URWOOD BEACH WASTEWATER REATMENT PLANT HEALTH …...burwood beach wastewater treatment plant health risk quantitative microbial risk assessment david roser, ben van den akker & richard

BURWOOD BEACH WASTEWATER TREATMENT PLANT HEALTH RISK QUANTITATIVE MICROBIAL RISK

ASSESSMENT

DAVID ROSER, BEN VAN DEN AKKER & RICHARD STUETZ (WRC KENSINGTON)

VERSION 5_3 – MAR 2010

WATER RESEARCH CENTRE SCHOOL OF CIVIL AND ENVIRONMENTAL ENGINEERING

VALLENTINE ANNEX (H22) UNSW SYDNEY 2052

PH (02) 9385 5097 FAX (02) 9313 8624

EMAIL [email protected] WEBSITE cwwt.unsw.edu.au

WRC Hunter Water Stage 2 Report final v5_3

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DOCUMENT STATUS RECORD

Project Title: BURWOOD BEACH WASTEWATER TREATMENT PLANT

HEALTH RISK QUANTITATIVE MICROBIAL RISK ASSESSMENT

Client Hunter Water Corporation Job No. 2010/1 Document Title BBWTP Risk Assessment Document File Name: WRC Hunter Water Stage 2 Report final v5_3

Signatures Issue No Date of Issue Description Authors Checked Approved 1 12/May/2009 Draft DR,BV,RS RS RS 2 1/Aug/2009 Galley DR,BV,RS RS DR 3 11/Sep/2009 Final draft DR,BV,RS BV DR 4 15/Mar/2010 Final

version DR,BV,RS BV DR

Notes: Issue 1 Draft report for internal review and discussion with clients Issue 2 Galley proof issued to client for proofing Issue 3 Final Draft version Issue 4 Final version Disclaimer:

1. The Water Research Centre has taken all reasonable steps to ensure that the information contained in this publication is accurate at the time of production. In some cases, we have relied on information supplied by the client.

2. This report has been prepared in accordance with good professional practice. No other warranty, expressed or implied, is made as to the professional advice given in this report.

3. The Water Research Centre maintains no responsibility for the misrepresentation of results due to incorrect use of information contained within this report.

4. This report should remain together and be read as a whole. 5. This report has been prepared solely for the benefit of the client listed above. No liability is accepted by the Water Research Centre with

respect to the use of this report by third parties without prior written approval. 6. A range of stakeholder organisations were consulted in the preparation of this document especially through the workshop described in the

Appendices and main text. But the opinions expressed herein and conclusions reached must not be taken as necessarily reflecting the current policies or opinions of representatives/experts within these organisations.

Copyright: © UNSW Water Research Centre


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Contents

Contents ...............................................................................................................................................3 Glossary ...............................................................................................................................................7 Executive Summary .............................................................................................................................8

Background ......................................................................................................................................8 Guidelines ......................................................................................................................................10 Risk Assessment Principles ...........................................................................................................10 Key Issues of Concern ...................................................................................................................11 Risk Assessment Strategy ..............................................................................................................12 Quantitative Risk Estimation .........................................................................................................13 Wastewater Quality........................................................................................................................13 Inactivation Experiments ...............................................................................................................14 Occurrence of Onshore Transport..................................................................................................15 ‘Exceedence Probability’ and Risk Estimation..............................................................................15 Key Findings ..................................................................................................................................17 Uncertainties ..................................................................................................................................18 Conclusions....................................................................................................................................20

1. Introduction................................................................................................................................22 1.1. Objective ............................................................................................................................22 1.2. Wastewater treatment plant description and study location...............................................22 1.3. Background ........................................................................................................................23 1.4. Scope of the study..............................................................................................................24

1.4.1. What this Assessment Involves in Summary.............................................................24 1.4.2. Typical Newcastle Beachwater Microbial Quality Based on Beachwatch Assessment 24 1.4.3. Risk Assessment Study Models .................................................................................25 1.4.4. Guidelines ..................................................................................................................26

1.5. Assessment Strategy ..........................................................................................................26 1.5.1. Risk Assessment Principles and Tasks ......................................................................26 1.5.2. Engagement................................................................................................................27 1.5.3. Issue Identification.....................................................................................................27 1.5.4. Hazard Identification..................................................................................................28 1.5.5. Dose Response Assessment .......................................................................................28 1.5.6. Exposure Assessment.................................................................................................28 1.5.7. Risk Characterization.................................................................................................29 1.5.8. Risk Management ......................................................................................................30 1.5.9. Hazardous Events.......................................................................................................30

1.6. Bathing Risk Benchmarks..................................................................................................32 1.6.1. Tolerable Illness Probability ......................................................................................32 1.6.2. Relating QMRA Outputs for Hazardous Events to Benchmarks...............................33

1.7. Structure of the Report.......................................................................................................33 2. Groundwork ...............................................................................................................................35

2.1. Introduction........................................................................................................................35 2.2. Issue Identification.............................................................................................................36

2.2.1. Problem Conceptualisation ........................................................................................36 2.2.2. Modelling Strategy.....................................................................................................38 2.2.3. Workplan Arising from QMRA Context and Stakeholder Discussions ....................38 2.2.4. Method for Quantifying Risk Arising from Hazardous Event Conditions ................40 2.2.5. Risk Assessment Scale and Logistics Issues..............................................................40


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2.3. Hazard Assessment ............................................................................................................46 2.3.1. Pathogen Concentrations and Loadings in Effluent and WAS..................................46 2.3.2. Dose Response Assessment .......................................................................................60

2.4. Exposure Assessment.........................................................................................................62 2.4.1. Exposure Locations....................................................................................................62 2.4.2. (Microbial) Particle Transport and Inactivation in Coastal Waters ...........................63 2.4.3. Consumption of Seawater ..........................................................................................65 2.4.4. Exposure and Risk Assessment Scenarios .................................................................66

2.5. Risk Characterization.........................................................................................................70 2.5.1. Analysis of the Historical Record and Previous BBWWTP Study Data...................70 2.5.2. Analysis of Primary Hydraulic Fate and Transport Pathway Data............................70 2.5.3. Basic QMRA modelling methodology ......................................................................71 2.5.4. Operational Integration of the QMRA and Hydraulic Models ..................................71 2.5.5. Reporting of Risk as Exceedence Probability............................................................73 2.5.6. Quantifying and Communicating Hazardous Event Consequence + Likelihood ......73

2.6. Uncertainty and Reality Checks.........................................................................................74 2.6.1. Considerations Check List .........................................................................................74 2.6.2. Hydraulic Modelling..................................................................................................74 2.6.3. Survey of Wastewater Quality and Discharge Hydrology.........................................74 2.6.4. Inactivation Studies – Microcosms and Water Transmissivity..................................75 2.6.5. Seasonality of Disease Burden and Outbreaks ..........................................................76 2.6.6. Validation and Calibration .........................................................................................77

3. Risk Characterisation .................................................................................................................79 3.1. Historical Data Analysis ....................................................................................................79

3.1.1. Evidence of Hazardous Events/Periods in Routine Indicator Monitoring Data ........79 3.1.2. Derivation of ‘Baseline’ Microbial Reduction Estimates ..........................................80 3.1.3. Previous Hydraulic Modelling and QMRA ...............................................................81

3.2. Hydraulic Modelling Output Statistics ..............................................................................82 3.2.1. Raw Output Summary Statistics ................................................................................82 3.2.2. What Analysis of Hydraulic Data Showed about Risk Reduction by Coastal Waters 83 3.2.3. Secondary Summary Statistics...................................................................................84

3.3. Assessment of Risk at Exposure Points under different Exposure Scenarios....................85 3.4. Variation in Waste Stream Dilution and Inactivation........................................................86

3.4.1. Exceedence Plots of Particle Reduction.....................................................................86 3.5. The Episodic Character of Contamination Events.............................................................87

3.5.1. Travel Times ..............................................................................................................89 3.5.2. Diurnal Timing of Events ..........................................................................................90 3.5.3. Influence of winds and currents .................................................................................92 3.5.4. Event Frequency and the Problem of Acceptable Bathing Hazardous Event Risk ...94

3.6. Infection and Illness Risks Associated With Hazardous Events and Circumstances ........95 3.6.1. Summary Percentiles..................................................................................................95 3.6.2. Baseline......................................................................................................................95 3.6.3. Effluent Under Baseline+Event Conditions...............................................................95 3.6.4. WAS under Baseline+Event Conditions....................................................................96 3.6.5. Plots of Baseline Risk to Normal Bathers from Secondary Effluent + WAS..........102 3.6.6. Plots of Baseline+ Event Summer Risks (Best Dominant Conditions for Shoreline Bathers) 103 3.6.7. Winter Risk ..............................................................................................................108 3.6.8. Risk from ‘All Pathogens’(as Enterococci) v. Index Pathogens..............................111 3.6.9. 2007 v. 2030.............................................................................................................111


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3.6.10. Seasonality ...............................................................................................................111 3.6.11. Between Beach Differences .....................................................................................112 3.6.12. Source Dominance Effluent or WAS?.....................................................................112 3.6.13. Effect of Solar Radiation..........................................................................................112 3.6.14. Sensitivity of Predicted Risk to Input Assumptions ................................................114 3.6.15. Seasonality and Outbreaks .......................................................................................118

4. Key Findings ............................................................................................................................121 5. Uncertainties ............................................................................................................................123

5.1. Model output prediction...................................................................................................123 5.2. Reliability of the Surfer sea water consumption (200mL per exposure) .........................123 5.3. Consumption of seawater in a single timestep.................................................................123 5.4. Clustering of hazardous (timestep) periods .....................................................................123 5.5. Assumed Level of Baseline Protection ............................................................................123 5.6. Number of Monte Carlo Iterations...................................................................................123 5.7. Data Set Numbers and Quality.........................................................................................124 5.8. Campylobacter Levels and Sensitivity Testing ...............................................................126 5.9. Water Transmissivity .......................................................................................................126 5.10. Increase in Illness Probability due to Seasonal and Outbreak Related Pathogen Peaks 126 5.11. Accommodating uncertainty ........................................................................................128

6. Conclusions..............................................................................................................................130 6.1. Primary Conclusions........................................................................................................130 6.2. Special Concerns..............................................................................................................130

7. References................................................................................................................................132 8. Appendices...............................................................................................................................145 Appendix 01 Newcastle Beachwater Quality and Baseline Reductions.......................................145 Appendix 02 Models for Microbial Risk Assessment in the Natural Environment in the Literature 155 Appendix 03 Justification for Project Design...............................................................................156 Appendix 04 Risk Management and HACCP ..............................................................................158 Appendix 05 Final Study Approach .............................................................................................160 Appendix 06 Other Strategic Elements ........................................................................................162 Appendix 07 Scope of Work Planned at Project Commencement ...............................................164 Appendix 08 Hazardous Events associated with Coastal Zone Outfalls ......................................167 Appendix 08a Acceptable Risk and Tolerable Risk .....................................................................170 Appendix 09 Illustrative Example of Risk Characterization via Microbial Risk Probability Calculation 176 Appendix 10 Seasonality, Outbreaks and Hazardous Events .......................................................180 Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation 185 Appendix 12 Guidelines, Combinatorial Explosion and the Scale of Risk Modelling ................209 Appendix 13 Selected Excerpts from WRL Modelling (Glamore et al., 2008) ...........................215 Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event Characterization ...............................................................................................................................221 Appendix 15 Hydraulic Modelling of Particle Transport and Inactivation in Coastal Waters.....231 Appendix 15a Plain English Explanation of Particle Fate and Transport Modeling Method ......234 Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity Experiments 243 Appendix 17 Example of Part of Simruns 2_1 Record Table ......................................................262 Appendix 18 Dose Response Assessment ....................................................................................263 Appendix 19 Assessment of Winter Model Input Data For Newcastle Coastal Waters ..............266


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Appendix 20 Hydraulic Statistics for BBWWTP Experimental Period.......................................268 Appendix 21 Hazardous Event Risks and the Guideline Water Quality Categories ....................271 Appendix 22 Exceedence Probability Statistics and Risk Benchmarking....................................273 Appendix 23 Wastewater and WAS Quality ................................................................................275 Appendix 24 Summary of Hydraulic Modeling Outputs..............................................................284 Appendix 25 Summary Tables for Hydraulic Modelling Statistics..............................................290 Appendix 26 Exceedence Probabilities ........................................................................................307 Appendix 27 Microbial Abundance and Risk Exceedence Plots I Nominal Dilution (i.e. Baseline) 313 Appendix 28a Microbial Abundance and Risk Exceedence Plots II Secondary Effluent Summer (Baseline + Event Scenario).............................................................................................................322 Appendix 28b Microbial Abundance and Risk Exceedence Plots II Secondary Effluent Winter (Baseline + Event Scenario).............................................................................................................338 Appendix 29a Microbial Abundance and Risk Exceedence Plots III WAS Summer (Baseline + Event Scenario) ................................................................................................................................351 Appendix 29b Microbial Abundance and Risk Exceedence Plots III WAS Winter(Baseline + Event Scenario) ................................................................................................................................359 Appendix 30 Reduction and Dilution Plots From Hydraulic Modelling......................................376 Appendix 31 Extracts for WRL Modelling of High Risk Periods................................................385 Appendix 32 Sensitivity Analysis Plots .......................................................................................388 Appendix 33 Selected Extracts from the WHO Recreation Guidelines (World Health Organization, 2003) .........................................................................................................................400 Appendix 34 Regressions describing WWTP flow and rainfall...................................................403 Appendix 35 Follow-up Work......................................................................................................405


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Glossary Term/Abbreviation Explanation/Full Wording

AFRI Acute Febrile Respiratory Illness (see NH&MRC 2008) Baseline and Hazardous Events

When estimating hazardous event risks we have modeled a ‘Baseline’ risk for the majority of model iterations, and a Hazardous Event risk for a minority of model iterations.

Benchmark As used here this is a numerical target associated with water quality. It could be a water quality target, a risk target or an exposure frequency.

CRG Community Reference Group CWWT Centre for Water and Waste Technology, University of NSW – name being changed to

Water Research Centre DECC NSW Department of Environmental and Climate Change DR Decimal Reduction = reduction by a factor of 10 Exceedence probability 1- the probability of an event expressed as a percentile. So the 99th percentile is the same as

an Exceedence Probability of 0.01. The latter is used because the percentile terminology is cumbersome and Exceedence Probabilty is a well developed communication and benchmark setting tool.

GSX Global Solar Exposure – terminology used by Australian Bureau of Meteorology to describe th entire solar spectrum at the earth’s surface (includes ultraviolet and infra-red as well

HWC Hunter Water Corporation Hydraulic Since the modelling included consideration of processes additional to ‘Hydrodynamic’

(water in motion) ones the generic water transport and mixing modelling has been described as ‘Hydraulic’ (pertaining to water) to distinguish from some of the sub-models which can be seen as purely ‘hydrodynamic’ models.

Hydrodynamic Water in motion Metamodel Term used to describe the QMRA modeling system. The basic modeling system is a simple

linear one based on exposure pathway analysis. But all the input assumptions are subject to change where the input algorithms describing each stage in the pathway are altered in the same manner as input values can be altered.

Microbial Water Quality Category A, B, C and D

Water quality based of recreational waters based on NHMRC (2008) Guideline Table 5.13

PDF Probability Density Function QMRA Quantitative Microbial Risk Assessment QRA Quantitative Risk Assessment generally e.g. for chemicals and pathogens Resampling Estimating the precision of sample statistics (medians, variances, percentiles) by using

subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping). The second approach is in effect what was done here when estimating risk probability.

Scenario Risks are estimates for a range of scenarios which are designed to explore different sources of risk variation e.g. between beaches, waste streams and populations. For each scenario a different model is constructed. Those scenarios modeled have been documented.

SD Standard Deviation WAS Waste Activated Sludge WRC University of NSW Water Research Centre which has several nodes including WRL and

the former CWWT. CWWT and WRL at the time of writing are in the process of merging so some reports are purely WRL authored and others are joint.

WRL Water Research Laboratory School of Civil and Environmental Engineering University of NSW

WWTP/BBWTP WasteWater Treatment Plant/Burwood Beach Wastewater Treatment Plant


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Executive Summary

Background

This study explores the fate, transport and risk posed to bathers using Newcastle Beaches from the pathogens in WAS (waste activated sludge) and (secondary treated sewage) effluent discharges from the Burwood Beach Wastewater Treatment Plant (BBWWTP) into the coastal waters off Burwood Beach. BBWWTP is the largest of Hunter Water’s wastewater treatment facilities and currently treats an average daily wastewater flow of 44 million litres. BBWWTP (Figure I). It has recently experienced process problems and requires a substantial upgrade to ensure reliable operation and to cater for growth. The plant is located approximately 4 km south of the Newcastle CBD adjacent to the coastal suburb of Merewether, and is surrounded by the Glenrock State Conservation Area. The plant services the Newcastle CBD and suburbs extending to Dudley in the south, Wallsend in the west and Mayfield in the north. The catchment produces mainly domestic wastewater, with a minor component of commercial and industrial wastewater. The plant processes the wastewater received to a secondary treatment level. The plant is licensed to discharge effluent and WAS generated by the WWTP to the ocean via a 1.5 km extended ocean outfall (Environment Protection Licence 1683). Hunter Water is planning a major upgrade of the plant to rectify process and reliability constraints and to ensure the plant operates sustainably into the future. The upgrade is being undertaken in two stages, namely the Stage 2 Upgrade, which will ensure the plant can operate to its original design capacity and meet current licence requirements; and the Stage 3 Upgrade which will provide the long-term sustainable strategy for the plant. The upgraded plant will have the capacity to treat an average daily wastewater flow of 53 million litres per day. Further information on the Stage 2 Upgrade can be found in Burwood Beach WWTW Stage 2 Upgrade - Review of Environmental Factors (CH2M HILL, 2009). Discharge occurs from a series of outfalls located 1.5 km off the south end of Burwood Beach. Burwood Beach and Dudley Beach are notable for having large forested catchments between the beach and the ridgeline above them and hence are less impacted by local urban stormwater during dry weather periods. An issue of concern raised by the Community Reference Group consulted about this upgrade is the potential health risk presented by the discharge from Burwood Beach WWTP. Notably the ability of the Beachwatch programme to characterise the health risk from the discharge, particularly with respect to viral contamination, was queried. Given the unique nature of the Burwood Beach discharge, a commitment was made to undertake a quantitative microbial health risk assessment (QMRA) to assess the human health risk presented by the discharge. A screening level health risk assessment was completed in 2008 by consultants CH2MHill using a desk-top approach of combining outputs from a hydrodynamic model of the discharges and literature sourced information on pathogen levels, inactivation rates and other broad assumptions. While indicating minimal risk under typical water quality conditions, the study did suggest that under some combinations of environmental and operational conditions elevated health risk to bathers could arise. A more detailed and documented investigation was recommended which led to the current study.


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Figure I. Location of the study beaches, the WWTP and the discharge points This document presents the background, rationale, design and results of the follow-up study. The study has involved:

• Estimation of index pathogen and indicator concentrations in source material; • Estimation of WWTP barrier effects and their moderation of source contaminant levels; • Estimation of the probability density functions describing the reduction in concentration of

conservative and non conservative contaminants between the outfalls and 8 assessment locations in the coastal waters off Newcastle (Figure II);

• Use of literature data to estimate pathogen ingestion; • Integration of the above data using Quantitative Microbial Risk Assessment techniques to

estimates infection and illness risk probabilities for pathogens overall and individually.


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Figure II. Exposure Assessment Points

Guidelines

Risk assessments relating to water quality are not undertaken in isolation but rather as part of a larger process of risk assessment and management. In the Australian context of Risk Assessment for water recreation there are three primary guiding documents:

• New natural bathing water recreation Guidelines especially the finalised NH&MRC (2008) document ; which is extensively based on

• The primary WHO documents (compare for example WHO, 2003); • EnHealth (EnHealth Council, 2002) guidelines which cover Health Risk Assessment and are

used as a general guide by Departments of Health when assessing health risks.

Risk Assessment Principles

A common feature of these Guideline documents is that they are based on, or promote, the application of risk assessment and management principles (AS/NZS 4360)(Standards Australia/Standards New Zealand, 1999). This marks a change from past assessment/ management frameworks for bathing water borne/related/associated risks. In the case of recreation, the most explicitly identified guiding principles appear to be Health Risk Assessment principles detailed in EnHealth (2002) as well as HACCP principles (NH&MRC, 2008, pp.14, 65, 68, 69):

• Hazard analysis; • Control points; • Critical limits; • Monitoring; • Management actions; • Validation/verification; • Record keeping.


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In EnHealth (2002) there is recommended process on how to undertake a health risk assessment (HRA) which is promoted by NSW Health and outlined in Figure III.

Engagement

Hazard Assessment

Risk Characterization

Risk Management

Exposure AssessmentLocations

Pathways

Exposed populations

Exposure concentration

Intakes

Hazard Identification

Issue Identification

Dose Response Assessment

Review Review

Figure III. Summary of Recommended Healthy Risk Assessment Process (EnHealth Council, 2002)

Key Issues of Concern

Beachwatch data (Table I) shows that the typical water quality of Newcastle’s Beaches is high (Microbial Category A) and indicates the ocean outfall disposal system is an effective barrier to contamination from both treated effluent and WAS most of the time. Table I. Selected Summary Statistics for Enterococci (cfu.100mL-1) Beachwatch data collected for Newcastle Beaches in the Vicinity of Burwood Beach and Supplied by Hunter Water

Period Statistic Dudley beach

Burwood Beach South

Burwood Beach North

Merewether Beach

Bar Beach

Percentile 0.95 23 52 41 43 42 All Data including Rainfall Impacted days 2001-2006 Median 0 1 1 2 2

Percentile 0.95 16 16 16 24 21 Data for days without Rainfall Impact 2001-2006 Median 0 1 1 1 1

However, supplementary hydraulic modeling has indicated that on occasion diluted contamination could find its way to the beaches owing to the occurrence of coastal water destratificiaton and strong on-shore currents (Figure IV). The question that needed to be answered in line with recommendations in the new NHMRC(2008) recreation guidelines was to what extent the pathogens in the WAS and effluent posed a risk to bathers under such ‘Exceptional Circumstances’. Of particular concern to the community were the following:

1. Did the WAS pose a risk higher than that arising from the effluent? 2. Does the solids loading or viral pathogens in the WAS discharge present an elevated health

risk to beach users compared to dose-response relationships developed for enterococcus in effluent discharges? and

3. Does the current Beachwatch monitoring program adequately characterise the risk from short duration high risk events?


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Figure IV. Illustrative Examples of Modelled Dispersion and Reduction of Thermotolerant Coliform numbers in Surfacing Effluent and WAS Plumes where plumes moved toward the beach zone (Glamore et al., 2008) Notes:

1. The initial assumed concentration in the effluent and WAS was 107.100mL-1. 2. The concentrations shown are illustrative models rather than actual measurements.

