Estimating Degradation Rates for Contaminants of

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We measured CECs with Triple Quadrupole HPLC/MS. The results of this study showed that acetaminophen, cotinine, caffeine, paraxanthine, and saccharin ...

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

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

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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 iii

into the graduate program of the College of Natural Resources and providing the funds for this study.

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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

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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 vi

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

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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 viii

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

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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

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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 volumeproportional 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. xi

′ 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.

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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

𝜀𝑚𝑜𝑑

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) xiii

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

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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 1

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.

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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).

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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.

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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.

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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 6

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,

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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. 8

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; FernandezFontaina 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

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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.

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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

11

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).

12

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.

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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.

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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.

15

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 Very toxic to aquatic organisms Toxic to aquatic organisms Harmful to aquatic organisms Non-classified

16

EC50 (mg L-1) 100

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.

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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

18

(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)

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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). 20

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.

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Table 4.2. Molecular structure, molecular weight (MW), Henry’s law coefficients (𝐾𝐻 ) at 25°C, and 𝐾𝑑 of the 13 CECs. MW (g mol-1)

𝑲𝑯 (atm m3 mol-1)

Acesulfame

163.15

9.63×10-9

Acetaminophen

151.17

8.93×10-10

Benzoylecgonine

289.33

1.03×10-13

194.19

-11

Compound

Structure

Caffeine

1.10×10

𝑲𝒅 (L kgMLSS-1) 10k 35k 47k 289b 19c 36h 84a 1160f

25l 233c*

14c 140a 537l 10a 20j 36d 66i 195l

15j 36g 43c 135e

Carbamazepine

236.27

1.08×10-7

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*

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Table 4.2. Continued.

Sucralose

Sulfamethazine

Sulfamethoxazole

397.64

3.99×10-19

278.34

-10

253.28

1.93×10

6.42×10-13

5.1b 24k 28k 34l 96k

13h 15c 100.5o

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), l Baalbaki 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.

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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 24

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).

Biosolids storage tanks

Final clarifiers

Lift station and fine screens Primary clarifiers Grit removal

Final clarifier (idle in 2015) Anaerobic basin

Aerobic basins Secondary digester

Primary digesters

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

25

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.

Final clarifiers

Aerobic ditch

Anoxic ditch Lift station and fine screens Cascade aerator

Biosolids storage tanks

Swale

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 26

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.

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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.

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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 29

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 Acesulfame Acetaminophen Benzoylecgonine Caffeine Carbamazepine Cotinine Paraxanthine Saccharin Sucralose Sulfamethazine Sulfamethoxazole Trimethoprim Venlafaxine

Concentration (μg mL-1) 400 200 100 200 100 200 400 1000 1000 100 100 100 100

30

Source Toronto Research Chemicals Inc. Sigma-Aldrich Corporation Grace Discovery Sciences Sigma-Aldrich Corporation Grace Discovery Sciences Sigma-Aldrich Corporation Sigma-Aldrich Corporation Sigma-Aldrich Corporation Toronto Research Chemicals Inc. Sigma-Aldrich Corporation C/D/N Isotopes Inc. Sigma-Aldrich Corporation Sigma-Aldrich Corporation

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 Acesulfame-D4 Acetaminophen-D4 Benzoylecgonine-D3 Caffeine-D9 Carbamazepine-D10 Cotinine-D4 Paraxanthine-D3 Saccharin-D4 Sucralose-D6 Sulfamethazine-D4 Sulfamethoxazole-D4 Trimethoprim-D9 Venlafaxine-D6

Concentration (μg mL-1) 40 20 50 20 10 20 40 100 100 20 20 20 20

Source Toronto Research Chemicals Inc. Sigma-Aldrich Corporation Grace Discovery Sciences Sigma-Aldrich Corporation Grace Discovery Sciences Sigma-Aldrich Corporation Toronto Research Chemicals Inc. Grace Discovery Sciences Toronto Research Chemicals Inc. Sigma-Aldrich Corporation C/D/N Isotopes Inc. Sigma-Aldrich Corporation 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 31

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 (ESIHPLC/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. 32

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 Lowest Std. (ng L-1) (μg L-1) Acesulfame 5.0 1.0 Acetaminophen 35.0 0.5 Benzoylecgonine 5.0 0.25 Benzoylecgonine-D3 5.0 0.25 Caffeine 12.0 0.5 Carbamazepine 2.0 0.25 Cotinine 3.0 0.5 Paraxanthine 5.0 1.0 Sucralose 25.0 2.5 Sulfamethazine 1.0 0.25 Sulfamethoxazole 5.0 0.25 Saccharin 25.0 2.5 Trimethoprim 5.0 0.25 Venlafaxine 5.0 1.0 *80.0 μg L-1 for the analytical runs in 2016.

