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ESTIMATING DEGRADATION RATES FOR CONTAMINANTS OF EMERGING
CONCERN IN ACTIVATED SLUDGE WITH LOW AND HIGH SOLIDS
RETENTION TIMES
By
Vadym Ianaiev
A Thesis
Submitted in partial fulfillment of the requirements of the degree
MASTER OF SCIENCE
IN
NATURAL RESOURCES
(WATER CHEMISTRY)
College of Natural Resources
UNIVERSITY OF WISCONSIN
Stevens Point, WI
June 2017
ii
APPROVED BY THE GRADUATE COMMITTEE OF:
_______________________________
Dr. Paul McGinley, Committee Chairman
Professor of Water Resources
_______________________________
Dr. Ronald Crunkilton
Professor of Water Resources
_______________________________
Dr. Daniel Keymer
Assistant Professor of Soil and Waste Resources
iii
ACKNOWLEDGEMENTS
I would like to thank, my advisor, Dr. Paul McGinley for his part in the
conceptual development of this study, for his critique, for his expert counsel, and his
support and encouragement throughout this project. His revisions of the thesis report
made this study to reach its full potential. I could not carry out this study and acquire my
Master’s degree without him taking a chance on me and acquiring financial support for
my studies. I would also like to thank the members of my graduate committee Dr. Daniel
Keymer and Dr. Ronald Crunkilton for the critique of the study’s scope, structure, and
reporting. I appreciate the time they committed to support this project. I would like to
acknowledge Chris Lefebvre and Adam Clark of the Stevens Point wastewater treatment
plant (WWTP) and Sam Warp, Joel Goham, Andrew Ott, and Jake Charron of the
Marshfield WWTP for providing wastewater samples and necessary information about
their respective WWTPs. I would like to acknowledge Dr. Hurlee Gonchigdanzan for his
guidance with a choice of statistical tests. I would like to thank Bill DeVita of University
of Wisconsin-Stevens Point’s Center for Watershed Science and Education for his
guidance with sample preparation and analysis as well as for his critique of this project. I
would like to thank Amy Nitka of University of Wisconsin-Stevens Point’s Center for
Watershed Science and Education for developing the analytical method for the analysis
of the contaminants of emerging concern and giving her guidance with the sample
preparation and analysis. I would like to thank Bill Cunningham of Siemens Water
Solutions and the Wisconsin Institute for Sustainable Technology for providing me with
the career-building employment and financial support for my graduate education. Lastly,
I would like to acknowledge the University of Wisconsin-Stevens Point for accepting me
iv
into the graduate program of the College of Natural Resources and providing the funds
for this study.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................................ iii
TABLE OF CONTENTS ................................................................................................................. v
LIST OF TABLES ........................................................................................................................ viii
LIST OF FIGURES ........................................................................................................................ ix
ABSTRACT .................................................................................................................................... xi
NOMENCLATURE ......................................................................................................................xiii
1. INTRODUCTION ....................................................................................................................... 1
2. LITERATURE REVIEW ............................................................................................................ 3
Importance of CECs ..................................................................................................................... 3
Generation of CECs ..................................................................................................................... 5
Treatment of CECs ....................................................................................................................... 6
Physical and Chemical Processes............................................................................................ 6
Biological Process ................................................................................................................... 8
3. OBJECTIVES ............................................................................................................................ 14
4. METHODS ................................................................................................................................ 15
Selection of CECs ...................................................................................................................... 16
Artificial Sweeteners .............................................................................................................. 17
Pharmaceuticals .................................................................................................................... 18
Psychoactive Drugs ............................................................................................................... 20
Site Description .......................................................................................................................... 24
Stevens Point WWTP .............................................................................................................. 24
Marshfield WWTP .................................................................................................................. 25
Stevens Point WWTP vs. Marshfield WWTP ......................................................................... 27
Analytical Methods .................................................................................................................... 29
Sample Collection .................................................................................................................. 29
Sample Preparation ............................................................................................................... 30
Sample Analysis ..................................................................................................................... 32
Analytical Results................................................................................................................... 35
Loading and Consumption ......................................................................................................... 38
Calculations ........................................................................................................................... 38
Statistics ................................................................................................................................. 39
vi
Attenuation Efficiency ................................................................................................................ 42
Calculation ............................................................................................................................. 42
Statistics ................................................................................................................................. 42
Kinetics ...................................................................................................................................... 44
Process Equation ................................................................................................................... 44
Active Biomass ....................................................................................................................... 47
Model 1: Steady State ................................................................................................................ 49
Model Description ................................................................................................................. 49
Parameter Estimation ............................................................................................................ 50
Model 2: Non-Steady State ........................................................................................................ 51
Model Description ................................................................................................................. 51
Parameter Estimation ............................................................................................................ 53
Sensitivity and Uncertainty .................................................................................................... 55
Statistics ................................................................................................................................. 57
Model 1 vs. Model 2 ................................................................................................................... 60
5. RESULTS AND DISCUSSION ................................................................................................ 61
Loading and Attenuation............................................................................................................ 61
Artificial Sweeteners .............................................................................................................. 63
Pharmaceuticals .................................................................................................................... 64
Psychoactive Drugs ............................................................................................................... 66
Drug Consumption ..................................................................................................................... 68
Biodegradation .......................................................................................................................... 71
Results of Model 1 .................................................................................................................. 71
Results of Model 2 .................................................................................................................. 72
Model 1 vs. Model 2 ............................................................................................................... 79
Comparison of WWTPs .......................................................................................................... 81
Sources of Error ......................................................................................................................... 86
Environmental Conditions ..................................................................................................... 86
Metabolites ............................................................................................................................. 87
Degradation in Sewer ............................................................................................................ 88
Sample Collection .................................................................................................................. 89
Sample Size ............................................................................................................................ 89
Sample Storage ...................................................................................................................... 90
vii
Processes ............................................................................................................................... 91
7. CONCLUSIONS........................................................................................................................ 92
Summary .................................................................................................................................... 92
Future Work ............................................................................................................................... 94
Implications ............................................................................................................................... 96
8. LITERATURE CITED .............................................................................................................. 99
A. APPENDIX A – Tables .......................................................................................................... 118
Analytical Results .................................................................................................................... 118
Initial Concentrations .............................................................................................................. 122
Skewness and Kurtosis ............................................................................................................. 124
Data Normality ........................................................................................................................ 125
B. APPENDIX B – Graphs .......................................................................................................... 126
Wastewater Flows .................................................................................................................... 126
Model 2 Results ........................................................................................................................ 127
Sensitivity Analysis ............................................................................................................... 127
Uncertainty Analysis ............................................................................................................ 131
Model Fit .............................................................................................................................. 135
Data Normality ........................................................................................................................ 139
Comparing Rate Constants ...................................................................................................... 143
viii
LIST OF TABLES
Table 4.1. Risk classes according to Commission of the European Communities
(1996)…………………………………………………………………………………….16
Table 4.2. Molecular structure, molecular weight, Henry’s law coefficients at 25°C, and
𝐾𝑑 of the 13 CECs……………………………………………….………………….…...22
Table 4.3. Concentrations and sources of standards for the spike mix……………..……30
Table 4.4. Concentrations and sources of the internal standards and the surrogate
standard, benzoylecgonine-D3…………………………….…………………………..…31
Table 4.5. Limit of detection, and the highest and lowest calibration standards for the 13
CECs in the analytical runs of 2015 and 2016……………………………………….…..33
Table 4.6. Percent differences for duplicate samples for analytical runs 2015 and
2016……………………………………………………………………………………....35
Table 4.7. Spike recoveries for the spike mix (not corrected for surrogate standard
recovery) and the surrogate standard (benzoylecgonine-D3) for analytical runs 2015 and
2016………………………………………………………………………………………36
Table 4.8. The process matrix for Model 2………………………………………………51
Table 5.1. Means, medians, and ranges of loading rates (mg day-1 per 1000 people) and
attenuation efficiencies for the 13 CECs of interest in the Stevens Point WWTP and
Marshfield WWTP…………….…………………………………………………...…….63
Table 5.2. CEC biodegradation/biotransformation rate constants – 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙 –
generated via Model 1 for the Stevens Point and Marshfield WWTPs, and the percent of
biodegradation/biotransformation to total attenuation………...…………………………68
Table 5.3. CEC biodegradation/biotransformation rate constants – 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙 –
generated by Model 2 for the Stevens Point and Marshfield WWTPs, and reference 𝑘𝑏𝑖𝑜𝑙
found in peer-reviewed journals for the 13 CECs of interest…………………………....77
Table A.1. Influent and effluent CEC concentrations (ng L-1) from the Stevens Point
WWTP generated through the analytical runs in 2015 and 2016……………………....118
Table A.2. Influent and effluent CEC concentrations (ng L-1) from the Marshfield WWTP
generated through the analytical runs in 2016……………………………………….…120
Table A.3. Modeled initial CEC concentrations in the Stevens Point WWTP’s anaerobic
tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖1), aerobic tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖2), and final clarifier (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖3)………...............122
Table A.4. Modeled initial CEC concentrations in the Marshfield WWTP’s anoxic ditch
(𝐶𝐶𝐸𝐶,𝑖𝑛𝑖1), aerobic ditch (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖2), and final clarifier (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖3)…………………..…123
Table A.5. Evaluating distributions of datasets for attenuation efficiencies and drug
consumption rates using skewness and excess kurtosis………………………………...124
Table A.6. Anderson Darling normality test was run for Models 2 residuals for the
Stevens Point and Marshfield WWTPs……………………………………...……….....125
ix
LIST OF FIGURES
Figure 4.1. The aerial view of the Stevens Point WWTP………………………….…….25
Figure 4.2. The aerial view of the Marshfield WWTP…………………………………..26
Figure 4.3. The flow of mobile phases versus sample run time……………………...….32
Figure 4.4. Schematics of biological treatment within the Stevens Point WWTP and
Marshfield WWTP………………………………,,………………………………….…..52
Figure 5.1. Mean loading rates of the most abundant CECs in the study calculated for the
Stevens Point and Marshfield WWTPs……………………………………………….….61
Figure 5.2. Mean loading rates of the least abundant CECs in the study calculated for the
Stevens Point and Marshfield WWTPs…………………………….…………………….62
Figure 5.3. Difference in median drug consumption rates between weekdays and
weekends in Stevens Point and Marshfield, WI…………………………………...…….69
Figure 5.4. Sensitivity functions for acesulfame data in the Stevens Point and Marshfield
WWTPs’ modeled effluent………………………………………………………………72
Figure 5.5. Error contribution functions for acesulfame data in the Stevens Point and
Marshfield WWTPs’ modeled effluent………………………………………….……….74
Figure 5.6. Model fits for acesulfame and benzoylecgonine data in the Stevens Point and
Marshfield WWTPs’ modeled effluent……….………………………………………….75
Figure 5.7. Association between first order biodegradation/biotransformation rate
constants generated by Model 1 and Model 2 for the Stevens Point WWTP……………79
Figure 5.8. Association between first order biodegradation/biotransformation rate
constants generated by Model 1 and Model 2 for the Marshfield WWTP…………....…80
Figure 5.9. Values of 𝑘𝑏𝑖𝑜𝑙′ and half-lives for the rapidly biodegrading CECs in the
Stevens Point and Marshfield WWTPs…………………………………………………..81
Figure 5.9. Values of 𝑘𝑏𝑖𝑜𝑙′ and half-lives for the slowly biodegrading CECs in the
Stevens Point and Marshfield WWTPs…………………………………………………..82
Figure B.1. Incoming and recirculation wastewater flows in biological treatment within
the Stevens Point WWTP and Marshfield WWTP……………………………………..126
Figure B.2. Graphs of sensitivity analysis for modeled concentrations of the Stephen
Point WWTP’s 13 CECs in the final clarifier…………………………………………..127
Figure B.3. Graphs of sensitivity analysis for modeled concentrations of the Marshfield
WWTP’s 13 CECs in the final clarifier………………………….……………………..129
Figure B.4. Graphs of uncertainty analysis for modeled concentrations of the Stephen
Point WWTP’s 13 CECs in the final clarifier………………………………………….131
Figure B.5. Graphs of uncertainty analysis for modeled concentrations of the Marshfield
WWTP’s 13 CECs in the final clarifier………………………………..……………….133
x
Figure B.6. Graphs for the Stephen Point WWTP’s 13 CECs comparing modeled CEC
concentrations in effluent with measured daily volume-proportional averages of CEC
concentrations in influent and effluent…………………………………………………135
Figure B.7. Graphs for the Stephen Point WWTP’s 13 CECs comparing modeled CEC
concentrations in effluent with measured daily volume-proportional averages of CEC
concentrations in influent and effluent…………………………………………………137
Figure B.8. Normal probability plots for the Stevens Point WWTP’s 13 CECs comparing
model residuals to estimated cumulative probability……………………………….…..139
Figure B.9. Normal probability plots for the Marshfield WWTP’s 13 CECs comparing
model residuals to estimated cumulative probability…………………………………...141
Figure B.10. Bar charts comparing first order biodegradation/biotransformation rate
constants for the CECs of interest in the Stevens Point and Marshfield WWTPs……...143
xi
ABSTRACT
The occurrence and fate of contaminants of emerging concern (CECs) during
wastewater treatment is of growing interest to water quality professionals. In this study,
we analyzed 13 CECs: acesulfame, acetaminophen, benzoylecgonine, caffeine,
carbamazepine, cotinine, paraxanthine, saccharin, sucralose, sulfamethazine,
sulfamethoxazole, trimethoprim, and venlafaxine. These CECs are detected in wastewater
because they pass through consumers’ digestive systems or are discarded unused into
wastewater. Even though these CECs are unlikely to pose an immediate threat to human
health at levels detected in the environment, it is not clear how they affect humans and
aquatic organisms in the long run. Wastewater treatment plants (WWTPs) have a
potential to treat these CECs before their release into the environment. The purpose of
this study was to understand how solids retention time (SRT) affects treatment of the
CECs within WWTPs. Although it would be useful to evaluate the efficacy of CEC
treatment by quantifying CEC biodegradation rates, analytical challenges, variations in
wastewater flows, and sorption of CECs to sludge make it difficult to develop an accurate
mass-balance analysis. This study used a non-steady state simulation model (AQUASIM
2.1) to generate first order biodegradation/biotransformation rate constants (𝑘𝑏𝑖𝑜𝑙′ ) for 13
CECs in two WWTPs operating with SRTs of 3 and 27 days. We used volume-
proportional composite samples from influent and effluent of the WWTPs’ activated
sludge systems for seven days. We measured CECs with Triple Quadrupole HPLC/MS.
The results of this study showed that acetaminophen, cotinine, caffeine, paraxanthine,
and saccharin exhibited the highest 𝑘𝑏𝑖𝑜𝑙′ , while carbamazepine, sulfamethazine,
sucralose, and venlafaxine showed little change in concentration during the treatment.
xii
The 𝑘𝑏𝑖𝑜𝑙′ values for acesulfame, benzoylecgonine, cotinine, caffeine, paraxanthine, and
saccharin were considerably and statistically higher at the 27-day than 3-day SRT. This
study found the WWTP with the SRT of 27 days achieved greater treatment of some
CECs compared to the WWTP with the SRT of 3 days. Although we cannot identify an
explanation in this study, the difference in 𝑘𝑏𝑖𝑜𝑙′ could reflect a difference in
microbiology such as the increase in 𝑘𝑏𝑖𝑜𝑙′ is possibly due to the biodegrading activity of
slow-growing microorganisms present at the SRT of 27 days.
xiii
NOMENCLATURE
AOP advanced oxidation process
𝑏ℎ𝑒𝑡 heterotrophic steady-state theory endogenous decay (d-1)
BOD5 5-day biological oxygen demand
𝐶𝐶𝐸𝐶 concentration of contaminants of emerging concern (ng L-1)
𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 initial CEC concentration in a compartment before simulation (ng L-1)
CEC contaminants of emerging concern
휀𝑚𝑜𝑑 model residual in Model 2
EBPR enhanced biological phosphorus removal
EC50 median effect concentration (mg L-1)
ESI electrospray ionization source
𝑓𝑎𝑐𝑡 fraction of MLSS that is active heterotrophic biomass (gACTIVE MLSS-1 gMLSS
-1)
HLB hydrophobic-lipophilic-balanced
Ha alternative hypothesis
Ho null hypothesis
HRT or 𝜃ℎ hydraulic retention time (hr)
HPLC high performance liquid chromatograph
ID inner diameter
𝑘𝑏𝑖𝑜𝑙′ first order rate constant of biodegradation/biotransformation (day-1)
𝑘𝑏𝑖𝑜𝑙 pseudo-first order rate constant of biodegradation/biotransformation
(L gMLSS-1 day-1)
𝐾𝑑 solid-water partition coefficients (L gMLSS-1 or L kgMLSS
-1)
𝑘𝑎𝑡𝑡′ first order rate constant of attenuation (day-1)
xiv
𝐾𝑜𝑤 octanol-water partition coefficient
log 𝐾𝑜𝑤 logarithm base 10 of 𝐾𝑜𝑤
MLSS mixed liquor suspended solids (mg L-1)
MGD millions of gallons per day
MS mass spectrometer
𝑟𝑎𝑡𝑡 CEC attenuation rate (ng L-1 day-1)
𝑟𝑠𝑙𝑢𝑑 CEC removal rate due to sorption and sludge removal (ng L-1 day-1)
RO reverse osmosis
𝑟𝑠 Spearman's rank correlation coefficient
𝑆𝐷 sample standard deviation
𝑆𝐸 standard error
𝑆𝑆 sum of squares
SPE solid-phase extraction
SRT or 𝜃𝑥 solids retention time (days)
𝑡1/2 half-life (days)
TKN Total Kjeldahl Nitrogen (mg N L-1)
𝑋𝑀𝐿𝑆𝑆 concentration of microbial biomass as MLSS (gMLSS L-1)
𝑋𝑎𝑐𝑡 concentration of active heterotrophic biomass as MLSS (gMLSS L-1)
USGS United States Geological Survey
UV ultraviolet
UWSP University of Wisconsin-Stevens Point
WWTP wastewater treatment plant
1
1. INTRODUCTION
A compound of emerging concern (CEC) is defined as “any synthetic or naturally
occurring chemical or any microorganism that is not commonly monitored in the
environment but has the potential to enter the environment and cause known or suspected
adverse ecological or human health effects, or both” (USGS, 2016). CECs include
thousands of compounds: artificial sweeteners, personal care products, over-the-counter
pharmaceuticals, prescription drugs, psychoactive licit and illicit drugs, and their
metabolites. CECs may cause a bodily response even when diluted to parts per billion to
parts per trillion concentrations (Khan and Nicell, 2015). Yet, these compounds are
almost entirely unregulated in the United States because it is challenging to detect them
and difficult to assess health risks associated with them. For the protection of human
health and the environment, the need to study the occurrence and fate of these
compounds in various environments becomes increasingly urgent.
A major way to reduce release of CECs into the environment is through their
treatment in municipal wastewater treatment plants (WWTPs). Wastewater
microorganisms may play a central role in reducing concentrations of CECs through
biodegradation. Even though some studies quantified CEC reductions in WWTPs, we
cannot fully evaluate the importance of these reductions because limited amount of
research exists on biodegradation rates for CECs in activated sludge.
Activated sludge is a conventional wastewater treatment technology. It maintains
a community of microorganisms by growing them and periodically removing a fraction
of them. Solids retention time (SRT) is the theoretical length of time a microbial cell
stays in activated sludge. Wastewater treatment plants control size and composition of
2
microbial population by adjusting SRT. Some studies have suggested that higher SRT
may lead to higher biodegradation rates for CECs, but these studies are scarce,
contradictory, and often not statistically rigorous (Clara et al., 2005; Majewsky et al.,
2011; Maeng et al., 2013; Vasiliadou et al., 2014; Chen et al., 2015).
In this study, we attempted to resolve some of the gaps in our understanding of
CEC occurrence and fate in activated sludge by measuring and modeling CECs in two
WWTPs. We calculated first order removal rate constants and overall treatment
efficiencies for a group of CECs to compare biodegradation rates between the two
WWTPs. In addition, we characterized generation of CECs by two communities in
central Wisconsin.
3
2. LITERATURE REVIEW
Importance of CECs
There is a sense of urgency to study public and ecological health risks associated
with release of CECs into the environment (Williams, 2005). A limited amount of acute
toxicity data has been used to assess health risks to humans and aquatic organisms
(Guillén et al., 2012). While the overwhelming majority of CECs are not likely to
jeopardize human health in environmental concentrations on their own, they can
endanger health of sensitive aquatic species (Khan and Nicell, 2015). These sensitive
species include algae, aquatic invertebrates, and fish (Williams, 2005). To date, little is
known about chronic exposure risks associated with individual CECs as well as mixtures
of them (Khan and Nicell, 2015). When used in lieu of chronic toxicity studies, acute
studies may underestimate long-term adverse impacts of CECs on aquatic organisms by
orders of magnitude (Williams, 2005).
Unfortunately, only a few studies have explored toxicity interactions for mixtures
of CECs (Guillén et al., 2012). Some CECs that are related in their pharmacological
effects exhibited synergetic toxicity (Sung et al., 2014), while others exhibited simple
additivity of individual toxicities (Liguoro et al., 2009). Metabolites of CECs produced as
a result of wastewater treatment may be more toxic to aquatic species or humans than
parent compounds (Noguera-Oviedo and Aga, 2016). Treatment of some CECs may
generate free radicals (Ren et al., 2016). In surface waters, free radicals may damage cell
components of aquatic organisms leading to diseases such as cancer (Bhattacharyya and
Saha, 2015).
4
It is impractical to monitor and regulate every CEC released into the environment.
Hence, a number of risk assessment models have been developed to prioritize CECs
based on levels of occurrence and hazard (Guillén et al., 2012; Khan and Nicell, 2015).
For most CECs, analytical methods for their detection have not been developed limiting
risk assessment to a narrow group of contaminants (Noguera-Oviedo and Aga, 2016).
However, models have been used to alleviate the lack of knowledge about environmental
levels of CECs (Guillén et al., 2012). Understandably, models of CEC generation have
their own limitations and cannot be used reliably without being calibrated to direct
measurements of CECs.
5
Generation of CECs
Contaminants of emerging concern are generated through human consumption
and subsequent excretion via urine and feces, or through discarding of used and unused
products into septic or sewer systems (Williams, 2005). From septic systems, CECs may
percolate through soils into groundwater. From sewers, CECs can either enter
groundwater through a leaky pipeline or pass altered or unaltered through a WWTP into
surface waters (Wolf et al., 2012). As the result of CEC environmental persistence, an
average concentration of 218 measured CECs in surface waters is 43 parts per trillion
(Williams, 2005).
