Temporal Records of Organic Contaminants in Lake Sediments, Their Bioconcentration and Effect on Daphnia Resting Eggs

Eawag_08974

Diss. ETH No. 21354 Temporal records of organic contaminants in lake sediments, their bioconcentration and effect on Daphnia resting eggs

A dissertation submitted to the ETH ZURICH

for the degree of Doctor of Sciences

presented by AUREA CECILIA HERNANDEZ RAMIREZ OTHER FORMATS: AUREA CECILIA CHIAIA-HERNANDEZ MSc. In Chemistry, Oregon State University born 7 August 1980 citizen of Mexico

accepted on the recommendation of Prof. Dr. Juliane Hollender, examiner Prof. Dr. Bernhard Wehrli, co-examiner Prof. Dr. Lee Ferguson, co-examiner PD. Dr. Piet Spaak, co-examiner

2014 ii Contents

Summary vii

Zusammenfassung xi

1 Introduction 1 1.1 Eutrophication and Anthropogenic Induced Environmental Changes . . . 2 1.2 Organic Pollutants in Sediments ...... 2 1.3 Analytical Procedures for the Detection and Identiﬁcation of Parent Com- pounds and Transformation Products ...... 4 1.4 Daphnia as a Model Organism ...... 5 1.5 Bioaccumulation and Effects of Organic Contaminants ...... 6 1.6 Objectives and Contents of the Thesis ...... 7

2 Screening of Lake Sediments for Emerging Contaminants by Liquid Chro- matography Atmospheric Pressure Photoionization and Electrospray Ion- ization Coupled to High Resolution Mass Spectrometry 15 2.1 Introduction ...... 16 2.2ExperimentalSection...... 18 2.2.1 Standards and Reagents ...... 18 2.2.2 Sample Collection and Preservation ...... 18 2.2.3ExtractionofSediments...... 19 2.2.4 Clean-up and Enrichment of Sediment Extracts ...... 19

iii 2.2.5 Liquid Chromatography Tandem High Resolution Mass Spectro- metricDetection...... 20 2.2.6AccuracyandPrecision...... 21 2.2.7 Limits of Detection and Quantiﬁcation ...... 21 2.2.8 Suspect Screening of Further Contaminants and Transformation Products ...... 21 2.2.9ProtonAfﬁnityData...... 22 2.3ResultsandDiscussion...... 23 2.3.1MethodPerformance...... 23 2.3.2 Optimization and Advantages of APPI ...... 25 2.3.3 Target Screening of Organic Contaminants in Sediment Cores of LakeGreifensee...... 27 2.3.4 Suspect Screening of Further Contaminants and Transformation products ...... 30 Supporting Information to Chapter 2 ...... 43

3 Suspect and Non-target Screening Approaches to Identify Records of Or- ganic Contaminants in Sediments 69 3.1 Introduction ...... 70 3.2ExperimentalSection...... 72 3.2.1 Sample Collection and Preservation ...... 72 3.2.2 Extraction, Clean-up and Enrichment of Sediment Extracts . . . . 73 3.2.3 Liquid Chromatography Tandem High Resolution Mass Spectro- metricDetection...... 73 3.2.4 Suspect Screening of Further Contaminants and Transformation Products ...... 74 3.2.5 Non-target Screening of Further Contaminants ...... 75 3.3ResultsandDiscussion...... 78 3.3.1 Target Screening of Organic Contaminants in Sediment Cores of Lake Lugano ...... 78

iv 3.3.2 Suspect Screening of Further Contaminants and Transformation Products ...... 78 3.3.3 Non-target Screening of Unknown Contaminants ...... 82 3.3.4InsightsandFutureResearch...... 85 Supporting Information to Chapter 3 ...... 97

4 Bioconcentration of Organic Contaminants in Daphnia Resting Eggs 125 4.1 Introduction ...... 126 4.2ExperimentalSection...... 128 4.2.1 Sample Collection and Preservation ...... 128 4.2.2 Ephippia Exposure: Uptake and Depuration Kinetics ...... 128 4.2.3 Clean-up and Enrichment of Extracts ...... 130 4.2.4 Liquid Chromatography Tandem High Resolution Mass Spectro- metricDetection...... 130 4.2.5BioconcentrationFactor(BCF)...... 131 4.2.6 Predicting Concentrations in Ephippia in the Environment . . . . . 133 4.3ResultsandDiscussion...... 134 4.3.1SedimentAnalysis...... 134 4.3.2 Uptake and Depuration Kinetics ...... 134 4.3.3 Ephippia Bioconcentration Factor (BCF) ...... 135 4.3.4 Estimated Concentrations in Ephippia in the Environment . . . . . 140 Supporting Information to Chapter 4 ...... 149

5 Environmental Organic Contaminants Inﬂuence Hatching from Daphnia Rest- ing Eggs and hatchling survival 163 5.1 Introduction ...... 164 5.2 Material and Methods ...... 167 5.2.1 Ephippia Collection and Preparation ...... 167 5.2.2 Standards and Reagents ...... 168 5.2.3HatchingExperiment...... 168

v 5.2.4 Ephippia and Egg Counting ...... 170 5.2.5StatisticalAnalysis...... 171 5.3ResultsandDiscussion...... 172 5.3.1HatchingSuccess...... 173 5.3.2HatchlingMortality...... 176 5.3.3Implications...... 178 Supporting Information to Chapter 5 ...... 191

6 Conclusion & Outlook 193 6.1Conclusion...... 194 6.2Outlook...... 196

Acknowledgements 203

Curriculum vitae 205

vi Summary

vii viii Summary

Emerging contaminants are important since they can have complex effects on ecosystems. However, the fate and occurrence of many organic contaminants could not be assessed in the past due to the lack of technology available. Measurements of emerging contaminants such as pharmaceutical and personal care products started in the 1990s, thus the exposure before this time is not well studied. Sediment cores have been shown to be useful to reconstruct environmental changes over time, providing historic records of natural events, changes in ecosystems, and climate. Therefore, sediments could be suitable to reconstruct the environmental contamination to provide records of anthropogenic impact. Chemicals can enter natural waters via wastewater treatment plant effluents, urban and industrial sewage, runoff, spray drift and leaching from agricultural areas and sorb to sediments. As a result, sediments are excellent archives of environmental contaminants if the chemicals persist over time since they can act as integrators of many inputs within a catchment. Until now, sediments have been mainly used to characterize the contamination of legacy compounds like polychlorinated biphenyls (PCBs) and polyaromatic hydrocarbons (PAHs) with highly hydrophobic characteristics. Therefore, the knowledge of long-term contamination with medium polar contaminants is not well explored. A multiresidue method was developed for the screening of more than 200 emerging contaminants and their transformation products with a broad range of physicochemical properties in lake sediments. Sediment cores were extracted by pressurized liquid extraction and liquid-liquid partitioning, followed by liquid chromatography high resolution tandem mass spectrometry (HRMS/MS) using electrospray ionization (ESI) and atmospheric pressure photoionization (APPI). Next, the spatial and temporal distribution of medium polar organic contaminants from two lakes in Switzerland were studied to understand the fate of less hydrophobic organic contaminants. The results show that biocides, musk fragrances, and other personal care products were the most frequently detected compounds, whereas none of the targeted pharmaceuticals were found. The concentrations trend of many urban contaminants originating from wastewater correlate temporally with the phosphorus inputs into the lake, which is a proxy for treatment efficiency of a wastewater treatment plant. Highest concentrations are observed at the beginning of the 1980s followed by rapid decline on Lake Greifensee while increasing concentrations in the late 1970s followed by a starting decline in concentrations starting in the 1990s is observed on Lake Lugano. The use of HRMS enabled a retrospective analysis of the full-scan data acquisition allowing the detection of suspect compounds like quaternary ammonium surfactants, the biocide triclocarban, the identification of the transformation products of triclosan and Summary ix

triclocarban, and furthermore the identification of different biocides and disinfectants in use in Switzerland. In addition, the use of HRMS/MS, together with computer assisted molecular formula generation and in silico fragmentation, enabled the identification of non-targeted compounds such as the mothproofing flucofuron and the disinfectant hexachlorophene. The compounds found can have complex effects on sediment ecosystems and constitute a primary source of exposure for benthic organisms. The effect of these organic contaminants was assessed by experimental studies measuring their bioconcentration factors (BCF) in Daphnia resting eggs (ephippia). The results show that personal care products, pesticides, pharmaceuticals, and biocides found in the sediments can bioaccumulate in ephippia. Furthermore, a model was developed to predict bioaccumulation of other compounds as a function of hydrophobicity. Analysis of organic contaminants in ephippia extracted from sediment cores is not possible due to the small number of ephippia available in the sediment extraction and the amount needed for the chemical analysis. Therefore, internal concentrations in ephippia in the environment were predicted based on sediment concentrations using the equilibrium partitioning model (EqP) and calculated BCFs. Estimated internal concentration values were obtained with triclocarban having the highest internal concentrations, followed by tonalide and triclosan. The outcomes show that contaminants can be taken up by ephippia from the water column or sediments. Additional experiments were performed to test ephippia hatching abilities and fitness after exposure to organic contaminants resulting in high mortality and deformation. The exposure concentrations were selected according to the predicted pore water maximum concentrations based on sediment analyses from Lake Greifensee and multiplied by a factor of 1,000. Resting egg banks are not only known for Daphnia but for many aquatic organisms and play a crucial role for their ecology and evolution. The significance of this project lies in the ability to study a wide range of organic contaminants with different physicochemical characteristics, including contaminants that could not be measured in the past but now can be measured thanks to the state of the art techniques available, and to provide present and historic records on the occurrence and fate of these organic contaminants. The findings provide information about the contamination of two lakes from the north and the south of Switzerland as well as insights to understand the potential impact of organic contaminants on aquatic organisms relying on resting stages during their life cycle. x Summary Zusammenfassung

xi xii Zusammenfassung

Verunreinigungen durch neuartige chemische Stoffe haben vielfaltige¨ Einflüss auf Oko-¨ systeme. In der Vergangenheit konnte das Auftreten von vielen organischen Verun- reinigungen jedoch nicht analysiert werden, da keine analytischen Messmethoden zur Verfugung ¨ standen. Die Messung von neuartigen Substanzen wie beispielsweise von Arzneimitteln oder von Korperpflegeprodukten¨ begann erst in den 90er Jahren. Daher istuber ¨ die Verbreitung dieser Chemikalien in Okosystemen¨ vor dieser Zeit nur wenig bekannt. Die Analyse von Sedimentbohrkernen hat sich als nützlich erwiesen, um Umweltereig- nisse der Vergangenheit sowie Veranderungen¨ in Okosystemen¨ oder des Klimas zu rekonstruieren. Insbesondere sind Sedimentbohrkerne geeignet, chemische Umwelt- belastungen der Vergangenheit sowie anthropogene Einflüsse zu beschreiben. Chemikalien konnen¨ uber ¨ Abflüsse von Klaranlagen,¨ stadtische¨ und industrielle Abwas-¨ ser oderuber ¨ Oberflachenabfluss,¨ Sprayverluste und Auswaschungen aus der Land- wirtschaft in natürliche Gewasser¨ gelangen und dort im Sediment sorbieren. Sedimente sind somit ein hervorragendes Archiv für langlebige Stoffe, da sie den Einflussuber ¨ ein ganzes Einzugsgebiet unduber ¨ eine gewisse Zeitspanne widerspiegeln. Bisher wurden Sedimentbohrkerne vor allem dazu verwendet, um die Kontamination durch Substanzen mit stark hydrophoben Eigenschaften, wie z. B. polychlorierte Biphenyle (PCB) und polyzyklische aromatische Kohlenwasserstoffe (PAK) zu charakterisieren. Die langfristige Kontamination durch mittel-polare Verunreinigungen wurde hingegen noch nicht ausführlich untersucht. Für das Sediment wurde eine Multirückstandsmethode zum screeninghaften Analy- sieren von mehr als 200 neuartigen Stoffen und deren Umwandlungsprodukten mit einem breiten Spektrum an physikalisch-chemischen Eigenschaften entwickelt. Dabei wurden die Stoffe durch druckinduzierte flüssig-Extraktion und flüssig-flüssig-Extraktion aus den Sedimentbohrkernen herausgelost¨ und anschliessend durch Flüssigchromato- graphie mittels eines hochauflosenden¨ Tandem-Massenspektrometers (HRMS/MS) mit Elektrospray-Ionisation (ESI) und Atmospharendruck-Photoionisation¨ (APPI) analysiert. Damit konnte die raumliche¨ und zeitliche Verteilung von mittel-polaren organischen Verunreinigungen in zwei Seen der Schweiz analysiert und das Verhalten von weniger hydrophoben organischen Verunreinigungen untersucht werden. Die Ergebnisse zeigen, dass Biozide, Moschus-Duftstoffe und andere Korperpflegeprodukte¨ am haufigsten¨ nachgewiesenen werden konnen.¨ Dagegen wurde keines der gesuchten Arzneimittel detek- tiert. Der Konzentrationstrend von vielen urbanen Verunreinigungen aus dem Abwasser korreliert zeitlich mit den Phosphor-Eintragen¨ in die Seen. Phosphor kann in diesem Zusammenhang als Proxy für die Behandlungseffizienz von Klaranlagen¨ betrachtet Zusammenfassung xiii

werden. Im Greifensee wurden die hochsten¨ Konzentrationen zu Beginn der 1980er Jahre gemessen. Danach folgte eine starke Abnahme der Konzentrationen. Im Lu- ganersee stiegen die Konzentrationen in den spaten¨ 1970er Jahren an. Ein Rückgang konnte erst zu Beginn der 1990er Jahre beobachtet werden. HRMS erlaubt eine retrospektive Datenanalyse, was die Detektion von gesuchten Verbin- dungen wie quaternaren¨ Ammonium-Tensiden, dem Biozid Triclocarban, die Identifika- tion der Umwandlungsprodukte von Triclosan und Triclocarban und weiteren, in der Schweiz eingesetzten Bioziden und Desinfektionsmitteln ermoglicht.¨ Darüber hinaus ermoglichte¨ die Kombination von HRMS/MS mit der computergestützten Generierung von molekularen Summenformeln und der in silico-Fragmentierung die Identifikation bisher unbekannter Verbindungen wie die des Mottenschutzmittels Flucofuron oder die des Desinfektionsmittels Hexachlorophen. Die nachgewiesenen Verbindungen haben vielfaltige¨ Auswirkungen auf das Okosystem¨ Sediment und bilden eine Hauptexpositionquelle für benthische Organismen. Die Wir- kung dieser organischen Verunreinigungen wurde experimentell untersucht, indem Bio- konzentrationsfaktoren (BCF) von organischen Schadstoffen in Dauereiern von Daph- nien (Ephippien) bestimmt wurden. Die Ergebnisse zeigen, dass die im Sediment gefundenen Korperpflegeprodukte,¨ Pestizide und Arzneimittel in Ephippien bioakku- mulieren konnen.¨ Aufgrund dieser Daten wurde ein Modell entwickelt, um auch die Bioakkumulation weiterer Substanzen als Funktion ihrer Hydrophobie vorherzusagen. Eine direkte Analyse der organischen Verunreinigungen in Ephippien aus Sediment- bohrkernen ist jedoch aufgrund ihres geringen Vorkommens im Sediment und der für die chemische Analyse benotigten¨ Mengen nicht moglich.¨ Deswegen wurden die internen Konzentrationen in Ephippien in der Umwelt aus den Konzentrationen im Sediment mittels des Gleichgewichtsverteilungsmodells (Eqp) und den berechneten BCF-Daten bestimmt. Die geschatzten¨ internen Konzentrationen sind für Triclocarban, gefolgt von Tonalid und Triclosan, am hochsten.¨ Das Ergebnis zeigt, dass Verunreinigungen in der Wassersaule¨ oder im Sediment-Porenwasser von Ephippien aufgenommen werden konnen.¨ Zusatzliche¨ Experimente wurden durchgeführt, um die Schlupffahigkeit¨ von Ephippien nach der Exposition mit organischen Verunreinigungen zu untersuchen. Diese zeigten eine hohe Sterblichkeit und mehr Deformation. Die Expositionskonzentrationen wurden gemass¨ den vorhergesagten maximalen Porenwasserkonzentrationen, basierend auf Sedimentanalysen aus den Greifensee, ausgewahlt¨ und mit einem Faktor von 1000 multipliziert. Dauereier sind auch für viele andere aquatische Organismen bekannt und spielen eine wichtige Rolle in der Okologie¨ und Evolution. xiv Zusammenfassung

Das Projekt zeigt, dass eine breite Palette von organischen Substanzen mit unter- schiedlichen physikalisch-chemischen Eigenschaften dank hochmodernen Techniken in See-Sedimenten gemessen werden kann. Damit lassen sich heute nachtraglich¨ Verunreinigungen analysieren, die in der Vergangenheit nicht gemessen werden kon- nten. Zwei Fallbeispiele von Seen aus dem Norden und dem S¨uden des Landes gaben Aufschl¨usseuber ¨ die Verunreinigungen in der Schweiz sowie einen Einblickuber ¨ die Auswirkungen von organischen Schadstoffen auf aquatische Organismen mit einem Dauerstadium wahrend¨ ihrer Entwicklungsphase. Chapter 1

Introduction

1 2 Chapter 1. Introduction

1.1 Eutrophication and Anthropogenic Induced Environ- mental Changes

During the last century, most European lakes went through a phase of eutrophication. Urban and industrial sewage, erosional runoff, and leaching from agricultural areas caused the increase of phosphorus in natural waters affecting species of higher trophic level, such as zooplankton and fish. The installation of sewage treatment plants around the 1980s made possible the recovery of many lakes to their original trophic state. 1 The eutrophication and later re-oligotrophication of lakes facilitated the invasion of several native and non-native species, and in many cases, the interspecific hybridization of invasive and indigenous taxa. Few studies have taken place on the reaction of biota to rapid environmental changes such as eutrophication. Brede et al. 2 studied the genetic architecture of Daphnia diapausing eggs and the changes in total phosphorus concentrations in lake sediments from two European lakes, demonstrating that anthropogenically induced temporal alterations of habitats are associated with a shift in species composition and the population structure of evolutionary lineages. Subsequently, studies have revealed that organic contaminants influence the aquatic food web, 3,4 but how this is happening and over which time-scales chemical pollutants are influencing the aquatic web, as well as the extent of the impact, is not well known. Currently, there is little knowledge about the influence and/or adaptation of aquatic organisms on the increase or decrease of organic pollutants. For many water contaminants, there is a lack of consistency in the quantitative data on the actual exposure status in water bodies and sediments. Measurements of emerging contaminants such as pharmaceutical and personal care products (PPCPs) started in the 1990s, thus the exposure pre-1990 is not well defined. Realistic exposure scenarios for benthic organisms in the past and present have to be reconstructed based on different sources of information, like consumption data, number and efficiency of connected waste water treatment plant (WWTPs) facilities, as well as land use within the catchments.

1.2 Organic Pollutants in Sediments

Today, approximately 300 million tons of synthetic compounds are used annually in industrial and consumer products globally. 3,5 Their manufacture, application and disposal are relevant to their fate, since natural waters provide a sink for a number of these 1.2. Organic Pollutants in Sediments 3

compounds. In order to assess an environmental risk, it is important to understand the fate, occurrence and effects of organic contaminants in the environment and in different organisms. In Switzerland, discharge from around 740 WWTPs enter surface waters. 6 Organic contaminants that are released via WWTPs effluents or with runoff from agricultural land after application can enter natural waters and sorb to sediments. Therefore, sediments act as archives of environmental contaminants, provided that the chemicals are persistent under generally anaerobic conditions. This is particularly true for hydrophobic organic compounds (HOC) which rapidly associate with sediments and suspended particles.7 Until now, such records have been used mainly to characterize the contamination of compounds with a long history of use and having highly lipophilic characteristics, such as polychlorinated biphenyl (PCBs) and polyaromatic hydrocarbons (PAH). 8,9 In contrast, the knowledge of long-term contamination of sediments with medium polar contaminants such as PPCPs, household chemicals or pesticides has not been well explored. Partitioning between aqueous solution and sediment of medium polar contaminants can be rather complicated compared to HOC due to the interaction with inorganic fractions, covalent binding and species-specific sorption mechanism.10 One parameter to predict the partitioning of organic pollutants based on Van der Waals interactions is the octanol-water partition coefficient (Kow). This parameter is widely used for esti- mating partitioning of neutral organic compounds between natural organic phases and water. Furthermore, sorption of many nonionic solutes to various matrices can be predicted by the organic carbon sorption coefficient (Koc). Although Koc cannot always accurately express sorption, linear absorption with macromolecular soil and sediment organic matter is nonetheless a major component of sorption that can dominate solute behavior under many circumstances. Sediment pore water also plays an important role in sediment-water sorption mechanisms and for bioavailability, given that it is captured during the sedimentation process and is essentially isolated from the water column. 7,11 Pore water concentration can be roughly estimated by standard approaches using Koc values, sediment concentrations, and the physical characteristics of the lake.4 4 Chapter 1. Introduction

1.3 Analytical Procedures for the Detection and Identi- ﬁcation of Parent Compounds and Transformation Products

The analysis of organic pollutants and transformation products in different matrices is challenging since they mostly occur in low concentrations, typically in the ng/L or ng/g range. Up until now, the method of choice for the analysis of polar molecules is liquid chromatography tandem mass spectrometry (LC-MS/MS). While LC-MS/MS has shown to be selective, sensitive and fast, there are still some challenges for the identification of micropollutants, especially in mixtures containing many known and unknown compounds at low concentrations and in complex matrices. Electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) are the most commonly used ionization technologies used in LC-MS/MS. Atmospheric pressure photo ionization (APPI) is a newer ionization technique for the analysis of poorly ionizable, less polar compounds. The suitability and universality of APPI has been demonstrated by the analysis of drugs, 12,13 PCBs and PAHs, 14,15 perfluorinated compounds, 16,17 steroids, 18,19 and fullerenes. 17 Therefore, the combination of ESI and APPI as complementary ionization techniques for the analysis of environmental samples could significantly expand the detection and quantification of a wide range of compounds in a single study. In addition to the difficulty in the ionization and detection of organic contaminants, there are additional challenges to be addressed, such as the lack of reference standards for potential contaminants, especially transformation products. Triple quadruple (QqQ) instruments have shown to be highly selective and sensitive when operating in selected ion monitoring (SIM) or selected reaction monitoring (SRM), but QqQ instruments can only measure nominal masses, and when they are operated in the full scan mode, the sensitivity is low, restraining the analysis to a given number of analytes. The use of high resolution mass spectrometry (HRMS) can overcome these challenges by studying parent compounds, metabolites and transformation products with and without reference standards based on accurate mass acquisition and fragmentation patterns. 20 HRMS instruments can reach resolving powers above 100,000, a high mass accuracy (<2ppm) and a sensitivity down in the femtogram to picogram range in full scan mode.20,21 The increased selectivity allows a reliable screening of molecular ions and their MS/MS fragments against a complex matrix background. Additionally, full-scan acquisition allows for a retrospective analysis for emerging contaminants years after the data has been 1.4. Daphnia asaModelOrganism 5

collected. The analysis of micropollutants in complex matrices, e.g. soils, sludge, and sediments can be difficult, since their composition of natural organic matter and biomolecules can interfere in the extraction process and later on, with the separation and detection. Many methods have been developed for the analysis of pharmaceuticals, pesticides, UV-light stabilizers, musk fragrances and surfactants in sediments. 22–25 Although these methods have been shown to be sensitive and selective, the methods aim at the quantification and study of only few numbers of compounds. The need of generic and non-compound class specific techniques for the analysis of a wider range of compounds is becoming essential, especially with the increasing number of chemicals produced annually. The development of techniques to identify compounds in a sample that were not set a priori (suspect screening) and identification and structure elucidation of potential contaminants based on accurate mass measurements and isotopic pattern without any previous information (non-target screening) are still in their infancies. Some methods for the identification of compounds without prior knowledge have been developed to assess the risk of emerging contaminants in surface water and wastewater. 20,26–30 How- ever, suspect and non-target screening has not yet been applied to sediments.