33

Highest Std. (μg L-1) 80.0 40.0 20.0 20.0 40.0 20.0 40.0 80.0 200.0 20.0 20.0 200.0 20.0 20.0*

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 𝑉𝑟𝑎𝑤 1000 𝑚𝐿 ∙ ∙ 𝜇𝑔 𝑉𝑠𝑡𝑑 + 𝑉𝑟𝑎𝑤 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

106 𝑛𝑔 𝑉𝑒𝑥𝑡 100 % = 𝐶𝑒𝑥𝑡 ∙ ∙ ∙ 𝑚𝑔 𝑉𝑟𝑎𝑤 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 34

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. 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 R E Caffeine 42.1 3.8 33.8E R E Carbamazepine 1.1 0.8 6.6E E E Cotinine 42.7 10.4 0.2E E R Paraxanthine 153.1 * 8.3 27.9R R R,D Sucralose 6.2 5.6 4.7R Sulfamethazine 43.5E 27.9E 26.5E R R Sulfamethoxazole 0.5 7.2 2.9R E E Saccharin 32.7 13.9 56.4E R R Trimethoprim 15.5 12.2 2.2R R R Venlafaxine 6.1 3.7 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. Analyte

35

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. 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 2.0 104.9 0.8 41.6 Carbamazepine 5.0 123.9*⁑ 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 1.3 70.1⁑ 0.5 27.2⁑ Sulfamethoxazole 7.5 186.6*⁑ 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. Analyte

In this study, very polar molecules were acesulfame, acetaminophen, benzoylecgonine, caffeine, cotinine, paraxanthine, saccharin, sucralose, sulfamethazine, 36

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

37

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 1000 𝑝𝑒𝑜𝑝𝑙𝑒 ÷ 18,620 𝑝𝑒𝑜𝑝𝑙𝑒 ∙ = 106 𝑛𝑔 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒

= 138.8 𝑚𝑔 𝐵𝐸/𝑑𝑎𝑦 𝑝𝑒𝑟 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒

38

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 1 ∙ = 289 𝑚𝑔 𝐵𝐸/𝑚𝑚𝑜𝑙 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 39

combined into a single dataset for each drug. The level of significance for the MannWhitney 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.

40

In this case, the two datasets were transformed using either reciprocal or cubic root transformations (Table A.5).

41

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 ∙ 100% 𝐶𝐶𝐸𝐶,𝑖𝑛𝑓

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 42

Point WWTP and the Marshfield WWTP. The level of significance for the MannWhitney 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.

43

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 ′ = − 𝑘𝑎𝑡𝑡 ∙ 𝐶𝐶𝐸𝐶 𝑑𝑡

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 sludgesorbed 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 𝐾𝑑 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑀𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 /𝑑𝑎𝑦 =− 𝑉𝑊𝑊 𝑉𝑊𝑊 =−

𝑏𝑢𝑡

𝜃𝑥 =

𝐾𝑑 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 𝑉𝑊𝑊

𝑉𝑊𝑊 ∙ 𝑋𝑀𝐿𝑆𝑆 𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 𝑋𝑀𝐿𝑆𝑆 , 𝑎𝑛𝑑 𝑡ℎ𝑒𝑛 = 𝑄𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 ∙ 𝑋𝑀𝐿𝑆𝑆,𝑜𝑢𝑡 𝑉𝑊𝑊 𝜃𝑥 44

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 ′ = − 𝑘𝑏𝑖𝑜𝑙 ∙ 𝐶𝐶𝐸𝐶 𝑑𝑡 ′ 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

45

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 ′ = −(𝑘𝑏𝑖𝑜𝑙 + 𝐾𝑑 /𝜃𝑥 ∙ 𝑋𝑀𝐿𝑆𝑆 ) ∙ 𝐶𝐶𝐸𝐶 𝑑𝑡