Risk Assessment Strategy

A survey of raw and treated effluent and waste activated sludge (WAS) quality was undertaken by local contract water quality laboratories as well as HWC. This was aimed at providing sufficient data to estimate probability density function (PDF) coefficients and defining the typical Baseline quality of the raw screened sewage, secondary treated effluent and WAS. PDFs were subsequently used in the QMRA modelling component of this study. The index pathogens assayed were:

• All gastrointestinal pathogens collectively (enterococci are used as a surrogate). • Adenovirus • Giardia lamblia • Cryptosporidium spp., • Campylobacter spp., • Rotavirus (risk not assessed ultimately as few were detected).

The following work was performed to support assessment of whether the WAS posed an enhanced pathogen related risk:

• Separate analysis of its pathogen content; • Comparison of this content data with that in the effluent stream for any inconsistency which

would suggest the concentrations were being underestimated; • Determination of likely pathogen fate and transport by hydraulic modelling and QMRA of

WAS as well as the secondary effluent streams; • Modelling of 2030 as well as the 2007 hydraulic loads to estimate the increase in risk due to

increased discharge rates; • Experimental measurements of WAS v. secondary effluent indicator inactivation.

Uncertainties about pathogen survival and inactivation were addressed by:

• Modelling inactivation rates ranging from the most rapid reasonably conceivable to the most conservative (dilution only);

• Experiments on indicator inactivation by solar radiation to determine what rates were likely to occur and how the rates varied between WAS and secondary effluent;

• Measurement of ocean water transmissivity, which would likely influence solar inactivation.


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Exposure pathways, leading from the pathogen source to bathers are summarised in Figure V. These are central to risk estimation and exposure Scenario construction. They are in line with the pathways considered in the earlier WRL report (Glamore et al., 2008). The assessment did not consider risks arising from the intrusion of stormwater, WWTP failure, flow bypass, bather shedding and exposure routes other than ingestion. Not does this study detail or characterise the effectiveness of potential management options. Rather it provides data designed to enable informed decisions regarding future risk management strategies.

Water Borne Pathogen in Raw Sewage

WAS Outfall Treated Effluent Outfall

Primary + Secondary Treatment

Bathers Surfers

Diffusers @15-20m depth, 1.5 km off shore

Surface Waters (1-2 m)

Figure V. Exposure Pathways Under Consideration Leading Potentially to Ingestion During Primary Contact Recreation

Quantitative Risk Estimation

The risk estimation process was structured as follows: 1. Address the gap in data on pathogen concentrations in Hunter Water sewage in line with

Guideline concerns by analysing the input and discharge waste streams at Burwood Beach WWTP and using this data to generate pathogen source concentration PDFs, the starting point for the QMRA;

2. Instead of directly modelling the behaviour of selected (indicator) model microorganisms (e.g. Thermo-tolerant coliforms or enterococci) as done previously (Glamore et al., 2008), modify the coding of the previously developed hydraulic models to calculate and extract 8500 fifteen minute timestep estimates of reduction, dilution and inactivation of microbial particles reflecting coastal zone processes under a range of Scenarios covering a range of inactivation rates;

3. Estimate the range of pathogen concentrations at 8 bathing locations along the coast; 4. Transform the the dilution plus mass reduction outputs from the hydraulic models into

barrier PDFs analogous to those used to define microbial removal by constructed barriers, such as a chlorination and sand filtration at water/wastewater treatment plants;

5. For each final QMRA model calculate bather infection and illness risk probability as a function of “Exceedence Probability.

Wastewater Quality

Data resampling was used to estimate the overall reduction in pathogen and indicator numbers as log10 Decimal Reduction (DR) values, and the percentage of each organism type partitioned into the WAS. Little difference was seen between input and output loads except in the case of the F-RNA


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coliphage where regrowth probably occurred. C. perfringens numbers were unchanged. Cryptosporidium and Campylobacter appeared to have undergone slight increases in numbers but this was not statistically significant. Indicator assays of E. coli, enterococci and C. perfringens, which were probably the most robust, pointed to only modest Decimal Reductions s in the range of 0.03 to 0.4. Compared to its volume (<10% of total) the WAS received a disproportionate load of the pathogens and indicators with the possible exceptions of E. coli and Campylobacter. Most pronounced was the partitioning of Giardia. This result should be viewed as tentative though due to the poor recovery of the protozoans. Following comparison of the data sets it was decided that the starting concentrations used in the QMRA should be those for secondary effluent and WAS (Table II). Table II. Final PDFs for Use in QMRA

Discharge Material Pathogen Parameter Log10 average Log10 Standard Deviation units enterococci 5.352 0.317 cfu/100mL

Cryptosporidium (total) 1.368 0.409 oocysts/L Giardia (total) adjusted 2.23 0.508 cysts/L

Campylobacter spp. 0.425 0.468 mpn/L

Secondary

Adenovirus 1.586 0.447 pfu/L enterococci 5.914 0.457 cfu/100mL

Cryptosporidium (total) 2.412 0.139 oocysts/L Giardia (total) adjusted 4.55 0.337 cysts/L

Campylobacter spp. -0.2 1.108 mpn/L

WAS

Adenovirus 1.948 0.28 pfu/L

Inactivation Experiments

Comparisons of the inactivation rates for effluent and WAS showed that there was no major difference between the solar inactivation rates of indicator microorganisms within diluted WAS to those that were particle-free (secondary treated) effluent diluted in seawater. The inactivation rates ( S90 values) observed with WAS indicator microorganisms were comparable to literature values obtained from other aquatic environments (Table III). Table III. Solar inactivation rates of indicators in surface waters estimated from the roof-top microcosm experiments.

S90 (MJ m-2)a

Treatment E. coli

b Total

Coliformsb Enterococcib C.

perfringensb,c F-RNA Phage

(MS2)

Dark control (WAS 1:500) (35.4) (46.6) (ND) (ND) (10.9)d Secondary treated effluent dil. in seawater (1:500)

2.8 5.6 7.4 38 6.8d

WAS diluted in seawater (1:500)

5.6 8.2 8.0 35 8.2d

WAS diluted in seawater (1:5000)

4.0 6.4 7.8 39 –

Notes: 1. aGSX S90 values: Cumulative Diurnal Global Solar Exposure at which 90% of organisms were inactivated. S90

values were calculated from the steep part of the die-off curves. The values shown have been doubled to correct for the reflectivity of the reactors which were originally design for studying drinking water sterilization.

2. bValues represent the average of duplicate experiments. 3. cPresumptive Clostridium perfringens (or sulphite reducing clostridia). 4. dDilution range was reduced from 1:500 to 1:200 due to low concentrations of native phage detected within the

secondary treated wastewater and WAS. 5. ND: no decay detected. i.e. slope does not deviate from zero (p.>0.05).


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Occurrence of Onshore Transport

Before undertaking QMRA the output from the hydraulic modeling was closely examined. These outputs provided an initial indication of the strength of the barrier effect provided by the coastal zone waters and where (modeled) risks were likely to be greatest. This pre-QMRA analysis was seen as indicating the extent to which QMRA was required and which scenarios would be most informative. This analysis (e.g. Figure VI) confirmed that on occasion (microbial) particles should find their way to the bathing zones in elevated numbers. The risk from such intrusion was offset by the peaks tending to be sporadic, of short duration and still characterized by marked dilution (90 particles per 15 minute timestep indicates a waste stream dilution of ca 102).

0

50

100

150

200

250

300

25/02/2007 4/03/2007 11/03/2007 18/03/2007 25/03/2007Date

No.

of P

artic

les

per T

imes

tep.

6 (Dudley)7 (Burwood)

Event 2a

Event 1a

Figure VI. Example Timeseries Plot Showing (Microbial) Particle Intrusion into Surfing Areas.

‘Exceedence Probability’ and Risk Estimation

Quantitative microbial risk statistics were generated for ca 200 selected exposure scenarios mainly for enterococci (surrogate for all pathogens), Adenovirus and Giardia which were identified as the pathogens of most concern. The output risks were calculated in terms of 1. the numbers of pathogens per L; 2. the probability of infection, and 3. the probability of illness. Risks were tabulated and plotted as a function of what is formally termed the ‘Exceedence Probability’. ‘Beachwatch’ monitoring has shown that typically, the dry weather ‘95th percentile’ of water quality (enterococci) measurements at Newcastle’s beaches falls within guideline targets. As a result the current assessment focused on quantifying the risk from rarer (probability < 5%), sporadic and transient periods of microbial particle intrusion into the bathing areas (Figure VI). The resulting statistics are expressed here in terms of ‘Exceedence Probability’. Exceedence Probability can be understood most simply by consideration of the analogy of ‘1 in 5 years’ and ‘1 in 100 years’ rainfall events. Heavy rainfall events happen very rarely but they can occur anytime and their impacts are potentially very costly e.g. flooding. Fortunately their magnitude is correlated to likelihood, so defining the relationship between likelihood and size is straitghtforward and the information is very valuable for management e.g. building codes, emergency response, community warnings. The following illustrates how this is done.


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To estimate rainfall related ‘event’ risks for a given location and event size, hydraulic engineers collect daily rainfall data and analyse them statistically and graphically to obtain a picture of risk variability. The starting point is to estimate the ‘percentile’ for daily rainfalls. Take a rainfall data set covering 100 years (36,500 days). To predict the magnitude of other future infrequent events hydrologists plot the rainfall (mm) against “1-percentile” on a graph. This ‘1- percentile’ value is termed the “Exceedence Probability” . To aid interpretation the Exceedence Probability is typically plotted in years instead of days and from this come figures like the “1 in 100 year, 24 h event” heard in weather reports (say 400 mm) corresponding to the 99.997th percentile. A common confusion is to think that the rainfall event identified will actually occur every 100 years. Strictly though what the plots tell us is the probability of there being a rainfall event tomorrow of next year equal to or exceeding 400 mm in 24 hours is 0.00003( 0.003%), hence the terminology. We analysed our data to obtain analogous risk estimates. Our Monte Carlo based risk model allowed us to simulate the concentrations of (STP microbial) particles in each bathing zone at ca 8500 fifteen minute timesteps over periods of 3 months during summer and winter. Bathers were assumed to consume seawater in one accidental ‘gulp’ during one 15 minute time and the hydraulic model outputs (Figure VI) were combined with other pathogen data (e.g. effluent composition, dose response algorithms, seawater consumption) to estimate for each timestep, 1. the concentration of the pathogen of concern, 2. the probability of infection, 3. the probability of illness. The output data was then plotted or tabulated as for the rainfall data example to generate ‘Exceedence Probability’ statistics and plots (Figure VII). These plots illustrate the extent to which the probability of illness is low for only one exposure but increases the more often a person bathes. These plots are not strictly reality but are the output of risk modeling for the Scenarios we constructed. For example the Figure VII plots show the risk levels estimated for surfers and shoreline bathers exposed to Giardia from treated effluent in the waters off Merewether Baths during the summer of 2007. They are not equivalent to statistics arising from actual water quality measurements or community illness levels. But they can allow us to understand rare high impact events better in order to manage them. For example plot b. indicates that the probability of a bather being infected by Giardia from the effluent from 10 swims is about 0.001% while the chance of getting ill after 1000 swims is about 1% (noting the precise meaning above).

a.


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b. Figure VII. Illustrative Exceedence Probability Plots for Giardia Risk. Notes:

1. Plot a. is for surfers, plot b. is for normal bathers.

Key Findings

1. The study method was effective at providing a detailed picture of the potential for bather under a range of scenarios (Note that the risk estimates (pathogen numbers, illness risk) are specific to the input assumptions used to construct the scenarios. It is emphasized that the risks are modeled ones developed to support decision making by project stakeholders. They should not be confused with observed risks of disease calculated from epidemiological studies and surveillance data.).

2. The risk estimates confirmed that under typical conditions the Newcastle beaches have very good quality bathing water consistent with Beachwatch results (the majority of the time no contaminant particles were modeled at travelling into the bathing zone). However, the study also found that under infrequent hazardous conditions the outfall discharge of effluent, and to a lesser extent WAS, does impact on the beaches and has the potential to present an elevated health risk to local beach users during these periods.

3. Under baseline conditions (summer, shoreline bathers) the infection and illness risk probability, modelled for all pathogens (enterococci) and individual pathogens (Adenovirus, Giardia in particular) at the 95th percentile (Exceedence probability 0.05), were generally < 0.01 (i.e. 1%). Again this was consistent with Beachwatch enterococci monitoring which indicated the beaches achieved Category A water quality status (infection prob. < 1%) based on the 95th percentile of the enterococci measurements taken during dry weather.

4. The study provided details of the risk associated with ‘Hazardous Events’ when there was sporadic mainly short duration on shore transport of outfall discharge material. These events were closely associated with a combination of destratification of the water column, on-shore currents, and strong or extended duration on-shore winds which varies from beach to beach.

5. Under Hazardous Event conditions: a. The risk estimates were highly dependent on the risk exposure scenario assumptions

in a particular season, discharge stream, solar inactivation and bather populations. b. For shoreline bathers the elevated gastrointestinal illness risk (probability >0.01) was

assessed to occur at an Exceedence Probability of 0.05 to 0.08 on sunny days. c. Under a worst case scenario (no sunlight) for surfers ingesting 200 mL of seawater

per occasion the elevated gastrointestinal illness risk (probability >0.01) was assessed to occur at an exceedence probability of 0.2 to 0.5.


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6. Surfers appear to be the higher risk group as: a. It was assumed they undertook more vigorous and extended bathing and would

consume 7X the seawater of normal bathers per exposure occasion; b. Their use of the ocean in winter and early morning could lead to higher risk due to

different contaminant transport patterns and solar inactivation being less effective. 7. Solar inactivation could be an effective moderator of risk during daylight hours. However, it

was less effective than might be expected because of short travel times (often less than 1 day) combined with solar inactivation occurring mainly around midday.

8. Events of poorer quality water were episodic and could occur at any time of the day. 9. For the most part the study found that the effluent discharge presented a larger health risk

than WAS. 10. Microcosm experiments found that following dispersion microbes in WAS and effluent

should show similar inactivation rates comparable to rates reported in the literature. 11. The study indicated that assessing the health risk based on predicted enterococcus levels did

not underestimate the potential health risk estimated for individual viral, bacterial or protozoan pathogens in the effluent and WAS discharges from the outfall. On this basis enterococcus appears to be a satisfactory indicator for Burwood effluent and WAS discharges under normal circumstances. A possible exception would be if there was a large outbreak of disease in the community. Of the index pathogens assessed Adenovirus and Giardia were the most significant.

12. The different beaches and swimming locations did not appear to differ greatly from one another in the levels of pathogens seen during a given season.

13. The impact of the BBWWTP upgrade and larger discharge volumes anticipated over the time seems very small compared to the other sources of risk variability and uncertainty.

Uncertainties

Model output prediction The illness risk estimates presented here were based on application of QMRA methods. The assessment explores risk options but the estimates are still model outputs. The use of the UK enterococci dose response curve was considered reasonable but it is still unproven in the Australian context. Reliability of the Surfer sea water consumption (200mL per exposure) This figure was based on qualitative assessment noting it is comparable to the normal maximum used for scoping exposure to chemical contaminants. Consumption of seawater in a single timestep In practice seawater consumption might occur over more than one timestep. Clustering of hazardous (timestep) periods Microbial ‘particle-rich’ timesteps were clearly clustered in the hydraulic model outputs. This should not greatly affect the infection risk probability estimation if it is assumed that the majority of accidental seawater ingestion occurred within a 15 minute period but this should be checked. Assumed Level of Baseline Protection The 105 and 106 .100 mL-1 assumptions for effluent and WAS for enterococci were estimated from the long-term enterococci measurements. Modelling indicated these were realistic but further verification is desirable.


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Number of Monte Carlo Iterations The number of Monte Carlo iterations per model run was set at 10,000 because of the available time and the number of simulations needing to be done. Data Set Numbers and Quality A concern noted by the assessment reviewer was the size of the data sets used to develop the PDFs. Though we concur that the larger the sample size the better, a review of the literature indicated that:

1. There is remarkably limited data on pathogen levels in sewage and this is even more limited when the range of different treatment processes is considered.

2. None of the studies identified approached the 20 samples per year for 5 years WHO model. 3. Survey philosophy ranged from studying a single sample type in much detail to a broad

reconnaissance survey. 4. Many surveys concurrently collected many more indicator measurements suggesting they

too were resource constrained and sample sizes were like ours a pragmatic compromise. 5. Few surveys collected a full range of pathogens from viruses, bacteria and protozoa. 6. Overall our survey scale of 54 samples was in fact quite comparable to this ‘best practice’.

Campylobacter Concentrations and Sensitivity Testing We were concerned that Campylobacter concentrations might be underestimated. However, it is unclear at this stage what would be an appropriate sensitivity factor to employ given the uncertainties associated with its concentrations in sewage (Section 2.3.1.5). Water Transmissivity The inactivation and transmissivity study showed that water might often be much less transmissive than was modelled. For this reason we recommended considering risks estimated for both the 15 MJ.m-2 and the Conservative scenarios. Because the survey was only of limited duration (one day) it cannot be considered to fully represent water transmissivity overall. Increase in Illness Probability due to Seasonal and Outbreak Related Pathogen Peaks Further QMRA could conceptually have been undertaken to detail the impact of seasonal and outbreak related pathogen peaks. Because seasonality and outbreak impacts are both largely unquantified, or where detected, they have a very high effect, a minimum sensitivity factor was considered to be 1 or 2 orders of magnitude as for Giardia and Adenovirus above (Section 3.6.14). It was not clear what inputs assumptions should be used and it did not appear that such an exercise would generate anything useful beyond what could be deduced from first principles. Accommodating uncertainty Uncertainty assessment, reality checks and related work were integrated into the general study design in a number of ways:

1. Reporting was designed to highlight how risk cannot be reduced to a single value; 2. Many scenarios were run so that decision making could be based on weight of evidence; 3. Data from each model run was compiled in database table format before being used in

modelling to allow efficient checks for data integrity. 4. The assessment of multiple scenarios covered a range of between factor variance; 5. Selection of the Baseline scenarios was based on assessment of the consistency of observed

water quality with that which would be expected from modelling; 6. Literature has been used to assess and identify possible underestimates of pathogen levels

and scenarios where sensitivity testing should be undertaken; 7. The project plan and the output results were reviewed by NSW Health & DECW; 8. The hydraulic models themselves are probabilistic;


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9. The risk assessment overall was based on the principle of knowing (defining quantitatively) the system as well as possible given available resources (financial, time, human);

10. The oceanographic data were compared with long term sets to assess if they are representative;

11. Experimental checks were used to assess inactivation rates in the waste streams, compare WAS with effluent, determine whether inactivation of different model microorganisms is consistent with literature values and determine how effective solar radiation is likely to be in the ocean based on measurement of the transparency of water samples.

Conclusions

The following are the central conclusions of this assessment. 1. Epidsodic periods of increased risk (i.e. >1% gastronintestinal illness probability) do occur. 2. Surfers were assessed to be a population at higher risk because of their more vigorous and

prolonged exposure to seawater and hence likely higher intakes. 3. Water quality degradation appears to be due to both the treated effluent and to a lesser extent

WAS discharges. The higher pathogen numbers in WAS are off-set by its smaller volume. 4. Following dilution, solar radiation can effectively inactivate microorganisms from WAS and

effluent to closely comparable degrees and at rates comparable to those reported in the literature.

5. However solar radiation’s protective effect is constrained by short travel times (often less than 24 h) of some plumes during onshore transport events, and during low light periods e.g. early morning.

6. Compared to other sources of risk variance and uncertainty the impact of the proposed Plant upgrade appears small if not trivial.

7. The impact of disease outbreaks in the community and the seasonality of pathogen loads remain for the moment unresolved.

8. The results of the risk assessment (pathogen numbers, illness risk) are specific to the input assumptions used to construct the exposure pathway based scenarios. It is emphasized that the risks are model based estimates developed to support decision making by project stakeholders. They should not be confused with directly measured illness rates.

In regard to the following final issues of concern we concluded:

1. Do the solids loading in the WAS pose a problem through shielding pathogens within their matrix? Do viral pathogens present an elevated health risk to beach users? Are enterococci a satisfactory indicator of risk and viral pathogens?

a. Once the WAS has been diluted by a factor >103 the microorganisms contained therein are as susceptible to inactivation via solar radiation as those in the effluent, at rates in line with those reported in the literature;

b. The enterococci ‘surrogate’ dose response curve appears to provide a conservative estimate of total gastrointestinal illness compared to illness risks estimated for individual pathogens, even allowing for pathogen assay limits. This included the index virus chosen Adenovirus.

2. Did the WAS pose a risk higher than that arising from the effluent? a. The WAS appears to pose in general a lower risk than the effluent especially during

summer. 3. How does the current work compare with outputs from the Beachwatch monitoring

program? a. Beachwatch addresses the primary question of overall water quality for shoreline

bathers.


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b. Beachwatch data can detect the occurrence of Hazardous Events via elevated indicator concentrations, notably stormwater inflows, but not characterize them in detail.

c. Beachwatch indicator monitoring data can detect other events by inference e.g. high concentrations of enterococci in the absence of heavy rainfall. However such monitoring data alone is insufficient per se to distinguish the possible sources e.g. on-shore transport of outfall discharges from bather shedding and sediment suspension and ‘dry weather’ stormwater flows.

d. The current study complements Beachwatch monitoring data. It charts the exposure pathway linking bathing sites to the outfalls and is able to explore less frequent high impact events than is logistically possible through current indicator sampling.

e. The current study also looked at the risks to other beach user populations using surfers as a conservative model.


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1. Introduction

1.1. Objective

The objective of this study was to conduct a quantitative microbial risk assessment (QMRA) to estimate gastrointestinal risks to bathers arising from the discharge of treated effluent and waste activated sludge (WAS) from Burwood Beach Wastewater Treatment Plant (BBWWTP). The risk assessment was based on normal dry weather conditions and selected ‘Hazardous Events’. The assessment did not consider risks arising from the intrusion of stormwater, WWTP failure, flow bypass, bather shedding and exposure routes other than ingestion. This study does not detail or characterise the effectiveness of potential management options. Rather it provides results to enable informed decisions to be made regarding future risk management strategies.