1.4 Daphnia as a Model Organism

Among the most important planktonic grazers in pelagic food webs are species of the genus Daphnia (Crustacea: Anomopoda; water ﬂeas). Daphnia species serve as food for ﬁsh and invertebrates, and feed on algae and bacteria.2 Daphnia normally reproduce clonally (parthenogenetic cycle) but they switch to sexual reproduction (sexual cycle) when environmental conditions are not ideal as in the case of change in food level, crowding and photoperiod.2,31 During sexual reproduction, Daphnia diapausing eggs are enclosed in a structure called an ephippium. Resting eggs can sink to the bottom of the lake and remain there until conditions become favorable or be transported by wind or aquatic birds to other water bodies and thus colonize new habitats. 32 In deeper parts of lakes, these diapausing eggs do not receive any hatching stimulus. They remain in the sediment, hence providing an unbiased archive of past populations. Once a sediment is extracted, diapausing eggs up to 40 years of age can be sampled and hatched for experimental purpose or directly analyzed with molecular genetic methods at least from 100-year-old resting eggs.33 When closely related Daphnia species 6 Chapter 1. Introduction

co-occur, they frequently produce hybrids. These hybrid lineages reproduce clonally again, and can therefore remain in the population. Because of the good knowledge and data available on the distribution of Daphnia in natural populations, the wealth of information on effects from chemicals and the available molecular markers, Daphnia is an ideal model system to understand the role of abiotic factors on parental and hybrids in lakes and in different time periods.

1.5 Bioaccumulation and Effects of Organic Contami- nants

The ability of organic contaminants to sorb to sediments constitutes a primary source of exposure for benthic organisms. Benthic organisms can accumulate xenobiotics from the particulate and interstitial components of sediments, as well as from the water column.7,34 Sediments contaminated with mutagenic substances are known to pose a hazard to indigenous biota including adverse effects such as DNA adducts, DNA strand breaks, chromosomal aberrations and cancer. 35 In addition, chemical pollutants have been associated with reproductive impairment, 36 emergence diseases37 and declines of non-targeted species. 30,38,39 Chemical contaminants can affect natural Daphnia populations either by direct toxin uptake, or by indirect ingestion of contaminated algae. Pollutants may bioaccumulate in resting eggs already in the water column or after sedimentation, inﬂuencing the ﬁtness and sexual reproduction, and therefore the evolutionary potential of an aquatic key species. However, there is almost no information about bioaccumulation in ephippia in literature. Analysis of organic contaminants in ephippia extracted from sediment cores is challenging due to the small number of ephippia available in the sediment extraction and the amount needed for the chemical analysis. Thus, a direct analysis of organic contaminants in ephippia has not yet been carried out. To evaluate the bioaccumulation of HOC from sediment, the equilibrium partitioning model (EqP) is often used. EqP assumes that organic contaminants are distributed between the lipids of the organisms, the pore water, and the organic carbon of the sediment, and that these compartments are in equilibrium.41,42 Therefore, based on this concept, the accumulation of a chemical in aquatic organisms can be estimated. 1.6. Objectives and Contents of the Thesis 7

1.6 Objectives and Contents of the Thesis

The primary goals of this research are (i) to obtain valuable information on the fate and occurrence of organic contaminants in lake sediments in Switzerland and (ii) to provide important information about the uptake and effects of organic contaminants in sediment by biota for future environmental risk assessments. To achieve the proposed goal, the work is divided into four major research tasks:

• Development of an analytical methodology to quantify organic contaminants in lake sediments by LC-ESI-APPI/HR-MS,

• Identiﬁcation of relevant organic contaminants and transformation products in sediments cores from two lakes in Switzerland, Lake Greifensee located in the north and Lake Lugano in the south, and to reconstruct the historical contamination of these lakes,

• Determination of the bioaccumulation of organic contaminants in Daphnia resting eggs as a function of hydrophobicity,

• Effects of organic contaminants towards Daphnia resting eggs and their impact on ﬁtness and hatching ability

The thesis is structured in five main chapters as described below: Chapter 2: Screening of Lake Sediments for Emerging Contaminants by Liquid Chromatography Atmospheric Pressure Photoionization and Electrospray Ion- ization Coupled to High Resolution Mass Spectrometry. In this section, a multiresidue method for the target and suspect screening of more than 180 pharmaceuticals, personal care products, pesticides, biocides, additives, corrosion inhibitors, musk fragrances, UV light stabilizers and industrial chemicals in sediments was developed. In addition, the method was used to provide the history of chemical deposition of different emerging contaminants in Lake Greifensee. Furthermore, the scope of the method was expanded by screening for suspected contaminants with similar characteristics as the detected targets and transformation products. Chapter 3: Suspect and Non-target Screening Approaches for Identification of Or- ganic Contaminants in Sediments. Following Chapter 2, the developed multi-residue method was applied for the screening of suspect and non-target candidates for the identification of compounds that were not originally in our target list. The suspected candidates were selected based on theoretical approaches followed by different filters 8 Chapter 1. Introduction

and confirmatory steps. HRMS in combination with screening approaches enabled the detection of suspected compounds, such as the tentative identification of different biocides and disinfectants in use in Switzerland. This also allowed the identification and confirmation of “unknown compounds”, with the aid of a molecular formula generator, and in silico fragmentation for computer assisted interpretation of mass spectra. Chapter 4: Bioaccumulation of Organic Contaminants in Daphnia Resting Eggs. In this section, organic contaminants detected in the sediments from Lake Greifensee and Lake Lugano, were selected to study the bioaccumulation of organic contaminants in Daphnia resting eggs (ephippia), and to develop models to predict the accumulation of other organic pollutants in ephippia. Predicted environmental internal concentrations in ephippia were obtained based on sediment concentrations using the equilibrium partitioning model and calculated bioconcentration factors. Chapter 5: Environmental organic contaminants influence hatching from Daph- nia resting eggs and hatchling survival Following Chapter 4, exposure experiments with Daphnia resting eggs were performed. Correlation between hatching ability and fitness was tested as a starting point to understand the potential impact of organic contaminants in aquatic organisms relying on resting eggs during their life cycle. Chapter 6: Conclusion and Outlook. The results from the above chapters are summarized, and the usefulness and effectiveness of the developed methodology, as well as the outcomes of the study are discussed. An outlook is given for future research in the development of organic contaminants screening approaches, as well as the need to understand the potential impact of organic contaminants on aquatic organisms relying on resting stages. Bibliography

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[23] Ferrer, I.; Hennion, M.-C.; Barcelo,´ D., Immunosorbents coupled on-line with liquid chromatography/atmospheric pressure chemical ionization/mass spectrometry for the part per trillion level determination of pesticides in sediments and natural waters using low preconcentration volumes. Analytical Chemistry 1997, 69, (22), 4508- 4514.

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Screening of Lake Sediments for Emerging Contaminants by Liquid Chromatography Atmospheric Pressure Photoionization and Electrospray Ionization Coupled to High Resolution Mass Spectrometry

Aurea C. Chiaia-Hernandez, Martin Krauss, and Juliane Hollender Environ. Sci. Technol., 2013, 47(2), pp 976-986

15 16 Chapter 2. Screening of Lake Sediments

Abstract We developed a multiresidue method for the target and suspect screening of more than 180 pharmaceuticals, personal care products, pesticides, biocides, additives, corrosion inhibitors, musk fragrances, UV light stabilizers and industrial chemicals in sediments. Sediment samples were freeze dried, extracted by pressurized liquid extraction and cleaned up by liquid-liquid partitioning. The quantification and identification of target compounds with a broad range of physicochemical properties (log Kow 0-12) was carried out by liquid chromatography followed by electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) coupled to high resolution Orbitrap mass spectrometry (HRMS/MS). The overall method average recoveries and precision are 103% and 9% (RSD), respectively. The method detection limits range from 0.010 to 4 ng/gdw, while limits of quantification range from 0.030 to 14 ng/gdw. The use of APPI as an alternative ionization source helped to distinguish two isomeric musk fragrances by means of different ionization behavior. The method was demonstrated on sediment cores from Lake Greifensee located in northeastern Switzerland. The results show that biocides, musk fragrances and other personal care products were the most frequently detected compounds with concentrations ranging from pg/gdw to ng/gdw, whereas none of the targeted pharmaceuticals were found. The concentrations of many urban contaminants originating from wastewater correlate with the highest phosphorus input into the lake as a proxy for treatment efficiency. HRMS enabled a retrospective analysis of the full-scan data acquisition allowing the detection of suspected compounds like quaternary ammonium surfactants, the biocide triclocarban, and the tentative identification of further compounds without reference standards, among others transformation products of triclosan and triclocarban.

2.1 Introduction

Today, approximately 300 million tons of synthetic compounds are used annually in industrial and consumer products. 1 These compounds can enter natural waters via waste water treatment plant efﬂuents, urban and industrial sewage, erosional runoff, and leaching from agricultural areas. Once in natural waters these compounds may sorb to sediments depending on their physical chemical properties. Sediments are excellent archives of environmental contaminants if the chemicals persist over time since they can act as integrators of many inputs within a catchment. This is particularly true for hydrophobic organic compounds (HOC) which rapidly sorb to sediments and suspended particles.2 Until recently, such records have been mainly used to characterize the contamination by legacy compounds with highly lipophilic characteristics such as 2.1. Introduction 17

polychlorinated biphenyls (PCBs) and polyaromatic hydrocarbons (PAH). 3,4 The long- term contamination of sediments with medium polar contaminants such as pharmaceuticals, personal care products, household chemicals or pesticides is not well explored. One example for the suitability of sediments for the study of less lipophilic compounds is nonylphenol, for which the dynamic of sediment loads could be clearly correlated to the restriction of their use in Switzerland at the end of the 1980s. Lara-Martin et al.5 studied the distribution of surfactants in sediment cores and detected degradation intermediates in deeper sediment layers and partly in pore water. Undisturbed cores are a prerequisite for construction of historic records. River sediments are usually not suitable because of turbulences and stronger influences of sampling locations as have been shown for human and veterinary antibiotics. 6 In the last decade, detection and quantification of polar to medium polar organic contaminants in the environment has typically been achieved by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Until recently, electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) are the most commonly used ionization technologies. Atmospheric pressure photo ionization (APPI) is a newer type of ionization technique for the analysis of poorly ionizable, less polar compounds. The suitability and universality of APPI has been demonstrated by the analysis of drugs, 7,8 PCBs and PAHs, 9,10 perfluorinated compounds, 11,12 steroids, 13,14 and fullerenes. 12 Therefore, the combination of ESI and APPI as complementary ionization techniques for the analysis of environmental samples could significantly expand the detection and quantification of a wide range of compounds in a single study. In addition to the difficulty in the ionization and detection of organic contaminants, there are additional challenges to be addressed such as the lack of reference standards for potential contaminants and especially transformation products. Triple quadruple (QqQ) instruments have shown to be highly selective and sensitive, and with a strong potential for quantitative analysis when operating in selected ion monitoring (SIM) or selected reaction monitoring (SRM), but QqQ instruments can only measure nominal masses, and when they are operated in the full scan mode, the sensitivity is low, restraining the analysis to a given number of analytes. The use of high resolution mass spectrometry (HRMS) can overcome these challenges by studying parent compounds, metabolites and transformation products with and without reference standards based on accurate mass acquisition and fragmentation patterns. 15 The most common HRMS instruments have resolving power of 10-20,000 while the new technologies reach values of 100,000 (Orbitrap) and up to 1,000,000 (FTICR), with high mass accuracy (<2 ppm) and still a sensitivity down in the femtogram to picogram range in full scan mode.15,16 The increased selectivity allows a reliable screening of molecular ions and their MS/MS frag- 18 Chapter 2. Screening of Lake Sediments

ments against a complex matrix background. Additionally, full-scan acquisition allows for a retrospective analysis for emerging contaminants years after the data has been acquired. The objectives of this study were (i) to develop a new generic analytical method for the screening of 180 pharmaceuticals, personal care products, pesticides, biocides, additives, corrosion inhibitors, musk fragrances, and industrial chemicals with a broad range of physicochemical properties in an under-investigated environmental compartment, (ii) to explore how APPI can expand the range of the method to less polar compounds as well as the potential of HRMS to screen retrospectively for suspected compounds with and without reference standards, and (iii) to demonstrate the utility of the method by providing a record of the historical contamination of a lake and discuss it with respect to usage pattern and transformation processes.

2.2 Experimental Section

2.2.1 Standards and Reagents

Details on the sources, preparation, and storage of reference standards and reagents are provided in the Supporting Information (SI).

2.2.2 Sample Collection and Preservation

Two sediment cores were taken from the deepest part of Lake Greifensee located in northeastern Switzerland during the summer of 2010. One sediment core was use for target and suspect screening of organic contaminants and analyzed in great detail while the second core was used for conﬁrmation purpose. Core samples were extracted by free fall gravity corer and stored vertically in the dark at 4 ◦C. Individual cores were dissected into approximately 2 cm slices and transferred to glass jars, closed airtight, and stored in the dark at −20 ◦C. Total phosphorus and nitrogen concentrations for each sediment layer were measured using peroxodisulfate oxidation as described by Ebina et al.17 Dating of Lake Greifensee sediment cores was performed by counting yearly laminations (varves) since the oxic and anoxic color composition of the lake can be easily identiﬁed. The results were compared with sedimentation rates and with another study that characterized lake Greifensee using 137Cs signals. 18 2.2. Experimental Section 19

2.2.3 Extraction of Sediments

Previously frozen, individual sediment layers were freeze-dried, homogenized with a mortar and pestle, weighed, and transferred to stainless steel cells prepared with a 27 mm glass fiber filter and a 16.2 mm cellulose filter (Dionex, Olten, Switzerland). The final amount of sediment delivered to the cell was between 4 to 6 g. In addition, 250 mg of diatomaceous earth was added to each extraction cell to increase solvent channeling (Hydromatrix, Resteck, Bellefonte, PA). The cells were then extracted by pressurized liquid extraction (PLE) using an ASE 350 system (Dionex, Sunnyvale, USA) at 80 ◦C using a mixture of the two polar aprotic solvents ethyl acetate and acetone with intermediate dielectric constants and polarity at a ratio of 70:30 (% v/v). Details on temperature and solvent ratio selection are provided in SI. Extracts of approximately 23 mL were spiked with 60 μL of 2.5 ng/μL internal standard mixture with an absolute amount of 150 ng of each compound. Afterwards, the extracts were gently evaporated to 100 μL with an automated evaporator system at a temperature of 30 ◦C (EZ-2 evaporation system from Genevac, Gardiner, USA). After evaporation, extracts were diluted to 2.5 mL with HPLC water.

2.2.4 Clean-up and Enrichment of Sediment Extracts

The removal of matrix from the sediment extracts was based on a modification of the multiresidue method “QuEChERS” (quick, easy, cheap, effective, rugged and safe) developed by Anastassiades et al. for the determination of polar pesticide residues in fruits and vegetables, 19 and has been previously applied in river sediments. 20 5mLoface- tonitrile was added to the extracts, followed by 1.6 g of MgSO4 to promote partitioning of less-polar analytes into the acetonitrile phase, and 0.4 g NH4Cl to initiate and influence liquid-liquid partitioning and thereby improving the recoveries of polar compounds. The addition of dispersive solid phase extraction material (d-SPE) containing primary secondary amine (PSA) was tested additionally to remove matrix interferences such as humic acids through anion exchange interactions. Since the use of d-SPE decreased recoveries for the more hydrophobic compounds up to 65% similar as described for steroids and drugs in soil,21 it was omitted from the final method. The mixture was vortexed and centrifuged for 10 min at 3500 rpm (1920 × g) (Megafuge 1.0R, Kendro laboratories, Langenselbold, Germany). After separation, the acetonitrile phase was transferred to a graduated centrifuge tube, evaporated to 50 μL and brought to a volume of 500 μL by adding methanol. The final extract was filtered into 2 mL 20 Chapter 2. Screening of Lake Sediments

autosample vials using 0.2 μm PTFE ﬁlters (BGB analytics, Boeckten, Switzerland).

2.2.5 Liquid Chromatography Tandem High Resolution Mass Spec- trometric Detection

The separation of all analytes was performed on a 2.1 × 10 mm C18 security guard cartridge connected to a 2.1 × 50 mm × 3.5 μm particle size X-bridge C18 column (Waters Corp., Milford, MA) at 35 ◦C. Two methods were developed with mobile phases consisting of HPLC water (A), methanol (B) and isopropanol (C). For method 1 (ESI) formic acid (0.1% v/v) was added to eluents A and B. Elution and details on the gradient are provided in SI. Detection of analytes was performed using a linear ion trap- Orbitrap (LTQ-Orbitrap-XL) mass spectrometer (Thermo Fisher Scientific Corp. USA) with two independent interfaces. ESI was used in positive and negative mode (method 1) and APPI was used in positive mode (method 2). APPI was used in the gas phase with the aid of an atmospheric pressure chemical ionization (APCI) probe. The ionization of analytes by APPI was performed using a PhotoMateTM light source (Syagen Technology, Tustin, CA) and a mixture of toluene:anisole at a ratio of 95:5 as dopant, delivered at a flow rate of 20 μL/min post separation with the aid of an external pump and a dynamic-mixer (Omnilab, Mett, Switzerland). Full scan accurate mass spectra range was acquired from 115 to 1000 Da with a nominal resolving power of 60,000 referenced to m/z 400 and a mass accuracy of ± 5ppm. Data-dependent high resolution product ion spectra (HR-MS/MS) were recorded at a resolving power of 7,500. Product ions were generated by higher energy collision dissociation (HCD) at collision energies calculated for each analyte based on its mass. In addition, as supplementary information, collision-induced dissociation (CID) was performed with a normalized collision energy of 35% and measured at unit resolution in the LTQ. Detector specifications and analysis settings are provided in the SI. HR-MS was used for detection and quantification of targets and suspects, whereas HR- MS/MS fragmentation were used as a confirmatory step for proper identification of compounds. Calibration standards (n=9) were made in methanol with concentrations ranging from 0.5 ng to 400 ng of standard mix solution (corresponding to nominal final concentration of 1 μg/L to 800 μg/L in vial). Full scan precursor ions (Table 2.4 in SI) were used for detection and quantification and product ions were used for confirmation of the iden- tity. Quality control accounted for more than 30% of the samples which included check standards and blank samples. Sample duplicates accounted for 20% of the total sam- 2.2. Experimental Section 21

ples and were run randomly in each sequence and during different days. Details on quantiﬁcation and quality control are reported in the SI.

2.2.6 Accuracy and Precision

Relative recoveries were determined by using uncontaminated lake sediment from sediment core layers belonging to the year 1920 or older. The sediment was thoroughly homogenized with a mortar and pestle and aliquots were distributed to extraction cells. Spike and recovery was performed by spiking samples with 25 μL of a standard mix solution with 10 ng/μL of each compound, corresponding to an absolute amount of 250 ng. A group of samples (n=4) were spiked at the top of the extraction cells (before PLE) and another group of samples (n=4) were spiked after extraction. All extracts were spiked after PLE extraction with internal standard mix solutions for quantiﬁcation. Additionally, spike addition was performed in different layers of the sediment core with the goal to explore the variability of the matrix within the core and accounted for 18% of the total samples. Different sediment layers were spiked with an absolute amount of 250 ng of each compound and the recoveries were calculated and compared with spiked blank sediment.

2.2.7 Limits of Detection and Quantiﬁcation

The method limits of detection (LOD) and quantification (LOQ) were calculated using signal to noise ratios (S/N). LOD was defined as the lowest concentration standard having a S/N ratio ≥ 3. LOQ was defined as the lowest point of the calibration curve with a S/N ratio ≥10 and with a satisfactory amount of ions to generate MS/MS fragmentation. The LOD and LOQ were then divided by a calculated matrix factor for each compound and subsequently multiplied by the dilution of the matrix sample (equations provided in SI). The matrix factor and calculated matrix effect (%) of each analyte are provided in Table 2.5.

