SORPTION AND BIODEGRADATION OF PHARMACEUTICAL COMPOUNDS IN BIOLOGICAL WASTEWATER TREATMENT PROCESS Except where reference is made to the work of others, the work described in this dissertation is my own or was done in collaboration with my advisory committee. This dissertation does not include proprietary or classified information. ____________________________________ Taewoo Yi Certificate of Approval: _____________________________ _____________________________ Clifford R. Lange Willie F. Harper Jr, Chair Associate Professor Associate Professor Civil Engineering Civil Engineering _____________________________ _____________________________ Yucheng Feng Orlando Acevedo Associate Professor Assistant Professor Agronomy and Soils Chemistry and Biochemistry _____________________________ Joe F. Pittman Interim Dean Graduate School SORPTION AND BIODEGRADATION OF PHARMACEUTICAL COMPOUNDS IN BIOLOGICAL WASTEWATER TREATMENT PROCESS Taewoo Yi A Dissertation Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Auburn, Alabama December 17, 2007 iii SORPTION AND BIODEGRADATION OF PHARMACEUTICAL COMPOUNDS IN BIOLOGICAL WASTEWATER TREATMENT PROCESS Taewoo Yi Permission is granted to Auburn University to make copies of this dissertation at its discretion, upon request of individuals or institutions and at their expense. The author reserves all publication rights. ______________________________ Signature of Author ______________________________ Date of Graduation iv VITA Taewoo Yi, son of JongWong Yi and MungSuk Lim, was born on Jan 12, 1972, in Boeun, Korea. He has one brother, Chanwoo Yi and one sister, Kyungkak Yi. After he completed elementary and high School in 1990, In March 1990 he entered Daejon University in Daejon, Korea and in February 1998 was awarded a Bachelor of Science degree in Environmental Engineering. He served as a military engineer in the Korean Army from April 1992 to June 1994. From September 1997, until July 1985, He attended the Graduate school in Biotechnology and Bioengineering at Korea University and then entered the Graduate studies at Iowa State University in September 2001 and received the degree of Master of Science (Civil and Environmental Engineering) in May 2003. In April 2007, he was awarded CH2M Hill Fellowship Award. Then, he came to Auburn University, Alabama and began work as a graduate student, in pursuit of the degree of Doctor of Philosophy in August 2003. v DISSERTATION ABSTRACT SORPTION AND BIODEGRADATION OF PHARMACEUTICAL COMPOUNDS IN BIOLOGICAL WASTEWATER TREATMENT PROCESS Taewoo Yi Doctor of Philosophy, December 17, 2007 (M.S., Iowa State University, 2003) (M.S., Korea University, Korea, 1999) (B.S., Daejon University, Korea, 1997) 215 Typed Pages Directed by Willie F. Harper Jr. This study was performed to investigate the removal mechanisms of pharmaceutical compounds (PhACs) in biological treatment processes. The removal efficiencies and byproducts of three model compounds including 17?-ethinylestradiol (EE2), Carbamazepine (CBZ), and Trimethoprim (TMP) were monitored in laboratory scale membrane bioreactor (MBR), sequencing batch reactor (SBR), and conventional bioreactor (CBR). Laboratory scale bioreactors were used to investigate sorption and biodegradation of EE2. Results showed that the sludge taken from the MBR had partitioning coefficient (K d ) that was more than twice that of biomass derived from SBRs. The MBR biomass had smaller particles and was more hydrophobic than the SBR biomass. Experiments with nitrifying sludge showed that sorption was more important when the initial ammonia concentration was 48 mg/L or less, but at higher initial vi ammonia concentrations the role of biodegradation became more important. The ammonia monooxygenase (AMO) containing protein extract removed EE2 in batch tests. The influence of biomass characteristics on K d and sorption-hysteresis of EE2 using MBR and SBR was investigated under normal and nutrient deficiency condition at different SRT. Under normal growth condition, the biomass mean particle size had a dramatic effect on K d and on sorption hysteresis index (HI). The EE2 partitioning coefficient and sorption hysteresis showed the considerable nonlinear relationship with the mean particle size. Visualization study confirmed this phenomenon. Although under nitrogen deficiency condition, K d and HI had weak correlation with particle size, overall results showed that the magnitude of the K d and sorption-hysteresis is affected by the particle size. This study also numerically explored the impacts of sorption hysteresis. Batch experiments showed that ring A of EE2 is the site of electrophilic initiating reactions, including conjugation and hydroxylation. Ring A was also cleaved before any of the other rings are broken, which is likely because the Frontier Electron Density (FED) of the ring A carbon units is higher than those of rings B, C, or D. EE2 and NH 3 were degraded in the presence of an AMO containing protein extract, and the reaction stoichiometry was consistent with a conceptual model. Continuous tests showed a linear relationship between nitrification and EE2 removal in enriched nitrifying cultures. Removal efficiencies of EE2, CBZ, and TMP were monitored in nitrifying sludge reactor and conventional bioreactor fed with toluene. EE2 was most efficiently removed in both reactors. The prediction tool combined with FED and degradation rules was applied to predict biodegradation reaction. Degradation reaction took place in the high FED region in three model compounds. vii ACKNOWLEDGMENTS I would like to thank the financial support to National Science Foundation, and Auburn University. I would like to thank the members of my committee, particularly Dr. Willie F. Harper Jr, for their guidance and valuable suggestions in this work. I would like to deeply express gratitude to my wife and sons for lovely support and prayer. I wish to thank my parents and other family members for endurance and their love. Finally, I give the heart full of thanks to my Lord Jesus Christ for His grace. May this work be to His Glory. viii Style manual or journal used: Water Environment Research Computer software used: Guassian 03; Amira4.0; Hyperchem 7.5; Microsoft Excel 2003; Microsoft Word 2003 . ix TABLE OF CONTENTS DISSERTATION ABSTRACT ...................................................................................... v LIST OF TABLES ........................................................................................................ xii LIST OF FIGURES ...................................................................................................... xiii I. INTRODUCTION........................................................................................................... 1 Objectives and scope............................................................................................... 3 Organization............................................................................................................ 4 II. LITERATURE REVIEW............................................................................................... 6 Full-scale studies of sorption and biodegradation. ................................................. 6 The role of extracellular polymeric substances in sorption .................................. 20 Influence of sorption/desorption hysteresis on biodegradation ............................ 21 Enzymes of interest............................................................................................... 22 Frontier electron density ....................................................................................... 28 III. THE ROLE OF PARTICLE SIZE AND AMMONIUM OXIDATION IN REMOVAL OF 17?-ETHINYL ESTRADIOL IN BIOREACTORS............................. 30 Abstract................................................................................................................. 30 Methodology......................................................................................................... 31 Results and Discussion ......................................................................................... 33 Conclusions........................................................................................................... 35 References............................................................................................................. 38 x IV. THE EFFECT OF BIOMASS CHARACTERISTICS ON THE PARTITIONING AND SORPTION HYSTERESIS OF 17?-ETHINYLESTRADIOL.............................. 41 Abstract................................................................................................................. 41 Methodology......................................................................................................... 45 Results and Discussion ......................................................................................... 50 Conclusions........................................................................................................... 74 Acknowledgements............................................................................................... 76 References............................................................................................................. 76 V. THE LINK BETWEEN NITRIFICATION AND BIOTRANSFORMATION OF 17?- ETHINYLESTRADIOL................................................................................................... 80 Abstract................................................................................................................. 80 Methodology......................................................................................................... 83 Results and Discussion ......................................................................................... 87 Conclusions......................................................................................................... 102 References........................................................................................................... 103 VI. DEGRADATION OF 17?-ETHINYLESTRADIOL, CARBAMAZEPINE, AND TRIMETHOPRIM IN NITRIFYING SLUDGE REACTOR AND CONVENTIONAL ACTIVATED SLUDGE FED WITH TOLUENE ......................................................... 107 Abstract............................................................................................................... 107 Methodology....................................................................................................... 109 Results and Discussion ....................................................................................... 113 Conclusions......................................................................................................... 126 References........................................................................................................... 126 VII. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH................ 131 Summary and Conclusions ................................................................................. 131 Suggestions for Future Work.............................................................................. 133 xi BIBLIOGRAPHY........................................................................................................... 135 APPENDICES ................................................................................................................ 149 An example of Gaussian Code for Structure optimization and FED calculation 150 Routine Operational Data ................................................................................... 156 Reactors and reactor configuration ..................................................................... 162 Degradation experiment data .............................................................................. 167 Sorption experiment data .................................................................................... 173 TLC plates for continuous reactor ...................................................................... 181 Full degradation pathways predicted using FED and degradation rules and 1H NMR data of byproducts..................................................................................... 188 xii LIST OF TABLES Table 2.1 Toluene monooxygenase enzymes ................................................................... 27 Table 3.1 Statistical comparison of the EE2 sorption data ............................................... 35 Table 4.1 The effect of nitrogen-limitation on exocelluar polymer production ............... 56 Table 4.2 The effect of SRT of the means of the particle size distribution ...................... 62 Table 5.1. EE2 biotransformation byproducts detected by NMR..................................... 96 Table 6.1 Structures and Properties of model compounds ............................................. 112 Table 6.2 Byproducts of 17?-EthinylEstradiol identified............................................... 118 Table 6.3 Byproducts of Trimethoprim identified.......................................................... 119 Table 6.4 Byproducts of Carbamazepine identified ....................................................... 121 Table B.1 Synthetic influent feed ................................................................................... 157 xiii LIST OF FIGURES Figure 2.1 AMO degradation mechanism......................................................................... 24 Figure 2.2 Reaction mechanism of Cytochrome P450 monooxygenase .......................... 26 Figure 3.1 Effect of initial ammonia concentration on the sorption and biodegradation of EE2............................................................................................................................. 36 Figure 3.2 Degradation of EE2 with an AMO-containing extract.................................... 37 Figure 4.1 An example showing a set of sorption and desorption isotherms for MBR and CBR biomass.............................................................................................................. 52 Figure 4.2 The effect of mean particle size and specific surface area on K d : normal growth conditions....................................................................................................... 53 Figure 4.3 The effect of mean particle size and specific surface area on K d : nitrogen- limited growth. ........................................................................................................... 55 Figure 4.4 The effect of mean particle size and surface area on HI: normal growth........ 58 Figure 4.5 The effect of mean particle size and surface area on HI: nitrogen-limited growth. ....................................................................................................................... 59 Figure 4.6 The effect of solids retention time on K d ......................................................... 60 Figure 4.7 The effect of sludge retention time on the hysteresis index. ........................... 61 Figure 4.8 Visualization of Sorption using Miroautoradiograpy...................................... 64 Figure 4.9 Visualization of Hysteresis using confocal microscope; Biomass (red) and green (Diclofop-methly)............................................................................................. 65 Figure 4.10 Typical sorption and desorption isotherms for CBR biomass: an example illustrating the loss of sorption capacity and the determination of ?. ........................ 67 Figure 4.11 Continuous flow (CSTR) activated sludge wastewater treatment plant schematic.................................................................................................................... 70 Figure 4.12 The effect of sorption hysteresis on relative normalized effluent quality..... 73 xiv Figure 5.1 Fluorescent in-situ hybridization of Nitrosomonas sp., Nitrosococcus sp. and Nitrosospira sp. cells from nitrifying sludge reactor. Cell hybridization was done using Nitri-VIT(Vermicon AG) ................................................................................. 88 Figure 5.2 Typical Thin Layer Chromatography Plate..................................................... 89 Figure 5.3 Example of an observed NMR spectrum: This shows that Ring A of EthinylEstradiol was cleaved ..................................................................................... 92 Figure 5.4 17?-Ethinylestradiol structure with electron density shown for the carbon units with the highest FED values ...................................................................................... 93 Figure 5.5 EE2 and E2 removal in the presence of an AMO-containing extract ............. 94 Figure 5.6 Conceptual model for AMO role in cometabolic transformation: Catalytic reaction cycle involving a binuclear copper site ........................................................ 97 Figure 5.7 Stoichiometry of EE2 and NADH removal..................................................... 99 Figure 5.8 Relationship between NH 3 -N and EE2 biotransformation rate..................... 100 Figure 5.9 Degradation of EE2 with inhibitor (allylthiourea) ........................................ 101 Figure 6.1 Degradation rules........................................................................................... 114 Figure 6.2 Removal Efficiency of model compounds .................................................... 117 Figure 6.3 Frontier Electron Density profile for 17?-EthinylEstradiol .......................... 120 Figure 6.4 Frontier Electron Density profile for (a) Trimethoprim (b) OH-TMP.......... 122 Figure 6.5 Frontier Electron Density profile for Carbamazepine................................... 124 Figure 6.6 Metabolic pathways of model compounds predicted by prediction tool; (a) Trimethoprim, (b) Carbamazepine, and (c) 17?-EthinylEstradiol........................... 125 Figure B.1 MLSS concentration for SBR and MBR ...................................................... 158 Figure B.2 MLSS and Effluent TSS concentration for Nitrifying sludge reactor .......... 159 Figure B.3 Effluent TSS concentration for SBR and MBR............................................ 160 Figure B.4 NH 3 -N Concentration in Influent and Effluent............................................. 161 Figure C.1 CSTR fed with toluene ................................................................................. 163 Figure C.2 Nitrifying membrane bioreactor ................................................................... 164 Figure C.3 Membrane bioreactor.................................................................................... 165 Figure C.4 Sequencing bioreactor................................................................................... 166 Figure D.1 Nitrobacter sp. FISH image of nitrifying sludge using vermicon kit ........... 168 xv Figure D.2 EE2 removed at different initial toluene concentration in batch tests.......... 169 Figure D.3 Degradation of EE2 with Toluene in batch tests (intial concentration of Toluene = 50 mg/L) ................................................................................................. 170 Figure D.4 Degradation of EE2 with whole cell............................................................. 171 Figrue D.5 Degradation Tests using nitrifying sludge (initial concentration of EE2 = 100ug/L, NH 3 -N = 30mg/L)..................................................................................... 172 Figure E.1 Relationship between protein conc. of EPS and Partitioning coefficient (K d ) .................................................................................................................................. 174 Figure E.2 The relationship between Carbohydrate conc. of EPS and partitioning coefficient (K d )......................................................................................................... 175 Figure E.3 Floc 3D Structure using confocal microscope .............................................. 176 Figrue E.4 Hydrophobicity change with/without sodium azide (control test)................ 177 Figure E.5 Partitioning coefficienty (K d ) vs. Surface area of floc.................................. 178 Figure E.6 Hysteresis Index (HI) vs. Mean particle size of floc..................................... 179 Figure E.7 EE2 romoved in batch test at different concentration of NH 3 -N .................. 180 Figure F.1 TLC plate for EE2 in Nitrifying sludge reactor ............................................ 182 Figure F.2 TLC plate for Trimethoprim in Nitrifying sludge reactor............................. 183 Figure F.3 TLC plate for Carbamazepin in Nitrifying sludge reactor ............................ 184 Figure F.4 TLC plate for EE2 in CSTR fed with toluene............................................... 185 Figure F.5 TLC plate for Carbamazepine in CSTR fed with toluene............................ 186 Figure F.6 TLC plate for Trimethoprim in CSTR fed with toluene ............................... 187 Figure G.1 EE2 degradation (full) pathway using MO theory and degradation rules.... 189 Figure G.2 Carbamazepine degradation (full) pathway using MO theory and degradation rules.......................................................................................................................... 192 Figure G.3 Trimethoprim degradation (full) pathway using MO theory and degradation rules.......................................................................................................................... 194 Figure G.4 The observed NMR spectrum: This shows that hydroxylation of EthinylEstradiol........................................................................................................ 196 Figure G.5 The observed NMR spectrum: This shows that conjugation of EthinylEstradiol........................................................................................................ 197 xvi Figure G.6 1H NMR spectrum for Trimethoprim byproduct; ring cleavage.................. 198 Figure G.7 1H NMR spectrum for byproduct of Trimethoprim; Hydroxylation ........... 