous flow of events, which acts as a continuous flow of water.

The same methodology was used to develop the ozone–biofiltration-based treatment process train. Because the process of recycling the water is slightly different in this train, a separate simulation model was created, as shown in Figure 5.4. Furthermore, different equipment is used in the ozone–biofiltration-based treatment process train, so its processing times are different in the Arena simulation model.

Figure 5.4. Arena simulation model ozone–biofiltration-based treatment process train.

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5.3.2 Performance Data

In reliability engineering the normal distribution is used to demonstrate the likelihood of performance of a component relative to a particular technical specification. Table 5.3 includes the analyzer processes and their corresponding PDF. Here, the normal distribution is defined as NORM (µ, σ), where NORM is normal distribution, µ is respective mean, and σ is respective standard deviation of the processes’ expected reliability. Table 5.3 includes the different analyzers used in the RO membrane-based treatment process train. The column labeled “Figure of Merit” describes the basis on which the PDFs of each analyzer were defined (i.e., accuracy of the analyzer). This information was extracted from the functional specification of the product specification sheets. Long-term observations of a given analyzer’s performance at a given facility would ideally be used to populate the reliability distribution, but because this sort of data is not often collected by utilities, manufacturer-provided data were used instead.

In order to determine the mean and standard deviation of each process, the information provided in each monitor’s data sheet (from the manufacturer) was extrapolated. The reported reliability information for each product was used to generate a mean and standard deviation of its performance over the duration of the simulation (1 year). This extrapolation was further used to define the uptime and downtime in case of a failure event, as described in Table 5.4.

An example of how this was applied can be seen in the Rosemount Flow Transmitter model 8705 analyzer. The spec sheet notes system accuracy of ±0.5% at a flow velocity from 3 to 30 ft/s. The minimum value from the reported range (i.e., 3) is used in this model as the mean for the process behavior. Furthermore, the specification sheet states the system has an accuracy of ±0.015 ft/s (i.e., ±0.5% of 3 ft/s). Therefore, ±0.5% is the deviation from the accurate behavior of analyzer. However, the research team’s simulation model is executed for one year with the base units in minutes (instead of seconds). Therefore, the stated accuracy of ±0.015 ft/s becomes 0.9 ft/min when converted (i.e., 0.15*60=0.9). The spec sheet also includes stability rate ±0.1% of rate over six months, which is translated to a value of 1.2 (0.1%×12 months) for a period of one year. Therefore, the standard deviation would be 0.75 for the flow meter. This resulted in processing the analyzer as NORM(3, 0.75). As another example, the chlorine analyzer has a reported reliability of 0.05%, but when extrapolated for one year of execution, the mean was estimated to be 2%, with a standard deviation of 0.5.

A similar approach is used to derive other analyzer behavior from a probabilistic standpoint. This method is subject to errors, which will thus impact the reliability results. For accurate results, the operational performance should be used to determine actual distribution functions that best represent a monitor’s performance.

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Table 5.3. Process and Expression Used for Each Analyzer for RO Membrane-Based Treatment Process

Name of Analyzer

Distribution (mean [µ], standard deviation [σ])

Analyzer Make and Model Figure of Merit

Prechloramination chlorine analyzer NORM(2, 0.5) chlorine (Wallace and

Tiernan Micro 2000)

accuracy of reliability ±0.05%

Prechloramination ammonia analyzer NORM(3, 0.75) ammonia (Rosemount model

1056)

accuracy of reliability ±0.1%

MF pressure transmitter analyzer NORM(1, 0.5) pressure transmitter (Rosemount model 3051TG1F2B21AB4M5Q4)

accuracy of reliability ±1.65%

MF flow transmitter analyzer NORM(2, 0.5)

flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4)

accuracy of reliability ±0.25%

MF temperature transmitter analyzer

NORM (0.5, 0.25)

temperature transmitter (Rosemount 3144PD1F1E5M5Q4XA with 0068N21N00A045T20X3)

accuracy of reliability ±0.25%

RO conductivity analyzer NORM (0.8, 0.3)

RO permeate conductivity (Rosemount XMT-C-FF-10)

accuracy of reliability ±0.7%

RO total organic carbon analyzer NORM(1, 0.2) RO permeate TOC (GE/Sievers Model 900 and 5310C)

accuracy of reliability ±0.05%

UV transmittance analyzer NORM (0.8, 0.3) UVT Trojan OptiView

standard reference accuracy of 0.5% of rate

UV flow transmitter analyzer NORM (1.5, 0.8)

flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4)

standard reference accuracy of 0.25% of rate

pH analyzer NORM (2.2, 0.7)

pH analyzer (Rosemount XMT-P-FF-10)

standard reference accuracy of 0.5% of rate

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Name of Analyzer

Distribution (mean [µ], standard deviation [σ])

Analyzer Make and Model Figure of Merit

Chlorine flow analyzer NORM(2, 0.5)

flow transmitter (Rosemount meter model 8705TSA120C1W0N0G1B3Q4 and instrument 8742CFACNAA01M5B4)

standard reference accuracy of 0.5% of rate

Chlorine analyzer NORM(1, 0.5) chlorine (Wallace and Tiernan Micro 2000)

standard reference accuracy of 0.5% of rate

Notes: MF=microfiltration; RO=reverse osmosis; TOC=total organic carbon; UV=ultraviolet; UVT=ultraviolet transmittance.

For failure modes, a normal distribution is also used with different means and standard deviations. For example, normal distribution is defined as NORM(µ, σ), where µ is mean, and σ is standard deviation. The normal distribution is presented in days, based on a total simulation duration of one year. These values are derived from functional specifications available for each model used to monitor the RO membrane-based treatment process train. Table 5.4 describes failures associated with each analyzer. All analyzers that are associated with Power Failure are modeled using a uniform distribution. Other failures are defined using normal distribution.

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Table 5.4. Failures Associated with Each Analyzer in RO Membrane-Based Treatment Process Train

Name Up Time Down Time

Power failure UNIF(90, 182) UNIF(1, 2)

Instrument defect chlorine monitor NORM(36.5, 0.1825) NORM(1, 0.5)

Instrument defect ammonia monitor NORM(36.5, 0.365) NORM(1, 0.5)

Instrument defect pressure transmitter monitor NORM(36.5, 0.60225) NORM(1, 0.5)

Instrument defect flow transmitter monitor NORM(36.5, 0.9125) NORM(1, 0.5)

Instrument defect temperature transmitter monitor NORM(36.5, 0.9125) NORM(1, 0.5)

Instrument defect conductivity monitor NORM(36.5, 0.255) NORM(1, 0.5)

Instrument defect TOC monitor NORM(36.5, 0.1825) NORM(1, 0.5)

Instrument defect UVT monitor NORM(36.5, 18.25) NORM(1, 0.5)

Instrument defect UV flow monitor NORM(36.5, 0.9125) NORM(1, 0.5)

Instrument defect pH monitor NORM(36.5, 18.25) NORM(1, 0.5)

Instrument defect chlorine flow monitor NORM(36.5, 18.25) NORM(1, 0.5)

Instrument defect flow monitor NORM(36.5, 18.25) NORM(1, 0.5)

Notes: RO=reverse osmosis; TOC=total organic carbon; UV=ultraviolet; UVT=ultraviolet transmittance.

In Table 5.4, the first column is the name of the failure as defined in the simulation model. The term UNIF represents uniform distribution, with a minimum of 90 days and a maximum of 182 days in this example. The Up Time column is the time in which a failure could be inactive or in sleep mode. The Down Time column is the duration in which a failure would be active. After Down Time is over, the system would resume monitoring the water treatment process. The Up Time and Down Time of failure modes replicate a scenario in which any analyzer is not working because of a defect. Down Time replicates the MTTF metric.

By way of example, the chlorine monitor could fail to analyze samples correctly one day (+/-half a day) out of the year (spread randomly throughout the year, so one is really looking at 86,400 seconds in a year of failure to analyze the “true” chlorine measurement), or 0.27% of the time. The Up Time measurement gives an indication of the likelihood of an analyzer’s sleep mode or time offline for calibration and maintenance (e.g., for the chlorine monitor, 36.5 days out of the year, or 10% of the time). The Down Time is the amount of time that the instrument is operating but failing to give a reading within the specified accuracy window.

Each process analyzer is associated with one or more failure modes and follows three different rules on how it would work during a failure. There are three failure rules used in the simulation model:

1. Ignore. Capacity goes down immediately, but work goes on until finished.

2. Preempt. Capacity goes down, and processing is interrupted immediately.

3. Wait. Capacity decreases until entity releases resource.

These failure modes describe how the water flowing through the train put monitors in “busy” mode during the water treatment process (Kelton, et al., 2007). The simulation model was configured to

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simulate one year of water treatment with 10 replications. In other words, the 10 replications simulate the execution of 10 plants recycling the water for one year. The simulation model of the RO membrane-based treatment process train was designed as a sequential model where only one instance of analyzer equipment is used.

5.4 Results

5.4.1 Simulation Results

The simulation software uses the assigned PDFs regarding accuracy and failure rates to determine the likely performance of a given analyzer over a given period of time. Of course, this would be much better characterized using actual performance data, but absent large data sets, manufacturer data were used to estimate failure rates and utilization.

The utilization of each analyzer can be defined as the scheduled utilization (Us), which is the ratio of the average number of busy minutes divided by the average number of available minutes. It represents the utilization of the analyzer based on its availability during the simulation run (e.g., the Us for the ammonia analyzer is 0.004, indicating it would be busy 0.4% of the time). For the purpose of simulation modeling, only one instance of the equipment is considered. The results for Us would change in cases where there is more than one instance of analyzer equipment. The Us for a single replication for the RO membrane-based treatment process train is listed in Table 5.5.

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Table 5.5. Scheduled Utilization (Us) for Single Replication for (a) RO Membrane-Based Treatment Process Train and (b) Ozone–Biofiltration-Based Treatment Process Train (a) (b)

Scheduled Utilization (RO Membrane-Based Treatment) Us Value

Scheduled Utilization (Ozone–Biofiltration-Based Treatment)

Us Value

Ammonia monitor 0.004024 Chlorine monitor 0.002603

Chlorine flow monitor 0.002704 Flow meter 0.001326

Chlorine monitor 0.002736 Flow monitor 0.001306

Conductivity monitor 0.001098 Magnetic flow 0.002645

Flow monitor 0.001369 Ozone dose 0.00391

Flow transmitter monitor 0.002686 Ozone dose 0.003937

pH monitor 0.002972 TOC–post-GAC 0.001042

Pressure transmitter monitor 0.001381 TOC–post-O3–BAC 0.001051

Temperature transmitter monitor 0.000693 UV transmitter 0.001946

TOC monitor 0.001338 UV process UVT 0.002857

UV flow monitor 0.002072 UVT monitor 0.002603

UVT monitor 0.001094

Notes: BAC=biological activated carbon; GAC=granular activated carbon; TOC=total organic carbon; Us=Scheduled Utilization; UV=ultraviolet; UVT==ultraviolet transmittance.

A frequency variable, which is the time persistent occurrence of a failed state of a monitor during the execution cycle occurred, was also defined. Standard percent (fp) is the average percentage of time the resource was in the failed state.

Figure 5.5 shows the Pareto analysis of the reliability of the various monitors throughout the RO membrane-based treatment process train. Shown on the far left, the least reliable monitor is the TOC monitor, which spent 4.84% of its time in a failed state (i.e., anything that corresponds to the unavailability of the analyzer such as power failure, defect, maintenance, calibration, being offline) over a one-year simulation run. The next least reliable analyzer in the RO membrane-based treatment process train is the pH analyzer, which has fp of 4.65%.

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Figure 5.5. Standard percentage (fp) for RO membrane-based treatment process train monitors.

The simulation results are entirely dependent on the input parameters. These results would vary under different simulation conditions or analyzers. For example, the chlorine analyzer in Figure 11, labeled Chlorine Monitor State, spends 4.09 % of time in a failed state. The same analyzer in an RO membrane-based treatment process simulation model spends 4.26% in a failed state. This variation between results, even with the same analyzer, is due to the modeling of the random behavior of analyzers. This randomness is due to stochastic behavior introduced in each analyzer, which is represented by its own PDF.

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Figure 5.6. Standard percentage (fp) for ozone–biofiltration-based treatment process train monitors.

Figure 5.6 shows Pareto analysis of the ozone–biofiltration-based treatment process train obtained from the simulation results. The post-GAC TOC analyzer had the highest failure during the simulation run, with 4.13% of time spent in a failed state over one year. It represents 12% of the total failure time spent by all analyzers in the ozone–biofiltration-based treatment configuration, making it the least reliable analyzer in the train.

Two observations are worth mentioning. First, the connection between Figures of Merit for each analyzer (e.g., 0.05% accuracy) and their estimated performance is an approximation that was necessary for the computer simulation. A more realistic approach would be to use actual performance data from a monitor so that the simulation can provide realistic outputs. Second, when comparing the results in the Pareto diagram shown in Figures 5.5 and 5.6, it is apparent that the difference in performance of the various analyzers is relatively similar. In other words, the performance of the analyzers is relatively uniform.

The results described in this section are only focused on individual analyzers. It is possible to also analyze the impact of an analyzer failure at the CCP level. To accomplish this, the analyzers should be bundled by CCP. The impact of failure on the overall system could be categorized as high risk in cases where all of the analyzers in a single CCP are at risk of failing at the same time. In different scenarios the failed analyzers could be placed in different CCPs. It is crucial to conduct a macrolevel analysis to identify where each individual analyzer lies within a CCP to assess its overall impact.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Standard Percentage (fp) - Ozone-Biofiltration Based Treatment

Standard Percent Cumulative %

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5.4.2 Risk Priority Number Tool

A distinct approach to evaluating the reliability of a water reuse treatment monitor is the RPN, a numeric assessment of risk assigned to steps in a process. It helps identify bottlenecks and potential problem areas through a numeric score that quantifies (a) likelihood that the failure will occur (“occurrence”), (b) the amount of harm or damage the failure mode may cause to a person or equipment (“severity”), and (c) likelihood that the failure will not be detected (“detection”). The product of these three scores is the RPN:

RPN=Occurrence Index x Severity Index x Detection Index (X.5)

A lower RPN indicates better reliability (McGraw et al., 2014). As an organization works to improve a process, it can anticipate and compare the effects of proposed changes by calculating hypothetical RPNs of different scenarios. It should be noted that the RPN is a measure for comparison within one process only; it is not a measure for comparing risk between processes. A numeric score is assigned to each category by using predefined levels of occurrence, severity, and detection. For the water reuse treatment plant, occurrence scores are shown in Table 5.6. The criteria for each level can be changed depending on the reliability performance of the system under evaluation. The key is to apply the scores consistently once they are established.

Table 5.6. Occurrence Ranking Index

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In the analysis of the water reuse processes, the lowest occurrence score corresponds to the highest reliability criteria (e.g., 99.999% reliability), whereas the highest occurrence score corresponds to the lowest reliability criteria (e.g., 70% reliability), meaning that there is a higher percentage of occurrence of a failure. Following a similar approach, severity rankings for the water reuse processes are shown in Table 5.7. Just like the occurrence table, the lowest severity ranking corresponds to the highest system performance (e.g., undetectable effect on system), whereas the highest severity ranking corresponds to the lowest severity performance (e.g., critical problem with serious safety and legal or compliance implications).

Table 5.7. Severity Ranking Index

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Table 5.8. Detection Ranking Index

Table 5.8 shows the detection ranking index developed for the water reuse process train. Just like the previous two tables, the lowest detection ranking corresponds to the highest certainty of detecting failure mode, whereas the highest detection ranking index corresponds to the lowest certainty of detecting the failure mode.

The utility of the RPN approach is twofold. First, the definition of the occurrence, severity, and detection indices help establish evaluation criteria that can be applied to all elements of the water reuse process. Second, the application of the three indices to determine the RPN helps identify the riskiest and least reliable component of the process that can be addressed to increase overall reliability.

To this end, the RPN approach was applied to two different water reuse configurations: RO membrane-based treatment process train and ozone–biofiltration-based treatment process train. The results of the RPN help identify the components that can be addressed in order to improve the overall reliability of the system. Tables 5.9 through 5.14 include the occurrence, severity, and detection for the monitors in the RO membrane-based treatment train, and Tables 5.15 through 5.20 show the ozone–biofiltration-based treatment train.

On the basis of the risk evaluation of the process monitors for the RO membrane-based treatment train, the five highest RPNs are listed following this paragraph. It is noteworthy that the chlorine analyzer has the same occurrence and detection rank for both the pre-membrane chloramination step and the post-treatment final disinfection step. However, the final chlorination step provides a barrier against an acute risk (illness caused by infection) and therefore a potentially more severe outcome if the chlorine analyzer fails relative to a potential failure of the analyzer in the chloramination step, which may lead to a temporary situation of additional DBP formation but not pose any acute health risk. As such, the RPN for the chlorine analyzer in the disinfection step is 144, whereas the RPN for the same analyzer in the chloramination step is only 96. Likewise, the magnetic flow meter for the UV–AOP step has a high RPN

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because of a higher severity score (6), yet a low occurrence score (4) because of the generally high reliability of such meters.

Chlorine analyzer, disinfection step (RPN=144)

Flow meter, disinfection step (RPN=144)

pH analyzer, stabilization step (RPN=96)

Chlorine analyzer, chloramination step (RPN=96)

Magnetic flow meter, UV–AOP step (RPN=90)

Based on the risk evaluation of the process monitors for the ozone–biofiltration-based treatment train, these are the four highest RPNs. In this process train, the major risk observed (and thus higher severity scores) is through inadequate disinfection. Thus, the highest RPNs for this process train are all related to the disinfection and filtration processes employed.

Ozone (RPN=144)

Ozone–BAC (RPN=144)

UV (RPN=144)

Chlorine (RPN=144)

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Table 5.9. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – Pre-Chloramination Step

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Chlorine analyzer

overdose disinfection byproduct formation

Chlorine analyzer reads false low result, leading to overdose.

4 6 4 96 Cross-check reading, calibrate instrument, and replace probe if necessary.

Any online analyzer will be configured so a power failure will result in an alarm of the control system (if the signal is <4mA or >20 mA, there is a failure). It is assumed that any self-diagnostic failures are noted by a signal to the PLC. Action will be taken in these cases. The more likely failure is analyzer drift or incorrect reading, which can occur with poor calibration or verification problems. Regular verification should mean this is detected with moderately high likelihood.

Ammonia analyzer

incorrect dose

disinfection byproduct formation

Ammonia analyzer reads false high result, leading to overdose.

2 5 4 40 Cross-check reading, calibrate instrument, and replace probe if necessary.

Any online analyzer will be configured so a power failure will result in an alarm of the control system (if the signal is

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Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

<4mA or >20 mA, there is a failure). It is assumed that any self-diagnostic failures are noted by a signal to the PLC. Action will be taken in these cases. The more likely failure is analyzer drift or incorrect reading, which can occur with poor calibration or verification. Regular verification should mean this is detected with moderately high likelihood. The consequence is less in this case, as high chlorine is more of a risk.

Notes: D=detection; O=occurrence; PLC=programmable logic controller; RO=reverse osmosis; S=severity.

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Table 5.10. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – Microfiltration–Ultrafiltration

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Pressure transmitter

membrane damage, seal breach, or O-ring breach

microorganism control

Pressure transmitter reading incorrectly leads to false result.

2 9 2 36 Regular (although not considered frequent) pressure transmitter cross-check.

Pressure decay test relies on an accurate pressure transmitter. Pressure transducers are typically very reliable, so this is a low likelihood of failure. Same notes as Table 5.9 for instrument power or loop failure.

Turbidity analyzer

membrane damage, seal breach, or O-ring breach

microorganisms and chemicals of concern

Turbidity analyzer reading incorrectly, leading to a false low result.

4 9 2 72 Regular cross-check of the analyzer. Compare also to pressure decay test results. Regular maintenance of analyzers (ensure probes regularly cleaned, flow to analyzer correct).

Same comments as Table 5.9 for analyzer power or loop failure.

Pressure decay automated sequence

membrane damage, seal breach, or O-ring breach

microorganisms and chemicals of concern

Failure of correct pressure decay test operating sequence.

2 9 2 36 Regular check of operating procedure to ensure this occurs correctly. Many systems have diagnostic steps to ensure sequence is correct, including start pressure checks, drain checks, and so on.

Sequence contains operating steps to drain liquid, pressurize fibers to correct test pressure, and ensure test is representative of all fibers. This sequence may provide a false result that masks true damage if the system doesn't adequately drain.

Notes: D=detection; O=occurrence; RO=reverse osmosis; S=severity.

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Table 5.11. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – Reverse Osmosis

Component Name

Cause of Failure Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Conductivity analyzer

membrane breach

microorganisms and chemicals of concern

failure of correct conductivity analyzer reading

2 9 2 36 Regular cross-check of RO conductivity by handheld instrument.

Same as Table 5.9 for instrument power or loop failure.

TOC analyzer

membrane breach

microorganisms and chemicals of concern

failure of correct TOC reading

4 6 2 48 Regular cross-check with laboratory instrument if available. Regular calibration and maintenance.

Severity is considered lower for TOC, as it will always be used in concert with conductivity. The analyzer is difficult to cross-check, as laboratory analysis does not have the low level of detection the TOC analyzer requires for this application. Instrument requires good maintenance. Same notes as Table 5.9 for instrument power or loop failure.

Notes: D=detection; O=occurrence; RO=reverse osmosis; S=severity; TOC=total organic carbon.

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Table 5.12. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – UV–H2O2

Component Name

Cause 0f Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action

Notes

Magnetic flow meter

insufficient dose of hydrogen peroxide

microorganisms and chemicals of concern

Failure of flow meter reading (lower than actual). UV dose lower than actual.

3 6 5 90 Regular calibration test of dosing pump to ensure dose is correct.

Failure would occur if dosing pump is incorrectly calibrated or fault is not detected. Dosing pumps can have fault detection for internal diagnostics, which would be provided to the PLC. Dosing line flow meters can be used as further confirmation of dose. Given that advanced oxidation is for chronic risks, this may not be necessary.

UV transmittance meter

insufficient dose of UV

microorganisms and chemicals of concern

Failure of UV transmittance analyzer reading higher than actual, resulting in UV underdose.

2 9 4 72 Regular cross-check of UVT. If UVT drops <95% following RO pretreatment, this will also trigger an alarm.

Same as Table 5.9 for instrument power or loop failure. Control can be such that if UVT < some figure, the control system can ramp up UV dose to full.

Notes: D=detection; O=occurrence; PLC=programmable logic controller; RO=reverse osmosis; S=severity; UV=ultraviolet; UVT=ultraviolet transmittance.

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Table 5.13. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – Stabilization

Component Name

Cause of Failure

Effect of Failure

Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

pH analyzer incorrect chemical dose

lead and copper in distribution system

failure of pH analyzer

4 6 4 96 Regular cross-check and calibration of pH analyzer. Replace probe if required.

Same as Table 5.9 for instrument power or loop failure. As this is a chronic issue rather than acute, brief periods out of specification can be tolerated.

Conductivity analyzer

insufficient hardness addition

lead and copper in distribution system

failure of correct conductivity analyzer reading

2 6 2 24 Regular cross-check of analyzer. Typically, conductivity instruments have a low rate of failure.

Same as Table 5.9 for instrument power or loop failure. As this is a chronic issue rather than acute, brief periods out of specification can be tolerated.

Notes: D=detection; O=occurrence; RO=reverse osmosis; S=severity.

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Table 5.14. RO Membrane-Based Treatment Train Risk Priority Numbers for Occurrence, Severity, and Detection – Chlorine Disinfection

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Chlorine analyzer

insufficient dose

microorganisms Chlorine analyzer reads false high result, leading to underdose.

4 9 4 144 Cross-check reading, calibrate instrument, and replace probe if necessary.

Any online analyzer will be configured so a loss of power failure will result in an alarm of the control system (if the signal is <4mA or >20 mA, there is a failure). Any self-diagnostic failures are assumed to be noted by a signal to the PLC. Action will be taken in these cases. This failure is analyzer drift or incorrect reading, which can occur with poor calibration or verification problems. Regular verification (cross-check) should mean this is detected with moderately high likelihood.

Flow meter dosing pump failure

microorganisms failure of flow meter reading (reading lower than actual)

2 9 8 144 Regular, if infrequent, test of flow meter. Flow meters are highly reliable.

Same as previous for power failure or loop failure. Magnetic flow meters are highly reliable, so this failure is very unusual and managed best with regular calibration. This issue occurs most often with poorly installed flow meters, poor initial calibration, or incorrect programming of the flow meter, handled by effective commissioning and documentation processes.

Notes: D=detection; O=occurrence; PLC=programmable logic controller; RO=reverse osmosis; S=severity.

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Table 5.15. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – Ozone

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

UV transmittance analyzer

overdose, insufficient dose

excessive ozone applied or insufficient ozone dose applied

Failure of one of two UVT analyzers that results in a higher ΔUVT than actual and underdosing of ozone.

2 9 4 72 Regular cross check of UVT analyzers, regular maintenance cleaning.

Any online analyzer will be configured so power failure will result in an alarm of the control system (if the signal is <4mA or >20 mA, there is a failure). Any self-diagnostic failures are assumed to be noted by a signal to the PLC. Action will be taken in these cases. This failure is analyzer drift or incorrect reading, which can occur with poor calibration or verification problems. Regular verification (cross-check) should mean this is detected with moderately high likelihood. Use of an ozone monitor will be a useful cross-check.

Ozone dose monitor

overdose, insufficient dose

DBP formation, microorganisms

Failure of ozone dose monitor, reading higher than actual, leading to underdosing.

4 9 4 144 Regular cross-check and calibration. Cross-check of ΔUVT if employed also.

Same comment for loss of power as previous. The failure is analyzer drift or incorrect reading or problems with verification. The use of ΔUVT can assist if ozone dose isn't correlating as usual.

Notes: D=detection; DBP=disinfection byproducts; O=occurrence; PLC=programmable logic controller; S=severity; UV=ultraviolet; UVT=ultraviolet transmittance.

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Table 5.16. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – Ozone–BAC

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Ozone dose monitor

insufficient dose, insufficient contact time with BAC

organic compounds

Failure of ozone dose monitor, reading higher than actual, leading to underdosing.

4 6 4 96 Regular cross-check and calibration.

Noted as lower severity because of ozone for BAC focusing on chronic rather than acute risks. Same comment for loss of power as Table 5.9. The failure is analyzer drift or incorrect reading or problems with verification.

Magnetic flow meter

insufficient dose, insufficient contact time with BAC

organic compounds

Failure of flow meter reading (reading lower than actual), resulting in insufficient dose and carbon contact time.

2 9 8 144 Regular, if infrequent test of flow meter. Flow meters are highly reliable.

Same as previous for power failure or loop failure. Magnetic flow meters are highly reliable, so this failure is very unusual and managed best with regular calibration. Most often, this issue occurs with poorly installed flow meters or poor initial calibration or incorrect programming of the flow meter, handled by effective commissioning and documentation processes.

Notes: BAC=biological activated carbon; D=detection; O=occurrence; S=severity.

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Table 5.17. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – Coagulant–BAC

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

TOC analyzer

insufficient coagulant dose

microorganisms Failure of correct TOC reading, not detecting when the BAC is being bypassed or the carbon is exhausted.

4 5 4 80 Regular cross-check with laboratory instrument if available. Regular calibration/maintenance. Severity is reduced, as the system can always be operated with a minimum coagulant dose to ensure filter performance.

This TOC is reading in a higher range than for RO permeate in the RO membrane-based treatment process, can be more easily cross-checked. Instrument requires good maintenance. Same notes as for previous for instrument power or loop failure.

Online turbidity analyzer

insufficient coagulant dose

microorganisms Turbidity analyzer reading incorrectly, leading to undetected filter breakthrough.

4 9 2 72 Regular cross-check of the analyzer. Regular maintenance of analyzers (ensure probes regularly cleaned, flow to analyzer correct).

Same notes as for previous for instrument power or loop failure.

BAC works as a biological filter, with turbidity breakthrough as trigger for filter backwash cycles and filter ripening: Incorrect turbidity readings can result in particle breakthrough.

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Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action Notes

Flow meter/flow switch

filter breakthrough

microorganisms failure of flow meter reading (reading lower than actual)

5 5 4 100 Regular calibration test of dosing pump to ensure dose is correct.

Failure would occur if dosing pump is incorrectly calibrated or a dosing pump fault is not detected. Dosing pumps can have fault detection for internal diagnostics, which would be provided to the PLC. Dosing line flow meters can be used as further confirmation of dose.

Notes: BAC=biological activated carbon; D=detection; O=occurrence; PLC=programmable logic controller; S=severity; TOC=total organic carbon.

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Table 5.18. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – GAC

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number

(O)*(S)*(D)

Recommended Corrective Action Notes

TOC analyzer carbon too old, filter bypass

organic chemical absorption

Failure of correct TOC reading, not detecting value, resulting in not detecting that GAC is being bypassed or carbon is exhausted.