Limited amount of research has been dedicated to measuring community
generation of CECs (Noguera-Oviedo and Aga, 2016). Community contribution to CEC
loadings in sewage can be specific to city, country, or day of a week (Reid et al., 2011;
Khan and Nicell, 2015). The information about community generation of CECs will
become more relevant when reduction of CEC levels in water resources becomes a
greater priority for state and federal agencies.
In sewage, previous studies have found an increase in residue levels of illegal
psychoactive drugs on weekends (Reid et al., 2011; Kinyua and Anderson 2012). These
concentrations have been used to estimate drug consumption of cocaine, amphetamine,
methamphetamine, ecstasy, and cannabis using parent and metabolized forms of these
compounds (Reid et al., 2011; Kinyua and Anderson 2012). The information about illicit
drug use by communities can help law enforcement identify epicenters of concern.
6
Treatment of CECs
Contaminants of emerging concern are introduced into the environment through
municipal WWTPs. Hence, it is important to study efficiency of WWTPs to attenuate
CECs. Attenuation is a term that quantifies an apparent reduction of a CEC as a result of
wastewater treatment. Attenuation includes all aspects of wastewater treatment including
chemical, physical, and biological fates of CECs.
Physical and Chemical Processes
Effect of AOPs
Complete attenuation via activated sludge may not be possible for all CECs due to
their recalcitrant molecular nature. In these cases, attenuation of CECs can be achieved
through other processes such as advanced oxidation techniques (AOPs). These techniques
include chlorination, sonolysis (i.e. ultrasound irradiation), ultraviolet (UV) irradiation,
UV photocatalysis using titanium dioxide (TiO2), oxidation with hydrogen peroxide
(H2O2), oxidation with Fenton’s reagent (i.e. Fe2+/H2O2), and ozonation (Ziylan and Ince,
2011; Noguera-Oviedo and Aga, 2016).
Effect of Hydrolysis and Volatilization
Because of hydrolysis or volatilization of CECs in wastewater, determination of
CEC biodegradation rates could be overestimated. Hydrolysis is chemical process in
which a CEC are transformed by reacting with water or its ionic species. Volatilization is
a physical process in which a CEC can be transferred from water into atmosphere. With
the exception of β-lactam antibiotics, hydrolysis is likely not a significant factor for CEC
7
degradation (Williams, 2005). Because most CECs have high aqueous solubility,
volatilization plays a limited role in CEC attenuation in WWTPs (Williams, 2005).
Effect of Photolysis
Photolysis (also called photodegradation) is a chemical process in which CECs
are transformed by photons through a direct reaction or indirect reaction involving other
dissolved species that absorb light such as dissolved organic matter. Photolysis of CECs
follows first order kinetics and can occur in a UV disinfection system or under sunlight
(Sang et al., 2014). Photolysis under sunlight can not only slowly transform CECs, but
can also initiate and accelerate biodegradation rates for otherwise recalcitrant CECs
(Calisto et al., 2011; Gan et al., 2014).
Effect of Sorption
Sorption of CECs to wastewater sludge can be an important mechanism of CEC
treatment in WWTPs (Williams, 2005). Sorption potential of a compound is often
characterized by an octanol-water partition coefficient (𝐾𝑜𝑤), which is an equilibrium
concentration ratio of a compound existing in the organic octanol phase versus water
phase. Many studies have ignored sorption effects on relatively hydrophilic CECs in
wastewater if a logarithm of the octanol-water partition coefficient (log 𝐾𝑜𝑤) for a CEC
was under 3 (Williams, 2005). However, it has been shown that sorption of more
hydrophilic CECs can vary widely from what is predicted by 𝐾𝑜𝑤 and depend heavily on
environmental conditions such as redox potential, pH, and ionic conductivity (Williams,
8
2005). For this reason, solid-water partition coefficients (𝐾𝑑) have been measured in
activated sludge to reflect an actual degree of CEC sorption.
A value of 𝐾𝑑 represents an equilibrium ratio of a compound’s concentration in
wastewater sludge over its concentration in water. The assumption of 𝐾𝑑 is that a
compound partitions mainly due to hydrophobic interactions in a linear fashion
(Williams, 2015):
𝐾𝑑 =𝐶𝑠
𝐶𝑤
where 𝐾𝑑 = linear solid-water partition coefficient
𝐶𝑠 = concentration of a CEC on the sludge
𝐶𝑤 = concentration of a CEC in water
It has been demonstrated that sorption of organic molecules in activated sludge
occurs rapidly (Modin et al., 2015). Therefore, removal of CECs via sludge sorption is
primarily limited by the net amount of new sludge generated by microbial biomass. At
steady-state, the biomass removed in a WWTP is equal to the new biomass generated
(Metcalf & Eddy et al., 2003). Therefore, SRT could be used to determine the amount of
CECs removed through sludge harvest each day.
Biological Process
Biological treatment of CECs involves biodegradation and biotransformation
reactions. Biodegradation entails microbially-mediated reactions that ultimately result in
breakdown of a molecule. Complete biodegradation is synonymous with mineralization.
9
Biotransformation is a microbially-mediated process that does not lead to breakdown of a
molecule, but yields a smaller change in a molecule such as an addition or subtraction of
a functional group. In WWTPs, biodegradation/biotransformation rates are influenced by
microbial kinetics, hydraulic retention time (HRT), biological oxygen demand (BOD),
pH, redox conditions, SRT, and temperature.
Effect of Kinetics
Microbial kinetics are determined by fitting a process equation to lab or field data.
There are two process equations that have been routinely used to model biodegradation of
organic molecules: Monod and first order rate equations (Schoenerklee et al., 2010;
Fernandez-Fontaina et al., 2014; Pomiès et al., 2015). Monod equation has been
historically used to describe biodegradation kinetics of human waste in activated sludge
models (Metcalf & Eddy et al., 2003). Monod equation assumes that substrate utilization
is a primary mechanism of biodegradation. This assumption may be appropriate for some
CECs but not the others (Schoenerklee et al., 2010; Pomiès et al., 2015; Fernandez-
Fontaina et al., 2014).
Typically, concentrations of CECs are too low for substrate utilization to occur
(Williams, 2005). For these CECs, biodegradation may be best described as
cometabolism, where biodegradation of a CEC by microorganisms is accidental rather
than deliberate. For cometabolism to occur, substrate should be present for
microorganisms to release enzymes and these enzymes should be effective at
biodegrading CECs. For example, nitrifiers require both presence of ammonia and
dissolved oxygen concentrations of 0.5 mg L-1 or more to release ammonia
10
monooxygenase enzymes that can oxidize CECs (Park and Noguera, 2008; Tran et al.,
2015). Consequently, nitrification and nitrifier cometabolism will not happen under
anoxic/anaerobic conditions or in absence of ammonia. Hence, not all microorganisms
that are present in a WWTP are capable of cometabolism at all times.
Effect of HRT
It is intuitive that higher HRT would increase attenuation of the CECs by
providing more contact time for microbial biodegradation/biotransformation to take
place. In fact, this effect of HRT has been confirmed by scientific research (Xia et al.,
2015). When calculating biodegradation rates, it is essential to account for HRT.
Effect of BOD
Biological oxygen demand (BOD) is an analytical test that is used to indirectly
measure the amount of readily-biodegradable organics present in wastewater. While an
increase in influent BOD loading has been shown to increase biodegradation of some
CECs in WWTPs (Xia et al., 2015), it has also been shown to decrease biodegradation of
other CECs (Vasiliadou et al., 2014). The CECs that are biodegraded more with
increasing BOD are not rapidly degrading compounds (Xia et al., 2015). It is possible
that BOD competes with CECs for biodegrading enzymes resulting in the negative
association between BOD and CEC biodegradation (Vasiliadou et al., 2014). The positive
association between BOD and biodegradation of CECs supports the assumption of
cometabolism. In any case, BOD appears to be an important factor to consider when
comparing biodegradation kinetics between WWTPs.
11
Effect of pH
Wastewater pH has influence on kinetics of biological reactions. This influence is
especially strong for nitrification (Metcalf & Eddy et al., 2003). In general, higher pH
will result in higher nitrification rates until pH of 8 (Shammas, 1986). This effect can be
explained by high level of acidity generated by nitrifying microorganisms. This acidity
should be neutralized for these microorganisms to survive.
Effect of Redox Conditions
The role of redox conditions on CEC biodegradation/biotransformation rates can
vary depending on a CEC (Noguera-Oviedo and Aga, 2016). In WWTPs, more
biodegradation was observed for some antibiotics under constant anaerobic conditions
(DO < 0.5 mg L-1) than under full aerobic or alternating anaerobic/aerobic conditions
(Stadler et al., 2015). Conversely, more biodegradation for other antibiotics was
measured under aerobic conditions (DO > 2 mg L-1) than anaerobic/aerobic or fully
anaerobic conditions (Stadler et al., 2015). In contrast, a degree of biodegradation of
other pharmaceuticals was found to be similar under aerobic, anaerobic, and
anaerobic/aerobic conditions (Stadler et al., 2015).
Effect of SRT
Some studies of CEC attenuation in activated sludge have suggested that higher
SRTs yield higher biodegradation/biotransformation rates for CECs than lower SRTs
(Oppenheimer et al., 2007; Göbel et al., 2007; Vasiliadou et al., 2014). A study by Clara
et al. (2014) demonstrated a positive association between biodegradation rates for several
12
CECs and SRT. Other studies have observed that lengthening SRT does not increase
(Chen et al., 2015), or that it even decreases biodegradation rates for some CECs
(Majewsky et al., 2011).
There are several reasons why solids retention time may be a key factor in
biodegradation/biotransformation rates for CECs. Changes in SRT may alter both
microbial diversity (Xia et al., 2016) and microbial abundance in activated sludge
(Metcalf & Eddy et al., 2003). Higher SRTs propagate higher fractions of slow-growing
microorganisms, and lower fractions of alive and active microbial biomass (Xia et al.,
2016; Metcalf & Eddy et al., 2003). Higher SRTs may promote microbial diversity and
growth of microbes that degrade compounds with high molecular weights such as CECs
(Xia et al., 2016). Lower SRTs propagate higher proportions of fast-growing
microorganisms and of active microbial biomass (Xia et al., 2016; Metcalf & Eddy et al.,
2003). SRTs of more than 8 days are associated with presence of slow-growing,
autotrophic microorganisms that perform nitrification reactions (Cirja et al., 2008). These
autotrophic microorganisms (i.e. nitrifiers) typically constitute a negligible fraction of
overall microbial biomass in WWTPs (Ubisi et al., 1997). However, nitrifiers release
highly-reactive, oxidative enzymes such as an ammonia monooxygenase that may
enhance biodegradation rates of CECs (Tran et al., 2015). Higher activity of nitrifiers in
activated sludge has been shown to increase biodegradation/biotransformation rates for
some CECs (Helbling et al., 2012; Tran et al., 2014; Tran et al., 2015).
13
Effect of Temperature
In general, higher temperatures are associated with increased metabolic rates of
microorganisms (Metcalf & Eddy et al., 2003) and increased
biodegradation/biotransformation rates for CECs (Cirja et al., 2008). However, CEC
sorption to sludge has been shown to decrease to some extent at higher temperatures
possibly due to an increase in water solubility of CECs (Cirja et al., 2008). For relatively
hydrophilic CECs, it can be expected that substantial increase in wastewater temperature
would generally elevate both biodegradation and attenuation of CECs.
14
3. OBJECTIVES
The review of the scientific literature demonstrates that we are just beginning to
understand the challenges associated with the release of CECs into the environment,
quantities of CECs, and effectiveness of WWTPs in their treatment. Some studies have
looked at attenuation of CECs in WWTPs and were able to link improvements in CEC
attenuation to a SRT increase (Oppenheimer et al., 2007; Göbel et al., 2007; Vasiliadou et
al., 2014). Many of these studies have not isolated the rate of removal from concentration
attenuation. Without knowledge of the rates of these processes, it is difficult to compare
WWTPs. In this study, we attempted to resolve some of the gaps in our knowledge about
generation of CECs and factors influencing their fate in WWTPs. The four objectives of
this study were:
To measure the attenuation of a group of CECs and compare it in two
WWTPs.
To calculate the first order rate constants for the attenuation of a group of
CECs and compare them in two WWTPs.
To compare loadings of the selected group of CECs to two WWTPs.
To compare psychoactive drug use in two cities of central Wisconsin
between weekdays and weekends.
15
4. METHODS
We selected a group of CECs that fit both our research goals and analytical
capabilities. We selected two WWTPs with contrasting SRTs to investigate the variation
between WWTPs. The two WWTPs were located in central Wisconsin: one in the City of
Stevens Point and another in the City of Marshfield. Wastewater samples were collected
at the WWTPs, prepared, and analyzed for the selected group of CECs using high
performance liquid chromatography/mass spectrometry (HPLC/MS). The analytical
method was developed by Nitka (2014).
To evaluate attenuation of the selected CECs, we computed attenuation
efficiencies for the CECs. To evaluate loadings of CECs to the WWTPs, we computed
loading rates for the CECs using CEC concentrations detected in influent wastewater and
normalized these rates to the population size of each city. Furthermore, we estimated
consumption rates for caffeine, cocaine, and nicotine for the two Wisconsin cities using
the loading rates of these compounds’ metabolites.
To evaluate biodegradation rates for the CECs, we estimated first order
biodegradation/biotransformation rate constants (𝑘𝑏𝑖𝑜𝑙′ ) in the activated sludge of two
WWTPs. We used two mathematical models. Model 1 was simple, steady state model
that can be used to estimate CEC reduction and verify the results of a more detailed
model. Model 2 was a non-steady state simulation model that varied wastewater inflow
rates, accounted for recirculation flows, and incorporated tank configurations of the
WWTPs.
16
Selection of CECs
We selected a suite of compounds that represent three groups of CECs commonly
found in the environment: artificial sweeteners, pharmaceuticals, and psychoactive drugs
(Khan and Nicell, 2015). The reason for their ubiquity in the environment is tendency of
these CECs to move freely due to their low sorption characteristics (Barron et al., 2009;
Subedi and Kannan, 2014; Baalbaki et al., 2015). Low sorption for these CECs is
exemplified by their Log 𝐾𝑜𝑤 values ranging from -1.3 to 3.2 and relatively low 𝐾𝑑
(National Library of Medicine, 2017; Table 4.2). The CECs chosen for this study range in
their biodegradation potential: from relatively recalcitrant to labile (Stevens-Garmon et
al., 2011; Subedi and Kannan, 2014; Baalbaki et al., 2015). Because of this
biodegradability spectrum, the CECs selected for this study may serve as useful
indicators of activated sludge’s efficacy to treat a variety of CECs.
In this study, CECs range widely in terms of their acute and chronic toxicity to
humans and aquatic organisms. Commission of the European Communities (1996)
classified environmental pollutants into four risk categories based on median effect
concentrations (EC50) values (Table 4.1). The magnitude of EC50 represents a substance
concentration at which 50% of test organisms are negatively affected in terms of survival,
motility, reproduction, and feeding.
Table 4.1. Risk classes according to Commission
of the European Communities (1996).
Risk Class EC50 (mg L-1)
Very toxic to aquatic organisms <1
Toxic to aquatic organisms 1-10
Harmful to aquatic organisms 11-100
Non-classified >100
17
This section of the research paper briefly reviews environmental occurrence and
ecotoxicological risks using risk classes in Table 4.1 associated with the selected CECs
by their categories: artificial sweeteners, pharmaceuticals, and psychoactive drugs.
Artificial Sweeteners
Three artificial sweeteners – saccharin, acesulfame, and sucralose – have been
used around the world as zero-calorie sugar substitutes and added many personal care
products, foods, and beverages (Table 4.2). As the result of ubiquitous use and
incomplete degradation, artificial sweeteners have been found in wastewater, fresh
surface waters, coastal waters, groundwater, tap water, precipitation, soil, and atmosphere
(Gan et al., 2013; Sang et al., 2014).
Since the early 1970s, studies of non-human mammals have raised concerns about
potential carcinogenetic and genotoxic effects of artificial sweeteners to humans (Cohen
et al., 1979; Mukherjee and Chakrabarti, 1997). Over time, additional studies have found
no acute or chronic threats refuting the initial health concerns (Takayama et al., 1998;
Turner et al., 2001; Weihrauch and Diehl, 2004; Schiffman and Rother, 2013). Because
of the studies in mammals and a few existing papers about their effects on aquatic
organisms (Sang et al., 2014; Stoddard and Huggett, 2014), the artificial sweeteners are
unlikely to pose a human health or ecological risk of any kind. Nonetheless, these
compounds are still viewed to be harmful by the public.
18
Pharmaceuticals
In this study, we investigated six over-the-counter and prescription
pharmaceuticals: acetaminophen (analgesic), carbamazepine (anticonvulsant), antibiotics
– sulfamethazine, sulfamethoxazole, and trimethoprim – and venlafaxine (antidepressant;
Table 4.2). The veterinary antibiotic, sulfamethazine, enters wastewater stream mainly
through public consumption of meat products (Ji et al., 2010). The rest of the compounds
are directly used by the public and hospitals. The residues of these compounds have been
found in drinking water, groundwater, lakes, seas, and streams (Boix et al., 2016; Sun et
al., 2015; Ferguson et al., 2013; Nödler et al., 2014; Veach and Bernot, 2011).
Analgesic
When compared to ibuprofen, in terms of toxicity to newly hatched marine green
neon shrimp (Neocaridina denticulate) or freshwater flea (Daphnia magna),
acetaminophen exhibited similar toxicity (Sung et al., 2014; Du et al., 2016). Lower
concentrations of acetaminophen affect reproductive capacity of female water flea (Du et
al., 2016). Based on the above studies, acetaminophen can be categorized as toxic to
aquatic organisms (Table 4.1).
Antibiotics
The antibiotics of interest exhibit non-classifiably low level of acute toxicity to D.
magna following this order: trimethoprim > sulfamethazine > sulfamethoxazole (Kolar et
al., 2014; De Liguoro et al., 2009; Mendel et al., 2015; Table 4.1). No synergy in toxicity
between sulfamethazine, sulfamethoxazole, and trimethoprim was observed in D. magna
19
(De Liguoro et al., 2009; Mendel et al., 2015). Contrary to D. magna test results,
sulfamethoxazole and trimethoprim decreased immune function of freshwater mussel
(Elliptio complanata) at levels classifiable as very toxic (Gagné et al., 2006; Table 4.1).
Occurrence of both trimethoprim and sulfamethoxazole in wastewater and
streams was shown to favor enteric bacteria (Escherichia coli) carrying antibiotic
resistance genes (Suhartono et al., 2016). Together, concentrations of trimethoprim (2 μg
L-1) and sulfamethazine (10 μg L-1) have been shown to inhibit growth of E. coli (Peng et
al., 2015). Favored antibiotic-resistant bacterium may harm human health if it is a human
pathogen. Humans may be exposed to these pathogens via recreational or potable waters
containing this antibiotic-resistant pathogen. Hence, the antibiotics of interest may
jeopardize health of both humans and aquatic ecosystems.
Anticonvulsant
Carbamazepine can be classified as very toxic to Elliptio complanata as it acutely
suppresses its immune system (Gagné et al., 2006; Table 4.1). Carbamazepine have been
also shown to induce chronic toxicity to a freshwater nonbiting midge (Chironomus
riparius) in parts per billion concentrations (Oetken et al., 2005). Carbamazepine was
shown to cross embryo brain barrier when pregnant mice were fed with water containing
100 μg L-1 of carbamazepine before and after conception (Kaushik et al., 2016). The
ability of carbamazepine to cross a brain barrier has potentially detrimental consequences
on the behavior of aquatic organisms. For instance, environmental concentrations of
carbamazepine (10 ng L-1) make D. magna more attracted to light (Rivetti et al., 2016)
20
Antidepressant
Fathead minnow larvae (Pimephales promelas) exposed to venlafaxine as
embryos or larvae at concentrations as low as 0.5 μg L-1 exhibit slower escape reflex
(Painter et al., 2009). In rainbow trout (Oncorhynchus mykiss), 1.0 μg L-1 of venlafaxine
disrupts liver and gill metabolism, lowers food intake, increases aggressive behavior of
dominant trout, and compromises metabolic response to danger (Melnyk-Lamont, 2014;
Best et al., 2014). Based on the above studies, venlafaxine is classifiable as very toxic to
aquatic organisms (Table 4.1).
Psychoactive Drugs
In this study, we looked at the fate of one natural stimulant – caffeine – and
metabolites of natural stimulants – paraxanthine (caffeine metabolite), cotinine (nicotine
metabolite), and benzoylecgonine (cocaine metabolite; Table 4.2). For the exception of
benzoylecgonine, these compounds have been found above detection limits in a variety of
environments: drinking water, groundwater, Great Lakes, seas, and streams (Sun et al.,
2015; Nitka, 2014; Ferguson et al., 2013; Nödler et al., 2014; Veach and Bernot, 2011).
In freshwater zebra mussel (Dreissena polymorpha), the environmental
benzoylecgonine concentration of 1.0 μg L-1 was found to reduce enzymatic protection
from oxidative stress and induce damage to DNA (Parolini et al., 2013). Exposure to
benzoylecgonine concentrations of 1-100 ng L-1 decreased activity of mitochondria and
quantity of DNA in soft shield-fern (Polystichum setiferum) spores (García-Cambero et
al., 2015). According to the above studies, benzoylecgonine is likely to be very toxic to
aquatic organisms (Table 4.1).
21
Both caffeine and cotinine cause a drop in immune function of E. complanata at
concentrations classifiable as harmful (Gagné et al., 2006; Table 4.1). Another study
found that 0.5-18.0 μg L-1 of caffeine exhibits chronic toxicity by inducing free radical
damage and disabling antioxidant enzymes in two marine benthic polychaete worms
(Diopatra neapolitana and Arenicola marina; Pires et al., 2016). To the best of our
knowledge, no toxicity studies are available for the caffeine metabolite, paraxanthine.
22
Table 4.2. Molecular structure, molecular weight (MW), Henry’s law coefficients (𝐾𝐻) at
25°C, and 𝐾𝑑 of the 13 CECs.
Compound Structure MW
(g mol-1)
𝑲𝑯
(atm m3 mol-1)
𝑲𝒅
(L kgMLSS-1)
Acesulfame
163.15 9.63×10-9
10k
35k
47k
289b
Acetaminophen
151.17 8.93×10-10
19c
36h
84a
1160f
Benzoylecgonine
289.33 1.03×10-13 25l
233c*
Caffeine
194.19 1.10×10-11
14c
140a
537l
Carbamazepine
236.27 1.08×10-7
10a 15j
20j 36g
36d 43c
66i 135e
195l
Cotinine
176.22 3.30×10-12 23l
34a
Paraxanthine
180.16 1.75×10-12 85a
Saccharin
183.18 1.23×10-9 4.1b
2.7n*
23
Table 4.2. Continued.
