Fate of Glucocorticoid Receptor Agonists During Water and Wastewater Treatment Processes
Item Type text; Electronic Dissertation
Authors Wu, Shimin
Publisher The University of Arizona.
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Download date 01/10/2021 18:33:56
Link to Item http://hdl.handle.net/10150/623167 FATE OF GLUCOCORTICOID RECEPTOR AGONISTS DURING WATER AND WASTEWATER TREATMENT PROCESSES
by
Shimin Wu
______Copyright © Shimin Wu 2016
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF CHEMICAL AND ENVIRONMENTAL ENGINEERING
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY WITH A MAJOR IN ENVIRONMENTAL ENGINEERING
In the Graduate College
THE UNIVERSITY OF ARIZONA
2016 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Shimin Wu, titled Fate of Glucocorticoid Receptor Agonists During Water and Wastewater Treatment Processes and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.
______Date: 11/29/2016 Dr. Shane A. Snyder
______Date: 11/29/2016 Dr. Avelino E. Saez
______Date: 11/29/2016 Dr. Robert G. Arnold
______Date: 11/29/2016 Dr. James Farrell
Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.
______Date: 11/29/2016 Dissertation Director: Dr. Shane A. Snyder
______Date: 11/29/2016 Dissertation Director: Dr. Avelino E. Saez
2 STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of the requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.
SIGNED: Shimin Wu
3 TABLE OF CONTENTS
List of Figures...... 9
List of Tables ...... 12
List of Abbreviations ...... 15
Abstract...... 17
1. Introduction...... 19
1.1. Glucocorticoid Receptor Agonists as An Emerging Concern ...... 19
1.2. Use of Corticosteroids...... 21
1.3. Toxicity of Corticosteroids ...... 26
1.4. Occurrence of Corticosteroids in the Aquatic Environment...... 29
1.5. Removal of Corticosteroids in Water and Wastewater Treatment Processes... 30
1.6. Research Objectives...... 32
2. Trace Analysis of Glucocorticoid Receptor Agonists in Environmental Waters by Liquid Chromatography-Tandem Mass Spectrometry ...... 33
2.1. Introduction...... 33
2.2. Experimental Section...... 36
2.2.1. Chemicals and Materials ...... 36
2.2.2. Sample Collection ...... 37
2.2.3. Sample Extraction and Cleanup ...... 37
2.2.4. Instruments and Operation Conditions...... 38
2.2.5. Data Analysis...... 39
2.3. Results and Discussion ...... 40
4 2.3.1. Optimization for Multiple Reaction Monitoring Conditions in MS/MS.... 40
2.3.2. Optimization for Liquid Chromatography Conditions ...... 44
2.3.3. Cleanup and Matrix Effects ...... 48
2.3.4. Method Validation ...... 49
2.4. Conclusions...... 53
3. Determination of Octanol-Water Partition Coefficients for Glucocorticoid
Receptor Agonists ...... 54
3.1. Introduction...... 54
3.2. Experimental Section...... 55
3.2.1. LogKow Calculation Programs...... 55
3.2.2. Experimental LogKow Dataset...... 57
3.3. Results and Discussion ...... 57
3.3.1. Performance of Different Computational Programs ...... 57
3.3.2. Prediciton of LogKow for A Wide Range of Corticosteroids...... 58
3.3.3. Comparison of LogKow for Different Classes of Steroid Hormones...... 65
3.4. Conclusions...... 66
4. Occurrence of Glucocorticoid Receptor Agonists in Surface Water and
Groundwater ...... 67
4.1. Introduction...... 67
4.2. Experimental Section...... 68
4.2.1. Chemicals and Materials ...... 68
4.2.2. Sample Collection ...... 68
4.2.3. Sample Preparation and LC-MS/MS Analysis...... 70
5 4.3. Results and Discussion ...... 70
4.3.1. GR Agonists in the Lower Santa Cruz River...... 70
4.3.2. GR Activity in the Lower Santa Cruz River ...... 72
4.3.3. GR Agonists in Groundwater Along the Lower Santa Cruz River...... 74
4.4. Conclusions...... 75
5. Occurrence and Fate of Glucocorticoid Receptor Agonists in Wastewater
Treatment Plants (WWTP)...... 76
5.1. Introduction...... 76
5.2. Experimental Section...... 77
5.2.1. Chemicals and Materials ...... 77
5.2.2. Sample Collection ...... 77
5.2.3. Enzymatic Hydrolysis of Conjugated Corticosteroids...... 78
5.2.4. Sample Preparation and LC-MS/MS Analysis...... 79
5.3. Results and Discussion ...... 79
5.3.1. Occurrence of Corticosteroids in WWTP ...... 79
5.3.2. Removal of Corticosteroids in WWTP ...... 82
5.3.3. Presence of Conjugated Corticosteroids in WWTP...... 85
5.3.4. Removal of GR Activity in WWTP ...... 87
5.4. Conclusions...... 90
6. Removal of Glucocorticoid Receptor Agonists during Water Treatment
Processes ...... 91
6.1. Introduction...... 91
6.2. Experimental Section...... 92
6 6.2.1. Chemicals and Materials ...... 92
6.2.2. Sample Collection ...... 92
6.2.3. Sample Preparation ...... 94
6.2.4. LC-MS/MS Analysis of Corticosteroids...... 95
6.3. Results and Discussion ...... 96
6.3.1. Method performance of LC-MS/MS Analysis ...... 96
6.3.2. Occurrence of GR Agonists in Water Treatment Processes ...... 100
6.3.3. Removal of GR Agonists in Water Treatment Processes...... 102
6.3.4. Contributions of GR Agonists to the GR Activity in Wastewater...... 106
6.3.5. Target CSs-Derived GR Activity in Environmental Waters...... 108
6.4. Conclusions...... 111
7. Removal of Glucocorticoid Receptor Agonists in Low-Pressure UV/AOP
Treatment ...... 112
7.1. Introduction...... 112
7.2. Experimental Section...... 114
7.2.1. Chemicals and Materials ...... 114
7.2.2. UV Dose Determination of LP-UV Collimated Beam Device ...... 115
7.2.3. Lab-Scale LP-UV/AOP Experiment...... 117
7.2.4. Direct Injection LC-MS/MS Analysis of Corticosteroids...... 118
7.3. Results and Discussion ...... 120
7.3.1. Removal of GR Agonists by Individual Treatment...... 120
7.3.2. Removal of GR Agonists by LP-UV/AOP Processes ...... 123
7.4. Conclusions...... 126
7 8. Removal of Glucocorticoid Receptor Agonists Using Powdered Activated
Carbon (PAC)...... 127
8.1. Introduction...... 127
8.2. Experimental Section...... 127
8.2.1. Activated Carbons...... 127
8.2.2. Slurry pH...... 128
8.2.3. Lab-Scale PAC Experiment ...... 128
8.2.4. Direct Injection LC-MS/MS Analysis of Corticosteroids...... 129
8.3. Results and Discussion ...... 129
8.4. Conclusions...... 132
9. Conclusions...... 133
Reference ...... 135
8 LIST OF FIGURES
Figure 1.1. Schematic representation of regulation of corticosteroid actions.8 ...... 20
Figure 1.2. The structure and nomenclature of corticosteroids...... 21
Figure 1.3. The sources and fate of natural and synthetic corticosteroids...... 30
Figure 2.1. The chromatographic separation of analytes (10 ng/mL) using acetonitrile and methanol...... 45
Figure 2.2. LC-MS/MS Chromatogram of all analytes at 10 ng/mL and surrogate standards at 20 ng/mL (HT is the counts of peak height)...... 48
Figure 2.3. The chromatograms of BUD-d8 and FTP-d5 in 500× concentrated WWTP influent extract before and after cleanup using Sep-Pak Silica cartridge...... 49
Figure 3.1. Relationships between the experimental n-octanol/water partition coefficients
(logKow) and calculated logKow for 50 validated steroid hormones...... 61
Figure 3.2. Relationships between the experimental n-octanol/water partition coefficients
(logKow) and calculated logKow for 32 validated steroid hormones...... 62
Figure 3.3. Prediction of logKow for 63 corticosteroids using all computational programs and the best three programs...... 65
Figure 3.4. Ranges of logKow values for five classes of steroid hormones...... 65
Figure 4.1. Sampling sites of the Lower Santa Cruz River, Pima County, Arizona...... 69
Figure 4.2. Concentration (ng/L) of corticosteroids in the Lower Santa Cruz River...... 72
Figure 4.3. Percent distribution (%) of corticosteroids in the Lower Santa Cruz River.. 72
Figure 4.4. Contribution (%) of detected corticosteroids to GR-agonistic activity in the
Lower Santa Cruz River...... 73
9 Figure 4.5. Concentration (ng/L) of corticosteroids in the groundwater along the Lower
Santa Cruz River...... 74
Figure 5.1. Flow scheme of Agua Nueva Water Reclamation Facility with sampling locations...... 78
Figure 5.2. LC-MS/MS chromatograms of detected analytes in a 24h-composite influent sample...... 80
Figure 5.3. Percent Distribution (%) of chemical concentrations (ng/L) and dexamethasone-equivalents (Dex-EQ, ng/L) of detected corticosteroids in WWTP...... 82
Figure 5.4. Correlations between the logKow values and corticosteroid removal in secondary treatment...... 85
Figure 5.5. Percent distribution of free and conjugated corticosteroids in WWTP influent and DAF effluent...... 87
Figure 6.1. Typical LC-MS/MS chromatograms of GCs. (Left) GCs standards at 20 ppb;
(Right) effluent sample (WWTP4). X-axis indicates retention time (min), and Y-axis indicates sample response (counts)...... 98
Figure 6.2. Removal of GR agonists by chlorination in WWTP2...... 103
Figure 6.3. Removal of GR agonists in WWTP2 effluent by UV at bench-scale...... 103
Figure 6.4. Removal of GR agonists in WWTP1 effluent by ozone at bench-scale...... 104
Figure 6.5. Removal of GR agonists in WWTP1 effluent by RO at bench-scale...... 105
Figure 6.6. Removal of GR agonists in full-scale WWRF by MF, RO, UV, and ozone.
...... 106
Figure 6.7. The percent distribution of detected GR agonists and their contributions to the predicted GR activity (Chem-DEQs) in tested effluents...... 107
10 Figure 6.8. Comparison of Bio-DEQs and Chem-DEQs for GR activity in all tested samples...... 109
Figure 6.9. Comparison of removal efficiency (%) in different treatment processes via total concentrations of detected corticosteroids ( ∑ CSs), GR activity, predicted from the chemical analysis (Chem-DEQ) and GR activity measured by GR bioassay (Bio-DEQ).
For chemicals or sample activities below the detection limit, 1/2MDL was used here for estimation...... 110
Figure 6.10. The removal efficiency (%) evaluated via in vitro GR activity and the chemical concentrations of four highly potent corticosteroids in different treatment processes...... 110
Figure 7.1. The UV lamp equipment and LP UV lamp for the experiment...... 115
Figure 7.2. Removal of GR agonists by UV...... 120
Figure 7.3. Mechanism of phototransformation of prednisolone (PNL)104 ...... 121
Figure 7.4. Removal of GR agonists by free chlorine...... 122
Figure 7.5. Removal of GR agonists by H2O2...... 122
Figure 7.6. Degradation of cortisone (group 1) and fluticasone propionate (group 2) in different UV/AOP processes...... 124
Figure 7.7. Removal of all 26 CSs by UV/Cl2 and UV/H2O2 treatment...... 126
Figure 8.1. Removal of target GR agonists by four types of PACs...... 132
11 LIST OF TABLES
Table 1.1. The physicochemical properties of typical corticosteroids...... 22
Table 1.2. Mode of action of compounds affecting the corticosteroid signaling pathway.
...... 27
Table 1.3. The GR-mediated activity of corticosteroids determined by in vitro bioassays.
...... 28
Table 1.4. The concentration of GR agonists and GR-mediated activity in environment water samples from various countries...... 30
Table 2.1. Chemical structures of target corticosteroids...... 33
Table 2.2. Timetable for LC gradient on Agilent 1290 UHPLC system...... 39
Table 2.3. Optimized MRM conditions of target corticosteroids and surrogate standards.
...... 41
Table 2.4. The relative recovery for target corticosteroids and absolute recovery for isotope surrogates at two fortified concentration levels in various water matrices (n = 4)...... 51
Table 2.5. IDLs and MDLs for target corticosteroids in various water matrices...... 52
Table 3.1. Experimental and theoretical partition coefficients (logKow) of 50 steroid hormones for validation ...... 59
Table 3.2. Model summary of linear regression of experimental logKow against calculated logKow...... 62
Table 3.3. Experimental and theoretical partition coefficients (logKow) of 63 corticosteroids for prediction ...... 63
12 Table 4.1. Concentration (ng/L) of corticosteroids in surface water samples collected from
Lower Santa Cruz River...... 71
Table 4.2. Relative potency (REP) of detected corticosteroids applied for calculating predicted dexamethasone equivalents...... 73
Table 5.1. Concentration (ng/L) and removal of target corticosteroids at different stages in the WWTP...... 79
Table 5.2. Concentration (ng/L) of target corticosteroids in wastewater samples with/without enzyme treatment...... 86
Table 5.3. Relative potency (REP) of detected corticosteroids applied for calculating predicted dexamethasone equivalents...... 89
Table 6.1. Water quality parameters of tested WWTP secondary effluents...... 92
Table 6.2. Timetable for LC gradient on Agilent 1260 UHPLC system...... 95
Table 6.3. IDLs, MDLs and recovery (corrected by isotope surrogates) of target corticosteroids in WWTP secondary effluent (n = 4, spiked concentration: 20 ng/L). .... 97
Table 6.4. Concentrations (ng/L) of detected corticosteroids in WWTP effluents and treated effluents...... 99
Table 6.5. Relative potency (REP) of detected GR agonists applied for calculating predicted dexamethasone equivalents...... 106
Table 7.1. The exposure time required for LPUV doses...... 117
Table 7.2. Initial and residual concentrations of free chlorine and H2O2 during LP-
UV/AOP experiment...... 118
Table 7.3. IDLs and MDLs of target corticosteroids in direct injection LC-MS/MS analysis.
...... 119
13 Table 8.1. Summary of activated carbon properties. Slurry pH was experimentally determined whereas other properties were found the referred references...... 128
14 LIST OF ABBREVIATIONS
FCH Fluocortolone 21-hexanoate ACD Alclometasone dipropionate FCP Fluocortolone 21-pivalate ACTH Adrenocorticotrophic hormone FCT Fluocortolone ALD Aldosterone FDL Flurandrenolide AMC Amcinonide FLA Fludrocortisone acetate ANWRF Agua Nueva Water FLC Fluocinonide Reclamation Facility FLN Flunisolide AOPs Advanced oxidation processes FLU 9α-Fludrocortisone BCM Beclomethasone FML Fluorometholone BDP Beclomethasone dipropionate FMP Flumethasone 21-pivalate BET Betamethasone FMS Flumethasone BMA Betamethasone 21-acetate FTP Fluticasone propionate BMB Betamethasone 17-benzoate GCs Glucocorticoids BMD Betamethasone dipropionate GR Glucocorticoid receptor BMV Betamethasone 17-valerate HAL Halcinonide BUD Budesonide HBP Halobetasol propionate CBB Clobetasone 17-butyrate HCA Hydrocortisone 21-acetate CBG Corticosteroid-binding HCB Hydrocortisone 17-butyrate globulin HCBP Hydrocortisone probutate CBP Clobetasol 17-propionate HCPA Hydrocortisone aceponate CIC Ciclesonide HCT Hydrocortisone (Cortisol) CLP Clocortolone pivalate HCV Hydrocortisone 17-valerate CNS Central nervous system IPM Interacting protein COR Cortisone modification CRF Corticotrophin-releasing factor LC- Liquid chromatography– CSs Corticosteroids MS/MS tandem mass spectrometry CTC Corticosterone MCs Mineralocorticoids CTX 11-Deoxycortisol MF Microfiltration (Cortexolone) MMF Mometasone furoate DCA Deoxycorticosterone acetate MPL 6α-Methylprednisolone DEX Dexamethasone MPLA Methylprednisolone 21-acetate DFD Diflorasone diacetate MR Mineralocorticoid receptor DFP Difluprednate PAC Powder activated carbon DFV Diflucortolone valerate PCN Prednicarbate DFZ Deflazacort PLA Prednisolone 21-acetate DMA Dexamethasone 21-acetate PLH Prednisolone hexanoate DSN Desonide PMA Paramethasone 21-acetate DSM Desoximetasone PMS Paramethasone EDCs Endocrine disrupting PNL Prednisolone compounds PNS Prednisone FCA Fluocinolone acetonide PTM Post-translational modification
15 RML Rimexolone RO Reverse osmosis SCR Santa Cruz River TAC Triamcinolone TCA Triamcinolone acetonide TCH Triamcinolone hexacetonide TRWRF Tres Ríos Water Reclamation Facility UV Ultraviolet WWTP Wastewater treatment plant
16 ABSTRACT
In recent years, endocrine disruption of corticosteroid signaling pathways in wildlife and humans by environmental chemicals have attracted increasing attention. The integrated potential of chemicals in the aquatic environment that disrupt corticosteroid actions have been evaluated using in vitro glucocorticoid receptor (GR) mediated bioassays. Exogenous natural and synthetic corticosteroids (CSs), which are widely used in human and animal therapeutic applications, were demonstrated to be the most important GR agonists, that can potentially cause adverse effects, especially on aquatic organisms. To date, only a few studies have investigated the occurrence and behavior of GR agonists in the aquatic environment and their removal in conventional wastewater treatment plants. Furthermore, there are hardly any data reported on the removal of GR agonists by advanced water and wastewater treatment, especially those synthetic CSs with high potency.
To further understand the fate of GR agonists in water and wastewater treatment processes, a sensitive and robust LC-MS/MS method was successfully developed for analyzing a wide range of GR agonists in various environmental waters. The occurrence of
GR agonists in surface water and groundwater was monitored along the Lower Santa Cruz
River (SCR). Several GR agonists were detected, and a trend of degradation was observed downstream the two WWTP outfalls for both surface water and groundwater. The fate of
GR agonists in a local wastewater treatment plant (WWTP) was investigated, and up to 14
GR agonists were detected at different stages. Highly potent synthetic CSs, including clobetasol propionate (CBP), fluticasone propionate (FTP), fluocinolone acetonide (FCA), and triamcinolone acetonide (TCA), were poorly removed in WWTP. Negative removal of
17 some CSs was observed in primary treatment, which may due to the deconjugation of CS conjugates. Removal of GR agonists in secondary effluent during various advanced water treatment processes, including UV, ozonation, MF, RO and chlorination, were studied. UV and RO appeared to be the most efficient treatment process for the attenuation of GR agonists, followed by ozone, while chlorination had little effects on GR agonists in water.
Bench-scale experiments were then carried out to investigate the removal of GR agonists by ultraviolet based advanced oxidation processes (UV/AOPs), and powder activated carbon (PAC). UV/chlorine and UV/H2O2 were demonstrated to be effective in removal
GR agonists in wastewater, and UV photolysis would be the predominant mechanism in
UV/AOP processes. Four types of PACs were tested for removing GR agonists in wastewater effluent, and Cabot HDB carbon was suggested, while Calgon PWA carbon was not recommended due to its low removal efficiency.
Keywords: Glucocorticoid receptor agonists; Corticosteroids; Liquid chromatography– tandem mass spectrometry; Wastewater treatment plant; Advanced water treatment;
Removal.
18 1. INTRODUCTION
1.1. Glucocorticoid Receptor Agonists as An Emerging Concern
The presence of endocrine disrupting compounds (EDCs) in the aquatic environment has caused a widespread of concerns for the past two decades, due to their potential ecological hazards. Research on environmental EDCs mainly focuses on the chemicals that disrupt the sex hormone signaling pathways, especially estrogenic and androgenic compounds, of which the adverse effects on aquatic organisms have been demonstrated, such as decreased fertility, sexual malformations, and intersex.1-6 In recent years, the disruption of corticosteroid signaling pathways in wildlife and humans from environmental chemicals have attracted increasing attention.7-9
Endogenous corticosteroids (CSs), compromising glucocorticoids (GCs) and mineralocorticoids (MCs), are a class of steroid hormones that play a vital role in regulating numerous fundamental physiological processes in vertebrates, which are synthesized by the adrenal cortex or its homologue—interrenal tissue—in non-mammals.10 GCs such as cortisol and corticosterone regulate glucose metabolism, immune functions, and stress response, while MCs such as aldosterone modulate electrolyte and water balance. CSs exert their biological effects mainly through binding to the glucocorticoid (GR) and mineralocorticoid receptor (MR) acting as ligand-activated transcriptional regulators, subsequent regulation of related gene expression.11, 12 Figure 1.1 depicts the regulation of corticosteroid actions. Any disturbances of the proteins involved in corticosteroid regulation, which can be caused by chemicals that mimic the endogenous corticosteroids,
19 or directly modify a given target, may induce various diseases, such as metabolic syndrome, immune disorders, and neurological impairments.8
Figure 1.1. Schematic representation of regulation of corticosteroid actions.8 CNS, central nervous system; CRF, corticotrophin releasing factor; ACTH, adrenocorticotrophic hormone; CBG, corticosteroid-binding globulin; IPM, interacting protein modification; PTM, post-translational modification.
Many studies have investigated the effects of corticosteroid disruption in the aquatic environment using in vitro GR receptor-mediated bioassays. The presence of GR agonist activity in surface water and wastewater was first detected in the Netherlands.13 It was not until later that they were discovered in other countries, such as the United States,14-16 the
Czech Republic17, Switzerland,17 Australia,18-20 and Japan.21 Although the compounds responsible for GR activity have not been fully identified; natural and synthetic CSs were
20 demonstrated to be the most important contributors.17, 22, 23 Therefore, their occurrence and fate in the aquatic environment has been increasingly concerned.
1.2. Use of Corticosteroids
Natural and synthetic CSs are frequently applied in human and veterinary medicine for the treatment of various inflammatory and autoimmune diseases, such as asthma, arthritis, and lupus.24 Of the natural CSs, cortisol (i.e., hydrocortisone) and cortisone are mainly used, whereas the synthetic CSs are more frequently employed nowadays, of which the stability and potency/efficacy are enhanced through structural modifications, such as halogenation (fluorination/chlorination) at the C-9/C-6 position of the B ring, and adding ester or acetal groups to the D-ring.25
Figure 1.2. The structure and nomenclature of corticosteroids.
The principal variants in the corticosteroid cyclopentanophenanthrene structure and substitutes are: A-ring, double bond C1-C2; B-ring, substitutions at C-6, C-9, C-7 (fluorine, chlorine, and methyl groups); D-ring-16, 17-cis-diol with cis-ketal formation and -16- methyl substitute; side chain, esterification C-17 and or C-21 (Figure 1.2). The physicochemical properties and abbreviations of typical CSs are listed in Table 1.1. In general, CSs are grouped into four classes, based on chemical structure26: Group A—
21 Hydrocortisone type (e.g., HCT, HCA, PNL, MPL, and PNS) (Short- to medium-acting glucocorticoids); Group B—Acetonides (and related substances) (e.g., TCA, MMF, AMC,
BUD, DSN, FLC, FCA, and HAL); Group C—Betamethasone type (e.g., BET, DEX, and
FCT); Group D—Esters, including Group D1—Halogenated (less labile) (e.g., HCV, HMS,
ACD, BMV, BMD, CBB, CBP, and FCP), Group D2—Labile prodrug esters (e.g., HCB,
HCPA, CIC and PCN).
There is a tremendous increase in the prevalence of natural and synthetic CSs for human disease treatment. In 2006, data from the United Kingdom showed that the total prescribed CSs were 9 and 14-fold higher than estrogens and androgens, respectively, which accounted for 64% of the total steroids in clinical use.27 The top prescribed CSs in the United Kingdom were HCT and PNL.27 In the United States, five prescribed medicines with synthetic CSs as the main ingredient were ranked among the top 100 most prescribed drugs,28 including FTP (Advair Diskus, ranked 6th in selling unit; Flovent HFA, 32th),
BUD (Uceris, 1st; Symbicort, 26th), and MMF (Nasonex, 24th), which have an annual sale value of more than nine billion US dollars.28 About 1.2% of the US population reported the oral use of CSs during 1999-2008, with the mean duration of usage more than four years.29
Table 1.1. The physicochemical properties of typical corticosteroids.