1.2. Wastewater treatment plant description and study location

This study concerns the fate, transport and risk posed to bathers using Newcastle Beaches from the pathogens in (secondary treated sewage) effluent and WAS discharges from the BBWWTP into the coastal waters off Burwood Beach. BBWWTP is the largest of Hunter Water’s wastewater treatment facilities and currently treats an average daily wastewater flow of 44 million litres. The plant is located approximately 4 km south of the Newcastle CBD adjacent to the coastal suburb of Merewether, and is surrounded by the Glenrock State Conservation Area. The plant services the Newcastle CBD and suburbs extending to Dudley in the south, Wallsend in the west and Mayfield in the north. The catchment produces mainly domestic wastewater, with a minor component of commercial and industrial wastewater. The plant processes the wastewater received to a secondary treatment level. The plant is licensed to discharge effluent and WAS generated by the WWTP to the ocean via a 1.5 km extended ocean outfall (Environment Protection Licence 1683). Hunter Water is planning a major upgrade of the plant to rectify process and reliability constraints and to ensure the plant operates sustainably into the future. The upgrade is being undertaken in two stages, namely the Stage 2 Upgrade, which will ensure the plant can operate to its original design capacity and meet current licence requirements; and the Stage 3 Upgrade which will provide the long-term sustainable strategy for the plant. The upgraded plant will have the capacity to treat an average daily wastewater flow of 53 million litres per day. Further information on the Stage 2 Upgrade can be found in Burwood Beach WWTW Stage 2 Upgrade - Review of Environmental Factors (CH2M HILL, 2009). The location of the plant and the ocean outfall in relation to the study beaches is shown in Figure 1-1. Discharge occurs from a series of outfalls located 1.5 km off the south end of Burwood Beach. Burwood Beach and Dudley Beach are notable for having large forested catchments between the beach and the ridgeline above them and hence are relatively un-impacted by local urban stormwater during dry weather periods.


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Figure 1-1. Location of the study beaches, the WWTP and the discharge points

Notes: 1. Coordinates are Australian map grid references and correspond to meters. 2. Images adapted from those in Glamore et al. (Glamore et al., 2008).

1.3. Background

BBWWTP has recently experienced process problems and requires a substantial upgrade to ensure reliable operation and to cater for growth. An issue of concern raised by the Community Reference Group (CRG) consulted about this upgrade is the potential health risk presented by the discharge from Burwood Beach WWTP. The community were also concerned about the ability of the Beachwatch monitoring programme to characterise the health risk from the discharge, particularly with respect to viral contamination. Given the unique nature of the Burwood Beach discharge, a commitment was made to undertake a quantitative microbial health risk assessment (QMRA) to assess the human health risk presented by the discharge. A screening level health risk assessment was completed in 2008 by consultants CH2MHill (CH2MHill, 2008) using a desk-top approach of combining outputs from a hydrodynamic model of the discharges (Glamore et al., 2008) and literature sourced information on pathogen levels, inactivation rates and other broad assumptions. While indicating minimal risk under typical hydrological conditions, the study did suggest that under some combinations of environmental and operational conditions an elevated health risk to bathers could arise. A more detailed and robust investigation was recommended to better characterise the suggested risks leading to this study. The current document is the primary study arising from that recommendation. It is complemented by two other related works on coastal zone hydrology (Pells et al., 2009; Rayner et al., 2009) as well as the previous WRL report(Glamore et al., 2008). The project was not be seen as replacing or contradicting the Beachwatch or its results. Beachwatch is designed to assess the general water quality and sanitary status of the Beaches. It can also detect the occurrence of atypically poor water quality irrespective of source. The current project by contrast was designed to investigate a specific “Exceptional Circumstance” - the potential for Burwood Beach WWTP WAS (and treated effluent) discharge under atypical environmental conditions and consequently would complement Beachwatch related management.


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1.4. Scope of the study

1.4.1. What this Assessment Involves in Summary

The current study is what is known as a Quantitative Microbial Risk Assessment. The basic principles involved are described by Haas et al. (Haas et al., 1999) and in a subsequent WHO publication (Fewtrell & Bartram, 2001). It aims to apply the new Australian recreational water quality guidelines (NH&MRC, 2008) consistent with more general risk assessment guidelines promoted by NSW Health (EnHealth Council, 2002). The study involved:

1. Characterising the load of representative(index) water borne pathogens emitted in treated effluent and WAS from BBWWTP outfalls;

2. Assessing the extent to which these pathogens are transported into the bathing zones of nearby Newcastle Beaches during periods when the coastal waters are not stratified and transport of contaminants on-shore can occur;

3. Estimating the risk of infection and illness posed to shoreline bathers and surfers by these pathogens;

4. Formatting and presenting the final risk estimates in a form suited to decision making and management by HWC, NSW Health and NSW Department of Environment, Conservation and Climate Change (DECC) and provision of the findings to the community.

1.4.2. Typical Newcastle Beachwater Microbial Quality Based on Beachwatch Assessment

When interpreting the current study it should be recognised that since the construction and previous upgrade to BBWWTP, bathing water quality at Newcastle’s beaches has been excellent by the older NHMRC guideline criteria (National Health and Medical Research Council, 1990). The beaches also achieve satisfactory water quality as judged by the new recreation guidelines (NH&MRC, 2008; WHO, 2003) which are designed to allow for poorer water quality periods through their focus on the 95th percentile water quality Benchmarks. Table 1-1 shows the median and 95th percentile enterococci numbers estimated from Beachwatch data collected from four Newcastle Beaches considered in this assessment over 6 recent years (further statistics are presented in Appendix 01 Newcastle Beachwater Quality). Also shown for comparison are the median and 95th percentile indicators levels estimated by WRL modelling of the discharge plumes. Note that the data set used to calculate the statistics differs from the original Beachwatch data because it is a subset thereof in which high rainfall days were excluded. It can be seen that:

1. The typical water quality corresponds to Microbial water quality assessment category A or B (NH&MRC, 2008) even during high rainfall days, which would likely be associated with the inclusion of stormwater inflows.

2. Once data from high rainfall periods are excluded, the 95th percentile corresponds to category A waters (<40 enterococci.100mL-1) and the worst percentile estimates are still well within those expected of Category B waters (40-200 enterococci.100mL-1).

3. Much the same water quality was estimated irrespective of the means by which the statistics are calculated.

4. The quality is substantially higher than reported for Sydney Beaches to the south in the 1990s (compare Armstrong et al., 1997).


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5. Median water quality in terms of enterococci is excellent at near or less than 1 cfu.100mL-1. This indicates that typically there is little conventional water quality analysis evidence of microbial contamination at these beaches under predominant dry weather conditions (i.e. little or no chronic stormwater inputs).

6. ‘Dry weather’ water quality is comparable to that predicted by WRL hydraulic modelling, lying as it does between the levels predicted under conservative and time decay conditions (see also Appendix 01 Newcastle Beachwater Quality).

Accordingly this health risk assessment was focused on characterizing the impact of rare but potentially concerning ‘Hazardous Events’ or hazardous circumstances (Section 1.5.9). Table 1-1. Selected Summary Statistics for Enterococci (cfu.100mL-1) Derived from Selected Beachwatch data collected for Newcastle Beaches in the Vicinity of Burwood Beach Provided by HWC

Period Statistic Dudley beach

Burwood Beach South

Burwood Beach North

Merewether Beach

Bar Beach

Percentile 0.95 (a) 23 52 41 43 42 Percentile 0.95 (b) 20 30 30 33 35

All Data including Rainfall Impacted days 2001-2006 Median (a) 0 1 1 2 2

Percentile 0.95 (a) 16 16 16 24 21 Percentile 0.95 (b) 11 14 14 16 17 Percentile 0.95 (c) 22 19 22 20 24 Median (a) 0 1 1 1 1

Data for days without Rainfall Impact 2001-2006

Median (c) 0.8 1.3 1.3 1.4 1.6 Notes:

1. Data marked: a. refer to statistics estimated on the primary data set using Excel functions such as PERCENTILE(). b. were calculated after log transforming the data. Below detection limit data were substituted with a

half detection limit (i.e. 0.5 enterococci per 100 mL). Where indicated they are still in log10 transformed format. In the case of the 95th percentile this was estimated using the (arithmetic) average and standard deviation and using the NORMINV() function to estimated the 95th percentile.

c. were estimated using Palisade @Risk curve fitting applied to the positive detection data.

1.4.3. Risk Assessment Study Models

As far as we have been able to determine from the literature a combination of full pathogen related QMRA together with detailed outfall discharge transport modelling has never been undertaken or at least reported in the literature. However, there is increasing use of a combination of hydrodynamic modelling with indicator behaviour (Falconer et al., 2001; Harris et al., 2004; Kashefipour et al., 2002; Kay et al., 2005; Lin et al., 2008). Such modelling focuses on understanding the generic movement of microorganisms overall, rather than estimating the quantitative risk posed by infrequent but high impact ‘Hazardous Events (Nadebaum et al., 2004)’. The work of Harris et al. illustrates the approach (Harris et al., 2004) and is essentially the same as that already applied in the case of Burwood Beach in a previous study (Glamore et al., 2008). There are a range of other analogous microbial risk assessment studies in the literature.1 The implication in the Guidelines is that hydrodynamic/hydraulic and QMRA modelling should be combined. This is in line with feedback from NSW Health and DECC and the Community Reference Group when the current project approach was formulated. 1 Rather than compile information in a single literature review we have developed a number of discussion sections on major issues as appendices or in context – see Section 2.2.5.4 Principle Groundwork Activities


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Accordingly the literature in the field of QMRA of water borne pathogens was examined to assess whether the style of risk assessment planned was likely to be considered best practice from a scientific literature standpoint and this appeared to be the case. Appendix 02 Models for Microbial Risk Assessment in the Natural Environment identifies a number of the references considered which illustrate that the current assessment is in line with previous studies.

1.4.4. Guidelines

Risk assessments relating to water quality are not undertaken in isolation but rather as part of a larger process of risk assessment and management based on government endorsed best practice Guidelines. In the Australian context of Risk Assessment for water recreation there are arguably three primary guiding documents:

1. New natural bathing water recreation Guidelines especially the recently finalised National Health and Medical Research Council (NH&MRC) document (NH&MRC, 2008);

which is extensively based on - 2. WHO documents developed for the same purpose (WHO, 2003); 3. EnHealth (EnHealth Council, 2002) which cover Health Risk Assessment generally and

are used as a primary source by Departments of Health when assessing health risks. Except where indicated further reference to the ‘Guidelines’ relates to the NH&MRC (2008) document. During the implementation of the project work further examination of Guidelines was undertaken to ensure that the risk outputs anticipated were consistent with its recommendations. The results of this examination are presented in Appendix 03 Justification for Project Design.

1.5. Assessment Strategy

1.5.1. Risk Assessment Principles and Tasks

The three key Guidelines are based on and promote the application of risk assessment and management principles (AS/NZS 4360)(Standards Australia/Standards New Zealand, 1999). This marks a change from past assessment/ management frameworks relating to water borne/related/associated risks. In the case of recreation, the most explicitly identified guiding principles appears to be general Risk Assessment Principles detailed in EnHealth (2002) as well as HACCP principles (NH&MRC, 2008, pp.14, 65, 68, 69):

1. Hazard analysis; 2. Control points; 3. Critical limits; 4. Monitoring; 5. Management actions; 6. Validation/verification; 7. Record keeping.

In EnHealth (2002) there are a range of other recommended actions as to how to undertake a health risk assessment (HRA). It is recommended that all such assessments:

• Identify Issues; • Identify Hazards; • Assess Exposure; • Assess Dose Response; • Characterise Risk; • Undertake Uncertainty Assessment;


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• Undertake Reality Checking between risk assessment and management. The latter framework provides both a check list and a consistent set of headings for any given risk assessment and sequence for assessment task implementation and documentation. This general scheme has been used in this assessment. The summary framework is shown in Figure 1-2. Further detail of how the assessment has been undertaken is provided elsewhere (Roser et al., 2007). This section outlines elements of the risk assessment strategy developed. Further information on assessment design and rationale are provided in Appendices 04 Risk Management and HACCP, 03 Justification for Project Design, 05 Final Study Approach, 06 Other Strategic Elements, and 07 Scope of Work Planned at Project Commencement.

Engagement

Hazard Assessment

Risk Characterization

Risk Management

Exposure AssessmentLocations

Pathways

Exposed populations

Exposure concentration

Intakes

Hazard Identification

Issue Identification

Dose Response Assessment

Review Review

Figure 1-2. Summary of Recommended Healthy Risk Assessment Process (EnHealth Council, 2002)

The remainder of this section outlines how each HRA task in this scheme was implemented. The assessment presentation also follows this scheme.

1.5.2. Engagement

In developing the risk assessment program meetings were held with NSW Health, NSW Department of Environment and Climate Change, and with the Community Reference Group whose formation was facilitated by HWC at the project’s inception.

1.5.3. Issue Identification

As part of the risk assessment plan development process, four sets of issues were identified: 1. Issues identified in the previous Screening Assessment and in consultation with the

community; 2. Issues identified specifically in the NSW Health Correspondence; 3. Issues arising from the special attributes of the discharge Scenarios/exposure situations of

concern identified in discussions with HWC and via examination of technical documents;


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4. Issues identified in the Guidelines relating to ocean discharge of sewage. These provided an in principle checklist for assessing whether the proposed work program met concerns detailed by NSW Health. The overarching issue of concern appeared to be infectious agents especially viruses.

1.5.4. Hazard Identification

During the engagement meetings it was proposed to focus on 3 index pathogens, the protozoan Cryptosporidium, the bacterial pathogen Campylobacter and Rotavirus. These 3 pathogens were proposed because they are highly infectious, are common causes of water borne disease, are theoretically measurable with technology available commercially/widely in Australia and represent the 3 main pathogen groups of concern. In addition enterococci were identified as a potential model surrogate for all gastrointestinal pathogens.2,3

1.5.5. Dose Response Assessment

Dose response information for all these pathogens is available in the QMRA literature and this was seen as addressing this need. The selected algorithms are detailed in this report.

1.5.6. Exposure Assessment

The primary hazard source location to be considered was agreed to be BBWWTP during dry weather. The primary exposure locations where risk was to be assessed were agreed to be the swimming and surfing zones of Dudley Beach, Burwood Beach, Merewether Beach and Bar Beach (Figure 1-3). A detailed exposure pathway was developed, which is summarised in Figure 1-4. The principle exposed populations identified were bathers swimming at Newcastle’s beaches located closest to the outfalls (i.e. those above as shown in Figure 1-1 and Figure 1-3). The CRG raised the issue of exposure to surfers being higher than the general population because:

1. They tend to spend more time in the water; 2. The vigorous nature of surfing means that they are more likely to swallow a larger volume

of water during contact periods; 3. They’re activities tend to be further off shore and hence they encounter less dilute waste.

For intake estimates it was agreed to review the literature and use the best available data for the general population. For the surfing community it was agreed to run models which assumed a higher intake per exposure.

2 Subsequently Adenovirus replaced Rotavirus as the model organism. A full explanation for this alteration are provided further down but the principle reason was that the rotavirus assay was not considered to be sufficiently reliable. 3 The laboratory enumerated Giardia routinely along with Cryptosporidium providing and opportunity for assessing the risk arising from another well recognised index pathogen (see Medema, G. J. & Schijven, J. F. (2001). Modelling the sewage discharge and dispersion of Cryptosporidium and Giardia in surface water. Water Research 35, 4307-4316).


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Figure 1-3. Exposure Assessment Points

Water Borne Pathogen in Raw Sewage

WAS Outfall Treated Effluent Outfall

Primary + Secondary Treatment

Bathers Surfers

Diffusers @15-20m depth, 1.5 km off shore

Surface Waters (1-2 m)

Figure 1-4.Overall Exposure Pathway

1.5.7. Risk Characterization

It was agreed to develop a series of quantitative/probabilistic risk models reflecting the overall exposure pathway presented in Figure 1-4 which we collectively term a metamodel. It was agreed that risks would be estimated in a format suited to comparison with Guideline benchmarks. Most QMRA to date has estimated risk in the form of annual infection probability


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(Haas & Eisenberg, 2001) or DALYs (e.g. Havelaar & Melse, 2003) for specific pathogens because of a focus on drinking and recycled water. This contrasts with the Guideline benchmarks which are cast in terms of total gastrointestinal illness and daily/swimming occasion exposure. To harmonise these different approaches:

1. Risks were estimated as probabilities per day which was taken as being equivalent to probability per swimming occasion;

2. The DALY literature was used to estimate factors for converting infection probabilities into gastrointestinal illness probabilities;

3. The empirical enterococci exposure dose response curve used to develop the Guidelines microbial categories (Kay et al., 2004; Kay et al., 1994) was adapted to estimating the total probability of gastrointestinal illness for a given exposure scenario to sewage enterococci.

In discussion with NSW Health and DECC no additional benchmark involving likelihood of exposure was identified other than the 95th percentile. So we undertook to develop an approach to facilitate documentation and communication of risks arising at are range of other likelihoods as well (e.g. 98th, 99th, 99.9th percentiles).

1.5.8. Risk Management

Risk Management options have not been fully scoped yet. Instead risks were characterised under the different exposure Scenarios which were designed with future management in mind e.g. they were designed to extensively characterise the varying level of risks, where, when and how often such risks occurred, which populations were exposed, what were the natures of the risks and what uncertainties existed as a prelude to management. This was seen as the best course because:

1. It was unclear whether and to what extent management was needed at the assessment outset; 2. It was seen as more appropriate to develop management options once risks were understood

quantitatively and insight into the likely effect of management options was also possible.

1.5.9. Hazardous Events

1.5.9.1. Relationship to Beachwatch Data

From an analysis of Beachwatch enterococci data and further to discussion in Section 1.4.2, under typical conditions (> 95% of the time) the water quality of the four Newcastle beaches corresponded to Microbial Category A and is therefore ‘Very Good’ as most of the time discharges from Burwood WWTP are transported out to sea away from the beaches (Glamore et al., 2008). However, there are several ways in which health risks to bathers could be higher at times than this rating indicates. Several are identified here and in the Guidelines and were discussed in consultations with DECC, NSW Health and the CRG:

1. Though Beachwatch sampling frequency is high it may be insufficient for fully characterising low frequency high impact health risk ‘events’ of concern notably periods of strong on-shore winds /currents leading to short travel times, limited dilution and pathogen inactivation, as suggested by earlier hydraulic modelling by Glamore et al, (2008), which is summarised in Section 1.5.9.2.;

2. The method used to estimate the 95th percentile could yield an incorrect value if not calculated appropriately (NH&MRC, 2008 p. 70);

3. There might be higher loads of viruses and other pathogens in the effluent discharges than indicated by enterococci concentrations e.g. during an outbreak or due to seasonal variation;

4. Pathogens might survive longer than indicators, especially viruses [compare coliform and viral inactivation rates in Table 5.8 (NH&MRC, 2008) with those summarised by USEPA (USEPA, 2001 ) and used by WRL in earlier modelling (Glamore et al., 2008)];

5. The WAS might have posed esspecially high risks;


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6. Between population dose response variation - In particular surfers might be exposed to a higher than median risk due to their greater exposure to the aquatic environment;

7. In the future, increased discharge loads may increase risk. This risk assessment was designed to address these issues and some others arising such as analytical resource constraints. This said the risk assessment does not invalidate the overall assessment undertaken by Beachwatch. Rather it complements the information available through this program.

1.5.9.2. Hazardous Events

The NH&MRC (2008, Table 5.13) Guidelines provide a scheme for assessing the overall microbial suitability of a recreation site. The current study, however, was concerned overall with the issue of what are termed ‘Exceptional Circumstances’ or ‘Hazardous Events’. In the Guidelines a Hazardous Event is defined as:

“An incident or situation that can lead to the presence of a hazard (what can happen and how).”

The Hazardous Event of most concern is illustrated in Figure 1-5. Most of the time the discharge diffusers are designed to inject and mix the treated effluent and WAS plumes into the coastal waters below the surface in the hypolimnion. On occasion though destratification can occur and plumes or a portion of the material may surface. Even then, most of the material is transported out to sea (a.). However, on occasion diluted waste plumes may be transported into the bathing zones (b.) as illustrated by these model contour plots (Glamore et al., 2008). A more detailed discussion on the concept of ‘Hazardous Events’ is provided in Appendix 08 Hazardous Events associated with Coastal Zone Outfalls.


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a.

b. Figure 1-5. Illustrative Examples of Modelled Dispersion and Reduction of Thermotolerant Coliform numbers in Surfacing Effluent and WAS Plumes (Glamore et al., 2008)

Notes: 1. The initial assumed concentration in the effluent and WAS was 107.100mL-1.

1.6. Bathing Risk Benchmarks

1.6.1. Tolerable Illness Probability

NH&MRC (2008) Table 5.13 is based on complementary sets of ‘Benchmarks’ notably those in Table 5.7 and Table 5.10. They respectively provide the basis for assigning a microbial water quality category to a location and determining the sanitary inspection category for outfalls. Of the two Benchmark types the more operationally convenient are the water quality based targets in Table 5.7 because they are defined quantitatively and can be objectively measured. The latter


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indicates that risk of gastrointestinal illness (GI) probability should not exceed 1% and/or 5% (i.e. total probability of gastrointestinal illness <0.01 or 0.05 .person-1.exposure-1) depending on beach classification aimed for and sanitary status and that this potential can be estimated from water quality data.

1.6.2. Relating QMRA Outputs for Hazardous Events to Benchmarks

The Guidelines promote QMRA which conceptually should be able to generate infection and illness probability estimates for comparison with these two benchmarks. However, the Guidelines are limited in several respects which affect the current assessment:

1. They do not indicate what the tolerable risk for any specific pathogen should be other than by implication that it should not exceed the 1% or 5% thresholds;

2. They do not indicate how to use QMRA to estimate Total Gastrointestinal Illness arising from all pathogens together.

3. The Guidelines give in principle support for Hazardous Event risk assessment but they do not provide further quantitative guides recommendations in the form of explicit and numerical ‘tolerable’ Hazardous Event risk magnitude.

4. While the Guidelines promote the need to consider ‘Consequence’ along with ‘Likelihood’ (Figure 1.2) in line with normal risk assessment practice (e.g. Tables 4a-4c Standards Australia/Standards New Zealand, 2006) no guidance on the acceptable Likelihood of such events is provided.

To address these limitations the following was proposed and implemented:

1. Adapt the DALY literature to the estimation of gastrointestinal illness probability for individual pathogens;

2. Adapt the mathematical relationships in the recreational water literature into a form yielding illness probability risk estimates;

3. Develop a method for efficiently reporting Hazardous Event risks in a manner which captured variations in, and the interrelationship between, Consequence and Likelihood.

Regarding the last task the following was seen as noteworthy. QMRA Scenario simulations which use inputs in the form of PDFs, also generate PDFs as their output. In some cases this PDF describes the uncertainty associated with estimates of the total risk. As a result it is common to see not only the median risk but also the 95th percentiles (Westrell et al., 2003) presented for a given Scenario. The same approach can be used to estimate the variation in risks associated with a class of Hazardous Events (e.g. Figure 3 Nilsson et al., 2007). The latter example concerns varying levels of failure in a chlorination system. The analogue in the present study is the variable magnitude and pattern of events illustrated in Figure 1-5.

