1

Regional Water Quality Issues:Algae and Associated Drinking Water Challenges

Workshop – September 2007

A Cooperative Research and Implementation ProgramArizona State University (Tempe, AZ)

Paul Westerhoff, Milton Sommerfeld, Susanne NeuerK.C. Kruger, Chao-An Chiu, and Marisa Masles

Salt River ProjectCentral Arizona ProjectCity of PhoenixCity of TempeCity of GlendaleCity of ChandlerASU NSF Water Quality Center

Agenda

Purpose: Provide a forum to review and discuss on-going regional water quality issues, in particular algae-associated issues.

8:30 Refreshments 8:45 Introductions9:00 Project overview, Past, Present, and Future9:15 Satellite Imaging of Algae in Reservoirs9:45 Break10:00 In-plant algae identification10:30 DBP Precursors & Modeling11:00 Future directions & discussion

2

The “State” of Water Supplies in 2007

2007 was more about dissolved organic carbon (DOC) issues than

about T&O levels

Workshop will present results as water

moves down through the watershed

3

Salt River Above Roosevelt

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

J-10 J-20 J-30 J-40 J-50 J-60 D-69 D-79 D-89 D-99 D-09

Flow

(cfs

)

Hydrology Affects Water Quality(conductance can affect algal dominance)

0

500

1000

1500

2000

2500

Jul-9

9Ju

l-00

Jul-0

1Ju

l-02

Jul-0

3Ju

l-04

Jul-0

5Ju

l-06

Jul-0

7

Con

duct

ivity

(μs/

cm)

Lake PleasantBartlett LakeSaguaro Lake

These were years with highest T&O

4

Lake Pleasant

0

10

20

30

40

50

60

0 5 10 15DO (mg/L)

Dep

th (m

)

1/8/073/12/077/9/07

0

10

20

30

40

50

60

10 15 20 25 30Temperature (C)

Dep

th (m

)

7/11/20068/1/067/9/07

Bartlett Lake

0

10

20

30

40

50

60

10 15 20 25 30

Temperature (°C)

Dep

th fr

om s

urfa

ce (m

)

1/9/072/6/073/12/074/10/075/8/078/6/07

010203040506070

0 3 6 9 12 15

DO (mg/L)

Dep

th fr

om s

urfa

ce (m

)

1/9/07 2/6/073/12/07 4/10/075/8/07 8/6/07

5

Arsenic is highest in Bartlett Reservoir

0

5

10

15

20

25

4/13/0

4

6/21/0

4

8/17/0

4

10/12

/04

12/7/

04

2/15/0

5

4/12/0

5

6/14/0

5

8/16/2

005

10/11

/2005

12/6/

2005

2/13/2

006

4/10/2

006

6/13/2

006

8/14/0

6

10/10

/06

12/5/

062/5

/074/9

/076/4

/07

Ars

enic

(μg/

L)

Pleasant HypoBartlett HypoSaguaro Hypo

MCL = 10 μg/L

Saguaro Lake

0

10

20

30

5 10 15 20 25 30 35 40

Temperature (°C)

Dep

th fr

om s

urfa

ce (m

)

1/9/072/6/073/12/074/10/075/8/078/6/07

0

10

20

30

40

0 3 6 9 12 15

DO (mg/L)

Dep

th fr

om s

urfa

ce (m

)

1/9/07 2/6/073/12/07 4/10/075/8/07 8/6/07

2008 Operation: Draw down Canyon Lake from Oct 07-Jan 08

2007 Operation: Apache Lake was drawn down during same periods

6

Dissolved Nitrogen Trends in Reservoirs

0.0

0.2

0.4

0.6

0.8

1.0

Aug-99

Aug-00

Aug-01

Aug-02

Aug-03

Aug-04

Aug-05

Aug-06

Aug-07To

tal d

isso

lved

nitr

ogen

(mg/

L)

Pleasant EpiPleasant Hypo

0.0

0.2

0.4

0.6

0.8

1.0

Aug-99

Aug-00

Aug-01

Aug-02

Aug-03

Aug-04

Aug-05

Aug-06

Aug-07

Tota

l dis

solv

ed n

itrog

en (m

g/L)

