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THE TOXICOLOGICAL IMPACT OF WASTEWATERS ON DRINKING WATER SOURCES
by
Mustafa Iqbal
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Department of Civil & Mineral Engineering University of Toronto
© Copyright by Mustafa Iqbal
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The Toxicological Impact of Wastewaters on Drinking Water Sources
Mustafa Iqbal
Master of Applied Science
Department of Civil & Mineral Engineering
University of Toronto
2018
ABSTRACT
Surface waters may contain wastewater effluent associated with de facto reuse which can
impact their toxicological properties both before and after treatment. This study examined the
genotoxic response of three surface waters containing a range of wastewater effluent (5%, 10%,
and 25% by volume). The SOS ChromotestTM was used to assay the genotoxicity of both
chlorinated and unchlorinated mixtures whereas toxicity index model based on the CHO comet
assay was used to predict the contribution of trihalomethanes (THM), haloacetonitriles (HAN),
and halonitromethanes (HNM) to overall genotoxicity of chlorinated mixtures. Wastewaters were
generally genotoxic whereas raw and chlorinated surface waters were not. Mixtures containing 5%
and 10% wastewater generally had similar responses to chlorinated surface waters alone;
significant effects were more common at higher ratios (≥ 25%). SOS genotoxicity correlated
strongly with predicted genotoxicity, DOC, and THM concentrations, suggesting that THMs may
potentially serve as surrogates for toxic disinfection by-products (DBPs).
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ACKNOWLEGEMENTS
This work was funded by the Natural Sciences and Engineering Research Council of
Canada (NSERC) Chair in Drinking Water Research at the University of Toronto, and the Ontario
Research Fund.
I would like to thank my supervisor Dr. Robert C. Andrews for having granted me this rare
opportunity to contribute to the growing field of water research. His close mentorship, guidance,
and commitment to quality were invariably some of the major driving forces behind the success
of this work, including a broader skillset that I will continue to cultivate.
I would also like to recognize Dr. Susan Andrews, who played an immense role in helping
me achieve the right chemistry for my experiments; Dr. Ron Hofmann, for elucidating how to
perform chlorine breakpoint experiments; Liz Taylor-Edmonds, for managing, enabling and
facilitating much of my work; Jim Wang, for his meticulous oversight and instruction in the lab;
Nicole Zollbrecht and Shelir Ebrahimi, for helping me conduct various analyses; Michael Mckie,
Corinne Bertoia, and Noreen Mian for helping me with my first steps in the lab; Kerry Evans-
Tokaryk, who, other than being on the watch for everyone’s safety, played the role of a friend; the
DWRG group, for fostering a spirit of community, comradery, and the very essence of a home
away from home; Husein Al Muhtaram, for putting up with my daily shenanigans – in and out of
work; Ryan Marchildon and Antonio Milos for the occasional heart-to-heart conversations that
broke the monotony of my routines; and John Forster, my best friend and confidante, who was
always there for me at the times I needed it most.
Finally, my unreserved love for my family who were there with me every step of the way,
and thus having played an essential role in this very important academic work. The success of this
thesis is as much as yours as it is mine.
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TABLE OF CONTENTS
ACKNOWLEGEMENTS .............................................................................................................. iii LIST OF TABLES ......................................................................................................................... vi
LIST OF FIGURES ....................................................................................................................... ix
NOMENCLATURE ....................................................................................................................... x
1.0 Introduction ............................................................................................................................... 1 1.1 Background ...................................................................................................................... 1 1.2 Objectives ......................................................................................................................... 2 1.3 Outline of Chapters .......................................................................................................... 3
2.0 Literature Review................................................................................................................. 4
2.1 Toxicological Evaluation of Drinking Water ................................................................... 4 2.2 Impact of Wastewater on Surface Water.......................................................................... 5
2.2.1 Volumetric Impact of Wastewater on Surface Water ............................................... 5 2.2.2 The Toxicological Impact of Wastewater on Surface Water .................................... 5 2.2.2.1 Effects of Wastewater on Drinking Water Formation Potential ........................... 7
3.0 Materials and Methods ......................................................................................................... 9
3.1 Experimental Protocols .................................................................................................... 9 3.2 Selection of Surface Waters and Wastewaters ................................................................. 9 3.3 Bench-Scale Treatment Sequence .................................................................................. 12 3.4 Sample Collection & Preparation ................................................................................... 13 3.5 Analytical Methods ........................................................................................................ 14
3.5.1 Dissolved Organic Carbon (DOC) .......................................................................... 14 3.5.2 Ultraviolet Absorbance at 254 nm .......................................................................... 15 3.5.3 Alkalinity ................................................................................................................ 16 3.5.4 Ammonia................................................................................................................. 16 3.5.5 Nitrate ..................................................................................................................... 16 3.5.6 Nitrite ...................................................................................................................... 17 3.5.7 pH Measurement ..................................................................................................... 17 3.5.8 Haloacetic Acid Analysis ........................................................................................ 17 3.5.9 Trihalomethanes, Haloacetonitriles, Halonitromethanes Analysis ......................... 18 3.5.10 Chlorine Demand Tests........................................................................................... 19 3.5.11 Genotoxicity Analysis with the SOS ChromotestTM ............................................... 20 3.5.12 Predicted Genotoxicity Based on Chinese Hamster Ovary Cell Comet Assay ...... 22
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3.6 Statistical Analysis ......................................................................................................... 22 4.0 Method Development of Chlorine Demand Tests for Impacted Surface Waters .............. 22
4.1 Breakpoint Chlorination Method ................................................................................... 22 4.2 Simplified Chlorine Demand Test ................................................................................. 26
5.0 Results and Discussion ...................................................................................................... 27 5.1.1 Comparison of Wastewaters ....................................................................................... 27 5.1.2 Comparison of Surface Waters ................................................................................... 28 5.1.3 Comparison of Wastewater–Surface Water Mixtures ................................................ 29 5.1.4 Comparison of Predicted and SOS ChromotestTM Genotoxicity................................ 31
6.0 Conclusions ........................................................................................................................ 33
6.1 Recommendations for Future Work................................................................................... 34
7.0 References .......................................................................................................................... 35 8.0 Appendix ............................................................................................................................ 42
8.1 Treatment Processes for WWTPs Duffins Creek and Otonabee River .......................... 42 8.2 Measured Water Quality Parameters for Mixtures ........................................................ 42 8.3 Relative Enrichment Factor, Induction Factor, and Cytotoxicity .................................. 47 8.4 Effect Concentration Calculations ................................................................................. 55 8.5 Predicted Genotoxicity Calculations .............................................................................. 56 8.6 Sample Quality Assurance/Quality Control Chart ......................................................... 70
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LIST OF TABLES
Table 3.1: Sampling Schedule for the Three Paired Wastewaters and Surface Waters ............... 10
Table 3.2: Surface Water Influent Characteristics for Three DWTPs .......................................... 10
Table 3.3: Wastewater Characteristics for Three WWTPs ........................................................... 10
Table 3.4: Summary of Sample Collection for All Experimental Analyses ................................. 13
Table 3.5: DOC Analyzer Conditions ........................................................................................... 14
Table 3.6: DOC Analysis Reagents .............................................................................................. 15
Table 3.7: GC-ECD Operating Conditions for HAA Analysis ..................................................... 18
Table 3.8: GC-ECD Operating Conditions for THM, HAN, and HNM Analyses ....................... 19
Table 8.1: Duffins Mixtures – Water Quality Parameters ............................................................ 42
Table 8.2: Erie Mixtures – Water Quality Parameters .................................................................. 43
Table 8.3: Ontario Mixtures – Water Quality Parameters ............................................................ 43
Table 8.4: Otonabee Mixtures – Water Quality Parameters ......................................................... 43
Table 8.5: Simcoe Mixtures – Water Quality Parameters ............................................................ 44
Table 8.6: Chlorine Residuals for all Chlorinated Mixtures ......................................................... 44
Table 8.7: R values for all Chlorinated Surface Waters (SW-E, SW-O, SW-S) .......................... 45
Table 8.8: R values for Chlorinated Lake Ontario Mixtures ........................................................ 45
Table 8.9: R Values for Chlorinated Lake Simcoe Mixtures ........................................................ 46
Table 8.10: R Values for Chlorinated Lake Erie Mixtures ........................................................... 46
Table 8.11: Unchlorinated Duffins Creek Mixtures – IF Values for Each Dilution step ............. 48
Table 8.12: Chlorinated Duffins Creek Mixtures – IF Values for Each Dilution Step ................ 48
Table 8.13: Unchlorinated Lake Erie Mixtures – IF Values for Each Dilution Step.................... 48
Table 8.14: Chlorinated Lake Erie Mixtures – IF Values for Each Dilution Step........................ 49
Table 8.15: Unchlorinated Lake Ontario Mixtures – IF Values for Each Dilution Step .............. 49
Table 8.16: Chlorinated Lake Ontario Mixtures – IF Values for Each Dilution Step .................. 49
Table 8.17: Unchlorinated Lake Otonabee Mixtures – IF Values for Each Dilution Step ........... 50
Table 8.18: Chlorinated Lake Otonabee Mixtures – IF Values for Each Dilution Step ............... 50
Table 8.19: Unchlorinated Lake Simcoe Mixtures – IF Values for Each Dilution Step .............. 50
Table 8.20: Chlorinated Lake Simcoe Mixtures – IF Values for Each Dilution Step .................. 51
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Table 8.21: Unchlorinated Duffins Creek Mixtures – Cytotoxicity Values for Each Dilution step
....................................................................................................................................................... 51
Table 8.22: Chlorinated Duffins Creek Mixtures – Cytotoxicity Values for Each Dilution Step 51
Table 8.23: Unchlorinated Lake Erie Mixtures – Cytotoxicity Values for Each Dilution Step ... 52
Table 8.24: Chlorinated Lake Erie Mixtures – Cytotoxicity Values for Each Dilution Step ....... 52
Table 8.25: Unchlorinated Lake Ontario Mixtures – Cytotoxicity Values for Each Dilution Step
....................................................................................................................................................... 52
Table 8.26: Chlorinated Lake Ontario Mixtures – Cytotoxicity Values for Each Dilution Step .. 53
Table 8.27: Unchlorinated Otonabee River Mixtures - Cytotoxicity Values for Each Dilution
Step ............................................................................................................................................... 53
Table 8.28: Chlorinated Otonabee River Mixtures – Cytotoxicity Values for Each Dilution Step
....................................................................................................................................................... 53
Table 8.29: Unchlorinated Lake Simcoe Mixtures – Cytotoxicity Values for Each Dilution Step
....................................................................................................................................................... 54
Table 8.30: Chlorinated Lake Simcoe Mixtures – Cytotoxicity Values for Each Dilution Step .. 54
Table 8.31: SOS Genotoxicity (slope) Values – Unchlorinated Mixtures .................................... 55
Table 8.32: SOS Genotoxicity (slope) Values – Chlorinated Mixtures........................................ 56
Table 8.33: Molecular weight and genotoxicity potency for the measured disinfection by-
products ......................................................................................................................................... 57
Table 8.34: Duffins Creek Mixtures – HAAs by Specific Compounds ....................................... 58
Table 8.35: Lake Erie Mixtures - HAAs by Specific Compounds .............................................. 58
Table 8.36: Lake Ontario Mixtures – HAAs by Specific Compounds ........................................ 59
Table 8.37: Otonabee River Mixtures – HAAs by Specific Compounds .................................... 59
Table 8.38: Lake Simcoe Mixtures – HAAs by Specific Compounds ........................................ 60
Table 8.39: Duffins Creek Mixtures – THMs by Specific Compounds ....................................... 60
Table 8.40: Lake Erie Mixtures – THMs by Specific Compounds ............................................. 61
Table 8.41: Lake Ontario Mixtures – THMs by Specific Compounds ......................................... 61
Table 8.42: Otonabee River Mixtures – THMs by Specific Compounds .................................... 62
Table 8.43: Lake Simcoe Mixtures – THMs by Specific Compounds ........................................ 62
Table 8.44: Duffins Creek Mixtures – HANs by Specific Compounds ....................................... 63
Table 8.45: Lake Erie Mixtures – HANs by Specific Compounds............................................... 63
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Table 8.46: Lake Ontario Mixtures – HANs by Specific Compounds ......................................... 64
Table 8.47: Otonabee River Mixtures – HANs by Specific Compounds .................................... 64
Table 8.48: Lake Simcoe Mixtures – HANs by Specific Compounds ......................................... 65
Table 8.49: Duffins Creek Mixtures – HNMs by Specific Compounds ....................................... 65
Table 8.50: Lake Erie Mixtures – HNMs by Specific Compounds .............................................. 66
Table 8.51: Lake Ontario Mixtures – HNMs by Specific Compounds ........................................ 66
Table 8.52: Otonabee River Mixtures – HNMs by Specific Compounds ................................... 67
Table 8.53: Lake Simcoe Mixtures – HNMs by Specific Compounds ......................................... 67
Table 8.54: Duffins Creek Mixtures – Predicted Genotoxicity Values ........................................ 68
Table 8.55: Lake Erie Mixtures – Predicted Genotoxicity Values ............................................... 68
Table 8.56: Lake Ontario Mixtures – Predicted Genotoxicity Values .......................................... 68
Table 8.57: Otonabee River Mixtures – Predicted Genotoxicity Values ...................................... 69
Table 8.58: Lake Simcoe Mixtures – Predicted Genotoxicity Values .......................................... 69
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LIST OF FIGURES
Figure 3.1: Generalized stages of bench-scale testing .................................................................... 9
Figure 3.2: Treatment processes for individual WWTPs. ............................................................. 11
Figure 3.3: Sample calibration curve – DOC (April 2018) .......................................................... 15
Figure 3.4: Sample dichloroacetic acid calibration curve (May 2018) ......................................... 18
Figure 3.5: Sample trichloromethane calibration curve (May 2018) ............................................ 19
Figure 4.1: The classic breakpoint curve – Zones 1 and 2 represent the formation of chloramines,
and Zone 3 represents the formation of free chlorine (Adapted from White, 2010). .................. 23
Figure 4.2: Sample chlorine breakpoint curve of WW-O (November, 2017) .............................. 24
Figure 5.1: SOS ChromotestTM IF values for three surface waters (SW) and wastewaters (WW)
under unchlorinated (no NC) and chlorinated (C) conditions at a relative enrichment factor
(REF) of 80-fold. An IF of 2.0 is indicative of genotoxicity (represented by the red line) and an
IF of 1.5 indicates a response that is different than the control but not positive (represented by the
green line). Vertical bars represent maximum and minimum values. .......................................... 27
Figure 5.2: SOS ChromotestTM IF values for three wastewater-surface water mixtures under
unchlorinated (NC) and chlorinated (C) conditions at a relative enrichment factor (REF) of 80-
fold. An IF of 2.0 is indicative of genotoxicity (represented by the red line) and an IF of 1.5
indicates a response that is different than the control but not positive (represented by the green
line). Vertical bars represent maximum and minimum values. .................................................... 30
Figure 5.3: Predicted genotoxicity and measured SOS ChromotestTM genotoxicity (slopes) of
various wastewater and surface water mixtures post-chlorination. The 100% wastewater sample
is presented for comparison. ......................................................................................................... 31
Figure 8.1: Treatment processes for usused WWTPs Duffins Creek and Otanabee River. ......... 42
Figure 8.2: IF vs. REF for duplicated unchlorinated SW-O (0%) samples. ................................. 47
Figure 8.3: Concentrations of measured disinfection by-products (DBPs) for all ratios of WW to
SW. The 100% wastewater (NC) has been included for comparison. .......................................... 57
Figure 8.4: Quality control chart for DOC analysis. ..................................................................... 70
x
NOMENCLATURE
~ Approximate
% Percent
< Less than
> Greater than
AWWA American Water Works Association
ANOVA Analysis of Variance
C Chlorinated sample
CHO Chinese Hamster Ovary
Cl2 Chlorine
°C Degrees Celsius
µg/L Micrograms per litre
4-NQO 4-Nitroquinoline 1-oxide
DBP Disinfection by-product
DFR De facto reuse
DNA Deoxyribonucleic Acid
DOC Dissolved organic carbon
DON Dissolved organic nitrogen
DWTP Drinking water treatment plants
E. coli Escherichia coli
EC Effect concentration
EfOM Effluent organic matter
FEEM Fluorescence excitation-emission matrices
FP Formation Potential
g Gram(s)
h Hours
GC Gas chromatography
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GC-MS Gas chromatography – mass spectrometry
GC-ECD Gas chromatography – electron capture detection
HAA(s) Haloacetic acid
HAN(s) Haloacetonitriles
HNM(s) Halonitromethanes
HLB Hydrophilic-lipophilic balance
HPLC High performance liquid chromatography
IF Induction factor
KHP Potassium hydrogen phthalate
L Litre(s)
LC-OCD Liquid chromatography – organic carbon detection
MDL Method detection limit
mg/L Milligram(s) per litre
min Minute(s)
mm millimetre
NC Not chlorinated sample
NOM Natural organic matter
OCD Organic carbon detection
OD Optical density
ON Ontario
pH -log (hydrogen ion concentration)
QA/QC Quality assurance/quality control
R Pearson correlation coefficient
REF Relative enrichment factor
sec Second(s)
SPE Solid phase extraction
SUVA Specific ultraviolet absorbance
SW Surface waters
xii
THM(s) Trihalomethane(s)
TOC Total organic carbon
UV254 UV absorbance at 254 nm
v/v % volume by volume percent
µm micrometer
WW Wastewater
WWTP Wastewater treatment plant
1
1.0 Introduction
1.1 Background
Disinfection of drinking water represents one of the greatest public health achievements of
the 20th century (Calderon, 2000), however surface waters that serve as drinking water sources
contain natural organic matter (NOM) (Matilainen et al., 2010) along with anthropogenic
compounds which can form disinfection by-products (DBPs) upon chlorination that may pose
health risks to consumers (Žegura et al., 2009). The toxicological potencies, type, and amount of
DBPs formed is specific for a given water matrix (Richardson et al., 2007). Genotoxicity assays
can be used to show whether individual or groups of compounds formed upon chlorination can
cause DNA damage (Quillardet et al., 1982). It is not apparent to what extent wastewater
discharges impact surface waters in terms of their overall bioanalytical reactivity when considering
the genotoxicity assays. A comprehensive study of major surface waters in the United States found
that under average conditions wastewater represents < 2% by volume of waters entering intakes
of drinking water treatment plants (DWTPs) (Rice et al., 2015). However, for some locations,
highly impacted surface waters were shown to contain > 50% wastewater by volume (Guo and
Krasner, 2009), with the highest being 100% (Rice et al., 2013).