2.2.8 Suspect Screening of Further Contaminants and Transfor- mation Products

In this study, suspect screening is deﬁned as the search of compounds that were not originally in our target list and was achieved by gathering information of the suspected 22 Chapter 2. Screening of Lake Sediments

compound such as molecular formula and structure. Suspect screening was performed by extracting from the HR-full scan chromatogram the exact mass of the expected ion with a mass window of ± 5 ppm with the assumption or knowledge of ionization behavior. This is possible since most of the ionization techniques produce molecular ions [M+H]+ or [M-H]−. Furthermore, assessing steps which include comparison of measured and predicted retention times (Rt) (Figure 2.8 in SI), isotope patterns, ionization efficiency, and fragmentation pattern were used (Figure 2.1) similar to Kern et al. 22 Product ions of an acquired MS/MS spectrum of a suspected compound peak were compared with the spectrum of a standard or a predicted fragmentation pattern (Mass- Frontier 6.0 software, Thermo Scientific Corp., USA). Unequivocal confirmation is only possible if a reference standard is available.

2.2.9 Proton Afﬁnity Data

Experimental proton afﬁnities (PAs) were obtained from Hunter et al. 23 As for tonalide and galaxolide no experimental values were available, PAs were estimated by computational methods using the Fireﬂy 7.1g software executed from the WebMO 12.1.004 interface.24,25 Initial geometries were fully optimized by density functional theory calculations using the B3LYP functional and the 6-31G(d) basis set. For the [M+H]+ species,

Figure 2.1: Workﬂows of target screening using reference standards and suspect screening without reference standards. 2.3. Results and Discussion 23

the oxygen atom was assumed as site of protonation. Proton afﬁnities at 298 K were

calculated using the following equation, where Ee is the electronic energy and ZPE is the zero-point energy.

+ + PA(M)=−Ee([M + H] )+Ee(M) − ZPE([M + H] )+ZPE(M)+2.5RT (2.1)

2.3 Results and Discussion

2.3.1 Method Performance

Pressurized liquid extraction followed by QuEChERS-like cleanup was developed as a new method to reduce the solvent consumption and to evade the use of cleanup techniques such as gel permeation chromatography (GPC) which uses non-polar solvents, generates large amounts of waste and can increase the loss of polar compounds. 18,26 Method recoveries were calculated by averaging the spike recoveries obtained from blank sediment samples in combination with spike addition recoveries obtained from different layers of the sediments core (Table 2.6 and 2.7 in SI). Figure 2.2 summa- rizes the results of the method performance of different groups of substances with their corresponding average recoveries and minimum and maximum values. The average deviation within spiked layers and spiked blank samples was 11%, indicating that the differences in matrix within a sediment core did not inﬂuence the performance of the extraction method. The overall average method recovery is 103% with the highest and lowest values of 139% and 57%, respectively. The overall method precision indicated by %RSD range from 1-30% with an average of 9%. There was no clear correlation between recoveries and log Kow values. The recoveries and precision of the method are consistent with the analysis of different organic contaminants in sediments using different extraction procedures. Lofﬂer¨ et al.27 reported recoveries between 38% to 152% using ultrasonication for a wide range of pharmaceuticals. Ferrer et al. 28 reported recoveries of different pesticides with a range of 42% to 103% using Soxhlet extraction and immunosorbent cleanup coupled to LC- APCI-MS. Using similar techniques, Reddy et al.29 showed recoveries between 90% to 110% for the analysis of UV-LS and Peck et al. 30 reported recoveries for the analysis of musk fragrances between 63 to 86%. Compared to other published methods, our fast method proved to be suitable for the analysis of a much broader range of compounds with similar or better recoveries. In addition, we have shown that the use of non-polar solvents such as dichloromethane or hexane, commonly used solvents in the extraction 24 Chapter 2. Screening of Lake Sediments

Figure 2.2: Method performance for the different substance classes. Average recoveries in percentage are shown in black squares with vertical lines illustrating the lowest and highest recoveries of compounds in each substance class (PCP=personal care products, Pest=pesticides, Pharma=pharmaceuticals, UV-LS=Ultraviolet-Light Stabiliz- ers, and Other= corrosion inhibitors, industrial chemicals, and additives). Average limits of quantiﬁcation in ng/gdw are illustrated in grey with the corresponding number of substances analyzed (n) and the standard deviation (STD) per group.

of sediments and known to be hazardous to human health and the environment, are not necessary for an efﬁcient extraction of target compounds up to a log Kow of 12, such as some UV-LS.

The method LOD ranged from 0.010 ng/gdw to 4 ng/gdw and LOQ ranged from 0.030 ng/gdw to 14 ng/gdw (Figure 2.2 and Table 2.5 in SI). The UV-LS, analyzed with LC- APPI-HRMS method, showed the highest LOD values with concentration up to 4 ng/gdw, however, the values obtained are still signiﬁcantly lower than in previous studies using GC-MS which reported LOD up to 20 ng/g. 26,29 The LOD and LOQ obtained in our method are similar or better when compared with other studies in the analysis of pesticides, pharmaceuticals and musk fragrances.20,28,31–35 The aid of QuEChERS and two different types of ionization (ESI and DA-APPI) enables a generic analysis covering a broad range of analytes in a single analytical procedure. 2.3. Results and Discussion 25

2.3.2 Optimization and Advantages of APPI

The mobile phase selection for the LC-APPI-HRMS method was performed by using toluene as a dopant to promote ionization. Two aqueous mobile phases were studied and included a combination of HPLC water with FA (0.1% v/v) and HPLC water alone. The organic mobile phase was tested with methanol, acetonitrile and isopropanol. Re- sults showed that the addition of FA to water resulted in lower ionization efficiency with an increase of background noise. The findings are consistent with Kostianinen et al. 36 which found that the addition of acids to buffers weakened the signal response due to the recombination of the negatively and positively charged species, thus resulting in fewer reactant ions available for the ionization of the analytes. Furthermore, methanol showed a higher ionization capacity over acetonitrile and isopropanol. This effect can be partially explained by the higher proton affinity (PA) of acetonitrile (779 kJ/mol) and isopropanol (793 kJ/mol) compared to methanol (754 kJ/mol), and as a consequence acetonitrile and isopropanol may suppress the ionization of low PA compounds.23 After defining the mobile phase, the optimization of the dopant was completed by testing different dopant flow rates and dopant mixtures. Dopant flow rate fractions between 5-10% with respect to the total solvent flow rate were tested. Higher analyte signal intensities were obtained at a dopant fraction of 10%. The results are consistent with studies by Robb et al. 37,38 which reports a plateau in ionization efficiency at about 5- 10% of the solvent flow. Additionally, a combination of toluene and anisole (95:5) exhi- bited an increase of analyte ionization in comparison with toluene or anisole alone. This is reasoned by an increase in ionization efficiency via charge transfer. 39 Dopant-assisted-APPI (DA-APPI) was successfully used to ionize two musk fragrances, eight UV-LS and two sunscreen cosmetics (Table 2.8), compounds reported to be detected mainly by GC/MS. 29,40–45 Although with our method the ionization of the selected UV-LS can be achieved using ESI and APPI, APPI was up to two orders of magnitude more sensitive and selective than ESI as illustrated in Figure 2.3. The ionization of the analytes was possible via charge and proton transfer mechanisms, thus masses of their molecular ions or protonated ions were detected according to their specific ionization behavior. The chromatographic method did not allow for sufficient separation of the isobaric compounds tonalide and galaxolide. Therefore DA-APPI was important in the identification and quantification of these two isomers based on their specific ion reaction pathway. Albeit both compounds share a common ionization pathway via charge transfer (M.+), tonalide could uniquely be detected and quantified via proton transfer [M+H]+ and by its corresponding 13C isotope pattern (Figure 2.4). For galaxolide a fragmentation or oxida- 26 Chapter 2. Screening of Lake Sediments

Figure 2.3: Comparison of DA-APPI (left) vs. ESI (right) for the analysis of three UV- light stabilizer standards. APPI is two orders of magnitude more sensitive compared to ESI.

tion product [M-H]+ ion was detected and quantiﬁed. This differential ionization pattern of tonalide and galaxolide can be explained by the difference in proton afﬁnities. The density functional theory calculated PA values of 917 kJ/mol for tonalide and 845 kJ/mol for galaxolide. Based on calculated PA values of acetophenone (884 kJ/mol; resembling a substructure of tonalide) and tetrahydropyran (834 kJ/mol for the lowest-energy conformer; resembling a substructure of galaxolide), which were within +25 kJ/mol of the experimental values (acetophenone 861 and tetrahydropyran 823 kJ/mol), 23 we as- sume that the real PA values of galaxolide and tonalide might be overestimated by 10-25 kJ/mol, which is also true for other species present (methanol calculated 748, experimental 724 kJ/mol, anisole calculated 868, experimental 840 kJ/mol). Thus the proton-donating species in the APPI source has likely a PA higher than galaxolide, but lower than tonalide. Such species could be protonated anisole or protonated methanol clusters, as those exhibit a much higher PA than the isolated gas phase molecules (e.g., 46 PA for (CH 3OH)2 of 886 kJ/mol). 2.3. Results and Discussion 27

Figure 2.4: Identiﬁcation of tonalide and galaxolide by three different ionization mechanisms and isotope pattern using DA-APPI.

2.3.3 Target Screening of Organic Contaminants in Sediment Cores of Lake Greifensee

The utility of the developed method was demonstrated by screening 180 target compounds with log Kow values ranging from 0 to 12 (Table 2.4) in sediment cores from the deepest part of Lake Greifensee and with no proximity to waste water treatment plants (WWTP). Although, 27% of the target compounds have a log Kow below 2, and are not expected to be in sediments, they were included in the analysis for the purpose of ex- panding the capabilities of the screening method. In total, 13 of 180 target compounds were detected with the most prominent substance classes corresponding to musk fragrances, personal care products (PCP), pesticides, and biocides. Figure 2.5 illustrates the organic contaminants showing temporal trends with concentrations ranging from pg/gdw to ng/gdw (for further compounds see Table 2.9 in SI). The musk fragrance tonalide, and three biocides, triclosan, terbutryn and prometryn, follow a similar pattern with a peak concentration in the late 1970s followed by a rapid decline. The highest concentrations of tonalide, triclosan, terbutryn and prometryn correlate with the highest total phosphorus input into the lake, which indicate input to surface waters via WWTP discharges or agricultural runoff after application. In the catchment area of Lake Greifensee, most treated wastewater as well as untreated wastewater from com- 28 Chapter 2. Screening of Lake Sediments

Figure 2.5: Temporal trends of triclosan (ng/gdw), tonalide (ng/gdw), irgarol (pg/gdw), μ terbutryn + prometryn (pg/gdw), and the total phosphorous concentration ( g/gdw)in sediment of Lake Greifensee. Highest concentration of phosphorus correlates with the highest concentration of the input of contaminants into the lake. *The isomers terbutryn and prometryn occurrence are reported as a sum.

bined sewers is discharged to two major inflows (Monchaltdorfer¨ Aa and Ustermer Aa) which cover input from treated wastewater of around 23,200 and 42,600 residents, and inputs from combined sewer overflows of 25,000 and 58,700 residents respectively. 47 Terbutryn has not been reported to be used as a pesticide in the surrounding areas,48 therefore terbutryn release to the lake most likely occurred via WWTP discharge after usage as biocide and not from agricultural runoff. The pattern of compounds showing temporal trends can be associated with an increased use in households and later the improved elimination in WWTPs. Chemical precipitation and filtration steps were introduced in WWTPs at the beginning of the 1980s and the sludge retention time was increased, leading to better phosphorus, dissolved organic carbon (DOC) and organic pollutants removal. In Lake Greifensee the total phosphorus decreased from 0.50 in 1976 to 0.07 mg/L in 2003 due to the construction of WWTPs, the ban of phosphates in detergents, and ecological measures taken in agriculture. 47 The pattern and concentrations of triclosan are in good agreement with the concentrations reported by Singer et al. 18 The pattern of the algicide irgarol shows the highest concentration in the most recent layers with an increase of two fold in the last 10 years. The increase might be caused by the ban of antifouling paints containing organotin compounds in Switzerland in the 1990s and subsequently their replacement with irgarol and copper-based products. In Switzerland, half of the authorized antifouling paints contain the combination of these two products. 49 Although the concentrations in the water phase were reported 2.3. Results and Discussion 29

to be relatively low in one catchment near Lake Greifensee,48 the sediment as an inte- grator in time and space, show clearly the increase of irgarol. Irgarol is a highly active compound with a lowest observed effect concentration (LOEC) in the range of 8 to 80 ng/L in water for phytoplankton50 and further monitoring is therefore recommended. Sediment layers from the lake, corresponding to the past 10 years, show the presence of PCPs such as octocrylene and the transformation product of galaxolide (galaxolidone) also in older layers (∼40 years). In addition, the biocide propiconazole and the pesticide prochloraz were found at the top layers of the sediment core (∼20 years). In 2004, propiconazole and prochloraz were in the top list of the azole compounds most frequently sold in Switzerland51 which is in good agreement with the results. Despite the low log Kow values of benzotriazole (1.42 ± 0.64) and tolyltriazole, a mixture of 4- and 5-methyl-benzotriazole (1.21 ± 0.24),52 these corrosion inhibitors were found at the top 8 cm of the sediment (∼ 20 years) with maximum concentrations of 1.4 and 0.8 ng/gdw respectively. Because they are widely used as dishwashing additives and they are persistence in the environment, benzotriazole and tolyltriazole have been reported to be present in the surface waters from Lake Greifensee in concentrations of 1.2 μg/L and 230 ng/L respectively.53 Benzotriazole and tolyltriazole are present in the neutral form or up to 50% deprotonated (Lake Greifensee pH=8.1 ± 0.3), making solid phase interactions unlikely. One explanation to the findings is that the corrosion inhibitors are mainly present in the pore water which is highest in the top layers. Pore water concentration can be roughly estimated by standard approaches54 using the organic carbon sorption coefficient values (Koc) for benzotriazole and tolyltriazole (87 and 53 L/kg), 55 the sediment concentration (Cs), and the physical characteristics of the lake (foc=0.4). Assuming a water content of 90% in the original sediment sample, the estimated concentration of 11 to 67 ng/L of the corrosion inhibitors in the pore water sup- ports this assumption. In addition, the formation of benzotriazole complexes, especially with cooper, is well known for its use as a corrosion inhibitor. 56 Therefore the findings could also suggest an interaction between the corrosion inhibitors with the sediment via surface complexation. If benzotriazole surface complexation dominates the overall sorption of benzotriazoles then log Kow values, in this context, are poor predictors of sorption. The results show that sediments can be integrators in time and space for emerging contaminants providing history of chemical deposition. This is a significant advantage compared to water samples, where usually no historical samples are available and emerging contaminants have not been studied in the past. 30 Chapter 2. Screening of Lake Sediments

2.3.4 Suspect Screening of Further Contaminants and Transfor- mation products

HRMS was used to expand the scope of a developed and validated method by use of full scan acquisition for the detection of compounds which were not part of our originally target screening list, but are expected to be present in sediments, based on data for consumption, discharge to the environment, and physical-chemical properties. Industrial products such as quaternary ammonium surfactants (QAs) and UV-LS (not covered by the target list) were selected due to their wide application but their fate not yet extensively studied. The complete list of suspect screening compounds is provided in Table 2.10 in SI. QAs are high production volume chemicals used in detergent formu- lation, fabric softener products, microbiocides and personal care products as well as in industrial processes. 57 Based on suspect screening filtered approaches, the QAs were identified since the permanent positive charge of the nitrogen atom allows the prediction of their ionization behavior (M.+). Several QA congeners were tentatively identified based on their accurate masses, and predicted retention time (Rt) obtained from the correlation between log Kow values and Rt from all reference standards in the method (Figure 2.8). Later on, QAs were confirmed by reference standards and the concentration pattern was determined (Figure 2.9, 2.10, 2.11 in SI). The most abundant QAs in Lake Greifensee were the benzalkonium and diallyldimethylammonium congeners with concentrations up to 130 and 300 ng/gdw respectively, and with again the highest inputs in the 1970s. Suspect screening was also applied to explore the fate of several UV-LS. Eight UV-LS (Table 2.8) were studied as part of our target list due to their wide use as additives in plastics, fibers, paints, cosmetics products, and recently reported to be detected in the Ariake sea in Japan. 26 The UV-LS chosen for the development of the screening method was limited to compounds having reference standards, however they are still a great number of UV-LS in the market lacking reference standard. Based on information on molecular formula and structure, 68 UV-LS (Table 2.11) and sunscreen additives were screened from the full scan chromatograms. Although these compounds are widely used, in our study no UV-LS were found. This could be due to relatively low consumption in Switzerland compared to Japan and the relatively low sensitivity of the method ≤ for this class of compounds (LODs 4ng/gdw) making their detection challenging, especially if they are present in the low pg/gdw. Furthermore, the scope of the developed method was expanded by screening of suspected contaminants with similar characteristics as the detected targets and transfor- 2.3. Results and Discussion 31

mation products. Methyltriclosan, a known transformation product formed via biological methylation and also a known impurity of triclosan, 58 was detected in three sediment core layers. Although the source of methyltriclosan in the sediment cannot be tracked, it was found in layers where the highest concentration of triclosan was reported. Methyltri- closan has been reported previously as a transformation product present in the surface waters of Lake Greifensee in the lower ng/L concentrations.59 Similar to triclosan, triclocarban (TCC) is a widely used biocide but yet not monitored at all in Switzerland. TCC was detected in sediment layers dated back to 1960s and follows the same pattern as triclosan. In the US, TCC has been employed since 1957 as an additive to antimicrobial soap, cosmetics, and other personal care products at levels of 0.5-5% (w/w). 60 Next to TCC, dichlorocarbanilide (DCC), a transformation product formed by dechlorination, was tentatively identiﬁed (Figure 2.6 and 2.12 in SI). DCC has been reported previously to occur in lake sediments at a ratio 1:5 TCC:DCC.58 Based on peak areas TCC:DCC ratios in Lake Greifensee range from 40:1 to 100:1, ratios similar to those found in surface waters. 58,61 This indicates that the presence of DCC in sediment layers mainly results from sedimentation and not from in-situ dechlorination in the sediment. The highly sensitive and selective multiresidue target analysis combined with the suspect screening of possible contaminants using HRMS provide a comprehensive picture of the overall contamination pattern with polar and medium polar emerging contaminants and possibly in the future non-targeted compounds. 32 Chapter 2. Screening of Lake Sediments

Figure 2.6: Temporal resolution and identiﬁcation of triclocarban (TCC) and its transformation product dichlorocarbanilide (DCC) in sediment of Lake Greifensee based on extracted ion chromatograms, predicted retention times, isotopic patterns and for TCC further conﬁrmation with a reference standard. 2.3. Results and Discussion 33

Acknowledgements The authors thank Heinz Singer and Philipp Longree´ for their advice on HRMS/MS, Joris Buiter for his help in the analysis of UV-LS, the AUA laboratory at Eawag for the analysis of total P and N concentrations, Markus Moest, Piet Spaak and Flavio Anselmetti for their help in the collection of sediment cores, and Damian Helbling and Carl Isaacson for comments and suggestions. Funding by the Swiss National Science Foundation (SNF CR32I3 125211) is gratefully acknowledged.

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[61] Sapkota, A.; Heidler, J.; Halden, R.U., Detection of triclocarban and two co- contaminating chlorocarbanilides in US aquatic environments using isotope dilution liquid chromatography tandem mass spectrometry. Environ. Res. 2007, 103, (1), 21- 29.

Supporting Information to Chapter 2

43 44 Supporting Information to Chapter 2

Standard and Reagents

Ethylacetate (99.5% purity), toluene (99.5%), anisole (99.5%) and formic acid (98- 100%) were purchased from Merck (Darmstadt, Germany). Acetone (99.5% purity), ethanol (99.5%), MgSO4 anhydrous (99.5% purity) and NH4Cl (99.5% purity) were purchased by Sigma Aldrich (Steinheim, Germany). HPLC water, acetonitrile (99.9% purity) and isopropanol (99.5% purity) were purchase from Acros Organics (New Jersey, USA). Methanol (99.9% purity) was purchased from Fisher Scientific (Wohlen, Switzer- land). The reference standards (purity ≥ 97%) were purchased from the following providers: Sigma-Aldrich (Steinheim, Germany), Alfa Aesar (Karlsruhe, Germany), TRC Canada (Toronto,Canada), Ciba (Basel, Switzerland), Dr. Ehrenstorfer (Augsburg Germany), LGC Standards (Wesel Germany), Novartis (Basel, Switzerland), ReseaLIFEchem (Burg- dorf Switzerland), Th. Geyer (Berlin, Germany), Altana AG (Wesel, Germany), Roche (Basel,Switzerland), ASCA (Berlin, Germany), CDN Isotopes (Augsburg, Germany), and Lipomed (Arlesheim, Switzerland). Galaxolidone was a gift from Dr. Jean-Daniel Berset (Amt für Gewasserschutz¨ und Abfallwirtschaft des Kantons Bern, Switzerland). N4-Acetyl-Sulfadimethoxine and N,N’-dimethyl-N-(4-methylphenyl)-sulfamide was synthesized at Eawag (Dübendorf, Switzerland). Detailed information is provided in Table 2.1 and Table 2.2. Table 2.1: Standards for the analysis of organic contaminants in lake sediments.