Bartlett Epi

Bartlett Hypo

0.0

0.2

0.4

0.6

0.8

1.0

Aug-99

Aug-00

Aug-01

Aug-02

Aug-03

Aug-04

Aug-05

Aug-06

Aug-07

Tota

l dis

solv

ed n

itrog

en (m

g/L)

Saguaro Epi

Saguaro Hypo

Total Phosphorous

0

20

40

60

80

100

Jun-99

Dec-99

Jun-00

Dec-00

Jun-01

Dec-01

Jun-02

Dec-02

Jun-03

Dec-03

Jun-04

Dec-04

Jun-05

Dec-05

Jun-06

Dec-06

Jun-07

Tota

l pho

sphr

ous

(μg/

L)

Pleasant EpiPleasant Hypo

0

100

200

300

400

500

Jun-99

Dec-99

Jun-00

Dec-00

Jun-01

Dec-01

Jun-02

Dec-02

Jun-03

Dec-03

Jun-04

Dec-04

Jun-05

Dec-05

Jun-06

Dec-06

Jun-07

Tota

l pho

sphr

ous

(μg/

L)

Bartlett Epi

Bartlett Hypo

0

40

80

120

160

200

Jun-99

Dec-99

Jun-00

Dec-00

Jun-01

Dec-01

Jun-02

Dec-02

Jun-0

3

Dec-03

Jun-04

Dec-04

Jun-0

5

Dec-05

Jun-06

Dec-06

Jun-0

7

Tota

l pho

sphr

ous

(μg/

L)

Saguaro Epi

Saguaro Hypo

7

Secchi Disk Depth Influenced by Inorganic Suspended Sediment and/or Organic Biomass

0

2

4

6

8

10

Aug-01

Jan-0

2Ju

l-02

Jan-0

3Ju

l-03

Jan-0

4Ju

l-04

Jan-0

5Ju

l-05

Jan-0

6Ju

l-06

Jan-0

7Ju

l-07

Dec-07

Secc

hi D

isk

Dep

th (m

) Lake PleasantBartlett LakeSaguaro Lake

Up-stream reservoirs attenuate DOC

0

2

4

6

8

10

08/01

/00

06/01

/01

04/01

/02

11/13

/02

07/14

/03

03/16

/04

02/15

/05

12/6/

2005

10/11

/2006

8/6/20

07

DO

C (m

g/L)

Lake PleasantBartlett LakeSaguaro Lake

8

Specific UV Absorbance at 254 nm

0.001.002.003.004.005.006.007.008.00

8/1/99

2/1/00

8/1/00

2/1/01

8/1/01

2/1/02

8/1/02

2/1/03

8/1/03

2/1/04

8/1/04

2/1/05

8/1/05

2/1/06

8/1/06

2/1/07

8/1/07

SUVA

(L/m

/mg)

Pleasant

Bartlett

Saguaro

DOC Removal by WTP

0

1

2

3

4

5

6

24thStreetWTP

DV WTP VV WTP GreenWTP

NP WTP SPT WTP UH WTP

Avg

Tre

ated

wat

er D

OC

(mg/

L)

Influent (2004) Effluent (2004) Influent (2005) Effluent (2005) Influent (2006) Effluent (2006) Influent (2007) Effluent (2007)

9

Geosmin Data

0

4

8

12

16

20

Jun-99

Jun-00

Jun-01

Jun-02

Jun-03

Jun-04

Jun-05

Jun-06

Jun-07

Geo

smin

(ng/

L)

Lake Pleasant EplimnionLake Pleasant Hypolimnion

0

4

8

12

16

20

24

28

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

Geo

smin

(ng/

L)

Bartlett Lake EplimnionBartlett Lake Hypolimnion

050

100150200250300350

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

Geo

smin

(ng/

L)

Saguaro Lake EplimnionSaguaro Lake Hypolimnion

10

MIB Data – Lake Pleasant

0

10

20

30

40

50

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)Lake Pleasant Eplimnion (R2A)

