Identification and Confirmation of the Environmental Risks of Emerging Pollutants in Surface Waters and Sediments

Identification and confirmation of the environmental risks of emerging pollutants in surface waters and sediments

Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH

Aachen University zur Erlangung des akademischen Grades eines Doktors der

Naturwissenschaften genehmigte Dissertation

Vorgelegt von

Shangbo Zhou, Master of engineering

aus Gansu, China

Berichter: Univ.-Prof. Dr. rer. nat. Henner Hollert

Univ.-Prof. Dr. rer. nat. Andreas Schäffer

Tag der mündlichen Prüfung: 24.09.2019

Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek online verfügbar.

Your teacher can open the door but you must enter by yourself.

– Chinese verb

Abstract Although the occurrence, the fate and the toxicology of emerging pollutants in the aquatic environment have been widely studied, there is still a lack in the correlation of the levels of pollutants with the possible adverse effects in wildlife. The shortcomings of traditional methods for risk assessment have been observed, and the contributions of the identified compounds to the observed risks are rarely confirmed. Therefore, the main purpose of this thesis was to develop reasonable methods for risk identification of single compounds and mixtures, and to identify and confirm environmental risks caused by non-specific and mechanism-specific toxicity in aquatic systems. In this thesis, optimized methods for risk identification of single compounds and mixtures were developed. For screening-level risk assessment of single compounds, an optimized risk quotient that considers not only toxicological data but also the frequency with which the detected concentrations exceeded predicted no-effect concentrations was used to screen candidate priority pollutants in European surface waters. Results showed that 45 of the 477 analyzed compounds indicated potential risks for European surface waters. For risk assessment of environmental samples, a risk quotient that considers the ratio of the BEQ to the environmental quality standard (EQS) and the frequency of BEQ exceeding EQS was recommended. Results showed that the highest risk of the anti-androgenic activities was presented at the site directly influenced by the effluents of wastewater treatment plants. To confirm the risk of selected pharmaceuticals, the three antimicrobials clarithromycin, triclosan and sulfamethoxazole, the two anti-inflammatories ibuprofen and diclofenac, the anticonvulsant carbamazepine, the lipid-lowering agent bezafibrate, and the stimulant caffeine were used to study their delayed toxicity using the zebrafish larvae behavioral test. Delayed hatch was observed for exposure to triclosan (1 μg/l) and ibuprofen (100 μg/l) in the early stages of development. In the early stages of development after hatching, the larval locomotor behavior following embryonic exposure to 0.1 μg/l triclosan and 1 μg/l caffeine was altered. Furthermore, for a mixture of the respective highest environmental concentrations of the 8 pharmaceuticals changes in larval behavior were observed. Mechanism-specific bioassays and chemical analyses were performed to identify and confirm endocrine disturbance activities (e.g., anti-androgenic activity) in surface waters and aryl hydrocarbon receptor (AhR)-mediated activities in sediments. Spatial

I and temporal variations of anti-androgenic activities and environmental risks were observed. High cytotoxicity and anti-androgenic activities were observed in surface waters that were directly influenced by the effluents of wastewater treatment plants. Although sediment samples from the upper Danube River were considered less contaminated, high AhR-mediated activities were observed. Furthermore, by combining bioassays, fractionation, chemical analysis and confirmation to an effect-directed analysis (EDA), fractions with adverse effects were screened and effects further associated to specific pollutants and mixtures. The comparison between measured bioanalytical equivalents (BEQs) of sediment samples and BEQs of synthetic mixtures revealed that the US EPA priority PAHs seem to account only to a minor extent for the AhR-mediated activities of the sediment samples, while non-priority substances in medium-polar and polar fractions were high inducers. In this thesis, induced EROD and endocrine disturbance activities were expressed as BEQs of the respective reference standards. In order to properly use the BEQs and confirm the contributions of chemically analyzed compounds to bioassay-derived BEQs, the factors causing the variations of BEQs and relative potencies (REPs) were mathematically analyzed. The effects of the effect levels, slopes and the maximum response of the concentration-response curves of the samples and reference compounds on REP and BEQ variations were confirmed. Although bioassay-derived BEQs (Bio-BEQs) and chemically estimated BEQs (Chem-BEQs) vary with the selected effect level, the explanation of Bio-BEQs by Chem-BEQs of certain compounds at the same effect level will be theoretically stable.

Zusammenfassung Obwohl das Vorkommen, der Verbleib und die Toxikologie sogenannter ―Emerging Pollutants‖ in der aquatischen Umwelt bereits weitgehend untersucht worden sind, mangelt es immer noch an der Korrelation der Schadstoffgehalte mit den möglichen negativen Auswirkungen auf die Tierwelt. Zudem konnten bereits die Defizite traditioneller Methoden der Risikobewertung beobachtet werden, und die Beiträge der identifizierten Verbindungen zu den beobachteten Risiken werden selten bestätigt. Folglich war es das Hauptanliegen dieser Arbeit eine angemessene Methode zur Risikenidentifizierung von Einzelsubstanzen und Substanzgemischen zu entwickeln, sowie die Risiken für die Umwelt zu identifizieren und bestätigen, die durch nicht-spezifische und Mechanismus-spezifische Toxizität in aquatischen Systemen verursacht werden. In dieser Dissertation wurden optimierte Methoden zur Risikoidentifizierung einzelner Verbindungen und Gemische entwickelt. Für die Risikobewertung einzelner Verbindungen auf Screening-Ebene wurde ein optimierter Risikoquotient verwendet, um prioritäre Schadstoffe in Europäischen Oberflächengewässern zu screenen, der nicht nur toxikologische Daten berücksichtigt, sondern auch die Häufigkeit, mit der die ermittelten Konzentrationen die prognostizierten Grenzwerte überschritten werden. Die Ergebnisse zeigten, dass 45 der 477 untersuchten Verbindungen ein potenzielles Risiko für europäische Oberflächengewässer darstellten. Für die Umweltrisikobewertung von Umweltproben wurde ein Risikoquotient empfohlen, der das Verhältnis der BEQ zum Umweltqualitätsstandard (Environmental Quality Standard, EQS) und die Häufigkeit von BEQ-Überschreitungen berücksichtigt. Die Ergebnisse zeigten, dass das höchste Risiko für antiandrogene Aktivitäten am Standort vorlag, welcher direkt von den Abwässern von Kläranlagen beeinflusst wurde. Um das Risiko ausgewählter Arzneimittel zu bestätigen, wurden die drei antimikrobiellen Wirkstoffe Clarithromycin, Triclosan und Sulfamethoxazol, die beiden Entzündungshemmer Ibuprofen und Diclofenac, das Antikonvulsivum Carbamazepin, das Lipidsenkungsmittel Bezafibrat und das Stimulans Coffein mittels eines Verhaltenstests mit Zebrabärblingslarven auf ihre verzögerten Toxizität untersucht. Bei der Exposition gegenüber Triclosan (1 μg/l) und Ibuprofen (100 μg/l) wurde in den frühen Entwicklungsstadien eine verzögerte Schlupfphase beobachtet. In den frühen Stadien der Entwicklung nach dem Schlüpfen war das Verhalten der Larvenlokomotorik

III nach Belastung mit 0,1 μg/l Triclosan und 1 μg/l Koffein verändert. Weiterhin wurden bei einer Mischung der jeweils höchsten Umweltkonzentrationen der acht Arzneimittel Veränderungen im Larvenverhalten beobachtet. Mechanismusspezifische Bioassays und chemische Analysen wurden durchgeführt, um endokrine Störungsaktivitäten (z. B. antiandrogene Aktivität) in Oberflächengewässern und Aryl-Hydrocarbon-Rezeptor (AhR) vermittelte Aktivitäten in Sedimenten zu identifizieren und zu bestätigen. Es wurden räumliche und zeitliche Variationen der antiandrogenen Aktivitäten und Umweltrisiken beobachtet. In Oberflächengewässern, die direkt von den Abwässern von Kläranlagen beeinflusst wurden, wurde eine hohe Zytotoxizität und antiandrogene Aktivität beobachtet. Obwohl Sedimentproben aus der oberen Donau als weniger kontaminiert angesehen wurden, wurden Aktivitäten mit hoher AhR-vermittelter Aktivität beobachtet. Weiterhin konnte durch die Kombination von Bioassays, Fraktionierung, chemischer Analyse und Bestätigung zu einer Effekt derigierten Analyse (EDA) Fraktionen mit negativen Auswirkungen gescreent und Effekte mit spezifischen Schadstoffen und Gemischen in Verbindung gebracht. Der Vergleich zwischen gemessenen bioanalytischen Äquivalenten (Bioanalytical equivalents, BEQs) von Sedimentproben und BEQs von synthetischen Gemischen ergab, dass die prioritären PAKs der US-amerikanischen EPA nur in geringem Maße für die AhR-vermittelten Aktivitäten der Sedimentproben verantwortlich zu sein scheinen, während nicht-prioritäre Substanzen in mittel-polaren und polaren Fraktionen hohe Induzierer waren. In dieser Arbeit wurden induzierte EROD- und endokrine Störungsaktivitäten als BEQs der jeweiligen Referenzstandards ausgedrückt. Um die BEQs angemessen zu verwenden und die Beiträge chemisch analysierter Verbindungen zu von Bioassays abgeleiteten BEQs zu bestätigen, wurden die Faktoren, die die Variationen der BEQs und relativen Potenzen (REPs) verursachen, mathematisch analysiert. Die Auswirkungen der Effektniveaus, Steigungen und der maximalen Wirkung der Konzentrations-Wirkungs-Kurven der Proben und Referenzverbindungen auf REP- und BEQ-Variationen wurden bestätigt. Obwohl von Bioassays abgeleitete BEQs (Bio-BEQs) und chemisch geschätzte BEQs (Chem-BEQs) mit dem ausgewählten Effektniveau variieren, ist die Erklärung von Bio-BEQs durch Chem-BEQs bestimmter Verbindungen mit demselben Effektniveau theoretisch stabil.

Table of Contents

Chapter 1 General Introduction ...... 1

1.1 Emerging pollutants in the aquatic environment ...... 2

1.2 Bioassays ...... 4 1.2.1 Behavioral test ...... 5 1.2.2 Aryl hydrocarbon receptor (AhR)-mediated activity ...... 5 1.2.3 Endocrine-disrupting activity ...... 7

1.3 Environmental risk assessment ...... 8

1.4 Structures and objectives ...... 9

Chapter 2 Optimization of screening-level risk assessment and priority selection of emerging pollutants – the case of pharmaceuticals in European surface waters ... 12

2.1 Introduction ...... 15

2.2 Methodology ...... 16 2.2.1 Data collection ...... 16 2.2.2 Risk quotient (RQ) ...... 17

2.2.3 Optimized risk quotient (RQf) ...... 18

2.2.4 Relationships between optimized (RQf) and traditional (RQ) risk quotients ...... 19

2.3 Results and Discussion ...... 20 2.3.1 Pharmaceuticals occurrence in European surface waters ...... 20 2.3.2 Screening-level risk assessment of pharmaceuticals in surface waters ...... 29

2.4 Conclusions ...... 34

Chapter 3 Behavioral profile alterations in zebrafish larvae exposed to environmentally relevant concentrations of eight priority pharmaceuticals ...... 36

3.1 Introduction ...... 39

3.2 Materials and methods ...... 40 3.2.1 Test pharmaceuticals ...... 40 3.2.2 Mixtures ...... 40 3.2.3 Fish embryo acute toxicity test up to 48 hpf ...... 41 3.2.4 Delayed effects followed by transference to clean water up to 118 hpf ...... 43 3.3.5 Statistical analysis ...... 44

3.3 Results ...... 45 3.3.1 Acute toxicity of pharmaceuticals ...... 45

Table of Contents

3.3.2 Hatching rates at 72 h ...... 45 3.3.4 Locomotion patterns after exposure to single compounds and their mixtures ...... 46 3.3.4 Swimming speed ...... 47 3.3.5 Absolute angular velocity ...... 49

3.4 Discussion ...... 50 3.4.1 Immediate toxicity to embryos ...... 50 3.4.2 Hatching rates and behavioral profiles after transference to clean water ...... 50 3.4.3 Relevance of delayed larval behavioral effects for toxicity assessment ...... 55

3.5. Conclusion ...... 57

Chapter 4 Identification and confirmation of aryl hydrocarbon receptor-mediated activities in sediment samples from the Upper Danube River by means of effect-directed analysis ...... 59

4.1 Introduction ...... 61

4.2 Materials and Methods ...... 63 4.2.1 Sediment samples ...... 63 4.2.2 Soxhlet-extraction and clean-up of sediment extracts ...... 64 4.2.3 Fractionation and chemical analysis ...... 65 4.2.4 Biological analysis by EROD assay ...... 66 4.2.5 Calculation of Bio-TEQ values ...... 66 4.2.6 Calculation of Chem-TEQ values ...... 67 4.2.7 Synthetic fractions and confirmation ...... 67

4.3 Results and Discussion ...... 68 4.3.1 AhR-mediated activities by EROD assays...... 68 4.3.2 Characterization of AhR agonists in selected fractions of Danube sediments by GC-MS ...... 75 4.3.3 Comparison of Bio-TEQs of fractions with Bio-TEQs of synthetic mixture and calculated Chem-TEQs ...... 76 4.3.4 Proposals of potential identity, sources and environmental effects of AhR-mediated substances ...... 83

4.4 Conclusions ...... 84

Chapter 5 Bioanalytical equivalents and relative potencies for predicting the biological effects of mixtures and environmental samples ...... 86

5.1 Introduction ...... 88

5.2 Theoretical analysis of BEQs and REPs ...... 88 5.2.1 CA model and calculation of mixture toxicity ...... 90

Table of Contents

5.2.2 The calculation of REPs ...... 90 5.2.3 The calculation of Bio-BEQs...... 93 5.2.4 The relationship between Bio-BEQs and Chem-BEQs obtained by using REPs ...... 94 5.2.5 Concentration-response curves of the mixture and its components ...... 96

5.3 Materials and Methods ...... 99 5.3.1 PAHs and mixtures preparation ...... 99 5.3.2 AhR-mediated EROD assay ...... 101

5.4 Results and discussion ...... 101 5.4.1 REPs of PAHs for the AhR-mediated activities ...... 101 5.4.2 Comparison of measured Bio-BEQs and calculated Chem-BEQs at different effect levels ... 104 5.4.3 Predicted BEQs based on CA model ...... 106

5.5 Conclusions ...... 107

Chapter 6 Spatial and temporal variations of anti-androgenic activities and their environmental risks in surface waters ...... 110

6.1 Introduction ...... 112

6.2 Methods and materials ...... 113 6.2.1 Sample collection ...... 113 6.2.2 CALUX assays ...... 113 6.2.3 In vitro phase I metabolism ...... 114 6.2.4 Risk assessment ...... 114

6.3 Results and discussion ...... 115 6.3.1 Cytotoxicity at three sites ...... 115 6.3.2 The spatial and temporal variations of anti-androgen receptor-mediated (anti-AR) activities 116 6.3.3 Compounds occurrence and distribution ...... 118 6.3.4 Risk assessment based on effect-based bioassays ...... 119 6.3.5 Endocrine-disrupting activity after metabolism ...... 120

6.4 Conclusions ...... 121

Chapter 7 Discussion, conclusions and outlook ...... 123

7.1 General conclusions and Discussion ...... 124 7.1.1 Bioanalytical equivalents (BEQs) for the risk identification ...... 125 7.1.2 Identification of aryl hydrocarbon receptor (AhR)-mediated activities ...... 125 7.1.3 Identification of endocrine-disrupting activities ...... 126 7.1.4 Risk assessment of the single compounds and samples ...... 126

7.2 Proposals for further research ...... 128

VII

Table of Contents

References ...... 130

Annex ...... 184

Contributions to the published articles and chapters...... 219

Acknowledgements ...... 221

Curriculum vitae ...... 223

Scientific Contributions ...... 225

VIII

List of Figures

List of Figures Figure 1.1 Diclofenac concentrations in European surface waters by country...... 2 Figure 1.2 Carbamazepine concentrations in European surface waters by country...... 3 Figure 1.3 Mechanism of AhR-mediated activity...... 6 Figure 1.4 The framework of environmental risk assessment for the compounds ...... 8 Figure 1.5 Basic structure of the thesis ...... 10 Figure 2.1 Therapeutic groups analyzed (A) and positively detected (i.e., conc. >LOD, B) in European surface waters...... 21 Figure 2.2 Total numbers of pharmaceuticals, metabolites and transformation products analyzed (A) and positively detected (i.e., conc. >LOD, B) in European surface waters by country, given as groups...... 27

Figure 2.3 The total numbers of pharmaceuticals with RQf above zero in each country...... 30 Figure 3.1 Percentage of hatched larvae at 72 hpf after 48 h exposure to the triclosan, ibuprofen and negative controls...... 46 Figure 3.2 Swimming speed (mm/s) of 5dpf larvae in each ten-minute light and dark periods after 48 h embryonic exposure to the single compounds diclofenac (panel A), triclosan (panel B), carbamazepine, (panel C), bezafibrate (panel D), sulfamethoxazole (panel E), ibuprofen (panel F), caffeine (panel G), and clarithromycin (panel H) at 6 concentrations (0, 0.01, 0.1, 1, 10 and 100 μg/l) or to low-, medium- and high-level mixtures (M)...... 49 Figure 3.3 Angular velocity (deg/s) of 5 dpf larvae in each ten-minute light and dark exposure to 8 single pharmaceuticals at 6 concentrations gradients (0, 0.01, 0.1, 1, 10 and 100 μg/l) or to low-, medium- and high-level mixtures...... 53 Figure 4.1 Sampling sites along the Upper Danube River...... 65 Figure 4.2 Comparison of the AhR-mediated activities of raw extracts of sediment samples collected at different sites along the Upper Danube River in 2004 (Keiter et al., 2008) and 2006 (this study), as determined with the EROD assay using RTL-W1 cells...... 71 Figure 4.3 Comparison of the AhR-mediated activities of the raw extracts as well as accelerated dialyzed extracts (i.e., AMD extracts) and their fractions (F) of Danube sediment samples collected at the sites at a) Sigmaringen, b) Lauchert, c) Lauchert-R (R = raw extract of the sediment sample collected at the tributary Lauchert in 2004) and d) Oepfingen, as determined with the EROD assays with RTL-W1 cells (left y-axis)...... 73 Figure 4.4 Contributions of Bio-TEQs of the synthetic mixtures (dotted; bioassay-derived toxic equivalents) and Chem-TEQs (gridded; chemically calculated toxic equivalents) to Bio-TEQs of their respective fractions (gray), as well as the contributions of the sum Bio-TEQs of synthetic fractions and the sum Chem-TEQs of fractions to the Bio-TEQs of the dialyzed extracts, and to the sum Bio-TEQs of fractions (Sum F) of the different sediments from the sites at a)

List of Figures

Sigmaringen, b) Lauchert and c) Oepfingen...... 83 Figure 5.1 The possible relationships between the concentration-response curve of the mixture and the curves of its components...... 99 Figure 5.2 Comparison of measured Bio-BEQs and calculated Chem-BEQs of the mixtures at different effect levels (EC5, EC10, EC25 and EC50)...... 106 Figure 5.3 Observed (solid lines) and predicted (dashed lines) concentration-response curves of the synthetic mixtures in the EROD assays...... 108 Figure 6.1 Cytotoxicity of water samples from 2014 to 2016 at three sites (Wernigerode, Silstedt and Nienhagen), the response was expressed as % of relative response to the DMSO control. 117 Figure 6.2 Anti-androgen receptor-mediated (anti-AR) activities from 2014 to 2016 at three sites (Wernigerode, Silstedt and Nienhagen)...... 119 Figure 6.3 Temporal variations in the potential risks of anti-AR activities at Silstedt and Nienhagen sites...... 121 Figure 6.4 Androgenic activities of stanozolol (STZ) and water sample S1 in or absence of S9...... 121 Figure 6.5 Estrogenic activities of single compounds and water sample S2 in or absence of S9...... 122 Figure 7.1 Conceptual framework for the risk identification and confirmation of emerging pollutants in ecosystems...... 130

List of Tables

List of Tables Table 2.1 Mean and maximum concentrations, the frequency of measured environmental concentrations (MECs) above the LODa, and calculated mean and maximum RQ values for the 45 most frequently analyzed pharmaceuticals in European surface waters...... 22

Table 2.2 Prioritized pharmaceuticals according to the RQf values in descending order (RQf > 0)...... 30 Table 3.1 Physicochemical characteristics of the 8 priority pharmaceuticals for European surface waters selected for behavioral tests in this study...... 41 Table 3.2 Composition of mixtures and concentrations of 8 priority selected pharmaceuticals in European surface waters...... 42 Table 4.1 Total concentrations of selected polycyclic aromatic compounds (PACs) given for selected fractions of dialyzed extracts of sediment samples from the Upper Danube River. .. 75 Table 5.1 The concentrations (ng/mL) of polycyclic aromatic compounds (PAHs) in synthetic mixtures...... 101 Table 5.2 US EPA priority PAHs that induced EROD activities in RTL-W1 cells after 72 h exposure...... 104 Table 5.3 The REPs based on molar concentrations for US EPA priority PAHs derived from EROD induction using RTL-W1 cells...... 104

Chapter 1

Chapter 1 General Introduction

Small parts of this chapter are based on a study published in Science of the Total Environment: Zhou, S., Chen, Q., Di Paolo, C., Shao, Y., Hollert, H., Seiler, T. B. 2019. Behavioral profile alterations in zebrafish larvae exposed to environmentally relevant concentrations of eight priority pharmaceuticals. Science of the Total Environment, 664, 89–98.

Chapter 1

1.1 Emerging pollutants in the aquatic environment Emerging pollutants include a wide range of substances (such as pharmaceuticals, personal and household care products, benzothiazoles, polychlorinated alkanes, benzotriazoles, endocrine disruptors and metabolites), which are in use worldwide (Thomaidis et al., 2012; Gavrilescu et al., 2015). Emerging pollutants are widely distributed in soil, air, water and living organisms, and have the potential to cause adverse ecological and human health effects (Geissen et al., 2015). The occurrence of emerging pollutants in aquatic ecosystems, such as streams, lakes, rivers, reservoirs, estuaries and marine waters, is of particular concern (e.g., Loos et al., 2009; Loos et al., 2008; Pal et al., 2010; Hughes et al., 2013; aus der Beek et al., 2015). Emerging pollutants are generally released into the aquatic environment through the effluents of wastewater treatment plants and runoff from urban and agricultural areas. More than 100 pharmaceuticals have been detected in the aquatic environment in the United States (aus der Beek et al., 2015) and European countries, with the concentrations ranging from ng/L to ug/L. Figures 1.1 and 1.2 show the concentrations of two frequently detected pharmaceuticals carbamazepine and ibuprofen in European surface waters, respectively.

Figure 1.1 Diclofenac concentrations (ng/L) in European surface waters by country. The publications used for data collection are categorized in Annex 1.1.

Chapter 1

Figure 1.2 Carbamazepine concentrations (ng/L) in European surface waters by country. The publications used for data collection are categorized in Annex 1.1. Emerging pollutants are present in low concentrations in surface waters, and thus the lethal toxicity of emerging pollutants is less likely to be observed (De Lange et al., 2006). However, many studies reported sublethal effects on non-target organisms at environmentally relevant concentrations for a variety of different emerging pollutants (Orvos et al., 2002; Oaks et al., 2004; Kidd et al., 2007; Triebskorn et al., 2007; Brodin et al., 2013). Erratic swimming of rainbow trout occurred after chronic exposure to 71 μg/L triclosan (Orvos et al., 2002). Exposure to low concentrations (10–100 ng/L) of fluoxetine and ibuprofen resulted in a significant decrease in the activity of Gammarus pulex (Crustacea, Amphipoda) (De Lange et al., 2006). The growth inhibition of three antibiotics (clarithromycin, azithromycin, and ciprofloxacin) to algae was observed in the range of ng/L to μg/L (Guo et al., 2016). Generally, effects of emerging pollutants on aquatic ecosystems were so far detected at three trophic levels, i.e. vertebrates (fish), invertebrates (crustaceans) and plants (algae). The detected concentration in aquatic system exceeding the predicted no effect concentration (PNEC) indicates a potential environmental risk. At present, the effects of emerging pollutants on ecosystems are still unclear. Also, information on the environmental behavior and the fate of emerging pollutants in the environment is still insufficient (Deblonde et al., 2011). In this thesis,

Chapter 1 the environmental risks of emerging pollutants for aquatic ecosystems were analyzed, and their potential effects were further confirmed using bioassays at different effect levels. 1.2 Bioassays Chemical analysis is widely used to identify contaminants and their metabolites in biological and environmental samples. The results of chemical analysis can reflect the occurrence of pollutants in the environment, but cannot directly reflect the toxic pressure of the respective pollutants on specific species or on the whole ecosystems. In vitro or in vivo bioassays at different organization levels, such as biochemical, molecular, organs, organism, and population levels, were developed and standardized to measure the total toxicologically relevant burden of chemicals and to assess the toxic effects of pollutants in different matrices such as air, soil, water and sediment (Brack et al., 2002; Giesy et al., 2002; Tiedeken and Ramsdell, 2009; Eichbaum et al., 2014; Neale et al., 2015; Legradi et al., 2015; Schiwy et al., 2015; Xiao et al., 2016; Heinrich et al., 2017). Bioassays indicative of activation of the aryl hydrocarbon receptor (AhR), activation of the androgen receptor (AR), activation of the estrogen receptor (ER), activation of the pregnane X receptor (PXR), acetylcholinesterase (AChE) inhibition, mutagenicity, genotoxicity and inflammation, algae growth inhibition and fish embryo toxicity test were developed and applied in aquatic systems (Otte et al., 2008; Neale et al, 2015, 2017; Xiao et al., 2016; Shao et al., 2019a, 2019b). Compared with chemical analyses, effect-based biossays are quite suitable for detecting the effects of mixtures and minimizing the risk of overlooking hazardous compounds, metabolites and transformation products (Brack et al., 2019). SOLUTIONS (http://www.solutions-project.eu/project/) is a European Union Seventh Framework Programme Project (EU-FP7) (Brack, et al. 2015). In 2013, this project was set up for identifying, assessing and prioritizing emerging pollutants that might pose risks to aquatic ecosystems and finally providing solutions for the prioritized pollutants in European water resources. The detailed description of SOLUTIONS project can be found in previous publication (Brack, et al. 2015). In SOLUTIONS, a wide range of effect-based in vitro and in vivo biossays at different effect levels has been applied successfully for both diagnostic and monitoring purposes to assess the possible effects of emerging pollutants (Brack et al., 2019).

Chapter 1

The bioassays involved in this thesis are part of SOLUTIONS project, and can be divided into two categories: non-specific bioassays and mechanism-specific bioassays. Non-specific bioassays, such as cytotoxicity, fish embryo test, behavioral test, daphnia immobilization assay and algae growth inhibition test, can be used to assess the non-specific toxicity of pollutants (Brack et al., 2016b). Mechanism-specific bioassays are based on specific modes of action, such as AhR-mediated activity and endocrine-disrupting activity, to assess the specific toxicity of pollutants. In this chapter, the main bioassays involved in this thesis are briefly being introduced. 1.2.1 Behavioral test Investigating changes in behavior gradually becomes a widely accepted method for studying the sublethal effects of compounds on non-target aquatic organisms (Tiedeken and Ramsdell, 2009; Ali et al., 2012; Legradi et al., 2015). Zebrafish (Danio rerio) is employed as a vertebrate model for investigating toxicological effects on behavior and neurodevelopment (Rihel et al., 2010; Padilla et al., 2011; Chen et al., 2017; Velki et al., 2017; Gauthier and Vijayan, 2018; Michelotti et al., 2018). Zebrafish embryos are small and transparent. Furthermore, zebrafish develop rapidly, the larvae hatch from chorion at 2 or 3 days post fertilization (dpf), and all major organs are well developed at 5 dpf (De Esch et al., 2012). These characteristics of zebrafish determine that behavioral assessment can be carried out during a short period with a small volume of test medium. Meanwhile, multi-endpoints, including swimming speed, feeding performance, angular velocity and hatching rate are complementary behavioral endpoints have been used in previous studies (Nassef et al., 2010; Chen et al., 2017; Velki et al., 2017; Michelotti et al., 2018), and when they are assessed together may help to analysis the effects of pollutants and to clarify involved mechanisms of behavioral alteration. 1.2.2 Aryl hydrocarbon receptor (AhR)-mediated activity AhR-mediated activity can be determined by in vivo or in vitro 7-ethoxyresorufin-O-deethylase (EROD) assays. For instance, Jönsson et al. (2006) developed an in vivo gill filament EROD assay to monitor AhR-mediated activity in caged fish; Otte et al. (2008) developed an in vivo EROD assay in distinct tissues of the zebrafish embryo at different developmental stages; Schiwy et al. (2014) developed an in vivo zebrafish embryo EROD assay to detect AhR-mediated toxicity. Compared with in vivo EROD assays, in vitro cell-based EROD assays are more frequently applied for the investigation of AhR-mediated activity (Villeneuve et al., 2000; Larsson et al., 2012,

Chapter 1

2014a; Wang et al., 2014; Xia et al. 2016) due to the optimized protocols for quick and relatively inexpensive measurement. Therefore, in this thesis, the in vitro EROD assay was performed using Rainbow Trout Liver (RTL-W1), which have high bio-transformation capacity, and thus allow to measure the activity of the detoxifying enzyme CYP1A. When ligands bind to the AhR-receptor (Figure 1.3), a transcription factor complex with an aryl hydrocarbon nuclear translocator protein (ARNT) is formed and then binds to specific DNA sequences (xenobiotic/dioxin responsive elements (X/DRE)). The binding leads to the transcription of several genes (the Ah gene battery), which in turn produces a variety of protein products (e.g., CYP1A) after translation. Induction of CYP1A was measured as the EROD activity in cultivated cells via catalysis of the artificial substrate 7-ethoxyresorufin to resorufin (Behrens et al., 1998) through fluorometrical measurement. In the aquatic environment, compounds with AhR-mediated activity are ubiquitous. 16 PAHs were designated as US-EPA priority pollutants (Bols et al., 1999), and these compounds with AhR-mediated activity are considered to be hazardous in aquatic ecosystems.

Figure 1.3 Mechanism of AhR-mediated activity (modified from Whyte et al. (2000)). Besides being obtained using bioassays, the AhR-mediated activity of single PAHs and their mixtures can be predicted by relative potencies (REPs) or toxicity equivalency

Chapter 1 factors (TEFs), which convert the concentration of the compound to the concentration of the reference standard for producing the same response (Safe, 1998; Bols et al., 1999; Van Den Berg et al., 1998, 2006; Villeneuve et al., 2000; Larsson et al., 2014a,b). However, it should be emphasised that this is only possible if REP or TEF values were determined for a certain substance using the same bioassay, and for the same effect level. This approach hence greatly relies on available data and is thus limited to substances where the necessary datasets are sufficiently strong. 1.2.3 Endocrine-disrupting activity Compounds with endocrine-disrupting ability refer to a large number of anthropogenic (e.g., nonylphenol, bisphenol A, polychlorinated biphenyls (PCBs), PAHs, and ethinylestradiol (EE2)) and naturally occurring compounds (e.g., daidzein, genistein, estrone (E1), 17-βestradiol (E2), and estriol (E3)). The observed endocrine disorders include cancerous tumors, birth defects, and alterations in reproductive characteristics. Kidd et al. (2007), conducting a 7-year whole lake experiment, showed that long-term exposure to low concentrations of ethynylestradiol (5 - 6 ng/L) led to the feminization of fathead minnow (Pimephales promelas), which brought the entire fish population close to collaps. Diamanti-Kandarakis et al. (2009) published a scientific statement presenting the effects of environmental endocrine disruptors on human health. The endocrine system of organisms can be disturbed by exogenous compounds with endocrine-disrupting abilities. Hence, many studies were conducted on the presence, properties, and activities of the compounds that can affect the endocrine pathways (Nadal et al., 2001; Windal et al., 2005; Chang et al., 2007; Van Der Linden et al., 2008; Wise et al., 2011; Schriks et al., 2013; Liu et al., 2015). Chemically Activated LUciferase gene eXpression (CALUX) assays that use genetically modified cells with a luciferase gene controlled by the promotor allow the detection of substances with – among others – potential (anti-)estrogenic or (anti-)androgenic activity (Sonneveld et al., 2004). Compounds with one of these properties bind to the respective receptor and then enter the nucleus. Quite similar to the induction of CYP1A upon ligand binding to the AhR (see 1.2.1 and Figure 1.3) the chemical-receptor complex to the responsive elements triggers the mRNA transcription and then translation. The translated luciferase enzyme then catalyzes the substrate (luciferine) to the oxyluciferin and produces light in a concentration-response relationship. The measured luminescence resulting from exposure to a given sample is

Chapter 1 converted into a bioanalytical equivalent (BEQ) value that is the concentration of a reference standard that elicits a response equivalent to the response for the sample (Escher et al., 2018). 1.3 Environmental risk assessment Environmental risk assessment evaluates the probability of adverse effects caused by pollutants, and provides support for decision making. The general steps for environmental risk assessment of the compounds are shown in Figure 1.4 (EC, 2001). According to the collected information of manufacture, use and effects of the compounds, the target compounds for environmental risk assessment can be determined in the first step. The environmental risk the compound represents depends on its inherent toxicity to non-target organisms and the concentrations in the environment. Effect characterization aims to determine the concentration-response relationship of the compounds, and acute or chronic toxicity data (LC50/EC50, LOEC, etc.) can be obtained at this stage. The main content of exposure characterization is to describe the exposure state that actually occurs.

Figure 1.4 The framework of environmental risk assessment for the compounds The risk assessments of compounds in aquatic systems are based on toxicity data of the compounds on fish, crustaceans and algae at three trophic levels (European Commission, 2003). The toxicity value is divided by various uncertainty factors to obtain PNECs in 8

Chapter 1 the aquatic environment (Palma et al., 2014). According to the results of effect and exposure characterizations, the risk quotient (RQ) of the compound can be determined by comparing PNEC with environmental concentrations (Houtman et al., 2014; Mendoza et al., 2014). Similarly, the RQ of a multi-component sample can be obtained by comparing BEQ with the proposed environmental quality standard. The main tasks of risk assessment are to record the findings, identify risk factors, to eventually help risk management to take measures to control the risks. 1.4 Structures and objectives This thesis is part of SOLUTIONS project, thus the objectives of this thesis are in accordance with the purposes of SOLUTIONS project to some extent. One main objective of this thesis was to develop an optimized method for risk identification of single compounds (Chapter 2) and mixtures (Chapters 5 and 6). Another main objective can be simply expressed as risk identification (Chapters 2, 5 and 6) and confirmation (Chapters 3, 4 and 5). Figure 1.5 shows the basic structure of the thesis. In Chapter 2, the environmental risks of pharmaceuticals were assessed based on data gained from the SOLUTIONS project and publications, and then the potential risks of pharmaceuticals to zebrafish larvae were further confirmed in Chapter 3. Two mechanism-specific bioassays, EROD (Chapter 3) and CALUX assays (Chapter 3), were conducted to identify anti-androgenic and aryl hydrocarbon receptor (AhR)-mediated activities in aquatic systems, respectively. The induced activities were expressed as BEQs of the reference standard, and many factors can result in the variations of BEQs. To ensure comparability between BEQs, the factors that may cause the TEQ variations were analyzed in Chapter 5.

Chapter 1

Figure 1.5 Basic structure of the thesis In more detail the purposes of each chapter were: Chapter 2 There is an urgent need to establish reasonable methods for risk identification and confirmation of the compounds. Therefore, the main objective was to develop an optimized method for conveniently assessing the environmental risk of a single compound. In order to confirm the reliability of the optimized method, environmental risk assessment of pharmaceuticals in European surface waters was conducted and the results were compared with previous accepted results. The second objective was to select emerging pollutants that would have priority in further analysis of toxicity individually and regarding their combined effects. Chapter 3 Based on the results of environmental risk assessment of the pharmaceuticals in European surface waters, eight pharmaceuticals were selected to confirm their potential effects on zebrafish embryos. Furthermore, mixtures with known components were prepared to investigate the combined effects at environmentally correlated concentrations. Chapter 4 Polycyclic aromatic compounds (PACs) are a group of widespread organic compounds, and an important pathway concerning AhR-mediated activities of PACs is the EROD

Chapter 1 assay. In Chapter 4, the primary purpose was to identify priority fractions and compounds that are responsible for the high AhR-mediated activities from the upper Danube River using an integrated effect-directed analysis (EDA). Furthermore, synthetic mixtures were prepared to confirm the contributions of the identified priority pollutants to the detected effects. Chapter 5 In Chapter 4, we found that many factors, including differences in the shape of the concentration-response curves and interactions of the chemicals, caused the uncertainties of the BEQ values. In Chapter 5, in order to properly calculate the BEQs, the factors causing the variations of the BEQs were analyzed mathematically, and the prerequisites for the application of REPs were clarified. Furthermore, synthetic mixtures with known components were prepared to compare the calculated BEQs and bioassay-derived BEQs to verify the reliability of optimized REPs for mixture toxicity prediction. Chapter 6 In our environment, a wide range of compounds are thought to cause endocrine disruption. The androgenic or estrogenic activities of the compounds are frequently observed, anti-androgenic activity is of little concern. The first aim was to observe the spatial and temporal variations of anti-androgenic activities. Risk assessments of endocrine disrupting compounds are complicated since these compounds exist in the environment as complex mixtures. The second aim was to quantify the risks of anti-androgenic activities of water samples caused by the external disturbance on the basis of the BEQs. The third aim was to identify the endocrine-disrupting activities of single compounds and environmental samples after in vitro metabolism. Chapter 7 Discussion, conclusions and outlook

Chapter 2

Chapter 2 Optimization of screening-level risk assessment and priority selection of emerging pollutants – the case of pharmaceuticals in European surface waters

This chapter is based on a study published in Environment International: Zhou, S., Di Paolo, C., Wu, X., Shao, Y., Seiler, T. B., Hollert, H. 2019. Optimization of screening-level risk assessment and priority selection of emerging pollutants – the case of pharmaceuticals in European surface waters. Environmental International, 128, 1–10.

Chapter 2

Abstract Pharmaceuticals in surface waters have raised significant concern in recent years for their potential environmental effects. This study identified that at present a total of 477 substances (including 66 metabolites and transformation products) have been analyzed in European surface waters. Around 60% (284) of these compounds belonging to 16 different therapeutic groups were positively detected in one or more of 33 European countries. To conveniently and effectively prioritize potential high-risk compounds, an optimized method that considers the frequency of concentrations above predicted no effects levels was developed on the basis of the traditional method, and it was then used to identify and screen candidate priority pollutants in European surface waters. The results proved the feasibility and advantages of the optimized method. Pharmaceuticals detected in European surface waters were classified into 5 categories (high, moderate, endurable, negligible and safe) depending on their potential environmental effects and the distribution of pharmaceuticals. Circa 9% (45 out of 477) analyzed compounds showed a potential environmental risk to aquatic ecosystems. Among these 45 compounds, 12 compounds were indicated to have high environmental risk in aquatic environments, while 17 and 7 compounds showed moderate and small-scale environmental risks, respectively.

Keywords: Pharmaceuticals; Emerging pollutants; Prioritization; Risk assessment

Chapter 2

2.1 Introduction

Pharmaceuticals have gained scientific and public attentions as one of the most important groups of aquatic emerging pollutants, since their occurrence and environmental concentrations have widely been reported in water bodies in Europe and worldwide (e.g., Ashfaq et al., 2017; aus der Beek et al., 2015; Brack et al., 2018; Hughes et al., 2012; Loos et al., 2007, 2009; Munthe et al., 2017; Zhang et al., 2018). Multiple sources contribute to the occurrence of pharmaceuticals in surface waters, such as effluents of wastewater treatment plants (WWTPs) and industries, and agricultural runoff. Pharmaceuticals used in human medicine and not entirely metabolized or incompletely eliminated in WWTPs are released into surface waters. Furthermore, large quantities of pharmaceuticals used in veterinary medicine are excreted as parent compounds and metabolites into the environment without any treatment. Parent compounds and metabolites can undergo structural changes in the environment, resulting in new chemical entities (Michael et al., 2014). In the last few years, some studies have already compiled, summarized and critically analyzed the measured concentrations of emerging contaminants in surface waters. For example, aus der Beek et al. (2015) and Hughes et al. (2012) analyzed the presence of pharmaceuticals and measured environmental concentrations at the global scale, and Loos et al. (2009) provided the occurrence of 35 organic compounds in European river waters. Some studies reported that pharmaceuticals could result in adverse effect on non-target organisms at environmentally relevant concentrations (e.g., Oaks et al., 2004; Kidd et al., 2007). However, the occurrence of metabolites and transformation products of pharmaceuticals in the environment was relatively less observed, and the effects of these compounds on non-target aquatic organisms at a large scale have not been fully understood yet. Measured environmental concentrations (MECs) combined with predicted no effect concentrations (PNECs) as proposed by European commission (2003) are commonly used to screen compounds with potential environmental risks (Desbiolles et al., 2018; Houtman et al., 2014; Mendoza et al., 2014; Palma et al., 2014; Sanderson et al., 2004;

Chapter 2

Thomaidi et al., 2017; Thomatou et al., 2013; Vazquez-Roig et al., 2012; Vryzas et al., 2011). Burns et al. (2018a) reviewed that 76 pharmaceutical prioritizations were undertaken covering 24 countries. Meanwhile, the limitations of this traditional screening method have been realized and refined methods were proposed to properly identify the priority pollutants that should be regularly monitored in surface waters (e.g., Altenburger et al., 2018; Brack et al., 2017, 2018; Tousova et al., 2017; von der Ohe et al., 2011, 2012). However, to our knowledge there is no study providing a broad screening on the current potential environmental risks, whether assessed by traditional or improved methods, posed by pharmaceuticals to aquatic ecosystems. Therefore, the main objectives of the present study were to: 1) analyze the presence of pharmaceuticals belonging to different therapeutic groups and respective concentrations in European surface waters; 2) develop a reasonable and feasible method that could be used for screening the compounds with potential environmental risks, especially in a wide spatial-scale (e.g., regions, river basins, nations); 3) select the pharmaceuticals that would have priority in risk management and in further analysis of chronic toxicity in aquatic systems.