′ With first order kinetics, 𝑘𝑏𝑖𝑜𝑙 can be converted into a half-life coefficient using

the following relationship:

′ ln(𝐶𝐶𝐸𝐶,𝑡2 /𝐶𝐶𝐸𝐶,𝑡1 ) = −𝑘𝑏𝑖𝑜𝑙 ∙ (𝑡2 − 𝑡1 )

𝑏𝑒𝑐𝑎𝑢𝑠𝑒

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

46

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

47

𝑤ℎ𝑒𝑟𝑒

𝑓𝑎𝑐𝑡 =

1 1 + 𝑏ℎ𝑒𝑡 ∙ 𝜃ℎ𝑒𝑡 𝑇−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).

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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 dailymeasured 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.

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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 ) 𝐾𝑑 ∙ 𝑋𝑀𝐿𝑆𝑆 − 𝜃ℎ 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

Rate

Biodegradation/biotransformation

-1

′ 𝑘𝑏𝑖𝑜𝑙 ∙ 𝐶𝐶𝐸𝐶

Removal due to sorption and sludge removal

-1

𝐾𝑑 /𝜃𝑥 ∙ 𝐶𝐶𝐸𝐶 ∙ 𝑋𝑀𝐿𝑆𝑆

Process

′ 𝐶𝐶𝐸𝐶 = CEC concentration (ng L-1), 𝑘𝑏𝑖𝑜𝑙 = first order biodegradation/ -1 biotransformation rate constant (day ), 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

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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

52

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:

𝑛 2 𝑆𝑆𝑝 = ∑(𝐶𝐶𝐸𝐶,𝑖 − 𝐶̂𝐶𝐸𝐶,𝑝 ) 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 𝐶̂𝐶𝐸𝐶,𝑝 = modeled effluent CEC concentrations as a function of a model parameter

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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 −

𝑢𝑛𝑡𝑖𝑙

𝑝𝑥 − 𝑝𝑥−1 𝑓(𝑝𝑥 ) − 𝑓(𝑝𝑥−1 )

𝑆𝑆𝑝𝑥−1 − 𝑆𝑆𝑝𝑥 ≤ 10−5 𝑆𝑆𝑝𝑥−1

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):

−1

𝑆𝑆𝑝 𝑑𝑓(𝑝) 𝑑𝑓 ′ (𝑝) 𝑆𝐸𝑝 = √ ∙( ∙ ) 𝑛−1 𝑑𝑝 𝑑𝑝

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𝑤ℎ𝑒𝑟𝑒

𝜕𝑓(𝑝) 𝑓(𝑝 + ∆𝑝) − 𝑓(𝑝) ≈ 𝜕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

55

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 ∙

𝑤ℎ𝑒𝑟𝑒

𝜕𝑓(𝑝) 𝜕𝑝

𝜕𝑓(𝑝) 𝑓(𝑝 + ∆𝑝) − 𝑓(𝑝) ≈ 𝜕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 (𝜀𝑝 ) 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:

𝑛

𝑆𝐸𝐶𝐸𝐶 = √∑ 𝜀𝑝2 = √(𝜀𝐾𝑏 )2 + √(𝜀𝐾𝑑 )2 = 𝑝=1

2

= √(

2

′ ) 𝜕𝑓(𝑘𝑏𝑖𝑜𝑙 𝜕𝑓(𝐾𝑑 ) √ ∙ 𝑆𝐸 ) + ( ∙ 𝑆𝐸𝐾𝑑 ) 𝐾𝑏 ′ 𝜕𝑘𝑏𝑖𝑜𝑙 𝜕𝐾𝑑

56

where

𝑆𝐸𝐶𝐸𝐶 = a standard error of modeled CEC concentrations ′ 𝜀𝐾𝑏 = error contribution function for 𝑘𝑏𝑖𝑜𝑙

𝜀𝐾𝑑 = 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.