199 1 I. INTRODUCTION The presence of pharmaceutical compounds (PhACs) and their byproducts in water bodies may cause reproductive anomalies in invertebrates and possibly humans. Public awareness and concern has grown significantly over the past three decades and has brought this issue to the forefront in the water quality community. These concerns have been intensified even more over the past 15 years as improvements in analytical methods coupled with larger scale surveys revealed the broad range of apparently persistent PhACs that are present in the water cycle (Ternes et al., 1999a). Toxicologists are now working to quantify the risks associated with long term exposure, and governmental agencies are positioning themselves to eventually develop policies and perhaps regulations. Removing PhACs during biological wastewater treatment is probably important for preventing the proliferation of these chemicals in our environment. Accordingly, the wastewater treatment community has responded to these concerns with a great deal of applied research. Analytical methods are now available for low level detection of PhACs in wastewater, and it is now known that many PhACs are removed only partially in the activated sludge process (Ternes et al, 1999b). It is essential to understand the fate and behavior of pharmaceutical compounds discharged into a wastewater treatment processes. PhACs in the wastewater treatment plants are controlled by several important mechanisms including sorption/desorption, aerobic and anaerobic 2 biotransformation, abiotic-biodegradation, and volatilization. Sorption and biodegradation are main removal mechanisms in activated sludge processes. It is well-known that operating conditions such as nutrient deficiency and solid retention time (SRT) affect biomass characteristics such as particle size and surface properties. Nitrogen deficiency causes the production of extracellular polymeric substances (EPS) (Ramirez-Castillo and Uriblarrea, 2004; Pratt et al., 2007). The relationship between SRT and floc size has been investigated (Mass? et al., 2006; Liao et al., 2006; Liao et al., 2002) and it was believed that SRTs have an effect on floc size. Surface properties usually were represented by hydrophobicity. Hydrophobic sludge properties can enhance the removal of organic compounds, especially since organic pollutants have a relatively high octanol/water coefficient (Holthaus et al., 2002). Properties of EPS could represent the surface properties of biomass. The biomass characteristics may have a significant effect on sorption. In addition, Particle size was considered as another main factor, because different size particle shows different sorbability to organic compounds. However, the relationship between sorption (or sorption-hysteresis) and surface properties or particle size have not been clearly investigated so far, although biomass characteristics have highly effect on sorption of pharmaceutical compounds. PhAC biodegradation is likely due to cometabolic activity because PhACs are not present in high enough concentration to support substantial biomass growth. Much of the previous work on PhAC biodegradation has focused on the disappearance of parent compounds and/or the detection of daughter products. This data has been used to try to construct metabolic pathways, but these pathways are incomplete with missing 3 intermediates. Recently, Haiyan et al. (2006) proposed a new degradation pathway in which ethynyl group in EE2 was important. Previously, phenolic ring has been considered as the most reactive part. Construction of a complete metabolic pathway requires a prediction tool with which to predict and understand transformation. Frontier electron density (FED) has great promise for providing the organizing principle that is needed. Fukui pioneered much of the early work connecting FED to chemical reactivity in aromatic hydrocarbons. Wheland and Pauling (1935) successfully used FED to explain the reactivity of substituted aromatics. More recently, Ohura et al. (2005) showed that air-bore polycyclic aromatic hydrocarbons were abiotically chlorinated in positions that corresponded to high FED. Lee et al. (2001) used Fenton oxidation to remove polycyclic aromatic hydrocarbons, and they successfully used FED to predict the order of daughter product production. Although these previous attempts focused on abiotic reactions, they bolster the promise for predicting biological oxidations in the same way. Prior efforts to conduct predictions of biodegradation have generally focused on readily degradable substrates (e.g. glucose) that enter well-characterized metabolic pathways (e.g. glycolysis). FED presents the promise of predicting biodegradation on complex organics like PhACs; a contribution here will make a significant impact, most certainly stretching well beyond issues related to wastewater treatment. Objectives and scope The objectives of this research were (1) to evaluate the effect of hydrophobicity of biomass on EE2 sorption and biomass characteristics and to characterize the effect of particle size on partitioning coefficients and sorption-hysteresis for EE2 and potential 4 effects of sorption-hysteresis on effluent water quality, (2) to investigate the degradation mechanism of three model compounds in nitrifying sludge and conventional bioreactor fed with toluene, and (3) to develop a prediction tool for predicting biologically-mediated PhACs transformation in biological treatment processes. An improved understanding of the fundamentals of the biodegradation and sorption is essential to develop economical design of processes and to efficiently operate already installed wastewater treatment plants. A specific objective of this paper is to determine the influence factors for sorption and desorption and the role of monooxygenase, especially ammonia monooxygenase and toluene monooxygenase, in degradation of model compounds. The specific tasks accomplished during this research are as follows: 1) Conducted a comprehensive literature search pertinent to research carried out on the removal mechanisms. 2) Compared the capacity of sorption and sorption-hysteresis of the sludge from different types of reactor under various operating conditions. 3) Investigated degradation mechanisms and applied frontier electron density to predict biotransformation. Organization This dissertation is formatted in journal paper except chapter 1 and 7. For the purpose of accomplishing the research objectives, it was needed to distinguish four separate subtasks reflecting characteristics of sorption and sorption-hysteresis and degradation mechanisms in biological treatment processes. Each subtask was carried out 5 as an independent investigation presented in Chapters 3 through 6. This dissertation is presented in the following order: Chapter 2 presents the currently investigated full-scale studies of sorption and biodegradation, cell properties effect on sorption, and interest enzymes which are well known in cometablic mechanism based on a review of the literature. Chapter 3 presents introductory experiments; sorption with MBR and SBRs and biodegradation using nitrifying sludge reactor and enzyme extracts. Chapter 4 describes the relationship between biomass properties under various operating conditions, including the SRT and nutrient limiting condition, and sorption (or sorption-hysteresis). Chapter 5 presents biotransformation mechanism of 17?-ethinylestradiol (EE2) in nitrifying sludge reactor. FED analyses was performed to predict biodegradation reaction and to investigate the applicability of FED to biological reaction. Chapter 6 discusses prediction tool and removal of carbamazepine, trimethoprim, and 17?-ethinylestradiol in nitrifying sludge reactor and conventional bioreactor fed with toluene. Finally, Chapter 7 presents the summary and conclusions for all studies done and recommendations for future study. 6 II. LITERATURE REVIEW Full-scale studies of sorption and biodegradation. Numerous reports have explored the removal of various classes of pharmaceutical compounds (PhACs) at full scale, generally attempting to evaluate whether municipal wastewater treatment plants (WWTPs) are acting as persistent point sources for PhACs discharge to the environment. Ternes (1998) showed that the removal efficiencies ranged from 10 to 90% in wastewater treatment plants in Germany, and Ternes et al. (1999) showed that removal efficiencies for polar PhACs varied from 12 to 90% for WWTPs in Brazil. Gomez et al. (2006) conducted a one-year monitoring study at a sewage treatment plant in Spain, and they found that the removal efficiencies for 14 organic micro- pollutants varied from 20% (carbamazepine) to 99% (acetaminophen). Joss et al. (2006) showed that only 4 out of 35 compounds are 90% removed using state-of-the-art biological treatment systems, and 17 out of 35 are removed at less than 50% efficiency. These studies are in addition to others that present high removal efficiencies. Oppenheimer and Stephenson (2006) found that removal efficiencies for frequently detected PhACs were generally high (>80%), and another study by Jones et al. (2006) found that ibuprofen, paracetamol, salbutamol and mefenamic acid were removed at approx. 90% within a large sewage treatment plant in England. Overall, these efforts have shown that the removal efficiencies vary greatly. 7 That conclusion that PhAC removal in full-scale systems varies considerably is further supported by Lishman et al. (2006) who investigated the presence of selected acidic drugs, triclosan, polycyclic musks, and selected estrogens in WWTP influent and effluent at sites in Canada. They found that three analytes were never detected during the survey (clofibric acid, fenoprofen, fenofibrate) and two analytes were always removed at high efficiency for all treatment configurations (ibuprofen, naproxen, triclosan). Two analytes were removed at a low efficiencies (gemfibrozil, diclofenac), but better removals were observed for treatment configurations with higher solids retention times. Five polycyclic musks were surveyed; general conclusions could not be reached because of the small data set and because of numerous nonquantifiable results, but removal efficiencies generally were variable. E2 and E1 were both removed at high efficiency for all treatment systems. The removal efficiencies for different PhACs can vary significantly. Diclofenac removal efficiency is negative, suggesting that diclofenac may be deconjugated during the treatment process (Zwiener et al., 2003). Generally, these full-scale studies have not collected the type and amount of data necessary to organize mass balances for specific PhACs, so that a clear articulation of the relative roles of sorption and biodegradation in the full-scale process is generally unavailable. Some studies have complemented full- scale studies with batch experiments, so that the potential for sorption and/or biodegradation at full-scale can be assessed. Removal efficiencies can vary as a function of the type of compound. Carballa et al. (2004) surveyed two cosmetic ingredients (galaxolide, tonalide), eight pharmaceuticals (carbamazepine, diazepam, diclofenac, ibuprofen, naproxen, roxithromycin, sulfamethoxazole and iopromide) and three hormones (estrone, 17?- 8 estradiol and 17?-ethinylestradiol) at municipal WWTPs in Spain. They found that the overall removal efficiencies ranged between 70?90% for the fragrances, 40?65% for the anti-inflammatories, approximately 65% for 17?-estradiol and 60% for sulfamethoxazole. However, the concentration of estrone increased along the treatment due to the partial oxidation of 17?-estradiol in the aeration tank. Nakada et al. (2006) measured a host of compounds, including six acidic analgesics or anti-inflammatories (aspirin, ibuprofen, naproxen, ketoprofen, fenoprofen, mefenamic acid), two phenolic antiseptics (thymol, triclosan), four amide pharmaceuticals (propyphenazone, crotamiton, carbamazepine, diethyltoluamide), three phenolic endocrine disrupting chemicals (nonylphenol, octylphenol, bisphenol A), and three natural estrogens (17?-estradiol, estrone, estriol) in 24 h composite samples of influents and secondary effluents from municipal WWTPs in Tokyo. They found that aspirin, ibuprofen, and thymol were removed efficiently during secondary treatment (>90% efficiency). They also found that amide-type pharmaceuticals, ketoprofen, and naproxen showed poor removal (<50% efficiency), probably because of their lower hydrophobicity (log K ow <3). This study was also the first to report the presence of crotamiton (a topical treatment for scabies), and to show that it is persistent during secondary treatment. Overall, these results reinforce the conclusion that removal efficiencies vary for the various PhACs and suggest that chemical characteristics also may play an important role in determining the fate of each compound in biological wastewater treatment. Removal efficiencies also can vary as a function of the sludge retention time (SRT). Oppenheimer and Stephenson (2006) studied the removal of 20 PhACs in full- scale and pilot scale WWTPs in the U.S, and they organized their data using a BIN 9 assignment system, which assigned each detected compound into a category related to the frequency of detection (i.e. infrequent, variable, and frequent) and into another category related to the removal efficiencies (excellent removal, moderate removal, poor removal). They found that half of the PhACs were frequently detected and were removed at less than 80% efficiency at a sludge retention time (SRT) of 5 days of less. Caffeine and ibuprofen were among 9 compounds that were both frequently detected and removed well for all the systems in the study. Galaxolide and musk ketone were also frequently detected but removed at 80% only when the SRT exceeded 25 days. Membrane bioreactor systems (MBRs) have been evaluated as a possibly better technology for removing PhACs. MBRs use a suspended growth bioreactor, like in conventional activated sludge, but replaces gravity sedimentation with micro- or ultra- filtration. The MBR is an attractive treatment configuration because it eliminates the need for secondary clarification, which in turn allows the overall treatment process to be sited on a much smaller footprint. Kim et al. (2007) found that the MBR system was efficient for hormones (e.g., estriol, testosterone, androstenedione) and certain pharmaceuticals (e.g., acetaminophen, ibuprofen, and caffeine) with approximately 99% removal, but MBR treatment did not decrease the concentration of erythromycin, trimethoprim, naproxen, diclofenac, and carbamazepine. Oppenheimer and Stephenson (2006) used a limited data set to suggest that MBR provided no additional PhACs removal, when compared to similarly operated conventional systems. Kimura et al. (2005) found that MBRs exhibited much better removal regarding ketoprofen and naproxen, but with respect to the other compounds, comparable removal was observed between the MBRs and conventional systems. These data suggest that MBRs likely offer no inherent 10 advantage over conventional systems for removing PhACs, but because MBRs are operated at long solids retention times and at high mixed liquor suspended solids (MLSS) concentrations, those operational factors are likely the cause of any measured differences in PhAC removal efficiencies. Finally, there remains a need to continue to conduct full-scale studies, with the goal of organizing accurate mass balance and fate data. To accomplish this, rigorous wastewater sampling methods must be employed. For example, these full scale studies collected data using time-weighted composite sampling using automatic samplers, equipped with sample storage in cooled compartments. This strategy allowed the reports to collect data that is likely to represent a reasonable estimate of the PhAC concentrations of interest, as well as the inherent variability; but this approach is not infallible. Many of the PhACs of interest are biodegradable, and may be transformed while the samples remain stored in the collection container. Still other compounds are highly hydrophobic and sorb strongly to biomass solids and colloidal materials that are also present in the original sample. In these cases, it is possible to underestimate the concentrations of interest, either because the solids are not properly re-suspended before sample analysis, or because of inadequate extraction techniques. Finally, time-weighted sampling collects a given wastewater volume at given time intervals, even if the wastewater flow is low. This means that time-weighted sampling may cause low-flow PhAC concentrations to be over-represented in the composite sample. For these reasons, future sampling campaigns should consider the use of flow-weighted sampling in combination with frequent grab sampling to minimize the error associated with sample collection. Each collected sample should also be mixed vigorously to resuspend settled material, and PhAC analysis should 11 be carried out on both the filtered and unfiltered samples. Improvements in sample collection methodology will strengthen the reliability of the data, which in turn will no doubt be the basis for future treatment plant optimization and regulatory action. Sorption. In general, the partitioning of organic compounds from water onto activated sludge biomass is referred to as adsorption, although it may be more appropriate to refer to this as sorption because there may be some uncertainty as to whether the compound is on the surface (adsorption) or partitioning into another phase (absorption). When sorption is of interest, it is important to establish a relationship between what is on the surface and what is in the aqueous phase, a relationship generally referred to as a sorption isotherm. The term isotherm comes from the idea that the equilibrium is reached at a constant temperature to distinguish this type of partitioning from condensation. These relationships are determined experimentally and then the data is used to determine a partitioning coefficient, which is a measure for the affinity of a given compound for the activated sludge biomass. Partitioning coefficients (K d ) have been determined in a number of studies to investigate PhACs sorption to activated sludge. Ternes et al. (2004) conducted a series of batch tests with primary and secondary sludge slurries to determine partitioning coefficients for a number of target PhACs. They found that the K d values of pharmaceuticals ranged from <1 to 500 L kg?1, while that of the polycyclic musk fragrances AHTN and HHCB proved to be much higher and up to 5300 and 4900 L kg ?1 , respectively. They also found significant differences between the K d values obtained between primary sludge and secondary sludge; for acidic pharmaceuticals and musk 12 fragrances, the K d values were higher when measured with primary sludge; the opposite was true with neutral pharmaceuticals, iopromide, and ethinyl estradiol. Clara et al.(2004) found that the log(K d ) for steroid estrogens was 2.84 (2.64?2.97) and 2.84 (2.71?3.00) for E2 and EE2, respectively. In the work by Ternes et al. (2004) the log(K d ) for EE2 was determined to be 2.54 (2.49?2.58). Andersen et al.(2005) determined distribution coefficients (K d ) with activated sludge biomass for the steroid estrogens , estrone (E1), 17?-estradiol (E2) and 17?-ethinylestradiol (EE2) in batch experiments, and they determined log K d values for steroid estrogens of 2.6, 2.7, 2.8 respectively. When Andersen et al. (2005) corrected their log(K d ) values to account for the organic carbon content of the sludge, they found that the log(K d ) values were 3.16, 3.24, 3.32 respectively. These values were remarkably consistent with the sorption partitioning coefficients determined where soil is used as the sorbate (Holthaus et al., 2002; Bowman et al., 2003; Casey et al., 2003). Taken together, these partitioning coefficients enable practitioners to model PhAC sorption in activated sludge processes, and numerically evaluate the importance of sorption as a removal mechanism. Sorption is not always an important removal mechanism. Ternes et al. (2004) found that, for compounds with the K d values less than 500 L/Kg, only 20% of the target compound mass was associated with the sludge solids, which showed that the majority of the mass of the target compounds remained in solution. This result supported the idea that sorption is not an important removal mechanism for many pharmaceutical compounds. Yu et al. (2006) conducted aerobic batch biodegradation (using activated sludge as microbial inocula) experiments to evaluate the biodegradation behavior of 18 target PhACs at initial concentrations of 50, 10, and 1 ?g/L. The target compounds included a 13 number of antiseptics, barbiturates, and anticonvulsants. Their sterile control studies showed no loss of target PhACs during the entire incubation period, and sorption to the biomass was found to be negligible for all testing conditions. Urase and Kikuta (2005) conducted a series batch experiment to examine the removal of three steroid estrogens (i.e. 17?-estradiol), two endocrine disruptors (i.e. bisphenol A), and 10 pharmaceutical substances by activated sludge. Many of the target PhACs in this study were hydrophilic, had lower water?sludge partition coefficients than the steroid estrogens, and remained in the aqueous phase, with only a small fraction partitioning to the activated sludge. When sorption is important, there is a sorption/desorption cycle that should be investigated experimentally. In some cases, desorption fails to restore the full capacity of the sorbent, and when this happens, some of the sorption sites remain occupied. This is referred to as sorption hysteresis, and this has been reported for many organic compounds where either soil or sludge acts as the sorbent (Kim et al., (2005); Huang et al., 2003). Hysteresis has thus far received little attention where PhAC sorption to sludge is concerned. Recently, Kim et al. (2005) showed sorption hysteresis in the case of tetracycline sorption/desorption with activated sludge, but this is probably because tetracycline forms strong complexes with Ca (II) and other divalent cations known to be important for floc stability (Sobeck and Higgins, 2002; Martin, 1979). PhAC sorption hysteresis is a basic and relevant process that has not received great attention to date. One cause of sorption hysteresis may be related to particle characteristics (e.g. size), and there is a need to study the possible fundamental connections. In general, activated sludge particles in conventional processes are typically 80-300 ?m in diameter (Ng et al., 2005), and this structure typically consists of smaller microcolonies (approx 8- 14 15 ?m) connected by exocellular polymeric and inorganic material, and with a few large flow channels that facilitate transport (Snidaro et al. 1997; Chu and Tay, 2005). Smaller activated sludge particles can be found in bioreactors like MBR (Ng and Hermanowicz, 2005) and smaller particles have less internal polymer, a higher number of cells per unit volume (Snidaro et al., 1997) and they do not have the large flow channels that facilitate transport. Biodegradation. Biodegradation is likely due to cometabolic activity because PhACs are not present in high enough concentration to support substantial biomass growth. This means that PhAC transformation is most likely to occur during exponential growth stages and during active degradation of the primary substrates present in wastewater. The published reports of cometabolism of PhAC are currently limited. Most of the published reports that concern cometabolism focus on the removal of xenobiotics that are produced as a result of industrial and military activity (e.g. chlorinated solvents such as trichloroethylene, nitroaromatic compounds explosives, dyes, polyurethane foams). These compounds may be present in the environment at much higher concentrations than PhACs are, but many industrial pollutants and PhACs share some of the same structural features (i.e polyaromatic rings), so there may be common reaction mechanisms. It is also known that cometabolism is often an initiating reaction, producing intermediates that may be more biodegradable (and therefore would participate in the central metabolic pathways), or that may be suspectible to adsorption or polymerization reactions and rendered nonbioavailable (i.e. dead end product). Quintana et al. (2005) observed the cometabolic transformation of four acidic pharmaceuticals in laboratory- 15 scale experiments. Although cometabolism is likely when biodegradation is occurring, there is only limited information that clearly connects cometabolism with the removal of PhACs. One interesting example comes from Alexy et al. (2005) who found that each of 18 antibiotics was not biodegraded, but some partial biodegradation was observed when sodium acetate also was present. This suggests that when sodium acetate is available as a primary substrate, the antibiotics may be subject to cometabolism. Biodegradation may sometimes result in the formation of a stable byproduct. Haib and Kummerer (2006) found that diatrizoate (found in X-ray contrast media) was biodegraded aerobically to 3,5-diamino-2,4,6-triodobenzoic acid which was not further degraded by bacteria. Quintana et al. (2005) also found that biotransformation of ketoprofen and bezafibrate produced more stable metabolites. A wide variety of mono and di-oxygenase enzymes can transform xenobiotics during exponential growth conditions (Schwarzenbach et al., 2005), but biotransformation of pollutants in the absence of bacterial growth also may occur as a result of enzymes previously produced by dead (non-viable) bacteria and as a result of extracellular enzymes excreted by viable bacteria (Madigan and Parker, 1997; Kragelund et al., 2005). Activated sludge communities are diverse and known to house a wide variety of nonspecific mono and di-oxygenase enzymes associated with both heterotrophic and autotrophic microorganism (Gessesse et al., 2003; Servos et al., 2004). There is circumstantial evidence linking nitrifiers to a unique capability to biologically (perhaps cometabolically) transform steroid estrogens such as EE2. Surveys of municipal WWTPs indicated that nitrifying sludges remove EE2 more efficiently than those that do not nitrify (Servos et al., 2004). Numerous experimental results further 16 supported this contention: Vader et al. (2000) degraded EE2 using nitrifying activated sludge, and they noted the presence of unidentified hydrophilic daughter products. Several groups (Shi et al., 2004; Dytczak et al., 2006) also biologically degraded EE2 using nitrifying mixed cultures. These combined results suggest that EE2 and NH 4 transformation rates are linked. A specific EE2 transformation mechanism may involve AMO, the key enzyme that catalyzes the conversion of ammonia to nitrite in nitrifying organisms. For example, AMO is also capable of co-metabolically oxidizing polycyclic aromatic rings (Chang et al., 2003; Vannelli and Hooper, 1995). The active site of AMO is buried in the core of the protein, where four neighboring ?