Wastewaters containing effluent organic matter (EfOM), and surface waters containing
NOM, both contribute to the formation of DBPs and genotoxicity of drinking water. Indeed,
periods of low wastewater dilution are generally associated with greater toxic effects (De Lemos
and Erdtmann, 2000). Wastewaters with higher dissolved organic nitrogen (DON) concentrations
are known to generate toxic nitrogenous DBPs upon chlorination (Krasner et al., 2009b; Lee et al.,
2007). A comparison of magnitude shows that nitrogenous DBPs have significantly higher cyto-
and genotoxicity than carbonaceous DBPs (Plewa et al., 2008), although they are produced in
much lower concentrations than regulated DBPs (Le Roux et al., 2016). In vitro mammalian tests
have demonstrated that emerging nitrogenous DBPs haloacetonitriles (HANs) and
halonitromethanes (HNMs) are far more cyto- and genotoxic than haloacetic acids (HAAs) as well
as trihalomethanes (THMs) (Plewa et al., 2008). Although THMs have low potencies and do not
typically drive toxicity (Li and Mitch, 2017), their concentrations are monitored for regulatory
compliance as they are thought to be good surrogates for overall DBPs formed upon chlorination
(EPA, 2006)
2
Surface waters containing varying amounts of wastewaters represent complex mixtures
whose toxicity cannot be evaluated by standard physio-chemical analysis alone (Helma et al.,
1998), considering the presence of countless constituents as well as micropollutants at trace levels
(Radić et al., 2010). In vitro bioassays however can provide effect-based measurements of toxicity
(Kocak et al., 2010). These bioanalytical tools are able to capture a response representative of all
chemicals present in the mixture (Escher et al., 2014; Neale et al., 2012). They are highly sensitive
(Escher et al., 2015) and provide readily observable responses (Slabbert and Morgan, 1982) that
reflect antagonistic, additive, or synergistic effects of compounds which are present (EBPI, 2016).
The SOS ChromotestTM was selected for this study due to its good reproducibility (Escher et al.,
2013) and high sensitivity to chlorinated waters (Guzzella et al., 2004).
1.2 Objectives
This study evaluated the genotoxic impact of three wastewaters on three surface waters at
bench-scale level. Concentrations of THMs, HANs, and HNMs were measured to complement the
effect-based responses provided by the SOS ChromotestTM. The primary objectives are mentioned
as follows:
1. Examine how wastewater effluents associated with different treatment processes affect the
genotoxic response of chlorinated surface waters over a range of dilutions
2. Calculate and compare toxicity based on indexed Chinese Hamster Ovary (CHO) comet
assay-derived DBP potencies to toxicity assayed with the SOS ChromotestTM
3. Assess the usefulness of THM levels for estimating toxicity of chlorinated mixtures
3
1.3 Outline of Chapters
Chapter 2– introduction to the toxicological evaluation of drinking water, volumetric impact
of wastewater on surface water, the toxicological impact of wastewater on surface
water, & the effects of wastewater on drinking water formation potential
Chapter 3– detailed methodology used in the preparation of wastewater-surface water
mixtures, subsequent chlorination, DBP formation, measurement of water
characteristics and genotoxic evaluation, including a brief section on statistical
analysis
Chapter 4– outline of the method development for the chlorination of wastewater-surface
water mixtures, detailing the process by which samples were dosed for a 1.0 ± 0.5
mg/L Cl2 residual and interference from chloramines was effectively eliminated
Chapter 5– evaluation of the experimental results with statistical correlations drawn between
water quality parameters and genotoxicity
Chapter 6– conclusions and recommendations for future work
Chapter 7– list of references used in this study
Chapter 8– appendices, raw data, and QA/QC data
4
2.0 Literature Review
2.1 Toxicological Evaluation of Drinking Water
DBPs are formed as a result of chlorine reacting with natural organic matter (NOM) which
can have varying constituents depending on the water matrix (Richardson et al., 2007). In the
context of drinking water, the discovery of DBPs led to an increased effort by researchers to
identify groups of chemicals and specific compounds that may exhibit toxicological properties as
their long-term exposure is not well understood (Betts, 1998) Of concern are DBPs suspected of
carcinogenic effects (Richardson et al., 2007). Due the presence of a vast number of DBPs that are
produced upon chlorination, the toxicological properties of a water sample can be measured using
in vitro tests that provide rapid effect-based measurements of a complex mixture of chemicals
(Kocak et al., 2010). In vitro tests complement physio-chemical analysis, which can only quantify
chemicals of concern (Helma et al., 1998). Used as a screening test, the SOS Chromotest is one
common in vitro test that is cost- and time-effective and can be used to evaluate samples for their
genotoxicity (Zheng et al., 2015). The SOS ChromotestTM has been noted for its consistent results
(Escher et al., 2013) and is highly sensitivity to chlorinated waters (Guzzella et al., 2004), making
it an invaluable tool in the toxicological assessment of drinking waters.
Researchers have studied the association between water quality parameters, regulated DBPs,
and genotoxicity. A study of seven water utilities by Wang et al. (2011) found that all waters
disinfected with chlorine tested positive for genotoxicity (an induction factor, IF, > 2.0), whereas
concentrated influent water was almost always negative (IF <2.0). Additionally, the analytical
results showed that TOC and UV254 had a weak but positive correlation with genotoxicity. A more
recent study found that the correlation extends to dissolved organic carbon (DOC), which itself
correlates with many DBP surrogates (Zheng et al., 2015). The generation of AOX in chlorinated
drinking waters is of particular interest, considering that ~25% to ~30% of this group is typically
made up of two well-known classes of regulated DBPs, trihalomethanes (THMs) and
haloaceticacids (HAAs) (Mitch et al., 2009; Krasner et al., 2006). In general, THMs by themselves
have been found to be inadequate in explaining the observed toxicity in samples (Takanashi et al.,
2009; Watson et al., 2012), although increases in genotoxicity, even if marginally, must be the
result of DBPs that have yet to be identified. THMs have low potencies and do not drive toxicity
(Li and Mitch, 2017) but their concentrations are monitored for regulatory compliance as they are
5
believed to be good surrogates for overall DBPs formed upon chlorination (EPA, 2006). It is not
clear if THMs are always useful indicators of toxicity, especially in scenarios where surface waters
have been impacted by wastewater.
2.2 Impact of Wastewater on Surface Water
2.2.1 Volumetric Impact of Wastewater on Surface Water
Water scarcity is attributed to a growing global economy and population, making water a
limited resource that necessitates sustainable practices (Rice et al., 2013). In this context, the de
facto reuse (DFR) or incidental presence of treated sewage in drinking water sources and the
current desire to expand the water supply through the planned reuse of municipal wastewater are
critical in addressing the impacts on drinking water (National Research Council, 2012). DFR
occurs when wastewater is discharged into surface waters, many of which serve as sources for
drinking water treatment plants (Metcalf et al., 2007). As an example, the wastewater contribution
to a major Chinese river was reported to be as much as 14%, and up to 20% for a related tributary
(Wang et al., 2017). A more comprehensive study of major surface waters in the USA found that
contributions varied on average as much as less than 2% at the intakes of drinking water treatment
plants (Rice et al., 2015).
2.2.2 The Toxicological Impact of Wastewater on Surface Water
The inherent risk of wastewater impact to surface waters was highlighted in a study by the
National Research Council (2012) that compared a drinking water containing a small amount of
DFR against two planned potable reuse scenarios for different classes of contaminants, including
disinfection by-products (DBPs), nitrosamines, hormones, pharmaceuticals, antimicrobials, flame
retardants, perfluorochemicals. It was found that the risk from the chemicals in the two planned
reuse scenarios did not exceed the risks present in common existing water supplies. The
experiment demonstrates that depending on the quality of the wastewater effluent and the level of
treatment offered by drinking water facilities, there is a potential for adverse health effects. This
is especially true for surface waters impacted by effluent discharged after secondary treatment,
compared to those that receive effluent discharged after tertiary treatment (Gerrity et al., 2013).
6
White and Rasmussen (1998) showed that greater volumetric discharges of wastewater into the
water supply exacerbate the genotoxicity of surface water; indeed, municipal wastewaters can have
genotoxic loadings several orders of magnitude greater than those of industrial wastewaters.
Therefore, the genotoxic risk posed by wastewater is a function of both its rate of discharge and
genotoxic potency (White and Rasmussen, 1998) Furthermore, periods of low flow generally
reflect greater toxic effects in the natural environment (de Lemos and Erdtmann, 2000). However,
whether this is mainly due to a greater combined concentration of DOC, other constituents, or both
remains unclear. On the other hand their toxicological impact have been found to be dampened
considerably when diluted by surface water (Fernández et al., 1995). The effect that dilution has
on the genotoxicity of wastewater can be observed on the assay level. Jolibois and Guerbet (2005)
found that unconcentrated influent samples used in the Ames test were genotoxic, whereas the
unconcentrated effluents were not genotoxic. Similarly, Dizer et al. (2002) noted that diluted
primary effluent samples were almost always geno- or cytotoxic and that diluted samples of
wastewaters that had undergone secondary treatment exhibited no geno- or cytotoxicity in the umu
assay. In contrast, Shen et al., 2000 showed that when samples were concentrated more than 5000-
fold in the Ames test, the surface water sample taken closest to a municipal wastewater discharge
showed greater genotoxic results. It is suspected that the seasonal changes in wastewater genotoxic
and mutagenic properties are likely due to the disinfectant dose and/or concentration of precursors
in the influent (Monarca et al., 2000). In fact, it has been demonstrated that depending on the
constituents that comprise secondary effluent, chlorination can have varying effects on its
genotoxic potential. Studies conducted on secondary effluents have determined that chlorination
can either result in an increase in genotoxicity or a decrease in genotoxicity (Meier et al., 1987;
Wang et al., 2007). Wang et al. (2007) found that chlorine-disinfected wastewaters with low NH3-
N (<10~20 mg/L) usually elicited lower genotoxicity, while those with high NH3-N (>10~20
mg/L) induced higher genotoxicity. Additionally, the authors showed that in the presence of low
NH3-N, hydrophilic substances (HIS) were the primary fraction of dissolved organic matter
(DOM) that may have caused a reduction in genotoxicity, whereas in the presence of high NH3-N,
hydrophobic substances (HOS) may have caused an increase in genotoxicity. Limited information
exists regarding how constituents affect the genotoxicity of secondary effluents treated under
different disinfection strategies.
7
2.2.2.1 Effects of Wastewater on Drinking Water Formation Potential
Wastewater effluent organic matter (EfOM) is a source of precursors for a wide range of
DBPs that have varying toxicological properties and potencies. In comparison to surface waters,
studies have demonstrated that wastewaters are higher in dissolved organic nitrogen (DON), and
in many cases, higher in bromide as well that can affect the type of DBPs formed (Krasner et al.,
2009b). Wastewaters with higher DON concentrations are known to generate nitrogenous DBPs
upon chlorination (Lee et al., 2007). The nitrogen to carbon ratio in wastewater effluent can be
more than twice as that of river water’s (Le Roux et al., 2017). On a purely nitrogen basis, one
study found that surface waters typically have organic nitrogen concentrations of ~0.2 mg/L N,
whereas municipal wastewater effluents can have up to ~3 mg/L N (Westerhoff and Heath, 2002).
While DOC can be removed in appreciable amounts using granular activated carbon (GAC),
bromide and nitrogenous compounds are largely unaffected, leading to the formation of geno- and
cytotoxic DBPs upon disinfection with chlorine (Krasner et al., 2016). For example, compounds
such as bromine-containing dihaloacetonitriles (DHANs) have been reported to account for a
significant amount of the geno- and cytoxicity of DBPs in one study (Krasner et al., 2016). Other
than commonly regulated DBPs, nitrogenous DBPs are currently under consideration for two main
reasons: 1) a current interest in the impacts of elevated organic nitrogen concentrations from
wastewater and algal blooms on source waters, and 2) a growing desire amongst drinking water
utilities to use chloramine disinfection to reduce the concentration of THMs (Mitch et al., 2009).
By their sheer toxicity, these DBPs include haloacetonitriles (HANs), haloacetamides (HAAms),
and halonitromethanes (HNMs). This argument is extended further by Plewa et al. (2008), who
made a broader comparison of carbonaceous and nitrogenous DBPs in which they found that the
cyto- and genotoxicity of the latter was greater by almost an order of about two. The implications
of this is clear as wastewater effluent tends to be more enriched in nitrogen than surface water, and
therefore is potentially a greater source of precursors for more toxic DBPs. It has been found that
HANs and HNMs are far more cyto- and genotoxic than THMs and HAAs (Muellner et al., 2007;
Plewa et al., 2004).
The process used to treat wastewater effluents prior to discharge may affect drinking water
sources and the subsequent formation potential of DBPs. It has been shown that complete
nitrification in wastewater facilities effectively removes precursors responsible for nitrogenous
8
DBPs, including HANs and NDMA, and a nonnitrogenous class of DBPs, trihaloacetaldehyde
from wastewater effluent (Krasner et al., 2009a). Carbonaceous DBP precursors, such as those
responsible for HAAs are removed as well, but to a much smaller extent which can be explained
by the limited degradation of humic substances. This is an important finding as humic substances
will readily react with chlorine to form appreciable amounts of AOX (Reckhow et al., 1990);
amongst other properties, the degree of aromaticity of these compounds have been shown to play
an important role in the amount of DBPs generated upon chlorination (Chin et al., 1994).
Interestingly, NH3 concentrations have been noted to inhibit the formation of AOX upon
disinfection with chlorine, although lower pH values can dramatically improve this yield (Schulz
and Hahn, 1998). AOX yield is maximized at low pH values in the total absence of NH3. Moreover,
THM precursors are not reduced by nitrification as they’re generally recalcitrant to biodegradation
(Krasner et al., 2009a). In one of the most comprehensive studies to date, Liew et al., (2016) noted
that N-DBP concentrations in treated drinking water, especially HANs, were strongly correlated
with DOC and NH3. While adverse health effects are associated with DBPs, the large-scale release
of micropollutants from wastewater treatment plants (Petrović et al., 2003) can have constituents
that are also genotoxic, such as polycyclic aromatic hydrocarbons (PAHs) (White and Rasmussen,
1998). Complex micropollutants are generally not removed in conventional activated sludge
processes due to factors ranging from poor biodegradability to poor biotransformation (Das et al.,
2017) and thus can persist into the drinking water supplies.