Name CAS-No Group Company Benzothiazol 95-16-9 Aditive Sigma-Aldrich Oxazepam 604-75-1 Anesthetic Sigma-Aldrich Triclosan 3380-34-5 Biocide Ciba Propiconazol 60207-90-1 Biocide Dr. Ehrenstorfer Te r bu t r y n 886-50-0 Biocide Dr. Ehrenstorfer Irgarol 28159-98-0 Biocide Sigma-Aldrich Diuron 330-54-1 Biocide Sigma-Aldrich Triclocarban 101-20-2 Biocide Sigma-Aldrich 2-n-Octyl-4-isothiazolin-3-on (OI) 26530-20-1 Biocide Sigma-Aldrich 4,5-Dichloro-2-n-octyl-isothiazol-3(2H)-one (DCOIT) 64359-81-5 Biocide Sigma-Aldrich Irgarol-descyclopropyl 30125-65-6 Biocide ASCA Benzotriazol 95-14-7 Corosion Inhibitor Sigma-Aldrich Methyl-Benzotriazol 136-85-6 Corosion Inhibitor Sigma-Aldrich 2-Naphthalenesulfonic acid 120-18-3 Industrial chemical Sigma-Aldrich Galaxolide 1222-05-5 Musk Fragance Dr. Ehrenstorfer Galaxolidone Musk Fragance LGC standards Tonalide 21145-77-7 Musk Fragance LGC standards Climbazol 38083-17-9 Personal Care Product Dr. Ehrenstorfer Benzophenon 3 (=2-Hydroxy-4-methoxybenzophenon) 131-57-7 Personal Care Product Th. Geyer Octocrylene (=2-Ethyl-2-cyano-3,3-diphenylacrylate 6197-30-4 Personal Care Product Sigma-Aldrich Galaxolidon Personal Care Product Provide Atrazine 1912-24-9 Pesticide Ciba 3,5-dibromo-4-hydroxybenzoic acid 3337-62-0 Pesticide Dr. Ehrenstorfer 3-Phenoxybenzoic acid 3739-38-6 Pesticide Dr. Ehrenstorfer Acetochlor 34256-82-1 Pesticide Dr. Ehrenstorfer Aldicarb Na 116-06-3 Pesticide Dr. Ehrenstorfer Azoxystrobin 131860-33-8 Pesticide Dr. Ehrenstorfer Bentazon 25057-89-0 Pesticide Dr. Ehrenstorfer Supporting Information to Chapter 2 45

Name CAS-No Group Company Bifenox acid 53774-07-5 Pesticide Dr. Ehrenstorfer Clomazone 81777-89-1 Pesticide Dr. Ehrenstorfer Cyproconazole 94361-06-5 Pesticide Dr. Ehrenstorfer Cyprodinil 121552-61-2 Pesticide Dr. Ehrenstorfer Desmedipham 13684-56-5 Pesticide Dr. Ehrenstorfer Dicamba 1918-00-9 Pesticide Dr. Ehrenstorfer Diﬂufenican 83164-33-4 Pesticide Dr. Ehrenstorfer Dimethylaminosulfanilide 4710-17-2 Pesticide Dr. Ehrenstorfer Dinoseb 201-861-7 Pesticide Dr. Ehrenstorfer Epoxyconazole 133855-98-8 Pesticide Dr. Ehrenstorfer Ethofumesate 26225-79-6 Pesticide Dr. Ehrenstorfer Fenpropimorph 67306-03-0 Pesticide Dr. Ehrenstorfer Fipronil 120068-37-3 Pesticide Dr. Ehrenstorfer Fipronil-sulﬁde 120067-83-6 Pesticide Dr. Ehrenstorfer Fipronil-sulfone 120068-36-2 Pesticide Dr. Ehrenstorfer Fluazifop (acid free) 69335-91-7 Pesticide Dr. Ehrenstorfer Fludioxonil 131341-86-1 Pesticide Dr. Ehrenstorfer Fluroxypyr (acid free) 69377-81-7 Pesticide Dr. Ehrenstorfer Flusilazole 85509-19-9 Pesticide Dr. Ehrenstorfer Ioxynil 1689-83-4 Pesticide Dr. Ehrenstorfer Kresoxim-methyl 143390-89-0 Pesticide Dr. Ehrenstorfer Linuron 330-55-2 Pesticide Dr. Ehrenstorfer MCPA 94-74-6 Pesticide Dr. Ehrenstorfer Mesotrion 104206-82-8 Pesticide Dr. Ehrenstorfer Metamitron 41394-05-2 Pesticide Dr. Ehrenstorfer Metribuzin 21087-64-9 Pesticide Dr. Ehrenstorfer N,N-diethyl-3-methylbenzamide (DEET) 134-62-3 Pesticide Dr. Ehrenstorfer Napropamid 15299-99-7 Pesticide Dr. Ehrenstorfer Orbencarb 34622-58-7 Pesticide Dr. Ehrenstorfer Pethoxamid 106700-29-2 Pesticide Dr. Ehrenstorfer Phenmedipham 13684-63-4 Pesticide Dr. Ehrenstorfer Primicarb 23103-98-2 Pesticide Dr. Ehrenstorfer Prochloraz 67747-09-5 Pesticide Dr. Ehrenstorfer Prometryn 7287-19-6 Pesticide Dr. Ehrenstorfer Propaquizafop 111479-05-1 Pesticide Dr. Ehrenstorfer Propazine-2-hydroxy (=Prometon-hydroxy) 7371-53-0 Pesticide Dr. Ehrenstorfer Pyrimethanil 53112-28-0 Pesticide Dr. Ehrenstorfer Simeton 673-04-1 Pesticide Dr. Ehrenstorfer Spiroxamin 118134-30-8 Pesticide Dr. Ehrenstorfer Sulcotrione 99105-77-8 Pesticide Dr. Ehrenstorfer Tebuconazole 107534-96-3 Pesticide Dr. Ehrenstorfer Te bu t a m 35256-85-0 Pesticide Dr. Ehrenstorfer Terbumeton 33693-04-8 Pesticide Dr. Ehrenstorfer Terbutylazin-2-hydroxy Pesticide Dr. Ehrenstorfer Terbutylazine 5915-41-3 Pesticide Dr. Ehrenstorfer Terbutylazine-desethyl Pesticide Dr. Ehrenstorfer Trinexapac-ethyl 95266-40-3 Pesticide Dr. Ehrenstorfer Acetochlor-ESA 187022-11-3 Pesticide Sigma-Aldrich Chloridazon 1698-60-8 Pesticide Sigma-Aldrich Isoproturon 34123-59-6 Pesticide Sigma-Aldrich Mecoprop 7085-19-0 Pesticide Sigma-Aldrich Dimethenamid-OXA 380412-59-9 Pesticide Novartis Metolachlor-ESA 171118-09-5 Pesticide Novartis Metolachlor-OXA 152019-73-3 Pesticide Novartis Alachlor-ESA 142363-53-9 Pesticide ReseaLIFEchem Alachlor-OXA 171262-17-2 Pesticide ReseaLIFEchem Acetochlor-OXA 194992-44-4 Pesticide Sigma-Aldrich Alachlor 15972-60-8 Pesticide Sigma-Aldrich Bromazil 314-40-9 Pesticide Sigma-Aldrich Bromoxynil 1689-84-5 Pesticide Sigma-Aldrich Carbetamide 16118-49-3 Pesticide Sigma-Aldrich Desethylatrazin 6190-65-4 Pesticide Sigma-Aldrich Desisopropylatrazine 1007-28-9 Pesticide Sigma-Aldrich Dichlorprop 204-390-5 Pesticide Sigma-Aldrich Dimethachlor 50563-36-5 Pesticide Sigma-Aldrich Flufenacet 142459-58-3 Pesticide Sigma-Aldrich Imidacloprid 138261-41-3 Pesticide Sigma-Aldrich Metalaxyl 57837-19-1 Pesticide Sigma-Aldrich Metazachlor 67129-08-2 Pesticide Sigma-Aldrich Metolachlor 51218-45-2 Pesticide Sigma-Aldrich Monuron 150-68-5 Pesticide Sigma-Aldrich Prometon 1610-18-0 Pesticide Sigma-Aldrich 46 Supporting Information to Chapter 2

Name CAS-No Group Company Propachlor 1918-16-7 Pesticide Sigma-Aldrich Prosulfocarb 52888-80-9 Pesticide Sigma-Aldrich Simazin 122-34-9 Pesticide Sigma-Aldrich 2,4-D 94-75-7 Pesticide Sigma-Aldrich Dimethenamid 87674-68-8 Pesticide Sigma-Aldrich Hexazinon 51235-04-2 Pesticide Sigma-Aldrich Pyraclostrobin 175013-18-0 Pesticide Sigma-Aldrich Chlortoluron 15545-48-9 Pesticide Dr. Ehrenstorfer Diuron-desdimethyl = 1-(3,4-Dichlorophenyl)urea 08.02.2327 Pesticide Dr. Ehrenstorfer Isoproturon-didemethyl = 1-(4-Isoprophenyl)urea 56046-17-4 Pesticide Dr. Ehrenstorfer Isoproturon-monodemethyl = 1-(4-Isoprophenyl)-3-methylurea 34123-57-4 Pesticide Dr. Ehrenstorfer Metolachlor-Morpholinon 120375-14-6 Pesticide Dr. Ehrenstorfer Pinoxaden 243973-20-8 Pesticide Dr. Ehrenstorfer Diuron-desmonomethyl (DCPMU) = 1-(3,4-Dichlorophenyl)-3- 3567-62-2 Pesticide Sigma-Aldrich methylurea 2,4-dimethylphenylformamide 60397-77-5 Pesticide Sigma-Aldrich Metamitron-Desamino Pesticide Dr. Ehrenstorfer Metribuzin-Desamino (DA) 35045-02-4 Pesticide Dr. Ehrenstorfer N,N-dimethyl-N’-(4-methylphenyl)-sulfamide Pesticide Synthesized Clofibrin acid 882-09-7 Pharmaceutical Sigma-Aldrich Iminostilbene 256-96-2 Pharmaceutical Alfa Aesar Fluconazole 86386-73-4 Pharmaceutical Dr. Ehrenstorfer Tramadol 27203-92-5 Pharmaceutical Dr. Ehrenstorfer Methylprednisolone 83-43-2 Pharmaceutical Sigma-Aldrich Sulfadimethoxine 122-11-2 Pharmaceutical Sigma-Aldrich Diazepam 439-14-5 Pharmaceutical Roche Bezafibrat 41859-67-0 Pharmaceutical Sigma-Aldrich Carbamazepin 298-46-4 Pharmaceutical Sigma-Aldrich Dexamethasone 50-02-2 Pharmaceutical Sigma-Aldrich Diclofenac 15307-86-5 Pharmaceutical Sigma-Aldrich Fenofibrate 49562-28-9 Pharmaceutical Sigma-Aldrich Fluoxetine 54910-89-3 Pharmaceutical Sigma-Aldrich Ifosfamid 3778-73-2 Pharmaceutical Sigma-Aldrich Indomethacin 53-86-1 Pharmaceutical Sigma-Aldrich Ketoprofen 22071-15-4 Pharmaceutical Sigma-Aldrich Mefenamic acid 61-68-7 Pharmaceutical Sigma-Aldrich Prednisolon 50-24-8 Pharmaceutical Sigma-Aldrich Primidon 125-33-7 Pharmaceutical Sigma-Aldrich Propanolol 04.10.4199 Pharmaceutical Sigma-Aldrich Reserpine 50-55-5 Pharmaceutical Sigma-Aldrich Sulfamethoxazole 723-46-6 Pharmaceutical Sigma-Aldrich Verapamil 152-11-4 Pharmaceutical Sigma-Aldrich Carbamazepine-10,11-epoxid 36507-30-9 Pharmaceutical TRC Canada Clarithromycin 81103-11-9 Pharmaceutical TRC Canada Clotrimazole 23593-75-1 Pharmaceutical TRC Canada Exemestane 107868-30-4 Pharmaceutical TRC Canada Fenofibric-Acid 42017-89-0 Pharmaceutical TRC Canada Lamotrigin 84057-84-1 Pharmaceutical TRC Canada Metoprolol 37350-58-6 Pharmaceutical TRC Canada Ritonavir 155213-67-5 Pharmaceutical TRC Canada Valsartan 137862-53-4 Pharmaceutical TRC Canada Pantoprazol 102625-70-7 Pharmaceutical Altana AG Phenazon (Antipyrine, ID 2519) 60-80-0 Pharmaceutical Dr. Ehrenstorfer Oseltamivir 196618-13-0 Pharmaceutical Roche Cyclophosphamide 50-18-0 Pharmaceutical Sigma-Aldrich Losartan 114798-26-4 Pharmaceutical Sigma-Aldrich Carbamazepin-10,11-dihydro-10,11-dihydroxy 58955-93-4 Pharmaceutical TRC Canada Cetirizine 83881-52-1 Pharmaceutical TRC Canada Clindamycin 18323-44-9 Pharmaceutical TRC Canada N,N-Didesvenlafaxin 93413-77-5 Pharmaceutical TRC Canada N-Desvenlafaxine 149289-30-5 Pharmaceutical TRC Canada O,N-Didesvenlafaxin 135308-74-6 Pharmaceutical TRC Canada O-Desvenlafaxine 93413-62-8 Pharmaceutical TRC Canada Venlafaxine 93413-69-5 Pharmaceutical TRC Canada Irbesartan 138402-11-6 Pharmaceutical TRC Canada N4-Acetyl-Sulfamethoxazole 21312-10-7 Pharmaceutical TRC Canada AMDOPH 519-65-3 Pharmaceutical TRC Canada N4-Acetyl-Sulfadimethoxine 24341-30-8 Pharmaceutical Synthesized Benzyldimethyldodecylammonium 139-07-1 Quaternary ammonium Sigma-Aldrich Didecyldimethylammonium 2390-68-3 Quaternary ammonium Sigma-Aldrich Dodecyltrimethylammonium 1119-94-4 Quaternary ammonium Sigma-Aldrich Benzyldimethylhexadecylammonium 122-18-9 Quaternary ammonium Sigma-Aldrich Supporting Information to Chapter 2 47

Name CAS-No Group Company Benzyl-dimethyl-tetradecylammonium 139-08-2 Quaternary ammonium Sigma-Aldrich Dihexadecyldimethylammonium 70755-47-4 Quaternary ammonium Sigma-Aldrich Dimethyditetradecylammonium 68105-02-2 Quaternary ammonium Sigma-Aldrich Dimethyldioctadecylammonium 3700-67-2 Quaternary ammonium Sigma-Aldrich Diodecyldimethylammonium 3282-73-3 Quaternary ammonium Sigma-Aldrich Hexadecyltrimethylammonium 57-09-0 Quaternary ammonium Sigma-Aldrich Myristyltrimethylammonium 1119-97-7 Quaternary ammonium Sigma-Aldrich Trimethyloctadecylammonium 1120-02-1 Quaternary ammonium Sigma-Aldrich 2-Ethylhexyl-4-methoxycinnamate 5466-77-3 Sunscreen cosmetic comp. Sigma-Aldrich 3-(4-Methylbenzylidene)-camphor 36861-47-9 Sunscreen cosmetic comp. Sigma-Aldrich 2-(2H-Benzotriazol-2-yl)-4,6-bis(1-methyl-1-phenylethyl)phenol 70321-86-7 UV-Light Stabilzer Sigma-Aldrich 2-(2H-Benzotriazol-2-yl)-4,6-di-tert-pentylphenol 25973-55-1 UV-Light Stabilzer Sigma-Aldrich 2-(2H-Benzotriazol-2-yl)-6-dodecyl-4-methylphenol 125304-04-3 UV-Light Stabilzer Sigma-Aldrich 2-(2-Hydroxy-5-methylphenyl)benzotriazole 2440-22-4 UV-Light Stabilzer Sigma-Aldrich 2-(4,6 Diphenyl-1,3,5-triazin-2-yl)-5-[(hexyl)oxy]-phenol 147315-50-2 UV-Light Stabilzer Sigma-Aldrich 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl)phenol 3864-99-1 UV-Light Stabilzer Sigma-Aldrich 2-tert-Butyl-6-(5-chloro-2H-benzotriazol-2-yl)-4-methylphenol 05.11.3896 UV-Light Stabilzer Sigma-Aldrich Bis(1-octyloxy-2,2,6,6-tetramethyl-4-piperidyl) sebacate 129757-67-1 UV-Light Stabilzer Sigma-Aldrich

Table 2.2: Internal standards for the quantiﬁcation of organic contaminants in lake sediments.

Internal Standard Company Internal Standard Company 2,4-D 13C6 Dr. Ehrenstorfer MCPA D6 Dr. Ehrenstorfer Acetylsulfamethoxazol-D4 TRC Canada Mecoprop D6 Dr. Ehrenstorfer Alachlor-D3 Dr. Ehrenstorfer Menfenamic acid-D3 TRC Canada Aldicarb-D3 Dr. Ehrenstorfer Mesotrion D3 Sigma-Aldrich (Syn) Atrazine-D5 Dr. Ehrenstorfer Methyl-benzotriazol-D6 TRC Canada Bentazon D6 Dr. Ehrenstorfer Metolachlor-D6 Dr. Ehrenstorfer Benzafibrat D4 Dr. Ehrenstorfer Metoprolol-D7 TRC Canada Benzotriazol-D4 TRC Canada N-Desvenlafaxin-D3 TRC Canada Charithromycin-D6 Dr. Ehrenstorfer O,N-Didesvenlafaxine-D3 TRC Canada Chlortoluron-D6 Dr. Ehrenstorfer OIT-D17 Sigma-Aldrich (Syn) Clarithromycin-D3 TRC Canada Phenazon-D3 TRC Canada Clofibrin acid D4 TRC Canada Primidon-D5 TRC Canada Cyclophosphamide-D4 TRC Canada Propanolol-D7 TRC Canada DEET-D7 Dr. Ehrenstorfer Propazin-D6 Dr. Ehrenstorfer Desethylatrazine-15N3 Sigma-Aldrich (Syn) Propiconazol-D5 Dr. Ehrenstorfer Desisopropylatrazine-D5 TRC Canada Ritonavir-D6 TRC Canada Diazepam-D5 Lipomed Simazin-D5 Dr. Ehrenstorfer Diazinon-D10 Dr. Ehrenstorfer Sufadimethoxine-D4 Dr. Ehrenstorfer Dichlorprop D6 Dr. Ehrenstorfer Sulcotrine-D3 Sigma-Aldrich (Syn) Diclofenac D4 TRC Canada Sulfamethoxazole-D4 Dr. Ehrenstorfer Diflufenican-D3 Dr. Ehrenstorfer Tebutam-D4 Sigma-Aldrich (Syn) Diuron-D6 Dr. Ehrenstorfer Terbutryn-D5 Dr. Ehrenstorfer Fenofibrate D6 TRC Canada Terbutylazin-D5 Dr. Ehrenstorfer Fluconazole-D4 Dr. Ehrenstorfer Tonalide D3 Dr. Ehrenstorfer Indomethacin-D4 TRC Canada Tramadol-D6 TRC Canada Irbesartan-D4 TRC Canada Triclosan 13C6 Dr. Ehrenstorfer Irgarol-D9 CDN Isotopes Venlafaxine-D6 TRC Canada Isoproturon-D6 Dr. Ehrenstorfer Verapamil-D6 TRC Canada 48 Supporting Information to Chapter 2

Extraction of Sediments

For PLE a mixture of the two polar aprotic solvents ethyl acetate and acetone with intermediate dielectric constants and polarity was tested in ratios of 50:50 and 70:30 (% v/v). A higher fraction of ethyl acetate resulted in recoveries between 30 to 70 % higher for most analytes (data not shown) and thus selected for the ﬁnal solvent-ratio. Tem- peratures ranging from 60 ◦C to 200 ◦C have been successfully reported in PLE for the extraction of a wide range of organic contaminants. 1–6 Based on this knowledge, tem- peratures of 80 ◦C and 100 ◦C were tested. An extraction temperature of 80 ◦C resulted up to 25 % higher area ratios for selected musk fragrances and UV stabilizers (UV-LS) (Figure 2.7) and was chosen for the method.

Liquid Chromatography Tandem High Resolution Mass Spectrometric Detection

4 min of each run, the eluent was diverted to waste to protect the ion source. Three injections were required. Two for positive and negative ionization of ESI and one for positive ionization of APPI. Due to the stability of the calibration of the instrument (LTQ- Orbitrap) switching between positive and negative mode is not possible. New HR-MS in the market allow the simultaneous acquisition of positive and negative mode, however, when scanning hundreds of compounds in full scan acquisition, switching modes is also not recommended due to the scanning time required for acquiring two modes. The electrospray, source fragmentation and capillary voltage in positive ion mode were set at 5 kV, 15 V and 25 V respectively, and the capillary temperature to 300 ◦C .Inthe negative ion mode the electrospray, source fragmentation and capillary voltage were set at -4 kV, -15 V and -20 V with a capillary temperature of 350 ◦C. Tube lens was set to 60 V for positive and -70 V in negative analysis. The sheath and auxiliary gas ﬂow were set at 50 and 20 arbitrary units respectively. APPI Krypton lamp was used in the gas phase with the aid of an atmospheric pressure chemical ionization (APCI) probe. The capillary voltage and temperature were set at 50 V and at 175 ◦C respectively. The vaporizer temperature was set at 250 ◦C and the tube lens voltage was set to 100 V. The sheath and auxiliary gas ﬂow rate was set at 25 and 8 arbitrary units respectively.

Table 2.3: Gradient program used for the separation of all analytes by ESI (left) and APPI (right).

No. Time A% B% C% μL/min No. Time A% B% C% μL/min 0 0 95 5 0 200 0 0 45 50 5 200 1 0.02 95 5 0 320 1 0.02 45 50 5 200 2 0.5 95 5 0 320 2 4 45 50 5 200 3 1 90 10 0 200 3 4.01 42.5 47.5 10 200 4 4 50 50 0 200 4 7 42.5 47.5 10 200 5 18 0 100 0 200 5 7.01 0 90 10 200 6 22 0 100 0 200 6 16 0 90 10 200 7 22.01 0 0 100 200 7 16.01 0 0 100 200 8 30 0 0 100 200 8 23 0 0 100 200 9 30.01 95 5 0 200 9 23.01 45 50 5 200 10 38 95 5 0 200 10 32 45 50 5 200

Quantiﬁcation and Quality Control

Calibration standards (n=9) were made in methanol with concentrations ranging from 0.5 ng to 400 ng of standard mix solution (corresponding to nominal ﬁnal concentrations of 1 μg/L to 800 μg/L in vial). Analytes were quantiﬁed from calibration standards based on the normalization of analyte responses to internal standards by linear least-squares 50 Supporting Information to Chapter 2

regression. Positive analyte identification required that its retention time differed not more than ± 0.5 min from the standards. Parent ions were used for quantification and products ions were used for confirmation. Quantified m/z used for the detection and quantification of analytes are shown in Table 2.4. Internal standards were selected by the best fit to retention time, structure similarity as well as considering linearity (>0.97) and recoveries. Calibration curves were run at the beginning and at the end of each run. Instrument blanks were run before and after each batch of ten samples as well as two check standards of 125 ng and 225 ng (nominal final concentration of 250 μg/L and 450 μg/L in vial). Method blanks were run randomly with each sequence. Qual- ity control accounted for more than 30% of the sample number and sample duplicates accounted for 20% and were run randomly with each sequences and at different days. Deviations of the quality control standards by more than 30% were rejected and samples were re-analyzed. In addition, two sediment cores were taken at different times of the year from the same location and analyzed independently. Data analysis was done by Xcalibur software (Thermo Scientific Corp., USA). Quan Browser was used for the quantification of analytes with reference standards and Qual Browser was used for the identification of substances without reference standards, as in the case of suspect analysis. Fragmentation of products ions was predicted using MassFrontier 6.0 software (Thermo Scientific Corp., USA).