1.7. Structure of the Report

This report is comprised of the following major Sections/Chapters: Section 1 of the report (this section) includes an introduction to the project, scope and objectives as well as a detailed background on the study area and risk assessment principles. Section 2 of the report details the ‘Groundwork’ required for the risk assessment, namely the materials, methods and information needed to undertake each of the key steps in the risk assessment framework. This section describes the methods employed for QMRA modelling and the quantification of risks arising from hazardous events. Methods for the hydraulic modelling, determination of pathogen concentrations and loadings, dose response assessment, and exposure assessment are also described here.


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Section 3 details the Risk Characterisation component of the study. It involved the estimation of health risks arising from recreational contact for different exposure scenarios (eg baseline and hazardous event conditions). It also considers the barrier effect provided by the coastal zone waters. Section 4 provides a summary of the key findings from Section 3: Risk Characterisation. It summarises the health risks presented to surfers and bathers presented by the discharge of WAS and treated effluent under normal and hazardous event conditions. Section 5 details a range of uncertainties that where encountered or identified during the risk assessment process. Section 6 outlines the major conclusions of the health risk assessment study. It includes a conclusive statement with respect to the risk of infection to bathers from pathogens from the Burwood Beach Outfalls under both normal and event conditions. Much of the work undertaken was modular or involved extensive interpretation of Guidelines and literature. This work has been extensively compiled in the form of fully or largely stand alone Appendices. These Appendices also outline the related WRL studies.


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2. Groundwork

2.1. Introduction

Prior to characterizing risk, a range of input information needed to be collected. The scale of such ‘Groundwork’ can be large so the process needs to be systematic and targeted. The end aim of the Groundwork was to leave the assessors at its end in a position to undertake a risk characterisation process similar to that illustrated in Appendix 09 Illustrative Example of Risk Characterization via Microbial Risk Probability Calculation. The HRA framework outlined above provides such a system and this has been used in the present instance to inform the structure of the Groundwork description. The main actions were as follows:

1. Issue Identification: a. Undertake Problem Conceptualisation; b. Develop a Modelling Strategy; c. Consult representative groups and develop a Workplan Arising from QMRA

Context and Stakeholder Discussions; d. Develop/Adapt a Method for Quantifying Risk Arising from Hazardous Event

Conditions; e. Identify Risk Assessment Scale and Logistics constraints and means for addressing

them as far as practical; 2. Hazard Assessment:

a. Measure Pathogen Concentrations and Loadings in Effluent and WAS; b. Identify and summarise algorithms most suited to Dose Response Assessment in the

bathing context; 3. Exposure Assessment:

a. Identify the Exposure Locations where risk is to be characterised; b. Adapt the previously constructed WRL (Microbial) Particle Transport and

Inactivation in Coastal Waters model (Glamore et al., 2008) so as to quantify the reduction in pathogens which can be achieved via the exposure pathways off Newcastle between the outfalls and the bathing exposure points, and generate outputs suited to QMRA;

c. Estimate the likely Consumption of Seawater at the exposure points from the literature and from consideration of the location populations exposure;

4. Risk Characterisation: a. Undertake an Analysis of the Historical Record and Previous BBWWTP Study

Data in line with WHO recommended practice; b. Undertake an Analysis of Primary Hydraulic Fate and Transport Pathway Data

generated by the hydraulic modelling with a view to integrating it into the larger QMRA;

c. Develop/Adapt an existing Basic QMRA modelling method to Newcastle bathing waters;

d. Develop a means for Operational Integration of the QMRA and Hydraulic Models;

e. Develop approaches for undertaking Reporting of Risk as Exceedence Probability;

f. Explain the process of Quantifying and Communicating Hazardous Event Consequence + Likelihood;


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g. Undertake Risk Assessment based on Exposure and Risk Assessment Scenarios as a basis for QMRA model input selection;

5. Uncertainty and Reality Checks: a. Summarise issues relating to Model output prediction; b. Discuss the Reliability of the Surfer sea water consumption (200mL per

exposure); c. Outline the limitations of assumptions relating to Consumption of seawater in a

single timestep and the Clustering of hazardous (timestep) periods; d. Explain the Assumed Level of Baseline Protection; e. Explain the selection of the Number of Monte Carlo Iterations; f. Explain the strengths and limits of Data Set Numbers and Quality; g. Discuss and outline issues relating to Campylobacter Levels and Sensitivity

Testing; h. Outline the limitations on inactivation assessment due to variations in Water

Transmissivity; i. Outline the potential Increase in Illness Probability due to Seasonal and

Outbreak Related Pathogen Peaks; j. Explain how we have gone about Accommodating uncertainty.

Groundwork is in one sense the Materials and Methods of the risk assessment. However, the components are complex in origin and often modular comprising as they do:

1. New discrete strategic monitoring studies e.g. to estimate contaminant concentrations and loads and evaluate the local effectiveness of solar radiation;

2. Adapting risk assessment methodologies to the special concerns of project stakeholders; 3. Critiquing and adapting information from the scientific and other technical literature; 4. Developing and documenting achievable terms of reference where the assessment is

logistically feasible, scientifically defensible and timely; 5. Developing a final work program which allows for unforseen constraints.

The report layout structure here was developed to account for this and continually draw the reader back to the aim of the current study i.e. systematic risk assessment.

2.2. Issue Identification

2.2.1. Problem Conceptualisation

The risk assessment approach being undertaken is based as discussed above on the new national water recreation guidelines (NH&MRC, 2008). Though comprehensive in scope these guidelines are less clear about how the risk assessment steps should be undertaken and what is the preferred output for regulators. However QMRA is increasingly well established with good models for/legitimating its application such as those found in the:

1. The main text of this subject (Haas et al., 1999); 2. World Health Organisation sponsored resources (Fewtrell & Bartram, 2001); 3. Many scientific papers (Gerba, 2000; Medema & Schijven, 2001; Schijven & de Roda

Husman, 2006; Schijven et al., 2005; Schijven et al., 2006); 4. Promotion in international and national guidelines (EnHealth Council, 2002; NH&MRC,

2008; NH&MRC/NRMMC, 2008; NRMMC/EPHC, 2005; World Health Organization, 2003).

In a previous risk assessment project (Khan et al., 2007b; Roser et al., 2006a) it was indicated by NSW Health that their preferred format for such work was one which followed in principle the style of the EnHealth Health Risk Assessment Guidelines (EnHealth Council, 2002). The EnHealth


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HRA Scheme proved serviceable for QMRA with slight adaptation/clarification (Roser et al., 2007), which is summarised in Figure 2-1. This scheme was proposed during the project development phase and has been subsequently followed in its implementation. Complementing this core HRA task work, we have been involved in:

1. Clarifying and establishing the context of the risk assessment with the CRG, DECC and NSW Health;

2. Undertaking support work such as developing exposure Scenarios, assessing inactivation rates for microorganisms in WAS, field reconnaissance of water clarity and re-examining the historical data record;

3. (to a lesser degree) Scoping the relationship between risk assessment and management. (Roser et al., 2007).

The rationale for the approach has been already described in Sections 1.1, 1.5 and 1.6 above. The remainder of this introduction summarises key features of the HRA task list (Figure 2-1). The full description of the finalised project plan can be found in previous reports (Roser & Stuetz, 2008a; Roser & Stuetz, 2008b; Roser et al., 2008).

Figure 2-1. Core HRA Tasks for Water Reuse Supporting QRA (Roser et al., 2007)

Notes: 1. Single direction arrows indicate order in which tasks should be undertaken. 2. Bidirectional arrows indicate need for feedback and refinement of tasks and task products. 3. Scheme adapted from Figure 1 in EnHealth Guidelines(EnHealth Council, 2002).


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2.2.2. Modelling Strategy

It was proposed for each risk exposure Scenario that a Monte Carlo model be constructed and used to generate a Probability Density Function (PDF) of estimated risk. Thus for each Scenario we collected statistics and constructed an exceedence probability plot for:

1. Pathogen concentration; 2. Risk of infection from that pathogen; 3. Risk of illness from that pathogen.

These plots covered ‘Baseline’ conditions and ‘Baseline+Hazardous Event’ conditions. Hazardous Events are not generally modelled in isolation. In QMRA modelling the system is considered to be under Baseline conditions or Hazardous Event conditions in proportion to the probability of Hazardous Event conditions prevailing – see Figure 1 in Appendix 09 Illustrative Example of Risk Characterization via Microbial Risk Probability Calculation for illustration. We also:

1. Calculated a range of statistics on the behaviour of the coastal waters related to their expected reduction of pathogen particle numbers;

2. Plotted the behaviour of the on-shore particle movement in respect to time to assess what patterns there might be relevant to future monitoring and management;

3. Proposed calculating a range of statistics for storage in a database and subsequent auditing/checking e.g.:

a. Average (aggregate) risk of illness or infection (incorporates low and high values together);

b. Upper 95th percentile risk of illness or infection; c. Median 85th, 90th, 99th and other percentiles.

It was planned that these risk estimates would then be compared with the risk benchmarks. The key benchmark proposed against which calculated risks would be compared was the 1% total GI illness risk. Further details on assessment strategy development can be found in Appendix 06 Other Strategic Elements.

2.2.3. Workplan Arising from QMRA Context and Stakeholder Discussions

Prior to the project commencement discussions were held with stakeholders about what information was desired from the risk assessment. The aspects provisionally agreed to are detailed in Table 2-1. These informed Scenarios design (Section 2.4.4) subject to modifications described in this text e.g. replacement of Rotavirus with Adenovirus. Table 2-1. Summary of Risk Assessment Input Information

Aspect of Risk Assessment

Model Inputs/Groundwork Implemented Notes

Hazard Assessment: Which Microorganisms

1. Cryptosporidium spp., 2. Campylobacter spp., 3. Giardia spp. (assayed along with Cryptosporidium

and found to be much more abundant and potentially a greater concern

4. Adenovirus (frequently detected in Rotavirus sample and widely recognised as an environmentally resilient pathogen of concern in bathing waters);

5. Enterococci (surrogate for all gastrointestinal pathogens).

Rotavirus were infrequently detected and replaced with Adenovirus. For further rationale on the latter’s selection see Appendix 10 Seasonality, Outbreaks and Hazardous Events


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Aspect of Risk Assessment

Model Inputs/Groundwork Implemented Notes

Hazard Assessment: Waste Streams

1. Secondary Treated Effluent 2. WAS

To be assessed separately. Secondary Effluent is dominant source.

Hazard Assessment: Inputs Data

PDFs for waste-streams developed from project monitoring data

See section 2.3

Dose Response: Dose Response Algorithms

Selected Literature Data See section 2.3.2

Exposure Assessment: Exposure Locations

50m and 200m vertical to the middle shoreline of: 1. Bar Beach; 2. Dudley Beach 3. Merewether Baths 4. Burwood Beach

See section 2.4

Exposure Assessment: Pathways

As conceived initially (see Section 1.5.6) Pathway to be modelled by a combination of three hydraulic models whose attributes are summarised in the methods below and detailed in other reports (Glamore et al., 2008; Rayner et al., 2009).

See Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation for further details

Exposure Assessment: Exposed Populations

1. Surfers at the above beaches located 200 m off shore;

2. ‘Average’ bathers nominally recreating 50 m off shore

The model exposed populations at the above beaches have been defined by agreement with project stakeholders. Surfers identified as having higher potential exposure.

Exposure Assessment: Exposure Concentration

Incremental concentration estimates at exposure points by combining discharge microbial concentrations and incremental dilution + inactivation estimates obtained through hydraulic modeling.

Each hydraulic simulation generates ca 8500 estimates of dilution + inactivation for microbial particles.

Exposure Assessment: Intakes

Selected Literature Data + Adaptation of Local Literature Values

See section 2.4.3

Risk Characterisation Scenarios: Seasons

Summer (3 month time-course) Winter (3 month time-course)

Separate WRL work(Rayner et al., 2009) showed that an older set of Burwood Beach hydrodynamic data was collected under conditions relatively representative of Winter

Risk Characterization Scenarios: inactivation rates

Conservative (dilution only) GSX S90 = 3 MJ.m-2 (maximum achievable) GSX S90 = 15 MJ.m-2(typical reported in situ rate) GSX S90 = 75 MJ.m-2 (low rate inactivation)

Dark inactivation not included as this would be different for each microorganisms and increased Scenarios by another factor of 5. Based on analysis of the literature little inactivation would be occur over 24 hours.

Risk Characterization Scenarios: Potential basic model Scenarios

2 Seasons(Winter & Summer) X 2 Populations (Bathers at 50 m off beach) and Surfers @ 200 m off beach) X 4 Beaches (Dudley, Burwood, Merewether, Bar) X 2 Waste streams (Treated effluent & WAS) X 2 Years (Current/2007, Future/2030) X 4 Inactivation rates (S90s of 3, 15, 75 MJ.m-2 and Conservative) X [= 256 hydraulic model output sets] 5 microorganisms [=1280]

Some Scenarios may be omitted where additional information obtained through modeling is minimal


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2.2.4. Method for Quantifying Risk Arising from Hazardous Event Conditions

Though the Guidelines identify Hazardous Events and circumstances as an issue of concern for bathing beaches near outfalls and identify QMRA as a npotentially powerful tool they do not detail how these issues should be considered together. Previously in the European Union MicroRisk project (Medema et al., 2006; Petterson et al., 2006; Roser et al., 2006b)and the Sydney Water Replacement Flows project(Khan et al., 2007b; Roser et al., 2006a) we developed a general approach where risk was quantified for Baseline conditions plus a definable Hazardous Event (Figure 2-2).

Figure 2-2. Procedure for Simulating the Risk Arising from Baseline+ Hazardous Event Conditions

In this scheme a Baseline risk model is identified and risks under event conditions replace the Baseline values in proportion to the likelihood of event conditions. Both Baseline and event conditions are expressed as PDFs. Different Baselines and events are modelled as separate Scenarios and the overall data provides the basis for decision making. The scheme shown was developed for drinking water risk assessment and focuses on annual risk statistics. The current recreation Guidelines focus on per exposure risk which is treated as equivalent to per day risk. An additional difference is that the infection estimates are used to estimate gastrointestinal illness risk as well. Other than these the approach was identical.

2.2.5. Risk Assessment Scale and Logistics Issues

2.2.5.1. Validation Arrangements

One reviewer of the draft of this assessment commented as follows:


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“I fully support the overall design aims. They are ambitious and appropriate but require good empirical calibration data for each stage in the work. In this section, the definition and description of data source and quality is not sufficiently detailed to allow the reader to evaluate whether the data to underpin the approach is …. In effect, one could conclude that the QMRA is using existing data because it is available without a full analysis of its quality and appropriateness. Both characteristics may, of course, be excellent but the reader does not have a clear audit trail to assess any such judgement at this stage in the report. [This may of course be clear from the parallel investigation cited in the reference listing as Glamore et al. (2008)]”

The major point to note here is highlighted in bold. The response to this is “Existing data must sometimes be used without as full an analysis of its quality and appropriateness as is desired”. The problem is that the larger and more complex and broad scale a risk assessment becomes, the more it must utilize external, ancillary or literature data sources whose validity and appropriateness may not be ideal but it appears to be logistically impractical or of little value to take the assessment significantly further. In our experience, this dilemma arises with risk assessments because:

1. The new class of guidelines based on risk assessment principles have so many heads of consideration that to fully address them all is often logistically infeasible.

2. Risk guidelines are necessarily open ended nature due to the need for comprehensively documenting uncertainties. Addressing a given uncertainty or identifying a new one through exposure pathway analysis always throws up another uncertainty which might be addressed, at least in our experience. This process leads to the question of where to cease an assessment and on what basis? In practice the decision point on where to stop is based on value judgements, issue scale, resource constraints, assessor decisions and reviewer concurrence rather than scientific analysis and can never be fully objective.

3. Depending on how many exposure pathways are considered, the number of Scenarios explored can increase explosively due to the phenomena related to ‘Combinatorial Explosion’ (discussed further below).

As a result validation must be reasonable, manageable and well targeted but it cannot be endless. In practice we have aimed to balance the need for the input data and assumption validation with what is logistically practical as follows:

1. The risk assessment terms of reference were structured so as to be clear and achievable, while not being excessively restrictive to assist broad conceptualisation of the problem (hence the comment about ambitious aims).

2. The terms of reference were developed in consultation with, and hence under the oversight of, potential reviewers, regulators and other stakeholders.

3. The terms of reference were based in part on issues identified by a previous independent screening level risk assessment (CH2MHill, 2008).

4. Quantitative techniques were employed as the assumptions to promote auditability of input assumptions, risk Scenario definition particularly for risk exposure patterns arising from Hazardous Events or circumstances and risk outputs.

5. Input assumptions were derived mainly from data and information sources which had been or were subject to an independent audit processes e.g.:

a. New water quality analyses was acquired from certified local laboratories (laboratory QA/QC);

b. Precursor studies of the Burwood Beach system had previously been reviewed by agencies and other stakeholders (previous reviewers);


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c. Literature data came mainly from refereed scientific literature or guidelines which had been assessed by paper referees etc..

6. The assessment was structured systematically to aid reviewers/readers to follow the risk assessment process.

7. Validation and reality checks on outputs have used the historical data (see Section 2.6.6); 8. The different risk assessments were scanned for consistency. 9. The limitations and uncertainties of the risk assessment identified are detailed in line with

Guideline recommendations. 10. The assessment was structured with a view to addressing the primary issues which led to it’s

the earlier screening level risk assessment (CH2MHill, 2008), and providing information for decision support and management actions provisionally identified by HWC (The driver for the present assessment was HWC’s need to determine whether the existing BBWWTP or increasing its size posed a marked or markedly increased pathogen risk to bathers of Newcastle’s beaches).

11. The assessment does not compromise further assessments as may be seen as necessary.

2.2.5.2. Assessment and Audit Scale, and Combinatorial Explosion Problems

To manage the scale of the QMRA we proposed to construct and explore Scenarios most likely to inform on patterns of risks important to Decision Support rather than exhaustively generating potential risk estimates. This is in line with current best Scenario assessment practice (The issue of modelling logistics is elaborated and discussed in Appendix 12 Guidelines, Combinatorial Explosion and the Scale of Risk Modelling). It was proposed that hydraulic modelling, however, be more exhaustive in the first instance because the programming was mature to a point where batch processing was possible with limited effort, and this would ensure any critical QMRA’s not done in the first iteration could be undertaken subsequently if necessary. The audit related concerns of the reviewer above reflected a related problem of how far the risk assessment needed to detail previous studies upon which it was based. To address this transparency issue we have provided further details on issues which were concerns for the reviewers. We have also detailed critical aspects of the assessment’s rationale in a range of Appendices such as the combinatorial problem and the seasonality/outbreak problem. We have also in some instances provided excerpts from significant studies which are not in the open literature A particular case in point is the hydraulic/hydrodynamic modelling component which was not been included in this document in detail but was a concern for the reviewer. The QMRA utilises outputs from a coastal waters particle transport model which had been developed for a previous related contract (Glamore et al., 2008), which was adapted to the present study (Rayner et al., 2009) and is understood to have been reviewed separately. Its use here without further review was seen as reasonable as:

1. The model is the outcome of a body of work that is too large to be reproduced in its entirety here but is available to the regulators on request/need.

2. The hydraulic modelling involves integration of 3 models which are extensively used commercially in Australia and overseas and the outputs and design have been the subject of various refereed papers. This indicates that the theory and approaches are accepted scientific practice in this field. Some references are:

a. For the JETLAG near-field model to simulate initial discharge plume mixing (Davidson & Pun, 1998);

b. For the RMA-10 Hydrodynamic model to simulate subsequent downstream dispersion (Fossati & Piedra-Cueva, 2008; Khangaonkar et al., 2006; King, 1998; Ramsey et al., 1996); and


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c. For the locally developed 3DRWALK model (Hughes et al., 2004; Miller et al., 2001; Wang & Miller, 1995).

3. The overall model has undergone validation and review in several independent commercial projects identified in the linked preceding study. The integrated model has been reviewed by an independent experts on a project by project basis. It is used routinely for commercial fate and transport work on the NSW coast and in other parts of Australia.

4. Illustrative excerpts from this most recent study are provided in Appendix 13 Selected Excerpts from WRL Modelling which indicate its degree of sophistication is high and its outputs are relevant to the current project and address the reviewer’s concerns.

5. Computation as

2.2.5.3. Manner of Integration of Hydraulic Modelling and QMRA

Building a single integrated QMRA + hydraulic model was initially considered but not undertaken. Instead the hydraulic modelling and QMRA were undertaken separately with the Hydraulic modelling providing data analogous to that collected for water treatment barrier effectiveness(e.g. Smeets et al., 2008). Reasons why the hydraulic modelling and the QMRA modelling were not fully integrated included:

1. At the project’s inception we had not determined precisely which microbial risk statistics were needed, what formats were required, or which estimates would be most informative. Nor had it been resolved whether and how to report infection or illness probability or how to reconcile the Guideline expression in terms of total gastrointestinal illness probability estimated from enterococci monitoring with infection probability estimation for specific model pathogens and more work on the QMRA model was required. As a result we were unable to provide the hydraulic modelling group with clear output specifications. The nature of this conundrum and how it was resolved is discussed in Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event Characterization.

2. A range of water quality data still remained to be obtained prior to the commencement of hydraulic modelling.

3. How integration was to be achieved needed to be explored. The two models were on different program platforms and based on different programming philosophies so it would require some work to integrate them. In contrast it appeared relatively straightforward to design the QMRA and hydraulic modelsseparately and integrate them subsequently. Keeping the modelling steps separate also simplified specification of modelling Scenarios.

4. WRC’s experience with risk characterisation is that stakeholders frequently request various model modification and Scenario revision which could be done rapidly with the QMRA system but appeared to be less easy to perform with the hydraulic models. The latter are designed to run as compiled code in batches to cope with the long run times.

A benefit of keeping the models separate was that the transport and inactivation of particles could be evaluated by themselves and the character of on-shore waste plumes could be determined from examination of different timeseries. The subsequent integration of the hydraulic model outputs into the QMRA is described in Section 2.5.4. Note that since the modelling included consideration of processes additional to ‘Hydrodynamic’ (water in motion) ones the generic modelling has been described as ‘Hydraulic’ (pertaining to water) to distinguish from some of the sub-models which can be seen as purely ‘hydrodynamic’ models.