4 6 4 96 Regular cross-check with laboratory instrument if available. Regular calibration and maintenance.

This TOC is reading in a higher range than for RO permeate in the RO membrane-based treatment process and can be more easily cross-checked. Instrument requires good maintenance. Same notes as Table 5.9 for instrument power or loop failure. Severity is consistent with the management of a chronic risk.

UV transmittance analyzer

carbon too old, filter bypass

organic chemical absorption

Failure of UV transmittance analyzer reading higher than actual value, resulting in not detecting that GAC is being bypassed or carbon is exhausted.

2 6 4 48 Regular cross-check of UVT. If UVT drops <95% following GAC pretreatment, this will also trigger an alarm.

Same as Table 5.9 for instrument power or loop failure. Control can be such that if UVT < some figure, the control system can ramp up UV dose to full.

Notes: D=detection; GAC=granular activated carbon; O=occurrence; S=severity; TOC=total organic carbon; UV=ultraviolet; UVT=ultraviolet transmittance.

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Table 5.19. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – UV

Component Name

Cause of Failure

Effect of Failure Failure Mode

O S

D

Risk Priority Number (O)*(S)*(D)

Recommended Corrective Action

Notes

UV transmittance analyzer

insufficient UV dose, poor transmissivity

microorganisms Failure of UV transmittance analyzer reading higher than actual value, resulting in UV underdose.

2 9 4 72 Regular cross check of UVT. If UVT drops <95% following GAC pretreatment, this will also trigger an alarm.

Same as Table 5.9 for instrument power or loop failure. If UVT < some figure, the control system can ramp up UV dose to full.

UV system power output

insufficient UV dose, poor transmissivity

microorganisms Failure of UV transmittance analyzer reading higher than actual, resulting in UV underdose.

2 9 7 126 Regular calibration of UV intensity analyzer.

Same as Table 5.9 for instrument power or loop failure. May be difficult to track; needs other regular cross-checks.

Flow rate meter

insufficient UV dose, poor transmissivity

microorganisms Failure of flow meter reading (reading lower than actual). Insufficient dose.

2 9 8 144 Regular, if infrequent, test of flow meter. Flow meters are highly reliable.

Same as Table 5.9 for power failure or loop failure. Magnetic flow meters are highly reliable, so this failure is very unusual and managed best with regular calibration. Most often, this issue occurs with poorly installed flow meters or poor initial calibration or incorrect programming of flow meter, handled by effective commissioning and documentation processes.

Notes: D=detection; O=occurrence; S=severity; UV=ultraviolet; UVT=ultraviolet transmittance.

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Table 5.20. Ozone–Biofiltration-Based Treatment Process System Risk Priority Numbers for Occurrence, Severity, and Detection – Chlorine

Component Name

Cause of Failure

Effect of Failure Failure Mode O S D

Risk Priority Number

(O)*(S)*(D)

Recommended Corrective Action Notes

Chlorine analyzer failure

insufficient dose, dosing pump failure

disinfection Chlorine analyzer reads false high result, leading to underdose or insufficient CT.

4 9 4 144 Cross-check reading, calibrate instrument, and replace probe if necessary.

Any online analyzer will be configured so power failure will result in an alarm of the control system (if the signal is <4mA or >20 mA, there is a failure). Any self-diagnostic failures are assumed to be noted by a signal to the PLC. Action will be taken in these cases. This failure is analyzer drift or incorrect reading, which can occur with poor calibration, or verification problems. Regular verification (cross-check) should mean this is detected with moderately high likelihood.

Flow meter failure

insufficient dose, dosing pump failure

disinfection failure of flow meter reading (lower than actual)

2 9 8 144 Regular, if infrequent, test of flow meter. Flow meters are highly reliable.

Same as previous for power failure or loop failure. Magnetic flow meters are highly reliable, so this failure is very unusual and managed best with regular calibration. Most often, this issue occurs with poorly installed flow meters or poor initial calibration or incorrect programming of the flow meter, handled by effective commissioning and documentation processes.

Notes: CT=concentration x time; D=detection; O=occurrence; PLC=programmable logic controller; S=severity.

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5.5 Recommendations

The simulation model results indicate that the TOC analyzers in general are the highest risk (in terms of failure to measure the true value) monitors in ozone–biofiltration-based treatment and RO membrane-based treatment process trains. These results are consistent with the input parameters used in the simulation model. The PDFs used to define the process, representing analyzers and their failure distribution function, are based on the functional specification information available in the product spec sheet. These values might not be the most accurate representations of each analyzer’s performance, and in order to better model the behavior of each analyzer, operational data should also be used. Plant production data collected over multiple years help capture the stochastic behavior and yield a precise simulation model.

For the RPN tool, highest RPNs should be considered as the riskiest components, and in this case, the severity of failure resulted in the disinfection processes having the highest RPNs. It is notable that the TOC analyzers seem to have the highest risk of failure to provide an accurate measurement, but the disinfection processes and associated monitors had the greatest impact on potential risk. This is important as it can help focus where calibration and verification need to be high-priority functions, and it may indicate where redundant monitors may provide additional security. However, it should be noted that this approach has a number of limitations. The first is that the development of the rating indices is a subjective process. The narrative definitions of occurrence, severity, and detection will differ based on the nature of the process under evaluation. Second, the application of the rating scores has reliability challenges of its own. One subject matter expert might interpret the likelihood of occurrence of failure differently than another based on their past experiences and technical knowledge about the context under evaluation.

Two actions can be taken in order to improve the validity of the RPN approach. The first is to make the rating levels as objective as possible in order to minimize the variability in interpretations among experts. For example, this can be accomplished by utilizing quantitative measures of reliability rather than relying on qualitative descriptions. As shown in Table 5.6, a score of 1 can be selected if there is a “remote chance for failure,” which corresponds to less than99.999% reliability.

The second action that can be taken to improve the validity of the RPN approach is to involve multiple subject matter experts in both the development of the scales and the application of the scales to the components. For example, one expert might assign a component’s detection a score of 1 in the belief that there is “almost certain detection of failure mode,” whereas another expert might assign it as 2, believing that there is a “very high likelihood of detecting failure mode.” In order to reconcile the difference of opinion, the experts should discuss the reasons for their rating. This opportunity will allow other experts to expose additional evidence and experiences about specific components. This process is known as the Wideband Delphi and has been applied to a variety of expert decision-making activities (Dalkey et al., 1969).

It should also be noted that any time there is a difference of opinion about a component’s rating, the worst-case scenario should be selected. For instance, a higher occurrence, severity, and detection index rating should be chosen to ensure that the RPN number is a pessimistic assessment. This approach will yield the highest utility for the improvement of the water reuse process.

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5.6 Conclusions

This chapter provides a mechanism to analyze how reliable water treatment process analyzers are in monitoring water that is safe for drinking. There are two approaches for determining the reliability of water reuse treatment facilities. The first approach uses a simulation model in Arena, which helps identify the performance of monitors throughout different treatment trains. By varying the input parameters and failure modes, the simulation models allow for the identification of least and most reliable analyzers.

The second approach is the use of an RPN tool to identify bottlenecks and potential problem areas. The three indices – severity, occurrence, and detection – are used to isolate high-risk analyzers. In this project expert opinion was used to estimate the values of the three indices. This helped identify the analyzers that are the critical components in the water reuse treatment plant and should be addressed in order to improve the overall reliability of the process train. However, it is important to note that this type of critical evaluation of analyzer failure will be different for each type of analyzer and should be conducted on a site-specific basis.

5.7 References

British Standards Institution: Quality vocabulary. Quality concepts and related definitions, BS 4778:1991.

Chang, D.; Zio, E. Application of Monte Carlo Simulation for the Estimation of Production Availability in Offshore Installations. In Simulation Methods for Reliability and Availability of Complex Systems; Chang, K. P. London: Springer, 2010.

Dalkey, N.; Brown, B.; Cochran, S. The Delphi Method, III: Use of Self-Ratings to Improve Group Estimates, Report No. RM-6115-PR, Rand Corporation, November 1969.

International Organization for Standardization. Quality management and quality assurance, ISO 8402:1994.

Kelton, W. D.; Sadowski, R. P.; Sturrock, D. T. Simulation with Arena. Boston: McGraw-Hill Higher Education, 2007.

McGraw, S. Risk Priority Number: A Method for Defect Report Analysis, 2014. http://resources.sei.cmu.edu/asset_files/Webinar/2014_018_100_428582.pdf. Software Engineering Institute, Carnegie Mellon University, 2014.

Morgenstern, N. Myth and Fact: Water Quality Measurements and Normal Distribution. WaterFront January 3, 2012.

Patev, R. C. Introduction to Engineering Reliability. Delivering Integrated, Sustainable, Water Resources Solutions, U.S. Army. https://www.palisade.com/downloads/pdf/EngineeringReliabilityConcepts.pdf (Last Accessed 5/31/2016)

Pham, H. System Software Reliability. Berlin: Springer, 2006.

Rausand, M.; Høyland, A. System Reliability Theory: Models, Statistical Methods, and Applications. Hoboken, NJ: Wiley-Interscience, 2004.

ReliaSoft Corporation. Life Data Analysis Reference Book, Retrieved June 2014 from ReliaWiki.org. Available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License Not Dated.

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Chapter 6

Full-Scale Challenge Testing at

Scottsdale Water Campus

6.1 Overview

A key focus of this project was to determine the reliability and robustness of CCPs, at full scale. In addition to the extensive operating data that have been gathered from plants for Monte Carlo analysis, presented in previous chapters, additional full-scale failure analysis was conducted to review the sensitivity and practicality of two selected CCPs and their respective monitoring parameters. For both CCPs, a set of physical failure conditions (i.e., O-ring breach, cut fibers, and loss of interconnects) were manually induced to determine how the monitors would respond to such events and what impacts the events may have on water quality. In this case, MF and RO CCPs that are used at Scottsdale for both IPR and nonpotable reuse applications were available for testing during nonpotable water production and formed the basis for this work.

As identified in Chapter 2, the hazards controlled and associated monitoring parameters at the two CCPs are listed in Table 6.1.

Table 6.1. Hazard Controlled and Associated Monitoring Parameters

Critical Control Point

Hazards Controlled

CCP Monitoring Parameters Comments

MF–UF microorganisms pressure decay integrity test and individual (or combined) filter effluent

Pressure decay integrity testing provides superior resolution; however, it is a discrete test that is conducted on a periodic, rather than continuous, basis. Turbidity can provide an effective continuous backup measure.

RO microorganisms and chemicals of concern

electrical conductivity online TOC

Electrical conductivity and online TOC are currently the most sensitive analyzers applied to this task.

Notes: CCP=critical control point; MF=microfiltration; RO=reverse osmosis; TOC=total organic carbon; UF=ultrafiltration.

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Testing was conducted at the Scottsdale Water Campus advanced treatment facility in Scottsdale, AZ, in February 2015. The Scottsdale Water Campus is an advanced treatment facility that has been operational since 1999 and was upgraded in 2012. The plant consists of two distinct treatment components: 1. A large surface water treatment facility that supplies drinking water to the city of Scottsdale

2. A wastewater plant with an advanced water treatment (AWT) facility that supplies purified recycled water to local golf courses for turf irrigation or to groundwater injection for IPR. When more water is available than the golf courses need, the additional flow is diverted to a series of vadose-zone injection wells and ultimately transferred to the local groundwater aquifer for IPR.

All testing was conducted at the AWT during a period of nonpotable reuse production only. The process treats tertiary effluent using ozonation, chloramination, UF, RO, and UV–advanced oxidation. Following advanced oxidation, the water is stabilized using decarbonation, followed by lime addition. The water is then finally delivered, either for golf course irrigation or to the vadose-zone well injection system.

A key aspect to utilizing the Scottsdale Water Campus as the test center is that both conventional 8 inch diameter and newer generation 16 inch diameter brackish water RO membranes are used side by side in the RO system. Because both are in use, this testing was able to compare integrity breach effects for 8 inch and 16 inch membrane sizes using the same feed water source.

Inducing failures at an operating treatment facility must be conducted within constraints that do not permanently damage equipment or lead to an adverse water quality impact. As a result, testing was conducted only when final treated water was being delivered for golf course irrigation. To induce failure in the RO process, this testing focused on the impact of O-ring breaches. O-rings are used extensively within both RO and MF–UF membrane modules and systems to provide seals between membrane modules and housings and provide a barrier between feed and treated water from those processes.

The failure of an RO barrier is usually the result of: A breach of the RO membrane itself (through physical tear, delamination of the membrane layer, or

oxidation of the membrane material)

Failure of glued seals on RO membrane envelopes (commonly referred to as glue lines)

Failure of O-ring seals (either improper installation or O-ring material failure)

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Although not the exclusive cause of a system breach, O-ring failures do occur in the field, with O-rings that roll out of their seal, break, or are sometimes not correctly installed. A key advantage to this testing is that the failure can be easily induced and quickly repaired with no permanent equipment damage. This test was considered the simplest and safest to conduct within the constraints of plant operation. Although this does not provide a comprehensive review of all possible failures, it is in many ways superior to pilot testing as it has been conducted at a realistic equipment scale, including real hydraulic operating conditions for full-scale membrane units.

For MF–UF systems, failures in the treatment barrier can be caused by O-ring failure, stuck or broken valves and valve block assemblies, broken fibers, or cracked potting material. As fiber breaks are relatively easy to produce, quantify, and repair, they were selected for evaluation of UF CCP monitoring procedures during this study.

In summary, the testing that was conducted included: For RO, the impact to online electrical conductivity (the CCP monitoring parameter) and other

measures from membrane element interconnector O-ring (and end vessel connector O-ring) failure

For MF–UF, a review of the impact of the pressure decay integrity test on fiber failure

6.2 Failure Analysis Methodology

Table 6.2 outlines the details of the membranes at Scottsdale Water Campus used for this testing.

Table 6.2. Scottsdale Water Campus Membrane Details Membrane 8 inch diameter RO 16 inch diameter RO Ultrafiltration

Membrane make Koch CSM Evoqua

Membrane model TFC-HR-Magnum RE16040-FE Memcor CP-L20N

Membrane material polyamide polyamide PVdF

Membrane type spiral-wound spiral-wound hollow-fiber

Minimum salt rejection 99.5% 99.7% N/A

Effective area per membrane (ft2)

575 1600 375

Notes: N/A=not applicable; RO=reverse osmosis.

The outcome of the testing was to review the sensitivity of each critical control monitoring point and the control and operational response.

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6.2.1 Reverse Osmosis System Testing: 8 Inch Membranes

RO Train 16 at Scottsdale, using 8-inch diameter RO membranes in a two-stage array, is shown in Figure 6.1. The facility has been in operation for approximately 15 years, with the current set of membranes older than five years.

RO units are designed in staged arrays in order to ensure that each individual membrane element in the train is operating within respected limits for individual element production, individual element recovery (permeate or feed), and ensuring a minimum element concentrate flow. RO elements are loaded into pressure vessels in series, with O-rings providing an interconnection on the permeate side between each subsequent element. The elements at each end of the pressure vessel connect from their respective permeate tubes to vessel end cap connections, from where water is piped to a common collection point. As each element produces some permeate, each subsequent element is fed less and less feed water until the minimum element cross-flow may not be respected. To balance this flow adequately, at the end of a first stage of pressure vessels, the concentrate flow from all vessels is collected and fed into a second stage of usually about half the number of original vessels. This may be repeated for a third stage and, infrequently, further stages.

Figure 6.1. 8 inch RO staging diagram, viewed from end cap. Note: Vessels with O-ring removal in orange, and vessels with interconnector removal in red.

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At the start of testing, the train was started and operated at normal production conditions for a period of 30 minutes to obtain normal steady-state operating conditions. Permeate was then sampled from each individual pressure vessel to obtain a performance baseline profile of the RO unit. Online instrumentation values were recorded, which included:

feed flow

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differential pressure

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In addition, grab samples were collected at the feed and combined permeate sampling points and analyzed at the onsite water quality laboratory by the city’s laboratory staff.

Specific water quality tests that were conducted and the minimum detection limit (MDL) are outlined in Table 6.3. In addition to these tests, samples were also collected and sent to Hazen and Sawyer’s laboratory in Raleigh, NC, for three-dimensional fluorescence excitation–emission matrix spectroscopy (EEMs) analysis (Stanford et al., 2011).

Figure 6.2. 16 inch RO staging diagram, viewed from end cap. Note: Vessels with O-ring removal in orange, and vessels with interconnector removal in red.

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The next component of testing was to shut down the RO system, release stored pressure, and remove an end cap from the feed end of a pressure vessel in the second stage. Two O-rings on the end cap connector were removed to affect a breach between second-stage feed water and permeate produced from that vessel. Table 6.5 shows a cutaway of the membrane pressure vessel internals and indicates the location of these O-rings. The end cap was reinstalled in the system minus O-rings, and the RO was restarted. The system was operated for a period of 30 minutes to reach a stabilized state before data and samples were collected.

The process of O-ring removal, system restart, and sample collection was repeated to remove O-rings on one end cap interconnector in a total of 1, 5, and 39 (total for the train) pressure vessels. The end caps removed were from the tail elements (opposite end to feed water inlet) in the first and third stages and the feed elements in the second stage. The selection of O-ring removal locations were chosen simply for ease of access.

To test a more significant integrity breach, an entire first-stage end cap interconnector was removed from the system. This provides a much more substantial breach than O-ring removal alone. The interconnector removal was evaluated as an additive breach, such that previously removed O-rings were not reinstalled during this test. Following the removal of the interconnector, the system was restarted and stabilized for a 30 minute period before samples were collected. The purpose of this test was to simulate the effect of a broken interconnector or a membrane installation error in which an interconnector was accidentally left from the system.

Following completion of this test, the interconnector was reinserted, and new O-rings were installed where they had been removed for testing. The system was restarted and tested for leaks before being returned to full service.

Table 6.3. Water Quality Test Methods Analyte Method MDL

Calcium EPA 200.8 0.10 mg/L

Sulfate EPA 300.0 50 mg/L

Caffeine LC/tandem mass spectroscopy 1 ng/L

Sucralose LC/tandem mass spectroscopy 10 ng/L

Total organic carbon Standard Methods 5310C 0.5 mg/L

Total dissolved solids Standard Methods 2540C 20 mg/L

Conductivity Standard Methods 2510B 2 uS/cm

UV254 Standard Methods 5910B 0.010 at 1 cm quartz cell

3D fluorescence EEMS Stanford et al. (2011) qualitative

Notes: EEMS=excitation–emission matrix spectroscopy; LC=liquid chromatography; UV=ultraviolet.

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6.2.2 Reverse Osmosis System Testing: 16 Inch Membranes

Following completion of the 8 inch RO testing, a similar process was followed to test 16 inch membranes. These have more membrane area per element, and fewer elements are needed to produce the same flow rates. Therefore, a breach in one element or its O-rings was expected to be more significant than for an 8 inch train. The 16 inch membranes, which are less than five years old, are newer than the 8 inch membranes and have not been operated as extensively as the 8 inch trains at the Scottsdale Water Campus.

RO Train 16 is a three-stage system with six elements in series per vessel, shown in Figure 6.2. The tested RO train contained 39 pressure vessels in total, with 24 vessels in Stage 1, 10 vessels in Stage 2 and 5 vessels in Stage 3. Table 6.4 shows RO Skid 21, a two-stage system used for testing with a total permeate production capacity of 2.3 mgd per day. The train has a total of 20 pressure vessels. The system was operated for a period of two hours before recording baseline conditions. Afterward, a testing approach similar to the one described previously for the 8 inch elements was used.

Testing the 16 inch RO train involved removing an end cap from the tail end of the first-stage pressure vessel. The end cap interconnector O-rings were removed, and then the end cap was reinstalled, and the system was re-started. Figure 6.6 shows an end cap that was removed from the pressure vessel; the O-rings can be seen on the top part of the permeate tube nozzle.

During 16 inch testing, the system was operated for a period of 30 minutes to reach a stabilized state before data and samples were collected. The procedure of O-ring removal, system restart, and sample collection was repeated to remove O-rings in a total of 1, 5, and 10 pressure vessels, all in the tail section of the first stage.

During testing there were concerns that removing the interconnecting adapter for the 16 inch vessels completely from the end cap could cause the membrane elements to move when started and result in damage. To continue to test, an interconnector was modified. The end nozzle component of the connector that inserts into the permeate tube was cut completely, and eight small holes were drilled into the side of the connector to allow feed water to bypass directly to the permeate tube, simulating removal of an interconnector while retaining the structural setup in the pressure vessel. Figure 6.7 provides a photograph of the modified interconnector. The O-rings that were previously removed remained out during this test to progressively capture the effect of change. Following completion of the testing, the modified end cap interconnector was replaced with a standard interconnector, and new O-rings were installed before the system was returned to full service.

Using the data collected for both 8-inch and 16-inch vessels, key membrane monitoring parameters were calculated such as recovery, transmembrane pressure, and differential pressure. The data collected for each case was normalized against the baseline data to evaluate normalized flow and salt passage.

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Figure 6.3. 8 inch RO Unit 16 at Scottsdale Water Campus.

Figure 6.4. 16 inch RO Unit 21 at Scottsdale Water Campus.

Figure 6.5. RO module cutaway (8 inch diameter).

Figure 6.6. End cap (16 inch diameter).

Figure 6.7. Modified interconnector.

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6.2.3 Ultrafiltration Membrane Filtration System Testing

Unit 6 at Scottsdale Water Campus, a pressurized Evoqua (Memcor) CP (UF) system, was used for UF testing. Figure 6.8 is a photo of the unit, which has a nominal membrane pore size of 0.04 µm. The system was operated initially for a period of 30 minutes to achieve steady-state operation and record the baseline operating conditions. The target monitoring parameters used to quantify membrane integrity were the PDT result, resulting calculated LRV, and online filtrate turbidity.

The PDT was performed according to manufacturer recommendations for this system, which is consistent with ASTM D6908 (06) 2010, Standard Practice for Integrity Testing of Water Filtration Membrane Systems, an operator-initiated automated test. The LRV was calculated internally in the system SCADA, provided from vendor-supplied calculations consistent with the aforementioned standard.

Filtrate turbidity was recorded using an online HACH Filtertrak 600; results from this instrument were confirmed using grab samples and a benchtop HACH 2100N. The UF system was taken offline, and a module was removed from the rack. Figure 6.9 shows the module after removal from the rack. Five fibers were cut using a pair of wire cutters, after which the module was reinstalled in the rack, the system was operated for a period of at least 30 minutes, and PDT, turbidity, and other system conditions were measured and recorded. The process of removing the module, cutting fibers, reinstalling the module, and running the system was repeated to produce data points at 5, 20, and 50 cut fibers from the same module.

The next test involved removing the two upper module O-rings and filtrate cup seals. Figure 6.10 illustrates a cutaway module that shows the location of the O-rings removed and the construction of the module at the filtrate cup area. The module was reinstalled in the rack, the system was operated for a period of 30 minutes, and PDT, filtrate turbidity, and system conditions were measured and recorded. Following testing, a new module was installed in the rack.

Figure 6.8. Ultrafiltration Unit 6 at Scottsdale Water Campus.

Figure 6.9. Module removed and ready for the fibers to be cut.

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Figure 6.10. MF–UF module.

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6.3 Results

6.3.1 Reverse Osmosis

RO permeate conductivity for both the 8 inch and 16 inch RO trains at baseline condition and during failure testing is shown in Figure 6.11, with progressive numbers of O-rings removed from interconnectors and finally an interconnector removed.

The 16 inch RO train had a lower permeate conductivity overall than the 8 inch system. This could be a combination of a lower membrane age and slightly different membrane material and rejection properties. It is noted that the nominal salt rejection of the 16 inch membrane is slightly higher than that of the 8 inch membrane (99.7% compared to 99.5%).

The system had a feed conductivity that varied by only 1.7% during the 24 hours of testing, yet the 8 inch RO train had a relatively small (though detectable) 5% increase in permeate conductivity through the removal of one set of O-rings and a 13% increase with five sets of removed O-rings. Removing O-rings from all vessels resulted in a noticeable 50% increase in the permeate conductivity. Finally, the removal of an interconnector had a substantial impact, with a threefold increase in permeate conductivity from the original baseline condition.

In contrast, the 16 inch RO train had a much greater change in permeate conductivity after a single set of O-rings were removed, with a 63% increase in conductivity. This is likely due to a larger and thicker O-ring in comparison to an 8 inch vessel, leading to a larger void for salt passage as well as one element representing a larger percentage of overall flow as compared to one element in an 8 inch system. The total flow through a 16 inch pressure vessel is also much higher than an 8 inch vessel, resulting in a higher overall influence.

Figure 6.11. RO permeate conductivity as measured by the conductivity analyzer on the array.

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RO system performance monitoring is often normalized to compare actual performance of the RO (in terms of permeability, differential pressure, and salt rejection) against a set of baseline conditions. A series of formulas are used to normalize performance for membrane characteristics impacted by water temperature, salinity, and flux rate, to provide a meaningful assessment of RO membrane performance. Normalized salt passage (sometimes reported as normalized salt rejection) is an ongoing, long-term monitoring parameter that provides a trend of ongoing performance over time. It is a useful normalization of online measurements to help anticipate when O-rings or membranes may require replacement and guard against unwanted breaches beyond critical limits by ensuring that proactive steps are taken to replace O-rings and membranes prior to a breach.

Figure 6.12 shows the normalized salt rejection for each condition of O-ring and interconnector removal. For both the 8 inch and 16 inch removal systems, a similar level of resolution is observed in the normalized data as for the raw online conductivity measurements.

Figure 6.12. Normalized salt passage from the array.

Water quality samples are not as useful for critical monitoring as significant time is required between sampling and obtaining a result. However, they are useful to validate performance and provide insight into longer term performance trends of membrane integrity. Based on laboratory data, the observed LRVs for TOC and conductivity are summarized in Table 6.4.

Calcium and sulfate were analyzed in the RO permeate to detect increases in salt passage. These divalent ions are well rejected by the RO membranes, and, as a result, a noticeable increase in their concentration is most likely the result of a membrane or O-ring breach. The levels of calcium and sulfate present in the RO permeate for the same set of operating conditions as shown previously are presented in Figures 6.13 and 6.14.

As dissolved ions, the results for sulfate and calcium are similar to the results for conductivity and salt passage, though there appears to be considerably less sensitivity than with conductivity. This is likely due to the presence of other divalent ions in addition to monovalent and trivalent ions, which are detected in conductivity measurements, whereas sulfate and calcium are measured individually and would require greater sensitivity to detect low concentrations.

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Table 6.4. Summary of Conductivity and TOC Removal Data

Baseline 1 O-ring

Removed 5 O-rings Removed

10 O-rings Removed

Interconnect Removed

8 inch RO

Conductivity % removal 93% 92% 92% 89% 65%

Conductivity log removal 1.14 1.12 1.09 0.94 0.45

TOC % removal 93% 94% 92% 90% 71%

TOC log removal 1.16 1.20 1.10 1.00 0.53

16 inch RO

Conductivity % removal 96% 94% 83% 73% 72%

Conductivity log removal 1.38 1.20 0.76 0.57 0.55

TOC % removal 94% 93% 81% 74% 69%

TOC log removal 1.26 1.13 0.72 0.58 0.50

Notes: RO=reverse osmosis; TOC=total organic carbon.

Figure 6.13. Lab results and log removal values for calcium (8 and 16 inch).

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Figure 6.14. Lab results and log removal values for sulfate (8 and 16 inch).

Figure 6.15. Lab results and log removal values for caffeine (8 and 16 inch).

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Figure 6.16. Lab results and log removal values for sucralose (8 and 16 inch).

Sucralose is an artificial sweetener used in food products that passes through the human body. Because of the small size of the molecule, it cannot be removed with conventional water and wastewater processes. RO is capable of removing sucralose, and this provides a good indicator of rejection performance. Caffeine is an indicator for wastewater contamination, providing similar results to fecal bacteria. Caffeine can be removed by RO and conventional wastewater processes. Caffeine rejection was reduced with increasing membrane integrity breaches, although the difference was not clearly distinguishable.

TOC is also used as an indicator of RO removal performance and has been identified as a possible CCP monitor. Online instruments can be expensive, however, and so may not be feasible at all facilities. These analyzers are able to operate at low levels of TOC (in the tens of μg/L range) and thus provide good resolution. Sampling of the RO permeate, however, cannot provide good resolution because of sampling contamination effects (absorption of TOC from other sources prior to analysis) and the resolution of the laboratory test itself (in the hundreds of μg/L range).

As seen in Figure 6.17, when conductivity and TOC are plotted together, both track each other closely for the 16 inch membrane and the 8 inch membrane paired tests, indicating that both parameters provide similar tracking of performance. However, it should be noted that lower level TOC readings from an online analyzer (as opposed to the grab samples and lab analyzer results presented here) may result in a higher resolution between baseline conditions and the removal of one O-ring, though this could not be tested without an available instrument.

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Figure 6.17. Conductivity and TOC.

Figure 6.18. Example excitation–emission matrix with regions outlined.