Sucralose
397.64 3.99×10-19
5.1b
24k
28k
34l
96k
Sulfamethazine
278.34 1.93×10-10
13h
15c 100.5o
Sulfamethoxazole
253.28 6.42×10-13
10a 11c
32h 33j
63j 77f
256e
Trimethoprim
290.32 2.39×10-14
14a 15h
61j 68c
90j 193g
208e 253f
3890l
Venlafaxine
277.40 2.04×10-11
72d
100m
1) Sources: aBlair et al. (2015), bSubedi and Kannan (2014), cBarron et al. (2009), dLajeunesse et
al. (2013), eGöbel et al. (2005), fRadjenović et al. (2009), gStevens-Garmon et al. (2011), hYu
et al. (2011), iUrase and Kikuta (2005), jFernandez-Fontaina et al. (2014), kTran et al. (2015), lBaalbaki et al. (2016), mHörsing et al. (2011), nIgnaz et al. (2011), and oBen et al. (2014).
2) Molecular structures were drawn in ChemDraw Professional 12.0.
3) Values of MW and 𝐾𝐻 are from Estimation Program Interface (EPI) SuiteTM (US EPA, 2016).
4) *Calculated using organic carbon-water partition coefficient assuming 30% organic content of
activated sludge (Barron et al., 2009; Stevens-Garmon et al., 2011).
5) Volatilization is negligible due to low 𝐾𝐻 of the CECs of interest.
24
Site Description
Stevens Point WWTP
The Stevens Point WWTP serves the City of Stevens Point, WI (Fig. 4.1).
Because the city is home to the University of Wisconsin-Stevens Point (UWSP), the
city’s population fluctuates depending on occupancy of the university campus (9700
students). The WWTP serves approximately 26,600 residents of Stevens Point when the
UWSP is in session (U.S. Census Bureau, 2015). The WWTP receives 3.0 million of
gallons per day (MGD) of wastewater on average and is designed for an average daily
flow of 4.6 MGD. The average annual BOD5 loading is 8100 lbs day-1.
In the Stevens Point WWTP, raw sewage goes through series of pretreatment
steps followed by the activated sludge system. Pretreatment includes fine screens to
remove debris, a grit removal system for sand, gravel, and other fine material; and
rectangular primary clarifiers where gravity settles finer solids and rakes skim grease,
oils, soaps, and plastics. The activated sludge system starts with an anaerobic basin (< 0.4
mg O2 L-1) where pretreated wastewater mixes with return activated sludge. The flow
then splits into three aerobic basins (1 mg O2 L-1) working in parallel. After that,
wastewater flows into two circular secondary clarifiers where the equivalent of 70 to 90%
of the inflow is pumped back into the anaerobic basin. After the clarifiers, effluent would
be disinfected with UV light in the summer and discharged into the Wisconsin River.
Because sample collection was done in November and December, the UV lamps were not
operational.
By removing sludge manually from return sludge throughout a day, the operators
of the WWTP achieve mixed liquor suspended solids (MLSS) of around 1250 mg L-1 and
25
SRT of around 3 days. The combined HRT for the anaerobic basin, the aerobic basins,
and the clarifiers is about 8-12 hours, while HRT for the entire facility including
pretreatment steps is approximately 14-18 hours. Wastewater temperatures averaged
around 13.9°C in 2015 and 16.5°C in 2016 data collection (overall average of 15.3°C).
Figure 4.1. The aerial view of the Stevens Point WWTP.
Marshfield WWTP
The Marshfield WWTP serves approximately 18,620 residents of the City of
Marshfield (Fig. 4.2; U.S. Census Bureau, 2015). Similar to the Stevens Point WWTP,
the Marshfield WWTP receives 3.0 MGD of wastewater on average and has a design
average daily flow of 4.6 MGD. The Marshfield facility has annual average BOD5
Primary
clarifiers
Grit removal
Primary
digesters
Final
clarifiers
Final
clarifier
(idle in 2015) Anaerobic
basin
Aerobic basins
Lift station and
fine screens
Secondary
digester
Biosolids
storage tanks
26
loading of 4,800 lbs day-1. In addition to residential and commercial wastewater,
Marshfield has a university campus (600 students), and a large regional hospital, clinic,
and medical research facility.
Figure 4.2. The aerial view of the Marshfield WWTP.
In the Marshfield WWTP, raw sewage goes through a single pretreatment step
shortly followed by the activated sludge system. Pretreatment uses fine screens to remove
debris, 3 mm or larger. After flowing through a splitter box, wastewater goes into an
anoxic ditch (0.1 mg O2 L-1) where it is mixed and aerated with a 125 hp mechanical
aerator. From the anoxic ditch, wastewater flows into an aerobic ditch (0.6 mg O2 L-1)
similar to the anoxic ditch where wastewater is mixed and aerated by the two mechanical
aerators. Next, wastewater flows into three circular final clarifiers where solids are
settled, partially removed, and largely returned to the anoxic ditch. The return flow is
Final
clarifiers
Swale
Cascade
aerator
Anoxic
ditch
Lift station and
fine screens
Biosolids
storage tanks
Aerobic ditch
27
about 70% of influent flow. After the final clarification, effluent passes through a cascade
aerator and flows into a constructed swale that connects the WWTP to Mill Creek.
By wasting mixed liquor biosolids manually four times a week from return
sludge, the operators of the Marshfield WWTP achieved average MLSS of 2600 mg L-1
and SRT of 27 days during the data collection period. The combined HRT for the anoxic
basin, aerobic basins, and clarifiers was about 44 hours. During the data collection
period, wastewater temperature averaged 14.3°C.
Stevens Point WWTP vs. Marshfield WWTP
The Stevens Point and Marshfield WWTPs are similar in their wastewater pH. In
both WWTPs, pH ranges between 6.8 and 7.2 for the entire facility. The two WWTPs are
also similar in their dissolved oxygen concentrations: below 0.4 mg O2 L-1 for the
anaerobic/anoxic tanks and 0.6-1.1 mg O2 L-1 for the aerobic tanks. Nitrate in the anoxic
tank of Marshfield WWTP may have similar effect of free oxygen on microbial kinetics
(Metcalf & Eddy et al., 2003). However, this nitrate is depleted promptly below 0.1 mg N
L-1. Therefore, the anoxic ditch in the Marshfield WWTP is closer to anaerobic than
aerobic conditions. In addition, aerobic conditions dominated the activated sludge system
in the two WWTPs making up 65-85% of the overall HRT.
The Stevens Point and Marshfield WWTPs both practice enhanced biological
phosphorus removal (EBPR). The EBPR stimulate bacteria to accumulate phosphorus by
alternating anaerobic and aerobic conditions. The sequence of the basins from the
anaerobic tank up to the final clarifiers is a part of EBPR.
28
The BOD5 loadings into the activated sludge system are similar between the two
WWTPs. The Marshfield WWTP does not have a primary treatment system, and
therefore the entire influent BOD5 loading enters its activated sludge system. Because the
Stevens Point WWTP employs primary treatment, its BOD5 loading to the activated
sludge is reduced by 40%. Therefore, the two WWTPs have BOD5 loadings to the
activated sludge close to 5000 lbs day-1.
Unlike the Stevens Point facility, the Marshfield WWTP has active nitrifying
microorganisms present in the activated sludge by design. On a typical day, total Kjeldahl
nitrogen (TKN) of 30.0 mg N L-1 (~75% NH4+-N) and nitrite/nitrate of < 1.0 mg N L-1 in
the influent is transformed into TKN of 4.0 mg N L-1 (~7.5% NH4+-N) and nitrite/nitrate
of 5.0 mg N L-1 in the effluent.
In summary, these two facilities are similar in BOD5 loading, redox sequencing,
and pH, but very different in HRT, SRT, and microbial communities.
29
Analytical Methods
Sample Collection
Wastewater influent and effluent was sampled for seven days in the Stevens Point
WWTP – Monday through Wednesday (December 12-14, 2016) and Thursday through
Sunday (November 19-22, 2015) – and for seven days in the Marshfield WWTP –
Monday through Sunday (December 5-11, 2016). In the Stevens Point WWTP, influent
was sampled at the outlet of primary clarifiers, and effluent was sampled at the outlet of
final clarifiers. In the Marshfield WWTP, influent was sampled at the splitter box prior to
the anoxic ditch and effluent was sampled at the outlet of the final clarifiers.
In the Stevens Point WWTP, automatic peristaltic water samplers (Isco 4700
Refrigerated Sampler) collected 20 mL discrete samples for 24 hours (approx. 140
samples per day): from 7:30 AM of one day to 7:30 AM of the next day. Sampling rate
was based on a measured wastewater flow rate: 20 mL discrete samples every 20,000
gallons (i.e. volume-proportional sampling). Within the sampler, these discrete samples
from one day are combined into a composite sample to a final volume close to 3 L.
Consequently, Sunday composite samples contain 7.5 hours of Monday sampling.
Because these 7.5 hours make up for the average travel time of wastewater within sewer
pipes, Sunday samples contain most of wastewater generated on Sunday.
In the Marshfield WWTP, automatic peristaltic water samplers (Sigma SD900
Refrigerated All Weather Sampler) collected 150 mL discrete samples for 24 hours
(approx. 127 samples per day): from 7:30 AM of one day to 7:30 AM of the next day. As
in the Stevens Point facility, sampling rate was volume-proportional sampling: 150 mL
discrete samples every 50,000 gallons. Within the sampler, these discrete samples are
30
combined into a composite sample to a final volume close to 19 L. All the composite
samples were transferred into 1000-mL brown glass bottles, filtered through a membrane
filter (0.45 μm pores) into 250-mL brown glass bottles, and stored at 4°C prior to
analysis.
Sample Preparation
The solid-phase extraction (SPE) method was used to concentrate samples before
analysis. Before the extraction, 0.2 µL (2015 analysis) or 0.4 µL (2016 analysis) of the
surrogate standard, benzoylecgonine-D3, were added for every 1 mL of sample (Table
4.4). The surrogate standard was used to test the efficiency of sample extraction and
matrix interferences during sample analysis. In addition to the surrogate standard, each
analytical run contained quality control measures: a blank, duplicate, and spike. The
spike consisted of 20 µL of the spike mix containing known concentrations of the
analytes dissolved in 100 mL of Milli-Q® reverse osmosis (RO) water (Table 4.3).
Table 4.3. Concentrations and sources of standards for the spike mix.
Compound Concentration (μg mL-1) Source
Acesulfame 400 Toronto Research Chemicals Inc.
Acetaminophen 200 Sigma-Aldrich Corporation
Benzoylecgonine 100 Grace Discovery Sciences
Caffeine 200 Sigma-Aldrich Corporation
Carbamazepine 100 Grace Discovery Sciences
Cotinine 200 Sigma-Aldrich Corporation
Paraxanthine 400 Sigma-Aldrich Corporation
Saccharin 1000 Sigma-Aldrich Corporation
Sucralose 1000 Toronto Research Chemicals Inc. Sulfamethazine 100 Sigma-Aldrich Corporation
Sulfamethoxazole 100 C/D/N Isotopes Inc.
Trimethoprim 100 Sigma-Aldrich Corporation
Venlafaxine 100 Sigma-Aldrich Corporation
31
A Thermo Scientific Dionex AutoTrace™ 280 was used to perform SPE. It
conditioned each Water Oasis® hydrophobic-lipophilic-balanced (HLB) cartridge (6 cc,
200 mg sorbent) with 5.0 mL of methanol (Fisher Scientific International Inc.) and 5.0
mL of RO water for one minute each. Then, it rinsed each sample-injection syringe with
5.0 mL of methanol. That was followed by loading each cartridge with 100.0 mL (2015
analysis) or 50.0 mL of sample (2016 analysis) at the flow rate of 5.0 mL min-1, drying
the cartridge with nitrogen gas for 15 minutes, and eluting 5.0 mL of a sample extract
with methanol at the flow rate of 5.0 mL min-1.
Table 4.4. Concentrations and sources of the internal standards and the surrogate
standard, benzoylecgonine-D3.
Compound Concentration (μg mL-1) Source
Acesulfame-D4 40 Toronto Research Chemicals Inc.
Acetaminophen-D4 20 Sigma-Aldrich Corporation
Benzoylecgonine-D3 50 Grace Discovery Sciences
Caffeine-D9 20 Sigma-Aldrich Corporation
Carbamazepine-D10 10 Grace Discovery Sciences
Cotinine-D4 20 Sigma-Aldrich Corporation
Paraxanthine-D3 40 Toronto Research Chemicals Inc.
Saccharin-D4 100 Grace Discovery Sciences
Sucralose-D6 100 Toronto Research Chemicals Inc. Sulfamethazine-D4 20 Sigma-Aldrich Corporation
Sulfamethoxazole-D4 20 C/D/N Isotopes Inc.
Trimethoprim-D9 20 Sigma-Aldrich Corporation
Venlafaxine-D6 20 Sigma-Aldrich Corporation
After the solid phase extraction, the methanol fraction was dried down to less than
0.1 mL with the TurboVap® nitrogen jet at 50°C. Dried sample extracts received 50 µL of
internal standard mix (Table 4.4). Sample extracts were brought up to 0.5 mL with 15
mM acetic acid in RO water, and were transferred into vials for analysis. Consequently,
the original sample concentration was increased 100 to 200 fold in the extracts. Raw
32
samples were analyzed as well. The raw samples were prepared for analysis by mixing 50
µL of the internal standard mix with 450 µL of raw sample.
Sample Analysis
Sample extracts and raw samples were analyzed for 13 CECs using an Agilent
1200 series high performance liquid chromatograph coupled to an Agilent 6430 series
triple quadrupole mass spectrometer with an electrospray ionization source (ESI-
HPLC/MS/MS). The liquid chromatography column was 4.6 ID × 50 mm Zorbax Eclipse
XDB-C8 (1.8 μm). The instrument altered flow rates of mobile phases A and B to a
combined flow rate of 0.5 mL min-1 following the programmed schedule (Fig. 4.3).
Mobile phase A consisted of 15 mM acetic acid in RO water, and mobile phase B
consisted of 15 mM acetic acid in methanol. Instrument conditions were as follows:
injection volume of 20 μL, column temperature of 50°C, gas temperature of 350°C, gas
flow of 10 L min-1, nebulizer pressure of 45 psi, and capillary voltage of ±4000 V.
Figure 4.3. The flow of mobile phases versus sample
run time.
33
Agilent LC/MS Mass Hunter® software was used to build five-point calibration
curves out of the calibration set (5 standards plus blank per each analyte) that was run in
the ESI-HPLC/MS/MS. For calibration to be accepted, calibration curves had to have a
coefficient of determination (R2) of 0.990 or higher. After running the calibration set, a
calibration verification standard and blank were run in order to confirm the calibration
accuracy. Subsequently, lower detection limits (LDLs) and upper detection limits (UDLs)
correspond to lowest and highest calibration standards (Table 4.5). In the case of sample
extracts, a UDL goes up 100- to 200-fold depending on the extraction ratio and a LDL
equals to a limit of detection (LOD; Nitka, 2014). A continuing verification standard and
a blank were run for every 10 samples in order to ensure that a shift in calibration did not
occur. After the analytical run, the recoveries of the surrogate standards and spikes were
calculated to ensure consistency in sample preparation and analysis.
Table 4.5. Limit of detection (LODs; Nitka, 2014), and the
highest and lowest calibration standards for the 13 CECs in the
analytical runs of 2015 and 2016.
LOD
(ng L-1)
Lowest Std.
(μg L-1)
Highest Std.
(μg L-1)
Acesulfame 5.0 1.0 80.0
Acetaminophen 35.0 0.5 40.0
Benzoylecgonine 5.0 0.25 20.0
Benzoylecgonine-D3 5.0 0.25 20.0
Caffeine 12.0 0.5 40.0
Carbamazepine 2.0 0.25 20.0
Cotinine 3.0 0.5 40.0
Paraxanthine 5.0 1.0 80.0
Sucralose 25.0 2.5 200.0
Sulfamethazine 1.0 0.25 20.0
Sulfamethoxazole 5.0 0.25 20.0
Saccharin 25.0 2.5 200.0
Trimethoprim 5.0 0.25 20.0
Venlafaxine 5.0 1.0 20.0*
*80.0 μg L-1 for the analytical runs in 2016.
34
After the ESI-HPLC/MS/MS analysis, Agilent LC/MS Mass Hunter® software
was used to determine analyte concentrations using the ratios of the signal of the internal
standards to the signal of the analytes. The results of ESI-LC/MS/MS analysis for the raw
samples were adjusted for the volume of the internal standard added:
𝐶𝐶𝐸𝐶 = 𝐶𝑟𝑎𝑤 ∙1000 𝑛𝑔
𝜇𝑔∙
𝑉𝑟𝑎𝑤
𝑉𝑠𝑡𝑑 + 𝑉𝑟𝑎𝑤 ∙
1000 𝑚𝐿
𝐿
where 𝐶𝐶𝐸𝐶 = CEC concentration in the original sample (ng L-1)
𝐶𝑟𝑎𝑤 = CEC concentration from the HPLC/MS/MS analysis of a raw sample
(µg L-1)
𝑉𝑟𝑎𝑤 = volume of a raw sample used for the analysis (mL) = 0.45 mL
𝑉𝑠𝑡𝑑 = volume of the internal standard added to a raw sample (mL) = 0.05 mL
The results of the analysis for the sample extracts were adjusted for the
concentration factor (𝑉𝑟𝑎𝑤/𝑉𝑒𝑥𝑡) in order to calculate concentrations of analytes in the
original samples. Additionally, surrogate standard recoveries were used to calculate
concentrations of the CECs in order to account for the concentrations lost during the
sample extraction process:
𝐶𝐶𝐸𝐶 = 𝐶𝑒𝑥𝑡 ∙106 𝑛𝑔
𝑚𝑔∙
𝑉𝑒𝑥𝑡
𝑉𝑟𝑎𝑤 ∙
100 %
𝑅𝑠𝑢𝑟
where 𝐶𝑒𝑥𝑡 = CEC concentration from the analysis of a sample extract (mg L-1)
𝑅𝑠𝑢𝑟 = recovery of the surrogate standard from a sample extract (%)
𝑉𝑒𝑥𝑡 = volume of a sample extract (mL) = 0.5 mL
35
𝑉𝑟𝑎𝑤 = volume of a raw sample used for the extraction (mL) = 100 mL
Analytical Results
For sample extracts, the average recovery of the surrogate standard was 37.3%
(𝑆𝐷±11.3) in the 2015 analytical run and 67.8% (𝑆𝐷±23.4) in the 2016 runs. The increase
in the surrogate recoveries between years was associated with a change in extraction
volume from 100 to 50 mL. This effect of extraction volume could be explained by
higher availability of sorption sites on a HLB cartridge when lower amounts of CECs are
run through the lipophilic media. Hence, higher proportions of CECs sorbed onto the
media if 50 mL of sample were extracted instead of 100 mL. All CEC concentrations
were adjusted for the surrogate recoveries to adjust for the differences in surrogate
recoveries between years.
Table 4.6. Percent differences for duplicate samples for
analytical runs 2015 and 2016.
Analyte Run in 2015 Runs in 2016
% % %
Acesulfame 4.6R 2.5R 3.1R
Acetaminophen 197.1E* 0E⁑ 0E⁑
Benzoylecgonine 23.2E 1.4E -8.8E
Caffeine 42.1R 3.8E 33.8E
Carbamazepine 1.1R 0.8E 6.6E
Cotinine 42.7E 10.4E 0.2E
Paraxanthine 153.1E* 8.3R 27.9R
Sucralose 6.2R 5.6R,D 4.7R
Sulfamethazine 43.5E 27.9E 26.5E
Sulfamethoxazole 0.5R 7.2R 2.9R
Saccharin 32.7E 13.9E 56.4E
Trimethoprim 15.5R 12.2R 2.2R
Venlafaxine 6.1R 3.7R 5.6R
1) RRaw samples. DDiluted raw samples. EExtracted samples.
2) *A possible carry-over from preceding samples in the run. The
duplicate was not rerun.
3) ⁑Below detection limit.
36
Low spike recoveries are commonly observed for very polar organic molecules
and considered to be acceptable as long as analytical results are reproducible with little
variance (Table 4.7; Nödler et al., 2010; Dasenaki and Thomaidis, 2015). For the
exception of a few analytes, reproducibility was acceptable for the CECs (Table 4.6).
Percent differences for duplicates were elevated for acetaminophen and paraxanthine in
the 2015 analytical run (Table 4.6). These differences might be explained by a carry-over
from the preceding samples with concentrations over the calibration ranges for these
CECs in the analytical run.
Table 4.7. Spike recoveries for the spike mix (not corrected for surrogate standard
recovery) and the surrogate standard (benzoylecgonine-D3) for analytical runs 2015
and 2016.
Analyte Run 2015 Runs 2016
ng mL-1 % ng mL-1 % ng mL-1 %
Acesulfame 1.0 6.2⁑ 1.9 25.3⁑ 1.3 17.5⁑
Acetaminophen 6.5 81.6 4.5 117.8 1.0 25.5
Benzoylecgonine 1.3 33.6 1.4 73.6 0.8 41.7
Benzoylecgonine-D3 3.5 87.6 1.5 75.6 0.8 43.5
Caffeine 10.8 135.0 9.8 256.4⁑ 2.9 76.5
Carbamazepine 5.0 123.9*⁑ 2.0 104.9 0.8 41.6
Cotinine 5.4 67.5 2.4 63.7 1.8 47.9⁑
Paraxanthine 13.4 83.8 9.4 122.8 3.6 46.6
Saccharin 0.4 0.9 6.7 34.7 2.2 11.5⁑
Sucralose 270.2 675.6*⁑ 22.8 118.9⁑ 2.0 10.7⁑
Sulfamethazine 2.5 62.4 1.7 88.4 0.8 43.4
Sulfamethoxazole 7.5 186.6*⁑ 1.3 70.1⁑ 0.5 27.2⁑
Trimethoprim 5.2 130.5*⁑ 1.0 50.3⁑ 0.5 28.2⁑
Venlafaxine 9.0 224.5*⁑ 2.6 136.1⁑ 2.2 115.2⁑
1) *Possible contamination of samples because blank and spike samples yielded
elevated concentrations both times it was run.
2) ⁑The results from the sample extracts in that extraction run were not used in the
study. The results from the raw samples were used instead.