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′ 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:

𝜀𝑚𝑜𝑑 = ln(𝐶𝐶𝐸𝐶,𝑡2 ) − ln(𝐶̂𝐶𝐸𝐶,𝑡2 ) ′ 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 ln(𝐶𝐶𝐸𝐶,𝑡2 ) = ln(𝐶̂𝐶𝐸𝐶,𝑡1 ) − 𝑘𝑏𝑖𝑜𝑙 ∙ (𝑡2 − 𝑡1 ) + 𝜀𝑚𝑜𝑑 ′ 𝑤ℎ𝑒𝑟𝑒, ln(𝐶̂𝐶𝐸𝐶,𝑡2 ) = ln(𝐶̂𝐶𝐸𝐶,𝑡1 ) − 𝑘𝑏𝑖𝑜𝑙 ∙ (𝑡2 − 𝑡1 )

where 𝜀𝑚𝑜𝑑 = model residual 𝐶̂𝐶𝐸𝐶,𝑡1 = modeled CEC concentration (ng L-1) in a clarifier at a time 𝑡1 𝐶̂𝐶𝐸𝐶,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 58

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.

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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.

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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.

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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. Stevens Point WWTP Marshfield WWTP Loading Rate Attenuation Loading Rate Attenuation CEC (mg day-1 per 1000) Efficiency (%) (mg day-1 per 1000) Efficiency (%) Range Range 𝝋𝟓𝟎% Range Mean 𝝋𝟓𝟎% Range Mean 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 A acesulfame, Bacetaminophen, Cbenzoylecgonine, Dcaffeine, Ecarbamazepine, Fcotinine, G paraxanthine, Hsaccharin, Isucralose, Jsulfamethazine, Ksulfamethoxazole, Ltrimethoprim, M venlafaxine.

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 63

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).

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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

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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

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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.

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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

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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).

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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.

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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).

CEC Acesulfame Acetaminophen Benzoylecgonine Caffeine Carbamazepine Cotinine Paraxanthine Saccharin Sucralose Sulfamethazine Sulfamethoxazole Trimethoprim Venlafaxine

Stevens Point WWTP % 𝒌′𝒃𝒊𝒐𝒍 𝒌𝒃𝒊𝒐𝒍 -1 -1 -1 biol (L g d ) (d ) MLSS 0.103 0.082 72.0 15.647 12.418 99.1 0.256 0.203 82.5 7.526 5.973 98.7 0.085 0.067 76.6 4.504 3.575 99.7 5.904 4.686 99.4 7.872 6.248 100.0 0.444 0.352 96.6 0.715 0.567 97.5 1.166 0.925 97.6 0.240 0.190 51.8 0.228 0.181 86.3

Marshfield WWTP % 𝒌′𝒃𝒊𝒐𝒍 𝒌𝒃𝒊𝒐𝒍 -1 -1 -1 biol (L g d ) (d ) MLSS 1.464 0.579 99.4 7.285 2.881 99.6 1.056 0.418 98.9 3.989 1.577 99.5 -0.020 -0.008 0.0 2.512 0.993 99.9 3.303 1.306 99.8 3.244 1.283 100.0 0.003 0.001 46.1 0.134 0.053 97.1 0.465 0.184 98.6 0.158 0.062 76.0 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

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′ 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.

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′ 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

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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

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′ 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. 75

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.

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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.

Stevens Point 𝒌′𝒃𝒊𝒐𝒍 𝒌𝒃𝒊𝒐𝒍 (L -1 gMLSS-1 d-1) (d ) -0.030 -0.024 61.987 49.196

Marshfield 𝒌′𝒃𝒊𝒐𝒍 𝒌𝒃𝒊𝒐𝒍 (L -1 gMLSS-1 d-1) (d ) 2.132 0.843 19.185 7.586

Ref. 𝒌𝒃𝒊𝒐𝒍 (L gMLSS-1 d-1) Acesulfame 0.029-0.060f,l, 1.27-1.57n Acetaminophen 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 i Sulfamethoxazole 0.722 0.573 0.331 0.131 0.60 , ≤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 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), j Yin 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). CEC

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

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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. 78

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 80

= 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 83

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).

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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 86

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 flowproportional 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

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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, aboverange 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. 92

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 nonsteady 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 96

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

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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.