0

10

20

30

40

50

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)

Lake Pleasant Hypolimnion (R2B)

MIB Data – Bartlett Lake

0

20

40

60

80

100

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)

Bartlett Lake Eplimnion (R6A)

0

20

40

60

80

100

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)

Bartlett Lake Hypolimnion (R6B)

11

MIB Data – Saguaro Lake

0

20

40

60

80

100

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)Saguaro Lake Eplimnion (R9A)

0

20

40

60

80

100

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

MIB

(ng/

L)

Saguaro Lake Hypolimnion (R9B)

Saguaro Lake had highly variable geomsin levels in 2007

321

143

340

238

11

190

7.2 42 2 11 21 11 5 2 316

0

50

100

150

200

250

300

350

400

Jan-07 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07

Geo

smin

Con

cent

ratio

n (n

g/L)

Eplimnion

Hypolimnion

12

In-Canal Production of T&O is seasonal

MIB Growth in AZ canal from below X-Con to DV Inlet

0

10

20

30

40

50

60

70

80

Aug-99

Feb-00

Aug-00

Feb-01

Aug-01

Feb-02

Aug-02

Feb-03

Aug-03

Feb-04

Aug-04

Feb-05

Aug-05

Feb-06

Aug-06

Feb-07

Aug-07

Cha

nge

in M

IB c

onc.

(ng/

L) . Less in-canal MIB

production in last 2 years – maybe due to more “consistent” water quality

13

MIB levels higher in AZ Canal system compared against South Canal system

2005-2007 have lower MIB levels

0

10

20

30

40

50

60

70

80

Jun-02

Nov-02

Apr-03

Sep-03

Feb-04Ju

l-04

Dec-04

May-05

Oct-05

Mar-06

Aug-06

Jan-07

Jun-07

WTP

Influ

ent M

IB (n

g/L)

Tempe North PlantTempe South Plant

Geosmin is more prevalent in AZ Canal

0

5

10

15

20

25

30

35

40

Jun-02

Nov-02

Apr-03

Sep-03

Feb-04Ju

l-04

Dec-04

May-05

Oct-05

Mar-06

Aug-06

Jan-07

Jun-07

WTP

Influ

ent G

eosm

in (n

g/L)

Tempe North Plant

Tempe South Plant

14

SummarySince heavy rains in winter 2005:

Conductance has decreasedMIB concentrations are lower in reservoirsMIB production in canal is minimal,

presumably due to less blending of water sourcesDOC is higherTradeoff between T&O and DOC

According to SRP we remain in the 13th

year of a drought, this may lead to higher conductance levels again Rainfall impacts the availability of water and water quality

Susanne Neuer, SoLS, ASU

Remote sensing of Salt River Reservoirs Remote sensing of Salt River Reservoirs and potential for an early detection and potential for an early detection

system of algal bloomssystem of algal blooms

--A proposal submitted for the internal A proposal submitted for the internal call of the Water Quality Centercall of the Water Quality Center--

Remote sensing of Salt River Reservoirs Remote sensing of Salt River Reservoirs and potential for an early detection and potential for an early detection

system of algal bloomssystem of algal blooms

Susanne Neuer, School of Life SciencesASU

15

Break

8:30 Refreshments 8:45 Introductions9:00 Project overview, Past, Present, and Future9:15 Satellite Imaging of Algae in Reservoirs9:45 Break10:00 In-plant algae identification10:30 DBP Precursors & Modeling11:00 Future directions & discussion

In-Plant AlgaeIdentification, Characterization and

Control

Arizona State UniversityMilton SommerfeldThomas DempsterPaul Westerhoff

&

Malcolm Pirnie Inc.