Limits of Detection and Quantiﬁcation

The method limits of detection (LOD) and quantification (LOQ) were calculated using signal to noise ratios (S/N). LOD was defined as the lowest concentration standard having a S/N ratio ≥ 3. LOQ was defined as the lowest point of the calibration curve with a S/N ratio ≥10 and with a satisfactory amount of ions to generate MS/MS fragmentation. The LOD and LOQ were then divided by a calculated matrix factor for each compound and subsequently multiplied by the dilution of the matrix sample (Formula 2.2-2.4). The matrix factor and calculated matrix effect (%) of each analyte are provided in Table 2.5.

(Area − Area ) MatrixFactor = spikes sample unspiked sampls (2.2) Areastamdard(as spiked concentration) LOD LOD matrix methanol × Diluttion factor = MatrixFactor (2.3)

LOD∗ LOQ matrix × Diluttion factor = MatrixFactor (2.4) Supporting Information to Chapter 2 51

*S/N≥ 10 and with a MS/MS fragmentation

Table 2.4: Analyte details for the detection and quantiﬁcation of analytes using LC- HRMS. Log Kow were calculated using VCCLAB (http://www.vcclab.org/online.html)

Name Formula Int. Standard RT Log Linearity Molecular Quantified Ionization (min) Kow* Weight m/z 2-(2H-Benzotriazol-2-yl)-4,6-bis(1- C30H29N3O Fenofibrate D6 12.04 7.67 0.99 447.2305 448.2383 APPI + methyl-1-phenylethyl)phenol 2-(2H-Benzotriazol-2-yl)-4,6-di-tert- C22H29N3O Tonalide D3 12.68 7.25 0.99 351.2305 352.2383 APPI + pentylphenol 2-(2H-Benzotriazol-2-yl)-6-dodecyl- C25H35N3O Fenofibrate D6 15.15 8.94 0.99 393.2775 394.2853 APPI + 4-methylphenol 2-(2-Hydroxy-5- C13H11N3O Tonalide D3 9.87 2.99 0.99 225.0897 226.0975 APPI + methylphenyl)benzotriazole 2-(4,6 Diphenyl-1,3,5-triazin-2-yl)- C27H27N3O2 Fenofibrate D6 14.98 6.24 0.99 425.2098 426.2176 APPI + 5-[(hexyl)oxy]-phenol 2,4-D C8H6Cl2O3 2,4-D 13C6 9 2,81 0.99 219.9689 218.9621 ESI - 2,4-dimethylphenylformamide C9H11NO Simazin-D5 6 1.5 0.99 149.0835 150.0913 ESI + 2,4-Di-tert-butyl-6-(5-chloro-2H- C20H24CIN3O Fenofibrate D6 12.92 6.91 0.97 357.1602 358.1681 APPI + benzotriazol-2-yl)phenol 2-Ethylhexyl-4-methoxycinnamate C18H26O3 Tonalide D3 10.51 5.8 0.99 290.1876 291.1955 APPI + 2-Naphthalinsulfon acid C10H8O3S 2-4-D 13C6 5.5 0.6 0.99 208.0189 207.0121 ESI - 2-n-Octyl-4-isothiazolin-3-one (OI) C11H19NOS OIT-D17 11.6 2.5 0.99 213.1182 214.126 ESI + 2-tert-Butyl-6-(5-chloro-2H- C17H18ClN3O Fenofibrate D6 12.36 5.55 0.99 315.1133 316.1211 APPI + benzotriazol-2-yl)-4-methylphenol 3-(4-Methylbenzylidene)-camphor C18H22O Tonalide D3 9.95 5.92 0.99 254.1665 255.1743 APPI + 3,5-dibromo-4-hydroxybenzoic acid C7H4Br2O3 2,4-D 13C6 7.3 3.2 0.99 293.8533 292.8454 ESI - 3-Phenoxybenzoic acid C13H10O3 Dichlorprop D6 10.8 3.9 0.99 214.0624 213.0557 ESI - 4,5-Dichloro-2-n-octyl-isothiazol- C11H17Cl2NOS Diazinon-D10 15.4 3.6 0.99 281.0402 282.0481 ESI + 3(2H)-one (DCOIT) Acetochlor C14H20ClNO2 Alachlor-D3 11.5 3 0.98 269.1177 270.1255 ESI + Acetochlor-ESA C14H21NO5S 2,4-D 13C6 7.6 1.8 0.99 315.1135 314.1068 ESI - Acetochlor-OXA C14H19NO4 2,4-D 13C6 7.5 1.6 0.99 265.132 264.1241 ESI - Alachlor C14H20ClNO2 Alachlor-D3 11.5 3.5 0.98 269.1177 270.1255 ESI + Alachlor-ESA C14H21NO5S 2,4-D 13C6 7.6 1.8 0.99 315.1135 314.1068 ESI - Alachlor-OXA C14H19NO4 2,4-D 13C6 7.5 1.6 0.99 265.132 264.1241 ESI - Aldicarb Na C7H14O2N2S1Na Aldicarb-D3 6.3 1.13 0.99 190.077 213.0668 ESI + AMDOPH C13H17N3O3 Metropolol-D7 5.4 0 0.99 263.127 264.1343 ESI + Atrazine C8H14Cl1N5 Atrazine-D5 8.5 2.6 0.99 215.0932 216.1011 ESI + Azoxystrobin C22H17N3O5 Diuron-D6 9.9 2.5 0.98 403.1163 404.1241 ESI + Bentazon C10H12N2O3S Bentazon D6 7.5 2.3 0.99 240.0563 239.0496 ESI - Benzophenon 3 (=2-Hydroxy-4- C14H12O3 Diazinon-D10 12.1 3.8 0.98 228.0781 229.0859 ESI + methoxybenzophenon) Benzothiazol C7H5NS Methyl-benzotriazol-D6 6.7 2 0.98 135.0137 136.0215 ESI + Benzotriazol C6H5N3 Benzotriazol-D4 4.8 1.4 0.99 119.0478 120.0556 ESI + Bezafibrat C19H20ClNO4 Benzafibrate-D4 10.6 4.3 0.98 361.1075 362.1154 ESI + Bifenox acid C13H7Cl2NO5 Mecoprop D6 11.5 3.9 0.98 326.9707 325.9629 ESI - Bis(1-octyloxy-2,2,6,6-tetramethyl- C44H84N2O6 Fenofibrate D6 18.9 12.4 0.98 736.6324 737.6402 APPI + 4-piperidyl) sebacate Bromazil C9H13BrN2O2 Chlortoluron-D6 7.3 2.1 0.99 260.0155 261.0233 ESI + Bromoxynil C7H3Br2N1O1 Bentazon D6 8.2 3.4 0.99 276.8561 275.8654 ESI - Carbamazepin C15H12N2O Verapamil-D6 7.8 2.5 0.99 236.0944 237.1022 ESI + Carbamazepin-10,11-dihydro- C15H14N2O3 Sulfamethoxazole-D4 6.1 0.9 0.99 270.1004 271.1077 ESI + 10,11-dihydroxy Carbamazepine-10,11-epoxid C15H12N2O2 Sulfamethoxazole-D4 6.5 1 0.99 252.0899 253.0972 ESI + Carbetamide C12H16N2O3 Mesotrion D3 6.6 1.6 0.99 236.1155 235.1088 ESI - Cetirizine C21H25ClN2O3 Clarithromycin-D3 9.3 2.28 0.98 388.1554 389.1627 ESI + Chloridazon C10H8Cl1N3O1 Metropolol-D7 5.5 1.1 0.98 221.035 222.0429 ESI + Chlortoluron C10H13ClN2O Chlortoluron-D6 8.4 2.4 0.99 212.0711 213.0789 ESI + Clarithromycin C38H69NO13 Clarithromycin-D3 9.7 3.2 0.98 747.4763 748.4842 ESI + Climbazol C15H17ClN2O2 DEET-D7 7.3 3.3 0.99 292.0979 293.1051 ESI + Clindamycin C18H33ClN2O5S1 Verapamil-D6 6.9 2.2 0.99 424.1799 425.1871 ESI + Clofibrin acid C10H11ClO3 Clofibrin acid D4 10.6 2.6 0.98 214.0391 213.0324 ESI - Clomazone C12H14ClNO2 Isoproturon-D6 9.5 2.5 0.99 239.0708 240.0786 ESI + Clotrimazole C22H17ClN2 Diuron-D6 8.7 6.3 0.98 344.1075 345.1153 ESI + Cyclophosphamide C7H15Cl2N2O2P Cyclphosphamide-D4 6.4 0.6 0.99 260.0248 261.0321 ESI + Cyproconazole C15H18ClN3O Terbutylazin-D5 11 2.9 0.99 291.1133 292.1211 ESI + Cyprodinil C14H15N3 Irgarol-D9 9.7 4 0.99 225.126 226.1339 ESI + 52 Supporting Information to Chapter 2

Name Formula Int. Standard RT Log Linearity Molecular Quantified Ionization (min) Kow* Weight m/z Desethylatrazin C6H10ClN5 Desethylatrazine-15N3 5.8 1.51 0.99 187.0619 188.0697 ESI + Desisopropylatrazin C5H8ClN5 Desisopropylatrazine- 4.5 1.2 0.98 173.0463 174.0541 ESI + D5 Desmedipham C16H16N2O4 Propazine-D6 9.5 3.4 0.99 300.1105 301.1183 ESI + Dexamethasone C22H29FO5 Diuron-D6 9.1 1.8 0.98 392.1999 393.2072 ESI + Diazepam C16H13ClN2O Diazepam-D5 10.1 2.8 0.97 284.0716 285.0789 ESI + Dicamba C8H6Cl2O3 2,4-D 13C6 9 2.2 0.99 219.9699 218.9621 ESI - Dichlorprop C9H8O3Cl2 Dichlorprp D6 10.5 3.4 0.99 233.9845 232.9778 ESI - Diclofenac C14H11Cl2N1O2 Diclofenac-D4 12.7 4.5 0.98 295.0161 296.024 ESI + Diflufenican C19H11F5N2O2 Diflufenican-D3 13.9 4.9 0.99 394.0735 395.0813 ESI + Dimethachlor C13H18ClNO2 Isoproturon-D6 9.1 2.2 0.99 255.1021 256.1099 ESI + Dimethenamid C12H18ClNO2S OIT-D7 10.1 2.2 0.99 275.0741 276.082 ESI + Dimethenamid-OXA C12H17N1O4S1 Diclofenac D4 7.2 0.8 0.96 271.0873 270.0806 ESI - Dimethylaminosulfanilid C8H12N2O2S Cyclophosphamide-D4 6.4 2.3 0.98 320.0692 321.0692 ESI + Dinoseb C10H12N2O5 Benzafibrat D4 12.8 3.6 0.98 240.0741 239.0673 ESI - Diuron C9H10Cl2N2O1 Diuron-D6 9.2 2.7 0.99 232.0165 233.0243 ESI + Diuron-desdimethyl = 1-(3,4- C7H6Cl2N2O Atrazine-D5 8.6 2.7 0.98 203.9852 204.993 ESI + Dichlorophenyl)urea Diuron-desmonomethyl (DCPMU) C8H8Cl2N2O Terbutylazin-D5 9.1 2.9 0.99 218.0008 219.0086 ESI + = 1-(3,4-Dichlorophenyl)-3-methylurea Epoxyconazol C17H13ClFN3O Propiconazol-D5 11.6 3.5 0.99 329.0726 330.0804 ESI + Ethofumesate C13H18O5S1 Terbutilazin-D5 10.2 2.7 0.98 286.0869 287.0948 ESI + Exemestane C20H24O2 Terbutilazin-D5 9.9 3 0.99 296.1771 297.1849 ESI + Fenofibrate C20H21Cl1O4 Fenofibrate-D6 15.5 5.2 0.98 360.1123 361.1201 ESI + Fenofibric-Acid C17H15ClO4 Mecoprop D6 12.2 4 0.99 318.0653 317.0586 ESI - Fenpropimorph C20H33NO Clarithromycin-D3 9.2 4.9 0.98 303.2557 304.2635 ESI + Fipronil C12H4Cl2F6N4O1S1 Bentazon D6 12.4 4.2 0.98 435.9387 434.9314 ESI - Fipronil-sulfide C12H4Cl2F6N4S1 Bentazon D6 12.7 5.1 0.97 419.9438 418.9365 ESI - Fipronil-sulfone C12H4Cl2F6N4O2S1 Benzafibrat D4 13.2 4.4 0.96 451.9336 450.9263 ESI - Fluazifop (acid free) C15H12F3NO4 Dichlorprop D6 10.6 3.2 0.99 327.0724 326.0646 ESI - Fluconazole C13H12F2N6O Fluconazole-D4 5.6 0.3 0.99 306.1035 307.113 ESI + Fludioxonil C12H6F2N2O2 Dichlorprop D6 10.5 4.1 0.98 248.0392 247.0325 ESI - Flufenacet C14H13F4N3O2S Metolachlor-D6 11.6 3.2 0.99 363.067 364.0737 ESI + Fluoxetine C17H18F3NO Clarithromycin-D3 9.2 4.1 0.98 309.1335 310.1413 ESI + Fluroxypyr (acid free) C7H5Cl2FN2O3 Benzafibrat D4 6.9 1.2 0.98 253.9667 252.9588 ESI - Flusilazole C16H15F2N3Si Ritonavir-D6 12.2 3.7 0.98 315.0998 316.1076 ESI + Galaxolide C18H26O Tonalide D3 10.4 5.9 0.99 258.1978 259.2056 APPI + Galaxolidon C18H24O2 Ritonavir-D3 14.7 5.3 0.99 272.1771 273.1849 ESI + Galaxolidone C18H24O2 Fenofibrate D6 9.86 5.26 0.99 272.1772 273.1849 APPI + Hexazinon C12H20N4O2 Chlortoluron-D6 7.3 1.9 0.99 252.1581 253.1659 ESI + Ifosfamid C7H15Cl2N2O2P Sulfamethoxazole-D4 6.4 1 0.99 260.0248 261.0321 ESI + Imidacloprid C9H10ClN5O2 Metoprolol-D7 5.1 1.1 0.99 255.0523 256.0596 ESI + Iminostilbene C14H11N1 Metolachlor-D6 11 4.1 0.99 193.0892 194.0964 ESI + Indomethacin C19H16ClNO4 Indomethacin-D4 12.6 4.3 0.98 357.0768 358.0841 ESI + Ioxynil C7H3I2NO Bentazon D6 9.2 3.4 0.99 370.8299 369.8231 ESI - Irbesartan C25H28N6O Irbesartan-D4 9.1 4.6 0.98 428.2325 429.2397 ESI + Irgarol C11H19N5S1 Irgarol-D9 9.1 4.1 0.99 253.1356 254.1434 ESI + Irgarol-descyclopropyl C8H15N5S Simazin-D5 6.9 2.7 0.99 213.1043 214.1121 ESI + Isoproturon C12H18N2O1 Isoproturon-D6 8.9 2.9 0.99 206.1414 207.1491 ESI + Isoproturon-didemethyl = 1-(4-Iso- C10H14N2O Chlortoluron-D6 8.3 2.2 0.98 178.1101 179.1179 ESI + prophenyl)urea Isoproturon-monodemethyl = 1-(4- C11H16N2O Isoproturon-D6 8.8 2.6 0.99 192.1257 193.1335 ESI + Isoprophenyl)-3-methylurea Ketoprofen C16H14O3 Propazin-D6 9.7 3.1 0.99 254.0937 255.1016 ESI + Kresoxim-methyl C18H19NO4 Charithromycin-D6 12.4 3.4 0.97 313.1309 314.1387 ESI + Lamotrigin C9H7Cl2N5 Desethylatrazine-15N3 5.7 1.9 0.97 255.0079 256.0151 ESI + Linuron C9H10Cl2N2O2 Propazin-D6 10.2 3.2 0.99 248.0114 249.0192 ESI + Losartan C22H23ClN6O Irgarol-D9 9.1 4.3 0.99 422.1622 423.1695 ESI + MCPA C9H9ClO3 MCPA D6 9.4 3.3 0.99 200.0235 199.0167 ESI - Mecoprop C10H11ClO3 Mecoprop D6 10.6 3.1 0.99 214.0391 213.0324 ESI - Mefenamic acid C15H15N1O2 Menfenamic acid-D 14.2 5.1 0.98 241.1097 242.1176 ESI + Mesotrion C14H13NO7S Cyclophosphamide-D4 6.5 1.5 0.98 339.0407 340.0485 ESI + Mesotrion C14H13NO7S Mesotrion D3 6.3 1.5 0.95 339.0407 338.034 ESI - Metalaxyl C15H21NO4 Isoproturon-D6 8.7 1.7 0.99 279.1465 280.1543 ESI + Metamitron C10H10N4O1 Metropolol-D7 5.2 0.8 0.98 202.086 203.0927 ESI + Metamitron-Desamino C10H9N3O1 Metropolol-D7 5.4 1.4 0.98 187.0746 188.0818 ESI + Metazachlor C14H16ClN3O Isoproturon-D6 8.6 2.1 0.99 277.0976 278.1055 ESI + Methyl-Benzotriazol C7H7N3 Methyl-benzotriazol-D6 6.1 1.7 0.99 133.0635 134.0713 ESI + Methylprednisolone C22H30O5 Irgarol-D9 9.3 1.8 0.99 374.2093 375.2166 ESI + Metolachlor C15H22ClNO2 Metolachlor-D6 11.6 3.1 0.99 283.1334 284.1412 ESI + Metolachlor-ESA C15H23N1O5S1 2,4-D 13C6 7.8 1.7 0.99 329.1291 328.1224 ESI - Supporting Information to Chapter 2 53