2.2 Methodology

2.2.1 Data collection The concentration data in surface waters was obtained from peer-reviewed publications and government reports published between 1998 and 2016 by performing searches in Web of Sciences, Scopus and Google Scholar using the keywords ''pharmaceutical'', ''drug'', ''pollutant'' or ''compound'' in combination with the keywords ''river'', ''lake'', ''stream'' or ''surface water''. The studies developed in European water bodies were then selected for this study. The publications used for data collection are categorized in Annex 1.1. The data was mainly generated from the chemical analysis of samples collected in rivers and streams, followed by lakes and estuaries. Parts of the data for surface waters in Romania, Serbia, Austria, Bulgaria, Hungary, Croatia, Moldova and Ukraine were gained from the SOLUTIONS project (Neale et al., 2015). We considered that the research done in WWTPs did not reflect the dilution and degradation processes

Chapter 2 of receiving waters, thus data from WWTPs effluent were excluded to avoid overestimating the pollution of surface waters. However, concentrations in receiving waters were included to reflect the worst case scenario in freshwater ecosystems. The data collected in this study was presented as the mean and maximum concentration values. The classification of pharmaceuticals was based on the PubChem database (Kim et al., 2015) and previous publications (e.g. aus der Beek et al., 2015; Hughes et al., 2012; Liu and Wong, 2013). In the present study, some compounds that are not pharmaceuticals for therapeutic, preventive, and diagnostic purposes were given due to their high detected frequencies together with pharmaceuticals. These compounds are metabolites and transformation products of pharmaceuticals, natural hormones and triclosan. Some metabolites and transformation products of pharmaceuticals still exhibit bioactivity, natural hormones are endocrine-disrupting compounds, and triclosan is an antimicrobial agent found in a variety of consumer products. 2.2.2 Risk quotient (RQ) Based on the concentration data for particular contaminants, a risk quotient (RQ) approach has often been used to screen the compounds with potential environmental risks in surface waters according to the respective EU guidelines (European Commission, 2003). The RQ is based on the consideration that detected environmental concentrations and chronic toxicity of pharmaceuticals to non-target aquatic organisms are crucial for the assessment of environmental risks. Accordingly, in the present study algae (phototrophic level), Daphnia magna (invertebrates) and fish (vertebrates) were selected as representative organisms of three different trophic levels in aquatic ecosystems to assess potential ecological effects (European Commission, 2003). Chronic no-observed effect concentrations (NOECs) or chronic lowest-observed effect concentrations (LOECs) values were used for the calculation of PNECs. In the absence of chronic toxicity data, short-term E(L)C50 data was used. Toxicity data was mainly obtained from the United States EPA ECOTOX database (https://cfpub.epa.gov/ecotox/quick_query.htm). When no data was found in the database, short and long-term toxicity data was obtained from published literature by performing searches in Web of Sciences and Google Scholar using the pharmaceutical 17

Chapter 2 and the organism names as keywords. Finally, when no experimental toxicity data was available, acute toxicity values of compounds were calculated using the ECOSAR™v. 1.11 and QSAR Toolbox 3.3 by importing chemical name and CAS number. When more than one dataset on toxicity was obtained at the same nutrient level, the one indicating the strongest effect was used. The toxic data that is selected as the toxicological benchmarks for the calculation of the PNECs is shown in Annex Table 2.1. The RQ was calculated as a ratio of the MEC (measured environmental concentration) and PNEC (RQ = MEC/PNEC) (Palma et al., 2014). An assessment factor was used to overcome the uncertainty related to the raw toxicity data and to derive the PNEC (Vryzas et al., 2011). According to the EU guidelines (European Commission, 2003): (i) an assessment factor (AF) of 1,000 was used in the cases where at least one short-term E(L)C50 from each of the three evaluated trophic levels was available; (ii) an AF of 100 was used when one long-term assay was available for either algae, crustaceans or fish; (iii) an AF of 50 was used in the case of existing two long-term assays in two different trophic levels; and (iv) an AF of 10 was used when three long-term assays in three different trophic levels were available. PNEC values were calculated by dividing the lowest NOEC, LOEC or E(L)C50 values of the most sensitive species by an appropriate AF. To calculate the RQ values under different scenarios, mean and maximum concentrations were used as MECs to reflect general and worst-case scenarios, respectively (Palma et al., 2014; Vryzas et al., 2011). If RQ is less than 1, it suggests that the compound is less likely to cause hazardous effects in the aquatic environment. If RQ is higher than or equal to 1, it indicates that the particular substance could pose potential adverse effects.

2.2.3 Optimized risk quotient (RQf) The current RQ approach as described above characterizes the toxicity of pharmaceuticals under the conditions of measured environmental concentrations, but ignores the possibility of aquatic organisms exposed to potentially unsafe levels. Since certain pharmaceuticals that are the long-term presence in water bodies have a greater impact than those pollutants that are the short-term presence on non-target organisms. That is to say, the risks of pharmaceuticals that were frequently detected and those were 18

Chapter 2 occasionally detected should be different. Therefore, it is a tendency to consider frequency during the high-risk compound screening (e.g. Desbiolles et al., 2018; Tousova et al., 2017; von der Ohe et al., 2011, 2012). When screening is given priority to pollutants that are widely distributed and frequently detected, the results could change considerably. Thus, a novel risk quotient (RQf) based on the mean RQ value and the frequency of MECs exceeding PNEC was used to evaluate the potential risks due to detected substances, which is close to the natural scenario and favors the selection of priority pollutants. The RQf value was calculated according to the following equations:

RQ RQ × F × F

O F 1 O2

RQf represents the optimized risk quotient after considering the frequency of MECs exceeding PNEC; RQ represents the ratio of the mean concentration and PNEC; F represents the frequency of MECs exceeding PNEC; NO1 represents the number of samples with concentrations higher than PNECs; NO2 represents the total number of samples. RQf was classified into 5 groups: if RQf is higher than 1 (RQf ≥ 1), high environmental risk is expected (high); if RQf lies between 0.1 and 1 (1 > RQf ≥ 0.1), moderate environmental risk is expected (moderate); if RQf is between 0.1 and 0.01

(0.1 > RQf ≥ 0.01), small-scale adverse effect is expected (endurable); if RQf is below

0.01 (0.01 > RQf > 0), the effect of this compound was quite limited (negligible); if RQf is equal to zero (RQf = 0), no risk is expected at present (safe).

2.2.4 Relationships between optimized (RQf) and traditional (RQ) risk quotients

According to the equations of RQf and RQ, traditional RQ can be regarded as a specific scenario of RQf. That is, only one environmental concentration is considered in RQf for risk assessment, and is assumed to be a concentration that may cause risks (F = 100%).

When only the mean concentration is considered, RQf is equal to mean RQ. When only the maximum concentration is considered, RQf is equal to maximum RQ. Maximum RQ is likely to overestimate the potential risk of compounds, but the mean RQ does not reflect the natural scenario precisely, since the detected concentrations show great spatial and temporal variation, and the concentrations probably causing the environmental risk

Chapter 2 do not stably occur.

By considering the variability of concentrations above PNECs the optimized RQf favors to screen the pollutants that are widely distributed and frequently detected. Furthermore, this method does not separate general and worst-case scenarios, but includes all possibilities of the detected concentrations above PNECs. Furthermore, RQf uses a graded system (high, moderate, endurable, negligible and safe) to categorize the potential risks of chemicals. Thus it is more convenient to select contaminants that should be prioritized in a large-scale water resources management.

2.3 Results and Discussion

2.3.1 Pharmaceuticals occurrence in European surface waters 2.3.1.1 Pharmaceuticals therapeutic groups Figure 2.1 shows the analyzed (a) and positively detected (b) therapeutic groups in European surface waters (detailed information is provided in the supplementary materials, Table S2). A total of 477 substances (411 pharmaceuticals and 66 metabolites and transformation products) belonging to 16 therapeutic groups were analyzed in European surface waters. Among these substances, 168 pharmaceuticals and 25 metabolites and transformation products were not present at concentrations above the detection limits of the analytical methods employed in the original publications. The remaining 284 substances (243 pharmaceuticals and 41 metabolites and transformation products) that were positively detected at concentrations above the detection limits belonged to 16 therapeutic groups: fungicides and antibiotics (73), analgesics and anti-inflammatories (30), anxiolytics and anticonvulsants (20), antihypertensives (19), antidepressants (19), opioids and morphine derivatives (18), stimulants (14), steroids and hormones (13), antihistamines (9), lipid-regulating drugs (8), antiviral drugs (7), β-blockers (7), diuretics (5), anaesthetics (3), antidiabetic drugs (3) and others (36). These groups were not only the most frequently detected compounds in Europe, but also were widely detected in other places throughout the world (aus der Beek et al., 2015; Bu et al., 2013; Burns et al., 2018b; Cantwell et al., 2018; Kolpin et al., 2002; Murata et al., 2011).

Chapter 2

Figure 2.1 Therapeutic groups analyzed (A) and positively detected (i.e., conc. >LOD, B) in

European surface waters. The number of pharmaceuticals, metabolites and transformation products in each group is expressed as a percentage relative to the total number and the detailed number are given in brackets.

Chapter 2

Table 2.1 Mean and maximum concentrations, the frequency of measured environmental concentrations (MECs) above the LODa, and calculated mean and maximum

RQ values for the 45 most frequently analyzed pharmaceuticals in European surface waters. Chemicals were ranked according to maximum RQ values from high to low.

Mean Maximum a Compounds CAS F1 (%) PNECs Mean Maximum concentrationsb concentrations RQc

(ng/L) (ng/L) (ng/L) RQ

Ethinylestradiol 57-63-6 2 10 57 0.002 4851.00 28500.00

Diclofenac 15307-86-5 63 247 18740 1 246.57 18740.00

Ibuprofen 15687-27-1 59 337 31323 10 33.71 3132.30

Carbamazepine 298-46-4 68 183 11561 10 18.32 1156.10

Atorvastatin 134523-00-5 14 26 128 0.26 101.88 492.31

Estriold 50-27-1 20 40 480 1 40.27 480.00

Sulfadiazine 68-35-9 35 952 19000 135 7.05 140.74

Ciprofloxacin 85721-33-1 35 657 13567 100 6.57 135.67

Caffeine 58-08-2 90 885 39813 320 2.76 124.42

Clarithromycin 81103-11-9 51 58 2403 20 2.89 120.15

Chapter 2

Paraxanthined 611-59-6 99 355 1412 14 25.38 100.86

Venlafaxine 99300-78-4 62 131 575 91.925 575.00 94.26

Ofloxacin 82419-36-1 37 540 8770 100 5.40 87.70

Atenolol 29122-68-7 n.a. 232 390 5 46.40 78.00

17-β-estradiold 50-28-2 9 9 120 2 5.70 75.00

Bezafibrate 41859-67-0 49 243 15060 230 1.06 65.48

Erythromycin 114-07-8 53 76 1700 40 1.89 42.50

Ranitidine 66357-35-5 57 34 136 5 6.82 27.69

Dextropropoxyphene 469-62-5 22 152 682 32 4.76 21.31

Amoxicillin 26787-78-0 31 201 622 37 5.44 16.81

Spiramycin 8025-81-8 43 31 74 5 6.19 14.84

Fluoxetine 54910-89-3 15 12 44 3 3.98 14.67

Triclosand 3380-34-5 51 35 223 20 1.74 11.15

Roxithromycin 80214-83-1 26 163 1100 100 1.63 11.00

Zidovudine 30516-87-1 100 57 170 20 2.84 8.50

Tramadol 27203-92-5 77 1127 7731 959 1.17 8.06

Sulfapyridine 144-83-2 84 1222 12000 1841 0.66 6.52

Chapter 2

Citalopram 59729-33-8 60 45 120 20 2.24 6.00

Gemfibrozil 25812-30-0 61 325 7780 1560 0.21 4.99

Sulfamethoxazole 723-46-6 49 200 11920 2400 0.08 4.97

Paracetamol 103-90-2 77 515 2382 500 1.03 4.76

Estroned 53-16-7 45 6 89 20 0.32 4.45

Telmisartan 144701-48-4 n.a. 110 110 26 4.23 4.23

Propyphenazone 479-92-5 56 1160 1970 571 2.03 3.45

4-Acetamidoantipyrined 83-15-8 90 1684 7239 2530 0.67 2.86

Oxytetracycline 79-57-2 24 101 680 310 0.33 2.19

4-Formylaminoantipyrined 1672-58-8 80 1728 3425 1690 1.02 2.03

Valsartan 137862-53-4 71 1507 7479 3865 0.39 1.94

Sulfamethoxypyridazine 80-35-3 48 379 3704 2085 0.18 1.78

Progesterone 57-83-0 n.a. 23 32 19 1.22 1.72

Propranolol 525-66-6 50 68 590 400 0.17 1.48

Pentoxifylline 6493-05-6 50 30 30 21 1.43 1.43

Tamoxifen 10540-29-1 8 25 71 60 0.42 1.18

Propiconazole 60207-90-1 n.a. 63 100 95 0.67 1.05

Chapter 2

Norfloxacin 70458-96-7 78 36 163 160 0.22 1.02 a: F1 represents the frequency of detected concentrations of compounds above the detection limit (>LOD). b: The LODs were different for different methods, only concentrations values above

c d LOD were considered to calculate mean value. : Maximum RQ higher than 1 means that potential environmental risk existed (i.e., RQf >0). : Metabolites and transformation products of pharmaceuticals, natural hormones and triclosan are not pharmaceuticals.

Chapter 2

2.3.1.2 Detection frequencies and concentrations of pharmaceuticals Figure 2.2 shows the numbers of pharmaceuticals that were analyzed (a) and positively detected (b) in surface waters of each country. In total, there are 33 countries for which pharmaceuticals have been reported in the literature, covering the majority of the European area. Approximately 72% (86 out of 119) of the analyzed pharmaceuticals in the United Kingdom were detected at the concentrations above the limit of detection levels, and circa 55% (182 out of 100) in Germany and 40% in both Sweden and France (51 out of 132 and 61 out of 152, respectively). The most were found in Spain where studies reported circa 67% (153 out of 227) of the analyzed pharmaceuticals and metabolites and transformation products with concentrations over the detection limit. The concentrations and detection frequencies of the top 45 most studied compounds in European surface waters are shown in Table 2.1. Among these compounds, seven compounds, including 2 non-steroid anti-inflammatory drugs (NSAIDs) (ibuprofen, and diclofenac), 2 lipid-regulating drugs (gemfibrozil and bezafibrate), 1 stimulant (caffeine), 1 anticonvulsant (carbamazepine) as well as 1 antibiotic (sulfamethoxazole), were frequently analyzed in more than 28 European countries. Great differences in national-weighted mean and maximum concentrations were observed for these compounds, which may be caused by many factors, including differences in use and release, removal efficiency of the WWTPs, degradation rate, temperature and dilution of receiving waters (Baker and Kasprzyk-Hordern, 2013).

Chapter 2

Figure 2.2 Total numbers of pharmaceuticals, metabolites and transformation products analyzed

(A) and positively detected (i.e., conc. >LOD, B) in European surface waters by country, given as groups.

Predominant antibiotic groups, such as macrolide antibiotics (e.g., erythromycin and its metabolite erythromycin–H2O), fluoroquinolones (e.g., ciprofloxacin, ofloxacin and norfloxacin), and sulfonamides (e.g., sulfadiazine, sulfamethoxazole and its metabolite acetyl-sulfamethoxazole, sulfamethazine and its metabolite N4-acetyl sulfamethazine), were present in surface waters with high detection frequency (24 to 84%) and concentrations (up to 19,000 ng/L). The most concerned and studied antibiotic sulfamethoxazole and its metabolite acetyl-sulfamethoxazole were positively detected in over 50% of 892 samples collected in 33 countries, with a mean concentration of 192 ng/L. The concentrations of sulfamethoxazole in European countries were comparable to those in China (up to 940 ng/L) (Bu et al., 2013), and the highest concentration in European surface waters (11,920 ng/L) was higher than the reported maximum concentration in rivers of Australia (2,000 ng/L) (Watkinson et al., 2009). Norfloxacin was one of the most reported fluoroquinolones, the concentrations of norfloxacin in surface waters in European countries were similar to those in Australia (Watkinson et al., 2009), but lower than those in China (up to 6,800 ng/L) (Bu et al., 2013). The most ubiquitous anti-inflammatories were ibuprofen and diclofenac. Ibuprofen was detected in 16 out of 31 countries with a concentration higher than 100 ng/L.

Chapter 2

Especially, diclofenac was found in Spain and Germany with a mean concentration of 514 and 1,022 ng/L, respectively. Ibuprofen and diclofenac were frequently detected not only in surface waters, but also in aquatic organisms. It has been reported that the observed concentrations of ibuprofen and diclofenac in the bile of wild fish caught from a lake that received treated municipal wastewater ranged from 15 to 34 ng/mL and 6 to 148 ng/mL, respectively (Brozinski et al., 2012). The metabolites of anti-inflammatories, such as 4-Acetamidoantipyrine and 4-Formylaminoantipyrine, were frequently detected. Some antidepressants, such as fluoxetine, sertraline, venlafaxine, were commonly detected in WWTP effluents and surface waters (Corcoran et al., 2010). Meanwhile, the antidepressants fluoxetine and sertraline and their respective metabolites norfluoxetine and desmethylsertraline were observed in the fillet, liver, muscle and brain of fish caught from effluent-dominated rivers (Brooks et al., 2005; Ramirez et al., 2009; Schultz et al., 2010). Hormones were frequently detected in European surface waters. Although natural steroid estrogens (e.g., estrone, estradiol and estriol) excreted by humans and animals actually cannot be regarded as pharmaceuticals, these compounds were considered for their potential endocrine disrupting effects. Estriol was found in 20% of the all samples with a concentration range of 0.33 – 480 ng/L in European surface waters. The most frequently studied synthetic steroid estrogen is ethinylestradiol (EE2) with a concentration range of 0.3 – 57 ng/L in European surface waters. The most ubiquitous β-blocker was metoprolol, occurring in 69% of 635 samples analyzed in 20 countries. Another β-blocker sotalol was often present and occurred in the Netherlands, Spain, Sweden, Switzerland and Germany. Although some of β-blockers (e.g. atenolol, bisoprolol and sotalol) were frequently detected in surface waters, they have not been detected in fish tissues in previous studies (Brozinski et al., 2012; Lahti et al., 2012; Ramirez et al., 2009). For other therapeutic groups, the anxiolytic drug oxazepam was detected in 27% of samples taken from France and 68% in the United Kingdom. The drug metabolite clofibric acid was detected in 32% out of 484 samples collected from European 28

Chapter 2 surface waters. Caffeine is the most frequently consumed psychoactive stimulant (Persad, 2011) and is found in foods, beverage and pharmaceuticals. Concentrations of caffeine higher than 100 ng/L were reported in 26 out of 30 countries, and the highest concentration was found in Belgium (39,813 ng/L). The anticonvulsant carbamazepine was detected in mean concentrations up to 572 ng/L in Belgium, up to

771 ng/L in Hungary, and up to 5,783 ng/L in Cyprus. It must be mentioned that mean concentrations in some countries (e.g., Greece, Turkey and Cyprus) are based on quite limited numbers of studies, in contrast to other countries such as Spain and Germany in which many measurements are available. Thus a direct comparison between different countries is not expedient. 2.3.2 Screening-level risk assessment of pharmaceuticals in surface waters 2.3.2.1 National-scale risk assessment The number of pharmaceuticals with potential environmental risk in each country is shown in Figure 2.3. In these countries, a total of 45 pharmaceuticals yielded an RQf above zero. The risk assessment in surface waters of Spain found 26 pharmaceuticals with RQf above zero. In France, Germany, the United Kingdom and Sweden, 11, 13,

13 and 16 potentially environmental risk compounds with RQf above zero were detected, respectively. The present results reveal high environmental risks in these countries. In contrast, few were detected in Greece, Finland and Ukraine, which does not mean a lower risk in these countries due to the fact that a comprehensive monitoring is often lacking or limited. For the most frequently detected compounds, concentrations have been measured above PNEC levels for caffeine, ibuprofen, diclofenac and carbamazepine in 16 out of 33, 26 out of 31, 28 out of 33, and 30 out of 32 detected European countries, respectively. Bezafibrate with concentrations higher than PNEC was only detected in

Belgium, Cyprus, France, and Spain. For sulfamethoxazole, RQf values above zero were only detected in Cyprus and Spain. Other widely detected compounds were paracetamol, triclosan and estradiol with RQf values above zero in more than five countries.

Chapter 2

Figure 2.3 The total numbers of pharmaceuticals with RQf above zero in each country. 2.3.2.2 Europe-wide ranking system

Table 2.2 Prioritized pharmaceuticals according to the RQf values in descending order (RQf > 0).

Log Name Therapeutic groups F (%) RQf Kow

High risks

Diclofenac Anti-inflammatories 4.51 62 153.65

Ethinylestradiol Hormones 3.67 2 109.89

Paraxanthinea Metabolites -0.22 92 23.35

Ibuprofen Anti-inflammatories 3.97 47 15.73

Lipid-lowering Atorvastatin 6.36 14 14.54 agent

Carbamazepine Anticonvulsants 2.45 55 10.13

Estriola Hormones 2.45 17 6.75

Venlafaxine Antidepressants 0.43 44 6.67

Ranitidine Antihistamines 0.27 42 2.85

Spiramycin Antibiotics 1.87b 40 2.46

Zidovudine Antiviral drugs 0.05 73 2.07

Amoxicillin Antibiotics 0.87 31 1.70

Moderate risks

Citalopram Antidepressants 3.74 40 0.90

Chapter 2

Dextropropoxyphene Analgesics 4.18 19 0.89

Ciprofloxacin Antibiotics 0.28 12 0.80

Caffeine Stimulants -0.07 28 0.77

Propyphenazone Analgesics 2.02 6 0.76

Ofloxacin Antibiotics -0.39 14 0.74

Peripheral Pentoxifylline 0.29 50 0.71 vasodilator

Fluoxetine Antidepressants 4.05 14 0.57

Sulfadiazine Antibiotics -0.09 7 0.50

Clarithromycin Antibiotics 3.16 14 0.41

Triclosana Antibacterial 4.76 21 0.37

Erythromycin Antibiotics 3.06 18 0.33

Paracetamol Analgesics 0.46 30 0.31

4-Formylaminoantipyrinea Metabolites -0.13 30 0.31

Tramadol Opioids drugs 3.01 25 0.29

17-β-estradiola Hormones 4.01b 9 0.17

4-Acetamidoantipyrinea Metabolites 0.50 20 0.13

Endurable risks

Sulfapyridine Antibiotics 0.35 14 0.090

Roxithromycin Antibiotics 2.75 6 0.090

Lipid-regulating Bezafibrate 4.25 5 0.051 drugs

Valsartan Antihypertensives 3.65b 12 0.046

Norfloxacin Antibiotics -1.03 6 0.010

Oxytetracycline Antibiotics -0.90 4 0.010

Estrone Hormones 3.13 3 0.010

Negligible risks

Sulfamethoxypyridazine Antibiotics 0.32 5 0.009

Tamoxifen Anti-estrogen drugs 6.30 1 0.006

Chapter 2

Propranolol β–blockers 3.48 3 0.005

Lipid-regulating Gemfibrozil 4.77 1 0.003 drugs

Sulfamethoxazole Antibiotics 0.89 1 0.001 a Metabolites of pharmaceuticals, natural hormones and triclosan are not pharmaceuticals for therapeutic, preventive, and diagnostic purposes; b LogKow reference from ECOSAR, and other LogKow from SRC (2016).

Table 2.2 shows prioritized pharmaceuticals according to RQf values in descending order. RQf ranged from 153.65 for the anti-inflammatory diclofenac to 0.001 for the antibiotic sulfamethoxazole. Compared with the original RQ value, RQf showed greater difference in potential environmental risks of the compounds after considering the frequency of MECs exceeding PNECs. For 12 compounds, namely diclofenac, ethinylestradiol, paraxanthine, ibuprofen, atorvastatin, carbamazepine, estriol, venlafaxine, ranitidine, spiramycin, zidovudine and amoxicillin, the RQf values were higher than 1.0, meaning high environmental risks in European surface waters according to our proposed approach. For 17 compounds the yielded RQf values were between 0.1 and 1.0, which would mean that moderate environmental risk was probable. Among these 29 compounds with high and moderate environmental risks, ethinylestradiol, diclofenac, and 17-β-estradiol have recently been included in the watch list by EU decision 2015/495 (EU, 2015). A previous study suggested that triclosan should be included as a priority pollutant in Europe (von der Ohe at al., 2012). Similarly, a European demonstration program (EDP) for chemical identification and effect-based monitoring of organic pollutants in surface waters prioritized 8 pharmaceuticals on the basis of their frequencies and extent of exceedance of PNECs (Tousova et al., 2017). Seven of them were ranked in our priority list, i.e. diclofenac, triclosan, ibuprofen, caffeine, erythromycin, carbamazepine and clarithromycin.

For 7 compounds the obtained RQf values were between 0.1 and 0.01, which we proposed indicated small-scale environmental risks in European surface waters. The

RQf values of 5 compounds were less than 0.01, in terms of the RQf indicating quite

Chapter 2 limited effects to the environment. In general, the selected priority pharmaceuticals were comparable to previous results. Actually, some of pharmaceuticals in our priority list were concerned worldwide. Previous studies suggested the potential ecological risks of erythromycin and oxytetracycline in Asia (Ji et al., 2012; Park and Choi, 2008; Yang et al., 2011). Bu et al. (2013) based on the RQ identified that 6 priority compounds (erythromycin, roxithromycin, diclofenac, ibuprofen, salicylic acid and sulfamethoxazole) posed moderate to high risks to aquatic organisms in surface waters in China. Finally, the RQf values of the remaining pharmaceuticals were zero, which would mean that no environmental risk was expected at detected environmental concentrations. Adverse effects of some high-risk pharmaceuticals on non-target organisms have been observed in the laboratory and field. For example, diclofenac has been shown to bioaccumulate in fish (Lahti et al., 2011) and poses distinct effects in liver, kidney, and gills of fish at environmentally relevant concentrations (Triebskorn et al., 2007). The chronic exposure of fathead minnow (Pimephales promelas) to low concentrations (5 - 6 ng/L) of the potent ethynylestradiol in surface water led to feminization of males (Kidd et al., 2007). Among these compounds, fungicides and antibiotics appear to be the most toxic in European surface waters. Antibiotics in the environment not only directly influence the health of organisms, but also promote the evolution and spread of antibiotic resistance genes, which can favor the spread of resistant pathogens (Ågerstrand et al., 2015; Pruden et al., 2013). It must be mentioned that propiconazole, progesterone, atenolol and telmisartan were only analyzed in limited sampling sites and the detection frequencies of those compounds were unavailable, thus these 4 chemicals

(RQf >0) were not in our priority list and further studies should be done. The environmental fate of pharmaceuticals can be affected by physicochemical properties such as water solubilities, lipophilic characteristics and adsorption coefficients. Therefore partition coefficients such as the octanol–water partition coefficient (Kow) should be considered. Some pharmaceuticals such as caffeine (LogKow= - 0.07) and paracetamol (LogKow = 0.46) are readily biodegradable, but 33

Chapter 2 other pharmaceuticals appear to be quite persistent (Stuer-Lauridsen et al., 2000). Compounds with logKow higher than 3.0 show hydrophobic behavior and have a high potential for bioaccumulation (Palma et al., 2014). For example, erythromycin (LogKow = 3.06) can persist in the environment for more than one year. As shown in Table 2.2, around 40% (12 out of 29) of the highly or moderately hazardous pharmaceuticals have high potentials for bioaccumulation and should be considered as priority at the same risk level. Traditional pharmaceutical prioritization usually assesses the risk of compounds under general and worst scenarios. A total of 45 chemicals revealed the potential existence of an environmental risk under the worst-scenario (maximum RQ >1), and 33 chemicals indicated the potential existence of an environmental risk under the general scenario (mean RQ >1). It is clear that the risk of compounds cannot be precisely reflected by the RQ values no matter based on general or worst scenarios due to the great variations in detected concentrations. For example, potential environmental risk really existed for pentoxifylline (RQ=1.43) and propranolol (RQ=1.48), but the possibility of organisms exposed to the unsafe levels were 50% and 3%, respectively.

2.4 Conclusions

Pharmaceuticals are ubiquitous in European surface waters and often detected at the ng/L range, and pose potential threats to aquatic organisms. Thus, in this study an optimized risk assessment method considering the frequency of concentrations above PNECs was recommended for screening-level risk assessment. Results showed that wide-scale water resource management should give priority to high-risk pollutants that are widely distributed and frequently detected. In European surface waters 12 compounds posed high risks to aquatic species and 17 caused moderate risks. Further investigations of the chronic effects of single pharmaceuticals or their mixtures need to be conducted in order to understand potential environmental effects.

Acknowledgements

Chapter 2

The present research has been supported by the SOLUTIONS project that is funded by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement number (603437) and a personal fellowship to the first author by the China Scholarship Council. We appreciate the very valuable comments and suggestions of Dr. Gerd Maack and Dr. Peter Carsten von der Ohe from the German Federal Environment Agency (UBA) in Germany. The authors thank all scientists and agencies who offered us the data and additional information.

Chapter 3

Behavioral profile alterations in zebrafish larvae exposed to environmentally relevant concentrations of eight priority pharmaceuticals

This chapter is based on a study published in Science of the Total Environment: Zhou, S., Chen, Q., Di Paolo, C., Shao, Y., Hollert, H., Seiler, T. B. 2019. Behavioral profile alterations in zebrafish larvae exposed to environmentally relevant concentrations of eight priority pharmaceuticals. Science of the Total Environment, 664, 89–98.

Chapter 3

Abstract

Although the effects of pharmaceuticals on aquatic organisms have been widely investigated during the last decades, toxic effects, especially delayed toxicity, during the developmental stage at environmental relevant concentrations were rarely known. In this study, a sensitive assay based on behavioral alterations was used for studying the delayed toxicity during the developmental stage on zebrafish embryos. Eight pharmaceuticals that were frequently detected with high concentrations ranging from ng/l to μg/l were screened for this study. These compounds were the three antimicrobials clarithromycin, triclosan and sulfamethoxazole, the two anti-inflammatories ibuprofen and diclofenac, the anticonvulsant carbamazepine, the lipid-lowering agent bezafibrate and the stimulant caffeine. Behavioral alterations of zebrafish at 118 hpf (hours post fertilization) after exposing to eight single pharmaceuticals with concentrations in the ranges of environmental detected and their mixtures during embryonic development (2 - 50 hours post fertilization, hpf) were observed. Multiple endpoints, including mortality, hatching rate, swimming speed and angular velocity were evaluated. Results showed that behavioral profile alterations in zebrafish larvae are promising for predicting delayed sublethal effects of chemicals. Delayed hatch was observed at 72 hpf following embryonic exposure to triclosan (1 μg/l) and carbamazepine (100 μg/l) up to 50 hpf. The zebrafish larval locomotor behavior following embryonic exposure to 0.1 μg/l triclosan and 1 μg/l caffeine in the early stages of development (2- 50 hpf) was altered. Furthermore, the effects of the mixture of 8 pharmaceuticals each with the highest environmental concentration on larval behavior were observed during embryonic development. Generally, this study showed that the effects of pharmaceuticals singly or their mixtures in surface waters cannot be ignored.

Keywords: Triclosan, Zebrafish behavior, Locomotion alterations, Mixture toxicity, Zebrafish development

Chapter 3

3.1 Introduction

Globally, pharmaceuticals are produced and used each year. A proportion of pharmaceuticals and their metabolites finally are released into the environment through a variety of pathways (Jaffrézic et al., 2017; Desbiolles et al., 2018). Consequently, pharmaceuticals have been observed in surface waters at concentrations ranging from ng/l to μg/l (Hughes et al., 2013; aus der Beek et al., 2015; Hossain et al., 2018; Fekadu et al., 2019). Although the concentrations of pharmaceuticals in surface waters are unlikely to result in lethal toxicity, the sublethal effects of emerging pollutants on locomotor activities, feeding and reproduction are an increasing concern (Brodin et al. 2013; Di Paolo et al., 2015). Visual motor response has already been used for assessing effects of chemical compounds on zebrafish behavior (Nüßer et al., 2016; Chen et al., 2017; Velki et al., 2017). Zebrafish are phototactic, and thus an alternating dark-and-light period enables the detection of behaviors under different stress scenarios (Legradi et al., 2015). During recent years, multi-endpoints, including swimming speed (Xia et al., 2017), moved distance (Chen et al., 2017; Velki et al., 2017; Michelotti et al., 2018), angular velocity (Michelotti et al., 2018), thigmotaxis preference (Schnörr et al., 2012) and feeding performance (Nassef et al., 2010) have been used in previous studies for fish behavioral assessment, and changes in these endpoints are correlated with external stress. Based on measured environmental concentrations of pharmaceuticals and the frequencies of their concentrations over predicted no effects levels in European surface waters, eight pharmaceuticals from five therapeutic categories with potential environmental risks were screened for zebrafish behavioral study. These pharmaceuticals included the three antimicrobials clarithromycin, triclosan and sulfamethoxazole, the two anti-inflammatories ibuprofen and diclofenac, the anticonvulsant carbamazepine, the lipid-lowering agent bezafibrate and the stimulant caffeine.

Chapter 3

Delayed toxicity that was viewed as a special case of chronic toxicity was observed with marine flatfish sole (Foekema et al., 2008, 2014) and freshwater zebrafish (Di Paolo et al., 2015; Lovato et al., 2016) during early-stage embryonic development. But, to our knowledge, the delayed effect of pharmaceuticals on fish species is still unknown. Furthermore, compounds often occur in the natural environment as complex mixtures rather than single alone, thus the knowledge of mixture cannot be excluded. The first aim of this study was to characterize the occurrence of delayed effects on zebrafish larvae up to 118 hpf after exposure to the pharmaceuticals during the embryonic development (2 to 50 hpf) followed by transference to clean artificial freshwater. The second aim of this study was to investigate the combined effects of the selected pharmaceuticals as mixtures under three different scenarios (worst, medium and best-case). Considering that the effects of the compounds could not be completely evaluated by one single endpoint assessment, we analyzed several endpoints (e.g., mortality, hatching rate, swimming speed and angular velocity).

3.2 Materials and methods

3.2.1 Test pharmaceuticals The 8 pharmaceuticals (ibuprofen, diclofenac, caffeine, carbamazepine, clarithromycin, sulfamethoxazole, bezafibrate and triclosan) were supplied by Sigma-Aldrich (Deisenhofen, Germany). Stock solutions of tested pharmaceuticals were prepared in dimethyl sulfoxide (DMSO). The relevant physicochemical characteristics of the test pharmaceuticals are presented in Table 3.1. Diclofenac and clarithromycin have low solubility in water. In order to observe the potential effects of pharmaceuticals, diclofenac and clarithromycin were tested using a maximum concentration without precipitation observed in DMSO-water miscible systems. The other pharmaceuticals were tested at concentrations below the solubility in water at 20℃. 3.2.2 Mixtures Mixtures of 8 pharmaceuticals at three concentration levels simulating worst, medium

Chapter 3 and best-case scenarios were designed. Concentrations were based on the highest measured concentrations in European surface waters (Table 3.2). In the synthetic mixture M1, the concentration of each compound was similar to the highest reported concentration in European surface waters, which was regarded as worst-case scenario in this study. In the mixture M2, the concentration of the each component was 10 times lower than the highest measured concentrations, and close to or little higher than the mean concentrations, representing the medium-case scenario. In the mixture M3 (best-case), the concentrations of the compounds were 100 times lower than the highest measured concentrations and similar or lower than the mean concentrations, representing the best-case scenario.

Table 3.1 Physicochemical characteristics of the 8 priority pharmaceuticals for European surface waters selected for behavioral tests in this study.

Highest test Compound Log Solubility a CAS Mode of Action concentration Kow (mg/l) (mg/l)

Ibuprofen 15687-27-1 Antiinflammatory 3.97 21 12.5

Diclofenac 15307-86-5 Antiinflammatory 4.51 2.37 50

Caffeine 58-08-2 Neuroactive -0.07 21600 100

Carbamazepine 298-46-4 Anticonvulsant 2.45 18 6.25

Clarithromycin 81103-11-9 Antibiotic 3.16 1.693 12.5

Sulfamethoxazole 723-46-6 Antibiotic 0.89 610 100

Triclosan 3380-34-5 Antibiotic 4.76 6.05 5

Lipid-lowering Bezafibrate 41859-67-0 4.25 - 6.25 agent a: LogKow and water solubility values were acquired from PubChem database. Water solubility of the bezafibrate was unknown.

3.2.3 Fish embryo acute toxicity test up to 48 hpf

Table 3.2 Composition of mixtures and concentrations of 8 priority selected pharmaceuticals in

Chapter 3

European surface waters.

Concentrations in the Mean Highest mixture (μg/l) Mixture Compound concentration concentration High Medium Low ratio (%) (μg/l) a (μg/l) a (M1) (M2) (M3)

Diclofenac 0.31 18.74 19 1.9 0.19 14.82

Triclosan 0.04 0.22 0.2 0.02 0.002 0.16

Carbamazepine 0.22 11.56 11 1.1 0.11 8.58

Bezafibrate 0.18 15.06 15 1.5 0.15 11.7

Sulfamethoxazole 0.3 11.92 12 1.2 0.12 9.36

Ibuprofen 0.38 31.32 30 3 0.3 23.4

Caffeine 0.75 39.81 40 4 0.4 31.2

Clarithromycin 0.17 1.31 1 0.1 0.01 0.78 a: The highest and mean concentrations in European surface waters were acquired from our unpublished manuscript Zhou et al. (under revision).

The zebrafish embryo acute toxicity test up to 48 hpf was applied to assess the lethal toxicity of the test compounds and their mixtures. Zebrafish eggs utilized in this study were produced by a wild-type zebrafish strain from West Aquarium GmbH (Bad Lauterburg, Germany). The fish rearing and acute toxicity test protocols were followed as described in the FET (Fish Embryo Test) OECD guideline (OECD, 2013) with slight modifications. Briefly, viable fertilized eggs at 2 hpf (64-cell stage) were exposed individually in 200 μl/well to freshly prepared test solutions in artificial freshwater in 96-well plates. Five nominal concentrations of each compound (dilution ratio 1:2) were used for LC50 determination. The maximum concentrations applied for the individual tests are shown in Table 3.2. For quality assurance an artificial freshwater control, a solvent control (0.5% DMSO), and a positive control (4 mg/l 3,4-dichloroaniline) were also performed in each experiment, and ten embryos were exposed per each test concentration or control condition. After 48 h of exposure at 26 ± 0.5 °C in the incubator under a 14 h light : 10 h dark cycle, lethal endpoints were

Chapter 3 recorded according to the guideline. Acute toxicity values obtained at 48 h were the fundamental for further behavioral test which was observed without lethal toxicity.