Three-dimensional EEMs were also quantified during this test to compare the sensitivity of 3D fluorescence relative to TOC and conductivity measurements. Figure 6.18 shows the regions on an organics fluorescence plot referenced against the fluorescence intensity and the wavelength, as described by Stanford et al. (2011). By integrating the area under the surface, fluorescence volume and volumes for Regions I, II, and III can be compared between samples.

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As shown in Figures 6.19 and 6.20, total fluorescence volume, Region II, and Region III were the most impacted by the O-ring removals and interconnection removals. The fluorescence results displayed similar trends to those presented for TOC and conductivity. Removal of O-rings results in a measurable breakthrough of fluorescence signal, but to a limited degree. The more O-rings that were removed from the system, the greater the fluorescence signal detected in the permeate. In particular, the 8 inch RO system showed a 5% change from baseline conditions even with O-rings removed from all vessels. By comparison, the larger 16 inch diameter RO resulted in up to a 25% change with O-rings removed from 1 to 10 end cap interconnectors. Removal of an interconnector approximately doubles the amount of Region II and III organics present in the permeate for both the 8 inch and 16 inch RO membranes.

Figure 6.19. Regional fluorescence volumes for 8 inch RO membrane tests.

Figure 6.20. Regional fluorescence volumes for 16 inch RO membrane tests.

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6.3.2 UF Membrane Filtration

During the course of the two-day test period, the raw water turbidity to the MF unit remained constant at 0.44 NTU for all tests. Data from the UF failure tests are presented in Table 6.5, showing a baseline PDT result of 0.15 psi/min, which was typical of historical system performance for membrane filtration units at the Scottsdale Water Campus and indicates a greater than 4.0 log removal of 3 μm particles (4.0 log removal is the minimum acceptable performance of the UF system). The number of broken fibers that had failed in the module prior to testing was unknown but not of relevance because the baseline conditions were recorded and showed acceptable performance. The number of fibers broken during the test period is assumed to be inclusive of an entire train, as the data were collected in terms of the overall train.

Increasing the number of cut fibers increased the PDT result, though it is noteworthy that under no conditions did the calculated LRV drop to less than an acceptable 4.0 log performance standard. Figure 6.21 shows a graphical comparison of the PDT and filtered turbidity during the test period, which demonstrates the increase with the varied test conditions. Fifty fibers cut increased the pressure decay to 0.28 psi/min, which represents an 86% increase over the baseline result. Removal of the filtrate cup O-rings gave a more distinguishable pressure decay result of 0.41 psi/min, which represents a 173% increase over the baseline test result. Note that the PDT results are a relative measure, and although the percent increase looks impressive, the total LRV only decreased from 4.68 log to 4.59 log under the most challenging conditions of 50 cut fibers and the loss of an O-ring.

Online effluent filtrate turbidity was also monitored, and the data are located in Table 6.5. The filtrate turbidity did not change a significant or distinguishable amount regardless of the fibers that were cut. At no point during testing did the filtrate turbidity exceed 0.033 NTU.

The data collected indicate that PDT provides superior resolution over filtrate turbidity for detection of change in the system, which is an expected result. This observation is perhaps better demonstrated in Figure 6.21, which is a graphical comparison of the PDT and filtered turbidity during the test period. The LRV reduced from 4.69 at the baseline measurement to 4.58 with 50 fibers cut and filtrate cup O-rings removed.

Table 6.5. Membrane Filtration Test Results

Test Baseline 5 fibers 20 fibers 50 fibers 50 fibers + O-rings

PDT (psi/min) 0.15 0.17 0.21 0.28 0.41

Feed water temperature (° C) 21.7 21.8 21.5 21.5 21.6

Feed water turbidity (NTU) 0.44 0.44 0.44 0.44 0.44

Unit flow rate (gpm) 1335 2127 1507 1717 1812

TMP (psi) 2.4 2.5 2.5 2.9 2.9

Flux (gfd) 23.1 29.1 25.1 29.9 28.7

Permeability @20C 8.78 8.64 9.04 9.26 8.94

LRV 4.69 4.65 4.61 4.63 4.58

Filtered turbidity (NTU) 0.019 0.021 0.020 0.018 0.033

Notes: LRV=log removal value; PDT=pressure decay test; TMP=transmembrane pressure.

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Figure 6.21. Membrane filtration pressure decay test results.

It should be noted that one UF module was altered out of a total of 204 on the membrane filtration unit. Further, if one assumes there are 10,000 hollow fibers in each module, then the percentage of the system changed with 50 fibers cut is 0.0025% of the entire unit (rack) and only 0.5% of any given module. The number of fibers cut (50) was assumed to be the upper limit of a typical event in a single Siemens membrane module. Experience during prior commissioning has suggested there is usually a portion of modules that require some pinning of fibers, which is a factor with membrane manufacture, transportation, and commissioning. The number of fiber breakages in a typical event varies between different membrane manufacturers as a function of the fiber strength.

6.4 Conclusions

Online electrical conductivity measurement of RO permeate appeared to provide the accuracy to detect changes in both 8 inch and 16 inch systems with only one set of O-rings removed (5% change) taken under steady-state conditions and with constant feed water temperature and feed conductivity. However, in normal operation over extended time periods, permeate conductivity is also affected by temperature and membrane fouling over as much as a 10% increase in permeate conductivity. Therefore, the impact of a single O-ring breach could be masked, and alarm triggers could not be practically set without the use of alarm management algorithms (see Chapter 7).

Large system breaches with changes greater than 10% increase in permeate conductivity could be detected by operations without alarm management algorithms and could be easily implemented as a functional control system alarm condition. Performing individual RO conductivity profiles in parallel with the online combined permeate could be used to identify a salt passage breach in a single module. Thus, permeate conductivity (and online TOC) appears to be a good CCP performance indicator for RO; at times when conductivity changes, individual conductivity profiling can be used to isolate the source of

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the salt passage increase. This is consistent with the RO system standard operational response procedures developed in Chapter 7.

An interesting observation was made in the difference between 8 inch and 16 inch RO. For the 16 inch RO, it could be inferred that void size and influence from removal of 10 O-rings was almost equal to that of an interconnector integrity breach. The 8 inch vessels appeared to have a closer tolerance between the connector and permeate tube, and thus the removal of the interconnector provided a larger passage for salt to pass. This observation is significant in that issues may be detected earlier on a larger system, but the influence to the permeate quality is more severe. The general conception among water industry professionals is that 16-inch vessels would be more resilient to integrity breaches, such as O-ring failure, because of a lower number of vessels and thus less likelihood for incorrect installation. This testing identified that even with one set of O-rings removed, simulating a failure, a large increase in salt passage was observed. The 8 inch RO, by comparison, showed only minor increases. The 8 inch system uses a thinner O-ring, which is more prone to breaking, wear and tear, and chemical attack compared to a thicker O-ring, which has more structure as more rubber is used. It is well documented that a 16 inch system has numerous benefits in terms of flow capacity and process footprint. The 16 inch system appears less resistant for errors in manufacturing tolerance, membrane and O-ring installation, and rubber–chemical compatibility.

Detectable changes in calcium and sulfate rejection were observed across the range of analysis. RO membranes typically reject divalent ions well, unless the membrane is scaled, even in mechanically damaged membranes. A noticeable change was observed in this study for both 8 inch and 16 inch RO with removal of more than five sets of O-rings. This follow-up sampling can be a useful additional cross-check of RO system performance.

Organic matter 3D fluorescence spectroscopy characterization provided distinguishable resolution that identified increased organics in permeate with only one O-ring removed. The 8 inch and 16 inch RO membranes showed visibly similar profiling results between the organics charts. The major drawback of this type of measurement is it is expensive, and continuous online monitoring has not been demonstrated in this setting.

Conductivity, TOC, and fluorescence measurements also tracked the observed breakthrough of caffeine and sucralose. Although individual trace organic contaminant monitoring is not recommended, the results provide confidence that conductivity and TOC can be reasonable surrogates for membrane performance, which is an important finding.

For membrane filtration, the use of online turbidity monitoring did not show significant changes with cut fibers. Even though the fibers were cut on one module, the changes were considered to be the total influence per train. The data obtained from the UF test indicated there was a reduction in the LRV with increasing fiber breaks in the system. The PDT results provided excellent resolution on changes to the system but is not a continuous online test. The filtered water turbidity was relatively consistent during all changes, suggesting this is not a good monitoring tool for small breakages but rather to diagnose large failures and significant process changes. The PDT provides a superior resolution but is a discrete test, thus turbidity or particle counts, which are both continuous online measures, are recommended as a backup monitor.

6.5 References

ASTM International. D 4516 Standard Practice for Standardizing Reverse Osmosis Performance Data. Conshohocken, PA, 2008.

Stanford, B. D.; Pisarenko, A. N.; Holbrook, R. D.; Snyder, S. A. Preozonation Effects on the Reduction of Reverse Osmosis Membrane Fouling in Water Reuse. Ozone-Sci. Eng. 2011, 33(5), 379–388.

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Chapter 7

Response Procedures and Design Guidance

7.1 Background Information

All water treatment facilities require high reliability to ensure water is delivered at an acceptable quality and risk to public health is minimized. This is underlined in the case of potable reuse, where the real risks of higher contaminant levels in plant feed water (e.g., during epidemics or after industrial accidents), along with perceived risks associated with public perception of reuse, require a high level of operational certainty. These can be met only with a holistic approach including a robust design, effective and transparent operational management, a carefully managed maintenance strategy, and proven response procedures.

The perceived “human element” in the process, operators must have robust and reliable plans, systems, and processes to ensure safety and reliability – essential for the public acceptance of potable use. Relative to existing treatment systems, operations of DPR systems are under greater scrutiny for performance and must have adequate training to successfully operate and manage water recycling systems.

As IPR and DPR schemes have begun to emerge, the challenges for treatment operators and operations teams are becoming increasingly apparent. For example:

• Many of the treatment technologies used in potable reuse applications are not well covered in existing water or wastewater training curricula.

• Stringent and intense requirements for sampling and analysis must be considered.

• An understanding of the impacts of upstream wastewater processes is vital; similarly, industrial pretreatment must be included in the HACCP risk assessment.

• Rapid and effective responses to system failures are critical.

• Treatment systems must have sufficient engineered storage to account for “failure response time” to ensure water that does not meet specifications is diverted from potable supply (Salveson et al., 2015).

• Management of downstream introduction of reuse water to environmental buffers, water treatment plants, or distribution systems must be understood.

To support the development of trained operations staff, this project, in addition to WRRF-13-13, Development of Operation and Maintenance Plan and Training and Certification Framework for Direct Potable Reuse (DPR) Systems, provides information on managing and responding to the various elements of process control, monitoring, and response for each type of unit process and combined process train discussed in this report. The HACCP framework discussed throughout this report is a valuable tool and framework for engineers and planners to use in evaluating the potential for DPR process selection, developing the protocols and focused analytical procedures for testing the process trains, and providing a framework to transparently challenge assumptions while offering solutions during the evaluation phase. Likewise, HACCP helps to provide the backbone for operational response procedures and corrective actions that will ultimately drive the success of the full-scale system.

The purpose of this chapter is to use the HACCP principles to provide the reader with an overview of control systems, alarms, and CCP-specific response procedures that will be needed to handle each CCP with the two identified process trains (MF–RO–UV–AOP–Cl2 and flocculation–sedimentation–ozone–BAC–GAC–UV–Cl2). However, it should be understood that the information presented in this chapter is inherently generic (i.e., providing the basic principles of response procedures that would be similar

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among different brands and designs of unit processes) and therefore should be used only as a blueprint for developing site-specific and process-specific response procedures.

7.2 Control Systems in Water Treatment

AWT facilities, including those used for recycled water, are designed and operated as highly automated systems. For both of the process trains under consideration, rapid and appropriate operational response is as much a function of correct controls programming, SCADA, and human–machine interface (HMI) configuration. The success of an effective and reliable operating response relies on the interface of the automation for the operator. The operations team must be diligent in ensuring that the controls and automation work correctly, and they must interact effectively to:

Proactively review performance to anticipate problems before they occur.

Respond effectively to alarms and shutdown conditions.

Provide a thorough investigation of why the problem occurred and transfer lessons learned to improve future operations.

Return systems safely and effectively to service in a timely manner.

Ensure transparency to provide trust with stakeholders.

Treatment systems and their process components are configured with PLCs connected to field devices (valve, pumps, analyzers, and switches) necessary for operation (Figure 7.1). The PLC executes a series of programmed instructions in a continuous loop, which are repeated (scanned) multiple times per second. Data are stored in PLC registers, which may contain simple binary (discrete) status (e.g., 1/0, on/off, run/stop, open/closed) or more complex analog process values (e.g., flow, pressure, level, turbidity, or conductivity). The PLC is able to communicate with a SCADA, which has the ability to access the PLC registers in a bidirectional (read/write) manner. An HMI is used to display the status and analog values in a visual format (typically a table or schematic) and allows the operator to enter binary or analog values into the PLC for subsequent execution range to prevent an inadvertent or unanticipated data entry. The HMI is usually configured to limit the value of an operator input to within a specified

Figure 7.1. Control system elements.

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Operational data from the PLC registers are retained in a database by time, events, or both and subsequently used to create operating trends or reports using SCADA itself or other supplementary software programs. Operator interaction at the HMI is often monitored as well and may be recorded as an event or change in operation. Programs that log events, alarms, and response (known as “historians”) provide a record of activities that can be used to troubleshoot events or other changes and a visual display of numeric values in a graphical format over a period of time.

Most control systems are accessible through an Internet connection behind a firewall, so the HMI may not be located at the facility. Likewise, most systems will have multiple HMIs with partial or complete views of the system. From an operations perspective, the link between process components, PLC, SCADA, and HMI must be maintained at all times to call relevant information to the attention of staff so that they can respond in a timely manner to events that range from minor changes for process optimization and production to catastrophic events that require system shutdown and repair. Likewise, management staff can access stored history to review operator response to various events that occur throughout the day.

When selecting control systems for water treatment, and specifically for DPR scenarios, the engineer should consider the amount of data being collected and analyzed, the frequency at which the data are produced and sent to the operations team, and the amount of time required to identify and respond to an alarm and true system failure (failure response time, as described in Salveson et al., 2015). The following sections of this chapter provide context on water quality alarms, strategies for identifying false alarms, response procedures for alerts and critical alarms, and guidance on how these principles can be applied in designing and operating DPR facilities.

7.3 Water Quality Alarms

Alarms are used to inform operations staff about recorded events within the system that may indicate an alert or critical notification about a given process or process monitor. Because of the highly configurable and programmable nature of control systems, the structure and management of alarms can be widely variable from facility to facility and are based upon the regulatory requirements, preferences of the utility, configuration and limitations of the PLC or programming software, and understanding of the control system programmer. Specific terminology and practices may vary; however, several common themes and practices exist.

In general, alarms are categorized in terms of severity and structure at the SCADA level. The assignment of severity to an alarm condition can be somewhat subjective in nature, with categorization established using project guidelines or prior experience of the programmer. A solid understanding of process function (e.g., determining whether it is a CCP), acceptable ranges of performance, and outcomes when the process or process monitor fails will help guide the development of alarm set points and severity levels.

Events are categorized as routine start and stop of a process based upon a field parameter (Hi or Lo). Events do not require acknowledgement or action by the operator. These are really not alarms but will result in the issuance of a notification.

Alerts (warning) are categorized as non-routine events that require acknowledgement by the operator. This alarm condition may require a field investigation and corrective action by the operator prior to placing equipment back into service. The typical convention for an alert alarm is Hi or Lo status indication.

Critical alarms (failure) are triggered by conditions that result in the disabling of a system or a loss of its functionality until the condition in the field is resolved. This is categorized as an abnormal condition, beyond the typical or anticipated operating parameter of the system, and is normally associated with a failure of the process. The typical convention for an alarm of this type is Hi-Hi or Lo-Lo condition.

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In a well-designed system, alarms are minimized to limit any nuisance, or transitional alarms. There are two common practices associated with the management of nuisance or transitional alarms: delay and disable. Delay of an alarm generally involves the establishment of an HMI adjustable time delay in the PLC. Disabling alarms may be possible by PLC programming or adjustable through the HMI; thus, restriction on access may be necessary.

Alarm set point values may also be adjusted through the HMI and may require similar restrictions on access, especially for critical alarms. With some control systems, it may be possible for the operator to overwrite or simulate a process value into a PLC register from the HMI on a temporary basis in order to disable an alarm.

Examples of questions that can be asked to determine if the programming of the PLC or HMI is well designed are as follows:

Can a process or subprocess be started or stopped without nuisance alarms or operator intervention to disable or modify set points in the control system?

Can process control sequences be completed without failure?

What are the number of critical alarms that occur in a day?

Is there a process in place to identify and manage false alarms?

A key consideration in the proper commissioning of any treatment system is whether the number of alarms is manageable for normal operation. Alarm flooding is an all-too-common and significant issue for operators, a condition with more alarms than can be reasonably managed by an operations team, where there is a high risk of missing very important alarms.

Alarms may also be process- or water quality–related. The remaining discussion will focus upon water quality–based alarms that are focused on CCPs in potable reuse and would result in a noncompliance regulatory permit violation.

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7.3.1 Alarm Types

Within the PLC, the process value from the field device can be scanned multiple times per second. The PLC may be programmed with specialized instructions to ensure that a true alarm condition exists before subsequent action is taken. An example of an alarm configuration strategy is called “Time Delay On” (Figure 7.2). The process value (indicated by the red line) has to be continuously greater than the threshold value (high set point) in order to initiate the timer. If the monitor or field device continues to produce a signal greater than the high set point beyond a predetermined period of time, the alarm notification is issued (true). In the event that the value drops to less than the threshold value for any period of time, the timer is reset (false) and will not time until the value is greater than its threshold value again. Additional programming may be used to ensure the alarm condition has cleared before the timer is reset.

Figure 7.2. Graphical depiction of a time delay alarm trigger (true) and a false event resulting in no alarm.

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A second alarm configuration is called the “Moving Average” (Figure 7.3). In this case the sample is averaged on a periodic basis (1/sec, 1/min), and the values reported are in reality an average of the past number of events over a predefined interval. At the next interval, the oldest value is discarded, other values indexed, and the most recent value added to calculate the moving average.

A third alarm configuration is the “Block Average” (Figure 7.4). In this case an input is averaged over a period of time (e.g., 30 seconds) and then averaged. All samples are discarded and the process repeated. In the following example, the alarm notification (Block Average > High Set Point) would occur for Block 3 of the true example.

The fourth alarm configuration is called “Point to Point” (Figure 7.5). In this case a single value from the PLC is used as the basis for alarm determination. Process values between the points of measurement are disregarded. An alarm is triggered when P1 and P2 are greater than the high set point for two consecutive measurements. Analyzer measurements between P1 and P2 are ignored.

Figure 7.3. Graphical depiction of a moving average alarm (true) and a false event resulting in no alarm.

Figure 7.4. Graphical depiction of a block average alarm trigger (true) and a false event resulting in no alarm.

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Figure 7.5. Graphical depiction of a point-to-point alarm trigger (true) and a false event resulting in no alarm.

The development of alarm configurations for water treatment has historically used the scenario of an operator recording data (by hand) and subsequently calculating compliance as a strategy for rule making. As a result, there are numerous potential water quality scenarios that are likely to occur but may not be captured from a compliance monitoring perspective. Such scenarios would result in excessively long failure response time intervals and be nearly impossible to manage from a production and CCP perspective.

Therefore, from a practical perspective, continuous online monitoring for process parameters for regulatory compliance, with data averaging via PLC and SCADA, offers the potential to improve the overall process reliability as increased monitoring is possible and vastly reduces the failure response time. Information about sampling frequency should be considered when developing alerts and alarms; continuous processes such as intermittent sampling (e.g., 15 minute point-to-point sampling or peak hour flow measurement) can be more challenging to monitor and respond to in a timely manner than an alarm configured in the manner typically associated with an online monitor and PLC system.

7.3.2 Alarm Management Considerations

Alarms are often not managed effectively in any of the process industries, including the water and wastewater industries. Often, little thought has been given to what constitutes an alarm, how it should be displayed, and how it should be managed. All modern HMI applications have the ability to select alarm levels within their analog point configuration database. In general, the event, alert, and alarm levels for these analog signals are selected during the initial configuration of the control system and rarely modified thereafter. The result is that the SCADA system will generate many alarms, only some of which are really important enough for operators to act upon. Excessive alarms can quickly overwhelm operators or hide critical process alarms within a long list of lower priority alarms, thereby extending response time. Excessive alarms can also lead to a condition of alarm flooding, and the natural human tendency in such cases is to simply ignore them. Following some incidents in the process industries where accidents occurred when operators missed critical alarms, a more concerted effort was made to develop standards for alarm management. The relevant standard for alarm management is ANSI/ISA-18.2-2009, Management of Alarm Systems for the Process Industries. One of the key concepts that came out of this effort was that, if a signal does not

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require an operator action, it is not an alarm. This concept helps to eliminate the bulk of the alarm “noise” at a facility so that those alarms that are integral to CCPs and COPs will be immediately apparent and quickly acted upon. Suppression of other alarms such as those associated with equipment that is out of service (or needing scheduled routine maintenance) further reduces the number of alarms generated by a SCADA system. The key to successful alarm management is the application of such principles in a systematic manner at each facility. The first step is to create an alarm philosophy to define what actually constitutes an alarm. For existing facilities, the amount and type of alarms must be analyzed, and those alarms that are truly critical enough to require operator action retained and others placed in the event category, meaning they are simply information. Another key concept of the standard is that the alarm management process continues throughout the life of the system. Alarms are constantly monitored, and those deemed no longer relevant are deleted from the system. It is recommended that DPR utilities implement an alarm management strategy based on the applicable portions of the ANSI/ISA-18.2 standard for all facilities (considering that it was written for a variety of process industries, not just the water industry). Additional information on this subject will be available in the future as part of WRRF-14-01, Integrated Management of Sensor Date for Real Time Decision Making in DPR Systems.

7.4 Response Procedures and Implementation of Alarm Strategies

An important and final piece of this study has been the development of operational response procedures as guidance for plant operations teams should one of the CCPs fail. CCP procedures provide a clearly articulated set of responses for plant operators to take if a critical monitoring point determines that a barrier is no longer fully intact. As a starting point for operational safety, each critical monitoring point has an alert limit that acts in the manner of a warning alarm, providing an indication that the monitoring limit is approaching the critical limit yet allowing time for both automated control responses and operational responses to occur in a proactive fashion to resolve the issue before a critical limit is breached. For more severe issues that may occur with or without a preceding alert notification, critical alarm limits indicate failure of a process or a monitor for a CCP that requires immediate attention to ensure water quality goals are met.

In most cases for recycled water facilities, processes and plants are highly automated. As a result, the operating procedures focus on ensuring that automated processes have operated correctly. There is a heavy reliance on instrumentation, and as a result, the procedures tend to focus heavily on checking instrumentation and verifying that the monitoring limit is real and analyzers and instruments are operating correctly.

Response procedures must also clearly articulate which stakeholders are to be notified, how to correct the issue, and how to return the equipment safely to service once the issue has been resolved. It is important to note that the procedures require an investigation to ensure the cause of the breach is understood so that actions can be taken to prevent it in the future.

As a general starting place when developing site- and process-specific response procedures, the following sequence of steps are suggested for the configuration of alerts (warning), critical alarms (failure), and responses. The information provided here is based upon work that has been previously conducted as a part of the Western Corridor Recycled Water Project Recycled Water Management Plan developed by Veolia Water Australia in conjunction with SEQWater.

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Alert (Warning)

Perform a water quality test if alert condition is obtained.

Validate that the instrumentation is properly functioning.

Repeat the test if the alert condition is confirmed.

Diagnose and repair the condition,

Record the event and remedial action.

Repeat the test with acceptable results.

Critical Alarm (Failure)

Process unit automatically shuts down and is taken out of service.

Perform a water quality test if alarm condition is obtained.

Validate that the instrumentation is properly functioning.

Repeat the test if the alarm condition is confirmed.

Notify supervision that a critical alarm condition exists.

Diagnose and repair the condition.

Repeat the test; when acceptable results are obtained, the unit may be returned to service.

Record the event and remedial action.

Notify regulatory authorities if water quality was compromised.

A generic flow diagram of response procedures for alerts and alarms is provided on Figures 7.6 and 7.7).

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Figure 7.6. Generic alert level response procedure.

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Figure 7.7. Generic critical alarm response procedure.

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7.4.1 Specific Alert and Alarm Procedures for Identified CCPs

In the following section, alert and critical alarm strategies are presented to illustrate how alarms are addressed for the CCPs identified in the two process trains. Although examples here are provided for the reader to see how response procedures can be developed for specific unit processes, it is important to realize that each facility will need to develop or modify its own procedures that are specific to its equipment, monitors, and water quality goals. However, there are a few key considerations that can be carried between systems, and they are included here based on professional judgment and experience with operating full-scale potable reuse facilities:

Control system Events should not be identified or included in the alarm historian, though they should be documented in the overall system historian.

Alerts (warning) should be used to address the normal issues that are associated with maintenance of repair of equipment.

Alerts (warning) should not shut down or disable the operation of the equipment.

Critical Alarms (failure) should be used to trigger immediate and automatic shutdown and disabling of equipment until corrective action is successful.

Critical Alarms should include parameters for method of measurement (time delay, moving average), including sampling frequency (min, sec) and time basis (min, hr) if deemed appropriate by the regulatory authority.

Critical Alarms (failure) should be structured to reflect an unusual or catastrophic occurrence, such as an equipment failure, resulting in equipment operation. The occurrence can ideally be captured as an alert (warning) before the critical condition is obtained.

Critical Alarms (failure) may or may not result in the loss of water quality. Notification of regulatory authorities shall only occur when the water quality from the system is compromised.

Critical Alarms (failure) should have restricted (supervisor) access at the HMI level or be programmed at the PLC level to limit inadvertent changes to the alarm set points.

Alarms should be redundant at the unit and system (common) level if possible.

A single analyzer located on the influent or effluent to a CCP should not be capable of triggering a critical system failure alone. Redundant instruments should be used around all CCPs.

7.4.1.1 RO Membrane-Based CCP Response Procedures

Chloramine Dosing Alert (Warning)

Figure 7.8 illustrates the proposed Chloramine Dosing Alert (warning) for the chloramination step. Chloramination is used to provide a disinfection residual through the membrane treatment processes, particularly the RO process, to minimize biological fouling. RO membranes will rapidly oxidize in the presence of free chlorine but have a substantially higher tolerance to chloramine. The biological fouling of membranes is not a health concern in itself, but rather there is a risk of chloramine dosing forming unwanted byproducts such as nitrosamines and other DBPs.

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The approach to chloramine dosing varies from plant to plant and depends in particular upon how much ammonia is in the secondary or tertiary effluent stream that feeds the advanced treatment process. In cases where there is no or incomplete nitrification, chlorine may be dosed directly into the effluent stream to form chloramine. This is the approach taken at the West Basin Municipal Water District’s Edward C Little Recycled Water facility, Orange County’s GWRS, and Scottsdale Water Campus. In other facilities where the effluent has been fully nitrified, ammonia (in the form of ammonium sulfate or ammonium hydroxide) is dosed to form chloramine. This is the approach taken at the Western Corridor Recycled Water Project in Brisbane, Australia.

In the case of the Bundamba AWTP (one of the three Western Corridor Recycled Water Plants), the original dosing configuration of ammonium sulfate followed immediately by sodium hypochlorite was shown to result in an increase of NDMA (Walker and Roux, 2009). The dosing approach was modified, whereby the ammonium sulfate and sodium hypochlorite were both dosed to form chloramine in a single carrier water stream of RO permeate, which was consequently dosed directly as chloramine. Although the exact reaction kinetics and pathway were not studied in this case, empirical evidence showed that NDMA formation effectively ceased. In cases where ammonia and chlorine must be added to the effluent stream, this is a recommended approach.

Chloramine formed with a background ammonia concentration in effluent is not conducted and would not be cost effective. In this case, it is recommended that chlorine is not overdosed beyond that required to inhibit biological growth on the membranes .

Dosing is typically targeted to provide a dose of between 2 and 5 mg/L chloramine, measured as Cl2 at a chlorine-to-ammonia ratio of 4:1.. The target dose may vary slightly from plant to plant, as dictated by different warranty requirements determined by individual RO membrane suppliers. The dose does not need to be fine-tuned, but rather control is aimed at maintaining an average dose (on a daily average basis) without exceeding a required maximum dose. Short-term losses of chloramine dosing (a few hours) provide no additional health risk but may increase the rate of membrane fouling and result in additional operating costs or a loss of plant production.

A higher than desired chlorine dose may increase the risk of unwanted byproduct formation. The impact of chlorine dose to byproduct formation will vary from plant to plant and may vary from day to day, and the best approach in terms of maximum chlorine dose may be determined empirically at each plant with a program of sampling and analysis. This may assist in the appropriate chlorine target set point.