Name Abbr. CAS No. Formula MW Structure LogKow Mineralocorticoids Natural Aldosterone ALD 52-39-1 C21H28O5 360.45 1.08
11-Deoxycorticosterone DOC 64-85-7 C21H30O3 330.47 2.88 (21-Hydroxyprogesterone)
Synthetic
22 Name Abbr. CAS No. Formula MW Structure LogKow 11-Deoxycortisol CTX 152-58-9 C21H30O4 346.47 3.08 (Cortexolone)
Deoxycorticosterone DCA 56-47-3 C23H32O4 372.51 3.08 acetate
9α-Fludrocortisone FLU 127-31-1 C21H29FO5 380.46 1.67
Fludrocortisone acetate FLA 514-36-3 C23H31FO6 422.49 NA
Glucocorticoids Natural Corticosterone CTC 50-22-6 C21H30O4 346.47 1.94
Cortisone COR 53-06-5 C21H28O5 360.45 1.47
Hydrocortisone HCT 50-23-7 C21H30O5 362.47 1.61 (Cortisol)
Synthetic Alclometasone ACD 66734-13-2 C22H29ClO5 521.05 NA dipropionate
Amcinonide AMC 51022-69-6 C28H35FO7 502.58 NA
Beclomethasone BCM 4419-39-0 C22H29ClO5 408.92 NA
Beclomethasone BDP 5534-09-8 C28H37ClO7 521.05 NA dipropionate
Betamethasone BET 378-44-9 C22H29FO5 392.47 1.94
Betamethasone 21-acetate BMA 987-24-6 C24H31FO6 434.50 2.77
Betamethasone 17-benzoate BMB 22298-29-9 C29H33FO6 496.58 NA
Betamethasone BMD 5593-20-4 C28H37FO7 504.60 4.07 dipropionate
Betamethasone 17-valerate BMV 2152-44-5 C27H37FO6 476.59 3.60
23 Name Abbr. CAS No. Formula MW Structure LogKow Budesonide BUD 51333-22-3 C25H34O6 430.54 NA
Clobetasol 17-propionate CBP 25122-46-7 C25H32ClFO5 466.97 3.50
Clobetasone 17-butyrate CBB 25122-57-0 C26H32ClFO5 478.99 3.76
Clocortolone pivalate CLP 34097-16-0 C27H36ClFO5 495.03 NA
Ciclesonide CIC 141845-82-1 C32H44O7 540.70 NA
Deflazacort DFZ 14484-47-0 C25H31NO6 441.52 NA
Desonide DSN 638-94-8 C24H32O6 416.51 NA
Desoximetasone DSM 382-67-2 C22H27FO4 376.47 2.35
Dexamethasone DEX 50-02-2 C22H29FO5 392.47 1.83
Dexamethasone 21-acetate DMA 1177-87-3 C24H31FO6 434.50 2.91
Diflorasone diacetate DFD 33564-31-7 C26H32F2O7 494.53 NA
Diflucortolone valerate DFV 59198-70-8 C26H34F2O6 478.58 NA
Difluprednate DFP 23674-86-4 C27H34F2O7 508.56 NA
Flumethasone FMS 2135-17-3 C22H28F2O5 410.46 1.94
Flumethasone 21-pivalate FMP 2002-29-1 C27H36F2O6 494.58 3.86
Flunisolide FLN 3385-03-3 C24H31FO6 434.50 NA
24 Name Abbr. CAS No. Formula MW Structure LogKow Fluocinolone acetonide FCA 67-73-2 C24H30F2O6 452.49 2.48
Fluocinonide FLC 356-12-7 C26H32F2O7 494.53 3.19
Fluocortolone FCT 152-97-6 C22H29FO4 376.47 NA
Fluocortolone 21-hexanoate FCH 303-40-2 C28H39FO5 474.61 NA
Fluocortolone 21-pivalate FCP 29205-06-9 C27H37FO5 460.59 NA
Fluorometholone FML 426-13-1 C22H29FO4 376.47 2.00
Flurandrenolide FDL 1524-88-5 C24H31FO6 434.50 NA
Fluticasone propionate FTP 80474-14-2 C25H31F3O5S 500.57 NA
Halcinonide HAL 3093-35-4 C24H32ClFO5 454.96 NA
Halobetasol propionate HBP 66852-54-8 C25H31ClF2O5 484.96 NA
Hydrocortisone aceponate HCPA 74050-20-7 C26H36O7 460.57 NA
Hydrocortisone 21-acetate HCA 50-03-3 C23H32O6 404.50 2.19
Hydrocortisone 17-butyrate HCB 13609-67-1 C25H36O6 432.56 3.18
Hydrocortisone probutate HCBP 72590-77-3 C28H40O7 488.62 NA
Hydrocortisone 17-valerate HCV 57524-89-7 C26H38O6 446.58 3.79
6α-Methylprednisolone MPL 83-43-2 C22H30O5 374.48 NA
25 Name Abbr. CAS No. Formula MW Structure LogKow Methylprednisolone 21- MPLA 53-36-1 C24H32O6 416.51 NA acetate
Mometasone furoate MMF 83919-23-7 C27H30Cl2O6 521.43 3.90
Paramethasone PMS 53-33-8 C22H29FO5 392.47 NA
Paramethasone 21-acetate PMA 1597-82-6 C24H31FO5 418.51 NA
Prednicarbate PCN 73771-04-7 C24H32O6 488.58 NA
Prednisolone PNL 50-24-8 C21H28O5 360.45 1.62
Prednisolone 21-acetate PLA 52-21-1 C23H30O6 402.49 2.40
Prednisolone hexanoate PLH 69164-69-8 C27H38O6 458.60 NA
Prednisone PNS 53-03-2 C21H26O5 358.43 1.46
Rimexolone RML 49697-38-3 C24H34O3 370.53 NA
Triamcinolone TAC 124-94-7 C21H27FO6 394.44 1.16
Triamcinolone acetonide TCA 76-25-5 C24H31FO6 434.50 2.53
Triamcinolone hexacetonide TCH 5611-51-8 C30H41FO7 532.65 NA
1.3. Toxicity of Corticosteroids
Although natural and synthetic CSs are widely used in human and veterinary medicine, exposure to exogenous CSs at low levels could potentially cause adverse effects to aquatic organisms on their reproduction, growth, and development.30-33
26 Previous studies demonstrated that exposure to CSs at low concentration levels could potentially cause adverse effects on fish reproduction, growth, and development. Exposure to 500 µg/L of DEX reduced fecundity, spawning, and increased the abnormal hatch fry in
Fathead minnow.30 Plasma glucose concentration and gene expression of liver phosphoenolpyruvate carboxykinase (PEPCK), which controls the gluconeogenesis rate, were significantly increased in fathead minnows, after 21 days of exposure to BDP at concentrations as low as 0.1 µg/L.31 A similar effect was also observed when exposing rainbow trout to 0.65 µg/L of BDP.32 Also, female fathead minnows exhibited male secondary sexual characters with low levels of BDP exposure, indicating that glucocorticoids may induce fish masculinization.31 The serum concentration of free amino acids in common carps significantly increased at 1 µg/L of CBP exposure, which suggests
CSs may enhance protein degradation in fish.33 Furthermore, CSs simulated aryl hydrocarbon receptor (AhR)-mediated transcription and the consequent CYP1A1 gene expression in rodents.34-36 However, CSs exhibited less effects, or even suppress the AhR expression in human cells.37 This suggests that CSs crosstalk with other pathways and could enhance/depress xenobiotic metabolism related toxicity in the aquatic ecosystem.
The mode of action of typical CSs and other steroids with respect to the corticosteroid signaling pathway are listed in Table 1.2.17
Table 1.2. Mode of action of compounds affecting the corticosteroid signaling pathway.17 Compounds Mode of action (with respect to corticosteroid signaling) Glucocorticoids Hydrocortisone (cortisol) endogenous hormone, GR-, MR-agonist Cortisone inactive form of hydrocortisone Amcinonide synthetic GR-agonist Beclomethasone synthetic GR-agonist Beclomethasone dipropionate synthetic GR-agonist Betamethasone synthetic GR-agonist Dexamethasone synthetic GR-agonist
27 Compounds Mode of action (with respect to corticosteroid signaling) Betamethasone 21-acetate prodrug of betamethasone, synthetic GR-agonist Dexamethasone 21-acetate prodrug of dexamethasone, synthetic GR-, MR agonist Budesonide synthetic GR-agonist Clobetasol propionate synthetic GR-agonist Clobetasone butyrate synthetic GR-agonist Corticosterone endogenous hormone (e.g., in rodents), GR-, MR-agonist Deflazacort prodrug of 21-desacetyldeflazacort Flumethasone synthetic GR-agonist Flunisolide synthetic GR-agonist Fluocinolone acetonide synthetic GR-agonist Fluocinonide synthetic GR-agonist Fluorometholone synthetic GR-agonist Fluticasone propionate synthetic GR-, MR-agonist 6α-Methylprednisolone prodrug of prednisolone, synthetic GR-agonist Mometasone furoate synthetic GR-agonist Prednisolone synthetic GR-agonist Prednisone prodrug of prednisolone Triamcinolone acetonide synthetic GR-agonist Mineralocorticoids and Other Steroids Aldosterone endogenous hormone, MR-agonist 11-Deoxycorticosterone MR-agonist; weak GR-agonist Deoxycorticosterone acetate MR-agonist Fludrocortisone acetate MR-, GR-agonist Eplerenone MR-antagonist Spironolactone (Aldactone) MR-antagonist Mifepristone (RU-486) GR-antagonist, alters GR and MR gene expression Medroxyprogesterone GR-agonist, MR-antagonist Progesterone GR-agonist, MR-antagonist, 11β−HSD2 inhibitor, alters GR, MR, CYP11B gene expression
The biological activity of CSs mediated via GR has been mostly studied, comparing with MR. Table 1.3 lists the GR-mediated activity and relative potencies (REPs) of some
CSs determined by in vitro bioassays.
Table 1.3. The GR-mediated activity of corticosteroids determined by in vitro bioassays. GR-GeneBlazer23 GR-CALUX CSs a EC50 (nM) REP (EC50) EC50 (nM) REP (EC50) Prednisone >500 <0.004 >50022 <0.00222 Cortisone >500 <0.004 >100022 <0.000822 Prednisolone 17.7 0.101 3.6822,3.9517, 5.721 0.222,0.1317, 0.1721 Triamcinolone 11.8 0.152 5.6722 0.222 Fludrocortisone acetate 9.67 0.185 1.6017 0.3317 Hydrocortisone 6.81 0.264 11.421, 14.717, 1421 0.0722, 0.03617, 0.06921 6α-methylprednisolone 6.79 0.264 2.2522, 0.9817, 2.421 0.421, 22, 0.5417 Betamethasone 2.83 0.634 1.0222, 0.9017, 1.321 0.822, 0.5917, 0.7521 Fluocinonide 1.89 0.948 NA b NA Dexamethasone 1.79 1.000 0.8422, 0.5317, 0.9721 1.0
28 GR-GeneBlazer23 GR-CALUX CSs a EC50 (nM) REP (EC50) EC50 (nM) REP (EC50) Triamcinolone acetonide 0.79 2.265 0.3722, 0.4717 2.322, 1.1217 Flumethasone 0.36 5.032 0.1317 4.017 Budesonide 0.26 6.895 0.0917 6.117 Fluocinolone acetonide 0.24 7.398 NA NA Clobetasol propionate 0.048 37.04 0.01417, 0.03521 3817, 2821 Fluticasone propionate 0.025 70.88 0.00917 5717 a REP (Relative potency) was expressed as ECdexamethasone/ECcompound. b NA: No reported.
1.4. Occurrence of Corticosteroids in the Aquatic Environment
After being ingested by human and animals, CSs are excreted directly or as conjugates,38, 39 and released into the aquatic environment from wastewater treatment plants (WWTPs) due to incomplete elimination.40, 41 Figure 1.3 diagrams the sources and fate of natural and synthetic CSs in the environment.
The occurrence of CSs in environmental waters has been reported in China,40-43
Japan,21, 44, 45 France,46 Hungary,47 the Netherlands,22 Spain,48 the Czech Republic,49 and
Switzerland,49 with total concentrations ranged between 0.03-29 ng/L and a maximum concentration of 230 ng/L. Most of these studies analyzed only natural CSs (i.e. HCT and
COR) and a limited number of synthetic CSs (e.g. DEX, PNS, and PNL).
Several previous studies also have used in vitro GR bioassays, which can be complementary to chemical analysis, to evaluate the cumulative bioactivity from GCs mixtures in water. GR activity was observed in 27% of the tested waters in the eastern US, though the causal compounds were not identified.14 Dexamethasone equivalents (Dex-EQ) were reported to be 16-90 ng/L in the US,15, 16 0.4-38 ng/L in Europe,13, 17, 50 <3-78 ng/L in
Japan21 and up to 81 ng/L in Australia.20
29 Table 1.4. The concentration of GR agonists and GR-mediated activity in environment water samples from various countries. Country Number of Concentration In vitro GR bioactivity investigated compounds (ng/L) (Dex-EQ, ng/L) Australia19 NA NA 81 USA15, 16 NA NA 16 – 90 China40, 41 7 Corticosteroids (CSs) Natural Synthetic Biosynthesis Drug Use Drug Use Production Biosynthesis Human Industry Animals Excretion Excretion Domestic Industrial Agricultural Solid Waste Sewage Sewage Sewage Irrigation Direct Discharge Wastewater Excess Soil Landfills Treatment Plant Sludge Effluent Irrigation Leachate Adsorption Soil Interstitial Groundwater Sediment Surface Water Water Desorption Drinking Drinking water Water Plant Figure 1.3. The sources and fate of natural and synthetic corticosteroids. 1.5. Removal of Corticosteroids in Water and Wastewater Treatment Processes The objective of wastewater treatment systems is to remove organic substances, phosphorus and nitrogen from wastewater, but research has discovered that natural steroid hormones can also be reduced by wastewater treatment systems. Among wastewater 30 treatment systems, the activated sludge process is the most widely used in the world, and as the proportion of removal by primary settling, chemical precipitation, aerating volatilization and sludge absorption was small, most steroid hormones in wastewater is regarded as removed by biodegradation. Relatively high and stable removal of 92 – 100% for COR, HCT, DEX, MPL and PNS was found among the seven STPs, while the removal of PNL was only 66 ‒ 90%, which was lower than those of other CSs.41 Ten CSs were investigated in the effluents after pretreatment, primary sedimentation, secondary sedimentation and chlorination, from a STP in Japan. Most of the target CSs were removed by activated sludge in the reactive tank, except for BMV, which remained 15% in the final effluent.44 The fate of steroid hormones in different treatment processes was assessed by Fan et al, and a mass balance analysis was made.40 CSs were mainly removed by degradation, with total removal of 85‒99%, while different behaviors were observed in the aerated grit chamber, anaerobic tank, anoxic tank, and aerobic tank units. Many of the detected CSs were eliminated in the anaerobic tank, but estrogens were largely degraded in the aerobic one. A similar study was carried out a mass balance analysis for steroid hormones in two different types of WWTPs.51 The anoxic treatment process had a dominant role in the removal of detected CSs, and the total removal of most GCs following the anoxic treatment exceeded 80%. A significant reduction after oxidation ditch treatment was observed for GCs, with removal up to 88.3%. The decomposition of CSs during activated sludge processing were investigated in bench-scale,52 and ten CSs were roughly classified into three groups: 1) PNL, TAC, BET, PLA, and HCA, which decomposed within 4 h; 2) FLN, BMV, and BUD of which more than 50% remained after 4 h, but almost all of which decomposed within 24 h; and 3) TCA, 31 and FCA of which more than 50% remained after 24 h. The decomposed ratio correlated with each CS’s log P, especially groups 2) and 3). Only sparse data have been produced investigating the CSs or GR activity during conventional and especially advanced wastewater recycle treatment. It has been previously reported that micro/ultra-filtration, chlorination, and ozonation might not yield significant GR activity removal while reverse osmosis (RO) and ultraviolet (UV) appear efficient.15, 20, 53 1.6. Research Objectives Due to lack of data about the occurrence and fate of GR agonists — especially highly potent synthetic CSs — in water and wastewater treatment processes, the following objectives were addressed in this study: (1) To develop a sensitive and robust LC-MS/MS method for analyzing a wide range of GR agonists in various environmental waters. (2) To investigated the occurrence of GR agonists in WWTP influent, effluent, surface water, groundwater and drinking water. (3) To investigate the fate of GR agonists, especially highly potent synthetic CSs, in a full-scale WWTP. (4) To investigate the removal of GR agonists in water treatment. (5) To investigate the removal of GR agonists by ultraviolet based advanced oxidation processes (UV/AOPs) at bench-scale. (6) To investigate the removal of GR agonists by powder activated carbon (PAC) at bench-scale. 32 2. TRACE ANALYSIS OF GLUCOCORTICOID RECEPTOR AGONISTS IN ENVIRONMENTAL WATERS BY LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY 2.1. Introduction Among the various analytical methods for CSs in environmental waters, liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) was the most commonly used, due to its high sensitivity and selectivity.54 There are many challenges associated with the analysis of CSs in wastewater matrices. Since CSs are usually present at low ng/L concentration levels in environmental waters, analytical methods need high sensitivity. Thus, a solid-phase extraction (SPE) pretreatment is often used to concentrate CSs before instrumental analysis. However, when the target CSs are concentrated, the matrix contents in the water are also condensed, which may cause severe ion suppression in LC-MS/MS equipped with electrospray ionization (ESI) source. The reduction of these matrix effects is a big challenge for analyzing CSs. Chromatographic separation of some isomers is another challenge, such as the two epimers ‒ dexamethasone and betamethasone, which only differ in the orientation of the methyl group on the C-16 position. When good separation is achieved, these two epimers can be quantified separately with a mass spectrometer. A list of 26 CSs were selected as target analytes, including 3 natural GCs, 20 synthetic GCs, 1 natural MC, and 2 synthetic MCs (Table 2.1). Table 2.1. Chemical structures of target corticosteroids. Compounds Abbr CAS Formula MW Structure logKow Glucocorticoids (GCs) Natural 33 Compounds Abbr CAS Formula MW Structure logKow Corticosterone CTC 50-22-6 C21H30O4 346.47 1.94 Cortisone COR 53-06-5 C21H28O5 360.45 1.47 Hydrocortisone (Cortisol) HCT 50-23-7 C21H30O5 362.47 1.61 Synthetic 51022-69- Amcinonide AMC C H FO 502.58 3.78 a 6 28 35 7 b Beclomethasone BCM 4419-39-0 C22H29ClO5 408.92 2.19 Beclomethasone BDP 5534-09-8 C H ClO 521.05 3.97 a dipropionate 28 37 7 Betamethasone BET 378-44-9 C22H29FO5 392.47 1.94 51333-22- Budesonide BUD C H O 430.54 2.55 b 3 25 34 6 Dexamethasone DEX 50-02-2 C22H29FO5 392.47 1.83 25122-46- Clobetasol propionate CBP C H ClFO 466.97 3.50 7 25 32 5 25122-57- Clobetasone butyrate CBB C H ClFO 478.99 3.76 0 26 32 5 14484-47- Deflazacort DFZ C H NO 441.52 1.97 b 0 25 31 6 Flumethasone FMS 2135-17-3 C22H28F2O5 410.46 3.86 b Flunisolide FNS 3385-03-3 C24H31FO6 434.50 2.51 34 Compounds Abbr CAS Formula MW Structure logKow Fluocinolone acetonide FCA 67-73-2 C24H30F2O6 452.49 2.48 Fluocinonide FLC 356-12-7 C26H32F2O7 494.53 3.19 Fluorometholone FML 426-13-1 C22H29FO4 376.47 2.00 80474-14- Fluticasone propionate FTP C H F O S 500.57 3.96 b 2 25 31 3 5 b 6α-Methylprednisolone MPL 83-43-2 C22H30O5 374.48 1.95 83919-23- Mometasone furoate MMF C H Cl O 521.43 2.73 b 7 27 30 2 6 Prednisolone PNL 50-24-8 C21H28O5 360.45 1.62 Prednisone PNS 53-03-2 C21H26O5 358.43 1.46 Triamcinolone acetonide TCA 76-25-5 C24H31FO6 434.50 2.53 Mineralocorticoids (MCs) Natural Aldosterone ALD 52-39-1 C21H28O5 360.45 1.08 Synthetic Deoxycorticosterone DCA 56-47-3 C H O 372.51 3.08 acetate 23 32 4 a Fludrocortisone acetate FLA 514-36-3 C23H31FO6 422.49 2.26 a Calculated based on EPI Suite from U.S. EPA (KowWIN v1.68). b Calculated based on XLOGP3. 35 2.2. Experimental Section 2.2.1. Chemicals and Materials All 26 target CSs standards, including aldosterone (ALD, ≥95%), amcinonide (AMC, ≥98%), beclomethasone (BCM, ≥99%), beclomethasone dipropionate (BDP, ≥99%), betamethasone (BET, ≥98%), budesonide (BUD, ≥99%), clobetasol propionate (CBP, ≥98%), clobetasone butyrate (CBB, ≥98%), corticosterone (CTC, ≥98.5%), cortisone (COR, ≥98%), deflazacort (DFZ, ≥98%), deoxycorticosterone acetate (DCA, ≥98%), dexamethasone (DEX, ≥98%), fludrocortisone acetate (FLA, ≥98%), flumethasone (FMS, ≥98%), flunisolide (FNS, ≥97%), fluocinolone acetonide (FCA, ≥97.5%), fluocinonide (FLC, ≥98%), fluorometholone (FML, ≥98%), fluticasone propionate (FTP, ≥98%), hydrocortisone (HCT, ≥98%), 6α-methylprednisolone (MPL, ≥98%), mometasone furoate (MMF, ≥98%), prednisolone (PNL, ≥99%), prednisone (PNS, ≥98%), and triamcinolone acetonide (TCA, ≥99%) were obtained as powders from Sigma-Aldrich (St. Louis, MO, USA). Aldosterone-d7 (ALD-d7, 95%), betamethasone-d5 (BET-d5, 97% D), budesonide- d8 (BUD-d8, >98% D), cortisone-d8 (COR-d8, 97.9% D), fluticasone propionate-d5 (FTP- 13 13 13 d5, 98.1% D), triamcinolone- C3 acetonide (TCA- C3, >98% C) were purchased as powders from Toronto Research Chemicals Inc. (Ontario, Canada). Dexamethasone-d4 (DEX-d4, 96% D), hydrocortisone-d2 (HCT-d2, 99% D), 6α-methylprednisolone-d2 (MPL- d2, 95.3% D), and prednisone-d8 (PNS-d8, 98.5% D) were purchased as powders from C/D/N Isotopes Inc. (Pointe-Claire, Canada). LC/MS grade acetonitrile (ACN), ethyl acetate (EA), hexane (Hex), and acetic acid (AA) were purchased from Fisher Scientific Co. (Fair Lawn, NJ, USA). Ultrapure water with a resistivity of 18.2 MΩ·cm was prepared using a Milli-Q system (Millipore, Billerica, MA). 36 Stock solutions of each analyte and isotope surrogate standard were prepared at 1.0 mg/mL in methanol. A mixed substock solution of all the analytes at 10 μg/mL and of the ten isotope surrogates at 1.0 μg/mL were prepared in methanol. Working standard solutions at concentrations of 0.010–200 ng/mL were prepared weekly by serially diluting the mixed substock solution of analytes in methanol, and then spiking isotope surrogates at 20 ng/mL afterward. All solutions were stored in the dark at −20 °C. 2.2.2. Sample Collection The local WWTP chosen for this study has a capacity of 32 million gallons per day (MGD) and uses a treatment train that involves flocculation/aerated grit removal, dissolved air flotation (DAF) clarification, 5-stage Bardenpho system, disk filtration (DF), and sodium hypochlorite disinfection. Grab samples of WWTP influent and effluent, surface water downstream of the WWTP outfall, as well as drinking water, were collected for analytical method development. All samples were collected in 4 L amber glass bottles and stored at 4 °C in the dark until extraction. 2.2.3. Sample Extraction and Cleanup Water samples were spiked with 20 ng of each isotope surrogate after being filtered with a 0.7-μm glass microfiber filters (GF/F, Whatman, Maidstone, UK), and then extracted by an AutoTrace™ 280 Solid-Phase Extraction (SPE) device (Dionex, Sunnyvale, CA) using Oasis HLB cartridges (500 mg, 6 mL, Waters Corp., Milford, MA). The cartridges were preconditioned with 6 mL of MTBE, 6 mL of methanol, and 12 mL of ultrapure water prior to loading 100 mL of influent, 200 mL of effluent, 500 mL of surface 37 water and 1 L of drinking water or ultrapure water at a flowrate of 10 mL/min onto the cartridge. The cartridges were subsequently rinsed with 10 mL of ultrapure water, and dried under a stream of nitrogen for 45 min. The cartridges were then eluted with 5 mL of methanol/MTBE (10:90, v/v) followed by 5 mL of methanol into 15 mL glass conical vials. The extracts were evaporated to dryness under a gentle stream of nitrogen and redissolved in 0.2 mL of ethyl acetate and 1.8 mL of hexane for further cleanup. The mixed solutions were then loaded onto Sep-Pak Silica cartridges (500 mg, 6 mL, Waters Corp., Milford, MA), which were preconditioned with 4 mL of water-saturated ethyl acetate and 4 mL of hexane/ethyl acetate (90:10, v/v). The conical vials were rinsed twice with 0.2 mL of ethyl acetate and 1.8 mL of hexane, and the rinsates were also loaded onto the cartridges. The analytes were eluted with 4 mL of water-saturated ethyl acetate. The eluates were evaporated to dryness and reconstituted in 1.0 mL of methanol for LC-MS/MS analysis. 2.2.4. Instruments and Operation Conditions The liquid chromatography (LC) separation was conducted using an Agilent 1290 UHPLC system (Agilent Technologies, Palo Alto, CA). Separation was achieved with an Agilent ZORBAX Eclipse Plus C8 RRHD column (2.1×100 mm, 1.8 µm, 1200 bar) maintained at 30 °C with a constant flow rate of 0.3 mL/min. Ultrapure water containing 0.1% (v/v) acetic acid (A) and methanol containing 0.1% (v/v) acetic acid (B) were used as mobile phases. The gradient was as follows: B was initiated at 10%, then linearly increased to 48% in 0.5 min and held for 11.5 min, then increased to 60% in 0.5 min and increased to 65% in another 3.5 min, and finally increased to 100% in 1.0 min and held for 38 4.0 min (Table 2.2). A 4.0 min equilibration at 10% B was used at the end of each run. The injection volume was 10 µL. Table 2.2. Timetable for LC gradient on Agilent 1290 UHPLC system Time (min) A (%) B (%) 0.0 90 10 0.5 52 48 12.0 52 48 12.5 40 60 16.0 40 60 17.0 0 100 21.0 0 100 21.1 90 10 Mass spectrometric detection was performed using an Agilent 6490 triple quadrupole mass spectrometer (Agilent Technologies, Palo Alto, CA) equipped with the Jet Stream dual electrospray source and iFunnel technology. The instrument was tuned and optimized to obtain sufficient sensitivity and resolution using the Agilent tune solution (p/n: G1969- 85000) in all peak windows. The instrument was operated using an electrospray ionization source (ESI) in both positive and negative mode simultaneously, with optimized parameters as follows: gas temperature, 275oC; gas flow, 15 L/min; sheath gas temperature, 350oC; sheath gas flow, 12 L/min; nebulizer, 45 psi; fragmentor, 380 V. The optimized MRM transition parameters for target CSs and surrogate standards are listed in Table 2.3. 