The mortalities of positive controls were higher than 40%, and all experiments were repeated independently three times. 3.2.4 Delayed effects followed by transference to clean water up to 118 hpf For the observation of delayed effects, namely hatching rate and behavioural tests, the exposure to the single compounds and mixtures was done up to 50 hpf, followed by transference of embryos to clean water up to 118 hpf. Hatching rate was observed daily and measurement of behavioral endpoints were carried out at 118 hpf. For each single pharmaceutical, five nominal concentrations (0.01, 0.1, 1, 10 and 100 μg/l) and one artificial freshwater control were tested. For the pharmaceutical mixtures, three concentration levels as described above and the artificial freshwater control were tested. There was no significant difference (p > 0.05) between behavioral profiles of larvae which were maintained up to 50 hpf in the solvent control condition (DMSO 0.1%) and the profiles of those larvae kept in artificial freshwater. For each test condition, 20 normally developed embryos at 2 hpf were exposed in glass beakers containing 40 ml of the respective exposure solution with final 0.1% DMSO, except for the negative control which contained artificial freshwater only. Chemical exposure solutions were prepared by diluting stock solutions containing 0.01, 0.1, 1, 10 and 100 mg/l of each compound in DMSO to 0.01, 0.1, 1, 10 and 100 μg/l in the exposure medium, respectively. The pH of all solutions was measured and then the glass beakers were sealed with parafilm to minimize evaporation. After 48 h of exposure, the solutions were removed, and then the embryos were gently washed three times with 20 ml artificial freshwater. Finally, 40 ml of artificial freshwater were refilled into the glass beakers. During the whole exposure period, exposure vessels were observed every 24 h. The hatching rate was recorded, and dead embryos or larvae were noted and removed immediately. Larvae exhibiting severe malformations such as curved body axis were excluded from the behavioral analysis to avoid the interference of morphological effects. The total numbers of dead or malformed larvae accounted for less than 10 % in all conditions. There was no significant between the 43

Chapter 3 treated groups and the controls in embryo mortalities or morphology defects. At 117 hpf, for each condition 16 larvae were transferred into 96-well microtiter plates as one larva in 300 μl of artificial freshwater per well. At 118 hpf, after 5 minutes of acclimation in the dark in the observation chamber DanioVision (Noldus, Netherlands), video recording of the behavior began in the dark, followed by 10-minute alternated light and dark periods for a total of 50 minutes at 26 °C. The test of behavior was carried out in a quiet environment to exclude external disturbance. In total, 48 larvae from three replicates at each concentration level for each pharmaceutical or their mixture were analyzed. The imaging of larval locomotion was analyzed with the EthoVision XT 10 software package (Noldus Information Technology, USA) with evaluation of the swimming speed and the angular velocity. Swimming speed is an indicative of mobile activity and absolute angular velocity based on the heading of the center-point reflects rotation activity of larvae, thus the changes in any of two endpoints are correlated with external stress. Zebrafish larvae below 120 hpf are not protected animal stages according to EU Directive 2010/63/EU (European Union, 2010). No animal test authorization was required for our experiments, since the final measurements were terminated before 120 hpf. At the end of the experiments the larvae were euthanized by a prolonged immersion in a benzocaine solution of 4 g/l in ethanol. Since larvae showed greatly individual variations in locomotion response, extra 3 replicates for high concentrations (1, 10 and 100 μg/ l) of each pharmaceutical (i.e., in total 6 replicates) were tested to certify the potential effects, data were not shown in this study. 3.3.5 Statistical analysis

For the acute toxicity tests, 48 h LC50 values (expressed in mg/l) were determined by four parameter logistic regression based on the grouped results for mortality endpoint assessment obtained from three independent experiments using GraphPad Prism 5 (GraphPad Software Inc., USA). For the behavioral endpoints, the significance of differences was analyzed using SPSS Statistics 17.0 (SPSS Inc., USA). Data were first analyzed using one way ANOVA, followed by a post hoc test (LSD test, two-tailed) to compare between groups. Data were presented as mean ± SEM 44

Chapter 3

(standard error of the mean), and a p-level of 0.05 was used as the minimal criterion of significance. Graphs were plotted using Origin 9.0 software (OriginLab Corp., USA).

3.3 Results

3.3.1 Acute toxicity of pharmaceuticals No significant mortality for 48 h of exposure individually to ibuprofen, caffeine, clarithromycin, sulfamethoxazole, and bezafibrate were observed compared with the controls. The most toxic compound was triclosan, with a 48 h LC50 value of 1.50 ±

0.48 mg/l for zebrafish embryos, followed by diclofenac with a 48 h LC50 of 14.15 ± 0.99 mg/l. The acute toxicity of carbamazepine showed an incomplete concentration-response curve in the range of tested concentrations, with circa 50 % of mortality in the highest tested concentration (6.25 mg/l). The three mixtures of the 8 pharmaceuticals did not produce any visible acute toxic effects in embryos up to 48 h. 3.3.2 Hatching rates at 72 h Significant reduction of hatching rates was observed at 72 h after exposure for 48 h (up to 50 hpf) to 1 μg/l triclosan (p < 0.05), which caused a tendency for a non-monotonic dose-response curve. This exposure condition showed only circa 25 % of hatched embryos, while circa 60 % and between 40 and 65 % of embryos were hatched in the water control and in the higher triclosan exposure concentrations (10 and 100 μg/l), respectively (Figure 3.1). Also, the embryos exposed to the highest concentration of ibuprofen (100 μg/l) caused significant hatching reduction at 72 hpf (p < 0.05, Figure 3.1). Hatching rates at 72 hpf after exposure to the other pharmaceuticals or to the mixtures showed no significant differences compared with these of the water controls (data not shown). At 96 hpf, the percentages of hatched larvae in all controls and exposed groups were above 90 % and no further statistically significant differences were found between each other.

Chapter 3

80 Triclosan

40 * Hatching(%) rate 20

0 NC 0.01 0.1 1 10 100 Concentrations (g/l)

Ibuprofen 80

60 *

40 Hatching(%) rate

0 NC 0.01 0.1 1 10 100 Concentrations (g/l) Figure 3.1 Percentage of hatched larvae at 72 hpf after 48 h exposure to the triclosan, ibuprofen and negative controls (i.e., water controls). Bars represent the mean values for 3 replicates and error bars stand for SEM. 20 embryos per replicate of each concentration were used for percentage calculation. Values that are significantly different from the control are indicated by asterisks (p < 0.05) 3.3.4 Locomotion patterns after exposure to single compounds and their mixtures Locomotor activity endpoints, namely swimming speed and angular velocity, were analyzed for 118 hpf zebrafish larvae to detect behavioral alterations and potential neurological impairments caused by the 48 h embryonic exposure to the single pharmaceuticals and their mixtures. The interference of morphological effects (e.g. malformations, edema) on behavioral assessment was avoided by selecting a concentration range which caused no observable lethal or sub-lethal toxicity. For all

Chapter 3 conditions, the swimming speeds in dark periods were significantly higher (p < 0.05) than respective values in light periods (Figure 3.2). In contrast, the angular velocity in dark periods was significantly lower (p < 0. 05) than respective values in light periods (Figure 3.3), which means that the larvae under visual conditions prefer to rotate in situ rather than directly move to response to external pressure. In general, high individual variability regarding the swimming speed and angular velocity was observed either in negative controls or in any other case of exposures. 3.3.4 Swimming speed Considering swimming speed, no significant effects compared to the control were observed for diclofenac (Figure 3.2A), bezafibrate (Figure 3.2D), sulfamethoxazole (Figure 3.2E), ibuprofen (Figure 3.2F), and caffeine (Figure 3.2G). Triclosan (Figure 3.2B), carbamazepine (Figure 3.2C), clarithromycin (Figure 3.2H) and the mixture under worst-case (Figure 3.2M) affected swimming speed during the dark periods, but no significant differences were found during the light periods. Larvae exposure to the highest carbamazepine concentration (100 μg/l) up to 50 hpf tended to swim faster (1.81 mm/s) than those from water control (1.41 mm/s) during dark periods, with significant differences (p < 0.05) in the first dark period. Exposure to the dilution series of clarithromycin (Figure 3.2H) produced an inhibitory dose– response pattern, in which increased concentrations caused decreased swimming speed. The swimming speed for 100 μg/l clarithromycin-exposed larvae (0.89 mm/s) was significantly lower (p < 0.05) than those presented by larvae from the control condition (1.31 mm/s). Swimming speeds at lower concentrations (0.01, 0.1, 1 and 10 μg/l) were slightly lower (1.29 to 1.07 mm/s) than the control, but no significant differences were found (p > 0.05). Although the decreased dose–response pattern was not particularly obvious for triclosan (Figure 3.2B), a reduced swimming speed (1.12 mm/s) was presented by the larvae exposed to the highest concentration of 100 μg/l triclosan when compared to the control (1.49 mm/s) during the third dark period (p < 0.05). For the mixture-exposed groups, there was no significant difference in swimming speed exposure to the low (M3) and medium (M2) concentration ranges relatively to the control, but a significant decrease (p < 0.05) in swimming speed for 47

Chapter 3 the larvae treated with the high-level concentration (M1) was found (Figure 3.2M).

4 4 A Diclofenac B Triclosan Control 0.01 μg/l 0.1 μg/l Control 0.01 μg/l 0.1 μg/l 1 μg/l 10 μg/l 100 μg/l 3 1 μg/l 10 μg/l 100 μg/l 3 *

2 2 Velocity(mm/s) Velocity(mm/s) 1 1

0 0 10 20 30 40 50 10 20 30 40 50 4 4 Time (min) Time (min) C Carbamazepine D Bezafibrate

3 3 *

2 Velocity(mm/s) Velocity(mm/s) 1 1

0 0 10 20 30 40 50 10 20 30 40 50

Time (min) Time (min)

4 4 E Sulfamethoxazole F Ibuprofen Control 0.01 μg/l 0.1 μg/l Control 0.01 μg/l 0.1 μg/l 3 1 μg/l 10 μg/l 100 μg/l 3 1 μg/l 10 μg/l 100 μg/l

2 2 Velocity(mm/s) Velocity(mm/s) 1 1

0 0 10 20 30 40 50 10 20 30 40 50 4 Time (min) 4 Time (min) G H Caffeine Clarithromycin 3 3 * *

2 2 * Velocity(mm/s) Velocity(mm/s) 1 1

0 0 10 20 30 40 50 10 20 30 40 50

Time (min) Time (min)

Chapter 3

4 M Mixture Control Low 3 Medium High

2 * Velocity (mm/s)

0 10 20 30 40 50

Times (min)

Figure 3.2 Swimming speed (mm/s) of 5dpf larvae in each ten-minute light and dark periods after

48 h embryonic exposure to the single compounds diclofenac (panel A), triclosan (panel B), carbamazepine, (panel C), bezafibrate (panel D), sulfamethoxazole (panel E), ibuprofen (panel F), caffeine (panel G), and clarithromycin (panel H) at 6 concentrations (0, 0.01, 0.1, 1, 10 and 100

μg/l) or to low-, medium- and high-level mixtures (M). The white and black bars at the bottom represent the 10-minute light and dark periods, respectively. Bars represent the mean swimming speed values for 3 experimental replicates (n= 42 - 48) ± SEM (standard error of mean). Asterisks indicate a significant difference from the control in the respective light or dark period (p < 0.05).

3.3.5 Absolute angular velocity Diclofenac, carbamazepine, bezafibrate, sulfamethoxazole, ibuprofen and clarithromycin and mixtures did not exert any significant effects on the absolute angular velocity of the larvae. For zebrafish larvae following embryonic exposure to triclosan (Figure 3.3B) a pattern of increasing absolute angular velocities at increasing concentrations occurred at the second and third dark periods. During the second dark period, larvae exposed to 1 and 100 μg/l triclosan performed turning angles higher than respective control (p < 0.05). During the third dark period, an increased absolute angular velocity was presented by larvae exposed to 0.1, 1, 10, and 100 μg/l triclosan when compared with control conditions (p < 0.05). For caffeine (Figure 3.3G), an 49

Chapter 3 increased absolute angular velocity, which were significantly higher than the control, was observed at 1, 10, and 100 μg/l during the second and third dark periods (p < 0.05). A slight but not significant reduction in absolute angular velocity was observed for larvae exposed to the mixture with the highest concentration range (M1) when compared with control condition.

3.4 Discussion

3.4.1 Immediate toxicity to embryos For these 8 pharmaceuticals, triclosan, diclofenac and carbamazepine were capable of causing acute toxicity at early embryonic stages. The 48 h LC50 values obtained for diclofenac (14.15 mg/l) and triclosan (1.50 mg/l) were in similar range as previously determined values for adult medaka (Oryzias latipes) after 96 h exposure (Nassef et al., 2009). It is of relevance to mention that diclofenac and triclosan, having relatively high logKow values (4.5 - 4.8, Table 3.1), are known to possibly cause delayed toxicity and have a tendency for bioaccumulation (Daley et al., 2009; Di Paolo et al., 2015). These aspects should be taken in consideration in future studies. Although the

LC50 value of carbamazepine failed to be detected, acute lethal effects for zebrafish were still observed at the highest tested concentration (6.25 mg/l). The fact that the mixtures did not cause lethality in 48 h embryos suggests that mortality might not be a concern for environmentally representative exposures to pharmaceuticals. Nevertheless delayed or chronic toxicity was still investigated to properly assess the hazard potential of these chemical mixtures. 3.4.2 Hatching rates and behavioral profiles after transference to clean water Generally, a tendency to increased or decreased locomotion was revealed and a significant difference was observed during the dark periods. The differences were not stably occurred during the whole dark periods (Chen et al., 2017; Velki et al., 2017), which is partly attributed to the high individual variations in locomotion response. Four out of eight pharmaceuticals were found with significant variations in behavioral profiles in larval stages, which is related to their respective modes of action, similar results were also found in previous studies (e.g., Drummond and Russom, 1990; Rihel

Chapter 3 et al., 2010). The antimicrobials triclosan and clarithromycin depressed the locomotor activity as measured by the swimming speed in zebrafish larvae. In contrary the stimulant caffeine accelerated erratic movement as indicated by increased angular velocity, and an increased swimming speed was observed for the anticonvulsant carbamazepine exposure. It is clear that not all of compounds were detected with stress-induced locomotion response, since compounds with a similar mode of action can also have differences in toxicity potency, time to effect onset, and duration of effects, inducing even different behavioral effect profiles (Chevalier et al., 2015). A significant hatch delay occurred at 72 hpf after exposure to ibuprofen (100 μg/L), similar results were reported previously (David and Pancharatna, 2009; Xia et al., 2017). Possible involved mechanisms are related to the fact that the nonsteroidal anti-inflammatory ibuprofen can inhibit cyclooxygenases, which catalyze the biosynthesis of prostaglandin (Cleuvers, 2004) and are necessary for the development of zebrafish embryos (Grosser et al., 2002; Cha et al., 2005). Although no delayed effects of ibuprofen in zebrafish locomotion were observed in this study, immediate effects were previously found for an invertebrate species. For example, acute exposure lasting for 1.5 h to ibuprofen 0.01 μg/l, a concentration lower than the observed mean concentration in European surface waters (0.38 μg/l), resulted in a significant decrease in the activity of amphipoda (Gammarus pulex) (De Lange et al., 2006). Therefore, there is indication that ibuprofen can cause delayed hatching in fish and also have immediate effects on other aquatic organisms in the environment. Both diclofenac and ibuprofen are nonsteroidal anti-inflammatory drugs and have the same mode of action, but no obvious effects of diclofenac on zebrafish were detected in the tested concentration ranges. Similarly, Nassef et al. (2010) found that exposure to 1 mg/l diclofenac for 9 days had no significant effect on the swimming speed of adult medaka fish. Lee et al. (2011) also reported no effects of 100 μg/l diclofenac to medaka after 3 months exposure from egg phase to adult phase, which is greater than respective observed concentrations in European surface waters near sewage treatment plants (18.74 μg/l). It is hence unlikely that this drug would pose potential environmental risks. 51

Chapter 3

3000 3000 Triclosan A Diclofenac B Control 0.01 μg/l 0.1 μg/l Control 0.01 μg/l 0.1 μg/l 2500 2500 1 μg/l 10 μg/l 100 μg/l 1 μg/l 10 μg/l 100 μg/l

2000 2000 * * * * * 1500 1500 *

1000 1000

Angularvelocity (deg/s) 500 500 Angularvelocity (deg/s)

0 0 10 20 30 40 50 10 20 30 40 50 3000 Time (min) 3000 Time (min)

C Carbamazepine D 2500 2500 Bezafibrate

2000 2000

1500 1500

1000 1000

500 500

Angularvelocity (deg/s) Angularvelocity (deg/s)

0 0 10 20 30 40 50 10 20 30 40 50 Time (min) Time (min)

3000 3000 E Sulfametholxazole F Ibuprofen 2500 Control 0.01 μg/l 0.1 μg/l 2500 Control 0.01 μg/l 0.1 μg/l 1 μg/l 10 μg/l 100 μg/l 1 μg/l 10 μg/l 100 μg/l 2000 2000

1500 1500

1000 1000

500 500

Angularvelocity (deg/s) Angularvelocity (deg/s)

0 0 10 20 30 40 50 10 20 30 40 50 4000 Time (min) 3000 Time (min) H 3500 G Caffeine Clarithromycin

3000 2000 2500

2000 * * * 1500 * * * 1000

1000 Angularvelocity (deg/s)

500 Angularvelocity (deg/s)

0 0 10 20 30 40 50 10 20 30 40 50 Time (min) Time (min)

Chapter 3

3000 M

Control Low Mixture 2500 Medium High

2000

1500

1000 Angular velocity (mm/s) velocity Angular

500

0 10 20 30 40 50 Time (min)

Figure 3.3 Angular velocity (deg/s) of 5 dpf larvae in each ten-minute light and dark exposure to 8 single pharmaceuticals at 6 concentrations gradients (0, 0.01, 0.1, 1, 10 and 100 μg/l) or to low-, medium- and high-level mixtures. Velocity was analyzed for 50 minutes in alternating dark and light. The white and black bars at the bottom represent 10-minute light and dark periods, respectively. Bars represent 3 replicates (n=42-48 larvae) ± SEM. Asterisks indicate a significant difference from control (p < 0.05).

The lowest carbamazepine concentration at which significant effects were detected for larval locomotion (100 μg/l) was one order of magnitude higher than the highest concentration detected in European effluent-influenced surface waters (11.56 μg/l). A decrease in feeding behavior and swimming speed was observed in adult Japanese medaka fish when exposed to 6.15 mg/l carbamazepine (Nassef et al., 2010), which was three orders of magnitude higher than the concentrations detected in surface waters. Previous studies did not show any adverse signal of carbamazepine at environmentally relevant concentrations (De Lange et al., 2006; Guler and Ford, 2010). Thus a preliminary conclusion should be that current carbamazepine concentrations in surface waters do not represent an urgent risk in terms of behavioral alterations. For caffeine, a stimulant of the central nervous system, concentration-dependent 53

Chapter 3 stimulation of angular velocity and hatching rate were detected. The LOEC (lowest observed effect concentration) for behavior (1 μg/l) was 40 times lower than the maximum concentration detected in European surface waters near wastewater treatment plants (39.81 μg/l), which indicates the potential environmental effects of caffeine on non-target species in aquatic ecosystem. Caffeine is highly water soluble (Log Kow =-0.07) and can be easily eliminated by the liver in adult fish. However, previous studies showed that the half-life time of caffeine in newborn infants (31-132 h) (Parsons and Neims, 1981) is longer than that in human adults (3–6 h) (Hering-Hanit and Gadoth, 2003). Prolonged half-life of caffeine may also exist in liver of other vertebrates, and thus absorbed caffeine during zebrafish embryonic developmental stages is not completely eliminated at 118 hpf due to the immature liver of larvae, and still causes stimulation of the central nervous system. Exposure to the antimicrobial triclosan in general caused a tendency for reduced swimming speed with increased exposure concentrations. The effects of triclosan on locomotion behavior were also found in the other fish. For example, the swimming speed of adult Japanese medaka was decreased by exposure to 0.17 mg/l triclosan for 9 days (Nassef et al., 2010) and erratic swimming in rainbow trout (Oncorhynchus mykiss) was observed exposure to 71 μg/L triclosan for 61 days (Orvos et al., 2002). Interestingly, a biphasic effect for triclosan occurred for the hatching rate, with a significant hatching delay at low concentrations (1 μg/l), but no significant effects at higher concentrations (10 and 100 μg/l). This complex non-monotonic concentration-related effects of triclosan on zebrafish early-life stages was also observed in previous studies. For example, Falisse et al., (2017) showed delayed hatching after 72 h exposure to 50 μg/L of triclosan, while no significant hatching delay appeared after exposure to 100 μg/L. Parenti et al.(2019) observed significantly increased activities of catalase (CAT), glutathione peroxidas (GPx), and glutathione S transferase (GST) at 0.1μg/L triclosan during the 24 h exposure, but no significant increase was shown at higher triclosan concentration. The other two antimicrobials showed different dose–response curves, larvae exposed to clarithromycin showed a reduction in swimming speed and no obvious trend were 54

Chapter 3 found for sulfamethoxazole exposure. In fact, despite the similar use the mechanisms of actions are different for these three antimicrobial compounds. Clarithromycin prevents bacteria by inhibiting the translation of peptides, sulfamethoxazole prevents bacteria by interfering with the synthesis of folate, and triclosan prevents bacteria primarily by inhibiting fatty acid synthesis (Finberg et al., 2004; Adzitey, 2015). Therefore changes in behavior pattern probably occur through different mechanisms of action (e.g., avoidance, repellency, neurotoxicity) (Nüßer et al., 2016). Behavioral effects following exposure of zebrafish to the mixture under the worst scenario (M1) were detected. Generally, the toxicity prediction of a mixture is based on concentration addition for its components with similar models of action and independent action for its components with dissimilar models of action (Faust et al., 2001; Altenburger et al., 2004). Although the concentrations of each component in the mixture M1 are lower than the lowest observed effect levels for swimming speed, the mixture M1 still caused decreased swimming activity, which could be explained by combined effects of mixture. Interestingly, for angular velocity the concentrations of two components (i.e. carbamazepine and caffeine) in mixture M1 were higher than the respective lowest observed effect levels for angular velocity, but no significant variation was detected relatively to control. One reason might be that the behavioral effects of carbamazepine and caffeine were counteracted by the combined effects of other components. Meanwhile, the existence of drug interactions could also alter behavior effects of the mixture. For example, erythromycin could interfere with the metabolism of carbamazepine, causing decreased carbamazepine clearance and elevated carbamazepine level (Stafstrom et al., 1995). Such hypothesis could be explored in a future study by investigating specifically designed mixtures. 3.4.3 Relevance of delayed larval behavioral effects for toxicity assessment The effects of compounds on the larval locomotion could be evaluated not only by swimming speed but also by angular velocity (e.g., Liu and Fetcho, 1999; Lorent et al., 2001). Absolute angular velocity characterizes the changes in direction of zebrafish movement and is a sensitive locomotor variable in motor function, especially the 55

Chapter 3 erratic swim pattern (Cachat et al., 2011; Rosemberg et al., 2012; Tran and Gerlai, 2013). In our results it is clear that angular velocity was sensitive to dark and light alterations, exhibiting significant difference between the light and dark period. Increased angular velocity indicates the increased escape behaviors, which often is correlation with increased erratic movement (Cachat et al., 2011). Still, angular velocity and swimming speed are complementary endpoints for selecting potentially toxic compounds, being of relevance for instance for chemicals that modulate anxiogenesis and escape response (Budick and O’Malley, 2000). Delayed effects in swimming speed and angular velocity could be detected in the larval stage after exposure to compounds during early stages of embryonic development (2-50 hpf). The occurrence of developmental toxicity in embryos may be related to disruption of the development of the nervous system. During the exposure period, neurons begin to be generated from neural stem cells (6 hpf) and are firstly connected by axons (48 hpf) (De Esch et al., 2012), while brain ventricles also are formed at 48 hpf (De Esch et al., 2012; Legradi et al., 2015). Pollutants could cross placenta and deposit in yolk affecting embryo developmental processes. Our further studies detected increased antioxidative capacity in larvae exposure to 0.1 μg/l triclosan from 2 hpf to 50 hpf (Annex Figure 3.1), which probably explains the increased locomotion at 118 hpf to some extent. Similarly, Parenti et al. (2019) reported an increase of activities of antioxidant enzymes in zebrafish embryos exposed for 24 h to triclosan at 0.1 μg/L. Although no significant increase in larval locomotion were detected for 1 μg/L carbamazepine, but increased antioxidative capacity in larvae was still observed. The LOECs of triclosan (0.1 μg/l) and caffeine (1 μg/l) in this study were in the range of environmentally relevant concentrations. Of particular interest for future investigations and risk assessment strategies is that the obtained LOEC of triclosan for locomotion is just slightly higher than the observed mean concentration in European surface waters (0.04 μg/l). Since environmental risk is often expressed as a ratio of measured environmental concentration and toxicologically effective concentration, studying the developmental effects of pharmaceuticals to zebrafish not only favors the 56

Chapter 3 understanding of chemical toxicity, but also provides reliable data for environmental risk assessment.

3.5. Conclusion

The present study demonstrated that delayed effects could occur in zebrafish larvae due to embryonic exposure to pharmaceuticals in the range of concentrations detected in effluent-influenced surface waters. Meanwhile, zebrafish embryos exhibited developmental toxicity of changes in behavior after exposure to single pharmaceuticals triclosan, carbamazepine, caffeine as well as clarithromycin, and the highest concentration of the mixture which represents the worst scenario occurred near WWTP effluents.

Chapter 3

Acknowledgements

The present research has been supported by the SOLUTIONS project (no.603437). Personally, Shangbo Zhou got financial support from China Scholarship Council (CSC). We want to thank Sarah Johann (RWTH-Aachen University, Germany) for the fish breeding and embryo test instruction, and Sina Volz for behavioral instructions (RWTH-Aachen University, Germany). We also thank Nüßer Leonie for data analysis. The authors thank Noldus for the DanioVision as a partner of the Students Lab ―Fascinating Environment‖ at Aachen Biology and Biotechnology

(ABBt).

Chapter 4

Identification and confirmation of aryl hydrocarbon receptor-mediated activities in sediment samples from the Upper

Danube River by means of effect-directed analysis

This chapter is based on a manuscript to be submitted to Environmental Pollution: Grund, S., Zhou, S., Krauss, M., Schulze,T., Varel, U.L., Winkens, K., Braunbeck, T., Brack, W., Seiler T-B., Hollert H. (to be submitted). Identification and confirmation of aryl hydrocarbon receptor-mediated activities in sediment samples from the Upper Danube River by means of effect-directed analysis. Parts of this chapter have also be used the PhD thesis of Stefanie Grund (2010).

Chapter 4

Abstract

High aryl hydrocarbon receptor (AhR)-mediated activities have been observed for highly contaminated locations in aquatic systems around the world. This study investigated the AhR-mediated activities of sediment extracts of less contaminated sites in the Upper Danube River, and identified specific fractions or pollutants responsible for the determined AhR-mediated activities by a complete effect-directed analysis (EDA). Based on the AhR-mediated activities for the raw extracts, sediment samples from four sites were chosen for fractionation. Purified sediment extracts and all fractions were tested with the 7-ethoxyresorufin-O-deethylase (EROD) assay using RTL-W1 cells, and the most active fractions were chemically analyzed. Then the contributions of detected chemicals to bioassay-derived toxicity equivalents (Bio-TEQs) were calculated. Furthermore, synthetic mixtures of the most active fractions were prepared to reflect the real contributions of detected chemicals to Bio-TEQs. The results from EROD assays clearly demonstrated high AhR-mediated activities in raw extracts, dialyzed extracts and different fractions of selected extracts of the sediment, and subsequently confirmed the presence of AhR agonists in these samples. The measured Bio-TEQs of sediment extracts from several sites are similar to those reported for samples from highly contaminated locations in other river systems. Besides non-polar fractions, medium-polar to polar fractions were also identified as priority fractions for the AhR-mediated activities of extracts, and the analyzed US EPA priority PAHs in non-polar fractions accounted only to a minor extent for the AhR-mediated activities of the dialyzed extracts as well as to the sum of the Bio-TEQs of the analyzed fractions. The results of the present study indicate that EDA using on-line fractionation procedure, bioassays, chemical analyses and confirmation is a powerful tool to screen sediment extracts for fraction-specific adverse effects and associate these effects to specific groups of pollutants.

Keywords: EDA; sediment extracts; fractionation; polycyclic aromatic hydrocarbons; RTL-W1; EROD

Chapter 4

4.1 Introduction

Due to the large-scale production and use of an increasing diversity of chemicals in modern society, surface waters and sediments are often contaminated with a multitude of known and unknown chemicals which are present in high concentrations. In such a mixture of thousands of contaminants it can be only a few which actually have a major deleterious impact on aquatic organisms. Therefore, it is crucial to identify the components responsible for adverse effects in contaminated environmental samples, to identify sources as well as assess and mitigate the associated risks to aquatic life. Particularly, sediments can accumulate and retain many of those pollutants released by human activities, and are well known to have the potential to negatively affect aquatic organisms (Boehler et al., 2018; Brack et al., 2007; Brinkmann et al., 2013; Karlsson et al., 2008; Qi et al., 2017; Yi et al., 2015). Previous investigations included meta analyses, toxicity assays and several lines of evidence clearly showed that acute (cytotoxicity, embryo toxicity) and specific toxic potentials (genotoxicity, mutagenicity, endocrine disruption and teratogenicity, AhR-mediated activity) are associated with local sediments, which seemed to be related to negative effects in situ (Boettcher et al., 2010; Grund et al., 2010a, 2010b; Keiter et al., 2006, 2008; Otte et al., 2008; Seitz et al., 2008).

Certain lipophilic pollutants, frequently found in aquatic sediments, such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs), are known to induce cytochrome P450 1A (CYP1A) by ligand-activation of the aryl hydrocarbon receptor (AhR) (Brack et al., 2005, 2007; Li et al., 2016; Xiao et al., 2016; Weber et al., 2018). The binding of xenobiotics to the intracellular AhR triggers numerous adverse effects in many organisms (Henkler and Luch, 2011; Henkler et al., 2012; Parzefall, 2002). The adverse effects of AhR agonists in various vertebrate species are similar. Up to date, a multitude of particular symptoms of toxicity in various species has been associated with AhR ligands, including effects on biochemistry, physiology and reproduction even in environmentally relevant concentrations (Birnbaum and

Chapter 4

Tuomisto, 2000; Giesy et al., 2002). Thus, in river basins and adjacent coastal areas, several attempts have been made to identify AhR-mediated toxicants (e.g., Brack et al., 2007; Keiter et al., 2008; Schiwy et al., 2015; Xiao et al., 2016; Wölz et al., 2010). These studies clearly showed that risk assessment based on chemical analysis, e.g. of priority pollutants, in sediments or water, is often not able to reflect the risk of the actual mixture of contaminants, but only the risk of those pre-selected toxicants (Brack et al., 2009; Li et al., 2016; von der Ohe et al., 2009). On the contrary, combined biological and chemical-analytical approaches have been shown to provide an important progress towards an identification of those toxicants that are relevant for site-specific risks and towards an estimation of the portion of an effect that can be explained by the analyzed chemicals (Brack et al., 2007; Hollert et al., 2002; Neale et al., 2015). This holds particularly true for responses triggered by the binding to a specific receptor such as the Ah-receptor with a limited number of compounds often explaining high portions of the effect. However, even for those effects a quantitative agreement between chemically derived potentials and measured effects as a crucial basis for reliable conclusions is not always achievable. This approach does not provide a possibility to identify unknown causes of effects. Effect-directed analysis (EDA), which combines effect-based bioassays, fractionation, chemical analysis and confirmation, has been demonstrated to be a suitable tool to identify hazardous compounds in complex environmental mixtures (Brack et al., 2008b, 2016, 2018; Hashmi et al., 2018). The potential of this approach was shown to allow for toxicant identification in many matrices and for many toxicological endpoints in several studies (Brack et al., 2007, 2008b; Xiao et al., 2016).

In the present study, 7-ethoxyresorufin-O-deethylase (EROD) activity in vitro in the rainbow trout liver cell line (RTL-W1) was evaluated for the determination of AhR-mediated activities of sediment extracts. Because of their speed, sensitivity, and reproducibility, in vitro EROD induction assays are powerful tools for the specific detection of AhR-binding toxicants in complex environmental samples (Brack et al., 2002, 2016; Giesy et al., 2002; Heinrich et al., 2017; Xiao et al., 2016). Prior to

Chapter 4 fractionation, an accelerated membrane-assisted clean-up technique was used to purify the complex matrix of the sediment extracts (Streck et al., 2008). Subsequently, an automated on-line fractionation procedure was applied using three coupled and automatically connected columns (Lübcke-von Varel et al., 2008). Purified sediment extracts and all fractions were tested with the EROD assay and the most active fractions were chemically analyzed by gas chromatography with mass-selective detection (GC-MS).

Confirmation proposed as a final step in EDA is a crucial element to confirm whether identified compounds contribute to in situ effects in aquatic organisms, populations and communities under realistic exposure conditions (Brack et al., 2008b, 2016), because present data on dioxin-related compounds provides limited information regarding measured effects in the environment. Confirmation favors to establish reliable cause–effect relationships and to explain the measured effects (Brack et al., 2008b).Although EDA has been widely used in many aquatic systems around the world, a complete EDA approach is still state of the art due to the complexity of the confirmation step. Many standard compounds that are required for effect confirmation need to be tested in the same bioassay (Brack et al., 2016). The goal of the present study was to investigate the AhR-mediated activities of sediment extracts from less contaminated sites along the Upper Danube River, and to identify specific fractions or pollutants responsible for the assessed AhR-mediated activities by a complete EDA.

4.2 Materials and Methods

4.2.1 Sediment samples Near-surface bottom sediment samples were collected at six locations along the Upper Danube River as well as at one artificial lake and at two tributary streams just upstream of their confluence with the Danube River, by means of a van-Veen-gripper or a stainless steel shovel. Sampling sites were chosen in accordance with a suspected gradient of sediment contamination at these locations (Boettcher et al., 2010; Keiter et al., 2006, 2008; Seitz et al., 2008) because of their exposure to sewage treatment plants effluents (Figure 4.1). Four samples were chosen for further investigations by 63

Chapter 4 means of fractionation in accordance to the following reasons: The first site (Sigmaringen) was the sampling site located the farthest upstream, and previous studies showed the highest AhR-mediated activities in this area (Keiter et al., 2008; Otte et al., 2008); the second site (Lauchert) located in tributary just upstream of its confluence with the Danube River was suspected to be an uncontaminated reference site (Keiter et al., 2008; Seitz et al. 2008); the third site (Oepfingen) is known to be a sedimentation pond for river load of the upstream parts of the Danube River; additionally, a sample at the second site collected in 2004 (Site 2R = Lauchert-R) was investigated again for direct comparison to the AhR-mediated activity of the sediment sample collected at the same site in 2006.

CZECH Neckar Regensburg Elbe GERMANY REPUBLIC 8 Weser Ingolstadt Rhine Stuttgart Danube

9 Main Ulm Passau Augsburg Danube 5 6 Lauchert 4 7* 1 * 3 Schwarzach München AUSTRIA 2* /R* Inn Lech Salzburg Lake Constance Isar SWITZERLAND 100 km

Figure 4.1 Sampling sites along the Upper Danube River (Redrawn according to Grund et al.

(2010a)): 1 = Sigmaringen, 2 = Lauchert (tributary), 3 = Riedlingen, 4 = Schwarzach (tributary),

5 = Rottenacker, 6 = Ehingen, 7 = lake Oepfingen, 8 = Jochenstein, 9 = Bad Abbach).

Sampling sites. Sewage treatment plants (> 10,000 residents according to (LFW 2005).

*sampling sites chosen for fractionation and chemical analyses. R = reference sediment sample collected at site Lauchert in 2004 (Keiter et al., 2008).

4.2.2 Soxhlet-extraction and clean-up of sediment extracts The freeze-dried, sieved (≤1.25 mm) and homogenized sediments were Soxhlet extracted for 14 h with dichloromethane and acetone (3:1 v/v, 400 mL) according to the method described by Hollert et al. (2005). After extraction, the extracts were concentrated first using a rotary evaporator and then evaporated close to dryness by a

Chapter 4 gentle stream of nitrogen. Residues from each sample were re-dissolved in 1 mL DMSO for EROD assays and in 1mL hexane and acetone (7:3; v:v) toluene. An accelerated membrane-assisted clean-up technique was used to purify the complex matrix of the sediment extracts according to an optimized protocol described previously (Streck et al., 2008). Briefly, an aliquot of the raw extract equivalent to 20 g of sediment (20 g sediment equivalents, SEQ) was transferred to dialysis membranes (Polymer-Synthese-Werk GmbH, Rheinberg, Germany) and dialyzed using an ASE 200 device (Dionex, Sunnyvale, CA). Dialysis extracts were collected in glass ASE vials and capped with a PTFE-coated screw cap. After evaporating the extracts to dryness, the residue was weighted and re-dissolved in DMSO for EROD assays and in 1mL Hx:DCM (9:1; v:v) to a final concentration of 25 g SEQ mL-1 for fractionation procedure.

4.2.3 Fractionation and chemical analysis Fractionation was performed using an automated on-line fractionation procedure for polycyclic aromatic compounds (PACs) in sediment extracts on three coupled normal-phase high-performance liquid chromatography columns, including cyanopropyl (CN), nitrophenyl (NO), and porous graphitized carbon (PGC), which has been described by Lübcke-von Varel et al., (2008). After fractionation, the fractions of each sample were evaporated to dryness and the residue was re-dissolved in DMSO for EROD assays and in toluene to a concentration of 5 g SEQ per mL solvent.

GC-MS analyses were carried out on a HP 6890 GC coupled to a HP MSD 5973 (Agilent, Palo Alto, USA), equipped with a 30 m x 0.25 mm I.D. x 0.25 µm film HP-5 MS fused capillary silica column, a 5 m pre-column (Agilent J&W, Folsom, USA) and a splitless injector with deactivated glass wool. The MS was operated in electron impact ionization mode (EI+, 70 eV) with a source temperature of 230 °C scanning from 30 to 500 amu (full-scan mode) or single ion monitoring (SIM) for quantification. The PAHs were quantified using external calibration with a mixture of PAHs (Promochem, Wesel, Germany) using single ion monitoring (SIM). The results

Chapter 4 were corrected with an internal standard containing deutered PAH (Mix 35, Promochem, Wesel, Germany).

4.2.4 Biological analysis by EROD assay Induction of the EROD enzyme was measured in the CYP1A-expressing permanent fish liver cell line RTL-W1 according to previous methods described by Behrens et al. (1998) with slight modifications (Seiler et al., 2006; Wölz et al., 2008). EROD assay was carried out without cytotoxic effects. Briefly, RTL-W1 cells were seeded in 96-well plates (TPP) and allowed to grow to 100% confluence for 72 h. Subsequently, the medium was removed and the cells were exposed for 72 h to the 2,3,7,8-tretrachlorodibenzo-p-dioxin (TCDD) standard as positive controls and to the samples. Then, induction was terminated by removing the medium and freezing at -70 °C to lyse the cells for 1 h. EROD activity was measured fluorometrically using a multiwell plate reader (Tecan, Crailsheim, Germany) with excitation/emission wavelength of 544/590 nm. Protein was determined fluorometrically using the fluorescamine method with excitation/emission wavelength of 355/390 nm. EROD activity was determined based on the quantity of produced resorufin per milligram protein per minute. 4.2.5 Calculation of Bio-TEQ values The concentration-response curves for EROD induction in the RTL-W1 cells were computed by the classic sigmoid curve as model equations using Prism 6.0 (GraphPad Software Inc., San Diego). The AhR-mediated potency of the samples was converted to bioassay-derived 2,3,7,8-TCDD equivalents (Bio-TEQs) by relating the ECF value of each sample (i.e., ECF,sample) to the mean ECF of TCDD (i.e., ECF,TCDD) using the fixed effect level quantification method (Equation (4.1); Brack et al., 2000). Briefly, the sample concentration that induced activity to F% of the TCDD-induced maximum

EROD activity was designated as the ECF,sample for that sample. Bio-TEQs based on

EC25 of samples were widely used (e.g., Wölz et al., 2008; Otte et al., 2013; Xiao et al., 2016), therefore it was also used in this study. Because most of the sediment extracts and multilayer fractions were relatively weak inducers, the EC25 was likely to

Chapter 4 be close to the turning point of the sigmoid curve, which allowed for assuming a linear concentration-response relationship. The Bio-TEQs given in this study were mean values of n = 3 (raw extracts, individual compounds, and synthetic mixtures), n = 2 (dialyzed extracts) and n = 1 (fractions) independent test runs with six replicate wells per tested concentration. Bio-TEQs of dialyzed extracts and fractions were calculated with less mean values due to insufficient volumes. Subsequently,

Bio-TEQs based on EC25 were given as picogram TCDD per gram SEQ. The TEQ concentrations were expressed as:

Q (4.1)

4.2.6 Calculation of Chem-TEQ values Safe (1994) described the AhR pathway as structure dependent, as the most potent congeners were 2,3,7,8-substituted tetra- and penta-chlorinated PCDD/Fs as well as meta- and para-substituted coplanar PCBs. Based on in vivo and in vitro data, relative toxic potencies (REP values) were assigned (Eadon et al. 1986; NATO/CCMS 1998). REPs were given as values which were related to the toxic potency of 2,3,7,8-TCDD (REP = 1). Aiming to explain the determined Bio-TEQ levels, chemically derived TEQs (Chem-TEQs) were calculated by multiplying compound concentrations and related REP values that were determined by Bols et al. (1999), specific for RTL-W1 cells and each compound assessed. Subsequently, Chem-TEQs were given as picogram TCDD per gram SEQ-EQ. The Chem-TEQ of a sample containing n components (n≥i> 0) can be expressed with the following equation (4.2):

Q ∑( × ) ( ) 1

Where: Ci is the concentrations of individual components and REPi is the correlated REP values.