-helices provide two histidine and four glutamic acids as iron ligands.( Zahn et al., 1996; Siegbahm et al., 1998). One face of the di-iron site contains a hydrophobic pocket, and may be well suited for organic substrates like EE2. These results support the conclusion that nitrifying activated sludge cultures may play a role in biotransforming pharmaceuticals in biological WWTPs, but heterotrophic organisms also likely play a key role. Furthermore, these results show that nitrifiers may, at a minimum, provide for initial degradation of PhACs like EE2 into an intermediate that can then be degraded further by heterotrophic organisms. The work of Shi et al. (2004) also supports this idea. They conducted EE2 biodegradation experiments with a nitrifying pure culture and a nitrifying mixed culture. They detected daughter products in the pure culture experiments but not in the mixed culture experiments, perhaps because the heterotrophs completely degraded the daughter products produced by the nitrifiers. At this point, it is not clear if AMO is kinetically dominant in full-scale WWTPs among all enzymes that might be capable of transforming pharmaceuticals, especially if the 17 enzymes are present in fast-growing heterotrophic organisms. Proof that nitrifiers are responsible for transformation of steroids in full-scale systems has not been shown definitively. It may be that nitrifiers will cometabolically transform pharmaceuticals containing aromatic structures when they are present in low organic carbon, ammonium enriched environments through the AMO. However, heterotrophic cultures also may contribute to and, in fact, may predominate these biotransformations if the sewage also contains mono- and dioxygenase inducers that function in heterotrophic bacteria. Other scavenging, biodegradative mechanisms are likely to exist and function among the complex collection of heterotrophic bacteria present in the low organic carbon environments found during the nitrification phase of bioreactors. Questions related to the relative importance and the potentially synergistic interplay between nitrifiers and heterotrophs need to be further elucidated to clarify this issue. Nyholm et al. (1996) suggested that biodegradation can be enhanced by operating at longer SRT. They operated laboratory-scale bioreactors over a range of SRT values (1- 32 days) and sludge loadings (0.1 ? 0.9 mgBOD5/mg MLSS/d), and they spiked five organic micropollutants (2,4-dichlorophenoxy acetic acid (2,4-D); 2,4,6-trichlorophenol (TCP); pentachlorophenol (PCP); 4-nitrophenol (4-NP) and lindane) into the influent. They found that adaptation was generally required, and that removal by biodegradation in successfully adapted systems were generally within a range of about 40 to about 95% except for 4-NP, which was degraded to concentration levels below the analytical detection limit. They found that PCP, TCP, and 2,4-D were degraded best at high sludge ages. 18 Biofilm experiments also have offered insight into the biodegradability of selected PhACs. For example, Boyd et al. (2005) investigated removal of naproxen and its chlorination products using a laboratory-scale biofilm bioreactor process. The bioreactor was a plug-flow bioreactor, and it used 31 m of polypropylene tubing as the support matrix for the biofilm. The bioreactor was fed a naproxen solution and then fed a solution at the same naproxen concentration following contact with free chlorine. Naproxen was not degraded biologically, and the naproxen solution containing products of chlorination caused biomass sloughing and discharge from the bioreactor. Zwiener and Frimmel (2003) investigated the biodegradation of three active compounds of pharmaceuticals (clofibric acid, ibuprofen, and diclofenac) in short-term tests with a miniaturized upflow biofilm bioreactor with an oxic/anoxic configuration. The biofilm reactor removed 85% of the applied dissolved organic carbon (DOC), but clofibric acid and diclofenac were not eliminated and were discharged at a level of approximately 95% of their initial concentration; they did find however that the elimination in the anoxic region of the biofilm reactor improved the removal efficiencies of clofibric acid and diclofenac to values between 60 and 80% of their initial concentration. Winkler et al. (2001) found that ibuprofen (as well as it?s hydroxylated and carboxyated metabolites) was biodegraded in a river biofilm reactor, but clofibric acid was not. Synthetic antibiotics, which do not appear to be readily biodegradable, deserve special attention. Ingerslev et al. (2001) studied the primary aerobic and anaerobic biodegradability of the antibiotics olaquindox (OLA), metronidazole (MET), tylosin (TYL) and oxytetracycline (OTC). They conducted batch experiments at intermediate concentrations (50?5000 ?g/l) using shake flasks inoculated with C14-labeled antibiotic 19 compounds and mixed with sediment or activated sludge. They found that these compounds were slowly biodegradable during aerobic conditions, with half life values that were typically between 1-5 weeks. During anaerobic conditions the biodegradation rates were slower, with half life values of up to 12 weeks. Alexy et al. (2005) studied the biodegradability of 18 clinically important antibiotics, and in addition to finding that none of them were readily biodegradable, they also found that half of the antibiotics tested inhibited biological activity when present at ppb levels. A study by Kummerer et al. (2000) also revealed that none of the test antibiotic compounds (ciprofloxacin, ofloxacin, and metronidazole) were biodegraded, and that, in addition, the genotoxicity was not eliminated during batch experiments. Zhou et al. (2006) treated a high-strength pharmaceutical wastewater with a pilot-scale system composed of an anaerobic baffled reactor followed by a biofilm airlift suspension reactor. They found that ampicillin and aureomycin, with influent concentrations of 3.2 and 1.0 mg/L, respectively, could only be partially degraded, with overall removal efficiencies of less than 10% at steady state. These results imply that biodegradation is not likely to play a large role in determining the ultimate fate of synthetic antibiotics in conventional biological wastewater treatment systems. Although a number of elucidating studies concerning biodegradation of PhACs have been conducted, research on the biodegradation of PhACs should continue, with particular attention to the identification of daughter products and the application of molecular methods to identify the important microorganisms and mechanisms. Currently, there are numerous examples in the literature reporting on the biodegradation of PhACs in biological treatment systems, but without any direct evidence of biotransformation (e.g. 20 metabolites). This is a weakness that currently exists in the literature, and it does not serve to clarify the dialogue concerning the fate of PhACs. There are also examples (Vader et al., 2000; Shi et al., 2004) of reports that show unidentified ?daughter products? ; these reports will be strengthened with clear identification of metabolites, which can be readily accomplished by combining the latest tools in HPLC/MS/MS technology with well established methods such as thin layer chromatography and NMR. The role of extracellular polymeric substances in sorption The distribution of pollutants in water is highly dependent on the processes at the solid-liquid interface. Sorption phenomena can be explained by some interaction mechanism; hydrophobic interaction, van der Waals forces and electrostatic interaction between adsorbate molecules and the adsorbent. To understand the mechanism of sorption between PhACs and floc, investigators have also focused on the physicochemical properties of cell surface. The solid phase is represented by many different materials, mostly minerals but also metals and organics cells can sorb inorganic and organic solutes and particles. As sorption sites can serve: extracellular polymeric substances (EPS), cell walls, cell membranes and cell cytoplasm. These sites display different sorption preferences, capacities and properties. EPS have been considered to be the most important sorption site as outmost component of cell. EPS as metabolic products are accumulated on the bacterial cell surface (Morgan et al., 1990). They are composed of a variety of organic substances (Fr?lund et al., 1996; Liao et al., 2001); carbohydrate, protein, humic substance, uronic acid and deoxyribonucleic acids (DNA). They are important from environmental and 21 engineering perspectives because they protected cells from phagocytosis in the environment and play a role in metal complexation. In addition, they affect microbial flocculation (Fr?lund et al., 1996; Rudd et al., 1984) and dewatering (Nielsen et al., 1996) in the activated sludge process. EPS components have different roles, depending on the physical and chemical properties. Liao et al. (2000) found hydrophobicity was not influenced by the total EPS content of sludge. The change in the hydrophobicity of sludge floc was affected by variations in the protein, total carbohydrate, and DNA content in the EPS. Jorand et al. (1998) also found that the hydrophobic fraction of EPS was affected only by proteins and not carbohydrates. Their results showed protein in cell surface is very important as binding sites. Yi and Harper (2005) showed that membrane bioreactor (MBR) sludge is more hydrophobic than that of sequencing batch reactor (SBR) even when both are operated at the same SRT and the linear sorption coefficient (K d ) measured from MBR was twice that of SBR biomass. In addition, Danielsson et al. (1996) reported that sorption of organic compounds which have high log K ow value ( > 3.5) to activated sludge was dominant as removal mechanism. Thus, it is concluded that cell hydrophobicity can be affected by the change of EPS composition such as protein and sorption capacity depends on cell hydrophobicity. These results also reinforce the hydrophobic interaction between organic compound and biomass flocs are dominant. Influence of sorption/desorption hysteresis on biodegradation The mechanism of sorption-hysteresis is usually explained by irreversible chemical binding, sequestration of a solute into specific components of biomass, (or 22 entrapment of the solute into micro-porous structures), and time-dependent hysteresis that is a consequence of slow kinetics at given experimental conditions (Ravikovitch and Neimark, 2005). Sorption/sorption-hysteresis which influences intracellular degradation rates may be related to availability of the organic compounds to the degrading organisms. When organic compounds are bonded irreversibly or trapped in dead biomass or non-living organic particles, they are isolated from the degrading microorganisms and thus are protected from biodegradation. Ogram et al. (1985) reported that 2, 4-Dichlorophenoxy sorbed to organic particles was completely protected from biodegradation. The study of Stringfellow and Alvarez-chozen (1999) also showed that degradation rate of fluoranthene in the presence of non-degrading biomass was significantly reduced. These studies suggest that organic sequestration by inactive- or dead biomass and inorganic particles reduces the bioavailability of organic pollutant to degrading biomass. Enzymes of interest Microorganisms in our environment play a very important role in oxidizing or mineralizing many natural products and PhACs to carbon dioxide or to intermediate byproducts. Most of these compounds newly appear in the environment and microorganisms in nature do not have removal mechanisms or proper enzyme systems. Thus, many PhACs are known to be persistent in the environment because they are stable and thus resistant to enzyme catalyzed reactions. In cometabolic transformation of PhACs, monooxygenase enzymes play a very important role by initiating biodegradation. They catalyze the insertion of oxygen into a substrate, then reduce stability and, in most cases, 23 increase hydrophilicity. Many researchers start to have a great interest in their property to increase removal of recalcitrant PhACs and eventually prevent harmful effects of PhACs. Ammonia monooxygenase. The autotrophic ammonia oxidizing bacteria such as Nitrosomonas sp. are the most extensively studied (Hooper et al., 1984) and nowadays have been investigated intensively because of the capability of ammonia monooxygenase (AMO). Ammonia is converted to nitrite by oxygen-dependent reaction of AMO, which oxidizes ammonia to hydroxylamine consuming two electrons, and hydroxylamine oxidoreductase (HAO) oxidizes hydroxylamine to nitrite with obtaining four electrons. Two electrons are returned to AMO and provide energy for oxidations. The oxidization by AMO is called cometabolic reaction because the byproducts formed do not act as growth substrates for the ammonia-oxidizing bacteria. It is found that AMO can oxidize a wide variety of PhACs because of the nonspecificity of AMO, including aromatics, ethers (Hyman et al., 1994), n-alkanes (Hyman and Wood, 1983), n-alkenes (Hyman et al., 1988), and thioethers (Juliette et al., 1993). In addition, the substrate range of AMO is extended to polycyclic aromatic hydrocarbons (PAH) (Chang et al., 2002) and the halogenated hydrocarbons (Rasche and Hyman, 1991). Some researchers (Shi et al., 2004; Vader et al., 2000) observed the biotransformation of estrogenic chemicals such as 17?- ethinylestradiol, estradiol, and estrone to hydrophilic daughter products in a highly enriched nitrifying culture. It is evident that ammonia oxidizing bacteria play an important role in natural bioremediation systems of recalcitrant chemicals. AMO is also induced by substrate (ammonia). It is found to increase AMO activity by at least 2-fold 24 Reaction: 1EE2+O 2 +2NADH = 1Oxided-EE2+H 2 O+2NAD + Figure 2.1 AMO degradation mechanism Hydroxylamine Oxidoreductase NAD+ NADH NH 3 NH 2 OH NH 2 OH NO 2 Cu + Cu 2+ O 2 H 2 O NADH NA Ammonia Monooxygenase O 2 Cu 2+ - Cu 2+ Cu + - Cu + Cu 2+ Cu 2+ Eletrophilic form of O2 2e - EE 2 OH-EE 2 , H 2 O 25 when ammonia is present (Arciero et al., 1989). In surface water and wastewater, NH 3 is abundant and its concentration is relatively high, compared to possible inducers for other monooxygenase such as Cytochrome P450 or toluene monooxygenase. Cytochrome P450s. Cytochrome P450 represents a huge family of enzymes and is found to catalyze the oxidation of a wide variety of chemicals, including many environmental pollutants such as PhACs and halogenated compounds. These enzymes are found in almost all living organisms. Especially, the P450s in drug industry was intensively investigated because they are deeply involved in the drug metabolism such as activation or inactivation and conversion of chemicals to reactive intermediates in the human body. It is known that the P450s can catalyze hydroxylation, epoxidation, sulfoxidation, or dealkylation reactions (Urlacher et al., 2004) of organic chemicals and convert toxic chemicals into less toxic intermediates (usually more hydrophilic forms) that are more easily removable from the body. In bacteria, P450s are involved in these catabolic reactions of pharmaceutical compounds and are very important in contaminant sites of toxic hydrocarbons. There are three different induction systems; substrate induction, non substrate induction, and induction by environmental factors such as temperature and pH. It was found that Pseudomonas sp. and Bacillus sp. frequently observed in activated sludge can produce the enzymes called the cytochrome P450 cam and P450 BM-3 , respectively. P450 BM-3 and P450 cam which are classified as substrate induction enzymes are induced by barbiturate and camphor, respectively. Although the roles of P450 were not fully or clearly investigated in biological treatment processes, when inducer is present in biological 26 Reaction: RH+O 2 +2NADH = ROH+H 2 O+2NAD + Figure 2.2 Reaction mechanism of Cytochrome P450 monooxygenase (Guengerich, 2007) ROH RH Fe 3+ Fe 3+ Fe 3+ Fe 3+ -O 2 2- Fe 3+ -O 2 - (Fe-O) 3+ Fe 3+ RH RH RH RH ROH RH 2H + H 2 O e- O 2 e- 27 processes P450s may be considered a degrader for degradation of pharmaceutical compounds. Toluene mono- and dioxygenase. It is known that toluene monooxygenases (TMOs) are able to hydroxylate toluene and a wide range of substrates including aromatic and phenolic compounds in metabolic pathways. TMOs initiate the oxidation of toluene by inserting oxygen into C-H bonds in toluene molecule, and enzymes have been classified according to the specificity in hydroxylation of a unique position C-H bond (Table 2.1), and the unique property of each TMO explains different removal efficiency for the same molecule. Table 2.1 Toluene monooxygenase enzymes Toluene monooxygenase Microorganisms References Toluene 4-monooxygnease Pseudomonas mendocina Whited et al., (1991) Toluene 3-monooxygease Ralstonia pickettii Fishman et al., (2004) Toluene 2-monooxygenase Burkholderia cepacia Newman et al., (1995) Toluene 2,3-dioxygenasse Pseudomonas putida F1 Jahng et al., (1994) For example, TMOs show different capability for the removal of trichloroethylene. Pseudomonas putida F1 (Toluene 2,3-dioxygenasse) and Burkholderia cepacia (Toluene 2-monooxygenase) are shown as the high TCE degrader, but Pseudomonas mendocina (Toluene 4-monooxygnease) is the poor degrader. 28 Frontier electron density Frontier electron density (FED) profile on a molecule provides a useful means for the detailed characterization of donor-acceptor interactions (that is, highest occupied molecular orbital (HOMO) - lowest unoccupied molecular orbital (LUMO) interaction). In 1952, the FED was proposed by Fukui and his coworker and at the present time the HOMO-LUMO interactions became widely accepted by researchers, providing an important means in interpretation of the pathway of chemical reactions. This FED can be applied to electrophilic, nucleophilic, and radical positions reactions as follows: 1. For an electrophilic reaction, HOMO densities are normalized by the energy of the frontier molecular orbitals at ground state: ?r = [2*?(C ri HOMO) 2 ]. 2. For an nucleophilic reaction, LUMO densities are normalized by the energy of the frontier molecular orbitals at ground state: ?r = [2*?(C ri LUMO) 2 ]. 3. For a radical reaction, the sum of HOMO and LUMO densities are normalized by the energy of the frontier molecular orbitals at ground state: ?r = [ ? (C ri HOMO) 2 +? (C ri HOMO) 2 ]. Where r is the number of carbon atoms in i: 2s, 2px, 2py, and 2pz. The highest ?r value indicates the most reactive position, that most likely to be attacked by oxygen of enzyme. The biochemical transformations of organic compounds in aerobic condition could be achieved by using enzymes carrying an electrophilic form of oxygen, which reacts with carbon atom in the highest ?r positions of the organic compound. Especially in cometabolism, hydroxylation can be predicted by considering monooxygenase- 29 mediated reactions of organic compounds, regarding where the initial point of attack in molecule is located. Fukui pioneered much of the early work connecting FED to chemical reactivity in aromatic hydrocarbons (Fukui, 1997). Wheland and Pauling (1935) successfully used FED to explain the reactivity of substituted aromatics. More recently, Ohura et al. (2005) showed that air-bore polycyclic aromatic hydrocarbons were abiotically chlorinated in positions that corresponded to high FED. Lee et al. (2001) used Fenton oxidation to remove polycyclic aromatic hydrocarbons, and they successfully used FED to predict the order of daughter product production. Although these previous attempts focused on abiotic reactions, they bolster the promise for predicting biological oxidations in the same way. 30 III. THE ROLE OF PARTICLE SIZE AND AMMONIUM OXIDATION IN REMOVAL OF 17?-ETHINYL ESTRADIOL IN BIOREACTORS Abstract Laboratory scale bioreactors were used to investigate sorption and biodegradation of 17?-ethinylestradiol (EE2). EE2 is among many emerging micropollutants that may cause endocrine disruption of aquatic organisms in the environment. Results showed that the sludge taken from the membrane bioreactor (MBR) had a sorption partitioning coefficient that was more than twice that of biomass derived from sequencing batch reactors (SBRs). The MBR biomass had smaller particles and was more hydrophobic than the SBR biomass. Experiments with nitrifying sludge showed that sorption was more important when the initial ammonia concentration was 48 mg/L or less, but at higher initial ammonia concentrations the role of biodegradation became more important. The ammonia monooxygenase enzyme extracted from a nitrifying mixed culture removed EE2 in batch experiments. These findings are the first that we are aware of to link biomass particle size, hydrophobicity, and sorption capacity. These results also support the notion that cometabolic biodegradation of EE2 can occur in nitrifying sludge. Keywords: Pharmaceuticals, Sorption, Hysteresis, Bioreactors, Nitrogen, Wastewater 31 The purposes of this work were to evaluate the effect of hydrophobicity of biomass on EE2 sorption and biomass characteristics, and to investigate the degradation of EE2 in nitrifying sludges. EE2 is more persistent than natural estrogens like estone (Ternes et al., 1999a), but it can be removed in biological wastewater treatment processes (Ternes et al., 1999b). Previous research showed that membrane bioreactors (MBRs) may be well suited to remove EE2 via sorption because of the hydrophobic properties of MBR sludge (Yi and Harper, 2005) and hydrophobic sludge properties can enhance the removal of EE2, especially since EE2 has a relatively high octanol/water coefficient (log K ow for EE2 is 3.9, Holthaus et al., 2002). MBRs are also often operated to nitrify, and there is circumstantial evidence linking nitrifying sludge to a unique capability to biologically degrade EE2 (Servos et al., 2004; Vader et al., 2000). A specific EE2 degradation mechanism may involve ammonia monooxygenase (AMO), the key enzyme that catalyzes nitrification. The active site of AMO is buried in the core of the protein, and one face of the di-iron site contains a hydrophobic pocket, and may be well suited for organic substrates like EE2. Methodology Bioreactor configuration and operation. Fully-automated laboratory-scale bioreactors were operated, including a MBR, an anaerobic/aerobic SBR (AASBR), an aerobic SBR (ASBR), and a nitrifying continuous stirred-tank reactor (NCSTR). The operating details for the four bioreactors were presented previously (Yi and Harper, 2005). Each bioreactor was operated at a SRT of 20 days. The primary substrate in the 32 synthetic wastewater for the MBR and SBRs was acetate (360 mg as COD/L). For the NCSTR, the primary substrate was ammonia (140 mg/L as N). Analytical methods. EE2 was detected by high pressure liquid chromatography (HPLC) (Hewlett-Packard, HP 1100) as explained previously (Yi and Harper, 2005). Surrogates samples were included in each batch of samples, and recovery always exceeded 90%. Total suspended solids (TSS), volatile suspended solids, and ammonia-N were analyzed according to Standard Methods (APHA 1992). The microbial adhesion to hydrocarbon test was used to measure sludge hydrophobicity (Guellil et al., 1998). Particle size distribution was determined using a Horiba LA-920 laser scattering particle size distribution analyzer (Delta Analytical Instruments, North Huntington, PA). Extracellular polymeric carbohydrate and protein were determined as described by Ng and Hermanowicz (2005). Biomass was microscopically observed for filamentous microorganisms as described by Jenkins et al (1993). Sorption Experiments. Sorption of EE2 onto the biomass was determined by adding EE2 at different concentrations ranging between 10 ?g/L and 1000 ?g/L. Time series sampling was done, beginning after 1 hour. Biomass inhibition was achieved with sodium azide at 0.2% w/v (Ning et al., 1996). Soluble EE2 was measured directly by HPLC, as described above. Solid phase EE2 was determined by mass balance; this is possible because biodegradation was inhibited, EE2 did not associate with the walls of the glass bottles (as determined by controls), and EE2 has a low vapor pressure (1.7x10-6 Pa) so that evaporation was negligible. 