Name Formula Int. Standard RT Log Linearity Molecular Quantified Ionization (min) Kow* Weight m/z Metolachlor-Morpholinon C14H19N1O2 Isoproturon-D6 8 2.5 0.99 233.141 234.1489 ESI + Metolachlor-OXA C15H21N1O4 2,4-D 13C6 8.8 1.4 0.99 279.1465 278.1398 ESI - Metoprolol C15H25NO3 Metropolol-D7 5.3 1.9 0.99 267.1829 268.1907 ESI + Metribuzin C8H14N4O1S1 Chlortoluron-D6 7.2 1.7 0.99 214.0883 215.0961 ESI + Metribuzin-Desamino (DA) C8H13N3OS Chlortoluron-D6 7.7 2.5 0.98 199.0774 200.0852 ESI + Monuron C9H11ClN2O Propanolol-D7 7.1 1.9 0.99 198.0554 199.0633 ESI + N,N-Didesvenlafaxin C15H23N1O2 N-Desvenlafaxin-D3 6.4 2.2 0.98 249.1729 250.1801 ESI + N,N-diethyl-3-methylbenzamide C12H17NO DEET-D7 8.6 2.2 0.99 191.1305 192.1383 ESI + (DEET) N,N-dimethyl-N’-(4-methylphenyl)- C9H14N2O2S Sulcotrine-D3 7.7 1.7 0.99 214.077 215.0849 ESI + sulfamide N4-Acetyl-Sulfadimethoxin C14H16N4O5S N-Desvenlafaxin-D3 6.5 1.5 0.99 352.0836 353.0914 ESI + N4-Acetyl-Sulfamethoxazole C12H13N3O4S Acetylsulfamethoxazol- 5.8 1.2 0.96 295.0621 296.07 ESI + D4 Napropamid C17H21NO2 Metolachlor-D6 11.5 3.4 0.99 271.1567 272.1645 ESI + N-Desvenlafaxine C16H25N1O2 Venlafaxine-D6 5.1 3.1 0.99 263.1885 264.1958 ESI + O,N-Didesvenlafaxin C15H23N1O2 0,N-Didesvenlafaxin- 5.2 2.4 0.98 249.1729 250.1801 ESI + D3 Octocrylene (=2-Ethyl-2-cyano-3,3- C24H27NO2 Fenofibrate-D6 17.3 6.5 0.99 361.2115 362.2036 ESI + diphenylacrylate O-Desvenlafaxine C16H25N1O2 O,N-Didesvenlafaxine- 6.3 2.7 0.98 263.1885 264.1958 ESI + D3 Orbencarb C12H16ClNOS Diazinon-D10 13.2 3.4 0.99 257.0647 258.0714 ESI + Oseltamivir C16H28N2O4 Verapamil-D6 7 1 0.99 312.2044 313.2122 ESI + Oxazepam C15H11ClN2O2 Isoproturon-D6 9.1 2.4 0.98 286.048 287.0582 ESI + Pantoprazol C16H15F2N3O4S Primidon-D5 6.7 1.3 0.98 383.0746 384.0824 ESI + Pethoxamid C16H22ClNO2 OIT-D17 11.4 2.7 0.99 295.1334 296.1412 ESI + Phenazon (Antipyrine) C11H12N2O Phenazon-D3 4.8 0.4 0.99 188.0944 189.1022 ESI + Phenmedipham C16H16N2O4 Propazin-D6 9.5 3.6 0.99 300.1116 301.1183 ESI + Pinoxaden C23H32N2O4 Ritonavir-D6 13.4 4.1 0.99 400.2368 401.2435 ESI + Prednisolon C21H28O5 Verapamil-D6 8.1 1.5 0.99 360.1937 361.201 ESI + Primicarb C11H18N4O2 Metropolol-D7 4.7 1.7 0.98 238.143 239.1503 ESI + Primidon C12H14N2O2 Primidon-D5 5.7 0.9 0.98 218.105 219.1128 ESI + Prochloraz C15H16Cl3N3O2 Propiconazol-D5 12 4.1 0.99 375.0303 376.0381 ESI + Prometon C10H19N5O Simazin-D5 6.8 3 0.99 225.1584 226.1662 ESI + Prometryn C10H19N5S Terbutryn-D5 8.5 3.5 0.98 241.1356 242.1434 ESI + Propachlor C11H14ClNO Isoproturon-D6 8.7 2.2 0.99 211.0758 212.0837 ESI + Propanolol C16H21NO2 Propanol-D7 6.71 2.79 0.99 260.1645 260.1645 ESI + Propaquizafop C22H22ClN3O5 Fenofibrate-D6 15.2 4.6 0.98 443.1242 444.1321 ESI + Propazine-2-hydroxy (=Prometon- C9H17N5O Metropolol-D7 5.9 2.5 0.98 211.1428 212.1506 ESI + Hydroxy) Propiconazol C15H17Cl2N3O2 Propiconazol-D5 12.8 3.7 0.99 341.0692 342.0771 ESI + Prosulfocarb C14H21NOS Ritonavir-D6 14.3 4.7 0.99 251.1349 252.1417 ESI + Pyraclostrobin C19H18ClN3O4 Diazinon-D10 13.1 4.2 0.99 387.0986 388.1059 ESI + Pyrimethanil C12H13N3 Atrazine-D5 7.7 3 0.99 199.1109 200.1182 ESI + Reserpine C33H40N2O9 Clarithromycin-D3 8.2 3.5 0.97 608.2734 609.2807 ESI + Ritonavir C37H48N6O5S2 Ritonavir-D6 13 6.3 0.99 720.3128 721.32 ESI + Simazin C7H12ClN5 Simazin-D5 7.2 2.2 0.99 201.0776 202.0854 ESI + Simeton C8H15N5O1 Metropolol-D7 5.1 2.7 0.99 197.1271 198.1349 ESI + Spiroxamin C18H35N1O2 Clarithromycin-D3 9.8 4.3 0.98 297.2668 298.2741 ESI + Sulcotrione C14H13Cl1O5S1 Sulcotrine-D3 7.1 2.3 0.99 328.0167 329.0245 ESI + Sulfadimethoxine C12H14N4O4S Sufadimethoxine-D4 6 1.6 0.99 310.073 311.0809 ESI + Sulfamethoxazole C10H11N3O3S1 Sulfamethoxazole-D4 5 0.9 0.99 253.0516 254.0594 ESI + Tebuconazole C16H23ClN3O Propiconazole-D5 12.8 3.7 0.99 307.1446 308.1524 ESI + Te bu t a m C15H23NO Te bu t a m- D4 11.6 3 0.99 233.1774 234.1852 ESI + Terbumeton C10H19N5O Simazin-D5 6.8 3.1 0.99 225.1584 226.1662 ESI + Te r bu t r y n C10H19N5S1 Terbutryn-D5 8.5 3.7 0.99 241.1356 242.1434 ESI + Terbutylazin-2-hydroxy C9H17N5O Terbutilazin-D5 5.9 2.6 0.99 211.1428 212.1506 ESI + Terbutylazine C9H16ClN5 Terbutylazin-D5 10.1 3.2 0.99 229.1089 230.1167 ESI + Terbutylazine-desethyl C7H12Cl1N5 Simazin-D5 7.5 2.2 0.99 201.0776 202.0854 ESI + Tonalide C18H26O Tonalide D3 10.4 6.35 0.98 258.1978 260.209 APPI + Tramadol C16H25NO2 Tramadol-D6 6.3 3 0.99 263.1885 264.1958 ESI + Triclosan C12H7Cl3O2 Triclosan 13C6 14.5 4.8 0.99 287.9506 286.9439 ESI - Trinexapac-ethyl C13H16O5 Irgarol-D9 9.1 1.6 0.98 252.0992 253.1071 ESI + Valsartan C24H29N5O3 Terbutylazin-D5 10.9 3.7 0.99 435.227 436.2343 ESI + Venlafaxine C17H27NO2 Venlafaxine-D6 6.3 3.3 0.99 277.2036 278.2115 ESI + Verapamil C27H38N2O4 Verapamil-D6 7.3 4.8 0.99 454.2826 455.2904 ESI + 54 Supporting Information to Chapter 2

Table 2.5: Matrix factor, matrix effect (%) and limits of detection (LOD) and quantiﬁcation (LOQ) of studied analytes using LC-ESI-HRMS

Name Matrix factor Matrix effect (%) LOD (pg/g) LOQ (pg/g) 2-(2H-Benzotriazol-2-yl)-4,6-bis(1-methyl-1-phenylethyl)phenol 0.29 -71 90 290 2-(2H-Benzotriazol-2-yl)-4,6-di-tert-pentylphenol 0.27 -73 90 310 2-(2H-Benzotriazol-2-yl)-6-dodecyl-4-methylphenol 0.18 -82 3500 12000 2-(2-Hydroxy-5-methylphenyl) benzotriazole 0.19 -81 660 2200 2-(4,6 Diphenyl-1,3,5-triazin-2-yl)-5-[(hexyl)oxy]-phenol 0.19 -81 130 440 2,4-D 0.6 -40 420 1400 2,4-dimethylphenylformamide 0.59 -41 210 710 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl)phenol 0.26 -74 2400 8000 2-Ethylhexyl-4-methoxycinnamate 0.53 -47 1200 4000 2-Naphthalinsulfonsaure¨ 0.38 -62 660 2200 2-n-Octyl-4-isothiazolin-3-on (OI) 0.68 -32 180 615 2-tert-Butyl-6-(5-chloro-2H-benzotriazol-2-yl)-4-methylphenol 0.2 -80 3100 10000 3-(4-Methylbenzylidene)-camphor 0.45 -55 60 190 3,5-dibromo-4-hydroxybenzoic acid 0.68 -32 920 3100 3-Phenoxybenzoic acid 0.58 -42 430 1400 4,5-Dichloro-2-n-octyl-isothiazol-3(2H)-one (DCOIT) 0.39 -61 320 1100 Acetochlor 0.57 -43 220 730 Acetochlor-ESA 0.48 -52 260 870 Acetochlor-OXA 0.44 -56 570 1900 Alachlor 0.59 -41 210 710 Alachlor-ESA 0.48 -52 520 1700 Alachlor-OXA 0.48 -52 520 1700 Aldicarb Na 0.42 -58 300 1000 AMDOPH 1.79 79 70 230 Atrazine 0.44 -56 280 950 Azoxystrobin 0.92 -8 140 450 Bentazon 1.01 1 250 830 Benzophenon 3 (=2-Hydroxy-4-methoxybenzophenon) 0.56 -44 220 740 Benzothiazol 0.95 -5 30 90 Benzotriazol 0.52 -48 480 1600 Bezafibrat 0.65 -35 190 640 Bifenox acid 0.66 -34 380 1300 Bis(1-octyloxy-2,2,6,6-tetramethyl-4-piperidyl) sebacate 0.03 -97 4200 14000 Bromazil 0.63 -37 400 1300 Bromoxynil 1.18 18 210 710 Carbamazepin 0.94 -6 130 440 Carbamazepin-10,11-dihydro-10,11-dihydroxy 0.74 -26 850 2800 Carbamazepine-10,11-epoxid 0.98 -2 260 850 Carbetamide 1.63 63 150 510 Cetirizine 0.97 -3 640 2200 Chloridazon 1.12 12 220 740 Chlortoluron 0.75 -25 170 560 Clarithromycin 1.58 58 80 260 Climbazol 0.73 -27 170 570 Clindamycin 1.18 18 110 350 Clofibrin acid 0.66 -34 380 1300 Clomazone 0.6 -40 210 690 Clotrimazole 0.96 -4 260 870 Cyclophosphamide 0.73 -27 170 570 Cyproconazole 0.61 -39 210 680 Cyprodinil 0.52 -48 50 160 Desethylatrazin 0.95 -5 260 880 Desisopropylatrazin 0.57 -43 1100 3700 Desmedipham 0.72 -28 170 580 Dexamethasone 0.64 -36 390 1300 Diazepam 0.81 -19 150 500 Dicamba 0.6 -40 420 1400 Dichlorprop 0.64 -36 400 1300 Diclofenac 0.39 -61 640 2100 Diflufenican 0.19 -81 3300 11000 Dimethachlor 0.88 -12 280 950 Dimethenamid 0.77 -23 160 540 Dimethenamid-OXA 0.21 -79 1200 4000 Dimethylaminosulfanilid 0.6 -40 210 690 Dinoseb 2.55 155 10 30 Diuron 0.82 -18 310 1000 Diuron-desdimethyl = 1-(3,4-Dichlorophenyl)urea 0.37 -63 680 2300 Supporting Information to Chapter 2 55

Name Matrix factor Matrix effect (%) LOD (pg/g) LOQ (pg/g) Diuron-desmonomethyl (DCPMU) = 1-(3,4-Dichlorophenyl)-3- 0.13 -87 1900 6400 methylurea Epoxyconazol 0.59 -41 200 700 Ethofumesate 0.5 -50 250 830 Exemestane 0.76 -24 160 550 Fenofibrate 0.31 -69 400 1300 Fenofibric-Acid 0.57 -43 440 1500 Fenpropimorph 1.19 19 20 70 Fipronil 0.82 -18 310 1000 Fipronil-sulfide 0.84 -16 150 500 Fipronil-sulfone 1 0 30 80 Fluazifop (acid free) 0.53 -47 470 1600 Fluconazole 1.05 5 240 800 Fludioxonil 0.52 -48 50 160 Flufenacet 0.62 -38 200 670 Fluoxetine 1.24 24 20 70 Fluroxypyr (acid free) 0.66 -34 950 3200 Flusilazole 0.69 -31 180 600 Galaxolide 0.87 -13 720 2400 Galaxolidon 0.52 -48 240 800 Hexazinon 0.82 -18 150 500 Ifosfamid 0.73 -27 340 1100 Imidacloprid 1.2 20 210 690 Iminostilbene 0.6 -40 40 140 Indomethacin 0.24 -76 100 350 Ioxynil 1.11 11 110 380 Irbesartan 0.3 -70 420 1400 Irgarol 0.44 -56 60 190 Irgarol-descyclopropyl 0.63 -37 200 660 Isoproturon 0.62 -38 200 670 Isoproturon-didemethyl = 1-(4-Isoprophenyl)urea 0.67 -33 190 620 Isoproturon-monodemethyl = 1-(4-Isoprophenyl)-3-methylurea 0.76 -24 160 550 Ketoprofen 0.79 -21 320 1100 Kresoxim-methyl 0.99 -1 2500 8400 Lamotrigin 1.17 17 110 350 Linuron 0.73 -27 170 570 Losartan 0.69 -31 40 120 MCPA 0.6 -40 420 1400 Mecoprop 0.66 -34 400 1300 Mefenamic acid 0.35 -65 360 1200 Mesotrion 0.42 -58 600 2000 Mesotrion 1.43 43 1800 5800 Metalaxyl 0.74 -26 170 560 Metamitron 1.05 5 240 800 Metamitron-Desamino 1.18 18 210 710 Metazachlor 0.85 -15 150 490 Methyl-Benzotriazol 0.96 -4 130 430 Methylprednisolone 0.68 -32 370 1200 Metolachlor 0.55 -45 230 760 Metolachlor-ESA 0.38 -62 660 2200 Metolachlor-Morpholinon 0.77 -23 160 540 Metolachlor-OXA 0.35 -65 360 1200 Metoprolol 1.08 8 20 80 Metribuzin 0.73 -27 170 570 Metribuzin-Desamino (DA) 0.83 -17 150 500 Monuron 0.69 -31 180 600 N,N-Didesvenlafaxin 1.21 21 100 340 N,N-diethyl-3-methylbenzamide (DEET) 0.38 -62 330 1100 N,N-dimethyl-N’-(4-methylphenyl)-sulfamide 0.4 -60 310 1000 N4-Acetyl-Sulfadimethoxin 1.32 32 100 300 N4-Acetyl-Sulfamethoxazole 1.32 32 190 630 Napropamid 0.61 -39 40 140 N-Desvenlafaxine 1.32 32 20 60 O,N-Didesvenlafaxin 1.28 28 100 330 Octocrylene (=2-Ethyl-2-cyano-3,3-diphenylacrylate 0.34 -66 1800 6100 O-Desvenlafaxine 1.37 37 90 300 Orbencarb 0.49 -51 260 850 Oseltamivir 1.44 44 90 290 Oxazepam 0.84 -16 150 500 Pantoprazol 0.43 -57 290 970 Pethoxamid 0.72 -28 170 580 Phenazon (Antipyrine, ID 2519) 1.03 3 240 810 56 Supporting Information to Chapter 2

Name Matrix factor Matrix effect (%) LOD (pg/g) LOQ (pg/g) Phenmedipham 0.71 -29 180 590 Pinoxaden 0.53 -47 50 160 Prednisolon 0.97 -3 260 860 Primicarb 1.17 17 210 710 Primidon 0.4 -60 1600 5200 Prochloraz 0.52 -48 240 800 Prometon 0.64 -36 40 130 Prometryn 0.55 -45 50 150 Propachlor 0.72 -28 170 580 Propanolol 0.86 -14 30 100 Propaquizafop 0.26 -74 480 1600 Propazine-2-hydroxy /Terbuthylazine-2-hydroxy 0.68 -32 180 610 Propiconazol 0.56 -44 220 740 Prosulfocarb 0.41 -59 310 1000 Pyraclostrobin 0.54 -46 230 770 Pyrimethanil 0.42 -58 60 200 Reserpine 1.28 28 100 320 Ritonavir 0.59 -41 1100 3500 Simazin 0.48 -52 50 170 Simeton 1.13 13 20 70 Spiroxamin 1.16 16 20 70 Sulcotrione 0.34 -66 740 2500 Sulfadimethoxine 1.23 23 200 680 Sulfamethoxazole 1.04 4 240 800 Tebuconazole 0.53 -47 240 790 Te bu t a m 0.56 -44 50 150 Terbumeton 0.54 -46 50 150 Te r bu t r y n 0.54 -46 50 150 Terbutylazine 0.65 -35 40 130 Terbuthylazine-desethyl 0.68 -32 190 610 Tonalide 0.55 -45 50 150 Tramadol 1.49 49 20 60 Triclosan 0.28 -72 890 3000 Trinexapac-ethyl 0.69 -31 360 1200 Valsartan 0.52 -48 240 800 Venlafaxine 1.39 39 20 60 Verapamil 1.21 21 100 340

Table 2.6: Method Performance: precision (% RSD) and recoveries (%) and deviation between different layers for samples analyzed by LC-ESI-HRMS. * A sediment core is not homogeneous along the years, therefore recovery between layers was performed to conﬁrm that the developed method worked in the entire sediment core.

Name Precision RSD Recoveries at Recoveries at Recoveries between Difference between (%) ASE (%) LLE% layers (%) layers (%) 2,4-D 5 ¡20 104 118 11 2,4-dimethylphenylformamide 6 69 81 83 4 2-Naphthalinsulfonsaure¨ 11 ¡20 65 73 16 2-n-Octyl-4-isothiazolin-3-on (OI) 5 78 89 97 5 3,5-dibromo-4-hydroxybenzoic acid 10 ¡20 115 115 11 3-Phenoxybenzoic acid 11 47 98 161 14 4,5-Dichloro-2-n-octyl-isothiazol-3(2H)-one (DCOIT) 5 47 73 64 25 Acetochlor 8 84 95 98 9 Acetochlor-ESA 5 ¡20 83 99 7 Acetochlor-OXA 11 ¡20 83 103 14 Alachlor 8 84 95 99 11 Alachlor-ESA 5 ¡20 83 100 6 Alachlor-OXA 11 ¡20 84 104 14 Aldicarb Na 7 84 106 116 2 AMDOPH 1 88 109 108 16 Atrazine 2 90 100 109 4 Azoxystrobin 4 109 112 127 9 Bentazon 4 69 100 109 2 Benzophenon 3 (=2-Hydroxy-4-methoxybenzophenon) 2 102 103 72 13 Benzothiazol 18 76 111 117 8 Benzotriazol 6 95 125 131 10 Bezaﬁbrat 3 50 1111 109 3 Supporting Information to Chapter 2 57

Name Precision RSD Recoveries at Recoveries at Recoveries between Difference between (%) ASE (%) LLE% layers (%) layers (%) Bifenox acid 11 20 116 111 10 Bromazil 12 93 91 92 18 Bromoxynil 4 60 117 102 10 Carbamazepin 8 79 74 72 7 Carbamazepin-10,11-dihydro-10,11-dihydroxy 11 67 70 89 15 Carbamazepine-10,11-epoxid 23 80 94 124 17 Carbetamide 20 77 88 138 10 Cetirizine 9 ¡20 69 72 10 Chloridazon 4 66 74 68 9 Chlortoluron 6 79 107 113 6 Clarithromycin 11 60 112 110 12 Climbazol 8 100 108 135 10 Clindamycin 17 65 90 86 28 Clofibrin acid 11 50 109 97 5 Clomazone 6 83 106 107 10 Clotrimazole 33 21 127 132 19 Cyclophosphamide 6 95 125 118 6 Cyproconazole 7 81 95 107 7 Cyprodinil 2 85 89 91 3 Desethylatrazin 6 84 107 116 5 Desisopropylatrazin 8 88 113 112 21 Desmedipham 5 93 106 123 8 Dexamethasone 12 60 81 90 7 Diazepam 7 90 120 112 5 Dicamba 5 ¡20 104 119 11 Dichlorprop 2 33 106 118 12 Diclofenac 11 73 100 96 6 Diflufenican 7 81 102 106 8 Dimethachlor 5 98 111 112 6 Dimethenamid 5 87 101 102 6 Dimethenamid-OXA 19 ¡20 82 77 17 Dimethylaminosulfanilid 21 89 110 92 8 Dinoseb 14 80 139 138 20 Diuron 6 75 101 107 10 Diuron-desdimethyl = 1-(3,4-Dichlorophenyl)urea 8 66 66 72 21 Diuron-desmonomethyl (DCPMU) = 1-(3,4- 2 67 104 103 13 Dichlorophenyl)-3-methylurea Epoxyconazol 8 97 108 118 5 Ethofumesate 4 96 112 99 9 Exemestane 7 104 113 133 5 Fenofibrate 7 94 127 120 4 Fenofibric-Acid 7 103 103 103 7 Fenpropimorph 6 62 84 90 6 Fipronil 8 58 82 87 19 Fipronil-sulfide 4 57 84 76 24 Fipronil-sulfone 16 83 134 124 14 Fluazifop (acid free) 8 42 89 107 8 Fluconazole 9 85 118 120 5 Fludioxonil 3 94 92 130 19 Flufenacet 5 85 93 93 9 Fluoxetine 7 ¡20 91 44 22 Fluroxypyr (acid free) 6 20 90 72 9 Flusilazole 10 89 97 97 5 Galaxolidon 10 96 73 66 16 Hexazinon 6 71 119 132 11 Ifosfamid 13 82 88 97 10 Imidacloprid 26 71 86 70 11 Iminostilbene 7 ¡20 93 95 10 Indomethacin 12 70 111 110 8 Ioxynil 3 54 111 100 18 Irbesartan 8 80 113 114 3 Irgarol 3 83 100 104 2 Irgarol-descyclopropyl 5 92 110 128 7 Isoproturon 5 80 96 103 2 Isoproturon-didemethyl = 1-(4-Isoprophenyl)urea 2 80 95 97 12 Isoproturon-monodemethyl = 1-(4-Isoprophenyl)-3- 6 81 98 106 3 methylurea Ketoprofen 8 84 118 126 15 Kresoxim-methyl 14 70 73 60 13 Lamotrigin 1 62 135 73 19 Linuron 3 92 106 99 11 Losartan 7 75 121 112 7 58 Supporting Information to Chapter 2

Name Precision RSD Recoveries at Recoveries at Recoveries between Difference between (%) ASE (%) LLE% layers (%) layers (%) MCPA 3 ¡20 107 114 8 Mecoprop 4 52 117 123 7 Mefenamic acid 10 77 127 122 5 Mesotrion 15 ¡20 80 69 16 Mesotrion 12 ¡20 93 114 7 Metalaxyl 5 74 98 103 7 Metamitron 10 79 74 69 5 Metamitron-Desamino 8 56 77 71 7 Metazachlor 3 93 109 114 6 Methyl-Benzotriazol 2 94 117 120 10 Methylprednisolone 13 61 118 132 16 Metolachlor 5 84 101 98 1 Metolachlor-ESA 7 ¡20 67 81 10 Metolachlor-Morpholinon 11 69 99 99 12 Metolachlor-OXA 13 ¡20 63 84 11 Metoprolol 12 23 121 115 2 Metribuzin 7 89 106 114 12 Metribuzin-Desamino (DA) 3 79 116 121 7 Monuron 2 61 78 67 7 N,N-Didesvenlafaxin 11 ¡20 110 77 27 N,N-diethyl-3-methylbenzamide (DEET) 7 95 102 113 9 N,N-dimethyl-N’-(4-methylphenyl)-sulfamide 19 60 82 78 39 N4-Acetyl-Sulfadimethoxin 13 82 116 129 15 N4-Acetyl-Sulfamethoxazole 12 76 129 124 21 Napropamid 3 86 92 99 5 N-Desvenlafaxine 13 81 115 129 15 O,N-Didesvenlafaxin 21 ¡20 119 144 13 Octocrylene (=2-Ethyl-2-cyano-3,3-diphenylacrylate 26 98 117 84 19 O-Desvenlafaxine 8 20 121 112 2 Orbencarb 3 83 92 80 16 Oseltamivir 13 20 98 51 24 Oxazepam 5 98 109 128 9 Pantoprazol 22 82 85 116 5 Pethoxamid 4 83 95 95 4 Phenazon (Antipyrine, ID 2519) 7 82 119 115 9 Phenmedipham 5 89 106 122 8 Pinoxaden 9 65 77 79 6 Prednisolon 14 66 66 72 13 Primicarb 20 86 80 82 16 Primidon 10 86 129 114 7 Prochloraz 5 77 97 114 4 Prometon 6 90 98 111 6 Prometryn 5 84 93 100 1 Propachlor 6 67 95 98 6 Propanolol 9 60 117 118 5 Propaquizafop 5 78 105 110 7 Propazine-2-hydroxy (=Prometon-Hydroxy) 8 82 106 125 23 Propiconazol 7 85 105 110 2 Prosulfocarb 13 65 60 53 15 Pyraclostrobin 3 92 99 99 10 Pyrimethanil 2 64 71 77 6 Reserpine 14 89 93 107 15 Ritonavir 13 95 111 110 6 Simazin 1 86 110 121 7 Simeton 20 73 76 72 10 Spiroxamin 6 66 82 87 2 Sulcotrione 6 ¡20 102 117 13 Sulfadimethoxine 12 91 130 123 3 Sulfamethoxazole 7 80 106 102 9 Tebuconazole 8 89 100 113 3 Te bu t a m 5 80 104 113 5 Terbumeton 6 90 96 111 6 Te r bu t r y n 5 84 91 99 1 Terbutylazin-2-hydroxy 4 93 103 113 5 Terbutylazine 5 83 101 108 2 Terbutylazine-desethyl 1 78 101 110 7 Tramadol 13 ¡20 121 111 9 Triclosan 7 90 123 119 8 Trinexapac-ethyl 5 60 119 109 5 Valsartan 12 ¡20 80 71 15 Venlafaxine 10 ¡20 121 113 5 Verapamil 8 60 114 114 3 Supporting Information to Chapter 2 59

Table 2.7: Method Performance: precision (% RSD) and recoveries (%) for samples analyzed by LC-APPI-HRMS.