4.2.7 Synthetic fractions and confirmation Stock solutions of the individual PACs and fifteen synthetic fractions composed of 2 to 6 PACs were prepared in DMSO. For the individual PACs, a stock concentration of

Chapter 4

5 µg/mL was prepared. Based on the assumption that detected PACs in the environment may account for the observed effects, components of the mixtures were combined according to compounds detected in the respective fractions, and the stock concentrations of individual components in the mixture were combined according to concentrations detected in 20 g SEQ (Table 4.1). AhR inductions of the synthetic mixtures were tested with the EROD assay using RTL-W1 cells in the same manner as the sediment extracts. The Bio-TEQs of the synthetic mixtures were calculated based on an effect level EC25. 4.3 Results and Discussion

4.3.1 AhR-mediated activities by EROD assays 4.3.1.1 AhR-mediated activities of the raw extracts All nine raw extracts of sediments collected at different sites along the Upper Danube River in 2006 as well as one extract from the tributary Lauchert collected in 2004 (Lauchert-R) caused a concentration-dependent increase in EROD activity, indicating the presence of AhR agonists in these samples (Figure 4.2). The highest mean Bio-TEQ was found for the sample from the site at Sigmaringen (13,733 pg/g SEQ) being between 3- to 45-fold greater than Bio-TEQs of the other sediment samples. Relatively high AhR-mediated activities were also determined for raw extracts from the sites at Lauchert, Schwarzach, Rottenacker and Oepfingen with mean Bio-TEQs between 2,240 and 5,196 pg/g SEQ.

AhR-inducing potency may vary depending on the cell system used, which could lead to divergent TEQ determinations for a given sample (Hilscherova et al., 2001; Keiter et al., 2008). Nevertheless, in the following the Bio-TEQs measured for the Upper Danube sediments are compared to some sediment data on TEQs determined by different bioassays: In the present study, Bio-TEQs were determined in the range of 306-13,733 pg/g SEQ. By using the DR-CALUX® assays using the rat hepatoma cell line H4IIE, AhR activities observed in river sediment in the Czech Republic (5,900– 48,200 pg/g SEQ; Vondráček et al,. 2001), in UK estuaries (1,100–154,000 pg/g SEQ; Hurst et al., 2004), and in harbor sediment in New Zealand (5,489 ± 402 pg/g SEQ;

Chapter 4

Houtman et al., 2006) were generally close to or higher than levels detected in the Upper Danube River sediments. Interestingly, even the Bio-TEQs of sediments from sampling sites at industrial hot spots in the River Elbe basin in Germany determined in the DR-CALUX® bioassay (9,700–16,600 pg/g SEQ) were in a similar range those calculated for the Danube River sediments in the present study. The Bio-TEQs reported in the present study are also in a similar range as Bio-TEQs of sediments collected from a coastal river in France measured by the EROD assay using the PLHC-1 cell line (2–14,000 pg/g SEQ; David et al., 2010) and those of sediments from a regularly flooded industrial area in the Czech Republic detected by the H4IIE-luc bioassay (1,000–8,700 pg/g SEQ; Hilscherova et al., 2010). The Bio-TEQs of sediments from the Tietê River in Brazil measured by the EROD assay using RTL-W1 cells ranged from 5,078 to 22,402 pg/g SEQ (Rocha et al., 2010). In an assessment of the potential influence of flood events on the toxicity of suspended particulate matter in Neckar River (Germany), Wölz et al. (2008) recorded a strong increase of EROD activity during a flood event in correlation with discharge and a maximum Bio-TEQ during the peak of the flood event of 8,300 pg/g SEQ. These values highlight the broad range of concentrations that could be analyzed by in vitro bioassays and demonstrate that even though the Upper Danube River was considered to be much less exposed to industrial and other anthropological influences compared to other German rivers systems (such as Rhine, Neckar and Elbe Rivers), the calculated Bio-TEQs of sediment extracts from several sites along the Upper Danube River are in range similar to those reported for samples from highly contaminated locations at other river systems around the world.

Great differences were determined, particularly for the extracts of the sediments from the sites at Sigmaringen, Lauchert and Riedlingen (Figure 4.2). Based on the results of previous studies, the site at the tributary Lauchert and the site at Riedlingen were suspected to be uncontaminated reference sites (Keiter et al., 2008; Seitz et al., 2008). In the present study, however, the Bio-TEQs for these samples were 7-fold and 55-fold greater than those of Lauchert and Riedlingen sediment extracts collected in

Chapter 4

2004, respectively. Polluted sediments in rivers can be subject to remobilization, transport, and redistribution during certain times of year because of periods of high flow, floods, or human activities (Hilscherova et al., 2010; Turowski et al., 2010; Wölz et al., 2008). Thus, the great differences in the AhR-mediated activities measured for some of the sediment samples taken at different years might be due to a dislocation of the sediments as a consequence of flood events. Furthermore, a potential reason for the great differences might also be a temporarily or constantly increased discharge of contaminants into the Upper Danube River at these locations.

100000 2004 2006 Lauchert-R* 10000

1000

100

Bio-TEQ [pg/g SEQ] [pg/g Bio-TEQ 10

n.e. 1 PrCo Ehingen Lauchert*Riedlingen Oepfingen* Sigmaringen* SchwarzachRottenacker JochensteinBad Abbach

river flow direction

Figure 4.2 Comparison of the AhR-mediated activities of raw extracts of sediment samples collected at different sites along the Upper Danube River in 2004 (Keiter et al., 2008) and 2006

(this study), as determined with the EROD assay using RTL-W1 cells. Biological toxicity equivalent (Bio-TEQ) values were calculated as the concentration giving 25% of the maximum induction of 2,3,7,8-TCDD. Data are presented as mean Bio-TEQ values ± standard error of the mean (n = 3). PrCo = procedural control. n.e. = no effect. R = raw extract of the sediment sample collected at the tributary Lauchert in 2004. * sediment samples used for fractionation and GC-MS.

4.3.1.2 AhR-mediated activities of the dialyzed extracts and their fractions (F) The dialyzed extracts from the sites Sigmaringen, Lauchert and Oepfingen revealed a high EROD potency, with Bio-TEQs of 7,061, 2,547 and 2,824 pg/g SEQ, respectively. By comparison of the sum of the Bio-TEQs of the 18 fractions with the

Chapter 4

Bio-TEQs of their dialyzed extracts, it became evident that for the samples from the sites Lauchert and Oepfingen the AhR-mediated activities of the extracts can be completely explained (102% and 106%) by the activities of their fractions (Figure 4.3).

The Bio-TEQs strongly differed among the fractions, but the distribution of their ranges followed the same pattern in all four samples. Generally, no or only very low AhR-mediated activities were measured in the EROD assay for fractions F1 to F5 which co-elute with typical AhR agonists, such as PCNs, coplanar PCBs, and PCDD/Fs (Villeneuve et al., 2000; Xiao et al., 2016), suggesting low levels of these compounds in the Danube sediments. This is in agreement with the results reported in a previous study by Keiter et al. (2008). In this study, chemical analyses of sediment samples collected at similar sampling sites along the Danube River in 2004 revealed only comparatively low concentrations of PCBs and PCDDs/PCDFs and only a minor portion of the total measured AhR-mediated activities in the EROD assay seemed to result from PCBs and PCDDs/PCDFs. The greatest Bio-TEQs were calculated for the fractions F9 to F11 and F13 to F15 of the extracts from the four sediments investigated. The sum of the Bio-TEQs of these fractions alone explained 45 and 64% of the Bio-TEQ of the Lauchert-R and the Sigmaringen extracts as well as 87 and 88% of the Oepfingen and Lauchert extracts, respectively. Thus, these fractions of the extracts from Sigmaringen, Lauchert and Oepfingen were chosen for chemical analyses.

Chapter 4

100000 100000 140 a) Bio-TEQ fraction(s) 120 b) Bio-TEQ fraction(s)

% rel. to AMD extract % rel. to AMD extract

%to relative AMD extract %to relative AMD extract 10000 10000 120 100 100 1000 80 1000 80 60 100 100 60 40 40

Bio-TEQ [pg/g SEQ] [pg/g Bio-TEQ

Bio-TEQ [pg/g SEQ] [pg/g Bio-TEQ 10 10 20 20

n.d. 1 0 1 n.d. n.d. 0

4 5 6 7 8 5 6 7 8 9* 12 16 17 18 9* 12 16 17 18 1-3 10* 11* 13* 14* 15* 1-4 10* 11* 13* 14* 15* 1 - 3 1 - 4

Sum F* SumSum F* F* Fractions (F) Fractions (F) RawRaw extract extract AMDAMD extractextractSumSum F1-F18 F1-F18 RawAMD extract extractSumSum F1-F18 F1-F18

Dialyzed extract 100000 100000 140 c) Bio-TEQ fraction(s) 180 d) Bio-TEQ fraction(s)

% rel. to AMD extract % rel. to AMD extract %to relative AMD extract 160 120 %to relative AMD extract 10000 10000 140 100 120 1000 1000 80 100

100 80 100 60 60 40

Bio-TEQ [pg/g SEQ] [pg/g Bio-TEQ

Bio-TEQ [pg/g SEQ] [pg/g Bio-TEQ 10 40 10 20 20

n.d. n.d. 1 0 1 n.d. 0 9 5 6 7 8 10 11 12 13 14 15 16 17 18 *9 12 16 17 18 1-8 1-4 *10 *11 *13 *14 *15 1 - 4

SumSum F* F* Fractions (F) Fractions (F) Raw extract Raw extract AMD extractSumSum F1-F18F1-F18 RawAMD extract extractSumSum F1-F18 F1-F18 Dialysed extract Dialyzed extract Figure 4.3 Comparison of the AhR-mediated activities of the raw extracts as well as accelerated dialyzed extracts (i.e., AMD extracts) and their fractions (F) of Danube sediment samples collected at the sites at a) Sigmaringen, b) Lauchert, c) Lauchert-R (R = raw extract of the sediment sample collected at the tributary Lauchert in 2004) and d) Oepfingen, as determined with the EROD assays with RTL-W1 cells (left y-axis). Additionally, the percentage contributions of the single fractions as well as of the fractions used for chemical analyses, to the total activity of the dialyzed extracts is shown (right y-axis). Biological toxicity equivalent (Bio-TEQ) values were calculated as the concentration giving 25% of the maximum induction of 2,3,7,8-TCDD. Data are presented as mean Bio-TEQ values ± standard error of the mean. n.d. = not detectable. *Fractions used for chemical analyses.

The distribution pattern of the range of calculated Bio-TEQs for the single fractions is in agreement with the results of other studies on the AhR-mediated activities of sediments also using the automated on-line fractionation method applied in the present study (Machala et al., 2001; Kaisarevic et al., 2009; Rocha et al., 2010; Lübcke-von Varel et al., 2011). In these studies, the AhR-mediated activities in 72

Chapter 4 sediment samples from sites at industrial hot spots in the River Elbe basin in Germany (Lübcke-von Varel et al., 2011), from two locations at the Tietê River Basin in Brazil (Rocha et al., 2010) and from a wastewater canal which drains into the Danube River in an industrial area in Serbia (Kaisarevic et al., 2009) were investigated by means of the EROD assay using RTL-W1 cells, the DR-CALUX® test using rat hepatoma H4IIE cells, and the MicroEROD assay using the H4IIE rat hepatoma cell line, respectively. Similar to our results, those studies reported the strongest AhR-mediated activities for F9 to F11 and F13 to F15 of the extracts, whereas no or only low AhR-mediated activities were measured for F1 to F7, F12 and F16 to F18, except for F5 of the sample from Bitterfeld which also showed a significant AhR-induction in the study by Lübcke-von Varel et al. (2011).

Chapter 4

Table 4.1 Total concentrations of selected polycyclic aromatic compounds (PACs) given for selected fractions of dialyzed extracts of sediment samples from the

Upper Danube River.

Sigmaringen Lauchert Oepfingen Compounds DL FS9 FS10 FS11 FS14 FS15 FL9 FL10 FL11 FL14 FL15 FO9 FO10 FO11 FO14 FO15 Naphthalenea 0.1

Unit: ng/g SEQ (sediment equivalent); a US EPA Priority PAHs (Callahan et al., 1979); b Sum of benzo[b]fluoranthene, benzo[j]fluoranthene and benzo[k]fluoranthene, which co-elute under the given chromatographic conditions and the used column; < DL = below determination limit.

Chapter 4

4.3.2 Characterization of AhR agonists in selected fractions of Danube sediments by GC-MS The fractions F9 to F11 and F13 to F15 of sediment samples from the sites at Sigmaringen, Lauchert and Oepfingen which exhibited greatest EROD induction activities were subject to chemical analysis. The analytical results confirmed the presence of several PACs expected in the respective fractions of the dialyzed extracts (Table 4.1). The concentrations of small substituted and non-substituted PACs and small-seized PAHs mainly eluting in F1 to F8, i.e. naphthalene, acenaphthene, acenaphthalyene, fluorene, fluoranthene, anthracene, penanthrene and pyrene were below the detection limit in the fractions of the extracts from all three sites, as expected for the selected fractions.

The greatest concentrations of the analyzed PAHs were measured for the sum of benzo[b+j+k]fluoranthene (111–145 ng/g SEQ) in F10 of the extracts from the sites at Sigmaringen and Lauchert. Additionally, the greatest concentrations of benzo[g,h,i]perylene (23–52 ng/g SEQ), dibenzo[a,h]anthracene (5–16 ng/g SEQ) and indeno[1,2,3-cd]pyrene (21–45 ng/g SEQ) were measured in F11 of all extracts as well as in comparable concentrations in F10 of the Sigmaringen extract. The non-U.S. EPA PAHs were almost exclusively detected in F14 and F15 of the extracts from all three sites, except for cyclopental[cd]pyrene which was only detected in F9 of the Sigmaringen extract. Among these compounds relatively great concentrations were discovered for 9,10-anthracen-dione (19–35 ng/g SEQ) in F14 of the extracts from all sites as well as for triphenylphosphat (16 ng/g SEQ) in F15 of the Oepfingen extract.

The sum of concentrations of the EPA PAHs (165.5-477.7 ng/g SEQ) measured in the sediment extracts analyzed in the present study, were significantly lower (up to 32-fold) compared to those measured in sediment extracts collected from similar sites at along the Danube River in 2004 (Keiter et al., 2008). However, it has to be considered, that in the present study PAHs were only analyzed in five selected fractions whereas the results reported by Keiter et al. (2008) are based on PAH analyses in whole sediment extracts, also considering small-sized PAHs. Nevertheless,

Chapter 4 even after subtraction of small-sized PAHs, the sum of concentrations of the remaining PAHs are still up to 5-fold greater than those found in the respective samples in the present study. This is surprisingly, since, in contrast, Bio-TEQs of the sediment raw extracts for the sites Sigmaringen, Lauchert and Oepfingen, calculated in this study were in a similar range or even significantly greater than those measured in the respective samples collected in 2004 (see Figure 2). Thus, the results might indicate a decrease of the EPA PAH concentrations in sediments from these locations since 2004, however, accompanied by an increase of other compounds with EROD-inducing potency.

Generally, with respect to results of other investigations on sediment samples from rivers around the world (Brack and Schirmer, 2003; David et al., 2010; Du and Wu, 2010; Hilscherova et al., 2010; Kaisarevic et al., 2009; Rocha et al., 2010), the sum of concentrations of the EPA PAHs measured in the present study (165.5-477.7 ng/g SEQ) are comparably low. For example, the sum of concentrations of the EPA PAHs in the most active fractions of sediment samples from locations at the Tietê River Basin in Brazil ranged from 198,480-1,913,940 ng/g SEQ (Rocha et al., 2010) and Kaisarevic et al. (2009) reported a maximum sum concentration of 29, 200 ng/g SEQ in the most active fractions of sediments from a wastewater canal in an industrial area in Serbia. Total concentration of 16 PAHs in sediments from lake Järnsjön in an industrial area in Sweden was 7,660 ng/g SEQ (Engwall et al., 1996), while in sediments from Morava River in the Czech Republic it was 1,569–18,728 ng/g SEQ (Hilscherova et al., 2010).

4.3.3 Comparison of Bio-TEQs of fractions with Bio-TEQs of synthetic mixture and calculated Chem-TEQs Benzo[b+j+k]fluoranthene is the sum of three PAHs benzo[b]fluoranthene, benzo[j]fluoranthene and benzo[k]fluoranthene. As individual concentrations of these PAHs in the samples were not quantifiable, benzo[b]fluoranthene and benzo[k]fluoranthene were used for obtaining Bio-TEQs of synthetic mixture and calculated Chem-TEQs of benzo[b+j+k]fluoranthene in the scenarios A and B,

Chapter 4 respectively (Figure 4.4). Since no EROD-inducing ability was observed for benzo[j]fluoranthene using the RTL-W1 cells, and thus the lowest contributions were obtained in this scenario. In this study, we aimed to find the most active fractions and components for the identified AhR activities in the sediment from Upper Danube River, thus detailed contributions under this scenario were not given. A comparison of the measured Bio-TEQs of fractions with the Bio-TEQs of synthetic mixture and the calculated Chem-TEQs revealed that the analyzed EPA PAHs seem to account only to a minor extent for the AhR-mediated activities of the dialyzed extracts as well as for the sum of the Bio-TEQs of the chemically analyzed fractions measured by the EROD assay, whereas non-priority substances were high inducers.

4.3.3.1 Comparison of measured Bio-TEQs of fractions and calculated Chem-TEQs In the scenario A, the contribution of the calculated Chem-TEQs of priority EPA PAHs to the biological response of dialyzed extracts and sum of related fractions was no more than 3%. By comparison of the Chem-TEQ of each fraction to the respective Bio-TEQ, it became evident that, with the exception of fractions F10 of all extracts and F11 of the extracts from site Lauchert and Oepfingen, up to 100% of the biological responses of the single fractions remained unexplained. The Chem-TEQ of the Lauchert fraction F10 contributed to 18% of the measured TEQ and the Chem-TEQ of the Oepfingen fraction F10 contributed to 7%. In the scenario B, the analyzed PAHs for the dialyzed extracts and the sum of selected fractions of the sediment from the site at the tributary Lauchert could explain as much as 8 and 9% of the measured EROD induction, respectively. Furthermore, for fraction F10 of Lauchert as much as 62% of the measured EROD activity can be explained by the calculated Chem-TEQ. Similarly, the Chem-TEQs of F10 of the sediment samples from the sites at Sigmaringen and Oepfingen contributed to as much as 12 and 22% of the measured EROD activities, respectively. For F10 of all extracts, there is a clear difference in the contributions of Chem-TEQs to measured Bio-TEQs under the scenarios A and B, which is caused by the great difference in REPs between benzo[b]fluoranthene (0.000193) representing the sum of benzo[b+j+k]fluoranthene

Chapter 4 in the scenario A and benzo[k]fluoranthene (0.001039) representing the sum of benzo[b+j+k]fluoranthene in the scenario B. The Chem-TEQs for fractions F11 from the sites at Lauchert and Oepfingen could explain as much as 17 and 14% of the measured activities, respectively. The PAHs benzo[a]anthracene and chrysene, detected in high concentrations in fraction F9, accounted for less than 2% of the Chem-TEQs in the respective fractions due to their considerably lower relative potency factors.

Therefore, results confirmed that non-identified compounds with EROD-inducing potency accounted for the majority of the determined EROD-inducing potential. The nonpolar fractions F9 to F11, as the most AhR-active fractions, most likely include non-priority PAHs with four to six aromatic rings (Lübcke-von Varel et al., 2008). However, additionally to these non-polar fractions, also medium-polar to polar fractions, mainly eluting mononitro PAHs (fraction F13) as well as (hydroxy-)quinones, keto-, dinitro-, hydroxy-PAHs and N-heterocycles (fractions F14 and F15) showed AhR-mediated effects to a similar extent. Thus, it might be hypothesized that these compounds do account for a major fraction of measured Bio-TEQs and potential hazards. However, chemical analysis applied in this study was capable to identify only few substances in F14 to F15 and non in F13. No EROD-inducing activity in RTL-W1 cells of the compounds detected in these fractions.

Generally, these findings are in accordance with results obtained in previous studies and emphasize the potential contribution of non-priority pollutants to environmental hazards. Although investigators found the polyaromatic fractions of extracts of sediment and/or settling particulate matter to be highly potent in inducing EROD activity (e.g., Lübcke-von Varel et al., 2011; Xiao et al., 2016), attempts to ascribe this potency to identified priority PAHs in most cases failed and, in fact, it turned out that so far unknown and usually not analyzed non-priority PAHs were of higher relevance (Brack et al., 2005; Brack and Schirmer, 2003; Hollert et al., 2002; Kaisarevic et al., 2009; Keiter et al., 2008; Otte et al., 2013; Rocha et al., 2010; Xiao

Chapter 4 et al., 2016; Wölz et al., 2010). Hence, the results of the present study demonstrate that an exclusive focus on prioritized pollutants may result in inadequate assessment of environmental samples.

4.3.3.2 Confirmation of AhR-mediated activities of the fractions The concentration-response curves of the synthetic fractions F9 of all samples failed to reach the TCDD-EC25 level and therefore no Bio-TEQ was calculated. Generally, no AhR-mediated activity was measured in the EROD assay for synthetic mixtures of medium-polar to polar fractions (F13, F14 and F15) of all samples (Figure 4.4), suggesting that measured AhR-mediated activities in these fractions were not contributed by the detected compounds, and the AhR agonists in these fractions still remain in question. In scenario A for all samples, the sum of the Bio-TEQs of the synthetic mixtures only explained no more than 1% of the Bio-TEQs of their dialyzed extracts and no more than 2% of sum of Bio-TEQs of their fractions. In scenario B, the highest percentage of biological activity of a synthetic mixture of a sediment fraction was observed for F10 of Sigmaringen, in which the 32% of fraction Bio-TEQ could be explained. The sum of Bio-TEQs of synthetic mixtures of the Sigmaringen explained 5 and 15% of the Bio-TEQs of sediment extract and the sum Bio-TEQs of their fractions, respectively. There is a clear difference between the Chem-TEQs of the fractions F10 of all samples and the Bio-TEQs of their respective synthetic fractions, which probably was caused by a potential non-additive behavior of the compounds in the mixtures. Such a non-additive behavior of PACs was observed in previous studies (Larsson et al., 2012, 2014).

Generally, confirmation showed that a major portion of the AhR-mediated potencies still could not be explained by synthetic mixtures no matter based on scenario A or scenario B. It can be hence concluded that unknown chemicals, especially those in the medium-polar and polar fractions, have primarily contributed to the observed activity. Previous studies reported that a large portion of AhR-mediated activities of environmental mixtures were contributed by methylated PAHs rather than by parent compounds alone (Brack and Schirmer, 2003; Hong et al., 2015, 2016; Kaisarevic et

Chapter 4 al., 2009). Brack and Schirmer (2003) reported that methylation of chrysene in the 1-position and benz[a]anthracene in the 9-position enhanced the AhR-mediated potency by 17- and 100-fold, respectively. These methylated chrysene and benz[a]anthracene, as well as other non-priority PACs were confirmed as major EROD-inducing compounds in a contaminated sediment in Germany. Hong et al. (2015) found that three alkyl-PAHs C1-chrysene, C3-chrysene and C4-phenanthrene explained 18% of the Bio-TEQs in coastal sediments contaminated by the oil spill. It is reported that plant-derived materials, such as wood components (Huuskonen et al., 1998), as well as humic and fulvic acids (Bittner et al., 2006; Doostdar et al., 2000) found in soils and sediments, have been identified as AhR ligands or as the products that can be converted into AhR ligands (Otte et al., 2013). Further confirmation including chemical analytical confirmation and effect confirmation using bioassays to establish causes for observed effects is still needed.

Chapter 4

Figure 4.4 Contributions of Bio-TEQs of the synthetic mixtures (dotted; bioassay-derived toxic equivalents) and Chem-TEQs (gridded; chemically calculated toxic equivalents) to Bio-TEQs of their respective fractions (gray), as well as the contributions of the sum Bio-TEQs of synthetic fractions and the sum Chem-TEQs of fractions to the Bio-TEQs of the dialyzed extracts, and to the sum Bio-TEQs of fractions (Sum F) of the different sediments from the sites at a) Sigmaringen, b) Lauchert and c) Oepfingen. The calculated contributions were given in percent at the top of each bar. The Bio-TEQs of synthetic fractions and Chem-TEQs of individual fractions were calculated based on scenarios A (Figure 4.4A) and B (Figure 4.4B), respectively. Chem-TEQs were calculated using the relative toxic potencies (REPs) taken from Bols et al. (1999).

Chapter 4

4.3.4 Proposals of potential identity, sources and environmental effects of AhR-mediated substances The combination of in vitro bioassays with fractionation techniques helps to associate effects to groups of contaminants with similar physico-chemical properties and thus to prioritize individual fractions for subsequent EDA. The on-line fractionation procedure applied in this study in combination with EROD assays allowed to screen sediment extracts from several sites for fraction-specific adverse effects, associating these effects to specific groups of pollutants. Besides non-polar fractions F9 to F11, medium-polar to polar fractions F13, F14 and F15 were identified as putative major inducers of the AhR-mediated activities measured in the sediment extracts investigated in the present study. These substances are emitted by multiple sources and enter the aquatic environment not only through the effluents of sewage treatment plants, but also through multiple other points and diffuse sources, e.g., through industrial discharges, petroleum pills, combustion of fossil fuels, and non-point sources such as urban runoff and atmospheric fall-out, making an estimation of potential sources difficult (Brack et al., 2007; Chen and Preston, 2004; Xiao et al., 2016).

The persistence and long-term toxicity of PAHs, which includes probable mutagenic and carcinogenic effects, raise a great concern about their biological effects on aquatic organisms (Simpson et al., 2005). Thus, analysis of PAHs in sediments can be used for assessment and interpretation of the impact of these anthropogenic pollutants on the aquatic environments (Olajire et al., 2005). PAHs and their derivatives have been shown to modulate AhR, but their toxicity in vivo directly mediated by AhR remains disputable (Brack et al., 2007). The principal effects currently associated with exposure to PAHs are carcinogenicity, immunosuppression and endocrine dysfunction (Blaha et al., 2006; Chen and White, 2004; Henkler and Luch, 2011; Henkler et al., 2012).

Nitrated polycyclic aromatic hydrocarbons (Nitro-PAHs) and N-heterocyclic aromatic hydrocarbons (aza-PAHs, azaarenes) in fractions 14 and 15 are as ubiquitous in the

Chapter 4 environment as their parent PAH compounds, although occurring at lower concentrations (Fent and Jung 2000; Sovadinova et al., 2006). Their availability to organisms may be higher than that of their homocyclic analogues based on the higher polarity of these substances, along with decreased log Kow values and higher bioavailability (Hong et al., 2016). Several nitro-PAHs are believed to be far more mutagenic or carcinogenic than unsubstituted PAHs (Ozaki et al., 2010). Some nitro-PAHs and heterocyclic PAHs were found to be mutagens and carcinogens and to cause non-genotoxic effects, such as (anti-)estrogenicity (Fent and Jung 2000; Sovadinova et al., 2006; Wahidulla and Rajamanickam 2010). However, to date, the knowledge about their occurrence, environmental fate, biological metabolism, and effects in vivo is still limited.

In the present study comparatively high AhR-mediated activities were determined in sediment extracts from several sites at the Upper Danube River. While the results of the in vitro assays applied in this study do not allow direct extrapolation to physiological in vivo responses, the results of the EROD assay documented the presence of compounds with the ability to bind to the AhR-receptor in Danube sediments. Endo- and epibenthic organisms such as oligochaetae or bottom-dwelling fish like flounders and barbels are directly exposed to contaminants of sedimentary origin (Egeler et al., 2001; Micheletti et al., 2007). Contact with, as well as ingestion of, contaminants absorbed to sediment particles or dissolved in pore water may result in their accumulation in organisms. Toxic effects can occur in the exposed organism (Bouché et al., 2000) and trophic transfer may constitute a threat to pelagic organisms (Egeler et al., 2001).

4.4 Conclusions

Overall, a complete EDA, including bioassays, fractionation, chemical analyses and confirmation, is a powerful tool to screen fractions with adverse effects and further associate these effects to some specific pollutants. EROD assay using RTL-W1 cells detected high AhR-mediated activities of sediment extracts of less contaminated sites in the Upper Danube River. The analytical results confirmed the presence of several 84

Chapter 4

PACs expected in the respective fractions of the dialyzed extracts, including PAHs and several other heterocyclic aromatic compounds. A comparison of the measured Bio-TEQs of the sediment samples with the Bio-TEQs of the synthetic mixtures and the calculated Chem-TEQs revealed that the analyzed US EPA priority PAHs seem to account only to a minor extent for the AhR-mediated activities of the dialyzed extracts as well as to the sum of the Bio-TEQs of the analyzed fractions, whereas non-priority substances in medium-polar to polar fractions were higher inducers. However, chemical analyses applied in this study were capable to identify only a few substances in these fractions and no EROD activity was observed for their synthetic mixtures. Thus, the identification of AhR-activity compounds mainly expected in the medium-polar to polar fractions should be further investigated.

Acknowledgements

This study was supported by SOLUTIONS project that is funded by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement number (603437). Personally, Shangbo Zhou and Stefanie Grund were supported by the scholarship program of China Scholarship Council (CSC) and the German Federal Environmental Foundation (DBU, Deutsche Bundesstiftung Umwelt), respectively. The authors would like to thank Drs. Niels C. Bols and Lucy Lee (University of Waterloo, Canada) for providing RTL-W1 cells. The authors thank Tecan Group Ltd. for providing laboratory instruments in this study as a partner of the Students Lab ―Fascinating Environment‖ at Aachen Biology and Biotechnology (ABBt).

Chapter 5

Chapter 5 Bioanalytical equivalents and relative potencies for predicting the biological effects of mixtures and environmental samples

This chapter is based on a manuscript to be submitted to Water Research: Zhou, S., Peng, S., Brack W., J.A, Doering, Seiler, T.-B., Hollert, H. (to be submitted). Bioanalytical equivalents and relative potencies for predicting the biological effects of mixtures and environmental samples. 86

Chapter 5

Abstract

Bioanalytical equivalents (BEQs) of mixtures and environmental samples are widely used to reflect the potential threat of pollutants in the environment and can be obtained by bioassays or using chemical analysis combined with relative potencies (REPs). In this study, the relationships between bioassay-detected BEQs (Bio-BEQs) and chemically analyzed BEQs (Chem-BEQs) were analyzed by mathematical analysis. BEQs and REPs are correlated with effect level and the concentration-response curves of the reference standard and sample. Thus, effect level

(e.g., EC10, EC25 and EC50) should be addressed for the BEQ values obtained from bioassays or chemical analyses. The previous prerequisites for REPs application (i.e., curves that are parallel and have the same maximum response) are redundant, and the use of REPs for the calculation of BEQs or in risk assessment should instead be based on the same effect level. For a mixture with many components, all active components can be regarded as dilutions of the standard compound for inducing a specific effect

ECF (0

BEQ calculation. Relative toxicity estimates based on EC50 ignore the contribution of weak-active components with maximum response below EC50 of the reference standard, especially in complex environmental samples. As an example, the aryl hydrocarbon receptor (AhR)-mediated activity of US EPA priority polycyclic aromatic hydrocarbons (PAHs) in RTL-W1 cells was used to assess the reliability of

REPs for mixture toxicity prediction based on the effect level EC10.

Keywords: Concentration addition; Effect level; Toxicity equivalency factor; Polycyclic aromatic hydrocarbon; Risk assessment

Chapter 5

5.1 Introduction

In vivo or in vitro bioassays, including acute toxicity, cytotoxicity, Ames, CALUX (Chemically Activated Luciferase gene eXpression), oxidative stress response, NF-κB (Nuclear Factor Kappa B), 7-ethoxyresorufin-o-deethylase (EROD), and other assays, are useful tools to measure the total toxicologically relevant burden of chemicals (Heinrich et al., 2017; Neale et al., 2015; Schiwy et al., 2015) and to detect the emerging pollutants in the natural environment (Neale et al., 2015). Meanwhile, biological effects of chemical mixtures are usually predicted by using different models, including the independent action (IA) model used for chemicals acting with different modes of action (MoAs), and the concentration addition (CA) model applying bioanalytical equivalents (BEQs) and relative potencies (REPs) for chemicals with similar MoAs (Altenburger et al., 2004; Larsson et al., 2014a; Neale et al., 2015). In environmental assessment, CA model is the most frequently used, even in the case of undefined MoA, since IA requires the availability of full concentration-response curves for the mixture and its components, which are rarely available (Belden et al., 2007). Based on CA but other than REPs considering different endpoints, toxicity equivalency factors (TEFs) have been invented for dioxin-like toxicity in several expert meetings since the early 1990s by the World Health Organization (WHO) to derive consensus TEFs for human, fish, and wildlife risk assessment (Van Den Berg et al., 1998, 2006). Although there is a lack of consistency in the literature, in this paper the term REP is used to describe relative potencies of a compound to a reference compound in a specific test system. REPs are based on concentration–response relationships of individual chemicals (Larsson et al., 2012, 2014a, 2014b; Villeneuve et al., 2000, 2002), and are widely used to evaluate the toxicity of mixtures not only for specific receptor-mediated effects (Bonefeld-Jørgensen et al., 2007; Brion et al., 2012; Larsson et al., 2012, 2014a, 2014b; Pillon et al., 2005), but also for acute toxicity (Neale et al., 2015; Tang and Escher, 2014), adaptive stress responses (Neale et al., 2015) and cytotoxicity (Neale et al., 2015). Meanwhile, REPs can also be used for

Chapter 5 bridging chemical analysis and bioassay results by mass balance analysis (Larsson et al., 2014b; Van Den Berg et al., 1998, 2006; Villeneuve et al., 2000, 2002). Meanwhile, many factors, including interactions between chemicals, differences in the shape of the concentration-response curves, and species responsiveness can result in uncertainties in mass balance analysis and have been discussed previously (Van Den Berg et al., 1998; Villeneuve et al., 2000). For example, Villeneuve et al. (2000) highlighted the possible effects of the response level on REPs and recommended a multiple-point estimate (a range from EC20 to EC80) approach to reflect the REPs variations. However, multiple-point REP estimation is a cumbersome and laborious approach and has been rarely applied. REPs are based on several assumptions and limitations. Concentration addition (CA) should be the correct mixture effect model and the concentration-response curves for all components should be parallel and exhibit similar efficacy (i.e., maximum response) (Billiard et al., 2008; Payne et al., 2000; Van Den Berg et al., 1998). Actually, these criteria are hardly met or can only be met to a limited degree in most studies, and thus are commonly violated or ignored

(Billiard et al., 2008; Van den Berg et al., 2006; Villeneuve et al., 2000). Bioanalytical equivalents (BEQs) are the concentrations of a reference standard that elicit a response equivalent to the response of the tested sample in a particular assay (Escher et al., 2018; Neale et al., 2015). BEQs can be directly obtained from the application of in vitro or in vivo bioassays (i.e., Bio-BEQ) or from detected chemical concentrations multiplied by REPs (i.e., Chem-BEQ). The comparison of Chem-BEQs and Bio-BEQs has been widely applied to quantify the contribution of identified compounds to the bioassay-derived effects (Brack et al., 2000; Hollert et al., 2002; Escher et al., 2014, 2018; Larsson et al., 2014b; Neale et al., 2015; Otte et al., 2013; Villeneuve et al., 2000; Xiao et al., 2016 ; Wölz et al., 2010). Gaps between Bio-BEQs and Chem-BEQs are typically interpreted to be caused by unidentified chemicals. However, interactions between components of the mixtures may also affect the contributions of detected chemicals to biological effects (Billiard et al., 2008; Hong et al., 2015; Larsson et al., 2012). A third reason for gaps apparently suggesting unidentified drivers may be artifacts of data interpretation ignoring the 89

Chapter 5 basic requirements of the model on the concentration-response relationships of the mixture components. Although potential deviations of Chem-BEQs calculated on the basis of different effect levels were mentioned (Larsson et al., 2014b; Villeneuve et al., 2000), it remains unclear how effect levels impact Chem-BEQs. Therefore, the first aim of this study was to theoretically unravel the impact of slopes, maximum effects and selected effect levels on REPs and BEQs, and to establish criteria for the scientifically sound application of the REPs in the calculation of Chem-BEQs by mass balance approaches. The second aim was to use an experimental study to compare calculated Chem-BEQs with experimentally derived Bio-BEQs in order to validate the theoretical concept. Specifically, an RTL-W1 cell assay was used to measure the aryl hydrocarbon receptor (AhR)-mediated mixture effects of polycyclic aromatic hydrocarbons (PAHs) based on mixtures detected in sediment extracts from the River Danube. This experimental study helped explain how the effect level under consideration affects the Chem-BEQ, evaluate the explanatory power of Chem-BEQ to Bio-BEQ, and verify the feasibility of optimized REPs for mass balance analysis.

5.2 Theoretical analysis of BEQs and REPs

5.2.1 CA model and calculation of mixture toxicity When the composition of mixture is known, CA model can be expressed using the following equation (5.1):

(∑ ) (5.1)

Where: ECF, mix is the predicted concentration of the mixture provoking F% response between the maximum response to the reference compound and the blank response;

ECF,i is the concentration of the ith mixture component provoking F% response between the minimum and the maximum induction by the reference compound when applied individually; n is the number of components of mixtures; Ri is the ratio of the ith component in the mixture (Berenbaum, 1985; Larsson et al., 2014a). 5.2.2 The calculation of REPs REPs were used to calculate the concentration of a reference standard that is

Chapter 5 equivalent to a given concentration of the sample inducing a specific absolute response level (Safe, 1998). There are different equations modeling concentration-response curves of the reference, but the most widely accepted one is the four-parameter logistic equation, which means that there is a plateau and reduction in response probably caused by toxic effects and which cannot be considered. For the reference this reads as given in equation (5.2):

(5.2) 1 1 ( )

Where: IF,ref. is the biological response to the reference compound with a concentration ECF,ref. (0< F<100, expressed as a percentage); Iblank is the blank response to the solvent; Imax,ref. is the maximum response to the reference compound in the solvent; EC50,ref. is the reference concentration that causes 50% response between Imax,ref. and Iblank; Href. represents the hill slope of the curve of the reference compound.

Similarly, the concentration-response curves of the samples (i.e., compounds, mixtures and complex environmental samples) can be expressed as the following equation (5.3):

(5.3) ( ) 1 1

Where: Isample is the response of the sample with a concentration Csample; Iblank is the blank response to the solvent; Imax,sample is the maximum response to the sample in the solvent; EC50,sample is the sample concentration that causes 50% response between

Imax,sample and Iblank; Hsample represents the hill slope of the curve.

REPs can be calculated according to the equation (5.4) being aware that Imax,sample and

Imax,ref. may differ significantly and F always represents an effect level relative to

Imax,ref..

R (5.4)

ECF,sample can be converted from EC50,sample using the following equation (5.5) that is converted from the equation (5.3).

Chapter 5

L L L ( ) (5.5)

For a single chemical, chemically derived BEQ based on ECF (i.e., Q ) can be calculated by multiplying the concentration with related REP (equation (5.6)).

Q × R (5.6)

When the maximum response of a compound is greater than half of the maximum

response of the standard, R was widely used in previous publications (Bols et al., 1999; Villeneuve et al., 2000). To minimize interpretation problems, the

relationship between R and R can be established using the equation

(5.7) derived from equations (5.2), (5.4) and (5.5).

( ) ( ) R ( ) ( ) R (5.7) ( ) ( )

Where: R is a relative potency on the basis of ECF,ref.; R is a relative

potency on the basis of EC50,ref.; is the half response between the maximum response of the reference compound and the blank response.

The maximum responses of the sample can be expressed as the relative percentage of the maximum response of the reference standard ( t o = Imax,sample/Imax,ref. ×

100%), and the response IF,ref. can be expressed as F% of Imax,ref. with a concentration

ECF,ref. (IF,ref. = F × 100%). Thus the relationship between R and R can be expressed using a simplified equation (5.8):

( ti ) R ( ) ( ) R (5.8) 1 ( ti )

Where: t o > 50 × 100%; 0

For R a great challenge is that this value cannot be obtained when the maximum response of the sample fails to reach half maximum response of the reference standard.

Chapter 5

Similar to equation (5.8), a relationship between R and R can be established by equation (5.9).

1 ( ti ) R ( ) ( ) R (5.9) 1 ( ti 1 )

Where: t o > 10×100%; 0

5.2.3 The calculation of Bio-BEQs

The bioassay-derived BEQ based on a fixed effect level ECF (i.e., Q ) was calculated by dividing ECF for the reference compound by the ECF,sample (equation (5.10); Brack et al., 2000).