33 Batch tests with nitrifying sludge. Batch tests with inhibited (with 0.2% sodium azide) and uninhibited NCSTR biomass were conducted. Biomass was washed and resuspended in fresh media with various initial ammonia concentrations ranging from 19 to 78 mg/L ammonia-N. The biomass was spiked with EE2 (1 ?gEE2 per mg TSS) and then samples were collected over 24 hours for soluble EE2 analysis. The amount of biodegraded EE2 was calculated by taking the difference between the total EE2 removed from solution and the bound EE2 (which was calculated using experimentally-determined distribution coefficients and the measured soluble EE2 concentration). Enzyme Experiments. AMO was extracted from NCSTR biomass as explained by Moir et al. 1996. The degradation of EE2 in the presence of the enzyme extract was conducted in 10mM Tris-HCl using eluted enzyme, 0.5mM NADH, 0.6 units diaphorase, 0.5mM duroquinone, and ammonia. A control test was also performed without the enzyme extract. Results and Discussion Sorption isotherms were developed using biomass from the MBR and SBRs. The linear isotherm model was used, q=K d ?C, where q is the equilibrium biomass-associated EE2 (?g/g), K d is the partitioning coefficient, and C is the equilibrium concentration of soluble EE2 (?g/L). The K d measured for the MBR biomass was more than twice that measured for the biomass from either SBR (Table 3.1). The partitioning coefficients (K d ) were typically higher for the sludges that were more hydrophobic, and the MBR K d and hydrophobicity values were statistically higher (at 99% confidence) than those of either 34 SBR, as determined by the one-tailed T test. This result supports the notion that sludge hydrophobicity affects the extent of EE2 sorption in bioreactors. MBRs appear to have a higher sorption capacity for EE2, and because MBRs remove TSS to very low levels, lower concentrations of EE2 can be expected from MBRs as compared to conventional bioreactors with gravity sedimentation. Sludge hydrophobicity is also affected by filamentous organisms and extracellular polymeric substances (EPS) (Jenkins et al., 1993), but the observed differences in this study can not be attributed to these factors because very few filaments were microscopically observed and the EPS contents of the three bioreactors were similar (typically 10-15% b. wt. extracellular polymeric carbohydrate, 13-18% b. wt. extracellular protein, for all bioreactors). Particle size is the key difference; the measured median particle diameter for the MBR sludge was 10 ?m, compared to 120 ?m for the ASBR and 123 ?m for the AASBR. Because of the smaller floc in the MBR, the exposed surface area was greater for MBR biomass, which in turn likely contributed to the difference in the measured sludge hydrophobicity, because of the nature of the cell wall. Figure 3.1 shows the effect of initial ammonia concentration on the removal of EE2 by sorption and biodegradation (the length of the error bars is twice the standard deviation). Sorption was most important when the initial ammonia concentration was 50 mg/L or less, but at higher initial ammonia concentrations the role of biodegradation became more important. These results appear reasonable because ammonia concentrations are known to regulate AMO activity (Sayavedra-soto et al., 1996). At lower initial ammonia concentrations, there is less of the co-metabolic activity likely 35 responsible for EE2 degradation. As the initial ammonia concentration increases, respiration and cometabolism become more important. Table 3.1 Statistical comparison of the EE2 sorption data Parameter K d Hydrophobicity MBR ASBR AASBR MBR ASBR AASBR No. values 12 12 12 19 19 19 Average 0.45 0.22 0.3 68% 22% 35% Std Dev 0.06 0.05 0.09 15% 6.80% 7.50% Direct comparison Parameter Kd Hydrophobicity MBR MBR ASBR MBR MBR ASBR vs ASBR vs AASBR vs AASBR vs ASBR vs AASBR vs AASBR T value 10.5 4.7 2.4 13.7 82 9 Confidence >99% >99% >97% >99% >99% >99% Figure 3.2 shows that EE2 was removed in the presence of the AMO enzyme, but not in the control. This was expected because AMO is capable of co-metabolically oxidizing polycyclic aromatic rings (Chang et al., 2003, Vannelli and Hooper, 1995). The action of AMO enzyme can remove EE2, but the mode of action is not yet clear. AMO may catalyze degradation of EE2, or it may covalently interact with EE2, acting more as a reagent. Future work must identify daughter products to investigate the metabolic details. Conclusions The MBR sludge had K d and hydrophobicity values that were significantly higher than those of SBR sludges. The MBR contained much smaller particles than SBRs, thus 36 Figure 3.1 Effect of initial ammonia concentration on the sorption and biodegradation of EE2 37 Figure 3.2 Degradation of EE2 with an AMO-containing extract 0 20 40 60 80 100 120 140 012345 Time(h) Concent r at i on ( ug/ L) EE2 Control Times (h) 38 providing more exposed surface area for sorption. Ammonia affected the relative amounts of EE2 removed via sorption and biodegradation in nitrifying sludge. Sorption was most important when the initial ammonia concentration was 48 mg/L or less, but at higher ammonia concentrations biodegradation became more important. EE2 was removed in the presence of the AMO enzyme in batch tests; this further suggests that AMO possesses co-metabolic capability. References American Public Health Association (APHA) (1992). Standard Methods for the Examination of Water and Wastewater. 18th Ed., American Public Health Association, American Water Works Association, Water Pollution Control Federation, Washington, D.C. Chang, S., Hyman, M, and Williamson, K. (2003). Cooxidation of napthalene and other polycyclic aromatic hydrocarbons of the nitrifying bacterium, Nitrosomonas europaea. Biodegradation, 13(6), 373. Guellil, A., Block, J., and Urbain, V. (1998). Adaptation of the microbial adhesion to hydrocarbon test (MATH) for measuring activated sludge hydrophobicity. Water Sci. Technol., 37(4-5), 359. Holbrook, R.D., Novak, J., Grizzard, T., and Love, N.G. (2002). Estrogen receptor agonist fate during wastewater and biosolids treatment processes: A mass balance analysis. Environ. Sci. Technol.; 36(21), 4533. 39 Holthaus, K.I.E., Johnson, A.C., Jurgens, M.D., Williams, R.J., Smith, J.J.L., and Carter, J. E. (2002). The potential for estradiol and ethinylestradiol to sorb to suspended and bed sediments in some English rivers. Environ. Toxicol. Chem., 21(12), 2526. Jenkins, D, Richard, M., and Daigger, G.T. (1993). Manual on the Causes and Control of Activated Sludge Bulking and Foaming. Lewis Publishers, Boca Raton, Fl.. Moir, J., Crossman, L., Spiro, S., and Richardson, D. (1996). The purification of ammonia monooxygenase from Paracoccus denitrificans. FEBS Lett., 387(1), 71. Ning, Z., Kennedy, K.J., and Fernandes, L. (1996). Biosorption of 2,4- dichlorophenol by live and chemically inactivated anaerobic granules. Water Res., 30(9), 2039. Ng, H. and Hermanowicz, S.W. (2005). Membrane bioreactor operation at short solids retention times: performance and biomass characteristics. Water Res., 39(6), 981. Parkkonen, J., Larsson, D., Adolfsson-Erici, M., Pettersson, M., Berg, A., Olsson, P., and F?rlin, L. (2000). Contraceptive pill residues in sewage effluent are estrogenic to fish. Mar. Environ. Res., 50(1-5), 198. Sayavedra-soto, L., Hommes, N., Russell, S., and Arp, D. (1996). Induction of ammonia monooxygenase and hydroxylamine oxidoreductase mRNAs by ammonium in Nitrosomonas europaea. Mol. Microbiol., 20, 541. Servos, M., Bennie, D., Burnison, B., Jurkovic, A., McInnis, R., Neheli, T., Schnell, A., Seto, P., Smyth, S., and Ternes, T. (2004). Distribution of estrogens, 17-estradiol and estrone, in Canadian municipal wastewater treatment plants. Sci. Total Environ., 336(1-3), 155. 40 Ternes, T.A., Kreckel, P., and Mueller, J. (1999a). Behavior and occurrence of estrogens in municipal sewage treatment plants ? II. Aerobic batch experiments with activated sludge. Sci. Total Environ., 225(1-2), 91. Ternes, T.A., Stumpf, M., Mueller, J., Haberer, K., Wilken, R.D., and Servos, M. (1999b). Behavior and occurrence of estrogens in municipal sewage treatment plants ? I. Investigations in Germany, Canada and Brazil. Sci. Total Environ., 225(1-2), 81. Vader, J., van Ginkel, C., Sperling, F., de Jong, F., de Boer, W., de Graaf, J., van der Most, M., and Stokman, P.G.W. (2000). Degradation of ethinyl estradiol by nitrifying activated sludge. Chemosphere, 41(8), 1239. Vannelli, T. and Hopper, A. (1995). NIH shift in the hydroxylation of aromatic compounds by the ammonia-oxidizing bacterium Nitrosomonas europaea. Evidence against an arene oxide intermediate. Biochemistry, 34(37), 11743. Yi, T. and Harper, Jr., W.F. (2005). Mechanisms for removal of 17?-ethinylestradiol in bioreactors. Proc., 78th Annual Water Environment Federation Technical Exposition and Conference, WEF, Washington, D.C., 5140. 41 IV. THE EFFECT OF BIOMASS CHARACTERISTICS ON THE PARTITIONING AND SORPTION HYSTERESIS OF 17?- ETHINYLESTRADIOL Abstract A membrane bioreactor (MBR) and a conventional bioreactor (CBR) were operated under various conditions to manipulate the biomass characteristics and evaluate the ensuing effects on the partitioning and sorption hysteresis of 17?-ethinylestradiol (EE2). When the biomass was grown without nitrogen limitation, the biomass mean particle size had a dramatic effect on the observed partitioning coefficient (K d ) and on sorption hysteresis index (HI). Visualization study confirmed this result. MBR K d (0.33? 0.57 L/g) values were equal to or larger than those of the CBR (0.25?0.33 L/g). Under nitrogen-deficient conditions, the correlations between the biomass particle size and K d and HI were poor, likely because of extracellular polymeric substances. The K d and HI were determined for initial EE2 concentrations between 100 and 1000?g/L. Changing the solid retention time (SRT) did not manipulate particle size, and the effects on K d and HI were not dramatic. This study also numerically explored the impacts of sorption hysteresis on the removal of pharmaceutical compounds. Keywords: Pharmaceuticals; Sorption; Hysteresis; Bioreactors; Nitrogen; Wastewater 42 The purposes of this work were to: (1) characterize the effect of particle size on partitioning coefficients and sorption-hysteresis for 17?-ethinylestradiol; (2) manipulate the biomass particle characteristics by changing the nutrient levels and operating solid retention time (SRT) and characterizing the ensuing effects on partitioning coefficients and sorption-hysteresis; and (3) investigate the potential effects of sorption-hysteresis on effluent water quality. It is now known that removal efficiencies for the various classes of PhACs can vary greatly (Clara et al., 2005; Joss et al., 2005). Carballa et al. (2004) found that the overall removal efficiencies ranged between 70% and 90% for the fragrances, 40?65% for the anti-inflammatories, around 65% for 17?-estradiol. Joss et al. (2006) showed that only 4 (ibuprofen, paracetamol, estradiol, estrone) out of 35 compounds are 90% removed using state-of-the-art biological treatment systems, and 17 out of 35 are removed at less than 50% efficiency. Sorption occurs when PhACs associate with activated sludge biomass. The importance of sorption depends on the chemical characteristics of the parent compound; some PhACs are relatively hydrophilic and have low partitioning coefficients, while others are hydrophobic and will partition strongly onto the solid phase. When sorption is the primary removal mechanism, there is a sorption/desorption cycle that should be investigated experimentally. In some cases, desorption fails to restore the full capacity of the sorbent, and when this happens, some of the sorption sites remain occupied. This is referred to as sorption hysteresis, and this has been reported for many organic compounds where either soil or sludge acts as the sorbent (Kim et al., 2005; Conrad et al., 2006; Huang et al., 2003). Hysteresis has thus far received little attention where PhACs 43 sorption to sludge is concerned. Recently, Kim et al. (2005) showed sorption hysteresis in the case of tetracycline sorption/desorption with activated sludge, but this is probably because tetracycline forms strong complexes with Ca(II) and other divalent cations known to be important for floc stability (Martin, 1979; Sobeck and Higgins, 2002). PhAC sorption hysteresis is a basic and relevant process, and neglecting it may cause an overestimation of the long-term sorption capacity of the activated sludge. One cause of sorption hysteresis may be related to particle characteristics (e.g. size), and there is a need to study the possible fundamental connections. The hypothesis of this work is that sorption hysteresis is more pronounced as the biomass particle size distribution shifts toward larger sizes. The rationale for this is smaller flocs are more dense and less permeable than larger floc (Snidaro et al., 1997; Chu et al., 2005) therefore allowing for much less intraparticle entrapment of PhACs. In general, activated sludge particles in conventional processes are typically 80-300?m in diameter (Metcalf and Eddy, 2003), and this structure typically consists of smaller microcolonies (approx. 8-15?m) connected by extracellular polymeric and inorganic material, and with a few large flow channels that facilitate transport (Snidaro et al., 1997; Chu et al., 2005). Smaller activated sludge particles can be found in bioreactors like membrane bioreactors (MBRs) (Yi et al., 2006; Ng and Hermanowicz, 2005), and smaller particles have less internal polymer, a higher number of cells per unit volume (Snidaro et al., 1997) and they do not have the large flow channels that facilitate transport. The implications of this hypothesis may easily reach practitioners. Bioreactor particle size distributions can be affected in the design stage (e.g. MBRs will produce smaller particles than conventional 44 bioreactors (CBRs), Yi et al., 2006) or through operational adjustments (e.g. changing the mixing intensity or addition of non-ionic polymer). Biomass particle characteristics may be affected by nutrient deficiency and the SRT. Nutrient deficiency is known to cause the production of extracellular polymeric substances (EPS), which can lead to viscous bulking (Jenkins et al., 1993). Liao et al. (2006) recently showed that the particle size distribution for non-bulking sludge fit a log- normal distribution, but for bulking sludge, they observed a bi-modal distribution. SRT may also affect biomass particle size and structure. Mass? et al. (2006) recently found that the floc size decreased as SRT increased. Liao et al. (2006) found that sludge flocs at low SRT were more irregular in shape and variable in size than those at higher SRT; this is probably because floc strength is greater at high SRT than at low SRT (Liao et al., 2002). These previous results show that nutrient deficiency and SRT affect biomass characteristics, and they raise the possibility that these factors may be manipulated to affect particle size. 17?-Ethinylestradiol (EE2) is an ideal compound used to study sorption hysteresis in activated sludge. EE2 is strongly hydrophobic (log K ow = 3.67), relative to other commonly detected PhACs, and it will therefore partition to activated sludge quite favorably. EE2 is also a synthetic steroid and the active ingredient in birth control pills; it is therefore of particular interest and concern because of the potential to cause endocrine disruption. Sorption hysteresis could affect EE2 bioavailability, which is important because EE2 can be biotransformed into estradiol and estrone which are more biodegradable. 45 Methodology Experimental overview. Two laboratory-scale bioreactor systems were operated, a MBR and CBR, both operated in continuous flow mode. Since MBRs have smaller particles than CBRs, it is possible to investigate the effect of particle size by operating these two bioreactors in parallel. Both bioreactors were originally seeded with mixed liquor from the City of Auburn Southside Wastewater Treatment Facility. The experimental strategy was to harvest biomass from the bioreactors for use in a series of sorption/desorption batch tests. The data retrieved from the batch tests was used to determine sorption and desorption isotherms, and the slopes of these isotherms were used to determine the partitioning coefficients for sorption (K d ) and desorption (K ds ), respectively (an example is presented in Figure 4.1, which is discussed in the Results and Discussion section). The sorption hysteresis index (HI) was calculated as follows: r d dds CT K KK HI , ? = (1) The subscript T (23 o C) and C r (C r level is 0.5) refer to specific conditions of constant temperature and residual solution phase concentration ratio, respectively. The partitioning coefficient determined from the sorption experiments is K d , and the partitioning coefficient determined from the desorption experiments is K ds . The bioreactor operating mode was adjusted in two ways, in an attempt to manipulate the biomass particle characteristics and investigate the possible effects on sorption and desorption. First, three different SRTs were used, 5, 10, and 20 days. Second, 46 nitrogen-limiting conditions were imposed on the bioreactors by decreasing the influent ammonia?N concentration from 40.3 mg N/L (at normal growth conditions) to 8 mg N/L (at nitrogen-limiting conditions). After the bioreactor operating condition was changed, the bioreactors were operated for three SRTs before samples were taken for analysis. Bioreactor operation. The MBR had a working volume of 60 L and was equipped with one, vertically mounted membrane module (pore size 0.08?m, physical size (0.55 m in total length), surface area (0.5m 2 ), courtesy of Vivendi/US Filter), completely submerged in a plexiglass vessel. The module was 56 cm in length, and had a 8 cm diameter. The height of the vessel was 91 cm, with 16 cm of freeboard, and a 76 cm water depth. The module was placed to allow a 10 cm clearance both from the vessel bottom and the water surface. The module was mounted in the middle of the vessel, and held in place by a plexiglass U-shaped support apparatus. The MBR SRT was varied (5, 10, and 20 days), and mixed liquor was withdrawn daily to control the SRT. Peristaltic pumps were used in order to control the influent and effluent flow. The airflow rate was maintained at 10 L/min to sustain a DO concentration of 2 mg/L and to provide mixing. The pressure drop across the membrane was monitored daily, and was typically 1.2 psi. The pH was controlled with an auto-pH meter (alpha pH 200 1/8-DIN pH/ORP Controller, EUTECH Instruments Pte Ltd, Singapore) and pH electrode (Thermo Orion Glass pH electrode, Orion Research, INC., Beverly, MA). The pH of each reactor was maintained in the range of 6.8?7.3 by the addition of 0.1 M HCL solution or 0.1 M NaOH solution. The volumes of acid and base were monitored daily and the addition of 47 new solution to the storage vials was recorded. The temperature was ambient (approx. 24 o C). The CBR had a working volume of 4 L. The SRT was varied (5, 10, and 20 days) and sludge was manually wasted each day to control the SRT. Aerobic conditions were maintained by bubbling ambient air through a porous diffuser. Aerobic conditions were verified by measuring dissolved oxygen (YSI Model 57 Oxygen Meter with YSI Model 5793 Standard Membranes, YSI Incorporated, Yellow Springs, OH) and redox potential (Eutech Instruments, Model 200, Singapore). The pH was controlled with an auto-pH meter (WDP Series Dual Input pH/ORP Controller, Walchem Corporation, Holliston, MA) and pH electrode (WEL-PHF-NN electrode, Walchem Corporation, Holliston, MA) with a protective housing (Model 102606, Walchem Corporation, Holliston, MA). The pH of each reactor was maintained in the range of 6.8?7.3 by the addition of 0.1 M HCL solution or 0.1 M NaOH solution. The volumes of acid and base were monitored daily and the addition of new solution to the storage vials was recorded. There was no pH control during the settling or effluent withdrawal phases. The temperature was ambient (approx. 