These studies serve as a good first step, but more are needed to determine how wastewaters
associated with different treatment processes impact DBP formation and the final genotoxicity of
drinking waters. In a rare study that examined the toxicological impact of surface water impacted
by 20% wastewater, Nakamuro (1989) noted that chlorination of the surface water resulted in a
decrease in mutagenicity of concentrated surface waters. It remains unclear what organic fractions
of DOC, DON, or other constituents were responsible for this phenomenon, and the resulting
distribution and occurrence of DBPs. A possible relationship may exist between different mixtures
of wastewaters and surface waters and DBP formation potential. Answering these questions may
inform the treatment processes of municipal wastewater facilities, disinfection strategies, and
discharge rate thereby ensuring safer DFR practice.
9
3.0 Materials and Methods
3.1 Experimental Protocols
A bench-scale experiment was used to investigate the toxicological impact of wastewater
on surface water on a volume by volume (v/v %) basis. A diagram of the general testing method
is shown in Figure 3.1
Step 1: Water Characteristics
Step 2: Cl2 Demand & FP
Step 3: Solid Phase Extraction
Step 4: Genotoxic Analysis
• Filter raw surface
waters and wastewaters; prepare 5%, 10%, and 25% v/v ww/sw mixtures.
• Analyze all mixtures for alkalinity, DOC, NH3, NO3-N, NO2-N, pH and UV254
• Establish Cl2 dose that
achieves 1.0 ± 0.5 mg/L Cl2 residual for all mixtures
• Use theoretical Cl2
doses to chlorinate 2.5 L mixtures
• Quench and reduce
mixture vol to 2 L; use 250 mL aliquots for DBP analysis
• Load unchlorinated
and chlorinated 2.5 L mixtures onto 6cc HLB cartridges using SmartPrep® Automated Cartridge Extractor II
• Dry samples to
dryness, reconstitute to 30 µL and put into micro volume inserts
• Assess toxicity of
samples using SOS Chromotest
Figure 3.1: Generalized stages of bench-scale testing
Raw surface and wastewaters were collected from various treatment plants across Ontario,
Canada (see Section 3.2) and were promptly placed in dark storage maintained at 4 oC. Prior to
beginning experiments, samples were brought back to room temperature, 22 oC.
3.2 Selection of Surface Waters and Wastewaters
Surface water samples were collected from Lake Ontario, Lake Simcoe, and Lake Erie Ontario,
Canada at the intakes of DWTPs; wastewater effluents were collected from wastewater treatment
plants (WWTPs) representing three different types of processes. A schedule for each pair of
wastewater and surface water is given in Table 3.1. Characteristics of the individual surface and
wastewaters are shown in Tables 3.2 and 3.3, respectively. Treatment processes for each WWTP
are shown in Figure 3.2. Characteristics of the two unused surface and wastewaters are given in
10
Appendix Tables 8.1 and 8.4, and the treatment processes of unused effluents are shown in
Appendix Figure 8.1. These samples were not assessed due to inconsistencies in the SOS
ChromotestTM results.
Table 3.1: Sampling Schedule for the Three Paired Wastewaters and Surface Waters
Sample Sampling Event
Lake Ontario April 9, 2018 Lake Simcoe May 11, 2018
Lake Erie June 27, 2018
Table 3.2: Surface Water Influent Characteristics for Three DWTPs
Samples pH Alkalinity
(mg/L CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA (L/mg/
m) Lake Ontario 8.07 97.50 2.01 0.01 0.75 0.01 0.04 2.20
Lake Simcoe 8.04 117.5 4.27 0.01 0.65 0.01 0.06 1.50
Lake Erie 8.41 100 2.36 0.01 0.25 0.00 0.03 1.47
Table 3.3: Wastewater Characteristics for Three WWTPs
Sample pH Alkalinity
(mg/L CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA (L/mg/
m) WWTP Lake Ontario 7.51 107.5 7.89 10.25 15.35 2.33 0.15 1.89
WWTP Lake Simcoe 7.60 159.5 6.13 0.04 7.40 0.01 0.11 1.74
WWTP Lake Erie 7.48 108.5 6.76 0.13 8.10 0.18 0.15 2.26
11
WWTP Lake Ontario
WWTP Lake Simcoe
WWTP Lake Erie
Figure 3.2: Treatment processes for individual WWTPs.
UV
Effluent
Al2(SO4)
Al2(SO4)
Grit Screen 2o Clarifier Aeration Tank Rotating Biological
Contactor 1o Clarifier
UV
Al2(SO4)
Effluent
1o Clarifier Grit Screen 2o Clarifier Aeration Tank
Cl2(g)
Effluent
FeCl2 FeCl2
1o Clarifier Grit Screen 2o Clarifier Aeration Tank
12
3.3 Bench-Scale Treatment Sequence
Mixtures 0%, 5%, 10%, and 25% v/v wastewater to surface water were treated under
chlorinated and unchlorinated conditions, each of which were performed in duplicate for DBP and
genotoxic analysis. Paired surface water and wastewaters were sourced from different regions of
Ontario, Canada and are listed in Section 3.2. Prior to mixing, all wastewater and surface water
samples were filtered using 0.45 µm pore size Supor® membrane filters (90 mm diameter, Pall
Corporation, Port Washington, NY). This level of filtration was chosen to prevent fouling of the
SmartPrep® Automated Cartridge Extractor II (Horizon Technology Inc., Salem, NH) during solid
phase extraction, and to remove any particulate organic matter that could affect chlorine demand
tests and sample extractions for DBP analysis.
Using two 1 L graduated cylinders, one for each type of water, mixtures were first prepared
in small batches in a 2 L beaker at the ratios specified above to complete chlorine demand tests as
well as water characteristic analyses. Mixtures were then poured into three 250 mL amber bottles,
each dosed with a different chlorine concentration for a range of chlorine residual and incubated
for 24 h at 22 ± 1 oC. This allowed the determination of an optimal chlorine dose that would
guarantee a chlorine residual of 1.0 ± 0.5 mg/L Cl2. More details on the chlorine demand test
procedure are given in Section 3.5.10 and the development of the method is given in Section 4.0.
Leftover mixture (500 mL) not used in the demand tests were used for measuring alkalinity, DOC,
NH3, NO-2, NO-
3, NO-2, pH, and UV254. With the optimal chlorine dose determined for the target
residual, mixtures intended for chlorination were prepared again in the same manner as before for
a volume of 2.5 L whereas unchlorinated mixtures were prepared for a volume of 2 L. The 2.5 L
mixtures were chlorinated with the optimal chlorine dose and were incubated at the same
conditions as those used in the chlorine demand tests. After 24 h, the mixtures were measured for
their chlorine residual (Appendix Table 8.6) and quenched accordingly; 250 mL aliquots were
collected from each sample for DBP analysis. The remaining sample of each chlorinated mixture
was then drained of excess and were reduced to 2 L. Next, both the chlorinated and unchlorinated
2 L mixtures were acidified to pH 2 and subsequently loaded onto to the Automated SPE Cartridge
Extractor II (Horizon Technology Inc., Salem, NH). The procedures for DBP and genotoxicity
analysis are found in Sections 3.5.8 and 3.5.9. It should be noted that the HAAs data were not used
due to poor results (Appendix Tables 8.34-8.38).
13
Between experiments, glassware was cleaned using a Miele Disinfektor G7736
dishwasher, which uses detergent water (LaboClean FT, Dr. Weigert, Germany) and an acid rinse
(Neodisher acid, Dr. Weigert). This was followed by a rinse with distilled water before being dried
in the oven for 6 h at 400 oC. Amber bottles used in chlorine demand and formation potential tests
were pretreated with chlorine by soaking 250 mL and 2.5 L bottles with 1.5 mL and 15 mL of
bleach solution, respectively, for a duration of 24 h. The bottles were then rinsed with MQ water
three times and set aside to dry. The oven was not used to prevent reactivation of the chlorine
demand sites.
3.4 Sample Collection & Preparation
As described in Section 3.3, aliquots were taken for water characteristic measurements as
well as DBP analyses at two separate stages of the experiment. An additional 500 mL was prepared
for each mixture that was subjected to the chlorine demand test to provide enough volume to
perform measurements of water characteristics on the same day; this volume was split between the
various quality tests. A summary of sample collection and preparation is provided in Table 3.4.
Note: for DBP analyses, 250 mL aliquots were acquired from each chlorinated 2.5 L mixture.
Table 3.4: Summary of Sample Collection for All Experimental Analyses
Analysis Type Volume # of Replicates Procedure Alkalinity 100 mL 2 -Filter with 0.45 µm membrane filter
DOC 30 mL 2 -Filter with 0.45 µm membrane filter -Acidify with 2 drops of H2SO4
NH3 25 mL 2 -Filter with 0.45 µm membrane filter NO3 25 mL 2 -Filter with 0.45 µm membrane filter NO2 25 mL 2 -Filter with 0.45 µm membrane filter
UV254 5 mL 2 -Filter with 0.45 µm membrane filter
Chlorine Demand 250 mL 0 -Filter with 0.45 µm membrane filter -Chlorinate at 3 different doses for 24 h -Determine dose for 1.0 ± 0.5 resid. Cl2
THMs/HAAs/ HANs/HNMs 250 mL 2
-Filter with 0.45 µm membrane filter -Chlorinate w/ calculated dose for 24 h -Quench with L-ascorbic acid
Genotoxicity 2.5 L 2 -Filter with 0.45 µm membrane filter -Chlorinate w/ calculated dose for 24 h -Quench with L-ascorbic acid
14
3.5 Analytical Methods
3.5.1 Dissolved Organic Carbon (DOC)
DOC was measured using an O-I Corporation Model 1030 TOC Analyzer with Model 1051
Vial Multi-Sampler (College Station, TX) as outlined in Standard Method 5310D (APHA, 2012).
Water samples were first vacuum filtered with 0.45 μm membrane filters and then prepared at the
specified proportions listed above after which they were put in 40 mL amber vials, capped with
Teflon®-lined septum screw caps, and stored in the dark at 4ºC until analysis. Samples were
acidified to pH ≤ 2 using concentrated sulfuric acid if samples were not analyzed immediately
after preparation. The calibration solutions were prepared at a concentration of 10 mg/L and diluted
by the instrument to concentrations of 0, 0.625, 1.25, 2.5 and 5 mg/L for a 6-point calibration
curve. A 10 mg/L calibration sample was prepared, diluted and analyzed before each sample set.
Check standards (C = 2.5 mg/L) were tested after every 10 samples, and at the end of every sample
set. Additionally, a minimum of three blank samples were tested after calibration, and before every
check standard sample. The DOC instrument conditions are given in Table 3.5. A reagent list and
a sample calibration curve are presented in Tables 3.6 and Figure 3.3, respectively. A sample
QA/QC chart is provided in Appendix Figure 8.4.
Table 3.5: DOC Analyzer Conditions
Parameter Description
Acid volume 200 μL of 5% phosphoric acid Oxidant volume 1000 μL of 100 g/L sodium persulphate Sample volume 15 mL Rinses per sample 1 Volume per rinse 15 mL Replicates per sample 3 Reaction time (min:sec) TIC 2:00; TOC 2:30 Detection time (min:sec) TIC 2:40; TOC 2:00 Purge gas Nitrogen Loop size 5 mL
15
Table 3.6: DOC Analysis Reagents
Reagent Supplier and purity Milli-Q® water Prepared in the laboratory Sulphuric acid, H2SO4 VWR International, 98%+ Sodium persulphate, Na2(SO4) Sigma Aldrich, 98%+, anhydrous Potassium hydrogen phthalate (KHP), C8H5KO4 Sigma Aldrich, 98%+ Phosphoric acid, H3PO4 Caledon, >85% Nitrogen gas, N2 Praxair, Ultra high purity (UHP)
Figure 3.3: Sample calibration curve – DOC (April 2018)
3.5.2 Ultraviolet Absorbance at 254 nm
UV254 was measured with a CE 3055 Single Beam Cecil UV/Visible Spectrophotometer
(Cambridge, England) (APHA, 2012). A 1 cm quartz cell (Hewlett Packard, Mississauga) was
used to take UV254 readings in accordance with Standard Method 5910B (APHA, 2012). Between
samples, the cuvette was rinsed with Mili-Q. Specific ultraviolent absorbance (SUVA) was
calculated by dividing UV254 value by DOC.
y = 0.0002x - 0.419R² = 0.9998
0
2
4
6
8
10
12
0 10000 20000 30000 40000 50000
DO
C (m
g/L)
Area
16
3.5.3 Alkalinity
Alkalinity was determined using an end-point colorimetric titration method as described in
Standard Method 2320B (APHA, 2012). First, a 100 mL of the water sample was transferred to a
250 mL beaker, followed by 5 drops of bromocresol green indicator (VWR International). A 0.02N
sulphuric acid titrant solution (VWR International, 98+%) prepared in Milli-Q® water was added
drop wise until the sample color changed from blue to yellow. The alkalinity of the sample was
calculated by:
Alkalinity �mgL
CaCO3� = A∗N∗50,000V
, (3.1)
where:
A = volume of H2SO4 titrated (mL),
N = normality of H2SO4 (N= 0.02)
V = volume of sample (V = 100 mL)
3.5.4 Ammonia
Ammonia readings were analyzed with the DR 2700 Spectrophotometer (Loveland, CO)
according to the Salicylate Method (HACH, 2017). Two 10 mL sample cells were filled with Milli-
Q® blank and sample water, followed by the addition of an Ammonia Salicylate Powder Pillow
(HACH) after which both were shaken and set aside for 3 minutes. Next, the cells received an
Ammonia Cyanurate Powder Pillow (HACH) and set aide for 15 minutes before having its reading
taken. A green color indicated the presence of nitrite.
3.5.5 Nitrate
Nitrate readings were annalyzed with the DR 2700 Spectrophotometer (Loveland, CO)
according to the Cadmium Reduction Method (HACH, 2017). The instrument was first blanked
with a 10 mL sample cell filled with Milli-Q® water. Next, a 10 mL sample cell was filled with
sample water, followed by the addition of a NitraVer® 5 Nitrate Reagent Powder Pillow (HACH)
after which it was shaken vigorously for 1 minute. The sample cell was set aside for 5 minutes
before having it’s reading taken. An amber color indicated the presence of nitrite.
17
3.5.6 Nitrite
Nitrite readings were analyzed with the DR 2700 Spectrophotometer (Loveland, CO)
according to the USEPA Diazotization Method (HACH, 2017). The instrument was first blanked
with a 10 mL sample cell filled with Milli-Q® water. Next, a 10 mL sample cell was filled with
sample water, followed by the addition of a NitriVer® 3 Nitrite Reagent Powder Pillow (HACH).
The sample cell was swirled briefly and allowed to sit for 20 minutes before having its reading
taken. A pink color indicated the presence of nitrite.
3.5.7 pH Measurement
The pH of water samples was taken with a Orion Star™ A111 pH meter (Thermo
ScientificTM, Waltham). Prior to measuring the pH of each sample, the device was calibrated using
pH buffer solutions at pH 4, 7, and 10 (VWR international). Readings were taken as the samples
were being mixed with a magnetic stirrer.
3.5.8 Haloacetic Acid Analysis
Nine different haloacetic acids (HAA9) were analyzed using a liquid-liquid extraction and
gas chromatography according to Standard Method 6251 (APHA, 2012). The compounds consist
of monochloroacetic acid (MCAA), monobromacetic acid (MBAA), dichloroacetic acid (DCAA),
trichloroacetic acid (TCAA), bromochlroacetic acid (BCAA), dibromoacetic acid (DBAA),
bromodichloroacetic acid (BDCAA), dibromochloroacetic acid (DBCAA), and tribromoacetic
acid (TBAA). Samples were analyzed using a Hewlett Packard 5890 Series II Plus gas
chromatograph (Mississauga, ON) with an electron capture detector (GC-ECD) and a DB 5.625
capillary column (Agilent Technologies Canada Inc., Mississauga, Ontario). The instrument
conditions used to conduct the experiments are presented below in Table 3.7. A sample calibration
curve is presented in Figure 3.4.