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Sun

CECs in Influent Acesulfame 43134.4R 45384.0R 45887.9R 44592.3R 43660.0R 38399.6R 36933.8R D D D R R R Acetaminophen 11164.6 89011.5 38805.0 15742.7 21179.8 2233.3 18610.7R E E E E E E Benzoylecgonine 228.4 238.5 211.5 100.4 199.5 261.1 239.7E E E E E E E Benzoylecgonine-D3 12.0 9.8 10.2 4.4 1.7 3.9 3.8E Caffeine 86005.2D 91166.9D 89829.4D 70293.9R,A 70900.1R,A 69806.1R,A 73950.3R,A Carbamazepine 193.5E 255.3E 216.1E 283.3R 262.3R,B 279.2R 302.1R R R R R R R Cotinine 2321.5 2279.9 2256.2 1621.0 1560.6 1559.8 1516.1R R R R R R R Paraxanthine 16132.2 18191.1 18298.6 13406.5 13679.8 12316.4 12118.8R R R R R R R Sucralose 92046.0 80471.6 65510.0 45206.9 44131.8 40361.1 40257.0R Sulfamethazine 19.9E 41.9E 43.0E 22.1E 212.8E 41.6E 10.3E R R R R R R Sulfamethoxazole 554.7 815.2 730.2 1097.5 963.9 1076.7 885.0R R R R R R R Saccharin 23131.0 24285.1 26151.3 19263.0 18045.0 17882.9 16656.3R R R R R R R Trimethoprim 673.8 648.6 795.8 402.2 376.7 400.3 274.5R,B R R R R R R Venlafaxine 3497.8 3518.6 3517.1 485.0 496.6 555.3 505.1R A Above upper detection limit. BBelow lower detection limit or limit of detection. EExtracted samples. RRaw samples.

2016 Analytical Runs Mon Tue Wed

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.

A. APPENDIX A – Tables

Analytical Results

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2015 Analytical Runs Fri Sat

Thu Sun CECs in Effluent Acesulfame 39931.4R 42731.2R 43786.4R 40998.9R 33849.1R 41206.9R 38099.2R Acetaminophen 1.7E,B 0.6E,B 2.8E,B 7.4E,B 37.5E 1.1E,B 17.1E,B E E E E E E Benzoylecgonine 257.5 164.6 198.9 111.4 129.4 215.3 240.2E Benzoylecgonine-D3 10.9E 13.2E 9.5E 3.5E 5.5E 3.2E 2.5E R R R R R R Caffeine 26305.8 35667.1 42555.9 3652.7 1529.1 431.1 1025.0E,A E E E R R,B R,B Carbamazepine 198.8 225.3 260.9 297.5 210.6 264.0 271.7R,B R R R E E E Cotinine 871.7 915.7 1040.4 194.8 150.4 217.0 143.1E R R R E,A E,A E Paraxanthine 4436.1 6866.8 9465.3 1404.3 843.0 512.8 237.9E Sucralose 56133.6R 42711.1R 54343.4R 46088.5R 35889.6R 37739.5R 44134.7R Sulfamethazine 13.1E 18.3E 32.6E 20.2E 59.3E 65.2E 15.2E R R R R R R Sulfamethoxazole 286.3 360.6 478.6 739.0 529.5 756.5 786.3R R R R E E E,B Saccharin 5691.2 6973.6 9678.7 1097.6 706.3 23.0 470.0E R R R R R,B R Trimethoprim 662.3 718.9 776.7 314.6 245.4 316.9 214.9R,B R R R R R R Venlafaxine 3238.0 3377.6 3478.3 481.4 352.4 488.4 480.5R A Above upper detection limit. BBelow lower detection limit or limit of detection. EExtracted samples. RRaw samples.

Table A.1. Continued.