Q (5.10)

For a sample containing only one active component, Bio-BEQ is equal to Chem-BEQ that can be calculated by multiplying the concentration by the corresponding REP (equation (5.6)). For a complex sample with more than one active component, Chem-BEQ of the mixture can be expressed as the sum of individual Chem-BEQs of these active components on the basis of CA model (Larsson et al., 2012).

Based on the equations (5.2), (5.5) and (5.10), a relationship between Q and Q can be expressed using the simplified equation (5.11):

( ti ) Q ( ) ( ) Q (5.11) 1 ( ti )

Where: t o > 50 × 100%; 0

Based on equations (5.7) and (5.11), it is clear that the variations of REPs and BEQs of the samples are correlated with the maximum responses and hill slopes of concentration-response curves of the compounds and reference standard. When the concentration-response curves of the sample and the reference standard are parallel

(i.e., Href.=Hsample) and have a same efficacy (i.e., Imax,ref.=Tmax,sample), Q and

R can be regarded as the values independent from the effect levels. 93

Chapter 5

Nevertheless, the previous REP estimates were calculated on the basis of these unproven or rarely existing prerequisites (Billiard et al., 2008; Villeneuve et al., 2000). Based on mass balance analysis, there is no doubt that the contribution of known components to the toxic potency of a complex mixture is a constant value, regardless of which effect level the contribution is analyzed. Mass balance calculations for toxicity estimates of the samples should be based on the same effect level, which was also recommended by Larsson et al. (2012, 2014b). Meanwhile, environmental risk assessment of different samples using the BEQs should be based on the same effect level to ensure data comparability.

Similar to the equation (5.11), a relationship between Q and

Q can be established using the following equation (5.12):

1 ( ti ) Q ( ) ( ) Q (5.12) 1 ( ti 1 )

Where: t o > 10×100%; 0

5.2.4 The relationship between Bio-BEQs and Chem-BEQs obtained by using REPs For a given mixture containing only one active component and no interactions between the components, the concentration-response curve of the mixture is determined by this active component ignoring the potential disturbance of cytotoxicity

of other non-active components. The ratio between R and R of the

active compound and that between Q and Q of the mixture are consistent according to equation (5.13) that is converted from equations (5.9) and (5.12). Equation (5.14) converted from the equations (5.6) and (5.13) shows that the ratio between Chem-BEQ and Bio-BEQ should be a constant value.

o (5.13) o

Chapter 5

Q Q ( ) Q Q

For complex mixtures with more than one active component, the basic assumptions of the BEQ calculation are that the combined effects of the components are dose additive and act in a similar manner without non-interaction (Larsson et al., 2014a; Payne et al., 2000; Van Den Berg et al., 1998). However, many other assumptions and limitations (i.e., a similar slope and efficacy) are not always met and based on an understanding of the REP concept that the components are supposed to behave as the dilutions of the standard compound (Billiard et al., 2008; Villeneuve et al., 2000). Actually, the REP concept was designed based on a specific effect level ECF,ref (equation (5.4)) and all active components of the mixture are supposed to behave as the dilutions of the standard compound for inducing this effect. Therefore, weak inducers with efficacies below the response level ECF cannot be regarded as the standard dilutions. To correctly calculate BEQs of the complex samples, a reasonable approach is that the BEQ calculations of the complex mixtures should be based on a lower effect level recommended by Belden et al. (2007) and Escher et al. (2018).

In previous studies, the sigmoidal concentration-response curve was used for BEQ calculation at an effect level higher than 30% of the maximum effect. However, a linear concentration-response curve was used at an effect level lower than 30% (Neale et al., 2015). Bio-BEQ calculations based on different curves are laborious and the comparability of Bio-BEQs would be affected.

In this study, REPs based on an effect level EC10 that can be significantly distinguished from solvent response are recommended for obtaining Chem-TEQs. If the maximum response of ith component in a mixture containing n components (n≥i>0) is less than 10% of the reference standard, the ith component can be regarded as a non-active component and be excluded from Chem-BEQ calculation.

Q of a mixture can be calculated by the sum of Q of its active components (10 ≤ F ≤ min (I1, I2, I3…Ii) and F<100×100%). Since environmental samples typically elicit weak responses, high enrichment is required to induce a 50%

Chapter 5 response of the reference standard, but lower enrichment is required to induce a 10% response. The contribution of the detected chemicals to bioassay-derived BEQ can be

determined by the ratio of Q to Q . EC10-based REPs can be used to calculate the Chem-BEQs of the mixtures with specific MoA (e.g., in vivo or in vitro inductions of endocrine disturbance and AhR activity) and other mixtures that can be simulated by the CA model, such as oxidative stress response, daphnia acute immobilization test and fish embryo toxicity.

5.2.5 Concentration-response curves of the mixture and its components The relationships of concentration-response curves between the mixture and its multiple components are quite complex and cannot be clearly characterized mathematically. However, the possible relationships and interactions of components can be reflected in Figure 5.1. When the concentration of the mixture is lower than the lowest concentration of its components alone to induce a specific effect level (Figure 5.1B), the synergistic interactions between components definitely exist. Conversely, when the concentration of the mixture is higher than the highest concentration of its components alone to produce a specific effect level without cytotoxicity (Figure 5.1C), the antagonist interactions between components must be present. However, when the concentration-response curve of the mixture is in a range composed by the curves of its components (Figure 5.1A), further judgment is needed.

Chapter 5

110 (A )

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n 80 a

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Chapter 5

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Figure 5.1 The possible relationships between the concentration-response curve of the mixture and the curves of its components. The concentration-response curves of the mixtures within, above and below the range consisting of the curves of its components are shown in Figure 5.1A, 5.1B and 5.1C, respectively. Components I, II and III represent the components of the mixture with the highest response, the weakest response and the steepest curve, respectively. The concentration of the mixture represents the sum of concentrations of all active components. In Figure 5.1A, the deviations of the environmental concentration away from the concentrations of the mixture for inducing EC10 and EC50 are indicated by the letters "a" and "b", respectively. The potential concentrations of the mixture inducing the effect levels EC10 and EC50 vary in the ranges of "c" and "d", respectively. In Figure 5.1B, the deviations between the concentration of the mixture and the lowest concentration of its components alone for inducing EC10 and EC50 are represented in the ranges of "e" and "f", respectively. In Figure 5.1C, the deviation between the concentration of the mixture and the highest concentration of its components alone for producing an effect level

EC10 is represented in the range of "g".

Based on the concept of additive behavior and non-interactions between components, the concentration-response curve of mixture is directly related to the concentration-response curve and the ratio of each active component in the mixture

Chapter 5

(Figure 5.1A). The mixture curve is steeper than the component curve with the minimum slope (component II), but flatter than the component curve with the maximum slope (component III). The efficacy of the mixture should be between the minimum (component II) and maximum efficacies (component I) of its components. It should be noted that the response of a mixture higher than the maximum response of the weakest component (i.e., component B) cannot be predicted by the CA model (Larsson et al., 2012; Rajapakse et al., 2001). Therefore, it is unreasonable to obtain BEQ based on high effect levels and even to extrapolate beyond the maximum response of the weakest component to get a CA-converted REP for toxicity estimates.

The potential concentrations of the mixture inducing a specific response (e.g., EC10) below the maximum response of the weakest component vary in a limited range (e.g., "c") due to variations in the slopes and efficacies of the components. However, the potential concentrations of a mixture inducing a response (e.g., EC50) above the maximum response of its weakest component vary in an infinite range (e.g., "d") without cytotoxicity. A wider range means a more ambiguous linkage between the results of chemical analyses and the potential effects of the mixtures. Environmental samples mostly are weak inducers, thus the linkage between the results of chemical analyses and the potential environmental risks of samples will be more ambiguous at a higher effect level. Therefore, it appears reasonable to calculate BEQ at a lower effect level.

5.3 Materials and Methods

5.3.1 PAHs and mixtures preparation AhR-mediated activities of seven US EPA priority PAHs (i.e., benzo[k]fluoranthene, dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene, benzo[b]fluoranthene, benzo[a]pyrene, benzo[a]anthracene, chrysene) were evaluated using the 7-ethoxyresorufin O-deethylase (EROD) assay in RTL-W1 cells. The remaining nine US EPA priority PAHs (i.e., benzo[g,h,i]perylene, naphthalene, phenanthrene, anthracene, pyrene, acenaphthylene, acenaphthene, fluorene, fluoranthene) were not evaluated individually since no EROD induction of these PAHs was observed in a

Chapter 5 previous study using similar conditions (Bols et al., 1999). Benzo[k]fluoranthene (≥98%), dibenzo[a,h]anthracene (≥98%), benzo[b]fluoranthene (≥98%), benzo[a]pyrene (≥97%), benzo[a]anthracene (≥98%) and chrysene (≥97%) were purchased from Sigma-Aldrich GmbH, and indeno[1,2,3-cd]pyrene (≥99.5%) was purchased from Dr. Ehrenstorfer GmbH. Meanwhile, six synthetic mixtures with 2 to

6 PAHs were prepared according to the detected concentrations in multilayer fractions of sediment samples from three sites at Sigmaringen (MS), Lauchert (ML) and Oepfingen (MO) in Upper Danube River (unpublished data). The concentrations of individual components in the mixtures are given in Table 5.1. One widely detected non-AhR-active PAH benzo[g,h,i]perylene (≥98%, Sigma) was also included in five of the synthetic mixtures to determine whether the presence of non-AhR-active PAHs affect the activities of the mixtures. Benzo[b]fluoranthene, benzo[k]fluoranthene and benzo[j]fluoranthene could not be chromatographically separated and thus their individual concentrations in the samples were not quantifiable. Thus, two mixtures MS1 and MS2 were prepared containing benzo[k]fluoranthene and benzo[b]fluoranthene, respectively. Benzo[j]fluoranthene was not included in any mixture because it did not elicit any AhR activity in the RTL-W1 cells (Bols et al.,

1999). MS3 was prepared based on the concentration of another fraction of sediment sample from the site at Sigmaringen.

Table 5.1 The concentrations (ng/mL) of polycyclic aromatic compounds (PAHs) in synthetic mixtures.

Sigmaringen Lauchert Oepfingen Compounds MS1 MS2 MS3 ML1 ML2 MO1

Benzo[b]fluoranthene 2216 0 0 0 0 0

Benzo[k]fluoranthene 0 2216 0 2902 0 0

Benzo[a]pyrene 1104 1104 476 1422 0 0

Dibenzo[a,h]anthracene 196 196 166 0 326 106

Indeno[1,2,3-cd]pyrene 384 384 420 0 902 488

Benzo[g,h,i]perylene 476 476 476 0 1046 454

100

Chapter 5

Sum of active components 3900 3900 1062 4324 1228 594

Sum of all components 4376 4376 1538 4324 2274 1048

The dose of a single PAH in 1 ml solvent is equal to that detected in 20 g sediment equivalent (SEQ). 5.3.2 AhR-mediated EROD assay EROD induction was measured in RTL-W1 cells according to methods described by Heimann et al. (2011) and Wölz et al. (2008) with slight modifications. Briefly, RTL-W1 cells were seeded in 96-well plates and allowed to grow to 90% confluence for 72 h. Following incubation, the medium was removed and the cells were exposed to samples diluted in medium using eight dilutions (less than 1% dimethyl sulfoxide (DMSO) per well). The compound 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) was used as a positive control and serially diluted to a final concentration range of 3.13-100 pM. After 72 h exposure, induction was stopped by removing the medium and freezing for at least 1 h at -70 °C to lyse the cells. Deethylation of exogenous 7-ethoxyresorufin (EXR) was initiated by adding EXR to each well and incubating the cells in the dark at room temperature for 10 min, followed by addition of NADPH solution and incubating for a further 10 min. The reaction was terminated by adding fluorescamine dissolved in acetonitrile and incubating for 15 min. EROD activity was measured fluorometrically using a multiwell plate reader (Tecan, Crailsheim, Germany) with an excitation/emission wavelength of 544/590 nm. Protein was determined fluorometrically using the fluorescamine method with an excitation/emission wavelength of 355/390 nm. The AhR-mediated activities of the chemicals and mixtures were converted to

Bio-BEQ by relating the ECF-sample of sample to the mean ECF of 2,3,7,8-TCDD using the equation (10) based on fixed effect level ECF.

5.4 Results and discussion

5.4.1 REPs of PAHs for the AhR-mediated activities Taking the AhR-mediated activities of PAHs as an example, REPs were obtained based on different effect levels EC5, EC10, EC25 and EC50. Biphasic concentration-response curves were found in earlier studies (Brack et al., 2000), but

101

Chapter 5 for the evaluation of AhR-activities the decrease trend probably caused by cytotoxicity was excluded. Full concentration-response curves were acquired for individual PAHs. These curves varied in hill slopes and the maximum responses after 72 h exposure using RTL-W1 cells. Most of the US EPA priority PAHs are relatively weak inducers, the derived EC50 values for US EPA priority PAHs were three orders of magnitude higher than the EC50 values for 2,3,7,8-TCDD standard (7.16 pM). Generally, the induced maximum EROD inductions of US EPA priority PAHs, except benzo[k]fluoranthene and indeno[1,2,3-cd]pyrene, were lower than the maximum induction of the reference standard (Table 5.2).

For a given PAH, REP is not a constant value and varies greatly, such as at different effect levels (Table 5.3). Furthermore, great variations in REPs for different PAHs were observed. From most to least potent, the order of REPs based on EC50 were: benzo[k]fluoranthene > indeno[1,2,3-cd]pyrene > dibenzo[a,h]anthracene > benzo[b]fluoranthene > benzo[a]pyrene > chrysene > benzo[a]anthracene, the potency rankings based on other effect levels (EC5, EC10 and EC25) were similar

(Table 5.3).

The REPs of benzo[k]fluoranthene were two orders of magnitude higher than that of the benzo[a]anthracene at a same effect level. Generally, REPs of the PAHs at an effective level EC50 were lower than those at lower effective levels (i.e., EC5, EC10

and EC25). The width of variation between R and R was correlated with the hill slopes and the maximum EROD inductions of individual PAHs. Besides the determination of the effect levels on REP values, earlier studies reported that REPs were different when the compound was tested using different methods and cell lines at different exposure times (Bols et al., 1999; Larsson et al., 2012, 2014b; Villeneuve et al., 2002). When the Bio-BEQ of a sample was obtained from one bioassay, Chem-BEQ calculation should use REPs obtained from the same assay using the same cell lines at the same exposure time to analyze the contribution of detected chemicals. In this study, the REPs of individual PAHs and Bio-BEQs of their mixtures were obtained under the same conditions, thus Chem-BEQs calculated from REPs can be 102

Chapter 5 directly compared with the Bio-BEQs. It is worth mentioning that regardless of which reference standard (e.g., 2,3,7,8-TCDD, benzo [a] pyrene, 17‐β‐estradiol), or cell line, or species (e.g., H4IIE rat hepatoma, RTL-W1, U2OS cell line) is used to assess the activity with a specific mode of action, REPs at different effect levels can be expressed by equation (5.7) under the same conditions.

Table 5.2 US EPA priority PAHs that induced EROD activities in RTL-W1 cells after 72 h exposure.

Maximum EROD Hill activity slopes EC50 (nM) (pmol/mgP/min) ± t o (Hsample PAHs ± SD SD a (%) )

Benzo[k]fluoranthene 9.98 ± 1.45 3.92 ± 0.27 103.52 0.81

Dibenzo[a,h]anthracene 12.13 ± 2.01 3.68 ± 1.28 92.49 1.07

Indeno[1,2,3-cd]pyrenguone 15.41 ± 1.65 4.45 ± 0.87 107.82 1.12

Benzo[b]fluoranthene 112.07 ± 31.55 3.46 ± 0.64 87.02 0.89

Benzo[a]pyrene 230 ± 61.53 2.94 ± 0.25 73.74 0.89

Benzo[a]anthracene 1097.51 ± 207.57 2.72 ± 0.21 68.26 0.90

Chrysene 1312.57 ± 140.47 3.03 ± 0.26 76.26 0.99 a t o was the maximum EROD induction of PAHs (Imax,sample) relative to the maximum induction induced by the reference 2,3,7,8-TCDD (Imax,sample). The maximum EROD induction of the reference was 3.98 ± 0.73 pM/mgP/min (n=21). The hill slope (Hsample) of the reference was 1.64.

Table 5.3 The REPs based on molar concentrations for US EPA priority PAHs derived from

EROD induction using RTL-W1 cells.

a R R R R R PAHs ×10-3 ×10-3 ×10-3 ×10-3 ×10-3 Benzo[k]fluoranthene 3.700 2.896 1.563 0.717 1.040 ±0.397 ±0.439 ±0.372 ±0.104

Indeno[1,2,3-cd]pyrene 0.661 0.657 0.558 0.453 0.278 ±0.366 ±0.313 ±0.155 ±0.045

Dibenzo[a,h]anthracene 0.839 0.655 0.484 0.361 0.350 ±0.014 ±0.030 ±0.046 ±0.06

Benzo[b]fluoranthene 0.183 0.175 0.131 0.069 0.193 ±0.014 ±0.030 ±0.046 ±0.028

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Benzo[a]pyrene 0.114 0.109 0.077 0.035 0.300 ±0.031 ±0.041 ±0.030 ±0.009

Chrysene 0.044 0.035 0.022 0.011 0.047 ±0.004 ±0.004 ±0.002 ±0.002 Benzo[a]anthracene 0.035 0.022 0.014 0.006 0.043 ±0.006 ±0.008 ±0.005 ±0.001

The units of EC5, EC10, EC25 and EC50 values were pM, and the mean EC5, EC10, EC25 and EC50 values for

2,3,7,8-TCDD standard were 1.19, 1.86, 3.67 and 7.16 pM, respectively; a The values from Bols et al. (1999).

5.4.2 Comparison of measured Bio-BEQs and calculated Chem-BEQs at different effect levels The effectiveness of the REPs at different effect levels for the evaluation of EROD activities was assessed using the RTL-W1 cell line (Figure 5.2). For the synthetic mixtures MS1 and MS2, there was no significant difference between the detected Bio-BEQs and Chem-BEQs. For the mixtures MS3, ML1, ML2 and MO1, there were significant differences between the calculated Chem-BEQs and the measured Bio-BEQs at some effect levels, which was caused by the potential non-additive behavior of the components (Larsson et al., 2014a). Non-addictive interactions could be identified to some extent based on the relationship of concentration-response

curves between the mixture and its components (Figure 1). Q of the

ML2 (14.55 ±1.67 ng/g SEQ) and MO1 (7.05 ± 2.84 ng/g SEQ) were lower than

± Q of their respective weakest components alone (25.64 3.20 and

13.45 ±1.67 ng/g SEQ) with the same mixture concentration, indicating antagonist interactions of the components. The maximum deviation between the Chem-BEQs and measured Bio-BEQs was observed for the two-component mixture ML1 in which Chem-BEQ was 1.5-3 times higher than the detected Bio-BEQ, depending on the different effect levels.

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80 800 MS1 Chem-TEQ MS2 70 Chem-TEQ Bio-TEQ Bio-TEQ 60 600 50

40 400 30

20 200 TEQs (ng/g SEQ) (ng/g TEQs 10 SEQ) (ng/g TEQs

0 0 EC5 EC10 EC25 EC50 EC5 EC10 EC25 EC50

40 MS3 1000 ML1 * 30 800 *

* 600 20 * 400 10

TEQs (ng/g SEQ) (ng/g TEQs 200 TEQs (ng/g SEQ) (ng/g TEQs

0 0 EC10 EC5 EC25 EC50 EC5 EC10 EC25 EC50

80 40 ML2 MO1

60 30 * *

40 * 20 *

20 10

TEQs (ng/g SEQ) (ng/g TEQs TEQs (ng/g SEQ) (ng/g TEQs

0 0 EC5 EC10 EC25 EC50 EC5 EC10 EC25 EC50

Figure 5.2 Comparison of measured Bio-BEQs and calculated Chem-BEQs of the mixtures at different effect levels (EC5, EC10, EC25 and EC50). Chem-BEQs were calculated using the relative potencies (REPs) based on mass concentrations. Error bars represent the standard deviations (n=3).

Asterisk (*) represents a significant difference between the measured Bio-BEQ and the calculated

Chem-BEQ (p<0.05). The unit ng/g SEQ means ng TCDD equivalent detected in g sediment equivalent (SEQ).

The ratio between Q / Q and Q /

Q should theoretically be a constant value (i.e., one), but the ratios for these mixtures varied between 0.6 and 1.9. These deviation ratios should be common since

105

Chapter 5 approximately 90% deviation ratios between the tested and CA-predicted toxicity varied in a similar range (0.5-2.0) in previous studies (Belden et al., 2007). These ratios may be caused by potential interactions of the components. Overall, the comparison of the measured Bio-BEQs with the calculated Chem-BEQs of the synthetic mixtures revealed that the REPs at a certain effect level can be used to analyze the contribution of detected compounds to mixture toxicity. The explanatory power of Bio-BEQ by Chem-BEQ will be covered by the artifact when both BEQs are obtained at different effect levels. For example, Bio-BEQ of the MS2 could be

completely explained by Chem-BEQ at the same effect level while Q could only account for approximately 15% of Bio-BEQ at an effect level EC5, resulting in an underestimated contribution.

The prediction of the effects of mixtures with many weak inducers, especially those with maximum response below half that of the reference standard, proved to be a challenge in earlier studies (Payne et al., 2000). Actually, this issue can be solved by

REPs at a lower effect level, such as EC10. 5.4.3 Predicted BEQs based on CA model

The predictability of the CA model for the activities of the mixtures of chemicals acting with similar MoAs has been demonstrated in other bioassays (e.g., Larsson et al., 2012, 2014a; Payne et al., 2000; Zhang et al., 2008). The relationship between the bioassay-derived BEQs and REP-predicted BEQs can be reflected by the status of experimental and CA-predicted concentration–response curves since REPs were designed based on the CA model. Regardless of whether Chem-BEQs were determined based on REPs or the CA model, the prediction of mixture activity was based on the additive interactions of components, and non-additive behavior of the components was not considered. Thus, the effects of non-additive behavior could be reflected by comparing the concentration–response curve of the tested with that of the CA-model predicted (Figure 5.3).

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M S 1 M S 2

) )

. .

x x a

a 1 2 0 1 2 0

M M

D 1 0 0 1 0 0

D D

C C

T T

8 0 8 0

f f

o o

% 6 0 6 0

( (

y y

t t i

i 4 0 4 0

v v

i i

t t c

c 2 0 2 0

a a

D D O

O 0 0 R

R -1 0 1 2 3 -1 0 1 2 3

E E C o n c e n tra tio n L o g (m g /m l) L o g (m g /m l)

M S 3 M L 1

) )

. .

x x a

a 1 2 0 1 2 0

M M

D 1 0 0 1 0 0

D D

C C

T T

8 0 8 0

f f

o o

% 6 0 6 0

( (

y y

t t i

i 4 0 4 0

v v

i i

t t c

c 2 0 2 0

a a

D D O

O 0 0 R

R -1 0 1 2 3 -1 0 1 2 3

E E C o n c e n tra tio n L o g (m g /m l) C o n c e n tra tio n L o g (m g /m l)

M L 2 M O 1

) )

. .

x x a

a 1 2 0 1 2 0

M M

D 1 0 0 1 0 0

D D

C C

T T

8 0 8 0

f f

o o

% 6 0 6 0

( (

y y

t t i

i 4 0 4 0

v v

i i

t t c

c 2 0 2 0

a a

D D O

O 0 0 R

R -1 0 1 2 3 -1 0 1 2 3

E E C o n c e n tra tio n L o g (m g /m l) C o n c e n tra tio n L o g (m g /m l)

Figure 5.3 Observed (solid lines) and predicted (dashed lines) concentration-response curves of the synthetic mixtures in the EROD assays. Observed data was calculated based on the average of three repetitions. Error bars represent the standard deviations. EROD activities of the synthetic mixtures were normalized against the maximum response observed for their corresponding standard. The unit mg/ml means mg sediment equivalent (SEQ) in 1 ml medium.

Generally, CA model predictions show almost the same results as biological detected, but slight inconsistencies between the predicted and observed results were also observed. The CA model tends to overestimate the toxicity of the mixtures, since the predicted effects of the mixtures (except MS2) were slightly higher than those observed effects. Similarly, Olmstead and LeBlanc (2005) showed that the CA model 107

Chapter 5 slightly overestimated the effects of the mixture of four PAHs on the growth rate of the crustacean Daphnia magna during sub-chronic exposure. On the contrary, underestimated toxicities were observed when REP and CA model were used to predict the toxicities of the mixtures containing two to four PAHs (Larsson et al. 2012). It is possible that the differences between predicted and observed results were caused by non-additive interactions. That is, synergistic effects lead to underestimated effects and antagonistic interactions lead to overestimated effects. It is also possible that the differences are caused by the bioassay (Larsson et al., 2012), taking into account the deviations of the EC50s of PAHs. The concentrations of chemicals in environmental samples are quite low and synergism is rare (Payne et al., 2000; Neale et al., 2015). Generally, CA model and CA-converted REP can be used to confirm the contribution of detected compounds to the effects observed in a complex sample.

5.5 Conclusions

BEQs and REPs varied at different effect levels (e.g., EC10-EC80) and these differences directly related to the slopes of the concentration-response curves of the sample and standard and to the efficacy of the sample relative to the standard. The effect level should be addressed when using REPs and BEQs for comparative, risk assessment or mass balance analysis. Although bioassay-derived and chemically estimated BEQs vary with the effect level selected, the ability of Chem-BEQ to interpret Bio-BEQ at the same effect level will be stable theoretically. Therefore, we recommended that Bio-BEQs should be calculated based on a lower effect level EC10 that can be significantly distinguished from the solvent response, and Chem-BEQs

calculated from R should be used to analyze the contributions of chemically analyzed compounds. The prerequisite for the application of REPs at a specific effect level is that the response of the sample can be simulated by the sigmoidal concentration-response curve. A full concentration-response curve containing the maximum response and the minimum response that is significantly different from the background value is required to obtain a reliable REP for a compound. In this study,

individual R of US EPA priority PAHs using RTL-W1 were obtained and 108

Chapter 5 these values accurately predicted the toxicity of mixtures containing various components.

Acknowledgements

This study was supported by SOLUTIONS project that is funded by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement number (603437). Personally, Shangbo Zhou was supported by the scholarship program of China Scholarship Council (CSC). The authors really appreciate the comments of Prof. Beate Escher and Dr. Maria Larsson. We thank Dr. Martin Krauss in UFZ Helmholtz Centre for Environmental Research in Germany for preparing PAHs and mixtures. The authors would like to thank Drs. Niels C. Bols and Lucy Lee (University of Waterloo, Canada) for providing RTL-W1 cells.

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Chapter 6

Spatial and temporal variations of anti-androgenic activities and their environmental risks in surface waters

This chapter is based on a manuscript to be submitted to Science of the Total Environment: Zhou, S., Krauss, M., Xiao, H., Schulze, T., Hollert, H., Seiler, T.-B. (to be submitted). Spatial and temporal variations of anti-androgenic activities and their environmental risks in surface waters.

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Abstract

The concentrations and combined effects of multiple compounds in the environment have been quantified. However, their spatial and temporal variations are rarely incorporated. In this study, the variations of anti-androgenic activities from 2014 to 2016 were observed using in vitro effect-based bioassays. Water samples were obtained from three sites of the river according to the disturbance of effluents of wastewater treatment plant (WWTP). Results showed that high cytotoxicity was observed at the site that was directly influenced by effluents. After approximately 21 km dilution and degradation, cytotoxicity at the downstream site decreased greatly and was close to that at the WWTP upstream site with the minimum human disturbance. Great spatial and temporal variations of anti-androgenic activities were found at all three sites, and the highest activity was obtained at the site directly influenced by effluents. Furthermore, a screening-level risk assessment showed that the highest risk of the anti-androgenic activities was presented at the site directly influenced by WWTP effluents, and the risks decreased greatly at the downstream site away from effluents. Meanwhile, an in vitro metabolism using rat liver S9 was applied to reflect endocrine-disrupting activities of single compounds and complex water samples after metabolism. Results indicated that androgenic or estrogenic abilities of the compounds and water samples could be altered after phase I metabolism. Keywords: Cell viability; Metabolism; Variation; Risk assessment

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6.1 Introduction

Androgen antagonists can disturb the development of male phenotype and secondary sexual characteristics in humans and other vertebrates (Di Paolo et al., 2016). Anti-androgen receptor-mediated (anti-AR) activities have been widely detected in the rivers, lakes and oceans (Van Der Linden et al., 2008; Weiss et al., 2009; Liscio et al., 2014; Alvarez-muñoz et al., 2015; Di Paolo et al., 2016). However, the spatial and temporal variations of anti-androgenic activities in aquatic systems are few observed. Among the in vitro assays for the detection of (anti-)androgenic or (anti-)estrogenic activities, Chemically Activated LUciferase gene eXpression (CALUX) assays can detect the activations of androgen or estrogen receptors at low concentrations (Weiss et al., 2009; van der Burg et al., 2010; Brand et al., 2013; Alvarez-muñoz et al., 2015; Välitalo et al., 2016). The bioassay-derived activities of the samples can be expressed as the equivalent concentration of a known reference standard that would elicit the same effect as the tested compound (Kase et al., 2018). The metabolic capacity of CALUX system is quite weak and can be neglected. The results of CALUX assays without metabolism step can reflect the endocrine-disrupting activities of the parent compounds. However, metabolism is quite important for detoxifying some compounds, and even activating some (Jacobs et al., 2013; Brack et al., 2016). The combination of in vitro metabolism and specific receptor-ligand binding assay was applied in previous studies (Escher et al., 2017; Mollergues, et al., 2017; van Vugt-Lussenburg et al., 2018). The liver containing cytochrome P450 (CYP450) enzymes is important for metabolizing many xenobiotics (Kojima et al., 2014). Thus an in vitro metabolic system using rat liver S9 fractions incorporated into the CALUX assays was recommended to observe the endocrine-disrupting activities of the compounds after metabolism (Gomez et al., 2010; van Vugt-Lussenburg et al., 2018). For the mixtures or natural samples, biological responses obtained by bioassays are of limited relevance for the potential impacts on non-target organisms, but these responses can be translated into risk quotients (RQs) that are directly correlated with

112

Chapter 6 potential environmental risk (Kase et al., 2018). The purposes of this study were to: observe the spatial and temporal variations of anti-AR activities after three years sampling from 2014 to 2016; quantify the risks of anti-AR activities caused by the external disturbance; study the endocrine-disrupting activities after in vitro metabolism.

6.2 Methods and materials

6.2.1 Sample collection Water samples were taken from 2014 to 2016 at three sites of Holtemme river- Wernigerode, Silstedt and Nienhagen. The first site was located at the Steinerne Renne upstream of the town of Wernigerode with minimum human disturbance, thus samples from this site were selected as references. The second Silstedt site was located at the approximately 1.2 km downstream of the effluents of wastewater treatment plant (WWTP). The third site was located at the downstream near the village of Nienhagen, approximately 22.5 km downstream of the WWTP effluents. Water samples were extracted on-site using a large volume solid phase extraction (LVSPE) device according to the methods reported earlier (Schulze et al., 2017; Välitalo et al., 2017). A total of 65 water samples were collected at three sites. The concentration of the sample was expressed as the relative enrichment factor (REF) that was the ratio of the volume of water sample prior to the enrichment using the LVSPE device to the volume of sample after enrichment. Blank control and process controls were also prepared. 6.2.2 CALUX assays MTT assays were performed prior to the CALUX assays to observe the cytotoxicity of water sample extracts, then CALUX assays were performed in a range of concentrations without cytotoxicity using the human osteosarcoma U2OS cell line, according to the BioDetection Systems (BDS) protocol with slight modifications (Di Paolo et al., 2016). Briefly, cells in the assay medium (DMEM/F12 supplemented with 5% of stripped FCS, 0.2% penicillin/streptomycin solution and 1% non-essential amino acids) were seeded into 96-well plates at a density of 10 000 cells/well. After

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24 h incubation (37 °C, 5% CO2), the medium was replaced with exposure medium containing tested samples, and the concentration of dimethyl sulfoxide (DMSO) was 1% (v/v). After 24 h incubation, the medium was removed and cells were lysed in 30 µL /well of Triton-lysis buffer. The amount of luciferase activity was quantified using a luminometer (GloMax®96 Microplate Luminometer, Germany). For the anti-AR CALUX assay, the same procedure as for the AR CALUX assay was followed, with the exception that the exposure medium was supplemented with dihydrotestosterone (DHT) at a concentration of 4.2 × 10−10 M. Three independent analyses were performed for all samples. 6.2.3 In vitro phase I metabolism Many xenobiotics can be metabolized by CYP450 enzymes contained in rat liver S9. Therefore, an in vitro phase I metabolism was established using rat liver S9 to evaluate the endocrine activity of chemicals after metabolism. For the CALUX assay considering metabolism, the same procedure was followed as the CALUX assay with the exception that the exposure medium was supplemented with rat liver S9 (0.01mg/ml) as well as co-factors nicotinamide adenine dinucleotide phosphate (NADP+, 0.33 mmol/l) and glucose-6-phosphate (G-6-P, 6.3 mmol/l) (Otto et al., 2008). 6.2.4 Risk assessment For the risk assessment of the single compound, the detected concentration exceeding predicated no effect concentration (PNEC) indicates a potential environmental risk. Similar to this traditional method, the risk assessment of a multi-component sample can be calculated by dividing the bioanalytical equivalent (BEQ) of the sample by the environmental quality standard (EQS) (Equation 6.1, Kase et al., 2018). In this study, the potential risks of anti-AR activities in surface waters were assessed. BEQ was the flutamide-equivalent (eq.) of water sample to inhibit agonistic activity as the reference flutamide at a specific effect level IC50. The maximum BEQ of the samples at the uncontaminated Wernigerode site was regarded as EQS for risk assessment. RQ ≥ 1 means significant external disturbance.

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RQ (6.1)

For a large-scale risk assessment or a risk assessment with many replicates, an optimized risk quotient (RQf) based on the ratio of mean BEQ (i.e., BEQmean) to EQS and the frequency (i.e., F) of BEQs exceeding EQS was recommended (Equation 6.2).

RQf > 0 means significant external disturbance.

RQ F (6.2)

6.3 Results and discussion

6.3.1 Cytotoxicity at three sites The results of MTT assays showed non-specific toxicity of water samples at the cellular level. For the Wernigerode and Nienhagen samples, the significant difference in cytotoxicity between the samples and the control was observed at a concentration of 100 REF (Figure 6.1). However, the significant difference between the Silstedt samples and the control was also observed at a lower concentration (50 REF). Among the three sites, the highest cytotoxicity of water samples was observed at the Silstedt site, approximately 1.2 km downstream of the WWTP, and the cytotoxicity of water samples at the Nienhagen site (22.5 km downstream of the WWTP) was close to the cytotoxicity of samples obtained at the Wernigerode site upstream of the WWTP.

130 DMSO 120 Wernigerode 110 *

100 90 80 70 60 50 40 Cell viability ( %) ( viability Cell 30 20 10 0 0.2 0.39 0.78 1.56 3.13 6.25 12.5 25 50 100 Concentrations (REF)

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130 DMSO Silstedt 120 110 100 * 90 80 * 70 60 50

Cell viability ( %) ( viability Cell 40 30 20 10 0 0.2 0.39 0.78 1.56 3.13 6.25 12.5 25 50 100 Concentrations (REF)

130 DMSO Wernigerode 120 110 *

100 90 80 70 60 50 40 Cell viability ( %) ( viability Cell 30 20 10 0 0.2 0.39 0.78 1.56 3.13 6.25 12.5 25 50 100 Concentrations (REF) Figure 6.1 Cytotoxicity of water samples from 2014 to 2016 at three sites (Wernigerode, Silstedt and Nienhagen), the response was expressed as % of relative response to the DMSO control. * indicates that a significant difference was observed between the control and the treatment.

6.3.2 The spatial and temporal variations of anti-androgen receptor-mediated (anti-AR) activities In this study, significant spatial and temporal variations of anti-AR activities from Apr to Nov in 2014, 2015 and 2016 were found at all three sites (Figure 6.2). Most water samples at the Silstedt site showed the highest anti-androgenic activity due to sustained wastewater discharge, with the flutamide-eq. in a range of 4.32 to 60.97 µg/l. The highest activity was also found at the Nienhagen site due to surface runoff. The

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Chapter 6 anti-AR activity at the unpolluted Wernigerode site was the lowest with a mean value of 6.77 µg/l flutamide-eq.. Generally, anti-AR activities were detected at three sites, indicating that in vitro effect-based bioassays are sensitive and suitable for assessing endocrine activities in surface waters. The levels of anti-androgenic activities in the Holtemme river were comparable with those reported in the literature. For example, anti-androgenic activities in water samples of the Lambro river in Italy were in the range of 370 to 4723 µg/l flutamide-eq.; anti-androgenic activities in surface water at a West Virginia injection well disposal site in the US reached 700 μg/L flutamide-eq. (Kassotis et al., 2016); the levels of anti-androgenic activities in surface water from the Pearl River in China were 20.4 to 935 µg/l flutamide-eq. (Zhao et al., 2011). Furthermore, the potential adverse effects of antagonists on the male gonads were observed at measured environmental concentrations (Jensen et al., 2004; Urbatzka et al., 2007). The spatial variations of anti-AR activities probably were caused by the release of pollutants from WWTPs. Anti-AR activities decreased at the downstream Nienhagen site due to the in-stream degradation and dilution. Reasons for temporal variations included variations in flow, usage, and degradation (Burns et al., 2018). High concentrations of the compounds in the environment can be caused by low flow and degradation. The variations of anti-AR activities were quite complex, high and low concentrations were observed during the same periods in different years. Further research is needed for the reasons of temporal variations.

70 A Wernigerode 60 Silstedt Nienhagen

40 eq. eq. µg/l

- 30

Flutimde 10

0 Apr May Jun Jul Aug Sep Oct Nov Time

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Wernigerode 50 B Silstedt

40 Nienhagen

30 eq. eq. µg/l

- 20

Flutimde 0 Apr May Jun Jul Aug Sep Oct Nov Time

50 C Wernigerode Silstedt 40

Nienhagen

30 eq. eq. µg/l

- 20

Flutimde 0 Apr May Jun Jul Aug Sep Oct Time

Figure 6.2 Anti-androgen receptor-mediated (anti-AR) activities from 2014 to 2016 at three sites

(Wernigerode, Silstedt and Nienhagen). Water samples at Wernigerode site were not available until

Aug 2014. Anti-AR activities were reported as µg of flutamide-eq. per L of water. Samples at

Nienhagen site in Jun 2016 failed to obtain flutamide-eq..

6.3.3 Compounds occurrence and distribution Annex Table 6.1 shows the concentrations (ng/L) and frequencies of compounds occurrence at three sites (Wernigerode, Silstedt and Nienhagen). 216 out of 401 analyzed compounds were positively detected with concentrations above LODs at three sites, only 45 compounds (11%) were positively detected at the Wernigerode site where was regarded to be less contaminated. The number of compounds with the concentrations higher than LODs increased greatly after the disturbance of WWTP effluents and approximately 50% compounds were positively detected at the Silstedt and Nienhagen sites. The highest concentration at the Silstedt site was 8437.4 ng/L of

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Chapter 6 acesulfame, and the highest concentration at the Nienhagen site was 1768.8 ng/L of N-Formyl-4-aminoantipyrine. It was clear that the concentrations of compounds decreased greatly after 21 km transformation and dilution. Escher et al. (2017) showed that genistein, daidzein, 2,4-dinitrophenol played an important role in inhibiting androgenic activities. In this study, the detected concentrations of genistein and daidzein were less than 4.0 ng/L. However, 2,4-dinitrophenol was observed at higher concentrations, and the highest concentration was observed at the Silstedt site (64.4 ng/L). Generally, genistein, daidzein and 2,4-dinitrophenol were present in low concentrations, which supports the hypothesis that unknown compounds may contribute to the induced activities. 6.3.4 Risk assessment based on effect-based bioassays Risk assessment based on effect-based bioassays can be used as a screening tool to identify polluted waterbodies (Kase et al., 2018). 15 out of 23 (i.e., 65%) Silstedt samples and 3 out of 23 (i.e., 13%) Nienhagen samples showed the RQ values above 1 (Figure 6.3). Although potential risks were observed at Silstedt and Nienhagen sites, the frequencies of risk occurrence were greatly different. In this study, multiple replicates were obtained, thus optimized RQfs were obtained for Silstedt and Nienhagen samples, with values of 0.67 and 0.06, respectively. It was clear that the potential risks of anti-AR activities decreased about 10 times after 21 km transportation and dilution from upstream Silstedt site to downstream Nienhagen site.

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Figure 6.3 Temporal variations in the potential risks of anti-AR activities at Silstedt and

Nienhagen sites.

6.3.5 Endocrine-disrupting activity after metabolism Figure 6.4 shows that androgenic activities of stanozolol (STZ) and water sample S1 were altered after co-exposure with S9 and cofactors. For DHT, no androgenic activity at a concentration of 4.2 × 10−10 M was observed after phase I metabolism, thus DHT is not a suitable agonist to test the anti-androgenic activity after considering metabolism. Therefore, estrogenic and androgenic activities of the compounds after in vitro metabolism were studied.