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2.2.5.4. Principle Groundwork Activities

The risk estimation modelling process was structured as follows: 1. Address the gap in data on pathogen concentrations in Hunter Water sewage in line with

Guideline concerns by analysing the input and discharge waste streams at Burwood Beach WWTP and using this data, generate pathogen source concentration PDFs, the starting point for the QMRA;

2. Instead of directly modelling the behaviour of a selected (indicator) model microorganism within the hydraulic model (e.g. Thermo-tolerant coliforms or enterococci) as done previously (Glamore et al., 2008), modify the coding of the previously developed hydraulic models to calculate and output a timeseries (15 minute timesteps) of statistics from which PDFs of overall reduction, dilution and inactivation of generic microbial ‘particles’ can be obtained for a range of Scenarios;

3. Compile the dilution plus mass reduction output from the hydraulic models in the same manner as PDFs of microbial removal performance data from conventional pathogen barriers such as a chlorination and sand filtration at water and wastewater treatment plants (e.g. Westrell et al., 2003);

4. Estimate the range of pathogen concentrations at preselected bathing points along the coast; (The aim was to gather detailed timeseries suited to QMRA rather than spatial data as previously undertaken. To do both spatial and timeseries in fine detail was judged to be computationally and analytically unmanageable and not essential as the previous work had already characterised spatial movement of plumes well.);

5. Using literature data estimate the doses and dose responses of the exposed populations; 6. For each final QMRA model calculate a spectrum of risk statistics of infection and illness

probability as a function of the probability of a bather being in the exposure zone using the QMRA metamodel for selected exposure Scenarios.

2.2.5.5. Literature Review

Rather than have a single literature review section, each issue has been discussed in the appropriate part of the assessment usually as a distinct section or as part of an Appendix. At such points the literature associated with each issue is discussed along with the related assessment data and perspective. The assessment sections in the main body of the report and the Appendices are listed in Table 2-2. Table 2-2. Location of Reviews of Literature on Central Risk Assessment Issues

Issue Literature Discussion Locations Conclusions and Comments Risk Assessment Style Models

Appendix 02 Models for Microbial Risk Assessment in the Natural Environment

The approach undertaken is in line with previous studies. Its main unusual feature is the integration with detailed hydraulic models

Pathogens and indicators in sewage or otherwise of concern

Section 2.3.1.5 Observed Microbial Numbers Compared to those reported in Sewage Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation Appendix 10 Seasonality, Outbreaks and Hazardous Events

Pathogens of interest are identified in the Guidelines. Most candidates were selected in earlier consultation (Roser & Stuetz, 2008a)and the issue had been reviewed further in a previous risk assessment which formed the basis of this one (Roser et al., 2006a) .

Hydraulic Modelling Section 2.2.5.3 Manner of Integration of Hydraulic Modelling and QMRA Section 2.5.4 Operational

Modelling appears to be a sound approach for estimating and modelling framework is well established


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Issue Literature Discussion Locations Conclusions and Comments Integration of the QMRA and Hydraulic Models Appendix 15 Hydraulic Modelling of Particle Transport and Inactivation in Coastal Waters

Solar radiation influence

Section 2.4.2.2 Solar Radiation and Selection of Inactivation Assumptions; Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity ; Appendix 15 Hydraulic Modelling of Particle Transport and Inactivation in Coastal Waters.

Solar radiation is an effective disinfectant but requires clear water and several days to be fully effective. So the Scenarios explored covered both best case and worst case inactivation.

Intake Section 2.4.3 Consumption of Seawater; Appendix 18 Dose Response Assessment

Intake based on small review of bathing water consumption literature and Guidelines

Dose Response Section 2.3.2 Dose Response Assessment; Appendix 18 Dose Response Assessment

Dose Response algorithms extracted from literature. They are generally conservative.

Exceedence Probability

Section 2.5.4 Reporting of Risk as Exceedence Probability Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event Characterization

This Appendix identifies illustrative literature on the communication of risk more generally probability using exceedence probability

Combinatorial explosion

Appendix 12 Guidelines, Combinatorial Explosion and the Scale of Risk Modelling

This section discusses the problem of assessment scale and the need to set limits on the work done and how Scenarios are selected.

Quality Assurance and Control 1. Assays of pathogens in wastewater

Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation

See QA/QC section and discussion of data reliability

Quality Assurance and Control 2. Applicability of Solar Inactivation Theory and Assumptions

Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity ;

This was the main work undertaken on this solar radiation. It complemented the literature review and assessed how likely solar radiation is likely to be.

Quality Assurance and Control 3. Applicability of Winter

Appendix 19 Assessment of Winter Model Input Data For Newcastle Coastal Waters

This is a summary. The full assessment is provided by Rayner et al. (Rayner et al., 2009)

Quality Assurance and Control 4. Assessing the Relationship between disease outbreaks and pathogen levels

Section 2.6.5 Seasonality of Disease Burden and Outbreaks; Appendix 10 Seasonality, Outbreaks and Hazardous Events

These sections discuss the seasonality and outbreak issues as they relate to water borne pathogens.

Notes: 1. See also Uncertainty discussions (Sections 2.6 & 5).


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2.3. Hazard Assessment

2.3.1. Pathogen Concentrations and Loadings in Effluent and WAS

2.3.1.1. Survey Rationale

A survey of wastewater and WAS quality was undertaken by local contract water quality laboratories as well as HWC. The survey aimed at providing sufficient data to estimate probability density function (PDF) coefficients defining the typical Baseline quality of the raw screened sewage, secondary treated effluent and WAS which could be used subsequently in QMRA modelling. The survey proposed was in situ by contrast with the desktop use of literature by CH2MHill (CH2MHill, 2008) . The collection of new data in situ led to the question of how long and how extensive the surveys should be? Models for the experimental work were:

1. Payment (Payment et al., 2001) for the survey of pathogens released by the WWTP; 2. Davies-Colley’s work (Davies-Colley et al., 1994) on seawater inactivation rates; 3. Falconer et al. (Falconer et al., 2001) on thermotolerant coliform inactivation in situ over a

large swathe of estuary. Experimental work at these scales in these documents was not proposed at this stage based on the following rationale:

1. It was not felt that such large surveys were needed to define sufficiently the risks arising from hydraulic Hazardous Events. This was because the primary concern was about a marginal upgrade of the WWTP which was understood to be operating satisfactorily rather than the installation of a completely new treatment system in a pristine environment.

2. BBWWTP was experiencing operational difficulties and determination of the impact of the proposed upgrade was urgent. Examination of the above studies on the other hand suggested comparable work to these models would require 2-3 years.

3. At the project commencement clear Hazardous Event water quality benchmarks were not available so it was unclear exactly what questions a very large experimental survey should be designed to address and what its end point should be.

4. It was unclear whether local pathogen analysis resources were sufficiently developed for reliable application to wastewater analysis as against surface and drinking waters.

5. It was unclear if such logistically feasible large surveys could actually greatly improve on existing data because the science in the field is still under development. For example our review of in situ measurement studies along the Auckland Harbour study lines (Davies-Colley et al., 1994) confirmed the dominance of solar radiation as the major disinfectant but it also indicated that comprehensive estimation of solar radiation potential across a wide stretch of water is not yet practical.

As a result we opted for an intermediate scale experimental survey based on the large model surveys above which still went well beyond the earlier desktop study (CH2MHill, 2008). It was designed primarily with support for decision making in mind, where existing scientific knowledge was exploited and experiments were designed to verify its applicability. This still left open the potential for further larger surveys to address unresolved questions in the future if required. Based on this reasoning and the model environmental studies above the following survey design principles were developed:

1. The survey would primarily: a. Collect local wastewater pathogen concentration data sufficient to estimate the

median/geometric mean concentration of pathogens and its variance in the discharge material under dry weather flow (in effect Baseline quality) for use in QMRA;


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b. Evaluate the degree of partitioning into WAS to assess which flow was of greater concern loading wise;

c. Evaluate whether there was evidence of temporal variance that might lead to our survey underestimating pathogen and indicator concentrations;

d. Be complemented by comparisons with data from other wastewater surveys to determine if the estimates were reasonable for use in QMRA or where sensitivity analysis should be applied.

2. Mesocosm scale solar inactivation and water transparency measurements would be undertaken to:

a. Assess if the inactivation of microorganisms in WAS took place at a markedly different rate to that occurring with treated effluent and to rates reported in the literature;

b. Assess the potential for light to penetrate seawater in situ compared to the transparency assumptions used in the hydraulic model;

c. Identify which of the inactivation rates used in the hydraulic models were most suitable.

3. A comparable scale of in situ hydraulic model tuning and validation had already been undertaken for BBWWTP. So work should focus on two more critical information gaps, a lack of knowledge of winter risks, and the construction of PDFs detailing the barrier effectiveness of the coastal zone) and involve:

a. Modification of the model coding to generate output data in a format suited to QMRA (Previously the model had been designed to generate a small number of summary statistics predicting indicator water quality and plot the spatial distribution of indicator concentrations over time as 2D contour plots.);

b. Verification and adaptation of a second set of coast zone input data (wind run, current speed and direction, water temperature etc.) which had been identified to cover the winter months (Earlier modelling had focused on the summer months as these are the peak use season. However we also wished to assess the risk to surfers who are active in the winter months as well.)

This said the scale of the survey was comparable to many other surveys of WWTP pathogens (see Section 5.7 for a discussion on the scale of the WWTP survey). The remainder of this section summarises the wastewater pathogen content survey. The full report for the latter can be found in Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation . Locations of this and other experimental work are also listed in Table 2-2.

2.3.1.2. Survey Design

The index pathogens proposed for analysis were: 1. Cryptosporidium spp., 2. Campylobacter spp., 3. Rotavirus. 4. Giardia lamblia 5. Adenovirus 6. (enterococci).

To complement a survey of these concentration, data on concurrent waste stream flows was also collected to allow estimation of pathogen loads (organisms discharged per unit time). Together these data were used to determine:


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1. Whether the measurements collected reflected those of normal dry weather flows or might have been markedly impacted by wet weather surges;

2. The reduction in pathogen numbers achieved by the secondary treatment processes; 3. The extent to which pathogens were concentrated in the WAS and partitioned between the

two discharge streams; 4. Whether the pathogen concentration PDFs were sufficiently representative to be used direct

in the QMRA; 5. Whether it was preferable to include simulation of WWTP treatment in the QMRA models

(Glamore et al., 2008) or simply use the discharge concentration estimates. Whether the WAS posed an elevated risk was addressed by:

1. Separate analysis of its pathogen content; 2. Comparison of this content data with that in the raw and treated effluent streams for any

inconsistency which would suggest the concentrations were being underestimated; 3. Construction of seperate WAS pathogen fate and transport Scenarios as part of the hydraulic

modelling for QMRA and for comparison with effluent fate and transport modelling; 4. Modelling of 2030 as well as the 2007 hydraulic loads to estimate the increase in risk due to

increased discharge rates and its relative importance compared to other source of variance in pathogen risks;

5. Experimental studies of indicator inactivation by solar radiation in WAS v. secondary effluent.

The issue of variable pathogen survival was addressed by:

1. Modelling inactivation rates covering rates ranging from the most rapid reasonably conceivable to the most conservative (dilution only);

2. The experiments on indicator and inactivation to determine at what rates this was likely to occur and how the rates varied between WAS and secondary effluent;

3. Measurement of ocean water transmissivity, which would likely influence solar radiation driven inactivation.

2.3.1.3. Methods

System Description Exposure pathways to be considered for QMRA assessment were identified in line with the original pathways considered in the WRL report (Glamore et al., 2008). Water Quality Analysis Survey It was initially planned that the concentrations of three reference or index pathogens would be measured. The three selected were:

• Cryptosporidium parvum/hominis • Campylobacter jejeuni • Rotavirus

Giardia spp. (assumed to be G. lamblia) were also being measured because their enumeration is straightforward to undertake when Cryptosporidium is measured. And based on another concurrent study (Roser & Ashbolt, 2008) it appeared that they are likely to be present in much higher numbers than Cryptosporidium spp., pose an even higher risk and therefore might be a more conservative index pathogen.


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Early in the monitoring program it appeared that there was little evidence of Rotavirus in the assays used by the analysts Environmental Pathogens. An alternative proposal was the measurent of Adenovirus concentrations in the event that overall few rotavirus were detected. Enterococci were included as a total gastrointestingal illness pathogen ‘surrogate’ when it was realized that enterococci guidelines had been be based on an empirical dose response relationship which related enterococci concentration to total gastrointestinal illness probability. Along with pathogen monitoring, bacterial indicators and FRNA bacteriophage were measured in the waste-streams over the course of three 24 hour periods so as to provide supplementary data which could be used to:

• assess treatment plant process effectiveness in reducing the numbers of the different microbial groups;

• assess how variable concentrations of micro-organisms are; • provide separate supporting data on how microorganisms behave generally.

A provisional timeline proposed at the commencement for the overall program was as follows:

• 1 month for preparation; • 3 months for data collection; • 1 month risk modelling with final data; • 1 month for report preparation; • 1 month contingency to allow for Christmas disruptions occurring in the latter part of the

program. Wastewater and WAS samples were analysed by:

1. Hunter Water Corporation Laboratory (Protozoa and bacterial indicators); 2. Australian Water Quality Centre (Campylobacter); 3. EML (F-RNA bacteriophage); 4. Environmental Pathogens (Rotavirus and Adenovirus).

Estimation of microorganisms used a number of different technologies including epifluorescence microscopy and nucleic acid based assessments as well as cultural methods. Quality control samples (replicates, spiked microorganisms, collection blanks) were included in the survey where available (replicates and blanks in all cases, and quantitative spikes where this was possible). Overall we used ‘Best Available’ assay technology. Nevertheless the reader must keep in mind that there are a range of limitations to the assays and they differ according to the microorganisms. Though reference materials were used these have limitations too. For this reason alone we would consider the risk modelling provides good order of magnitude estimates of pathogen numbers and risk rather than precise numbers desired for developing compliance based rules. Discussion of this aspect of QA/QC is provided in Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation. So we stress to the reader that while we used ‘best available technology’ these technologies have clear limitations. BBWWTP Hydraulics Hunter Water Corporation provided 30 minute and daily total flows for screened primary sewage, secondary treated effluent and WAS as well as daily rainfall data for the period of the water quality monitoring survey. The daily rainfall data measurements were taken as reflecting general Newcastle rainfall as well as rainfall in the Burwood Beach area. This data was used for the following:


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1. Assessment of the diurnal flow patterns to determine where in the WWTP wastewater hydrograph the sample collections tended to occur;

2. Calculations of pathogen and indicator loads into and out of the WWTP, partitioning into WAS and the total removal of pathogens achieved by the WWTP processes;

3. Identification of when rainfall event surges occurred so as to: a. Identify which flows could be considered ‘dry weather’; b. Document what was meant in practice by dry weather flows following removal of

high flow period data. Ancillary Analyses The following was undertaken to support the risk modelling:

1. As part of the verification work, measurements of seawater transmissivity to solar radiation were undertaken.

2. Concurrently seawater, treated water and WAS were collected and analysed at the laboratory to determine the transmissivity of different dilutions of wastewater and WAS.

3. The literature was surveyed for data on the fate/reduction of pathogens in treated effluent and WAS/sludge.

4. Inactivation rates were measured to determine if the coastal seawater inactivation modelling assumptions ranging from high solar radiation to conservative covered reasonably the full range of possible inactivation.

Data Management and Analysis All data were collated in an MS Access database to facilitate calculations, data filtering, summarisation, editing and data management. Prior to entry all data were screened for quality and if necessary edited with MS Excel (especially in the case of water quality data). Where possible and appropriate pathogen concentration data were adjusted based on the recovery of spikes. Based on recovery of spiked material the enterococci and E. coli concentration data were unchanged. Adjustment was undertaken in the cases of Cryptosporidium, Giardia, Adenovirus, Rotavirus and F-RNA bacteriophage. Campylobacter and C. perfringens data were unadjusted because of the unavailability of suitable recovery data. In environmental sampling of pathogens it had previously been found that the data were generally consistent with a log10 normal PDF (Roser & Ashbolt, 2007). So for QMRA purposes the initial (source) pathogen concentrations were assumed to follow this distribution form. Checks of complete data sets for goodness of fit using the Kolmogorov-Smirnov Test (Massey, 1951) indicated this was the case. Where data was partially censored the log10 normal PDF coefficients were estimated using the data distribution fitting tool in Palisade @Risk 4.5. The probability density functions describing microbial loads into and out of the WWTP were estimated by:

1. assuming there was no correlation between pathogen content and flow 2. resampling randomly the concentration PDFs and overall flow PDFs and 3. combining these values using @Risk 4.5.

2.3.1.4. Results and Discussion

Microbial Concentrations Sewage and WAS The weekly monitoring data was used to assemble a summary PDF set (Table 2-3) describing the source material water quality. Rotavirus has been omitted as there were insufficient detects to generate a usable PDF. Its replacement with Adenovirus was considered reasonable as this group is a very common in faecal matter and sewage and is explicitly identified as a major cause of illness/ outbreaks in bathing situations (World Health Organisation, 2006; World Health Organization, 2003). Overall the data suggested that there was limited or no reduction in pathogen concentrations


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by the treatment. There was some accumulation in the WAS most notably in the case of Giardia. Giardia were the most abundant pathogen. Adenovirus were notable for the small standard deviation (SD) values. The preferred data sets for QMRA were PDFs of pathogens and enterococci in the treated effluent and the WAS. The reasons that we sampled the raw waste stream as well were:

1. At the commencement of the study we did not know how effective the secondary treatment would be in reducing microbial concentrations and how this would impact on pathogen enumeration. Data from other secondary treated plants had indicated that more reduction via the activated sludge might be happening than we expected and the BBWWTP design was relatively unusual. In the event that this occurred the concentrations of pathogens might have been too low to measure and we would have had to include barrier functions describing the performance of the BBWWTP.

2. Data on treatment effectiveness would tell us how much poorer the microbiological quality of the discharge might be if the activated sludge process were to fail e.g. due to an incursion of toxins.

3. It was unclear how much further the study would go and whether at some stage or in a future study in Newcastle whether estimates of pathogens and indicators in raw sewage would be needed e.g. for assessing the impact of sewage overflows and leakage.

4. We wished to have an estimates of the extent to which microorganisms partitioned between WAS and secondary effluent as part of determining how different the hazard posed by WAS was compared to the effluent.

The reliability of such PDFs decreases with the proportion of non detects. A detection rate of 50% is tolerable in our experience but less than this is problematic. Based on this criterion it can be seen that the estimated numbers of Campylobacter and Cryptosporidium in the WAS were less than ideal. This is most evident in the extreme SDs. Table 2-3. PDFs of pathogens and enterococci from Weekly Sampling Data

Waste Stream Microorganism Log10 average

Log10 SD

minimum maximum Count of detects

units

Screened Primary

Cryptosporidium total adjusted

1.203 0.31 BDL 1.7 4 of 12 oocysts/L

Screened Primary

Giardia total adjusted 3.661 0.385 3.00 4.4 12 of 12 cysts/L

Screened Primary

enterococci 5.608 0.44 5.04 6.6 11 of 11 cfu/100mL

Screened Primary

Campylobacter spp. -0.066 1.084 BDL 2.5 5 of 12 mpn/L

Screened Primary

Adenovirus adjusted 2.225 0.143 BDL 2.4 10 of 12 pfu/L

Secondary Cryptosporidium total adjusted

1.368 0.409 BDL 2.1 9 of 12 oocysts/L

Secondary Giardia total adjusted 2.23 0.508 1.43 3.2 12 of 12 cysts/L Secondary enterococci 5.352 0.317 4.86 6 11 of 11 cfu/100mLSecondary Campylobacter spp. 0.425 0.468 BDL 3.3 7 of 12 mpn/L Secondary Adenovirus adjusted 1.586 0.447 BDL 2.4 10 of 12 pfu/L WAS Cryptosporidium total

adjusted 2.412 0.139 BDL 2.8 4 of 12 oocysts/L

WAS Giardia total adjusted 4.55 0.337 4 5.1 12 of 12 cysts/L WAS enterococci 5.914 0.457 4.95 6.4 11 of 11 cfu/100mL


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Waste Stream Microorganism Log10 average

Log10 SD


units

WAS Campylobacter spp. -0.2 1.108 BDL 1.8 4 of 12 mpn/L WAS Adenovirus adjusted 1.948 0.28 BDL 2.3 12 of 12 pfu/L

Notes: 1. BDL = Below Detection Limit 2. Total assays was generally 12.

As with pathogens a summary PDF set (Table 2-4) was constructed using the diurnal sampling data. Of the indicators FRNA were the most problematic. They showed high standard deviations and there was a strong suggestion that they were multiplying in the WAS. Table 2-4. PDFs for Indicators from Diurnal Sampling Data

Material Microorganism Log10 average

Log10 SD


units

Screened Primary

enterococci 5.687 0.182 5.30 6 19 of 19 cfu/100mL

Screened Primary

E. coli 6.834 0.211 6.23 7.3 18 of 18 mpn/100mL

Screened Primary

C. perfringens 5.232 0.615 4.11 6.1 19 of 18 cfu/100mL

Screened Primary

FRNA Coliphage adjusted

2.853 0.422 BDL 3.5 9 of 18 pfu/L

Secondary enterococci 5.18 0.38 4.43 5.7 19 of 19 cfu/100mL Secondary E. coli 6.434 0.253 5.98 6.8 18 of 18 mpn/100mL Secondary C. perfringens 5.076 0.542 3.60 6.1 18 of 18 cfu/100mL Secondary FRNA Coliphage

adjusted 3.762 0.614 2.24 4.3 12 of 12 pfu/L

WAS enterococci 6.028 0.285 5.30 6.4 18 of 18 cfu/100mL WAS E. coli 6.67 0.316 6 7.4 17 of 17 mpn/100mL WAS C. perfringens 5.849 0.285 5.17 6.3 17 of 17 cfu/100mL WAS FRNA Coliphage

adjusted 4.998 0.2 4.69 5.2 11 of 12 pfu/L

Screened Primary


2.424 0.62 1.11 3.5 12 of 12 pfu/L

Screened Primary


1.955 1.247 0.619 3.5 18 of 18 pfu/L

Secondary FRNA Coliphage adjusted

3.585 0.61 2.24 4.3 18 of 18 pfu/L

WAS FRNA Coliphage adjusted

3.662 1.926 0.619 5.2 17 of 17 pfu/L

Notes: 1. The in FRNA coliphage data from diurnal run 1 were suspect. The PDF shown in normal

fonts comes from a data set with these data removed. The PDFs from the whole data set are shown in italics and illustrate the resulting very high variance probably reflecting variations in method effectiveness.

Quality Control and Abundance Variability In the case of the pathogen measurements the average ratio of replicate 1 to replicate 2 ranged from 0.55 to 1.48. In the case of indicator measurements the average ratio ranged from 0.5 to 1.58. These illustrate the limits of reproducibility of assays between samples collected concurrently. However the numbers were sufficiently close to indicate good replication sufficient for log transformed data.