24 o C). Synthetic wastewater. The synthetic wastewater used for the MBR and the CBR was the same. The organic substrate (acetic acid) and inorganic nutrients were added in separate feed streams. The composition of the synthetic feed was (on mg COD/L basis: acetate (360), casamino acids (20), yeast extract (<1)). The inorganic salts content was (as mg/L total influent concentration) KCl (210), MgCl 2 ?6H 2 O (394), MgSO 4 ?7H 2 O (26), CaCl 2 (80), H 3 BO 3 (0.11), ZnSO 4 ?7H 2 O (0.0.50), KI (0.027), CuSO 4 ?5H 2 O (0.11), 48 Co(NO 3 ) 2 ?6H 2 O (0.135), NaMoO 4 ?2H 2 O (0.056), MnSO 4 ?H 2 O (0.62), and FeSO 4 ?7H 2 O (0.55). The influent P concentration was supplied as NaH 2 PO 4 ?2H 2 O and was always 8.0 mg P/L. The influent N was supplied as NH 4 Cl and was always 40.3 mg N/L. The synthetic wastewater used for nitrogen-limiting conditions was the same, except that the influent nitrogen concentration was reduced to 8 mg N/L. Experimental protocol for sorption and desorption. The EE2 sorption experiments were conducted using 200 mL biomass samples taken from the CBR and MBR. Sorption of EE2 onto the biomass was determined by adding EE2 into glass bottles at different concentrations ranging between 100 and 1000?g/L. To prevent biodegradation, sodium azide was added at 0.2% w/w; this concentration inhibited biodegradation (as confirmed with respirometry), did not cause cell lysis (as confirmed microscopically and with soluble carbohydrate measurements), and did not change the sludge hydrophobicity (as determined by the MATH Test protocol described by Guellil et al., 1998). Samples were mixed on an orbital shaker at 200 rpm at 23 o C, including additional control bottles without biomass to make sure that EE2 was not being lost to the glassware. Samples were taken after 1 h; preliminary kinetic tests indicated that equilibrium was reached in this time. Filtered supernatants (0.2-?m Teflon filter) were used for EE2 analysis. Biomass-bound EE2 concentrations were determined by mass balance. All sorption tests were done in triplicate. After the sorption experiment, desorption experiments were conducted as follows: the glass bottles were agitated in the dark at 200 rpm, 23 o C for 5 h then centrifuged for 20 min at 3000xg. Next, 100 mL of the supernatant was removed, and the supernatant was replaced with fresh background 49 solution, and the tubes were further agitated at 200 rpm, 23 o C for 1 h. Filtered supernatants (0.2-?m Teflon filter) were used for EE2 analysis. All desorption tests were done in triplicate. Visualization of sorption-hystersis: A 250mL glass bottle with a working volume of 200mL was used for this test. Sorption of Diclofop-Methyl (DM) onto the biomass was determined by adding DM into glass bottles at the final concentration of 500ug/L. Sorption tests were conducted in an orbital shaker at 250rpm at 23 o C for 1day. Biomass inhibition was achieved using sodium azide (final concentration of 200mg/L). After sorption experiment, the glass bottles were centrifuged for 10 min at 2,000xg. Next, 100 mL of the supernatant were removed. The removed supernatant was replaced with fresh background solution (effluent) and the bottles were further agitated under the same conditions for 1day. After each step (sorption and desorption), biomass samples were taken and dried on the microscope slide, and then mount with Propidium Iodide was applied to slide. Accumulation of DM by biomass floc was visualized using confocal microscope. Setup of confocal microscope followed the study of Wolfaardt et al. (1995) Analytical methods. EE2 was detected by HPLC (Hewlett-Packard, HP 1100). The system consisted of a degasser (G1322A), a Quaternary pump (G1311A), an ALS auto-sampler (G1313A), a Colcomp column oven (G1316A) and Variable Wavelength UV?VIS Detector (G1314A). A Hypersil ODS C18, (125x46mm,5 ?m) column was used. HPLC operating conditions were as follows; UV detector wavelength, 197 nm and mobile phase, acetonitrile and water (40:60) with solvent delivered at a constant flow rate of 1 mL/min. The total runtime of the HPLC analysis was 10 min. Each batch of samples 50 included spiked surrogate samples, and recovery always exceeded 90%. Total suspended solids (TSS), volatile suspended solids, and ammonia?N were routinely analyzed according to Standard Methods (APHA, 1992). Particle size distribution and total specific surface area were determined on 15 mL samples from the MBR and CBR sludges utilizing a Horiba LA-920 laser scattering particle size distribution analyzer (Delta Analytical Instruments, North Huntington, PA). The measurement range for this instrument is 0.02?2000 microns. Extracellular polymeric substances (EPS) were extracted chemically, using formaldehyde and NaOH, as follows; 10 mL sludge was washed with DI water three times. Formaldehyde was added to the rinsed sludge and incubated for 1h at 4?C and then incubated for 3h with NaOH. The extracted EPS were harvested using centrifugation for 10 min at 6000xg, followed by membrane filter (0.2?m membrane) to remove solid materials. The extracted EPS was purified with a dialysis membrane of 3500 Da (Pierce, USA) for 24h. The extracted EPS was analyzed for total carbohydrates and protein. The carbohydrate and protein concentration in EPS were determined by the anthrone method (Gaudy, 1962) and Lowery method (Lowry et al., 1951), respectively. Results and Discussion EE2 partitioning. Figure 4.1 shows a typical sorption/desorption result for two different biomass floc suspensions. The suspension taken from the MBR had a mean particle size of 10?m, while that of the CBR had a mean particle size of 120?m. In this example, the sorption/desorption experiment yielded K d and K ds values of 0.47 and 0.56 L/g for the MBR biomass, and 0.32 and 0.61 L/g for the CBR biomass, respectively. 51 These K d and K ds values are the slopes of the respective isotherms. Using these values, the HI values for the MBR and CBR were 0.19 and 0.89, respectively. Results like this suggested that the particle size influenced the HI for EE2 sorption, and that the extent of the observed hysteresis (as reflected by the difference between the sorption and desorption lines) increased as the soluble EE2 concentration increases. As EE2 concentration increases, more intrafloc mass transfer and entrapment may occur. Figure 4.2 shows the effect of the biomass mean particle size and specific surface area on the measured K d values; the results from both the MBR and CBR sorption experiments are included. The mean particle size data points are shown with vertical bars that have a length equal to the standard deviation of the particle size distribution. K d values increased as the mean particle size decreased and as the specific surface area increased. The observed trends were best approximated with a power law equation, which indicates that there was a scale invariant relationship between dependant and independent variables. In this case, the variable on the x-axis is the dependant variable, the partitioning coefficient, K d , and the independent variables are the mean particle size and the specific surface area. Figure 4.2 indicates that the mean particle size and specific surface area can have a dramatic and nonlinear effect of the observed K d . The reason for this nonlinear relationship may lie in the fact that a number of factors that affect the K d also change as the biomass particle size changes (e.g. porosity, density, available surface area, the protein and carbohydrate content of the floc). These results suggest that the cumulative effect of the change in all of these factors produces the observed nonlinearity. Figure 4.2 also shows that the K d values measured for the MBR were always equal to or greater than those measured for the CBR, because of the difference in the particle sizes. 52 0 20 40 60 80 100 120 140 0 50 100 150 200 250 300 Aqueous EE2 (ug/L) Bi omass- associat ed EE2 ( ug/ g) MBR- desorp MBR-sorp CBR-sorp CBR - desorp Arrows show extend of hysteresis Figure 4.1 An example showing a set of sorption and desorption isotherms for MBR and CBR biomass. 53 Figure 4.2 The effect of mean particle size and specific surface area on K d : normal growth conditions. y = 243558x 4.4652 R 2 = 0.8972 y = 1.0922x -3.8695 R 2 = 0.9143 0 50 100 150 200 250 300 350 0 0.10.20.30.40.50.60.7 K d (L/g) Mean par t icle size (um ) 0 5000 10000 15000 20000 25000 Specific surf a ce a r e a (cm2/cm3) ?? MBR; ?? CBR filled points are mean particle size; open points are specific surface area Error bar length equal to the standard deviation of the particle size distibution 54 The CBR K d values ranged between 0.25 and 0.33 L/g, while those of the MBR were between 0.33 and 0.57 L/g. This means that the EE2 sorption capacity of MBR sludge was up to twice that of CBR sludge. This result corroborates previous findings which show that MBR K d values for EE2 are higher than those of CBR sludges (Yi et al., 2006). Figure 4.3 shows the effect of the biomass mean particle size and specific surface area on the measured K d values in the case where the biomass was grown under nitrogen-limiting conditions. The relationships between measured K d values and the particle size and specific surface area were not approximated well by the power law (or by any other linear or nonlinear model). There are weak trends showing that K d increased as mean particle size decreased and as specific surface area increased, but the data scatter and poor curve fit show that nitrogen-limiting conditions significantly interfere with the trends otherwise present under normal growth conditions. Figure 4.3 also shows that the CBR K d values sometimes exceeded those of the MBR, another trend different from that observed in Figure 4.2. Under nitrogen-limiting conditions, the slime production was noticeable and the EPS-associated carbohydrate levels were significant (Table 4.1). EPS is known to alter the surface characteristics of biomass (Liao et al., 2001) and is therefore a plausible cause for the poor correlations. Hysteresis index. There were good correlations between the HI and both the mean biomass particle size and specific surface area (Figure 4.4). The MBR HI values ranged from 0.15 to 0.67, while those of the CBR were from 0.65 to 0.92, so the HI was nearly always higher for CBR biomass than for MBR biomass. The power law was the best model with which to approximate the data, which again showed the nonlinear effect 55 Figure 4.3 The effect of mean particle size and specific surface area on K d : nitrogen- limited growth. y = 38137x 3.433 R 2 = 0.2694 y = 4.3696x -3.166 R 2 = 0.2755 0 50 100 150 200 250 300 0.2 0.25 0.3 0.35 0.4 0.45 0.5 K d (L/g) M e an par t i c l e si ze ( u m ) 0 1000 2000 3000 4000 5000 6000 7000 8000 S p e c ific s u r f a c e a r e a (c m 2 /c m 3 ) ?? MBR; ?? CBR filled points are mean particle size; open points are specific surface area Error bar length equal to the standard deviation of the particle size distibution 56 Table 4.1 The effect of nitrogen-limitation on exocelluar polymer production Extracellular protein/carbohydrate (average of three measurements) Bioreactor SRT Normal Nitrogen-limiting conditions 5 9.3/23 9.1/ a 10 16/18 20/28 MBR 20 7.5/24 7.8/44 5 6.6/17 13/23 10 13/14 16/24 CBR 20 6.4/29 7.3/37 The production of extracellular polymer substances was significant. a Data not available. that biomass particle size has on HI values. For example, as the mean particle size increased from 50 to 150?m, the HI values take a dramatic increase from approximately 0.50 to 0.80; this is particularly interesting given that the 50-150?m range is the particle size range that represents the transition from those particle sizes typically found in MBRs to those commonly found in more conventional activated sludge systems. Selection of MBRs in the design of biological wastewater treatment systems appears to offer the advantage of lower sorption hysteresis of EE2, and this result may also apply to other micropollutants with similar chemical characteristics. 57 This result shows that the biomass particle size can have a dramatic effect on the entrapment of EE2 within activated sludge floc, which in turn may affect the ultimate fate of EE2. Figure 4.5 shows the relationship between the HI and the mean biomass particle size and specific surface area, when the biomass was initially grown under nitrogen- limiting conditions. The correlations were not as good as those derived when the biomass was grown under normal conditions, but the larger particles tended to have higher HI values. Under nitrogen limited conditions, the MBR HI values ranged from 0.32 to 0.6, while those of the CBR were between 0.53 and 0.9, so that the MBR biomass tended to have lower HI values. The overall range of HI values measured for the MBR and CBR were similar under normal growth and nitrogen-limited conditions. The effect of SRT. Figure 4.6 shows the effect of SRT and nitrogen limitation on the measured K d . When the biomass was grown under normal growth conditions the average MBR K d generally decreased with the increase of SRT. When the biomass was grown under nitrogen-limiting conditions, the average MBR K d values were relatively stable, 0.45 L/g at a 5 day SRT, 0.45 L/g at a 10 day SRT, and 0.35 L/g at a 20 day SRT. Under normal conditions the average CBR K d values were 0.47 L/g at a 5 day SRT, 0.64 L/g at a 10 day SRT, and 0.25 L/g at a 20 day SRT. The average CBR K d value measured at a 10 day SRT was surprising, given that previous results have shown that MBR K d values are higher than those of comparably operated CBRs (Yi et al., 2006). The CBR K d values measured during nitrogen-limiting conditions were lower than those measured during normal growth conditions, except for when the SRT was 20 days. Overall, different K d values were observed at different SRTs, but these results did not 58 y = 5.3106e 4.0509x R 2 = 0.8723 y = 35112e -4.4793x R 2 = 0.8591 0 50 100 150 200 250 300 350 0 0.2 0.4 0.6 0.8 1 Hysteresis Index (unitless) Me an part icle size (um) 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Specific surfac e are a (cm 2 /cm 3 ) ?? MBR; ?? CBR filled points are mean particle size; open points are specific surface area Error bar length equal to the standard deviation of the particle size distibution Figure 4.4 The effect of mean particle size and surface area on HI: normal growth. 59 Figure 4.5 The effect of mean particle size and surface area on HI: nitrogen-limited growth. y = 242.74x 2.0925 R 2 = 0.6847 y = 475.53x -2.238 R 2 = 0.7211 0 50 100 150 200 250 300 00.20.40.60.81 Hysteresis Index (unitless) Mean p a rticle size (u m) 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Spe c ific surfac e ar ea (cm2/cm3 ) ?? MBR; ?? CBR filled points are mean particle size; open points are specific surface area Error bar length equal to the standard deviation of the particle size distibution 60 Figure 4.6 The effect of solids retention time on K d 0 0.2 0.4 0.6 0.8 51020 Solids Retention Time (days) Par t itio ni ng Coeffi cient, Kd (L/g) MBR normal MBR limiting CBR normal CBR limiting Error bars length equal to 2 x standard deviation, as determined on triplicate samples 61 Figure 4.7 The effect of sludge retention time on the hysteresis index. 0 0.2 0.4 0.6 0.8 1 1.2 51020 Solids Retention Time (days) H yster es is Inde x (unitless ) MBR-Normal condition MBR-Nitrogen limiting condition CBR-Normal condition CBR-Nitrogen limiting condition Error bars length equal to 2 x standard deviation, as determined on triplicate samples 62 Table 4.2 The effect of SRT of the means of the particle size distribution Mean of the particle size distribution Bioreactor SRT Average Standard deviation 5 18 14 10 36 16 MBR 20 30 14 5 129 46 10 94 54 CBR 20 170 44 produce clear and generally applicable relationships that applied to both the MBR and CBR biomass. Under non-nitrogen-limiting conditions, the CBR K d values did not show clear relationships with SRT, and the K d value determined at a 10 day SRT was surprisingly high. MBR K d values decreased as the SRT increased; this might be because, as the SRT increases, the MBR may have been retaining more colloidal material that had different surface properties than the activated sludge biomass. Under nitrogen-limiting conditions, there were also no clear relationships that can be applied to both the MBR and CBR biomass. Nitrogen-limitation is known to induce EPS production, but how this affects the structural and chemical properties that bear directly on K d is still not well understood. Future research should endeavor to fill this knowledge gap. Figure 4.7 shows the effect of SRT and nitrogen limitation on the HI. At a 5 day SRT, the average HI values were lower for the MBR, and the nitrogen-limiting conditions increased the HI for 63 both bioreactors. At a 10 day SRT, the average HI for the bioreactors were more similar, except that the CBR had a lower than expected HI under normal growth conditions. At a 20 day SRT, the MBR again maintained a lower HI than the CBR, and the nitrogen limitation caused a significant change in the HI for the CBR but not the MBR. Overall, the HI appeared to increase gradually with SRT for both bioreactors, perhaps because of a general effect that SRT has on the structure of activated sludge floc. There is recent evidence that shows that, as the SRT is increased, the floc surface becomes covered with tightly bound EPS (Liao et al., 2002; Li and Yang, 2006); this tightly bound polymer may be trapping of compounds like EE2. SRT proved to be a less effective means of directly manipulating the biomass characteristics (Table 4.2). The mean particle size of the MBR was unaffected by SRT, and the CBR mean particle size was not affected in a systematic way. Therefore, it follows that the effects of SRT on the K d and the HI, although present, were not as dramatic. When the SRT was 5 or 20 days, nitrogen limitation increased the HI values; this result implies that it is possible to cause increased accumulation of EE2 in activated sludge floc during nitrogen-limiting conditions. Visualization of sorption/desorption Hysteresis in sludge flocs. The fluorescence intensity observed in the study was typically sustained for 1 day using the sludge from MBR and CBR, after which no further accumulation was observed. Figure 4.9 shows the confocal microscopic images from the sludge flocs of SBR and MBR. The red staining highlighted the position of bacteria and the green staining represented diclofop-methyl (DM) accumulation. After 1day of sorption, DM was completely diffused into floc regardless of the floc size, and even distributed. After desorption, 64 (a) SBR particle (b) MBR particle Figure 4.8 Visualization of Sorption using Miroautoradiograpy 65 (a) After sorption in SBR particles (b) After desorption in SBR particles (a) After sorption in MBR particles (b) After desorption in MBR particles Figure 4.9 Visualization of Hysteresis using confocal microscope; Biomass (red) and green (Diclofop-methly) 66 intensity of DM in different size of flocs was different, that is, the bigger flocs still have DM inside. In smaller floc, most of DM was released and not observed (Figure 4.8). This experiment clearly showed that previous results that particle size affects desorption processes. The bigger particles are, the bigger HI values. Another sorption test using micro autoradiography showed how sorption is affected by particle size. Cell surface was absolutely shown as a good sorption site (Figure 4.9). The connection between the HI and the loss of sorption capacity. When sorption hysteresis occurs, the removal capacity of the biomass is reduced because some of the surface sites are unavailable. This reduction in the removal capacity cannot be ignored, because neglecting it may cause an overestimation of the sorption capacity of the activated sludge. Before the operating implications of sorption hysteresis can be quantitatively explored, the HI and the loss of sorption capacity must be connected. The HI is a measure of the potential for PhAC entrapment, but the actual loss of sorption capacity occurs when the biomass, having been previously exposed to a higher soluble PhAC concentration, is exposed to a lower PhAC concentration, and then again to a higher PhAC concentration. Figure 4.10 illustrates an example, which is formulated using data collected in this study. Consider point #1, which corresponds to a biomass- associated PhAC concentration of 76?g/g(q1) and suspended in wastewater with a soluble PhAC concentration of 230?g/L(c1). When this biomass is exposed to a lower PhAC concentration (e.g.100?g/L(c2), located at point #2 in Figure 4.10), hysteresis may occur; so that the amount of the PhAC associated with the biomass will be 64?g/g(q2 ? ) located at point #2 ? , instead of 32?g/g(q2) located at point #2. The quantity q2 ? -q2 67 Figure 4.10 Typical sorption and desorption isotherms for CBR biomass: an example illustrating the loss of sorption capacity and the determination of ?. 0 20 40 60 80 100 120 0 50 100 150 200 250 Aqueous EE2 (ug/L) B i o m a ss-a sso c i ated E E 2 (u g / g ) point #1, (c1,q1) = (230, 76) point #3, (c3,q3) = (210,68) point #2, (c2,q2) = (100,32) point #0, (0,0) point #2', (c2',q2') = (100,64) Kdes = 0.61 L/g Kd = 0.32 L/g H.I. = 0.89 c2-c0 = 100 ug/L q3-q2 = 36 ug/g ? = 0.89 f = 0.79 68 represents the PhAC amount that remains associated with biomass, possibly entrapped within the floc. The loss of sorption capacity is realized when an ?upshift? occurs, or in other words, when the biomass is now exposed to a higher PhAC concentration, c3 (210?g/L) at point #3, where the biomass can only remove q3-q2 ? (as opposed to q3-q2). In this case, the fractional loss of sorption capacity (f) is 23 )2'2( qq qq ? ? (2) Therefore, f is a function of q2 ? -q2, which can be quantitatively related to the HI. The geometries of the sorption and desorption lines can be exploited to show )23( )02( qq cc KHIf d ? ? ??= (3) or f=HI*?, (4) where )23( )02( qq cc K d ? ? ?=? (5) The ? term is therefore dependent on four factors: (1) the slope of the sorption line, which determines K d , (2) the relationship between the sorption and desorption lines, which determines the c2-c0 term, (3) the magnitude of the ?upshift?, which determines the q3- q2 term, and (4) the magnitude of c2-c0, which depends on the location of point #2. The 69 example shown in Figure 4.10 produced ?=0.89, and with HI=0.89, this produced f=0.79. In general, f is proportional to HI, and their respective values are normally on the same order of magnitude, but the exact relationship between f and HI depends on the relationship between the sorption and desorption lines and the nature of the transient phenomena. Operating implications. The operating implications of sorption hysteresis depend on the configuration of the treatment plant in question. To quantify the operating implications for a single CSTR, the following schematic is considered (Figure 4.11). The mass balance for a given PhAC is as follows: sludgebiolprimprimprimsludgewaswasoi XVCkCXQCXQQCQC ???+??+??+= 0 (6) where Q is the wastewater flow (volume/time); C i the influent PhAC concentration (mass/volume); C 0 the effluent PhAC concentration (mass/volume); Q was the waste activated sludge flow (volume/time); X was the waste activated sludge concentration (mass/volume); C sludge the PhAC concentration in sludge (g/g); Q prim the primary sludge flow (volume/time); X prim the primary sludge concentration (mass/volume); C prim the PhAC concentration in primary sludge (mass/volume); k biol the rate of co-metabolic PhAC biotransformation resulting in CO 2 production (volume per mass per time); X sludge the mixed liquor suspended solids concentration (mass/volume); and V the bioreactor volume (volume). In Eq. (6), the pharmaceutical concentrations in the waste activated sludge and in the mixed liquor are assumed to be the same (C sludge ). Also, loss of PhACs 70 Figure 4.11 Continuous flow (CSTR) activated sludge wastewater treatment plant schematic. 71 due to volatization is neglected. The next step is to connect the aqueous and biomass phase PhAC concentration using the appropriate partitioning coefficient 0 CKC dsludge ?= (7) idprim CKC ?= ' (8) where K d and K d ? represent the partitioning coefficients associated with the mixed liquor and the primary solids, respectively. Finally, sorption equilibrium is assumed and Eq. (6) is rearranged and then normalized with respect to the influent concentration to produce the following: VXkKXQQ KXQQ C C sludgebioldwaswas dprimprim i ??+??+ ??? = ' 0 (9) The K d term can now be replaced with (1-f) K d , VXkKfXQQ KXQQ C C sludgebioldwaswas dprimprim i ??+???+ ??? = )1( ' 0 (10) The f term is introduced to represent the fraction of the adsorptive capacity lost due to hysteresis, and theoretically it can hold values between 0 and 1. When f is equal to 0, the full sorption capacity is available, and no sorption hysteresis is observed; very high values of f indicate that a larger fraction of the sorption capacity has been lost. An expression similar to Eq. (10) can be derived for a system of CSTRs operating in series. Joss et al. (2006) recently presented the appropriate expression, which accounted for 72 recycle streams and is fundamentally based on the same mass balance presented in Figure 4.11: ))1(1/())1(1()1( 1 )1(1 1 ' 0 ssddsludged primd i XKfSPKfRXKf XK C C ??+?+++ ? ??+ ?? = ? (11) Where [] ? ? ? ? ? ? ? ? ??++ ??? += b ssd hrtssbiol nXKfR Xk ))1(1)(1( 1? (12) SP is the specific sludge production (mass/volume); R the recycle rate (unitless); ? hrt the hydraulic retention time (time); and n the number of reactors in series. Figure 4.12 presents the quantitative effect of sorption hysteresis. The relative normalized effluent quality is shown on the y-axis, and it is the ratio of the normalized effluent quality (at a given value of f) divided by the normalized effluent quality when f is zero: 00 0 )( )/( = = fi fii CC CC ? (13) The relative normalized effluent quality is calculated for four cases; cases 1?3 are for a single CSTR, and for the case when biotransformation is important (#1), when biotransformation is negligible and sorption is favorable K d =2L/G (#2), and when biotransformation is negligible and sorption is very strong K d =10L/G (#3). Eq. (10) is used for the first three cases. Eq. (11) is used for the fourth case, which is when three CSTRs in series are considered, and both sorption (K d =0.5) and biotransformation play a 73 0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.2 0.4 0.6 0.8 1 Fraction of sorption capacity lost due to hysteresis (unitless) R e l a t i v e nor m a l i z e d e f f l ue nt qua l i t y f o r ca ses 1 - 3 ( u n i t l es s) 0 5 10 15 20 25 30 R e l a t i v e nor m a l i z e d e f f l ue nt qua l i t y f o r case 4 ( u n i t l es s) Case 1 Case 2 Case 3 Case 4 Figure 4.12 The effect of sorption hysteresis on relative normalized effluent quality. 