1 2 0 1 2 0 D H T

S TZ ) )

1 0 0 1 0 0 %

% D H T

(

S T Z + S 9

n n

o S 1 o 8 0

8 0 i

c u

u S 1 + S 9 d

6 0 d 6 0

e i 4 0 i

v 4 0

l e

2 0 e 2 0

R R

0 0 -1 .5 -1 .0 -0 .5 0 .0 0 .5 1 .0 1 .5 2 .0 -1 .5 -1 .0 -0 .5 0 .0 0 .5 1 .0 1 .5 2 .0 L o g (n M ) L o g (n M )

Figure 6.4 Androgenic activities of stanozolol (STZ) and water sample S1 in or absence of S9. S1 was the water sample obtained at Nienhagen site in Jun 2016.

The estrogenic activities of estradiol (E2), bisphenol S (BPS) and water sample varied greatly after in vitro metabolism (Figure 6.5). Although flutamide was inactive in estrogenic activity, it showed slightly activity after phase I metabolism. Generally, metabolism is important for testing hormonally active chemicals. Ignoring metabolism may underestimate or overestimate the full potential of endocrine activities of the samples (Kojima et al., 2014; Mollergues et al. 2017; van

Vugt-Lussenburg et al., 2018). This study proved that the combination of in vitro metabolism and CALUX assays was effective in obtaining androgenic or estrogenic activity of the compounds, as recommended in previous studies (Mollergues et al., 2017; van Vugt-Lussenburg et al., 2018). The in vitro metabolism in this study is the phase I metabolism, which is catalyzed by the CYP450 enzymes for the purpose of readily excreting xenobiotics. The main reactions include hydrolysis (epoxide hydrolase, amidase, etc.), oxidation (peroxidases, monoamine oxidase, alcohol 120

Chapter 6 dehydrogenase, aldehyde dehydrogenase, etc.) and reduction (NADPH-cytochrome P450 reductase, etc.) (Burkina et al., 2015).

E s tra d io l ( E 2 ) 1 2 0 B is p h e n o l S (B P S )

E 2 1 4 0 E 2 )

) 1 0 0 %

% E 2 + S 9 1 2 0 (

( B P S

o o

8 0 i 1 0 0 i

t B P S + S 9

c u

u 8 0 d

d 6 0

6 0

e i

i 4 0

t a

a 4 0

l e

e 2 0 R R 2 0

0 0 -0 .5 0 .0 0 .5 1 .0 1 .5 2 .0 2 .5 0 2 4 6 8 C o n c e n tra tio n (L o g p M ) C o n c e n tra tio n (L o g p M )

F lu ta m id e 1 2 0 E 2 E 2 1 2 0 S 2

) S 2

1 0 0 F lu ta m id e )

% 1 0 0

( %

S 2 + S 9

(

n F lu ta m id e + S 9 n

o 8 0 i

o 8 0

u d

6 0 d 6 0

e i

4 0 i 4 0

l e

e 2 0 2 0

R R

0 0 0 2 4 6 8 -4 -2 0 2 C o n c e n tra tio n (L o g p M ) C o n c e n tra tio n (L o g R E F )

Figure 6.5 Estrogenic activities of single compounds and water sample S2 in or absence of S9.

The response was expressed as % of relative response to the reference E2. S2 was the water sample obtained at the Silstedt site in Sep. 2014.

6.4 Conclusions

Spatial and temporal variations of the anti-androgenic activities were observed from upstream to downstream of Holtemme river. The risk assessment based on the detected anti-androgenic activities of the samples over the EQS was used to predict the effects of human disturbance. The optimized risk assessment considering the frequencies of risk occurrence showed that the RQf at the site near the WWTP was 10 times higher than that at the Nienhagen site away from WWTP disturbance. In vitro metabolism can be used to observe the endocrine-disrupting activities of single compounds and complex natural samples. Results showed that endocrine-disrupting activities of most compounds were decreased after metabolism.

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Acknowledgements The study was funded by the SOLUTIONS project supported by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement no. 603437. Personally, the first author was supported by the China Scholarship Council. We thank BioDetection Systems BV (BDS, Amsterdam, The Netherlands) for supplying the cell line and respective culture and method protocols.

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Discussion, conclusions and outlook

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7.1 General conclusions and Discussion

In this thesis, optimized methods for risk identification of single compounds (Chapter 2) and multi-component samples (Chapter 6) have been developed and proposed the necessity for confirmation (Chapters 3, 4 and 5). In European surface waters, 284 compounds belonging to 16 different therapeutic groups were positively detected. 45 of 477 analyzed compounds showed a potential environmental risk to aquatic ecosystems (Chapter 2). Diclofenac, ethinylestradiol, paraxanthine, ibuprofen, atorvastatin, carbamazepine, estriol, venlafaxine, ranitidine, spiramycin, zidovudine and amoxicillin were dentified as emerging compounds with high environmental risks in European surface waters. Triclosan and caffeine showed the moderate risks and their delayed effects (Chapter 3) on zebrafish larval locomotor behavior were observed at the environmental relevant concentrations. The studies of delayed toxicity of emerging pollutants on non-target species are still insufficient. In Chapter 3, 8 of these 45 compounds with potential environmental risks were screened for studying the delayed effects in swimming speed, hatching rate and angular velocity of the zebrafish larvae. Of particular interest for future investigations and risk assessment strategies is that the obtained lowest observed effect concentration (LOEC) of triclosan (0.1 μg/L) for larval locomotor behavior is just slightly higher than the observed mean concentration in European surface waters (0.04 μg/L). The mixture of 8 compounds each with the highest environmental concentration during embryonic development reduced the swimming speed. Early developmental stages of organisms are sensitive to toxicants (Di Paolo et al., 2015), thus zebrafish behavior test can act as a sensitive biological system for screening the pharmaceuticals that may exert effects on the nervous system and for early detecting the potentially high-risk substance. Furthermore, zebrafish display a series of behavioral patterns which are comparable to rodents and other species in terms of their genome, brain patterning and neurological system (Ali et al., 2012; Roberts et al., 2013; Legradi et al., 2015). Thus compounds that affect zebrafish in the early stage may also effects on other species.

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7.1.1 Bioanalytical equivalents (BEQs) for the risk identification Bioanalytical equivalents (BEQs) are widely used to describe the response of the tested sample in in vitro or in vivo bioassays, such as EROD (Chapter 4) and CALUX (Chapter 6) assays involved in this thesis. Meanwhile, chemically analyzed BEQs are obtained by using the relative potencies (REPs) to quantify the contributions of identified compounds to the bioassay-derived BEQs. In Chapter 5, the factors affecting BEQ values and REP application were analyzed. BEQ and REP variations at a range of effect levels (e.g., EC10-EC80) are directly related to the slopes of concentration-response curves of the sample and standard, and to the efficacy of the sample relative to the standard. The previous prerequisites for REPs application (i.e., parallel and the same maximum response of the curves) are redundant, and the use of REPs for the calculation of BEQs or the risk assessment should be based on the same effect level. The interactions between components of the mixtures and artifacts of data interpretation ignoring the basic requirements for the calculation of BEQs affect the contributions of detected chemicals to the biological response. Although Chem-BEQs can reveal the contributions of detected compounds to the bioassay-derived toxicity of complex environmental samples, the most accurate method is to prepare synthetic mixtures with known components to analyze their contributions, and thus potential interactions of the components were considered. Polycyclic aromatic compounds and endocrine disturbance compounds are widespread organic compounds in aquatic systems, and thus BEQs of the aryl hydrocarbon receptor (AhR)-mediated and endocrine-disrupting activities were identified in Chapter 4 and Chapter 6, respectively. 7.1.2 Identification of aryl hydrocarbon receptor (AhR)-mediated activities In Chapter 4, effect-directed analysis (EDA) was used to identify specific fractions and pollutants that are responsible for the AhR-mediated activities of sediment extracts at the less contaminated sites. 7-ethoxyresorufin-O-deethylase (EROD) activities were evaluated for the determination of AhR-mediated activities. Sediment samples from four sites with high activities were chosen for fractionation. Besides the non-polar fractions, the medium-polar and polar fractions were identified as priority fractions for the AhR-mediated activities. The BEQs of the US EPA priority PAHs accounted only to a 125

Chapter 7 minor extent for the equivalents of sediment extracts. However, chemical analyses applied in this study were capable to identify only a few nitrated polycyclic aromatic hydrocarbons (Nitro-PAHs) and N-heterocyclic aromatic hydrocarbons (aza-PAHs, azaarenes) in the medium-polar and polar fractions and no EROD activity of their synthetic mixtures was observed. Some nitro-PAHs and heterocyclic PAHs were found to be far more mutagenic or carcinogenic than unsubstituted PAHs (Sovadinova et al., 2006; Ozaki et al., 2010; Wahidulla and Rajamanickam 2010). However, the relationships between the AhR-mediated activities of samples and their toxic effects in situ are still not clear.

7.1.3 Identification of endocrine-disrupting activities In Chapter 6, the spatial and temporal variations of anti-androgenic activities in aquatic systems were observed. The anti-androgenic activities of water samples were expressed as the BEQs of flutamide and high activities were observed from 2014 to 2016. The potential high risk caused by endocrine disturbance was present at the site directly influenced by the effluents of wastewater treatment plants, and the risks decreased greatly at the site away from wastewater treatment plants. The metabolic capacity of CALUX system is quite weak. The combination of in vitro metabolic system using rat liver S9 fractions and CALUX assays was effective in obtaining the endocrine activities after metabolism. Estrogenic activities of estradiol, bisphenol S and water sample decreased greatly after in vitro metabolism. Flutamide was estrogen-inactive, but showed slightly estrogenic activity after metabolism in the ER CALUX® assay. In this study, only phase I metabolism that involves oxidation, reduction and hydrolysis was considered, but the phase II metabolism that involves conjugation cannot be ignored. 7.1.4 Risk assessment of the single compounds and samples Optimized methods for screening-level risk assessment of single compounds and environmental samples were developed and applied. Specifically, for the risk assessment of a single compound, an optimized risk quotient (RQf) that considers the toxicological data and the frequency of detected concentrations over predicted no-effect level was recommended to screen candidate priority pollutants in Chapter 2, and the reliability

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and advantages were confirmed. When the RQf value of a compound is higher than zero, potential environmental risk is expected. The environmental risks of compounds can be classified into five categories (high, moderate, endurable, negligible and safe) depending on potential negative effects of compounds and the frequency of risk occurrence. In European surface waters, 12 compounds were indicated to have high environmental risk in aquatic environments, 17 and 7 compounds showed moderate and small-scale environmental risks, respectively. Brack et al. (2017) believed that traditional monitoring and assessment tend to emphasize well known and regulated chemicals and to overlook emerging compounds. They recommended that an integrated strategy for prioritization of contaminants should be used. Tousova et al. (2017) also said that novel tools and approaches are required to monitor micropollutants and their effects in the aquatic environment. Furthermore, the lack of chronic toxicological data and the consequent use of ECOSAR and QSAR estimates often require a high assessment factor, the risk levels of compounds based on acute toxicity data were probably overestimated, leading to a higher priority ranking. Environmental risk assessment of pharmaceuticals should consider their metabolites and transformation products, since the degradation of pharmaceuticals in the environment may form persistent and toxic transformation products (Escher and Fenner, 2011). Although metabolites and transformation products of pharmaceuticals were detected in European surface waters, most of their toxicological data could not be acquired. For example, whether the occurrence of several metabolites of carbamazepine (e.g., 10,11-dihydro-10,11-dihydroxy-carbamazepine and 10,11-epoxy-carbamazepine) can pose a threat to aquatic systems is still uncertain. In Chapter 6, a screening-level risk assessment of multi-component samples was developed and used, and the risk quotient (RQ) was calculated by dividing BEQ by the proposed environmental quality standard (EQS). For a large-scale risk assessment, an optimized RQf based on the mean RQ and the frequency of BEQs exceeding EQS was recommended. In this study, the highest risk for anti-androgenic activities was found at the site directly influenced by WWTP effluents, and the risks decreased greatly at the downstream site further away from effluents. The optimized method for risk assessment of single compounds can be used for the 127

Chapter 7 priority selection of emerging pollutants with potential environment risks in surface waters and sediments. The optimized method for risk assessment of multi-component samples based on mechanism-specific bioassays (e.g., CALUX assays) helps to find the samples with certain inducing ability (e.g., endocrine disturbance ability) beyond the environmental quality standard. It needs to be embrassed that the risks of samples in the aquatic environment correlate with the length of time of aquatic organisms exposed to hazardous compounds, which means that the potential risks of pharmaceuticals in aquatic systems rely on chronic toxicity with long-term exposures rather than acute one with short-time exposures (Brausch and Rand, 2011). Further studies should confirm the risk of pharmaceuticals with RQf above zero. 7.2 Proposals for further research

Risk identification and confirmation of emerging pollutants in ecosystems is a complex progress (Figure 7.1). Further research should consider a more comprehensive testing strategy, especially for in situ effects. The combination of bioassays using model test species and chemical analysis of the environmental samples is the first step in identifying compounds with negative effects on non-target species. Then the compounds with potential risks should be prioritized based on a screening-level risk assessment. Compounds exist in the natural environment as complex mixtures rather than single alone, thus the knowledge of mixtures cannot be excluded. The grouping of compounds, especially the high-risk pollutants, according to modes of action can support the selection of the single compounds that should compose chemical mixtures for further investigations such as by mechanism-specific bioassays. For example, the potential estrogenic effects of pharmaceuticals in surface waters were directly correlated with three compounds (ethinylestradiol, 17-β-estradiol, estrone), rather than other endocrine disrupting compounds (e.g. 17α-estradiol, testosterone, progesterone, gestoden and bisphenol A) (Kase et al., 2018; Könemann et al., 2018).

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Figure 7.1 Conceptual framework for the risk identification and confirmation of emerging pollutants in ecosystems. By further considering the involved mechanisms in mixture design (Busch et al., 2016; Kienzler et al., 2016), an in-depth confirmation of the toxicity of prioritized compounds and their mixtures should be conducted at different endpoints of the different species. Thus, compounds with potential risks for aquatic organisms at environment-related concentrations can be identified, while the results obtained in the laboratory are not directly related to the in situ hazard effects. Therefore, in order to confirm the in situ effects of these compounds or mixtures, bioassays of compounds and their mixtures should be further conducted in situ for multiple endpoints at different levels.

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161

Annex

Annex 1.1 Reference used for collecting the pharmaceuticals concentrations and toxicity data.

Annex 1.1.1 References for pharmaceuticals in each country

Austria

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335.

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Belgium

Janssen, C., Vincx, M., De Brabander, H., Roose, P., Vethaak, D., 2007. Endocrine disruptors in the

Scheldt estuary: distribution, exposure and effects. Sci. Support plan 106.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

Van De Steene, J.C., Stove, C.P., Lambert, W.E., 2010. A field study on 8 pharmaceuticals and 1

pesticide in Belgium: Removal rates in waste water treatment plants and occurrence in surface

water. Sci. Total Environ. 408, 3448–3453.

Verliefde, A., Cornelissen, E., Amy, G., Van der Bruggen, B., van Dijk, H., 2007. Priority organic

micropollutants in water sources in Flanders and the Netherlands and assessment of removal

possibilities with nanofiltration. Environ. Pollut. 146, 281–289.

Bulgaria 162

Annex

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335.

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Croatia

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Cyprus

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

Czech Republic

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Denmark 163

Annex

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Matamoros, V., Arias, C.A., Nguyen, L.X., Salvadó, V., Brix, H., 2012. Occurrence and behavior of

emerging contaminants in surface water and a restored wetland. Chemosphere 88, 1083–1089.

TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report.

https://doi.org/http://dx.doi.org/10.6027/TN2012-519

Estonia

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Faroe Islands

Huber, S., Remberger, M., Kaj, L., Schlabach, M., Jörundsdóttir, H.T., Vester, J., Arnórsson, M.,

Mortensen, I., Schwartson, R., Dam, M., 2016. A first screening and risk assessment of

pharmaceuticals and additives in personal care products in waste water, sludge, recipient water

and sediment from Faroe Islands, Iceland and Greenland. Sci. Total Environ. 562, 13–25.

TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report, Available at:

http://www.nordicscreening.org/index.php?module=Pagesetter&type=file&func=get&tid=5&fi

d=reportfile&pid=16.

Finland

Lindholm-Lehto, P.C., Ahkola, H.S.J., Knuutinen, J.S., Herve, S.H., 2015. Occurrence of

pharmaceuticals in municipal wastewater, in the recipient water, and sedimented particles of

northern Lake Päijänne. Environ. Sci. Pollut. Res. 22, 17209–17223.

Lindholm-Lehto, P.C., Ahkola, H.S.J., Knuutinen, J.S., Herve, S.H., 2016a. Widespread occurrence

and seasonal variation of pharmaceuticals in surface waters and municipal wastewater 164

Annex

treatment plants in central Finland. Environ. Sci. Pollut. Res. 23, 7985–7997.

Lindholm-Lehto, P.C., Ahkola, H.S.J., Knuutinen, J.S., Koistinen, J., Lahti, K., Vahtera, H., Herve,

S.H., 2016b. Suitability of passive sampling for the monitoring of pharmaceuticals in Finnish

surface waters. Environ. Sci. Pollut. Res. 23, 18043–18054.

Lindqvist, N., Tuhkanen, T., Kronberg, L., 2005. Occurrence of acidic pharmaceuticals in raw and

treated sewages and in receiving waters. Water Res. 39, 2219–2228.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report, Available at:

http://www.nordicscreening.org/index.php?module=Pagesetter&type=file&func=get&tid=5&fi

d=reportfile&pid=16.

Vieno, N.M., Tuhkanen, T., Kronberg, L., 2006. Analysis of neutral and basic pharmaceuticals in

sewage treatment plants and in recipient rivers using solid phase extraction and liquid

chromatography-tandem mass spectrometry detection. J. Chromatogr. A 1134, 101–111.

Vieno, N.M., Tuhkanen, T., Kronberg, L., 2005. Seasonal Variation in the Occurrence of

Pharmaceuticals in Effluents from a Sewage Treatment Plant and in the Recipient Water

Seasonal Variation in the Occurrence of Pharmaceuticals in Effluents from a Sewage Treatment

Plant and in the Recipient Water. Environmantal Sci. Technol. 39, 8220–8226.

Vieno, N.M., Härkki, H., Tuhkanen, T., Kronberg, L., 2007. Occurence of pharmaceuticals in river

water and their elimination in a pilot-scale drinking water treatment plant. Environ. Sci.

Technol. 41, 5077–5084.

France

Bouissou-Schurtz, C., Houeto, P., Guerbet, M., Bachelot, M., Casellas, C., Mauclaire, A.C., Panetier,

P., Delval, C., Masset, D., 2014. Ecological risk assessment of the presence of pharmaceutical

residues in a French national water survey. Regul. Toxicol. Pharmacol. 69, 296–303.

Celle-Jeanton, H., Schemberg, D., Mohammed, N., Huneau, F., Bertrand, G., Lavastre, V., Le

Coustumer, P., 2014. Evaluation of pharmaceuticals in surface water: Reliability of PECs

compared to MECs. Environ. Int. 73, 10–21. 165

Annex

Coetsier, C.M., Spinelli, S., Lin, L., Roig, B., Touraud, E., 2009. Discharge of pharmaceutical

products (PPs) through a conventional biological sewage treatment plant: MECs vs PECs?

Environ. Int. 35, 787–792.

Comoretto, L., Chiron, S., 2005. Comparing pharmaceutical and pesticide loads into a small

Mediterranean river. Sci. Total Environ. 349, 201–210.

Dévier, M.H., Le Menach, K., Viglino, L., Di Gioia, L., Lachassagne, P., Budzinski, H., 2013.

Ultra-trace analysis of hormones, pharmaceutical substances, alkylphenols and phthalates in

two French natural mineral waters. Sci. Total Environ. 443, 621–632.

Feitosa-Felizzola, J., Chiron, S., 2009. Occurrence and distribution of selected antibiotics in a small

Mediterranean stream (Arc River, Southern France). J. Hydrol. 364, 50–57.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Rabiet, M., Togola, A., Brissaud, F., Seidel, J.L., Budzinski, H., Elbaz-Poulichet, F., 2006.

Consequences of treated water recycling as regards pharmaceuticals and drugs in surface and

ground waters of a medium-sized mediterranean catchment. Environ. Sci. Technol. 40, 5282–

5288.

Tamtam, F., Mercier, F., Le Bot, B., Eurin, J., Tuc Dinh, Q., Clément, M., Chevreuil, M., 2008.

Occurrence and fate of antibiotics in the Seine River in various hydrological conditions. Sci.

Total Environ. 393, 84–95.

Vulliet, E., Cren-Olivé, C., 2011. Screening of pharmaceuticals and hormones at the regional scale,

in surface and groundwaters intended to human consumption. Environ. Pollut. 159, 2929–2934.

Germany

Christian, T., Schneider, R.J., Färber, H.A., Skutlarek, D., Meyer, M.T., Goldbach, H.E., 2003.

Determination of antibiotic residues in manure, soil, and surface waters. Acta Hydrochim.

Hydrobiol. 31, 36–44.

Hass, U., Duennbier, U., Massmann, G., 2012. Occurrence and distribution of psychoactive

compounds and their metabolites in the urban water cycle of Berlin (Germany). Water Res. 46, 166

Annex

6013–6022.

Heberer, T., Heberer, T., 2002. Occurrence, fate, and removal of pharmaceutical residues in the

aquatic environment: a review of recent research data. Toxicol. Lett. 131, 5–17.

Heberer, T., Verstraeten, I.M., Meyer, M.T., Mechlinski, A., Reddersen, K., 2000. Occurrence and

Fate of Pharmaceuticals During Bank Filtration – Preliminary Results From Investigations in

Germany and the United States 4–17.

Hirsch, R., Ternes, T., Haberer, K., Kratz, K.L., 1999. Occurrence of antibiotics in the aquatic

environment. Sci. Total Environ. 225, 109–118.

Jux, U., Baginski, R.M., Arnold, H.-G., Krönke, M., Seng, P.N., 2002. Detection of pharmaceutical

contaminations of river, pond, and tap water from Cologne (Germany) and surroundings. Int. J.

Hyg. Environ. Health. 205, 393–398.

Kunkel, U., Radke, M., 2012. Fate of pharmaceuticals in rivers: Deriving a benchmark dataset at

favorable attenuation conditions. Water Res. 46, 5551–5565.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335.

Prasse, C., Schlüsener, M.P., Schulz, R., Ternes, T.A., 2010. Antiviral drugs in wastewater and

surface waters: A new pharmaceutical class of environmental relevance? Environ. Sci. Technol.

44, 1728–1735.

Reddersen, K., Heberer, T., Dünnbier, U., 2002. Identification and significance of phenazone drugs

and their metabolites in ground- and drinking water. Chemosphere 49, 539–544.

Ruff, M., Mueller, M.S., Loos, M., Singer, H.P., 2015. Quantitative target and systematic non-target

analysis of polar organic micro-pollutants along the river Rhine using high-resolution

mass-spectrometry - Identification of unknown sources and compounds. Water Res. 87, 145–

154.

Scheurer, M., Sacher, F., Brauch, H. J., 2009. Occurrence of the antidiabetic drug metformin in 167

Annex

sewage and surface waters in Germany. J. Environ. Monit. 11, 1608–1613.

Scheurer, M., Michel, A., Brauch, H.J., Ruck, W., Sacher, F., 2012. Occurrence and fate of the

antidiabetic drug metformin and its metabolite guanylurea in the environment and during

drinking water treatment. Water Res. 46, 4790–4802.

Ternes, T. a, 1998. Occurrence of drugs in German sewage treatment plants and rivers1Dedicated to

Professor Dr. Klaus Haberer on the occasion of his 70th birthday.1. Water Res. 32, 3245–3260.

Vallejo, A., Prieto, A., Moeder, M., Usobiaga, A., Zuloaga, O., Etxebarria, N., Paschke, A., 2013.

Calibration and field test of the Polar Organic Chemical Integrative Samplers for the

determination of 15 endocrine disrupting compounds in wastewater and river water with special

focus on performance reference compounds (PRC). Water Res. 47, 2851–2862.

Von, V.V., 2012. Analysis , Occurrence and fate of antiviral drugs in the aquatic environment.

Wolf, L., Zwiener, C., Zemann, M., 2012. Tracking artificial sweeteners and pharmaceuticals

introduced into urban groundwater by leaking sewer networks. Sci. Total Environ. 430, 8–19.

Greece

Arditsoglou, A., Voutsa, D., 2008. Determination of phenolic and steroid endocrine disrupting

compounds in environmental matrices. Environ. Sci. Pollut. Res. Int. 15, 228–236.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Hungary

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335. 168

Annex

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Iceland

TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report, Available at:

http://www.nordicscreening.org/index.php?module=Pagesetter&type=file&func=get&tid=5&fi

d=reportfile&pid=16.

Ireland

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

McEneff, G., Barron, L., Kelleher, B., Paull, B., Quinn, B., 2014. A year-long study of the spatial

occurrence and relative distribution of pharmaceutical residues in sewage effluent, receiving

marine waters and marine bivalves. Sci. Total Environ. 476–477, 317–326.

Italy

Baronti, C., Curini, R., D’Ascenzo, G., Di Corcia, A., Gentili, A., Samperi, R., 2000. Monitoring

natural and synthetic estrogens at activated sludge sewage treatment plants and in a receiving

river water. Environ. Sci. Technol. 34, 5059–5066.

Calamari, D., Zuccato, E., Castiglioni, S., Bagnati, R., Fanelli, R., 2003. Strategic survey of

therapeutic drugs in the rivers Po and lambro in Northern Italy. Environ. Sci. Technol. 37,

1241–1248.

Castiglioni, S., Fanelli, R., Calamari, D., Bagnati, R., Zuccato, E., 2004. Methodological approaches

for studying pharmaceuticals in the environment by comparing predicted and measured

concentrations in River Po, Italy. Regul. Toxicol. Pharmacol. 39, 25–32.

Ferrari, F., Gallipoli, A., Balderacchi, M., Ulaszewska, M.M., Capri, E., Trevisan, M., 2011.

Exposure of the main Italian river basin to pharmaceuticals. J. Toxicol. 2011. 169

Annex

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Wollgast, J., Huber, T., Hanke, G., 2007. Polar herbicides, pharmaceutical products,

perfluorooctanesulfonate (PFOS), perfluorooctanoate (PFOA), and nonylphenol and its

carboxylates and ethoxylates in surface and tap waters around Lake Maggiore in Northern Italy.

Anal. Bioanal. Chem. 387, 1469–1478.

Mainero Rocca, L., Gentili, A., Caretti, F., Curini, R., Pérez‐Fernández, V., 2015. Occurrence of

non-steroidal anti-inflammatory drugs in surface waters of Central Italy by liquid

chromatography–tandem mass spectrometry. Int. J. Environ. Anal. Chem. 95, 685–697.

Meffe, R., de Bustamante, I., 2014. Emerging organic contaminants in surface water and

groundwater: A first overview of the situation in Italy. Sci. Total Environ. 481, 280–295.

Verlicchi, P., Al Aukidy, M., Jelic, A., Petrović, M., Barceló, D., 2014. Comparison of measured and

predicted concentrations of selected pharmaceuticals in wastewater and surface water: A case

study of a catchment area in the Po Valley (Italy). Sci. Total Environ. 470–471, 844–854.

Zuccato, E., Castiglioni, S., Bagnati, R., Melis, M., Fanelli, R., 2010. Source, occurrence and fate of

antibiotics in the Italian aquatic environment. J. Hazard. Mater. 179, 1042–1048.

Zuccato, E., Castiglioni, S., Fanelli, R., 2005. Identification of the pharmaceuticals for human use

contaminating the Italian aquatic environment. J. Hazard. Mater. 122, 205–209.

Zuccato, E., Castiglioni, S., Fanelli, R., Reitano, G., Bagnati, R., Chiabrando, C., Pomati, F., Rossetti,

C., Calamari, D., 2006. Pharmaceuticals in the environment in Italy: causes, occurrence, effects

and control. Environ. Sci. Pollut. Res. Int. 13, 15–21.

Lithuania

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

170

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Luxembourg

Krein, A., Keßler, S., Meyer, B., Pailler, J.Y., Guignard, C., Hoffmann, L., 2013. Concentrations and

loads of dissolved xenobiotics and hormones in two small river catchments of different land use

in Luxembourg. Hydrol. Process. 27, 284–296.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

Pailler, J.Y., Krein, A., Pfister, L., Hoffmann, L., Guignard, C., 2009. Solid phase extraction coupled

to liquid chromatography-tandem mass spectrometry analysis of sulfonamides, tetracyclines,

analgesics and hormones in surface water and wastewater in Luxembourg. Sci. Total Environ.

407, 4736–4743.

Malta

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Moldova

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

The Netherlands de Jongh, C.M., Kooij, P.J.F., de Voogt, P., ter Laak, T.L., 2012. Screening and human health risk

assessment of pharmaceuticals and their transformation products in Dutch surface waters and

drinking water. Sci. Total Environ. 427–428, 70–77.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European 171

Annex

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Stolker, A.A.M., Niesing, W., Fuchs, R., Vreeken, R.J., Niessen, W.M.A., Brinkman, U.A.T., 2004.

Liquid chromatography with triple-quadrupole and quadrupole-time-of-flight mass

spectrometry for the determination of micro-constituents - A comparison. Anal. Bioanal. Chem.

378, 1754–1761. ter Laak, T.L., van der Aa, M., Houtman, C.J., Stoks, P.G., van Wezel, A.P., 2010. Relating

environmental concentrations of pharmaceuticals to consumption: A mass balance approach for

the river Rhine. Environ. Int. 36, 403–409.

Verliefde, A., Cornelissen, E., Amy, G., Van der Bruggen, B., van Dijk, H., 2007. Priority organic

micropollutants in water sources in Flanders and the Netherlands and assessment of removal

possibilities with nanofiltration. Environ. Pollut. 146, 281–289.

Norway

Langford, K., Thomas, K. V, 2011. Input of selected human pharmaceutical metabolites into the

Norwegian aquatic environment. J. Environ. Monit. 13, 416–21.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report, Available at:

http://www.nordicscreening.org/index.php?module=Pagesetter&type=file&func=get&tid=5&fi

d=reportfile&pid=16.

Vasskog, T., Anderssen, T., Pedersen-Bjergaard, S., Kallenborn, R., Jensen, E., 2008. Occurrence of

selective serotonin reuptake inhibitors in sewage and receiving waters at Spitsbergen and in

Norway. J. Chromatogr. A 1185, 194–205.

Poland

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide 172

Annex

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Kasprzyk-Hordern, B., Dinsdale, R.M., Guwy, A.J., 2007. Multi-residue method for the

determination of basic/neutral pharmaceuticals and illicit drugs in surface water by solid-phase

extraction and ultra performance liquid chromatography-positive electrospray ionisation

tandem mass spectrometry. J. Chromatogr. A 1161, 132–145.

Wagil, M., Kumirska, J., Stolte, S., Puckowski, A., Maszkowska, J., Stepnowski, P., Białk-Bielin´ska,

A., 2014. Development of sensitive and reliable LC-MS/MS methods for the determination of

three fluoroquinolones in water and fish tissue samples and preliminary environmental risk

assessment of their presence in two rivers in northern Poland. Sci. Total Environ. 493, 1006–

1013.

Portugal

Madureira, T.V., Barreiro, J.C., Rocha, M.J., Rocha, E., Cass, Q.B., Tiritan, M.E., 2010. Spatio

temporal distribution of pharmaceuticals in the Douro River estuary (Portugal). Sci. Total

Environ. 408, 5513–5520.

Paíga, P., Santos, L.H.M.L.M., Amorim, C.G., Araújo, A.N., Montenegro, M.C.B.S.M., Pena, A.,

Delerue-Matos, C., 2013. Pilot monitoring study of ibuprofen in surface waters of north of

Portugal. Environ. Sci. Pollut. Res. 20, 2410–2420.

Paíga, P., Santos, L.H.M.L.M., Ramos, S., Jorge, S., Silva, J.G., Delerue-Matos, C., 2016. Presence

of pharmaceuticals in the Lis river (Portugal): Sources, fate and seasonal variation. Sci. Total

Environ. 573, 164–177.

Romania

Chițescu, C.L., Nicolau, A.I., 2014. Preliminary survey of pharmaceutical residues in some important

Romanian rivers. Toxicol. Environ. Chem. 96, 1333–1345.

Chitescu, C.L., Kaklamanos, G., Nicolau, A.I., Stolker, A.A.M.L., 2015. High sensitive multiresidue

analysis of pharmaceuticals and antifungals in surface water using U-HPLC-Q-Exactive

Orbitrap HRMS. Application to the Danube river basin on the Romanian territory. Sci. Total 173

Annex

Environ. 532, 501–511.

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335.

Moldovan, Z., 2006. Occurrences of pharmaceutical and personal care products as micropollutants in

rivers from Romania. Chemosphere 64, 1808–1817.

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Serbia

Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

https://doi.org/10.2788/29668

Loos, R., Locoro, G., Contini, S., 2010. Occurrence of polar organic contaminants in the dissolved

water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

Res. 44, 2325–2335.

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

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pharmaceutical drugs by high performance liquid chromatography coupled to mass

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Slovenia

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water phase of the Danube River and its major tributaries using SPE-LC-MS2 analysis. Water

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Spain

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surface waters of Central Spain. Sci. Total Environ. 466–467, 939–951. 175

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Fernández, C., González-Doncel, M., Pro, J., Carbonell, G., Tarazona, J. V., 2010. Occurrence of

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Gros, M., Rodríguez-Mozaz, S., Barceló, D., 2012. Fast and comprehensive multi-residue analysis of

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liquid chromatography-triple quadrupole-tandem mass spectrometry in combination with ultra

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López-Serna, R., Petrović, M., Barceló, D., 2012. Occurrence and distribution of multi-class

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lagoon (Mar Menor, SE Spain): Sources and seasonal variations. Sci. Total Environ. 490, 59–

72.

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modeling of pharmaceuticals on a sewage-impacted Mediterranean river and their dynamics

under different hydrological conditions. Sci. Total Environ. 440, 3–13.

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Ebro river basin, Spain. Chemosphere 85, 1331–1339.

Sweden

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active compounds in the environment, a case study: Höje River in Sweden. J. Hazard. Mater.

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Commission, Joint Research Centre, Institute for Environment and Sustainability.

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TemaNord, 2012. PPCP monitoring in the Nordic Countries – Status Report.

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Woldegiorgis, A., Kaj, L., Dye, C., 2007. Results from the Swedish screening 2006, Sub report 4:

Pharmaceuticals. Stock. IVL Swedish.

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Alder, A.C., Schaffner, C., Majewsky, M., Klasmeier, J., Fenner, K., 2010. Fate of β-blocker human

pharmaceuticals in surface water: comparison of measured and simulated concentrations in the

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122, 195–204.

Berset, J.-D., Brenneisen, R., Mathieu, C., 2010. Analysis of llicit and illicit drugs in waste, surface

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river-lake system. Chemosphere 80, 301–309.

Daneshvar, A., Svanfelt, J., Kronberg, L., Weyhenmeyer, G.A., 2010b. Winter accumulation of

acidic pharmaceuticals in a Swedish river. Environ. Sci. Pollut. Res. 17, 908–916.

Golet, E.M., Alder, A.C., Giger, W., 2002. Environmental exposure and risk assessment of

fluoroquinolone antibacterial agents in wastewater and river water of the Glatt Valley

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Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

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analysis of polar organic micro-pollutants along the river Rhine using high-resolution 179

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monitoring survey of polar persistent pollutants in European river waters, European

Commission, Joint Research Centre, Institute for Environment and Sustainability.

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Ukraine

Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K.,

Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I.,

2015. Linking in vitro effects and detected organic micropollutants in surface water using

mixture-toxicity modeling. Environ. Sci. Technol. 49, 14614–14624.

Ashton, D., Hilton, M., Thomas, K. V., 2004. Investigating the environmental transport of human

pharmaceuticals to streams in the United Kingdom. Sci. Total Environ. 333, 167–184.

Baker, D.R., Kasprzyk-Hordern, B., 2013. Spatial and temporal occurrence of pharmaceuticals and

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Sci. Total Environ. 454–455, 442–456.

Blackwell, P.A., Lützhøft, H.C.H., Ma, H.P., Sørensen, B.H., Boxall, A.B.A., Kay, P., 2004. Fast and

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water using a tandem solid-phase extraction with high-performance liquid chromatography–UV

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Bound, J.P., Voulvoulis, N., 2006. Predicted and measured concentrations for selected

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Hilton, M.J., Thomas, K. V., 2003. Determination of selected human pharmaceutical compounds in 180

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effluent and surface water samples by high-performance liquid chromatography-electrospray

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Loos, R., Gawlik, B.M., Locoro, G., Rimaviciute, E., Contini, S., Bidoglio, G., 2008. EU wide

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Commission, Joint Research Centre, Institute for Environment and Sustainability.

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Roberts, P.H., Thomas, K. V., 2006. The occurrence of selected pharmaceuticals in wastewater

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Environ. Toxicol. Chem. 26, 601–606.

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Christensen, A.M., Faaborg-Andersen, S., Ingerslev, F., Baun, A., 2007. Mixture and

single-substance toxicity of selective serotonin reuptake inhibitors toward algae and

crustaceans. Environ. Toxicol. Chem. 26, 85–91.

De Liguoro, M., Di Leva, V., Dalla Bona, M., Merlanti, R., Caporale, G., Radaelli, G., 2012.

Sublethal effects of trimethoprim on four freshwater organisms. Ecotoxicol. Environ. Saf. 82,

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Dzialowski, E.M., Turner, P.K., Brooks, B.W., 2006. Physiological and reproductive effects of beta

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Han, S., Choi, K., Kim, J., Ji, K., Kim, S., Ahn, B., Yun, J., Choi, K., Khim, J.S., Zhang, X., Giesy,

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Japanese medaka (Oryzias latipes) and freshwater cladocerans Daphnia magna and Moina

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Ji, K., Kim, S., Han, S., Seo, J., Lee, S., Park, Y., Choi, K., Kho, Y.L., Kim, P.G., Park, J., Choi, K.,

2012. Risk assessment of chlortetracycline, oxytetracycline, sulfamethazine, sulfathiazole, and

erythromycin in aquatic environment: Are the current environmental concentrations safe?

Ecotoxicology 21, 2031–2050. 182

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Kim, Y., Choi, K., Jung, J., Park, S., Kim, P.G., Park, J., 2007. Aquatic toxicity of acetaminophen,

carbamazepine, cimetidine, diltiazem and six major sulfonamides, and their potential ecological

risks in Korea. Environ. Int. 33, 370–375.

Park, S., Choi, K., 2008. Hazard assessment of commonly used agricultural antibiotics on aquatic

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Stuer-Lauridsen, F., Birkved, M., Hansen, L.P., Lützhøft, H.C., Halling-Sørensen, B., 2000.

Environmental risk assessment of human pharmaceuticals in Denmark after normal therapeutic

use. Chemosphere 40, 783–93.

Yamamoto, H., Nakamura, Y., Nakamura, Y., Kitani, C., Imari, T., Sekizawa, J., Takao, Y.,

Yamashita, N., Hirai, N., Oda, S., Tatarazako, N., 2007. Initial ecological risk assessment of

eight selected human pharmaceuticals in Japan. Environ. Sci. 14, 177–93.

Yang, L.-H., Ying, G.-G., Su, H.-C., Stauber, J.L., Adams, M.S., Binet, M.T., 2008.

Growth-inhibiting effects of 12 antibacterial agents and their mixtures on the freshwater

microalga Pseudokirchneriella subcapitata. Environ. Toxicol. Chem. 27, 1201–1208.