In this study, very polar molecules were acesulfame, acetaminophen,
benzoylecgonine, caffeine, cotinine, paraxanthine, saccharin, sucralose, sulfamethazine,
37
sulfamethoxazole, and trimethoprim. Their log 𝐾𝑜𝑤 values range from -1.3 to 0.9
(National Library of Medicine, 2017). These CECs had fluctuating, low spike recoveries
(Table 4.7). On the other hand, relatively less polar venlafaxine had sufficiently high
spike recoveries and log 𝐾𝑜𝑤 of 3.2 (Table 4.7; National Library of Medicine, 2017). The
SPE method should be modified in the future to achieve greater recoveries of the very
polar CECs.
It is common to observe recoveries above 100% by 10-20% for acetaminophen
and caffeine (Dasenaki and Thomaidis, 2015). However, spike recoveries for some CECs
exceeded 130% (Table 4.7). Concentrations generated from samples in extraction runs
with spike recoveries this high were not used in the study. In this case, concentrations
from raw sample runs were used instead. There was possibly a contamination of samples
in the 2015 analytical run because both the spike and blank samples had elevated
concentrations for some CECs (Table 4.7). The results from the sample extracts in that
extraction run were not used in the study. The results from the raw samples were used
instead.
Appendix A contains tables of the CEC concentrations measured in this study
from the Stevens Point WWTP (Table A.1) and the Marshfield WWTP (Table A.2).
Some effluent concentrations for acetaminophen and saccharin were below LOD even
after sample extracts were used. In these cases, their LODs were used for CEC
concentrations
38
Loading and Consumption
Calculations
Concentrations of the CECs in influent wastewater were used to calculate loading
rates for the 13 CECs in units of milligrams per day per thousand inhabitants of Stevens
Point and Marshfield. Additionally, influent concentrations of the human metabolites of
psychoactive drugs – paraxanthine, benzoylecgonine, and cotinine – were used to
calculate consumption rates for caffeine, cocaine, and nicotine in units of milligrams per
day per thousand inhabitants of the two cities. To calculate drug consumption rates,
measured concentrations of drug metabolites were adjusted for a literature-based fraction
metabolized by a drug user. For example, a drug user reportedly excretes approximately
45% of consumed cocaine as benzoylecgonine, 80% of consumed caffeine as
paraxanthine, and 80% of consumed nicotine as cotinine (Ambre et al., 1988; Martınez
Bueno et al., 2011).
The following example illustrates the calculation procedure used to calculate
loading rates and drug consumption rates. The example uses an influent benzoylecgonine
concentration from Monday in the Marshfield WWTP. Benzoylecgonine and cocaine are
denoted as BE and CE, respectively. Loading rates of drugs to the WWTP were
calculated normalizing to the population size of Marshfield:
𝐿𝑜𝑎𝑑𝑖𝑛𝑔 𝑅𝑎𝑡𝑒 =
= 239.7 𝑛𝑔 𝐵𝐸/𝐿 ∙ 10780853 𝐿/𝑑𝑎𝑦 ∙1 𝑚𝑔
106 𝑛𝑔÷ 18,620 𝑝𝑒𝑜𝑝𝑙𝑒 ∙
1000 𝑝𝑒𝑜𝑝𝑙𝑒
𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒=
= 138.8 𝑚𝑔 𝐵𝐸/𝑑𝑎𝑦 𝑝𝑒𝑟 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒
39
Then, the drug consumption rate was calculated using the mass ratio of the parent
drug to metabolite, fraction of the metabolite excreted, and loading rate of the metabolite:
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 =
= 138.8 𝑚𝑔 𝐵𝐸/𝑑𝑎𝑦 𝑝𝑒𝑟 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒 ∙303 𝑚𝑔 𝐶𝐸/𝑚𝑚𝑜𝑙
289 𝑚𝑔 𝐵𝐸/𝑚𝑚𝑜𝑙∙
1
0.45=
= 323.4 𝑚𝑔 𝐶𝐸/𝑑𝑎𝑦 𝑝𝑒𝑟 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒
Where 0.45 is the fraction of the CEC excreted as benzoylecgonine. Finally, the
drug consumption rate was expressed in terms of a drug dose. A typical drug dose for
cocaine is 100 mg, for caffeine is 100 mg (i.e. one cup), and for nicotine is 1 mg (i.e. one
cigarette; National Library of Medicine, 2017).
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 = 323.4 𝑚𝑔 𝐶𝐸/𝑑𝑎𝑦 ÷ 1000 𝑝𝑒𝑜𝑝𝑙𝑒 ÷ 100 𝑚𝑔 𝐶𝐸/𝑑𝑜𝑠𝑒 =
= 3.2 𝑑𝑜𝑠𝑒/𝑑𝑎𝑦 𝑝𝑒𝑟 1000 𝑝𝑒𝑜𝑝𝑙𝑒
Statistics
Comparing Drug Consumption
Minitab 17 was used to compute medians and standard deviations for drug
consumption rates as well as to run non-parametric two-tail Mann-Whitney U test
(Mendenhall et al., 2008). The Mann-Whitney test was used to find a statistical difference
between the medians of drug consumption rates on the weekdays – Monday through
Friday – and the weekend – Saturday and Sunday – in Stevens Point and Marshfield. In
order to test the medians, the consumption rates from Stevens Point and Marshfield were
40
combined into a single dataset for each drug. The level of significance for the Mann-
Whitney test was set at 5% (α = 0.05). The null (Ho) and alternative (Ha) hypotheses were
as follows:
Ho: The rate of drug consumption during the weekend was not significantly higher
than during the weekdays in Stevens Point and Marshfield.
Ha: The rate of drug consumption during the weekend was significantly higher
than during the weekdays in Stevens Point and Marshfield.
The assumptions of the Mann-Whitney test were continuous dependent variables,
two categorical and independent groups, independence of data, and similar shapes of
distributions for the two datasets.
Comparing Distributions
Values of skewness and kurtosis were computed using Minitab 17 to test the
Mann-Whitney U test’s assumption of similar distributions for drug consumption rates
from the Stevens Point and Marshfield datasets. The rule of thumb is that a skewness
value should be within ±2 (a tolerance range of 4) and an excess kurtosis value should be
within ±3 (a tolerance range of 6) for a distribution to be distinctly non-normal (Westfall
and Henning, 2013). Based on this rule, we generated more stringent criteria for tolerated
differences of skewness and excess kurtosis between two distributions. If difference
between skewness values of two distributions was more than 2 and difference between
excess kurtosis values was more than 4, the two distributions were considered different.
41
In this case, the two datasets were transformed using either reciprocal or cubic root
transformations (Table A.5).
42
Attenuation Efficiency
Calculation
Attenuation efficiencies were calculated to evaluate attenuation of CECs for the
Stevens Point and Marshfield WWTPs. Attenuation efficiencies were calculated
according to the following formula:
𝐴𝑡𝑡𝑒𝑛𝑢𝑎𝑡𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 − 𝐶𝐶𝐸𝐶,𝑒𝑓𝑓
𝐶𝐶𝐸𝐶,𝑖𝑛𝑓∙ 100%
where 𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 = influent CEC concentration (ng L-1)
𝐶𝐶𝐸𝐶,𝑒𝑓𝑓 = effluent CEC concentration (ng L-1)
For this formula, the influent and effluent concentrations were determined from
volume-proportional composite samples taken during the same time interval and on the
same day. Therefore, 𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 represents an average influent CEC concentration of a day
and 𝐶𝐶𝐸𝐶,𝑒𝑓𝑓 represents an average effluent CEC concentration of a day. Attenuation
efficiencies were calculated for each day in the Stevens Point and Marshfield WWTPs.
Statistics
Comparing Attenuation Efficiencies
Minitab 17 was used to compute medians and standard deviations of attenuation
efficiencies as well as to run non-parametric two-tail Mann-Whitney U test (Mendenhall
et al., 2008). The Mann-Whitney test was to find a statistical difference between the
medians of percent attenuation efficiencies for the CECs of interest between the Stevens
43
Point WWTP and the Marshfield WWTP. The level of significance for the Mann-
Whitney test was set at 5% (α = 0.05). The null (Ho) and alternative (Ha) hypotheses were
as follows:
Ho: The percent attenuation efficiencies were not statistically different for the
CEC of interest between the Stevens Point and Marshfield WWTPs.
Ha: The percent attenuation efficiencies were statistically different for the CEC of
interest between the Stevens Point and Marshfield WWTPs.
Comparing Distributions
Values of skewness and kurtosis were computed using Minitab 17 and compared
as they were for drug consumption rates in Loading and Consumption section of the
report.
44
Kinetics
Process Equation
It is convenient to express the rate of change in the CEC mass or attenuation rate
of any CEC per unit volume as a product of a CEC concentration, and a first order rate
constant of attenuation (Yu et al., 2011):
𝑟𝑎𝑡𝑡 =𝑑𝐶𝐶𝐸𝐶
𝑑𝑡= − 𝑘𝑎𝑡𝑡
′ ∙ 𝐶𝐶𝐸𝐶
where 𝑟𝑎𝑡𝑡 = CEC attenuation rate (ng L-1 day-1)
𝑡 = time a CEC molecule spends in an activated sludge system (days)
𝐶𝐶𝐸𝐶 = CEC concentration (ng L-1)
𝑘𝑎𝑡𝑡′ = first order attenuation rate constant (day-1)
The CEC attenuation occurs from both biodegradation and removal of sludge-
sorbed CECs through sludge harvest (Joss et al., 2006). Because sorption of organics to
activated sludge is nearly instantaneous (Modin et al., 2015) and can be characterized by
linear partition coefficient, 𝐾𝑑, the removal of sludge-sorbed CECs can be described
using first order kinetics:
𝑟𝑠𝑙𝑢𝑑 = −𝑀𝐶𝐸𝐶,𝑜𝑢𝑡/𝑑𝑎𝑦
𝑉𝑊𝑊= −
𝐾𝑑 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑀𝑀𝐿𝑆𝑆,𝑜𝑢𝑡/𝑑𝑎𝑦
𝑉𝑊𝑊
= −𝐾𝑑 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡
𝑉𝑊𝑊
𝑏𝑢𝑡 𝜃𝑥 =𝑉𝑊𝑊 ∙ 𝑋𝑀𝐿𝑆𝑆
𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡, 𝑎𝑛𝑑 𝑡ℎ𝑒𝑛
𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡
𝑉𝑊𝑊=
𝑋𝑀𝐿𝑆𝑆
𝜃𝑥
45
𝑡ℎ𝑒𝑟𝑒𝑓𝑜𝑟𝑒, 𝑟𝑠𝑙𝑢𝑑 = − 𝐾𝑑/𝜃𝑥 ∙ 𝑋𝑀𝐿𝑆𝑆 ∙ 𝐶𝐶𝐸𝐶
where 𝑟𝑠𝑙𝑢𝑑 = CEC removal rate due to sorption and sludge removal (ng L-1 day-1)
𝐾𝑑 = solid-water partitioning coefficients (L gMLSS-1)
𝑀𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 = mass of MLSS lost due to sludge removal (g)
𝑀𝐶𝐸𝐶,𝑜𝑢𝑡 = mass of a sorbed CEC lost due to sludge removal (ng)
𝑉𝑊𝑊 = volume of wastewater in a tank (L)
𝐶𝐶𝐸𝐶 = CEC concentration (ng L-1)
𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 = MLSS concentration in a sludge removal flow (g L-1)
𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 = removal flow of MLSS from a tank (L day-1)
𝑋𝑀𝐿𝑆𝑆 = MLSS concentration in a tank (gMLSS L-1)
𝜃𝑥 = solids retention time (days)
A first order rate equation can also be used to describe metabolism of CECs by
activated sludge (Fernandez-Fontaina et al., 2014):
𝑑𝐶𝐶𝐸𝐶
𝑑𝑡= − 𝑘𝑏𝑖𝑜𝑙
′ ∙ 𝐶𝐶𝐸𝐶
where 𝑘𝑏𝑖𝑜𝑙′ = first order biodegradation/biotransformation rate constant (day-1)
𝐶𝐶𝐸𝐶 = dissolved concentration of a CEC (ng L-1)
𝑡 = time a CEC molecule spends in an activated sludge system (days)
Because both CEC biodegradation/biotransformation and CEC removal due to
sorption and sludge harvest can be described by first order kinetics, 𝑘𝑎𝑡𝑡′ can be regarded
46
as a sum of two first order rate constants. Hence, the process equation for 𝑟𝑎𝑡𝑡 can be
rewritten as follows:
𝑏𝑒𝑐𝑎𝑢𝑠𝑒, 𝑘𝑎𝑡𝑡′ = 𝑘𝑏𝑖𝑜𝑙
′ + 𝐾𝑑/𝜃𝑥 ∙ 𝑋𝑀𝐿𝑆𝑆
𝑡ℎ𝑒𝑛, 𝑑𝐶𝐶𝐸𝐶
𝑑𝑡= −(𝑘𝑏𝑖𝑜𝑙
′ + 𝐾𝑑/𝜃𝑥 ∙ 𝑋𝑀𝐿𝑆𝑆) ∙ 𝐶𝐶𝐸𝐶
With first order kinetics, 𝑘𝑏𝑖𝑜𝑙′ can be converted into a half-life coefficient using
the following relationship:
𝑏𝑒𝑐𝑎𝑢𝑠𝑒 ln(𝐶𝐶𝐸𝐶,𝑡2/𝐶𝐶𝐸𝐶,𝑡1
) = −𝑘𝑏𝑖𝑜𝑙′ ∙ (𝑡2 − 𝑡1)
𝑡ℎ𝑒𝑛, ln(1/2) = −𝑘𝑏𝑖𝑜𝑙′ ∙ 𝑡1/2
𝑡ℎ𝑒𝑟𝑒𝑓𝑜𝑟𝑒, 𝑡1/2 =0.693
𝑘𝑏𝑖𝑜𝑙′
where 𝐶𝐶𝐸𝐶,𝑡1 = CEC concentration (ng L-1) in a tank at a time 𝑡1 (days)
𝐶𝐶𝐸𝐶,𝑡2= CEC concentration (ng L-1) in a tank at a time 𝑡2 (days)
𝑡1/2 = half-life (days)
Discussion of half-lives can be useful when talking to the audience that does not
have intuitive perception of 𝑘𝑏𝑖𝑜𝑙′ units. In this study, 𝑘𝑏𝑖𝑜𝑙
′ characterizes all the processes
that attenuate CECs except sorption. In scientific literature, 𝑘𝑏𝑖𝑜𝑙′ is often normalized to
MLSS as a way to adjust for the amount of biological activity in the system:
𝑘𝑏𝑖𝑜𝑙 = 𝑘𝑏𝑖𝑜𝑙′ /𝑋𝑀𝐿𝑆𝑆
47
where 𝑘𝑏𝑖𝑜𝑙 = pseudo-first order biodegradation/biotransformation constant (L gMLSS-1
day-1)
𝑋𝑀𝐿𝑆𝑆 = concentration of microbial biomass (gMLSS L-1) as MLSS
The issue with this normalization is that it does not correct for an inert portion of
microbial biomass. Some authors tried to remedy this issue through the expression of
microbial biomass as active heterotrophic biomass by performing respirometric studies
on activated sludge (Majewsky et al., 2011). However, this approach does not
discriminate against microorganisms that do not participate in biodegradation of CECs.
Therefore, the normalization of 𝑘𝑏𝑖𝑜𝑙′ to microbial biomass is of questionable
significance. Nevertheless, 𝑘𝑏𝑖𝑜𝑙′ was normalized to average 𝑋𝑀𝐿𝑆𝑆 for the sake of
comparing 𝑘𝑏𝑖𝑜𝑙′ to the existing body of work.
Active Biomass
Parameter 𝑋𝑀𝐿𝑆𝑆 includes inert and active microbial biomasses in terms of BOD
removal. The proportion of active biomass (𝑓𝑎𝑐𝑡) as MLSS varies as a function of SRT
(Ubisi et al., 1997). When comparing magnitudes of 𝑘𝑏𝑖𝑜𝑙′ between WWTPs, it is helpful
to consider magnitude of heterotrophic active biomass, which constitutes an
overwhelming majority of the total active biomass (Ubisi et al., 1997):
𝑋𝑎𝑐𝑡 = 𝑋𝑀𝐿𝑆𝑆 ∙ 𝑓𝑎𝑐𝑡
48
𝑤ℎ𝑒𝑟𝑒 𝑓𝑎𝑐𝑡 =1
1 + 𝑏ℎ𝑒𝑡 ∙ 𝜃ℎ𝑒𝑡𝑇−20 ∙ 𝜃𝑥
where 𝑋𝑎𝑐𝑡 = concentration of active heterotrophic biomass (gMLSS L-1)
𝑓𝑎𝑐𝑡 = fraction of MLSS that is active heterotrophic biomass (gACTIVE MLSS-1 gMLSS
-1)
𝑏ℎ𝑒𝑡 = heterotrophic steady-state theory endogenous decay at 20ºC (d-1)
= 0.06-0.24 (Metcalf & Eddy et al., 2003; Sözen et al., 1998; Dold et al.,
1980)
𝜃ℎ𝑒𝑡 = temperature dependence coefficient for 𝑏ℎ𝑒𝑡 for a temperature (𝑇; ºC)
= 1.03-1.08 (Metcalf & Eddy et al., 2003; Dold et al., 1980)
In our study, the value of 𝑏ℎ𝑒𝑡 was set at 0.10 (Karahan et al., 2008), which is a
typical value for municipal WWTPs (Metcalf & Eddy et al., 2003). The value of 𝜃ℎ𝑒𝑡 was
set at 1.03 (Dold et al., 1980).
49
Model 1: Steady State
Model Description
Model 1 is a steady state model suitable for quantifying
biotransformation/biodegradation rates for a batch or plug-flow system. In Model 1, 𝑘𝑏𝑖𝑜𝑙′
values are calculated using the integrated form of the process equation assuming first
order kinetics:
𝐶𝐶𝐸𝐶,𝑒𝑓𝑓 = 𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 ∙ 𝑒− (𝑘𝑏𝑖𝑜𝑙′ +𝐾𝑑/𝜃𝑥∙𝑋𝑀𝐿𝑆𝑆)∙𝜃ℎ
where 𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 = daily average of influent CEC concentrations (ng L-1)
𝐶𝐶𝐸𝐶,𝑒𝑓𝑓 = daily average of effluent CEC concentrations (ng L-1)
𝜃ℎ = hydraulic retention time (days)
𝜃𝑥 = solids retention time (days)
𝑘𝑏𝑖𝑜𝑙′ = first order biodegradation/biotransformation rate constant (day-1)
𝐾𝑑 = solid-water partition coefficient (L gMLSS-1)
𝑋𝑀𝐿𝑆𝑆 = concentration of microbial biomass in activated sludge (gMLSS L-1)
𝐶𝐶𝐸𝐶,𝑖𝑛𝑓 and 𝐶𝐶𝐸𝐶,𝑒𝑓𝑓 were concentrations from volume-proportional composite
samples taken during the same time interval and on the same day. 𝑋𝑀𝐿𝑆𝑆 was a daily-
measured MLSS concentration. 𝐾𝑑 was based on the literature values. 𝜃𝑥 was based on
amount of activated sludge removed and remaining MLSS concentrations. The equation
was solved for each of the seven days in the Stevens Point and Marshfield WWTPs.
50
Parameter Estimation
Taking natural logarithm of both sides and rearranging the exponential equation
discussed in the previous section, 𝑘𝑏𝑖𝑜𝑙′ values can be calculated using natural logarithms
of influent and effluent CEC concentrations, and residence time for the entire activated
sludge system:
𝑘𝑏𝑖𝑜𝑙′ =
𝑙𝑛(𝐶𝐶𝐸𝐶,𝑖𝑛𝑓) − 𝑙𝑛(𝐶𝐶𝐸𝐶,𝑒𝑓𝑓)
𝜃ℎ−
𝐾𝑑 ∙ 𝑋𝑀𝐿𝑆𝑆
𝜃𝑥
The values of 𝑘𝑏𝑖𝑜𝑙′ were calculated for each pair of influent and effluent CEC
concentrations. A mean and a standard error of the mean were determined for a set of
seven calculated 𝑘𝑏𝑖𝑜𝑙′ values for each WWTP in Minitab 17.
Sampling time between influent and effluent was not lagged. This sampling
protocol creates an uncertainty in calculated 𝑘𝑏𝑖𝑜𝑙′ values from Model 1 because sampling
does not account for wastewater HRT. Accounting for HRT in sampling may not be as
beneficial in a completely mixed system as in a plug flow system because effluent in a
mixed system contains influent wastewater from different days. This mixing of
wastewaters from different days is more profound in the Marshfield WWTP with the
HRT of nearly 2 days than in the Stevens Point WWTP with the HRT of about a half of a
day. Some of this uncertainty in 𝑘𝑏𝑖𝑜𝑙′ values from Model 1 is likely to be captured by
calculating standard errors of the results. Consequently, this uncertainty would result in
the calculation of larger standard errors making the statistical comparison of CEC
treatment between the two WWTPs more difficult.
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Model 2: Non-Steady State
Model Description
Model 2 used a numerical simulation that included wastewater inflow and
recirculation flow rates as well as basin volumes and spatial configurations of the
WWTPs. A non-steady state simulation model was built in computer program
AQUASIM 2.1 (Reichert, 1994). In addition to the simulation of WWTPs, AQUASIM
2.1 provides tools for parameter estimation, sensitivity analysis, and uncertainty analysis.
Table 4.8 shows the process matrix of Model 2 used. In the model, 𝐶𝐶𝐸𝐶 was a
state variable defining CEC concentrations to be computed by the model simulation.
Constant variables were 𝑘𝑏𝑖𝑜𝑙′ , which was a parameter estimated by the model, and 𝐾𝑑,
which was a parameter based on values from studies reported in peer-reviewed journals.
Characteristics of WWTPs 𝜃𝑥 and 𝑋𝑀𝐿𝑆𝑆 were input as real list variables that varied
daily.
Table 4.8. The process matrix for Model 2.
𝐶𝐶𝐸𝐶 = CEC concentration (ng L-1), 𝑘𝑏𝑖𝑜𝑙′ = first order biodegradation/
biotransformation rate constant (day-1), 𝐾𝑑 = solid-water partition
coefficient (L gMLSS-1), 𝜃𝑥 = solids retention time (days), and 𝑋𝑀𝐿𝑆𝑆 =
concentration of microbial biomass in a tank (gMLSS L-1).