The protection of membranes from free chlorine oxidation is also required and may utilize free chlorine, ammonia, and oxidation reduction potential analyzers to detect the presence of free chlorine. This is, however, related to protecting the membrane asset and not directly related to mitigating a health risk and so is not considered an aspect of the CCP. This will be considered a COP.

In contrast to most of the selected CCPs, the health risk that is mitigated is a chronic chemical risk rather than an acute microbiological risk. As a result, actions that are required need not be as immediate, which is reflected in the response procedures. In this case, a 24 hour or similar average period can be taken to assess this parameter.

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Chloramine Dosing Alarm (Failure)

Figure 7.9 illustrates the alarm (failure) procedures for the Chloramine Dosing step. Critical failure alarms for chloramine processes are configured in a manner similar to the alert (warning) status alarm. The primary difference is that a failure will result in a stoppage of the chloramine dosing, whereas an alert will allow for operator intervention before this controlled action takes place. As for the warning alarm condition, as this is not an acute health risk, a longer time period prior to failure alarm will be provided (Table 7.1).

Table 7.1. Chloramine Alert and Alarm Example Set Points Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

Total chlorine

Cl2>1 mg/L from set point 1hr moving average

Cl2>1 mg/L from set point 24 hr moving average

A high level of chlorine is an indication of poor dosing control. The ideal chlorine set point is a balance of both membrane warranty requirements and empirical data from sampling and analysis at the individual plant. As the health risks from chloramine are not acute, but rather chronic, the control action is to shut down the chloramine system rather than the entire plant.

Microfiltration–Ultrafiltration Alert (Warning)

Figure 7.10 illustrates the proposed alert (warning) scenario for hollow-fiber (MF–UF) membrane integrity verification. For these systems, turbidity is a continuously available water quality parameter that has been demonstrated to be less sensitive than the pressure decay rate (PDR; U.S. EPA, 2005) but can provide continuous information to the SCADA system. However, PDR testing is performed on a periodic basis (typically 1/day) and thus does not provide the confidence of continuous monitoring of a CCP. Under normal operation, it is recommended that turbidity or a particle counter should be included in online monitoring such that if turbidity is observed greater than a trigger set point, then the following actions would be to investigate the turbidimeter and conduct an off-cycle PDR test. During normal turbidity meter operation and reading, a PDT would be performed on a scheduled basis.

With the exception of some small plants, most plants have multiple MF or UF units operating in parallel. It should be noted that the alert and critical alarms are monitored at the individual unit level. This is an inherently conservative approach as the alert and critical alarms are set to achieve the full removal requirements, ignoring that any issue will be diluted by other units that are operating correctly.

In the event that high turbidity is confirmed, the unit would be removed from service and the PDR established. If the PDR indicates an unacceptable test result, the unit would remain out of service, and diagnostic and repair activities would be performed to obtain an acceptable PDR test result prior to returning the unit to service.

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Microfiltration–Ultrafiltration Alarm (Failure)

Figure 7.11illustrates the critical (failure) response procedures for a hollow-fiber (MF–UF) membrane system. The first action for a critical (failure) limit exceedance is to inhibit the operation of the unit or system. The next action is to confirm that the critical (failure) limit has been exceeded, and that, once confirmed, managers and supervisors are notified of the failure condition. The second difference is that, once the corrective action step has been performed, an assessment is made to determine if water quality was actually compromised during the failure event. If water quality was compromised, additional actions and interaction with external entities may be required.

It should be noted that for Figure 7.11, a filtrate turbidity critical condition is highly unlikely and will most likely only exist with failure of the turbidimeter. Membrane units often have redundant turbidimeters that would also have to fail in order for the common filtrate to fail critically. Recall that from Chapter 5 the RPN of 72 was assigned to the turbidimeter, whereas an RPN of only 36 was applied to the PDR test. In both cases the occurrence (O) number was 2, indicating a low likelihood of failure, whereas severity (S) was 9, indicating that the monitor provides a critical function and is key to public health protection. Because of the automation of the turbidimeter, the detection (D) number was 4, but it was only 2 for the PDR test. The take-away message is that the two tests (turbidity and PDR) are highly reliable in terms of being able to detect and trigger alarm events with a low likelihood of “failure to notice failure.”

Table 7.2. Microfiltration–Ultrafiltration Alert and Alarm (Summary) Monitoring Parameter

Alert Level Critical (Failure) Level Notes

Unit pressure decay rate (daily integrity test)

PDR (in psi/min) target calculated to achieve slightly > (LRV+0.2) target LRV of 3 μm particles, based on calculation in U.S. EPA Membrane Filtration Guidance Manual. PDR target calculated will account for membrane type, unit configuration, flow rate, water temperature, and degree of fouling.

PDR (in psi/min) target calculated to achieve desired LRV of 3 μm particles, based on calculation in U.S. EPA Membrane Filtration Guidance Manual.

LRV targeted is typically 4.0 for 3 μm particles for Giardia, Cryptosporidium, and bacteria. Lower targets are sometimes used if LRV requirements can be achieved with other processes. As LRV is logarithmic, a PDR target slightly > (0.1–0.2 log higher than target) is sufficient to provide sufficient buffer between the alert and critical levels.

Unit filtrate turbidity (15 min moving average)

Typically >0.2 NTU or similar depending on accuracy of the analyzer. If the analyzer is a laser turbidimeter, consideration of a lower figure may be suitable.

Typically >0.5 NTU or similar, depending on accuracy of the analyzer. If the analyzer is a laser turbidimeter, consideration of a lower figure may be suitable.

Turbidity is far less sensitive relative to PDR. This backup provides a continuous monitor in case of gross failure and does not provide a guarantee of log removal. Specific targets may be adjusted from site to site.

Notes: LRV=log removal value; PDR=probability density function.

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RO Membrane Alert (Warning)

Figure 7.12 illustrates the proposed alert (warning) scenario for an RO membrane system using electrical conductivity percent removal as the method to trigger an alert response. Electrical conductivity is a continuously available water quality parameter that feeds data to the PLC. Under normal operation, if a low percent removal is observed, actions would include investigating the feed and permeate conductivity. RO membranes have conductivity-based water quality monitors located on the influent as well as the permeate (effluent) that are used to determine the percent removal. The percent removal is calculated simply as the relationship between feed and permeate (i.e., a more complex calculation involving temperature compensation or normalization of water quality is not being used). Percent removal is used to represent the actual (non-normalized) removal across the membrane system.

TOC can be used to provide an additional monitoring technique to indicate the level of organic material removal across the RO system. RO permeate has a relatively low level of TOC, and as such, a low detection analyzer such as UV–persulfate oxidation must be used to achieve consistent results in the less than 100 ppb range. These instruments are relatively costly, and it is generally uneconomical to include one analyzer per RO train; rather, one is installed on the common RO permeate line. This approach has been taken at Orange County Water District’s GWRS, Western Corridor Recycled Water Project, and Singapore’s Newater facility. Although this instrument can be used as a CCP monitor, it has most often been used as an indicator of upstream process upset or an unanticipated pollution spill to the sewershed.

Current research is working toward improvements in RO system integrity monitoring (Frenkel and Cohen, 2014; Jacangelo, ongoing research). This work may result in the potential for alternative monitoring approaches with higher sensitivity. Some approaches under current investigation include rhodamine dye injection, uranine injection, and sulfate monitoring, among others. Once validated, these may be substituted as monitors for this CCP.

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RO Membrane Alarm (Failure)

Figure 7.13 illustrates the critical (failure) response procedures for an RO membrane system. The first action for the critical (failure) limit exceedance is to inhibit the operation of the unit or system. The next action is to confirm that the critical (failure) limit has been exceeded, and that, once confirmed, managers and supervisors are notified of the failure condition. The second difference is that, once the corrective action step has been performed, an assessment is made to determine if water quality was actually compromised during the failure event. If water quality was compromised, additional actions and notification may be required.

Table 7.3. Reverse Osmosis Membrane Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

RO EC percent removal (15 min moving average) (common feed/unit permeate)

< 94% removal for a target LRV of 1.2

<90% removal for a target LRV of 1.0

EC removal is calculated from a common RO feed and an individual unit. Targets can be adjusted for higher or lower LRV and may depend on the specific RO membrane rejection.

RO TOC percent removal (15 min moving average) (common feed/common permeate)

<98 % for a salt rejection LRV of 1.7

<95% for a target LRV of 1.2

Analyzers for permeate measurement are expensive and thus used on the common RO permeate line. These analyzers have typically been used for monitoring of upstream process upset or sewershed contamination.

Notes: EC=electrical conductivity; LRV=log removal value; RO=reverse osmosis; TOC=total organic carbon. .

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UV Advanced Oxidation Alert (Warning)

Figure 7.14 illustrates the proposed alert (warning) scenario for UV–H2O2AOP. In a UV–AOP system, three modes of action are occurring simultaneously for chemical and microbial control (Kruithof et al., 2007). First, UV irradiation is being used to disinfect the water and, in the case of UV–AOP, the energy is typically at least 10 times greater than that used for UV disinfection. Second, the high-energy UV light will photolyze some compounds such as NDMA that may be less amenable to removal by other treatment technologies (Plumlee et al., 2008; Steinle-Darling et al., 2007). Third, hydrogen peroxide is added to the water and reacts with the high-energy UV light to form into hydroxyl radicals. The hydroxyl radical acts as a strong oxidant that reduces the amount of other chemical constituents, such as 1,4-dioxane, in the water (Stefan and Bolton, 1998). Hydrogen peroxide is metered as a liquid into the process stream and can lose flow through off-gassing.

The amount of light exposure achieved in a UV–AOP reactor can be measured as a dose (mJ/cm2) or as a reaction energy equivalent per log order of reduction (EEO) for a target contaminant. Additional parameters such as UV transmittance, UV lamp intensity, and flow are used to calculate the dose or EEO. Under certain circumstances, including end of useful life, UV lamps may fail, resulting in decreased dosage or EEO. However, a UV–oxidation control system that is operating based on a target EEO will be able to adjust the delivered UV energy to maintain the target EEO. The contaminant log reduction (comparison of target and calculated actual log reduction) is referred to as the electrical energy dose.

Likewise, the amount of peroxide added can impact the degree of production of hydroxyl radicals formed and UV photolysis and disinfection that can occur. Because only about 10% of the peroxide is reacted to create hydroxyl radicals, too little peroxide addition will result in a low hydroxyl radical yield and potentially insufficient advanced oxidation (Rosenfeldt and Linden, 2007). Conversely, because peroxide absorbs UV light, adding too much peroxide will decrease the UV dose energy delivered and may compromise photolysis (Sgroi et al., 2015). Finally, careful attention must also be given to the amount of quenching agent or chlorine that must be used to eliminate the 90% of peroxide still remaining after UV–AOP. Thus, careful design and validation must be used to determine the optimal dose of hydrogen peroxide or chlorine (in the case of UV–chlorine-based AOP systems) to achieve the desired formation of hydroxyl radicals and subsequent contaminant oxidation while minimizing chemical costs and impacts on downstream chlorine addition.

In addition, UV lamps require some time to warm up to reach full operational status and may also operate at reduced efficiency if there is a sudden increase or decrease in flow through the system. These systems are usually flow controlled during startup, operation, or shutdown to ensure that water passing through the system receives the required UV energy. The monitors for operation of the UV system may include various combinations of power, UV fluence (or intensity), UV transmittance of the feed water, peroxide flow, feed water flow, and turbidity.

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UV Advanced Oxidation Alarm (Failure)

Figure 7.15 illustrates the proposed critical (warning) scenario for UV–H2O2–AOP. Treatment is provided both by UV light and hydrogen peroxide, so the loss of hydrogen peroxide feed or operating lamps will reduce treatment effectiveness. For these systems, loss of hydrogen peroxide results in the process becoming ineffective. Other failures may occur as a result of lamp failure or mechanical issues. Specific response procedures would need to be developed for the exact sensor combinations used at a given facility, though this diagram will provide a basic overview of potential alarm response procedures.

Table 7.4. UV–Advanced Oxidation Alert and Alarm Level Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

Hydrogen peroxide (1 min moving average)

25% < target dose level

50% < target dose level.

This same approach can be used if sodium hypochlorite is used instead of hydrogen peroxide.

Electrical energy dose (measured EEO to target EEO)

<105% target <100% target This ratio takes into account UVT, UV intensity, flow, and power. Calculations are contained in proprietary vendor control systems.

UV lamp failure >10% >15% This figure may be adjusted for each reactor at an individual site for a discrete number of lamps.

Notes: EEO=energy equivalent per log order of reduction; UV=ultraviolet; UVT=ultraviolet transmittance.

Chemical Disinfection (Chlorine) Alert (Warning)

Figure 7.16 illustrates the proposed Chemical Disinfection alert (warning) for the post-treatment chlorination step. Chemical disinfection processes are common to water treatment and reuse applications. Under most circumstances a disinfectant (e.g., chlorine) is added to the water and allowed to react for a period of time to inactivate any remaining target pathogens (e.g., Giardia, coliform bacteria, and viruses). The disinfection residual is normally, but not always, measured at the end of the process together with temperature, pH, and flow.

Disinfection is normally expressed as a function of the chemical residual concentration (C) multiplied by the contact time (T), or CT. Because pH and water temperature affect the disinfection process, they are also monitored. Under some circumstances the volume of the disinfection structure may also be a variable and will require a methodology to calculate the compliance flow (e.g., min, avg. hour, peak hour) established by the regulatory authority to be used in the disinfection calculation.

It should be noted that many water treatment facilities calculate CT based upon peak hourly flow at the outlet condition. This approach correlates to an operator hand measurement, and the calculation may be cumbersome using the PLC. Using this approach, a scenario can exist where there is a gap between the availability of the calculation parameter (e.g., peak hourly flow) necessary to calculate the CT parameter. Thus, alternative online measurement and averaging strategies may be more appropriate to monitor a system under continuous flow conditions. This will allow the calculation to be performed in real time. The online approach was used to model the chlorine disinfection and CT data presented in Chapters 3 and 4.

The most common problem associated with this type of equipment is loss of an accurate analyzer signal through drift, sample flow, loss of reagent, or other issue with the instrumentation. Because disinfection

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structures are generally common to the overall facility, it may be advisable to locate redundant analyzers in a common structure. This is consistent with the highest observed RPN of 144 presented in Chapter 5 for the final disinfection step chlorine analyzer. The high RPN is a product of both the criticality of the process step (disinfection) and the likelihood of instrument failure.

Chemical Disinfection (Chlorine) Alarm (Failure)

Figure 7,17 illustrates the alarm (failure) procedures for the Chemical Disinfection step. Critical failure alarms for disinfection processes are configured in a manner similar to the alert (warning) status alarm. The primary difference is that a failure will result in an interruption to the process, whereas an alert will allow for operator intervention before a compliance issue arises.

Table 7.5. Chlorine Disinfection Alert and Alarm Level Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

CT (mg/L/min) specific targets follow specific targets follow

CT is calculated according to the U.S. EPA Disinfection Guidance Manual. Specific requirements for chlorine residual, flow rate, temperature, and pH are managed as individual alerts as a part of this CCP.

Chlorine (mg/L) <25% of target 15 min moving average

<50% of target 15 min moving average

Target that is required to achieve chlorine CT for given range of temperature, flow, and pH.

Temperature (oC or oF)

<110% of min design temperature 15 min moving average

< design min temperature 15 min moving average

Alarm should be set at minimum temperature of design at which CT can be achieved based on EPA guidance manual.

Flow (gpm) 10% > maximum flow 20% > maximum flow

CT will be designed on a maximum flow rate at which chlorine dosing can achieve required dose. This is not a likely scenario, as the RO process typically produces a steady flow that cannot increase beyond the physical limitations of the system

pH <6.5 or >8.7 <6.0 or >9.0 EPA CT figures are only valid for this pH range. pH outside this range would also likely trigger alarms in the RO system upstream

Notes: CCP=critical control point; CT=concentration x time.

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Chemical Stabilization Alert (Warning)

Figure 7.18 illustrates the proposed Chemical Stabilization alert (warning). Chemical stabilization is commonly applied in RO-based treatment plants in order to restore a sufficient balance of calcium hardness, alkalinity, and pH to minimize corrosion of plant and distribution network infrastructure, in particular cement lining and copper services. Although this dose is primarily concerned with protecting assets, from a health standpoint it is considered a CCP to protect against the mobilization of lead and copper in distribution systems, which may occur if water is not suitably stabilized.

Chemical stabilization can be achieved in several ways, including:

lime (calcium hydroxide) dosing

calcium carbonate contactor (calcite filter)

carbon dioxide dosing

mineral acid (sulfuric acid, hydrochloric acid)

The intent of these dosing regimens is to ensure the balance of pH, alkalinity, ionic strength, and, in particular, calcium hardness to manage chemical erosion of cement-based materials or corrosion and mobilization of metallic substances. A number of different indices are used to target an appropriate balance of these parameters, including the Langelier Saturation Index and calcium carbonate precipitation potential. In both cases, the most significant contributors to these indices are the levels of pH and calcium hardness. From a CCP standpoint, there is a health risk if the water is in a more aggressive state (i.e., more likely to solubilize metals), and, as such, controls will be in place to minimize the risks of extended periods at this condition. Online pH monitoring, with a check on calcium hardness, is used as a monitoring parameter. Calcium hardness is monitored as a dose flow (if lime is used) and supported by regular sampling and analysis for hardness.

Where used, lime dosing is typically achieved by batching a slurry of dry hydrated lime (Ca(OH)2) with water into a suspension known as milk of lime. This suspension, which can contain various concentrations of lime, is either dosed directly or fed to a lime saturator to remove impurities where the clarified lime solution is dosed. A dose flow rate requirement will therefore need to be calibrated for the concentration of calcium that is dosed in the slurry liquid or supernatant.

Calcite contactors, sometimes called calcite filters, contain small chips or pellets of calcium carbonate. Water flows through these contactors to dissolve sufficient calcium hardness to achieve desired levels and stability indices. To target a required concentration, a portion of the water is bypassed. The response procedure has been written assuming a lime dosing–carbon dioxide or mineral acid dosing combination. However, this could also be used for calcite contactors, with the lime dose flow monitor substituted for a flow-to-bypass flow ratio through the contactor.

In contrast to most of the selected CCPs, the health risk that is mitigated is a chronic chemical risk. As a result, required actions need not be as immediate, which is reflected in the response procedures. In this case, a 24-hour or similar average period can be taken to assess this parameter.

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Chemical Stabilization Alarm (Failure)

Figure 7.19 illustrates the alarm (failure) procedures for the Chemical Stabilization step. Critical failure alarms for chemical stabilization processes are configured in a manner similar to the alert (warning) status alarm. The primary difference is that a failure will result in an interruption to the process, whereas an alert will allow for operator intervention before a compliance issue arises. As for the warning alarm condition, as this is not an acute health risk, a longer time period prior to failure alarm will be provided.

Table 7.6. Chemical Stabilization Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

pH (24 hr moving average)

pH <0.5 units from set point

pH <1.0 units from set point

pH is the most sensitive parameter with respect to water stability. The set point for pH will be set at individual plants based upon final treated water chemistry and distribution network requirements.

Lime dose (24 hr moving average)

25% < target dose level

50% < target dose level

Lime dose can be provided from a dose flow meter or ion selective electrode with laboratory checks on alkalinity and calcium carbonate precipitation potential (CCPP)

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Figure 7.8. Chloramine dosing alerts (warning) response.

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Figure 7.9. Chloramine dosing critical alarm (failure) response.

Critical limit

triggered?

Notification to plant manager and supervisors

Automated shutdown of chloramine dosing

system.

Review Alert response steps

Record EventRestart process under Supervisor direction

Normal Operations

Y

Incident Response Process:• Verbal notification to authorities and customers• Initial investigation and risk assessment (potential

exposure, process failure analysis)• Incident report• Engagement with authorities to agree subsequent steps

Process engineer to carry out investigation and implement maintenance and

corrective actions.

Continue monitoring of trends (including

sampling if required)

Complete Incident Report (<24hrs)

-> Preliminary risk assessment

Complete Investigation Report (<1wk)

-> Corrective Actions

Parameter Critical limit

Total Chlorine24 hr moving average Cl2 > 1 mg/L from setpoint

Repairs Successful

?

Water Quality Failure?

Y

N

N

Y

N

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Figure 7.10. MF–UF alerts (warning) response.

Turbidity Alert

triggered?

Review combined and individual SCADA trends (Feed, filtrate turbidity,

cleaning process, TMP, flow)

Check analyzer(flow, chamber

cleanliness , signal, calibration)

Validate actual turbidity using

handheld analyzer

Alert is real?

Conduct risk assessment with supervisor and review

corrective actions

Normal Operations

Y

Y

N

Notify as per Incident

Response Plan

Remove Unit from Service

Investigation (Diagnostic membrane bubble testing, equipment fault

identification) and implement maintenance/corrective actions

Record Event

Parameter Alert level

Unit Pressure Decay Rate(Daily Integrity Test)

PDR > 0.2 psi/min(eq 4 LRVs)

Unit Filtrate Turbidity(15 min moving average)

> 0.2 NTU (unit/combined)

Plan analyzermaintenance/calibration

Remove individual analyzer/unit from

service

PDR Alert triggered?

N

N

Y Is PDR Alert

confirmed?

Y

Repeat PDR Test

N

PDT Level back to normal?

N

Perform PDR Test

Y

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Figure 7.11. MF–UF critical alarm (failure) response.

Critical limit

triggered?

Notification to plant manager and supervisors

Automated MF unit shutdown and supply

interruption

Review Alert response steps (including validation of

combined analyzer signal)

Record EventRestart process under Supervisor direction

Normal Operations

Y

Incident Response Process:• Verbal notification to authorities and customers• Initial investigation and risk assessment (potential

exposure, process failure analysis)• Incident report• Engagement with authorities to agree subsequent

steps

Run PDT on unit or confirm validity of combined turbidity

Failure investigation (Membrane bubble and equipment testing, review

trends, fault identification) and implement maintenance/corrective

actions

Continue monitoring of trends (including

manual testing if required)

Complete Incident Report (<24hrs)

-> Preliminary risk assessment

Complete Investigation Report (<1wk)

-> Corrective Actions

Parameter Critical limit

Unit Pressure Decay Rate(Daily Integrity Test)

Decay Rate in psi/min calculated at LRV above target

Unit Filtrate Turbidity(15 min moving average)

Typical > 0.5 NTU (unit or combined)

Repairs Successful

?

Critical limit

Confirmed?

Water Quality Failure?

Y

Y

N

N

N

Y

N

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Figure 7.12. RO alert (warning) response.

Alert Level

triggered?

Review combined and individual

SCADA trends (EC

LRV, RO feed and RO permeate)

Check analyzer(flow, chamber,

signal, calibration)

Validate feed and permeate EC using handheld analyzer

Alert is real?

Conduct risk assessment with supervisor and review

corrective actions

Normal Operations

Y Y

N Notify as per Incident

Response Plan

Rotate RO units using standby to restore LRV

Process engineer to carry out investigation (conductivity profiling,

review normalised trends, fault

identification) and implement maintenance/corrective actions

Level back to normal?

Continue monitoring of

trends

Y

N

Critical limit

triggered?

Critical response process

Y

N

Parameter Alert level Critical limit

RO EC Removal <94% for LRV of 1.2 (15 min rolling Average) 90% for LRV of 1.0 (15 min rolling average)

Unit Filtrate TOC <98% for LRV of 1.7 (15 min rolling average) <95% for LRV of 1.2 (15 min rolling average)

Note Can be adjusted for specific RO membrane rejection selected

Plan analyzermaintenance/calibration

Shift to individual analyzers and calculated

combined EC LRV

N

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Figure 7.13. RO critical alarm (failure) response.

Critical limit

triggered?

Automated RO shutdown and supply

interruption

Notification to plant manager and supervisors

Review Alert response steps (including validation of

combined analyzer signal)

Critical limit

breach is real?

Restart process under Supervisor direction

Normal Operations

Y Y

N

Incident Response Process:• Verbal notification to authorities and customers• Initial investigation and risk assessment (potential

exposure, process failure analysis)• Incident report• Engagement with authorities to agree subsequent

steps

Restart RO units with LRV>1.2

Process engineer to carry out investigation (conductivity profiling,

review normalised trends, fault identification) and implement

maintenance/corrective actions

Continue monitoring of trends (including

manual testing if required)

Complete Incident Report (<24hrs)

-> Preliminary risk assessment

Complete Investigation Report

(<1wk)-> Corrective Actions

Alert level response process

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Figure 7.14. UV–AOP alert (warning) response.

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Figure 7.15. UV–AOP critical alarm (failure) response.

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Figure 7.16. Disinfection system (chlorine) alert (warning) response.

Alert Level

triggered?

Review SCADA trends (Flow, Cl2, dose, ammonia)

Check analyzer(flow, chamber

cleanliness, signal,

calibration)

Place redundant analyzer into

service

Alert is real?

Conduct risk assessment with supervisor and review

corrective actions

Normal Operations

Y

Y

N

Notify as per Incident

Response Plan

Staff to carry out investigation and implement maintenance/corrective

actions

Level back to normal?

Record Event Continue monitoring of trends

Y

N

Parameter (location) Alert level

Chemical Disinfection (combined)15 min moving average

Chlorine ResidualTemperature

FlowpH

less than 0.7 LRV Giardia

Plan analyzermaintenance/

calibration

Shift to calculated combined LRV

N

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Figure 7.17. Chemical disinfection (chlorine) alarm (failure) response.

Critical limit

triggered?

Notification to plant manager and supervisors

Review Alert response steps (including validation of

combined analyzer signal)

Repairs Successful?

Record Event Restart process under Supervisor direction

Normal Operations

Y

Y

N

Incident Response Process:• Verbal notification to authorities and customers• Initial investigation and risk assessment (potential

exposure, process failure analysis)• Incident report• Engagement with authorities to agree subsequent steps

Process engineer to carry out investigation and implement

maintenance/corrective actions

Continue monitoring of trends (including

manual testing if

required)

Complete Incident Report (<24hrs)

-> Preliminary risk assessment

Complete Investigation Report

(<1wk)-> Corrective Actions

Critical limit

breach is

real?

N

Y

Water Quality Failure?

N

Y

N

Parameter (location) Alert level

Chemical Disinfection (combined)15 min moving average

Chlorine ResidualTemperature

FlowpH

less than 0.5 LRV Giardia

Automated shutdown and supply

interruption

Confirm Event is real with redundant

analyzer

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Figure 7.18. Chemical stabilization alert (warning) response.

pH Alert triggered?

Review SCADA trends (Flow, pH, lime dose

flow, CO2 or acid dose flow)

Check analyser(flow, chamber

cleanliness , signal, calibration)

Validate actual pH using handheld

analyzer

Alert is real?

Conduct risk assessment with supervisor and review corrective

actions

Normal Operations

Y

Notify as per Incident Response

Plan

Staff to carry out investigation and implement maintenance/corrective actions

Record Event

Parameter Alert level

pH24 hr moving average

pH < 0.5 units from setpoint

Lime Dose Flow24 hr moving average

flow < 25 % of setpoint

Plan analyzermaintenance/calibration

Remove analyzer from service

N

Level back to

normal ?

Y

N

Lime dose alert

triggered?

Y

Measure Ca Hardness,

Alkalinity, pH and TDS and calculate

stability index

N

NY

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Figure 7.19. Chemical stabilization alarm (failure) response.

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7.4.1.2 Ozone–Biofiltration -Based CCP Response Procedures

Ozone Low CT Alert (Warning) Response

In some cases there may be one or two ozone injection points (one prior to BAC and possibly one prior to flocculation–sedimentation). In this case, the CCP response procedures are identical for both injection points, but the alert/alarm set points will need to be adjusted according to dose requirements and function of the process.

In general, ozone systems that are designed for disinfection use the CT concept similar to that explained for chlorine. The U.S. EPA has a guidance manual (U.S. EPA, 2010) for the use of ozone for inactivation of pathogens and viruses, including the calculation of disinfection CT for various ozone contactor configurations. Utilities desiring to obtain the pathogen or virus inactivation credit for ozone in drinking water must utilize one of the methods presented in the guidance manual for calculating the overall CT, unless the state has adopted alternative methods for calculation of CT. In the absence of regulatory guidance from a DPR perspective, it is likely that this guidance will be used by states in that application as well. A common theme among the options presented in the guidance manual is the requirement to sample for residual ozone concentration at multiple points within the contactor and at the contactor effluent.

The LT2ESWTR Toolbox Guidance Manual provides the following considerations regarding ozone residual monitoring locations and CT calculations:

In ozone dissolution chambers (other than the first chamber in a contactor), CT credit is only allowed if there is a measurable ozone residual in the influent to the given dissolution chamber. The CT calculation then uses the effluent ozone residual concentration from the given dissolution chamber.

In the reaction chambers (no ozone addition), CT credit calculations depend on the number of reaction chambers.

If there are less than two reaction chambers, then the CT calculation relies on the effluent ozone residual concentration from each reaction chamber.