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The three bacterial indicators were detected in similar and consistent concentrations. FRNA coliphage were not detected consistently in diurnal variation sample run 1. This was considered unlikely and so load estimation utilised only runs 2 and 3. Enterococci Bioballtm spike recovery was very good with an average of 27 compared to expect 30 cfu.100mL-1. Similarly E. coli Bioball spike counts were 36, 21, 28 compared to an expected value of 30. Recovery of Giardia and Cryptosporidium Colorseedtm on the other hand was very poor and ranged from 0 to 41% depending on the matrix. Compared to Giardia, Cryptosporidium detection tended to be more sporadic. The suboptimal recovery of the protozoans indicated that the PDF estimates were somewhat uncertain and sensitivity analysis should be undertaken on this pathogen. Poliovirus recovery (used to assess virus assay reliability) was very good noting the spiked numbers were relatively high at ca 106. One of the three FRNA spike assays was unsuccessful. Recovery data from runs 2 and 3 were used to adjust the counts of these indicator bacteriophage. Of 55 blanks for pathogens two failed in the case of Adenovirus. The concentration of one was comparable to that in the samples suggesting a mix up at the subcontracting laboratory. Carryover occurring in the autosampler appeared to be low and comparable to that observed in the blanks collected concurrently. So the use of the autosampler was seen as reasonable and the data used. Microbial numbers in wastewater have been reported to undergo seasonal variation (Payment et al., 2001). Examination of the data collected in this study showed however:

1. No clear trend over the course of the study in the daily pathogen measurements with the possible except of Giardia in WAS;

2. No trend in the indicator counts over the course of each day during the diurnal variation study;

3. No relationship of note between instantaneous flow and microbial numbers. WWTP Hydraulics Burwood Beach WWTP primary and secondary treated flows responded strongly to rainfall, increasing by a factor of up to 3 during a moderately large event. The correlations (r2) increased from ca 0.3 between flow and rainfall on the same day to ca 0.6 in relation to the following day’s flow and decreased to 0.3 again for flow two days after the rainfall. Graphic plots showed that response to rainfall was still evident several days later (Figure 2-3). WAS flow, however, was not significantly correlated with rainfall. Regression graphs yielded r2 values in the range 0.01 to 0.04. Diurnal flow variation showed the well known ‘morning flush’ and ‘evening flush’ (Figure 2-4). The 8-9 am sampling time corresponded to the rising portion of the main peak. Nevertheless the diurnal indicator sampling runs indicated that water quality samples taken at 8 to 9 am were still representative of that over the whole day, flows were not fully representative. For this reason when calculating loads we did not combine paired concentration and flow values but instead resampled the flow PDF.


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Figure 2-4. Average Diurnal Flow at Hydraulic Monitoring Points within the WWTP

To simplify analysis of data, sampling was only undertaken on dry weather days. The rationale was that:

1. Wet weather days and 1 to 2 days following were when stormwater impacts generally were already a concern and there would already be warnings to the public about the unsuitability of conditions for bathing issued by Beachwatch in any case (Armstrong et al., 1997).

2. WRL modelling had already focused on Average Dry Weather Flow and it was preferable to keep a consistent terms of reference.

Based on the plot of sampling occasion v. flow, the flow conditions identified as those most characteristic of the sampling days were considered to be those on dry days + some moderately rainy days when the influence of large events had dissipated to the point it was not evident in the flow statistics. The complete table of flow data was examined (Appendix 20 Hydraulic Statistics for BBWWTP Experimental Period) and those days corresponding to the two largest events and the two days immediately following were removed. The statistics for the resulting dry plus moderate rainfall days are shown in Table 2-5. These were used subsequently in load and partitioning


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estimation. These were seen as most appropriate because sampling was undertaken predominantly on Dry Weather days (see Figure 2-3). Table 2-5. Flow representative of the sampling period

Material Average SD Minimum Maximum units Screened Primary 51.01 9.21 41.70 102.60 ML/dSecondary 47.36 9.05 37.29 98.18 ML/dWAS 2.11 0.95 0.50 4.01 ML/d

Microbial Loads Statistics for waste stream loading PDFs were obtained by combining the concentration and flow data (Table 2-6 and Table 2-7). Average and standard deviations for enterococci were closely comparable. However standard deviation and 95th percentiles tend to be larger in the weekly data. Table 2-6. Pathogen Loadings (Organisms per day)

Waste Stream Pathogen Average Standard deviation Median 0.95 percentile Cryptosporidium total 1.11E+09 9.21E+08 8.41E+08 2.84E+09 Giardia total 3.63E+11 4.10E+11 2.41E+11 1.06E+12 Enterococci 3.60E+14 4.66E+14 2.12E+14 1.12E+15 Campylobacter spp. 8.98E+08 6.01E+09 4.64E+07 2.56E+09

Screened Primary

Adenovirus 9.51E+09 3.52E+09 8.96E+09 1.61E+10 Cryptosporidium total 1.81E+09 2.27E+09 1.12E+09 5.42E+09 Giardia total 1.66E+10 2.66E+10 8.37E+09 5.58E+10 Enterococci 1.47E+14 1.28E+14 1.10E+14 3.87E+14 Campylobacter spp. 2.38E+08 3.63E+08 1.30E+08 8.02E+08

Secondary Effluent

Adenovirus 3.27E+09 4.56E+09 1.91E+09 9.97E+09 Cryptosporidium total 5.84E+08 3.00E+08 5.32E+08 1.15E+09 Giardia total 1.02E+11 9.66E+10 7.34E+10 2.88E+11 Enterococci 3.05E+13 4.45E+13 1.59E+13 1.03E+14 Campylobacter spp. 3.84E+07 4.99E+08 1.21E+06 9.06E+07

WAS

Adenovirus 2.34E+08 1.98E+08 1.83E+08 6.12E+08 Table 2-7. Indicators Loadings (Organisms per day)

Material Indicator Average SD Median 0.95 percentile Enterococci 2.84E+14 1.29E+14 2.58E+14 5.27E+14 E. coli 4.12E+15 2.18E+15 3.62E+15 8.03E+15 C. perfringens 2.46E+14 5.58E+14 9.13E+13 8.65E+14

Screened Primary

FRNA Coliphage 6.17E+10 7.95E+10 3.85E+10 1.96E+11 enterococci 1.10E+14 1.18E+14 7.42E+13 3.04E+14 E. coli 1.60E+15 1.04E+15 1.32E+15 3.55E+15 C. perfringens 1.27E+14 2.24E+14 5.83E+13 4.50E+14

Secondary

FRNA Coliphage 7.61E+11 1.63E+12 2.79E+11 3.03E+12 enterococci 2.82E+13 2.37E+13 2.12E+13 7.31E+13 E. coli 1.31E+14 1.29E+14 9.22E+13 3.82E+14 C. perfringens 1.89E+13 1.70E+13 1.43E+13 5.02E+13

WAS

FRNA Coliphage 2.36E+11 1.47E+11 1.99E+11 5.36E+11 Decimal Reductions and Partitioning of Pathogens into Effluent and WAS Resampling of PDFs with the @Risk software was used to estimate the overall reduction in pathogen and indicator numbers as log10 Decimal Reduction (DR) values and the percentage of each organism type partitioned into the WAS. There was little difference between input and output load except in the case of the F-RNA coliphage where it seems likely that regrowth occurred. C. perfringens numbers appear to have remained


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unchanged. Cryptosporidium and Campylobacter appear to have undergone slight increases but this was unlikely to be statistically significant given the high standard deviation and poor/unknown recovery from water samples of markedly different types. These indicate modest DRs in the range of 0.03 to 0.4. Compared to its volume (<10% of total) the WAS received a disproportionate load of the pathogens and indicators with the possible exceptions of E. coli and Campylobacter. Most striking was the partitioning of Giardia. Nevertheless the majority of the microorganisms on a loading basis were discharged in the treated effluent and hence this was seen provisionally as the greater risk. A possible exception was Giardia but the recovery of cysts was relatively poor so this estimate is less credible. The WAS was diluted with treated effluent to aid pumping so the numbers detected could not be compared directly with the literature on sludge and biosolids. For the most abundant and reliably measured indicators (E. coli, enterococci, C. perfringens) only 9 to 27 % of the total load was released in the form of WAS.

2.3.1.5. Observed Microbial Numbers Compared to those reported in Sewage

Constant enterococci concentrations in the range of 105- 106 .100mL-1 were consistent with generic numbers reported generically (Medema et al., 2003) , at two large Canadian STPs (Payment et al., 2001), in the US (He & Jiang, 2005) and in Europe (Vilanova et al., 2004). Protozoa Cryptosporidium and Giardia were comparable to the numbers seen in polluted rivers under storm conditions in Australia (Roser & Ashbolt, 2007), at two southern highland WWTPs (SCA internal report prepared by UNSW), in sewage in the Netherlands (Medema & Schijven, 2001) and Canada(Payment et al., 2001). But these concentrations were 1 to 2 log10 units below those reported by some old US work (Madore et al., 1987) under presumably normal social circumstances, and were much less still than in the case of an outbreak in a small communities (Lee et al., 2001). These data suggested the Baseline concentrations in effluent may be higher at times and Scenario sensitivity modelling using an assumed concentration higher by a factor of 101 might be desirable for the protozoa. On the other hand Cryptosporidium viability assessments of Southern Highlands isolates indicated the viability of oocysts may only be ca 5% in fresh sewage. So the estimated concentrations might have been reasonable. Viruses Rotavirus were rarely detected. At first this seemed anomalous as Rotavirus are reported in the literature as occurring reasonably frequently (Bosch et al., 1988b; Medema et al., 2003; Smith & Gerba, 1982 ). However several of these reports are from developing South American countries where the levels of disease would be expected to be higher and hence the virus more frequent (Mehnert & Stewien, 1993; Oragui et al., 1989; Toranzos et al., 1988) and hence this data is of uncertain applicability to Australia. Other studies indicate Rotavirus occurrence is sporadic (Bates et al., 1984; Hejkal et al., 1984; Petrinca et al., 2009). One group suggested this was due to ‘seasonality’ (Hejkal et al., 1984). This may be the case but fluctuations in the timeseries were such that to draw this conclusion with the typical limited data sets reported would seem unwise. More recent PCR based assays do not appear to have settled the matter. Rotavirus have been reported to be common(Lodder & de Roda Husman, 2005), puzzlingly absent (Pusch et al., 2005) or occur sporadically (Petrinca et al., 2009). Our conclusion is that the use of the Rotavirus numbers was problematic. It is unclear why this the numbers were low but this observation is not inconsistent with the literature.


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This problem with using Rotavirus as the index was identified early in sampling program by the laboratory. In discussions between HWC and the analysts (Environmental Pathogens) it was suggested that Adenovirus might be substituted and the previously processed sampled could be re-assayed. Adenovirus was identified in the Guidelines as one of the virus pathogens of concern. Further a conservative dose response curve is available for Adenovirus (Haas & Eisenberg, 2001), as is an estimate of infection:illness ratio (Van Heerden et al., 2005b). Other ‘Best Practice’ indicating the suitability of Adenovirus as a model virus are:

1. The numbers detected were in the range identified in other Australian Guidelines (NRMMC/EPHC, 2005) and comparable to those detected in sewage using culture based assays (He & Jiang, 2005);

2. Adenovirus has been used for previous sewage related QMRA (Westrell et al., 2004). 3. Adenovirus has been assayed as an index for disinfection control contamination surveys in

the Australasian region (Watercare Services Limited, 2003). Further rationale for its selection can be found in Appendix 10 Seasonality, Outbreaks and Hazardous Events. So Adenovirus was substituted as the model virus choice because of concerns. It main limitation was considered to be that cultural assays may detect numbers substantially lower than via PCR based assays (He & Jiang, 2005). Bacteria The Campylobacter numbers observed (geometric mean of effluent ca 3.L-1) were lower than expected. Examination of the literature did not resolve this uncertainty however. In the Netherlands (Koenraad et al., 1994) these pathogens have been reported to be detected in sewage at concentrations in the range of 102-105.L-1. Betaieb and Jones (Betaieb & Jones, 1990) detected 104 to 105. L-1 of raw and treated/undisinfected sewage. Skelly et al. (Skelly & Weinstein, 2003) reviewed possible sources because of concerns about disease rates in New Zealand. Their review indicated only a limited number of literature reports on wastewater content with numbers. Nevertheless numbers reportedly ranged from ca 101 to 106. L-1. Seasonal peaks seemed to occur not at all or around the season in which our survey took place. Review of sewage constituents (Skelly & Weinstein, 2003) similarly showed with possible maxima in late autumn to early summer. Jones et al. (Jones et al., 1990) also identified strong seasonal peaks in Libyan wastewaters for the same period. Other data (Skelly & Weinstein, 2003) indicated that the seasonality was non existent, vague or source specific. In respect to QMRA of sewage we identified one paper on sludge and biosolids from Gale (Gale, 2005a) who is well known as an applied specialist working on many microbial risk assessment projects (Gale, 2000; 2002; 2003; 2005b). Interestingly he did not tabulate any sewage concentration data on Campylobacter in order to understand partitioning effects. No data on wastewater was identified in the literature from Australia and we have not identified any relating to sewage but we felt that this still did not settle the matter. In previous work on storm-runoff from an urban catchment (Roser & Ashbolt, 2007) we have observed by contrast with the observed sewage levels an overall 150 Campylobacter.L-1 during high run-off periods. This finding is all the more noteworthy because the sample were analysed by the same laboratory that undertook analyses in the current instance. Two credible reasons for these anomalous results were identified which may also be inter-related. The first is high variance in the environmental occurrence of Campylobacter illustrated by the following:

1. Reported concentrations in the literature ranged apparently over 6 orders of magnitude;


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2. Average sludge concentrations can also be highly variable ranging by 2.5 orders of magnitude (Jones et al., 1990);

3. From our previous catchment survey study (Roser & Ashbolt, 2007) we observed a 10 fold difference between the geometric means of dry and wet samples and a 30 fold difference between the flow weighted average means in different run-off events.

4. Estimated variance of the log10 transformed concentration estimates from the urban catchment was 0.86, about 4 times that seen with indicators (Roser & Ashbolt, 2007).

5. In the current survey, many samples were below the detection limit, but one sample (for both the treated effluent and WAS) had >1100 .L-1.

A second more disturbing reason may be that the high numbers and older environmental data may be reporting false positives arising from applying assay techniques, developed presumably for food, to wastewater and the limited number of critical studies on this topic. Like the other literature Diergardt et al. (Diergaardt et al., 2003 ; Diergaardt et al., 2004) reported 102 and 108 .L-1 of ‘Campylobacter like’ organisms in sewage in initial analyses using ‘standard’ techniques. However, subsequent genetic analyses and other characterisation of the isolates indicated much overlap and problematic identification. In the first of these papers genetically characterised sewage isolates were classified as Arcobacter, Acinetobacter and Paracraurococcus and no confirmed Campylobacter were detected in other water samples. The subsequent survey confirmed Campylobacter in only 3 samples and only 1 sewage sample (104.L-1). The authors suggested South Africa’s warmer climate (which is comparable to that of southern Australia) might have a role in reducing numbers. Another possibility is that Campylobacter may enter the viable non culturable state (Oliver, 2005). A third more serious source of uncertainty which may have been encountered by Diergardt et al. (2004) is microbial biodiversity in the natural environment which is finally being better appreciated through Metagenomics based on 16S RNA. This rising field appears to be finally quantifying the long suspected (Staley, 1997) astronomical diversity of bacteria in the environment. Pertinent to faecal pathogens is that even the early/recent/ limited surveys of gut microbes have identified over 106 ! different ‘operational taxonomic units’ in humans alone (Ley et al., 2008; Turnbaugh et al., 2009). How much biodiversity occurs in the rich active microbial soup of wastewaters or is generated in the complex microbial ecosystem, that is an Activated Sludge, remains to be determined. But irrespective the observed biodiversity indicates that the potential for false positive detects of pathogens in environmental samples is much higher than previously appreciated, whether differentiation is via culture or genetic techniques is substantial on first principles. We concluded that the literature data indicated for Campyloacter that: 1. The low numbers observed at BBWWTP in wastewater were still ‘within in range’ and our

selected sampling period was not inappropriate; but 2. There is substantial uncertainty associated with both literature estimates of Campylobacter

numbers in wastewater and probably the data collected in this study; 3. It was not clear what should be an alternative numbers per L assumption value to use or

even what sensitivity factor to use in QMRA. Conclusions Overall from this review of the literature and comparison with our data we concluded that:

1. The levels of pathogens observed in the effluent and WAS were consistent with and only marginally below those in the screened raw sewage. So it was reasonable to use the data from these streams in the current QMRA.

2. Indicator counts were of the numbers expected. So we could use effluent and WAS data for enterococci as the starting point for the QMRA.


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3. Cryptosporidium and Giardia were present at levels consistent with literature reports. Giardia were present in substantially higher levels than Cryptosporidium so these might be preferable as the model protozoan parasite. There was uncertainty about the assay precision due to the suboptimal recovery of spike material. On the other hand the counts reported were ‘Totals’ and hence were conservative and it was possible that the degree of viability was low. It was concluded that sensitivity analysis assuming a higher level by a factor of 101 should be undertaken.

4. Adenovirus was a reasonable and credible substitute for Rotavirus and the reported levels were consistent with sewage content reported in the literature based on culture assays. However it was unclear whether their numbers were underestimated by this older assay approach. Accordingly it was concluded that sensitivity analysis assuming a higher level by a factor of 102 should be undertaken.

5. Due to the uncertainties identified in the literature estimates of Campylobacter numbers it was decided to only undertake limited risk estimation using the data estimated in our survey. Sensitivity analysis was considered but the difficulty was how much larger to assume levels might be. The literature indicated the levels might be comparable or higher by a factor of 106 which would lead to an extreme illness risk. This issue is discussed further in the Uncertainty Section 5.

The WAS sludge cannot be directly compared to normal primary and activated sludges. The latter are typically thickened before separation to minimise volume where as the WAS is diluted to aid its flow (HWC pers. Com.). As a result microbial numbers in WAS would be expected to be intermediate between wastewater and sludge. This appears to be the case.

2.3.1.6. Source Pathogen Levels for QMRA

Final PDFs estimated for the simulations are shown in Table 2-8. With the except of F-RNA bacteriophage the levels of microorganisms in the discharged wastes were consistent with those in the screened primary sewage. PDFs estimated for secondary effluent and WAS appeared satisfactory for exposure pathway modelling with the possible exception of Campylobacter. Other notable points which should be recognised in interpreting QMRA data were:

1. Secondary treatment had little effect on the load of pathogens. Average Decimal Reductions were at best 0.6 in the case of Adenovirus. Decimal reductions and standard deviations were small for both protozoa assays.

2. Of the two waste-streams, the secondary effluent appeared to be the major source of pathogens and indicators discharged into the coastal waters off Burwood Beach.


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Table 2-8. Final Source Material PDFs

Material Pathogen Parameter Log10 average Log10 SD units enterococci 5.352 0.317 cfu/100mL Cryptosporidium (total) adjusted 1.368 0.409 oocysts/L Giardia (total) 2.23 0.508 cystsl/L Adenovirus 1.586 0.447 pfu/L

Secondary Effluent

Campylobacter spp. 0.425 0.468 mpn/L enterococci 5.914 0.457 cfu/100mL Cryptosporidium (total) adjusted 2.412 0.139 oocysts/L Giardia (total) adjusted 4.55 0.337 cystsl/L Adenovirus 1.948 0.28 pfu/L

WAS

Campylobacter spp. -0.2 1.108 mpn/L

2.3.2. Dose Response Assessment

When the pathogen and enterococci data for the two discharge streams were combined with the timestep reduction data obtained via hydraulic modelling it was possible to obtain bathing contact zone pathogen estimates for these microorganisms for the occasions/periods when diluted discharge plumes reached the shoreline waters. From this data and the consumption data (Section 2.4.3) doses could estimated. This data was then in turn used to estimate values for the probability of total and pathogen specific gastrointestinal infection and illness depending on location, population, pathogen, intake volume, and intake Scenario based on the application of dose response information from the literature. Full details of the input algorithms are presented in Appendix 18 Dose Response Assessment. The key information is summarised below.

2.3.2.1. General Approach

For drinking water the emerging best practice is to transform pathogen level data into infection probability estimates and disability life adjusted year estimates or DALYs (Haas & Eisenberg, 2001; Havelaar & Melse, 2003; Pruss & Havelaar, 2001 ; Vijgen et al., 2007). The process involves starting with estimating infection probability and estimating the likelihood of different diseases, residual symptoms and death which may ensure (Havelaar & Melse, 2003; Pruss & Havelaar, 2001 Figure 3.1 ). The situation with bathing water is slightly different because the Guidelines focus on the probability of total gastrointestinal illness per exposure. In line with this manner of expressing risk the two following procedures were implemented:

1. Infection probability was estimated for each model pathogen using infection rate algorithms (Haas & Eisenberg, 2001 Table 8.1) and multiplying these estimates by illness:infection ratios available from the DALY development process to obtain gastrointestinal illness probability estimates; and

2. Total gastrointestinal illness probability was estimated using the ‘pathogen surrogate’ algorithm developed by Kay and his co-workers based on enterococci numbers (Kay et al., 2004; Kay et al., 1994) to provide a quantitative basis for the current WHO and Australian recreation guidelines (NH&MRC, 2008; World Health Organization, 2003).

2.3.2.2. Estimating Infection Probability

The basic techniques for estimating illness probability for individual pathogens are described in Haas et al. (1999)(Haas et al., 1999) and Fewtrell and Bartram (Fewtrell & Bartram, 2001). There are also examples of their use in the recreation context (Gerba, 2000; Schets et al., 2004).


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The dose response relationships defining the probability of infection following different doses are shown in Table 2-9. These dose response algorithms were those selected based in part on their use in the EU MicroRisk project (Medema et al., 2006) which reviewed a range of alternate algorithms. Table 2-9. Infection Probability Algorithms for selected pathogens

Pathogen Dose Response Equation Equation coefficients

Reference

Cryptosporidium alpha= 0.115, beta=0.176 (Teunis et al., 2002a; Teunis et al., 2002b)

Campylobacter

alpha=0.024, beta=0.011 (Evans, 1996; Van den Brandhof et al., 2003)

Rotavirus

Beta-Poisson with unit dose threshold: Probability of infection =1-(1+dose/beta)-alpha

alpha = 0.253, beta = 0.422

(Teunis et al., 2002a; Teunis et al., 2002b; Ward et al., 1986)

Giardia Exponential: Probability of infection = 1-exp(-gamma*dose)

gamma = 1.99 E-02 (Rendtorff, 1954) cited in(Haas & Eisenberg, 2001)

Adenovirus Exponential: Probability of infection = 1-exp(-r*dose)

r= 1/k and k = 2.397 (Haas & Eisenberg, 2001)

All pathogens1 Probability of ‘excess’ gastroenteritis infection/ illness= (1/1+e-d))-0.0866 and d =a*((b-32))0.5-c

a=0.20102, b = numbers per 100mL of enterococci, c= 2.3561

(Kay et al., 2004; Kay et al., 1994)

Notes: 1. Regarding the use of the enterococci surrogate illness algorithm:

a. This relationship is an empirical one and only applies between levels of 32 and 158 enterococci per 100 mL.

b. The curve rises steeply from zero and P. of illness is assumed to reach a maximum of 0.388 at this point though in fact it may continues to increase.

c. It was necessary for QMRA purposes to convert the levels of enterococci into doses. It was assumed in line with the estimates of water consumed during bathing (see above) that the amount of water consumed by the bathers averaged 30mL and hence the threshold dose was ca 10 enterococci.