Each WWTP was simulated as three model compartments: anaerobic/anoxic
tank/ditch, aerobic tanks/ditch, and clarifier (Fig. 4.4). The measured average flow of a
Process 𝑪𝑪𝑬𝑪 Rate
Biodegradation/biotransformation -1 𝑘𝑏𝑖𝑜𝑙′ ∙ 𝐶𝐶𝐸𝐶
Removal due to sorption and
sludge removal -1 𝐾𝑑/𝜃𝑥 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑋𝑀𝐿𝑆𝑆
52
CEC for each day was used throughout each day. Throughout that day, the influent
concentration of a CEC was a measured value. The influent CEC concentration was
assumed to be constant for each 24-hour period. The measured effluent CEC
concentrations were treated as clarifiers’ CEC concentrations in the middle of each day
(i.e. 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, and 6.5 days).
Figure 4.4. Schematics of biological treatment within the Stevens
Point WWTP (in 2015 denoted as “SP1” and in 2016 denoted as
“SP2”) and Marshfield WWTP (denoted as “M”). Boxes represent
model compartments.
The purpose of AQUASIM 2.1 was to vary 𝑘𝑏𝑖𝑜𝑙′ in such a way as to produce the
best fit between measured and modeled effluent CEC concentrations. Before model
simulation can begin, initial concentrations of CECs (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in each compartment had
53
to be estimated. The parameter estimation feature of AQUASIM 2.1 was used to estimate
𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 values.
Model 2 simulates CEC concentrations in each compartment every 1.4 minute
based on influent CEC concentrations and defined processes (Table 4.6). The Marshfield
WWTP model begins simulation on day 1 (i.e. Monday) and stops at day 7 (i.e. Sunday).
The Stevens Point WWTP model has data from different years 2015 and 2016. Hence, it
starts on day 1 (i.e. Monday) and ends on day 3 (i.e. Wednesday). Then, it restarts on day
4 (i.e. Thursday) and ends on day 7 (i.e. Sunday).
Parameter Estimation
In AQUASIM 2.1, values of 𝑘𝑏𝑖𝑜𝑙′
were estimated using non-linear regression
through a numerical analysis approach that minimizes a sum of squares (𝑆𝑆) between
measured and calculated effluent CEC concentrations. The following least squares
formula calculates a sum of squares:
𝑆𝑆𝑝 = ∑(𝐶𝐶𝐸𝐶,𝑖 − �̂�𝐶𝐸𝐶,𝑝)2
𝑛
𝑖=1
where 𝑆𝑆𝑝 = sum of squares as a function of a model parameter
𝐶𝐶𝐸𝐶,𝑖 = measured effluent CEC concentrations
𝑛 = total number of measured effluent CEC concentrations
�̂�𝐶𝐸𝐶,𝑝 = modeled effluent CEC concentrations as a function of a model
parameter
54
The secant method was a numerical method of chose for minimizing a sum of
squares because this method calculates a standard error of the modeled parameters
(Reichert, 1994). The secant method requires two initial guesses of a parameter value
(Gill et al., 1981). The lowest and highest guesses were set to allow the secant method’s
root-finding algorithm to converge. If the algorithm converged at a guessed value, then
this guess was readjusted and the parameter estimation was restarted. The secant method
uses an equation of a secant line to adjust guesses of a parameter until a convergence
criteria was met:
𝑝𝑥+1 = 𝑝𝑥 −𝑝𝑥 − 𝑝𝑥−1
𝑓(𝑝𝑥) − 𝑓(𝑝𝑥−1)
𝑢𝑛𝑡𝑖𝑙 𝑆𝑆𝑝𝑥−1
− 𝑆𝑆𝑝𝑥
𝑆𝑆𝑝𝑥−1
≤ 10−5
where 𝑝𝑥 = previous parameter estimate
𝑝𝑥−1 = parameter estimate before 𝑝𝑥
𝑝𝑥+1 = new parameter estimate
If the convergence criteria was not met, the root-finding algorithm was stopped
after one thousand iterations. An asymptotic standard error of a model parameter was
calculated using the covariance matrix using the Gauss-Newton algorithm (Ralston and
Jennrich, 1978; Ruckstuhl, 2010):
𝑆𝐸𝑝 = √𝑆𝑆𝑝
𝑛 − 1∙ (
𝑑𝑓(𝑝)
𝑑𝑝∙
𝑑𝑓′(𝑝)
𝑑𝑝)
−1
55
𝑤ℎ𝑒𝑟𝑒 𝜕𝑓(𝑝)
𝜕𝑝≈
𝑓(𝑝 + ∆𝑝) − 𝑓(𝑝)
∆𝑝
where 𝑆𝐸𝑝 = standard error of a model parameter
𝑝 = model parameter (𝑘𝑏𝑖𝑜𝑙′ or 𝐾𝑑)
∆𝑝 = 1% of the standard deviation of the parameter
𝑛 = total number of measured effluent CEC concentrations
𝑓(𝑝) = modeled CEC concentration as a function of the model parameters
This procedure estimates 𝑘𝑏𝑖𝑜𝑙′ and its standard error. Initial CEC concentrations
in the anaerobic/anoxic tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖1) and clarifier (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖3) were set using measured
influent and effluent CEC concentrations for the first day in the simulation, respectively.
Initial CEC concentration in the aerobic tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖2) was set using averages of
measured influent and effluent CEC concentrations for the first day in the simulation. The
parameter 𝐾𝑑 and its standard error were estimated from the range of partition
coefficients for activated sludge found in the scientific literature.
Sensitivity and Uncertainty
In AQUASIM 2.1, sensitivity and uncertainty analyses were used to characterize
degree to which potential sources of variation in the model parameters – 𝑘𝑏𝑖𝑜𝑙′ , 𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 ,
and 𝐾𝑑 – may have influenced modeled CEC concentrations (Reichert, 1994). The less
sensitive a modeled CEC concentration is to an estimated parameter, the less certainty
and importance exists in the estimate of that parameter. An absolute-relative sensitivity
56
function was used to carry out sensitivity analysis. The absolute-relative sensitivity
function (𝛿𝑝) measures an absolute change in modeled effluent CEC concentrations as a
function of change in an estimated parameter:
𝛿𝑝 = 𝑝 ∙𝜕𝑓(𝑝)
𝜕𝑝
𝑤ℎ𝑒𝑟𝑒 𝜕𝑓(𝑝)
𝜕𝑝≈
𝑓(𝑝 + ∆𝑝) − 𝑓(𝑝)
∆𝑝
Two parameters are said to be unidentifiable if the shapes of their sensitivity
functions are similar. More unidentifiability indicates greater uncertainty in estimated
parameters.
The linearized error propagation method in AQUASIM 2.1 was used to assess
uncertainty in the results of Model 2 (Gujer, 2008). The error propagation method
determines error contribution functions (휀𝑝) of each model parameter neglecting
correlation of these parameters in the model. The error propagation formula determines a
standard error of modeled CEC concentrations by summing the error contributions of
each parameter:
𝑆𝐸𝐶𝐸𝐶 = √∑ 휀𝑝2
𝑛
𝑝=1
= √(휀𝐾𝑏)2 + √(휀𝐾𝑑)2 =
= √(𝜕𝑓(𝑘𝑏𝑖𝑜𝑙
′ )
𝜕𝑘𝑏𝑖𝑜𝑙′ ∙ 𝑆𝐸𝐾𝑏)
2
+ √(𝜕𝑓(𝐾𝑑)
𝜕𝐾𝑑∙ 𝑆𝐸𝐾𝑑)
2
57
where 𝑆𝐸𝐶𝐸𝐶 = a standard error of modeled CEC concentrations
휀𝐾𝑏 = error contribution function for 𝑘𝑏𝑖𝑜𝑙′
휀𝐾𝑑 = error contribution function for 𝐾𝑑
𝑆𝐸𝐾𝑏 = a standard error of 𝑘𝑏𝑖𝑜𝑙′
𝑆𝐸𝐾𝑑 = a standard error of 𝐾𝑑
Statistics
Comparing Rate Constants
Ninety-five percent confidence intervals (𝐶𝐼95%) were constructed for 𝑘𝑏𝑖𝑜𝑙′
estimates for the Stevens Point and Marshfield WWTPs (Mendenhall et al., 2008). For
the intervals, t-distribution was used with the significance level of 5% (α = 0.05) and
degrees of freedom (𝑑𝑓) of 6:
𝐶𝐼95% = 𝑘𝑏𝑖𝑜𝑙′ ± 2.447 ∙ 𝑆𝐸𝐾𝑏
The values of 𝑘𝑏𝑖𝑜𝑙′ and their corresponding standard errors (𝑆𝐸𝐾𝑏) were generated
using AQUASIM 2.1 as described in Parameter Estimation section. For a pair of 𝑘𝑏𝑖𝑜𝑙′
values to be statistically different from each other, their confidence intervals should not
overlap. The null (Ho) and alternative (Ha) hypotheses were as follows:
Ho: The values of 𝑘𝑏𝑖𝑜𝑙′ for a CEC were not statistically different between the
Stevens Point and Marshfield WWTPs.
58
Ha: The values of 𝑘𝑏𝑖𝑜𝑙′ for a CEC were statistically different between the Stevens
Point and Marshfield WWTPs.
Normality Test
The use of t-distribution in constructing 95% confidence intervals requires values
of 𝑘𝑏𝑖𝑜𝑙′ to be normally distributed. It is typically assumed that the distribution of an
estimated parameter using non-linear regression follows asymptotic normal distribution,
which approaches normal distribution when sample size is large (Ruckstuhl, 2010).
However, the sample size is small (i.e. 7 data points) in this study. For this reason,
Anderson-Darling normality test was run in Minitab 17 to justify the assumption of
normal distribution for 𝑘𝑏𝑖𝑜𝑙′ (Mendenhall et al., 2008).
In the test, model residuals were statistically compared to the fitted line of normal
cumulative distribution using least squares regression. The normality of model residuals
should indicate the normality of 𝑘𝑏𝑖𝑜𝑙′ values. Model residuals were computed in the
following way:
휀𝑚𝑜𝑑 = ln(𝐶𝐶𝐸𝐶,𝑡2) − ln(�̂�𝐶𝐸𝐶,𝑡2
)
𝑏𝑒𝑐𝑎𝑢𝑠𝑒 ln(𝐶𝐶𝐸𝐶,𝑡2) = ln(�̂�𝐶𝐸𝐶,𝑡1
) − 𝑘𝑏𝑖𝑜𝑙′ ∙ (𝑡2 − 𝑡1) + 휀𝑚𝑜𝑑
𝑤ℎ𝑒𝑟𝑒, ln(�̂�𝐶𝐸𝐶,𝑡2) = ln(�̂�𝐶𝐸𝐶,𝑡1
) − 𝑘𝑏𝑖𝑜𝑙′ ∙ (𝑡2 − 𝑡1)
where 휀𝑚𝑜𝑑 = model residual
�̂�𝐶𝐸𝐶,𝑡1 = modeled CEC concentration (ng L-1) in a clarifier at a time 𝑡1
�̂�𝐶𝐸𝐶,𝑡2= modeled CEC concentration (ng L-1) in effluent at a time 𝑡2
𝐶𝐶𝐸𝐶,𝑡2 = measured CEC concentration (ng L-1) in effluent at a time 𝑡2
59
In addition to Anderson-Darling test, normal probability plots were generated
using Minitab 17 to aid in the justification of normality. The linearity of the model
residuals in this plot would indicate that the distribution of 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 is
normal. If the normality of 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 was not justified, then justified
𝑘𝑏𝑖𝑜𝑙′ values from Model 1 were used for the comparison of rate constants. For these 𝑘𝑏𝑖𝑜𝑙
′
values from Model 1, Anderson-Darling test was conducted and normal probability plots
were generated using a set of seven modeled 𝑘𝑏𝑖𝑜𝑙′ values instead of model residuals.
60
Model 1 vs. Model 2
Model 1 could serve as a useful check of Model 2 results. Plotting 𝑘𝑏𝑖𝑜𝑙′ values
from Model 1 versus 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 should yield a positive linear relationship
for each WWTP. If the slope of this line is close to one, then 𝑘𝑏𝑖𝑜𝑙′ values from Model 1
could be used in lieu of 𝑘𝑏𝑖𝑜𝑙′ values from Model 2. If the slope is not one, the values of
𝑘𝑏𝑖𝑜𝑙′ generated using Model 1 should be adjusted. The linear relationship between 𝑘𝑏𝑖𝑜𝑙
′
values from Models 1 and 2 could be used to predict 𝑘𝑏𝑖𝑜𝑙′ from Model 2 based on 𝑘𝑏𝑖𝑜𝑙
′
from Model 1.
To test the strength of the relationship between 𝑘𝑏𝑖𝑜𝑙′ values generated by Model 1
and Model 2, simple linear regression was run using Minitab 17 removing data points
with large residuals and unusual observations detected by the software. The assumptions
of simple linear regression are linear relationship, multivariate normality, insignificant
multicollinearity, no auto-correlation, and homoscedasticity (Mendenhall et al., 2008).
The null (Ho) and alternative (Ha) hypotheses of linear regression were:
Ho: There is no statistically significant linear correlation between 𝑘𝑏𝑖𝑜𝑙′ values
generated by Model 1 and Model 2.
Ha: There is a statistically significant linear correlation between 𝑘𝑏𝑖𝑜𝑙′ values
generated by Model 1 and Model 2.
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5. RESULTS AND DISCUSSION
Loading and Attenuation
This section discusses both loading rates and attenuation efficiencies for the CECs
in the Stevens Point and Marshfield WWTPs. Figures 5.1 and 5.2 show loading rates for
the target CECs as well as proportion of the loading rates attenuated by the two WWTPs.
Acetaminophen, caffeine, paraxanthine, and the three artificial sweeteners (acesulfame,
saccharin, and sucralose) had the highest loading rates to the two WWTPs (Fig. 5.1;
Table 5.1). Cotinine, venlafaxine, carbamazepine, benzoylecgonine, and the three
antibiotics (sulfamethoxazole, trimethoprim, and sulfamethazine) had the lowest loading
rates to the two WWTPs (Fig. 5.2; Table 5.1).
Figure 5.1. Mean loading rates of the most abundant CECs in the study
calculated for the Stevens Point and Marshfield WWTPs. Error bars indicate
± one standard deviation. Stripes represent mean attenuated proportions of
the loading rates.
62
The Stevens Point and Marshfield WWTPs had the ability to attenuate more than
90% of caffeine, cotinine, paraxanthine, and saccharin (Fig. 5.1 and 5.2; Table 5.1).
Other WWTPs exhibited similar efficiency in attenuating these CECs (Huerta-Fontela et
al., 2008; Martínez Bueno et al., 2011; Ziylan and Ince, 2011; Subedi and Kannan, 2014).
On average, the two WWTPs attenuated less than 15% of carbamazepine,
sulfamethazine, sucralose, and venlafaxine (Fig. 5.1 and 5.2; Table 5.1). As in our study,
recalcitrant nature of these CECs in WWTPs has been observed in other studies (Behera
et al., 2011; Lester et al., 2013; Ryu et al., 2014; Subedi and Kannan, 2014).
Figure 5.2. Mean loading rates of the least abundant CECs in the study calculated
for the Stevens Point and Marshfield WWTPs. Error bars indicate ± one standard
deviation. Stripes represent mean attenuated proportions of the loading rates.
63
Table 5.1. Means, medians (𝜑50%), and ranges (maximum/minimum) of loading rates
(mg day-1 per 1000 people) and attenuation efficiencies (%) for the 13 CECs of interest in
the Stevens Point WWTP and Marshfield WWTP.
CEC
Stevens Point WWTP Marshfield WWTP
Loading Rate
(mg day-1 per 1000)
Attenuation
Efficiency (%)
Loading Rate
(mg day-1 per 1000)
Attenuation
Efficiency (%)
Mean Range 𝝋𝟓𝟎% Range Mean Range 𝝋𝟓𝟎% Range
A 16669 19799/13350 6 22/-7 19029 22808/16007 93 94/92
B 10800 33061/855 >99 >99 62322 71252/53371 >99 >99
C 81 100/45 6 35/-13 136 179/66 87 90/72
D 30664 33862/26722 95 99/53 37781 40740/31774 >99 >99
E 100 126/73 5 20/-21 542 1448/359 -23 70/-47
F 727 872/548 86 91/54 1299 1465/1178 99 >99/99
G 5791 6833/4380 89 98/48 7163 7864/6485 >99 >99
H 8085 9765/6021 94 >99/63 9971 10907/8971 >99 >99
I 22550 34581/14551 17 47/-10 28074 36849/20537 4 40/-62
J 23 91/4 24 72/-57 4 6/2 -4 66/-41
K 345 487/208 34 55/11 701 931/466 57 74/35
L 198 297/99 21 35/-11 397 439/366 31 39/21
M 679 1314/183 5 29/1 1439 1719/1317 4 24/-1 Aacesulfame, Bacetaminophen, Cbenzoylecgonine, Dcaffeine, Ecarbamazepine, Fcotinine, Gparaxanthine, Hsaccharin, Isucralose, Jsulfamethazine, Ksulfamethoxazole, Ltrimethoprim, Mvenlafaxine.
The next three subsections discuss the loading rates and attenuation efficiencies
for the target CECs by their categories: artificial sweeteners, pharmaceuticals, and
psychoactive drugs.
Artificial Sweeteners
Loading rates of artificial sweeteners to the WWTPs in the literature: acesulfame
> saccharin > sucralose (Gan et al., 2013). Loading rates of the target artificial sweeteners
in the Stevens Point and Marshfield WWTPs followed this order: sucralose > acesulfame
> saccharin (Fig. 5.1 and Fig. 5.2). Higher abundance of sucralose in influent wastewater
was potentially due to the United States being a higher consumer of sucralose (Sang et
al., 2014). Because CECs can be biodegraded in sewers (Thai et al., 2014), the observed
64
abundance of sucralose could also be explained by the low biodegradability of sucralose
(Buerge et al., 2011).
Attenuation efficiencies for saccharin are typically above 80% in WWTPs, while
it is common to observe low or negative attenuation efficiencies for acesulfame and
sucralose (Table 5.1; Ryu et al., 2013; Subedi and Kannan, 2014). This pattern was
observed in the Stevens Point WWTP, but not in the Marshfield WWTP. At the
Marshfield WWTP, acesulfame attenuation efficiencies of more than 90% were observed
(Fig. 5.1; Table 5.1). Furthermore, the Marshfield WWTP had statistically higher median
attenuation efficiencies than the Stevens Point WWTP for acesulfame (𝑊 = 28, 𝑛1 & 𝑛2
= 7, 𝑝 < 0.01) and saccharin (𝑊 = 35, 𝑛1 & 𝑛2 = 7, 𝑝 = 0.03), whereas attenuation
efficiencies for sucralose (𝑊 = 61, 𝑛1 & 𝑛2 = 7, 𝑝 > 0.1) were not statistically different
between the two WWTPs.
Pharmaceuticals
Antibiotics
Loading rates for the target antibiotics to the two WWTPs followed the order of
concentrations typically found in fresh and salt waters: sulfamethoxazole > trimethoprim
> sulfamethazine (Table 5.1; Ferguson et al., 2013; Nödler et al., 2014). A possible
reason for higher loading rates for sulfamethoxazole than trimethoprim is that these
antibiotics are often formulated together at a 5:1 ratio, respectively (De Liguoro et al.,
2009). Sulfamethazine has lower loading rates than sulfamethoxazole and trimethoprim
because it originates from trace amounts of sulfamethazine found in meat products (Ji et
al., 2010).
65
Sulfamethoxazole was the most attenuated antibiotic in this study as well as in
previous studies (Fig. 5.2; Table 5.1; Yu et al., 2011). There is a wide range of reported
attenuation efficiencies for trimethoprim in scientific literature. Average attenuation
efficiencies of 13% and 31% in our study have also been observed by other studies
(Göbel et al., 2007; Ryu et al., 2013), but attenuation efficiencies as high as 69% have
also been reported (Behera et al., 2011). Moreover, the Marshfield WWTP had
statistically higher median attenuation efficiencies than the Stevens Point WWTP for
sulfamethoxazole (𝑊 = 33, 𝑛1 & 𝑛2 = 7, 𝑝 = 0.015) and trimethoprim (𝑊 = 35, 𝑛1 & 𝑛2
= 7, 𝑝 = 0.03), while attenuation efficiencies for sulfamethazine (𝑊 = 52, 𝑛1 & 𝑛2 = 7, 𝑝
> 0.1) were not statistically different between the two WWTPs.
Other Pharmaceuticals
Loading rates for acetaminophen and carbamazepine to the Marshfield WWTP
were approximately 6 times higher than the loading rates to the Stevens Point WWTP
(Table 5.1). This outcome can be explained by the large Marshfield Clinic in Marshfield.
Loading rates for venlafaxine to the Stevens Point WWTP had risen 7-fold from year
2015 to 2016. This increase in venlafaxine loadings could be due to the fact that a
prescribed dosage of venlafaxine could vary from 37.5 to 225 mg per day (National
Library of Medicine, 2017).
Mean attenuation efficiencies calculated in the current study were analogous to
the efficiencies found for acetaminophen (> 95%), carbamazepine (< 10%), and
venlafaxine (< 10%) in the scientific literature (Lester et al., 2013; Blair et al., 2015;
Table 5.5). However, other studies have also reported mean carbamazepine attenuations
66
of about 20-30% and mean venlafaxine attenuations of about 30-50% (Behera et al.,
2011; Ryu et al., 2013; Rúa-Gómez et al., 2012). Furthermore, attenuation efficiencies for
acetaminophen (𝑊 = 52.5, 𝑛1 & 𝑛2 = 7, 𝑝 > 0.1), carbamazepine (𝑊 = 58, 𝑛1 & 𝑛2 = 7,
𝑝 > 0.1), and venlafaxine (𝑊 = 59, 𝑛1 & 𝑛2 = 7, 𝑝 > 0.1) were not statistically different
between the Stevens Point and Marshfield WWTPs.
Psychoactive Drugs
Loading rates for the target psychoactive drugs to the Stevens Point and
Marshfield WWTPs followed the order of concentrations found in fresh and marine
surface waters: caffeine > paraxanthine > cotinine > benzoylecgonine (Fig 5.1 and 5.2;
Table 5.1; Martínez Bueno et al., 2011; Ferguson et al., 2013; Nödler et al., 2014). In
humans, 80% of caffeine is metabolized into paraxanthine while 10% is metabolized to
theobromine (Martínez Bueno et al., 2011). Although formation of paraxanthine by
bacteria have been previously reported, a more common metabolic route for bacteria is
through formation of theobromine (Gummadi et al., 2012). Therefore, it can be assumed
that paraxanthine loading in our study was mostly generated through human consumption
of caffeine, and caffeine loading came from discarded caffeinated products. Moreover,
cotinine and benzoylecgonine loadings mostly reflected human consumption of tobacco
and cocaine, respectively (Reid et al., 2011; Senta et al., 2015).