2.2.5. Data Analysis Identification of the target analytes was accomplished by comparing the retention time and the relative intensities (RI) of the two detected product ions (Table 2.3). The tolerance of relative retention time (RRT) was ±2%. The tolerances for various RIs were ±20% for RI >50%, ±25% for RI >20–50%, ±30% for RI >10–20%, and ±50% for RI ≤10% 55. All 39 target analytes were quantified internally using isotopically labeled surrogate standards. The surrogate standard applied for each analyte is shown in Table 1. At least one lab blank and one lab fortified blank sample using ultrapure water were carried out for every 10 samples. Data acquisition and analysis were carried out by the Agilent MassHunter Workstation Software (Ver. B.06.01). 2.3. Results and Discussion 2.3.1. Optimization for Multiple Reaction Monitoring Conditions in MS/MS CSs can be ionized using either ESI or APCI sources,54, 56 but ESI is preferred since it offers higher signal intensity than APCI.57-59 Therefore, ESI was chosen as the ionization source in this study. Previous studies have reported that in the presence of organic acids (RCOOH), highly abundant base adduct ions of CSs ([M+RCOO]−) are formed in ESI 60 negative ion mode , which can be selected as precursor ions. Acetic acid (CH3COOH) was used as the mobile phase additive in this study, which has been widely used for the analysis of CSs in biological and water samples.40, 42, 61 Agilent MassHunter Optimizer Software (Ver. B.07.01) was used to investigate the most abundant MRM transition (precursor ion > product ion) and optimize the collision energy for each transition. A 1 μg/mL solution of individual analyte was infused into the mass spectrometer in both ESI positive and negative modes using water/methanol (50/50, v/v) with 0.1% (v/v) acetic acid as the mobile phase. A maximum of four product ions was investigated for each CS, and those with a mass (m/z) lower than 30 were excluded. 40 Table 2.3. Optimized MRM conditions of target corticosteroids and surrogate standards. Compound Abbr. Ret Time Surrogate Prec Ion Prod Ion a CE (V) ESI Mode (min) Assignment m/z (Formation) m/z (RI, %) b Aldosterone ALD 5.36 ALD-d7 359.2 331.2 (100) 12 Neg ([M−H]− ) 189.1 (35) 16 Prednisolone PNL 6.24 HCT-d2 419.2 329.3 (100) 20 Neg − ([M+CH3COO] ) 295.1 (17) 36 Prednisone PNS 6.37 PNS-d8 417.2 327.2 (100) 12 Neg − ([M+CH3COO] ) 357.3 (36) 4 Hydrocortisone HCT 6.38 HCT-d2 421.2 331.3 (100) 16 Neg − ([M+CH3COO] ) 297.3 (22) 36 Cortisone COR 6.70 COR-d8 419.2 329.1 (100) 12 Neg − ([M+CH3COO] ) 359.2 (33) 4 6α-Methylprednisolone MPL 8.87 MPL-d2 433.2 343.1 (100) 20 Neg − ([M+CH3COO] ) 309.1 (24) 36 Betamethasone BET 9.43 BET-d5 451.2 361.1 (100) 12 Neg − ([M+CH3COO] ) 307.1 (23) 36 Dexamethasone DEX 9.94 DEX-d4 451.2 361.1 (100) 16 Neg − ([M+CH3COO] ) 307.1 (19) 36 Flumethasone FMS 10.42 BET-d5 469.2 379.1 (100) 12 Neg − ([M+CH3COO] ) 305.0 (17) 40 Corticosterone CTC 10.44 MPL-d2 347.2 329.2 (100) 12 Pos ([M+H]+) 120.9 (75) 24 Beclomethasone BCM 11.25 MPL-d2 467.2 377.1 (100) 8 Neg − ([M+CH3COO] ) 341.0 (50) 20 13 Triamcinolone acetonide TCA 12.52 TCA- C3 493.2 413.2 (100) 20 Neg − ([M+CH3COO] ) 337.1 (51) 20 13 Flunisolide FNS 13.00 TCA- C3 493.2 375.1 (100) 12 Neg − ([M+CH3COO] ) 59.2 (44) 32 13 Fluocinolone acetonide FCA 14.95 TCA- C3 511.2 431.1 (100) 20 Neg − ([M+CH3COO] ) 355.1 (57) 28 13 Fluorometholone FML 15.85 TCA- C3 435.2 58.9 (100) 16 Neg − ([M+CH3COO] ) 355.1 (55) 12 Fludrocortisone acetate FLA 15.95 MPL-d2 481.2 421.3 (100) 8 Neg − ([M+CH3COO] ) 59.0 (28) 28 Deflazacort DFZ 16.21 BUD-d8 500.2 59.0 (100) 12 Neg − ([M+CH3COO] ) 440.2 (46) 8 Budesonide BUD 16.73 BUD-d8 489.2 357.2 (100) 12 Neg − ([M+CH3COO] ) 339.2 (26) 16 Fluocinonide FLC 17.35 BUD-d8 553.2 375.2 (100) 20 Neg 41 Compound Abbr. Ret Time Surrogate Prec Ion Prod Ion a CE (V) ESI Mode (min) Assignment m/z (Formation) m/z (RI, %) b − ([M+CH3COO] ) 355.1 (51) 24 Deoxycorticosterone acetate DCA 17.89 BUD-d8 373.2 97.1 (100) 28 Pos ([M−H]+) 108.9 (82) 20 Amcinonide AMC 18.09 FTP-d5 561.2 357.1 (100) 16 Neg − ([M+CH3COO] ) 481.2 (21) 16 Clobetasol propionate CBP 18.20 FTP-d5 525.2 429.2 (50) 12 Neg − ([M+CH3COO] ) 465.2 (100) 8 Mometasone furoate MMF 18.33 FTP-d5 519.1 35.2 (100) 8 Neg − ([M+CH3COO] ) 483 (28) 12 Fluticasone propionate FTP 18.37 FTP-d5 559.2 413.2 (100) 24 Neg − ([M+CH3COO] ) 59.0 (80) 44 Beclomethasone dipropionate BDP 18.91 FTP-d5 579.2 34.8 (40) 16 Neg − ([M+CH3COO] ) 519.2 (100) 4 Clobetasone butyrate CBB 19.38 FTP-d5 479.2 71.1 (85) 24 Pos ([M+H]+) 42.9 (100) 56 Surrogate Aldosterone-d7 ALD-d7 4.91 − 366.2 338.1 16 Neg Prednisone-d8 PNS-d8 6.30 − 425.2 333.2 16 Neg Hydrocortisone-d2 HCT-d2 6.38 − 423.2 333 16 Neg Cortisone-d8 COR-d8 6.62 − 427.2 335.2 8 Neg Methylprednisolone-d2 MPL-d2 8.83 − 435.2 343.2 12 Neg Betamethasone-d5 BET-d5 9.25 − 456.2 364.1 16 Neg Dexamethasone-d4 DEX-d4 9.82 − 455.2 363.1 16 Neg 13 13 Triamcinolone acetonide- C3 TCA- C3 12.52 − 496.2 416.1 16 Neg Budesonide-d8 BUD-d8 16.70 − 497.3 357.1 12 Neg Fluticasone propionate-d5 FTP-d5 18.35 − 564.2 417.1 16 Neg a The product ion in the first row is used as quantifier, and second row as qualifier. b RI: Relative intensity (%, based on peak area) of the main product ions formed in ESI-MS/MS of the analytes. 42 − In ESI negative mode, the acetate adduct ion [M+CH3COO] was selected as the precursor ion for most of the analytes, except ALD and CBB, where it was a deprotonated molecular ion [M−H]−, while SPL failed to form sufficiently intense precursor ions. − − [M+CH3COO] generated highly abundant product ions [M−H] , which were predominant − for BDP, CBP, FLA, and MMF. [M−H−CH2O] was the most abundant ion for BCM, BET, COR, DEX, FMS, HCT, MPL, PNL and PNS. Various product ions the high abundance were formed for other CSs, such as [M−H−HF]− for FCA and TCA, and − − [M−H−(CH3)2CO] for FNS. For ALD and CBB, where [M−H] was the precursor, [M−CHO]– and [M−H−HCl]− were the major product ions, respectively. In ESI positive mode, the [M+H]+ ion was selected as the precursor ion for all target CSs. The product ions in ESI positive mode was highly dependent on the chemical structure + of each CS. At low collision energy (< 20 V), loss of H2O ([M−H2O+H] ) was observed for CSs with an OH group in C-11 (ALD, BCM, BDP, BUD, CTC, MPL, PNL, and PNS), + + while loss of fluorine ([M−F] ) and spontaneous loss of H2O with F ([M−H2O−F+H] ) occurred for fluorinated CSs (BET, DEX, and TCA). At higher collision energy, product ions were generated by cleavages in the B, C and D rings, such as m/z 121 (for BCM, BUD, CBP, DFZ, FLA, FMS, FNS, FCA, FLC, FTP, HCT, and PNL), 171 (for FNS, PNL, and PNS), 237 (for DEX) and 279 (for BCM, BET, and FML).62 A product ion with a m/z of 147 was formed from CSs without substitution in C-6 (BCM, BET, BUD, CBP, DEX, PNL, PNS, and TCA)and a product ion with a m/z of 161 was produced by 6α-methyl CSs (MPL and FML). All potential (i.e., the top three abundant) MRM transitions in both ESI positive and negative modes were further tested for their instrumental detection limits (IDLs) and 43 performance under the influence of water matrix. WWTP influent extracts were used to determine the best MRM transitions. Two MRM transitions were finally selected for each analyte, and the most abundant MRM transitions were used for isotopically labeled surrogates. [M+CH3COO]− was selected as precursor ion for most of the target CSs, while CTC, DCA, and CBB were monitored in ESI positive mode using [M+H]+ as precursor ion. For some CSs, such as CBP, BDP, and CBB, the MRM transition with higher intensity showed a worse signal-to-noise (S/N) ratio at low concentration level due to the high baseline noise. Therefore the second abundant MRM transition was selected as quantifier (Table 2.3). 2.3.2. Optimization for Liquid Chromatography Conditions A big challenge for optimizing LC conditions is the chromatographic separation of the isomers, including COR and PNL (C21H28O5, MW = 360.2), BET and DEX (C22H29FO5, MW = 392.2), FNS and TCA (C24H31FO6, MW = 434.2). Most of the studies failed to quantify BET and DEX separately in environmental water samples,44, 45, 49 and a few 48 studies accomplished this task by partial separation. In addition, MMF (C27H30Cl2O6, MW = 520.1) and BDP (C28H37ClO7, MW = 520.2) have very close molecular weights, which could not be differentiated by a quadrupole mass spectrometer, so as FML (C22H29FO4, MW = 376.2) and the deuterated surrogate MLP-d2 (C22H28D2O5, MW = 376.2). Cross-interference was observed during the instrumental analysis when they were not well separated. Quantification of FNS will be largely affected if FNS is not completely separated from TCA, since the MRM transitions of quantifier and qualifier for FNS are 44 also produced by TCA. However, the MRM transitions for TCA are more specific, and will not be affected by FNS. In this study, acetonitrile and methanol were both tested on their optimized conditions, and all isomers could be baseline separated using either of the solvents. However, acetonitrile was finally chosen, since it obtained better separation for those strongly retained CSs (logKow > 3), and higher signals for all analytes except for ALD and DFZ (Figure 2.1). The performance of chromatographic separation is shown in Figure 2.2. Figure 2.1. The chromatographic separation of analytes (10 ng/mL) using acetonitrile and methanol. 45 100 ALD 5.36 100 MPL 8.87 359.2 > 331.2 433.2 > 343.1 % 50 % 50 HT: 2.52e4 HT: 2.56e5 0 0 3 4 5 6 7 8 5 6 7 8 9 10 100 ALD 5.36 100 MPL 8.87 359.2 > 189.1 433.2 > 309.1 % 50 % 50 HT: 9.44e3 HT: 6.22e4 0 0 3 4 5 6 7 8 5 6 7 8 9 10 100 4.91 100 8.83 ALD-d7 MPL-d2 366.2 > 338.1 435.2 > 343.1 % 50 % 50 HT: 6.38e4 HT: 5.70e5 0 0 3 4 5 6 7 8 5 6 7 8 9 10 100 PNL 6.25 100 BET 9.43 419.2 > 329.3 451.2 > 361.1 % 50 % 50 HT: 3.48e5 HT: 4.11e5 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 PNL 6.25 100 BET 9.43 419.2 > 295.1 451.2 > 307.1 % 50 % 50 HT: 6.31e4 HT: 7.39e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 6.38 100 9.25 HCT BET-d5 421.2 > 331.3 456.2 > 364.1 % 50 % 50 HT: 3.01e6 HT: 5.46e5 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 6.38 100 9.82 HCT DEX-d4 421.2 > 297.3 455.2 > 363.1 % 50 % 50 HT: 6.74e4 HT: 6.22e5 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 6.38 100 9.94 HCT-d2 DEX 423.2 > 333.0 451.2 > 307.1 % 50 % 50 HT: 5.28e6 HT: 7.95e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 PNS 6.39 100 DEX 9.94 417.2 > 327.2 451.2 > 361.1 % 50 % 50 HT: 1.60e6 HT: 3.89e5 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 PNS 6.39 100 FMS 10.42 417.2 > 357.3 469.2 > 379.1 % 50 % 50 HT: 5.90e5 HT: 3.68e5 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 6.30 100 10.42 PNS-d8 FMS 425.2 > 333.2 469.2 > 305.0 % 50 % 50 HT: 1.69e6 HT: 6.06e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 COR 6.70 100 CTC 10.44 419.2 > 329.1 347.2 > 329.2 % 50 % 50 HT: 3.79e5 HT: 8.74e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 COR 6.70 100 CTC 10.44 419.2 > 359.2 347.2 > 120.9 % 50 % 50 HT: 9.69e4 HT: 6.62e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 100 6.63 100 11.25 COR-d8 BCM 427.2 > 335.2 467.2 > 377.1 % 50 % 50 HT: 5.00e6 HT: 8.93e4 0 0 3 4 5 6 7 8 7 8 9 10 11 12 Retention time (min) Retention time (min) 46 100 BCM 11.25 100 BUD 16.73 467.2 > 341.0 489.2 > 357.2 % 50 % 50 HT: 4.79e4 HT: 1.88e5 0 0 7 8 9 10 11 12 15 16 17 18 19 20 100 TCA 12.52 100 BUD 16.73 493.2 > 413.2 489.2 > 339.2 % 50 % 50 HT: 7.70e4 HT: 4.69e4 0 0 10 11 12 13 14 15 15 16 17 18 19 20 100 12.52 100 16.70 TCA BUD-d8 493.2 > 337.1 497.3 > 357.1 % 50 % 50 HT: 3.98e4 HT: 2.52e5 0 0 10 11 12 13 14 15 15 16 17 18 19 20 100 13 12.52 100 17.35 TCA- C3 FLC 496.2 > 416.1 553.2 > 375.2 % 50 % 50 HT: 1.34e6 HT: 8.73e5 0 0 10 11 12 13 14 15 15 16 17 18 19 20 100 FNS 13.00 100 FLC 17.35 493.2 > 375.1 553.2 > 355.1 % 50 % 50 HT: 1.15e5 HT: 4.54e5 0 0 10 11 12 13 14 15 15 16 17 18 19 20 100 FNS 13.00 100 DCA 18.98 493.2 > 59.2 373.2 > 97.1 % 50 % 50 HT: 5.03e4 HT: 8.55e5 0 0 10 11 12 13 14 15 15 16 17 18 19 20 100 FCA 14.95 100 DCA 18.98 511.2 > 431.1 373.2 > 108.9 % 50 % 50 HT: 1.21e5 HT: 7.87e5 0 0 13 14 15 16 17 18 15 16 17 18 19 20 100 FCA 14.95 100 AMC 18.09 511.2 > 355.1 561.2 > 481.2 % 50 % 50 HT: 6.96e4 HT: 8.85e4 0 0 13 14 15 16 17 18 15 16 17 18 19 20 100 FML 15.85 100 AMC 18.09 435.2 > 58.9 561.2 > 357.1 % 50 % 50 HT: 9.30e5 HT: 6.83e4 0 0 12 13 14 15 16 17 15 16 17 18 19 20 100 FML 15.85 100 CBP 18.20 435.2 > 355.1 525.2 > 465.2 % 50 % 50 HT: 5.38e5 HT: 4.73e5 0 0 12 13 14 15 16 17 15 16 17 18 19 20 100 FLA 15.95 100 CBP 18.20 481.2 > 421.3 525.2 > 429.2 % 50 % 50 HT: 2.25e5 HT: 2.54e5 0 0 12 13 14 15 16 17 15 16 17 18 19 20 100 FLA 15.95 100 MMF 18.33 481.2 > 59.0 519.1 > 35.2 % 50 % 50 HT: 6.05e4 HT: 1.42e4 0 0 12 13 14 15 16 17 15 16 17 18 19 20 100 DFZ 16.21 100 MMF 18.33 500.2 > 59.0 519.1 > 483.0 % 50 % 50 HT: 1.42e5 HT: 4.04e3 0 0 12 13 14 15 16 17 15 16 17 18 19 20 100 16.21 100 18.35 DFZ FTP-d5 500.2 > 440.2 564.2 > 417.1 % 50 % 50 HT: 7.00e4 HT: 1.60e5 0 0 12 13 14 15 16 17 15 16 17 18 19 20 Retention time (min) Retention time (min) 47 100 FTP 18.37 100 BDP 18.91 559.2 > 413.2 579.2 > 34.8 % 50 % 50 HT: 2.05e5 HT: 3.28e4 0 0 15 16 17 18 19 20 15 16 17 18 19 20 100 FTP 18.37 100 CBB 19.38 559.2 > 59.0 479.2 > 42.9 % 50 % 50 HT: 1.60e5 HT: 2.90e5 0 0 15 16 17 18 19 20 15 16 17 18 19 20 100 BDP 19.44 100 CBB 19.38 579.2 > 519.2 479.2 > 71.1 % 50 % 50 HT: 8.34e4 HT: 2.39e5 0 0 15 16 17 18 19 20 15 16 17 18 19 20 Retention time (min) Retention time (min) Figure 2.2. LC-MS/MS Chromatogram of all analytes at 10 ng/mL and surrogate standards at 20 ng/mL (HT is the counts of peak height). 2.3.3. Cleanup and Matrix Effects When analyzing environmental water samples, the ion suppression or enhancement of target CSs would be caused by the co-eluted water matrices in LC-ESI-MS/MS. To reduce the matrix effects, different concentration factors were chosen for various kinds of water samples, and a modified cleanup procedure using Sep-Pak Silica cartridges from previous studies was applied.23, 41 The cleanup procedure could effectively reduce the signal suppression, as shown in Fig. S3. In 500×concentrated WWTP influent extract, the peak of spiked BUD-d8 clearly appeared, and the signal of FTP-d5 increased by more than 10 times after cleanup (Figure 2.3). The matrix effects of different water matrices can be indicated by the absolute recovery of the fortified isotope surrogates (Table 2.4). The relatively low recovery (< 50%) for PNS-d8, COR-d8, BUD-d8 and FTP-d5 were obtained in WWTP influent samples, indicating that target CSs were still significantly suppressed by the co-eluted matrices after cleanup. Since accurate quantification was very important for assessing the potential risk 48 of highly potent GCs in wastewater, isotope surrogates were highly recommended. Signal 13 enhancement up to 20% was observed for TCA- C3 in WWTP effluent and influent. 100 100 16.70 BUD-d8 BUD-d8 497.3 > 357.1 497.3 > 357.1 % 50 % 50 HT: 2.17e3 0 0 15 16 17 18 19 20 15 16 17 18 19 20 100 18.35 100 18.35 FTP-d5 FTP-d5 564.2 > 417.1 564.2 > 417.1 % 50 % 50 HT: 7.34e2 HT: 7.97e3 0 0 15 16 17 18 19 20 15 16 17 18 19 20 Retention time (min) Retention time (min) Figure 2.3. The chromatograms of BUD-d8 and FTP-d5 in 500× concentrated WWTP influent extract before and after cleanup using Sep-Pak Silica cartridge. 2.3.4. Method Validation 2.3.4.1. Detection Limits Instrumental detection limits (IDLs) were determined as the minimum injected mass (pg) that gives a signal-to-noise ratio of S/N ≥ 3. IDLs ranged from 0.05 to 1.0 pg for each target CS (Table 3). The method detection limit (MDL) was determined by processing seven fortified replicate samples, at concentrations of 2–5 times the estimated MDL in 1 L of ultrapure water and spiked with 20 ng of surrogate standards. The MDL was calculated as the product of the standard deviation (s) of the measured concentrations and the Student’s t critical value for the 99% confidence level with 6 degrees of freedom (for seven replicate determinations, the t-value is 3.143).63 The fortification level (FL, i.e. spike level) and calculated MDL for each analyte are listed in Table 2.5. The MDLs for analytes ranged from 0.008 to 0.15 ng/L, where HCT was the lowest and MMF was the highest. Since many target analytes were expected to occur in environmental water matrices, and the “true” MDL could be impaired by particular water matrix, the practical MDL of 49 each CS in the four types of water matrices (drinking water, surface water, WWTP effluent and influent) were calculated using the MDL obtained from ultrapure water, divided by relative concentration factor (1 for drinking water, 0.5 for surface water, 0.2 for WWTP effluent, and 0.1 for WWTP influent) and the absolute recovery of its corresponding isotope surrogate. The practical MDLs for the target CSs were 0.008−0.16 ng/L in finished drinking water, 0.019−0.50 ng/L in surface water, 0.047−1.5 ng/L in WWTP effluent, and 0.10−3.1 ng/L in WWTP influent, respectively. 2.3.4.2. Linearity Calibration curves of at least nine points were constructed over the concentration range of 0.010–200 ng/mL for each analyte and were spiked with 20 ng/mL of each. All analytes were calibrated internally using linear regression with 1/x weighting based on the relative response factors (RRFs) for the analytes and their corresponding isotopically labeled surrogate standards. Excellent linearity was achieved in this concentration range with the correlation coefficients (R2) higher than 0.99 for all the analytes (Table 2.5). 2.3.4.3. Recovery Recovery experiments were carried out using wastewater influent, wastewater effluent, surface water and finished drinking water samples collected in Tucson, AZ. Four fortified replicate samples were prepared by spiking two different fortified concentration levels of all analytes, and 20 ng of surrogate standards into each water matrix. The recovery was calculated as the difference in measured concentration between the fortified and unfortified sample divided by the fortified concentration expressed as a percentage. 50 Table 2.4. The relative recovery for target corticosteroids and absolute recovery for isotope surrogates at two fortified concentration levels in various water matrices (n = 4). Drinking Water (1 L) Surface Water (500 mL) WWTP Effluent (200 mL) WWTP Influent (100 mL) Analyte 2 ng/L 10 ng/L 5 ng/L 20 ng/L 20 ng/L 100 ng/L 50 ng/L 200 ng/L ALD 105.9±5.7 103.8±4.2 108.2±8.1 107.2±2.8 101.0±4.5 96.5±8.3 85.7±1.9 80.8±3.5 PNL 106.9±3.9 98.8±3.5 96.9±4.0 100.5±4.6 109.3±5.4 110.4±5.3 98.9±3.3 100.9±8.0 PNS 93.1±3.8 87.0±3.6 83.4±3.1 81.3±1.9 81.0±5.9 84.0±2.5 82.3±1.2 85.2±2.0 HCT 108.7±2.5 100.4±1.5 100.8±4.1 106.2±3.2 106.6±3.2 108.7±2.7 98.1±10.4 86.8±6.4 COR 104.3±3.7 100.2±3.9 92.2±3.8 90.3±3.4 79.7±1.9 84.9±2.6 109.9±12.9 100.9±5.5 MPL 91.9±6.0 102.7±2.4 102.1±4.7 107.4±4.8 104.5±6.3 105.8±3.3 111.6±5.4 111.3±4.7 BET 103.9±4.9 102.9±4.8 102.2±3.0 99.9±2.3 96.4±2.0 98.4±2.7 94.1±1.0 90.3±2.8 DEX 102.6±3.0 96.8±3.5 107.2±5.3 101.9±4.0 96.7±3.6 99.1±2.2 89.2±5.7 98.2±7.9 FMS 103.1±3.6 97.8±4.8 105.2±5.6 108.6±2.9 106.6±2.8 111.7±2.3 125.7±2.4 125.4±4.0 CTC 100.8±6.5 96.8±6.0 104.5±7.6 83.3±1.8 89.2±3.1 75.7±4.1 77.5±1.9 82.9±2.6 BCM 102.1±6.3 96.6±1.4 92.2±2.9 87.5±1.4 90.6±2.2 91.7±3.9 97.2±1.7 93.4±2.8 TCA 107.0±8.3 105.2±4.0 111.9±2.2 104.9±4.4 109.9±5.2 107.3±4.7 105.2±1.4 101.1±3.4 FNS 88.6±3.1 99.1±4.6 110.4±4.7 101.9±7.5 91.6±3.7 88.2±3.8 94.6±1.6 84.6±4.6 FCA 111.9±3.0 101.5±3.7 106.9±9.2 112.3±4.1 112.3±4.2 112.8±3.7 111.9±2.5 107.0±5.6 FML 110.4±6.3 108.5±4.8 115.6±4.8 108.4±4.1 93.4±8.0 94.7±4.4 93.0±5.4 92.8±5.8 FLA 110.2±1.7 98.3±4.1 114.6±3.8 96.0±7.9 95.3±6.4 86.9±5.1 79.0±2.5 73.0±1.9 DFZ 97.0±3.1 103.2±3.9 100.9±9.3 84.0±1.9 93.5±9.2 103.2±3.7 82.0±5.2 82.0±6.9 BUD 104.1±9.8 102.9±10.1 95.5±9.5 90.5±6.6 88.2±3.4 95.7±5.4 96.6±9.6 84.5±6.0 FLC 94.9±5.4 101.0±4.0 106.6±7.5 99.8±7.4 106.4±4.4 106.4±3.7 98.5±8.8 105.8±4.2 DCA 97.2±2.9 93.9±4.0 102.3±0.6 99.1±8.7 85.5±8.7 91.4±4.5 72.0±3.8 72.8±6.3 AMC 93.6±8.7 91.9±8.0 67.0±3.0 66.7±3.9 64.2±6.8 65.0±2.5 66.4±5.7 65.3±3.3 CBP 96.2±7.6 98.8±6.6 94.6±3.0 91.6±3.9 86.2±2.8 88.7±2.7 99.3±5.7 91.4±1.6 MMF 91.7±2.0 88.4±2.2 93.6±2.4 86.6±2.5 97.5±2.2 92.3±4.0 98.5±6.7 103.4±2.5 FTP 100.4±6.0 101.0±3.4 107.7±1.9 103.3±3.9 101.0±3.0 95.0±2.0 95.2±3.9 95.6±9.7 BDP 100.9±7.5 90.3±9.0 79.4±4.0 78.8±3.0 97.2±7.9 95.6±3.7 77.2±6.7 75.7±4.9 CBB 93.8±3.9 90.3±6.5 101.2±6.6 93.5±2.1 104.4±12.2 98.0±4.0 129.6±4.9 129.4±4.0 Surrogate ALD-d7 94.3±6.0 97.2±5.0 79.4±9.9 85.5±7.8 84.5±10.2 81±5.6 84.9±4.4 88.2±5.3 PNS-d8 82.1±10.7 89.2±5.7 60.3±13.2 60.8±8.8 59.9±7.2 63.1±4.6 39.0±2.6 43.8±7.5 COR-d8 89.8±11.3 93.3±5.8 65.0±6.4 68.5±13.7 70.3±10.4 68.6±6.9 40.5±1.6 41.0±8.8 HCT-d2 90.7±6.9 95.4±8.5 77.9±9.2 87.5±9.5 77±8.8 78.8±13.3 78.0±2.7 82.0±10.5 BET-d5 90.4±6.0 96.1±8.4 80.7±10.3 91.9±10.1 92.4±7.5 90.3±9.7 97.3±7.4 96.1±8.8 DEX-d4 87.3±6.2 93.0±10.3 71.6±8.9 81.9±8.1 101.5±13.6 98.1±8.2 78.0±5.1 81.9±8.8 MPL-d2 89.2±6.1 91.4±8.6 71.8±9.1 79.4±8.8 82.6±8.6 79.9±12.2 73.1±2.9 75.5±6.9 13 TCA- C3 91.9±6.7 96.1±11.2 93.2±9.9 97.2±11.0 114.2±8.0 118.2±9.1 115.3±9.5 113.4±10.6 BUD-d8 93.1±7.4 100.8±11.9 61.8±8.0 57.3±11.1 56.2±10.0 61.8±5.7 43.8±5.6 44.3±6.7 FTP-d5 95.1±7.7 94.3±7.0 62.0±7.8 59.7±9.8 51.5±8.9 47.9±4.5 50.1±7.0 48.1±5.0 51 Table 2.5. IDLs and MDLs for target corticosteroids in various water matrices. Practical MDL (ng/L) Precision (RSD, %) IDL FLa MDLb Analyte R2 Drinking Water Surface Water WWTP Effluent WWTP Influent Intra-day Inter-day (pg) (ng/L) (ng/L) (1 L) (500 mL) (200 mL) (100 mL) (n = 6) (n = 12) ALD 0.9955 0.50 0.20 0.036 0.037 0.087 0.22 0.41 4.5 5.1 PNL 0.9990 0.10 0.10 0.012 0.013 0.029 0.078 0.15 5.3 6.5 PNS 0.9954 0.10 0.10 0.012 0.014 0.039 0.10 0.29 5.0 5.2 HCT 0.9990 0.05 0.10 0.008 0.008 0.019 0.050 0.10 3.9 4.6 COR 0.9987 0.20 0.10 0.012 0.013 0.035 0.083 0.28 3.4 4.0 MPL 0.9963 0.05 0.10 0.019 0.021 0.050 0.12 0.26 7.3 7.7 BET 0.9980 0.05 0.10 0.015 0.016 0.036 0.084 0.16 4.4 4.4 DEX 0.9983 0.10 0.10 0.009 0.010 0.023 0.047 0.12 4.3 5.4 FMS 0.9984 0.20 0.10 0.011 0.012 0.025 0.055 0.10 4.8 5.6 CTC 0.9958 1.0 0.20 0.041 0.045 0.11 0.25 0.55 5.9 6.3 BCM 0.9984 0.50 0.10 0.020 0.022 0.052 0.12 0.27 5.2 6.3 TCA 0.9976 0.50 0.10 0.026 0.028 0.055 0.11 0.23 5.8 7.0 FNS 0.9958 2.5 0.20 0.019 0.021 0.041 0.083 0.17 5.7 7.2 FCA 0.9945 0.20 0.20 0.019 0.020 0.039 0.080 0.16 6.0 6.7 FML 0.9980 1.0 0.20 0.040 0.042 0.084 0.17 0.35 4.9 5.8 FLA 0.9941 2.0 0.50 0.11 0.12 0.30 0.69 1.5 9.0 10.3 DFZ 0.9988 0.50 0.20 0.019 0.020 0.065 0.16 0.44 4.6 5.2 BUD 0.9965 0.20 0.10 0.023 0.023 0.076 0.19 0.51 6.4 11.3 FLC 0.9986 0.20 0.10 0.017 0.017 0.057 0.14 0.38 5.6 6.9 DCA 0.9992 0.50 0.10 0.023 0.023 0.076 0.19 0.51 7.2 11.4 AMC 0.9973 0.20 0.10 0.024 0.025 0.079 0.24 0.49 8.0 8.7 CBP 0.9993 0.20 0.10 0.024 0.025 0.078 0.24 0.49 7.1 8.2 MMF 0.9977 2.0 0.50 0.15 0.16 0.50 1.53 3.1 10.1 12.3 FTP 0.9966 0.20 0.10 0.026 0.027 0.085 0.26 0.52 6.4 10.7 BDP 0.9915 1.0 0.20 0.061 0.065 0.20 0.62 1.24 9.8 10.3 CBB 0.9923 1.0 0.20 0.044 0.047 0.15 0.45 0.91 7.8 8.1 aFortification level bMDL for 1 L of ultrapure water 52 As shown in Table 2.4, the recoveries for target CSs obtained from the two different fortified concentration levels in each water matrix were very close. The mean recoveries for all analytes were 80.2–108.5% with RSDs of 1.4–10.1% in the finished drinking water, 67.0–112.3% with RSDs of 0.6–9.5% in the surface water, 64.2–112.8% with RSDs of 1.9–12.2% in the wastewater effluent, and 66.4–129.6% with RSDs of 1.0–12.9% in the wastewater influent. AMC was the only analyte with a mean recovery less than 70% in water matrices other than drinking water, while the mean recoveries for FMS and CBB were higher than 120% in the wastewater influent samples. This phenomenon is likely due to the different degree of ion suppressions between the analytes and surrogates. 