Name Precision (RSD%) Recoveries (%) Bis(1-octyloxy-2,2,6,6-tetramethyl-4-piperidyl) 17 105 sebacate 2-(2H-Benzotriazol-2-yl)-4,6-bis(1-methyl-1- 17 123 phenylethyl)phenol 2-(2H-Benzotriazol-2-yl)-6-dodecyl-4- 16 97 methylphenol 2-(2H-Benzotriazol-2-yl)-4,6-di-tert-pentylphenol 16 109 2-tert-Butyl-6-(5-chloro-2H-benzotriazol-2-yl)-4- 5 102 methylphenol 2-(2-Hydroxy-5-methylphenyl)benzotriazole 17 105 2-(4,6 Diphenyl-1,3,5-triazin-2-yl)-5-[(hexyl)oxy]- 20 101 phenol 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2- 20 98 yl)phenol 3-(4-Methylbenzylidene)-camphor 15 109 2-Ethylhexyl-4-methoxycinnamate 23 81 Galaxolidone 14 102 Galaxolide 18 106 Tonalide 16 101

Table 2.8: List of ionized compounds by LC-dopant assistant-atmospheric pressures photoionization (DA-APPI) coupled to HRMS.

Name CAS-No Group Formula Exact Mass Exact Mass (M+H) Bis(1-octyloxy-2,2,6,6-tetramethyl-4-piperidyl) sebacate 129757-67-1 UV-Light Stabilzer C44H84N2O6 736.6324 737.6402 2-(2H-Benzotriazol-2-yl)-4,6-bis(1-methyl-1- 70321-86-7 UV-Light Stabilzer C30H29N3O 447.2305 448.2383 phenylethyl)phenol 2-(2H-Benzotriazol-2-yl)-6-dodecyl-4-methylphenol 125304-04-3 UV-Light Stabilzer C25H35N3O 393.2775 394.2853 2-(2H-Benzotriazol-2-yl)-4,6-di-tert-pentylphenol 25973-55-1 UV-Light Stabilzer C22H29N3O 351.2305 352.2383 2-tert-Butyl-6-(5-chloro-2H-benzotriazol-2-yl)-4- 05.11.3896 UV-Light Stabilzer C17H18ClN3O 315.1133 316.1211 methylphenol 2-(2-Hydroxy-5-methylphenyl)benzotriazole 2440-22-4 UV-Light Stabilzer C13H11N3O 225.0897 226.0975 2-(4,6 Diphenyl-1,3,5-triazin-2-yl)-5-[(hexyl)oxy]-phenol 147315-50-2 UV-Light Stabilzer C27H27N3O2 425.2098 426.2176 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2- 3864-99-1 UV-Light Stabilzer C20H24CIN3O 357.1602 358.1681 yl)phenol 3-(4-Methylbenzylidene)-camphor 36861-47-9 Sunscreen cosmetic C18H22O 254.1665 255.1743 2-Ethylhexyl-4-methoxycinnamate 5466-77-3 Sunscreen cosmetic C18H26O3 290.1876 291.1955 Galaxolidon Musk Fragance C18H24O2 272.1772 273.1849 Galaxolide 1222-05-5 Musk Fragance C18H26O 258.1978 259.2056 Tonalide 21145-77-7 Musk Fragance C18H26O 258.1978 259.2056

Table 2.9: Temporal resolution and concentrations of organic contaminants in sediments from Lake Greifensee.

Year Benzotriazol Tolytriazole Propiconazole Prochloraz Octocrylene Galaxolidon (pg/gdw) (pg/gdw) (pg/gdw) (ng/gdw) (ng/gdw) (ng/gdw) 2002-2010 1400 820 620

Year Benzotriazol Tolytriazole Propiconazole Prochloraz Octocrylene Galaxolidon (pg/gdw) (pg/gdw) (pg/gdw) (ng/gdw) (ng/gdw) (ng/gdw) 1970-1973 ND ND ND ND ND 20

Table 2.10: List of quaternary ammonium surfactants (QAs) used for suspect screening in sediments from Lake Greifensee. *Log Kow were calculated using VCCLAB (http://www.vcclab.org/online.html)

Name Acronym Exact Mass log Kow* Expected Rt (min) Dodecyltrimethylammonium AT MAC 12 228.2686 3.77 ± (1.90) 10.8 Myristyltrimethylammonium AT MAC 14 256.2999 4.51 ± (1.98) 12.6 Hexadecyltrimethylammonium AT MAC 16 284.3311 5.31 ± (2.23) 14.5 Trimethyloctadecylammonium AT MAC 18 312.3625 6.27 ± (2.28) 16.7 Benzyldimethyldodecylammonium BAC 12 304.2999 5.26 ± (1.97) 14.4 Benzyl-dimethyl-tetradecylammonium BAC 14 332.3312 6.37 ± (2.69) 17 Benzyldimethylhexadecylammonium BAC 16 360.3625 7.30 ± (2.79) 19.2 Didecyldimethylammonium DADMAC 326.3781 6.66 ± (2.24) 17.7 (C10:C10) Diodecyldimethylammonium DADMAC 382.4407 8.85 ± (3.96) 22.8 (C12:C12) Dimethyditetradecylammonium DADMAC 438.5033 10.56 ± (3.96) 26.9 (C14:C14) Dihexadecyldimethylammonium DADMAC 494.5659 11.59 ± (3.71) 29.3 (C16:C16) Dimethyldioctadecylammonium DADMAC 550.6285 13.17 ± (4.30) 33 (C18:C18)

Table 2.11: List of UV-LS used for suspect screening in sediments from Lake Greifensee.

Name Exact Mass Expected Rt Name Exact Mass Expected Rt (min) (min) Actipol 137.0471 8.4 Drometrizole trisiloxane 349.0729 12.0 UV Check AM 300 143.0245 8.5 Tinuvin 1130 353.1734 12.1 Kernisorb 10 160.0519 8.8 Eusolex OCR 361.2036 12.2 Eusoolex T series 168.1145 9.0 Tinuvin 363 363.1133 12.3 o-Benzoylphenol 198.0675 9.5 Tinuvin 315 368.2094 12.4 Uvinul 400 214.0624 9.7 IRGANOX 1135 376.2972 12.5 Uvinul M-40 228.0781 10.0 Tinuvin 213 383.1840 12.6 Cyasorb UV 24 244.0730 10.2 Tinuvin 386 385.1915 12.6 Uvinul D 50 246.0523 10.3 Tinuvin 780 396.2983 12.8 Isoamyl-p- 247.1329 10.3 BTZ-4 399.2305 12.9 methoxycinnamate 2-Ethylhexylsalicylate 249.1485 10.3 Tinuvin 1577FF 425.2098 13.3 Eusolex OS 250.1563 10.4 Tinuvin R 432.1693 13.4 Tinuvin 744 261.1723 10.5 Tinuvin 120 438.3128 13.5 Eusolex HMS 262.1563 10.6 Tinuvin 928 441.2775 13.6 Tinuvin PS 267.1366 10.6 Tinuvin 900 447.2305 13.7 Cyasorb UV9 272.0679 10.7 Tinuvin 384 451.2829 13.8 Eusolex 232 274.0407 10.8 Tinuvin 109 485.2440 14.3 Uvinul D 49 274.0836 10.8 Tinuvin 238 503.2931 14.7 Supporting Information to Chapter 2 61

Name Exact Mass Expected Rt Name Exact Mass Expected Rt (min) (min) 2-Ethylhexyl-4- 276.1958 10.8 Tinuvin 292 508.4235 14.7 dimethylaminobenzoate Uvinul N-35 277.1097 10.8 BTZ-2 1 547.2888 15.4 Eusolex 6007 277.2036 10.8 ECAMSULE 562.1690 15.7 Tinuvin 282 281.1523 10.9 HPT-2 583.3405 16.0 2-Ethylhexyl-4- 289.1798 11.0 BTZ-2 2 591.3150 16.2 methoxycinnamate Tinuvin 622LD 297.1809 11.2 Cyasorb’ UV 3346 628.4105 16.8 Tinuvin 301 301.0976 11.2 HPT-1 653.4187 17.2 Benzophenone-4 308.0349 11.3 Tinuvin 360 658.3990 17.3 Eusolex 9020 310.1563 11.4 HPT-3 677.3248 17.6 Tinuvin 312 312.1468 11.4 Tinuvin 440 684.5436 17.7 Tinuvin 622 319.1989 11.5 Tinuvin 144 684.5436 17.7 Tinuvin 320 323.1992 11.6 Tinuvin-1130 704.3317 18.1 Tinuvin 531 326.1876 11.6 Chimassorb 944 706.4705 18.1 Goodrite UV 3034 338.2676 11.8 Tinuvin 840 760.3943 19.0 Tinuvin 510 339.1941 11.9 HPT-4 993.5345 23.0 (OX-1) 340.1781 11.9 Irganox 1010 1176.7835 26.1

Figure 2.7: Example on the inﬂuence of extraction temperature by pressurized liquid extraction (PLE) during method development. 80 ◦C resulted up to 25 % higher area ratios for selected UV-light stabilizers and musk fragrances than an extraction temperature of 100 ◦C. 62 Supporting Information to Chapter 2

Figure 2.8: Linear correlation between measured retention time and Log Kow for 164 reference standards with the dashed lines representing the 95% prediction interval. The retention time acceptability limit was deﬁned as the upper limit of the 95% prediction interval and should not be exceeded for a retention time to be satisfactory. rse=residual standard error Supporting Information to Chapter 2 63

Figure 2.9: Several quaternary ammonium surfactant congeners (BAC=benzyldimethylammonium compounds) were tentatively identified based on accurate masses, ionization efficiency, retention times and confirmed by purchased reference standards. 64 Supporting Information to Chapter 2

Figure 2.10: Temporal resolution of quaternary ammonium surfactant congeners and phosphorus in Lake Greifensee. Supporting Information to Chapter 2 65

Figure 2.11: Temporal resolution of the benzalkonium quaternary ammonium (BAC) congeners in Lake Greifensee with concentrations up to 12 ng/gdw. 66 Supporting Information to Chapter 2

Figure 2.12: Identiﬁcation of dichlorocarbanilide (DCC) in sediments of Lake Greifensee. The tentative identiﬁcation of DCC was made using accurate mass information, predicted retention time and isotopic pattern. Bibliography

[1] Simonich, S.L.; Begley, W.M.; Debaere, G.; Eckhoff, W.S., Trace Analysis of Fra- grance Materials in Wastewater and Treated Wastewater. Environ. Sci. & Technol. 2000, 34, (6), 959-965.

[2] Singer, H.; M¨uller, S.; Tixier, C.; Pillonel, L., Triclosan: Occurrence and Fate of a Widely Used Biocide in the Aquatic Environment: Field Measurements in Wastewater Treatment Plants, Surface Waters, and Lake Sediments. Environ. Sci. & Technol. 2002, 36, (23), 4998-5004.

[3] Carafa, R.; Wollgast, J.; Canuti, E.; Ligthart, J.; Dueri, S.; Hanke, G.; Eisenreich, S.J.; Viaroli, P.; Zaldivar, J.M., Seasonal variations of selected herbicides and related metabolites in water, sediment, seaweed and clams in the Sacca di Goro coastal lagoon (Northern Adriatic). Chemosphere 2007, 69, (10), 1625-1637.

[4] Burkhardt, M.R.; ReVello, R.C.; Smith, S.G.; Zaugg, S.D., Pressurized liquid extraction using water/isopropanol coupled with solid-phase extraction cleanup for industrial and anthropogenic waste-indicator compounds in sediment. Anal. Chim. Acta 2005, 534, (1), 89-100.

[5] Martens, D.M.; Gfrerer, M.G.; Wenzl, T.W.; Zhang, A.Z.; Gawlik, B.G.; Schramm, K.W.S.; Lankmayr, E.L.; Kettrup, A.K., Comparison of different extraction techniques for the determination of polychlorinated organic compounds in sediment. Anal Bioanal Chem 2002, 372, (4), 562-568.

[6] Vazquez-Roig, P.; Segarra, R.; Blasco, C.; Andreu, V.; Pico,´ Y., Determination of pharmaceuticals in soils and sediments by pressurized liquid extraction and liquid chromatography tandem mass spectrometry. J Chromatogr A 2010, 1217, (16), 2471- 2483.

67 68 BIBLIOGRAPHY Chapter 3

Suspect and Non-target Screening Approaches to Identify Records of Organic Contaminants in Sediments

Aurea C. Chiaia-Hernandez, Emma L. Schymanski, Kumar Praveen, and Juliane Hollender

69 70 Chapter 3. Suspect and Non-target Screening

Abstract Until now, sediments have been mainly used to characterize the contamination of compounds with highly lipophilic characteristics, whereas the knowledge of long-term contamination with more polar contaminants is not well explored. Thus, a multiresidue method was applied for the screening of lake sediments using pressurized liquid extraction and liquid-liquid partitioning followed by liquid chromatography tandem high resolution mass spectrometry (LC-HRMS/MS). Besides target analysis of 200 emerging contaminants with a broad range of physicochemical properties, suspect screening was performed on 849 biocides, pesticides and pharmaceuticals used in Switzerland. Different filter steps, including retention time based on log Kow values, ionization behavior, background subtraction, isotopic pattern and intensity cutoff were applied to the most contaminated layers of two lakes in Switzerland (around the 1970s for Lake Greifensee and 1980s for Lake Lugano). Non-target candidates were selected from the same contaminated layers using different background subtractions to match the matrix and to focus on relevant compounds. The developed filtering approaches and subsequently structure elucidation using MS/MS spectra and fragmentation prediction tools enabled the tentative identification of 23 suspect compounds with the biocides chlorophene and bromochlorophene occurring in both lakes. Furthermore, the non-target approach allowed the identification and confirmation of the mothproofing agent flucofuron and the disinfectant hexachlorophene. The results show that sediments provide historical records of chemical contamination and that suspect and non- target screening is possible even in complex matrices such as sediments.

3.1 Introduction

The analysis of micropollutants in complex matrices, e.g. soils, sludge, and sediments can be difﬁcult since the presence of natural organic matter and biomolecules can interfere with the extraction and later on with the separation and detection. Different methods have been developed for the analysis of organic contaminants like pharmaceuticals, pesticides, UV-light stabilizers and musk fragrances in sediments.1–4 Although these methods have been shown to be sensitive and selective, they aim to quantify and study only a small number of compounds. With the increasing number of synthetic compounds produced annually,5,6 generic analytical techniques to measure a broader range of compounds in a single study are becoming essential to overcome the challenges when analyzing complex matrices, containing many targets and non-target compounds at different concentrations. High resolution mass spectrometry (HRMS) using the orbitrap and time-of-ﬂight (TOF) instruments has emerged as a very power- 3.1. Introduction 71

ful tool due to its high mass accuracy, sensitivity in the full scan and resolving powers above 100,000. 7,8 Therefore, HRMS is preferable to assess the fate of a broad range of organic contaminants in environmental compartments since the analysis is much more selective and not limited to nominal masses. Generic analytical methods in combination with HRMS have been proven to be a reliable tool to screen molecular ions and their MS/MS fragments against a complex matrix background. 9 Recently, approaches have been developed for the identification of compounds without prior knowledge to assess the risk of emerging contaminants in surface water and wastewater. 7,10–14 These studies have focused on developing techniques to identify compounds in a sample without reference standards that were potential contaminants but not set apriori(i.e., suspect screening) and in the identification and structure elucidation of potential contaminants without any previous information. A suspect screening approach was performed by Ibańez˜ et al. 10 by comparing experimental data with a homemade library containing 500 compounds. Using experimental accurate masses in water samples, several pesticides, antibiotics and drugs of abuse were identified. Many studies in literature for the analysis of non-target screening, without any aprioriinformation, propose the use of different steps for the structure elucidation. Krauss et al, 7 proposed a series of steps which include prediction of molecular formula from exact mass, followed by structure search in databases, matching retention time (RT) and MS/MS fragmentation. Similar approaches for the detection of suspect and unknown compounds have been proposed by Hogenboom et al., 11 for the identification of microcontaminants in groundwater. In addition, elemental composition has been used to restrict molecular formulas based on isotopic pattern such as Cl, Br, or S due to their distinct isotope signal at M+2. 14,15 Suspect and non-target screening have developed as approaches to unravel the contamination in the environment by helping to find emerging contaminants and prevent unwanted disclosures in the future. However, the identification of suspects and non- target compounds is still an analytical challenge since software and methods to predict fragmentation patterns, ionization behavior and retention time are still under development. Commercial software are available such as Mass Frontier and Mass Fragmenter to predict mass spectral fragments using different fragmentation rules. 16,17 However, Mass Frontier, for example, does not contain many rules for negative ionization. Hill et al., 17 proposed the determination of unknown chemical structures by matching their experimental collision-induced dissociation (CID) fragmentation spectra with computational fragmentation spectra queried from chemical databases. Wolf et al. 18 combined bond-disconnection in silico fragmentation (MetFrag) with compound database search- 72 Chapter 3. Suspect and Non-target Screening

ing using KEGG, PubChem and ChemSpider and provided this as an open-access tool. Additional challenges still remain, such as the lack of comprehensive mass spectral libraries for high accuracy MS/MS and the limited comparability between CID and HCD spectra, which makes the identification of unknown compounds more challenging. Re- cent contributions to the open spectral libraries MassBank and METLIN are a step in the right direction. 19 The use of new methods for structure elucidation and further exclusion steps based on physicochemical characteristics and patterns, MS/MS fragmentation pattern and ultimately confirmation with the purchase of reference standards could facilitate the identification of non-target compounds as has been shown for GC-MS. 20 Previously a multiresidue method for the target screening of more than 200 compounds with a broad range of physicochemical properties (log Kow 0-12) was developed. 9 The method was applied to identify target compounds and to screen for some important suspects as a first step to identify potential organic contaminants that were not originally in the target list. The objectives of this study were to extend this and (i) to explore suspect screening further using around 850 compounds used in Switzerland and (ii) to develop screening approaches to identify non-target compounds in lake sediments to provide records of the historical contamination of Lake Greifensee and Lake Lugano. We aimed to clarify whether recently developed methods for suspect and non-target screening7,10–12 could also work successfully for very complex samples like sediment extracts.

3.2 Experimental Section

Details on the sources, preparation, and storage of reference standards and reagents are given elsewhere.9 Flucofuron and hexachlorophene reference standards (purity ≥ 97%) were purchased from Dr. Ehrenstorfer (Augsburg Germany).

3.2.1 Sample Collection and Preservation

Sediment cores were taken from Lake Greifensee (47◦20’58”N, 8◦40’49”E) and Lake Lugano (N45◦57’31.5”/E008◦53’38.3”) located in the north and south of Switzerland, during the summer of 2010. Sediment core samples were obtained using a free fall gravity corer and stored vertically in the dark at 4 ◦C until analysis. Dating of the sediment cores was performed by counting yearly laminations and by using 137Cs signals from Chernobyl 1986 and atomic bomb tests from 1963 as has been described else- 3.2. Experimental Section 73

where.9 Total phosphorus concentrations for each sediment layer were measured using peroxodisulfate oxidation as described by Ebina et al.21

3.2.2 Extraction, Clean-up and Enrichment of Sediment Extracts

The extraction, enrichment and analysis of the sediment samples are reported elsewhere.9 Briefly, previously frozen, individual sediment layers were freeze-dried, homogenized, weighed, and transferred to stainless steel cells. The cells were then extracted by pressurized liquid extraction (PLE) using an ASE 350 system (Dionex, Sunnyvale, USA) with a mixture of ethyl acetate and acetone in a ratio of 70:30 (% v/v) and a temperature of 80 ◦C. Extracts were spiked with 60 μL of 2.5 ng/μL internal standard mixture with an absolute amount of 150 ng of each compound. Afterwards, the extracts were gently evaporated to 100 μL with an automated evaporator system at a temperature of 30 ◦C (EZ-2 evaporation system from Genevac, Gardiner, USA). After evaporation, extracts were diluted to 2.5 mL with HPLC water. The removal of matrix from the sediment extracts was achieved by adding 5 mL of acetonitrile, followed by 1.6 g of MgSO4 and 0.4 g NH4Cl. The mixture was vortexed and centrifuged. After separation, the acetonitrile phase was transferred to a graduated centrifuge tube, evaporated and brought to a volume of 500 μL by adding methanol. The final extract was filtered into 2 mL autosample vials using 0.2 μm PTFE filters (BGB analytics, Boeckten, Switzerland).