183

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Annex Annex Table 2.1 Toxicological data (mg/L) for alage, crustaceans and fish and calculated PNECs (ng/L) for detected compounds. Algae Crustaceans Fish PNEC Name CAS AF Selected Data (mg/L) (mg/L) (mg/L) (ng/l) Analgesics and anti-inflammatories

Fenoprofen 29679-58-1 40.592 26.36 38.945 1000 26.36 EC50 2.64E+04 Tolfenamic acid 13710-19-5 4.189 7.4 1.999 1000 1.999 EC50 2.00E+03 Nimesulide 51803-78-2 2.107 63.44 63.842 1000 2.107 EC50 2.11E+03 Dextropropoxyphene 469-62-5 0.032 0.081 0.466 1000 0.032 EC50 3.20E+01 Dimethylaminophenazone 58-15-1 1.639 44.626 7.032 1000 1.639 EC50 1.64E+03 Paracetamol/Acetaminophen 103-90-2 0.005 0.32 500 10 0.005 NOEC 5.00E+02 Mefenamic acid 61-68-7 2.5a 10a 3.4a 100 2.5 NOEC 2.50E+04 Diclofenac 15307-86-5 0.005 0.00001 0.0015 10 0.00001 NOEC 1.00E+00 Ibuprofen 15687-27-1 2a 1b 0.0001b 10 0.0001 NOEC 1.00E+01 Indomethacin 53-86-1 2.9 27 44 100 2.9 NOEC 2.90E+04 Ketoprofen 22071-15-4 3.282 1.24* 22.8* 1000 1.24 EC50 1.24E+03 Naproxen 22204-53-1 40 82 36.7* 1000 36.7 EC50 3.67E+04 Propyphenazone 479-92-5 0.571 9.487 2.126 1000 0.571 EC50 5.71E+02 Acetylsalicylic acid 50-78-2 777.332 0.061c 0.1 50 0.061 NOEC 1.22E+03 4-Acetyl aminoantipyrine 83-15-8 2.53 6.91 6.05 1000 2.53 EC50 2.53E+03 4-Formyl aminoantipyrine 1672-58-8 1.69 4.51 3.13 1000 1.69 EC50 1.69E+03 Salicylic acid 69-72-7 3.453 52.493 156.341 100 3.453 NOEC 3.45E+04 4-acetylaminoantipyrine 519-98-2 1.769 46.91 7.863 1000 1.769 EC50 1.77E+03 4′-Hydroxy Diclofenac 64118-84-9 142.668 34.803 68.207 1000 34.803 EC50 3.48E+04 Piroxicam 36322-90-4 0.289 11.6 4.22 1000 0.289 EC50 2.89E+02 Meloxicam 71125-38-7 0.184 3.93 1.39 1000 0.184 EC50 1.84E+02

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Etodolac 41340-25-4 1.82 13 1.94 1000 1.82 EC50 1.82E+03 Meclofenamic acid 644-62-2 1.7 0.491 0.597 1000 0.491 EC50 4.91E+02 Phenazone 60-80-0 1.31 3.47 2.32 1000 1.31 EC50 1.31E+03 Flunixin 38677-85-9 10.2 4.51 6.08 1000 4.51 EC50 4.51E+03 4-Aminoantipyrine 83-07-8 n.a. n.a. n.a.

Hydroxy-ibuprofen n.a. n.a. n.a. n.a.

Carboxy-ibuprofen 15935-54-3 n.a. n.a. n.a.

1-acetyl-1-methyl-2-dimethyl-oxamoyl-2-phenylhydrazid n.a. n.a. n.a. n.a. e 1-acetyl-1-methyl-2-phenylhydrazide 38604-70-5 n.a. n.a. n.a.

Antibiotics

Nalidixic acid 389-08-2 1800 3290 4420 1000 1800 EC50 1.80E+06 Ofloxacin 82419-36-1 0.005d 10d 101* 50 0.005 NOEC 1.00E+02 Sulfadiazine 22199-08-2 0.135e 1.88 1360* 1000 1.88 EC50 1.88E+03 Sulfamethazine 57-68-1 1f 50 > 500 50 1 NOEC 2.00E+04 Trimethoprim 738-70-5 16f 3.12g 0.157 10 0.157 NOEC 1.57E+04 Lincomycin 154-21-2 0.07h 7.2h 1000h 100 0.07 NOEC 7.00E+02 Oxytetracycline 79-57-2 0.0031i 3.08j 1000h 10 0.0031 NOEC 3.10E+02 Sulfamethoxazole 723-46-6 0.5 0.12 0.533 50 0.12 NOEC 2.40E+03 Clarithromycin 81103-11-9 0.002f 8.16f 1000f 100 0.002 NOEC 2.00E+01 Tylosin 1401-69-0 0.064f 79.099 164.658 100 79.099 NOEC 7.91E+05 Tetracycline 60-54-8 0.05 0.01 0.0005 10 0.01 NOEC 1.00E+03 Chlortetracycline 64-72-2 0.5f 0.41j 78.9k 50 0.41 NOEC 8.20E+03 Ciprofloxacin 85721-33-1 0.005l 60l 100l 50 0.005 EC50 1.00E+02 Norfloxacin 70458-96-7 0.0016i 0.12 0.014 10 0.014 NOEC 1.40E+03 Triclosan 3380-34-5 0.0002f 0.006 0.00045 10 0.00045 NOEC 4.50E+01

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Triclocarban 101-20-2 0.01f 0.0004 0.00079 10 0.0004 NOEC 4.00E+01 Amoxicillin 26787-78-0 0.0037e >1000 1 100 1 EC50 1.00E+04 Flumequine 42835-25-6 0.159e 0.75 783.59 100 0.75 EC50 7.50E+03 Oxolinic acid 14698-29-4 0.18e 0.38 0.035 50 0.035 NOEC 7.00E+02 Sarafloxacin 91296-87-6 0.015e 1.1 3016.736 100 1.1 EC50 1.10E+04 Sulfadimethoxine 122-11-2 7.8i 183.9 >100 100 183.9 NOEC 1.84E+06 Azythromycin 83905-01-5 1.847 3.023 21.945 1000 1.847 EC50 1.85E+03 Roxithromycine 80214-83-1 0.01 6.717 51.64 100 0.01 NOEC 1.00E+02 Sulfapyridine 144-83-2 5.28 1.841 377.595 1000 1.841 EC50 1.84E+03 Olaquindox 23696-28-8 911.555 1000 70661.43 1000 911.555 EC50 9.12E+05 Sulfamethizole 144-82-1 24.94 2.001 491.915 1000 2.001 EC50 2.00E+03 Sulfamethoxypyridazine 80-35-3 3.820 2.085 719.0237 1000 2.085 EC50 2.09E+03 Sulfisoxazole 127-69-5 18.98 1.951 180.221 1000 1.951 EC50 1.95E+03 Clindamycine 18323-44-9 7.936 9.634 80.751 1000 7.936 EC50 7.94E+03 Azithromycin 83905-01-5 1.874 3.023 18.822 1000 1.874 EC50 1.87E+03 Bronopol 52-51-7 0.012 0.560 1.94 10 0.012 NOEC 1.20E+03 Cefalotin 153-61-7 9075.162 6704.915 6293.479 1000 6293.479 EC50 6.29E+06 Cefotaxime 63527-52-6 109.478 33.555 5794.246 1000 33.555 EC50 3.36E+04 Metronidazole 443-48-1 12.5 100 1000 50 12.5 EC50 2.50E+05 Ceftriaxone 73384-59-5 400.273 42.709 175000 1000 42.709 EC50 4.27E+04 Spiramycin 8025-81-8 0.005 96.469 56.118 1000 0.005 EC50 5.00E+00 Oleandomycin 3922-90-5 17.083 19.919 85.05 1000 17.083 EC50 1.71E+04 Tilmicosin 108050-54-0 0.517 1.022 6.665 1000 0.517 EC50 5.17E+02 Erythromycin 114-07-8 0.002 25 100 10 0.002 NOEC 2.00E+02 Vancomycin 1404-90-6 7944.607 17564.133 205000 1000 7944.607 EC50 7.94E+06 Enrofloxacin 93106-60-6 0.049 10 5 50 0.049 EC50 9.80E+02 Chloramphenicol 56-75-7 2.5 2 38.821 50 2 NOEC 4.00E+04

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Clotrimazole 23593-75-1 0.04 0.105 0.036 1000 0.036 EC50 3.60E+01 Miconazole 22916-47-8 0.049 0.128 0.044 1000 0.044 EC50 4.40E+01 Flucloxacillin 5250-39-5 5.602 75.132 131.18 1000 5.602 EC50 5.60E+03 Enoxacin 74011-58-8 2204.185 1606.716 17428.467 1000 1606.716 EC50 1.61E+06 Florfenicol 73231-34-2 2.5 337 1791.05 50 2.5 NOEC 5.00E+04 Fluconazole 86386-73-4 3.06271 > 100 0.0306271 50 0.0306271 NOEC 6.13E+02 Propiconazole 60207-90-1 0.00095 0.06 0.0058 10 0.00095 NOEC 9.50E+01 N4-acetylsulfamethazine 100-90-3 6.329 314.851 225.099 1000 6.329 EC50 6.33E+03 Doxycycline 564-25-0 1998.994 117.345 13860.99 1000 117.345 EC50 1.17E+05 Josamycin 16846-24-5 2.374 3.757 11.699 1000 3.757 EC50 3.76E+03 Sulfathiazole 72-14-0 13.1 2.22 >500 100 2.22 NOEC 2.22E+04 Sulfasoxazole 127-69-5 18.98 1.951 5.172 1000 1.951 EC50 1.95E+03 Sulfisomidin 515-64-0 0.527 2.045 291.394 1000 0.527 EC50 5.27E+02 Sulfanitran 122-16-7 2.183 64.244 65.669 1000 2.183 EC50 2.18E+03 Sulfamerazine 127-79-7 11.900 277 > 100 1000 277 EC50 2.77E+05 Sulfaquinoxaline 59-40-5 6.459 131 275.952 1000 6.459 EC50 6.46E+03 Sulfadoxine 2447-57-6 1601.588 2.331 12.037 1000 2.331 EC50 2.33E+03 Sulfabenzamide 127-71-9 4.625 2.006 121.892 1000 2.006 EC50 2.01E+03 Acetyl-Sulfamethoxazole 21312-10-7 8.62 515.82 325.206 1000 8.62 EC50 8.62E+03 Griseofulvin 126-07-8 163 281 412 1000 163 EC50 1.63E+05 Prochloraz 67747-09-5 0.192 1.29 0.161 1000 0.161 EC50 1.61E+02 Cyproconazole 94361-06-5 4.34 6.14 7.01 1000 4.34 EC50 4.34E+03 Flusilazole 85509-19-9 0.895 3.59 2.53 1000 0.895 EC50 8.95E+02 Flutriafol 76674-21-0 14.1 26.4 32 1000 14.1 EC50 1.41E+04 Tebuconazole 107534-96-3 0.871 3.49 2.45 1000 0.871 EC50 8.71E+02 Carbendazim 10605-21-7 1.49 23.2 28.5 1000 1.49 EC50 1.49E+03 Cefalexin 15686-71-2 n.a. n.a. n.a.

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Danofloxacin 112398-08-0 n.a. n.a. n.a.

Erythromycin-H2O 23893-13-2 n.a. n.a. n.a. Anhydro-erythromycin-A 23893-13-2 n.a. n.a. n.a. Tiamulin 55297-95-5 n.a. n.a. n.a.

Antidepressants

Trazodone 19794-93-5 0.978 1.57 11.4 1000 0.978 EC50 9.78E+02 Diazepam 439-14-5 0.983 9.2 12.7 1000 0.983 EC50 9.83E+02 Lorazepam 846-49-1 1.683 44.712 70.5* 1000 1.683 EC50 1.68E+03 Paroxetine 61869-08-7 1.6 0.22m 3.293 100 1.6 NOEC 2.20E+03 Citalopram 59729-33-8 1.6n 0.8m 0.001 50 0.001 NOEC 2.00E+01 Fluvoxamine 54739-18-3 0.062n 0.366m 0.603 100 0.062 EC50 6.20E+02 Fluoxetine 54910-89-3 0.072 0.01 0.00003 10 0.00003 NOEC 3.00E+00 Venlafaxine 99300-78-4 265.634 141.28 0.000305 50 0.000305 NOEC 6.10E+00 Amitriptyline 50-48-6 0.777 1.15121 0.616 1000 0.616 EC50 6.16E+02 Dosulepin/dothiepin 113-53-1 0.094 0.203 1.271 1000 0.094 EC50 9.40E+01 Nortriptyline 72-69-5 0.058 0.132 0.805 1000 0.058 EC50 5.80E+01 Desmethyl diazepam 1088-11-5 0.73 14.156 19.22 1000 0.73 EC50 7.30E+02 Moclobemide 71320-77-9 19.9 19.9 184 1000 19.9 EC50 1.99E+04 Sertraline 79617-96-2 0.028 0.071 0.408 1000 0.028 EC50 2.80E+01 O-desmethylvenlafaxine 93413-62-8 n.a. n.a. n.a.

Norfluoxetine 83891-03-6 n.a. n.a. n.a.

N,N-Didesmethylvenlafaxine 93413-77-5 n.a. n.a. n.a. N-Desmethylvenlafaxine 149289-30-5 n.a. n.a. n.a. O,N-Didesmethylvenlafaxine 135308-74-6 n.a. n.a. n.a. Anxiolytics and anticonvulsants

Pentobarbital 76-74-4 0.017 7.641 246.6343 100 0.017 EC50 1.70E+02 Nordiazepam 1088-11-5 0.73 14.156 19.22 1000 0.73 EC50 7.30E+02

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Phenobarbital 50-06-6 0.00232 5 500 10 0.00232 NOEC 2.32E+02 Temazepam 846-50-4 2.281 72.175 70.23 1000 2.281 EC50 2.28E+03 Oxcarbazepine 28721-07-5 0.812 203.47 387.348 1000 0.812 EC50 8.12E+02 Gabapentin 60142-96-3 7770.997 4338.242 8561.85 100 4338.242 EC50 4.34E+07 Primidone 125-33-7 12.692 1052.044 531.259 1000 12.692 EC50 1.27E+04 Butalbital 77-26-9 0.017 10.369 32.548 1000 0.017 EC50 1.70E+01 Oxazepam 604-75-1 1.698 47.787 50.358 1000 1.698 EC50 1.70E+03 7-aminoflunitrazepam 34084-50-9 6.293 2.081 286.046 1000 2.081 EC50 2.08E+03 Oxazepam glucuronide 6801-81-6 n.a. n.a. n.a. Phenylethylmalonamide 7206-76-0 69.4 1270 1030 1000 69.4 EC50 6.94E+04 Carbamazepine 298-46-4 0.5 0.0001 0.1 10 0.0001 NOEC 1.00E+01 2-Hydroxycarbamazepine 68011-66-5 n.a. n.a. n.a.

Carbamazepine-10,11-epoxide/Epoxycarbamazepine 36507-30-9 n.a. n.a. n.a.

10,11-Dihydro-10,11-dihydroxy-carbamazepine 58955-93-4 n.a. n.a. n.a.

10,11-Dihydro-10-hydroxycarbamazepine 29331-92-8 n.a. n.a. n.a.

3OH carbamazepine 68011-67-6 n.a. n.a. n.a.

Anaesthetics Lidocaine 137-58-6 107.562 8.643 75.438 1000 8.643 EC50 8.64E+03 Ketamine 6740-88-1 0.722 1.134 8.344 1000 0.722 EC50 7.22E+02 Norketamine/N-desmethylketamine 35211-10-0 n.a. n.a. n.a. Lamotrigine 84057-84-1 15 4.83 126 1000 4.83 EC50 4.83E+03 Others

Olanzapine 132539-06-1 2.37 3.27 25.7 1000 2.37 EC50 2.37E+03 Tamoxifen 10540-29-1 0.006 0.021 0.01 100 0.006 EC50 6.00E+01 Pentoxifylline 6493-05-6 0.021 12.014 90.166 1000 0.021 EC50 2.10E+01 Sildenafil 139755-83-2 2.91 7.166 5.798 1000 2.91 EC50 2.91E+03 Genistein 446-72-0 22.251 31.893 500 100 22.251 EC50 2.23E+05

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Flubendazole 31430-15-6 0.234 1.764 3.718 1000 0.234 EC50 2.34E+02 4-Formyl-antipyrine 950-81-2 1.509 40.794 0.43 1000 0.43 EC50 4.30E+02 2-(Thiocyanatomethylthio)benzothiazole 21564-17-0 0.029 0.00089 0.0051 10 0.00089 EC50 8.90E+01 4-chloro-3-methylphenol 59-50-7 1.9 1.3 2.21 50 1.3 NOEC 2.60E+04 Cyclophosphamide/cytophosphane 50-18-0 161.232 0.026 4 100 0.026 EC50 2.60E+02 Salbutamol/Albuterol 18559-94-9 41.552 36.801 361.764 1000 36.801 EC50 3.68E+04 Methotrexate 59-05-2 202.8 34.926 124000 1000 34.926 EC50 3.49E+04 Crotamiton 483-63-6 0.163 0.891 2.129 1000 0.163 EC50 1.63E+02 Fenoterol 13392-18-2 20.425 20.683 190.009 1000 20.425 EC50 2.04E+04 Clenbuterol 37148-27-9 5.24 6.351 53.278 1000 5.24 EC50 5.24E+03 Trimetazidine 5011-34-7 46.645 41.261 405.858 1000 41.261 EC50 4.13E+04 Naftidrofuryl 31329-57-4 0.03 0.08 0.452 1000 0.03 EC50 3.00E+01 Lansoprazole 103577-45-3 0.192 1.067 1.483 1000 0.192 EC50 1.92E+02 Mesalazine /5-Aminosalicylic acid 89-57-6 154.687 11.191 65.624 1000 11.191 EC50 1.12E+04 Acridone 578-95-0 14.495 3.419 7.817 1000 3.419 EC50 3.42E+03 Acridin 260-94-6 0.26 0.625 0.91 100 0.26 NOEC 2.60E+03 Ephedrine 299-42-3 n.a. n.a. n.a. 0

Mebeverine 2753-45-9 2.93 9.17 5.37 1000 2.93 EC50 2.93E+03 Clopidogrel 113665-84-2 0.315 0.579 3.93 1000 0.315 EC50 3.15E+02 Thiabendazole 148-79-8 0.979 13.2 10.9 1000 0.979 EC50 9.79E+02 Levamisole 14769-73-4 0.943 1.39 10.6 1000 0.943 EC50 9.43E+02 Xylazine 7361-61-7 1.39 0.714 0.997 1000 0.714 EC50 7.14E+02 Methalaxyl 57837-19-1 53.4 120 55.8 1000 53.4 EC50 5.34E+04 Ioxitalamic acid 28179-44-4 1880 32800 26800 1000 1880 EC50 1.88E+06 Iomeprol 78649-41-9 3200 144000 89300 1000 3200 EC50 3.20E+06 Pantoprazole 102625-70-7 1.47 18.4 12.4 1000 1.47 EC50 1.47E+03 Levetiracetam 102767-28-2 205 5940 4190 1000 205 EC50 2.05E+05

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Dipyridamole 58-32-2 97 75.8 794 1000 75.8 EC50 7.58E+04 4-(Trifluoromethyl)phenol 402-45-9 n.a. n.a. n.a.

Sitagliptin 486460-32-6 n.a. n.a. n.a.

Amisulpride 82248-59-7 n.a. n.a. n.a.

Clopidogrel carboxylicacid 144750-42-5 n.a. n.a. n.a.

Dextromethorphan 6700-34-1 n.a. n.a. n.a.

Lipid-regulating agents

Bezafibrate 41859-67-0 1.352 0.023 26.435 100 0.023 NOEC 2.30E+02 Atorvastatin 134523-00-5 0.103 0.001 0.000013 50 0.000013 NOEC 2.60E-01 Sulfasalazine 599-79-1 75.394 19.551 39.84 100 19.551 EC50 1.96E+05 Gemfibrozil 25812-30-0 3.125 0.078 6.728 50 0.078 NOEC 1.56E+03 Pravastatine 81093-37-0 85.494 239.102 129.393 1000 85.494 EC50 8.55E+04 Clofibric acid 882-09-7 75o 0.64o 70o 10 0.64 NOEC 6.40E+04 Fenofibric acid 42017-89-0 10.85 4.9 0.7 100 0.703 EC50 7.03E+03 Simvastatin 79902-63-9 6.25 1.129 1.25 100 1.129 EC50 1.13E+04 Antidiabetic drugs

Metformin 657-24-9 >320 64 27737.17 1000 64 EC50 6.40E+04 Glyburide 10238-21-8 0.086 0.454 0.05 100 0.049 NOEC 4.90E+02 Glibenclamide 10238-21-8 0.308 0.585 0.308 1000 0.308 EC50 3.08E+02 Antihistamines

Cimetidine 51481-61-9 0.787 271.3p >100p 1000 0.787 EC50 7.87E+02 Cetirizine 83881-51-0 5127.551 3409.267 38741.023 1000 3409.267 EC50 3.41E+06 Omeprazole 73590-58-6 0.21 1.271 4.998227 100 0.21 EC50 2.10E+03 Ranitidine 66357-35-5 797.927 0.31 0.000246 50 0.000246 NOEC 4.92E+00 Chlorocycline 82-93-9 0.414 0.727 5.059 1000 0.414 EC50 4.14E+02 Famotidine 76824-35-6 478.143 > 100 > 100 1000 478.143 EC50 4.78E+05 Loratadine 79794-75-5 0.062 0.1 0.038288 100 0.038288 EC50 3.83E+02

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4-OH-omeprazole 301669-82-9 n.a. n.a. n.a. Ranitidine N-oxide 73857-20-2 n.a. n.a. n.a. Antiviral drugs

Acyclovir 59277-89-3 2.615 69.166 1764.524 1000 2.615 EC50 2.62E+03 Stavudine 3056-17-5 0.017 4742.621 19.295 1000 0.017 EC50 1.70E+01 Nevirapine 129618-40-2 0.167 1.621 3.523 1000 0.167 EC50 1.67E+02 Oseltamivir 196618-13-0 32.369 30.84 170.556 1000 30.84 EC50 3.08E+04 Zidovudine 30516-87-1 0.02 539.698 318.28 1000 0.02 EC50 2.00E+01 Penciclovir 39809-25-1 n.a. n.a. n.a.

Oseltamivir carboxylate 187227-45-8 n.a. n.a. n.a.

Beta–blockers for heart failure

Atenolol 29122-68-7 10 9.45* 5 50 5 NOEC 1.00E+05 Metoprolol 37350-58-6 7.3 6q 138* 100 7.3 NOEC 7.30E+04 Propranolol 525-66-6 0.1a 1 0.004 10 0.004 NOEC 4.00E+02 Sotalol 3930-20-9 26.386 60.809 616.625 1000 26.386 EC50 2.64E+04 Bisoprolol 66722-44-9 8.009 9.351 79.937 1000 8.009 EC50 8.01E+03 Pindolol 13523-86-9 15.82 8.416 10.874 1000 8.416 EC50 8.42E+03 Atenolol acid 56392-14-4 n.a. n.a. n.a.

Diuretics

Furosemide 54-31-9 19.797 0.156r 588.118 100 19.797 NOEC 1.98E+05 Hydrochlorothiazide 58-93-5 56.184 8125.047 2808.512 1000 56.184 EC50 5.62E+04 Bendroflumethiazide 73-48-3 5.1 201.236 168.464 1000 5.1 EC50 5.10E+03 Alfuzosin 81403-80-7 4.787 2.793 69.581 1000 2.793 EC50 2.79E+03 Torasemide 56211-40-6 0.141 54.5 51.2 1000 0.141 EC50 1.41E+02 Stimulants

1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine 69610-10-2 n.a. n.a. n.a. Benzoylecgonine 519-09-5 71406.47 6805.16 33458.81 1000 6805.164 EC50 6.81E+06

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Caffeine 58-08-2 0.005 0.005 0.0032 10 0.0032 NOEC 3.20E+02 Benzylpiperazine 2759-28-6 13.935 13.794 47.4 1000 13.794 EC50 1.38E+04 1,7-Dimethylxanthine/Paraxanthine 611-59-6 0.014 17.796 223.802 1000 0.014 EC50 1.40E+01 Nicotine 20033 1589.854 0.07 4.2 50 0.07 NOEC 1.40E+03 Amphetamine 300-62-9 3.803 4.357 37.602 1000 3.803 EC50 3.80E+03 Methamphetamine 537-46-2 1.907 2.509 20.511 1000 1.907 EC50 1.91E+03 Cocaine 50-36-2 4.35 5.482 45.092 1000 4.35 EC50 4.35E+03 1-(3-trifuoromethylphenyl)piperazine 15532-75-9 1.906 2.596 20.532 100 1.906 EC50 1.91E+04 Continine 486-56-6 17.815 1917.981 1 100 1 NOEC 1.00E+04 Norbenzoylecgonine n.a. n.a. n.a. n.a. Cocaethylene 529-38-4 n.a. n.a. n.a. 1189805-46- Mephedrone (4-Methylmethcathinon) n.a. n.a. n.a. 6 Opioids and morphine derivative

2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine 30223-73-5 n.a. n.a. n.a. Tramadol 27203-92-5 0.959 1.467 10.238 1000 0.959 EC50 9.59E+02 Buprenorphine 52485-79-7 0.509 0.187 0.079 1000 0.079 EC50 7.90E+01 Codeine 76-57-3 18.345 0.976 7.438 1000 0.976 EC50 9.76E+02 Fentanyl 437-38-7 3.718 0.552 0.295 1000 0.295 EC50 2.95E+02 Morphine 57-27-2 43.555 39.284 1 100 1 NOEC 1.00E+04 Norcodeine 467-15-2 24.653 0.983 225.374 1000 0.983 EC50 9.83E+02 Oxycodone 76-42-6 52.515 46.786 458.553 1000 46.786 EC50 4.68E+04 Oxymorphone 76-41-5 124.994 97.874 1023.936 1000 97.874 EC50 9.79E+04 Normorphine 466-97-7 58.39 50.167 500.789 1000 50.167 EC50 5.02E+04 Dihydrocodeine 125-28-0 13.018 14.037 124.924 1000 13.018 EC50 1.30E+04 Methadone 76-99-3 0.172 0.344 2.242 1000 0.172 EC50 1.72E+02 6-Acetylmorphine 2784-73-8 n.a. n.a. n.a.

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Norpropoxyphene 3376-94-1 n.a. n.a. n.a. Nortramadol n.a. n.a. n.a. n.a. O-Desmethyltramadol 80456-81-1 n.a. n.a. n.a. Norbuprenorphine 78715-23-8 n.a. n.a. n.a. 2-ethyl-5-methyl-3,3-diphenylpyrroline n.a. n.a. n.a. n.a. Steroids and hormones

Bicalutamide 90357-06-5 9.54 65.5 70.5 1000 9.54 EC50 9.54E+03 Estrone 53-16-7 8.74 0.001 0.198 50 0.001 NOEC 2.00E+01 Gestoden 60282-87-3 8.465 0.601 3.888 1000 0.601 EC50 6.01E+02 17-α-ethinyl-estradiol/Ethinylestradiol 57-63-6 0.84 0.0000001 0.0000002 50 0.0000001 NOEC 2.00E-03 17α-estradiol/Alfatradiol 57-91-0 4.299 1.129 1.578 1000 1.129 EC51 1.13E+03 Estriol 50-27-1 22.252 5.235 0.0001 100 0.0001 NOEC 1.00E+00 17b-Estradiol/Estradiol 50-28-2 0.008 0.006 0.000016 10 0.000016 NOEC 1.60E+00 Androstenedione 63-05-8 27.731 0.7 2291.2 50 0.7 NOEC 1.40E+04 Androsterone 53-41-8 18.457 16.494 26.326 1000 16.494 EC50 1.65E+04 Testosterone 58-22-0 10.739 0.1 0.5 50 0.1 NOEC 2.00E+03 Progesterone 57-83-0 5.573 0.1 0.00093 50 0.00093 NOEC 1.86E+01 Norethindrone 68-22-4 18.768 500 0.00074 50 0.00074 NOEC 1.48E+01 Levonorgestrel 797-63-7 7.848 9.914 23.626 1000 7.848 EC50 7.85E+03 Antihypertensives

Diltiazem 42399-41-7 2.151 8.2p 0.5 100 0.5 NOEC 5.00E+03 Enalapril 76420-72-9 2203.95 4855.842 57350.95 1000 2203.95 EC50 2.20E+06 Valsartan 137862-53-4 3.865 44.337 86.094 1000 3.865 EC50 3.87E+03 Telmisartan 144701-48-4 0.171 0.241 0.026 1000 0.026 EC50 2.60E+01 Candesartan 139481-59-7 1.197 4.827 3.666 1000 1.197 EC50 1.20E+03 Verapamil 52-53-9 0.091 0.21 0.0262 100 0.0262 NOEC 2.62E+02 Acebutolol 37517-30-9 10.059 23.846 219.776 1000 10.059 EC50 1.01E+04

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Propranolol hydrochloride 318-98-9 0.16 0.4 0.0013 10 0.0013 NOEC 1.30E+02 Losartan 114798-26-4 1.897 2.001 2.151 1000 1.897 EC50 1.90E+03 Carazolol 57775-29-8 1.929 2.721 21.144 1000 1.929 EC50 1.93E+03 Timolol 26839-75-8 8.972 10.267 88.665 1000 8.972 EC50 8.97E+03 Betaxolol 63659-18-7 1.176 1.787 13.375 1000 1.176 EC50 1.18E+03 Cilazapril 88768-40-5 50.852 65.58 533.141 1000 50.852 EC50 5.09E+04 Eprosartan 133040-01-4 0.48 0.677 0.382 1000 0.382 EC50 3.82E+02 Lisinopril 83915-83-7 2556.705 5633.592 66157.75 1000 2556.705 EC50 2.56E+06 Irbesartan 138402-11-6 n.a. n.a. n.a.

Nadolol 42200-33-9 22.6 22.6 209 1000 22.6 EC50 2.26E+04 Amiloride 2016-88-8 97 75.8 794 1000 75.8 EC50 7.58E+04 Enalaprilat 76420-72-9 57350.95 4855.84 2203.95 1000 2203.95 EC50 2.20E+06

Toxicity values without any marks represent short-term L(E)C50; Values in italics represent Long Term NOEC; Values with asterisk represent predicted QSAR values;

L(E)C50 values marked with underline were estimated from ECOSAR; Toxicity data without asterisk and underline were collected from ECOTOX Database of US EPA;

Toxicity data marked with capital were collected from following references: a: Yamamoto et al., 2007; b: Han et al., 2010; c: Stuer-Lauridsenetal et al., 2000; d: Ferrari et al.,

2003; e: Holten--Lützhøft et al., 1999; f: Yang et al., 2008; g: De Liguoro et al., 2012; h: Isidori et al., 2005; i: Ando et al., 2007; j: Ji et al., 2012; k: Park and Choi, 2008; l:

Halling-Sorensen et al., 2000; m: Henry et al., 2004; n: Christensen et al., 2007; o: Ferrari et al., 2003; p: Kim et al., 2007; q: Dzialowski et al., 2006; r: Isidori et al., 2006.

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Supplementary Table S2.2. Summary of pharmaceuticals analyzed and detected above the LOD in European surface water and the therapeutic classes. Maximum Name CAS concentrations Analgesics and anti-inflammatories Fenoprofen 29679-58-1 >LOD Tolfenamic acid 13710-19-5 >LOD Nimesulide 51803-78-2 >LOD Dextropropoxyphene 469-62-5 >LOD Dimethylaminophenazone 58-15-1 >LOD Paracetamol/Acetaminophen 103-90-2 >LOD Mefenamic acid 61-68-7 >LOD Diclofenac 15307-86-5 >LOD Ibuprofen 15687-27-1 >LOD Indometacin 53-86-1 >LOD Ketoprofen 22071-15-4 >LOD Naproxen 22204-53-1 >LOD Propyphenazone 479-92-5 >LOD Acetylsalicylic acid 50-78-2 >LOD 4-Acetyl aminoantipyrine 83-15-8 >LOD N-Formyl-4-aminoantipyrine 1672-58-8 >LOD 4-Aminoantipyrine 83-07-8 >LOD Salicylic acid 69-72-7 >LOD 4-acetylaminoantipyrine 519-98-2 >LOD 4′-Hydroxy Diclofenac 64118-84-9 >LOD Hydroxy-ibuprofen n.a. >LOD Carboxy-ibuprofen 15935-54-3 >LOD 1-acetyl-1-methyl-2-dimethyl-oxamoyl-2-phenylhydrazide n.a. >LOD 1-acetyl-1-methyl-2-phenylhydrazide 38604-70-5 >LOD Flunixin 38677-85-9 >LOD Piroxicam 36322-90-4 >LOD Meloxicam 71125-38-7 >LOD Etodolac 41340-25-4 >LOD Meclofenamic acid 644-62-2 >LOD Phenazone 60-80-0 >LOD Niflumic acid 4394-00-7 LOD Sulfadiazine 22199-08-2 >LOD Sulfamethazine 57-68-1 >LOD

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Trimethoprim 738-70-5 >LOD Lincomycin 154-21-2 >LOD Oxytetracycline 79-57-2 >LOD Sulfamethoxazole 723-46-6 >LOD Clarithromycin 81103-11-9 >LOD Tylosin 1401-69-0 >LOD Tetracycline 60-54-8 >LOD Chlortetracycline 64-72-2 >LOD Ciprofloxacin 85721-33-1 >LOD Norfloxacin 70458-96-7 >LOD Triclosan 3380-34-5 >LOD Triclocarban 101-20-2 >LOD Amoxicillin 26787-78-0 >LOD Flumequine 42835-25-6 >LOD Oxolinic acid 14698-29-4 >LOD Sarafloxacin 91296-87-6 >LOD Sulfadimethoxine 122-11-2 >LOD Azythromycin 83905-01-5 >LOD Roxithromycine 80214-83-1 >LOD Sulfapyridine 144-83-2 >LOD Olaquindox 23696-28-8 >LOD Sulfamethizole 144-82-1 >LOD Sulfamethoxypyridazine 80-35-3 >LOD Sulfisoxazole 127-69-5 >LOD Clindamycine 18323-44-9 >LOD Azithromycin 83905-01-5 >LOD Bronopol 52-51-7 >LOD Cefalotin 153-61-7 >LOD Cefotaxime 63527-52-6 >LOD Metronidazole 443-48-1 >LOD Ceftriaxone 73384-59-5 >LOD Spiramycin 8025-81-8 >LOD Oleandomycin 3922-90-5 >LOD Tilmicosin 108050-54-0 >LOD Vancomycin 1404-90-6 >LOD Enrofloxacin 93106-60-6 >LOD Chloramphenicol 56-75-7 >LOD Clotrimazole 23593-75-1 >LOD Miconazole 22916-47-8 >LOD Flucloxacillin 5250-39-5 >LOD Enoxacin 74011-58-8 >LOD Florfenicol 73231-34-2 >LOD Fluconazole 86386-73-4 >LOD Propiconazole 60207-90-1 >LOD

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N4-acetylsulfamethazine 100-90-3 >LOD Doxycycline 564-25-0 >LOD Josamycin 16846-24-5 >LOD Sulfathiazole 72-14-0 >LOD Sulfasoxazole 127-69-5 >LOD Sulfisomidin 515-64-0 >LOD Sulfanitran 122-16-7 >LOD Sulfamerazine 127-79-7 >LOD sulfaquinoxaline 59-40-5 >LOD Sulfadoxine 2447-57-6 >LOD Sulfabenzamide 127-71-9 >LOD Danofloxacin 112398-08-0 >LOD

Erythromycin-H2O 23893-13-2 >LOD Tiamulin 55297-95-5 >LOD Nalidixic acid 389-08-2 >LOD Erythromycin 114-07-8 >LOD Griseofulvin 126-07-8 >LOD Prochloraz 67747-09-5 >LOD Cyproconazole 94361-06-5 >LOD Flusilazole 85509-19-9 >LOD Flutriafol 76674-21-0 >LOD Tebuconazole 107534-96-3 >LOD Carbendazim 10605-21-7 >LOD Anhydro-erythromycin-A 23893-13-2 >LOD Cefalexin 15686-71-2 >LOD Thiamphenicol 15318-45-3

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Oxolinic acid 14698-29-4 LOD Lorazepam 846-49-1 >LOD Paroxetine 61869-08-7 >LOD

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Citalopram 59729-33-8 >LOD Fluvoxamine 54739-18-3 >LOD Fluoxetine 54910-89-3 >LOD Venlafaxine 99300-78-4 >LOD Amitriptyline 50-48-6 >LOD Dosulepin/dothiepin 113-53-1 >LOD Nortriptyline 72-69-5 >LOD Desmethyl diazepam 1088-11-5 >LOD Norfluoxetine 83891-03-6 >LOD Moclobemide 71320-77-9 >LOD Sertraline 79617-96-2 >LOD Trazodone 19794-93-5 >LOD N,N-Didesmethylvenlafaxine 93413-77-5 >LOD N-Desmethylvenlafaxine 149289-30-5 >LOD O,N-Didesmethylvenlafaxine 135308-74-6 >LOD O-Desmethylvenlafaxine 93413-62-8 >LOD Levomeprazine 60-99-1 LOD 2-Hydroxycarbamazepine 68011-66-5 >LOD Carbamazepine-10,11-epoxide/Epoxycarbamazepine 36507-30-9 >LOD 10,11-Dihydro-10,11-dihydroxy-carbamazepine 58955-93-4 >LOD 10,11-Dihydro-10-hydroxycarbamazepine 29331-92-8 >LOD 3OH carbamazepine 68011-67-6 >LOD Pentobarbital 76-74-4 >LOD Nordiazepam 1088-11-5 >LOD Phenobarbital 50-06-6 >LOD Temazepam 846-50-4 >LOD Oxcarbazepine 28721-07-5 >LOD Gabapentin 60142-96-3 >LOD Primidone 125-33-7 >LOD Butalbital 77-26-9 >LOD Oxazepam 604-75-1 >LOD 7-aminoflunitrazepam 34084-50-9 >LOD

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Oxazepam glucuronide 6801-81-6 >LOD Lamotrigine 84057-84-1 >LOD Oxacepam 604-75-1 >LOD Phenylethylmalonamide 7206-76-0 >LOD Alprazolam 28981-97-7 LOD Ketamine 6740-88-1 >LOD Norketamine/N-desmethylketamine 35211-10-0 >LOD Phencyclidine 77-10-1 LOD Atorvastatin 134523-00-5 >LOD Sulfasalazine 599-79-1 >LOD Gemfibrozil 25812-30-0 >LOD Pravastatine 81093-37-0 >LOD Clofibric acid 882-09-7 >LOD Fenofibric acid 42017-89-0 >LOD Simvastatin 79902-63-9 >LOD Mevastatin 73573-88-3

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Antidiabetic drugs Metformin 657-24-9 >LOD Glyburide 10238-21-8 >LOD Glibenclamide 10238-21-8 >LOD Amlodipine 88150-42-9 LOD Cetirizine 83881-51-0 >LOD Omeprazole 73590-58-6 >LOD Ranitidine 66357-35-5 >LOD Chlorocycline 82-93-9 >LOD Famotidine 76824-35-6 >LOD Loratadine 79794-75-5 >LOD 4-OH-omeprazole 301669-82-9 >LOD Ranitidine N-oxide 73857-20-2 >LOD 5-O-desmetyl omeprazole 151602-49-2 LOD Stavudine 3056-17-5 >LOD Nevirapine 129618-40-2 >LOD Oseltamivir 196618-13-0 >LOD Zidovudine 30516-87-1 >LOD Penciclovir 39809-25-1 >LOD Oseltamivir carboxylate 187227-45-8 >LOD Docetaxel 114977-28-5 LOD Metoprolol 37350-58-6 >LOD Propranolol 525-66-6 >LOD Sotalol 3930-20-9 >LOD Bisoprolol 66722-44-9 >LOD Pindolol 13523-86-9 >LOD Atenolol acid 56392-14-4 >LOD

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Propranolol-β-D-glucuronide 87102-70-3 LOD Hydrochlorothiazide 58-93-5 >LOD Bendroflumethiazide 73-48-3 >LOD Alfuzosin 81403-80-7 >LOD Torasemide 56211-40-6 >LOD Tamsulosin 106133-20-4 LOD Benzylpiperazine 2759-28-6 >LOD 1,7-Dimethylxanthine/Paraxanthine 611-59-6 >LOD Nicotine 20033 >LOD Amphetamine 300-62-9 >LOD Methamphetamine 537-46-2 >LOD Cocaine 50-36-2 >LOD Benzoylecgonine 519-09-5 >LOD 1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine 69610-10-2 >LOD Norbenzoylecgonine n.a. >LOD Cocaethylene 529-38-4 >LOD 1-(3-trifuoromethylphenyl)piperazine 15532-75-9 >LOD Continine 486-56-6 >LOD 1189805-46- Mephedrone (4-Methylmethcathinon) >LOD 6 Ecgonidine 484-93-5 LOD Buprenorphine 52485-79-7 >LOD Codeine 76-57-3 >LOD Fentanyl 437-38-7 >LOD Morphine 57-27-2 >LOD Norcodeine 467-15-2 >LOD Oxycodone 76-42-6 >LOD Oxymorphone 76-41-5 >LOD Normorphine 466-97-7 >LOD Dihydrocodeine 125-28-0 >LOD Methadone 76-99-3 >LOD

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6-Acetylmorphine 2784-73-8 >LOD Norpropoxyphene 3376-94-1 >LOD Nortramadol n.a. >LOD O-Desmethyltramadol 80456-81-1 >LOD Norbuprenorphine 78715-23-8 >LOD 2-ethyl-5-methyl-3,3-diphenylpyrroline n.a. >LOD 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine 30223-73-5 >LOD 3-methoxy-6-acetyl-(5a,6a)-7,8-didehydro-4,5-epoxy-3-metho 6703-27-1 LOD Gestoden 60282-87-3 >LOD 17-α-ethinyl-estradiol/Ethinylestradiol 57-63-6 >LOD 17α-estradiol/Alfatradiol 57-91-0 >LOD Estriol 50-27-1 >LOD 17b-Estradiol/Estradiol 50-28-2 >LOD Androstenedione 63-05-8 >LOD Androsterone 53-41-8 >LOD Testosterone 58-22-0 >LOD Progesterone 57-83-0 >LOD Norethindrone 68-22-4 >LOD Levonorgestrel 797-63-7 >LOD Bicalutamide 90357-06-5 >LOD 4-t-octylphenol 140-66-9 LOD Enalapril 76420-72-9 >LOD Valsartan 137862-53-4 >LOD Telmisartan 144701-48-4 >LOD