2.3.4.4. Precision The methods intra- and inter-day reproducibility as indicated by relative standard deviation (RSD, %) was studied for target CSs across a three-day period. Six duplicate fortified samples spiked with 10 ng/L of analytes in ultrapure water were prepared and analyzed on the first day. Triplicate fortified samples were processed daily on the next two days. The intra-day RSD (n = 6) was 3.4−10.1%, and inter-day RSD (n = 12) was 4.0−12.3% (Table 2.5), which indicated that the method had acceptable precision. 2.4. Conclusions A sensitive and reliable method of extracting and analyzing sub-ng/L concentrations of 26 CSs in highly complex natural water matrices was successfully developed. 53 3. DETERMINATION OF OCTANOL-WATER PARTITION COEFFICIENTS FOR GLUCOCORTICOID RECEPTOR AGONISTS 3.1. Introduction The octanol-water partition coefficient (Kow or P, normally expressed in logarithmic form as logKow or logP), used as a measure of lipophilicity or hydrophobicity for organic compounds, is a very important index in biological, toxicological and environmental studies, which has been widely used in numerous Quantitative Structure–Activity Relationship (QSAR) models for predicting pharmaceutical, environmental, biochemical 64 and toxicological properties of chemicals. LogKow can be measured experimentally; however, experimental data of logKow exist for only about 30 000 organic structures, which is negligible compared to the exponentially increasing number of compounds.65 Therefore, theoretical approaches for predicting the logKow from chemical structure have been developed. Since the publication of theoretical fundamentals of logKow prediction, many commercial software solutions are available. Since a variety of synthetic CSs been have been synthetized by the pharmaceutical industry, experimentally measured logKow is not available for many of them. In this study, the performance of nine computational programs (ACD/LogP, ALOGPS 2.1, CLOGP, JChem, KOWWIN, MiLogP, MolLogP, MOSES.logP, and XLOGP3) was evaluated for predicting logKow of CSs. The best programs were selected for predicting logKow of synthetic CSs. 54 3.2. Experimental Section 3.2.1. LogKow Calculation Programs The following programs were used to calculate the logKow values of 63 CSs: ACD/LOGP: ACD/LOGP (embedded in ACD/Labs 12.0, Advanced Chemistry Development) uses atom contribution methods in order to estimate missing fragmental increments, in addition to fragmental constants.66 ACD/LOGP uses rules similar to those for CLOGP in order to calculate correlation factors. However, compared with CLOGP, these rules are simplified. Another difference from the CLOGP program is that the correlation factors for ACD/LOGP are determined by regression analysis. ALOGPS 2.1: ALOGPs was developed with 12908 molecules from the PHYSPROP database using 75 E-state indicies. 64 neural networks were trained using 50% of molecules selected by chance from the whole set. The logKow prediction accuracy is root mean squared error rms=0.35 and standard mean error s=0.26.67, 68 CLOGP: CLOGP (embedded in ChemBioDraw 14.0) was developed by the Medicinal Chemistry Project and BioByte, which is based on the fragmental method utilizing the constructionism approach.69 The method of calculating correlation factors in the CLOGP program is the most detailed compared with other models based on the fragmental method. For example, a correlation factor for an electronic aromatic interaction is calculated from sigma and rho constants, similar to those of Hammett rule assigned to each substituent. JChem: The logKow prediction is based on a modified version of the method of Viswanadhan et al., where the predicted partition coefficients are composed of the molecules’ atomic increments.70 The applied modifications include the redefinition of 55 selected atom types to accommodate electron delocalization (in particular for sulfur, carbon, nitrogen, and metal atoms), and the addition of contributions of ionic forms. KOWWIN (v1.68): KOWWIN (embedded in EPA EPI Suite v4.11) is an atom- fragment contribution method for logKow calculation. This method bases on data for 13058 organic compounds, for which accurate experimental partition coefficients logKow are published.71 MiLogP: This method for logKow prediction developed at Molinspiration (miLogP 2.2) is based on group contributions. These have been obtained by fitting calculated logKow with experimental logKow for a training set more than twelve thousand, mostly drug-like molecules. In this way hydrophobicity values for 35 small simple “basic” fragments have been obtained, as well as values for 185 larger fragments, characterizing intramolecular 72 hydrogen bonding contribution to logKow and charge interactions. MolLogP: MolLogP (provided by Molsoft L.L.C.) is based fragment-based contributions. An original method was developed for splitting a molecule into a set of linear or non-linear fragments of different length and representation levels and counting the number of occurrences of each chemical pattern found. A Partial Least Squares (PLS) regression model was built and optimized for a particular property using a leave-50%-out cross-validation calculation.73 MOSES.logP: MOSES.logP (embedded in ChemBioDraw 14.0) for predicting acid dissociation constants, aqueous solubility and octanol/water distribution coefficients of chemical compounds are computational calculator modules based on Molecular Networks’ cheminformatics platform MOSES. MOSES is developed, maintained and owned by Molecular Networks GmbH, Erlangen, Germany.74 The limitation of the method is that 56 only the following atom types and hybridization states are parameterized: Csp3, Csp2, Caromatic, Csp, Nsp3, Nsp2, Namide, Naromatic, Nsp, Osp3, Osp2, Psp3, Ssp3, Ssp2, Ssulfoxide, Ssulfone, F, Cl, Br, I. XLOGP3: XLOGP3 has implemented an optimized atom typing scheme based on its predecessor — XLOGP75 and XLOGP276, which are atom-additive methods with well- defined correction factors, and is calibrated on a much larger training set.More importantly, based on the assumption that compounds with similar structures have similar properties, XLOGP3 introduces a new strategy by predicting the logP value of a query compound based on the known logP value of a structural analog. This new strategy improves the accuracy of XLOGP3 remarkably. It also provides users the possibility of utilizing their in-house logP data.77 3.2.2. Experimental LogKow Dataset Fifty known steroid hormones (including 7 androgens, 6 progestogens, 5 estrogens, 6 mineralocorticoids, and 26 glucocorticoids) with experimentally validated logKow values were selected for program validation. The experimental data were provided by EPA EPI Suite v4.11. 3.3. Results and Discussion 3.3.1. Performance of Different Computational Programs The nine programs were validated using all 50 steroid hormones with experimentally validated logKow values. Besides 32 CSs, other types of steroid hormones were also included to expand the datasets, due to their structural similarity with CSs. The 57 experimental and calculated logKow values are shown in Table 1. Linear regression of experimental logKow values against calculated logKow values obtained from each program was carried out for both 50 validated steroid hormones (Figure 3.1) and 32 validated CSs (Figure 3.2). The model summary of linear regression of experimental logKow on calculated 2 logKow is shown in Table 3.2. Adjusted R was used to assess the goodness-of-fit (model accuracy) for linear models. Considering both the adjusted R2 and the difference between slope value and 1, XLOGP3 (adjusted R2 = 0.9872, slope = 1.0000) was the best program for predicting 2 logKow values of steroid hormones (including CSs), followed by MiLogP (adjusted R = 0.8995, slope = 0.9739) and KOWWIN (adjusted R2 = 0.8827, slope = 0.9862), while MOSES.logP (adjusted R2 = 0.6779, slope = 1.2219) was the worst. 3.3.2. Prediciton of LogKow for A Wide Range of Corticosteroids The logKow values of 63 CSs were calculated using 9 computational programs, and the results are lists in Table 3.3. The distribution of calculated results is depicted in Figure 3.3. The mean and median of calculated values obtained from all tested programs and the best three programs (XLOGP3, MiLogP, and KOWWIN) were compared with the experimental values. For some of the CSs, such as ACD, BDP, DFP, and FTP, the predicted values shows large variation. Therefore, the median of predicted logKow values calculated by XLOGP3, MiLogP, and KOWWIN is suggested to use for future studies. 58 Table 3.1. Experimental and theoretical partition coefficients (logKow) of 50 steroid hormones for validation Exp. ACD/ ALOGPS JChem KowWIN MOSES. Compounds Abbr. Formula MW CLogP MiLogP MolLogP XLOGP3 logKow LogP 2.1 3.2 v1.68 logP Androgens Natural Androstenedione (4- ADD C H O 286.42 2.75 2.90 2.93 3.01 3.93 2.76 3.06 2.97 3.53 2.75 Androstene-3,17-dione) 19 26 2 Testosterone TTR C19H28O2 288.43 3.32 3.48 2.99 3.41 3.37 3.27 3.25 3.53 3.69 3.32 Androsterone (cis- ADR C H O 290.45 3.69 3.75 3.71 3.55 3.77 3.07 3.43 4.22 3.84 3.69 Androsterone) 19 30 2 Dehydroepiandrosterone DHEA C19H28O2 288.43 3.23 3.42 3.53 3.07 3.36 2.98 3.25 3.01 3.80 3.23 Synthetic 17α-Methyltestosterone MTT C20H30O2 302.46 3.36 4.02 3.61 3.93 3.65 3.72 3.69 3.95 4.19 3.36 Nandrolone (19- NDL C H O 274.40 2.62 3.00 2.60 2.89 3.07 2.82 3.00 3.11 3.33 2.62 Nortestosterone) 18 26 2 5α-Dihydrotestosterone DHT C H O 290.45 3.55 3.75 3.37 3.55 3.41 3.07 3.43 4.44 3.77 3.66 (Androstanolone, Stanolone) 19 30 2 Progestogens Natural Progesterone PGT, P4 C21H30O2 314.47 3.87 4.04 3.58 3.96 4.15 3.67 3.81 3.47 4.21 3.87 17α-Hydroxyprogesterone 17-HPT C21H30O3 330.47 3.17 2.89 2.99 3.34 3.40 3.08 2.86 2.96 3.42 3.17 Pregnenolone PGL, P5 C21H32O2 316.49 4.22 4.52 4.06 4.03 3.58 3.89 3.99 3.51 4.47 4.22 Synthetic Ethisterone ETH C21H28O2 312.45 3.11 3.86 3.44 3.31 3.52 3.44 3.48 3.56 4.07 3.11 19-Norethisterone NET, NTD C H O 298.43 2.97 3.38 2.72 2.79 3.22 2.99 3.23 3.14 3.71 2.97 (Norethindrone) 20 26 2 Etonogestrel ENG C22H28O2 324.46 3.16 4.23 3.19 3.35 3.60 3.89 3.88 3.31 4.60 3.16 Estrogens Natural Estrone E1 C18H22O2 270.37 3.13 3.69 4.03 3.38 4.31 3.43 3.24 3.60 4.05 3.13 17β-Estradiol E2β C18H24O2 272.39 4.01 4.13 3.57 3.78 3.75 3.94 3.43 4.17 4.21 4.01 Estriol E3 C18H24O3 288.39 2.45 2.94 2.54 3.20 2.67 2.81 2.51 3.59 3.17 2.45 Synthetic 17α-Ethynylestradiol EE2 C20H24O2 296.41 3.67 4.52 3.63 3.68 3.90 4.12 3.66 4.19 4.59 3.67 Diethylstilbestrol DES C18H20O2 268.36 5.07 5.93 4.62 4.96 5.19 5.64 4.30 5.45 5.41 5.07 Mineralocorticoids Natural Aldosterone ALD C21H28O5 360.45 1.08 0.73 1.54 1.76 1.06 1.23 1.16 0.22 0.85 1.08 11-Deoxycorticosterone (21- DOC C H O 330.47 2.88 3.41 3.10 3.44 3.33 3.12 2.80 2.43 3.23 2.88 Hydroxyprogesterone) 21 30 3 Synthetic 11-Deoxycortisol CTX C H O 346.47 3.08 2.74 2.79 2.81 2.58 3.15 2.53 2.36 2.44 2.52 (Cortexolone) 21 30 4 Deoxycorticosterone acetate DCA C23H32O4 372.51 3.08 4.53 3.09 3.98 3.77 3.71 3.50 3.17 3.80 3.08 9α-Fludrocortisone FLU C21H29FO5 380.46 1.67 1.23 1.35 1.73 1.32 1.52 1.60 0.32 1.36 1.67 Spironolactone SPL C24H32O4S 416.58 2.78 3.12 3.10 2.84 3.64 2.88 3.03 3.39 3.54 2.93 Glucocorticoids Natural Corticosterone CTC C21H30O4 346.47 1.94 1.76 2.09 2.51 2.02 1.99 1.88 1.64 2.19 1.94 59 Exp. ACD/ ALOGPS JChem KowWIN MOSES. Compounds Abbr. Formula MW CLogP MiLogP MolLogP XLOGP3 logKow LogP 2.1 3.2 v1.68 logP Cortisone COR C21H28O5 360.45 1.47 1.44 1.98 1.49 1.66 1.81 1.43 0.12 1.33 1.47 Hydrocortisone (Cortisol) HCT, CRL C21H30O5 362.47 1.61 1.43 1.79 1.89 1.28 1.62 1.62 0.50 1.40 1.61 Synthetic Betamethasone BET C22H29FO5 392.47 1.94 1.87 1.93 1.79 1.68 1.72 2.06 1.14 2.04 1.94 Betamethasone 21-acetate BMA C24H31FO6 434.50 2.77 2.96 2.60 2.32 2.12 2.46 2.76 1.45 2.62 2.77 Betamethasone dipropionate BMD C28H37FO7 504.60 4.07 4.42 3.38 3.92 3.96 3.66 4.18 2.70 3.87 4.07 Betamethasone 17-valerate BMV C27H37FO6 476.59 3.60 3.78 3.76 3.91 3.71 3.94 4.18 2.81 3.77 3.60 Clobetasol 17-propionate CBP C25H32ClFO5 466.97 3.50 3.98 3.49 3.49 4.18 2.98 3.68 2.99 3.99 3.83 Clobetasone 17-butyrate CBB C26H32ClFO5 478.99 3.76 4.82 3.77 4.21 5.19 3.67 4.05 2.97 4.61 3.76 Desoximetasone DSM C H FO 376.47 2.35 2.20 2.13 2.41 2.35 2.09 2.32 2.28 2.82 2.35 (Dexamethasone 21-desoxy) 22 27 4 Dexamethasone DEX C22H29FO5 392.47 1.83 1.87 1.93 1.79 1.68 1.72 2.06 1.14 2.04 1.94 Dexamethasone 21-acetate DMA C24H31FO6 434.50 2.91 2.96 2.60 2.32 2.12 2.46 2.76 1.45 2.62 2.77 Flumethasone FMS C22H28F2O5 410.46 1.94 1.61 1.91 1.83 1.34 1.59 2.07 1.06 2.17 1.94 Flumethasone 21-pivalate FMP C27H36F2O6 494.58 3.86 3.93 3.21 3.60 3.58 3.69 4.27 2.12 3.96 3.86 Fluocinolone acetonide FCA C24H30F2O6 452.49 2.48 2.24 2.47 2.25 1.60 2.56 2.57 1.19 2.65 2.48 Fluocinonide FLC C26H32F2O7 494.53 3.19 3.36 2.93 2.79 2.05 2.77 3.27 1.93 3.23 3.19 Fluorometholone FML C22H29FO4 376.47 2.00 2.02 2.34 2.11 2.42 2.06 2.38 1.93 3.03 2.00 Hydrocortisone 21-acetate HCA C23H32O6 404.50 2.19 2.51 2.31 2.42 1.72 2.36 2.32 0.81 1.98 2.19 Hydrocortisone 17-butyrate HCB C25H36O6 432.56 3.18 2.81 3.21 3.48 2.86 3.34 3.24 1.62 2.64 3.18 Hydrocortisone 17-valerate HCV C26H38O6 446.58 3.79 3.34 3.62 4.01 3.31 3.83 3.75 2.17 3.13 3.79 Mometasone furoate MMF C27H30Cl2O6 521.43 3.90 4.27 4.27 4.12 5.06 3.38 4.27 3.29 4.71 3.87 Prednisolone PNL C21H28O5 360.45 1.62 1.49 1.66 1.42 1.27 1.40 1.59 1.01 1.69 1.62 Prednisolone 21-acetate PLA C23H30O6 402.49 2.40 2.58 2.17 1.96 1.71 2.14 2.30 1.32 2.27 2.40 Prednisone PNS C21H26O5 358.43 1.46 1.57 2.07 1.66 1.66 1.59 1.41 0.63 1.62 1.46 Triamcinolone TAC C21H27FO6 394.44 1.16 0.83 0.84 0.71 0.24 0.96 0.67 -0.17 0.62 1.16 Triamcinolone acetonide TCA C24H31FO6 434.50 2.53 2.50 2.31 2.21 1.94 2.69 2.56 1.27 2.53 2.53 60 Figure 3.1. Relationships between the experimental n-octanol/water partition coefficients (logKow) and calculated logKow for 50 validated steroid hormones. 61 Figure 3.2. Relationships between the experimental n-octanol/water partition coefficients (logKow) and calculated logKow for 32 validated steroid hormones. Table 3.2. Model summary of linear regression of experimental logKow against calculated logKow. Intercept Slope Statistics Program Value SE a Value SE Adjusted R2 SSE b 50 Steroid Hormones ACD/LogP -0.4482 0.1801 1.2218 0.0601 0.8939 0.2889 ALOGPs 0.3745 0.1407 0.8658 0.0469 0.8739 0.3150 CLogP 0.1378 0.1601 0.9729 0.0534 0.8711 0.3185 JChem -0.4086 0.2738 1.1487 0.0913 0.7624 0.4323 KowWIN 0.0259 0.1538 0.9862 0.0513 0.8827 0.3038 MiLogP 0.1228 0.1393 0.9739 0.0465 0.8995 0.2812 MolLogP -0.1112 0.2252 1.1325 0.0751 0.8221 0.3741 MOSES.logP -1.1150 0.3591 1.2219 0.1198 0.6779 0.5034 XLOGP3 -0.0006 0.0488 1.0000 0.0163 0.9872 0.1004 32 Corticosteroids ACD/LogP -0.4724 0.2103 1.2122 0.0778 0.8864 0.2944 ALOGPs 0.3588 0.1580 0.8566 0.0585 0.8733 0.3109 CLogP 0.0330 0.2042 1.0013 0.0755 0.8493 0.3392 JChem -0.6102 0.3324 1.1941 0.1230 0.7506 0.4363 KowWIN 0.0896 0.1535 0.9415 0.0568 0.8984 0.2785 MiLogP -0.1971 0.1227 1.1043 0.0454 0.9502 0.1950 MolLogP -0.1322 0.2451 1.0849 0.0907 0.8210 0.3696 MOSES.logP -0.7657 0.3087 0.9481 0.1142 0.6868 0.4890 XLOGP3 0.0083 0.0700 0.9951 0.0259 0.9794 0.1254 a SE: Standard error b SSE: Standard error of the estimate 62 Table 3.3. Experimental and theoretical partition coefficients (logKow) of 63 corticosteroids for prediction Exp. ACD/ ALOGPS JChem KowWIN MOSES. b c Name Abbr. a CLogP MiLogP MolLogP XLOGP3 Mean Median logKow LogP 2.1 3.2 v1.68 logP Aldosterone ALD 1.08 0.73 1.54 1.76 1.06 1.23 1.16 0.85 0.22 1.08 1.16 1.16 11-Deoxycorticosterone (21- DOC 2.88 3.41 3.10 3.44 3.33 3.12 2.80 3.23 2.43 2.88 2.93 2.88 Hydroxyprogesterone) 11-Deoxycortisol CTX 3.08 2.74 2.79 2.81 2.58 3.15 2.53 2.44 2.36 2.52 2.73 2.53 (Cortexolone) Deoxycorticosterone acetate DCA 3.08 4.53 3.09 3.98 3.77 3.71 3.50 3.80 3.17 3.08 3.43 3.50 9α-Fludrocortisone FLU 1.67 1.23 1.35 1.73 1.32 1.52 1.60 1.36 0.32 1.67 1.60 1.60 Fludrocortisone acetate FLA NA 2.32 2.16 2.27 1.76 2.26 2.31 1.94 0.63 1.69 2.09 2.26 Corticosterone CTC 1.94 1.76 2.09 2.51 2.02 1.99 1.88 2.19 1.64 1.94 1.94 1.94 Cortisone COR 1.47 1.44 1.98 1.49 1.66 1.81 1.43 1.33 0.12 1.47 1.57 1.47 Hydrocortisone (Cortisol) HCT 1.61 1.43 1.79 1.89 1.28 1.62 1.62 1.40 0.50 1.61 1.62 1.62 Alclometasone dipropionate ACD NA 4.38 3.28 3.91 3.97 3.94 4.52 4.09 2.75 3.23 3.90 3.94 Amcinonide AMC NA 4.09 3.16 2.88 3.20 3.78 3.92 3.90 2.32 3.57 3.76 3.78 Beclomethasone BCM NA 2.04 2.12 2.13 2.15 2.03 2.36 2.36 1.28 2.19 2.19 2.19 Beclomethasone dipropionate BDP NA 4.59 3.69 4.26 4.43 3.97 4.49 4.19 2.84 3.05 3.84 3.97 Betamethasone BET 1.94 1.87 1.93 1.79 1.68 1.72 2.06 2.04 1.14 1.94 1.91 1.94 Betamethasone 21-acetate BMA 2.77 2.96 2.60 2.32 2.12 2.46 2.76 2.62 1.45 2.77 2.66 2.76 Betamethasone 17-benzoate BMB NA 4.26 3.75 3.97 4.18 3.92 4.48 3.98 3.19 3.89 4.10 3.92 Betamethasone dipropionate BMD 4.07 4.42 3.38 3.92 3.96 3.66 4.18 3.87 2.70 4.07 3.97 4.07 Betamethasone 17-valerate BMV 3.60 3.78 3.76 3.91 3.71 3.94 4.18 3.77 2.81 3.60 3.91 3.94 Budesonide BUD NA 3.14 2.42 2.90 2.73 2.18 3.19 3.17 1.92 2.55 2.64 2.55 Clobetasol 17-propionate CBP 3.50 3.98 3.49 3.49 4.18 2.98 3.68 3.99 2.99 3.83 3.50 3.68 Clobetasone 17-butyrate CBB 3.76 4.82 3.77 4.21 5.19 3.67 4.05 4.61 2.97 3.76 3.83 3.76 Clocortolone pivalate CLP NA 4.36 4.17 4.57 4.72 3.82 4.84 5.05 3.83 4.49 4.38 4.49 Ciclesonide CIC NA 6.13 4.08 5.25 5.32 5.02 5.65 5.11 4.25 5.28 5.32 5.28 Deflazacort DFZ NA 2.02 2.56 2.86 1.80 1.31 2.11 2.02 1.94 1.97 1.80 1.97 Desonide DSN NA 2.62 2.31 2.37 1.90 2.79 2.57 2.57 1.45 2.74 2.70 2.74 Desoximetasone DSM 2.35 2.20 2.13 2.41 2.35 2.09 2.32 2.82 2.28 2.35 2.25 2.32 (Dexamethasone 21-desoxy) Dexamethasone DEX 1.83 1.87 1.93 1.79 1.68 1.72 2.06 2.04 1.14 1.94 1.91 1.94 Dexamethasone 21-acetate DMA 2.91 2.96 2.60 2.32 2.12 2.46 2.76 2.62 1.45 2.77 2.66 2.76 Diflorasone diacetate DFD NA 3.10 2.85 2.90 2.22 2.54 3.48 3.00 2.11 2.44 2.82 2.54 Diflucortolone valerate DFV NA 4.65 3.95 4.58 4.04 3.62 4.46 4.98 4.30 4.21 4.10 4.21 Difluprednate DFP NA 3.67 3.28 3.44 3.00 3.11 3.92 3.59 2.60 1.84 2.96 3.11 Flumethasone FMS 1.94 1.61 1.91 1.83 1.34 1.59 2.07 2.17 1.06 1.94 1.87 1.94 Flumethasone 21-pivalate FMP 3.86 3.93 3.21 3.60 3.58 3.69 4.27 3.96 2.12 3.86 3.94 3.86 Flunisolide FLN NA 2.16 2.20 2.41 1.56 2.66 2.58 2.69 1.15 2.51 2.58 2.58 Fluocinolone acetonide FCA 2.48 2.24 2.47 2.25 1.60 2.56 2.57 2.65 1.19 2.48 2.54 2.56 Fluocinonide FLC 3.19 3.36 2.93 2.79 2.05 2.77 3.27 3.23 1.93 3.19 3.08 3.19 Fluocortolone FCT NA 1.86 2.09 2.61 2.08 2.06 2.35 2.98 2.38 2.38 2.26 2.35 Fluocortolone 21-hexanoate FCH NA 5.10 3.67 5.26 4.45 4.20 4.98 5.50 5.03 4.87 4.68 4.87 Fluocortolone 21-pivalate FCP NA 4.20 3.05 4.38 4.34 3.60 4.55 4.77 3.87 4.36 4.17 4.36 Fluorometholone FML 2.00 2.02 2.34 2.11 2.42 2.06 2.38 3.03 1.93 2.00 2.15 2.06 Flurandrenolide FDL NA 2.16 2.20 2.41 1.56 3.00 2.58 2.40 1.15 2.51 2.70 2.58 Fluticasone propionate FTP NA 3.73 3.69 3.03 3.72 2.49 4.61 4.59 3.39 3.96 3.69 3.96 Halcinonide HAL NA 3.32 3.24 3.31 3.58 2.93 3.37 3.43 2.36 3.49 3.26 3.37 63 Exp. ACD/ ALOGPS JChem KowWIN MOSES. b c Name Abbr. a CLogP MiLogP MolLogP XLOGP3 Mean Median logKow LogP 2.1 3.2 v1.68 logP Halobetasol propionate HBP NA 3.73 3.81 3.53 3.84 2.85 3.69 4.12 2.91 3.75 3.43 3.69 Hydrocortisone aceponate (Hydrocortisone 17-propionate HCPA NA 3.45 3.26 3.49 2.86 3.06 3.39 2.74 1.80 3.27 3.24 3.27 21-acetate) Hydrocortisone 21-acetate HCA 2.19 2.51 2.31 2.42 1.72 2.36 2.32 1.98 0.81 2.19 2.29 2.32 Hydrocortisone 17-butyrate HCB 3.18 2.81 3.21 3.48 2.86 3.34 3.24 2.64 1.62 3.18 3.25 3.24 Hydrocortisone probutate (Hydrocortisone 17-butyrate HCBP NA 4.51 3.83 4.55 4.00 4.05 4.30 3.71 2.61 4.22 4.19 4.22 21-propionate) Hydrocortisone 17-valerate HCV 3.79 3.34 3.62 4.01 3.31 3.83 3.75 3.13 2.17 3.79 3.79 3.79 6α-Methylprednisolone MPL NA 1.99 2.06 1.74 1.56 1.82 2.07 2.09 1.51 1.95 1.95 1.95 Methylprednisolone 21-acetate MPLA NA 3.07 2.58 2.28 2.00 2.56 2.77 2.67 1.81 2.73 2.69 2.73 Mometasone furoate MMF 3.90 4.27 4.27 4.12 5.06 3.38 4.27 4.71 3.29 3.87 3.84 3.87 Paramethasone PMS NA 1.53 1.51 1.99 1.30 1.68 2.08 2.21 1.03 1.95 1.90 1.95 Paramethasone 21-acetate PMA NA 2.97 2.37 3.15 2.51 2.43 3.05 2.78 3.12 2.95 2.81 2.95 Prednicarbate PCN NA 4.02 3.08 3.50 3.83 3.20 3.50 3.23 2.93 4.22 3.64 3.50 Prednisolone PNL 1.62 1.49 1.66 1.42 1.27 1.40 1.59 1.69 1.01 1.62 1.54 1.59 Prednisolone 21-acetate PLA 2.40 2.58 2.17 1.96 1.71 2.14 2.30 2.27 1.32 2.40 2.28 2.30 Prednisolone hexanoate PLH NA 4.71 3.91 4.08 3.75 4.11 4.23 4.21 3.23 4.31 4.22 4.23 Prednisone PNS 1.46 1.57 2.07 1.66 1.66 1.59 1.41 1.62 0.63 1.46 1.49 1.46 Rimexolone RML NA 4.01 3.64 4.14 4.52 3.28 4.09 4.79 4.17 3.45 3.61 3.45 Triamcinolone TAC 1.16 0.83 0.84 0.71 0.24 0.96 0.67 0.62 -0.17 1.16 0.93 0.96 Triamcinolone acetonide TCA 2.53 2.50 2.31 2.21 1.94 2.69 2.56 2.53 1.27 2.53 2.59 2.56 Triamcinolone hexacetonide TCH NA 5.37 3.90 4.60 4.12 4.76 4.71 4.87 3.95 4.76 4.74 4.76 a NA: not available. b Mean: the average of predicted logKow values calculated by XLOGP3, MiLogP, and KOWWIN. c Median: the median of predicted logKow values calculated by XLOGP3, MiLogP, and KOWWIN. 64 Figure 3.3. Prediction of logKow for 63 corticosteroids using all computational programs and the best three programs. Figure 3.4. Ranges of logKow values for five classes of steroid hormones. 