18
Table 3.7: GC-ECD Operating Conditions for HAA Analysis
Parameter Conditions System Hewlett Packard 5890 Series II Plus Column DB 5.625 capillary column (30m x 0.25 mm x 0.25 µm ID) Injector Temperature 200 oC Detector Temperature 300 oC Temperature Program 35 oC for 10 min
2.5 oC/min temperature ramp to 65 oC 10 oC/min temperature ramp to 85 oC 20 oC/min temperature ramp to 205 oC, hold for 7 minutes
Carrier Gas Helium Flow Rate 1.2 mL/min at 35 oC
Figure 3.4: Sample dichloroacetic acid calibration curve (May 2018)
3.5.9 Trihalomethanes, Haloacetonitriles, Halonitromethanes Analysis
THMs (trichloromethane (TCM), tribromomethane (TBM), bromodichloromethane
(BDCM), chlorodibromomethane(CDBM)), HANs (bromochloroacetonitrile (BCAN),
trichloroacetonitrile (TCAN), dichloroacetonitrile (DCAN), dichloroacetonitrile(DCAN)), and
HNMs (bromodichloronitromethane (BDCNM), dibromochloronitromethane (DBCNM),
trichloronitromethane (TCNM)) were analyzed according to Standard Method 6232B (APHA,
2012), using a 5890 Series II Plus Gas Chromatograph (Hewlett Packard, Mississauga, ON) with
an electron capture detector (GC-ECD) and a DB 5.625 capillary column (Agilent Technologies
y = 43.14x - 0.0811R² = 0.9996
0
5
10
15
20
25
30
35
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Dich
loro
acet
ic a
cid
(ug/
L)
Ratio Response
19
Canada Inc., Mississauga, ON). The instrument conditions used to conduct the experiments are
presented below in Table 3.8. A sample calibration curve is presented in Figure 3.5.
Table 3.8: GC-ECD Operating Conditions for THM, HAN, and HNM Analyses
Parameter Conditions System Hewlett Packard 5890 Series II Plus Column DB 5.625 capillary column (30m x 0.25 mm x 0.25 µm ID) Injector Temperature 200 oC Detector Temperature 300 oC Temperature Program 40 oC for 4 min
4 oC/min temperature ramp to 95 oC 60 oC/min temperature ramp to 200 oC
Carrier Gas Helium Flow Rate 1.2 mL/min at 35 oC
Figure 3.5: Sample trichloromethane calibration curve (May 2018)
3.5.10 Chlorine Demand Tests
Chlorine demand tests were performed on surface water, wastewater, and mixtures to
determine the chlorine dose that would yield a 1.0 ± 0.5 mg/L Cl2 residual. In addition, the residual
was achieved after an incubation period of 24 ± 1 h, under a constant temperature of 22 ± 1.0 oC.
Tests were conducted on three 250 mL aliquots of each prepared mixture, 0%, 5%, 10%, and 25%
y = 67.665x + 1.2979R² = 0.9956
0
10
20
30
40
50
60
70
80
90
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Dich
loro
acet
ic a
cid
(ug/
L)
Ratio Response
20
v/v% wastewater to surface water, using a broad range of chlorine doses. Samples were chlorinated
using sodium hypochlorite solution (12% NaOCl stock, BioShop Canada, Inc., Burlington, ON).
Residuals and all relevant chlorine concentrations were measured using the DR2700
Spectrophotometer (Loveland, CO) in accordance with the DPD colorimetric Standard Method
4500-C1 G (APHA, 2012). Prior to analysis, the chlorine kit was blanked with Mili-Q® water.
Mixtures with concentrations that exceeded the HACH chlorine kit’s range (>2.0 mg/L Cl2) were
diluted with Mili-Q®, after which they were measured and subsequently adjusted to account for
dilution.
3.5.11 Genotoxicity Analysis with the SOS ChromotestTM
Genotoxicity was quantified using EBPI’s (Environmental Bio-Detection Products Inc.,
Mississauga, Ontario, Canada) SOS Chromotest™, a colorimetric method marked by endpoint
DNA damage, the severity of which determines how genotoxic a substance or mixture is. Tests
were conducted using PQ37, a mutant strain of Escherchia coli (E. coli) which has the β-
galactosidase (β-gal) gene fused to the bacterial sfiA SOS operon (Quillardet et al., 1982). Lesions
to the genetic material activate the DNA repair mechanism; consequently, the SOS promoter
induces lacZ expression followed by the synthesis of β-gal, an enzyme capable of degrading
lactose. A more genotoxic substance manifests greater β-gal activity, which is quantifiable by a
change in color (blue) caused by the presence of a chromogenic substrate (Kocak et al., 2010).
However, it is possible to underestimate the induction of β-gal as some chemicals may inhibit
protein synthesis. To correct for this, the PQ37 strain was made constitutive for alkaline
phosphatase synthesis. As such, the specific activity of β-gal or the induction factor is the ratio of
β-gal to alkaline phosphatase activities. Chlorinated samples (2 L) were quenched with L-ascorbic
acid (20 mg per 1 mg/L of Cl2) and acidified to pH 2 with concentrated sulfuric acid, after which
they were extracted using Oasis HLB cartridges (6cc, 200 mg, Waters Corporation) with a Horizon
Technology Automated Cartridge Extractor II (Salem, New Hampshire). Samples were eluted with
2 x 5 mL of acetone, evaporated to dryness under nitrogen, and reconstituted in 30 μL of dimethyl
sulfoxide (DMSO). 66 equivalent mL of sample was added to the SOS Chromotest™ and
subjected to serial dilution for a total of 8 test concentrations. 100 μL of diluted bacterial
suspension (prepared overnight and diluted to 0.05 optical density at 600 nm) was added to each
well and incubated at 37 oC for 2 h. Following incubation, 100 μL of chromogen for β-gal and
21
alkaline phosphatase (AP) was added to each well and incubated at 37 oC for an additional hour.
A microplate reader (Infinite 200, Tecan, Morrisville, NC) was used to read the activity of β-gal
(OD620) and AP (OD405). Equations to calculate β-gal activity (Rc), AP reduction activity (Ro), and
induction factor (IF) are given below:
RC = Sample OD620−Blank OD620Control OD620− Blank OD620
(3.2)
RO = Sample OD405−Blank OD405Control OD405− Blank OD405
(3.3)
IF = RcRo
(3.4)
A serial, step-wise dilution (total of eight dilutions for each sample) was used in the SOS
Chromotest™ bioassay. For each dilution, the amount of sample was quantified with a unitless
relative enrichment factor (REF) as defined by Escher and Leusch (2011):
REF = enrichment factordilution factor
(3.5)
Enrichment factor = concentration achieved during sample extraction, Dilution factor = dilution
amount for each step in the bioassay.
Using IF and REF values, results that exhibited a dose-response relationship for at least
two consecutive samples and having an IF greater than 2 were considered as genotoxic (IF2.0).
The REF needed to elicit this response, also defined as the effect concentration (EC), was derived
from a linear concentration-response curve or slope of dose-dependent response, for cells that
exhibited 70% or greater viability and is expressed as REFIF2.0 As such, less genotoxic samples
have higher REFs as more concentration is required to elicit a genotoxic response (REF > 1). In
contrast, samples that are more genotoxic require fewer concentration steps; a REF of 1 indicates
that a sample requires no enrichment to induce a genotoxic response. Toxicity can also be
presented as 1/REFIF2.0, which is equivalent to the slope of a concentration-response curve; higher
values represent greater genotoxic effects. In this study, samples were considered different than
the control but not positive if they had IF values that ranged from 1.5 to < 2 (Cachot et al., 2006).
22
REF 80 were used to qualify the data and when necessary, slopes were used to complement
comparisons.
3.5.12 Predicted Genotoxicity Based on Chinese Hamster Ovary Cell Comet Assay
The predicted genotoxicity of measured THMs, HANs, and HNMs was calculated by
dividing the measured concentration of each DBP by its published genotoxicity potency data based
on the Chinese Hamster Ovary (CHO) cell comet assay, yielding a unitless value (Wagner and
Plewa, 2017). Potency is defined as the effect concentration (EC50) required to elicit a toxic
response in 50% of cells (Krasner et al., 2016). Total predicted genotoxicity (X-DBP) was
calculated by summing the values for each class of DBPs, where a predicted genotoxicity value of
1 signifies a sample by itself contains enough DBPs to elicit an EC50. The method detection limit
(MDL) for all measured DBPs was ≤ 1 μg/L. A list of the measured DBPs and their potencies are
provided in Appendix Table 8.33.
3.6 Statistical Analysis
All statistical analyses were achieved with Microsoft Excel 2016 (Microsoft Corporation,
Redmond, WA). Pearson correlation analyses were performed to see how well measured water
quality characteristics, DBP concentrations, and predicted genotoxicity were associated with SOS
genotoxicity (represented by the slopes of chlorinated mixtures listed in Appendix Table 8.32). A
Pearson coefficient of r = 1 represents a perfect linear relationship and can either be negative or
positive, whereas coefficients that tend towards r = 0 imply no association between variables.
4.0 Method Development of Chlorine Demand Tests for Impacted Surface Waters
4.1 Breakpoint Chlorination Method
Mixtures of 0%, 5%, 10%, and 25% vol/vol wastewater to surface water were chlorinated
for a 1.0 ± 0.5 mg/L free chlorine residual. While achieving this target residual for surface water
is well understood for drinking waters, the method can be complicated by the presence of
wastewater, which can contain elevated levels of ammonia. Ammonia, as well as other reducing
substances, can make it difficult to asses what chlorine dose yields a measurable free chlorine
23
residual, thereby rendering the normal method by which chlorine demand tests are performed
difficult. This necessitated the formulation of a new method that would eliminate the effects of
ammonia and provide a true measure of a sample’s chlorine demand.
Chlorine breakpoint curves were first developed for pure wastewater effluents, requiring
the measurement of both free and total chlorine. If the wastewater contained NH3-N concentrations
in appreciable amounts, the sample was first diluted to an appropriate level (~0.5 mg/L NH3-N) in
preparation for the chlorine breakpoint experiment. The reason for this was to make dosing easier
as chlorine will react with NH3-N at low dose concentrations to form monochloramine, and at
moderate concentrations to form dichloramines (and trichloramines), both of which were observed
in the lab to produce interferences in free chlorine measurements. In effect, this method not only
reduced the amount of chlorine needed for each dose, but the number of samples needed to
generate the breakpoint curve as well.
The production of chloroamines is shown in the classic breakpoint chlorination curve in
Figure 4.1, below. In Zone 1, chlorine oxidizes organic compounds to form monochloroamines.
However, with increasing doses of chlorine, the system transitions into Zone 2 in which
monochloroamines are destroyed, and dichloramines and trichloramines are produced; as a result,
measured chlorine residual drops until the stoichiometric ratio of 7.6 Cl2 to 1 NH3-N is reached.
Figure 4.1: The classic breakpoint curve – Zones 1 and 2 represent the formation of chloramines, and Zone 3 represents the formation of free chlorine (Adapted from White, 2010).
Past this point, the system transitions into Zone 3 in which the first measurable free chlorine
residual is produced. It has been generally observed that 3 mg/L monochloramine results in an
Zone 1 Zone 2 Zone 3
Res
idua
l Chl
orin
e C
once
ntra
tion
(mg/
L)
Cl2 to NH3-N Ratio (wt)
24
increase of less than 0.1 mg/L free chlorine reading (Spon, 2008), though many samples handled
in the lab showed to spike upwards in their free chlorine readings with little to no predictability.
The general procedure of generating a chlorine breakpoint is as follows.
In a typical breakpoint chlorination experiment, treated wastewater was first filtered using
a 0.45 µm pore size Supor® membrane filters (90 mm diameter, Pall Corporation, Port
Washington, NY). Next, six Erlenmeyer flasks were filled with 100 mL of pure or diluted
wastewater, depending on the concentration of NH3-N in the sample. As discussed above,
wastewater samples that had high levels of ammonia were diluted such that the final ammonia
concentration was reduced to ~0.5 mg/L NH3-N. The flasks were then dosed with increasing
amounts of chlorine with 5 minutes between each dosing event; each chlorinated flask was then
placed on a magnetic stirrer for that duration, and subsequently allowed to sit idle for the remainder
of the experiment. After 30 minutes, the free and total chlorine residual were measured for each
sample. The experiment was repeated until 3-4 sample points after noting the breakpoint, the point
at which a measurable free chlorine residual is produced. If the wastewater was diluted, all chlorine
measurements were adjusted to account for dilution. An example of a chlorine breakpoint curve is
shown for wastewater Ontario (WW-O) in Figure 4.2, below. The breakpoint curve provides a
clear indication of where the breakpoint begins, specifically, the point on the x-axis to which a
straight line (R = 0.996) is extrapolated from the last six points on the “Free Cl2” curve. This value
was then used as a starting point for subsequent chlorine demand tests of various wastewater to
surface water mixtures.
Figure 4.2: Sample chlorine breakpoint curve of WW-O (November, 2017)
y = 0.5349x - 4.6175R² = 0.996
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Mea
sure
d C
l 2 (m
g/L)
Cl2 Dose (mg/L)
Total Cl2
Free Cl2
Total Cl2 - Free Cl2
Linear (Predicted Free Cl2)
25
Next, the chlorine demand of pure surface water and wastewater were conducted to determine the
Cl2 dose concentration needed to achieve a 1.0 ± 0.5 mg/L Cl2 residual. Other requirements include
that the residual is achieved after an incubation period of 24 ± 1 h, under a constant temperature
of 22 ± 1.0 oC. For surface water samples, two pairs of bottles were used: the first pair was
chlorinated at a low dose and the second at a higher dose. Chlorine dose concentrations were based
on McKie et al.'s (2015) assessments of various Ontario surface waters. For wastewaters, three
sets of two 250-mL amber bottles were used, enabling a range of low to high dosing conditions
given the unpredictable nature of wastewater effluent. Additional bottles were used in case there
were concerns regarding the DOC concentration. The chlorine dose concentration that gives the
target residual was estimated for each sample water by means of linear interpolation:
y-y1=y2-y1x2-x1
(x-x1) (4.1)
where, x1 = high Cl2 residual, y1 = high Cl2 dose, x2 = low Cl2 residual, y2 = low Cl2 dose, x = 1.0
mg/L Cl2, and y = interpolated Cl2 dose. With the known chlorine dose for wastewater, it was
possible to derive the theoretical chlorine dose for 5%, 10%, and 25% v/v wastewater to surface
water mixtures. These values were determined using a two-step procedure. First, the chlorine
demand of wastewater effluent was calculated:
Raw WW Cl2Demand = (Cl2 Dose- 1 mgL
Cl2) (4.2)
Finally, the theoretical chlorine dose was found by adjusting the chlorine demand for a specific
volume ratio of wastewater to Milli-Q®, as shown in Equation 4.3:
Theoretical Cl2 Dose=(Raw WW Cl2Demand * WW volumeMili-Q volume
+ 1 mgL
Cl2) (4.3)
The theoretical chlorine doses for 5%, 10%, and 25% v/v wastewater to Milli-Q® provided
sufficient information to estimate a broad range of Cl2 dose concentrations that should contain the
target chlorine residual for each mixture. For example, if a 5% v/v wastewater to MQ has a chlorine
demand of A, and the chlorine demand of surface water is B mg/L Cl2, then for that 5% v/v mixture
the chlorine dose concentration can be no less than the chlorine demand of the 5% v/v wastewater
to MQ mixture, A mg/L Cl2, which forms the lower limit, but can be no more than this value plus
95% of the chlorine demand of pure surface water, A mg/L Cl2 + 0.95*(B mg/L Cl2), constituting
26
the upper limit. The optimal chlorine dose concentration then lies somewhere between those
values. Like before, two pairs of 250-mL amber bottles were filled halfway with the prepared
sample. Next, the set of bottles were dosed at the calculated low and high chlorine dose
concentrations. The bottles were then subsequently filled headspace-free with the remaining
sample and incubated for 24 h, after which they were measured for their chlorine residual. Like
before, Equation 4.1 was used to interpolate for the target chlorine dose for each mixture. Though
the chlorine breakpoint method offered an accurate and reliable way to determine the chlorine
dose, this method was simplified by combining chlorine demand tests and breakpoint chlorination
into one step.