Average attenuation efficiencies of more than 75% for caffeine, paraxanthine, and
cotinine in this study were also reported by others (Table 5.1; Oppenheimer et al., 2007;
Martinez Bueno et. al., 2011; Blair et al., 2015). The average benzoylecgonine
attenuation efficiency of 85% in the Marshfield WWTP was also reported by Rodayan et
67
al. (2014), but 9% attenuation in the Stevens Point WWTP was unusually low for
municipal WWTPs (Huerta-Fontela et al., 2008; Rodayan et al., 2014). The Marshfield
WWTP had statistically higher median attenuation efficiencies (𝑊 = 28, 𝑛1 & 𝑛2 = 7, 𝑝 <
0.01) than the Stevens Point WWTP for caffeine, paraxanthine, cotinine, and
benzoylecgonine.
68
Drug Consumption
Caffeine consumption rates in our study were unrealistically low considering that
a majority of U.S. adults consumes caffeine on the daily basis (National Library of
Medicine, 2017): 65-92 caffeine doses for every thousand people every day. This
underestimation of caffeine consumption rates could be explained by rapid
biodegradation of caffeine in sewers (Senta et al., 2015; more in Sources of Error
section). For every thousand people every day, an average of 837 nicotine doses were
consumed in Stevens Point and 1546 nicotine doses were consumed in Marshfield.
Nicotine consumption rates in our study seem to be reasonable considering that 21% of
the entire U.S. population uses tobacco products and about 40% tobacco smokers
consume 20 cigarettes or more every day (Substance Abuse and Mental Health Services
Administration, 2014).
Cocaine consumption rates were also reasonable considering that 0.5% of the
entire U.S. population uses cocaine (Substance Abuse and Mental Health Services
Administration, 2014). For every thousand people every day, an average of 2 cocaine
doses were consumed in Stevens Point and 3 cocaine doses were consumed in
Marshfield. Cocaine consumption rates for the small municipalities in our study were low
compared to larger study populations around the world. In this city of Lubbock, TX
(269,000 inhabitants), the mean cocaine consumption rate was 43 doses per day per 1000
people (Kinyua and Todd, 2012). In northeastern Spain (2.5 million inhabitants), the
mean cocaine consumption rate was 14 doses per day per 1000 people (Huerta-Fontela et
al., 2008). Nevertheless, cocaine consumption rates in the city of Oslo, Norway (620,000
69
inhabitants) were closer to our study: 5.5-7.5 doses per day per 1000 people (Reid et al.,
2011).
It has been previously reported but not statistically verified that caffeine and
nicotine consumption rates decrease on weekends (Senta et al., 2015). In our study, the
median consumption rate for caffeine (𝑊 = 93, 𝑛1 = 10, 𝑛2 = 4, 𝑝 = 0.013) was
statistically higher on weekdays than on weekends in Stevens Point and Marshfield (Fig.
5.3). This difference can be explained by a higher use of stimulants during work hours.
Moreover, the median consumption rate for nicotine was higher during weekdays than
weekends in the two cities, but this difference was not statistically significant (𝑊 = 85,
𝑛1 = 10, 𝑛2 = 4, 𝑝 > 0.10; Fig. 5.3).
Figure 5.3. Difference in median drug consumption rates
between weekdays and weekends in Stevens Point and
Marshfield, WI. Error bars indicate ± one standard
deviation. Different letters indicate statistically significant
differences (α = 0.05).
70
Previous studies have found that cocaine consumption increases during weekends
(Kinyua and Anderson 2012; Reid et al., 2011). However, our study could not
substantiate this claim statistically (𝑊 = 70, 𝑛1 = 10, 𝑛2 = 4, 𝑝 > 0.10). It is possible that
cocaine consumption rates in our study were too low to discern statistical differences
between days of a week. More sampling should be done to ascertain potential differences
between cocaine and nicotine consumption rates on weekends and weekdays in Stevens
Point and Marshfield.
71
Biodegradation
Results of Model 1
Rate Constants for Biodegradation
Table 5.2 lists 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙 generated by Model 1 for the Stevens Point and
Marshfield WWTPs, and the ratio of biodegradation/biotransformation to total
attenuation expressed as percent. The lower values of this ratio for acesulfame,
carbamazepine, sucralose, trimethoprim, and venlafaxine demonstrate that sorption plays
an important role in CEC attenuation (Table 5.2).
Table 5.2. CEC biodegradation/biotransformation rate constants – 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙
– generated via Model 1 for the Stevens Point and Marshfield WWTPs, and the
percent of biodegradation/biotransformation to total attenuation (% biol).
Stevens Point WWTP Marshfield WWTP
CEC 𝒌𝒃𝒊𝒐𝒍
′
(d-1)
𝒌𝒃𝒊𝒐𝒍
(L gMLSS-1 d-1)
%
biol
𝒌𝒃𝒊𝒐𝒍′
(d-1)
𝒌𝒃𝒊𝒐𝒍
(L gMLSS-1 d-1)
%
biol
Acesulfame 0.103 0.082 72.0 1.464 0.579 99.4
Acetaminophen 15.647 12.418 99.1 7.285 2.881 99.6
Benzoylecgonine 0.256 0.203 82.5 1.056 0.418 98.9
Caffeine 7.526 5.973 98.7 3.989 1.577 99.5
Carbamazepine 0.085 0.067 76.6 -0.020 -0.008 0.0
Cotinine 4.504 3.575 99.7 2.512 0.993 99.9
Paraxanthine 5.904 4.686 99.4 3.303 1.306 99.8
Saccharin 7.872 6.248 100.0 3.244 1.283 100.0
Sucralose 0.444 0.352 96.6 0.003 0.001 46.1
Sulfamethazine 0.715 0.567 97.5 0.134 0.053 97.1
Sulfamethoxazole 1.166 0.925 97.6 0.465 0.184 98.6
Trimethoprim 0.240 0.190 51.8 0.158 0.062 76.0
Venlafaxine 0.228 0.181 86.3 0.025 0.010 75.6
One of 𝑘𝑏𝑖𝑜𝑙′ values for carbamazepine was a negative number (Table 5.2). It is
possible that this negative 𝑘𝑏𝑖𝑜𝑙′ value indicates net production of carbamazepine from a
carbamazepine metabolite in the Stevens Point WWTP (Blair et al., 2014). It is also
possible that the negative 𝑘𝑏𝑖𝑜𝑙′ value is merely a reflection of analytical errors in
72
measured benzoylecgonine concentrations. The negative 𝑘𝑏𝑖𝑜𝑙′ might indicate
unsuitability of the sampling procedure used for this model (discussed in detail in
Methods). Because Model 1 was a check of Model 2, and Model 2 was used to draw main
conclusions, the shortcomings of Model 1 are not of critical importance to this study.
Results of Model 2
Sensitivity Analysis
Sensitivity analysis was used to evaluate relative importance of the three model
parameters – 𝐶𝐶𝐸𝐶,𝑖𝑛𝑖, 𝑘𝑏𝑖𝑜𝑙′ , and 𝐾𝑑 – in Model 2. AQUASIM 2.1 determines the
sensitivity functions numerically by calculating derivatives with respect to each
parameter. Examples of the sensitivity functions in the modeled effluent are shown in
Figure 5.4. The rest of the sensitivity functions can be found in Appendix B in Figures
B.2 and B.3.
Figure 5.4. Sensitivity functions for acesulfame data in the Stevens Point (left
graph) and (right graph) Marshfield WWTPs’ modeled effluent. The graphs for the
rest of the CECs are displayed in Fig. B.2 and B.3.
73
The sensitivity analysis shows that the parameters 𝑘𝑏𝑖𝑜𝑙′ and 𝐾𝑑 are completely
unidentifiable from each other because they exhibit identical shapes of the sensitivity
functions (Fig. 5.4). It is virtually impossible to estimate both 𝑘𝑏𝑖𝑜𝑙′ and 𝐾𝑑 using the
same model. That is why 𝐾𝑑 values were not estimated with the model and instead were
entered as values found in the scientific literature. The parameter unidentifiability
translates into significant uncertainty in the estimated parameters.
Note that sensitivity functions for 𝑘𝑏𝑖𝑜𝑙′ , and 𝐾𝑑 can have opposite signs (Fig. 5.4).
If the sensitivity function for 𝑘𝑏𝑖𝑜𝑙′ is positive, then 𝑘𝑏𝑖𝑜𝑙
′ must be negative suggesting net
generation of a CEC. Fig. 5.4 shows the relevance of 𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 to the model decreases
exponentially as time progresses. Because 𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 has low significance to the model
results, this parameter could be estimated using measured CEC concentrations for
influent and effluent (discussed in detail in Methods). Appendix A contains tables
showing the 𝐶𝐶𝐸𝐶,𝑖𝑛𝑖 values in the three compartments of Model 2 for the Stevens Point
(Table A.3) and Marshfield (Table A.4) WWTPs.
Because acesulfame attenuation in the Stevens Point WWTP was below 10%, the
effect of 𝐾𝑑 was significant for this CEC’s 𝑘𝑏𝑖𝑜𝑙′ estimation. About a half of acesulfame
attenuation in the Stevens Point WWTP may be attributed to CEC sorption to harvested
sludge (Fig. 5.4). This case as well as other cases (e.g. benzoylecgonine, carbamazepine,
and trimethoprim, and venlafaxine) demonstrate the importance of 𝐾𝑑 as a model
parameter for slowly degrading CECs (Fig. B.2 and B.3).
Sensitivity functions for rapidly degrading CECs tend to indicate that 𝐾𝑑 was not
a significant parameter in modeling CEC concentrations, and hence, was not important in
estimating 𝑘𝑏𝑖𝑜𝑙′ (Fig. B.2 and B.3). Because acesulfame was biodegrading much faster
74
in the Marshfield WWTP than Stevens Point WWTP, the estimate of 𝐾𝑑 for acesulfame
was not as important in the Marshfield WWTP (Fig. 5.4).
Uncertainty Analysis
In our study, error contribution functions exhibited similar trends as sensitivity
functions in terms of sign and shapes (Fig. B.4 and B.5). This observation is not
surprising because the only difference between these functions is the replacement of
𝑘𝑏𝑖𝑜𝑙′ , and 𝐾𝑑 in sensitivity functions with standard errors of these parameters in error
contribution functions. Examples of the uncertainty functions in the modeled effluent are
shown in Figure 5.5. The rest of the uncertainty functions can be found in Appendix B in
Figures B.4 and B.5.
Figure 5.5. Error contribution functions for acesulfame data in the Stevens Point (left
graph) and Marshfield (right graph) WWTPs’ modeled effluent. The graphs for the
rest of the CECs are displayed in Fig. B.4 and B.5.
For the majority of simulations, 𝑘𝑏𝑖𝑜𝑙′ contributed most and 𝐾𝑑 contributes least to
the uncertainty of modeled effluent CEC concentrations. Yet, there are cases when 𝐾𝑑
75
contributed considerably to the uncertainty when compared to error contribution of 𝑘𝑏𝑖𝑜𝑙′
(Fig. B.4 and B.5). For example, the magnitude of 𝐾𝑑 error contribution to modeled
effluent concentrations of acesulfame was considerable in the Stevens Point WWTP (Fig.
5.5). However, the magnitude of 𝐾𝑑 error contribution to modeled effluent concentrations
of the same CEC was relatively insignificant in the Marshfield WWTP (Fig. 5.5).
Examples of the model fits in the modeled effluent are shown in Figure 5.6. The
rest of the model fits can be found in Appendix B in Figures B.6 and B.7. As shown in
Figure 5.6, modeled effluent CEC concentrations matched measured CEC concentrations
well.
Figure 5.6. Model fits for acesulfame and benzoylecgonine data in the Stevens
Point (left graph) and Marshfield (right graph) WWTPs’ modeled effluent. The
graphs for the rest of the CECs are displayed in Fig. B.6 and B.7.
76
For most simulations, error bounds around modeled effluent CEC concentrations
were narrowest at the beginning of model simulations (Fig. 5.6; Fig. B.6 and B.7). As
time progressed, uncertainty in modeled CEC concentrations increased and error bounds
got wider (Fig. 5.6). Error bounds for some CECs were narrow to the point of invisibility
indicating a high degree of certainty in the model results (Fig. 6). In general, the
uncertainty in modeled CEC concentrations yielded considerable standard errors for the
model parameters and considerable error bounds for modeled CEC concentrations (Fig.
B.6 and B.7). To reduce uncertainty, more sampling could be done in the future,
preferably in a single span of time to lessen effects of extraneous variables on
uncertainty.
Limitations of Model 2
One of the limitations of the model is that it does not currently include return
flows from sludge process. Many WWTPs have digesters for their harvested sludge.
These digesters reduce volume of the harvested sludge, but also return CECs that are
sorbed to sludge that get solubilized and returned to the activated sludge system. In this
study, the Stevens Point and Marshfield WWTPs did not return flows from the digesters.
Another limitation of the model is that the model likely underestimates 𝑘𝑏𝑖𝑜𝑙′ if a
CEC is getting consistently attenuated at nearly 100% as acetaminophen in this study.
This limitation can be addressed by sampling earlier in the activated sludge system.
However, this change in a sampling location narrows evaluation of the entire system to a
part of the system.
77
Rate Constants for Biodegradation
Table 5.3 shows 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙 values generated by Model 2 for the Stevens Point
and Marshfield WWTPs as well as 𝑘𝑏𝑖𝑜𝑙 values found in research articles.
Table 5.3. CEC biodegradation/biotransformation rate constants – 𝑘𝑏𝑖𝑜𝑙′ and 𝑘𝑏𝑖𝑜𝑙 –
generated by Model 2 for the Stevens Point and Marshfield WWTPs, and reference (ref.)
𝑘𝑏𝑖𝑜𝑙 found in peer-reviewed journals for the 13 CECs of interest.
1) Sources: aAymerich et al. (2016), bBlair et al. (2015), cClara et al. (2005), dMajewsky, et al.
(2011; adjusted from gACTIVE MLSS-1 to gMLSS
-1 using data from the article), eKruglova et al.
(2014), fTran et al. (2014), gJoss et al. (2006), hPlosz et al. (2013), iSuárez et al. (2012), jYin et al. (2014), kUrase and Kikuta (2005), lTran et al. (2015), and nCastronovo et al.
(2017).
2) Normal distribution for 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 was justified for all the target CECs
(Table A.6; Fig. B.8 and B.9).
Biodegradation rate constants are usually reported as 𝑘𝑏𝑖𝑜𝑙 in scientific literature.
Hence, 𝑘𝑏𝑖𝑜𝑙 will be discussed in lieu of 𝑘𝑏𝑖𝑜𝑙′ in this section. In the Stevens Point and
Marshfield WWTPs, the highest 𝑘𝑏𝑖𝑜𝑙 values were for acetaminophen followed by
saccharin, caffeine, paraxanthine, and cotinine (Table 5.3). The lowest 𝑘𝑏𝑖𝑜𝑙 values in the
Stevens Point Marshfield
CEC 𝒌𝒃𝒊𝒐𝒍
′
(d-1)
𝒌𝒃𝒊𝒐𝒍 (L
gMLSS-1 d-1)
𝒌𝒃𝒊𝒐𝒍′
(d-1)
𝒌𝒃𝒊𝒐𝒍 (L
gMLSS-1 d-1)
Ref. 𝒌𝒃𝒊𝒐𝒍
(L gMLSS-1 d-1)
Acesulfame -0.030 -0.024 2.132 0.843 0.029-0.060f,l, 1.27-1.57n
Acetaminophen 61.987 49.196 19.185 7.586 58.1-240.0g, 53.5-73.2b,
2.4-25.0d
Benzoylecgonine -0.003 -0.002 1.256 0.497 7.9h
Caffeine 3.543 2.812 14.765 5.838 39.6-50.1b, 9.1-30.7d
Carbamazepine -0.028 -0.022 0.027 0.011 ≤0.24b,c,d, 0.048k, 0.20e
Cotinine 2.539 2.015 6.460 2.554 16.6-17.5b
Paraxanthine 3.065 2.433 10.000 3.954 33.8-48.5b
Saccharin 4.182 3.319 9.530 3.768 0.16-0.55f,l
Sucralose 0.399 0.317 -0.028 -0.011 0.002-0.050f,l
Sulfamethazine 1.190 0.944 0.063 0.025 0.13j
Sulfamethoxazole 0.722 0.573 0.331 0.131 0.60i, ≤0.24b, 1.4-4.6d
Trimethoprim -0.298 -0.237 0.073 0.029 0.65i, ≤0.24b
Venlafaxine -0.148 -0.117 -0.010 -0.004 0.21a
78
two WWTPs were determined for carbamazepine, sucralose, trimethoprim, and
venlafaxine (Table 5.3).
The values of 𝑘𝑏𝑖𝑜𝑙 measured for saccharin in our study were substantially higher
than the values found in other studies (Table 5.3; Tran et al., 2014; Tran et al., 2015).
While 𝑘𝑏𝑖𝑜𝑙 for acesulfame measured was analogous to the range of values found in
scientific literature (Table 5.3; Tran et al., 2014; Tran et al., 2015; Castronovo et al.,
2017). Nevertheless, the values of 𝑘𝑏𝑖𝑜𝑙 determined for sucralose in our study were
comparable to the values found in other studies (Table 5.3; Tran et al., 2014; Tran et al.,
2015).
The 𝑘𝑏𝑖𝑜𝑙 values for acetaminophen, carbamazepine, sulfamethoxazole,
trimethoprim, and venlafaxine in this study were comparable to 𝑘𝑏𝑖𝑜𝑙 values of previous
studies (Table 5.3; Urase and Kikuta, 2005; Joss et al., 2006; Majewsky et al., 2011;
Kruglova et al., 2014; Suarez et al., 2012; Yin et al., 2014; Blair et al., 2015), while the
𝑘𝑏𝑖𝑜𝑙 values for sulfamethazine were much higher in the Marshfield WWTP than
previously reported (Aymerich et al., 2016). In addition, the 𝑘𝑏𝑖𝑜𝑙 values for
benzoylecgonine, caffeine, cotinine, and paraxanthine were sizably lower than the values
reported in other studies (Plosz et al., 2013; Blair et al., 2015; Table 5.3).
One reason for the stark contrast in the reported values could be temperature
differences. In our study, temperatures ranged from 13.4 to 16.9°C while both studies by
Blair et al. (2015) and Plosz et al. (2013) conducted experiments at room temperatures
(typically, 20-22°C). Because higher temperatures typically promote higher 𝑘𝑏𝑖𝑜𝑙 values
(Suarez et al., 2012), it could be concluded that the differences in the 𝑘𝑏𝑖𝑜𝑙 values
between our study and reference studies were partially temperature-related.
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Model 1 vs. Model 2
Positive linear relationships were established by plotting 𝑘𝑏𝑖𝑜𝑙′ values from Model
2 versus 𝑘𝑏𝑖𝑜𝑙′ values from Model 1 using the Stevens Point and Marshfield WWTPs’
datasets (Fig. 5.7 and 5.8). These linear plots demonstrate that Model 2 generated
reasonable 𝑘𝑏𝑖𝑜𝑙′ values and there is no substantial miscalculation by AQUASIM 2.1.
However, 𝑘𝑏𝑖𝑜𝑙′ values at the upper range had to be excluded from the regression to
achieve strong linear correlations between the two models (Fig. 5.7 and 5.8).
Figure 5.7. Association between first order biodegradation/
biotransformation rate constants (𝑘𝑏𝑖𝑜𝑙′ ) generated by Model 1
and Model 2 for the Stevens Point WWTP. Model 1 𝑘𝑏𝑖𝑜𝑙′ are
averages for the seven days.
Completely mixed and plug-flow tanks have comparable CEC concentrations
throughout the WWTPs when CEC concentrations throughout activated sludge system do
not drop too rapidly from CEC concentrations in influent wastewater. Conversely,
rapidly degrading CECs would greatly differ in concentrations between the two systems.
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Naturally, this point explains accelerating disagreement in 𝑘𝑏𝑖𝑜𝑙′ values between the two
models in the upper range of 𝑘𝑏𝑖𝑜𝑙′ values (Fig. 5.7 and 5.8). Differences between Model
1 and 2 could also be explained by how bias is spread. Model 1 spreads bias uniformly
throughout data points, while Model 2 is more biased toward higher effluent CEC
concentrations.
Figure 5.8. Association between first order biodegradation/
biotransformation rate constants (𝑘𝑏𝑖𝑜𝑙′ ) generated by Model 1
and Model 2 for the Marshfield WWTP. Model 1 𝑘𝑏𝑖𝑜𝑙′ are
averages for the seven days.
For the Stevens Point WWTP, the linear correlation was statistically significant
(𝐹 = 9.7, 𝑑𝑓 = 7, 𝑝 = 0.021). Even though the regression slope was close to 1 in the 𝑘𝑏𝑖𝑜𝑙′
range of 0-1.2 day-1, the y-intercept was too large for the results of Model 1 and 2 to be
used interchangeably for the Stevens Point WWTP (Fig. 5.7). The 𝑘𝑏𝑖𝑜𝑙′ values from
Model 1 could be used to estimate the 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 if adjusted for the y-
intercept. For the Marshfield WWTP, the linear correlation was statistically significant (𝐹
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= 195.4, 𝑑𝑓 = 7, 𝑝 < 0.001). The slope of the regression line was 1.4 and the y-intercept
was small (Fig. 5.8). Hence, 𝑘𝑏𝑖𝑜𝑙′ values from Model 1 for the Marshfield WWTP could
be used to estimate 𝑘𝑏𝑖𝑜𝑙′ values from Model 2 if adjusted for the slope and as long as
𝑘𝑏𝑖𝑜𝑙′ from Model 1 stays within 0-1.5 day-1.
Comparison of WWTPs
Figures 5.9 and 5.10 display half-lives of CECs in addition to 𝑘𝑏𝑖𝑜𝑙′ values for the
Stevens Point and Marshfield WWTPs, because half-lives provide a more intuitive
representation of 𝑘𝑏𝑖𝑜𝑙′ values. Values of 𝑘𝑏𝑖𝑜𝑙
′ for acesulfame, benzoylecgonine, caffeine,
cotinine, paraxanthine, and saccharin were statistically and considerably higher in the
Marshfield WWTP than Stevens Point WWTP (Fig. 5.9 and 5.10; Fig. B.10).
Figure 5.9. Values of 𝑘𝑏𝑖𝑜𝑙
′ and half-lives for the rapidly
biodegrading CECs in the Stevens Point and Marshfield WWTPs.
Error bars indicate ± one standard error. Different letters indicate
statistically significant differences (α = 0.05).