If there are three or more reaction chambers, then the Extended-CSTR Method can be used. This requires at least three reaction chambers with a detectable ozone residual for the calculation.

Based on this guidance, operators must monitor the ozone residual concentration from at least four locations within each ozone contactor; thus, it is paramount that site-specific response procedures be developed. However, the generalized response procedures presented in this chapter can be used as a starting place for developing customized response procedures.

Figures 7.20 through 7.22 illustrate the proposed alert (warning) scenario for investigation of a low disinfection CT alert within the ozonation process. As disinfection CT is affected by both flow and ozone residual, and the latter by the applied ozone dose, there are three alert level response steps. The first step, as depicted in Figure 7.20, is to identify if the individual ozone contact flow is within the design range. Ozone contactor flow is a continuously available parameter and can be reviewed by operators and engineers via the plant SCADA system. Under normal operations, if the operator or engineer reviews the SCADA trends and observes that the flow is greater than the design rating of the reactor, the flow to the reactor should be reduced. This may entail either a redistribution of flow to the other online reactors or a decrease in the overall DPR plant flow.

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If the reactor flow is within the design range according to SCADA, or a reduction of contactor flow does not increase disinfection CT to the desired level, then the next step is to investigate the ozone residual measurements at each stage in the contactor. This is depicted in Figure 7.21. The first step is to review SCADA trends for ozone residual at each monitoring point to identify if any residual measurements are less than the target value. At each identified low residual measurement point, staff should take a handheld ozone residual analyzer and validate the low residual measurement at the point of sampling. If the online measurement is validated, then the ozone dose to the reactor should be increased and reasons for the increase in required ozone dose investigated (see Figure 7.21). If the online measurement is shown to be inaccurate, the affected ozone residual analyzer should be taken offline for recalibration or repair.

When online ozone residual analyzers are taken out of service for maintenance, the impact on the CT calculation needs to be assessed and the calculation method revised, if needed. As noted previously in this section, CT credit for ozone dissolution chambers is only granted when there is measurable ozone residual in the influent to the chamber and the effluent from the dissolution chamber, if used for the CT calculation. If one of the impacted analyzers taken out of service is the influent analyzer to a dissolution chamber, then the operator or engineer must exclude that particular chamber from the overall CT calculation. A similar revision to the CT calculation must be performed depending on the number of reaction chambers and associated online residual analyzers. If the target CT cannot be maintained when the calculation method is revised, then the contactor flow must be reduced in an attempt to achieve the target CT or the overall DPR plant flow reduced and the particular ozone contactor taken offline while the analyzers are recalibrated or repaired.

Figure 7.22 illustrates the final step in the ozone alert level response process, namely evaluation of root causes for the required increase in ozone dose. Ultraviolet light transmissivity (UVT) is an indirect measure of organic matter content in the influent to the ozone reactor. The lower the UVT, the greater the amount of organic matter present in the influent. This organic matter can increase ozone demand, requiring a higher ozone dose to achieve the target residual concentration in the ozone contactor. If a reduction in UVT is observed through SCADA, operations staff should discuss potential process performance issues with the upstream wastewater treatment plant operations staff to identify potential measures to improve upstream organics removal. In addition, staff should assess whether the ozone diffusers are functioning properly, and if they are not, incorporate the affected diffusers into the plant’s preventative maintenance program. This latter item can be verified by residual ozone concentration levels or pressure in the ozone feed line. The ozone concentration in the contactor headspace should be measured prior to the destruct units and examined for long-term and short-term trends relative to flow and CT. If the residual ozone concentration increases, then there is an issue with one or more diffusers. Likewise, it there is clogging or other issues with the diffusers themselves, the pressure in the ozone feed line will increase.

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Ozone Low CT Critical Alarm (Failure) Response

Figure 7.23 illustrates the critical (failure) response procedures for the ozonation system. The first step in the response is an automatic shutdown of the affected ozone contactor. Following shutdown of the contactor, plant staff should notify the plant manager or supervisor and review the alert level response procedures to confirm that the critical (failure) limit breach is real. If the breach is real, then staff should perform the following actions:

• Verify the calibration status of the flow meters and online ozone residual analyzers and collect confirmatory grab samples to validate the online residual analyzer values.

• Verify that no upstream process upsets have occurred that might impact ozone demand.

• Review SCADA trends for ozone dose and residual, flow, and influent UVT.

• Review ozone diffuser performance.

• Verify the CT calculation performed in the PLC by hand.

• Assess the water quality of the contactor effluent.

• Perform incident reporting to appropriate stakeholders.

If the water quality was actually compromised during the critical limit breach, additional actions and interaction with external entities may be required.

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Ozone–BAC Alert (Warning) Response

The ozone–BAC combined process and CCP is intended to reduce the concentration of organic contaminants in the recycled water, including emerging contaminants of concern that may not be captured in the initial risk assessment. The process has two key operating parameters associated with it: ozone dose and empty bed contact time (EBCT). Ozone dose is applied in the ozone contactor to oxidize the organic compounds, and EBCT is the time that the oxidized organic compounds in the water being treated reside within the carbon bed of the biological activated filter. The contact time allows for both adsorption of organic compounds to the open pore spaces in the carbon media and degradation of those compounds via biological activity. Sometimes dose is determined by (a) a desired residual concentration for CT credit, (b) a ratio of ozone to TOC, or (c) an observed change in UVT across the ozone contactors (Serna et al., 2014; Snyder et al., 2013; Wert et al., 2009). All three dosing strategies have their merits and may be synergistically used together for multiple treatment objectives.

Table 7.7. Ozone Low CT Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

CT (mg/L/min) overall monitor

Flow (gpm) per contactor

10% > maximum flow for each contactor (15 min moving average)

20% > maximum flow for each contactor (15 min moving average)

This is performed on a per-contactor basis.

Ozone residual (mg/L) per contactor

<25% of target (15 min moving average)

<50% of target (15 min moving average)

Target that is required to achieve ozone CT for given range of flow for each contactor.

ΔUVT (%) across ozone contactor

<110% of minimum set point (15 min moving average)

minimum set point (15 min moving average)

Actual set point will be site-specific based on design and operating experience. The intent is to detect a change to normal conditions.

Notes: CT=concentration x time; UVT=ultraviolet transmittance.

It should be noted here that in the case of an ozone–BAC combined process, there are varying objectives, including disinfection (which is handled by the ozone CCP alone) and the oxidation and biodegradation of dissolved organic constituents in the water, which is handled by the ozone–BAC CCP. In this case, other measures of process performance may be needed besides a simple CT calculation. UV transmittance has been shown to be a good surrogate for contaminant oxidation as well as disinfection and provides a useful monitoring tool for ozone–BAC applications (Gerrity et al., 2011; Pisarenko et al., 2012; Snyder et al., 2013; Wert et al., 2009). Thus, there will be a need for separate site-specific alert (and alarm) procedures to be developed for UVT-based monitoring and control systems that may be applied in water reuse applications.

Figure 7.24 depicts the response procedures associated with alert (warning) level notifications for either of the two key operating parameters. In the case of a high ozone dose alert, the plant operator or engineer should follow the high ozone dose investigative procedures depicted in Figure 7.20 and described in this chapter. In the case of a low EBCT alert, plant staff should identify through SCADA if the flow to the BAC filter is outside of the design range. If the flow is too high to achieve the target EBCT, then the flow

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to the BAC filter should be reduced until the target is met. This can entail either a redistribution of flow to the online BAC filters or a reduction in the overall DPR plant flow. If the individual BAC filter flow is too high and cannot be reduced, then a critical (failure) condition exists, and those procedures should be followed.

Ozone–BAC Critical (Failure) Response

Figure 7.25 illustrates the critical (failure) response procedures for the ozone–BAC CCP. The first step in the response is an automatic shutdown of the affected ozone contactor and BAC filter. Following shutdown of the contactor and filter, plant staff should notify the plant manager or supervisor of the shutdown and review the alert level response procedures to confirm that the critical (failure) limit breach is real. If the breach is real, then staff should perform the following actions:

• Verify the calibration status of the flow meters and online UVT analyzers and collect confirmatory grab samples to validate the online UVT analyzer values.

• Verify that no upstream process upsets have occurred that might impact ozone demand.

• Review SCADA trends for ozone dose, flow, and influent UVT.

• Review ozone diffuser performance.

• Assess the water quality of the ozone–BAC process effluent.

• Perform incident reporting to appropriate stakeholders.

If the water quality was compromised during the critical limit breach, additional actions and interaction with external entities may be required.

Table 7.8. Ozone–BAC Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

Ozone dose (mg/L)

90% of maximum dose (15 min moving average)

maximum dose (15 min moving average)

Ozone dose is controlled to match inlet flow and influent UVT. If ozone dose required is higher than maximum, this may be due to a high flow or higher UVT than design capability. A loss of ozone dose will be captured as a part of the individual ozone disinfection CCP.

EBCT (flow per filter)

10% > maximum flow for each contactor (15 min moving average)

20% > maximum flow for each contactor (15 min moving average)

This is performed on a per-contactor basis.

Notes: CCP=critical control point; EBCT=empty bed contact time; UVT=ultraviolet transmittance.

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Coagulant–BAC Alert (Warning) Response

The coagulant–BAC combined process and CCP is intended to provide control for the removal of pathogenic cysts (e.g., Cryptosporidium and Giardia) from the DPR process stream. This CCP includes the processes of rapid mixing and coagulation, flocculation (perhaps) and settling, and BAC filtration. The pretreatment processes are required upstream of the filtration process to condition the cysts and other particles in the water for removal via interception on the individual carbon grains. The upstream coagulation process is also required by the U.S. EPA to receive credit for filter performance in removing pathogenic microorganisms. Because it is difficult and expensive to quantify pathogenic cyst removal across a filter (and impossible to do so in real time), achieving a target filter effluent turbidity goal is used as a surrogate. As long as the effluent turbidity from the BAC filter is less than the target value (generally 0.1 NTU), then the pathogen LRV across the filter can be reasonably assumed to have been achieved.

Figure 7.26 depicts the alert (warning) level response process for this CCP associated with a high filter effluent turbidity reading. The first step in the response process is for the plant operator or engineer to review the SCADA flow readings to verify that the processes are operating within their design loading rates. If not, then the process flow rate should be reduced to achieve the target rates and avoid stressing the unit processes beyond their design capacity. Next, staff should review the SCADA trends for individual and combined BAC filter effluent turbidity to identify if this is an isolated or plant-wide problem. Grab samples should be collected from each sampling point that indicates an elevated turbidity for validation on a bench-top turbidimeter. If the online turbidimeter readings are shown to be correct, then the affected BAC filter should be taken out of service and placed into the backwash rotation for cleaning.

If backwashing the affected filter does not resolve the high turbidity alert, then plant staff need to evaluate the coagulation process. Coagulation neutralizes the surface charges on the particles and cysts in the water so that they will effectively adhere to the individual BAC filter media grains. The first step in the coagulation assessment is to verify through SCADA that no changes in upstream pH have occurred. If they have, than a pH adjusting chemical is needed to reduce the pH into the optimum range for proper coagulation to occur. Following this, the operations staff should perform jar tests to verify the amount of coagulant to be delivered to the process and adjust the coagulant dose full-scale. Staff should then continue to monitor SCADA trends to ensure that the issue is resolved and repeat these steps if needed to resolve the issue. Other steps that can be taken to evaluate the alert include assessing background coagulant demand (via online UVT analysis), chemical dosing pump calibration, and verification of the coagulant dose control algorithm in the plant’s PLC.

Finally, long-term monitoring of filter performance, including filter run times, individual filter turbidity, head loss, filter profiles of turbidity breakthrough over time, unit filter run volumes, backwash settings, and backwash turbidity, among others, can all be used to assist in the diagnosis and repair of filter inefficiencies or issues that may trigger an alert before they escalate into failure and alarm status. It is key that operations teams collect and review such data throughout the life of the plant to better learn from past experience and use that information to assist in decision making and asset maintenance and repair.

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Coagulant–BAC Critical (Failure) Response

Figure 7.27 illustrates the critical (failure) response procedures for the coagulant–BAC CCP. The first step in the response is an automatic shutdown of the affected BAC filter and incorporation of said filter into the overall backwash queue. Following filter backwash, plant staff should notify the plant manager or supervisor of the process unit shutdown and review the alert level response procedures to confirm that the critical (failure) limit breach is real. If the breach is real, then staff should perform the following actions: • Verify the calibration status of the flow meters and online turbidity analyzers and collect

confirmatory grab samples to validate the online turbidimeter values.

• Verify that no upstream process upsets have occurred that might impact coagulant demand.

• Review SCADA trends for coagulant dose, flow, and influent pH.

• Review filter backwash history and perform additional jar tests.

• Assess the water quality of the coagulant–BAC process effluent.

• Perform incident reporting to appropriate stakeholders.

If the water quality was actually compromised during the critical limit breach, additional actions and interaction with external entities may be required.

Table 7.9. Coagulation–BAC Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level

Notes

Filtered water turbidity (NTU)

90% of maximum turbidity (15 min moving average)

maximum turbidity (typically 0.1 NTU) (15 min moving average)

Laser turbidimeter can be considered for higher resolution of turbidity.

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GAC Alert (Warning) Response

The GAC contact process step CCP is intended to reduce the concentration of organic matter in the GAC contactor effluent. By using GAC as a sorption-based process for contaminant control, it will lose its sorptive capacity over time and need to be replaced or regenerated on a regular basis. The regeneration of GAC is typically triggered by observations of organic matter breakthrough beyond a set point (e.g., 1 mg/L TOC or 50% breakthrough of C/C0) that is site-specific or state-specific. The removal of organic matter can be evaluated in one of two ways: • By measuring the percent reduction (Δ) in TOC concentration across the GAC contactor

• By measuring the percent reduction in UVT across the GAC contactor

The former can be more expensive and time consuming to run from an analysis standpoint, whereas the latter can be rapidly performed on a bench-top spectrophotometer. Both can be monitored via a continuous, online analyzer with regular, scheduled verification using grab samples for laboratory analysis. Although TOC is a direct measurement of organic matter concentration, UVT is more of a surrogate monitoring parameter. Common practice is to monitor TOC on a weekly basis, using UVT for the more frequent analysis of organic matter removal. A site-specific correlation needs to be developed between TOC and UVT for this method to work, which requires more frequent initial monitoring of TOC.

Figure 7.28 provides a depiction of the alert (warning) level response process to address a low TOC or low ΔUVT alert. The first step in the response process is to review SCADA trends for changes in TOC or UVT removal over time, as measured by influent and effluent analyzers. If possible, any observed reductions in percent removal across the GAC contactor should be correlated with DPR plant or upstream wastewater treatment plant performance (i.e., increased TOC loading onto the GAC filters). Grab samples for GAC contactor influent and effluent should be collected to allow validation of online instrument readings on bench-top analyzers. If the organics removal alert is shown to be real, then plant staff should review the flow being sent to the GAC contactor to verify the EBCT is on target and review the GAC replacement history to identify the age of the carbon in the affected contactor. If the carbon life alert is triggered based on this review, the affected contactor should be taken out of service and the media replaced with virgin or regenerated carbon.

If the EBCT and carbon life are acceptable, but organics removal performance is still low, then a forensic evaluation of the GAC contactor itself should be performed. This filter condition assessment involves taking the GAC contactor offline and performing multiple evaluation steps to identify potential problems with it other than media life. Items to be evaluated include a sieve analysis to confirm that the installed media meet the effective size and uniformity coefficient design parameters for the contactor and a media profile analysis to ensure that the proper bed depth is provided and that there are no mud balls or other deleterious matter in the carbon bed. The historical filter run length should also be assessed and a chlorinated backwash performed to control any biological growth that might have occurred in the contactor. Any issues identified through the condition assessment should be performed prior to returning the contactor to service.

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GAC Critical (Failure) Response

Figure 7.29 illustrates the critical (failure) response procedures for the GAC CCP. The first step in the response is an automatic shutdown of the affected GAC contactor and incorporation of said contactor into the overall backwash queue. Following contactor backwash, plant staff should notify the plant manager or supervisor of the process unit shutdown and review the alert level response procedures to confirm that the critical (failure) limit breach is real. If the breach is real, then staff should perform the following actions: • Verify the calibration status of the flow meters and online TOC or UVT analyzers and collect

confirmatory grab samples to validate the online analyzer values.

• Verify that no upstream process upsets have occurred that might impact organics loading to the process.

• Review SCADA trends for contactor flow and influent and effluent TOC and UVT and review carbon replacement history.

• Perform filter condition assessment and implement corrective actions.

• Assess the water quality of the GAC contactor effluent.

• Perform incident reporting to appropriate stakeholders.

If the water quality was actually compromised during the critical limit breach, additional actions and interaction with external entities may be required.

UV disinfection process targets the inactivation of pathogens in the reuse process flow. Beams of UV light generated by the lamps in the closed vessel UV reactor strike the pathogenic cysts in the water and inhibit their reproduction. If a cyst cannot reproduce, then it cannot infect a host. From a process operation standpoint, the key to the UV disinfection process is to operate the UV reactor within its validated range. Because the process does not physically remove pathogenic cysts from the process flow, the measurement of pathogen inactivation must be evaluated via performance monitors and full-scale validation. At installation, UV reactors are tested full-scale by manufacturers over a specific range of parameters. If the UV reactor is operated within an established range of tested parameters (i.e., the “validated range”), then the UV reactor can be assumed to have achieved the pathogen inactivation associated with this range.

Table 7.10. GAC Alert and Alarm Summary Monitoring Parameter

Alert Level Critical (Failure) Level Notes

TOC (mg/L) 0.4 mg/L (1 hr moving average)

0.5 mg/L (1 hr moving average)

Target for TOC taken from California Title 22 regulations.

ΔUVT% 80% of critical set point (1 hr moving average)

critical set point (1 hr moving average)

Whether ΔUVT% is a site-specific correlation to be determined from piloting or full-scale operation.

Notes: TOC=total organic carbon; UVT=ultraviolet transmissivity.

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The validated range consists primarily of a range of UV doses, as measured by UV intensity sensors in the UV reactor, and a range of flow conditions, as measured by effluent flow meters. The number of UV intensity sensors can vary from reactor to reactor depending on the particular manufacturer’s dose control algorithm. In some cases, the UVT and turbidity of the reactor influent are also measured. These parameters are used to indicate how clean the water is, which can be attributed to how easily the UV light passes through the water to the target organisms. By monitoring all of these parameters (as appropriate to the particular model of UV reactor), plant operations staff can ensure that the process is operating within its validated range and the required level of pathogen inactivation is being achieved.

Figure 7.30 depicts the response procedures associated with alert (warning) levels for the UV disinfection process for UV dose, UV reactor flow, and influent UVT. (Note that turbidity should also be included in response procedures and can be combined with the same evaluation steps as UVT. However, turbidity was not included in this diagram for simplicity.) If a low UV dose alert is triggered, the first step involves a review of existing SCADA data to compare what UV dose has been achieved over time versus the target inactivation dose. The calibration of each UV intensity sensor should be checked, and if out of calibration, the affected sensor should be replaced with a spare calibrated sensors. If the UV intensity sensors are all reading properly, then the condition of the UV reactor equipment should be assessed. This can include reviewing the lamp age data, cleaning the quartz sleeves that protect the UV lamps, replacing aged lamps, and planning for preventive maintenance on the UV lamp ballasts.

In the event of a high reactor flow alert, plant operations or engineering staff should review SCADA data for individual reactor flows and adjust them until each reactor is operating within its validated range. This may necessitate reducing the overall DPR plant flow.

Finally, if a low UVT alert (or high turbidity alert) is triggered, plant staff should review the SCADA trend of influent UVT over time to identify if any specific observations can be made. Staff should simultaneously review the performance of upstream treatment processes to ensure that they are operating properly, such that organics and other compounds that absorb UV light are being removed prior to the UV disinfection step (i.e., GAC contactors). Grab samples should also be collected to validate that the online UVT (or turbidity) measurements are accurate. If not, the affected reactor should be removed from service and the associated UVT (or turbidity) monitor recalibrated.

UV Disinfection Critical (Failure) Response

Figure 7.31 illustrates the critical (failure) response procedures for the UV disinfection CCP. The first step in the response is an automatic shutdown of the affected UV disinfection reactor. Following reactor shutdown, plant staff should notify the plant manager or supervisor of the shutdown and review the alert level response procedures to confirm that the critical (failure) limit breach is real. If the breach is real, then staff should perform the following actions:

• Verify the calibration status of the flow meters, UV intensity sensors, and online UVT and turbidity analyzers and collect confirmatory grab samples to validate the online analyzer values.

• Verify that no upstream process upsets have occurred that might impact organics loading to the process that would result in a reduced UVT or increased turbidity in the reactor influent.

• Review SCADA trends for reactor flow, influent UVT, and UV dose, and review reactor maintenance history (lamp age, ballast repair history).

• Assess the water quality of the UV reactor discharge.

• Perform incident reporting to appropriate stakeholders.

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If the water quality was actually compromised during the critical limit breach, additional actions and interaction with external entities may be required.

Table 7.11. UV Disinfection Alert and Alarm Summary Monitoring Parameter Alert Level Critical (Failure) Level Notes

UV dose (mJ/cm2) (per UV reactor)

<110% of validated dose for targeted LRV (15 min moving average)

< validated dose for targeted LRV (15 min moving average)

Flow (gpm) <110% of validated flow for reactor. (15 min moving average)

< validated flow for reactor (15 min moving average)

minimum flow at which individual reactor is validated

UVT% <5% > minimum UVT% (15 min moving average)

minimum UVT% (15 min moving average)

UVT% minimum at which reactor has been sized and validated

Turbidity (NTU) 90% of maximum turbidity (15 min moving average)

maximum turbidity (typically 0.1 NTU) (15 min moving average)

Laser turbidimeter can be considered for higher resolution of turbidity.

Notes: LRV=log removal value; UV=ultraviolet; UVT=ultraviolet transmissivity.

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Figure 7.20. Ozone low CT and low flow alert procedures.

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Figure 7.21. Ozone low CT and low ozone residual alert procedures.

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Figure 7.22. Ozone low CT and low UVT alert procedures.

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Figure 7.23. Ozone low CT critical failure response.

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Figure 7.24. Ozone–BAC alert response procedures.

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Figure 7.25. Ozone–BAC critical failure response procedures.

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Figure 7.26. Coagulant–BAC alert response procedures.

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Figure 7.27. Coagulant–BAC critical failure response procedures.

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Figure 7.28. GAC alert response procedures.

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Figure 7.29. GAC critical failure response procedures.

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Figure 7.30. UV disinfection alert response procedures.

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Figure 7.31. UV disinfection critical failure response procedures

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7.5 Equipment Design Standards and Guidance for DPR Systems

7.5.1 Water Quality Instrumentation

The success of the CCP approach relies on the effective design and reliability of water quality and other process instrumentation. A discussion of instrumentation and reliability was provided in Chapter 5; the discussion that follows is focused more on the function of the instruments, ranges of normal operation, and process of identifying instrument and process failure.

7.5.1.1 Instrumentation Analyzer Concepts

The type and placement of an instrument may have an effect upon the measured result and subsequent output to the PLC and SCADA systems. Many types of instruments are used to determine water quality performance, and a cross-section of these have been recommended as a part of the CCP response procedures. The following important performance characteristics are associated with all instruments.

Accuracy

Accuracy is the measure of the output of the instrument against a reference or standard method of measurement. In practice, accuracy also includes the effects of variations in water quality (pH, temperature, and chemical constituents that may interfere with the method of measurement). The accuracy of an analyzer is of particular importance in terms of its resolution. For example, a turbidity analyzer is limited in monitoring the filtrate of MF and UF membranes because it does not have a level of accuracy sufficient to measure the very low levels that are produced. More accurate laser turbidimeters are often used to provide a higher resolution.

Accuracy is also managed by good instrument maintenance, validation, and calibration. Validation is a term often used to mean cross-checking an analyzer against a known reference – for example, cross-checking a chlorine analyzer against a grab sample and test. Calibration is the deliberate resetting of an analyzer against known standards. A thorough program of validation and calibration is essential for the success of this process.

Response Time

Instruments and analyzers may have a nearly continuous output that is updated multiple times per second, or they can be configured in a semi-batch combination of flushing, reagent addition, reaction, and measurement, which can delay the response of the instrument for several minutes as the sample is processed.

Even though an analyzer may have a continuous output and theoretically a fast response time, the method of sample measurement may delay the response of the device. Sample sensors may be placed in line or connected by a sample line (sidestream). The addition of a sidestream sample line between the process and the analyzer and the sensor delays response time and may cause erroneous readings if the sample line loses flow.

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Examples of analyzers with continuous output include:

pH

conductivity

chlorine residual (amperometric)

UV transmissivity

Example of analyzer with intermittent output:

TOC (online)

Examples of analyzers with semi-batch operation include:

chloramine analyzers

chlorine residual (DPD Method) nitrate analyzers

Where possible, design should try work to minimize the delay, taking into account other design considerations such as analyzer access for maintenance and any specific climate control or housing requirements or appropriate configuration.

Drift

Drift is an important consideration in the use of analyzers. Some methods and types of sensors are more prone to drift after recalibration than others. Drift can be caused by the accumulation of foulant on the sensor or a change in electrical potential across the sensor and is a primary consideration that determines when recalibration is needed. In some cases a simple prefilter can help manage this; in other cases prefiltration interferes with the actual reading. Drift and sample handling can be potential issues with a number of analyzers. Regular review of data trending can often determine where drift is occurring, especially if instrument maintenance, cleaning, and calibration returns the analyzer to previous readings without any change to the process performance. An assessment of regular drift should be used to inform the appropriate timing of regular system calibration. This underlines the importance of a thorough validation and calibration program.

Reagents

Reagents are used to improve the accuracy of the analyzer and condition the sample to allow more accurate measurement of the water quality parameter desired. Reagents can include buffer solutions, pH adjustment solution, and specific chemicals that react with the sample, allowing subsequent measurement. As a general statement, a higher level of accuracy and a more stable output are associated with the use of a chemical reagent. The drawback is the cost and maintenance associated with the replacement of the chemical reagents, in addition to the longer response time.

Where reagents are required for analyzers, it is important to maintain inventory, ensuring enough is available for operation and that reagents used have not expired. Out-of-date reagents may lead to erroneous readings.

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Interference

With many of the sample measurements, there is the possibility of interference from other constituents in the water, which may lead to an erroneous result. Interferences are specific to the constituent being measured or the analyzer being used, and it may be necessary to validate or correlate the output of the instrument against a laboratory method to assure that the result is representative.

A careful assessment of analyzers should be considered in the design of the plant. In the case of most instruments assessed as monitoring parameters for CCPs, interference is not a significant issue for the location of the analyzer relative to its specific CCP.

7.5.1.2 Process Control and Process Compliance

Regulatory language established at the federal or state level usually establishes when a water quality violation is determined. Implementation of the regulatory requirements, however, may require additional effort.

For example, the regulatory compliance requirement for turbidity of the effluent from a membrane filter is established in the following manner:

95% of samples must have less than 0.1 NTU.

No two consecutive samples may have greater than 0.15 NTU.

A sample frequency of at least once every 15 minutes is required.

This example is a regulatory one, but for the selection of CCPs, there are also specific considerations that must be addressed when the requirements of monitoring and control are integrated into a plant control system.

The term upper control limit (UCL) is used to describe the exceedance criteria for a violation from a process compliance perspective. The term lower control limit (LCL) is any other criterion established for monitoring or operation that is used by the utility. The UCL is the process compliance limit, whereas the LCL is a process control limit.

In the case of the CCP approach, the CCP monitoring point is synonymous with the UCL. Other important operational parameters for the correct operation of the plant, maintenance of flow production, and protection of assets are referred to as COPs. These are synonymous with LCLs.

Configuration of control limits into control systems (CCP, COP, LCL, or UCL) for operation and compliance may require additional information in order to facilitate implementation. From a programming perspective, configuration of alarms to satisfy regulatory requirements can be challenging, as interpretation of the alarm requirement does not always match the available information, commands, or common algorithms that can be configured in the PLC. The following section provides an introductory review as to how data from analyzers can be configured to create alarms that track process activity in a more realistic manner.

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7.5.2 Design Issues – Basic Questions to Validate Design

In the development of a control system that incorporates alert (warning) and critical (failure) analysis, a review of the system should be performed during design to assure that it is possible to perform testing and repairs that are likely to be encountered. The following questions are typical issues that may be encountered during a review of the operation and development of control strategies.

Is the system designed to minimize nuisance and transitional alarms upon system start/stop and during intermittent processes?

Some systems require a period of time to attain stable operation during startup and shutdown. If required, provisions to waste effluent water (e.g., filter to waste) that do not meet specified requirements may be required for the process. For example, filtration systems may use a filter-to-waste step, and RO systems commonly operate to waste until permeate conductivity levels meet requirements.

Is the water quality sampling point for compliance consistent with the definition of a unit?

A unit is defined as the point at which there is a common feed water quality and a common filtrate quality that can be completely isolated by valves for the purposes of testing and operation. A unit also has the capability to be placed into or removed from service as required and has a defined production capacity and water quality characteristics.