4.2 Simplified Chlorine Demand Test
Due to time constraints and other limiting factors, breakpoint chlorination and the chlorine
demand test were merged into one. With the ammonia concentration of pure wastewater known,
the concentration of ammonia present in each mixture was simply assumed to be a fraction of the
original value based on the wastewater to surface water volume ratio. Additionally, because the
stoichiometric breakpoint is usually achieved at 7.6 Cl2:NH3-N in a pure system, a conservative
estimate of the chlorine concentration needed to satisfy breakpoint was taken at 8-10 times more
than the concentration of NH3-N to account for any nonidealities. Furthermore, it was assumed
that because surface waters in Ontario typically have demands that range from 2 to 4 mg/L, even
the highest wastewater to surface volume ratio of 25% would cause only a moderate increase in
demand. Therefore, the chlorine dose required for a minimum chlorine free residual for any
mixture was simply the stoichiometric amount of chlorine needed to achieve breakpoint plus an
estimate of the natural chlorine demand of the mixture. The general procedure is outlined as
follows and is identical to how a normal chlorine demand test for a surface water would be
conducted, except that a broader range of chlorine doses were used.
In the revised chlorine demand test, mixtures of 0%, 5%, 10%, and 25% v/v wastewater to
surface water were first prepared. Next, each sample was poured into three 250 mL amber bottles,
filled halfway before receiving their respective chlorine doses. The amber bottles were then filled
head-space free and placed inside an incubator at a stable temperature of 22 oC for 24 h. After the
time had elapsed, the chlorine residual was measured. A linear relationship between chlorine
residual and dose, or a lack of interference, or both, confirmed that breakpoint had been achieved.
27
5.0 Results and Discussion
5.1.1 Comparison of Wastewaters
Wastewater effluent obtained from three different WWTPs located on Lake Ontario (WW-
O), Lake Simcoe (WW-S), and Lake Erie (WW-E) was assayed prior to lab chlorination. The
genotoxicity of wastewaters can be represented as the dose dependent relationship between
response (IF) and concentration (REF), as shown in Figure 5.1. At a REF of 80, WW-O and WW-
E exceeded an IF of 2.0, and as such are positive in the genotoxicity assay. In comparison, WW-S
was not genotoxic but was different than the control as it exceeded an IF of 1.5. The large
difference in the IF values for the UV treated wastewaters (1.86 for WW-S vs. 2.51 for WW-E)
may be explained by the fact that WW-S has an additional coagulation step following the activated
sludge process and a rotating biological contactor after secondary clarification, typically used by
wastewater facilities to provide additional removal of organic compounds (Hiras et al., 2004).
Despite these additional processes, the DOC concentration (6.13 to 6.76 mg/L) and UV254 values
(0.11 to 0.15 cm-1) for the WWTPs that incorporated UV (WW-S, WW-E) were similar.
Figure 5.1: SOS ChromotestTM IF values for three surface waters (SW) and wastewaters (WW) under unchlorinated (no NC) and chlorinated (C) conditions at a relative enrichment factor (REF) of 80-fold. An IF of 2.0 is indicative of genotoxicity (represented by the red line) and an IF of 1.5 indicates a response that is different than the control but not positive (represented by the green line). Vertical bars represent maximum and minimum values.
The impact of chlorination on UV disinfected wastewater was of interest as studies have
been only conducted on secondary effluents, some which reported an increase in genotoxicity
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Erie Ontario Simcoe
Indu
ctio
n Fa
ctor
@ 8
0 RE
F
SW-NC SW-C WW-NC WW-C
28
(Meier et al., 1987), while others observed a decrease (Wang et al., 2007). Wang et al. (2007) also
reported that chlorine-disinfected wastewaters with low NH3-N (<10~20 mg/L) exhibited lower
genotoxicity, while those with high NH3-N (>10~20 mg/L) exhibited higher genotoxicity. WW-E,
which had the highest SOS response (IF 2.51) and a very low NH3-N concentration, 0.13 mg/L,
was selected for subsequent chlorination. Chlorination decreased genotoxicity, as indicated by an
IF value that decreased from 2.51 to 2.14 (to achieve the same response). As such, the results of
this study show that the presence of low ammonia concentrations may also cause a decrease in
genotoxicity for UV disinfected wastewaters upon chlorination. A comparison of the cytotoxicity
of three wastewaters at REF 318 shows that WW-E had the highest survival rate of 79% whereas
those for WW-S and WW-O were 61% and 59%, respectively (Appendix Tables 8.21-8.30). This
finding is of interest as it shows that although WW-E was the most genotoxic, it was also the least
cytotoxic.
5.1.2 Comparison of Surface Waters
Raw and chlorinated surface waters did not elicit a genotoxic response at even the highest
REF; subsequently slopes (1/REFIF2.0) of the dose dependent response were extrapolated to
determine the REF at which the samples would reach an IF of 2.0. As such, very high REFIF2.0
values were obtained for SW-E (1540), SW-S (1233), and SW-O (378). Upon chlorination,
REFIF2.0 values decreased to 681 (75%), 313 (55%), and 198 (48%), respectively. In terms of their
REFIF2.0, SW-S, SW-E, and SW-O were associated with a reduction of 75%, 55%, and 48%
respectively, corresponding to increases in slope of 400%, 250%, and 92%, respectively. The
slopes of the chlorinated samples were higher than those of their non-chlorinated counterparts,
indicating that the SOS test responded to the presence of chlorinated DBPs.
Genotoxicity of the surface waters was compared at REF 80 (Figure 5.1). The IFs for SW-
E, SW-S, and SW-O ranged from 0.86 to 1.30, and did not exceed an IF 1.5, confirming that all
surface waters were not genotoxic nor were they different from the control. While the chlorinated
surface waters exhibited higher IFs, they were also not genotoxic (Figure 5.1), supporting results
of Guzzella et al. (2004) who reported higher responses of surface waters after chlorination. In
contrast, WW-E and WW-O exceeded an IF of 2.0, highlighting that unchlorinated wastewater
effluents were more reactive in the genotoxicity assay than both unchlorinated and chlorinated
surface waters. When considering surface waters, nitrogenous concentrations NO2-N (R > 0.80)
29
and NO3-N (R > 0.90) were strongly correlated to genotoxicity, as well as SUVA (R > 0.90),
indicating that the aromatic character of DOC, NO3-N, and NO2-N were shared factors that may
have driven genotoxicity (correlations for the chlorinated surface waters are provided in Appendix
Table 8.7).
5.1.3 Comparison of Wastewater–Surface Water Mixtures
At REF 80, none of the unchlorinated and chlorinated mixtures gave a genotoxic response
(Figure 5.2). In general, both were observed to give higher responses with increasing ratios of
wastewater, which became more noticeable at the 25% ratios. Antagonistic effects between surface
water and wastewater are suspected for the initial decrease in genotoxicity for the chlorinated Lake
Ontario and Lake Simcoe mixtures. Although it is not readily clear from Figure 5.2, DOC (R >
0.80-0.90) and UV254 (R > 0.80-0.90) showed strong correlations with genotoxicity (slope) for all
chlorinated Lake Ontario and Lake Erie mixtures (Appendix Tables 8.8 and 8.10). However, DOC
(R > 0.60) and UV254 (R > 0.70) correlated less with genotoxicity for chlorinated Lake Simcoe
mixtures (Appendix Table 8.9). SUVA correlated strongly (R > 0.70) with genotoxicity for all
mixtures, except those associated with Lake Ontario, which was caused due to a decreasing
UV254/DOC ratio with increasing wastewater ratio. These correlations suggest that the
genotoxicity of chlorinated wastewater-surface waters mixtures is dependent on the organic
loading associated with wastewater. Interestingly, Lake Simcoe’s unchlorinated and chlorinated
mixtures had on average the lowest slopes and responses at REF 80, never exceeding an IF of 1.5.
This is explained by WW-S’s low reactivity that may have resulted from the additional organic
removal steps in its treatment process.
Chlorinated mixtures were generally more reactive than their unchlorinated counterparts,
except for the 5%, 10%, and 25% ratios of Lake Ontario and the 25% ratio of Lake Simcoe (Figure
5.2). These were the only mixtures that agreed with the study by Nakamuro (1989) which showed
that chlorination caused a decrease in the mutagenicity of a surface water composed of 20%
wastewater using the Ames test. Of Lake Ontario’s mixtures, the 25% and 10% unchlorinated
ratios and the 25% chlorinated ratio were different than the control (IF > 1.5).
30
Figure 5.2: SOS ChromotestTM IF values for three wastewater-surface water mixtures under unchlorinated (NC) and chlorinated (C) conditions at a relative enrichment factor (REF) of 80-fold. An IF of 2.0 is indicative of genotoxicity (represented by the red line) and an IF of 1.5 indicates a response that is different than the control but not positive (represented by the green line). Vertical bars represent maximum and minimum values.
However, it was surprising to see that the same was not observed for the Lake Erie
mixtures, despite being mixed with a more genotoxic wastewater (WW-E). Lake Erie’s chlorinated
25% ratio was the only mixture that exceeded an IF of 1.5. In fact, both chlorinated and
unchlorinated mixtures for Lake Ontario were consistently more reactive than Lake Erie’s. One
possible explanation for Lake Erie’s unchlorinated mixtures being less genotoxic than Lake
Ontario’s is that Lake Erie’s surface water (SW-E) has a lower genotoxicity than Lake Ontario’s
(SW-O), and therefore the additive contribution of genotoxicity was less overall. However, this
alone does not account for the major differences when comparing the two chlorinated sets. Overall,
these results show that genotoxicity was more likely to be significant at 25% wastewater/surface
ratios (IF > 1.5), while smaller ratios of 5% and 10% generally had responses similar to those of
chlorinated surface waters (IF < 1.5).
0.0
0.5
1.0
1.5
2.0
2.5
3.0
NC C
Indu
ctio
n Fa
ctor
(IF) 0%
5%
10%
25%
100%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
NC C
Indu
ctio
n Fa
ctor
(IF) 0%
5%
10%
25%
100%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
NC C
Indu
ctio
n Fa
ctor
(IF)
0%
5%
10%
25%
100%
Lake Ontario Lake Simcoe
Lake Erie
31
5.1.4 Comparison of Predicted and SOS ChromotestTM Genotoxicity
Predicted genotoxicity is based on the sum of all measured DBPs and the published
genotoxicity potency data from the CHO comet assay (Wagner and Plewa, 2017). A value of 1
indicates that the concentrations of DBP species summed together (X-DBPs) can induce a
genotoxic response in 50% of cells (EC50). Individual DBP potencies varied widely both between
classes and within classes; for example, the potency of BDCNM, 6.32 x 10-5, is nearly two orders
of magnitude greater than that of TCAN, 1.01 x 10-3 M. A strong linear relationship (R2 > 0.90)
was observed to exist between wastewater/surface water ratio of the chlorinated mixtures and the
predicted genotoxicity (Figure 5.3).
Figure 5.3: Predicted genotoxicity and measured SOS ChromotestTM genotoxicity (slopes) of
various wastewater and surface water mixtures post-chlorination. The 100% wastewater sample is
presented for comparison.
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.0E+00
5.0E-04
1.0E-03
1.5E-03
2.0E-03
2.5E-03
3.0E-03
0 10 20 30 40
SOS
Gen
otox
icity
Pred
icte
d G
enot
oxic
ity
Vol/Vol% Wastewater to Surface Water
X-DBPs HANsBCAN HNMsTHMs SOS Genotoxicity
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.0E+00
5.0E-04
1.0E-03
1.5E-03
2.0E-03
2.5E-03
3.0E-03
0 10 20 30 40
SOS
Gen
otox
icity
Pred
icte
d G
enot
oxic
ity
Vol/Vol% Wastewater to Surface Water
X-DBPs HANsBCAN HNMsTHMs SOS Gentoxicity
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.0E+00
5.0E-04
1.0E-03
1.5E-03
2.0E-03
2.5E-03
3.0E-03
0 10 20 30 40
SOS
Gen
otox
icity
Pred
icte
d G
enot
oxic
ity
Vol/Vol% Wastewater to Surface Water
X-DBPs HANsBCAN HNMsTHMs SOS Genotoxicity
Lake Ontario
100%
Lake Simcoe
100%
Lake Erie
100%
32
Of the measured DBPs, HANs represented the primary driver of overall predicted
genotoxicity that increased with greater ratios of wastewater. Although HANs and HNMs are more
genotoxic than most regulated carbon-based DBPs, the predicted genotoxicity of measured N-
DBPs were all in the order of 10-4. No genotoxicity associated with THMs was predicted for any
sample despite THMs representing 72% to 95% of measured DBP concentrations (Appendix
Figure 8.3). This agrees with the well-established observation that THMs produced upon
chlorination constitute a significant portion (~14%) of total halogenated compounds (Mitch et al.,
2009). The correlation between THM concentrations and predicted genotoxicity were strong (R >
0.9). Similarly, the SOS genotoxicity was strongly correlated with both predicted genotoxicity
(R > 0.70 – 0.90) and THM concentrations (R > 0.70 – 0.90). This demonstrates that THMs serve
as a good surrogate for the total amount of observed DBPs that contribute to SOS genotoxicity.
Furthermore, in contrast to the chlorinated mixtures, all wastewaters had very low DBPs
concentrations (≤ 1 μg/L), and therefore low predicted genotoxicity (≤ 1.6 ⋅ 10-6), suggesting that
wastewater-associated DBPs were not a major contributor to genotoxicity. Chemicals like N-
nitroso compounds and PAHs are not considered to be DBPs yet are potent genotoxins that are
known to be present in municipal wastewaters (White and Rasmussen, 1998). Compared to the
calculated toxicity model, the SOS ChromotestTM measured whole mixture effects and provided
an estimate of each wastewater’s overall reactivity in cells. These results highlight the limitation
of the predicted genotoxicity model when assessing wastewaters.
33
6.0 Conclusions
This study evaluated the SOS genotoxicity of wastewaters (WW-E, WW-O, WW-S) and
surface waters (SW-E, SW-O, and SW-S) alone as well as chlorinated and unchlorinated mixtures
at bench scale. Water samples were analyzed for NOM characteristics using parameters DOC,
UV254, and SUVA as well as nitrogenous parameters NH3-N, NO3-N, and NO2-N. Chlorinated
waters and mixtures were analyzed for their THM, HAN, and HNM concentrations which were
used to predict genotoxicity.
Wastewaters were typically genotoxic, the extent of which is partly determined by the
treatment they are subjected to prior to discharge; genotoxicity can potentially be reduced by the
presence of additional organic removal steps. In contrast, neither unchlorinated or chlorinated
surface waters were genotoxic. This study showed that wastewaters increased the genotoxicity of
surface waters pre- and post-chlorination, however low wastewater ratios (v/v) of 5% and 10%
were generally similar to those of chlorinated surface waters. The 25% ratios were more likely to
show significantly higher genotoxic effects but still did not meet the criteria for being genotoxic.
Overall, the genotoxicity of chlorinated wastewaters/surface water mixtures increased with greater
contributions of wastewater. To provide further insight, THM concentrations were correlated to
predicted genotoxicity based on the CHO comet assay and SOS genotoxicity. Although the
predicted genotoxicity of THM was zero, THM concentrations correlated strongly with predicted
genotoxicity and SOS genotoxicity for the chlorinated mixtures, suggesting that THMs may serve
as surrogates for both known and unknown DBPs that drive genotoxicity when considering
chlorinated waters. Bioassays such as the SOS ChromotestTM are powerful tools that may be used
to assess whole mixture effects and should therefore be considered as part of long-term monitoring
plans of sources of drinking waters. More studies need be performed on different wastewater
effluents to understand how micropollutants impact the final genotoxicity of drinking waters.
34
6.1 Recommendations for Future Work
The results of this study suggest that any future work with a similar experimental design
could greatly benefit from the following recommendations:
1. Additional data is required for a more thorough statistical analysis. In this experiment,
mixtures were composed of a wastewater and surface water. These mixtures were then
subjected to chlorinated and unchlorinated conditions that were duplicated to provide
a measure of variance. However, more observations are required to perform analyses
as basic as the two-way ANOVA and linear regression.
2. When multiple factors can influence the outcome of a result, a full factorial experiment
should be considered as it can be used to investigate more than two factors at no
additional cost. It can also help determine conditions that yield an optimal result.
Accordingly, a full factorial experiment is better suited for evaluating whether the DOC
and its organic fractions associated with wastewater, surface water, or both play a
statistically significant role in a mixture’s final genotoxicity.