74 role. For case 1, sorption hysteresis has a negligible effect on ?, which is to be expected since biotransformation (and not sorption) is the principle removal mechanism. For the second case, the effect of sorption hysteresis is moderate, and it is only at very high values of f that the relative normalized effluent quality begins to approach 1.5. For case 3, sorption hysteresis causes a more dramatic increase in the relative normalized effluent quality, which is greater or equal to 1.8 when f is 0.6 or greater. These first three cases are in sharp contrast to case 4. When f is 0.5, ? is 2, and as f increases, more dramatic increases in normalized effluent quality occur. Values of f greater than 0.9 cause the effluent quality to deteriorate by a factor of at least 9. Thus, in some bioreactor configurations and for chemicals like EE2, severe cases of sorption hysteresis can cause the effluent concentrations to increase dramatically. The results of the current work show that severe levels of sorption hysteresis are possible with larger biomass particle sizes typically found in CBR systems. For example, the CBR HI values observed during this study were between 0.53 and 0.92; this translates into f values between 0.47 and 0.82 (when ?=0.89). This loss of sorption capacity would result in values between 2 and 5 for a series of three CSTRs (case #4). MBRs alleviate this problem by producing smaller biomass particles, less amenable to sorption hysteresis. Conclusions The conclusions from this study are as follows: ? Biomass characteristics can have an important effect of sorption of EE2 to activated sludge biomass. The mean particle size and specific surface area can have a dramatic and 75 nonlinear effect of the observed EE2 partitioning coefficient and on sorption hysteresis. As the mean particle size decreases, more EE2 partitions to activated sludge biomass and less sorption hysteresis is observed. The partitioning coefficient and the HI both correlated well with mean particle size, and the relationships were best approximated by a power law expression. MBR sludge generally has higher EE2 partitioning coefficients and smaller HI values than CBR sludge. ? When the biomass was grown under nitrogen-limited conditions, the observed partitioning coefficients and HI values did not correlate as well with biomass size. ? Changing the SRT did not prove to be an effective means of directly manipulating the biomass characteristics, or the measured EE2 partitioning coefficients or HI values. ? Visualization study proved the effect of particle size on sorption hysteresis. ? Severe levels of sorption hysteresis may potentially lead to significant increases in the effluent concentrations of PhACs, depending on the process configuration and the relative roles of sorption and biodegradation. When sorption is the most important removal mechanism, severe levels of sorption hysteresis can cause a significant increase in the effluent concentration of PhACs in a single CSTR system. When both biodegradation and sorption are important removal mechanisms for three CSTRs in series, severe levels of sorption hysteresis can cause the relative effluent concentrations of PhACs to increase by at least a factor of 9. 76 Acknowledgements The authors thank Jinling Zhuang for experimental assistance, and the National Science Foundation (BES-0546388) for financial support. The authors thank the staff at the City of Auburn Wastewater Treatment Plant for their assistance, and the anonymous reviewers for their suggestions. References APHA, 1992 APHA, 1992. Standard Methods for the Examination of Water and Wastewater, 18th ed., American Public Health Association, American Water Works Association, Water Pollution Control Federation, Washington, DC, 1268. Carballa, M., Omil, F., Lema, J., Llompart, M., Garc??a-Jares, C., Rodr??guez, I., Go?mez, M., and Ternes, T. (2004). Behavior of pharmaceuticals, cosmetics and hormones in a sewage treatment plant. Water Res., 38 (12), 2918. 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THE LINK BETWEEN NITRIFICATION AND BIOTRANSFORMATION OF 17?-ETHINYLESTRADIOL Abstract Biological treatment processes are probably important for preventing the proliferation of steroidal compounds in the environment, and a growing number of reports suggest that nitrification may play a role in removing these chemicals from wastewater. The link between nitrification and biotransformation of 17?-ethinylestradiol (EE2) was investigated using enriched cultures of autotrophic ammonia-oxidizers. Batch experiments showed that ring A of EE2 is the site of electrophilic initiating reactions, including conjugation and hydroxylation. Ring A was also cleaved before any of the other rings are broken, which is likely because the frontier electron density of the ring A carbon units is higher than those of rings B, C, or D. EE2 and NH 3 were degraded in the presence of an ammonium monooxygenase (AMO) containing protein extract, and the reaction stoichiometry was consistent with a conceptual model involving a binuclear copper site located at the AMO active site. Continuous tests showed a linear relationship between nitrification and EE2 removal in enriched nitrifying cultures. Taken together, these results support the notion that EE2 biotransformation can be cometabolically mediated under operating conditions that allow for enrichment of nitrifiers. 81 Keywords: Frontier electron density, Nitrifying bacteria, 17a-EthinylEstradiol, Carbamazepine, Trimethorprim Previous research (Shi et al., 2004; Vader et al., 2005; Kim et al., 2005; Yi et al., 2006; Dytczak et al., 2006) has provided useful information about the removal of EE2 in nitrifying cultures, but there remain important and unanswered three questions. The first unanswered question concerns the reaction mechanisms (e.g. ring cleavage, hydroxylation) which are at work when EE2 is initially biotransformed. Most previous experiments were not conducted in the manner that allowed for the identification of metabolites, leaving the nature of the chemical reactions unclear. There has also been little effort dedicated toward the development of a better fundamental and conceptual understanding of EE2/NH 3 cometabolism. This need raises the second unanswered question, which concerns identifying conceptual model can be used to understand the stoichiometry of EE2/NH 3 cometabolism. There is also not enough information in the literature to analytically characterize the relationship between nitrification and EE2 biotransformation, which are presumably linked cometabolically. The third question concerns the relationship between EE2 and NH3 biotransformation rates. The purposes of this study are to answer these questions using laboratory-scale experimentation. Recent applied research has demonstrated that micropollutant removal efficiencies depend on a number of factors including the chemical characteristics of the compound(s) (e.g. log K ow ), the operating solids retention time, and the biomass particle characteristics (Lishman et al., 2006;Clara et al., 2005; Yi et al., 2006; Oppenhiemer and Stephenson, 2006). One class of micropollutants that has attracted considerable attention 82 in the literature is the steroidal compounds, including natural estrogens such as 17?- estradiol (E2) and estrone (E1), and the synthetic steroid 17? ethinylestradiol (EE2). These compounds tend to adsorb strongly onto activated sludge particles, and much of the previous work has determined equilibrium partitioning coefficients (K d ). The values available in literature generally show good agreement. Clara et al. (2004) found that the ?log(K d )? for steroid estrogens was 2.84 (2.64?2.97) and 2.84 (2.71?3.00) for E2 and EE2, respectively (Clara et al., 2004). In the work by Ternes et al. (2004), the ?log(K d )? for EE2 was determined to be 2.54 (2.49?2.58). Yi et al. (2006) found that the ?log(K d )? for EE2 was 2.7 for membrane bioreactor sludge and 2.3 when the sludge was taken from a sequencing batch reactor. Andersen et al. (2005) determined distribution coefficients (K d ) with activated sludge biomass for the steroid estrogens , E1, E2 and EE2 in batch experiments, and they determined ?log(K d )? values for steroid estrogens of 2.6, 2.7, 2.8 respectively (Andersen et al., 2005). Taken together, these partitioning coefficients enable practitioners to model sorption in activated sludge processes, and numerically evaluate the importance of sorption as a removal mechanism. Biotransformation of steroidal compounds is an area where the consensus is still developing. Biotransformation is likely due to cometabolic activity because steroidal compounds (like other micropollutants) are not present in high enough concentration to support substantial biomass growth. Clear evidence of cometabolism is still needed, but progress is being made, as there is a growing body of reports suggesting that EE2 can be biotransformed in enriched autotrophic nitrifying cultures. Vader et al. (2000) degraded EE2 using nitrifying activated sludge, and they noted the presence of unidentified hydrophilic daughter products. Yi et al. (2006), Shi et al. (2004), and Dytczak et al. 83 (2006) also biologically degraded EE2 using nitrifying mixed cultures, and in each case, the simultaneous disappearance of EE2 and ammonia was reported. Cometabolic activity requires a catalyst, and Yi et al., (2006) investigated the possibility that ammonium monooxygenase (AMO) can mediate EE2/NH 3 cometabolism. Their data suggested that AMO can remove EE2 and ammonia simultaneously, but their results were in conflict with others that suggested that AMO may be inhibited by acetylene (an analogue of the C17 EE2 function group) (Teissier and Torre, 2002; Bollmann and Conrad, 1997; Hyman and Arp, 1990); since EE2 contains an acetylene group (at C17), the possibility of EE2 inhibiting AMO must be considered. Methodology Experimental overview. A nitrifying completely-mixed stirred tank reactor (NCSTR) with sludge recycle was operated to cultivate an enriched nitrifying microbial community. This bioreactor was originally seeded with mixed liquor from the H.C. Morgan Water Pollution Control Facility in Auburn, Alabama. The experimental strategy was to use the waste activated sludge for a series of batch experiments. The batch experiments involved extracting the AMO enzyme from the biomass and incubating the protein extract with EE2, E2 and NH 3 . These experiments were done to investigate reaction stoichiometry and to determine whether the EE2 acetylene group at the C17 inhibited nitrification. These batch tests were done three times, and samples were collected and analyzed in triplicate. Another series of batch tests involved incubating whole cells with EE2, NH 3 , and sometimes allylthiourea to confirm the link between nitrification and EE2 removal. After the batch tests were completed, the NCSTR was 84 used for continuous experiments in which EE2 was included in the influent and the effluent was collected for metabolite detection. During the continuous experiments, the influent NH4 + concentration was varied in order to change the nitrification rate and to evaluate the ensuing effects on the rate of EE2 biodegradation. Finally, computational experiments were carried out in order to determine with electron density of the EE2 compound, and to investigate whether the electron density of EE2 is related to the sites where the initial biotransformation steps take place. Nitrifying bioreactor. The NCSTR (with sludge recycle) was operated at a HRT of 0.75 d and an SRT of 20 d. Peristaltic pumps were used to control the flow of influent and effluent, and the pH was controlled between 7.5 and 8.5. The SRT was maintained by manual wasting of solids directly from the bioreactor, and in most cases the waste sludge was used for analytical purposes. The influent feed consisted of the following (as mg/L total influent concentration): (NH 4 ) 2 SO 4 (660), MgSO 4 (40), KH 2 PO 4 (83.3), CaCl 2 (34), CuSO 4 (0.2), NaHCO 3 (1500), FeCl 3 (0.4). The influent EE2 concentration was 300 ug/L and influent ammonium concentrations were between 100 ? 400 mg/L as NH 4 + -N. Ammonia was used as the primary substrate to select for an autotrophic microbial community, and fluorescence in situ hybridization (FISH) was done at each operating condition to confirm the structure of the bioreactor population. FISH tests were performed using the hybridization and washing buffers provided by Vermicon AG (Munich, Germany) and as described by Tarre and Green (2004). The hybridized samples were analysed with a BioRad Laser Scanning Confocal Microscope, and the results confirmed the presence of autotrophic nitrifiers (Figure 5.1). 85 Standard analytical methods. EE2 and E2 were detected by HPLC (Hewlett- Packard, HP 1100). The system consisted of a degasser (G1322A), a quaternary pump (G1311A), an ALS auto-sampler (G1313A), a colcomp column oven (G1316A) and variable wavelength UV-VIS detector (G1314A). A Hypersil ODS C18, (125x46 mm, 5um) column was used. HPLC operating conditions were as follows; UV detector wavelength, 197nm and mobile phase, acetonitrile and water (40:60) with solvent delivered at a constant flow rate of 1mL/min. The total runtime of the HPLC analysis was 10min. Total suspended solids (TSS), volatile suspended solids (VSS), and ammonia-N were analyzed according to Standard Methods (APHA 1992). NADH was measured colorimetrically as described by Hage and Hartmans, (1999). Thin layer chromatography. The effluent from nitrifying sludge reactor was collected and metabolites were extracted using solid phase extraction (SPE). Extracted samples were dried in Savant Speed Vac System (GMI, USA). Solid samples were dissolved in methanol. Chromatography was conducted using TLC plates of Silica Gel (Whatman, no. 4745-010, Florham Park, NJ) with a solvent system of hexane/ethyl acetate (3:1 v/v). The metabolites on the TLC plate were visualized by exposure to iodine vapors (see Figure 5.2). Column chromatography. SPE was carried out as described above and metabolites were eluted using methanol. The eluted samples were mixed with fresh silica gel (Whatman 4745-010, Kent, UK) and then dried in vacuum evaporator. A glass column was packed with silica gel. Silica gel containing dried metabolites was loaded on 86 the top of the silica gel column. The column was eluted with solvent mixture of hexane/ethyl acetate (3:1 v/v) by gravity and the eluting solvent was collected at the bottom of the silica gel column in each 50mL fraction. Total volume of eluting solvent was 1L. Each fraction of solvent was dried in vacuum evaporator and then sample was dissolved in acetone-D6 for NMR tests. Nuclear magnetic resonance spectroscopy (NMR). The 1H NMR of purified metabolites was obtained using Bruker 400 MHz model (Bruker-Oxford Imaging Comp, Oxford, UK). The samples were dissolved in acetone-D6 (Aldrich chemical, no. 29621-0, Milwaukee, WI). 600 ul samples were placed in a new NMR tube. Figure 5.3 shows an example of an NMR spectrum that was observed. AMO extraction and activity. AMO enzyme extraction was explained previously (Yi and Harper, 2006; Schwarzenbach et al., 2003). Briefly, biomass from the NCSTR was harvested by centrifugation at 5000xg at 4?C for 30min and resuspended in 10mM Tri-HCl (pH 8.0). The resuspended pellet was sonicated for 10min at 40% amplitude in an ice bath using Fisher scientific Sonic Dismembrator (model 550, maximum power of 500W at a frequency of 20 kHz). The particulate fraction of the sonicated product was separated by centrifugation at 5000xg and the pellet was reconstituted in 10mM Tris-HCl (pH 8.0) supplemented with 1% dodecyl-?-D-maltoside. The resuspended pellet, which contained the membrane-bound AMO was investigated in 10mM Tris-HCl using eluted enzyme, electron donors (0.5mM NADH, 0.6 units diaphorase and 0.5mM duroquinone), and ammonia. Control tests were always performed 87 in the same way without the enzyme extract; these controls confirmed the absence of abiotic transformation of EE2. Protein was incubated for 1h, and then separated by centrifugation at 5000xg at 4?C for 1hour. Chromatography was performed on the supernatants and the resuspended pellet in a glass column (1cm x 10cm) packed with a DEAE Sepharose CL6B (weak anion exchange resin). The column was eluted with 140mM NaCl in 10mM Tris-HCl (pH 9.0) + 0.02% dodecyl- ?-D-maltoside. The removal of EE2 or E2 was investigated in 10mM Tris-HCl using eluted enzyme, electron donors (0.5mM NADH, 0.6 units diaphorase and 0.5mM duroquinone), and ammonia. Control tests were always performed in the same way without the enzyme extract; these controls confirmed the absence of abiotic transformation of EE2 or E2. FED analysis. Frontier electron density (FED) values were obtained by using Gaussian 03 program on the supercomputer in Alabama supercomputer center. Optimization of the EE2 structure was carried out with STO-3G basis set at level of Unrestricted Hartree-Fock (UHF). Based on optimized structure, highest occupied molecular orbital (HOMO) were calculated using the same method and basis set. The frontier electron density ?r can be calculated as ?r = [2*? (C ri HOMO) 2 ] Where r is the number of carbon atoms in i: 2s, 2px, 2py, and 2pz. Results and Discussion Electrophilic initiating reactions and Ring A cleavage. EE2 is polycyclic with a single aromatic ring A, and it includes polycyclic rings B, C, and D; ring D carries a 88 Figure 5.1 Fluorescent in-situ hybridization of Nitrosomonas sp., Nitrosococcus sp. and Nitrosospira sp. cells from nitrifying sludge reactor. Cell hybridization was done using Nitri-VIT(Vermicon AG) 89 Figure 5.2 Typical Thin Layer Chromatography Plate 90 hydroxyl group and acetylene group at C17. The electron density associated with ring A is significantly higher than in other rings of the compound (Figure 5.4). The pi electrons associated with this ring are sterically unhindered (i.e. accessible to attacking reagents because of their location in the circular clouds above and below the plane of ring A). This leaves ring A vulnerable to electrophilic substitutions that may serve as initiating reactions. Therefore, we expected to identify daughter products that showed electrophilic substitution, and we further expected to identify daughter products that demonstrated ring A cleavage. Using NMR, three primary daughter products were identified: ETDC (3- ethynyl-3a,6,7-trimethyl-2,3,3a,4,5,5a,8,9,9a,9b-decahydro-1H-cyclopenta[a]naphthalen- 3-ol), EE2-OH, and EE2-SO4 (Table 5.1). The first, ETDC, shows that ring A was removed, which was expected since the electron density around the EE2 ring A was relatively high. The second, EE2-OH, is hydroxylated at the C-2, and EE2-SO 4 is conjugated at C3, and C3 and C2 are also high FED carbon units. These results show that the high FED regions of the EE2 structure are involved in initiating reactions. These results also show ring A cleavage can occur before modification of ring B or C; this latter finding is different from what was found by Haiyan et al. (2006). They used a Sphingobacterium sp. JCR5 to degrade EE2, and based on the daughter products they detected, they proposed that EE2 is initially oxidized to E1, and that the pathway continues with ring opening oxidation reactions on ring B, leaving ring A initially intact. These current results offer another view of these initiating reactions by whole cells because they show that ring A cleavage can occur before ring B is broken. 91 Acetylene group inhibition. Previous results have shown that acetylene (an analogue of EE2) irreversibly inactivates AMO (Teissier and Torre, 2002; Bollmann and Conrad, 1997; Hyman and Arp, 1990); since EE2 contains an acetylene group (at C17), the possibility of EE2 inhibiting AMO must be considered. EE2 and E2 were both incubated in the presence of an AMO-containing extract and with NH 4 + . Figure 5.5 shows that the rate of ammonia removal for both incubations is similar. When EE2 is present, the ammonia removal efficiency is 90% and with E2 the ammonia removal efficiency is 85%. The EE2 removal efficiency was 70% and that of E2 was 63%. Since the only difference between EE2 and E2 is the C-17 acetylene group of EE2, and the action of AMO does not appear to be inhibited by the C-17 acetylene group. This result makes sense in light of the previously shown evidence (from Table 5.1) that initial transformations occur at ring A of the EE2 structure and not at C17. Whole cell incubations with E2 and EE2 also did not show any inhibition of NH 3 removal rates (Figure 5.9). This result shows that the C-17 group does not affect AMO activity or nitrification. Cometabolic reaction. Figure 5.6 is a conceptual picture showing a detailed example of how EE2 and nitrification may be connected. AMO converts NH 3 to NH 2 OH in the presence of oxygen. This step requires reducing power that is regenerated as NH 2 OH is oxidized to NO 2 - by hydroxylamine oxidoreductase. Electrons then enter a catalytic cycle involving a binuclear copper site located at the AMO active site. Oxygen reacts to convert the Cu(I) to Cu(II), but the oxygen remains bound as an electrophilic radical. This oxygenated form of the enzyme then reacts with organic substrates to 92 Figure 5.3 Example of an observed NMR spectrum: This shows that Ring A of EthinylEstradiol was cleaved 92 93 Figure 5.4 17?-Ethinylestradiol structure with electron density shown for the carbon units with the highest FED values 94 Figure 5.5 EE2 and E2 removal in the presence of an AMO-containing extract 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 123456 Time (hours) R e l a t i ve concent r at i o n ( C / C o) EE2 E2 NH4 with EE2 NH4 with E2 The EE2 acetylene group at C- 17 does not appear to ihibiAMO 95 produce the Cu(II) form. This conceptual model is based on monooxygenase activity, but dioxygenase enzymes must be considered also, and for two reasons in particular. First, dioxygenase enzymes are capable of mediating cometabolic biotransformation of polyaromatic compounds (Schwarzenbach et al., 2003). Second, the microorganisms in this study were harvested from an enriched (not pure) culture of nitrifiers, so it is possible that heterotrophic activity may be present, which would increase the possibility that dioxygenase activity may be occurring. Fortunately, cometabolic dioxygenase-mediated biotransformation of EE2 can be distinguished from monooxygenase-mediated activity because the NADH/EE2 molar ratio of the former is 1:1 (as opposed to 2:1). Therefore, in order to investigate whether the biotransformation of EE2 was monoxygenase or dioxygenase mediated, the ratio of EE2/NADH removed was determined by incubating EE2 and NADH in the presence of an AMO-containing enzyme extract. The molar ratio of NADH/EE2 determined during the incubation was 2.2, which is consistent with the action of monooxygenase-mediated biotransformation. This result demonstrates the potential for monooxygenase-mediated EE2 biotransformation in vitro and it also provides a conceptual model which could be useful for the design of future experimental efforts. Nitrification and EE2 biotransformation. The aforementioned whole cell cultures were used as a resource for further evaluating links between nitrification and EE2 biotransformation. One of the objectives of this work is to characterize the relationship between nitrification rate and EE2 biotransformation rate. Figure 5.7 shows the relationship between the measured NH 3 removal rate and the measured EE2 96 Table 5.1. EE2 biotransformation byproducts detected by NMR biotransformation rate. Current data, as well as data taken from Shi et al. (2004) and Vader et al. (2000) is shown. The continuous experiments showed a linear relationship between nitrification and EE2 biodegradation rates over the range of NH 3 and EE2 biotransformation rates tested. The EE2 biotransformation rate increased from 1.1 to 4.1 ?mol EE2/g VSS/h, while the NH 3 biotransformation rate increased from 0.3 to 3.1 mmol NH 3 /VSS/h. The current data agree well with the results of Shi et al. (2004), who measured an EE2 biotransformation rate of 1.5 ?mol/g VSS/h at a nitrification rate of 0.1 mmol/g VSS/h. The EE2 biotransformation rates observed by Vader et al. (2000) where less than those reported by Shi et al. (2004) and by the current work, but the Vader et al. (2000) work 97 Figure 5.6 Conceptual model for AMO role in cometabolic transformation: Catalytic reaction cycle involving a binuclear copper site 98 showed a trend that is consistent with the current data. These data taken together strongly show a linear link between nitrification and EE2 removal in enriched nitrifying cultures, and therefore supports the notion that EE2 biotransformation can be cometabolically mediated in bioreactors that are enriched for autotrophic nitrifiers. Results from the whole cell experiments provide evidence for AMO involvement in EE2 biotransformation. The appearance of EE2-OH as a metabolite (from Table 5.1) is consistent with monooxygenase activity, and the correlation between the EE2 and NH3 biotransformation rate (from Figure 5.7) are both strongly suggestive. Also, nitrifying whole cells were incubated with NH4+-N and EE2, both with and without allylthiourea (a nitrification inhibitor). Nitrification and EE2 removal were observed without the inhibitor, but in the presence of the inhibitor, EE2 was not removed and nitrification did not occur (Figure 5.9). This result also supports the idea that AMO is involved because allylthiourea inhibits nitrification by reacting with AMO (Bedard and Knowles, 1989). There are also other reports presenting similar evidence for AMO oxidation of organic compounds (Wahman and Speitel, 2005). Taken together these observations offer a considerable body of evidence. The current and previous results make it tempting to assert that autotrophic cultures are keys to controlling the passage of micro pollutants through full scale biological wastewater treatment plants, but the role of heterotrophic organisms must be considered. There are many fast-growing heterotrophic microorganisms in the activated sludge processes that have a variety of mono- and dioxygenase enzymes; these cultures may contribute to or even dominate micro pollutant biotransformations. The current results show that nitrifiers can initially degrade EE2 into intermediates; these compounds 99 Figure 5.7 Stoichiometry of EE2 and NADH removal 0 0.5 1 1.5 2 2.5 3 EE2 NADH Component E E 2 o r NADH re m o v e d ( u M ) 100 Figure 5.8 Relationship between NH 3 -N and EE2 biotransformation rate. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.5 1 1.5 2 2.5 3 3.5 4 NH3-N biotransformation rate (mmol NH4-N/gVSS/h) EE2 biotra nsfor m ation r a te x 10 0 (umol EE 2/gVSS /h) current research from Shi et al 2004 from Vader et al 2000 y = 0.9284x + 0.9496 R2 = 0.9432 101 Figure 5.9 Degradation of EE2 with inhibitor (allylthiourea) 400 450 500 550 600 650 0 1020304050607080 Time (h) C o nc . of E E 2 ( ug/ L) with inhibitor Control 102 may serve as a substrate for heterotrophic organisms. The work of Shi et al. (2004) also supports this idea. They conducted EE2 biodegradation experiments with a nitrifying pure culture and a mixed culture of nitrifiers and heterotrophs. They detected daughter products in the pure culture experiments but not in the mixed culture experiments, perhaps because the heterotrophs completely degraded the daughter products. It is not clear that autotrophic nitrifiers can play a significant role in transforming micropollutants in systems that support significant heterotrophic populations. Extracellular enzymes and other scavenging, biodegradative mechanisms are also present in bioreactors. The relative importance of nitrifiers and heterotrophs remains open for future investigations. Conclusions ? FED profiles of EE2 showed that ring A is the most reactive region in EE2 molecule. Batch experiments using whole cell taken from nitrifying sludge reactor showed that ring A of EE2 is the site of electrophilic initiating reactions, including conjugation and hydroxylation. Ring A was also cleaved before any of the other rings are broken, which is likely because the frontier electron density of the ring A carbon units is higher than those of rings B, C, or D. ? EE2 and NH 3 were degraded in the presence of AMO containing protein extract, and the reaction stoichiometry was consistent with a conceptual model ? Continuous experiments showed a linear relationship between nitrification and EE2 removal in enriched nitrifying cultures. These results support the notion that EE2 biotransformation can be cometabolically mediated under operating conditions that allow for enrichment of nitrifiers. 103 References Andersen, H., Hansen, M., Kj?lholt, J., Stuer-Lauridsen, F., Ternes, T., and Halling- S?rensen, B.(2005). Assessment of the importance of sorption for steroid estrogens removal during activated sludge treatment. Chemosphere, 61(1), 139. APHA (1992). Standard Methods for the Examination of Water and Wastewater. 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DEGRADATION OF 17?-ETHINYLESTRADIOL, CARBAMAZEPINE, AND TRIMETHOPRIM IN NITRIFYING SLUDGE REACTOR AND CONVENTIONAL ACTIVATED SLUDGE FED WITH TOLUENE Abstract Prediction of biological metabolic pathway is important for understanding the fate of pharmaceutical compounds (PhACs) in biological processes. A metabolic pathway prediction tool was developed and predictive accuracy was assessed using two bioreactors fed with monooxygenase inducers; ammonia and toluene. The removal of 17?-EthinylEstradiol (EE2), Carbamazepine (CBZ), and Trimethoprim (TMP) were monitored and byproducts were detected and identified. Removal efficiencies of EE2 were relatively higher in both reactors. Removal efficiencies were 60 % and 40% in nitrifying sludge reactor and conventional bioreactor fed with toluene, respectively. And TMP and CBZ were not significantly removed in both reactors. Byproducts detected in continuous experiments clearly showed that biotransformation took place in the high frontier electron density region of EE2 and TMP. Although byproduct of carbamazepine (CBZ) was not detected, previous study showed biotransformation of CBZ was well correlated with FED profile. However, we could not prove whether or not the full degradation pathway developed by prediction tool was accurate due to not having enough substantial byproducts data in this research and previous studies. 108 Keywords: Frontier electron density, Degradation rules, Degradation pathway, monooxygenase enzyme The purposes of this study were to develop and prove the prediction tool and the capability of FED analysis in the biological systems. In this study, we tried to give a more accurate and logical way to predict degradation pathway by applying theoretical based approach (frontier electron density analysis) and degradation rules developed based on previously established metabolic pathways. The recent studies conducted by few researchers (Shi et al., 2006; Yi and Harper, 2007; Kim et al., 2005; Batt et al., 2006) have shown that some pharmaceuticals undergo a transformation in activated sludge treatment processes in the presence of nitrifying bacteria. It is well known that monooxygenase enzymes such as Cytochrome P450, ammonia monooxygenase, or toluene monooxygenase which are the highly non-specific enzyme are capable of co-metabolizing a variety of compounds. Shi et al. (2004) and Yoshimoto et al. (2004) reported the appearance of 17?-ethinylestradiol (EE2) degradation products and the study of Kim et al. (2006) suggested carbamazepine (CBZ) and trimethoprim (TMP) degradation in enriched nitrifying sludge reactor were observed. Yi and Harper (2007) and Haiyan et al. (2007) investigated the degradation mechanism and proposed the pathway of EE2, although each study suggested different degradation mechanism. Therefore, it is believed that some bacteria in presence of inducers are capable of producing monooxygenase enzyme and oxidizing pharmaceutical chemicals. However, more effort is needed to reveal degradation mechanisms. To experimentally develop a complete degradation pathway in most cases is generally very difficult because 109 byproducts are quickly removed and concentration of byproducts is usually under detection limit. Several degradation prediction tools (Pathway prediction systems, METEOR, MetabolExpert, and META) have been introduced for xenobiotic metabolism (Hou et al., 2004; Darvas, 1988; Greene, 1999; Klopman and Tu, 1997; Klopman et al., 1995; Long, 2002). However, these prediction tools for microbial metabolism of pharmaceutical compounds need huge database collected from previously established pathways. This system is not able to theoretically explain the degradation pathway because empirical data are used and pathway is predicted based on functional group. Thus, if a compound has several different functional groups, many different pathways are proposed by prediction system. To simply and logically predict the first reaction in cometabolism and whole metabolic pathway, the detail of how enzyme affects the chemically different transformation of pollutants and how microorganisms deal with oxidated or hydroxylated products at the first step are really of interest to study the fate of pharmaceuticals. Methodology Experimental overview. Two different types of bioreactor were operated; a nitrifying sludge reactor (NSR) fed with NH 3 -N and conventional bioreactor (CBR) fed with toluene. These bioreactors were originally seeded with mixed liquor from the H.C. Morgan Water Pollution Control Facility in Auburn, Alabama. These two reactors were operated continuously and EE2, CBZ, and TMP were fed to each reactor to investigate and prove the proposed degradation pathway which was developed by the combination tool of FED theory and degradation rules. 110 Reactor configuration and operation. NSR and CBR were fed with ammonia and toluene as an induer and substrate, respectively. Both reactors were operated at a HRT of 2 day and an SRT of 20 day. The flow of influent and effluent was controlled by peristaltic pumps, and the pH was controlled between 7.5 and 8.5. The SRT was controlled by manually wasting of mixed liquor directly from the bioreactor. The composition of the NSR influent feed (as mg/L total influent concentration) consisted of (NH 4 ) 2 SO 4 (660), MgSO 4 (40), KH 2 PO 4 (83.3), CaCl 2 (34), CuSO 4 (0.2), NaHCO 3 (1500), FeCl 3 (0.4). The influent concentration of model compounds (EE2, CBZ, and TMP) was fixed at 300 ?g/L. The primary substrate in the synthetic wastewater for the CBR was acetate (360 mg as COD/L) and influent ammonium concentration for NSR was 100mg/L as NH 3 -N. The operating details for the bioreactors were presented previously (Yi and Harper, 2007 and Yi and Harper, 2005). Standard analytical methods. EE2, CBZ and TMP were detected by HPLC (Hewlett-Packard, HP 1100). The system consisted of a degasser (G1322A), a quaternary pump (G1311A), an ALS auto-sampler (G1313A), a colcomp column oven (G1316A) and variable wavelength UV-VIS detector (G1314A). A Hypersil ODS C18, (125x46 mm, 5um) column was used. HPLC operating conditions were as follows; UV detector wavelength, 197nm, mobile phase, acetonitrile and water (40:60 for EE2 and CBZ; 10:90 for TMP) with solvent delivered at a constant flow rate of 1.5mL/min. The total runtime of the HPLC analysis was 30min. Total suspended solids (TSS), volatile suspended solids (VSS), and ammonia-N were analyzed according to Standard Methods (APHA 1992). 111 Column chromatography. Solid phase extraction (SPE) was carried out using C18 disc and then metabolites were eluted using methanol. The eluted samples were mixed with fresh silica gel (Whatman 4745-010, Kent, UK) and then dried in vacuum evaporator. A glass column was packed with fresh silica gel. Silica gel containing dried metabolites was loaded on the top of the silica gel column. The column was eluted with solvent mixture of hexane/ethyl acetate (3:1 v/v for EE2 and 1:1 v/v for CBZ and TMP) by gravity and the eluting solvent was collected at the bottom of the silica gel column in each 50mL fraction. Total volume of eluting solvent was 2L. Each fraction of solvent was dried in vacuum evaporator and then sample was dissolved in methanol-D4 for nuclear magnetic resonance spectroscopy (NMR) tests. Nuclear magnetic resonance spectroscopy. After column chromatography, the 1H NMR of purified metabolites was obtained using Bruker 400 MHz model (Bruker- Oxford Imaging Comp, Oxford, UK). The samples were dissolved in Methanol-D4 (Aldrich chemical, no. 29621-0, Milwaukee, WI). 0.6 ml samples were placed in a clean NMR tube. An example of an NMR spectrum was observed. FED analysis. Frontier electron density (FED) analyses were performed to determine highest electron density site in a compound, using Gaussian 03 program on the supercomputer in Alabama supercomputer center. Structure optimization of the model compounds was conducted with 6-31G(d) basis set at level of Unrestricted Hartree-Fock (UHF). After structure optimization, FEDs of carbon atoms in the model compounds 112 were calculated in the same method and basis set. The frontier electron density ?r can be calculated as ?r = [2*? (C ri HOMO) 2 ] Where r is the number of carbon atoms in i: 2s, 2px, 2py, and 2pz. Degradation rules. FED values were used to determine the location of the most reactive part in the model compounds. To determine what happens at the reactive position, we invoke five degradation rules (Figure 6.1) as follows (Kamath and Vaidyanathan, 1990; Hay and Focht, 1998; Nosova et al., 1997; Steffan et al., 1997; Casellas et al., 1997; Dean-Ross et. al., 2001; Brzostowicz et. al., 2005; Nakazawa and Hayashi, 1978; Olsen et. al., 1994); Rule 1 - Enzyme attacks carbon atom at the highest f r value position. Rule 2 - The phenol ring is cleaved after oxidized to catechol. The oxygenolytic cleavage Table 6.1 Structures and Properties of model compounds 113 of the phenol ring occurs via Ortho- or meta-cleavage. Ortho- or meta-cleavage was determined by f r value of adjacent carbon atom which has hydroxylated carbon with highest f r value. Ring cleavage takes place between hydroxylated carbon with highest f r value and carbon with higher f r out of two adjacent carbons. Rule 3 - The cyclohexane and cyclopentane ring are open after oxidized to cyclohexanone and cyclopentanone, respectively. Ring cleavage of cylcohexanone and cyclopentanone are determined by the same rule with phenol ring cleavage. Rule 4 - After ring opening, carbon chains are degraded to hydroxyl-, ketone, and carboxylic acid and then de-carboxylation step is followed. Rule 5 ?Ring cleavage of heterocyclic ring occur as shown in Figure 6.1 and hydrolytic de-amination and dehalogenation are followed. Rule 6 - Although a carbon is in the highest f r value position, if not applicable to degradation rules (rule 1 to 5), metabolic reaction occurs at the a carbon atom which has second highest f r value. Results and Discussion Degradation of model compound in nitrifying sludge reactor and activated sludge reactor fed with toluene. CBZ, EE2, and TMP were separately fed into both reactors. The influent concentrations of NH 3 -N for NSR and toluene for CBR were fixed at 100 mg/L and 520 mg/L, respectively. After a few days, NH 3 -N and toluene concentration in each reactor was stabilized at 5mg/L (? 1.7mg/L) and 3.5 mg/L (? 1mg/L), respectively. To determine the volatilized amount of toluene, control test was performed and toluene concentration was stabilized at 5mg/L. 114 RCH 3 R OH R O R O OH RH OH OH OH COOH COOH OH O COOH CH 2 OH OH O COOH CH 2 OH NH 2 CH 3 OH CH 3 N N OH OH N OH OH NH OH OH O O NH 2 OH OH O Figure 6.1 Degradation rules 115 EE2 was most easily degraded in both reactors and removal efficiencies were 60% and 40% in NSR and CBR, respectively (Figure 6.2), and disappearance of EE2 was not observed in control tests. Previous studies (Vader et al., 2000; Shi et al., 2004; Yi and Harper, 2006) showed that enriched bacterial culture or monoculture of nitrifiers can remove or initiate degradation of EE2. The current data agree well with previous finding. TMP and CBZ were highly persistent in both reactors. Removal efficiencies of CBZ and TMP were 10 % and 5 % in NSR, respectively and close to zero in CBR. Batt et al. (2006) achieved 70 % removal of TMP in fresh activated sludge fed with NH 3 -N as substrate. However, Khunjar et al. (2007) observed no removal of TMP and CBZ in the monoculture of nitrifying bacteria and concluded the various removal efficiencies of TMP were attributed to other heterotrophic bacteria which can not produce monooxygenase. Biomass composition may explain differences in the observed CBZ removal efficiencies. Batt et al. (2006) used fresh activated sludge, which is presumably diverse in terms of microbial community composition. The sludges used in the current study and by Khunjar et al. (2007) were taken from the bioreactor which was operated on synthetic wastewater, and the community composition was not likely to maintain a comparable level of microbial diversity. A diverse microbial community is more likely to have a broader pool of enzymes capable of transforming micro-pollutants. Removal efficiencies of three model compounds observed in both reactors were in the following order: EE2 > CBZ > TMP. This order was well correlated with log K ow values order (Table 6.1). Danielsson et al. (1996) reported that biotransformation rates of organic compounds increase with increase of log K ow value and reach the maximum at around log K ow 3.5, and then fall into a decline. It is note worthy to mention sorption is 116 often the first step of biotransformation and increase of log K ow has the influence on sorption. Theoretical studies and detected byproducts. Molecular orbital (MO) calculations were carried out to calculate FED for EE2, CBZ, and TMP. FED profiles show that which region or position in a molecule is most reactive. However, it does not tell how reaction will go or what will happen at the high FED position. We developed the prediction tool based on FED theory and degradation rules. FED indicates reactive part or position in a molecule and degradation rules designate what happens in this region or position. The prediction tool was applied to each degradation step. Degradation pathways of EE2, CBZ, and TMP were theoretically developed. All intermediates were derived on the basis of combination of FED and degradation rules. If the highest FED position was not applicable in degradation rules, the degradation rules were applied to the next highest FED position. The brief pathway is shown in Figure 6.6. Although steric hiderance effect is important in chemical reactions, it was not applied in degradation rules, because the framework needed for this is still missing. EE2: These FEDs analysis are found to be high at phenolic group in EE2, especially at the atom No. 13, followed by atom No. 16, 15, and 17 (Figure 6.3). It is determined that the first reaction takes place at atom No. 13 and then oxidation of Atom No. 15 takes place next (Figure 6.6). However, byproduct 1 clearly shows that first reaction occurred at atom No. 15. It is expected that atom No. 15, 16 or 17 may be more vulnerable than atom No. 13 because atom No. 13 associated with the ring structure can 117 0 20 40 60 80 100 17a-EthinylEstradiol Carbamazpeine Trimethoprim Model Compounds Rem o v a l ( % ) Nitrifying sludge CSTR with Toluene Figure 6.2 Removal Efficiency of model compounds 118 be sterically hindered. Figure 6.6 shows that after the hydroxylation of atom No. 15 ring A cleavage occurs. Byproduct 2 also demonstrates that ring A cleavage take place ahead of ring cleavage of B, C, D, and the elimination of ethinyl group. However, Haiyan et al. (2006) proposed the oxidation of EE2 was initialized at ethinyl group and then ring B cleavage took place earlier than ring A. Yi and Harper (2007) investigated EE2 and estradiol (E2) removal rates in enzyme extract to estimate the inhibition of AMO by Table 6.2 Byproducts of 17?-EthinylEstradiol identified Parent compounds Byproducts Byproduct No. Reference CH 3 CH OH OH 17a-EthinylEstradiol (EE2) CH 3 CH OH OH OH CH 3 CH 3 CH 3 CH OH CH 3 CH OH O S OH O O OH OH CH 3 OH O CH 3 1 2 3 4 5 Yi and Harper (2007) Yi and Harper (2007) Yi and Harper (2007) Hayian et al. (2007) Hayian et al. (2007) 119 Table 6.3 Byproducts of Trimethoprim identified Parent compounds Byproducts Byproduct No. Reference N N O O O NH 2 NH 2 CH 3 CH 3 CH 3 Trimethiprim (TMP) N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 OH N N NH 2 NH 2 O OH O CH 3 CH 3 OH O 6 7 This Study This Study ethinyl group in EE2 molecule because only structural difference between EE2 and E2 is ethinyl group of EE2. In their study, they found that biotransformation rates of both compounds were very similar. It is indicative that there was no or very little effect by ethynyl group on biotransformation and Shi et al. (2006) also found no significant difference of biotransformation rates among the four different estrogens including EE2 and E2 using Nitrosomonas europaea. Brzozowski et al. (1997) reported that phenol group in Estradiol (E2) has a significant effect on interaction with human estrogen receptor (hER). It is determined that FED profile of E2 is very similar with EE2 (data not shown). The study of Brzozowski et al. (1997) well agrees with FED profile of E2 and it indicates that phenol group plays an important role in the interaction between EE2 (or E2) and an enzyme. Therefore, it is expected the complex between enzyme and EE2 is associated with phenol group and it is highly possible that first hydroxylation occurs in phenol group. 120 13 12 14 17 15 16 4 10 5 11 2 3 7 6 1 8 9 OH 22 OH 18 CH 3 21 19 CH 20 Figure 6.3 Frontier Electron Density profile for 17?-EthinylEstradiol Atom No. 2FEDhomo 1 0.000371005 2 0.000553011 3 0.002074564 4 0.015643466 5 0.023331228 6 0.066403301 7 0.018288454 8 2.08166E-05 9 0.000467225 10 0.003232121 11 0.00569899 12 0.089349825 13 0.377681475 14 0.085472475 15 0.149679794 16 0.26059811 17 0.103886755 18 6.66368E-05 19 0.000355099 20 0.000391955 21 0.000214882 22 0.182670974 A B C D 121 Byproduct 3 also shows conjugation occurred at high FED region. After the cleavage of ring A, the next degradation occurs in where the carbon chain generated by phenol cleavage was degraded to short chain. The predicted pathway shows that the elimination of ethinyl group takes place next, followed by the cleavage of ring B, C, and D (Figure 6.6). Table 6.4 Byproducts of Carbamazepine identified Parent compounds Byproducts Byproduct No. Reference N O NH 2 Carbamazepine (CBZ) N O NH 2 O N O NH 2 OH OH N O NH 2 OH N O NH 2 OH N O NH 2 OH 8 9 10 11 12 Miao et al. (2005), Masubuchi et al.,(2001) By Cytochrom P450 Miao et al. (2005) Miao et al. (2005) Miao et al. (2005) Miao et al. (2005) 122 10 11 15 12 14 13 9 N 3 2 4 N 1 5 6 NH 2 8 NH 2 7 O 18 O 16 O 20 CH 3 21 CH 3 19 CH 3 17 (a) OH-TMP (b) Figure 6.4 Frontier Electron Density profile for (a) Trimethoprim (b) OH-TMP Atom No. (a) (b) 1 0.24741766 0.00449815 2 0.08417355 0.00104102 3 0.027575708 0.00187971 4 0.055900837 0.00261573 5 0.49185134 0.0538014 6 0.051353883 0.00381706 7 0.248716172 0.00137362 8 0.109231687 0.00035407 9 0.017677641 0.01452419 10 0.0030356 0.28086663 11 0.007253743 0.03636656 12 0.01931808 0.14960749 13 0.002774946 0.35524935 14 0.009524638 0.05285957 15 0.020068421 0.12729231 16 0.005108807 0.04885564 17 0.001546981 0.00973651 18 0.000195218 0.09631899 19 0.000608386 0.02665585 20 0.002096012 0.01628797 21 0.000958108 0.00313194 A B N N NH 2 NH 2 O O OCH 3 CH 3 CH 3 OH 123 Trimethoprim: FED profile of TMP is shown in Figure 6.3. High FED region is located in Ring B and the highest FED position is atom No. 5 and atom No. 6 is the second highest FED position. Accordingly, we expected that first reaction occurs by adding oxygen into atom No. 5. However, byproduct 6 shows first hydroxylation reaction took place at atom No. 6. This result indicated that first hydroxylation reaction may be affected by steric hindrance effect. High FED profile is significantly changed after the first hydroxylation reaction (Figure 6.4). It causes the ring A cleavage ahead of the cleavage of ring B. Byproduct 7 supported this prediction. Carbamazepine: It is determined that ring B is the most reactive part. The FED associated with atom No. 4 is significantly higher than that of other atoms of the compounds and atom No. 5, 6, and 9 are also high FED positions (Figure 6.5). It is predicted that first hydroxylation reaction occurs in atom No. 4, followed by atom No. 5 and then ring cleavage take place at ring B. Ring cleavage A and C follows ring B (Figure 6.6). Although byproducts were not detected in this study, Miao et al. (2005) reported the metabolic byproducts of CBZ detected in wastewater treatment plants. And these byproducts agree well with FED profile of CBZ. Byproduct 8, 9, 11, and 12 reported by Mia et al. (2005) shows that hydroxylation reaction of CBZ took place at high FED position. Most dominant metabolites are associated with the highest or second highest FED positions (atom No. 4 and 5). Masubuchi et al. (2001) also proposed 2 and 3-Hydroxy CBZ and 10, 11-Epoxide (byproduct 8) as CBZ metabolites using cytochrome P450. 