16

Strategies for Controlling and Mitigating Algal Growth Within Water Treatment

Plants

AWWARF Project (RFP 3111)

Malcolm Pirnie, Inc.Sunil KommeneniShahnawaz SinhaKristen Amante

Arizona State UniversityMilton SommerfeldThomas Dempster

Paul Westerhoff

Goal

Identify and recommend strategies for controlling algae growth within water treatment plants

17

Project Objectives

Gather Background Information (literature review)

Utility Survey (e-mail surveys)

Case Study of Selected Plants (on-site visits)

Identify/Document Dominant Algae Types

Identify Optimal Algae Control Strategies

Develop Guidance Document for Utilities

Participating UtilitiesCa. 200 utilities were solicitated to participate in survey

76 utilities completed website survey

Survey contained questions about demographics, algae occurrence and characterization, algae control strategies

18

Example Survey Questions

Do you have in-plant algae problems?Where do you have algae growth? Do you analyze algae samples?What kind of algae occur at your plant?When do you have algae problems?What are some of the issues caused by algae growth?What operational practices are used to control algae growth?

US Census Regions Classification

Northeast RegionWest Region

Midwest RegionSouth Region

STATESinclude

Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, MissouriNebraska, North Dakota, Ohio

South Dakota, Wisconsin

STATESinclude

Connecticut, New Jersey, New York, Maine, Massachusetts, New Hampshire, Pennsylvania,

Rhode Island, Vermont

STATESinclude

Alabama, Arkansas,DC,Delaware, Florida, Georgia,

Kentucky, Lousiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennesse, Texas, Virginia,

West Virginia

STATESinclude

Alaska, Arizona, California, Colorado,

Hawai, Idaho, Montana, Nevada, New Mexico,

Oregon, Utah, Washington,Wyoming

19

Plant capacities for the utilities taking the survey

31-50 mgd22%

51-100 mgd27%

> 100 mgd25%

0-30 mgd26%

Total Responses: 73

Combinations of Surface Source Waters

3%

3%

3%

1%

12%

12%

23%

36%

7%River

Lake

Canal

Reservoir

River, Lake

River, Lake, Canal

River, Canal

Lake, Canal

River, Reservoir

Total Responses: 74

20

Algae Related Issues

No Algae Related Issues13%

Algal Related Issues87%

Total Responses: 76

Algae Related Issue by Location

Both48%

In Plant14%

No Algae Related Issues13% Source/Intake

25%

Total Responses: 76

21

Algae Growth by Season

Total Responses: 64

Summer49%

Unpredictable5%

All year round5%

Fall19%

Spring22%

Algae Events

Total Responses: 68

More than three34%

None25%

One22%

Two9%

Three10%

22

Occurrence of Algae Events

Total Responses: 63

All year round7%

Continuous37%

Intermittent56%

Algal Analysis

Total Responses: 67

Send them to an outside lab

9%

None36%

Both9%

In-house46%

23

Control Strategies for Algal GrowthNo. of Responses Summary of Responses

28 Operational practices to mitigate algae such as cleaning basins, and other mechanical equipment prone to algae

27 Chlorine use for algae mitigation (chlorinating for disinfection, and shock chlorination)

17 Copper sulfate use in source water or in treatment plant to mitigate algae and T&O

12 Potassium permanganate in source or in treatment plant to mitigate algae and T&O

9 PAC addition for algae mitigation and T&O.

8 Limit nutrients at source (i.e. use multiple sources, water shedprotection).

6 Coagulant or polymer to restrict nutrient growth.

5 Cover portions of treatment train.

5 Aeration in source water

3 Algaecide use in source or within treatment train.

3 Ozonation for algae mitigation and T&O.

3 Chlorine dioxide in source water or within treatment train.

2 pH adjustment primarily used for softening, but is effective at mitigating algae.

1 Dissolved Air Flotation flocculation for algae mitigation.

1 Ultrasonic device for algae mitigation.

1 Minimize retention time.

Case Studies

WTPs Selected and Visited in Arizona, California, Oklahoma, Utah, Ohio, Indiana, Florida and Pennsylvania

24

Algae Sampling Methods

Sampling Locations in Treatment Plant

Presedimentation BasinsSedimentation BasinsFiltration Basins

Chlorine Sodium hydroxide HFS

GACBar Screen Pre-Sed Basin

Chlorine Polymer

Flocculation/Sedimentation Filtration Reservoir

Distribution

CoagulantPolymerChlorine

25

Types of Samples Collected

Plankton or SuspendedFloating (paddies or mats)Periphyton (attached to walls)Benthic (sediment from bottom surfaces)