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Candesartan 139481-59-7 >LOD Verapamil 52-53-9 >LOD Acebutolol 37517-30-9 >LOD Propranolol hydrochloride 318-98-9 >LOD Losartan 114798-26-4 >LOD Carazolol 57775-29-8 >LOD Timolol 26839-75-8 >LOD Betaxolol 63659-18-7 >LOD Cilazapril 88768-40-5 >LOD Eprosartan 133040-01-4 >LOD Lisinopril 83915-83-7 >LOD Irbesartan 138402-11-6 >LOD Enalaprilat 76420-72-9 >LOD Nadolol 42200-33-9 >LOD Amiloride 2016-88-8 >LOD Felodipine 72509-76-3 LOD Pentoxifylline 6493-05-6 >LOD Sildenafil 139755-83-2 >LOD Genistein 446-72-0 >LOD Flubendazole 31430-15-6 >LOD 4-Formyl-antipyrine 950-81-2 >LOD 2-(Thiocyanatomethylthio)benzothiazole 21564-17-0 >LOD 4-chloro-3-methylphenol 59-50-7 >LOD Cyclophosphamide/cytophosphane 50-18-0 >LOD Salbutamol/Albuterol 18559-94-9 >LOD Methotrexate 59-05-2 >LOD Crotamiton 483-63-6 >LOD Fenoterol 13392-18-2 >LOD Clenbuterol 37148-27-9 >LOD Trimetazidine 5011-34-7 >LOD Naftidrofuryl 31329-57-4 >LOD Lansoprazole 103577-45-3 >LOD Mesalazine /5-Aminosalicylic acid 89-57-6 >LOD Acridone 578-95-0 >LOD Acridin 260-94-6 >LOD Ephedrine 299-42-3 >LOD Dipyridamole 58-32-2 >LOD Pantoprazole 102625-70-7 >LOD

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Mebeverine 2753-45-9 >LOD Clopidogrel 113665-84-2 >LOD Thiabendazole 148-79-8 >LOD Levamisole 14769-73-4 >LOD Xylazine 7361-61-7 >LOD Methalaxyl 57837-19-1 >LOD Ioxitalamic acid 28179-44-4 >LOD Iomeprol 78649-41-9 >LOD Amisulpride 82248-59-7 >LOD Sitagliptin 486460-32-6 >LOD Dextromethorphan 6700-34-1 >LOD Clopidogrel carboxylicacid 144750-42-5 >LOD Olanzapine 132539-06-1 >LOD Levetiracetam 102767-28-2 >LOD 4-(Trifluoromethyl)phenol 402-45-9 >LOD Digoxin 20830-75-5

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Ethambutol 74-55-5

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Annex 3.1 Total antioxidant capacity In order to verify our results, two chemicals (triclosan and carbamazepine) with potential behavioral effects and one chemical (diclofenac) without any behavioral effects were selected for further antioxidant capacity assays. The methods were performed according to previous publication (Chen et al., 2017). Briefly, the larvae that were exposed to pharmaceuticals from 2 to 50 hpf were euthanized using benzocaine water solution (0.4 g/L) at 120 hpf, and then washed two times with normal saline (0.9 % sodium chloride). After that, the larvae were homogenized on ice in 1.2 mL of normal saline using a tissue homogenizer (VDI 12, VWR, Germany). After centrifugation at 5000g for 5 min, the supernatant was transferred and then frozen using liquid nitrogen. Finally, the supernatant were stored in −80 °C before test. Each chemical was tested with five concentrations (0, 0.1, 1 10, and 100 μg/L). Thirty larvae were pooled for each treatment, 1350 larvae were used in total (3 replicates). The activity of total protein and total antioxidant capacity (TAC) in the supernatant were measured with bicinchoninic acid protein assay kit (Keygen Biotech, China) according to the manufacturer's protocol. The enzyme activities are expressed in enzyme units (U) per mg of protein. Results:

90 *

80 Carbamazepine Triclosan 70 Diclofenac * 60 Control 50

30 TAC (U/mg TAC protein) 20

0 0.1 1 10 100 0.1 1 10 100 0.1 1 10 100 Concentrations (g/l)

Annex Figure 3.1 Changes in total antioxidant capacity (TAC) of zebrafish larvae at 120

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Annex Table 6.1 Minimum (Min.), median (Med.) and maximum (Max.) concentrations (ng/L) of compounds in 2014 and the frequency (F) of concentrations above the LOD (Limit of Detection) in percent. Wernigerode Silstedt Nienhagen Compounds Min. Med. Max. F Min. Med. Max. F Min. Med. Max. F Dimethoate n.d. n.d. n.d. n.d. 1.1 1.9 2.9 45 1.0 1.4 2.1 30 Isoproturon n.d. n.d. n.d. n.d. 0.7 2.4 369.1 100 0.7 2.8 138.8 100 Myclobutanil n.d. n.d. n.d. n.d. 0.9 0.9 0.9 9 n.d. n.d. n.d. n.d. Diflufenican n.d. n.d. n.d. n.d. 10.5 10.5 10.5 9 n.d. n.d. n.d. n.d. Metribuzin n.d. n.d. n.d. n.d. 1.1 1.3 2.3 36 1.0 1.3 1.3 60 Dimethenamid n.d. n.d. n.d. n.d. 1.1 10.3 12.4 27 9.1 9.3 9.5 20 Cyproconazole n.d. n.d. n.d. n.d. 0.8 7.5 13.6 73 0.9 2.1 5.1 70 Imidacloprid n.d. n.d. n.d. n.d. 1.0 2.6 6.9 91 1.7 2.9 5.2 80 Dimethachlor n.d. n.d. n.d. n.d. 8.7 43.9 216.1 27 2.3 30.3 118.6 30 Flufenacet n.d. n.d. n.d. n.d. 33.5 35.0 36.5 18 15.5 17.1 18.7 20 Prosulfocarb n.d. n.d. n.d. n.d. 1.6 2.2 2.2 36 1.4 1.8 3.8 50 Pethoxamid n.d. n.d. n.d. n.d. 1.5 2.8 4.1 18 2.3 2.9 3.5 20 Ethofumesate n.d. n.d. n.d. n.d. 1.5 7.4 61.7 82 1.0 7.1 31.4 80 Metamitron n.d. n.d. n.d. n.d. 1.6 7.0 609.6 91 1.2 2.1 44.3 90 Spiroxamine n.d. n.d. n.d. n.d. 3.7 4.5 5.4 18 n.d. n.d. n.d. n.d. Fenpropimorph n.d. n.d. n.d. n.d. 5.9 8.5 11.1 18 3.2 3.2 3.2 10 Epoxiconazole n.d. n.d. n.d. n.d. 1.6 3.4 47.7 45 1.2 1.6 31.1 40 Desethylatrazine 0.8 1.0 1.4 100 3.1 6.2 8.9 100 1.7 3.9 7.9 100 Desethylterbutylazine 1.2 1.2 1.2 25 1.1 4.5 16.7 100 2.0 2.9 10.4 100 Desisopropylatrazine n.d. n.d. n.d. n.d. 3.0 4.9 9.7 64 3.9 5.7 7.5 20 Terbuthylazine 1.1 1.8 3.9 100 1.5 3.6 11.9 100 1.2 5.8 13.6 90 Atrazine n.d. n.d. n.d. n.d. 1.7 3.9 6.1 100 1.2 2.9 5.1 100 Propiconazole n.d. n.d. n.d. n.d. 4.3 18.6 139.3 100 2.5 13.2 35.0 100

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Metazachlor 0.7 0.7 0.7 25 0.8 5.0 11.3 36 0.7 1.2 10.2 50 Pirimicarb n.d. n.d. n.d. n.d. 0.5 1.0 1.9 64 0.7 1.0 1.4 40 Metolachlor n.d. n.d. n.d. n.d. 0.8 1.8 1.8 27 1.1 2.0 3.1 40 Simazine 1.1 1.2 2.6 100 4.0 8.7 21.9 100 3.7 7.8 13.9 100 Tebuconazole n.d. n.d. n.d. n.d. 2.2 15.6 31.5 100 3.5 6.5 12.7 100 Thiacloprid n.d. n.d. n.d. n.d. 1.7 5.2 59.3 27 2.0 4.2 11.9 30 Fenuron 1.1 1.1 1.1 25 3.5 10.4 24.5 100 0.9 3.7 14.8 100 Lenacil n.d. n.d. n.d. n.d. 1.0 3.0 27.2 100 1.3 2.0 9.8 100 Quinoxyfen 1.2 1.2 1.2 25 n.d. n.d. n.d. n.d. 5.8 5.8 5.8 10 Clomazone n.d. n.d. n.d. n.d. 2.4 13.9 52.5 27 10.3 19.8 29.2 20 Boscalid n.d. n.d. n.d. n.d. 1.2 2.5 43.0 91 0.7 1.6 31.9 90 Prothioconazole-desthio n.d. n.d. n.d. n.d. 0.9 1.7 5.3 45 0.7 1.0 4.8 30 Flurtamone n.d. n.d. n.d. n.d. 54.5 56.0 57.6 18 16.3 19.1 21.9 20 Triadimenol n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2.9 2.9 2.9 10 Acetamiprid n.d. n.d. n.d. n.d. 1.1 1.1 1.1 9 0.6 0.6 0.6 10 Terbuthylazine-2-hydroxy n.d. n.d. n.d. n.d. 5.3 7.3 11.0 82 4.7 6.2 11.8 80 Cyromazine n.d. n.d. n.d. n.d. 2.2 2.2 2.2 9 n.d. n.d. n.d. n.d. Fenpropidin n.d. n.d. n.d. n.d. 1.4 2.9 4.4 18 n.d. n.d. n.d. n.d. Propyzamide n.d. n.d. n.d. n.d. 2.5 2.5 2.5 9 2.3 2.3 2.3 10 Quinmerac n.d. n.d. n.d. n.d. 8.4 8.4 8.4 9 1.1 4.4 7.6 20 Thiacloprid amide n.d. n.d. n.d. n.d. 15.5 15.5 15.5 9 3.5 3.5 3.5 10 Cyclophosphamide n.d. n.d. n.d. n.d. 1.6 1.7 2.0 36 1.3 1.4 2.4 40 Primidone n.d. n.d. n.d. n.d. 155.6 528.5 2173.7 100 17.0 396.4 1647.7 100 EDDP n.d. n.d. n.d. n.d. 17.3 32.1 66.8 100 16.2 22.8 44.6 100 Loperamide n.d. n.d. n.d. n.d. 1.4 1.9 2.8 55 n.d. n.d. n.d. n.d. Celecoxib n.d. n.d. n.d. n.d. 1.4 2.2 7.8 82 1.1 3.4 4.9 90 Clopidogrel n.d. n.d. n.d. n.d. 1.2 4.0 8.4 91 1.2 2.5 6.2 100

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Mebendazole n.d. n.d. n.d. n.d. 1.7 1.9 1.9 27 16.1 16.1 16.1 10 Ambroxol n.d. n.d. n.d. n.d. 1.8 5.0 22.4 73 1.5 5.2 11.0 80 Melperon n.d. n.d. n.d. n.d. 146.4 309.7 487.5 100 8.5 87.4 151.1 100 Pipamperone n.d. n.d. n.d. n.d. 63.9 451.5 1251.4 100 41.8 111.4 448.5 100 Nitrendipin n.d. n.d. n.d. n.d. 1.4 2.7 8.6 82 1.0 2.6 4.2 40 Naproxen n.d. n.d. n.d. n.d. 19.5 99.9 305.5 55 19.2 63.1 103.5 50 Phenazone n.d. n.d. n.d. n.d. 6.3 17.9 69.1 100 3.7 15.5 58.1 100 Propyphenazone n.d. n.d. n.d. n.d. 1.0 1.2 3.1 36 0.8 0.8 0.8 10 Metoprolol 3.4 3.4 3.4 25 138.5 345.9 1015.4 100 85.3 236.6 600.1 100 Erythromycin n.d. n.d. n.d. n.d. 7.2 16.1 67.9 91 6.2 9.4 60.2 80 Ofloxacin n.d. n.d. n.d. n.d. 4.9 53.9 140.4 64 4.5 26.7 42.9 60 Gemfibrozil n.d. n.d. n.d. n.d. 55.8 64.4 67.6 27 n.d. n.d. n.d. n.d. Paroxetine n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2.0 2.0 2.0 10 Propranolol n.d. n.d. n.d. n.d. 8.6 24.1 55.2 100 4.9 11.7 22.5 100 Verapamil n.d. n.d. n.d. n.d. 5.2 11.1 33.2 100 2.9 4.8 16.1 100 N-Acetyl-4-aminoantipyrine n.d. n.d. n.d. n.d. 70.1 290.6 840.5 100 38.3 216.1 823.7 90 Diazepam n.d. n.d. n.d. n.d. 0.8 1.0 1.7 55 1.0 1.1 1.3 30 Clarithromycin n.d. n.d. n.d. n.d. 7.0 23.3 119.1 100 7.2 15.4 107.4 100 Ketoprofen n.d. n.d. n.d. n.d. 1.2 3.6 13.5 100 2.1 5.1 7.1 80 Sulfamethoxazole n.d. n.d. n.d. n.d. 4.7 20.0 82.1 100 2.3 23.0 70.3 90 Sulfapyridine n.d. n.d. n.d. n.d. 15.3 45.9 113.8 100 7.0 40.6 79.0 90 2-(2-(Chlorophenyl)amino)benzaldehyde n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. Carbamazepine n.d. n.d. n.d. n.d. 163.9 350.6 1016.0 100 168.2 349.6 747.2 100 Diclofenac 2.7 2.7 2.7 25 199.1 502.6 1256.4 100 105.8 366.5 714.9 100 Roxithromycin 2.8 2.8 2.8 25 5.8 40.9 747.6 100 5.3 20.3 408.8 100 Lidocaine n.d. n.d. n.d. n.d. 12.9 31.6 58.0 100 10.5 25.7 60.3 100 Tramadol n.d. n.d. n.d. n.d. 85.6 171.5 241.0 100 39.2 143.1 201.0 100

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Mycophenolic acid 1.1 1.1 1.1 25 6.0 9.2 24.2 45 2.8 5.3 8.9 60 Trimethoprim n.d. n.d. n.d. n.d. 6.6 11.1 30.5 100 2.4 6.1 29.3 100 Crotamiton n.d. n.d. n.d. n.d. 1.0 2.6 7.4 91 1.1 1.9 3.5 80 10,11-Dihydro-10,11-dihydroxycarbamazepine 3.0 3.0 3.0 25 213.3 563.6 2152.7 100 2.2 520.6 1092.4 100 Irizine n.d. n.d. n.d. n.d. 13.9 22.2 80.7 100 7.0 19.3 33.7 100 2-Hydroxycarbamazepine n.d. n.d. n.d. n.d. 22.3 56.7 193.5 100 3.8 45.1 93.0 100 10,11-Dihydro-10-hydroxycarbamazepine n.d. n.d. n.d. n.d. 24.8 56.4 373.3 100 2.0 59.7 185.9 100 Amantadine n.d. n.d. n.d. n.d. 12.2 21.4 60.2 82 12.0 16.7 58.6 70 N-Formyl-4-aminoantipyrine n.d. n.d. n.d. n.d. 227.6 775.4 2119.7 91 11.9 552.1 1768.8 100 Diphenhydramine n.d. n.d. n.d. n.d. 5.5 18.9 836.9 100 8.0 18.9 473.2 100 Acetyl-sulfamethoxazole n.d. n.d. n.d. n.d. 1.0 3.6 12.8 100 1.4 4.1 6.3 40 Citalopram n.d. n.d. n.d. n.d. 22.0 58.5 212.7 100 12.3 25.3 50.5 100 Amitriptyline n.d. n.d. n.d. n.d. 8.0 17.9 45.0 100 2.9 6.5 13.6 100 Azithromycin n.d. n.d. n.d. n.d. 16.7 42.0 345.7 100 12.7 15.4 53.7 60 Bisoprolol n.d. n.d. n.d. n.d. 16.9 64.1 151.2 100 15.1 39.5 168.4 100 Bupropion n.d. n.d. n.d. n.d. 4.1 4.3 5.1 36 n.d. n.d. n.d. n.d. Fluconazole n.d. n.d. n.d. n.d. 7.4 18.5 446.3 100 7.0 18.7 38.7 100 Lorazepam n.d. n.d. n.d. n.d. 2.0 3.7 8.6 82 2.1 2.3 5.5 80 Memantine n.d. n.d. n.d. n.d. 5.5 16.4 33.1 100 7.2 9.3 28.6 100 Mirtazapine n.d. n.d. n.d. n.d. 8.6 18.2 32.1 100 5.9 10.3 18.2 90 Oxazepam n.d. n.d. n.d. n.d. 2.2 7.0 20.8 100 2.6 6.9 19.1 100 Temazepam n.d. n.d. n.d. n.d. 1.4 4.7 10.3 100 1.3 3.3 8.4 100 Losartan n.d. n.d. n.d. n.d. 4.8 20.3 41.5 100 3.7 12.4 36.3 90 Clozapine n.d. n.d. n.d. n.d. 3.0 4.9 6.9 82 2.7 3.5 4.1 30 Indometacin n.d. n.d. n.d. n.d. 6.1 16.1 43.5 100 4.9 10.8 18.6 100 Tetracain n.d. n.d. n.d. n.d. 1.1 1.6 3.9 73 1.0 1.2 2.0 60 Benzocain n.d. n.d. n.d. n.d. 2.7 8.6 23.5 100 2.5 5.4 10.4 90

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Metoprolol acid 24.2 31.3 36.0 100 23.8 62.9 207.6 100 24.7 76.8 276.0 90 Atenolol 1.2 1.2 1.3 50 6.9 28.0 54.5 100 2.3 16.1 109.5 90 2-Aminobenzimidazole 1.3 1.3 1.3 25 2.2 4.7 21.6 73 1.7 5.7 21.8 80 4'-Aminoacetanilide 1606.4 1606.4 1606.4 25 89.5 237.8 385.7 91 16.7 174.1 417.7 90 4-Aminobenzamide n.d. n.d. n.d. n.d. 39.5 104.3 279.7 73 36.3 73.4 1321.4 60 4-Aminoantipyrine n.d. n.d. n.d. n.d. 99.6 241.9 729.0 100 34.8 230.7 1408.1 100 Chlormequat n.d. n.d. n.d. n.d. 50.4 50.4 50.4 9 n.d. n.d. n.d. n.d. Caffeine 31.7 31.7 31.7 25 15.3 38.9 110.2 100 17.4 29.7 198.4 90 Acetaminophen n.d. n.d. n.d. n.d. 21.5 123.7 276.9 82 37.2 117.1 201.2 70 Ranitidine 6.6 6.6 6.6 25 7.2 10.1 24.5 91 7.0 11.3 22.1 90 Metformin n.d. n.d. n.d. n.d. 21.0 108.8 386.6 100 12.8 116.5 915.4 100 4-Hydroxybenzotriazole n.d. n.d. n.d. n.d. 20.0 38.2 142.2 82 13.3 22.3 55.6 60 Dimethylaminophenazone n.d. n.d. n.d. n.d. 1.6 2.2 3.5 100 1.1 2.0 7.7 100 Cotinine n.d. n.d. n.d. n.d. 15.8 29.0 65.9 100 3.4 20.7 57.4 100 Sotalol n.d. n.d. n.d. n.d. 14.7 44.2 192.0 100 3.9 51.3 205.4 100 Propamocarb n.d. n.d. n.d. n.d. 1.1 1.1 1.3 36 1.4 1.4 1.4 10 Mepiquat n.d. n.d. n.d. n.d. 21.1 47.9 152.6 100 27.8 45.4 120.4 90 Theophyllin n.d. n.d. n.d. n.d. 16.8 52.4 88.0 18 11.5 27.8 135.0 50 Benzophenone-3 n.d. n.d. n.d. n.d. 1.5 3.7 7.4 100 1.3 2.5 7.4 100 Octocrylene n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 14.9 14.9 14.9 10 Tonalide n.d. n.d. n.d. n.d. 3.7 8.8 27.3 100 2.1 6.5 23.1 80 ISO E Super n.d. n.d. n.d. n.d. 185.7 306.5 1023.2 100 153.5 203.1 811.4 60 Canrenone n.d. n.d. n.d. n.d. 2.6 10.2 17.7 18 7.6 7.6 7.6 10 Diuron n.d. n.d. n.d. n.d. 1.6 1.9 2.4 45 1.4 2.1 2.2 40 Diazinone n.d. n.d. n.d. n.d. 0.7 0.8 1.2 36 0.7 0.8 1.0 60 Iminostilbene n.d. n.d. n.d. n.d. 1.2 1.9 3.6 45 0.8 1.0 2.1 60 N,N-Dimethyldodecylamine N-oxide n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2.5 2.7 2.9 20

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Triethylcitrate 5.4 5.4 5.4 25 15.8 46.9 69.7 100 6.8 23.5 43.6 100 2-Mercaptobenzothiazole n.d. n.d. n.d. n.d. 117.9 163.7 404.6 64 n.d. n.d. n.d. n.d. 2-Methylbenzothiazole 1.3 1.3 1.3 50 1.7 2.1 4.9 82 1.3 1.5 2.1 80 Thiabendazole n.d. n.d. n.d. n.d. 2.7 4.0 6.9 100 2.8 4.6 43.2 90 N-Butylbenzenesulfonamide 60.4 327.3 594.1 50 475.0 1073.9 2033.0 73 444.7 1248.2 1559.2 70 Tris(2-chloroethyl)phosphate n.d. n.d. n.d. n.d. 6.2 20.3 30.1 100 6.1 14.6 23.7 100 Carbendazim n.d. n.d. n.d. n.d. 3.1 7.7 15.4 100 1.3 5.3 12.0 90 1,2-Benzisothiazolinone 2.1 2.1 2.1 25 2.3 8.0 15.3 100 3.6 4.6 14.1 80 terbutryn n.d. n.d. n.d. n.d. 1.0 5.0 6.9 100 1.2 4.4 8.1 100 Triphenylphosphate n.d. n.d. n.d. n.d. 0.9 2.2 3.9 73 0.7 1.5 2.6 60 DEET n.d. n.d. n.d. n.d. 6.6 14.5 46.9 100 4.4 12.3 47.0 100 Irgarol n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 1.1 1.1 1.1 10 1H-Benzotriazole 10.1 12.3 117.0 75 174.8 732.0 1529.7 100 51.6 555.8 1363.0 100 5-Methyl-1H-benzotriazole 4.9 4.9 4.9 25 293.5 580.7 1350.9 100 130.1 474.7 1029.3 100 Benzethonium n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2.1 2.1 2.1 10 Benzyldimethyldodecylammonium n.d. n.d. n.d. n.d. 22.5 22.5 22.5 9 n.d. n.d. n.d. n.d. Hexadecyltrimethylammonium n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. Didecyldimethylammonium 24.9 29.1 33.3 50 17.2 32.3 33.9 45 18.8 23.7 30.0 40 Hexadecylpyridinium n.d. n.d. n.d. n.d. 21.5 21.5 21.5 9 n.d. n.d. n.d. n.d. p-Toluenesulfonamide n.d. n.d. n.d. n.d. 13.4 55.4 187.6 100 13.0 59.3 116.5 100 Tri(butoxyethyl)phosphate n.d. n.d. n.d. n.d. 6.3 18.7 28.6 100 5.0 25.1 64.1 100 Trimethyloctylammonium n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 4.4 4.4 4.4 10 TDCPP n.d. n.d. n.d. n.d. 8.7 24.7 57.8 100 10.1 19.1 43.5 100 Tris(1-chloro-2-propyl)phosphate n.d. n.d. n.d. n.d. 50.4 173.5 554.4 100 34.8 121.4 320.0 100 Hexa(methoxymethyl)melamine 1.3 1.3 1.3 25 29.1 112.8 145.7 100 17.3 82.6 119.3 100 Triphenylphosphine oxide 15.0 18.9 37.7 100 21.3 46.9 363.5 100 9.4 31.0 287.0 100 Diphenylphosphate n.d. n.d. n.d. n.d. 2.1 4.6 15.7 100 3.0 5.4 16.2 80

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N-Ethyl-o-toluenesulfonamide n.d. n.d. n.d. n.d. 9.5 23.2 80.8 100 11.6 18.8 60.3 100 2-(Methylthio)benzothiazole 34.8 45.9 85.8 100 77.3 127.1 290.7 100 17.6 42.9 105.7 100 Benzothiazole 26.7 32.6 38.5 50 69.0 257.6 720.9 100 63.7 102.4 251.3 50 2,6-Xylidine n.d. n.d. n.d. n.d. 61.5 128.7 498.5 100 7.0 30.3 79.0 90 Icaridin n.d. n.d. n.d. n.d. 1.3 2.4 5.4 82 1.5 2.7 4.1 40 Genistein n.d. n.d. n.d. n.d. 1.0 1.4 3.4 73 1.8 2.3 2.8 20 Daidzein n.d. n.d. n.d. n.d. 1.2 1.2 1.2 9 n.d. n.d. n.d. n.d. Amidosulfobetaine-14 n.d. n.d. n.d. n.d. 2.5 3.1 3.8 18 n.d. n.d. n.d. n.d. triethylphosphate 3.4 3.5 4.7 75 37.9 179.8 393.6 100 11.7 80.9 178.8 100 Bis(2-ethylhexyl)phosphate n.d. n.d. n.d. n.d. 2.3 6.7 15.4 100 2.6 4.2 8.2 70 Tetraglyme 3.5 7.9 21.3 100 4.3 13.9 26.0 100 3.8 8.4 27.0 100 Denatonium n.d. n.d. n.d. n.d. 27.1 51.6 121.4 100 15.5 39.6 123.5 100 4-(Dimethylamino)pyridine n.d. n.d. n.d. n.d. 16.7 31.0 44.4 100 6.6 15.2 39.4 100 7-Amino-4-methylcoumarin 1.8 1.8 1.8 25 276.7 603.2 1198.9 100 68.5 195.5 361.0 100 2(4-morpholinyl)benzothiazole n.d. n.d. n.d. n.d. 1.4 2.6 5.0 91 0.9 1.4 2.2 80 7-Diethylamino-4-methylcoumarin n.d. n.d. n.d. n.d. 198.1 850.4 3592.6 100 118.1 208.8 1757.2 100 N-cyclohexyl-2-benzothiazole-amine 1.3 1.3 1.3 25 1.7 3.1 4.4 91 1.2 1.9 2.8 60 Hydrochlorothiazide 4.9 4.9 4.9 25 157.7 834.7 2071.6 100 1.3 547.6 1708.5 100 Acesulfame 20.1 20.5 20.9 50 37.4 124.1 8437.4 100 18.1 168.0 1062.6 100 Sucralose n.d. n.d. n.d. n.d. 210.5 655.4 2219.7 100 68.6 542.9 1660.7 90 Saccharin 18.3 43.6 131.0 75 35.8 96.7 795.0 100 38.6 85.4 686.8 90 Cyclamate n.d. n.d. n.d. n.d. 6.9 21.8 267.8 100 7.2 63.6 227.8 100 Metolachlor OA n.d. n.d. n.d. n.d. 1.1 1.1 1.1 9 1.2 2.9 3.6 30 Metolachlor ESA n.d. n.d. n.d. n.d. 1.5 2.3 6.1 73 2.4 5.4 10.5 80 Acyclovir n.d. n.d. n.d. n.d. 30.2 30.2 30.2 9 n.d. n.d. n.d. n.d. 6-Propyl-2-thiouracil 20.9 29.2 32.5 75 19.2 29.7 33.1 27 15.8 17.7 25.9 80 Dimethachlor OA n.d. n.d. n.d. n.d. 7.9 10.0 12.0 18 8.4 8.6 8.9 20

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Metazachlor ESA 1.5 1.5 1.5 25 6.7 33.8 125.3 100 8.9 45.1 102.5 90 Dimethachlor ESA n.d. n.d. n.d. n.d. 1.1 2.7 9.5 73 2.1 3.7 9.3 80 2,4-Dichlorophenoxyacetic acid n.d. n.d. n.d. n.d. 1.4 5.2 11.5 91 1.2 2.5 4.3 90 Bezafibrate 2.4 2.4 2.4 25 9.9 26.2 112.0 100 4.7 14.8 59.4 90 Bentazone n.d. n.d. n.d. n.d. 1.1 1.2 33.5 36 4.8 8.5 35.4 80 Pravastatin n.d. n.d. n.d. n.d. 2.4 3.3 4.2 18 n.d. n.d. n.d. n.d. Fipronil n.d. n.d. n.d. n.d. 0.6 2.1 6.8 100 0.7 1.7 5.2 100 Phenylbenzimidazole sulfonic acid n.d. n.d. n.d. n.d. 123.0 181.1 506.6 100 40.3 150.1 405.1 90 Dichlorprop n.d. n.d. n.d. n.d. 2.1 4.4 6.7 18 1.6 2.3 2.9 20 Perfluorooctanesulfonic acid 1.1 1.1 1.3 100 1.3 2.3 3.6 100 1.1 1.8 4.7 100 Perfluorohexanoic acid n.d. n.d. n.d. n.d. 2.2 2.5 3.0 45 2.2 2.6 3.0 20 Mecoprop n.d. n.d. n.d. n.d. 1.2 3.7 17.9 100 1.3 3.1 9.4 90 MCPA n.d. n.d. n.d. n.d. 3.7 14.0 47.6 100 3.8 9.2 23.6 90 Benzophenone-4 1.6 1.6 1.6 25 38.1 122.6 359.4 100 18.9 120.6 338.5 90 Chlorothalonil-4-hydroxy n.d. n.d. n.d. n.d. 1.1 1.3 2.3 55 1.0 1.4 1.7 20 Triclocarban n.d. n.d. n.d. n.d. 1.1 1.7 2.6 73 1.5 1.9 2.6 30 2-Naphthalene sulfonic acid n.d. n.d. n.d. n.d. 12.0 23.0 604.6 64 10.8 20.9 147.2 70 2,4-Dinitrophenol n.d. n.d. n.d. n.d. 4.1 25.6 64.4 100 2.9 43.8 52.4 90 2-Benzothiazolesulfonic acid 3.5 5.3 18.1 75 88.6 299.0 874.6 100 30.2 290.6 712.3 90 3,5,6-Trichloro-2-pyridinol n.d. n.d. n.d. n.d. 1.2 4.2 7.2 91 1.1 1.3 2.3 50 6:2 fluorotelomer sulfonic acid 1.0 1.4 1.9 50 1.0 1.4 25.6 82 1.1 1.3 2.5 40 Valsartan 1.8 1.8 1.8 25 20.6 92.4 574.6 100 2.5 205.6 528.5 100 Bicalutamide n.d. n.d. n.d. n.d. 14.6 37.3 103.1 100 16.5 24.4 88.1 100 Fipronil sulfide n.d. n.d. n.d. n.d. 0.9 1.3 1.8 18 0.7 1.1 1.6 20 Decyl sulfate 11.8 14.4 16.6 100 1.5 7.8 17.2 100 1.1 6.7 37.7 70 Furosemide n.d. n.d. n.d. n.d. 48.7 87.7 342.8 100 15.7 44.4 86.8 90 Triclosan n.d. n.d. n.d. n.d. 1.8 2.5 3.2 36 1.6 1.7 3.4 30

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Chlorophene n.d. n.d. n.d. n.d. 2.4 2.9 6.9 73 2.2 3.1 5.9 40 2,4-Dichlorophenol n.d. n.d. n.d. n.d. 35.0 48.3 156.0 73 n.d. n.d. n.d. n.d. Bisphenol S n.d. n.d. n.d. n.d. 2.2 2.2 2.2 9 0.7 1.1 1.5 20 Glimepiride n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 1.1 1.2 1.5 30

218

Contributions to the published articles and chapters

Chapter 1 Conception and writing of this chapter was done by SBZ. Chapter 2 Zhou et al. (2019): SBZ, TBS and HH designed the study. SBZ collected and then evaluated the data and wrote the manuscript. XDW made substantial contributions to the interpretation of data. CD, SY, TBS and HH made substantial contributions to the revision of the manuscript. MG and CP gave the suggestions for the revision. All authors read and approved the final manuscript. Chapter 3 Zhou et al. (2019): SBZ, TBS and HH designed the study. SBZ conducted 90% experiments, CQ and SY performed the remaining 10% experiment. SBZ evaluated the data and wrote the manuscript. CQ, CD TBS and HH made substantial contributions to the interpretation of data and to the revision the manuscript. All authors read and approved the final manuscript. Chapter 4 Grund et al. (to be submitted): SBZ, SG, MK, ULV, KW, TB, WB, TBS and HH designed the study. SG conducted three steps of effect-directed analysis (bioassays, fractionation, chemical analysis). SBZ conducted bioassays and confirmation. SG evaluated the data and wrote the 60% manuscript. SBZ further evaluated the data and wrote 40% manuscript. MK, ULV, KW, TB, WB, TBS and HH made substantial contributions

219

Contributions to the published articles and chapters

to the interpretation of data and to the revision of the manuscript. All authors read and approved the final manuscript. Chapter 5 Zhou et al.(to be submitted): SBZ, TBS and HH designed the study. SBZ conducted the experiments, evaluated the data and wrote the

manuscript. BE and ML gave great suggestions to the manuscript revision. MK prepared PAHs and mixtures. SCP, WB, DJA, TBS, and HH made substantial contributions to the interpretation of data and to the revision of the manuscript. All authors read and approved the final manuscript. Chapter 6 Zhou et al. (to be submitted): SBZ, HX, TS, HH and TBS designed the study. SBZ conducted most of the experiments, evaluated the data and wrote

the manuscript. HX made substantial contributions to the design of the metabolism study. TBS, and HH made substantial contributions to the the revision of the manuscript. All authors read and approved the final manuscript. Chapter 7 Conception and writing of this chapter was done by SBZ.

220

Acknowledgements

First of all, I would like to express my extreme gratitude to my supervisor Prof. Dr. Henner Hollert for giving me the chance to conduct my PhD studies in the Institute for Environmental Research, RWTH Aachen University. It is my honor to be his Ph.D. student. I would like to express my sincere gratitude for his support, motivation and patience. His guidances and passion for the research will greatly benefit my whole life. This is the power of a great supervisor! I am profoundly grateful to co-supervisor assistant Prof. Dr. Thomas-Benjamin Seiler for his support and contributions during the last fours years. He was quite patient and taught me a lot in research and life. My experimental designs and manuscripts improved greatly after receiving his instructions and revisions. To be one of his students, this must be God’s care! I am grateful to Prof. Dr. Andreas Schäffer for having supervised my dissertation and being my examiner of ecotoxicology. Thanks also go to assistant Prof. Dr. Martina Roß-Nickoll. Her enthusiasm for research greatly influenced my choice of studying in Germany. This study was supported by SOLUTIONS project that is funded by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement number (603437), which is gratefully acknowledged. Personally, I got financial support from China Scholarship Council (CSC). I was lucky to be one of the CSC funders. Thanks a lot to CSC and my homeland-People Republic of China. I am grateful to SOLUTIONS project partners PD Dr. Werner Brack, Dr. Tobias Schulze and Dr. Martin Scholze, they helped me a lot in the experiments and gave me good advice in the experiments. I would like to thank my colleagues Dr.Ying Shao and MSc Björn Deutschmann, they also helped me a lot. Special thanks Dr. Stefanie Grund for establishing the foundation for a complete effect-directed analysis. Special thanks MSc Carina Lackmann translated the abstract into German.

221

Acknowledgements

The members of the Institute of Environmental Research have contributed immensely to my professional time in the lab. I sincerely thank Simone Hotz, Dr. Sabrina

Schiwy, Dr. Niehus Nora, Dr. Sebastian Heger, MSc Leonie Nuesser, MSc Sina Volz, MSc Sarah Johann, Msc Christoph Kämpfer and MSc Yvonne Müller for their support of my work in the lab. Thanks Dr. Carolina Di Paolo for her language corrections and revisions to several chapters. Thanks to my Chinese colleagues Dr.

Hongxia Xiao, Dr. Qiqing Chen, Dr. Yunlu Jia, Dr. Miaomiao Du and Dr. Linyan Zhu for their assistance with my experiments and daily life. I would like to thank my Chinese friends Bo Yang, Fei Guo, Jin Yu, Hongpo Wu, Lina Zhou, Qiang Wang, Jiayu Sui, Zhongli Chen and Zemin Tian. I really enjoyed the time with you. Most of all, I would like to express my gratitude to my parents, my wife and young sister. They always make my life colorful and meaningful.

222

Curriculum vitae

Name：Zhou Shangbo Gender：Male

Date of birth: 1989.01.12 Nationality: Chinese

Address: Aachen, Germany

Email：[email protected] Tel：17347918869

Education

Since 2015.09 Ph.D student in Ecotoxicology and Toxicology Institute for Environmental Research (Biology V), RWTH Aachen University, Germany Research direction: Ecotoxicology and risk assessment Supervisor: Prof. Dr. Henner Hollert 2012.09-2015.06 Chongqing University, Chongqing, China Master in Environmental Science and Engineering Research direction: Ecotoxicology and risk assessment Research direction: Environmental ecology Supervisor: Prof. Dr. Xingzhong Yuan 2008.09-2012.06 Jilin University, Jilin, China Bachelor in Environmental Science

Research Experience

1) SOLUTIONS project - for present and future emerging pollutants in land and water resources management. It is funded by the European Union Seventh Framework Programme (FP7-ENV-2013-two-stage Collaborative project) under grant agreement number (603437) 2) Studying the functional group of invertebrate in the hyporheic zone under the natural and human disturbance that was supported by National Natural Science Foundation of China.

Awards and Honors 223

Curriculum vitae

CSC Scholarship 2015-2019 National Scholarship for Master's student 2015 Postgraduate scholarship, Chongqing University 2012-2015 Merit Student and Second Scholarship, Jilin University 2010 Second Scholarship of Environment Protection Forum, Jilin province, 2010

224

Scientific Contributions

Research articles published in international peer-reviewed journals Zhou, S., Di Paolo, C., Wu, X., Shao, Y., Seiler, T. B., Hollert, H. 2019. Optimization of screening-level risk assessment and priority selection of emerging pollutants – the case of pharmaceuticals in European surface waters. Environmental International, 128, 1–10.

Zhou, S., Chen, Q., Di Paolo, C., Shao, Y., Hollert, H., Seiler, T. B. 2019. Behavioral profile alterations in zebrafish larvae exposed to environmentally relevant concentrations of eight priority pharmaceuticals. Science of the Total Environment, 664, 89–98.

Zhou, S., He, Y., Yuan, X., Peng, S., Yue, J. 2017. Greenhouse gas emissions from different land-use areas in the Littoral Zone of the Three Gorges Reservoir, China. Ecological Engineering, 100, 316–324.

Zhou, S., Yuan, X., Peng, S., Yue, J., Wang, X., Liu, H., Williams, D.D. 2014. Groundwater-surface water interactions in the hyporheic zone under climate change scenarios. Environmental Science and Pollution Research, 21, 13943– 13955.

Thiäner J.B., Nett, L., Zhou S., Hotz S., Preibisch Y., Hollert H., Achten C. 2019. Identification of 7-9 ring polycyclic aromatic hydrocarbons in coals and petrol coke using high pressure liquid chromatography – diode array detection coupled to atmospheric pressure laser ionization - mass spectrometry (HPLC-DAD-APLI-MS). Environmental Pollution. 252, 723–732.

Shao, Y., Chen, Z., Hollert, H., Zhou, S., Deutschmann, B., Seiler, T.B. 2019. Toxicity of 10 organic micropollutants and their mixture: Implications for aquatic risk assessment. Science of the Total Environment, 666, 1273–1282.

Research articles to be submitted for publication in international peer-reviewed journals

225

Scientific Contributions

Zhou, S., Peng, S., Krauss, M., Brack W., Seiler, T. B., Hollert, H. 2019. Bioanalytical equivalents and relative potencies for predicting the biological

effects of mixtures and environmental samples. Water Research.

Zhou, S., Krauss, M., Brack W., Xiao, H., Seiler T.B., Hollert, H., Schulze T. 2019. Spatial and temporal variations of anti-androgenic activities and their environmental risks in surface waters. Science of the Total Environment.

Grund, S., Zhou, S., Krauss, M., Schulze,T., Varel, U.L., Winkens, K., Braunbeck, T., Brack, W., Seiler T-B., Hollert H. 2019. Identification and confirmation of aryl hydrocarbon receptor-mediated activities in sediment samples from the Upper Danube River by means of effect-directed analysis. Environmental Pollution.

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