3.3.3. Comparison of LogKow for Different Classes of Steroid Hormones The ranges of logKow values for five classes of steroid hormones, which include androgens (A), progestogens (P), estrogens (E), mineralocorticoids (M) and 65 glucocorticoids (G), were examined in Figure 3.4. Generally, MCs and GCs have relatively lower logKow than sex hormones, which implies their lower sorption to sludge. 3.4. Conclusions Nine computational programs (ACD/LogP, ALOGPS 2.1, CLOGP, JChem, KOWWIN, MiLogP, MolLogP, MOSES.logP, and XLOGP3) were evaluated for predicting logKow of CSs. XLOGP3, MiLogP, and KOWWIN showed the best performance. The median of predicted logKow values calculated by these three programs is suggested to use for synthetic CSs. 66 4. OCCURRENCE OF GLUCOCORTICOID RECEPTOR AGONISTS IN SURFACE WATER AND GROUNDWATER 4.1. Introduction The Lower Santa Cruz River (SCR) is a wastewater effluent-dominant stream in Pima County, Arizona. It has been ephemeral — only flowing during large rain events — since the early 1900s due to a lowered water table because of groundwater withdraws. Pima County discharges wastewater effluent from two water reclamation facilities (WRFs) into the SCR, which flows for approximately 20-40 km before it completely infiltrates. The most-upstream WRF along the SCR is the Agua Nueva Water Reclamation Facility (ANWRF), and approximately 7.6 km downstream enters the effluent from the Tres Ríos WRF (TRWRF). Both facilities replaced outdated treatment systems as a part of the Pima County Regional Optimization Master Plan in 2012 (Jenkins, 2014). ANWRF now has the capacity to treat 32 million gallons of water per day (MGD) with grit removal, dissolved air flotation thickeners, disk filters, sodium hypochlorite disinfection, and Bardenpho nutrition and biochemical oxygen demand removal bioreactors and clarifiers. TRWRF utilizes expanded primary treatment, Bardenpho nutrient removal, biological nutrient return activated sludge (BNRAS), and anaerobic digestion. TRWRF is now capable of treating 50 MGD, increased from 37.5 MGD pre-optimization plan. A connection pipeline for centralized sludge wasted treatment transports sludge from ANWRF to TRWRF. Since the lower SCR is generally comprised of 100% WRF effluent for most of the time in a year, it is an exceptional site to investigate how wastewater effluents and their 67 constituents behave in and interact with, the environment without the dilution of perennial surface water flow. In this study, the occurrence of GR agonists was investigated in both surface water and groundwater of Lower SCR. The trends of GR agonists in a wastewater effluent-dependent surficial flow of the Lower SCR was characterized. 4.2. Experimental Section 4.2.1. Chemicals and Materials Chemicals standards and solvents used in this study were described in Section 2.2.1. 4.2.2. Sample Collection The surface water samples of Lower SCR were collected at 9 sites in four seasons from 2014 to 2015 (05/12/2014, 09/22/2014, 12/01/2015, 02/12/2015). Sampling events were conducted in an upstream to downstream (South to North) manner in the absence of surficial flow beginning with the ANWRF outfall (Figure 4.1). Sites SCR-01 and SCR-04 are located at the base of the ANWRF and TRWRF outfalls, respectively. Reach 1 of the study flows from the ANWRF outfall to the downstream-most site upstream of the TRWRF outfall (SCR-03, or if dry then SCR-02). Reach 2 of the study flows from the first mixing zone site (SCR-05) to the farthest downstream site (SCR-10, or if dry then SCR-08). The distances between SCR-01 (0 km) and other sites are listed in Table 4.1. The groundwater samples were collected at 10 sites from the monitoring wells along the Santa Cruz River between August and September in 2015 (Figure 4.1). Sites SC-01 and SC-03 are located nearby the ANWRF and TRWRF outfalls, respectively. Samples were 68 collected thrice at site SC-01 on different days, and twice at SC-03. Sampling dates are shown in Table 4.2. TRWRF Outfall ANWRF Outfall Figure 4.1. Sampling sites of the Lower Santa Cruz River, Pima County, Arizona Water was captured either in a polypropylene bucket then poured into pre-kilned, amber glass bottles or captured directly into the bottles. All sampling equipment was triple- rinsed with the sample water at each site. Each sampling event included one Milli-Q water field blank and a true triplicate sample from one site. Samples were preserved in coolers with ice until the end of the sampling day, after which they were stored at 4 ºC for up to one week before extraction. 69 4.2.3. Sample Preparation and LC-MS/MS Analysis The details of samples preparation and LC-MS/MS analysis of 26 target CSs are described in Chapter 2. 4.3. Results and Discussion 4.3.1. GR Agonists in the Lower Santa Cruz River The analytical results are shown in Table 4.1. Of 26 target CSs, 9 CSs were detected in SCR surface water, including 2 natural CSs (COR and HCT) and 7 synthetic CSs, with concentrations ranging from 3.5 to 44.9 ng/L. TCA and PNL were present in all the SCR surface water samples, while BET, CBP, COR, FCA, FTP, and HCT were detected in over 80% of samples. Highest concentrations occurred at the WRF outfalls (ANWRF at SCR- 01 and TRWRF at SCR-04). The CS concentrations in December and February were in general higher than May and September, which may be due to the runoff dilution. A trend of degradation was observed downstream the ANWRF and TRWRF outfalls (Figure 4.2), which could be caused by biodegradation or solar photodegradation, but less likely by sorption to organic matter and sediments due to the relatively lower logKow of CSs (see Chapter 3). It has been demonstrated that CSs can be effectively removed by activated sludge.40 Also, a study reported that DEX (1 mg/L) was degraded within 6 hours,78 while effluents flow for hours to days in the SCR channel allowing sufficient sunlight exposure.79 70 Table 4.1. Concentration (ng/L) of corticosteroids in surface water samples collected from Lower Santa Cruz River. Data Sample Distance (km) BET CBP COR FCA FTP HCT MPL PNL TCA ∑CS Chem-DEQ Bio-DEQ 80 5/12/2014 FB – < 0.04 < 0.06 < 0.09 < 0.07 < 0.06 < 0.03 0.10 < 0.07 < 0.14 SCR-01 0.0 0.07 1.50 0.28 0.56 0.30 0.36 0.64 0.37 16.9 20.96 101.6 114.7 SCR-02 1.3 0.06 1.05 < 0.11 0.45 0.28 0.55 0.10 0.45 13.3 16.28 78.9 64.8 SCR-03 6.4 0.13 0.64 0.29 0.39 0.29 0.21 2.41 0.61 4.57 9.55 49.1 57.9 SCR-04 7.6 0.07 1.48 0.15 0.78 0.48 0.23 1.24 0.38 21.7 26.53 122.4 175.5 SCR-05 9.7 0.05 0.60 0.16 0.62 0.43 0.29 0.88 0.49 13.5 17.00 74.4 97.8 SCR-06 13.0 0.09 0.35 0.21 0.48 0.28 0.46 1.26 0.64 8.57 12.32 47.5 60.9 SCR-08 17.0 0.08 0.50 < 0.12 0.67 0.34 < 0.07 0.08 0.92 5.14 7.74 49.4 42.0 SCR-09 24.0 0.12 0.22 0.29 0.56 0.29 0.22 2.70 0.93 3.46 8.78 34.4 42.9 SCR-10 28.8 0.06 < 0.09 < 0.11 < 0.19 < 0.09 0.20 1.56 0.75 2.15 4.72 5.0 0.2 9/22/2014 FB – < 0.04 < 0.09 < 0.10 < 0.07 < 0.09 < 0.03 < 0.05 < 0.07 < 0.15 SCR-01 0.0 0.28 1.13 0.28 0.91 1.01 0.98 0.28 0.87 22.7 28.47 144.4 142.0 SCR-02 1.3 0.26 0.91 < 0.12 0.59 0.64 1.12 0.22 0.62 13.1 17.48 94.9 82.9 SCR-03 6.4 NA NA NA NA NA NA NA NA NA NA NA NA SCR-04 7.6 0.13 1.28 0.45 0.84 0.32 0.28 1.30 1.23 23.9 29.76 112.7 104.7 SCR-05 9.7 0.10 0.73 0.47 0.79 0.44 0.32 0.12 0.89 18.2 22.09 89.8 71.9 SCR-06 13.0 0.07 0.57 0.22 0.56 0.47 0.09 < 0.10 1.40 7.42 10.80 63.0 33.1 SCR-08 17.0 0.13 0.57 < 0.12 0.59 0.52 0.08 < 0.08 0.69 15.0 17.59 81.7 71.9 SCR-09 24.0 0.07 0.21 0.30 0.18 < 0.15 < 0.07 0.17 1.13 3.72 5.79 15.6 0.1 SCR-10 28.8 < 0.05 < 0.18 0.46 < 0.16 < 0.18 0.13 0.11 1.39 1.42 3.51 3.1 0.1 12/1/2014 FB – < 0.04 < 0.06 < 0.08 < 0.04 < 0.06 < 0.02 0.14 < 0.05 < 0.07 SCR-01 0.0 0.47 1.89 0.52 1.30 0.62 2.05 0.24 0.75 37.0 44.86 178.4 180.8 SCR-02 1.3 0.54 1.48 0.39 1.43 0.54 1.58 0.08 0.64 33.3 39.95 154.5 161.2 SCR-03 6.4 NA NA NA NA NA NA NA NA NA NA NA NA SCR-04 7.6 0.22 1.91 0.21 1.63 0.46 0.55 0.16 0.84 37.7 43.64 172.8 157.0 SCR-05 9.7 0.26 1.67 0.19 1.50 0.45 0.61 0.14 0.86 34.6 40.29 157.7 142.0 SCR-06 13.0 0.35 0.87 0.16 1.21 0.21 0.59 0.19 0.65 26.6 30.87 101.5 90.4 SCR-08 17.0 0.43 0.99 0.27 1.24 0.31 0.63 0.24 0.85 29.5 34.46 116.9 82.9 SCR-09 24.0 0.31 0.64 0.15 0.82 0.12 0.31 0.11 0.59 15.1 18.13 63.2 47.0 SCR-10 28.8 0.24 0.32 0.11 0.57 0.14 0.30 0.07 0.54 7.29 9.58 36.8 30.3 2/12/2015 FB – < 0.04 < 0.09 < 0.06 < 0.03 < 0.09 < 0.02 < 0.07 < 0.04 < 0.08 SCR-01 0.0 0.29 2.61 0.69 1.42 0.68 1.92 0.20 0.96 28.9 37.69 188.4 161.2 SCR-02 1.3 0.81 1.26 0.44 1.07 0.66 1.74 0.45 0.66 23.4 30.46 131.7 142.0 SCR-03 6.4 0.29 0.40 0.42 0.53 0.38 1.36 0.10 0.48 8.64 12.60 55.0 53.7 SCR-04 7.6 0.16 1.18 0.27 1.22 0.49 0.71 0.21 1.06 31.2 36.50 135.8 170.4 SCR-05 9.7 0.20 0.94 0.25 1.09 0.36 0.83 0.46 0.93 26.9 32.00 112.2 157.0 SCR-06 13.0 0.22 1.00 0.20 0.93 0.52 0.71 0.29 0.84 18.5 23.21 104.3 106.5 SCR-08 17.0 0.30 0.68 < 0.10 0.89 0.59 0.73 0.22 0.84 19.9 24.18 100.7 90.4 SCR-09 24.0 NA NA NA NA NA NA NA NA NA NA NA NA SCR-10 28.8 NA NA NA NA NA NA NA NA NA NA NA NA NA: Not available. 71 Figure 4.2. Concentration (ng/L) of corticosteroids in the Lower Santa Cruz River. Figure 4.3. Percent distribution (%) of corticosteroids in the Lower Santa Cruz River. The percent distribution of detected CSs is depicted in Figure 4.3. TCA had the highest concentrations (1.4-38 ng/L), which accounted for 39-86% of total CS concentrations. This is consistent with the findings from Jia et al. (2016) in which TCA accounted for 49-77% of total CS concentration.23 4.3.2. GR Activity in the Lower Santa Cruz River The GR-agonistic activity (Bio-DEQ) of surface water samples was previously determined as dexamethasone equivalents (ng DEX-EQ/L) using in vitro GR-GeneBlazer 72 bioassay (Table 4.1).80 Dexamethasone equivalents were also predicted from chemical analysis (Chem-DEQ) (Table 4.1), using the equation: Chem-DEX = ∑REPi × (Ci/Mi) × MDEX (unit: ng DEX-EQ/L), where REPi is the relative potency (REP) of each CS compared with DEX, Ci is measured concentration (ng/L), Mi is molecular weight, and MDEX is the molecular weight of DEX. The REP values used for calculation are listed in Table 4.2. Both predicted Chem-DEQs and Bio-DEQs are plotted in Figure 4.2, and good mass balance is shown. However, Chem-DEQs of many samples were greater than Bio- DEQs. Since no GR antagonistic activity was detected by in vitro anti-GR bioassay,80 this phenomenon may be due to the normal errors of bioanalytical and chemical analyses. Four synthetic CSs — TCA, FCA, CBP, and FTP — accounted for over 90% of Chem-DEQs, which agrees our recent study.23 Figure 4.4. Contribution (%) of detected corticosteroids to GR-agonistic activity in the Lower Santa Cruz River. Table 4.2. Relative potency (REP) of detected corticosteroids applied for calculating predicted dexamethasone equivalents. Compounds REP (EC50) 23 BET 0.634 CBP 37.04 COR 0.004 DEX 1.0 73 Compounds REP (EC50) 23 FCA 7.398 FTP 70.88 HCT 0.264 MPL 0.264 PNL 0.101 TCA 2.265 4.3.3. GR Agonists in Groundwater Along the Lower Santa Cruz River Only 3 CSs were detected in groundwater samples, with total concentrations ranging from 0.32 to 1.38 ng/L, including HCT (up to 0.20 ng/L), MPL (up to 0.43 ng/L) and PNL (up to 0.96 ng/L). This result implies that infiltration process may effectively remove CSs. A trend of decrease in CS concentrations was also observed downstream the ANWRF and TRWRF outfalls (Figure 4.5). The difference with the trend for surface water was that the highest total concentration downstream the TRWRF outfall occurred at site SC-05, instead of SC-03, which showed a delay. Figure 4.5. Concentration (ng/L) of corticosteroids in the groundwater along the Lower Santa Cruz River 74 4.4. Conclusions Several GR agonists were detected in SCR surface water, and groundwater in well monitoring along the SCR, with total concentrations of 3.5–44.9 ng/L and 0.32–1.38 ng/L, respectively. A trend of degradation was observed downstream the ANWRF and TRWRF outfalls for both surface water and groundwater. The very low concentration of GR agonists in groundwater implied the removal of GR agonists in infiltration process. The results indicated the discharge of GR agonists from the two WRFs, and the occurrence of GR agonists in the WRF need to investigate further. 75 5. OCCURRENCE AND FATE OF GLUCOCORTICOID RECEPTOR AGONISTS IN WASTEWATER TREATMENT PLANTS (WWTP) 5.1. Introduction WWTPs are a major source of GR agonists in environmental waters; nevertheless, only a few studies have reported the removal of GR agonists in WWTPs, not to mention highly potent CSs.17, 40, 41, 44, 46, 48, 51 Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) is most commonly used for analyzing CSs in environmental waters,54 which encounters challenges brought by matrix ion suppression/enhancement and presence of CSs at sub-ng/L concentration levels. In this study, the sensitive and robust LC-MS/MS method established in Chapter 2 was applied to investigate the occurrence and fate of 26 CSs, particularly highly potent GCs in WWTP, target CSs were quantified in wastewater at different stages of Agua Nueva Water Reclamation Facility (ANWRF) and their possible conjugates were determined indirectly using enzymatic hydrolysis. In addition, the behavior of highly potent CSs was compared with natural and less potent synthetic GCs, and the removal of integrated GR- agonistic activity during wastewater treatments was assessed. 76 5.2. Experimental Section 5.2.1. Chemicals and Materials Chemicals standards and solvents used in this study were described in Section 2.2.1. L-ascorbic acid (crystalline), and formaldehyde solution (37 wt. % in H2O) were purchased from Fisher Scientific Co. (Fair Lawn, NJ, USA). β-Glucuronidase from Helix pomatia (Type HP-2, ≥100,000 units/mL, containing ≤7,500 units/mL sulfatase), sodium acetate, and calcium carbonate were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water with a resistivity of 18.2 MΩ·cm was prepared using a Milli-Q system (Millipore, Billerica, MA). 5.2.2. Sample Collection The Agua Nueva Water Reclamation Facility (ANWRF) is a newly built 32-mgd wastewater treatment facility that employs dissolved air flotation (DAF) for primary treatment, 5‐Stage Bardenpho activated sludge secondary treatment with step‐feed aeration for nitrification/denitrification (nutrient removal), filtration, and chloramination for disinfection. The effluent from the ANWRF flows by gravity to Tucson Water’s Sweetwater Effluent Pump Station (SEPS) which is designed to pump the effluent to the Tucson Reclaimed Water Treatment Plant and/or distribute the water to the Tucson Water’s Sweetwater Recharge Basins. 24-h composite wastewater samples were collected each day during three days (May 9–12th, 2016) from four different points that included plant influent, DAF clarifier effluent, DF influent, and final effluent (Figure 5.1). The effluent after chlorination contained about 3 mg/L of total residual chlorine (TRC) and was quenched with sodium bisulfite prior to 77 discharging. All samples were collected in 4 L amber glass bottles and stored at 4 °C in the dark until extraction. The samples were analyzed for 26 target CSs using the analytical method described in Chapter 2. Flocculation/Aerated Dissolved Air Flotation Influent Coarse Grit Chambers (DAF) Clarifiers Screens Final Clarifiers 5-Stage Bardenpho Bioreactor Basins Disk Filtration Units Chlorine Contact Basins NaClO Na SO Return Activated Sludge (RAS) 2 3 24h composite sample Figure 5.1. Flow scheme of Agua Nueva Water Reclamation Facility with sampling locations. 5.2.3. Enzymatic Hydrolysis of Conjugated Corticosteroids The presence of conjugated CSs was determined indirectly by subjecting each wastewater sample to enzymatic hydrolysis and measuring the increase of free CSs compared with the control sample without enzyme. Enzyme hydrolysis was performed by adding 2 mL of 0.2 M sodium acetate, 2 mL of 1 mg/mL ascorbic acid and 0.10 mL of β- glucuronidase to 20 mL of sample as described by Hutchins.81 The samples were incubated at 37 °C for 16 h, and mixed with 200 mg of calcium carbonate and 0.5 mL of 37% formaldehyde before extraction. 78 5.2.4. Sample Preparation and LC-MS/MS Analysis The details of samples preparation and LC-MS/MS analysis of 26 target CSs are described in Chapter 2. 5.3. Results and Discussion 5.3.1. Occurrence of Corticosteroids in WWTP Of the 26 target analytes, 13 GCs and 1 MC were detected in the plant influent and DAF effluent, while 10 GCs were detected in DF influent and final effluent. The concentrations of detected CSs at different stages in the WWTP are shown in Table 5.1. Figure 5.2 depicts typical LC-MS/MS chromatograms of target CSs detected in a composite influent sample. Table 5.1. Concentration (ng/L) and removal of target corticosteroids at different stages in the WWTP. Concentration (ng/L) Removal during Treatment (%) a Compounds Plant Inf DAF Eff DF Inf Plant Eff 1st b 2nd c 3rd d Overall ALD 13.9 ± 1.5 13.0 ± 1.3 < 0.22 < 0.22 6 99 0 99 BET 2.54 ± 0.22 2.38 ± 0.18 0.23 ± 0.02 0.16 ± 0.01 6 90 26 93 BUD 2.47 ± 0.42 2.33 ± 0.55 0.19 ± 0.03 0.06 ± 0.03 4 91 67 97 CBP 2.16 ± 0.09 1.62 ± 0.06 1.08 ± 0.02 1.00 ± 0.09 25 34 7 53 COR 195 ± 33 225 ± 25 0.21 ± 0.01 0.18 ± 0.02 −18 100 15 100 CTC 24.2 ± 5.2 25.6 ± 6.4 < 0.25 < 0.25 −6 99 0 99 DEX 2.02 ± 0.42 1.86 ± 0.28 0.05 ± 0.01 0.04 ± 0.00 3 97 12 98 FCA 0.59 ± 0.08 0.58 ± 0.07 0.50 ± 0.02 0.49 ± 0.04 1 13 2 16 FTP 1.62 ± 0.37 1.49 ± 0.36 0.76 ± 0.06 0.76 ± 0.06 7 46 0 50 HCT 328 ± 27 341 ± 9 1.00 ± 0.05 0.98 ± 0.10 −4 100 1 100 MPL 8.03 ± 1.65 9.7 ± 2.0 0.17 ± 0.01 0.13 ± 0.03 −21 98 23 98 PNL 19.5 ± 1.3 25.2 ± 5.9 < 0.08 < 0.08 −28 100 0 100 PNS 10.0 ± 1.7 10.8 ± 0.1 < 0.10 < 0.10 −11 100 0 100 TCA 17.0 ± 1.5 18.5 ± 2.5 16.3 ± 0.4 15.4 ± 0.5 −10 10 6 9 ΣCSs 627 ± 63 679 ± 31 20.5 ± 0.5 19.2 ± 0.5 −9 97 7 97 Chem-DEQ 315 ± 15 298 ± 24 114 ± 5 109 ± 4 5 61 5 65 a For non-detected CSs, 1/2 MDL was used for calculating the removal. b 1st: Primary treatments, including flocculation/aerated grit removal and DAF clarification. c 2nd: Secondary treatments, including 5-Stage modified Bardenpho process and final clarification. d 3rd: Tertiary treatments, including disk filtration, and sodium hypochlorite disinfection. 79 Quantifier Qualifier 100 5.34 100 ALD ALD 5.36 359.2 > 331.2 359.2 > 189.1 % 50 % 50 0 0 3 4 5 6 7 8 3 4 5 6 7 8 100 PNL 100 PNL 6.25 419.2 > 329.3 6.25 419.2 > 295.1 % 50 % 50 0 0 3 4 5 6 7 8 3 4 5 6 7 8 100 HCT 6.38 100 HCT 6.38 421.2 > 331.3 421.2 > 297.3 % 50 % 50 0 0 3 4 5 6 7 8 3 4 5 6 7 8 6.39 100 PNS 100 PNS 6.39 417.2 > 327.2 417.2 > 357.3 % 50 % 50 0 0 3 4 5 6 7 8 3 4 5 6 7 8 100 COR 6.70 100 COR 6.70 419.2 > 329.1 419.2 > 359.2 % 50 % 50 0 0 3 4 5 6 7 8 3 4 5 6 7 8 100 MPL 8.87 100 MPL 8.87 433.2 > 343.1 433.2 > 309.1 % 50 % 50 0 0 5 6 7 8 9 10 5 6 7 8 9 10 100 BET/DEX 9.43 9.94 100 BET/DEX 9.43 451.2 > 361.1 451.2 > 307.1 9.94 % 50 % 50 0 0 7 8 9 10 11 12 7 8 9 10 11 12 100 CTC 10.44 100 CTC 10.44 347.2 > 120.9 347.2 > 329.2 % 50 % 50 0 0 7 8 9 10 11 12 7 8 9 10 11 12 100 TCA 12.52 100 TCA 12.52 493.2 > 413.2 493.2 > 337.1 % 50 % 50 0 0 10 11 12 13 14 15 10 11 12 13 14 15 100 FCA 100 FCA 14.95 511.2 > 431.1 14.95 511.2 > 355.1 % 50 % 50 0 0 13 14 15 16 17 18 13 14 15 16 17 18 100 BUD 16.73 100 BUD 16.73 489.2 > 357.2 489.2 > 339.2 % 50 % 50 0 0 15 16 17 18 19 20 15 16 17 18 19 20 100 CBP 18.20 100 CBP 18.20 525.2 > 465.2 525.2 > 429.2 % 50 % 50 0 0 15 16 17 18 19 20 15 16 17 18 19 20 100 FTP 18.37 100 FTP 18.37 559.2 > 413.2 559.2 > 59.0 % 50 % 50 0 0 15 16 17 18 19 20 15 16 17 18 19 20 Retention time (min) Retention time (min) Figure 5.2. LC-MS/MS chromatograms of detected analytes in a 24h-composite influent sample. 80 In the plant influent, a total CSs concentration of 627 ± 63 ng/L was detected. HCT and COR, both of which are natural GCs, occurred in the plant influent at concentrations of 328 ± 27 ng/L and 195 ± 33 ng/L, respectively, which were higher than those reported in China (up to 120 ng/L for HCT, and up to 86 ng/L for COR)40, 41, 43 and Switzerland (160 ng/L for COR+HTC),49 but similar to Spain (136–270 ng/L for HCT, and 122–280 ng/L for COR).48 The other two natural CSs, CTC and ALD, were also detected in the plant influent at 13.9 ± 1.5 ng/L and 24.2 ± 5.2 ng/L, respectively, which were comparable to Switzerland (19 ng/L for ALD, and 21 ng/L for CTC).49 The concentrations of common synthetic GCs (19.5 ± 1.3 ng/L for PNL, 10.0 ± 1.7 ng/L for PNS, 2.54 ± 0.22 ng/L for BET, and 2.02 ± 0.42 ng/L for DEX) were much lower than Switzerland (336 ng/L for PNL+PNS, and 106 ng/L for BET+DEX), while highly potent synthetic GCs have comparable concentrations (2.16 ± 0.09 ng/L for CBP and 1.62 ± 0.37 ng/L for FTP) to Switzerland (7 ng/L for CBP, and 4 ng/L for FTP).49 The percent distribution of detected CSs in the plant influent is profiled in Figure 5.3. Natural CSs accounted for 90% of total CSs, where HCT had the highest proportion (52%), followed by COR (31%), CTC (4%) and ALD (2%), which was similar to the percent distribution of CSs in influent reported in China.40, 41 Among synthetic CSs, PNL, TCA, PNS, and MPL each contributed 1–3%, while others were far below 1%. In the plant effluent, the total CS concentration decreased to 19.2 ± 0.7 ng/L, and highly potent synthetic GCs became predominant. TCA contributed to 80% of total CSs, followed by CBP (5%), FTP (4%), and FCA (3%). HCT and COR made up only 5% and 1% of the total 81 CSs, respectively. These results follow the same trend as our previous investigations in multiple WWTP effluents.23 The concentration level and percent distribution of CSs were very similar between the plant influent and DAF effluent, as well as the DF influent and plant effluent, which suggested that primary and tertiary treatment has limited performance for the removal of CSs, and secondary treatment is the most efficient process for CSs removal. Figure 5.3. Percent Distribution (%) of chemical concentrations (ng/L) and dexamethasone-equivalents (Dex-EQ, ng/L) of detected corticosteroids in WWTP. 5.3.2. Removal of Corticosteroids in WWTP The treatment train of the WWTP can be divided into three stages: (1) primary treatment, including flocculation/aerated grit removal and DAF clarification; (2) secondary treatment, including 5-Stage modified Bardenpho process and final clarification; (3) tertiary treatment, including disk filtration and sodium hypochlorite disinfection. The percent removal of detected CSs was calculated for the plant influent and effluent 82 concentrations at each stage of treatment. As shown in Table 5.1, natural CSs (ALD, COR, CTC, and HCT) were almost completely removed (>99%) in the final effluent, as well as PNL and PNS. High removal efficiency in overall treatment was also achieved for synthetic GCs—BET (93%), BUD (97%), DEX (98%), and MPL (98%). This high removal of GCs with low potency agrees with previous studies.40, 41, 48 However, highly potent synthetic GCs were poorly removed in the WWTP, with removals of 53% for CBP, 50% for FTP, 16% for FCA, and only 9% for TCA. A previous dstudy reported good removal of highly potent GCs (>84.6% for CBP, >71.4% for FTP, and 83.3% for TCA) in WWTP Turgi, Switzerland, which also applies primary mechanical treatment and secondary biological treatment (activated sludge operated in anaerobic, anoxic and aerobic mode).17 