Floating/mats

Periphyton/Attached

Benthic (Sediment from bottom)

Plankton/Suspended

Sample Collection Procedure

Plankton or suspendedthree 500 ml samples composited in 1,500 ml (X2)

Floating (paddies or mats)three mats composited in 1,500 ml basin water (X2)

Periphytonthree wall scrapings composited in 1,500 ml basin water (X2)

Benthicthree bottom sediment samples composited in 1,500 ml of basin water (X2)

Floating/mats

Periphyton/Attached

Benthic (Sediment from bottom)

Plankton/Suspended

26

Plankton Collection with Sludge Judge® II and 1 L Nalgene bottle in GAC filter beds

Benthic Sample Collected in GAC Filter with Sludge Judge® II

27

Periphyton Collected in GAC Filters with Telescopic Pole and Brush

A. Location in plant train (basin)

B. Habitat in basin1. Plankton2. Floating3. Periphyton4. Benthic

C. Form (unicellular, colonial or filamentous)D. SizeE. Color

F. Relative abundance (dominant, abundant, frequent, common or rare)

G. Potential to produce off-flavors/odors and toxins

H. Scientific name

Algae Characterization

28

Algae Characterization at 9-CA on June 23, 2006

Location Habitat Organism Growth Form Color Relative Abundance

Potential Problems

Flocculation Basin phytoplankton Fragilaria sp. filamentous golden-

brown abundant filter-clogging

Flocculation Basin phytoplankton Navicula sp. unicellular golden-

brown frequent none

Flocculation Basin phytoplankton Synedra sp. unicellular golden-

brown frequent filter-clogging

Flocculation Basin phytoplankton Chlorella sp. unicellular green frequent none

Flocculation Basin phytoplankton Scenedesmus sp. colonial green frequent none

Flocculation Basin phytoplankton Melosira varians filamentous golden-

brown frequent filter-clogging

Flocculation Basin phytoplankton Planktothrix

aghardhii filamentous blue-green common MIB production

Flocculation Basin phytoplankton Pseudanabaena sp. filamentous blue-green common MIB/Geosmin

production

Flocculation Basin phytoplankton Amphora sp. unicellular golden-

brown rare none

Flocculation Basin periphyton Oscillatoria spp. filamentous blue-green dominant MIB/Geosmin

production

Flocculation Basin periphyton Anabaena sp. filamentous blue-green frequent MIB/Geosmin

production

Comprehensive List Of Algae Taxa Observed At Participating WTPs

Water Treatment Plant

CYANOPHYTA TAXA Belmont 4-FL 5-IN 5-OH 6-OK CUWCD 9-AZ 9-CA

Anabaena sp. X X X

Aphanizomenon sp. X

Lyngbya sp. X X X X

Microcystis sp. X

Planktothrix aghardhii X X

Oscillatoria sp. X X X X X

Oscillatoria splendida X X X

Oscillatoria spp. X X X X X

Pseudanabaena sp. X X X X X X X

Tolypothrix sp. X

Trichodesmium sp. X X

29

CHLOROPHYTA TAXA Belmont 4-FL 5-IN 5-OH 6-OK CUWCD 9-AZ 9-CA

Ankistrodesmus sp. X

Chlamydomonas sp. X

Chlorella sp. X X

Cladophora sp. X

Closterium sp. X

Cosmarium sp X X X X

Eudorina sp. X

Microspora sp. X

Mougeotia sp. X X X

Oedogonium sp. X X X

Oocystis sp. X

Ophiocytium sp. X X X

Pediastrum sp. X

Scenedesmus sp. X X X X X X

Spirogyra sp. X

Stigeoclonium sp. X X

Tetrahedron sp. X

Ulothrix sp. X X

BACILLARIOPHYCEAE TAXA Belmont 4-FL 5-IN 5-OH 6-OK CUWCD 9-AZ 9-CA

Achnanthes minutissima X X X X X X XAchnanthes sp. X

Amphora sp. X XAsterionella formosa X X

Aulacoseira sp. X XCocconeis pediculus X

Cocconeis sp. X X X XCyclotella sp. X X X X

Cymatopleura solea XCymbella sp X X X XDiatoma sp. X XEunotia sp. X

Fragilaria crotonensis XFragilaria leptostauron X

Fragilaria sp. X XGomphonema sp. X X X X

Gyrosigma sp. X XMastogloia sp.