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Tue

Wed

2016 Analytical Runs Thu Fri

Sat Sun CECs in Influent Acesulfame 30098.2R 32292.3R 41080.9R 35010.9R 38294.4R 35790.7R 31915.0R Acetaminophen 92178.6D 114812.5R,A 107873.5R,A 123714.1R,A 127401.2R,A 115351.8R,A 121068.9R,A Benzoylecgonine 239.7E 315.7E 228.3E 120.8E 312.6E 256.8E 267.7E E E E E E E Benzoylecgonine-D3 10.2 12.9 11.4 13.0 12.5 14.1 19.6E D D D D D D Caffeine 70363.0 68784.1 72314.0 67274.4 72708.2 62542.2 70837.2D R R R R R R Carbamazepine 710.2 729.0 2607.3 724.2 718.5 706.2 733.4R R R R R R R Cotinine 2244.2 2311.8 2332.1 2479.6 2619.2 2357.4 2340.6R Paraxanthine 11200.0R 12133.3R 13946.6R 14434.1R 14061.3R 13351.8R 13029.2R R R D R R R Sucralose 38794.9 48253.2 47348.9 62702.1 51695.8 40423.7 73471.5D E E E E E E Sulfamethazine 7.8 11.1 7.4 5.2 6.4 5.8 5.5E R R R R R R Sulfamethoxazole 887.2 1638.3 1508.6 1087.3 1442.8 917.4 1521.0R R R R R R R Saccharin 16682.5 17743.8 19256.5 18314.8 19502.2 18780.5 17886.9R R R R R R R Trimethoprim 644.6 772.1 700.4 741.9 724.2 720.1 800.9R Venlafaxine 2427.9R 2526.4R 2469.2R 2573.2R 2548.2R 3384.2R 2626.3R A Above upper detection limit. The linearity of calibration curve above this level has been confirmed through check standards 80 and 160 μg L-1 check standards with the recoveries of 111% and 102%, respectively. BBelow lower detection limit or limit of detection. DDiluted raw samples. EExtracted samples. RRaw samples.

Mon

Table A.2. Influent and effluent CEC concentrations (ng L-1) from the Marshfield WWTP generated through the analytical runs in 2016.

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Mon

Tue

Wed 2295.8R 0.0E,B 34.0E 10.8E 40.0E 1065.0R 28.8E 21.2E 55112.2R 7.3E 560.4R 76.0E 509.9R 2523.0R

2627.2R 0.0E,B 31.2E 10.0E 47.7E 1010.1R 29.3E 49.5E 46823.6R 8.1E 579.5R 91.8E 527.8R 2556.9R

2016 Analytical Runs Thu Fri

CECs in Effluent Acesulfame 2088.4R 2282.4R 2405.8R E,B E,B Acetaminophen 0.0 0.0 11.8E,B E E Benzoylecgonine 30.8 38.4 24.4E E E Benzoylecognine-D3 21.7 20.9 11.0E E E Caffeine 36.0 42.4 80.8E Carbamazepine 773.4R 802.2R 764.1R E E Cotinine 22.3 21.7 19.4E E E Paraxanthine 21.3 27.6 34.7E R R Sucralose 56929.9 47469.9 45294.9R E E Sulfamethazine 3.8 3.8 4.0E Sulfamethoxazole 402.9R 427.3R 578.1R E E Saccharin 46.5 24.2 30.7E R R Trimethoprim 456.4 474.3 444.4R R R Venlafaxine 2308.2 2434.1 2311.2R D Diluted raw samples. EExtracted samples. RRaw samples.

Table A.2. Continued.

2733.8R 0.0E,B 49.1E 13.1E 56.9E 984.1R 30.0E 43.9E 65396.4R 6.0E 600.2R 76.3E 571.5R 2586.0R

Sat

2570.5R 0.0E,B 41.3E 10.4E 48.3E 900.5R 26.8E 44.2E 43706.0R 6.5E 659.5R 54.5E 511.5R 2642.8R

Sun

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 Acesulfame in 2015 in 2016 Acetaminophen in 2015 in 2016 Benzoylecgonine in 2015 in 2016 Caffeine in 2015 in 2016 Carbamazepine in 2015 in 2016 Cotinine in 2015 in 2016 Paraxanthine in 2015 in 2016 Saccharin in 2015 in 2016 Sucralose in 2015 in 2016 Sulfamethazine in 2015 in 2016 Sulfamethoxazole in 2015 in 2016 Trimethoprim in 2015 in 2016 Venlafaxine in 2015 in 2016

𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟏 (ng L-1)

𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟐 (ng L-1)

𝑪𝑪𝑬𝑪,𝒊𝒏𝒊𝟑 (ng L-1)