The contrary results need to be further studied. The different results observed between these two studies might due to the different setups between the two secondary treatment processes, and additional studied are still needed to further evaluate how activated sludge setup may affect CSs removal. In primary treatment, negative removal was observed for several CSs, including COR (−18%), CTC (−6%), HCT (−4%), MPL (−21%), PNL (−28%), PNS (−11%), and TCA (−10%), while other CSs were only slightly removed. The elevated concentration of CSs after aerated grit chambers has also been reported in other studies.40 The phenomenon might be caused by the deconjugation of CS conjugates (glucuronides and sulfates),38, 39 which was further investigated in this study. Secondary treatment contributed greatly to the removal of CSs, with over 90% of CSs removed in this stage, except for highly potent GCs including CBP, FTP, FCA, and TCA (10%-46%). Anaerobic and anoxic processes have also proved effective in removing low 83 potent CSs in previous studies.40, 41, 51 The correlation between the removal of CSs in secondary treatment and their logKow were further explored. As shown in Figure 5.4a, the removal of CSs was in significant negative correlation with their logKow (r = −0.697, p < 0.01). When assuming that the biodegradation of CSs follows first order kinetics, the concentration of CSs in the DAF effluent (CDAF eff) can be expressed as: CDAF eff = Cinf·exp(−kt), where Cinf is the CSs concentration in the plant influent (ng/L), k is the first- order rate constant, and t is the contact time. It is verified with the observed removal, and as shown in The constant k, which represents the degradation rate, has a negative linear correlation with ln(CDAF eff /Cinf). The correlation of CS’s logKow and ln(CDAF eff /Cinf) is also plotted in Figure 5.4b, which produced a higher linear correlation (r = −0.794, p < 0.01). A lab-scale study investigated the degradation of CSs during activated sludge 52 process, and the decomposed ratio was also negatively correlated with each CS’s logKow. However, it should also be noted that, the detected CSs can be roughly divided into three groups: (1) CSs with low logKow (< 2) and high removal, such as ALD, COR, HCT, PNL and PNS; (2) CSs with high logKow (2–4) and high removal, including BET, BUD, CTC, DEX and MPL; (3) CSs with high logKow (2–4) and low removal, including CBP, FTP, FCA, and TCA. CSs in group 1 have no heteroatoms or structural modifications on C-17 OH group (Table 2.1). CSs in groups 2 are modified at only one position compared with CSs in group 1, including esterified C-17 OH group, induced fluorine atom or methyl group at C-6. CSs in group 3 contain both an esterified C-17 OH group and fluorine atom at C-9. The structural information of synthetic CSs can be useful to predict their removal in biological treatment process.82 The low removal of FCA and TCA in secondary treatment, which is consistent with their slowest degradation during activated sludge process, was 84 attributed to their high structural stability brought from the C-16,17 acetonide group and fluorine atom at C-9.52 The results suggested that while biodegradation favors those CSs with lower logKow characteristics, structure stability also played important role in CSs attenuation during WWTP treatment. In tertiary treatment, no significant removal was found for most of CSs, except for BUD (67%), BET (26%), and MPL (23%), which indicates that disk filtration and sodium hypochlorite disinfection could not effectively remove CSs. This agrees with our previous study that chlorination could not appreciably attenuate the observed GR activity or GCs.23 Figure 5.4. Correlations between the logKow values and corticosteroid removal in secondary treatment. 5.3.3. Presence of Conjugated Corticosteroids in WWTP To explain the negative removal of some CSs in primary treatment, an enzymatic hydrolysis experiment was carried out to investigate the presence of conjugated CSs in the WWTP. The concentrations of target CSs in all wastewater samples with/without enzyme treatment are given in Table 5.2. Since the crude extract of Helix pomatia used in this 85 experiment contained low units of sulfatase (≤7,500 units/mL), the conjugated CSs measured would be mainly glucuronides. Percent distribution of free and conjugated CSs in plant influent and DAF effluent is depicted in Figure 5.5. In plant influent, conjugates were found for all detected CSs, which accounted for as high as 69% of total CTC, 31% of CBP, 30% of MPL, 29% of DEX, 28% of PNS, 25% of FCA, 23% of HCT, 19% of COR, 18% of FTP, 17% of BET, 15% of PNL, 13% of ADL, and 11% for TCA. After primary treatment, conjugates of some CSs were mostly deconjugated, such as COR, DEX, FCA, PNL, and TCA, while the ratio of free and conjugated forms for ALD, CBP, CTC was maintained. The total conjugated CSs was estimated as 219 ng/L in plant influent and 155 ng/L in DAF effluent. Table 5.2. Concentration (ng/L) of target corticosteroids in wastewater samples with/without enzyme treatment. Without Enzyme Treatment Compounds Plant Inf DAF Eff DF Inf Plant Eff ALD 14.4 ± 2.3 13.7 ± 1.5 ND ND BET 3.23 ± 0.40 2.66 ± 0.16 ND ND BUD 2.76 ± 0.66 2.19 ± 0.27 ND ND CBP 2.63 ± 1.75 1.49 ± 0.17 1.21 ± 0.72 0.91 ± 0.18 COR 188 ± 13 197 ± 30 ND ND CTC 23.0 ± 9.6 22.3 ± 8.2 ND ND DEX 2.01 ± 0.94 1.77 ± 0.87 ND ND FCA 0.66 ± 0.18 0.60 ± 0.09 0.44 ± 0.17 0.59 ± 0.10 FTP 2.02 ± 1.31 2.04 ± 0.14 1.09 ± 0.96 1.03 ± 0.39 HCT 314 ± 25 326 ± 13 ND ND MPL 9.1 ± 1.6 9.4 ± 1.5 0.09 ± 0.06 0.08 ± 0.03 PNL 17.3 ± 4.3 18.5 ± 2.4 ND ND PNS 9.3 ± 2.4 10.3 ± 1.3 ND ND TCA 17.5 ± 2.8 18.8 ± 1.2 16.0 ± 2.4 15.8 ± 0.1 With Enzyme Treatment Compounds Plant Inf DAF Eff DF Inf Plant Eff ALD 16.6 ± 2.4 15.7 ± 1.8 ND ND BET 3.88 ± 0.08 3.02 ± 0.14 ND ND BUD 2.88 ± 1.01 2.23 ± 0.62 ND ND CBP 3.80 ± 0.85 2.13 ± 0.28 1.39 ± 0.10 0.99 ± 0.14 COR 232 ± 22 199 ± 7 ND ND CTC 78.5 ± 6.5 75.5 ± 9.5 ND ND DEX 2.82 ± 1.02 1.82 ± 0.86 ND ND 86 With Enzyme Treatment Compounds Plant Inf DAF Eff DF Inf Plant Eff FCA 0.89 ± 0.33 0.61 ± 0.11 0.58 ± 0.02 0.59 ± 0.04 FTP 2.46 ± 1.14 2.40 ± 0.30 1.24 ± 0.82 1.08 ± 0.28 HCT 408 ± 1 405 ± 7 ND ND MPL 12.9 ± 5.7 12.4 ± 3.3 0.10 ± 0.04 0.08 ± 0.04 PNL 20.4 ± 1.2 18.7 ± 10.2 ND ND PNS 12.9 ± 3.0 12.8 ± 2.9 ND ND TCA 19.6 ± 0.1 19.0 ± 0.3 16.85 ± 0.75 16.1 ± 1.4 Figure 5.5. Percent distribution of free and conjugated corticosteroids in WWTP influent and DAF effluent. To our best knowledge, this is the first study to quantify the conjugated CSs in WWTP, and the cause of negative CS removal was proved to be the deconjugation of CS conjugates. The presence of a large mass of conjugated CSs implies that the removal efficiency of wastewater treatment can be underestimated using free CS concentrations. 5.3.4. Removal of GR Activity in WWTP To evaluate the removal of integrated GR-agonistic activity in WWTP, dexamethasone equivalents (Dex-EQ) of wastewater samples were estimated based on the 87 measured concentration of CSs and their relative potency (REP) compared with DEX. The predicted Dex-EQ (Chem-DEX) was calculated as: Chem-DEQ = ∑REPi × (Ci/Mi) × MDEX (unit: ng DEX-EQ/L), where REPi is the REP of each CS i, Ci is measured concentration (ng/L), Mi is molecular weight, and MDEX is the molecular weight of DEX. The REPs applied for calculation (Table S6) were all obtained in our previous study using the GR- GeneBlazer bioassay,23 expect for ALD and CTC, which were reported in a study using the GR-CALUX bioassay.17 The predicted Chem-DEQs of wastewater samples at different stages are listed in Table 5.1, and the contribution of each CS to the GR activity is profiled in Figure 5.3. In the plant influent, HCT (REP = 0.26), which accounted for 52% of the total chemical concentration, contributed to 30% of GR activity. FTP (REP = 71) and CBP (REP = 37), which accounted for only 0.3% of the chemical concentration, contributed 29% and 21% to the total GR activity, respectively. TCA (REP = 2.3) and BUD (REP = 6.9), which accounted for 3% and 0.4% of the chemical concentration, contributed 11% and 5% to the total GR activity, respectively. In the plant effluent, the GR activity was mainly contributed by high potent GCs including FTP (39%), TCA (29%), CBP (29%), and FCA (3%, REP = 7.4), which accounted for 4%, 80%, 5%, and 3% of the chemical concentration, respectively. This was consistent with our previous investigations in several WWTP effluents.23 The Chem-DEQ was 315 ± 15 ng DEX-EQ/L in the plant influent, and 109 ± 4 ng DEX-EQ/L in the plant effluent. The predicted DEX-EQ in plant effluent was similar to the DEX-EQ levels reported previously on the same plant (89.4 ng DEX-EQ/L).23 Same as chemical concentrations, secondary treatment predominantly contributes to the removal of 88 GR activity. The overall removal of total CSs in WWTP was 97%, while the removal of Chem-DEQ in the WWTP was only 65%, which was due to the low removal of highly potent GCs—FTP, TCA, CBP, and FCA (9%-53%, Table 5.1), which mainly contribute to the GR activity in plant effluent. This can explain the phenomenon observed in WWTP Turgi, Switzerland, where approximately 50% of GR activity was removed in treated effluent, whereas 92% of CSs measured by chemical analysis was removed.17 Table 5.3. Relative potency (REP) of detected corticosteroids applied for calculating predicted dexamethasone equivalents. Compounds REP (EC50) ALD 0.0037 17 BET 0.634 23 BUD 6.895 23 CBP 37.04 23 COR 0.004 23 CTC 0.033 17 DEX 1.0 23 FCA 7.398 23 FTP 70.88 23 HCT 0.264 23 MPL 0.264 23 PNL 0.101 23 PNS 0.004 23 TCA 2.265 23 Since highly potent GCs, such as FTP and CBP, are more persistent during WWTP treatment, and their contributions to GR-agonistic activity can be considerable even at sub- ng/L concentration levels, requiring the need to pay additional attention to in the future studies. In addition, a highly sensitive and robust analytical method, which is capable of reliably quantifying such GCs at sub-ng/L level, is necessary. 89 5.4. Conclusions The occurrence and fate of 26 target CSs in a local WWTP were investigated using a highly sensitive and reliable LC-MS/MS analytical method that is capable of quantifying CSs at sub-ng/L level in complex water matrices. Thirteen GCs and one MC were detected in the plant influent and DAF effluent, while ten GCs were detected in DF influent and final effluent. While high removal efficiency was achieved for natural and low potent synthetic CSs, highly potent synthetic GCs, including CBP, FTP, FCA, and TCA, were poorly removed in WWTP. Negative removal of CSs was observed in primary treatment including flocculation/aerated grit removal and DAF clarification, which was proved to be caused by the deconjugation of CS conjugates, using an enzymatic hydrolysis experiment. The removal of predicted GR activity in the WWTP was only 65%, whereas 97% of total CSs were removed, which was due to the low removal of highly potent GCs. The highly potent GCs should be paid more attention in the future studies. 90 6. REMOVAL OF GLUCOCORTICOID RECEPTOR AGONISTS DURING WATER TREATMENT PROCESSES 6.1. Introduction According to the results in Chapter 5, we know that conventional wastewater treatment cannot effectively remove high potent GR agonists, such as CBP, FTP, FCA, and TCA that are usually the main contributors to GR activity in environmental waters. Therefore, advanced treatments which can achieve good removal of these compounds need to be studied. However, there is a lack of data about the removal of CSs in advanced wastewater recycle treatments. It has been previously reported that micro/ultra-filtration, chlorination, and ozonation might not yield significant GR activity removal while reverse osmosis (RO) and ultraviolet (UV) appear efficient.15, 20, 53 In this study, we first investigated the occurrence of GR agonists in four WWTP effluents and their attenuation in different water treatment processes including chlorination, UV, ozonation, and membranes. A list of 26 CSs was selected as target analytes, including 3 natural GCs, 20 synthetic GCs, 1 natural MC, and 2 synthetic MCs, were analyzed simultaneously using a sensitive LC-MS/MS method. The target CSs were chosen either because they were reported previously, or they have the potential to be used in medications, and the standards are commercially available. The measured CS concentrations and their relative GR activity induction potency were then used to estimate their relative contribution to the integrated effects. To further investigate the removal efficiency of CSs in different treatment processes, bench- and pilot-scale experiments were also conducted. 91 6.2. Experimental Section 6.2.1. Chemicals and Materials Chemicals standards and solvents used in this study were described in Section 2.2.1. 6.2.2. Sample Collection Secondary effluent samples were collected from three municipal wastewater treatment plants in Tucson, Arizona (WWTP1, WWTP2, and WWTP3) and one wastewater reuse facility (WWRF) (WWTP4) in Los Angeles, California. Treatment experiments at bench-scale were carried out on the same day after the effluents were collected. The water quality parameters of tested WWTP secondary effluents are shown in Table 6.1. Table 6.1. Water quality parameters of tested WWTP secondary effluents. Parameters WWTP WWTP WWTP WWTP WWTP WWTP 1-1 1-2 2-1 2-2 3 4 Sampling Date 12/5/2013 06/04/2014 10/29/2014 11/17/2014 06/25/2013 07/01/2013 TSS (mg/L) 16 pH 8.0 7.8 7.4 7.3 8.2 6.8 Conductivity (µS/cm) 1206 1108 1520 TOC (mg/L) a 6.0 4.4 7.9 6.7 10.4 11 -1 UV254 (cm ) 0.16 0.14 - NO3 (mg/L) 3.3 5.4 3.5 0.05 3.9 5.54 - NO2 (mg/L) <0.1 <0.1 <0.1 0.79 Ammonia (mg/L) 1.46 0.75 a Water was filtered with 0.7 μm glass microfiber filters (GF/F, Whatman) before TOC measurement. The first WWTP1 secondary effluent (i.e., WWTP1-1) was collected on 12/5/2013, and three different ozone doses based on the TOC value of the effluent (i.e., ozone:TOC 0.25, 0.5 and 1.0) were applied using a bench-scale ozone generator (Xylem Inc., Germany). The WWTP1 effluent was re-collected on 06/04/2014 (i.e., WWTP1-2) and the attenuation 92 of reverse osmosis for GR was tested using a spiral-wound RO membrane system (ESPA2, Hydronautics, Nitto Denko), where both permeate and concentrated brine were collected. The secondary effluent before chlorination (pre-chlorination, i.e., WWTP2-1), post- chlorination (free chlorine residue 2.59 mg/L), and dechlorination (with sodium sulfite) were first collected from WWTP2 on 10/29/2014. A second WWTP2 secondary effluent (i.e., WWTP2-2) was collected on 11/17/2014, and the GR attenuation under UV radiation was studied using a bench-scale monochromatic low-pressure UV collimated beam device (Wedeco, Xylem Inc., Germany) with the UV fluence ranging between 20 to 400 mJ/cm2. WWRF receives treated wastewater effluent (WWTP4, 200 MGD capacity) as its source water and produces recycled water for indirect potable reuse (via groundwater direct injection). It serves about four million people and receives sewer mainly from domestic discharge. The plant scheme consists of preliminary treatments, primary sedimentation, secondary treatment, and clarification. In WWRF, the secondary effluent was pretreated by microfiltration (MF), followed by reverse osmosis (RO), and disinfected with ultraviolet and peroxide treatment (UV-AOP) before groundwater recharge. The UV-AOP system is comprised of four reactors connected in series, with each reactor housing 72 low pressure (LP) lamps, generating a UV fluence of ~1000 mJ/cm2. 3 mg/L of H2O2 feed was injected right before the reactors for UV oxidation. Grab samples were collected at the outlet of each unit including the treated wastewater. The treated wastewater which goes through a parallel full-scale ozone system (ozone dose 6 mg/L, equals ozone:TOC 0.55) was also collected. 93 6.2.3. Sample Preparation All samples were collected in 4 L amber glass bottles, which had previously been rinsed with methanol and ultrapure water, and then dried before use. Upon collection, the samples were first filtered with a glass microfiber filter (GF/F 0.7 μm, Whatman, Maidstone, UK) and then processed immediately. The extraction was performed using an AutoTrace™ 280 Solid-Phase Extraction (SPE) device (Dionex, Sunnyvale, CA). In brief, 1 L of each sample was passed through the Oasis Hydrophilic-Lipophilic Balance (HLB, 500 mg/6cc) cartridge (Waters Corporation, Milford, MA), which was preconditioned with 5 mL of MTBE, 5 mL of methanol, and 5 mL of ultrapure water. The samples were loaded onto the cartridges at 10 mL/min, rinsed with 10 mL of ultrapure water, and then dried under a stream of nitrogen for one hour. Then the cartridges were eluted with 5 mL of methanol followed by 5 mL of 10/90 (v/v) methanol/MTBE into a 15 mL glass conical vial. The extracts were then concentrated with a gentle stream of nitrogen to an approximate volume of 0.2 mL and brought to a final volume of 1.0 mL using methanol. The methanol extracts of all samples were split into two aliquots, one of which was solvent-exchanged to DMSO at 4000-fold enrichment for the bioassay test and the other was used for chemical analysis. To reduce the matrix effects, the methanol extracts were further cleaned-up according to a simplified method based on a previous publication.41 In brief, the 0.2mL solution spiked with isotope surrogates was evaporated to dryness with gentle nitrogen and then re-dissolved in 0.2 mL of ethyl acetate and 1.8 mL of hexane. The resulting solution was then loaded onto a Silica cartridge (500mg/6cc, Waters) which was preconditioned with 4 mL of water-saturated ethyl acetate and 4 mL of hexane/ethyl acetate (90:10, v/v). The cartridges were then rinsed with 3mL of hexane/ethyl acetate (90:10, v/v), 94 and eluted with 3mL of hexane/ethyl acetate (38:62, v/v) followed by 3 mL of water- saturated ethyl acetate. The extracts were combined, evaporated to dryness, and reconstituted into 0.2 mL of methanol to make a final sample concentration factor of 1000 for LC-MS/MS analysis. The details of in vitro GR-GeneBlazer bioassay and the measured GR activity (ng DEX-EQ/L) of all the samples have been published by Jia et al. (2016).23 6.2.4. LC-MS/MS Analysis of Corticosteroids In this study, the LC separation was conducted using an Agilent 1260 UHPLC system (Agilent Technologies, Palo Alto, CA), which is different from Chapter 2. Separation was achieved with an Agilent ZORBAX Eclipse Plus C8 RRHT column (2.1×100 mm, 1.8 µm, 600 bar) maintained at 30 °C with a constant flow rate of 0.3 mL/min. Water with 0.1% acetic acid (A) and acetonitrile (B) were used as mobile phases. The gradient was as follows: the initial 28% B for 8 min, and then increased to 40% in 0.1 min and held for 4 min, 40% B was then increased to 60% in 0.1 min and linearly increased to 70% in another 4 min. 70% B was further increased to 100% in 0.5 min and held for 4.0 min (Table 6.2). A 5-min equilibration at 28% B was used at the end of each run. The injection volume was 3 µL . Table 6.2. Timetable for LC gradient on Agilent 1260 UHPLC system Time (min) A (%) B (%) 0.0 52 28 8.0 52 28 8.1 60 40 12.0 60 40 12.1 40 60 16.0 30 70 16.5 0 100 20.5 0 100 20.6 52 28 95 Mass spectrometric detection was performed using an Agilent 6490 triple quadrupole mass spectrometer (Agilent Technologies, Palo Alto, CA) equipped with the Jet Stream dual electrospray source and iFunnel technology, which is the same as Chapter 2. The instrument was operated using an electrospray ionization source (ESI) in both positive and negative mode simultaneously, with optimized parameters as follows: gas temperature, 275 °C; gas flow, 15 L/min; sheath gas temperature, 350 C; sheath gas flow, 12 L/min; nebulizer, 45 psi; fragmentor, 380 V. The details of the optimized MRM transition parameters for target CSs and surrogate standards are listed in Table 2.3 in Chapter 2. One thing to note is that the retention times of target CSs in this study were different from the values in Table 2.3, due to the use of different UHPLC system, LC column, and solvent gradient. 