3. While being able to measure the genotoxicity of a sample is beneficial, understanding
the mode by which DNA damage occurs can be just as important, and may even help
in the classification of specific groups of compounds. Moreover, various kinds of
toxicological tests should be considered, as some pure wastewater samples in this
experiment showed noticeable cytotoxic responses at very concentrated conditions.
4. The wastewater source is a significant factor that influences DBP formation, but the
identities of the organic precursors responsible for the distribution were not assessed.
Analytical tools, like FEEM and Liquid Chromatography – Organic Carbon Detection
(LC-OCD) can be used to relate precursors to DBPs, thereby providing greater insight
into the effectiveness of wastewater treatment trains.
35
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8.0 Appendix
8.1 Treatment Processes for WWTPs Duffins Creek and Otonabee River
WWTP Duffins Creek
WWTP Otonabee River
Figure 8.1: Treatment processes for usused WWTPs Duffins Creek and Otanabee River.
8.2 Measured Water Quality Parameters for Mixtures
Table 8.1: Duffins Mixtures – Water Quality Parameters
Sample Type (v/v % ww/ss) pH
Alkalinity (mg/L
CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA
(L/mg/m)
0% 7.83 102.0 1.78 0.01 0.95 0.00 0.03 1.71
5% 7.64 102.5 1.91 0.04 1.45 0.03 0.03 1.75
10% 7.72 103.0 2.13 0.09 1.55 0.07 0.04 1.83
25% 7.53 110.0 2.57 0.10 4.00 0.20 0.05 2.00
100% 7.22 140.5 4.94 0.29 10.55 0.71 0.11 2.28
UV Effluent
Facultative Lagoon
NaOCl
Effluent
FeCl2 FeCl2
1o Clarifier Grit
2o Clarifier Aeration Tank
43
Table 8.2: Erie Mixtures – Water Quality Parameters
Sample Type (v/v % ww/ss) pH
Alkalinity (mg/L
CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA
(L/mg/m)
0% 8.41 100.0 2.36 0.01 0.25 0.00 0.03 1.47
5% 8.19 100.0 2.60 0.02 0.85 0.01 0.04 1.62
10% 8.09 100.5 2.82 0.02 1.1 0.01 0.05 1.78
25% 8.14 102.5 3.56 0.05 2.1 0.03 0.07 1.97
100% 7.48 108.5 6.76 0.13 8.1 0.18 0.15 2.26
Table 8.3: Ontario Mixtures – Water Quality Parameters
Sample Type (v/v % ww/ss) pH
Alkalinity (mg/L
CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA
(L/mg/m)
0% 8.07 97.50 2.01 0.01 0.75 0.01 0.04 2.20
5% 8.13 97.50 2.23 0.47 1.35 0.15 0.05 2.22
10% 8.23 98.25 2.49 0.92 1.85 0.34 0.05 2.13
25% 8.03 101.50 3.31 2.43 3.20 0.78 0.07 2.14
100% 7.51 107.50 7.89 10.25 15.35 2.33 0.15 1.89
Table 8.4: Otonabee Mixtures – Water Quality Parameters
Sample Type (v/v % ww/ss) pH
Alkalinity (mg/L
CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA
(L/mg/m)
0% 7.68 96.5 5.22 0.03 0.65 0.01 0.14 2.75
5% 7.72 96.5 5.35 0.13 0.45 0.01 0.14 2.67
10% 7.75 99.5 5.51 0.23 0.40 0.02 0.14 2.57
25% 7.88 103.0 5.99 0.49 0.50 0.03 0.14 2.31
100% 7.89 123.0 8.59 1.70 0.90 0.17 0.12 1.42
44
Table 8.5: Simcoe Mixtures – Water Quality Parameters
Sample Type (v/v % ww/ss) pH
Alkalinity (mg/L
CaCO3)
DOC (mg/L)
NH3-N (mg/L)
NO3-N (mg/L)
NO2-N (mg/L)
UV254 (cm-1)
SUVA
(L/mg/m)
0% 8.04 117.50 4.27 0.01 0.65 0.01 0.06 1.50
5% 8.11 122.00 4.46 0.02 0.90 0.01 0.06 1.44
10% 8.21 125.00 4.51 0.02 1.30 0.01 0.07 1.53
25% 8.15 129.50 4.78 0.02 2.05 0.01 0.08 1.59
100% 7.60 159.50 6.13 0.04 7.40 0.01 0.11 1.74
Table 8.6: Chlorine Residuals for all Chlorinated Mixtures
Sample Type (v/v % ww/ss) Duffins Erie Ontario Otonabee Simcoe
0% 1.38 1.36 1.09 0.94 1.26 1.45 1.40 1.02 0.99 1.26
5% 1.61 1.50 1.88 1.02 1.18 1.60 1.44 1.88 1.07 1.17
10% 1.39 1.43 2.66 1.70 1.19 1.41 1.38 2.68 1.72 1.22
25% 1.66 1.33 2.86 1.09 1.42 1.71 1.33 3.04 1.00 1.42
100%
1.08 0.95
45
Table 8.7: R values for all Chlorinated Surface Waters (SW-E, SW-O, SW-S)
DOC UV254 SUVA NH3-N NO2-N NO3-N Genotoxicity DOC 1.00
UV254 0.89 1.00
SUVA -0.60 -0.17 1.00
NH3-N N/A N/A N/A 1.00
NO2-N 0.37 0.75 0.52 N/A 1.00
NO3-N 0.19 0.61 0.68 N/A 0.98 1.00
Genotoxicity -0.16 0.30 0.90 N/A 0.86 0.94 1.00 Table 8.8: R values for Chlorinated Lake Ontario Mixtures
DOC UV254 SUVA NH3-N NO2-N NO3-N THMs HANs HNMs Pred. Genotoxicity Meas. Genotoxicity DOC 1.00 UV254 0.99 1.00 SUVA -0.67 -0.62 1.00 NH3-N 1.00 0.99 -0.66 1.00 NO2-N 0.99 0.99 -0.71 0.99 1.00 NO3-N 0.99 0.99 -0.69 0.99 0.99 1.00 THMs 0.99 0.99 -0.67 1.00 0.99 0.99 1.00 HANs 0.99 1.00 -0.64 0.99 0.99 0.99 0.99 1.00 HNMs 0.94 0.95 -0.45 0.94 0.91 0.90 0.93 0.95 1.00 Pred. Genotoxicity 0.99 0.99 -0.64 0.99 0.99 0.98 0.99 0.99 0.96 1.00 Meas. Genotoxicity 0.87s 0.86 -0.55 0.86 0.85 0.82 0.84 0.86 0.95 0.88 1.00
46
Table 8.9: R Values for Chlorinated Lake Simcoe Mixtures
DOC UV254 SUVA NH3-N NO2-N NO3-N THMs HANs HNMs Pred Genotoxicity Meas. Genotoxicity DOC 1.00 UV254 0.94 1.00 SUVA 0.74 0.92 1.00 NH3-N 0.75 0.53 0.21 1.00 NO2-N 0.75 0.53 0.21 1.00 1.00 NO3-N 0.98 0.99 0.86 0.63 0.63 1.00 THMs 0.99 0.93 0.72 0.72 0.72 0.97 1.00 HANs 0.97 0.96 0.80 0.60 0.60 0.98 0.98 1.00 HNMs 0.99 0.97 0.79 0.72 0.72 0.99 0.99 0.97 1.00 Pred. Genotoxicity 0.99 0.96 0.78 0.69 0.69 0.99 0.99 0.99 0.99 1.00 Meas. Genotoxicity 0.67 0.71 0.71 0.08 0.08 0.70 0.71 0.81 0.65 0.71 1.00
Table 8.10: R Values for Chlorinated Lake Erie Mixtures
DOC UV254 SUVA NH3-N NO2-N NO3-N THMs HANs HNMs Pred. Genotoxicity Meas. Genotoxicity DOC 1.00 UV254 0.99 1.00 SUVA 0.97 0.98 1.00 NH3-N 0.98 0.97 0.93 1.00 NO2-N 0.99 0.99 0.95 0.98 1.00 NO3-N 0.99 0.99 0.98 0.98 0.97 1.00 THMs 0.99 0.99 0.97 0.98 0.99 0.98 1.00 HANs 0.99 0.99 0.96 0.99 0.99 0.99 0.99 1.00 HNMs 0.99 0.99 0.98 0.99 0.98 0.99 0.99 0.99 1.00 Pred. Genotoxicity 0.99 0.99 0.97 0.99 0.99 0.99 0.99 0.99 0.99 1.00 Meas. Genotoxicity 0.99 0.99 0.99 0.97 0.97 0.99 0.99 0.99 0.99 0.99 1.00
47
8.3 Relative Enrichment Factor, Induction Factor, and Cytotoxicity
REF is defined as the unitless relative enrichment factor (Escher & Leusch, 2011):
REF= enrichment factordilution factor
,
A sample calculation is shown for the first dilution:
REF= enrichment factorextractiondilution factorbioassay
= 2000 mL
60 µL *1000 µL1 mL
105 µL=317.50
Induction factors (IF) corresponding to each dilution step for all unchlorinated and chlorinated
mixtures are listed in Appendix Tables 8.11-8.20. An example of the IF vs REF plot for
unchlorinated SW-O (duplicates) is presented in Appendix Figure 8.2. Cytotoxicity (survivability
rate %) data for all mixtures are listed in Appendix Tables 8.21-8.30.
Figure 8.2: IF vs. REF for duplicated unchlorinated SW-O (0%) samples.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0 50 100 150 200 250 300 350
Indu
ctio
n Fa
ctor
(IF)
Relative Enrichment Factor (REF)
0% - 1
0% - 2
48
Table 8.11: Unchlorinated Duffins Creek Mixtures – IF Values for Each Dilution step REF 0% NC
(v/v % WW/SW) 5% NC
(v/v % WW/SW) 10% NC
(v/v % WW/SW) 25 % NC
(v/v % WW/SW) 100 % NC
(v/v % WW/SW) 317.46 0.84 1.01 1.15 1.10 1.21 1.28 1.63 1.82 1.30 0.90 158.73 0.94 0.84 1.09 0.97 1.23 1.19 1.46 1.42 2.23 2.04 79.37 0.86 0.87 1.05 0.98 1.08 1.02 1.28 1.18 1.72 1.96 39.68 0.93 0.80 0.99 0.97 1.03 1.01 1.23 1.07 1.61 1.59 19.84 0.82 0.83 0.89 0.97 0.98 1.05 1.19 1.08 1.24 1.43 9.92 0.78 0.76 0.79 0.82 0.83 0.84 0.96 0.92 1.07 1.14 4.96 0.92 0.82 0.97 0.89 0.92 0.93 0.84 0.94 1.12 1.05 2.48 0.88 0.87 0.94 0.92 0.87 0.94 1.00 0.99 1.00 1.03
Table 8.12: Chlorinated Duffins Creek Mixtures – IF Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 1.09 1.07 1.35 1.38 1.59 1.32 1.40 1.55 158.73 1.00 0.97 1.08 1.18 1.11 1.16 1.10 1.22 79.37 0.94 1.00 1.01 1.07 1.12 1.02 1.29 1.09 39.68 0.95 1.00 1.07 0.87 0.98 1.04 1.05 1.11 19.84 0.89 0.89 1.06 0.61 0.97 0.97 0.95 0.96 9.92 0.78 0.81 0.80 0.88 0.81 0.83 0.83 0.88 4.96 0.79 0.77 0.87 0.89 0.86 0.81 0.83 0.81 2.48 0.87 0.83 0.92 0.89 0.83 0.84 0.87 0.85
Table 8.13: Unchlorinated Lake Erie Mixtures – IF Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 0.81 0.81 1.05 1.07 1.38 1.33 1.92 2.02 2.99 3.03 158.73 0.86 0.87 1.01 1.01 1.11 1.21 1.75 1.68 2.86 2.88 79.37 0.86 0.85 0.90 0.91 0.99 0.99 1.34 1.36 2.54 2.48 39.68 0.86 0.85 0.87 0.88 0.95 0.93 1.14 1.13 2.06 2.07 19.84 0.81 0.82 0.84 0.86 0.88 0.87 0.99 0.96 1.63 1.67 9.92 0.82 0.86 0.84 0.84 0.85 0.90 0.94 0.96 1.26 1.31 4.96 0.84 0.89 0.86 0.85 0.84 0.86 0.89 0.88 1.12 1.07 2.48 0.82 0.83 0.84 0.83 0.83 0.82 0.88 0.88 1.01 1.02
49
Table 8.14: Chlorinated Lake Erie Mixtures – IF Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
100% C (v/v % WW/SW)
317.46 1.50 1.59 1.95 1.82 2.11 1.96 2.67 2.60 4.51 3.83 158.73 1.20 1.39 1.49 1.50 1.67 1.55 1.79 1.85 3.06 3.03 79.37 1.19 1.19 1.34 1.31 1.38 1.35 1.51 1.53 2.27 2.01 39.68 1.04 1.18 1.24 1.23 1.35 1.12 1.29 1.31 1.68 1.54 19.84 1.07 1.15 1.15 1.15 1.15 1.12 1.19 1.22 1.42 1.32 9.92 1.13 1.12 1.15 1.14 1.17 1.08 1.11 1.11 1.30 1.24 4.96 1.14 1.11 1.11 1.11 1.11 1.05 1.05 1.05 1.10 1.18 2.48 1.07 1.09 1.07 1.06 1.08 1.02 1.08 1.07 1.13 1.06
Table 8.15: Unchlorinated Lake Ontario Mixtures – IF Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 1.90 1.78 2.20 2.01 2.10 2.03 2.08 1.82 0.58 0.77 158.73 1.65 1.43 1.62 1.69 1.66 1.72 2.03 1.87 1.83 2.05 79.37 1.37 1.22 1.49 1.45 1.54 1.48 1.77 1.83 1.99 2.16 39.68 1.30 1.18 1.41 1.28 1.41 1.33 1.65 1.71 1.93 2.20 19.84 1.08 1.09 1.18 1.22 1.28 1.29 1.46 1.47 1.79 1.95 9.92 1.02 1.07 1.16 1.23 1.20 1.23 1.35 1.31 1.48 1.70 4.96 1.12 1.07 1.17 1.17 1.22 1.22 1.27 1.19 1.57 1.58 2.48 1.05 1.11 1.11 1.19 1.21 1.32 1.23 1.36 1.53 1.46
Table 8.16: Chlorinated Lake Ontario Mixtures – IF Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 1.92 2.14 2.12 1.91 2.22 1.88 2.29 2.27 158.73 1.33 1.67 1.61 1.56 1.84 1.77 1.94 1.98 79.37 1.43 1.36 1.37 1.40 1.30 1.49 1.77 1.77 39.68 1.22 1.24 1.22 1.14 1.24 1.36 1.58 1.59 19.84 1.11 1.14 1.11 1.05 1.33 1.18 1.26 1.48 9.92 1.01 1.08 1.09 0.98 1.14 1.06 1.09 1.34 4.96 1.17 1.07 1.07 1.02 1.06 1.04 1.08 1.12 2.48 1.04 1.09 1.06 1.07 1.04 1.11 1.04 1.07
50