Although the definition of a unit should be self-explanatory, consolidation of equipment and various approaches used in design and operation of a system may lead to a different interpretation from a compliance perspective. For example, a group of membranes share a common filtrate turbidity meter; however, integrity testing is performed on a smaller section of membranes. From a unit perspective, the unit is defined as including the units that form the larger group and not the smaller section of membranes.

Some units may include intermediate sampling points (e.g., stage flow and conductivity analyzers) that are used for process monitoring purposes. These devices may facilitate identification of a section of a unit for alert purposes or other process control–related activities.

In the case of the processes reviewed, a number of the designated CCPs are configured in units, including:

MF units, commonly in trains of parallel units.

RO units

BAC filters

GAC filters

UV reactors (for disinfection and advanced oxidation)

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In these cases, an alert or critical failure is often related to an individual unit rather than the entire process barrier. This provides an additional level of safety, as the failure of one unit alone may be ameliorated by dilution of parallel, compliant units. In addition, loss of production of a single unit may have a minimum impact to overall production. Control and operational response procedures have been written with this approach.

It should be noted that process monitors at CCPs should be combined with double or triple validation. That is, either consider having redundant analyzers that look for the same parameter or redundant analyzers that look for different parameters (e.g., TOC and conductivity analyzers for RO permeate). By having double or triple validation, the likelihood of failing to notice a failure event drops substantially.

Can the staff perform confirmation testing for alerts or critical alarms without compromising the final product water quality? (e.g., Are there filter-to-waste or other diversion strategies that allow the unit to be operated for a period of time?)

Some systems may not have the required design elements to facilitate testing for diagnosis and repair of faulty equipment (membranes, valves, piping), which resulted in the alert (warning) or critical (failure) alarm condition.

Filter to waste should be considered for all filtration processes, including MF and UF. RO typically is designed with a permeate-to-waste stream, which should be considered in the design. AOP and UV disinfection processes should consider an off-specification water diversion prior to final product delivery, or, because these are used toward the end of the process, a final product tank (or engineered storage) can be used to divert any off-specification product.

What is the response time (delay) between the measurements of the sample until notification?

For this example, the plausible configuration is a disinfection approach using a large reservoir with significant detention time. If there is a significant time between the application of a treatment (i.e., chemical) and the measurement of a compliance residual, a violation may occur. Thus, in order to assure that compliance is continuously met, an intermediate sample and dosing point or surrogate measure may be required to assure that targeted residuals are obtained. Another example of this issue involves the use of an online turbidimeter for the continuous monitoring of MF–UF water quality in the absence of the continuous direct integrity test as a historical practice.

Under most conditions a time period of five to 15 minutes or less is acceptable based upon instrument response time and historical practices for the measurement of water quality for compliance. Consideration of sample supply tubing diameter and flow rate should be considered to ensure that the delay time from sample point to the analyzer is understood and minimized. Recent research into this area can provide additional insight into response time and engineered storage buffers (Salveson et al., 2015).

If calculations are required to determine process compliance, are the calculations performed at a frequency acceptable for regulatory compliance?

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Some systems may calculate regulatory compliance parameters using historical data that are stored. In this case a noncompliance would be determined after the fact and result in a violation. However, most alarm systems will be designed such that the range of acceptable performance is predetermined by calculating various compliance scenarios such that a historical analysis of data would not result in any “surprise” violations.

7.6 References

Frenkel, V.; Cohen, Y. New Techniques for Real-Time Monitoring of Membrane Integrity for Virus Removal: Pulsed-Marker Membrane Integrity Monitoring System. WRRF-09-06b, WateReuse Reserach Foundation, Alexandria, VA, 2014.

Gerrity, D.; Gamage, S.; Holady, J. C.; Mawhinney, D. B.; Quiñones, O.; Trenholm, R. A.; Snyder, S. A. Pilot-Scale Evaluation of Ozone and Biological Activated Carbon for Trace Organic Contaminant Mitigation and Disinfection. Water Res. 2011, 45(5), 2155–2165.

Jacangelo, J. Standard Methods for Integrity Testing and on-Line Monitoring of Nf and Ro Membranes. WRRF-12-07, WateReuse Research Foundation, Alexandria, VA (ongoing research).

Kruithof, J. C.; Kamp, P. C.; Martijn, B. J. UV/H2O2 Treatment: A Practical Solution for Organic Contaminant Control and Primary Disinfection. Ozone-Sci. Eng. 2007, 29(4), 273–280.

Pisarenko, A. N.; Stanford, B. D.; Yan, D.; Gerrity, D.; Snyder, S. A. Effects of Ozone and Ozone/Peroxide on Trace Organic Contaminants and NDMA in Drinking Water and Water Reuse Applications. Water Res. 2012, 46(2), 316–326.

Plumlee, M. H.; López-Mesas, M.; Heidlberger, A.; Ishida, K. P.; Reinhard, M. N-Nitrosodimethylamine (NDMA) Removal by Reverse Osmosis and UV Treatment and Analysis Via LC–MS/MS. Water Res. 2008, 42(1–2), 347–355.

Rosenfeldt, E. J.; Linden, K. G. The Roh,UV Concept to Characterize and the Model UV/H2O2 Process in Natural Waters. Environ. Sci. Technol. 2007, 41(7), 2548–2553.

Salveson, A.; Steinle-Darling, E.; Trussell, S.; Trussell, B.; McPherson, L. Guidelines for Engineered Storage for Direct Potable Reuse: Final Report of Project WRRF-12-06. WateReuse Research Foundation, Alexandria, VA, 2015.

Serna, M.; Trussell, R. S.; Gerringer, F. W. Ozone Pretreatment of a Non-Nitrifed Secondary Effluent before Microfiltration. WRRF-10-11. WateReuse Research Foundation, Alexandria, VA, 2014.

Sgroi, M.; Roccaro, P.; Oelker, G. L.; Snyder, S. S. N-Nitrosodimethylamine (NDMA) Formation at an Indirect Potable Reuse Facility. Water Res. 2015, 70, 164–183.

Snyder, S. A.; Korshin, G.; Gerrity, D.; Wert, E. Use of UV and Fluorescence Spectra as Surrogate Measures for Contaminant Oxidation and Disinfection in the Ozone/H2O2 Advanced Oxidation Process. WateReuse Research Foundation, Alexandria, VA, 2013, p 361.

Stefan, M. I.; Bolton, J. R. Mechanism of the Degradation of 1,4-Dioxane in Dilute Aqueous Solution Using the UV Hydrogen Peroxide Process. Environ. Sci. Technol. 1998, 32(11), 1588–1595.

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Steinle-Darling, E.; Zedda, M.; Plumlee, M. H.; Ridgway, H. F.; Reinhard, M. Evaluating the Impacts of Membrane Type, Coating, Fouling, Chemical Properties and Water Chemistry on Reverse Osmosis Rejection of Seven Nitrosoalklyamines, Including NDMA. Water Res. 2007, 41(17), 3959–3967.

U.S. EPA. Long Term 2 Enhanced Surface Water Treatment Rule Toolbox Guidance Manual. EPA 815-R-09-016. Office of Water, Washington, D.C., 2010.

EPA (2005). USEPA Membrane Filtration Guidance Manual. Washington, DC, United States Environmental Protection Agency Office of Water.

Walker, T.; Roux, A. Australia’s Western Corridor Recycled Water Project – Regulation of an Indirect Potable Recycling Scheme Down Under. AWWA/AMTA Membrane Technology Conference. Memphis, TN, American Membrane Technology Association and American Water Works Association, 2009.

Wert, E. C.; Rosario-Ortiz, F. L.; Snyder, S. A. Using Ultraviolet Absorbance and Color to Assess Pharmaceutical Oxidation During Ozonation of Wastewater. Environ. Sci. Technol. 2009, 43(13), 4858–4863.

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Appendix A

Literature Review of Contaminant

Concentrations in Wastewater Effluents

A literature review was conducted to examine broadly the range of potential contaminant concentrations that may be present in wastewater effluents. The purpose of this review was to provide a basis for checking the assumptions and starting concentrations (based on full-scale and pilot-scale data from participating utilities) in the risk assessment document to ensure risks were not significantly higher (or lower) than had been assumed during the assessment.

The literature review consisted of studies documenting contaminants from the risk analysis in primary, secondary, and tertiary effluents. The range of literature concentrations is presented in Table A.1. What is not shown here is a list of maximum contaminant concentrations from the final recycled water effluent from four full-scale facilities and one pilot used in this project, which form the basis of the risk register. The data presented in Table A.1 also provide a column of “maximum secondary or tertiary concentrations” for each contaminant. Primary effluent data were excluded from this in an effort to recognize that direct potable reuse (DPR) facilities were not likely to rely upon primary wastewater. However, where final recycled water effluent exceeded the literature values for the wastewater effluents (e.g., tritium), the recycled water values were used to populate the maximum concentration column. In addition, when a specific contaminant concentration appeared abnormally high, the source document was checked to determine if this was the result of a specific industrial application. In those cases, the industrial concentrations were also excluded. This highlights the importance of conducting a sewershed-specific risk assessment rather than relying on broad ranges of concentrations from the literature.

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Table A.1. Summary of Contaminant Concentrations in Wastewater Effluents asReported in the Literature Literature Concentration

Primary Effluent

Secondary Effluent

Tertiary Effluent

Microorganisms UNITS MAX Sec./ Tert. CONC.

Cryptosporidium oocysts/L 10.0 67–333 1

0–9 1 0–10 13

0–0.4 1

Giardia lamblia cysts/L 16,500.0 533–2,033 1

0–32 1 50–16,500 13

0–2.1 1

Heterotrophic plate count (HPC)

CFU/mL

Legionella 340.0 340 33 2.5–6 33

Total Coliforms (incl. fecal coliform and E. Coli)

CFU/mL 6896.0 29,000–48,000 1

~10,000 2

7600

33

41–78 1

~100 2 1.1–4.8 1

~1 2

Viruses (enteric) - PFU/mL 13,000.0 0.6–13,000 13

Bacteria (Salmonella, Shigella, etc.)

PFU/mL 21 24

Inorganics and metals

Aluminum (Al) mg/L 9.7 0.55–0.799 38

48 4 0.01 27a 0.060–0.280 38

0.03 27b

Antimony (Sb) μg/L 17.1 90 30 ND 39 0.327–5.420 5a

0.326–6.110 5b

Arsenic (As) μg/L 1850.0 1 30

442,000 32

1850 32

0.8–1 38 50

39

0.919–2.866 5a

0.832–3.569 5b 100 32

Asbestos MFL 0.2 <0.2 48

Barium (Ba) μg/L 468.0 0.001 27a 0–5.800 5a

0–4.610 5b

0.06 27b

Beryllium (Be) μg/L 14.1 5 30 0-0.041 5a

0–0.086 5b

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Cadmium (Cd) μg/L 55.0 1–46

20

2.3

25 14 30

8.4 3 5–10 19 8 24 1.5 25

4.86 39

0–0.040 5a

0–0.050 5b

Chloride (Cl) mg/L 2640.0 276–553 23

213 24 97–135 15

Chromium (Cr) μg/L 350.0 37–170

20 25

25

520 30

55.2 3 50–140 19 20 25

0.699–4.430 5a

0.650–4.190 5b

Copper (Cu) μg/L 368.0 30–81 20

58 25

40 30

146 3 10–120 19 123 24 33 25

0.0027 31

Cyanide (Cn) mg/L 0.7 22 30 0.053–0.68 21

<0.005 48

Fluoride (F) mg/L 5.4 0.39–0.72

23 2 24 ND–0.1 48

Iron (Fe) μg/L 108,000.0 450

25 <100 4

310–990 19

380 25

30–79 15

Lead (Pb) μg/L 190.0 31 25 139 3 70–190 19 27 25

0.052–0.951 5a

0.037–1.186 5b

Manganese (Mn) μg/L 15,600.0 26 25 80–310 19 0-1.050 5a

0–2.760 5b

Mercury (Hg) μg/L 5.1 0.3 30

5.1 3 ND–0.1 48

Nickel (Ni) μg/L 2140.0 15–650

20

600 25

154 3 40–130 19 430

25

2.451–9.170 5a

2.451–9.510 5b

Nitrate (NO3 as N) mg/L 48.8 0.082–0.67 18

0.0006 4

1390 10

5900–14,000 12

0.11–32.5 18

0.57 2

8.1–11.7 15

Nitrate (as NO3) mg/L 216.0 ND–0.042 15

Nitrite (NO2 as N) μg/L 8880.0 20 12 <50-<1000 18

0.08 4 350 10 10–209 12

<130–5300

18

Silver (Ag) μg/L 19.8 9 30 <0.5 6 6.23 39

<1 48

Sulfate (SO2) mg/L 2600.0 107 24 68–99 15

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223 31

Total Nitrate/Nitrite (as N) mg/L 34.2 48.4 12

0.01–42.3

12 9.9–14.1 15

Perchlorate (ClO4) mg/L 14.1 0.00131–0.107 34

ND 15

Selenium (Se) mg/L 0.3 2.28 32

0.03 32

0.00328 39 0.03 32

Thallium (Tl) μg/L 187.0 2 30 ND 39

<0.011 42 ND–0.5 48

Zinc (Zn) μg/L 2460.0 130–280

20

380

25 57

30

887 3 60–200 19 270

25

5.374–71.700 5a

5.471–57.550 5b

0.0563 31

Radionuclides

Uranium (U) μg/L 2.8 66 mBq/L 26

0–1.050 5a

0–2.760 5b

130 mBq/L 26

Combined Radium - 226+228 pCi/L 2.6 NF–2.6 44 <1 48

Gross Alpha particle activity (excluding radon & uranium)

mBq/L pCi/L (used for other utilities)

129.0 15–45 26

31–90 26 27–129 26

Gross Beta particle activity mBq/L pCi/L (used for other utilities)

21.9 554–878 26

ND–5.4 pCi/L 44

477–696

26

Strontium-90 (Sr-90) mg/L 1.0 0.3 27a 0.96 27b

Tritium pCi/L 7390.0 <1000 48

VOCs

Benzene (C6H6) μg/L 4.3 1 30 0.12–0.30 7

<0.005–0.06 8 0 22

<10–44,000 28

ND 39

<0.005 8

Carbon Tetrachloride (CCl4) μg/L 38.0 0.13–0.17 7

38 22

<1000–44,000 28

ND 39 <1 42

ND-0.5 48

Chlorobenzene mg/L 0.0 <0.01–0.01 28

ND–0.0005 48

1,2-Dichlorobenzene mg/L 0.5 <1-1 28 ND 39

ND–0.5 48

1,4-Dichlorobenzene ng/L 500.0 1300 17

3000 22 10–2500 28

ND–500 48

Water Environment & Reuse Foundation 311

75,000 39

1,1-Dichloroethane (1,1-DCA) μg/L 3.5 3.5 36 <1 42 ND–0.5 48

1,2-Dichloroethane μg/L 2.0 2 22 ND–0.5 48

1,1-Dichloroethylene (1,1-DCE)

μg/L 10.0 <10-20 37

<10 37 average of 5 37 ND 39

ND–0.5 48

cis-1,2-Dichloroethylene μg/L 0.5 ND 39 ND–0.5 48

trans-1,2-Dichloroethylene μg/L 0.5 0 22 ND 39 ND–0.5 48

Dichloromethane mg/L 0.2 0.003–0.23 28

ND–0.0005 48

1,3-Dichloropropene μg/L 0.5 0 or no data 40 <1 42

ND–0.5 48

1,2-Dichloropropane μg/L 0.5 0 22 ND–0.5 48

Ethylbenzene μg/L 0.5 0.11–0.53 7

<0.005–0.12 8 2 22

ND 39

<0.005–0.13 8

ND–0.5 48

Methyl-tert-butyl ether (MTBE)

μg/L 123.3 <0.5–123.3

41 <4 43

Monochlorobenzene μg/L same as chlorobenzene

0.5 ND 39 ND–0.5 48

Styrene μg/L 0.5 ND 39 ND–0.5 48

1,1,2,2-Tetrachloroethane mg/L 0.0 <0.001 42 ND–0.0005 48

Tetrachloroethylene μg/L 0.5 4.39-5.9 38

1–69 22

100–36,000 28 1.18–1.5 38

3.50 38

ND–0.5 48

Toluene μg/L 2.7 20 30 0.17–0.81 7

<0.005–2.7

9 2–110 22

10–12,000

28

0.52–1.9 8

1,2,4 Trichlorobenzene μg/L 0.5 <LOQ 7

10–109 28 ND–0.5 48

1,1,1-Trichloroethane μg/L 0.5 2-57 22 <1

42 ND–0.5 48

1,1,2-Trichloroethane μg/L 0.5 <1 42 ND–0.5 48

Trichloroethylene μg/L 1.3 1.5-1.7 38

2-3 22 500–13,000 28

1.12 38

1.26 38

Trichlorofluoromethane (freon)

mg/L 0.0 <0.004 43 ND–0.0005 48

1,1,2-Trichloro-1,2,2-Trifluoroethane (freon 113)

μg/L 0.5 ND 45 ND–0.5 48

312 Water Environment & Reuse Foundation

Vinyl chloride mg/L 0.0 <0.005 42 ND–0.0005 48

Xylenes μg/L 1.2 0.10–1.21 7

<0.005–0.45 8 10–2800 28

<0.005–0.35 8

SOCs

Acrylamide

Alachlor mg/L 0.0 <0.0015–0.0015 28

ND 36

ND–0.00005 48

Atrazine mg/L 0.2 <0.2 28 ND–0.0001 48

Bentazon μg/L 62.5 62.5 46 ND–1 48

Benzo(a) Pyrene μg/L 0.0 0.01–0.04 7

<200 28 ND

39

ND–0.1 48

Carbofuran μg/L 1.0 4200 50 ND–1 48

Chlordane mg/L 120.0 ND 39

<0.000005

43

ND–0.0001 48

Dalapon μg/L 5.5 0.000704 51 ND–1 48

Dibromochloropropane (DBCP)

μg/L 0.0 0.01 47 ND–0.01 48

Di(2-ethylhexyl)adipate [Bis(2-ethylhexyl) adipate]

μg/L 54.0 54 55 ND–2 48

Di(2-ethylhexyl)phthalate mg/L 0.3 <0.25–0.25 28 ND 39

ND–0.002 48

2,4-D mg/L 0.0 0.02275 42 ND–0.0005 48

Dinoseb μg/L 0.2 ND–0.05 48

Diquat μg/L 4.0 1630 52 ND–4 48

Endothall μg/L 45.0 ND–45 48

Endrin μg/L 0.3 0 22 ND–0.03 48

Epichlorohydrin (ECH)

Ethylene Dibromide (1,2-Dibromoethane)

μg/L 0.0 ND–0.01 48

Glyphosate (Roundup) μg/L 25.0 0–1 54 ND–25 48

Heptachlor μg/L 0.4 0.02–0.447 22 ND 39

ND–0.01 48

Heptachlor Epoxide μg/L 0.0 0.018–0.03 22 ND 39

ND–0.01 48

Hexachlorobenzene mg/L 0.1 <0.005–0.005 28

ND–0.1 48

Water Environment & Reuse Foundation 313

Hexachlorocyclopentadiene mg/L 0.1 <0.001 42 ND–0.1 48

Lindane μg/L 25.8 41.8 29

25.8 29 ND–0.1 48

Methoxychlor μg/L 0.8 0.84 53 ND–0.1 48

Molinate μg/L 0.1 2220 49 ND–0.1 48

Oxamyl μg/L 2.0 ND–2 48

Pentachlorophenol μg/L 148.0 0.04–0.21 7

3–148 28 ND–0.1 48

Picloram μg/L 0.5 ND–0.5 48

Polychlorinated Biphenyls ng/L 500.0 ND 39 <500

42 ND 48

Simazine mg/L 4.0 0.1–4 28 ND–0.1 48

Thiobencarb (Benthiocarb) μg/L 0.5 ND–0.5 48

Toxaphene μg/L 2.9 87.5 29

2.9 29 ND 39 ND–1 48

2,3,7,8-TCDD (Dioxin) pg/L 5.0 ND 39 <5 42 ND–5 48

2,4,5-TP (Silvex) μg/L 0.5 ND–0.5 48

DBPs and Disinfectants

Bromate (BrO3) μg/L 6.7 ND–6.7 16 ND–5 48

Chloramines (as Cl2) mg/L 7.0 10 12 4.1–7 12

Chlorine (as Cl2) mg/L 302.5 1.7–18 12 3.6 48

Chlorine dioxide (as ClO2)

Chlorite μg/L 10.0 ND–10 48

NDMA ng/L 1700.0 3–11 11

0.89–1 14 <0.28–0.3 14 88–480 15

Haloacetic acids (five) μg/L 480.0 ND 48

Dichloroacetic acid μg/L 76.2 ND-1 48

Trichloroacetic acid μg/L 17.5 ND-1 48

Monochloroacetic acid μg/L 1.0 ND-1 48

Total Trihalomethanes (THMs)

μg/L 526.0 0 12a 2–57 11 2–10 12a <10–526 12b

29–93 15

ND–0.5 48

Bromodichloromethane μg/L 44.3 0.12–44.25 7 1–3 22

ND–0.5 48

Bromoform μg/L 110.0 0.19–70.79 7 3–110 22

ND–0.5 48

Dibromochloromethane μg/L 80.3 0.14–80.31 7 2–15 22

ND–0.5 48

Chloroform μg/L 136.0 5 30 3–45 22 ND–0.5 48

Physical parameters

314 Water Environment & Reuse Foundation

Color 50.0

Corrosivity S.I 1.0

Odor Total Odor Number (TON)

3.0

pH 10.0 7.67 4 10 7.0

9 8–9.0 24 7.4 14 6.9–7.1 15

TDS mg/L 7530.0 1000 17

450–800 24 496–586 15

Turbidity NTU 5.2 5.2 9 0.64–1.11 15

1.7 14

0.16–0.43 15

Water Environment & Reuse Foundation 315

Table Footnotes/Sources Cited

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2. Zhang, K.; Farahbakhsh, K. Removal of native coliphages and coliform bacteria from municipal wastewater by various wastewater treatment processes: Implications to water reuse. Water Res. 2007, 41, 2816–2824.

3. Santos, A.; Judd, S. The fate of metals in wastewater treated by the activated sludge process and membrane bioreactors: A brief review. J. Environ. Monitor. 2010, 12, 110–118.

4. Obek, Erdal; Sasmaz, A. Bioaccumulation of Aluminum by Lemna gibba L. from Secondary Treated Municipal Wastewater Effluents. B. Environ. Contam. Tox. 2011, 86, 217–220.

5. Arevao, J.; Ruiz, L. M.; Perez, J., Moreno, B.; Gomez, M.A. Removal Performance of heavy metals in MBR systems and their influence in water reuse. Water Sci. Technol. 2013, 67(4), 894–900.

6. Sasmaz, A.; Obek, E. The accumulation of silver and gold in Lemna gibba L. exposed to secondary effluents. Chemie der Erde 2012, 72, 149–152.

7. Barco-Bonilla, N.; Romero-Gonzalez, R.; Plaza-Bolanos, P.; Vidal, L. M.; Castro, A. J.; Martin, I.; Salas, J. J.; Frenich, A. G. Priority organic compounds in wastewater effluents from the Mediterranean and Atlantic basins of Andalusia (Spain). Environ. Sci. Proc. Impacts 2013, 15, 2194–2203.

8. Fatone, F.; Fabio, S. D.; Bolzonella, D.; Cecchi, F. Fate of aromatic hydrocarbons in Italian municipal wastewater systems: An overview of wastewater treatment using conventional activated-sludge processes (CASP) and membrane bioreactors (MBRs). Water Res. 2011, 45, 93–104.

9. Hatt, J. W.; Lamy, C.; Germain, E.; Tupper, M.; Judd, S. J. NDMA formation in secondary wastewater effluent. Chemosphere 2013, 91, 83–87.

10. Huang, H.; Wu, Q.; Hu, H.; Mitch, W.A. Dichloroacetonitrile and Dichloroacetamide Can Form Independently during Chlorination and Chloramination of Drinking Waters, Model Organic matters, and Wastewater Effluents. Environ. Sci. Technol. 2012, 46, 10624–10631.

11. Krasner, S.; Westerhoff, P.; Chen, B.; Rittman, B. E.; Amy, G. Occurrence of Disinfection Byproducts in United States Wastewater Treatment Plant Effluent. Environ. Sci. Technol. 2009, 43, 8320–8325.

12. Rebhun, M.; Heller-Grossman, L.; Manka, J. Formation of Disinfection Byproducts during Chlorination of Secondary Effluent and Renovated Water. Water Environ. Res. 1997, 69(6), 1154–1162.

13. Danielson, R. E.; Pettegrew, L. A.; Soller, J. A.; Olivieri, A. W.; Eisenberg, D. M.; Cooper, R. C. A Microbiological comparison of a drinking water supply and reclaimed wastewater for direct potable reuse. Paper presented at the Joint AWWA and WEF Water Reuse 1996 Conference, San Diego, CA, January 1996.

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14. Sundaram, V.; Emerick, R. W.; Shumaker, S. E. Field evaluation of MF-Ozone-BAC process train for the removal of microconstituents from wastewater effluent. 24th Annual WateReuse Symposium, Seattle WA, 2009.

15. Halliwell, D. et al. Water Reuse Research Foundation Project #09-03. Utilization of Hazard Analysis and Critical Control Points Approach for Evaluating Integrity of Treatment Barriers for Reuse. WRRF Final Report 2014.

16. Kim, H. S.; Yamada, H.; Tsuno, H. Control of bromate ion and brominated organic compounds formation during ozone/hydrogen peroxide treatment of secondary effluent. Water Sci. Technol. 2006, 53(6) 169–174.

17. Drewes, J. E. et al. Water Reuse Research Foundation Project #03-014. Development of Indicators and Surrogates for Chemical Contaminant Removal during Wastewater Treatment and Reclamation. WRRF Final Report 2008.

18. Stephenson, R.; Oppenheimer, J. Fate of Pharmaceuticals and Personal Care Products through Municipal Wastewater Treatment Processes. WERF Final Report 2007.

19. El-Nennah, M.; El-Kobbia, T. Evaluation of Cairo Sewage Effluents for Irrigation Purposes. Environ. Pollut. B. 1983, 5, 233–245.

20. Nielsen, J. S.; Hrudey, S. E. Metal Loadings and Removal at a Municipal Activated Sludge Plant. Water Res. 1983, 17(9) 1041–1052.

21. Wild, S. R.; Rudd, T.; Neller, A. Fate and effects of cyanide during wastewater treatment processes. Sci. Total Environ. 1994, 156, 93–107.

22. Stubin, A. I.; Brosnan, T. M.; Porter, K. D.; Jimenez, L.; Lochan, H. Organic Priority Pollutants in New York City Municipal Wastewaters: 1989-1993. Water Environ. Res. 1996, 68(6), 1037–1044.

23. Vengosh, A.; Pankratov, I. Chloride/Bromide and Chloride/Fluoride Ratios of Domestic Sewage effluent and Associated Contaminated Ground Water. Groundwater 1998, 36(5), 815–824.

24. Emongor, V. E.; Ramolemana, G. M. Treated sewage effluent (water) potential to be used for horticultural production in Botswana. Phys. Chem. Earth 2004, 29, 1101–1108.

25. Karvelas, M.; Katsoyiannis, A.; Samara, C. Occurrence and fate of heavy metals in the wastewater treatment process. Chemosphere 2003, 53, 1201–1210.

26. Camacho, A. Behavior of natural radionuclides in wastewater treatment plants. J. Environ. Radioactiv. 2012, 109, 76–83.

27. Xu, P.; Bellona C.; Drewes, J. E. Fouling of nanofiltration and reverse osmosis membranes during municipal wastewater reclamation: Membrane autopsy results from pilot-scale investigations. J. Membrane Sci. 2010, 353, 111–121.

28. Marti, N.; Aguado, D.; Segovia-Martinez, L.; Bouzas, A.; Seco, A. Occurrence of priority pollutants in WWTP effluents and Mediterranean coastal waters of Spain. Mar. Pollut. Bull. 2011, 62, 615–625.

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29. Petrasek, A. C.; Kugleman, I. J.; Austern, B. M.; Pressley, T. A.; Winslow, L. A.; Wise, R. H. Fate of Toxic Organic Compounds in Wastewater Treatment Plants. J. Water Pollut. Control Fed. 1983, 55(10), 1286–1296.

30. Slater, C. S.; Ahlert, R. C.; Uchrin, C. G. Applications of Reverse Osmosis to Complex Industrial Wastewater Treatment. Desalination 1983, 48, 171–187.

31. Hamoda, M. F.; Al-Ghusain, I.; Al-Mutairi, N. Z. Sand filtration of wastewater for tertiary treatment and water reuse. Desalination 2004, 164, 203–211.

32. Mavrov, V.; Stamenov, S.; Todorova, E.; Chmiel, H.; Erwe, T. New hybrid electrocoagulation membrane process for removing selenium from industrial wastewater. Desalination 2006, 201, 290–296.