6.3. Results and Discussion 6.3.1. Method performance of LC-MS/MS Analysis The calibration curve was set at the concentrations at 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10, 20, 50, and 100 μg/L. The instrument detection limits (IDLs) were determined by the lowest standard in calibration curve with signal to noise ratio (S/N) of at least 3 and 80% accuracy. The IDLs for all target analytes were between 0.03 to 3.0 pg (Table 6.3). Method detection limits (MDLs) of all CSs in the effluent were calculated based on IDLs, sample concentration factor of 1000, and the recovery of isotope-labeled surrogates in effluent. The recovery of all analytes in WWTP effluents ranged between 53% and 109%, with MDLs between 0.02 to 5.0 ng/L (Table 6.3). All analytes were calibrated externally using 96 linear or power regression with 1/x weighting. Correlation coefficients were required to be at least 0.990 and typically exceeded 0.995. Each sample was injected twice and the average peak abundance was used for quantitation. Quality control samples at low, medium and high (random) concentrations were injected every 10 samples to ensure the integrity of mass spectrometric analysis. At least one lab blank and one lab fortified blank sample were also carried out for every 10 samples. The data was processed with MassHunter Quantitative Analysis B.06.00. Table 6.3. IDLs, MDLs and recovery (corrected by isotope surrogates) of target corticosteroids in WWTP secondary effluent (n = 4, spiked concentration: 20 ng/L). Compounds Surrogate Assignment IDLs (pg) MDLs (ng/L) Recovery (%) ALD ALD-d7 0.30 0.15 95.8±6.6% AMC FTD-d5 0.15 0.80 52.9±4.8% 13 BCM TCA- C3 0.30 0.10 88.4±3.2% BDP FTD-d5 0.30 0.50 93.5±6.0% BET BET-d5 0.15 0.06 94.4±2.5% BUD BUD-d8 0.30 0.20 89.2±4.6% CBP FTD-d5 0.30 0.20 80.0±2.8% CBB FTD-d5 0.60 0.40 98.2±8.3% CTC DEX-d4 3.0 1.2 80.0±3.7% COR COR-d8 0.15 0.12 79.8±2.3% 13 DFZ TCA- C3 0.30 0.16 95.4±6.6% DCA FTD-d5 0.15 0.12 91.9±5.4% DEX DEX-d4 0.15 0.06 95.0±3.0% 13 FLA TCA- C3 1.5 0.50 88.4±5.9% 13 FMS TCA- C3 0.06 0.02 105.9±2.6% 13 FNS TCA- C3 3.0 1.0 87.2±3.9% 13 FCA TCA- C3 0.30 0.10 109.2±4.1% FLC BUD-d8 0.30 0.20 103.2±4.2% 13 FML TCA- C3 0.60 0.20 91.2±6.4% FTD FTD-d5 0.15 0.15 95.1±2.6% HCT HCT-d2 0.03 0.03 104.4±3.0% MPL MPL-d2 0.03 0.03 102.0±5.0% MMF FTD-d5 3.0 5.0 92.0±3.2% PNL PNS-d8 0.03 0.03 106.6±5.5% PNS PNS-d8 0.03 0.05 80.0±4.3% 13 TCA TCA- C3 0.60 0.20 105.3±5.1% 97 10,000 (Left) 600 (Right) 15,000 (Left) 800 (Right) 600 400 10,000 5,000 Hydrocortisone Hydrocortisone Fluocinolone 400 Fluocinolone 200 5,000 421>297 421>297 acetonide 511>355 200 acetonide 511>355 0 0 0 0 30,000 60,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 173,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 171,500 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20,000 40,000 2,000 Fluocinolone 1,000 Fluocinolone 10,000 20,000 Hydrocortisone 1,000 Hydrocortisone acetonide 500 acetonide 421>331 421>331 511>431 511>431 0 0 0 0 60,000 1,500 3,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 150 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 40,000 Prednisolone 1,000 2,000 100 Prednisolone Budesonide Budesonide 20,000 500 1,000 50 Cortisone 419>329 419>329 489>339 489>339 0 0 0 0 10,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 400 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 10,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17300 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 300 200 5,000 Prednisolone 200 Prednisolone 5,000 Budesonide Budesonide 100 419>295 100 419>295 489>357 489>357 0 0 0 0 6,000 300 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 15,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17150 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 4,000 200 Methylprednisolone Methylprednisolone 10,000 Fluocinonide 100 Fluocinonide 100 2,000 5,000 433>309 433>309 553>355 50 553>355 0 0 800 0 0 20,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 30,000 150 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 15,000 600 20,000 100 10,000 Methylprednisolone 400 Methylprednisolone Fluocinonide Fluocinonide 200 10,000 50 5,000 (counts) Response Response Response (counts) 433>343 433>343 553>375 553>375 0 0 0 0 15,000 300 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 15,000 1,000 3 Betamethasone4 5 6 7 8 9 10 11 12 13 14 15 16 17 Betamethasone 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 10,000 200 10,000 Dexamethasone Clobetasol 500 Clobetasol 5,000 100 5,000 451>307 451>307 propionate 525>429 propionate 525>429 0 0 0 0 80,000 3Betamethasone4 5 6 7 8 9 10 11 12 13 14 15 16 17800 3 Betamethasone4 5 6 7 8 9 10 11 12 13 14 15 16 17 15,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 172,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 60,000 600 1,500 Dexamethasone 10,000 40,000 400 Clobetasol 1,000 Clobetasol 5,000 20,000 451>361 200 451>361 propionate 525>465 500 propionate 525>465 0 0 0 0 6,000 8,000 10,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 800 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 6,000 4,000 Triamcinolone Flunisolide Triamcinolone Fluticasone 600 Fluticasone 4,000 5,000 400 2,000 acetonide acetonide propionate propionate 493>59 2,000 493>59 559>59 200 559>59 0 0 0 0 4,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 6,00017 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 15,000 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 600 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3,000 4,000 10,000 400 2,000 Triamcinolone Triamcinolone Fluticasone Fluticasone acetonide 2,000 acetonide 5,000 200 1,000 493>337 493>337 propionate 559>413 propionate 559>413 0 0 0 0 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Retention Time (min) Retention Time (min) Figure 6.1. Typical LC-MS/MS chromatograms of GCs. (Left) GCs standards at 20 ppb; (Right) effluent sample (WWTP4). X- axis indicates retention time (min), and Y-axis indicates sample response (counts). 98 Table 6.4. Concentrations (ng/L) of detected corticosteroids in WWTP effluents and treated effluents. Sample BET BUD CBP COR DEX FCA FLC FTP HCT MPL PNL TCA ∑CSs WWTP1-1 Effluent 0.18 <0.20 1.04 <0.12 <0.06 2.55 <0.20 <0.15 0.22 <0.03 0.19 13.8 18.0 RO permeate <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 <0.03 0.99 <0.03 <0.20 0.99 RO brine 0.49 <0.20 3.44 <0.12 <0.06 8.81 <0.20 <0.15 1.09 0.72 0.35 53.1 68.0 WWTP1-2 Effluent 0.37 0.29 1.54 <0.12 0.16 2.59 <0.20 0.52 0.48 1.14 0.17 14.0 21.2 Ozone:TOC 0.25 0.33 <0.20 0.98 <0.12 <0.06 2.64 <0.20 0.29 0.44 1.06 0.22 12.5 18.5 Ozone:TOC 0.5 0.20 <0.20 0.79 <0.12 <0.06 1.48 <0.20 <0.15 0.19 2.36 0.26 6.07 11.4 Ozone:TOC 1.0 <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 0.06 0.74 <0.03 <0.20 0.81 WWTP2-1 Effluent (Pre-Chlorination) 0.21 <0.20 1.05 <0.12 <0.06 0.91 <0.20 0.34 0.43 0.61 <0.03 6.03 9.58 Post-Chlorination 0.23 <0.20 1.14 <0.12 <0.06 0.77 <0.20 0.39 0.42 1.68 0.14 5.27 10.0 Dechlorination 0.20 <0.20 1.24 <0.12 <0.06 1.06 <0.20 0.42 0.75 0.63 0.06 7.64 12.0 WWTP2-2 Effluent 0.26 <0.20 1.11 <0.12 <0.06 1.06 <0.20 0.52 0.75 0.79 0.27 5.75 10.5 UV 20 mJ/cm2 0.12 <0.20 0.58 <0.12 <0.06 0.68 <0.20 0.33 0.93 0.87 0.23 3.05 6.79 UV 40 mJ/cm2 <0.06 <0.20 0.29 <0.12 <0.06 0.55 <0.20 0.20 0.77 <0.03 0.11 1.80 3.73 UV 80 mJ/cm2 <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 0.28 <0.03 0.17 <0.20 0.45 UV 100 mJ/cm2 <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 0.26 <0.03 0.12 <0.20 0.39 WWTP3 Effluent 0.43 <0.20 2.35 0.51 0.11 1.91 <0.20 1.43 1.57 1.53 0.34 9.60 19.8 WWTP4 Effluent 0.66 0.36 2.15 <0.12 <0.06 3.69 0.27 0.69 0.76 0.96 0.22 10.5 20.3 MF 0.38 0.32 1.17 <0.12 <0.06 2.80 0.33 0.39 0.54 0.16 0.16 7.31 13.6 RO <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 0.14 0.17 <0.03 <0.20 0.31 UV <0.06 <0.20 <0.20 <0.12 <0.06 <0.10 <0.20 <0.15 0.05 <0.03 <0.03 <0.20 0.05 Ozone 0.40 0.22 1.10 <0.12 <0.06 2.37 0.26 0.42 0.54 0.62 0.09 6.20 12.2 99 6.3.2. Occurrence of GR Agonists in Water Treatment Processes Of the 27 target analytes, 12 were detected in WWTP effluents including: PNL, HCT, COR, MPL, BET, DEX, TCA, FCA, BUD, FLC, CBP, and FTP (Table 6.4). A typical LC MRM chromatogram obtained from the standard and the effluent sample was shown in Figure 6.1. Eight out of 12 CSs were detected in at least five of the six effluents, including TCA (5.75–14.0 ng/L), FCA (0.91–3.69 ng/l), CBP (1.04–2.35 ng/L), MPL (0.61–1.53 ng/L), FTP (0.34-1.43 ng/L), HCT (0.22–1.57 ng/L), BET (0.18–0.66 ng/L), and PNL (0.17–0.34 ng/L). TCA had the highest concentration (accounting for 49–77% of the total CS concentration in WWTP effluents), followed by FCA (10–18%), CBP (6–12%), MPL (5–8%), FTP (3–7%), HCT (1–8%), BET and PNL (1–3%). TCA has been found in Swiss and France WWTPs with a lower concentration of 1 ng/L and 3 ng/L in effluent, respectively,46, 49 and in the Netherlands at the concentration of 14 ng/L,22 which is at similar level to the US effluents in this study. TCA was not in the US top 100 drug list; however, it is the main ingredient of several brands that have already been approved as over-the-counter (OTC) drugs by the US Food and Drug Administration (FDA).83 Therefore, high concentration of TCA could either suggest a huge offline usage of the drug, or poor degradation characteristics compared with other CSs. PNL (0.07–1.7 ng/L) and HCT (0.13–6.6 ng/L) were detected in most tested WWTP secondary effluents in Asia,40-42, 45, 84 at similar concentrations as in this study. However, COR and/or HCT were detected at much higher concentration in Europe, 26 ng/L in Swiss49 and up to 229 ng/L in France.46 A similar situation was also observed for BET/DEX. The concentration of BET and DEX was between 0.11-0.66 ng/L in this study, which is close to Asian WWTPs concentrations (ND-1.7 ng/L),42, 84 but far lower than Swiss and 100 France WWTP effluents (7–15 ng/L).46, 49 This is probably due to the different drug usage between North America, Asia, and Europe. It has been reported that HCT, PNL, and BET were the top 3 prescribed CSs in UK (2006),85 which accounted for 82% of the total prescribed CSs,85 while none of them were within the top 100 drug list in the US. Besides, COR, which was frequently detected in China (0.15-0.88 ng/L),40, 42 was also found in WWTP3 in this study with a similar concentration (0.51 ng/L). However, MPL, which was barely found in China, was frequently detected in this study, suggesting the difference between these two countries. It should also be noted that BET and DEX have exactly the same mass transitions, and cannot be qualified or quantified separately in previous studies.44, 45, 49 This study showed that BET had much higher detection frequency as well as abundance than BEX through a complete LC separation (Figure 6.1). FCA was the second most abundant compound detected in this study with 100% occurrence in all six effluents (up to 3.7 ng/L and 18% of the total detected CSs). To our knowledge, the occurrence of this chemical in water was only reported in one French effluent.46 CBP was also found in all six effluents, and it was the third most abundant chemical (up to 2.4 ng/L) in the list and accounted for 6–12% of the summed CSs concentrations. However, it was only reported in Japan (ND–4.9 ng/L)45, 84 and one Czech effluent17 with the concentration close to detection limit (1 ng/L). Similarly, FTP was reported in five of the six effluents with concentrations up to 1.5 ng/L, though it was found in one Swiss influent with the concentration of 4 ng/L.17 The ubiquitous detection of these two CSs in this study was probably due to a low method detection limit (<0.2 ng/L, Table 6.4), compared with previous literature method (~1 ng/L).17 Besides, BUD and FLC were detected in one or two of the effluents, which suggests their usage as a medical application 101 in the US region. It should be noted that FTP, BUD, and MMF were all listed in the top 100 pharmaceuticals used in the USA; however, MMF was not detected in this study. Epoxy-MMF can be formed within one hour in human plasma,86 suggesting that this parent compound might be effectively metabolized and excreted. The total concentrations of all detected CSs were 9.6–21.2 ng/L in four WWTP effluents. WWTP4 showed the highest concentration (20.3 ng/L), followed by WWTP3 (19.8 ng/L), WWTP1 (18–21.2 ng/L), and WWTP2 (9.6–10.5 ng/L) (Table 6.4). 6.3.3. Removal of GR Agonists in Water Treatment Processes 6.3.3.1. Chlorination The removal of GR agonists by chlorination in WWTP2 is shown in Figure 6.2. In WWTP2 post-chlorinated and de-chlorinated samples, the total CS concentration was 10.0 ng/L and 12.0 ng/L, respectively. When compared to the pre-chlorinated effluent (9.6 ng/L), almost all detected CSs are still present after chlorination. Therefore, chlorination has no effect on CS attenuation, which is consistent with the results in GR bioassay.23 6.3.3.2. UV Figure 6.3 depicts the removal of GR agonists in WWTP2 effluent by UV at bench- scale. Compared with chlorine disinfection, UV reduced the total CS concentration from 10.5 ng/L to 6.79 ng/L at 20 mJ/cm2 (35% removal), 3.73 ng/L at 40 mJ/cm2 (65% removal), and 0.45 ng/L at 80 mJ/cm2 (96% removal). When the UV dose reached 80 or 100 mJ/cm2, most CSs detected in the WWTP2 effluent were not found except PNL and HCT (0.12– 0.28 ng/L). This further indicates that UV at a disinfection dose of over 80 mJ/cm2 can be 102 very efficient for CSs attenuation. Although a lot environmental emerging contaminants are not able to be significantly transformed by UV without peroxide,87 it has also been observed that PNL had the highest photolytic degradation rate constant among 40 pharmaceuticals.88 Most targeted CSs have a maximum UV absorbance around 240-250 nm,89-91 which can possibly explain why monochromatic UV lamp at 254 nm has high efficiency for removal of CSs. Figure 6.2. Removal of GR agonists by chlorination in WWTP2. Figure 6.3. Removal of GR agonists in WWTP2 effluent by UV at bench-scale. 103 6.3.3.3. Ozonation Figure 6.4 shows the removal of GR agonists in WWTP1 secondary effluent by ozone at bench-scale. At lower ozone doses (ozone:TOC at 0.25 and 0.5), the total CS concentrations decreased from 21.2 ng/L to 18.5 ng/L (13% removal) and 11.4 ng/L (47% removal), respectively. When the ozone:TOC value reached 1.0, most of the CSs except HCT and MPL were attenuated with a total CS removal rate of 96%. This observation was very similar to the GR bioassay results.23 Figure 6.4. Removal of GR agonists in WWTP1 effluent by ozone at bench-scale. 6.3.3.4. RO The removal of GR agonists in WWTP1 secondary effluent by RO at bench-scale is depicted in Figure 6.5. RO removed 95% of the target CSs with only MPL (0.99 ng/L) above the detection limits. In the concentrated RO brine, the total CS concentration of 68 ng/L was around 4-fold higher than the original WWTP1 feed effluent (18 ng/L), which is reasonable considering the RO recovery rate of approximately 75%. The elevated CSs in brine could be a concern in the future, and further treatment might be needed. 104 Figure 6.5. Removal of GR agonists in WWTP1 effluent by RO at bench-scale. 6.3.3.5. MF, RO, UV and Ozonation in Full-Scale WWRF Figure 6.6 shows the removal of GR agonists in full-scale WWRF by MF, RO, UV, and ozone. In the full-scale WWRF (WWTP4), the total CS concentration decreased from 20.3 ng/L to 13.6 ng/L after MF, and further decreased to 0.31 and 0.05 ng/L after RO and UV-AOP, respectively (Table 3). Trace concentrations of HCT (0.14 ng/L) and MPL (0.17 ng/L) were found in RO permeate, with only 0.05 ng/L of HCT remaining in the UV-AOP effluent. These results are consistent with the bench-scale RO and UV experiments. Similarly, ozone scheme removed 40% of the total CSs at an ozone:TOC 0.55 dose, which was consistent with the bench-scale ozone experiment (WWTP1). These results suggested that water treatment facilities utilizing advanced treatment techniques such as RO and UV- AOP could significantly decrease the exposure to CSs. 105 Figure 6.6. Removal of GR agonists in full-scale WWRF by MF, RO, UV, and ozone. Table 6.5. Relative potency (REP) of detected GR agonists applied for calculating predicted dexamethasone equivalents. Compounds REP (EC10) 23 BET 0.470 BUD 6.035 CBP 35.66 COR 0.001 DEX 1.000 FCA 6.580 FLC 3.131 FTP 62.58 HCT 0.235 MPL 0.214 PNL 0.078 TCA 1.621 6.3.4. Contributions of GR Agonists to the GR Activity in Wastewater To evaluate the contribution of detected GR agonists to the GR activity in tested water samples, the predicted Dex-EQ (Chem-DEX) was calculated as: Chem-DEX = ∑REPi × (Ci/Mi) × MDEX (unit: ng DEX-EQ/L), where REPi is the REP of each CS i, Ci is measured concentration (ng/L), Mi is molecular weight, and MDEX is the molecular weight of DEX. Since many environmental samples do not have the potency to reach the EC50 dose, EC10 based REPs were used in this study. The REPs (EC10) of CSs applied for calculation are 106 listed in Table 6.5, which were reported by Jia et al..23 The percent distribution of detected GR agonists and their contributions to the predicted GR activity (Chem-DEQs) in tested effluents are profiled in Figure 6.7. Figure 6.7. The percent distribution of detected GR agonists and their contributions to the predicted GR activity (Chem-DEQs) in tested effluents. Due to their vast difference on activity induction, the contributions of GR agonists to GR activity largely changed when compared with their chemical concentrations in effluent samples (Figure 6.7). CBP (REP = 36), which accounted for 6–12% of the total CS concentrations in effluents, contributed to almost half (42–50%) of the GR activity among all detected agonists. FTP (REP = 63), which accounted for 2–7% of the chemical concentrations, contributed 25–36% of the GR activity (except for WWTP1-1, where FTP was not detected). Due to a relatively low REP (1.6), TCA, which was present as the predominant CS in effluents (48–77% of the total CS concentrations), only accounted for 10–29% of the GR activity. Moreover, FCA (REP = 6.6) contributes to 8–22% of the 107 summed activity, which was similar to its chemical contributions (9–18%). In sum, these four GR agonists contributed to 97.7% to 99.8% of Chem-DEQs. All other eight detected CSs in the effluents including MPL (<7.7% of the total CS concentrations), HCT (1–8%), and BET (1–3%), totally contributed 0.2–2.3% of Chem-DEQs. A similar result was also observed in the concentrated RO brine, where CBP (46%), TCA (32%), and FCA (22%) accounted for almost 100% of Chem-DEQs. The results obtained in the current study were different from a previous study in Switzerland where BET/DEX accounted for the main Dex-EQ contributions (34 ± 28%), followed by FMS (17 ± 14%), CBP (13 ± 22%), BET/BMA (i.e. dexamethasone-21-acetate) (13 ±5%), and MPL (9 ±4%).92 The reason may be due to the different drug usage between these two countries, or the differences in the instrument detection sensitivity among the two laboratories. The MDLs for two highest potent CSs—CBP and FTP—were 0.20 ng/L and 0.15 ng/L in effluent in this study, which were five times more sensitive compared with the reported values in literature (1 ng/L).49 CBP and TCA were thought to be the main contributor to the GR agonistic activity among target GR agonists in the effluents in Japan45 and Netherlands,22 respectively. With a systematic target compounds analysis in this study, at least four agonists with low concentrations and high potency include CBP, FTP, FCA, and TCA were all identified as significant contributors to GR activity among the target compound lists and are suggested to be monitored and assessed in future studies. 6.3.5. Target CSs-Derived GR Activity in Environmental Waters Comparing the calculated Chem-DEQs with the GR activities determined by in vitro GR bioassays (Bio-DEQ), excellent agreement was found in all tested samples (Figure 6.8). 108 These data demonstrate that the detected CSs can entirely explain the observed biological GR activity. An additional agreement was further observed when evaluating the different treatment efficiencies via the total CS concentrations, Chem-DEQ and Bio-DEQ (Figure 6.9). Specifically, the four major GR activity contributors—CBP, FTP, FCA, and TCA all followed the same attenuation trend as the sample GR activities in different treatment processes (Figure 6.10). This could also explain why the total CSs and Chem-DEQ showed a comparable trend in treatment efficacy. These four CSs should be prioritized in future monitoring programs. This is particularly the case for TCA and FTP, which have recently been approved by the US FDA for OTC purchase.93, 94 To the best of our knowledge, limited studies have investigated the in vivo effect of these chemicals in aquatic organisms,95 and fish exposure, especially under their environmentally relevant concentration, should be conducted in the future. Figure 6.8. Comparison of Bio-DEQs and Chem-DEQs for GR activity in all tested samples. 109 Figure 6.9. Comparison of removal efficiency (%) in different treatment processes via total concentrations of detected corticosteroids (∑CSs), GR activity, predicted from the chemical analysis (Chem-DEQ) and GR activity measured by GR bioassay (Bio-DEQ). For chemicals or sample activities below the detection limit, 1/2MDL was used here for estimation. Figure 6.10. The removal efficiency (%) evaluated via in vitro GR activity and the chemical concentrations of four highly potent corticosteroids in different treatment processes. 110 6.4. Conclusions Overall, with the sensitive measurement of trace level of CSs in environmental waters, up to 12 GR agonists were determined and their removal efficiency varied depending on the treatment techniques applied. To the best of our knowledge, these data provide the first occurrence information for FLC and FTP in WWTP effluents. UV and RO appeared to be the most efficient treatment process for the attenuation of CSs, followed by ozone, while chlorination had little effect on targeted CSs in water. Future studies are still required to explore their degradation mechanism and treatment kinetics especially under UV and ozone process. The similar trend between the target CS concentrations and GR activity in different treatments suggested that the assay response in water samples is attributable to the detected CSs through targeted analysis. Four highly potent synthetic CSs—TCA, FCA, CBP, and FTP—were responsible for the majority of in vitro GR activity in environmental waters. With the continuous consumption and release of these drugs, these substances should be prioritized in future monitoring programs, especially with potential for increased use following over-the-counter approval for purchase in the USA. 111 7. REMOVAL OF GLUCOCORTICOID RECEPTOR AGONISTS IN LOW-PRESSURE UV/AOP TREATMENT 7.1. Introduction In Chapter 6, we have discovered that UV at a disinfection dose of over 80 mJ/cm2 can effectively remove CSs in WWTP effluent, which indicated that UV based advanced oxidation processes might achieve a high removal efficiency of CSs, especially highly potent synthetic CSs, which have poor removal in conventional wastewater treatment. Within the past few years, there have been numerous studies on the removal of trace organic compounds (TOrCs) by use of different chemical oxidants, known as advanced oxidation processes (AOPs). The essential mechanisms of AOPs are mineralization of pollutants in wastewater to CO2 or transference of pollutants to some other metabolite 2− products by some strong oxidizers, such as H2O2, O3, and S2O8 , through oxidation- reduction reactions. To increase the removal efficiency, some combinations such as UV/O3, UV/H2O2, and UV/Fenton have been widely applied to the removal of TOrCs. More recently, several studies have investigated the combination of aqueous chlorine and ultraviolet light (UV/Cl2) as an alternative to traditional AOPs. The main interest in UV/Cl2 AOPs is that this process is expected to provide substantial cost savings over traditional AOPs.96, 97 During UV/chlorine process, the main reactions occur are shown below:98 Cl22 H O HOCl HCl (1) HOCl H OCl (pKa 7.6 at 20 C) (2) HOClh OH Cl (3) 112 OClh O Cl (4) O H2 O OH OH (5) It is commonly agreed that UV/chlorine process has higher •OH yield comparing to 98, 99 UV/H2O2. Also a study shows that compared to UV/H2O2 process, UV/chlorine is much more efficient in producing hydroxyl radicals at pH below 5.5.100 Another important advantage of UV/chlorine process is that it provides multiple-barrier disinfection system. In a multiple-barrier disinfection system, chlorine is dosed as a preoxidant while UV is applied as a primary disinfectant. Then along with the residual chlorine, additional chlorine can be applied for residual disinfection.100-102 UV/chlorine processes can be impacted by many factors, including UV dose, chlorine species, pH and water quality. The pH value of the water has a profound impact on UV/chlorine processes since it determines the ratio of two free chlorine species—HOCl and OCl−, of which the absorbance profiles differed significantly. Therefore, adjusting pH is crucial for optimizing UV/HOCl performance.100 Water quality also impacts the performance of UV/chlorine mainly by pH, ammonium concentration, and natural organic matter (NOM) affections. Several types of chlorine species, including hypochlorous acid − (HOCl), hypochlorite (OCl ), monochloramine (NH2Cl) and chlorine dioxide (ClO2), can be applied to UV/chlorine process. Hypochlorite and hypochlorous acid exist in aqueous solution together, and their relative concentration depends on pH (pKa=7.5). Hypochlorite is commonly used for water disinfection. Chloramine can be used as the primary chemical oxidant for drinking water treatment because it has less risk to form harmful disinfection byproducts comparing to hypochlorite. 113 In this study, three types of UV/AOP processes—UV/HOCl, and UV/NH2Cl, and UV/H2O2 were investigated on their removal efficiency for a wide range of GR agonists. 