2.3.2.3. Estimating Illness Probability

The literature developed in support of DALYs includes, for most listed pathogens, estimates of the likelihood of gastrointestinal illness following infection. These data have been used to calculate illness likelihood as a fraction of infection based on our interpretation of the data presented in the references in Table 2-10.

Table 2-10. Illness Probability Factors for Selected Pathogens

Pathogen Illness:Infection Ratio Reference Cryptosporidium 0.7 (Havelaar & Melse, 2003) Campylobacter 0.33 (Havelaar & Melse, 2003) Rotavirus 0.9 (Havelaar & Melse, 2003) Giardia 0.7 (Andersson & Bohan, 2001) Adenovirus 0.5 (Van Heerden et al., 2005b) All pathogens 1.0 (Kay et al., 2004; Kay et al., 1994)

2.3.2.4. Hazardous Event Risks and the Guideline Water Quality Categories

The ‘enterococci’ dose response algorithm is based on a range of epidemiology + water quality data collected at the 4 beaches in the United Kingdom. Discussion with HWC indicated some potential for confusion about the risk predicted by the algorithm and the risk categories in the Guidelines which were derived from the same data.


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This superficial discrepancy arises because the algorithm predicts the probability of gastrointestinal illness at a given, in effect median, enterococci level. The Guidelines on the other hand are based on the 95th percentile of the enterococci level PDF at a bathing site, implying that most samples will contain much fewer enterococci than this value and risk will be lower overall. The Guidelines give credit for when the water quality is better the 95th percentile. The dose response algorithm on the other hand focuses on periods when the water quality is typically much higher. This matter is discussed further in Appendix 21 Hazardous Event Risks and the Guideline Water Quality Categories.

2.4. Exposure Assessment

2.4.1. Exposure Locations

Exposure assessment has been based on the system outline presented in Figure 2-5. The target localities are Bar Beach, Merewether Baths, Burwood Beach & Dudley Beach. The nominal Target Populations are healthy adult swimmers in the surf zone (assumed location 50 m vertical to the beach shoreline) & surfers 200 m perpendicular to the beach shoreline in the surf zone location (Table 2-11). Table 2-11. Exposure Locations Used in WRL Hydraulic Modelling

Location Name Location Number Easting Northing Distance from Shore (m) Depth Merewether Baths (50m) 1 383800 6353300 50 -0.5 Dudley Beach (50m) 2 381365 6350825 50 -0.5 Burwood Beach (50m) 3 382445 6352410 50 -0.5 Bar Beach (50m) 4 384255 6353990 50 -0.5 Merewether Baths (200m) 5 383905 6353175 200 -0.5 Dudley Beach (200m) 6 381478 6350763 200 -0.5 Burwood Beach (200m) 7 382535 6352323 200 -0.5 Bar Beach (200m) 8 384370 6353891 200 -0.5


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Raw Primary Sewage

Fine Screen

ABF Secondary Treatment Tower

Diluted Wastewater in Epilimnion Diluted WAS in Epilimnion

Degritting

Burwood Beach Bathers

Burwood Beach Surfers

Merewether Baths Bathers

Merewether Baths Surfers

Bar Beach Bathers

Bar Beach Surfers

Dudley Beach Bathers

Dudley Beach Surfers

Sludge

Wastewater

Other Pathways Not considered•Fish consumption•Other beaches•Stormwater

&•Baseline conditions

Drainage linesSeptics, animals, exfiltration

Treated effluent Ocean Discharge Point

Diluted Wastewater

WAS Ocean Discharge Point

Diluted WAS

Clarifiers

Dilution

Confinement in hypolimnion

Currents

Clarifier‘Compartment’

Solar Radiation

Barrier Dilution

Time

Currents

Solar Radiation

Time

Dilution Dilution

Time

Biological ProcessesAeration Chamber

Future?Time

Biological Processes

Wet Weather Bypass

Figure 2-5. Exposure Pathways Under Consideration Leading Potentially to Ingestion or Inhalation During Primary Contact Recreation

2.4.2. (Microbial) Particle Transport and Inactivation in Coastal Waters

2.4.2.1. Hydraulic Modelling(Rayner et al., 2009)

Hydraulic modelling involved integrating three distinct sub-models: 1. The JETLAG near-field model to simulate initial discharge plume mixing; 2. The RMA-10 Hydrodynamic model to simulate subsequent downstream dispersion; and 3. The 3DRWALK model to simulate ‘pathogen’ movement along the coast and into the

bathing zones. Hydrodynamic modelling of both summer (2007) and winter (1998) periods was modelled using RMA-10. This involved the modification of the winter ADCP and Thermistor data into a suitable format. The near-field modelling for the ADWF effluent and WAS cases was undertaken using JETLAG for the prescribed flow 2007 and 2030 rates using the 2007 and 1998 ambient conditions. The modelling work has allowed the following statistics to be calculated for each inactivation Scenario at 15 minute timesteps:

• total number of particles; • the median particle mass; • the 90th percentile particle mass; • the 99th percentile particle mass; and,


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• the sum of mass for all particles. Further explanation of this work can be found in Appendix 15 Hydraulic Modelling of Particle Transport and Inactivation in Coastal Waters. An explanation of the concept of ‘particle mass’ and how and why it is used to estimate inactivation rates is provided in Section 3.2.1. Briefly it is used to accelerate the process of concurrently tracking both particle movement/dilution and inactivation to a point where it is computationally practical.

2.4.2.2. Solar Radiation and Selection of Inactivation Assumptions

In earlier WRL modelling (Glamore et al., 2008) which in part led to the current assessment a microbial inactivation rate (T90 ) of ca 20 or 50 hours was adopted for enterococci. This general assumption was not, however, seen as ideal for characterising the survival potential of pathogens. This was because survival of pathogens is now well recognised to be highly variable depending on the microorganism and waterbody as well as the illumination conditions. Depending on the situation solar radiation may be negligible or greatly exceed ‘dark inactivation’(Davies-Colley et al., 2000; Guillaud et al., 1997; Johnson et al., 1997). In the NH&MRC guidelines (NH&MRC, 2008), polio and echoviruses are identified as having T90s of ca 30 and 100 h respectively in seawater and Cryptosporidium (river water at <20 oC) appears to have dark inactivation T90s of the order of 40 to 100 days (Medema et al., 1997). These are primarily examples of dark inactivation. By contrast T90 values can be increased enormously in the presence of sunlight. Microbial inactivation under optimum conditions can be theoretically > >10 orders of magnitude over the few hours around midday at the surface of a waterbody on a summer’s day (Davies et al., 2008b). Overall depending on water depth, transparency, time of day and year and cloud cover, solar radiation dominates over dark inactivation (USEPA, 2001 ) under the kinds of environmental conditions prevailing at Newcastle beaches. The result is that inactivation rates measured as T90s can be expected to range from those corresponding to dark inactivation to values up to 1000 times these (Davies et al., 2008b; Evison, 1988). So single invariant inactivation rate constants were not seen to be sufficient to cover the full range of inactivation possible. To better understand the potential of solar radiation we undertook:

1. Experimental measurements of inactivation rates on microbial indicators in diluted WAS and effluent;

2. Measurements of the transmissivity of diluted WAS and effluent; The results of this work are detailed in Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity Experiments. Uncertainties are outlined in Section 5.9.. The next step was to identify the most appropriate inactivation rate parameters. Davies-Colley et al. (Davies-Colley et al., 2000) identified Sarikaya and Saatci (1987)’s model as an option for combining light and dark inactivation. However, there were proved to be several difficulties with applying this model:

1. Analysis of measurements of the transparency of surface and deeper waters off Newcastle indicated that there was another potential source of combinatorial explosion. This was that total light penetration was far more variable than had been assumed in the hydraulic model (Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity Experiments);

2. This experimental work also indicated that the extents of dilution of the WAS and the effluent were likely to affect transparency in the UV part of the spectrum (<400 nm) and this was likely to vary irrespective.


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3. We had 5 microorganisms to consider and different inactivation rates that might be applied for both dark and light conditions to each. To model all combinations and permutations would have led to further unmanageable ‘Combinatorial Explosion’ in the number of Scenarios we needed to consider.

To address these issues which were provisionally identified before the hydraulic modelling commenced:

1. We assumed that dark inactivation would be small in the surface waters of marine environment and could be assumed to be zero. This introduced a degree of conservatism into the risk estimates which in hindsight was reasonable because of the rapid particle travel times modelled;

2. We identified four inactivation rates that covered the range of likely solar inactivation rates which would be experienced:

a. S90 (Global solar exposure)= 3 MJ.m-2 [maximum observed for indicators under optimal conditions (Davies-Colley et al., 1994)]

b. S90 = 15 MJ.m-2 [inactivation in sewage, and seawater under limited light (Davies-Colley et al., 1997; Sinton et al., 1999) (Sinton et al., 2007)];

c. S90 = 75 MJ.m-2 [2 X worst observed winter inactivation (Sinton et al., 1999)]; d. S90 = ∞ MJ.m-2 [=conservative worst case of dilution only].

2.4.3. Consumption of Seawater

A range of possible consumption volume per exposure values are shown in Table 2-12. In the Guidelines(NH&MRC, 2008) no specific value is proposed for ocean bathing though conservative conceptual consumption volume for chemicals appears to be around 100-200 mL. More recent data suggests smaller ingestion volumes are more typical around 50 mL or less. The most recent estimates based on systematic survey data (Schijven & de Roda Husman, 2006) and analysis of chloroisocyanurates (Dufour et al., 2006) indicate a much smaller volume still. In recognition of the vigorous nature of ocean bathing on Australia’s east coast we adopted a triangular distribution (min=10 mL, mode = 30 mL, max = 50mL) for the nominal health average bather. Surfers on the CRG requested that elevated risk they might be exposed to be considered. As no specific data on surfer water consumption we undertook discussion with CRG members as to how much would be consumed during a dump from their experience. 500 mL was felt to be too high and 50 mL too low. A value proposed as a credible modal value for seawater consumed by surfers, was suggested as 200 mL per day. Subsequent discussions were undertaken within WRC with 3 regular surfers involved in the project (Rayner, Glamore, van den Akker) who through their experience are familiar with measurement volumes. This figure was also the upper limit of consumption identified in the Guidelines for chemicals (NH&MRC, 2008) so it was settled on as working value for in effect sensitivity testing. When considering the risks estimated for surfers, readers should recognize that this estimate is necessarily semi-quantitative. Table 2-12. Intake During Recreation

Intake Estimate1 Circumstances Primary reference/Source ≤100 mL2 Full body immersion (swimming,

children playing, body washing) (DWAF, 1996);(World Health Organization, 2003) (Genthe & Rodda, 1999);(Haas et al., 1999)

100-200mL Conceptual consumption (NH&MRC, 2008) 50 mL2 Skiing, windsurfing canoeing with

repeated immersion (Medema et al., 2001)


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Intake Estimate1 Circumstances Primary reference/Source 10 mL2 Accidental during laundry, fishing and

irrigation related activities (Medema et al., 2001); (Genthe & Rodda, 1999)

30 mL3 Recreational swimming (Crabtree et al., 1997); 37/16 mL Recreational swimming for adults and

non-adults in swimming pools (Dufour et al., 2006)

5.7 mL Swallowed volume per freshwater diver- professional divers

(Schijven & de Roda Husman, 2006)

3.4 -13 mL Sports divers with ordinary masks (Schijven & de Roda Husman, 2006) 10 (min), 30 (mode), 50 (maximum) mL

General population, this study Reasonable mid range PDF based on consideration of literature values.

200 mL Surfers, this study Upper value based on discussions and conservative ingestion values in the literature.

Notes: 1. All estimates were developed by their authors with quantitative risk assessment in mind. 2. Adapted from Table 1 of Steyn et al. (Steyn et al., 2004). 3. Cited in Van Heerden et al. (Van Heerden et al., 2005a; Van Heerden et al., 2005b).

2.4.4. Exposure and Risk Assessment Scenarios

The focus of this risk assessment was Hazardous Events associated with hydrodynamic conditions which led to surfacing and rapid on-shore transport into bathing areas. Modelling needed to consider:

1. The difference between beaches; 2. The difference between the surf and shoreline zones and their populations; 3. Whether the waste was effluent or WAS; 4. The season (which impacts of population numbers and climatic variable including

current movement; 5. The different potential inactivation rates in particular as they relate to solar radiation.

This led to an explosion of possible condition combinations. For the hydraulics alone these totalled 256. For microbial risk the potential was for thousands. This issue has been identified above and is discussed in detail in Appendix 12 Guidelines, Combinatorial Explosion and the Scale of Risk Modelling. It is raised again here because recognition of this logistical issue necessitated the Scenario modelling strategy used here. This strategy was ‘Adaptive’ in that as Scenarios were modelled they revealed information about the system which indicated which further Scenarios were most important and which were less so. Hydraulic conditions were the critical factor determining whether Scenarios were relatively high risk or not and the largest source of combinatorial explosion arose was the number of Scenarios which were proposed for exploration. Accordingly hydraulic Scenario modelling was implemented first starting with the two extreme S90 cases (3 MJ.m-2 and conservative). Virtually all the mooted Scenarios were modelled. The options identified and those modelled in the end are shown in Table 2-13. This process took several months and to date 224 Hydraulic Scenarios have been completed. The gaps relate mainly to the effluent and S90 of 75MJ.m-2. This is not considered a problem because it was found that the characteristics of those 75MJ.m-2 Scenarios completed were sufficiently close to the ‘Conservative’ Scenario equivalents that little benefit would likely be gained by further modelling. QMRA modelling was commenced once the first hydraulic models were completed and all the water quality data had been edited, and compiled and the PDFs and algorithms had been finalised. A total of 233 pathogen exposure Scenarios have been modelled to date. The primary ones relating to ‘normal’ exposure are shown in Table 2-14.


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The ‘Adaptive’ procedure for constructing and selecting QMRA Scenarios to model was as follows:

1. Baseline (typical) reductions was identified through examination of the earlier WRL project results (Glamore et al., 2008) as being of the order of >106 to 104 (corresponding to the 50th and 80th percentiles Table 14 of this report). Based on this we concluded that the order of magnitude of Baseline dilution+inactivation barrier DR values for effluent and WAS risk modelling could be assumed conservatively to be 4, 5, 6, or 7.

2. ‘Baseline’ Risk exceedence probability statistics were calculated and plotted for each of the 5 microorganisms and two waste-streams and these 4 Baseline DR options. This process:

a. Generated PDFs of enterococci levels which we could compare with actual observations to determine what were the most likely DR factors. 105 for effluent and 106 for WAS were identified as closest and were adopted. ( Though these assumed Baselines were clearly approximate their use was seen as more credible than using zero values generated for the majority of timesteps by the hydraulic model. Assuming 104 and 105 led to a prediction of far higher numbers of enterococci than were actually observed in the swimming zones.);

b. Indicated the risks from Campylobacter and Cryptosporidium would be much lower than those estimated for enterococci, Adenovirus and Giardia, and the latter pathogens would dominate the gastrointestinal illness risks v. benchmark comparisons for the primary Scenarios.

3. Examination of the raw hydraulic data (conservative and 3 MJ.m-2) showed that all else being equal:

a. The exceedence probability PDFs of the reduction (dilution+inactivation) in particle concentrations were very similar for all beaches with Merewether Baths and Bar Beach being marginally more at risk.

b. The increased flows corresponding to 2007 v. 2030 (ca 20 and 40% for effluent and WAS respectively) introduced only a trivial degree of variation compared to other sources which dominated the overall between Scenario variation of 3 to 4 logs(orders of magnitude). Hence the proposed expansion would be unlikely to greatly influence conclusions on relative risk.

c. Effluent was dominant over WAS especially in the summer months. As a result in the draft modelling:

d. These two beaches were selected as the primary locations for assessing the order of magnitude risks to surfers and shoreline bathers in the first draft of this assessment;

e. Because the hydraulic data was available earlier, analysis focused on 2007 Scenarios;

f. Modelling effluent Scenarios was done in preference to those for WAS; 4. Following review, additional modelling was identified to supplement that done for the draft

report notably: a. 15 MJ.m-2 Scenarios were simulated as this inactivation rate seemed closest to those

observed in the microcosm experiments; b. Selected 2030 Scenarios were explored because even though they were only likely to

yield slightly more conservative risk estimates this degree should be documented; c. Limited modelling of risks at Dudley and Burwood Beach were undertaken as reality

checks; d. Combinations of Surfers and WAS in winter were closely examined because of the

much greater chance of WAS on-shore transport, the greater risk to surfers and their reported use of the beaches all year round.

5. Miscellaneous Scenarios were constructed to provide information of selected uncertainties (see Risk Characterisation).

a. Comparison of risks for all 5 pathogens to confirm differences;


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b. Decreasing Adenovirus removal by 102 to simulate assay inefficiency; c. Decreasing Giardia removal by 101 to simulate assay inefficiency;

Table 2-13. Hydraulic Modelling Scenarios

S90 / Year 3 MJ/m2 15 MJ/m2 75 MJ/m2 (Conservative)

Location Location Name

Discharge type

Season

2007 2030 2007 2030 2007 2030 2007 2030 Summer 8512 8512 8512 8512 8512 8512 Effluent

Winter 8516 8516 8516 8516 8516 8516 Summer 8516 8516 8516 8516 8516 8516 8516 8516

1

Merewether Baths (50m)

WAS Winter 8516 8516 8516 8516 8516 8516 8516 8516

Summer 8168 8512 8512 8512 8512 8512 Effluent Winter 8516 8516 8516 8516 8516 8516

Summer 8516 8516 8516 8516 8516 8516 8516 8516

2

Dudley Beach (50m)

WAS Winter 8516 8516 8516 8516 8516 8516 8516 8516


Summer 8516 8516 8516 8516 8516 8516 8516 8516

3

Burwood Beach (50m)

WAS Winter 8516 8516 8516 8516 8516 8516 8516 8516


Summer 8516 8516 8516 8516 8516 8516 8516 8516

4

Bar Beach (50m)

WAS Winter 8516 8516 8516 8516 8516 8516 8516 8516


Summer 8516 8516 8516 8516 8516 8516 8516 8516

5

Merewether Baths (200m) WAS

Winter 8516 8516 8516 8516 8516 8516 8516 8516 Summer 8516 8512 8512 8512 8512 8512 Effluent

Winter 8516 8516 8516 8516 8516 8516 Summer 8516 8516 8516 8516 8516 8516 8516 8516

6

Dudley Beach (200m) WAS


Winter 8516 8516 8516 8516 8516 8516 Summer 8516 8516 8516 8516 8516 8516 8516 8516

7

Burwood Beach (200m) WAS


Winter 8516 8516 8516 8516 8516 8516 Summer 8516 8516 8516 8516 8516 8516 8516 8516

8

Bar Beach (200m)

WAS Winter 8516 8516 8516 8516 8516 8516 8516 8516

The issue of variable pathogen survival was addressed by:

1. Modelling inactivation rates covering rates ranging from the most rapid reasonably conceivable to the most conservative (dilution only);

2. The experiments on indicator and inactivation to determine at what rates this was likely to occur and how the rates varied between WAS and secondary effluent;

3. Measurement of ocean water transmissivity, which would likely influence solar radiation driven inactivation.

Table 2-14. Primary QMRA Modelling Scenarios

S90 / Year (Conservative) 3 MJ/m2 15 MJ/m2

Beach Population Waste stream

Season Pathogen

2007 2007 2007 2030


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Season Pathogen

2007 2007 2007 2030 Merewether Effluent Summer Adenovirus 1 1 1 enterococci 1 1 1 Giardia 1 1 1 Winter Adenovirus 1 1 enterococci 1 1 1 Giardia 1 1 WAS Summer Adenovirus 2 1 enterococci 2 2 1 Giardia 2 1 Winter Adenovirus 1 1 enterococci 1 1 Giardia 1 1 Dudley Effluent Summer Adenovirus 1 enterococci 1 Giardia 1Burwood Effluent Summer Adenovirus 1 enterococci 1 Giardia 1Bar Effluent Summer Adenovirus 1 1 enterococci 1 1 Giardia 1 1 Winter Adenovirus 1 1 enterococci 1 1 Giardia 1 1 WAS Summer Adenovirus 1 1 enterococci 1 1 Giardia 1 1 Winter Adenovirus 1 1 enterococci 1 1

Shoreline bathers

Giardia 1 1 Merewether Effluent Summer Adenovirus 1 1 1 enterococci 2 2 1 Giardia 1 2 1 Winter Adenovirus 1 1 1 enterococci 1 1 2 Giardia 1 1 1 WAS Summer Adenovirus 1 1 enterococci 1 1 1 Giardia 1 1 Winter Adenovirus 1 1 1 1 enterococci 1 1 1 1 Giardia 1 1 1 1Dudley Effluent Summer Adenovirus 1 enterococci 1 Giardia 1 Winter Adenovirus 1 enterococci 1 Giardia 1 WAS Winter Adenovirus 1 1 enterococci 1 1 Giardia 1 1Burwood

Surfers

Effluent Summer Adenovirus 1


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Season Pathogen

2007 2007 2007 2030 enterococci 1 1 1 Giardia 1 Winter Adenovirus 1 enterococci 1 Giardia 1 WAS Winter Adenovirus 1 1 enterococci 1 1 Giardia 1 1Bar Effluent Summer Adenovirus 1 1 enterococci 1 1 Giardia 1 2 Winter Adenovirus 1 1 enterococci 1 1 Giardia 1 1 WAS Summer Adenovirus 1 enterococci 1 Giardia 1 Winter Adenovirus 1 1 enterococci 1 1

Giardia 1 1

2.5. Risk Characterization

2.5.1. Analysis of the Historical Record and Previous BBWWTP Study Data

A key proposal in the Guidelines is that the first step in assessing bathing risks should be to review the historical data records. This involved two activities:

1. Analysis of Beachwatch data collected for the beaches between 1996 and 2006 and supplied by HWC especially post 2001 by which time the BBWWTP was operating in much the manner and loading rate as at present;

2. Analysis and review of earlier WRL studies which characterised risk based on the earlier water quality guidelines.

Details of historical data analysis are presented in/via Section 3.1 Historical Data Analysis .