33. Bataller, M.; Veliz, E.; Fernandez, L.A.; Hernandez, C.; Fernandez, I.; Alvarez, C.; Sanchez, E. Secondary Effluent Treatment with Ozone. Conference Proceedings from IOA 17th World Ozone Congress, Strasbourg, 2005.

34. Qin, X.; Zhang, T.; Gan, Z.; Sun, H. Spatial distribution of perchlorate, iodide, and thiocyanate in the aquatic environment of Tianjin, China: Environmental source analysis. Chemosphere 2014, 111, 201–208.

35. Agency for Toxic Substances and Disease Registry (ATSDR). Toxicological profile for 1,1-Dichloroethane. (Draft for Public Comment). U.S. Department of Health and Human Services, Public Health Service. Atlanta, GA, 2013.

36. Robles-Molina, J.; Gilbert-Lopez, B.; Garcia-Reyes, J. F. Molina-Diaz, A. Determination of organic priority pollutants in sewage treatment plant effluents by gas chromatography high-resolution mass spectrometry. Talanta 2010, 82, 1318–1324.

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38. Government of Canada. Municipal Wastewater Effluent Characterization and Loadings. Environment and Climate Change Canada. http://www.ec.gc.ca/eu-ww/default.asp?lang=En&n=4F4513C8-1 (2013; Last Accessed 5/31/2016)).

39. United States Section, International Boundary and Water Commission (USIBWC). Nogales International Wastewater Treatment Plant Maximum Allowable Headworks Loading Development. Final Report 2009.

40. Agency for Toxic Substances and Disease Registry (ATSDR). Toxicological profile for Dichloropropenes. (Draft for Public Comment). U.S. Department of Health and Human Services, Public Health Service. Atlanta, GA, 2013.

41. Brown, J. S.; Bay, S. M.; Greenstein, D. J.; Ray, W.R. Concentrations of methyl-tert-butyl ether (MTBE) in inputs and receiving waters of Southern California. Marine Pollution Bulletin 42 (10):957-66, (2001)

42. California Regional Water Quality Control Board. Waste Discharge Requirements for the United States Section of the International Boundary and Water Commission, South Bay International Wastewater Treatment Plant, Discharge to the Pacific Ocean via the South Bay Ocean Outfall. 2014.

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43. Capital Regional District, Victoria, British Columbia. Gulf Islands and Port Renfrew Wastewater and Marine Environment Program: Annual Report, 2012.

44. Dixon-Solano Water Authority. Drinking Water Quality Report 2012. http://www.sidwater.org/DocumentCenter/View/352 (Last Accessed 5/31/2016)

45. Big Bear Regional Wastewater Agency. Recycled Water Master Plan Draft Preliminary Engineering Report. Tom Dodson and Associates, 2005.

46. Yang, B.; Ying, G.; Zhao, J.; Liu, S.; Zhou, L.; Cheng, F. Removal of selected endocrine disrupting chemicals (EDCs) and pharmaceuticals and personal care products (PPCPs) during ferrate (VI) treatment of secondary wastewater effluents. Water Res. 2012, 46, 2194–2204.

47. Chung, J.; Rittmann, B. E.; Wright, W. F.; Bowman, R. H. Simultaneous bio-reduction of nitrate, perchlorate, selenite, chromate, arsenate, and dibromochloropropane using a hydrogen-based membrane biofilm reactor. Biodegradation 2007, 18, 199–209.

48. Markus, M. R.; Deshmukh, S. S. An Innovative Approach to Water Supply – The Groundwater Replenishment System. World Environmental and Water Resources Congress 2010 Conference Proceedings, 2010, 3264–3639.

49. Phyu, Y. L.; St. Warne, M. J.; Lim, R. P. Toxicity of Atrazine and Molinate to the Cladoceran Daphnia carinata and the Effect of River Water and Bottom Sediment on their Bioavailability. Environ. Contam. Toxicol. 2004, 46, 308–315.

50. Madhubabu, S.; Kumar, M.; Philip, L.; Venkobachar, C. Treatment of carbofuran-bearing synthetic wastewater using UASB process. J. Environ. Sci. Heal. B 2007, 42(2), 189–199.

51. Hawker, D. W.; Cumming, J. L.; Neale, P. A.; Bartkow, M. E.; Escher, B. I. A screening level fate model of organic contaminants from advanced water treatment in a potable water supply reservoir. Water Res. 2011, 45, 768–780.

52. Randall, C. W.; Cokgur, E. U.; Kisoglu, Z.; Punrattanasin, W.; Erdal, U.; Sriwiriyarat, T. The Effects of Diquat Dibromide on Biological Wastewater Treatment Plants. J. Environ. Sci. Heal A 2003, 38(10), 2453–2463.

53. Kumar, Y.; Byrtus, G. Monitoring of Methoxychlor Residues in the Athabasca River System in Northern Alberta after Treatment for Control of Black Fly Larval Populations. Environ. Monit. Assess. 1993, 28, 15–32.

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Water Environment & Reuse Foundation 319

Appendix B

Risk Register Format and Detailed Analysis

Inherent Risk and Assessment of Treatment Barriers: RO Membrane-Based TreatmentThese assessments determine the hazards in the source at an unacceptable level and whether the treatment process is adequate to treat them.

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Biological

Cryptosporidium 0 Acute HealthDomestic waste - human andanimal faecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ● ● ◑ ● 10 log UF, RO, UV, Chlorine Insignificant Rare Low (A1) Significant treatment redundancy

Giardia lamblia 0 Acute HealthDomestic waste - human andanimal faecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ● ● ◕ ● 10 log UF, RO, UV, Chlorine Insignificant Rare Low (A1) Significant treatment redundancy

Total Coliforms (incl. fecal coliformand E. Coli)

0 Acute HealthDomestic waste - human andanimal faecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ● ● ● ● 10 log UF, RO, UV, Chlorine Insignificant Rare Low (A1) Significant treatment redundancy

Viruses (enteric) 0 Acute HealthDomestic waste - human andanimal faecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◑ ● ● ● ● 12 log UF, RO, UV, Chlorine Insignificant Rare Low (A1)Main barriers are RO, UV, and chlorine. Whilst there is

redundancy (RO LRVs), all barriers are required to meettreatment requirements

Inorganics and metals

Metals are removed in the biological wastewater treatmentprocess mainly by adsorption and complexation of themetals with microorganisms. Soluble metal removal hasbeen observed from 50% to 98% depending on the initialconcentration, solids concentration and system solidsretention time (Metcalf & Eddy 2003).

Aluminum 1 48 mg/L 48 Chronic HealthAlum-based Coagulation; pHControl

CA primary at 1mg/L but onlysecondary at 0.2 mg/L

Moderate Possible High (C3) Estimate ◕ ● ○ ○ ● N/A None as this is post treatment Minor PossibleModerate

(C2)Effluent pH may impact downstream processes at thedrinking water treatmetn plant, inlcuding coagulation

Antimony 0.006 0.09 mg/L 15 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Arsenic 0.01 1.85 mg/L 185 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ◑ ● ○ ○ ● N/A RO Moderate Rare Low (A3)

Asbestos 7 0.2 MFL 0.028571 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Major Rare High (A4) Estimate ◑ ● ○ ○ ● N/A RO Insignificant Rare Low (A1)

Barium 2 0.006 mg/L 0.003 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Beryllium 0.004 0.005 mg/L 1.25 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Cadmium 0.005 0.01 mg/L 2 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Chloride 250 553 mg/L 2.212 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Secondary at 250mg/L Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A

None necessary but RO doremove some chloride

Chromium 0.05 0.14 mg/L 2.8 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Copper 1.3 0.146 mg/L 0.112308 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Cyanide 0.2 0.68 mg/L 3.4 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Fluoride 4 2 mg/L 0.5 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source -Limit is high at 2mg/L

Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A

Iron 0.3 0.99 mg/L 3.3 Chronic HealthTrade waste, Domestic waste,Illegal discharge, Coagulation

Secondary Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A

Lead 0.015 0.19 mg/L 12.66667 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A

Manganese 0.05 0.31 mg/L 6.2 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Secondary Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Insignificant Rare Low (A1)

Mercury 0.002 0.005 mg/L 2.5 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source asfor other divalents

Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Removal

Inherent Risk(based on drinking feedwater directly at 2L

per day)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

B-1

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Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Nickel 0.1 0.43 mg/L 4.3 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source asfor other divalents

Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Nitrate (as N) 10 32 mg/L 3.2 Acute HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ◕ ○ ○ ◕ N/A RO Minor Unlikely Low (B2)

Nitrate (as NO3) 45 mg/L Acute HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ◕ ○ ○ ◕ N/A RO Minor Unlikely Low (B2) Rejections to be confirmed

Nitrite (as N) 1 5.3 mg/L 5.3 Acute HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ◕ ○ ○ ◕ N/A RO Minor Unlikely Low (B2) Rejections to be confirmed

Silver 0.1 0.0062 mg/L 0.062 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Secondary Minor Unlikely Low (B2) Estimate ○ ● ○ ○ ● N/A

Sulfate 250 107 mg/L 0.428 Chronic HealthTrade waste, Domestic waste,Illegal dischargeAlso by SBS quenching

Secondary at 250 mg/L. Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A

None necessary but RO doremove some

Control quenching processInsignificant Rare Low (A1)

Need more data on typical source concentrations to assessability to use sulfate as RO LRV indicator

Total Nitrate/Nitrite (as N) 10 42.3 mg/L 4.23 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2) Rejections to be confirmed

Perchlorate 0.006 0.107 mg/L 17.83333 Chronic HealthTrade waste, Domestic waste,Illegal discharge - Added/formedduring treatment and disinfection

Need more data on sourceand process

Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/ARO

Quality control hypoMinor Rare Low (A2)

Selenium 0.05 0.03 mg/L 0.6 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ● ○ ○ ● N/A RO Moderate UnlikelyModerate

(B3)

Thallium 0.002 0.00001 mg/L 0.005 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor Unlikely Low (B2) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Zinc 5 0.887 mg/L 0.1774 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Secondary Minor PossibleModerate

(C2)Estimate ○ ● ○ ○ ● N/A RO Insignificant Rare Low (A1)

Radionuclides Estimate

Uranium 20 ug/L Chronic Health Major Unlikely High (B4) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Combined Radium - 226+228 5 pCi/L Chronic Health Major Unlikely High (B4) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Gross Alpha particle activity(excluding radon & uranium)

15 pCi/L Chronic Health Major Unlikely High (B4) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

Gross Beta particle activity 4 mRem/y Chronic Health Major Unlikely High (B4) Estimate ○ ● ○ ○ ● N/A RO Minor Rare Low (A2)

VOCs

VOC are classed as having a boiling point less than 100oCand a vapour pressure > 1 mm Hg at 25oC (Metcalf & Eddy2003). These compounds are readily released to theatmosphere in the wastewater treatment process throughvolatilisation and gas stripping. No VOCs have beendetected in WWTP effluent greater than health limits.

Benzene 0.001 0.0003 mg/L 0.3 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Minor Unlikely Low (B2)

Carbon Tetrachloride 0.0005 0.038 mg/L 76 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

Chlorobenzene 0.1 0.01 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Minor Unlikely Low (B2)

1,2-Dichlorobenzene 0.6 1 mg/L 1.666667 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

1,4-Dichlorobenzene 0.005 0.075 mg/L 15 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

1,1-Dichloroethane 0.005 0.001 mg/L 0.2 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Minor Unlikely Low (B2)

1,2-Dichloroethane 0.0005 0.002 mg/L 4 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

1,1-Dichloroethylene 0.006 0.01 mg/L 1.666667 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Minor Unlikely Low (B2)

Domestic waste(radiopharmaceuticals, diffuse load) -Environmental contribution

Need more data on source

B-2

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Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

cis-1,2-Dichloroethylene 0.006 0.0005 mg/L 0.083333 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

trans-1,2-Dichloroethylene 0.01 0.0005 mg/L 0.05 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

Dichloromethane 0.005 0.23 mg/L 46 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

1,3-Dichloropropene 0.0005 0.001 mg/L 2 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

1,2-Dichloropropane 0.005 0.0005 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Minor Unlikely Low (B2)

Ethylbenzene 0.3 0.002 mg/L 0.006667 Chronic Health Insignificant Unlikely Low (B1) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

Methyl-tert-butyl ether (MTBE) 0.013 0.123 mg/L 9.461538 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)Unclear whether primary or secondary MCLs (earlier 0.005)

Monochlorobenzene 0.07 0.0005 mg/L 0.007143 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

Styrene 0.1 0.0005 mg/L 0.005 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

1,1,2,2-Tetrachloroethane 0.01 0.0005 mg/L 0.05 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

Tetrachloroethylene 0.005 0.069 mg/L 13.8 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

Toluene 0.15 0.11 mg/L 0.733333 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Moderate Unlikely

Moderate(B3)

1,2,4 Trichlorobenzene 0.005 0.0005 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Moderate Unlikely

Moderate(B3)

1,1,1-Trichloroethane 0.2 0.057 mg/L 0.285 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Moderate Unlikely

Moderate(B3)

1,1,2-Trichloroethane 0.005 0.001 mg/L 0.2 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Moderate Unlikely

Moderate(B3)

Trichloroethylene 0.005 0.003 mg/L 0.6 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A None Moderate Unlikely

Moderate(B3)

Trichlorofluoromethane 0.15 0.004 mg/L 0.026667 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

1,1,2-Trichloro-1,2,2-Trifluoroethane

1.2 0.0005 mg/L 0.000417 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

Vinyl chloride 0.0005 0.005 mg/L 10 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A None Moderate UnlikelyModerate

(B3)

Xylenes 1.75 0.0012 mg/L 0.000686 Chronic Health Insignificant Unlikely Low (B1) Estimate ○ ◑ ○ ○ ◑ N/A None Insignificant Unlikely Low (B1)

SOCs

Users of herbicides/pesticides such as pest controlcompanies and distributors of products could illegally oraccidentally discharge products to sewer.Low levels (below guidelines) of soluble compounds havebeen detected in WWTP effluent as they are not wellremoved by this process.

Acrylamide 0.002 N/A mg/L N/A Chronic Health Polyacrylamide Use Treatment Technique Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/ARO is a barrier, but bestcontrolled by polymericcoagulant aid dosing

Minor Unlikely Low (B2)

Alachlor 0.002 0.0015 mg/L 0.75 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Atrazine 0.001 0.001 mg/L 1 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Bentazon 0.018 0.0625 mg/L 3.472222 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Benzo(a) Pyrene 0.0002 0.00004 mg/L 0.2 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Carbofuran 0.018 4.2 mg/L 233.3333 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Trade waste, Domestic waste,Illegal discharge

Need more data on source -As these compounds are notwell rejected by RO but mostlyremoved during the wastewater treatment processupstream, it is critical tocharacterise their occurence inthe actual feedwater to theadvanced treatment plant.Based on rejection duringwaste water treatment, alikelihood of "unlikely" hasbeen used.Other facilities have been ableto claim 90% removal bywaste water treatment.

B-3

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Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Chlordane 0.0001 0.0001 mg/L 1 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Dalapon 0.2 0.001 mg/L 0.005 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Dibromochloropropane 0.0002 0.00001 mg/L 0.05 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Di(2-ethylhexyl)adipate 0.4 0.054 mg/L 0.135 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Di(2-ethylhexyl)phthalate 0.004 0.002 mg/L 0.5 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

2,4-D 0.07 0.02275 mg/L 0.325 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Dinoseb 0.007 0.00005 mg/L 0.007143 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Diquat 0.02 1.63 mg/L 81.5 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Endothall 0.1 0.045 mg/L 0.45 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Endrin 0.002 0.00003 mg/L 0.015 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Epichlorohydrin TT N/A N/A N/A Chronic Health Polymeric Coagulant AidsTreatment Technique; Nostandard analytical method

Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/ARO is a barrier, but bestcontrolled by polymericcoagulant aid dosing

Minor Unlikely Low (B2)

Ethylene Dibromide 0.00005 0.00001 mg/L 0.2 Chronic Health Trade waste and run-off Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Glyphosate 0.7 0.001 mg/L 0.001429 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Heptachlor 0.00001 0.00045 mg/L 45 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Heptachlor Epoxide 0.00001 0.00003 mg/L 3 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Hexachlorobenzene 0.001 0.005 mg/L 5 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Hexachlorocyclopentadiene 0.05 0.001 mg/L 0.02 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Lindane 0.0002 0.026 mg/L 130 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Methoxychlor 0.03 0.0008 mg/L 0.026667 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Molinate 0.02 2.22 mg/L 111 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Oxamyl 0.05 0.002 mg/L 0.04 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Pentachlorophenol 0.001 0.00021 mg/L 0.21 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Picloram 0.5 0.0005 mg/L 0.001 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Polychlorinated Biphenyls 0.0005 0.0005 mg/L 1 Chronic HealthTrade waste, Domestic waste,Illegal discharge

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Simazine 0.004 0.1 mg/L 25 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Thiobencarb 0.07 0.0005 mg/L 0.007143 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Toxaphene 0.003 0.0029 mg/L 0.966667 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

2,3,7,8-TCDD (Dioxin) 3E-08 5E-09 mg/L 0.166667 Chronic HealthTrade waste, Domestic waste,Illegal discharge, run-off

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

2,4,5-TP (Silvex) 0.05 0.0005 mg/L 0.01 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

B-4

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Lik

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No

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Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

DBPs and Disinfectants

Organohalides commonly formed after chlorination ofWWTP effluent are trihalomethanes and halo acetic acids.The WWTP effluent offtakes to the AWTPs need to bedesigned to prevent chlorinated effluent from entering theAWTPs, therefore the primary source of these hazards isthe chloramination step in the AWTPs. Halo-acetic acidsare much better rejected through reverse osmosismembranes than trihalomethanes and the indicatorcompounds could be trihalomethane with the mostprevalent liekly to be chloroform.THMs are low at the plants where chloramines arepreformed.

Bromate 0.01 0.0067 mg/L 0.67 Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Advanced oxidation can lead to formation of bromate frombromide but there is no target for bromide

Chloramines (as Cl2) 4 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Chlorine (as Cl2) 4 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Chlorine dioxide (as ClO2) 0.8 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Chlorite 1 0.01 mg/L 0.01 Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

NDMA 10 11 ng/L 1.1 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ● ○ ● N/A UV Minor Unlikely Low (B2)Pre-MF chloramination step is key to controlling/limiting

NDMA formation

Haloacetic acids (five) 0.06 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Dichloroacetic acid 0 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Trichloroacetic acid 0.02 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Monochloroacetic acid 0.07 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A RO Minor Unlikely Low (B2)

Total Trihalomethanes 0.08 0.526 mg/L 6.575 Chronic Health Moderate Possible High (C3) Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Bromodichloromethane 0 0.0445 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Bromoform 0 0.11 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Dibromochloromethane 0.06 0.08 mg/L 1.333333 Chronic Health Moderate Possible High (C3) Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Chloroform 0.07 0.045 mg/L 0.642857 Chronic Health Minor PossibleModerate

(C2)Reliable ○ ◑ ○ ○ ◑ N/A RO Minor Unlikely Low (B2)

Precursors in trade and domesticwaste - Byproduct chlor(am)ination

DBPs and DBP formationshould always be evaluated ona site-specific basis and incontext with site-specificblending water quality anddisinfection practices. Ingeneral, DBP precursors arewell-controlled by ROmembranes and overallformation is expected to below. NDMA and other pre-formed DBPs may still passthrough subsequent treatmentstages and, in the case ofNDMA, need furhter treatmentsuch as with UV/AOP.

B-5

Inherent Risk and Assessment of Hazardous Events: RO Membrane-Based TreatmentThis considers hazardous events that may cause the treatment process to fail.

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WWTPDischarge of large quantities of organicchemicals leads to sudden spikes in organics

VOCs Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP aslarge discharges would likely disrupt the WWTPprocesses.

Trade and domestic waste management

Regular source water monitoring

WWTP processes(CCP)WQ monitoring atAWTP inlet

Minor Unlikely Low (B2) As low as reasonably practicable Reliable Source control measures

WWTPIllegal discharge of large quantities of organicchemicals leads to sudden spikes in organics

SOCs Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP aslarge discharges would likely disrupt the WWTPprocesses.

Trade and domestic waste management

Regular source water monitoring

WWTP adsorption andbiological degradation

RO and UV/AOP

Minor Unlikely Low (B2) Reliable Source control measures

WWTPOutbreak of infectious disease in the communityleads to higher than usual source pathogenconcentration

Pathogens CatastrophicAlmostCertain

Very High(E5)

WWTP and AWTP designed to deal withpathogen concentrations found in wastewaterduring outbreaks, including multiple disinfectionsteps

Link to departments of health and early warningnetworks

WWTPMF-RO-UV/AOP-Chlorine

Minor Unlikely Low (B2) Estimate

WWTPFailure of biological processes leads to poorquality sewage (eg. failure of aeration)

Pathogens Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP(CCP)

Communication protocols between operators ofWWTP and AWTP

MF-RO-UV/AOP-Chlorine

Minor Unlikely Low (B2) Estimate

WWTP High rainfall events leading to bypass Pathogens Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP(CCP)

Communication protocols between operators ofWWTP and AWTP leading to shutdown of AWTPduring bypass

UF-RO-UV/AOP-Chlorine

Moderate UnlikelyModerate

(B3)Estimate

WWTPFailure to shut off pumps in response to adversemonitoring/ bypass signals at the interface point,leading to water quality that does not comply

Pathogens Moderate UnlikelyModerate

(B3)

Shut off trigger systems supported bymaintenance, calibration and validation;Failsafe on instrumentation;CCP response procedure;Additional staff training.

UF-RO-UV/AOP-Chlorine

Moderate Rare Low (A3) Reliable

MF/UFFailure of chloramine pre-dosing (under-dosing)leading to fouling

No public health risk,reduction in capacity

Insignificant Possible Low (C1)Automated dosing systems with shut-off triggerssupported by maintenance and calibrationOperational monitoring of UF performance

Not applicable (nohealth hazards)

Insignificant Unlikely Low (B1) Confident

MF/UFFailure of chloramine pre-dosing (over-dosing)leading to increased DBPs

DBPs Minor PossibleModerate

(C2)

Preformation of chloramines where possibleOptimisation of chloramine doseLocation of chlorine dosing point to minimisecontact timeLimit on dosing pump size

Monitoring of THMs in RO feed water

ROUV/AOP

Minor Rare Low (A2) Confident

MF/UFCatastrophic integrity breach (fiber breakage,tears, holes) leads to reduced removal

Pathogens Moderate UnlikelyModerate

(B3)

Validated CCP with direct and indirect integritymonitoring

Turbidity online monitoring, regular integrity tests(~24h) and maintenance inspections

ROUV/AOPChlorine disinfection

Minor Unlikely Low (B2) Confident

Maximum Risk. The

inherent hazard risk or an

assessment of risk without

preventive measures if the

hazard is added in the

process.

Residual Risk. The risk

posed by each hazardous

event, if the water was

consumed directly.

B-6

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MF/UF Membrane fouling leads to performance lossNo public health risk,reduction in capacity

Insignificant Possible Low (C1)Operational performance monitoringAntifouling strategies (eg. chloramine dosing

Not applicable (nohealth hazards)

Insignificant Unlikely Low (B1) Confident

ROFailure of antiscalant dosing leads to saltpassage

No public health risk,increase in TDS

Insignificant Possible Low (C1)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationOperational monitoring of RO membraneperformance and regular cleaning

Not applicable (nohealth hazards)

Insignificant Unlikely Low (B1) Confident

RO Biofouling leads to reduced performanceNo public health risk,reduction in capacity

Insignificant Possible Low (C1)Operational monitoring of RO membraneperformance and regular cleaning

Not applicable (nohealth hazards)

Insignificant Unlikely Low (B1) Confident

ROCatastrophic integrity breach (glue lines, o-rings)leads to reduced removal

All hazards Moderate Possible High (C3)

Advanced water treatment plant automatedprocess control systems with shut-off triggers(including EC, sulphate and TOC - CCP)supported by maintenance and calibration.

UV/AOPChlorine disinfection

Minor Unlikely Low (B2) Confident

RODeterioration of membranes leads to reducedremoval performance

Chemicals Minor PossibleModerate

(C2)

Advanced water treatment plant automatedprocess control systems with shut-off triggers(including EC, sulphate and TOC - CCP)supported by maintenance and calibration.Operational monitoring of RO membraneperformance

UV/AOPChlorine disinfection

Minor Unlikely Low (B2) Confident

RO

Cleaning or preservation chemicals notadequately removed and pass through themembrane, leading to water quality that does notcomply

Chemicals Minor Unlikely Low (B2)

Automatic cleaning process validated during plantcommissioning

Advanced water treatment plant automatedprocess control systems with shut-off triggers(including EC, sulphate and TOC - CCP)supported by maintenance and calibration.

UV/AOPChlorine disinfection

Minor Unlikely Low (B2) Confident

RO

Storage of membranes during periods of down-time resulting in deterioration of membraneefficiency, leading to water quality that does notcomply when membranes return to service.

All hazards Minor PossibleModerate

(C2)

Membrane preservation proceduresAutopsy programs with suppliers or third partiesAdvanced water treatment plant automatedprocess control systems with shut-off triggers(including EC, sulphate and TOC - CCP)supported by maintenance and calibration.

UV/AOPChlorine disinfection

Minor Unlikely Low (B2) Confident

UV/AOP

Failure to reduce pathogen and organiccompound concentration across the advancedoxidation process, e.g. due to loss of lampfunction or under-dosing of hyrdogen peroxide,leading to water that does not comply

NDMA, 1,4-dioxaneOther chemicals

Major PossibleVery High

(C4)

Automated process control systems with shut-offtriggers (Present Power Ratio CCP, automatedalarm/triggers on dosing pump), supported bymaintenance and calibration;

Chlorine disinfection Minor Unlikely Low (B2) Confident

UV/AOPFormation of disinfection byproducts duringadvanced oxidation leading to water that doesnot comply

Inorganic DBPs(bromate)

Minor PossibleModerate

(C2)Removal or precursors (bromide)Regular monitoring for precursors

WWTPMF-RO-UV/AOP

Minor PossibleModerate

(C2)More data required on formation potential insource water

UncertainRegular source monitoringfor bromide

StabilisationHigh lime dose rates results in high levels ofinorganic contaminants

Metals Minor PossibleModerate

(C2)

Cleaning treated water tank;Optimisation of dosing control;Control of operation of treated water tank -maintain stable flow;Use of lime saturators

WWTPMF-RO

Minor Unlikely Low (B2)Very dependent on the design and control of thestabilisation process

UncertainProduct specifications andquality control

StabilisationIneffective stabilisation leads to corrosive waterand release of heavy metals in distributionnetwork

Heavy metals Moderate Possible High (C3)Corrosivity to be monitored and calculatedcontinuously with CCP (triggers if outside oftarget range for significant period of time)

WWTPMF-RO

Minor Unlikely Low (B2) Uncertain

Post-treatmentdisinfection

Underdosing leads to low free chlorine residualand non-compliant water

Pathogens Moderate Possible High (C3)

Automated process control systems with shut-offtriggers, supported by maintenance andcalibration.Optimised chlorine dosing regulationPlant shut-down on low free chlorine.

WWTPMF-RO-UV/AOP

Minor Unlikely Low (B2) Uncertain

Post-treatmentdisinfection

Overdosing leads to non compliant water DBPs, free chlorine Minor PossibleModerate

(C2)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous monitoring

Minor Unlikely Low (B2) Uncertain

B-7

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Treated waterstorage

Contamination with fauna and pathogens Pathogens Minor PossibleModerate

(C2)Regular inspectionPhysical protection

Chlorine disinfection Minor Unlikely Low (B2) Uncertain

OverallOnline instrument failure leading to water thatdoes not comply

All hazards Minor PossibleModerate

(C2)

External calibration and servicing program;Regular operator checks (instruments monitoringCCPs checked daily).

Minor Unlikely Low (B2) Uncertain

OverallPower failure or partial power failure acrosssystem resulting in water quality that does notcomply

All hazards Insignificant Unlikely Low (B1)

Inherently controlled by the pumps being requiredto maintain water flows and the presence of back-up power supplies.

Regular inspection program

Insignificant Rare Low (A1) Uncertain

OverallSabotage, vandalism or terrorism causingunkown hazards to be added

Chemicals, pathogens Moderate UnlikelyModerate

(B3)

Security measure such as fencing and lockedgates around pump stations and water treatmentplants.Regular inspections of infrastruture and securiymeasures.

Online monitoring of conductivity at deliverypoints may detect some irregularities.

If contamination ofsource water, WWTP-MF-RO-UV/AOP-Chlorine

Moderate Rare Low (A3) Uncertain

OverallContaminants in bulk chemicals added duringthe treatment or post-treatment lead to noncompliance

Chemicals Minor PossibleModerate

(C2)

Contracts with suppliers specify maximumcontaminant limits. Require test results to bedelivered with each batch.