7.2. Experimental Section 7.2.1. Chemicals and Materials All 26 target CSs standards were obtained as powders from Sigma-Aldrich (St. Louis, MO, USA), Ten isotopically labeled CSs were purchased as powders from Toronto Research Chemicals Inc. (Ontario, Canada) and C/D/N Isotopes Inc. (Pointe-Claire, Canada). LC/MS grade methanol, acetonitrile, formic acid, acetic acid and sodium hypochlorite solution (NaOCl) (6% available Cl2, Ricca Chemical) were purchased from Fisher Scientific Co. (Fair Lawn, NJ, USA). Ammonium chloride (NH4Cl), sodium hydroxide (NaOH) (pellets, anhydrous), and sodium thiosulfate (Na2S2O3) (anhydrous) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water with a resistivity of 18.2 MΩ·cm was prepared using a Milli-Q system (Millipore, Billerica, MA). DPD free chlorine reagent powder pillows (10 mL, pk/100), DPD total chlorine reagent powder pillows (10 mL, pk/100), and monochlor F reagent pillows (10 mL, pk/100) were also purchased from Hach Company (Loveland, CO, USA). Hydrogen peroxide Vacu-vials instrumental kit (0-6 ppm, 30 ampoules) were purchased from CHEMetrics, Inc (Calverton, Virginia). 114 7.2.2. UV Dose Determination of LP-UV Collimated Beam Device A collimated beam UV apparatus (CBD 11-1, ITT Water & Wastewater Herford GmbH, Wedeco) equipped with 4 LP-UV Hg lamps (NLR2036, radiation flux of 9 Watt) was used in this experiment (Figure 7.1), which emitted monochromatic light at 254 nm. Figure 7.1. The UV lamp equipment and LP UV lamp for the experiment. The UV irradiance was measured by a calibrated UV detector (SED 240, International Light) connected to a radiometer (IL 1700, International Light), which was multiplied by correction factors based on:103 EEFFFFavg 0 Petri Reflection Water Divergence (1) −2 where Eavg is the average UV irradiance (mW cm ); E0 is the radiometer reading at the −2 surface of liquid in the Petri dish and the center of the UV beam (mW cm ); FPetri is the Petri factor (non-uniformity of radiation field across the dish); FReflection is the reflection factor (reflection of UV radiation at the liquid surface); FWater is the water factor (attenuation of the UV beam within the liquid due to the solution absorption); and FDivergence 115 is the divergence factor (attenuation of the UV beam due to the distance between light source and solution). The Petri factor (FPetri) is defined as the ratio of the average of the incident irradiance over the area of the Petri dish to the irradiance at the center of the dish and is used to correct the irradiance reading at the center of the Petri dish to more accurately reflect the average incident fluence rate over the surface area. In general, a well-designed collimated beam apparatus should be able to deliver an FPetri of greater than 90%. The reflection factor (FReflection) is defined as the rest fraction of the beam is reflected off the interface between the media when a beam of light passes from one medium to another. For air and water, the FReflection is 0.975 and represents the fraction of the incident beam that enters the water. The water factor (FWater) is defined as 1 10al F (2) Water al ln10 where a is decadic absorption coefficient (cm-1) or absorbance for a 1 cm path length and l is vertical path length (cm) of the water in the Petri dish. The divergence factor (FDivergence) is defined as L F (3) Divergence Ll where L is the length (cm) from the UV lamp to the surface of the cell suspension and l is vertical path length (cm) of the water in the Petri dish. The average germicidal fluence (UV dose), H (J m-2 or mJ cm-2), is then given by H Eavg t (4) where t is the exposure time t (s). 116 Since the UV irradiation dose is proportional to the irradiation time, samples’ exposure time to the UV irradiation was used to control the UV dose. The LP UV doses used in this study was 0, 100, 200, 400, 600 and 800 mJ/cm2. Table 7.1. The exposure time required for LPUV doses. Desired UV dose (mJ/cm2) Irradiation time (s) 0 0 100 94 200 188 400 376 600 565 800 753 7.2.3. Lab-Scale LP-UV/AOP Experiment The experiment was carried out using pre-chlorinated secondary effluent collected from Agua Nueva Water Reclamation Facilities (ANWRFs) located in Arizona, which employs dissolved air flotation (DAF) for primary treatment, 5‐stage Bardenpho activated sludge secondary treatment with step‐feed aeration for nitrification/denitrification (nutrient removal), disk filtration, and chloramination for disinfection. The effluent water sample was adjusted to pH 7.5 using a phosphorous buffer (10 mM) and spiked with 26 target CSs standards at concentrations ranging from 300 to 500 ng/L. The designed dosages of free chlorine were 0, 2.5 mg/L and 5 mg/L. The designed H2O2 dosages were 0, 3 mg/L and 7 mg/L. During the experiment, 200 mL of prepared effluent water was transferred to a Petri dish (inner diameter 12.5 cm), and the desired dosage of oxidant was added prior to being placed in the UV apparatus. Initial and residual concentrations of free chlorine were determined by a digital Hach Portable DR/890 Colorimeter (Hach Company, Loveland, CO) using Method 8021 (Chlorine, Free, 0 to 2.00 mg/L). Initial and residual concentrations of H2O2 were determined by the Peroxide Vacu- 117 vials Kit (CHEMetrics) using a HACH spectrophotometer (DR/2500, HACH). The initial and residual concentration of oxidants in all experiment groups are listed in Table 7.2. After UV/AOP treatment, sodium thiosulfate was added to the sample at 50 mg/L and 10 mL of sample was collected for direct injection LC-MS/MS analysis of 26 target CSs. The samples were stored in the dark at 4 °C to prevent degradation. Table 7.2. Initial and residual concentrations of free chlorine and H2O2 during LP- UV/AOP experiment Cl2 Dose (mg/L) UV Dose 0.0 2.5 5.0 (mJ/cm2) Before After Before After Before After 0 0.03 0.03 0.18 0.13 0.36 0.24 100 0.03 0.03 0.19 0.14 0.36 0.22 200 0.03 0.03 0.17 0.11 0.38 0.20 400 0.03 0.03 0.17 0.14 0.36 0.21 600 0.03 0.03 0.18 0.13 0.38 0.13 800 0.03 0.03 0.18 0.12 0.36 0.17 H2O2 Dose (mg/L) UV Dose 0.0 3.0 7.0 (mJ/cm2) Before After Before After Before After 0 0.49 0.43 3.27 3.22 6.92 6.90 100 0.49 0.43 3.23 3.21 7.30 7.08 200 0.49 0.43 3.24 3.17 7.04 6.88 400 0.49 0.43 3.21 3.14 7.16 6.90 600 0.49 0.43 3.25 3.15 7.02 6.88 800 0.49 0.43 3.24 3.11 7.16 6.94 7.2.4. Direct Injection LC-MS/MS Analysis of Corticosteroids Due to the limited sample volume and large throughput of samples, a rapid direct injection LC-MS/MS method was developed in this study for the simultaneous analysis of 26 target CSs in environmental waters, which provides a less labor-intensive sample preparation and increased sample throughput while being sensitive enough for the lab-scale experiment. 118 A 990-μL amount of sample was spiked with 10 μL of surrogate standard mixture (in 50:1 water/methanol solution) containing 20 μg/L of each isotopically labelled CSs (which made the final concentration of isotope surrogates as 200 ng/L), and filtered through 0.2- μm filters (Agilent Captiva PES filters; p/n 5190-5096). The sample was analyzed by an Agilent 6490 triple quadrupole mass spectrometer coupled to an Agilent Infinity 1290 UHPLC system (Agilent Technologies, Palo Alto, CA). The injection volume was 80 μL. Other parameters of LC and MS were the same as described in Chapter 2. The calibration curve was set at the concentrations at 1.0, 2.0, 5.0, 10, 20, 50, 100, 200, 500, 1000, and 2000 ng/L. The instrument detection limits (IDLs, pg) and method detection limits (MDLs) of target CSs are listed in Table 7.3. Table 7.3. IDLs and MDLs of target corticosteroids in direct injection LC-MS/MS analysis. Compounds Surrogate Assignment IDLs (pg) MDLs (ng/L) ALD ALD-d7 0.5 5 AMC FTD-d5 0.2 1 13 BCM TCA- C3 0.5 5 BDP FTD-d5 1.0 5 BET BET-d5 0.05 1 BUD BUD-d8 0.2 1 CBP FTD-d5 1.0 1 CBB FTD-d5 0.2 2 CTC DEX-d4 0.2 5 COR COR-d8 1.0 2 13 DFZ TCA- C3 0.5 5 DCA FTD-d5 0.1 2 DEX DEX-d4 0.5 1 13 FLA TCA- C3 0.2 5 13 FMS TCA- C3 2.0 1 13 FNS TCA- C3 0.2 2 13 FCA TCA- C3 1.0 5 FLC BUD-d8 0.2 1 13 FML TCA- C3 2.5 5 FTD FTD-d5 0.2 1 HCT HCT-d2 0.05 1 MPL MPL-d2 2.0 1 MMF FTD-d5 0.05 10 PNL PNS-d8 0.1 1 PNS PNS-d8 0.1 2 13 TCA TCA- C3 0.5 2 119 7.3. Results and Discussion 7.3.1. Removal of GR Agonists by Individual Treatment 7.3.1.1. LP-UV The removal efficiencies of all target GR agonists in UV irradiation are diagrammed in Figure 7.2. The compounds are sequenced clockwise by their removal at UV dosage of 100 mJ/cm2. Several GR agonists were completed removed by 100 mJ/cm2 of UV dose, including BUD, DFZ, FNS, MPL, PNL and PNS. Good removal efficiency was achieved for the four highly potent CSs, with removal of 91% for CBP, 90% for TCA, 84% for FTP and 82% for FCA. Figure 7.2. Removal of GR agonists by UV. The target CSs were divided into two groups: (1) UV sensitive: AMC, BET, BUD, CBB, CBP, DFZ, DEX, FMS, FNS, FCA, FLC, FML, FTP, MPL, MMF, PNL, PNS, TCA. (2) UV insensitive: ALD, BCM, BDP, COR, CTC, DCA, FLA, HCT. Comparing their chemical structures, we found that the key difference was that CSs in Group 1 (UV sensitive) all have two double bond carbon in ring A (Δ1,4), while CSs in Group 2 (UV 120 insensitive) have only one double bond carbon in ring A (Δ4) (Table 1.1). The mechanism of phototransformation of PNL (prednisolone) by solar light has been previously studied, and the scheme was suggested in Figure 7.3.104 Figure 7.3. Mechanism of phototransformation of prednisolone (PNL)104 7.3.1.2. Free Chlorine Figure 7.4 shows the removal of all target GR agonists in tested water treated by only free chlorine. Most of GR agonists were poorly removed by free chlorine. At free chlorine dosage of 5 mg/L, BCM had the highest removal of 42%, followed by FLA (23%), and CBB (22%), while other GR agonists were removed by less than 20%. This is consistent with our previous results that chlorination had little effects on CS attenuation in wastewater. 121 Figure 7.4. Removal of GR agonists by free chlorine. Figure 7.5. Removal of GR agonists by H2O2. 7.3.1.3. H2O2 Figure 7.5 shows the removal of all target GR agonists in tested water treated by only H2O2, which is similar to Figure 7.4. Most of GR agonists were also poorly removed by H2O2. At H2O2 dosage of 7 mg/L, BDP had the highest removal of 36%, followed by FLA 122 (32%), CTC (31%), and DCA (20%), while other GR agonists were removed by less than 20%. 7.3.2. Removal of GR Agonists by LP-UV/AOP Processes The performance of combined UV/AOP processes was compared in Figure 7.6. Two CSs—FTP (i.e. fluticasone propionate) in group 1 (UV sensitive) and COR (i.e. cortisone) in group 2 (UV insensitive)—were selected as representatives. It showed that there was no significant difference in removal efficiency between UV/HOCl and UV/H2O2 for the two selected CSs. High dose of oxidant (HOCl and H2O2) would help increase the removal, but to a very limited extent. This indicated that UV photolysis would be the predominant mechanism in LP-UV/HClO and LP-UV/H2O2 processes for removing GR agonists in wastewater. Figure 7.7 depicts the removal of all 26 CSs in UV/HOCl and UV/H2O2 treatment. The similarities between the contour of UV/HOCl and UV/H2O2 also indicated their close performance on removing GR agonists. It should be noted that the combination of UV and oxidant (HOCl and H2O2) did improve the removal of UV insensitive CSs, such as CTC and HCT, but at a very high dosage of UV and oxidant. Fluticasone propionate (FTP) 123 Cortisone (COR) Figure 7.6. Degradation of cortisone (group 1) and fluticasone propionate (group 2) in different UV/AOP processes. UV/HOCl UV/H2O2 UV/HOCl UV/H2O2 ALD Removal (%) ALD Removal (%) AMC Removal (%) AMC Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl 1 20 20 Cl 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) BCM Removal (%) BCM Removal (%) BDP Removal (%) BDP Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl 1 20 20 Cl 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) BET Removal (%) BET Removal (%) BUD Removal (%) BUD Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl 1 20 20 Cl 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) CBB Removal (%) CBB Removal (%) CBP Removal (%) CBP Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl 1 20 20 Cl 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) 124 UV/HOCl UV/H2O2 UV/HOCl UV/H2O2 COR Removal (%) COR Removal (%) CTC Removal (%) CTC Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) DFZ Removal (%) DFZ Removal (%) DCA Removal (%) DCA Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) DEX Removal (%) DEX Removal (%) FLA Removal (%) FLA Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) FMS Removal (%) FMS Removal (%) FNS Removal (%) FNS Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) FCA Removal (%) FCA Removal (%) FLC Removal (%) FLC Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) FML Removal (%) FML Removal (%) FTP Removal (%) FTP Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O O 2 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) 125 UV/HOCl UV/H2O2 UV/HOCl UV/H2O2 HCT Removal (%) HCT Removal (%) MPL Removal (%) MPL Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O 2 O 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) MMF Removal (%) MMF Removal (%) PNL Removal (%) PNL Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O 2 O 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) PNS Removal (%) PNS Removal (%) TCA Removal (%) TCA Removal (%) 5 100 7 100 5 100 7 100 6 6 4 80 80 4 80 80 5 5 3 60 4 60 3 60 4 60 2 40 3 40 2 40 3 40 Dose (mg/L) Dose Dose (mg/L) Dose 2 2 Dose (mg/L) Dose (mg/L) Dose 2 2 O 2 O 2 2 2 Cl Cl 1 20 20 1 20 20 H H 1 1 0 0 0 0 0 0 0 0 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 0 200 400 600 800 UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) UV Dose (mJ/cm2) Figure 7.7. Removal of all 26 CSs by UV/Cl2 and UV/H2O2 treatment. 7.4. Conclusions No significant differences in removal efficiency of GR agonists were observed between UV/HOCl and UV/H2O2. UV photolysis would be the predominant mechanism in LP-UV/HClO and LP-UV/H2O2 processes for removing GR agonists in wastewater. GR agonists can be divided into two groups: UV sensitive CSs, which have two double bond carbon in ring A (Δ1,4), and UV insensitive CSs, which have only one double bond carbon in ring A (Δ4). The four highly potent CSs (CBP, FCA, FTP, and TCA) are all UV sensitive. 126 8. REMOVAL OF GLUCOCORTICOID RECEPTOR AGONISTS USING POWDERED ACTIVATED CARBON (PAC) 8.1. Introduction Use of activated carbon (AC) is a well-known process for removing various organic contaminants. AC is most commonly applied as a powdered feed (powder activated carbon, PAC) or in a granular form (granular activated carbon, GAC) in packed bed filters. Several authors have demonstrated the efficiency of AC, both as PAC and GAC, for the removal of trace organic pollutants from water.105 Powdered activated carbon (PAC), adsorption processes in drinking water treatment, can effectively control problems related to trace organic contaminants (TOrCs) such as taste- and odor-causing compounds, volatile organic compounds, pesticides, and pharmaceutics. PAC in drinking water treatment has gained popularity because it can be applied only when it is needed, can be fed in many drinking water treatment plants following a relatively small capital investment, and is cheaper than granular activated carbon (GAC).106 In this study, four types of PACs, including Cabot 20BF, Cabot HDB, Calgon PWA, and Calgon WPH, were investigated for their removal efficiencies of 26 GR agonists. 8.2. Experimental Section 8.2.1. Activated Carbons Four PACs were used in this study: Calgon WPH, Calgon PWA, Cabot HDB and Cabot 20BF. These carbons were provided by Cabot Corporation (Boston, MA, USA) and 127 Calgon Carbon Corporation (Pittsburgh, PA, USA). Their adsorption characteristics are summarized in Table 8.1. Table 8.1. Summary of activated carbon properties. Slurry pH was experimentally determined whereas other properties were found the referred references. BET surface Iodine number Carbon Precursor Slurry pHa Reference area (m2/g) (mg/g) Cabot 20BF Bituminous coal 864 8.50 ± 0.07 800 107, 108 Cabot HDB Lignite coal 546 10.9 ± 0.13 540 109 Calgon PWA Bituminous coal with low 1142 8.58 ± 0.36 900b 110 acid soluble iron content Calgon WPH Bituminous coal 1027 7.64 ± 0.18 800 109, 111 a. Triplicate measurements for each carbon. b. The value was found in the data sheet from the manufacturer. 8.2.2. Slurry pH In order to understand surface charge of PACs, slurry pH was measured 112. Slurry pH is a pH value at which net charge of carbon surface is neutral. Carbon surface is charged negatively if slurry pH is less than solution pH and positively charged vice versa. 0.010 M KCl solution was prepared by addition of KCl (Certified ACS, Fisher Scientific) into nitrogen-sparged Milli-Q water. PAC was added to 15 mL of the prepared KCl solution and mixed for 24 hours using a magnetic stir plate. pH of supernatant was measured for slurry pH. 8.2.3. Lab-Scale PAC Experiment Secondary effluent samples (DOC = 6.5 mg/L) was collected from the Agua Nueva Water Reclamation Facility (ANWRF), and filtered using glass fiber filters with 0.7 μm pore size (Whatman GF/F, GE Healthcare Bio-Sciences). The tested wastewater was spiked with target CSs at approximately 500 ng/L. 128 Experiments were conducted using a jar test apparatus (PB-900 Programmable Jar Tester, Phipps & Bird) containing 6 reaction vessels. The dimensions of the vessels were 11.5 cm × 11.5 cm × 21 cm. Each PAC was added to 2 L of wastewater effluent in a jar to obtain 50 mg/L dose. The PAC containing wastewater was mixed at 300 rpm using a jar tester (Phipps & Bird PB900) after dosing the activated carbon. The applied contact times were 0, 5, 10, 20, 30, 40, 50, 60, 90, 120, 150, 180, 240, 300, and 360 min. At the desired contact time, 20 mL samples were collected and filtered using a syringe filter with 0.45 μm pore size (PVDF Millex-HV, Billerica, MA, USA). The filtered water samples were placed in amber glass scintillation vials and refrigerated at 4ºC until chemical analyses. 8.2.4. Direct Injection LC-MS/MS Analysis of Corticosteroids The samples were analyzed for 26 CSs using the direct injection LC-MS/MS method described in Section 7.2.5. 8.3. Results and Discussion Figure 8.1 depicts the removal of 24 GR agonists by four types of PAC. As figures shown, HDB carbon was the fastest to reach 90% removal of the majority of target CSs, followed by WPH carbon and 20BF carbon, while PWA carbon was the slowest. However, four tested PACs achieved 90% removal within 180 min. HDB had the highest removal efficiency for the four highly potent CSs, which obtained 90% removal of CBP at 15 min, FTP at 10 min, FCA, and TCA at 50 min. CBP and FTP were also effectively removed by WPH carbon and 20BF carbon. FCA and TCA were relatively difficult to remove. This 129 was probable because that the logKow of FCA (logKow = 2.48) and TCA (logKow = 2.53) are lower than CBP (logKow = 3.50) and FTP (logKow = 3.96) (Table 2.1). 130 131 Figure 8.1. Removal of target GR agonists by four types of PACs. 8.4. Conclusions Four types of PACs were tested for removing GR agonists in wastewater effluent. Cabot HDB carbon was suggested to use for the treatment of GR agonists. Calgon WPH and Cabot 20BF carbon also had good performance in removing GR agonists. Calgon PWA carbon was not recommended due to its low removal efficiency. 132 9. CONCLUSIONS The occurrence and fate of natural and synthetic CSs — especially highly potent synthetic CSs — in water and wastewater treatment processes were investigated in this study, several results were obtained: (1) A sensitive and reliable method of extracting and analyzing sub-ng/L concentrations of 26 CSs in highly complex natural water matrices was successfully developed. (2) Nine computational programs (ACD/LogP, ALOGPS 2.1, CLOGP, JChem, KOWWIN, MiLogP, MolLogP, MOSES.logP, and XLOGP3) were evaluated for predicting logKow of CSs. XLOGP3, MiLogP, and KOWWIN showed the best performance. The median of predicted logKow values calculated by these three programs is suggested to use for synthetic CSs. (3) Several GR agonists were detected in SCR surface water, and groundwater in well monitoring along the SCR, with total concentrations of 3.5–44.9 ng/L and 0.32–1.38 ng/L, respectively. A trend of degradation was observed downstream the ANWRF and TRWRF outfalls for both surface water and groundwater. The very low concentration of GR agonists in groundwater implied the removal of GR agonists in infiltration process. The results indicated the discharge of GR agonists from the two WRFs, and the occurrence of GR agonists in the WRF need to investigate further. (4) Up to 14 GR agonists were detected at different stages in a local WWTP. While high removal efficiency was achieved for natural and low potent synthetic CSs, highly potent synthetic GCs, including CBP, FTP, FCA, and TCA, were poorly removed in 133 WWTP. Negative removal of CSs was observed in primary treatment including flocculation/aerated grit removal and DAF clarification, which was proved to be caused by the deconjugation of CS conjugates, using an enzymatic hydrolysis experiment. The removal of predicted GR activity in the WWTP was only 65%, whereas 97% of total CSs were removed, which was due to the low removal of highly potent GCs. (5) UV and RO appeared to be the most efficient treatment process for the attenuation of CSs, followed by ozone, while chlorination had little effect on GR agonists in water. The similar trend between the target CS concentrations and GR activity in different treatments suggested that the assay response in water samples is attributable to the detected CSs through targeted analysis. Four highly potent synthetic CSs—TCA, FCA, CBP, and FTP—were responsible for the majority of in vitro GR activity in environmental waters. (6) UV/AOP treatments, including UV/HOCl and UV/H2O2, were all demonstrated to be effective in removal GR agonists in wastewater. UV photolysis would be the predominant mechanism in UV/AOP processes for removing GR agonists. GR agonists can be divided into two groups: UV sensitive CSs, which have two double bond carbon in ring A (Δ1,4), and UV insensitive CSs, which have only one double bond carbon in ring A (Δ4). The four important highly potent CSs (CBP, FCA, FTP, and TCA) are all UV sensitive. (7) Four types of PACs were tested for removing GR agonists in wastewater effluent. Cabot HDB carbon was suggested to use for the treatment of GR agonists. Calgon WPH and Cabot 20BF carbon also had good performance in removing GR agonists. Calgon PWA carbon was not recommended due to its low removal efficiency. 134 REFERENCE (1) Jobling, S.; Nolan, M.; Tyler, C. R.; Brighty, G.; Sumpter, J. P., Widespread sexual disruption in wild fish. Environ. Sci. Technol. 1998, 32, (17), 2498-2506. (2) Lange, A.; Paull, G. C.; Coe, T. S.; Katsu, Y.; Urushitani, H.; Iguchi, T.; Tyler, C. R., Sexual reprogramming and estrogenic sensitization in wild fish exposed to ethinylestradiol. Environ. Sci. 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