X X

Melosira varians X X X X X XNavicula sp. X X X X X X X

Nitzchia palea XNitzschia dissapata XNitzschia sigmoidea X

Nitzschia sp. X X X X X X XPinnularia sp. X X X

Rhoicosphenia curvata XRhopalodia gibba X

Stephanodiscus sp. X XSurirella sp. X

Synedra affinis XSynedra sp. X X X X X X X

Synedra ulna X

30

Visual Characterization of Dominant Algae

Field image of dominant algae in treatment plantImages of collected macroformsMicroscopic images

Floating Paddies In The Sedimentation Basin Contributed To High Algae Biomass At WTP 9-AZ

31

Photomicrograph of Pseudanabaena sp. from Sedimentation Basin Phytoplankton Sample at 4-FL

Sedimentation Basin Periphyton Contributed To High Algae Biomass At 9-AZ

32

Pseudanabaena sp. Was Prevalent Throughout WTP 9-AZ

Flocculation Basin At WTP 9-CA. A) Oscillatoria spp. Mat From Redwood Diffuser Wall, B) Photomicrograph Of Oscillatoria sp., and C) Photomicrograph Of Planktothrix aghardhii

A

C

B

33

A) Oscillatoria spp. Paddy Floating In Sedimentation Basin

B) Oscillatoria spp. Paddy Collected On Wire Brush

BA

Oscillatoria splendida, a KnownGeosmin Producer, Observed In Floating

Paddies in Filter Basins At WTP 6-OK

34

Planktothrix aghardhii Collected From The Flocculation And Filter Basin Periphyton At UVWTP

Photomicrograph of Pseudanabaena sp. from Sedimentation Basin Phytoplankton Sample at 4-FL

35

Summary

In-plant algae growth is common

Most water treatment plants report algae in flocculation/sedimentation basins

Most common algae mitigation measures were physical cleaning, chlorination, addition of copper sulfate and potassium permanganate

Based on 8 case studies, 50 genera (61 sp.) of in-plant algae were identified and characterized

Nine (9) genera identified were potentially producers of off-flavor compounds or toxins

DBP Precursors & Modeling2006-2007 SRP funded a project: Predicting Organic Carbon and Disinfection By Product Precursors in Metro-Phoenix Surface Water ReservoirsConduct laboratory experiments on water from the three terminal reservoirs (Bartlett Lake, Saguaro Lake, Lake Pleasant) Use data to validate models for municipal users of water (DOC removal models, DBP formation models). Models will be useful in years to come for SRP to decide with the cities when certain reservoir water qualities are particular troublesome or desirable to assist cities in complying with DBP regulations.

36

THM formation is dependent on several water quality parameters

Background

263.0601.1609.0068.0152.02

098.12 )()()()()()(10121.4 timepHTempBrClTOCTHM −−⋅=

3

2

2

3

CHBrClCHBr

CHBrClCHCl

HOBrHOCl

DOC →⎭⎬⎫

⎩⎨⎧

+HOBrBrHOCl →+ −

0.091 – 0.1500.105 – 0.1130.070 – 0.098Br- (mg/L)

106 – 147121 – 150188 – 239Alkalinity (mg/L as CaCO3)

8.2 – 8.88.0 – 8.48.3 – 8.6pH

1.5 – 2.11.1 – 1.51.4 – 3.7SUVA (L/mg-m)

0.096 – 0.1060.042 – 0.0520.043 – 0.071UV254 (1/cm)

0.23 – 0.400.28 – 0.390.16 – 0.22TDN (mg/L)