44592.3 43134.4

42795.6 41532.9

40998.9 39931.4

15742.7 11164.6

7888.8 5599.8

35.0 35.0

100.4 228.4

105.9 243.0

111.4 257.5

70293.9 86005.2

36973.3 56155.5

3652.7 26305.8

283.3 193.5

290.4 196.2

297.5 198.8

1621.0 2321.5

907.9 1596.6

194.8 871.7

13406.5 16132.2

7405.4 10284.2

1404.3 4436.1

19263.0 23131.0

10180.3 14411.1

1097.6 5691.2

45206.9 92046.0

45647.7 74089.8

46088.5 56133.6

22.1 19.9

21.2 16.5

20.2 13.1

1097.5 554.7

918.2 420.5

739.0 286.3

402.2 673.8

358.4 668.0

314.6 662.3

485.0 3497.8

483.2 3367.9

481.4 3238.0

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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 Acetaminophen Benzoylecgonine Caffeine Carbamazepine Cotinine Paraxanthine Saccharin Sucralose Sulfamethazine Sulfamethoxazole Trimethoprim Venlafaxine

30098.2 92178.6 239.7 70363.0 710.2 2244.2 11200.0 16682.5 38794.9 7.8 887.2 644.6 2427.9

16093.3 46106.8 135.3 35199.5 741.8 1133.3 5610.6 8364.5 47862.4 5.8 645.0 550.5 2368.0

2088.4 35.0 30.8 36.0 773.4 22.3 21.3 46.5 56929.9 3.8 402.9 456.4 2308.2

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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. CEC Attenuation Efficiencies Acesulfame Acetaminophen Benzoylecgonine Caffeine Carbamazepine Cotinine Paraxanthine Saccharin Sucralose Sulfamethazine Sulfamethoxazole Trimethoprim Venlafaxine

Dataset 1†

Dataset 2†

𝑵

𝑺𝒌𝒆𝒘

𝑲𝒖𝒓𝒕𝒐𝒔𝒊𝒔

𝑵

𝑺𝒌𝒆𝒘

𝑲𝒖𝒓𝒕𝒐𝒔𝒊𝒔

7 7 7 7 7 7 7 7 7 7 7 7 7

0.63 -1.46* -0.42* -0.57 -0.97⁑ -0.44* -0.81 -0.56 0.35 -0.52 -0.60 -0.29 1.89

1.35 2.03* -2.11* -2.01 0.06⁑ -2.40* -0.92 -1.85 -1.05 -1.15 0.49 -0.92 3.79

7 7 7 7 7 7 7 7 7 7 7 7 7

0.08 -1.46* -1.63* -1.27 -0.60⁑ 0.36* 0.21 0.27 -0.69 0.21 -0.44 -0.25 2.12

0.29 2.03* -2.58* 0.87 -0.73⁑ -0.66* -1.67 -1.53 -0.42 -2.19 1.31 -0.77 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 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.

124

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 Acesulfame Acetaminophen Benzoylecgonine Caffeine Carbamazepine Cotinine Paraxanthine Saccharin Sucralose Sulfamethazine Sulfamethoxazole Trimethoprim Venlafaxine

Stevens Point WWTP 𝑵 7 7 7 7 7 7 7 7 7 7 7 7 7

𝑨𝑫 0.276 0.370 0.274 0.406 0.209 0.598 0.464 0.405 0.160 0.531 0.223 0.334 0.471

125

𝒑 0.536 0.314 0.541 0.249 0.800 0.072 0.171 0.251 0.909 0.112 0.722 0.393 0.163

Marshfield WWTP 𝑵 7 7 7 7 7 7 7 7 7 7 7 7 7

𝑨𝑫 0.178 0.293 0.390 0.348 0.199 0.313 0.571 0.579 0.255 0.524 0.228 0.297 0.488

𝒑 0.872 0.504 0.276 0.361 0.810 0.447 0.086 0.082 0.599 0.117 0.702 0.491 0.146

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”).

126

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.

127

Figure B.2. Continued.

128

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.

129

Figure B.3. Continued.

130

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.

131

Figure B.4. Continued.

132

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.

133

Figure B.5. Continued.

134

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.

135

Figure B.6. Continued.

136

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.

137

Figure B.7. Continued.

138

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.

139

Figure B.8. Continued.

140

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.

141

Figure B.9. Continued.

142

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).

143

Figure B.10. Continued.

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