Testing of new chemical batches at delivery

Depending on wherechemicals areintroduced

Minor Rare Low (A2) Uncertain

OverallOverdosing of any chemical leading to hazardand non compliance

Chemicals Minor PossibleModerate

(C2)

Automatic dosing control and regularmaintenance and callibration of onlineintrumentation that controls dosing.Regular monitoring of treated water for knownchemical risks

Depending on wherechemicals areintroduced

Minor Rare Low (A2) Uncertain

OverallPotential introduction of hazards duringmaintenance processes

Chemicals Minor Unlikely Low (B2) Appopriate procedures and trainingDepending on wherechemicals areintroduced

Minor Rare Low (A2) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

B-8

Inherent Risk and Assessment of Treatment Barriers: Ozone-BAC-Based TreatmentThese assessments determine the hazards in the source at an unacceptable level and whether the treatment process is adequate to treat them.

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Biological

Cryptosporidium 0 Acute HealthDomestic waste - human and animalfaecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ◕ ○ ○ ◑ ◕ ● 10 log Floc/Sed,O3, BAC, UV Insignificant Rare Low (A1)LRVs achieved to be confirmed (BAC and ozone). Requires

pre-ozone filtration to remove particles which have beenshown to introduce shielding.

Giardia lamblia 0 Acute HealthDomestic waste - human and animalfaecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ◑ ○ ○ ◕ ● ● 10 logFloc/Sed,O3, BAC, UV,

ChlorineInsignificant Rare Low (A1)

LRVs achieved to be confirmed (BAC and ozone). Requirespre-ozone filtration to remove particles which have been

shown to introduce shielding.

Total Coliforms (incl. fecal coliformand E. Coli)

0 Acute HealthDomestic waste - human and animalfaecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ◕ ○ ○ ● ● ● 10 logFloc/Sed,O3, BAC, UV,

ChlorineInsignificant Rare Low (A1)

LRVs achieved to be confirmed (BAC and ozone). Requirespre-ozone filtration to remove particles which have been

shown to introduce shielding.

Viruses (enteric) 0 Acute HealthDomestic waste - human and animalfaecal matterContamination of storage reservoirs

CatastrophicAlmostCertain

Very High(E5)

Certain ◕ ◕ ○ ○ ● ● ● 12 logFloc/Sed,O3, BAC, UV,

ChlorineInsignificant Rare Low (A1)

LRVs achieved to be confirmed (BAC and ozone). Requirespre-ozone filtration to remove particles which have been

shown to introduce shielding.

Inorganics and metals

Metals are removed in the biological wastewater treatmentprocess mainly by adsorption and complexation of themetals with microorganisms. Soluble metal removal hasbeen observed from 50% to 98% depending on the initialconcentration, solids concentration and system solidsretention time (Metcalf & Eddy 2003).

Aluminum 1 48 mg/L 48 Chronic Health Alum-based Coagulation; pH ControlCA primary at 1mg/L but onlysecondary at 0.2 mg/L

Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ ○ N/A pH Control at floc/sed step Minor PossibleModerate

(C2)Effluent pH may impact downstream processes at thedrinking water treatmetn plant, inlcuding coagulation

Antimony 0.006 0.09 mg/L 15 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◕ ○ ○ ○ ○ ○ ◕ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Need more data on performance of BAC and GAC forremoval of metals

Arsenic 0.01 1.85 mg/L 185 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◕ ○ ● ○ ○ ○ ● N/APre-O3 coagulation

BACModerate Unlikely Moderate (B3)

Need more data on performance of BAC and GAC forremoval of metals

Asbestos 7 0.2 MFL 0.028571 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Major Rare High (A4) Estimate ◕ ○ ○ ○ ○ ○ ◕ N/APre-O3 coagulation

BACInsignificant Unlikely Low (B1)

Barium 2 0.006 mg/L 0.003 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Pre-O3 coagulationBAC

Minor Unlikely Low (B2)Need more data on performance of BAC and GAC for

removal of metals

Beryllium 0.004 0.005 mg/L 1.25 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◕ ○ ○ ○ ○ ○ ◕ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Need more data on performance of BAC and GAC forremoval of metals

Cadmium 0.005 0.01 mg/L 2 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◕ ○ ○ ○ ○ ○ ◕ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Need more data on performance of BAC and GAC forremoval of metals

Chloride 250 553 mg/L 2.212 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Secondary at 250mg/L Minor PossibleModerate

(C2)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

None necessary but thisprocess train does not address

TDS which is an aestheticconsideration

Chromium 0.05 0.14 mg/L 2.8 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Need more data on performance of BAC and GAC forremoval of metals

Copper 1.3 0.146 mg/L 0.112308 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Pre-O3 coagulationBAC

Minor Unlikely Low (B2)Need more data on performance of BAC and GAC for

removal of metals

Cyanide 0.2 0.68 mg/L 3.4 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ○ ○ ○ ◕ ○ ○ ◕ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Need more data on performance of BAC and GAC forremoval of metals and inorganics

Fluoride 4 2 mg/L 0.5 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source -Limit is high at 2mg/L

Minor PossibleModerate

(C2)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Iron 0.3 0.99 mg/L 3.3 Chronic HealthTrade waste, Domestic waste, Illegaldischarge, Coagulation

Secondary Minor PossibleModerate

(C2)Estimate ◕ ◕ ○ ○ ○ ○ ● N/A

Pre-O3 coagulation could beexpected to treat some iron out

Lead 0.015 0.19 mg/L 12.66667 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◑ ○ ○ ○ ○ ○ ◑ N/A

Manganese 0.05 0.31 mg/L 6.2 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Secondary Moderate Possible High (C3) Estimate ◕ ◕ ○ ○ ○ ○ ● N/APre-O3 coagulation

BACInsignificant Unlikely Low (B1)

Removal

(Empty = Insufficient Data)

● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

B-9

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● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

Mercury 0.002 0.005 mg/L 2.5 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source asfor other divalents

Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Nickel 0.1 0.43 mg/L 4.3 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source asfor other divalents

Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Nitrate (as N) 10 32 mg/L 3.2 Acute HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Needs to be managed atWWTP or by Blending

Minor PossibleModerate

(C2)

Nitrate (as NO3) 45 mg/L Acute HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Needs to be managed atWWTP or by Blending

Minor PossibleModerate

(C2)

Nitrite (as N) 1 5.3 mg/L 5.3 Acute HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ◕ ● ○ ○ ○ ● N/A

Needs to be managed atWWTP or by Blending

Minor PossibleModerate

(C2)

Silver 0.1 0.0062 mg/L 0.062 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Secondary Minor Unlikely Low (B2) Estimate ◑ ○ ○ ○ ○ ○ ◑ N/A

Sulfate 250 107 mg/L 0.428 Chronic HealthTrade waste, Domestic waste, IllegaldischargeAlso by SBS quenching

Secondary at 250 mg/L. Insignificant Possible Low (C1) Estimate ○ ○ ○ ○ ○ ○ ○ N/ANo stated barrier; secondary

MCL onlyInsignificant Possible Low (C1)

Total Nitrate/Nitrite (as N) 10 42.3 mg/L 4.23 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Major PossibleVery High

(C4)Estimate ○ ○ ○ ○ ○ ○ ○ N/A

Needs to be managed atWWTP or by Blending

Minor PossibleModerate

(C2)

Perchlorate 0.006 0.107 mg/L 17.83333 Chronic HealthTrade waste, Domestic waste, Illegaldischarge - Added/formed duringtreatment and disinfection

Need more data on source andprocess

Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ ○ N/A

No specific removal; avoidanceis key via proper storage and

use of chlorine or chlorinedioxide

Minor Unlikely Low (B2)

Selenium 0.05 0.03 mg/L 0.6 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ◕ ○ ○ ○ ○ ○ ◕ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Thallium 0.002 0.00001 mg/L 0.005 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor Unlikely Low (B2) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Zinc 5 0.887 mg/L 0.1774 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Secondary Minor PossibleModerate

(C2)Estimate ◑ ○ ○ ○ ○ ○ ◑ N/A

Pre-O3 coagulationBAC

Minor Unlikely Low (B2)

Radionuclides Estimate

Uranium 20 ug/L Chronic Health Major Unlikely High (B4) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Combined Radium - 226+228 5 pCi/L Chronic Health Major Unlikely High (B4) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Gross Alpha particle activity(excluding radon & uranium)

15 pCi/L Chronic Health Major Unlikely High (B4) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

Gross Beta particle activity 4 mRem/y Chronic Health Major Unlikely High (B4) Estimate ○ ○ ○ ○ ○ ○ ○ N/APre-O3 coagulation

BACMinor Unlikely Low (B2)

VOCs

VOC are classed as having a boiling point less than 100oCand a vapour pressure > 1 mm Hg at 25oC (Metcalf & Eddy2003). These compounds are readily released to theatmosphere in the wastewater treatment process throughvolatilisation and gas stripping. No VOCs have beendetected in WWTP effluent greater than health limits.

Benzene 0.001 0.0003 mg/L 0.3 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Carbon Tetrachloride 0.0005 0.038 mg/L 76 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

Chlorobenzene 0.1 0.01 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,2-Dichlorobenzene 0.6 1 mg/L 1.666667 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

1,4-Dichlorobenzene 0.005 0.075 mg/L 15 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

1,1-Dichloroethane 0.005 0.001 mg/L 0.2 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ○ ○ ○ ◑ N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,2-Dichloroethane 0.0005 0.002 mg/L 4 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ○ ◑ N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

1,1-Dichloroethylene 0.006 0.01 mg/L 1.666667 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

cis-1,2-Dichloroethylene 0.006 0.0005 mg/L 0.083333 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Radionuclides are very likely removed in the sludge at thewastewater treatment plant

Domestic waste(radiopharmaceuticals, diffuse load) -Environmental contribution

Need more data on source

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● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

trans-1,2-Dichloroethylene 0.01 0.0005 mg/L 0.05 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Dichloromethane 0.005 0.23 mg/L 46 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

1,3-Dichloropropene 0.0005 0.001 mg/L 2 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

1,2-Dichloropropane 0.005 0.0005 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Ethylbenzene 0.3 0.002 mg/L 0.006667 Chronic Health Insignificant Unlikely Low (B1) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Insignificant Unlikely Low (B1)

Methyl-tert-butyl ether (MTBE) 0.013 0.123 mg/L 9.461538 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ○ ○ ○ ◑ N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

Monochlorobenzene 0.07 0.0005 mg/L 0.007143 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Styrene 0.1 0.0005 mg/L 0.005 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,1,2,2-Tetrachloroethane 0.01 0.0005 mg/L 0.05 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Tetrachloroethylene 0.005 0.069 mg/L 13.8 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

Toluene 0.15 0.11 mg/L 0.733333 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,2,4 Trichlorobenzene 0.005 0.0005 mg/L 0.1 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,1,1-Trichloroethane 0.2 0.057 mg/L 0.285 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,1,2-Trichloroethane 0.005 0.001 mg/L 0.2 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Trichloroethylene 0.005 0.003 mg/L 0.6 Chronic Health Minor PossibleModerate

(C2)Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Trichlorofluoromethane 0.15 0.004 mg/L 0.026667 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ○ ○ ○ ◑ N/A O3, BAC, GAC Minor Unlikely Low (B2)

1,1,2-Trichloro-1,2,2-Trifluoroethane 1.2 0.0005 mg/L 0.000417 Chronic Health Minor Unlikely Low (B2) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Minor Unlikely Low (B2)

Vinyl chloride 0.0005 0.005 mg/L 10 Chronic Health Moderate Possible High (C3) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Moderate Unlikely Moderate (B3)

Xylenes 1.75 0.0012 mg/L 0.000686 Chronic Health Insignificant Unlikely Low (B1) Estimate ○ ◑ ◕ ○ ○ ● N/A O3, BAC, GAC Insignificant Unlikely Low (B1)

SOCs

Users of herbicides/pesticides such as pest controlcompanies and distributors of products could illegally oraccidentally discharge products to sewer.Low levels (below guidelines) of soluble compounds havebeen detected in WWTP effluent as they are not wellremoved by this process.

Acrylamide 0.002 N/A mg/L N/A Chronic Health Polyacrylamide Use Treatment Technique Moderate Possible High (C3) Estimate ○ ◑ ○ ◑ N/ATreatment Technique based onuse of polyacrylamide polymer

Minor PossibleModerate

(C2)

Alachlor 0.002 0.0015 mg/L 0.75 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Atrazine 0.001 0.001 mg/L 1 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Bentazon 0.018 0.0625 mg/L 3.472222 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Benzo(a) Pyrene 0.0002 0.00004 mg/L 0.2 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Carbofuran 0.018 4.2 mg/L 233.3333 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Chlordane 0.0001 0.0001 mg/L 1 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Dalapon 0.2 0.001 mg/L 0.005 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Treatment efficiency may vary depending on ozonationpractices, GAC type, GAC age, and operation of biofiltration

process. Most VOCs likely to be removed at upstreamwastewater treatment plant

Trade waste, Domestic waste, Illegaldischarge

Need more data on source - Asthese compounds are not wellrejected by RO but mostlyremoved during the wastewater treatment processupstream, it is critical tocharacterise their occurence inthe actual feedwater to theadvanced treatment plant.Based on rejection duringwaste water treatment, alikelihood of "unlikely" hasbeen used.Other facilities have been ableto claim 90% removal by wastewater treatment.

B-11

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● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

Dibromochloropropane 0.0002 0.00001 mg/L 0.05 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Di(2-ethylhexyl)adipate 0.4 0.054 mg/L 0.135 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Di(2-ethylhexyl)phthalate 0.004 0.002 mg/L 0.5 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

2,4-D 0.07 0.02275 mg/L 0.325 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Dinoseb 0.007 0.00005 mg/L 0.007143 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Diquat 0.02 1.63 mg/L 81.5 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Endothall 0.1 0.045 mg/L 0.45 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Endrin 0.002 0.00003 mg/L 0.015 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Epichlorohydrin TT N/A N/A N/A Chronic Health Polymeric Coagulant AidsTreatment Technique; Nostandard analytical method

Moderate Possible High (C3) Estimate ○ ◑ ○ ◑ N/AGAC is a barrier, but bestcontrolled by polymericcoagulant aid dosing

Minor PossibleModerate

(C2)

Ethylene Dibromide 0.00005 0.00001 mg/L 0.2 Chronic Health Trade waste and run-off Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Glyphosate 0.7 0.001 mg/L 0.001429 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Heptachlor 0.00001 0.00045 mg/L 45 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Heptachlor Epoxide 0.00001 0.00003 mg/L 3 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Hexachlorobenzene 0.001 0.005 mg/L 5 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Hexachlorocyclopentadiene 0.05 0.001 mg/L 0.02 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Lindane 0.0002 0.026 mg/L 130 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Methoxychlor 0.03 0.0008 mg/L 0.026667 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Molinate 0.02 2.22 mg/L 111 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Oxamyl 0.05 0.002 mg/L 0.04 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Pentachlorophenol 0.001 0.00021 mg/L 0.21 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Picloram 0.5 0.0005 mg/L 0.001 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Polychlorinated Biphenyls 0.0005 0.0005 mg/L 1 Chronic HealthTrade waste, Domestic waste, Illegaldischarge

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Simazine 0.004 0.1 mg/L 25 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

Thiobencarb 0.07 0.0005 mg/L 0.007143 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

Toxaphene 0.003 0.0029 mg/L 0.966667 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Moderate Possible High (C3) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor PossibleModerate

(C2)

2,3,7,8-TCDD (Dioxin) 3E-08 5E-09 mg/L 0.166667 Chronic HealthTrade waste, Domestic waste, Illegaldischarge, run-off

Need more data on source Minor PossibleModerate

(C2)Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

2,4,5-TP (Silvex) 0.05 0.0005 mg/L 0.01 Chronic HealthIllegal dicharges and run-off/infiltration

Need more data on source Minor Unlikely Low (B2) Estimate ○ ◕ ○ ◕ N/A O3, BAC, GAC Minor Unlikely Low (B2)

DBPs and Disinfectants

Organohalides commonly formed after chlorination ofWWTP effluent are trihalomethanes and halo acetic acids.The WWTP effluent offtakes to the AWTPs need to bedesigned to prevent chlorinated effluent from entering theAWTPs, therefore the primary source of these hazards isthe chloramination step in the AWTPs. Halo-acetic acids aremuch better rejected through reverse osmosis membranesthan trihalomethanes and the indicator compounds could be

Significant uncertainty in terms of the actual removal thatcan be achieved reliably through BAC and GAC for thisrange of compounds (likely to be significant variations

between parameters, GAC type and age/operation, andremoval at upstream wastewater treatment plant).

There is also some uncertainty regarding the typicalconcentrations in the effluent of the WWTP

It is unclear whether any indicator or surrogate can becontinuously monitored to identify any significant leakage of

any of these contaminants

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● = Excellent (90-100%)

◕ = Good (60-90%)

◑ = Fair (20 - 60%)

○ =Poor (0-20%)

Barrier Assessment(based on drinking the product water

assuming all barriers worked as designed)

Inherent Risk(based on drinking feedwater directly at 2L

per day)

Bromate 0.01 0.0067 mg/L 0.67 Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ○ ○ ○ ○ ○ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Chloramines (as Cl2) 4 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A Dosing Control Minor Unlikely Low (B2)

Chlorine (as Cl2) 4 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A Dosing Control Minor Unlikely Low (B2)

Chlorine dioxide (as ClO2) 0.8 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable N/A Dosing Control Minor Unlikely Low (B2)

Chlorite 1 0.01 mg/L 0.01 Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ○ ○ ○ ○ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

NDMA 10 11 ng/L 1.1 Chronic Health Moderate Possible High (C3) Estimate ○ ○ ○ ○ ○ ○ N/Aozone-coagulation-filtration to

remove NOM (DOC)BAC-GAC

Minor PossibleModerate

(C2)

Haloacetic acids (five) 0.06 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ◑ ◑ ○ ◕ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Dichloroacetic acid 0 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ◑ ◑ ○ ◕ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Trichloroacetic acid 0.02 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ◑ ◑ ○ ◕ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Monochloroacetic acid 0.07 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ◑ ◑ ○ ◕ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Total Trihalomethanes 0.08 0.526 mg/L 6.575 Chronic Health Moderate Possible High (C3) Reliable ○ ○ ○ ◑ ○ ◑ N/Aozone-coagulation-filtration to

remove NOM (DOC)BAC-GAC

Minor PossibleModerate

(C2)

Bromodichloromethane 0 0.0445 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ○ ◑ ○ ◑ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Bromoform 0 0.11 mg/L Chronic Health Minor PossibleModerate

(C2)Reliable ○ ○ ○ ◑ ○ ◑ N/A

ozone-coagulation-filtration toremove NOM (DOC)

BAC-GACMinor Unlikely Low (B2)

Dibromochloromethane 0.06 0.08 mg/L 1.333333 Chronic Health Moderate Possible High (C3) Reliable ○ ○ ○ ◑ ○ ◑ N/Aozone-coagulation-filtration to

remove NOM (DOC)BAC-GAC

Minor PossibleModerate

(C2)

Precursors in trade and domesticwaste - Byproduct chlor(am)ination

DBPs and DBP formationshould always be evaluated ona site-specific basis and incontext with site-specificblending water quality anddisinfection practices. Ingeneral, DBP precursors arewell-controlled by ROmembranes and overallformation is expected to below. NDMA and other pre-formed DBPs may still passthrough subsequent treatmentstages and, in the case ofNDMA, need furhter treatmentsuch as with UV/AOP.

B-13 WateReuse Research Foundation

Inherent Risk and Assessment of Hazardous Events: Ozone-BAC-Based TreatmentThis considers hazardous events that may cause the treatment process to fail.

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WWTPDischarge of large quantities of organic chemicalsleads to sudden spikes in organics

VOCs Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP aslarge discharges would likely disrupt the WWTPprocesses.

Trade and domestic waste management

Regular source water monitoring

WWTP processes(CCP)WQ monitoring at AWTPinlet

Minor Unlikely Low (B2) As low as reasonably practicable Reliable Source control measures

WWTPIllegal discharge of large quantities of organicchemicals leads to sudden spikes in organics

SOCs Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP aslarge discharges would likely disrupt the WWTPprocesses.

Trade and domestic waste management

Regular source water monitoring

WWTP adsorption andbiological degradation

RO and UV/AOP

Minor Unlikely Low (B2) Reliable Source control measures

WWTPOutbreak of infectious disease in the communityleads to higher than usual source pathogenconcentration

Pathogens CatastrophicAlmostCertain

Very High(E5)

WWTP and AWTP designed to deal withpathogen concentrations found in wastewaterduring outbreaks, including multiple disinfectionsteps

Link to departments of health and early warningnetworks

WWTPMF-RO-UV/AOP-Chlorine

Minor Unlikely Low (B2) Estimate

WWTPFailure of biological processes leads to poor qualitysewage (eg. failure of aeration)

Pathogens Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP(CCP)

Communication protocols between operators ofWWTP and AWTP

MF-RO-UV/AOP-Chlorine

Minor Unlikely Low (B2) Estimate

WWTP High rainfall events leading to bypass Pathogens Moderate Possible High (C3)

Monitoring of WQ (Ammonia) from the WWTP(CCP)

Communication protocols between operators ofWWTP and AWTP leading to shutdown of AWTPduring bypass

UF-RO-UV/AOP-Chlorine

Moderate UnlikelyModerate

(B3)Estimate

WWTPFailure to shut off pumps in response to adversemonitoring/ bypass signals at the interface point,leading to water quality that does not comply

Pathogens Moderate UnlikelyModerate

(B3)

Shut off trigger systems supported bymaintenance, calibration and validation;Failsafe on instrumentation;CCP response procedure;Additional staff training.

UF-RO-UV/AOP-Chlorine

Moderate Rare Low (A3) Reliable

Pre-treatmentUnderdosing ozone leads to poor coagulation andreduced removal of contaminants and DBPsprecursors

MetalsDBPsTDS

Minor PossibleModerate

(C2)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous monitoring

O3-BAC-GAC-UV-Cl2 Insignificant Unlikely Low (B1) Impact on DMF and capacity is not considered here Estimate

Pre-treatmentUnderdosing of coagulant leads to poor coagulationand reduced removal of contaminants and DBPsprecursors

MetalsDBPsTDS

Minor PossibleModerate

(C2)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous operational monitoring of DMF

O3-BAC-GAC-UV-Cl2 Insignificant Unlikely Low (B1) Impact on DMF and capacity is not considered here Estimate

Pre-treatmentCatastrophic failure of DMF with unfiltered waterpassing through

PathogensMetalsDBPsTDS

Moderate Possible High (C3)Continuous operational monitoring of DMF andshutdown/alarm triggers in the event of pressureloss

O3-BAC-GAC-UV-Cl2 Moderate Rare Low (A3)ALARP - Ozone would still provide somedisinfection but would no longer be integral due toparticles in the water

Uncertain

OzonationUnderdosing ozone leads to reduced pathogenremoval

Pathogens Major PossibleVery High

(C4)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous dose and flow monitoring (CCP)

BAC-GAC-UV-Cl2 Insignificant Rare Low (A1)As ozonation is critical to achieving the removalneeded (not as much redundancy when comparedto the UF-RO-UV/AOP-Cl2 train)

Estimate

Ozonation Overdosing of ozoneNo health hazard (onlyenergy consumptionissue)

Maximum Risk. The

inherent hazard risk or an

assessment of risk without

preventive measures if the

hazard is added in the

process.

Residual Risk. The risk

posed by each hazardous

event, if the water was

consumed directly.

B-14

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BACMaximum load reached leading to contaminantbreakthrough

All hazards Moderate Possible High (C3)Surrogate continuous monitoring (CCP) andoperational monitoring; operation to achieve 0.1NTU turbidity goal; backwashing

O3-GAC-UV-Cl2 Moderate UnlikelyModerate

(B3)Uncertain

BACCatastrophic failure of filter with unfiltered waterpassing through

Moderate Possible High (C3)

Automated shut-off/alarm triggers on pressuresupported by maintenance and calibration (CCP)Continuous pressure, turbidity, and flowmonitoring

O3-GAC-UV-Cl2 Moderate Rare Low (A3) Uncertain

BACBiology is inactivated leading to passage ofuntreated contaminants

Minor PossibleModerate

(C2)Surrogate continuous monitoring (CCP) andoperational monitoring

O3-GAC-UV-Cl2 Minor Unlikely Low (B2)

GACMaximum load reached leading to contaminantbreakthrough

ModerateAlmostCertain

High(E3)Surrogate continuous monitoring (CCP) andoperational monitoring; miantain GACreplacement frequency

O3-BAC-UV-Cl2 Minor Unlikely Low (B2) Uncertain

GACCatastrophic failure of filter with unfiltered waterpassing through

Moderate UnlikelyModerate

(B3)

Automated shut-off/alarm triggers on pressuresupported by maintenance and calibration (CCP)Continuous pressure, UVT,TOC, and flowmonitoring

O3-BAC-UV-Cl2 Minor Rare Low (A2) Uncertain

UVUV dose is not sufficient leading to poorcontaminant removal

Pathogens, DBPs Major PossibleVery High

(C4)

Automated UV dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous dose and flow monitoring (CCP)Alerts associated with lamp failures

O3-BAC-GAC-Cl2 Minor Unlikely Low (B2) Uncertain

UVUpstream filters leak turbidity/colour leading to highUVT and poor contaminant removal by UV

Pathogens, DBPs Moderate Possible High (C3)

Automated shut-off/alarm triggers on UVTsupported by maintenance and calibration (CCP)Continuous UVT and flow monitoring (CCP)Alerts associated with filter failures

O3-BAC-GAC-Cl2 Minor Unlikely Low (B2) Uncertain

Post-treatmentdisinfection

Underdosing leads to low free chlorine residual andnon-compliant water

Pathogens Moderate Possible High (C3)

Automated process control systems with shut-offtriggers, supported by maintenance andcalibration.Optimised chlorine dosing regulationPlant shut-down on low free chlorine.

WWTPDrinking watertreatmnet?

Moderate UnlikelyModerate

(B3)Uncertain

Post-treatmentdisinfection

Overdosing leads to non compliant water DBPs, free chlorine Minor PossibleModerate

(C2)

Automated dosing systems with shut-off/alarmtriggers supported by maintenance andcalibrationContinuous monitoring

Minor Unlikely Low (B2) Uncertain

Treated waterstorage

Contamination with fauna and pathogens Pathogens Minor PossibleModerate

(C2)Regular inspectionPhysical protection

Chlorine disinfection Minor Unlikely Low (B2) Uncertain

OverallOnline instrument failure leading to water that doesnot comply

All hazards Minor PossibleModerate

(C2)

External calibration and servicing program;Regular operator checks (instruments monitoringCCPs checked daily).

Minor Unlikely Low (B2) Reliable

OverallPower failure or partial power failure across systemresulting in water quality that does not comply

All hazards Insignificant Unlikely Low (B1)

Inherently controlled by the pumps being requiredto maintain water flows and the presence of back-up power supplies.

Regular inspection program

Insignificant Rare Low (A1) Reliable

OverallSabotage, vandalism or terrorism causing unkownhazards to be added

Chemicals, pathogens Moderate UnlikelyModerate

(B3)

Security measure such as fencing and lockedgates around pump stations and water treatmentplants.Regular inspections of infrastruture and securiymeasures.

Online monitoring of conductivity at delivery pointsmay detect some irregularities.

If contamination ofsource water, WWTP-O3-BAC-GAC-UV-Chlorine, blending

Moderate Rare Low (A3) Reliable

OverallContaminants in bulk chemicals added during thetreatment or post-treatment lead to non compliance

Chemicals Minor PossibleModerate

(C2)

Contracts with suppliers specify maximumcontaminant limits. Require test results to bedelivered with each batch.

Testing of new chemical batches at delivery

Depending on wherechemicals areintroduced

Minor Rare Low (A2) Reliable

OverallOverdosing of any chemical leading to hazard andnon compliance

Chemicals Minor PossibleModerate

(C2)

Automatic dosing control and regularmaintenance and callibration of onlineintrumentation that controls dosing.Regular monitoring of treated water for knownchemical risks

Depending on wherechemicals areintroduced

Minor Rare Low (A2) Reliable

OverallPotential introduction of hazards duringmaintenance processes

Chemicals Minor Unlikely Low (B2) Appopriate procedures and trainingDepending on wherechemicals areintroduced

Minor Rare Low (A2) Reliable

B-15

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Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

Insignificant Rare Low (A1) Insignificant Rare Low (A1) Uncertain

B-16

Water Environment & Reuse Foundation 1199 North Fairfax Street Alexandria, VA 22314-1177

Phone: 571-384-2100 Fax: 703-299-0742 Email: werf@werf.org www.werf.org

WE&RF Stock No. Reuse-13-03 WE&RF ISBN: 978-1-94124-243-8

Co-published by IWA Publishing

Alliance House, 12 Caxton Street London SW1H 0QS, United Kingdom Phone: +44 (0)20 7654 5500 Fax: +44 (0)20 7654 5555 Email: publications@iwap.co.uk

www.iwapublishing.com IWAP ISBN: 978-1-78040-850-7

October 2016