Table 8.17: Unchlorinated Lake Otonabee Mixtures – IF Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 1.104 1.024 1.125 1.143 1.108 1.015 1.268 1.108 1.640 1.742 158.73 0.977 1.033 0.953 0.997 1.003 0.968 1.089 1.060 1.265 1.137 79.37 0.956 0.900 0.928 0.911 0.908 0.940 1.029 1.013 1.214 1.151 39.68 0.934 0.910 0.949 0.903 0.903 0.886 0.962 0.925 1.079 1.034 19.84 0.906 0.892 0.871 0.893 0.879 0.888 0.909 0.978 1.038 1.049 9.92 0.900 0.839 0.895 0.937 0.919 0.915 0.894 0.985 1.048 1.019 4.96 0.877 0.887 0.873 0.888 0.880 0.864 0.906 0.940 0.956 0.969 2.48 0.858 0.851 0.846 0.869 0.855 0.864 0.856 0.906 0.939 0.953
Table 8.18: Chlorinated Lake Otonabee Mixtures – IF Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 4.857 5.221 4.816 4.689 4.567 4.521 3.339 5.148 158.73 2.792 2.660 2.731 2.810 2.749 2.601 2.000 2.561 79.37 1.754 1.719 1.916 1.767 1.914 1.762 1.478 1.875 39.68 1.382 1.176 1.345 1.302 1.377 1.290 1.121 1.429 19.84 1.119 1.112 1.132 1.085 1.156 1.081 0.946 1.138 9.92 1.009 0.930 1.009 1.030 2.338 0.953 0.932 1.027 4.96 0.930 0.969 0.957 0.966 2.321 0.918 0.876 0.938 2.48 0.873 0.985 0.938 0.927 0.927 0.891 0.907 0.891
Table 8.19: Unchlorinated Lake Simcoe Mixtures – IF Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 1.22 1.14 1.17 1.21 1.27 1.25 1.52 1.53 2.24 2.37 158.73 1.06 0.96 1.10 1.13 1.15 1.12 1.34 1.42 1.96 2.15 79.37 0.97 0.95 1.00 1.08 0.98 0.97 1.19 1.27 1.79 1.92 39.68 0.96 0.92 1.00 0.97 0.95 0.93 1.09 1.11 1.58 1.67 19.84 0.93 0.89 0.92 0.89 0.97 0.90 0.98 0.98 1.37 1.40 9.92 0.94 0.92 0.96 0.96 0.91 0.93 0.94 0.99 1.18 1.29 4.96 0.94 0.92 0.95 0.98 0.94 0.90 1.02 0.97 1.21 1.16 2.48 0.95 1.02 0.99 0.87 0.94 0.98 0.98 1.00 1.17 1.11
51
Table 8.20: Chlorinated Lake Simcoe Mixtures – IF Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 2.12 2.03 1.79 1.89 1.64 1.94 2.11 2.05 158.73 1.35 1.35 1.29 1.26 1.26 1.40 1.39 1.42 79.37 1.08 1.10 1.04 1.05 1.00 1.05 1.16 1.14 39.68 1.02 1.09 0.95 1.02 0.95 1.10 0.98 1.06 19.84 0.93 0.95 0.89 0.93 0.91 0.92 0.94 0.95 9.92 0.94 0.98 0.93 0.91 0.91 0.90 0.93 0.95 4.96 0.98 1.02 0.94 0.95 0.95 0.93 0.94 0.99 2.48 1.01 1.05 1.00 0.94 0.96 1.01 0.96 1.03
Table 8.21: Unchlorinated Duffins Creek Mixtures – Cytotoxicity Values for Each Dilution step REF 0% NC
(v/v % WW/SW) 5% NC
(v/v % WW/SW) 10% NC
(v/v % WW/SW) 25 % NC
(v/v % WW/SW) 100 % NC
(v/v % WW/SW) 317.46 74 81 74 72 76 75 68 71 57 52 158.73 86 79 74 73 79 74 76 65 76 77 79.37 90 89 83 87 91 87 78 86 96 87 39.68 89 86 81 75 90 82 75 81 81 90 19.84 90 89 85 93 82 85 78 85 78 87 9.92 91 94 87 84 96 88 88 85 90 95 4.96 90 91 86 82 88 86 90 85 86 94 2.48 96 107 94 93 89 90 87 91 88 92
Table 8.22: Chlorinated Duffins Creek Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 101 96 92 101 89 98 84 95
158.73 109 105 107 107 102 107 93 101
79.37 111 112 107 107 100 103 102 99
39.68 107 107 106 107 100 108 101 104
19.84 126 109 104 94 105 100 98 105
9.92 129 121 116 111 107 117 109 115
4.96 120 115 109 112 112 114 117 114
2.48 128 117 120 117 118 121 117 123
52
Table 8.23: Unchlorinated Lake Erie Mixtures – Cytotoxicity Values for Each Dilution Step REF 0% NC
(v/v % WW/SW) 5% NC
(v/v % WW/SW) 10% NC
(v/v % WW/SW) 25 % NC
(v/v % WW/SW) 100 % NC
(v/v % WW/SW) 317.46 97 90 87 87 89 87 90 88 82 77 158.73 93 88 86 86 84 86 89 87 86 88 79.37 94 88 85 86 83 87 91 87 85 94 39.68 94 85 85 86 87 90 88 88 92 91 19.84 91 86 89 89 87 89 87 90 90 91 9.92 90 86 88 86 86 93 90 90 89 87 4.96 96 91 92 89 88 93 91 91 90 91 2.48 84 87 98 89 97 90 99 99 97 93
Table 8.24: Chlorinated Lake Erie Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
100% C (v/v % WW/SW)
317.46 98 91 86 85 85 85 86 91 68 63
158.73 99 91 87 89 89 87 84 90 89 92
79.37 101 90 90 90 88 91 91 92 91 92
39.68 99 88 88 87 93 89 90 89 90 92
19.84 99 89 92 88 89 90 91 89 88 92
9.92 97 88 88 90 90 92 87 84 90 94
4.96 99 92 96 93 97 97 97 90 92 96
2.48 105 105 108 102 106 104 107 103 106 105
Table 8.25: Unchlorinated Lake Ontario Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 54 64 66 60 65 65 72 75 62 57
158.73 67 80 77 73 79 77 71 75 82 77
79.37 77 86 82 81 81 85 78 76 91 82
39.68 86 90 89 89 89 94 84 79 93 81
19.84 93 93 92 86 86 93 84 90 90 82
9.92 97 96 93 88 92 94 87 88 99 87
4.96 92 96 91 91 90 94 89 99 92 88
2.48 100 98 95 92 90 92 93 93 94 91
53
Table 8.26: Chlorinated Lake Ontario Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 63 69 64 68 71 67 70 65
158.73 85 86 75 79 83 75 74 76
79.37 91 79 86 87 85 92 82 79
39.68 103 89 93 102 99 96 89 83
19.84 102 90 92 95 103 85 90 90
9.92 104 101 99 98 108 99 99 104
4.96 102 90 100 98 100 97 105 99
2.48 98 98 99 99 97 102 99 103
Table 8.27: Unchlorinated Otonabee River Mixtures - Cytotoxicity Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 69 80 73 70 77 73 81 74 74 68
158.73 77 81 76 82 82 78 84 82 78 78
79.37 83 82 81 80 79 85 89 83 83 85
39.68 88 85 88 87 86 85 93 81 88 84
19.84 94 90 91 90 92 86 88 83 80 90
9.92 96 90 92 95 90 91 87 90 80 91
4.96 99 95 98 98 97 99 100 93 82 94
2.48 98 102 106 104 102 106 104 103 103 98
Table 8.28: Chlorinated Otonabee River Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 68 82 77 74 74 75 90 76 158.73 103 102 98 95 92 95 95 93 79.37 99 99 99 95 100 99 102 97 39.68 102 99 100 100 101 96 96 100 19.84 100 101 100 99 102 101 93 100 9.92 103 95 104 104 132 99 99 102 4.96 110 106 108 111 139 109 101 107 2.48 116 117 117 119 117 115 116 113
54
Table 8.29: Unchlorinated Lake Simcoe Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% NC (v/v % WW/SW)
5% NC (v/v % WW/SW)
10% NC (v/v % WW/SW)
25 % NC (v/v % WW/SW)
100 % NC (v/v % WW/SW)
317.46 76 72 68 67 66 66 72 80 58 64
158.73 90 83 84 85 84 83 88 89 83 91
79.37 97 88 87 101 90 87 89 90 91 101
39.68 98 92 93 90 95 92 95 91 96 102
19.84 101 94 94 95 99 95 98 99 101 102
9.92 102 97 101 99 98 94 97 93 92 94
4.96 104 102 101 99 100 98 96 97 97 97
2.48 107 98 97 99 102 98 100 103 100 101
Table 8.30: Chlorinated Lake Simcoe Mixtures – Cytotoxicity Values for Each Dilution Step
REF 0% C (v/v % WW/SW)
5% C (v/v % WW/SW)
10% C (v/v % WW/SW)
25 % C (v/v % WW/SW)
317.46 72 70 70 69 76 74 66 71
158.73 93 93 95 89 97 92 88 96
79.37 101 92 94 96 93 97 96 98
39.68 100 103 100 105 96 100 95 100
19.84 103 99 103 104 102 105 104 100
9.92 102 101 105 108 104 106 104 102
4.96 108 107 111 113 108 109 111 107
2.48 116 107 117 115 115 115 118 110
55
8.4 Effect Concentration Calculations
The effect concentration is defined as the relative enrichment factor (REF) required to elicit a
genotoxic response, IF = 2.0. This value is extrapolated from the linear concertation-response
curves for cells with 70% or greater viability and is expressed as REFIF2.0 or SOS genotoxicity,
1/REFIF2.0 (slope).
REFIF2.0 = 1𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
,
where slope is equal to the ΔIF/ΔREF. A sample calculation is shown for the first duplicate of
the unchlorinated SW-O sample plotted in Appendix Figure 8.2:
𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = (1.43−1.22)(158.73−79.37)
= 0.0023
REFIF2.0 = 1𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠0% 𝑛𝑛𝐶𝐶𝑠𝑠2
= 10.0023
= 440
The SOS genotoxicity for all unchlorinated and chlorinated mixtures is reported in Appendix
Tables 8.31-8.32.
Table 8.31: SOS Genotoxicity (slope) Values – Unchlorinated Mixtures
Sample Type (v/v % ww/ss) Duffins Erie Ontario Otonabee Simcoe
0% 0.0060 0.0008 0.0023 0.0019 0.0008 0.0016 0.0005 0.0030 0.0027 0.0008
5% 0.0077 0.0012 0.0032 0.0031 0.0008 0.0070 0.0011 0.0033 0.0015 0.0010
10% 0.0043 0.0016 0.0050 0.0015 0.0012 0.0031 0.0014 0.0036 0.0015 0.0012
25% 0.0285 0.0028 0.0067 0.0034 0.0019 0.0047 0.0032 0.0086 0.0047 0.0019
100% 0.0258 0.0270 0.0150 0.0019 0.0053 0.0222 0.0288 0.0180 0.0027 0.0058
56
Table 8.32: SOS Genotoxicity (slope) Values – Chlorinated Mixtures
Sample Type (v/v % ww/ss) Duffins Erie Ontario Otonabee Simcoe
0% 0.0026 0.0015 0.0060 0.0153 0.0030 0.0031 0.0015 0.0041 0.0163 0.0034
5% 0.0067 0.0027 0.0036 0.0170 0.0030 0.0088 0.0023 0.0036 0.0185 0.0032
10% 0.0066 0.0032 0.0047 0.0171 0.0027 0.0079 0.0030 0.0048 0.0158 0.0030
25% 0.0083 0.0050 0.0098 0.0107 0.0041 0.0102 0.0046 0.0062 0.0156 0.0036
100%
0.0128 0.0120
8.5 Predicted Genotoxicity Calculations
The predicted genotoxicity of each class of DBPs was calculated by dividing the measured
concentration of each species by its published genotoxicity potency data based on the CHO comet
assay, yielding a unitless value. A sample calculation is shown for the predicted genotoxicity of
HANs in the chlorinated 0% Ontario sample:
Predicted HANs genotoxicity = BCAN (M)BCAN Potency (M)
+ TCAN (M)TCAN Potency (M)
+ DCAN (M)DCAN Potency (M)
Predicted HANs genotoxicity = 9.52∗10−09 𝑀𝑀3.24∗10−04 𝑀𝑀
+ 0 𝑀𝑀1.01∗10−03𝑀𝑀
+ 6.18∗10−5𝑀𝑀2.75∗10−03𝑀𝑀
Predicted HANs genotoxicity =2.99 ∗ 10−5
The molecular weight and genotoxic potency based on the CHO assay for each measured DBP are
listed in Appendix Table 8.33. The DBP concentrations and predicted genotoxicity for all
chlorinated mixtures are reported in Appendix Tables 8.34-8.53 and Tables 8.54-8.58,
respectively. A summary of measured DBP concentrations for each set of mixtures is given in
Appendix Figure 8.3.
57
Table 8.33: Molecular weight and genotoxicity potency for the measured disinfection by-products
Compound Molecular Weight (g/mol) Genotoxicity Potency (M) THMS TCM 119.4 NP
BDCM 163.8 NP CDBM 208.3 NP TBM 252.7 NP HANs BCAN 154.4 3.24 x 10 -4 TCAN 144.4 1.01 x 10-3 DCAN 109.9 2.75 x 10-3 HNMs
BDCNM 208.8 6.32 x 10-5 DBCNM 253.3 1.43 x 10-4 TCNM 164.4 9.34 x 10-5
*NP = no potency (Wagner and Plewa, 2017)
Figure 8.3: Concentrations of measured disinfection by-products (DBPs) for all ratios of WW to SW. The 100% wastewater (NC) has been included for comparison.