124 6 7 11 8 10 9 54 14 13 15 12 3 2 N 1 16 NH 2 18 O 17 Figure 6.5 Frontier Electron Density profile for Carbamazepine. Atom No. 2FEDhomo 1 0.028598102 2 0.053538417 3 0.086247973 4 0.193466787 5 0.154936677 6 0.142871562 7 0.060326586 8 0.041501186 9 0.142826907 10 0.015609155 11 0.074303102 12 0.035776825 13 0.092644276 14 0.006538912 15 0.057707472 16 0.011709527 17 0.002420261 18 0.002528383 A B C 125 N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 OH N N NH 2 NH 2 O OH O CH 3 CH 3 OH O N N NH 2 NH 2 O OH OH CH 3 OH O OH O O O OH NH 2 N NH 2 NH 2 O OH OH CH 3 OH O O O O O OH O OH NH 2 OH O O O OH CO 2 (a) N O NH 2 N O NH 2 OH N O NH 2 OHOH N O NH 2 O O OH N O NH 2 O O OH O OH OH OH OH N O NH 2 O O OH O OH OH OH OH O O OOH N O NH 2 O O OH O OH O OH O O OH OH OH O CO 2 (b) (c) Figure 6.6 Metabolic pathways of model compounds predicted by prediction tool; (a) Trimethoprim, (b) Carbamazepine, and (c) 17?-EthinylEstradiol OH CH OH CH 3 OH CH 3 OH O CH O CH 3 OH O OH OH O OH CH CH 3 OH O OH OH O OH OH OH CH 3 OH O OH OH OH O OH CH 3 OH O O O OH OH OH CH 3 OH O O O OH OH OH OH CO 2 126 Conclusions ? Degradation of EE2, TMP, and CBZ was investigated using two reactors fed with ammonia and toluene. Results indicated that EE2 among three model compounds was most efficiently removed in both reactors. The removal efficiencies of EE2 were 60% and 40% in nitrifying sludge reactor and conventional bioreactor fed with toluene respectively. However, CBZ and TMP were not removed significantly in both reactors. ? FED theory was applied to predict biodegradation reaction. Byproducts detected in this research and reported in previous studies clearly shows that biotransformation reaction takes place in high FED position or region. However, we could not prove if the prediction tool well predict degradation pathway of pharmaceutical compounds through full pathways due to not having enough substantial data of byproducts in this and previous studies. References APHA (1992). Standard Methods for the Examination of Water and Wastewater. 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Env. Eng., 132(11), 1527. 131 VII. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH Summary and Conclusions Pharmaceutical compounds have been detected in relevant amounts in surface and wastewater. Biological treatment process plays an important role in preventing the negative effects from pharmaceutical compounds in the environment. The improved understanding of fundamental of removal mechanisms of these compounds in biological system is essential to increase removal efficiency. Three different types of bioreactor were continuously operated: membrane bioreactor (MBR), sequencing batch reactor (SBR), and conventional reactor (CBR). Sorption is one of main mechanisms responsible for removal of pharmaceutical compounds from wastewater. Biomass characteristics such as hydrophobicity and particle size were manipulated by operating MBR and CBR under various conditions (nitrogen limitation and SRTs) and monitored to determine the effects on K d and sorption-hysteresis of EE2. MBR always had smaller particle than that of SBR, and MBR K d was equal to or larger than that of SBR. SRT change does not manipulate particle size, and the effects on K d and HI were not dramatic. The biomass particle size had a dramatic effect on the observed K d under normal conditions. K d values of MBR and CBR were 0.33-0.57 and 0.25-0.33, respectively. Under nitrogen-limiting conditions, the correlations between the biomass particle size and K d and HI were weak. It was found that the hysteresis index was greater for biomass suspensions with larger particle sizes. 132 This means that the selection of a MBR may potentially result in the less entrapment of EE2 with activated sludge biomass. These results showed that the magnitude of the partitioning coefficient and sorption-Hysteresis depended on the particle size characteristics (i.e. specific surface area). Visualization study showed that after desorption, in bigger particles from SBR, large amounts of substrate remained inside particles. The result clearly confirms particle size has an effect on sorption-hysteresis. This study also numerically explored the impacts of sorption hysteresis on the removal of pharmaceutical compounds. Cometabolic reaction is important in degradation of pharmaceutical compounds. An enriched nitrifying sludge reactor was prepared and tested to estimate the role of nitrifying bacteria in degradation of EE2. Frontier electron density (FED) analysis was carried out to determine the highest FED region or position for electrophilic reaction using Gaussian 03 program. FED analysis shows that ring A of EE2 is the highest FED region and it is predicted that ring A is cleaved before rings B, C, or D was cleaved and ethinyl group was eliminated. Byproducts suggested that oxidation reaction and ring cleavage occurred at ring A of EE2 and Batch experiments using nitrifying sludge indicated that disappearance of EE2 was attributed to the presence of an ammonium monooxygenase (AMO) containing enzyme extract and EE2 degradation was improved with increase of removal of NH 3 , simultaneously. There are good linear relationship between nitrification and disappearance of EE2. The reaction stoichiometry between a binuclear copper site located at the AMO active site of ammonia monooxygenase enzyme and EE2 was consistent with a proposed conceptual model. Removal mechanism proved in these experiments suggested EE2 biotransformation may be cometabolically mediated 133 enzyme reaction mainly involving nitrifying bacteria. Two reactors that have different inducer conditions were tested to investigate the removal of EE2, TMP and CBZ in different monooxygenase condition. Removal efficiencies of the model compounds which have different chemical properties were monitored in each reactor. Removal efficiency results showed that EE2 was most efficiently removed in the nitrifying sludge reactor and conventional stirred tank reactor fed with toluene. Removal efficiencies of EE2 were 60% and 40%, respectively. CBZ and TMP were not removed significantly in both reactors. FED was applied to predict biodegradation reaction. Results clearly showed that degradation reaction took place in the high frontier electron density region. The full degradation pathway was developed using frontier electron density theory and degradation rules. It was determined that the first reaction was well matched with experimental results. However, we could not prove if the prediction tool predict degradation pathway of pharmaceutical compounds well due to not having enough substantial data of the byproducts in this research and references. Suggestions for Future Work This study raises two questions; (1) why the different structure of chemicals are removed at different removal efficiencies and (2) how the microbial community structure affects removal of pharmaceutical compounds. The complex between enzyme and organic compounds should be investigated because favorable complex shows higher removal of organic compounds. In cometabolism, the first reaction is very important because usually the first reaction is rate-limiting step. The study of a complex between monooxygenase enzyme and a organic compound will be more informative and can 134 determine why the various compounds which have different structures have different removal efficiencies even if in same condition. Docking Simulation between target monooxygenase enzymes and pharmaceutical compounds can be very nice tool to answer this question. The microbial structural diversity should be investigated using molecular techniques such as denaturing gradient gel electrophoresis (DGGE) or terminal- restriction fragment length polymorphism (T-RFLP). In this and previous studies, it is believed that microbial community structure of other heterotrophic bacteria as well as monooxygenase production bacteria such as ammonia monooxygenase is important for degradation of pharmaceutical compounds. In addition, more degradation byproducts should be detected to prove the capability of the prediction tool. 135 BIBLIOGRAPHY Alleman B.C., logan, B.E., and Cilbertson, R.L.(1994) Degradation of pentachorophenol by Fixed Films of White Rot Fungi in Rotating Tube Bioreactors. Wat. Res., 29. American Public Health Association (APHA) (1992). Standard Methods for the Examination of Water and Wastewater. 18th Ed., American Public Health Association, American Water Works Association, Water Pollution Control Federation, Washington, D.C. 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Science of the Total Environment, 309, 201. 149 APPENDICES 150 Appendix A An example of Gaussian Code for Structure optimization and FED calculation 151 %chk=Trimethoprim # opt uhf/6-31g(d) Trimethoprim 0 1 C C 1 B1 C 2 B2 1 A1 C 3 B3 2 A2 1 D1 C 4 B4 3 A3 2 D2 C 1 B5 2 A4 3 D3 H 2 B6 1 A5 6 D4 H 4 B7 3 A6 2 D5 C 3 B8 2 A7 1 D6 C 9 B9 3 A8 2 D7 C 3 B10 2 A9 1 D8 H 11 B11 3 A10 2 D9 H 11 B12 3 A11 2 D10 N 9 B13 3 A12 2 D11 N 10 B14 9 A13 3 D12 O 5 B15 4 A14 3 D13 O 6 B16 1 A15 2 D14 O 1 B17 2 A16 3 D15 C 16 B18 5 A17 4 D16 H 19 B19 16 A18 5 D17 H 19 B20 16 A19 5 D18 H 19 B21 16 A20 5 D19 C 18 B22 1 A21 2 D20 H 23 B23 18 A22 1 D21 H 23 B24 18 A23 1 D22 H 23 B25 18 A24 1 D23 C 15 B26 10 A25 9 D24 C 17 B27 6 A26 1 D25 H 28 B28 17 A27 6 D26 H 28 B29 17 A28 6 D27 H 28 B30 17 A29 6 D28 C 14 B31 9 A30 3 D29 H 32 B32 14 A31 9 D30 N 10 B33 9 A32 3 D31 H 34 B34 10 A33 9 D32 H 34 B35 10 A34 9 D33 O 27 B36 15 A35 10 D34 H 37 B37 27 A36 15 D35 B1 1.40140033 B2 1.40140226 B3 1.40140141 B4 1.40140135 B5 1.40140152 B6 1.07000086 B7 1.07000356 B8 2.51481727 B9 1.39494235 B10 1.54000157 B11 1.06999988 B12 1.07000097 B13 2.35905254 B14 1.33786616 B15 1.43000187 B16 1.43000087 B17 1.42999959 B18 1.43000244 152 B19 1.07000386 B20 1.06999779 B21 1.07000481 B22 1.43000099 B23 1.06999711 B24 1.07000035 B25 1.06999867 B26 1.34423457 B27 1.43000467 B28 1.07000012 B29 1.07000164 B30 1.07000036 B31 1.33786909 B32 1.06999955 B33 1.47000000 B34 1.00000000 B35 1.00000000 B36 1.43000000 B37 0.96000000 A1 120.00011735 A2 120.00005254 A3 119.99986978 A4 119.99980714 A5 119.99994519 A6 120.00016019 A7 114.09464701 A8 147.80491235 A9 119.99979053 A10 109.47118080 A11 109.47122415 A12 117.44431413 A13 119.34891330 A14 120.00003861 A15 119.99987921 A16 120.00014364 A17 109.47121850 A18 109.47121632 A19 109.47129493 A20 109.47117564 A21 109.47122769 A22 109.47117925 A23 109.47118265 A24 109.47120075 A25 120.74844672 A26 109.47118580 A27 109.47120989 A28 109.47122403 A29 109.47122921 A30 31.02568391 A31 120.32564913 A32 120.32552716 A33 109.47120255 A34 109.47120255 A35 119.46609692 A36 109.50000006 D1 0.00000000 D2 0.00000000 D3 0.00000000 D4 -180.00000000 D5 180.00000000 D6 -140.76720889 D7 -54.58115252 D8 -180.00000000 D9 -150.00016776 D10 -30.00018780 153 D11 87.79703810 D12 147.19310163 D13 180.00000000 D14 -180.00000000 D15 -180.00000000 D16 -90.00005520 D17 -59.99917473 D18 60.00080217 D19 -180.00000000 D20 90.00274192 D21 -180.00000000 D22 -59.99861809 D23 60.00150599 D24 0.00000000 D25 -89.99822483 D26 -180.00000000 D27 -59.99899057 D28 60.00110472 D29 18.98143950 D30 180.00000000 D31 -32.80550585 D32 132.60073377 D33 -107.39925144 D34 -180.00000000 D35 -66.93506356 --link1-- %chk=Trimethoprim # uhf/6-31g(d) guess=(read,only) Trimethoprim 0 1 C C 1 B1 C 2 B2 1 A1 C 3 B3 2 A2 1 D1 C 4 B4 3 A3 2 D2 C 1 B5 2 A4 3 D3 H 2 B6 1 A5 6 D4 H 4 B7 3 A6 2 D5 C 3 B8 2 A7 1 D6 C 9 B9 3 A8 2 D7 C 3 B10 2 A9 1 D8 H 11 B11 3 A10 2 D9 H 11 B12 3 A11 2 D10 N 9 B13 3 A12 2 D11 N 10 B14 9 A13 3 D12 O 5 B15 4 A14 3 D13 O 6 B16 1 A15 2 D14 O 1 B17 2 A16 3 D15 C 16 B18 5 A17 4 D16 H 19 B19 16 A18 5 D17 H 19 B20 16 A19 5 D18 H 19 B21 16 A20 5 D19 C 18 B22 1 A21 2 D20 H 23 B23 18 A22 1 D21 H 23 B24 18 A23 1 D22 H 23 B25 18 A24 1 D23 C 15 B26 10 A25 9 D24 C 17 B27 6 A26 1 D25 H 28 B28 17 A27 6 D26 H 28 B29 17 A28 6 D27 154 H 28 B30 17 A29 6 D28 C 14 B31 9 A30 3 D29 H 32 B32 14 A31 9 D30 N 10 B33 9 A32 3 D31 H 34 B34 10 A33 9 D32 H 34 B35 10 A34 9 D33 O 27 B36 15 A35 10 D34 H 37 B37 27 A36 15 D35 B1 1.40140033 B2 1.40140226 B3 1.40140141 B4 1.40140135 B5 1.40140152 B6 1.07000086 B7 1.07000356 B8 2.51481727 B9 1.39494235 B10 1.54000157 B11 1.06999988 B12 1.07000097 B13 2.35905254 B14 1.33786616 B15 1.43000187 B16 1.43000087 B17 1.42999959 B18 1.43000244 B19 1.07000386 B20 1.06999779 B21 1.07000481 B22 1.43000099 B23 1.06999711 B24 1.07000035 B25 1.06999867 B26 1.34423457 B27 1.43000467 B28 1.07000012 B29 1.07000164 B30 1.07000036 B31 1.33786909 B32 1.06999955 B33 1.47000000 B34 1.00000000 B35 1.00000000 B36 1.43000000 B37 0.96000000 A1 120.00011735 A2 120.00005254 A3 119.99986978 A4 119.99980714 A5 119.99994519 A6 120.00016019 A7 114.09464701 A8 147.80491235 A9 119.99979053 A10 109.47118080 A11 109.47122415 A12 117.44431413 A13 119.34891330 A14 120.00003861 A15 119.99987921 A16 120.00014364 A17 109.47121850 A18 109.47121632 A19 109.47129493 155 A20 109.47117564 A21 109.47122769 A22 109.47117925 A23 109.47118265 A24 109.47120075 A25 120.74844672 A26 109.47118580 A27 109.47120989 A28 109.47122403 A29 109.47122921 A30 31.02568391 A31 120.32564913 A32 120.32552716 A33 109.47120255 A34 109.47120255 A35 119.46609692 A36 109.50000006 D1 0.00000000 D2 0.00000000 D3 0.00000000 D4 -180.00000000 D5 180.00000000 D6 -140.76720889 D7 -54.58115252 D8 -180.00000000 D9 -150.00016776 D10 -30.00018780 D11 87.79703810 D12 147.19310163 D13 180.00000000 D14 -180.00000000 D15 -180.00000000 D16 -90.00005520 D17 -59.99917473 D18 60.00080217 D19 -180.00000000 D20 90.00274192 D21 -180.00000000 D22 -59.99861809 D23 60.00150599 D24 0.00000000 D25 -89.99822483 D26 -180.00000000 D27 -59.99899057 D28 60.00110472 D29 18.98143950 D30 180.00000000 D31 -32.80550585 D32 132.60073377 D33 -107.39925144 D34 -180.00000000 D35 -66.93506356 156 Appendix B Routine Operational Data 157 Table B.1 Synthetic influent feed Feed Stock Solution Influent Solution Carbon Feed CH 3 COONa?3H 2 O Casamino Acids 21240 mg/L 1500 mg/L 425 mg/L 30 mg/L Nutrient Feed KCl MgCl 2 ? 6H 2 O MgSO 4 ? H 2 O CaCl 2 Yeast Extract 10% HCl Trace element solution FeSO 4 solution NH 4 Cl NaH 2 PO 4 ?2H 2 O 1600 mg/L 3000 mg/L 200 mg/L 620 mg/L 112.5 mg/L 3.25 mg/L 2.75 mg/L 2 mg/L 1548 mg/L 186 mg/L 118.4 mg/L 222 mg/L 14.8 mg/L 45.9 mg/L 83 mg/L 0.24 mg/L 0.2 mg/L 0.15 mg/L 114.5 mg/L 13.8 mg/L Trace Elements Solution H 3 BO 3 ZnSO 4 ?7H 2 O KI CuSO 4 ? 5H 2 O Co(NO 3 ) 2 ? 6H 2 O Na 2 MoO 4 ? 2H 2 O MnSO 4 ?H 2 O 300 1500 75 300 367.1 150 1700 0.061 mg/L 0.305 mg/L 0.015 mg/L 0.061 mg/L 0.075 mg/L 0.031 mg/L 0.342 mg/L FeSO 4 Solution FeSO 4 ? 7H 2 O 2054 mg/L 0.304 mg/L Note: 500mL of influent solution consists of 10mL of carbon feed, 37mL nutrient feed, 453mL deionized water 158 0 500 1000 1500 2000 2500 3000 102030405060 Day of operation C o nc e n t r a t i on ( m g/ L) MBR Aero SBR Figure B.1 MLSS concentration for SBR and MBR 159 0 100 200 300 400 500 600 0 20 40 60 80 100 Date of Operation(days) M L SS a n d T SS c o n c . ( m g / L ) MLSS SS Figure B.2 MLSS and Effluent TSS concentration for Nitrifying sludge reactor 160 0 5 10 15 20 25 30 102030405060 Day of operation C o n cen t r at i o n ( m g / L ) MBR Aero SBR Figure B.3 Effluent TSS concentration for SBR and MBR 161 0 20 40 60 80 100 120 140 2040608010 Date of Operation (day) C o n c .( m g /L ) Eff Inf Figure B.4 NH 3 -N Concentration in Influent and Effluent 162 Appendix C Reactors and reactor configuration 163 Figure C.1 CSTR fed with toluene pH control effluent Air Influent 164 Figure C.2 Nitrifying membrane bioreactor pH control effluent Air Influent membrane 165 Figure C.3 Membrane bioreactor pH effluen Air Influe memb rane 166 Figure C.4 Sequencing bioreactor pH control effluent Air Influent Stirrer 167 Appendix D Degradation experiment data 168 Figure D.1 Nitrobacter sp. FISH image of nitrifying sludge using vermicon kit 169 0 50 100 150 200 250 300 350 0 100 200 300 400 500 600 Initial toluene Conc. (mg/L) EE2 C o n c . r e m o v e d ( u g/ L) Figure D.2 EE2 removed at different initial toluene concentration in batch tests 170 540 560 580 600 620 640 660 0 5 10 15 20 Time(h) C o n c . o f E E 2 Figure D.3 Degradation of EE2 with Toluene in batch tests (intial concentration of Toluene = 50 mg/L) 171 0 0.2 0.4 0.6 0.8 1 1.2 0 102030405060 Time(h) C/ Co EE2 with NH4-N EE2 without NH4-N NH3-N Figure D.4 Degradation of EE2 with whole cell 172 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0382 Time (h) C/ Co 0 50 100 150 200 250 300 C onc . ( m g/ L) EE2 NH4-N Bioma ss Conc. Figrue D.5 Degradation Tests using nitrifying sludge (initial concentration of EE2 = 100ug/L, NH 3 -N = 30mg/L) 173 Appendix E Sorption experiment data 174 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 Protein Conc. (mg/g) Kd ( L /g ) 0 0.2 0.4 0.6 0.8 HI ( % ) Kd vs Protein (Normal) Kd vs Protein (Nitrogen limiting) HI vs Protein (Nitrogen limiting) HI vs Protein (Normal) Figure E.1 Relationship between protein conc. of EPS and Partitioning coefficient (K d ) 175 0 0.2 0.4 0.6 0.8 1 102030405060 Carbohydrate conc. (mg/g) Kd ( L / g ) 0 0.2 0.4 0.6 0.8 HI ( % ) Kd vs Carbohydrate (Normal) Kd vs Carbohydrate (Nitrogen limiting) HI vs Carbohydrate (Nitrogen limiting) HI vs Carbohydrate(Normal) Figure E.2 The relationship between Carbohydrate conc. of EPS and partitioning coefficient (K d ) 176 Figure E.3 Floc 3D Structure using confocal microscope 177 0 20 40 60 80 100 MBR SBR H y dr ophobi c i t y ( % ) without Sodium Azide with Sodium Azide Figrue E.4 Hydrophobicity change with/without sodium azide (control test) 178 y = 0.0665x 0.2115 R 2 = 0.8415 0 0.1 0.2 0.3 0.4 0.5 0.6 0 5000 10000 15000 20000 Surface area (cm 2 /cm 3 ) K d ( L /g ) Figure E.5 Partitioning coefficienty (K d ) vs. Surface area of floc 179 y = 0.2137Ln(x) - 0.2738 R 2 = 0.816 0 0.2 0.4 0.6 0.8 1 0 50 100 150 200 250 Mean particle size(um) HI (% ) Figure E.6 Hysteresis Index (HI) vs. Mean particle size of floc 180 0 20 40 60 80 0 50 100 150 200 250 300 Initial Conc. of NH3-N E E 2 r e m o ve d ( u g / L ) Removed by Sorption Total removed Biotransformed Figure E.7 EE2 romoved in batch test at different concentration of NH 3 -N 181 Appendix F TLC plates for continuous reactor 182 Figure F.1 TLC plate for EE2 in Nitrifying sludge reactor Byproducts EE2 183 Figure F.2 TLC plate for Trimethoprim in Nitrifying sludge reactor Byproducts Trimethoprim 184 Figure F.3 TLC plate for Carbamazepin in Nitrifying sludge reactor Byproducts Carbamazepine 185 Figure F.4 TLC plate for EE2 in CSTR fed with toluene EE2 Byproducts 186 Figure F.5 TLC plate for Carbamazepine in CSTR fed with toluene Carbamazepine Byproduct 187 Figure F.6 TLC plate for Trimethoprim in CSTR fed with toluene Trimethoprim 188 Appendix G Full degradation pathways predicted using FED and degradation rules and 1H NMR data of byproducts 188 189 Figure G.1 EE2 degradation (full) pathway using MO theory and degradation rules OH CH OH CH 3 OH CH O CH 3 OH OH CH 3 OH O CH O CH 3 OH O OH OH O OH CH CH 3 OH O OH OH O OH OH CH 3 OH O OH OH O OH OH OH CH 3 OH O OH OH O OH O OH CH 3 OH O OH OH O OH O OH OH CH 3 OH O OH OH O OH O O OH CH 3 OH O OH OH O OH O OH CH 3 OH O OH OH O OH O OH OH CH 3 OH O OH OH O OH OH CH 3 OH O OH OH OH CH 3 OH O OH OH OH OH CH 3 OH O OH OH OH OH CH 3 OH O OH OH OH O 189 190 Figure G.1 EE2 degradation (full) pathway using MO theory and degradation rules (continued) CH 3 OH O OH OH OH O O CH 3 OH O OH OH OH O OH O OH CH 3 OH O OH OH OH O OH CH 3 OH O OH OO O OH OH O CH 3 OH O OH OH OH O O OH OH CH 3 OH O OH OH OH O OH CH 3 OH O OH OH OH O OH OH CH 3 OH O OH OH OH O OH O CH 3 OH O OH OH OH O OH CH 3 OH O OH OH OH O OH OH CH 3 OH O OH OH OH O OH OH OH CH 3 OH O OH OH OH OH OH CH 3 OH O OH OH OH O OH OH CH 3 OH O OH O OH O OH OH CH 3 OH O OH O OH O OH OH OH CH 3 OH O OH O OH O OH OH O CH 3 OH OO O OH OH O CH 3 OH OO O OH OH O OH CH 3 OH O O OH OH O CH 3 OH O O OH OH O OH 190 191 Figure G.1 EE2 degradation (full) pathway using MO theory and degradation rules (continued) CH 3 OH O O O OH OH OH OH OH CH 3 OH O O OH OH CH 3 OH O O O OH OH OH CH 3 OH O O O OH OH O CH 3 OH O O O OH OH O OH OH CH 3 OH O O O OH OH OH OH CH 3 O O O O OH OH OH OH OH CH 3 O O O O OH OH O OH OH CH 3 O O O O OH OH O OH OH OH CH 3 O O O O OH OH OH OH CH 3 O O OH OH OH CH 3 O OH OH OH CH 3 O O OH OH CH 3 O OH CO 2 191 192 N O NH 2 N O NH 2 OH N O NH 2 OH OH N O NH 2 O O OH N O NH 2 O O OH OH N O NH 2 O O OH OH OH N O NH 2 O O OH OH OH OH N O NH 2 O O OH O OH OH OH OH N O NH 2 O O OH O OH OH OH OH OH N O NH 2 O O OH O OH OH OH OH O N O NH 2 O O OH O OH OH OH OH O OH N O NH 2 O O OH O OH OH OH OH O OH OH OH N O NH 2 O O OH O OH OH OH OH O O OOH N O NH 2 O O OH O OH OH OH OH O O OOH OH N O NH 2 O O OH O OH OH OH OH O O OOH OH OH N O NH 2 O O OH O OH O OH OH O O OOH OH OH N O NH 2 O O OH O OH O O OH O O OOH OH OH N O NH 2 O O OH O OH O O OH O O OOH OH OH OH N O NH 2 O O OH O OH O O OH O O OOH OH OH OH OH Figure G.2 Carbamazepine degradation (full) pathway using MO theory and degradation rules 192 193 N O NH 2 O O OH O OH O O OH O O OOH OH OH OH O N O NH 2 O O OH O OH O O OH O O OOH OH OH OH O OH N O NH 2 O O OH O OH O O OH O O OOH OH OH OH O O N O NH 2 O O OH O OH O O OH O O O OH OH OH OH O O OH N O NH 2 O O OH O OH O O OH O O OH OH OH OH O O N O NH 2 O O OH O OH O OH O O OH OH OH O O OH N O NH 2 O O OH O OH O OH O O OH OH OH O CO 2 Figure G.2 Carbamazepine degradation (full) pathway using MO theory and degradation rules (continued) 193 194 N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 OH N N NH 2 NH 2 O OH O CH 3 CH 3 OH N N NH 2 NH 2 O O O CH 3 CH 3 OH OH OH N N NH 2 NH 2 O O O CH 3 CH 3 OH OH OH OH N N NH 2 NH 2 O O O CH 3 CH 3 OH OH OH O N N NH 2 NH 2 O O O CH 3 CH 3 OH OH OH O O N N NH 2 OH O O O CH 3 CH 3 OH OH OH O O OH N N OH OH O O O CH 3 CH 3 OH OH OH O O OH N N OH OH O O O CH 3 CH 3 OH OH OH O O OH OH N N OH OH O O O CH 3 OH OH OH O O OH OH N N OH OH O O CH 3 OH OH O O OH OH NH 2 N OH OH O O CH 3 OH OH O O O OH NH 2 OH O O CH 3 OH OH O O O OH OH O O CH 3 OH OH O O O OH O O O CH 3 OH OH O O O OH Figure G.3 Trimethoprim degradation (full) pathway using MO theory and degradation rules 194 195 O OH O OH OH O O O OH OH O O OH O O O OH OH OH O O OH O O O OH OH OH OH O OH O O O OH OH OH CO 2 Figure G.3 Trimethoprim degradation (full) pathway using MO theory and degradation rules (continued) 195 196 Figure G.4 The observed NMR spectrum: This shows that hydroxylation of EthinylEstradiol OH CH 3 CH OH OH CH 3 CH OH OH Hydroxylation 196 197 Figure G.5 The observed NMR spectrum: This shows that conjugation of EthinylEstradiol CH 3 CH OH OH CH OH CH 3 O S OH O OConjugation 197 198 Figure G.6 1H NMR spectrum for Trimethoprim byproduct; ring cleavage N N NH 2 NH 2 O O O OH CH 3 CH 3 CH 3 Ring cleavage N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 Ring Cleavage 198 199 Figure G.7 1H NMR spectrum for byproduct of Trimethoprim; Hydroxylation N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 OH N N NH 2 NH 2 O O O CH 3 CH 3 CH 3 Hydroxylation 199