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?