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The statistical difference in 𝑘𝑏𝑖𝑜𝑙′ for acetaminophen between the two WWTPs
was likely erroneous because acetaminophen was attenuated nearly 100% on average in
the two WWTPs (Fig 5.9; Fig. B.10). The statistical difference in 𝑘𝑏𝑖𝑜𝑙′ for sulfamethazine
between the two WWTPs was invalid because sulfamethazine concentrations in the
Marshfield WWTP’s influent were close to LOD (Fig 5.10; Fig. B.10). The statistical
difference in 𝑘𝑏𝑖𝑜𝑙′ for venlafaxine between the two WWTPs was not a significant
difference because 𝑘𝑏𝑖𝑜𝑙′ for venlafaxine is either negative or close to zero (Fig 5.10; Fig.
B.10; Table A.2).
Figure 5.10. Values of 𝑘𝑏𝑖𝑜𝑙
′ and half-lives for the slowly biodegrading CECs in the
Stevens Point and Marshfield WWTPs. Error bars indicate ± one standard error.
Different letters indicate statistically significant differences (α = 0.05).
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Effect of SRT
It appears that higher 𝑘𝑏𝑖𝑜𝑙′ values for 6 out of 13 target CECs were associated
with the longer SRT (i.e. Marshfield WWTP) in our study (Fig. 5.9 and 5.10). The
association between SRTs and biodegradation rates have been suggested by previous
studies (Clara et al., 2005; Oppenheimer et al., 2007; Göbel et al., 2007; Cirja et al.,
2008; Vasiliadou et al., 2014). This association is unlikely to be caused by differences in
the magnitude of active heterotrophic biomasses between the Stevens Point and
Marshfield WWTPs. Active biomass concentrations were estimated to be 996 mgMLSS L-1
(𝑓𝑎𝑐𝑡 = 0.79) in the Stevens Point WWTP and 759 mgMLSS L-1 (𝑓𝑎𝑐𝑡 = 0.30) in the
Marshfield WWTP. Hence, normalization of 𝑘𝑏𝑖𝑜𝑙′ to active biomass would not have
changed the conclusions of this study. It is more likely that the higher SRT yielded
greater diversity of wastewater microorganisms that exhibited a greater variety of
biochemical pathways for the biodegradation/biotransformation of CECs in the
Marshfield WWTP (Metcalf & Eddy et al., 2003; Xia et al., 2016).
There is a notable contradiction between the viewpoints of two studies – Clara et
al. (2005) and Majewsky et al. (2011) – about the effect of SRT on 𝑘𝑏𝑖𝑜𝑙. According to
Majewsky et al. (2011), higher 𝑘𝑏𝑖𝑜𝑙 were generated at a lower SRT. However, Clara et
al. (2005) produced results that contradict Majewsky et al. (2011) in principal rather than
directly. Not directly, because the two studies differed in their target CECs and yielded
no change in 𝑘𝑏𝑖𝑜𝑙 by varying SRT for the only CEC they shared – carbamazepine.
According to Clara et al. (2005), bezafibrate, ibuprofen, and bisphenol-A have
higher 𝑘𝑏𝑖𝑜𝑙 at higher SRTs (2-82 days). Whereas according to Majewsky et al. (2011),
acetaminophen, caffeine, and sulfamethoxazole have higher 𝑘𝑏𝑖𝑜𝑙 at lower SRTs (6 vs. 54
84
days). The differences in the results of these two studies could be due to specificity of
compounds tested. However, the results of our study contradict the results and
conclusions of Majewsky et al. (2011) and support the conclusions of Clara et al. (2005).
The explanation of the contradiction could be that Majewsky et al. (2011) considered
only heterotrophic microorganisms and inhibited activity of nitrifiers in experiments,
whereas Clara et al. (2005) used all wastewater microorganisms including nitrifiers.
Effect of Nitrifiers
The Stevens Point WWTP’s SRT of 3 days was too short to support a stable
population of nitrifiers, while the Marshfield WWTP’s SRT of 27 days was sufficient to
support nitrifying microorganisms, specifically ammonia-oxidizers. In lab studies, higher
activity of ammonia-oxidizing nitrifiers was positively and linearly correlated with 𝑘𝑏𝑖𝑜𝑙′
values for acesulfame, saccharin, and sucralose (Tran et al., 2014). In our study, the
Marshfield WWTP had higher 𝑘𝑏𝑖𝑜𝑙′ values for acesulfame and saccharin than the Stevens
Point WWTP. However, 𝑘𝑏𝑖𝑜𝑙′ for sucralose did not differ between the two WWTPs likely
due to recalcitrant nature of sucralose relative to the other artificial sweeteners (Soh et al.,
2011) and presence of other wastewater compounds competing for oxidation in our study.
In addition, higher activity of nitrifiers was linked to an increase in biodegradability of
venlafaxine (Helbling et al., 2012; Rúa-Gómez and Püttmann, 2012) and trimethoprim
(Cirja et al., 2008).
Recall that Majewsky et al. (2011) demonstrated that shortening SRT increases
𝑘𝑏𝑖𝑜𝑙 for some CECs. This trend can be explained by higher proportion of fast-growing
heterotrophs with high metabolic rates at lower SRTs (Metcalf & Eddy et al., 2003).
85
Therefore, it is possible that this trend is both generalizable and correct for activated
sludge as long as nitrifiers are not present. Presence of nitrifiers is important for CEC
biodegradation because Maeng et al. (2013) found that nitrification constitutes 22-77% of
total biodegradation of CECs. Hence, evidence presented by other studies that higher
SRTs induced an increase or no change in 𝑘𝑏𝑖𝑜𝑙 for CECs can be potentially explained by
elevated presence of nitrifiers at SRTs above 8 days and their diminished presence at
SRTs below 8 days (Clara et al., 2005; Oppenheimer et al., 2007; Göbel et al., 2007;
Cirja et al., 2008; Vasiliadou et al., 2014).
The significant contribution of nitrifiers to the degradation of CECs has been
disputed in Castronovo et al. (2017). In this study, presence of nitrifiers did not change
𝑘𝑏𝑖𝑜𝑙 for acesulfame, and acesulfame was biodegraded efficiently under aerobic and
anoxic conditions, but not under anaerobic conditions (Castronovo et al., 2017).
Therefore, it is possible that other microorganisms besides nitrifiers are also responsible
for higher 𝑘𝑏𝑖𝑜𝑙 values in the Marshfield WWTP.
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Sources of Error
Environmental Conditions
Besides being influenced by the parameters of interest in this study, 𝑘𝑏𝑖𝑜𝑙′ values
are also affected by redox conditions, pH, and temperature. Even though these parameters
were similar between the two WWTPs and varied little from day to day, they still have
their influence on the observed effluent CEC concentrations. To minimize their effects on
the results, the WWTPs in this study differed in their operational SRTs by an order of
magnitude.
Redox Conditions
The primary assumption is that biodegradation kinetics for all the CECs of
interest are similar in the Marshfield and Stevens Point WWTPs. This assumption may
not be true for all the CECs in this study because some may be sensitive to varied redox
conditions between the two WWTPs. However, it is a necessary assumption to make for
the comparison of the two WWTPs. Slight differences in redox conditions between the
two WWTPs are not likely to affect all the target CECs in the same way. For example,
biodegradation rates for benzoylecgonine are similar for both aerobic and anaerobic
conditions (Plosz et al., 2013). Overall, aerobic conditions dominated the activated sludge
systems in the two WWTPs.
pH
Values of pH below 6.0 can increase sorption of CECs to sludge if molecular
structures of these CECs have electron rich functional groups such as a carboxylic group
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of benzoylecgonine (Stadler et al., 2015). However this effect of pH was not applicable to
our study because wastewater pH is neutral (6.8-7.2) throughout the two WWTPs under
the examination.
Even though pH in our study varies between 6.8 and 7.2, slight variations in pH
between these two values may have a considerable impact on nitrification rates. In
general, higher pH will result in higher nitrification rates (Shammas, 1986). In our study,
nitrifying microorganisms are present only in one WWTP. Therefore, relatively narrow
pH variation should not be an obstacle in the comparison of 𝑘𝑏𝑖𝑜𝑙′ values between the two
facilities.
Temperature
Temperatures varied more for sampling days in the Stevens Point WWTP than
Marshfield WWTP. The reason for the variation of 1-3ºC was that data from two
different years was used for evaluation of the Stevens Point WWTP. Variance in 𝑘𝑏𝑖𝑜𝑙′ or
attenuation efficiencies for caffeine, cotinine, paraxanthine, and saccharin in the Stevens
Point WWTP could be partially explained by these temperature variations. Because
average temperatures between the two WWTPs differed by only 1ºC, the variance did not
hinder finding statistical differences in 𝑘𝑏𝑖𝑜𝑙′ or attenuation efficiencies for these CECs
between the two WWTPs.
Metabolites
Human metabolites of sulfamethoxazole, N4-acetylsulfamethoxazole and
sulfamethoxazole-glucuronide, have been shown to convert into the parent compound
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through deconjugation reactions (Stadler et al., 2015). Both metabolites account for about
60% of the administered antibiotic (Göbel et al., 2005). These two metabolites are
partially responsible for negative or low attenuations of sulfamethoxazole observed in
municipal WWTPs (Stadler et al., 2015). Therefore, the reported 𝑘𝑏𝑖𝑜𝑙′ values for
sulfamethoxazole in this study are likely to be underestimated (Table 5.5).
In WWTPs, influent concentrations of venlafaxine’s dominant metabolite,
desvenlafaxine, are typically 2-6 times higher than influent levels of venlafaxine
(Aymerich et al., 2016; Rúa-Gómez and Püttmann, 2012). Desvenlafaxine is also a
prescribed antidepressant, which can further complicate a mass balance analysis for
venlafaxine in WWTPs its metabolites are considered (Stadler et al., 2015). Furthermore,
there is some evidence to suggest that carbamazepine can be reconstituted from its
metabolites into the original form as the result of wastewater treatment (Blair et al.,
2015).
Degradation in Sewer
Because both paraxanthine and cotinine biodegrade fairly fast in the Stevens Point
and Marshfield WWTPs, they are also likely to biodegrade in sewers prior to their arrival
to the treatment facilities. Hence, both caffeine and nicotine consumption rates are
underestimated in our study (Fig. 5.3). However, this underestimation should not affect
the comparison of the drug use rates between the weekdays and weekends.
Contrary to caffeine and cotinine, benzoylecgonine is stable up to 4 days in
sewers (Kinyua and Anderson, 2012). Because cocaine is unstable within sewer, the
amount of benzoylecgonine produced through cocaine degradation in sewer is roughly
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20% of degraded cocaine (Thai et al., 2014). Yet, this amount is tolerable because levels
of benzoylecgonine in sewer are typically 2.5-5 times higher than levels of cocaine (Thai
et al., 2014). Therefore, cocaine consumption rates calculated in our study should be
reflective accurate.
Sample Collection
Two factors decrease a composite sample’s representativeness when sampling for
CECs: decreased sampling frequency and decreased number of pulses containing a CEC
of interest (Ort et al., 2010). The first issue is bypassed by using discrete flow-
proportional sampling mode with high sampling frequency of less than 15 minutes during
peak flows (Ort et al., 2010). The second issue is more difficult to control because it
depends on the use of CEC source by city residents. Chemicals such as carbamazepine,
trimethoprim, and venlafaxine are widely-used in the public generating multiple pulses
during the day (Ort et al., 2010). Still, sampling variation is a major source of uncertainty
in carbamazepine, trimethoprim, and venlafaxine results even in larger gravity-fed sewer
systems than the ones in Stevens Point and Marshfield (Ort et al., 2010). In contrast,
variation due to chemical analysis, not sampling variation, is a major source of
uncertainty in acetaminophen, caffeine, and sulfamethoxazole results (Ort et al., 2010).
Sample Size
The small sample size makes the results of this study more vulnerable to outliers.
The calculation of standard errors and use of the nonlinear regression in Model 2 helps to
account for some influence caused by potential outliers. Unfortunately, it is not always
90
practical or cost-effective to take many samples. With the improvements in analytical
techniques in the future, taking more samples may become more manageable.
Sample Storage
Even when such precautions as immediate refrigeration and microfiltration are
taken, biotransformation and biodegradation of CEC residues in wastewater samples is a
potential issue during storage. An analytical issue occurred during the 2016 analytical
run. When running influent raw samples from the Marshfield WWTP, acetaminophen
concentrations were above the calibration range. A week later, the raw samples were
rerun with two different dilutions two times. Both times the acetaminophen
concentrations were below the calibration range. Later, the analysis of sample extracts
has revealed that influent concentrations were close to the effluent concentrations for the
Marshfield WWTP. These results contradict both the original analytical results and
common sense because one sample from the Marshfield set yielded levels of
acetaminophen consistent throughout the reruns. Therefore, this phenomenon can be
explained by rapid biodegradation/biotransformation of acetaminophen in sample bottles
even after membrane filtration. Substantial microbial growth in these sample bottles was
observed upon the end of the experimental phase of the project. The original, above-
range concentrations of acetaminophen were used in the simulation model. The linearity
of the instrument calibration for acetaminophen beyond the calibrated range was
confirmed with check standards (Table A.2 in Appendix A). Sample preservation with
acid or via freezing is a potential solution to this issue. However, the addition of acid into
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samples or freezing samples would require reevaluation of the analytical method used in
this study.
Processes
Out of all unaccounted physical or chemical processes in this study, photolysis via
sunlight is the most influential one in degrading CECs. Photolysis has been shown to play
a significant part in degradation of carbamazepine and acesulfame (Calisto et al., 2011;
Gan et al., 2014). Eight products of acesulfame photolysis under sun and UV light have
been detected and identified (Ren et al., 2016; Gan et al., 2014). However, the role of
photolysis in degradation of CECs is diminished in winter months because of low light
intensity and short day length. Therefore, it can be assumed that the role of photolysis
was negligible in our study.
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7. CONCLUSIONS
Summary
Higher loading rates were observed for CECs that are consumed in large
quantities by the public: the pain killer, caffeine and its metabolite, and artificial
sweeteners. Lower loading rates were observed for CECs that have more limited use: the
nicotine metabolite, anticonvulsant, antidepressant, cocaine metabolite, and antibiotics.
Even though previous studies have observed an increase in use of many
psychoactive drugs on weekends, our study found the increase only in caffeine
consumption on weekdays. Cocaine consumption rates in Stevens Point and Marshfield
were low when compared to other cities around the world. The low cocaine consumption
rates could be the reason for the failure to establish statistical difference in cocaine
consumption between weekdays and weekends. Our study showed that the amount of
nicotine consumed by users in Stevens Point and Marshfield was equivalent to the
amount of nicotine consumed if everyone in the two cities smoked one cigarette a day.
We have found that the WWTP with the SRT of 27 days had higher
biodegradation rate constants (i.e. 𝑘𝑏𝑖𝑜𝑙′ ) for acesulfame, benzoylecgonine, caffeine,
cotinine, saccharin, and paraxanthine than the WWTP with an SRT of 3 days. Because of
this increase in biodegradation rates, attenuation efficiencies for these CECs were also
higher at the SRT of 27 days. However, higher attenuation efficiencies, but not
biodegradation rates were also observed for sulfamethoxazole and trimethoprim at the
SRT of 27 days. This result indicates that the biodegradation rate is not the only factor
influencing attenuation of CECs, and other factors such as HRT and sorption are also
important.
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For the most part, related studies produced similar 𝑘𝑏𝑖𝑜𝑙 values to our study
confirming validity of the non-steady state model. The advantage of using the non-steady
state model over the laboratory batch experiments is that it allows to evaluate an entire
activated sludge system versus a part of that system. In addition, the non-steady state
model could save time for the evaluation of biodegradation rates of many CECs at once.
This greater efficiency will become more important when release of CECs into the
environment become regulated by governments.
Although the 𝐾𝑑 values for the target CECs were relatively low in our study, the
effect of harvesting sludge-sorbed CECs on CEC reduction was still important for slowly
degrading CECs.
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Future Work
Even though this study have demonstrated efficacy and usefulness of the non-
steady state model built in AQUASIM 2.1, the model cannot be a complete substitute for
investigating 𝑘𝑏𝑖𝑜𝑙′ values for all CECs. Because of a more precise control over
extraneous variables, laboratory batch experiments are still worth the effort. This point is
especially true for CECs with remarkably high 𝑘𝑏𝑖𝑜𝑙′ such as acetaminophen (Aymerich et
al., 2016). Yet, statistically significant differences in 𝑘𝑏𝑖𝑜𝑙′ for 6 out of 13 CECs were
achieved between the Stevens Point and Marshfield WWTPs.
The results of our research support the positive association between higher
biodegradation rates and higher SRTs for some CECs. This association can be explained
by the presence of stable nitrifying communities at SRTs above 8 days (Cirja et al.,
2008). The results of our study are directly supported by Clara et al. (2005), and
supported indirectly by others who used attenuation to draw their conclusions about
biodegradation. However, Majewsky et al. (2011) have demonstrated that heterotrophic
bacteria biodegrades certain CECs faster at lower SRTs rather than higher SRTs. Both of
these points are not sufficiently researched.
Future research should address the effect of nitrifying microorganisms on CEC
biodegradation rates as well as the effect of their absence at different SRTs. In order to
produce more generalizable results, future studies need to greatly expand the number of
WWTPs under the evaluation. The researchers could save time and resources by using
the non-steady state model created in our study. The sampling size at each WWTP can be
increased to generate narrower confidence intervals for modeled 𝑘𝑏𝑖𝑜𝑙′ . This sampling
could be done earlier in the wastewater treatment process to quantify 𝑘𝑏𝑖𝑜𝑙′ for rapidly
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biodegrading compounds with greater statistical confidence. For instance, 𝑘𝑏𝑖𝑜𝑙′ for
acetaminophen in our study could be modeled more accurately if the additional sampling
was done after the anaerobic/anoxic tanks of the Stevens Point and Marshfield WWTPs.
This change in our sampling protocol would have exerted greater control over redox
conditions in this study. Introducing the additional sampling point could be used to
evaluate influence of dissolved oxygen concentrations on the magnitude of 𝑘𝑏𝑖𝑜𝑙′ .
The future research needs to address other strategies that might increase
biodegradation rates for relatively recalcitrant CECs. Perhaps, it is possible to increase
biodegradation by using AOPs in tandem with activated sludge system. This strategy has
been used for decontamination of soils affected by chemical spills (Sutton et al., 2014). In
these remediation projects, chemical oxidizers such as Fenton’s reagent, persulfate, and
permanganate have been used to increase biodegradability of pollutants in soils (Sutton et
al., 2014). This experience could be adopted for WWTPs. Otherwise, the use of AOPs
alone to deal with CEC treatment might prove itself to be too costly.
There is a general lack of scientific literature on the topic of biodegradation rates
for CECs during wastewater treatment. Although nitrifier activity is likely to be a
significant factor in the SRT-induced increase of CEC biodegradation rates (Maeng et al.,
2013), it may not be the only factor responsible for higher CEC biodegradation rates in
the Marshfield WWTP. More laboratory- or field-based research should be conducted to
investigate links between microbial communities, SRT, and biodegradation rates.
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Implications
Studying loadings of CECs to WWTPs can expand our understanding of relative
occurrences of these CECs in surface waters and groundwater. Acetaminophen had the
highest loading rate out of all CECs tested in our study. This result explains why
acetaminophen is one of the most abundant CECs detected in surface waters (Williams,
2005). Studying attenuations of the CECs can also shine the light on occurrences of
CECs. Carbamazepine had the lowest attenuation in our study, which explains why
carbamazepine is one of the most abundant CECs detected in drinking water (Williams,
2005). Hence, increasing attenuation of CECs in WWTPs is the key to reduce
environmental concentrations of these compounds.
Studying CEC loadings to WWTPs can also aid in wastewater-based
epidemiology. In our study, we were able to quantify the use of cocaine as well as
nicotine and caffeine in two cities of central Wisconsin throughout a week. The
knowledge about the illicit drug use can help law enforcement officials prioritize the
afflicted locations based on incidence of drug use. The knowledge about patterns of drug
use throughout a week can help health professionals prevent abuse of licit and illicit
drugs.
It takes more than just looking at attenuation efficiencies to be able to find ways
to improve treatment of CECs. Increased attenuation efficiency at the higher SRT does
not mean that biodegradation rates are also increased. Attenuation can also be increased
by increasing HRT or increasing sludge harvest. In fact, the increased attenuation of
human antibiotics – sulfamethoxazole and trimethoprim – can be attributed to higher
HRT in the Marshfield WWTP than the Stevens Point WWTP. In addition, the entire
97
attenuation of carbamazepine in the Stevens Point WWTP or of sucralose in the
Marshfield WWTP can be attributed to the combined effect of CEC sorption and removal
of sludge.
In some cases, biodegradation of CECs does not lead to toxicity reduction of CEC
residues. Metabolites of acetaminophen, p-aminophenol and p-benzoquinone, are more
toxic than their parent compound (Liang et al., 2016). Carbamazepine’s metabolite,
acridine is both recalcitrant to biodegradation (Bahlmann et al., 2014) and carcinogenic to
humans (Jelic et al., 2013). For these CECs, partial biodegradation is not a solution to the
toxicity problem and mineralization is warranted.
Computation of biodegradation/biotransformation rates for CECs does not fully
describe the efficacy of WWTPs at mineralization of potentially harmful CECs. In some
cases, degradation of a CEC leads to a much more biodegradable metabolite such as a
human carcinogen sulfamethoxazole’s metabolite, N4-acetylsulfamethoxazole (Aymerich
et al., 2016). But in other cases, metabolites are less biodegradable than parent CECs
such as venlafaxine’s metabolite, desvenlafaxine (Rúa-Gómez and Püttmann, 2012).
Increasing biodegradation rates for CECs may ultimately be not enough to
ultimately mineralize CECs. In the future, combination of biological and chemical
degradation through AOPs should be considered for the efficient treatment of CECs.
AOPs such as ozonation, UV photolysis, and UV/H2O2 oxidation have been shown to be
highly effective in degrading the CECs selected in our study: the artificial sweeteners
(Soh et al., 2011; Sang et al., 2014), the antibiotics (Schaar et al., 2010; Baeza and
Knappe, 2011), benzoylecgonine (Russo et al., 2016), and venlafaxine (Lester et al.,
2013). In addition, more unconventional AOPs such as sonolysis and TiO2-photocatalysis
98
can mineralize highly-recalcitrant CECs such as carbamazepine up to 40% (Jelic et al.,
2013).
Previous studies demonstrated that attenuation of CECs in activated sludge can be
increased by increasing HRT, wastewater temperature, and physical removal of CECs
through increased sludge harvest (Cirja et al., 2008; Xia et al., 2015). Increasing MLSS
does not necessarily result in higher attenuation, because not all biomass is active
(Metcalf & Eddy et al., 2003). Our study demonstrates that higher attenuation of CECs
may be achieved through the elevation of SRT from 3 days to 27 days and resulting
increase in biodegradation rates. The issue of CEC treatment is likely to become more
relevant in the future as new discharge regulations are passed by state and federal
governments.