4.84 – 6.203.07 – 3.721.93 - 3.07DOC (mg/L)SaguaroPleasantBartlettParameter

Raw Water Quality Ranges During This Study

37

Jar and SDS Tests

0 mg/L 20 mg/L 40 mg/L 60 mg/L 80 mg/L

Alum Dose (mg/L of Al2(SO4)3)

1

Filtration through GF/F filter with 0.7μm pore size

2

Measure THMs using EPA method 551.1

5

Measure DOC, TDN, UV254, alkalinity, pH and Br-

3

Chlorination such that free chlorine conc. after 24 hours is 1±0.2 mg/L

4

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 20 40 60 80 100

Alum Dose (mg/L)

DO

C (m

g/L)

BartlettPleasantSaguaro

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 20 40 60 80 100Alum Dose (mg/L)

UV

(1/c

m)

BartlettPleasantSaguaro 55%

reduction

36% reduction

May 2007 Jar Tests

38

0204060

80100120

10 60 1440Chlorine Contact Time (min)

THM

Con

c. (µ

g/L) 80 mg/L

40 mg/L0 mg/L

Kinetics of THM formationSaguaro Lake May 2007

Coagulation does NOT remove Bromide

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 10 20 30 40 50 60 70 80 90Alum Dose (mg/L)

Bro

mid

e (m

g/L)

Oct-06 Jan-07Mar-07 Mar-07 pH 7.0

May-07 May-07 pH 6.5Jul-07

39

0

20

40

60

80

100

120

0 20 40 60 80

TTH

M (p

pb)

0.00.20.40.60.81.01.21.41.61.82.0

BIF

CHBr3CHBr2ClCHBrCl2CHCl3BIF

TTHMLmol

TTHMBrL

mole

BIFμ

μ

= ∑=

−×=3

13

iii BrCHCliTHMBr

Saguaro Lake May 2007

SDS Conditions

WTP model•Originally developed for the USEPA in 1992 as an empirically based model to predict DBP formation, NOM removal, and disinfectant decay

•Updated in 1999 to include increased data availability and knowledge of treatment processes and to include additional disinfectants

Treatment Process Disinfectants Coagulation / Flocculation. / Sedimentation Chlorine Precip. Softening / Clarification / Filtration Chloramines

GAC Adsorption Ozone Membranes Chlorine Dioxide

Biotreatment

40

WTP.exe model

WTP.exe input parameters

41

Representative Model Outputs

WTP Validation for DOC prediction

DOC (mg/L)

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7

Predicted Values

Mea

sure

d Va

lues

BartlettPleasantSaguaro

42

Model Predictions

24 Hour Chlorine Demand (mg/L as Cl2)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Predicted Values

Mea

sure

d V

alue

s

BartlettPleasantSaguaro

Total THM4 (ppb)

0

50

100

150

200

250

0 50 100 150 200 250

Predicted Values

Mea

sure

d V

alue

s

BartlettPleasantSaguaro

Total THM4 (ppb)0

50

100

150

200

250

0 50 100 150 200 250

Predicted Values

Mea

sure

d V

alue

s

BartlettPleasantSaguaro

THMs extracted with pentane

Validate WTP modelFull Scale WTP

Total THM4 (µg/L)

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9

Sample

THM

Con

c. (µ

g/L)

. Measured ValuesPredicted Values

43

SummaryWTP.exe model is appropriate for use with waters from Verde & Salt Rivers and Lake Pleasant to predict ability to remove DOC by coagulation.WTP.exe model accurately predicts THM formationWith a minimal number of data inputs (TOC, pH, alkalinity, bromide, temperature) the model can be used to estimate the treatbilityof different source waters before arriving at WTPs

Planning for the FutureContinue baseline monitoringApply WTP.exe model seasonally to predict “treatability” of organics in the three reservoirs2007-08 will include sampling for trace organics (EDC/PPCPs) in the SRP watershed and maybe beyondWould like to use satellite imaging to evaluate past data and as a real-time monitoring tool on several lakesASU/Carollo/Phoenix is conducting seasonal RSSCTsusing Granular Activated Carbon to remove TOC and DBP precursorsWhat would you like to see in the next year?