0102030405060708090
100
0 5 10 25 100NC
0 5 10 25 100NC
0 5 10 25 100NC
WW-O WW-S WW-E
Con
cent
ratio
n (μ
g/L)
Ratio of Wastewater to Surface Water (%)THMs HANs HNMs
58
Table 8.34: Duffins Creek Mixtures – HAAs by Specific Compounds
Treatment
Sample Type
(v/v % WW/SW)
MCAA (μg/L)
MBAA (μg/L)
DCAA (μg/L)
TCAA (μg/L)
BCAA (μg/L)
DBAA (μg/L)
BDCAA (μg/L)
CDBAA (μg/L)
TBAA (μg/L)
Average (μg/L)
NC 0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL 0.00 <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% 5.19 <MDL 2.89 6.62 <MDL <MDL <MDL <MDL <MDL 14.72 5.78 <MDL 3.26 5.69 <MDL <MDL <MDL <MDL <MDL
C
0% 2.87 6.10 15.45 4.48 1.95 4.50 2.85 <MDL <MDL 38.84 3.24 6.27 15.52 4.74 2.26 4.66 2.80 <MDL <MDL
5% 5.30 7.50 17.16 6.23 2.28 5.01 3.49 <MDL <MDL 46.64 5.06 7.35 17.32 6.37 2.33 4.33 3.54 <MDL <MDL
10% 7.97 8.42 19.35 7.74 2.90 5.15 3.95 <MDL <MDL 53.21 7.36 7.92 17.79 6.74 2.21 5.12 3.81 <MDL <MDL
25% 17.33 11.00 24.86 11.85 3.16 7.16 5.86 <MDL <MDL 83.64 16.97 12.37 27.23 13.24 3.79 6.51 5.95 <MDL <MDL
Table 8.35: Lake Erie Mixtures - HAAs by Specific Compounds
Treatment
Sample Type
(v/v % WW/SW)
MCAA (μg/L)
MBAA (μg/L)
DCAA (μg/L)
TCAA (μg/L)
BCAA (μg/L)
DBAA (μg/L)
BDCAA (μg/L)
CDBAA (μg/L)
TBAA (μg/L)
Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL 0.00 <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL 3.58 <MDL <MDL <MDL <MDL <MDL 3.57 <MDL <MDL <MDL 3.57 <MDL <MDL <MDL <MDL <MDL
C
0% 0.86 1.42 7.75 1.20 1.17 <MDL <MDL <MDL <MDL 14.16 0.94 1.91 9.88 1.67 1.52 <MDL <MDL <MDL <MDL
5% 2.26 2.80 12.44 3.30 2.10 1.92 <MDL <MDL <MDL 26.66 2.75 3.07 14.76 3.71 2.35 1.87 <MDL <MDL <MDL
10% 3.88 3.26 15.47 5.21 2.72 1.69 0.73 <MDL <MDL 31.15 3.13 3.02 14.24 4.55 2.34 1.44 0.61 <MDL <MDL
25% 5.13 4.46 19.96 8.38 3.08 2.33 1.32 <MDL <MDL 43.75 4.91 4.19 19.53 7.87 2.85 2.15 1.33 <MDL <MDL
59
Table 8.36: Lake Ontario Mixtures – HAAs by Specific Compounds
Treatment
Sample Type
(v/v % WW/SW)
MCAA (μg/L)
MBAA (μg/L)
DCAA (μg/L)
TCAA (μg/L)
BCAA (μg/L)
DBAA (μg/L)
BDCAA (μg/L)
CDBAA (μg/L)
TBAA (μg/L)
Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL 0.00 <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% 0.80 <MDL <MDL 3.32 <MDL <MDL <MDL <MDL <MDL 4.37 1.22 <MDL <MDL 3.40 <MDL <MDL <MDL <MDL <MDL
C
0% 2.46 6.01 9.86 2.42 1.42 3.84 2.70 <MDL <MDL 28.05 2.50 5.50 9.47 2.31 1.35 3.62 2.62 <MDL <MDL
5% 6.18 8.94 17.17 6.42 2.15 5.11 3.68 <MDL <MDL 49.55 6.16 9.00 17.40 6.68 2.21 4.37 3.63 <MDL <MDL
10% 11.00 11.02 23.39 10.02 2.88 5.58 4.58 <MDL <MDL 68.32 10.06 11.17 23.33 10.00 2.97 5.76 4.88 <MDL <MDL
25% 23.93 16.74 37.90 19.44 4.44 6.49 7.74 <MDL <MDL 119.54 27.72 16.77 38.86 19.40 4.27 7.75 7.61 <MDL <MDL
Table 8.37: Otonabee River Mixtures – HAAs by Specific Compounds
Treatment
Sample Type
(v/v % WW/SW)
MCAA (μg/L)
MBAA (μg/L)
DCAA (μg/L)
TCAA (μg/L)
BCAA (μg/L)
DBAA (μg/L)
BDCAA (μg/L)
CDBAA (μg/L)
TBAA (μg/L)
Average (μg/L)
NC
0% <MDL <MDL 6.83 <MDL <MDL <MDL <MDL <MDL <MDL 6.24 <MDL <MDL 5.65 <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL 3.25 <MDL <MDL <MDL <MDL <MDL 3.46 <MDL <MDL <MDL 3.66 <MDL <MDL <MDL <MDL <MDL
C
0% 9.98 1.10 26.47 21.64 2.85 5.55 3.55 <MDL <MDL 69.97 10.39 1.00 24.79 21.65 2.84 5.05 3.08 <MDL <MDL
5% 12.92 0.90 28.65 21.69 2.95 4.95 4.95 <MDL <MDL 76.17 11.52 1.05 26.87 22.65 2.76 5.30 5.17 <MDL <MDL
10% 13.45 0.98 30.55 25.65 3.29 5.86 5.49 <MDL <MDL 82.86 13.35 1.00 28.65 23.65 3.05 5.65 5.08 <MDL <MDL
25% 13.53 1.00 34.64 28.35 3.65 7.55 6.85 <MDL <MDL 95.57 14.80 0.50 35.64 27.68 3.55 6.94 6.46 <MDL <MDL
60
Table 8.38: Lake Simcoe Mixtures – HAAs by Specific Compounds
Treatment
Sample Type
(v/v % WW/SW)
MCAA (μg/L)
MBAA (μg/L)
DCAA (μg/L)
TCAA (μg/L)
BCAA (μg/L)
DBAA (μg/L)
BDCAA (μg/L)
CDBAA (μg/L)
TBAA (μg/L)
Average (μg/L)
NC
0% <MDL <MDL 3.90 <MDL <MDL <MDL <MDL <MDL <MDL 4.06 <MDL <MDL 4.23 <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL 0.00 <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% 9.20 9.42 34.99 27.37 2.94 4.74 9.17 <MDL <MDL 97.04 9.96 9.47 34.01 26.27 2.84 4.86 8.84 <MDL <MDL
5% 10.87 10.60 34.81 26.99 3.03 5.05 9.42 <MDL <MDL 99.44 10.10 10.74 33.90 26.45 3.00 4.76 9.15 <MDL <MDL
10% 10.63 11.32 34.46 27.25 3.13 5.22 9.48 <MDL <MDL 102.26 9.39 11.66 35.48 28.44 3.22 4.81 10.02 <MDL <MDL
25% 14.42 15.61 39.75 32.92 3.74 6.78 13.18 <MDL <MDL 123.39 13.44 15.04 38.68 31.47 3.38 6.38 11.98 <MDL <MDL
Table 8.39: Duffins Creek Mixtures – THMs by Specific Compounds
Treatment Sample Type (v/v % WW/SW) TCM (μg/L) BDCM (μg/L) CDBM (μg/L) TBM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% 1.55 0.59 0.28 <MDL 2.19 1.22 0.51 0.23 <MDL
C
0% 10.04 11.89 5.14 0.53 28.41 10.71 12.60 5.42 0.49
5% 13.80 15.74 5.97 0.50 33.30 11.66 13.41 5.09 0.43
10% 12.51 13.54 5.14 0.43 32.11 13.01 13.88 5.27 0.44
25% 17.24 16.35 5.55 0.43 41.74 19.07 18.26 6.12 0.46
61
Table 8.40: Lake Erie Mixtures – THMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) TCM (μg/L) BDCM (μg/L) CDBM (μg/L) TBM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL 0.18 0.31 0.04 <MDL <MDL
C
0% 14.79 14.27 4.96 <MDL 34.33 15.50 14.12 5.02 <MDL
5% 18.53 15.23 4.76 <MDL 40.00 20.22 16.25 5.03 <MDL
10% 23.29 18.20 4.94 <MDL 46.74 23.57 18.44 5.04 <MDL
25% 38.42 26.12 5.86 <MDL 65.92 34.24 22.01 5.18 <MDL
100% 89.83 48.64 8.86 <MDL 106.87 9.31 48.32 8.78 <MDL
Table 8.41: Lake Ontario Mixtures – THMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) TCM (μg/L) BDCM (μg/L) CDBM (μg/L) TBM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% 0.47 <MDL <MDL <MDL 0.48 0.49 <MDL <MDL <MDL
C
0% 10.03 13.32 7.18 0.88 25.06 6.00 7.87 4.28 0.55
5% 12.73 14.38 6.01 0.49 30.62 11.40 10.50 5.29 0.44
10% 13.78 12.59 5.26 0.38 35.32 15.33 16.34 6.47 0.48
25% 24.91 21.38 6.88 0.44 50.93 22.22 19.45 6.19 0.39
62
Table 8.42: Otonabee River Mixtures – THMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) TCM (μg/L) BDCM (μg/L) CDBM (μg/L) TBM (μg/L) Average (μg/L)
NC
0% 40.42 <MDL <MDL <MDL 40.06 39.70 <MDL <MDL <MDL
100% 41.51 <MDL <MDL <MDL 39.11 36.70 <MDL <MDL <MDL
C
0% 146.15 8.88 <MDL <MDL 155.40 146.53 9.23 <MDL <MDL
5% 140.37 10.11 <MDL <MDL 152.98 145.38 10.11 <MDL <MDL
10% 130.05 9.99 <MDL <MDL 136.24 122.46 9.97 <MDL <MDL
25% 121.68 11.85 <MDL <MDL 134.45 123.93 11.44 <MDL <MDL
Table 8.43: Lake Simcoe Mixtures – THMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) TCM (μg/L) BDCM (μg/L) CDBM (μg/L) TBM (μg/L) Average (μg/L)
NC
0% 2.30 1.77 0.29 <MDL 3.95 1.94 1.43 0.17 <MDL
100% 0.71 0.19 <MDL <MDL 0.80 0.57 0.13 <MDL <MDL
C
0% 29.83 15.25 2.38 <MDL 50.30 32.85 17.38 2.90 <MDL
5% 30.31 17.10 2.89 <MDL 54.90 36.26 20.01 3.22 <MDL
10% 32.23 18.22 2.96 <MDL 55.52 34.42 19.75 3.46 <MDL
25% 34.99 22.00 4.53 <MDL 62.65 36.37 22.98 4.43 <MDL
63
Table 8.44: Duffins Creek Mixtures – HANs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% 0.67 <MDL 1.47 1.84 0.61 <MDL 0.93
C
0% 2.57 <MDL 0.41 3.09 2.78 <MDL 0.41
5% 3.63 <MDL 0.56 3.88 3.18 <MDL 0.38
10% 3.47 <MDL 0.66 4.17 3.69 <MDL 0.52
25% 5.99 <MDL <MDL 6.06 6.13 <MDL <MDL
Table 8.45: Lake Erie Mixtures – HANs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% 2.18 <MDL 5.34 7.71 2.30 <MDL 5.59
5% 2.88 <MDL 9.00 11.12 3.09 <MDL 7.28
10% 3.48 <MDL 8.26 12.21 3.80 <MDL 8.87
25% 6.48 <MDL 15.64 20.66 5.31 <MDL 13.89
100% 26.05 <MDL 74.83 103.65 30.16 <MDL 76.27
64
Table 8.46: Lake Ontario Mixtures – HANs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL 0.46 0.48 <MDL <MDL 0.50
C
0% 1.70 <MDL 0.34 1.64 1.24 <MDL <MDL
5% 3.35 <MDL 0.63 3.82 3.06 <MDL 0.59
10% 4.52 <MDL 0.25 5.53 5.94 <MDL 0.34
25% 13.50 <MDL 0.57 13.06 11.54 <MDL 0.50
Table 8.47: Otonabee River Mixtures – HANs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% 7.59 <MDL 28.87 39.50 8.32 <MDL 34.21
5% 9.49 <MDL 36.86 47.39 10.22 <MDL 38.21
10% 10.64 <MDL 37.00 48.54 11.17 <MDL 38.28
25% 16.76 <MDL 49.51 64.71 16.73 <MDL 46.42
65
Table 8.48: Lake Simcoe Mixtures – HANs by Specific Compounds
Treatment Sample Type
(v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% 0.08 <MDL 0.75 0.70 0.05 <MDL 0.52
100% 0.09 <MDL <MDL 0.07 0.05 <MDL <MDL
C
0% 2.75 <MDL 13.02 16.45 3.00 <MDL 14.13
5% 3.51 <MDL 13.18 17.77 3.89 <MDL 14.97
10% 3.76 <MDL 13.52 18.25 3.80 <MDL 15.42
25% 4.99 <MDL 17.76 22.55 5.87 <MDL 16.49
Table 8.49: Duffins Creek Mixtures – HNMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BDCNM (μg/L) DBCNM (μg/L) TCNM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
5% <MDL <MDL 0.15 0.14 <MDL <MDL 0.13
10% <MDL <MDL 0.18 0.19 <MDL <MDL 0.19
25% <MDL <MDL 0.38 0.41 <MDL <MDL 0.43
66
Table 8.50: Lake Erie Mixtures – HNMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BDCNM (μg/L) DBCNM (μg/L) TCNM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% 0.42 <MDL <MDL 0.43 0.44 <MDL <MDL
5% 0.63 <MDL <MDL 0.87 1.11 <MDL <MDL
10% 0.99 <MDL 0.27 1.12 0.73 <MDL 0.24
25% 1.56 <MDL 0.53 2.03 1.44 <MDL 0.52
100% 19.14 <MDL <MDL 17.32 15.51 <MDL <MDL
Table 8.51: Lake Ontario Mixtures – HNMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BDCNM (μg/L) DBCNM (μg/L) TCNM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
5% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
10% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
25% <MDL <MDL 0.21 0.21 <MDL <MDL 0.20
67
Table 8.52: Otonabee River Mixtures – HNMs by Specific Compounds
Treatment Sample Type
(v/v % WW/SS) BCAN (μg/L) TCAN (μg/L) DCAN (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL 0.00 <MDL <MDL <MDL
100% <MDL <MDL <MDL 0.00 <MDL <MDL <MDL
C
0% 0.40 <MDL 0.24 0.63 0.60 <MDL 0.02
5% 1.45 <MDL <MDL 1.01 0.57 <MDL <MDL
10% 0.68 <MDL <MDL 0.64 0.59 <MDL <MDL
25% 1.49 <MDL <MDL 1.49 1.49 <MDL <MDL
Table 8.53: Lake Simcoe Mixtures – HNMs by Specific Compounds
Treatment Sample Type (v/v % WW/SS) BDCNM (μg/L) DBCNM (μg/L) TCNM (μg/L) Average (μg/L)
NC
0% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
100% <MDL <MDL <MDL <MDL <MDL <MDL <MDL
C
0% 0.45 <MDL <MDL 0.55 0.65 <MDL <MDL
5% 0.92 <MDL <MDL 0.90 0.88 <MDL <MDL
10% 1.22 <MDL <MDL 1.15 1.09 <MDL <MDL
25% 1.34 <MDL <MDL 1.71 2.08 <MDL <MDL
68
Table 8.54: Duffins Creek Mixtures – Predicted Genotoxicity Values
Treatment Sample Type (v/v % WW/SS) THM HANs HNMs BCAN Total
NC 0% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 100% 0.00E+00 1.68E-05 0.00E+00 1.28E-05 1.68E-05
C
0% 0.00E+00 5.48E-05 0.00E+00 5.35E-05 5.48E-05 5% 0.00E+00 6.96E-05 9.12E-06 6.81E-05 7.87E-05
10% 0.00E+00 7.35E-05 1.20E-05 7.16E-05 8.56E-05 25% 0.00E+00 1.21E-04 2.64E-05 1.21E-04 1.48E-04
Table 8.55: Lake Erie Mixtures – Predicted Genotoxicity Values
Treatment Sample Type (v/v % WW/SS) THM HANs HNMs BCAN Total
NC 0% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 100% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
C
0% 0.00E+00 6.29E-05 3.27E-05 4.48E-05 9.56E-05 5% 0.00E+00 8.66E-05 6.59E-05 5.97E-05 1.53E-04
10% 0.00E+00 1.01E-04 8.19E-05 7.28E-05 1.83E-04 25% 0.00E+00 1.67E-04 1.48E-04 1.18E-04 3.15E-04
Table 8.56: Lake Ontario Mixtures – Predicted Genotoxicity Values
Treatment Sample Type (v/v % WW/SS) THM HANs HNMs BCAN Total
NC 0% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 100% 0.00E+00 1.59E-06 0.00E+00 0.00E+00 1.59E-06
C
0% 0.00E+00 2.99E-05 0.00E+00 2.94E-05 2.99E-05 5% 0.00E+00 6.61E-05 0.00E+00 6.41E-05 6.61E-05
10% 0.00E+00 1.06E-04 0.00E+00 1.05E-04 1.06E-04 25% 0.00E+00 2.52E-04 1.34E-05 2.50E-04 2.65E-04
69
Table 8.57: Otonabee River Mixtures – Predicted Genotoxicity Values
Treatment Sample Type (v/v % WW/SS) THM HANs HNMs BCAN Total
NC 0% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 100% 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
C
0% 0.00E+00 2.63E-04 4.61E-05 1.59E-04 3.10E-04 5% 0.00E+00 3.21E-04 7.65E-05 1.97E-04 3.98E-04
10% 0.00E+00 3.42E-04 4.82E-05 2.18E-04 3.91E-04 25% 0.00E+00 4.93E-04 1.13E-04 3.35E-04 6.06E-04
Table 8.58: Lake Simcoe Mixtures – Predicted Genotoxicity Values
Treatment Sample Type (v/v % WW/SS) THM HANs HNMs BCAN Total
NC 0% 0.00E+00 3.36E-06 0.00E+00 1.26E-06 3.36E-06 100% 0.00E+00 1.42E-06 0.00E+00 1.42E-06 1.42E-06
C
0% 0.00E+00 1.02E-04 4.17E-05 5.75E-05 1.44E-04 5% 0.00E+00 1.21E-04 6.81E-05 7.40E-05 1.89E-04
10% 0.00E+00 1.23E-04 8.74E-05 7.56E-05 2.11E-04 25% 0.00E+00 1.65E-04 1.29E-04 1.08E-04 2.94E-04
70
8.6 Sample Quality Assurance/Quality Control Chart
As seen in Appendix Figure 8.4, a QA/QC chart for DOC was generated to measure and
assure the quality of the readings from the DOC instrument. This was done by analyzing 8 samples
prepared with KHP solution at the expected DOC concentration (2.5 mg/L) of the water samples
for the mean and standard deviation. After every 10 real samples, check standards, also prepared
at the expected sample concentration, were assessed against the criteria outlined in Standard
Method 1020 (APHA, 2012). The QA/QC results are acceptable as none of the following criteria
were met:
1. 2 consecutive measurements fall outside of the control limit (CL) of the mean +/- 3 times
the standard deviation,
2. 3 out of 4 consecutive measurements were outside the warning limits (WL) of the mean
+/- 2 times the standard deviation,
3. 5 out of 6 consecutive measurements were outside of the mean +/- standard deviation,
4. 5 out of 6 consecutive measurements exhibited an increasing or decreasing trend or,
5. 7 consecutive samples were greater, or less, than the mean
Figure 8.4: Quality control chart for DOC analysis.
2.10
2.20
2.30
2.40
2.50
2.60
2.70
2.80
2.90
3.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
DO
C C
once
ntra
tion
(mg/
L)
Series1 Series11
4/01 4/09 4/25 5/10 6/17 7/2
Check Stds. QA/QC
Upper CL Upper WL
Lower WL Lower CL