99
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118
A. APPENDIX A – Tables
Analytical Results
Tab
le A
.1. I
nfl
uen
t an
d e
fflu
ent
CE
C c
once
ntr
atio
ns
(ng L
-1)
from
the
Ste
ven
s P
oin
t W
WT
P g
ener
ated
thro
ugh t
he
anal
yti
cal
runs
in 2
015 a
nd 2
016.
2016 A
naly
tica
l R
un
s 2015 A
naly
tica
l R
un
s
Mon
T
ue
Wed
T
hu
F
ri
Sat
Su
n
CE
Cs
in I
nfl
uen
t
Ace
sulf
ame
43134.4
R
45384.0
R
45887.9
R
44592.3
R
43660.0
R
38399.6
R
36933.8
R
Ace
tam
inophen
11164.6
D
89011.5
D
38805.0
D
15742.7
R
21179.8
R
2233.3
R
18610.7
R
Ben
zoyle
cgonin
e 228.4
E
238.5
E
211.5
E
100.4
E
199.5
E
261.1
E
239.7
E
Ben
zoyle
cgonin
e-D
3
12.0
E
9.8
E
10.2
E
4.4
E
1.7
E
3.9
E
3.8
E
Caf
fein
e 86005.2
D
91166.9
D
89829.4
D
70293.9
R,A
70900.1
R,A
69806.1
R,A
73950.3
R,A
Car
bam
azep
ine
193.5
E
255.3
E
216.1
E
283.3
R
262.3
R,B
279.2
R
302.1
R
Coti
nin
e 2321.5
R
2279.9
R
2256.2
R
1621.0
R
1560.6
R
1559.8
R
1516.1
R
Par
axan
thin
e 16132.2
R
18191.1
R
18298.6
R
13406.5
R
13679.8
R
12316.4
R
12118.8
R
Sucr
alose
92046.0
R
80471.6
R
65510.0
R
45206.9
R
44131.8
R
40361.1
R
40257.0
R
Sulf
amet
haz
ine
19.9
E
41.9
E
43.0
E
22.1
E
212.8
E
41.6
E
10.3
E
Sulf
amet
hoxaz
ole
554.7
R
815.2
R
730.2
R
1097.5
R
963.9
R
1076.7
R
885.0
R
Sac
char
in
23131.0
R
24285.1
R
26151.3
R
19263.0
R
18045.0
R
17882.9
R
16656.3
R
Tri
met
hopri
m
673.8
R
648.6
R
795.8
R
402.2
R
376.7
R
400.3
R
274.5
R,B
Ven
lafa
xin
e 3497.8
R
3518.6
R
3517.1
R
485.0
R
496.6
R
555.3
R
505.1
R
AA
bove
upper
det
ecti
on l
imit
. BB
elow
low
er d
etec
tion l
imit
or
lim
it o
f det
ecti
on
. EE
xtr
acte
d s
ample
s. R
Raw
sam
ple
s.
119
Tab
le A
.1. C
onti
nued
.
2016 A
naly
tica
l R
un
s 201
5 A
naly
tica
l R
un
s
Mon
T
ue
Wed
T
hu
F
ri
Sat
Su
n
CE
Cs
in E
fflu
ent
Ace
sulf
ame
39931.4
R
42731.2
R
4378
6.4
R
40998.9
R
33
849.1
R
4120
6.9
R
380
99.2
R
Ace
tam
inophen
1.7
E,B
0.6
E,B
2
.8E
,B
7.4
E,B
37.5
E
1.1
E,B
17.1
E,B
Ben
zoyle
cgonin
e 257.5
E
164
.6E
198.9
E
111.4
E
129.4
E
215
.3E
240
.2E
Ben
zoyle
cgonin
e-D
3
10.9
E
13.2
E
9.5
E
3.5
E
5.5
E
3.2
E
2.5
E
Caf
fein
e 26305.8
R
35667.1
R
4255
5.9
R
365
2.7
R
1529
.1R
431.1
R
1025.0
E,A
Car
bam
azep
ine
198.8
E
225
.3E
260.9
E
297.5
R
210.6
R,B
264.0
R,B
271.7
R,B
Coti
nin
e 871.7
R
915.7
R
1040.4
R
194.8
E
150.4
E
217
.0E
143
.1E
Par
axan
thin
e 4436.1
R
686
6.8
R
9465.3
R
1404.3
E,A
843.0
E,A
512
.8E
237
.9E
Sucr
alose
56133.6
R
42711.1
R
5434
3.4
R
46088.5
R
35
88
9.6
R
3773
9.5
R
441
34.7
R
Sulf
amet
haz
ine
13.1
E
18.3
E
32.6
E
20.2
E
59.3
E
65.2
E
15.2
E
Sulf
amet
hoxaz
ole
286.3
R
360.6
R
478.6
R
739.0
R
529.5
R
756.5
R
78
6.3
R
Sac
char
in
5691.2
R
697
3.6
R
9678.7
R
1097
.6E
706.3
E
23.0
E,B
470
.0E
Tri
met
hopri
m
662.3
R
718.9
R
776.7
R
314.6
R
24
5.4
R,B
316.9
R
214.9
R,B
Ven
lafa
xin
e 3238.0
R
337
7.6
R
3478.3
R
481.4
R
352.4
R
488.4
R
48
0.5
R
AA
bove
upper
det
ecti
on l
imit
. BB
elow
low
er d
etec
tio
n l
imit
or
lim
it o
f det
ecti
on
. EE
xtr
acte
d s
ample
s. R
Raw
sam
ple
s.
120
Tab
le A
.2. I
nfl
uen
t an
d e
fflu
ent
CE
C c
once
ntr
atio
ns
(ng L
-1)
from
the
Mar
shfi
eld
WW
TP
gen
erat
ed t
hro
ugh t
he
anal
yti
cal
runs
in 2
016.
2
01
6 A
na
lyti
cal
Ru
ns
Mo
n
Tu
e W
ed
Th
u
Fri
S
at
Su
n
CE
Cs
in I
nfl
uen
t
Ace
sulf
ame
30
09
8.2
R
32
29
2.3
R
41
08
0.9
R
35
01
0.9
R
38
29
4.4
R
35
79
0.7
R
31
91
5.0
R
Ace
tam
ino
ph
en
92
17
8.6
D
11
48
12
.5R
,A
10
78
73
.5R
,A
12
37
14
.1R
,A
12
74
01
.2R
,A
11
53
51
.8R
,A
12
10
68
.9R
,A
Ben
zoyle
cgo
nin
e 2
39
.7E
31
5.7
E
22
8.3
E
12
0.8
E
31
2.6
E
25
6.8
E
26
7.7
E
Ben
zoyle
cgo
nin
e-D
3
10
.2E
12
.9E
11
.4E
13
.0E
12
.5E
14
.1E
19
.6E
Caf
fein
e 7
03
63
.0D
68
78
4.1
D
72
31
4.0
D
67
27
4.4
D
72
70
8.2
D
62
54
2.2
D
70
83
7.2
D
Car
bam
azep
ine
71
0.2
R
72
9.0
R
26
07
.3R
72
4.2
R
71
8.5
R
70
6.2
R
73
3.4
R
Co
tinin
e 2
24
4.2
R
23
11
.8R
23
32
.1R
24
79
.6R
26
19
.2R
23
57
.4R
23
40
.6R
Par
axan
thin
e 1
12
00
.0R
12
13
3.3
R
13
94
6.6
R
14
43
4.1
R
14
06
1.3
R
13
35
1.8
R
13
02
9.2
R
Su
cral
ose
3
87
94
.9R
48
25
3.2
R
47
34
8.9
D
62
70
2.1
R
51
69
5.8
R
40
42
3.7
R
73
47
1.5
D
Su
lfam
eth
azin
e 7
.8E
11
.1E
7.4
E
5.2
E
6.4
E
5.8
E
5.5
E
Su
lfam
eth
ox
azo
le
88
7.2
R
16
38
.3R
15
08
.6R
10
87
.3R
14
42
.8R
91
7.4
R
15
21
.0R
Sac
char
in
16
68
2.5
R
17
74
3.8
R
19
25
6.5
R
18
31
4.8
R
19
50
2.2
R
18
78
0.5
R
17
88
6.9
R
Tri
met
ho
pri
m
64
4.6
R
77
2.1
R
70
0.4
R
74
1.9
R
72
4.2
R
72
0.1
R
80
0.9
R
Ven
lafa
xin
e 2
42
7.9
R
25
26
.4R
24
69
.2R
25
73
.2R
25
48
.2R
33
84
.2R
26
26
.3R
AA
bo
ve
up
per
det
ecti
on
lim
it. T
he
lin
eari
ty o
f ca
lib
rati
on
cu
rve
abo
ve
this
lev
el h
as b
een
co
nfi
rmed
th
rou
gh
ch
eck
stan
dar
ds
80
an
d 1
60 μ
g L
-1 c
hec
k s
tan
dar
ds
wit
h t
he
reco
ver
ies
of
11
1%
an
d 1
02%
, re
spec
tivel
y.
BB
elo
w l
ow
er
det
ecti
on
lim
it o
r li
mit
of
det
ecti
on
. DD
ilu
ted
raw
sam
ple
s. E
Ex
trac
ted
sam
ple
s. R
Raw
sam
ple
s.
121
Ta
ble
A.2
. Co
nti
nu
ed.
2016 A
naly
tica
l R
un
s
Mon
T
ue
Wed
T
hu
F
ri
Sat
Su
n
CE
Cs
in E
fflu
ent
Ace
sulf
ame
2088.4
R
2282.4
R
2405.8
R
2295.8
R
2627.2
R
2733.8
R
2570.5
R
Ace
tam
inophen
0.0
E,B
0.0
E,B
11.8
E,B
0.0
E,B
0.0
E,B
0.0
E,B
0.0
E,B
Ben
zoyle
cgonin
e 30.8
E
38.4
E
24.4
E
34.0
E
31.2
E
49.1
E
41.3
E
Ben
zoyle
cognin
e-D
3
21.7
E
20.9
E
11.0
E
10.8
E
10.0
E
13.1
E
10.4
E
Caf
fein
e 36.0
E
42.4
E
80.8
E
40.0
E
47.7
E
56.9
E
48.3
E
Car
bam
azep
ine
773.4
R
802.2
R
764.1
R
1065.0
R
1010.1
R
984.1
R
900.5
R
Coti
nin
e 22.3
E
21.7
E
19.4
E
28.8
E
29.3
E
30.0
E
26.8
E
Par
axan
thin
e 21.3
E
27.6
E
34.7
E
21.2
E
49.5
E
43.9
E
44.2
E
Sucr
alose
56929.9
R
47469.9
R
45294.9
R
55112.2
R
46823.6
R
65396.4
R
43706.0
R
Sulf
amet
haz
ine
3.8
E
3.8
E
4.0
E
7.3
E
8.1
E
6.0
E
6.5
E
Sulf
amet
hoxaz
ole
402.9
R
427.3
R
578.1
R
560.4
R
579.5
R
600.2
R
659.5
R
Sac
char
in
46.5
E
24.2
E
30.7
E
76.0
E
91.8
E
76.3
E
54.5
E
Tri
met
hopri
m
456.4
R
474.3
R
444.4
R
509.9
R
527.8
R
571.5
R
511.5
R
Ven
lafa
xin
e 2308.2
R
2434.1
R
2311.2
R
2523.0
R
2556.9
R
2586.0
R
2642.8
R
DD
ilute
d r
aw s
ample
s. E
Extr
acte
d s
ample
s. R
Raw
sam
ple
s.
122
Initial Concentrations
Table A.3. Modeled initial CEC concentrations in the Stevens Point
WWTP’s anaerobic tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖1), aerobic tank (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖2), and final
clarifier (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖3) in AQUASIM 2.1.
CEC 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟏 (ng L-1) 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟐 (ng L-1) 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟑 (ng L-1)
Acesulfame
in 2015
in 2016
44592.3
43134.4
42795.6
41532.9
40998.9
39931.4
Acetaminophen
in 2015
in 2016
15742.7
11164.6
7888.8
5599.8
35.0
35.0
Benzoylecgonine
in 2015
in 2016
100.4
228.4
105.9
243.0
111.4
257.5
Caffeine
in 2015
in 2016
70293.9
86005.2
36973.3
56155.5
3652.7
26305.8
Carbamazepine
in 2015
in 2016
283.3
193.5
290.4
196.2
297.5
198.8
Cotinine
in 2015
in 2016
1621.0
2321.5
907.9
1596.6
194.8
871.7
Paraxanthine
in 2015
in 2016
13406.5
16132.2
7405.4
10284.2
1404.3
4436.1
Saccharin
in 2015
in 2016
19263.0
23131.0
10180.3
14411.1
1097.6
5691.2
Sucralose
in 2015
in 2016
45206.9
92046.0
45647.7
74089.8
46088.5
56133.6
Sulfamethazine
in 2015
in 2016
22.1
19.9
21.2
16.5
20.2
13.1
Sulfamethoxazole
in 2015
in 2016
1097.5
554.7
918.2
420.5
739.0
286.3
Trimethoprim
in 2015
in 2016
402.2
673.8
358.4
668.0
314.6
662.3
Venlafaxine
in 2015
in 2016
485.0
3497.8
483.2
3367.9
481.4
3238.0
123
Table A.4. Modeled initial CEC concentrations in the Marshfield WWTP’s
anoxic ditch (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖1), aerobic ditch (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖2), and final clarifier (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖3)
in AQUASIM 2.1.
CEC 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟏 (ng L-1) 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟐 (ng L-1) 𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟑 (ng L-1)
Acesulfame 30098.2 16093.3 2088.4
Acetaminophen 92178.6 46106.8 35.0
Benzoylecgonine 239.7 135.3 30.8
Caffeine 70363.0 35199.5 36.0
Carbamazepine 710.2 741.8 773.4
Cotinine 2244.2 1133.3 22.3
Paraxanthine 11200.0 5610.6 21.3
Saccharin 16682.5 8364.5 46.5
Sucralose 38794.9 47862.4 56929.9
Sulfamethazine 7.8 5.8 3.8
Sulfamethoxazole 887.2 645.0 402.9
Trimethoprim 644.6 550.5 456.4
Venlafaxine 2427.9 2368.0 2308.2
124
Skewness and Kurtosis
Table A.5. Evaluating distributions of datasets for attenuation efficiencies and drug
consumption rates using skewness and excess kurtosis. The table displays sample
sizes (𝑁), skewness values (𝑠𝑘𝑒𝑤), and excess kurtosis (𝑘𝑢𝑟𝑡𝑜𝑠𝑖𝑠) values for each
dataset.
1) † For testing attenuation efficiencies, dataset 1 and 2 are attenuation efficiencies for the
Stevens Point and Marshfield WWTPs, respectively. For drug consumption rates,
dataset 1 and 2 are drug consumption rates for weekdays and weekends, respectively.
2) *Cube root transformation was applied to the datasets to generate similar skewness and
kurtosis.
3) ⁑Reciprocal transformation was applied to the datasets to generate similar skewness and
kurtosis.
CEC Dataset 1† Dataset 2†
𝑵 𝑺𝒌𝒆𝒘 𝑲𝒖𝒓𝒕𝒐𝒔𝒊𝒔 𝑵 𝑺𝒌𝒆𝒘 𝑲𝒖𝒓𝒕𝒐𝒔𝒊𝒔
Attenuation Efficiencies
Acesulfame 7 0.63 1.35 7 0.08 0.29
Acetaminophen 7 -1.46* 2.03* 7 -1.46* 2.03*
Benzoylecgonine 7 -0.42* -2.11* 7 -1.63* -2.58*
Caffeine 7 -0.57 -2.01 7 -1.27 0.87
Carbamazepine 7 -0.97⁑ 0.06⁑ 7 -0.60⁑ -0.73⁑
Cotinine 7 -0.44* -2.40* 7 0.36* -0.66*
Paraxanthine 7 -0.81 -0.92 7 0.21 -1.67
Saccharin 7 -0.56 -1.85 7 0.27 -1.53
Sucralose 7 0.35 -1.05 7 -0.69 -0.42
Sulfamethazine 7 -0.52 -1.15 7 0.21 -2.19
Sulfamethoxazole 7 -0.60 0.49 7 -0.44 1.31
Trimethoprim 7 -0.29 -0.92 7 -0.25 -0.77
Venlafaxine 7 1.89 3.79 7 2.12 4.95
Drug Consumption Rates
Caffeine 10 -0.05 0.17 4 -0.73 0.73
Cocaine 10 0.54 -0.91 4 -0.25 -4.31
Nicotine 10 -0.05 -2.06 4 -0.01 -5.88
125
Data Normality
Table A.6. Anderson Darling normality test was run for Models 2 residuals
for the Stevens Point and Marshfield WWTPs. The table displays test sample
size (𝑁), test statistic (𝐴𝐷), and p-values (𝑝) for the tested dataset (α = 0.05).
CEC Stevens Point WWTP Marshfield WWTP
𝑵 𝑨𝑫 𝒑 𝑵 𝑨𝑫 𝒑
Acesulfame 7 0.276 0.536 7 0.178 0.872
Acetaminophen 7 0.370 0.314 7 0.293 0.504
Benzoylecgonine 7 0.274 0.541 7 0.390 0.276
Caffeine 7 0.406 0.249 7 0.348 0.361
Carbamazepine 7 0.209 0.800 7 0.199 0.810
Cotinine 7 0.598 0.072 7 0.313 0.447
Paraxanthine 7 0.464 0.171 7 0.571 0.086
Saccharin 7 0.405 0.251 7 0.579 0.082
Sucralose 7 0.160 0.909 7 0.255 0.599
Sulfamethazine 7 0.531 0.112 7 0.524 0.117
Sulfamethoxazole 7 0.223 0.722 7 0.228 0.702
Trimethoprim 7 0.334 0.393 7 0.297 0.491
Venlafaxine 7 0.471 0.163 7 0.488 0.146
126
B. APPENDIX B – Graphs
Wastewater Flows
Figure B.1. Incoming and recirculation wastewater flows in biological treatment
within the Stevens Point WWTP (denoted as “SP”; the left side of the graph represents
2015 data and the right side presents 2016 data) and Marshfield WWTP (denoted as
“M”).
127
Model 2 Results
Sensitivity Analysis
Figure B.2. Graphs of sensitivity analysis for modeled concentrations of the Stephen
Point WWTP’s 13 CECs in the final clarifier with respect to an estimated first order
rate constants of CEC biodegradation (𝑘𝑏𝑖𝑜𝑙′ ), an estimated sludge-water partitioning
coefficient (𝐾𝑑), and an estimated initial CEC concentration (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in effluent.
First set of graphs is for rapidly degrading CECs and last set is for slowly degrading
CECs.
128
Figure B.2. Continued.
129
Figure B.3. Graphs of sensitivity analysis for modeled concentrations of the
Marshfield WWTP’s 13 CECs in the final clarifier with respect to an estimated first
order rate constants of CEC biodegradation (𝑘𝑏𝑖𝑜𝑙′ ), an estimated sludge-water
partition coefficient (𝐾𝑑), and an estimated initial CEC concentration (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in
effluent. First set of graphs is for rapidly degrading CECs and last set is for slowly
degrading CECs.
130
Figure B.3. Continued.
131
Uncertainty Analysis
Figure B.4. Graphs of uncertainty analysis for modeled concentrations of the
Stephen Point WWTP’s 13 CECs in the final clarifier with respect to an estimated
first order rate constants of CEC biodegradation (𝑘𝑏𝑖𝑜𝑙′ ), an estimated sludge-water
partitioning coefficient (𝐾𝑑), and an estimated initial CEC concentration (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in
effluent. First set of graphs is for rapidly degrading CECs and last set is for slowly
degrading CECs.
132
Figure B.4. Continued.
133
Figure B.5. Graphs of uncertainty analysis for modeled concentrations of the
Marshfield WWTP’s 13 CECs in the final clarifier with respect to an estimated first
order rate constants of CEC biodegradation (𝑘𝑏𝑖𝑜𝑙′ ), an estimated sludge-water
partitioning coefficient (𝐾𝑑), and an estimated initial CEC concentration (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in
effluent. First set of graphs is for rapidly degrading CECs and last set is for slowly
degrading CECs.
134
Figure B.5. Continued.
135
Model Fit
Figure B.6. Graphs for the Stephen Point WWTP’s 13 CECs comparing modeled
CEC concentrations (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in effluent with measured daily volume-proportional
averages of CEC concentrations in influent and effluent. Dotted red lines indicate
error bounds (± 1 SE) for the modeled CEC concentrations in effluent determined
through uncertainty analysis. First set of graphs is for rapidly degrading CECs and
last set is for slowly degrading CECs.
136
Figure B.6. Continued.
137
Figure B.7. Graphs for the Marshfield WWTP’s 13 CECs comparing modeled CEC
concentrations (𝐶𝐶𝐸𝐶,𝑖𝑛𝑖) in effluent with measured daily volume-proportional
averages of CEC concentrations in influent and effluent. Dotted red lines indicate
error bounds (± 1 SE) for the modeled CEC concentrations in effluent determined
through uncertainty analysis. First set of graphs is for rapidly degrading CECs and
last set is for slowly degrading CECs.
138
Figure B.7. Continued.
139
Data Normality
Figure B.8. Normal probability plots for the Stevens Point WWTP’s 13 CECs
comparing model residuals to estimated cumulative probability. The displayed results
of Anderson-Darling normality test were generated through Minitab 17 (α = 0.05).
The first set of graphs is for rapidly degrading CECs and the last set is for slowly
degrading CECs.
140
Figure B.8. Continued.
141
Figure B.9. Normal probability plots for the Marshfield WWTP’s 13 CECs comparing
model residuals to estimated cumulative probability. The displayed results of
Anderson-Darling normality test were generated through Minitab 17 (α = 0.05). The
first set of graphs is for rapidly degrading CECs and the last set is for slowly
degrading CECs.
142
Figure B.9. Continued.
143
Comparing Rate Constants
Figure B.10. Bar charts comparing first order biodegradation/biotransformation rate
constants (𝑘𝑏𝑖𝑜𝑙′ ) for the CECs of interest in the Stevens Point and Marshfield
WWTPs. The error bars represent 95% confidence intervals. Different letters above
bars indicate a statistical difference between two 𝑘𝑏𝑖𝑜𝑙′ values (α = 0.05). The first set
of graphs is for rapidly degrading CECs and the last set is for slowly degrading CECs.
Numbers by the bars represent values of 𝑘𝑏𝑖𝑜𝑙′ (± standard error).
144
Figure B.10. Continued.