2.5.2. Analysis of Primary Hydraulic Fate and Transport Pathway Data

Prior to setting up the QMRA models, the primary timeseries outputs from the hydraulic modelling Scenarios were examined. This involved:

1. Collation of all raw timeseries data and statistics (location, date/time, particle count, particle mass, particle travel time) and importation into the project MS Access database;

2. Cursory examination of the beginning and end of data series for possible errors and missing values;

3. Conversion of incremental particle ‘mass’ and ‘count’ statistics into inactivation and dilution time increments using select queries.

4. Running and examination of summary queries designed to generate statistics for each primary data field which could be checked for large scale errors e.g. missing timeseries, duplicate records, timeseries behaving out of character e.g. very different shoreline and surfing zone records;


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5. Plotting of selected timeseries data (diurnal and full timeseries); 6. Plotting of selected exceedence probability/percentile graphs.

The results of this preliminary hydraulic modelling data processing and its analysis are presented in Section 3.2 Hydraulic Modelling.

2.5.3. Basic QMRA modelling methodology

Source levels, reduction estimates for the coastal zone exposure Scenarios, ingestion volumes, and dose responses were integrated using an MS Excel based spreadsheet metamodel using inbuilt functions and additional Wizards from Palisade @Risk v 4.5 (Roser et al., 2006b). The model treated the ocean as a barrier analogous to an anthropogenic wastewater treatment process. The barrier PDFs for each Scenario were described by the timestep particle concentration data estimates. The model is summarised graphically in Figure 1-4. How risk was calculated for each Scenario is described generically in Appendix 09 Illustrative Example of Risk Characterization via Microbial Risk Probability Calculation. These QMRA models were used to calculate probabilities of infection and probabilities of illness per bathing exposure in line with the Guideline model. The outputs for each Scenario form a probability density function themselves. The main statistics (mostly percentiles/exceedences) were collated in the project database and concurrently used to generate Exceedence probability plots which was identified as an effective format for communicating the infection and illness risk probability PDFs.

2.5.4. Operational Integration of the QMRA and Hydraulic Models

Hydraulic modelling was done first: 1. Modelling Scenario specifications were developed based on the risk assessment plan (Waste

stream type, discharge rate; season; S90s; exposure point coordinates for accumulation of timeseries data – 2x2x2x4x8 = 256);

2. The winter or summer 15 minute field data (current speed and direction at different depths, water temperature, coastal current, wind speed and direction, water temperature) were compiled together with water transmissivity and the finite element model mesh to create the virtual coastal zone model;

3. Virtual particles were added to the model at a rate of ca 40,000 per 15 minute timestep and their individual movements from element mesh cell to cell were modelled over the following 2 to 7 days depending on the Scenario;

4. Each particle commenced with a ‘mass’ of 1.9x109 which was incrementally reduced in response to the cumulative solar radiation dose which it was estimated to intercept and the Scenario’s S90.

5. Statistics on the numbers (total) of particles, their masses (50th, 90th, 99th percentiles, and their travel times (median) were collected for each 15 minute increment and mesh cell corresponding to an exposure location as an ASCI file;

6. Particle numbers and average mass were combined to obtain incremental dilution + inactivation estimates by comparison with the model’s starting mass and discharge concentration/rate assumptions.

Three months of 15 minute increment data yielded timeseries of ca 8516 incremental estimates per Scenario of particle reduction, inactivation and travel time. Each was in effect a PDF of the barrier potential of the coastal water zone dispersion, dilution and inactivation mechanisms. Further details of the model are provided in Appendix 15 Hydraulic Modelling of Particle Transport and Inactivation in Coastal Waters.


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QMRA modelling was then undertaken in the same manner as for previous projects (Roser et al., 2006a; Roser et al., 2006b) based on the approach outlined by Haas and his colleagues (Haas & Eisenberg, 2001; Haas et al., 1999) except that the endpoints reached were exposure infection/illness probability estimates. Further to this:

1. The overall process is illustrated in Appendix 09 Illustrative Example of Risk Characterization via Microbial Risk Probability Calculation where the model adaptation is also discussed;

2. Initial levels of pathogens and enterococci were those estimated for the secondary treated effluent and WAS (Section 2.3.1);

3. For each Scenario, the reductions under ‘Baseline’ conditions were assumed to be factors of 105 (effluent) or 106(WAS) corresponding to the normal reduction observed to be achieved by the coastal zone based on Beachwatch monitoring data. The reduction under Hazardous Event conditions were those from the modelling Scenario being considered where simulated particles had actually occurred. The basis and rationale for using these Baseline reduction factors is described in Appendix 01 Newcastle Beachwater Quality and Baseline Reductions;

4. Intake estimation is explained in Section 2.4.3; 5. Pathogen dose response algorithm selection is explained in Section 2.3.2.

Calculation of risk statistics was undertaken using a Monte Carlo meta-model (Harvey, 2005). constructed using Palisade @Risk4.5 + MS Excel. The metamodel was adapted from earlier versions developed for drinking water and water reuse risk assessment (Roser et al., 2006b) and worked as follows:

1. Probability density functions were assembled in tables which defined: a. microbial source wastestream levels for the pathogen of interest; b. coastal zone barrier reductions (Baseline or Baseline+Event) for the exposure point

of interest; c. accidental water consumption rates for the population of interest; d. algorithms defining dose responses; and e. miscellaneous adjustment algorithms (e.g. sensitivity factor values).

2. For each Scenario the appropriate PDFs were applied to the metamodel in a manner analogous to spreadsheet lookup tables to temporarily create the specific model applicable to each Scenario of interest;

3. @Risk model simulation settings were checked prior to each simulation run which used 10,000 iterations and the Latin Hypercube calculation method.

4. For each iteration, point estimate values corresponding to microbial waste stream level, dilution + infection, consumption, infection probability and gastrointestinal illness probability and other statistics were randomly selected and applied in sequence in the model;

5. When simulating the coastal zone, the Monte Carlo modules sampled either the applicable Baseline reduction values or one of the incremental dilution+inactivation estimates.

6. The @Riskpercentile() function was used to collect likelihood statistics for pathogen level, infection probability and illness probability for each model run and generate exceedence probability plots.

7. Model inputs and outputs for each run were copied to an MS Access Datatable for further tabulation and checking if necessary (Appendix 17 Example of Part of Simruns 2_1 Record Table). Exceedence Probability plots were printed as Adobe PDF files. Further details are provided in Section 2.5.4.


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2.5.5. Reporting of Risk as Exceedence Probability

In discussions with Hunter Water the reporting of two or three ‘decision making’ statistics infection/illness probability statistics (e.g. 95th percentile, 99th percentile) was suggested. But this compilation format alone was seen to have severe limitations. Such arbitrary statistics would not necessarily show well or in context the variable likelihood of rarer ‘hazardous’ events or what level of risk was typically associated with event impacts. We considered that to capture the range of Hazardous Events, and for the risk communication process to be transparent, a full range of percentile statistics needed to be collected (this was a consideration in modifications to the hydraulic model) and presented in a concise or at least accessible format. As part of this we considered there was a need to develop or identify a graphic method to illustrate the degree to which increasing risk of illness from an exposure occurred along with decreasing likelihood of exposure occurring. As a result in general, risks are reported as Exceedence Probability (1- risk estimate PDF percentile). Background to the development and adaptation of the Exceedence Probability approach is described in Appendix 22 Exceedence Probability Statistics and Risk Benchmarking. The operational application of this approach is described in the next section.

2.5.6. Quantifying and Communicating Hazardous Event Consequence + Likelihood

Operationally collecting percentile statistics on event recurrence in QMRA was straightforward with the @Risk software. In addition after some trials we identified ‘1 – percentile’ or Exceedence Probability plots as a simple but effective way of presenting the range of risks faced and the impact of Hazardous Events. This format appears novel in QMRA but it is commonly used with hydrological risk data e.g. rainfall and flood exceedence probability plots risk plots which are widely used by Australian engineers due to their use in Australian Rainfall and Run-off (Pilgrim & Doran, 1997). The latter are used for capturing concurrently the increasing impact and decreasing likelihood of sporadic hazardous (hydrological) events. In constructing the probability plots and the percentile statistics generally we assumed that sea water consumption during any given exposure occurred in one discrete event corresponding to a 15 minute timestep period per bathing period. Provided this is accepted as a reasonable approximation to reality it makes no difference to the risk that inactivation/dilution timesteps with high contamination levels tended to occurred in groups (this occurred due to plume pushed on-shore by the prevailing current, wind and tidal conditions). In practice for each Scenario a total of individual 8516 fifteen minute timestep estimates were available. So estimating risks between the 50th and the 99.99th percentile dilution/inactivation was judged reasonable using the QMRA model. This range was seen as also having the benefit of covering risk recurrence intervals in the range of <1 per year to 1 per 100 years, the likelihood benchmark relative to which risks are common assessed [see AS 4360(Standards Australia/Standards New Zealand, 2004a)]. This corresponds approximately to individuals bathing 50 to 100 times per year. Details of method by which these statistics were calculated and plotted are provided in Appendix 14 Operational Application of Exceedence Probability Analysis To Hazardous Event.


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2.6. Uncertainty and Reality Checks

2.6.1. Considerations Check List

A range of actions were undertaken in the process of risk characterisation with uncertainty assessment and reality checks in mind these are listed in Section 5.11

2.6.2. Hydraulic Modelling

All risk assessments have limitations and uncertainties. In the case of the present study, input data variability and uncertainties were identified and documented which relate to:

• Source microbial level data; • WAS being a novel contaminated material in this context; • Hydrodynamic reference data; • The issue of reactive management; • Other sources of pathogens in particular bather shedding and stormwater; • Bypasses; • Accidental major rupturing of the diffuser lines; • Periods where there is a widespread gastroenteritis in the community.

The significance of most of these uncertainties is identified and discussed following each risk characterisation and in Section 5. Recognition of them is essential when acting on them. If they are seen as trivial on balance then no further action but other uncertainties may indicate the need for further analysis or data collection.

2.6.3. Survey of Wastewater Quality and Discharge Hydrology

Source quality data (effluent and WAS) uncertainties primarily related to: 1. Measurement reliability; 2. Representivity of the survey data set sizes of the quality of the overall waste streams; 3. Patterns of variation in water quality over the course of a day, over the three month period

and beyond; 4. The impact of seasonality and outbreaks.

To assess data reliability QA/QC was undertaken. Details of this work are provided in Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation. Further information in the form of raw data provided in Appendix 23 Wastewater and WAS Quality. General data representivity was assessed by comparison with literature on wastewater contaminants (Section 2.3.1.5). Diurnal and longer term variation were assessed by measuring the variance of indicator numbers over these periods and examining the data for clear trends. Details of this work are provided in Appendix 11 Water Quality and Hydrological Strategic Monitoring and Project Implementation. The impact of seasonality patterns in source quality and outbreaks could only be addressed to accounted for via the ‘what-if’ sensitivity testing compared to the primary model scenarios. More extensive/exhaustive modelling of seasonal and outbreak scenarios was not considered practical. Why this was considered the case is addressed in Section 2.6.5, Section 5.10 and Appendix 10 Seasonality, Outbreaks and Hazardous Events.


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2.6.4. Inactivation Studies – Microcosms and Water Transmissivity

Burwood Beach wastewater treatment plant (WWTP) does not undertake disinfection prior to discharge, and therefore the key pathogen removal barriers are those provided by natural mechanisms which occur out in the ocean, particularly solar inactivation. Reviews of relevant literature in other sections additional to this one (see Table 2-2). Solar inactivation of free-living planktonic microorganisms is widely documented and relatively well quantified. However, the impact of particulates in effluent and WAS, and the effect of limited dilution and absorption of sunlight wavelengths by sewage organics is not well quantified. This was a concern for NSW Health/DECC and the CRG. In response inactivation potential was experimentally assessed in two ways additional to consideration of literature inactivation rates:

1. Inactivation rates in effluent and WAS diluted at a rate comparable to field conditions were measured in mesocosm scale reactors;

2. Water samples were collected from the coastal waters off the beaches and their transmissivities, along with those of effluent and WAS amended samples were measured using a scanning spectrophotometer.

This Section and the next summarise the conclusions reached in regard to these two issues. The methodology was based on the approach of Sinton, Davies –Colley and their colleagues (Davies-Colley et al., 1994) (Davies-Colley et al., 2000; Sinton et al., 1999). A full description of the sub-study can be found in Appendix 16 Inactivation Studies Covering Microcosms and Water Transmissivity (for literature relating to inactivation see Section 2.2.5.4). The experimental results and literature were together used to identify solar inactivation scenarios most likely to be applicable in the current project. Through the aid of microcosm experiments, the study quantified the solar inactivation rates of indicator microorganisms within WAS and secondary treated effluent, diluted with seawater by factors characteristic of on-shore plumes (1:200 to 1:5000). Specific conclusions from this study are:

1. The microcosm dilutions considered of 1:200 to 1:5000 were comparable to those expected from discussions with the hydraulic modelling group on hazardous plume events (subsequent modelling confirmed the appropriateness of these dilution factors);

2. Under optimal conditions, our data showed at worst only a marginal difference between the solar inactivation in effluent compared to WAS;

3. Inactivation S90 values, obtained for effluent and WAS indicator microorganisms were comparable to literature values obtained from other studies of marine waters;

4. Dark inactivation rates were low, consistent with the expectation that little or no inactivation would take place over several hours following discharge. The data also indicated worst case inactivation rates of T90s of several days consistent with Guideline concerns (NH&MRC, 2008) and other literature indicating if solar inactivation is constrained, reduction in pathogen numbers will be limited at best;

5. S90 values obtained here were within in range of the rate constants values (3, 15, 75 MJ m-2) proposed for WRL’s 3DRWALK hydraulic modelling scenarios based on examination of the literature;

6. Superficially the diluted WAS and treated effluent were very transparent. In reality though only high dilution material (1:5000) was of comparable transmissivity to neat seawater;

7. Where effluent or WAS dilution is <1:1000, or seawater is otherwise poorly transmissive, light would attenuated rapidly with depth so that only pathogens the top 10 to 100 cm of the ocean surface would likely experience effective solar inactivation in the first day or two after emission.


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In regard to the selection of hydraulic modelling Scenarios the following were concluded as most likely:

1. The solar inactivation rate S90s selected for modelling were in line with the inactivation rates seen in the microcosm systems;

2. Given reduced transmissivity, the 3 MJ.m-2 modelling scenario was probably the maximum inactivation S90 that could be expected under optimal conditions. The 15 and 75 MJ.m-2 were probably closer to best achievable inactivation rates even on sunny days. Given the poor water transmissivity of many field samples the dilution alone Scenarios (conservative models) appear credible as well;

3. The applicability of the different inactivation rates to the risk assessment was in summary seen as follows:

a. 3 MJ.m-2 : highest achievable rate – probably only occurs for clear shallow waters on full bright sunshine day where there is very effective dilution in the first place; (best case)

b. 15 MJ.m-2 : of the order expected during sunny days – this inactivation rate seems more in line with those seen in wastewaters and where transmissivity is reduced these scenarios; (middle)

c. 75 MJ.m-2 : probably reflects overcast days for typical pathogens; and d. ∞ MJ.m-2 (conservative): worst case of dilution – this is conceivable given the low

transmissivity observed in situ under suboptimal illumination conditions. (worst case)

4. Not using the previous T90 approach appeared justified. Our reasoning in focusing on the seawater inactivation Scenarios was as follows:

1. The dilution and mixing components of the hydraulic model appear to be well developed and calibrated but the solar radiation is likely to be so variable its influence could not be modelled without an extensive survey of the transparency of coastal waters. Further, both intensity and irradiance vary with time of day, season, cloud cover and water transparency and also impact on inactivation. The first two variables can be satisfactorily modelled but the latter much less well.

2. Nevertheless solar radiation in surface coastal waters appears to drive inactivation compared to ‘dark processes’ so it cannot be discounted. As a result pathogen inactivation must be considered but it cannot be incorporated in modelling as a single ‘variable’.

3. So rather than modelling inactivation using a single ‘ideal’ value the model should be precautionary and cover a range of credible solar radiation intensities and these should be covered in the inactivation scenarios.

4. A range of scenarios should be constructed and ‘decision support’ should be based on consideration of both optimistic and conservative inactivation scenarios.

2.6.5. Seasonality of Disease Burden and Outbreaks

A limitation of this study was that gastrointestinal disease prevalence can often be ‘Seasonal’ (Payment et al., 2001) and a number of authors have commented on the need to include this factor and ‘outbreaks’ in risk assessments (Ashbolt et al., 2001; Griffin et al., 1999). This risk assessment has been focused on contaminant ‘plume’ Hazardous Events. Two other types of ‘Hazardous Events’ are also of concern to NSW Health and the draft report’s independent reviewer, which are associated with elevate pathogen levels, and hence risk:

1. ‘Seasonality’; 2. ‘Outbreaks’.


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‘Seasonality’ in water borne disease outbreaks has often been identified as a factor to consider in risk assessment (Ashbolt et al., 2001). The suggests the classic four climatic seasons of spring, summer, autumn and winter when for reasons partly understood there are peaks in disease rates, as for example with the ‘winter’ (swine) influenza peak discussed at the time of writing (note that human influenza is not strictly considered a water borne pathogen though wildfowl excrete avian influenza into water bodies). The desirability of considering ‘Outbreaks’ is also self-evident as these are the outcome of abnormally high source levels of pathogens, breakdowns in the barriers separating pathogen sources from exposed populations, enhanced intake, or increased exposure to a vulnerable sub-population. The term ‘Outbreaks is widely used to refer to sudden unexpected outbreaks of disease in the community like the ‘Walkerton Outbreak’. Incorporating ‘Seasonal’ and ‘Outbreak’ associated pathogen level peaks into the current risk assessment in a precise way has been undertaken to an extent depending on data availability as follows:

1. Seasonality is addressed in the primary QMRA in the following ways: a. Behaviour of plumes in summer compared to winter is modelled. b. The greatest disease risk overall is the ‘Seasonal’ risk arising from the general

bathing population using the beaches in the summer months. So summer has been the focus of Scenario modelling.

c. This summer population probably includes children and the elderly more than the surfing population who would be expected to comprise mainly young to middle age adults who are relatively healthy.

2. We have included selected ‘Sensitivity analyses’ addressing the generic question of what would be the risks if selected pathogens were higher in numbers by one or two orders of magnitude for certain more definable scenarios. The risk estimates made can also be used to assess the impact of seasonal or outbreak risk which led to a similar increase.

3. Source water quality variability has not been explicitly assessed because of data limitations. The issues relating to this are discussed in Section 5.10 and Appendix 10 Seasonality, Outbreaks and Hazardous Events.

2.6.6. Validation and Calibration

2.6.6.1. Overall

QMRAs are necessarily difficult to validate as a whole because pathogens tend to be present in low levels at exposure points due to dilution, and they are difficult to detect. The best that could be done within a constrained survey such as the present one, was to calibrate/validate the model steps and input assumptions as follows:

1. Source numbers – comparison against literature data, extensive QA/QC; 2. Hydraulics and hydraulic modelling – check against previous studies and refereed literature; 3. Consumption assumptions – check against reality; 4. Overall –:

a. comparison of modelled indicator numbers against observed numbers; b. use of a large number Scenarios and comparison for consistency; c. use of best practice based on examination of guidelines and previous modelling of

comparable situations.

2.6.6.2. Hydraulic Model

One reviewer of the draft report commented: “I fully support the overall design aims. They are ambitious and appropriate but require good empirical calibration data for each stage in the work. In this section, the definition and


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description of data source and quality is not sufficiently detailed to allow the reader to evaluate whether the data to underpin the approach is suitable (e.g. see bullet 2 on page 31 which further relates to footnote 1 in this review above). In effect, one could conclude that the QMRA is using existing data because it is available without a full analysis of its quality and appropriateness. Both characteristics may, of course, be excellent but the reader does not have a clear audit trail to assess any such judgement at this stage in the report. [This may of course be clear from the parallel investigation cited in the reference listing as Glamore et al. (2008)]”

Our assessment of the latter report was that it was satisfactory. In lieu of reproducing the whole report here we have in support of auditing:

1. Provided a summary of its structure and features in Appendix 13 Selected Excerpts from WRL Modelling (Glamore et al., 2008);

2. Identified previous work using the hydraulic model components with an emphasis on refereed literature ( Section 2.2.5.2).

2.6.6.3. Overall Consistency of data with previous model findings

The previous WRL reports provided a semi-independent assessment which could be compared for consistency with the current one. This report was generally found to provide a similar assessment. Most notably the levels of enterococci predicted by modelling appear to be comparable to those actually observed – see Appendix 01 Newcastle Beachwater Quality and Baseline Reductions (Predicted v. Observed Water Quality).

2.6.6.4. Report Differences

Disparities in focus and conclusions in the WLR report compared to the current one arose not because of fundamental differences in the data and modelling but because:

1. Additional criteria and newer pathogen risk assessment approaches have been introduced in the current assessment in response to stakeholders wishing to know more about the discharge impacts:

a. Of pathogens specifically (viruses); b. On a subpopulation (surfers); c. As assessed by newer methods (QMRA);

2. Assessment requirements in the Guidelines used to set the current project’s terms(NH&MRC, 2008) of reference are only currently becoming clear. This includes the need to account more fully for Hazardous Events;

3. Bathing water quality Benchmarks in the new guidelines are in fact much higher than in the earlier Guidelines;

4. The special case of surfers and their higher risk was not previously considered nor was this required nor has it been standard assessment practice to our knowledge (see Beachwatch reports).

5. The water quality objective for primary recreation in the earlier guidelines (National Health and Medical Research Council, 1990) used a considerably lower primary benchmark for enterococci (33 enterococci as a geometric mean v. 40 enterococci at the 95th percentile.

6. The previous report(Glamore et al., 2008) addressed risks with a recurrence up to the 99th percentile whereas in this follow-up project the sensitivity explored was extended to consider up to the 99.99th percentile. So the current project was more likely to detect hazardous conditions albeit with a lower likelihood. The reason in part lies in the structure. The previous study focused on understanding spatial movement of contaminant plumes along the coast rather than model bathing points. To usefully do both is not readily feasible.

7. The older guidelines focused on water quality objectives as the decision making point whereas the current project are focused on calculated risk of infection/illness.

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URWOOD BEACH WASTEWATER REATMENT PLANT HEALTH …...burwood beach wastewater treatment plant health risk quantitative microbial risk assessment david roser, ben van den akker & richard