3.2.3 Liquid Chromatography Tandem High Resolution Mass Spec- trometric Detection

The separation and detection techniques are described elsewhere. 9 In short, separation of all analytes was performed on a 2.1 x 10 mm C18 security guard cartridge connected to a 2.1 x 50 mm x 3.5 μm particle size X-bridge C18 column (Waters Corp., Milford, MA) at 35 ◦C. The mobile phase consisted of HPLC water (A), methanol (B) and isopropanol (C). Formic acid (0.1% v/v) was added to eluents A and B. Detection of analytes was performed with a combination of a linear ion trap Orbitrap (LTQ-Orbitrap-XL) and a high-performance benchtop quadrupole Orbitrap (Q-Exactive) mass spectrometer (Thermo Fisher Scientiﬁc Corp. USA). Electrospray ionization (ESI) was used in the positive and negative mode and the analysis was performed in multiple and independent injections. Full scan accurate mass spectra were acquired from 115 to 1000 Da with a nominal 74 Chapter 3. Suspect and Non-target Screening

resolving power of 60,000 referenced to m/z 400 and a mass accuracy of ± 5ppm. Data-dependent high resolution product ion spectra (DD-HR-MS/MS) were recorded at a resolving power of 7,500 for LTQ-Orbitrap-XL. Q-Exactive spectra were acquired at a nominal resolving power of 140,000 with a mass accuracy of ± 5ppm and a DD- HR-MS/MS of 17,500. Product ions were generated using energy collision dissociation (HCD) at collision energies calculated for each analyte based on its mass. In addition, collision-induced dissociation (CID) was performed with a normalized collision energy of 35%, measured at low resolution. Calibration standards, quality controls and quantification criteria used in the analysis are reported in Supporting Information (SI). The method limits of detection (LOD) was defined as the lowest concentration standard having a S/N ratio ≥ 3. Limits of quantification (LOQ) was defined as the lowest point of the calibration curve with a S/N ratio ≥10 and with a satisfactory amount of ions to generate MS/MS fragmentation. The LOD and LOQ were then divided by a calculated matrix factor for each compound and subsequently multiplied by the dilution of the matrix sample. The matrix factor and calculated matrix effect (%) of each analyte have been reported elsewhere.9

3.2.4 Suspect Screening of Further Contaminants and Transfor- mation Products

Suspect screening was used to search for compounds that were not originally in the target list, and a suspect list of 849 compound was compiled from a theoretical assessment based on consumption data. The list of suspects consists of all registered organic synthetic insecticides, fungicides, biocides and acaricides as well as all major metabolites of the most commonly used insecticides and fungicides in Switzerland. 22 The pharmaceuticals suspect list was compiled from the consumption data of Switzer- land, Germany, France and the United States (Wossner¨ et al., in preparation). Fur- thermore, the list of suspect compounds was narrowed to candidates with predicted log Kow values higher than 1 using VVCLAB software.23 Further, suspect compounds were eliminated if they were not likely to be ionized by ESI using rules (presence or absence of functional groups) similar to Moschet et al. 22 The complete list of suspect compounds, estimated to fall in the domain of the analytical method, are provided in Table 3.2 in SI. Suspect screening was performed in the top layer (∼past 10 years) and in the most contaminated sediment core layer (∼1970s) from Lake Greifensee and in one layer (∼1980) from Lake Lugano. When a suspect compound was found, the complete core 3.2. Experimental Section 75

was screened for this compound. The most contaminated layer was chosen using data for the total phosphorus and target screening analysis from Lake Greifensee, where a clear distinction between the highest period of contamination in the lake and later a fast improvement in phosphorus and organic contaminants removal is observed. 9 In Lake Lugano, a clear increase in phosphorus concentration is detected at the beginning of 1970 followed by a steady input until today. Therefore based in the target chemical analysis a layer corresponding to the 1980s was selected as illustrated in Figure 3.3. Suspect screening was performed using ToxID 2.0 (Thermo Fisher Scientific Corp., USA) to extract the exact mass of the expected ion from the HR full scan chromatogram with a mass window of ± 5 ppm. This approach is possible since the ionization with ESI produces molecular ions [M+H]+ or [M-H]−.24 Due to the difficulty of predicting ionization behavior, all suspected targets were assumed to be ionized in positive as well as in negative ionization. This generalization was applied to avoid losing compounds due to an incorrect prediction in ionization. Additionally, a retention time (RT) matching factor with a window of ± 4minwasin- cluded in the analysis. Predicted RTs were obtained using a linear correlation between measured retention time and predicted log Kow values (RT=1.65*log Kow + 4.36) obtained from 164 reference standards. 9 Data analysis was done using Xcalibur (Thermo Fisher Scientific Corp., USA) using Qual Browser for the identification of substances without reference standards. Un- equivocal confirmation is only possible if a reference standard is available.

3.2.5 Non-target Screening of Further Contaminants

Non-target candidates were obtained from an automated exact mass filtering, followed by background subtraction and recalibration of masses using internal standards by En- viMass 1.2 (Eawag, Switzerland) in combination with the peak picking software Formu- lator (Thermo Fisher Scientific Corp., USA). A sediment layer (∼100 years ago) within each core was set as a background sample where contamination was not likely to be present, to eliminate matrix peaks. In addition, a background subtraction was performed using sediment core layers adjacent to the most contaminated layer to match the sediment matrix as well as possible and identify relevant unknown masses. For example, the layer corresponding to the 1990s was subtracted from the first layer of each sediment core (last 10 years) as the background. Based on isotopic information obtained from EnviMass 1.2, the non-target list was reduced by selecting the top 10 most intense masses from each sediment layer and the 76 Chapter 3. Suspect and Non-target Screening

top 100 masses containing Cl, Br, S and/or Si. Masses higher than m/z 600 were excluded since most contaminants have lower molecular masses and only 3% of the target and suspect list were above this mass. Furthermore, the non-target list was reduced by keeping candidates containing only mass defects of 0.0, 0.1, 0.2, 0.8, and 0.9. The trend of loss mass defects was shown by Kind et al., 25 formassesupto300Da, while our sample data shows the same trend for masses up to 1000 Da, as illustrated in Figure 3.1 and 3.2 and in Figure 3.13, 3.14 and 3.15 in SI.

Figure 3.1: Lake Greifensee sediment matrix (black dots) in negative ionization with their mass defect (mDA) plotted in the y-axis against nominal (m/z) masses at the x-axis. Masses of suspect compounds are plotted as red dots, tentatively detected suspects after several ﬁlters according to Fig. 3 are marked with blue stars.

For further identiﬁcation, the ﬁnal list of non-targets were re-injected and measured in data dependant mode at different collision energies (HCD) of 15, 35 and 55% for targeted MS/MS fragmentation. 3.2. Experimental Section 77

Figure 3.2: Lake Greifensee sediment matrix (black dots) in positive ionization with their mass defect (mDA) plotted in the y-axis against nominal (m/z) masses at the x-axis. Suspect compunds are plotted in red dots.

Structure elucidation using the full scan and MS/MS data was performed using the molecular formula generator MOLGEN-MS/MS26 and MetFrag18 using ChemSpider and PubChem databases to perform compound database searches and in silico fragmentation. MOLGEN-MS/MS was used with a mass accuracy of 5ppm and 10 ppm in the MS and MS/MS spectra, respectively. Additionally, elemental restrictions, charge and exact mass were used in the analysis. MetFrag was employed using the neutral exact mass or molecular formula and by specifying the ionization mode, charge and a mass error of 5 ppm (mzppm) and an absolute error of 0.001 (mzabs). 78 Chapter 3. Suspect and Non-target Screening

3.3 Results and Discussion

3.3.1 Target Screening of Organic Contaminants in Sediment Cores of Lake Lugano

In total, 18 of 200 target compounds were detected in Lake Lugano with the most prominent substance classes corresponding to musk fragrances, personal care products (PCP), pesticides, and biocides. Figure 3.3 illustrates the organic contaminants showing temporal trends with concentrations in the ng/gdw range. The musk fragrance tonalide, the biocides triclosan and triclocarban and the quaternary ammonium surfactants congeners benzyl-dimethyl-tetradecylammonium (BAC 14) and diodecyldimethylammonium (DADMAC C12:C12), show similar pattern with increasing concentrations in the late 1970s, followed by a decline in concentrations starting in the 1990s. The biocides, irgarol, terbutryn and prometryn, and the transformation product of the musk fragrance galaxolide (galaxolidone) were detected in the ng/gdw to the pg/gdw range with increasing concentrations through the early 1990s to the highest concentrations in the most recent layers. In addition, the biocide propiconazole, the pesticides ﬂudioxonil, DEET, the isobaric compounds acetochlor and alachlor, and the corrosion inhibitor benzotriazole were detected in layers of the past 10 years. The measured concentration for all positive ﬁndings in Lake Lugano are reported in Table 3.3 in SI. The results are consistent with the screening of organic contaminants performed previously for Lake Greifensee, where the most detected compounds were biocides, musk fragrances and other personal care products. 9 Little information is available about the historical contamination of Lake Lugano, although the lake was highly contaminated in the 1960s and 1970s.27 The results from both lakes show that sediments can be integrators in time and space for emerging contaminants within a catchment, providing history of deposition over time and allowing the reconstruction of past environmental concentrations of different organic contaminants.

3.3.2 Suspect Screening of Further Contaminants and Transfor- mation Products

Following target analysis, emphasis was shifted towards the identiﬁcation of 849 suspected candidates using different ﬁltering steps as illustrated in Figure 3.4 to expand the detection of organic contaminants beyond the target list. 3.3. Results and Discussion 79

Figure 3.3: Target analysis of organic contaminants in sediments from Lake Lugano showing the temporal resolved concentrations of different personal care products (A), biocides (B) quaternary ammoniums congeners (C), and the total phosphorus concentration (D). Scales in red in graph A and B correspond to tonalide and irgarol, respectively.

The matrix from sediment cores corresponding to 1950 for Lake Greifensee and Lake Lugano were studied by plotting the mass defect (mDa) vs nominal masses (m/z). Most matrix ions show a slightly positive mass defect which is consistent with natural compounds containing C, H, N and O atoms. Figure 3.1 and 3.2 and Figure 3.14 and 3.15 in SI show that for most of the suspect compounds, there is an overlap with the sediment matrix as illustrated with black dots (sediment matrix) and red dots (suspect compounds). In addition, the matrix is more predominant in negative vs. positive ionization. Nevertheless, the number of detected masses was not signiﬁcantly different for both ionization modes ranging from 80 to 262 detected peaks out of the 849 masses. Peaks with a good chromatographic shape were selected visually following the automated data processing, eliminating an average of 80 % of possible suspect matches. The use of predicted RT with a large error window narrowed the list of suspects by an 80 Chapter 3. Suspect and Non-target Screening

Figure 3.4: Flowchart for the suspect screening of 849 compounds using exact mass data, retention time (RT), ionization behavior, blank subtraction, isotopic patterns, and intensity cutoff. Black bold numbers indicate the number of peaks filling specific criteria, while red numbers shows the number of suspects excluded per step. In addition, suspects in red are more challenging to identified and additional confirmatory steps are necessary.

additional average of 9 %. Exclusion by RT has its limitations, since there is already an error in the predicted log Kow, especially for ionic compounds. However, based on our experience with target analysis using different software to predict log Kow, the target analytes consistently fell in the RT window of ± 4min which is also in the boundaries of the 95% prediction interval of the linear correlation used. 9 In the automated data processor, 3 different ions can be assigned for detection, in this case [M+H]+,M.+ and [M+NH4]+ were used in positive ionization. The output of the results are organized according to the intensity of the adducts, with the adduct having the highest intensity is reported ﬁrst. Therefore, a ﬁlter step was included for the correct ionization ([M+H]+,M.+ and [M-H]−) status of suspects. For example, a suspect candidate could be categorized as detected at M.+, however, after looking at the functional groups, the detected peak could be rejected if the molecule was not likely to be protonated. Good chromatographic shape, 3.3. Results and Discussion 81

matching retention time with predicted log Kow and correct ionization behavior, were performed as first filter steps since no additional software tools were necessary. These three partially automatic and fast steps led already to an average elimination of around 700 exact candidate masses per sediment core layer. Subsequently, after reducing the list of suspects significantly, a manual check by visual inspection of individual extracted ion chromatograms to confirm absence in the blank (sediments from 100 years ago) was performed. Parallel to this step, isotopic patterns of Cl, Br and S were controlled and suspects for which no isotopic pattern match were found or the intensity ratio differed more than 50% from the theoretical pattern were eliminated. In the absence of a distinct isotopic signal (i.e., compounds with only C, H, O, N) a cutoff of 106 was performed to focus on the most relevant suspect compounds. In total 23 of 849 exact mass matches were selected for further analysis as illustrated in Figure 3.4 and Table 3.1. Masses matching the pharmaceuticals docosahexanoix acid and linoleic acid were detected in positive and negative ionization with different RT, and therefore additional evaluation is needed. In addition, the isobaric pharmaceuticals, alitretinoin/isotretinoin and D-fenchone/camphor, respectively, were filtered and their identification is challenging since it might be not possible to differentiate the similar structures by MS/MS fragmentation. Blue marked red dots in Figure 3.3, show the mass defect for the suspects detected in the sediment cores indicating that most of the compounds identified are at the edges of the matrix. Based on the developed filtering approaches (Figure 3.4), the biocides chlorophene and bromochlorophene were detected in both lakes and investigated further. Chlorophene could be tentatively identified using MetFrag by matching 4 fragments in the MS/MS spectrum and with a score of 0.987 (maximum value=1) as illustrated in Figure 3.5. The match of 4 characteristic fragments makes the correct identification of chlorophene most likely, however, unique identification is only possible with a purchased reference standard. The pattern of chlorophene in both lakes with time is illustrated in Figure 3.6 and 3.7 showing its highest input in the ∼1980s and ∼1990s for Lake Greifensee and Lake Lugano, respectively. However, this is based only on intensities and varying ionization efficiency in the extracts from the different layers cannot ruled out. Bromochlorophene could not be identified since a MS/MS spectrum was not available for the relatively low intense peaks. Identification of further suspects reported in Table 3.1 will be performed by comparison of acquired MS/MS spectra with predicted MS/MS fragmentation using tools such as Mass Frontier (Thermo Fisher Scientific Corp. USA) in combination with MOLGEN-MS/MS and confirm by the purchase of reference stan- 82 Chapter 3. Suspect and Non-target Screening

dards whenever available.

Figure 3.5: Spectrum of the tentatively identiﬁed biocide chlorophene in layers (∼ 1970s) from Lake Greifensee. MetFrag helped to identify four fragments, matches are marked in red and the structures are shown in the top left. A zoom out of the spectra is shown in the top right illustrating the original compound at [M-H]−.

3.3.3 Non-target Screening of Unknown Contaminants

The use of EnviMass as a ﬁrst step for the identiﬁcation of non-target candidates was useful as background subtraction and noise removal eliminated up to 60% of the picked peaks, as shown in Figure 3.8. The overall results illustrate that only 1% of the picked masses are known masses (target compounds and internal standards), and that 33% of the masses remain unknown. The results are similar for negative and positive ionization for both lakes although slightly more unknowns (52%) were found for the negative mode in Lake Greifensee. In all sediment analysis, the non-target candidates contained between 9-18% peaks with isotopic pattern, 1-5% adducts and around 80% monoisotopic peaks (Figure 6). Additionally, the overall analysis reports in negative ionization more non-target candidates containing Cl, Br or S than in positive ionization. For example, 3.3. Results and Discussion 83

Figure 3.6: Temporal resolution of suspected compunds and non-target compunds (ﬂu- cofuron and hexachlorophene) in sediments of Lake Greifensee.

in Lake Greifensee only 30 exact masses contained Cl, Br or S were found in positive ionization while 740 were found in negative. Non-targets candidates with a distinct isotope signal as in the case of Cl, Br and S were prioritized for structure elucidation and further analysis. The successful identification of flucofuron and hexachlorophene was performed starting with more than 1000 possible molecular formulas (without restrictions) for each compound. Furthermore MS and MS/MS spectra were gathered and used in MOLGEN-MS/MS and MetFrag working in parallel as illustrated for hexachlorophene and flucofuron in Figure 3.9 and 3.10. For hexachlorophene, two molecular formulas were assigned using MOLGEN MS/MS MOLGEN-MS/MS and candidates with 6 different molecular formulas were retrieved using MetFrag. Five of these formulas could be eliminated since the isotopic pattern obtained did not match the MS spectra. The correct molecular formula assigned was obtained based on the highest ranked formula of the 2 candidates using MOLGEN- MS/MS with a score of 0.96 for C13H6Cl6O2 and the remaining candidate in MetFrag having the same molecular formula. Furthermore, 11 structures were proposed using MetFrag, and MS/MS fragmentation was examined. MOLGEN-MS/MS could ex- plain 4 fragments based on the MS/MS spectra, while MetFrag explained only up to 3 84 Chapter 3. Suspect and Non-target Screening

Figure 3.7: Temporal resolution of suspected compunds and non-target compunds (ﬂu- cofuron and hexachlorophene) in sediments of Lake Lugano.

fragments within the proposed structures, with 2 fragments common to all the 11 candidates. Consequently, predicted log Kow values based on RT were controlled, and the non-target candidates were arranged according to the number of references and articles in ChemSpider and PubMed. Orthogonal filters using a variety of databases sources to refine the number of associated reference has shown to be very useful for the identification of non-targets. 28 Hexachlorophene ranked in the top of the list with 909 references in ChemSpider and 657 in PubMed, and was confirmed finally by the purchase of a reference standard as shown in Figure 3.11. Figure 3.6 and 3.7 show the pattern of intensities over time in the sediment cores. The mothproofing agent flucofuron was identified following similar approaches. How- ever, at the beginning of the workflow no clear molecular formula could be assigned due to the low match scores obtained using the two programs, and with only one prominent fragment in the MS/MS spectrum. Therefore the exact mass was explored using Met- Frag adduct options ([M+Cl]−), resulting in 298 molecular formulas with higher score matches and hits in molecular formula containing F. Consequently, the correct exact mass (414.9846) was searched in the MS spectrum and a more intense peak (106 vs 105), matching retention time and fragmentation of its Cl adduct, was observed. The 3.3. Results and Discussion 85

Figure 3.8: Pie chart of Analysis of sediment layers of Lake Greifensee (∼1970s) and Lake Lugano using EnviMass. Red and black dots represent target compounds and internal standards respectively.

new exact mass, MS and MS/MS spectra as well as elemental restrictions containing F were used in MOLGEN-MS/MS resulting in more than 140 molecular formulas with a high rank number for C15H8Cl2F6N2O with a score 0.96. Furthermore, the proposed molecular formula was used in MetFrag, ranking flucofuron No.1 with a score of 1.0 within 5 proposed structures. Further, MS/MS fragmentation, predicted log Kow and database search, helped to refined the non-target candidates as illustrate in Figure 3.12. Flucofuron ranked in the top of the list with 22 and 2 references in ChemSpider and PubMed, respectively, and confirmation was reached using the purchased reference standards as shown in Figure 3.12. Additionally, Figure 3.6 and 3.7 illustrates the temporal pattern in sediments of the mothproofing agent flucofuron.

3.3.4 Insights and Future Research

The analysis of micropollutants in sediments is more challenging compared to water samples since the high occurrence of natural organic matter and biomolecules containing mainly C, H, O, and N can interfere with the analysis of organic contaminants. However, in this study, suspect and non-target screening was possible with the successful identiﬁcation of 2 non-target compunds and the tentative identiﬁcation of 18 suspects. The combination of suspect and non-target screening gave insight to the detection of two disinfectants; chlorophene and hexachlorophene. Based on the results, penta, tetra, and di-chlorophene were screening in Lake Lugano and Lake Greifensee, result- 86 Chapter 3. Suspect and Non-target Screening

Figure 3.9: Flowchart for the identiﬁcation of the disinfectant hexachlorophene. Criteria used for the identiﬁcation are illustrated at each step.

ing in the tentative identification of dichlorophene which is also a disinfectant in use (Figure 3.6 and 3.7). This shows that the results of the target, suspect and non-target screening can aid to focus on specific compounds with similar characteristics as the ones already identified, as in the case of chlorophene and its different chlorine congeners. In this study, the identification of suspect and non-target candidates is biased towards structures containing halogens or a clear isotopic pattern (Cl, Br and S) and a negative mass defects which are outside the range where the matrix is very prominent (Fig. 3.1 and 3.2 ). Both characteristics facilitate the suspect and non-target screening approaches significantly. As many anthropogenic compounds and toxic compounds in the environment contain halogens this bias is reasonable but future research should address also the challenge in identification of toxicants without halogens and S. 3.3. Results and Discussion 87

Figure 3.10: Flowchart for the identification of the mothproofing agent flucofuron. Cri- teria used for the identification is illustrated at each step. 88 Chapter 3. Suspect and Non-target Screening al .:Fnlls fdtce upce opud codn oteflwhr rmFgr . n diinlexclu- additional and 3.4 Figure from flowchart the to according K compounds log suspected steps. detected sion of list Final 3.1: Table 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 No ioecacid Linoleic acid Docosahexanoic Chlorophene Bromochlorophene Erlotinib acid Eicosapentaenoic acid Docosahexanoic D-fenchone Chlorphenamine Chlorophene Camphor Bromochlorophene Alitretinoin Soneclosan Simvastatin Oxetacaine alanine (methoxyacetyl) 6-dimethylphenyl)-N- N-(2 Mefloquine Lufenuron acid Linoleic Ketoconazole Isotretinoin Hexadecyl(2-ethylhexanoate) acid Gamolenic Fluticasone Flunarizine acid Flufenamic Name ow auswr rdce sn VVCLAB. using predicted were values C14H19NO4 oeua Formula Molecular C22H23N3O4 C10H16O C18H32O2 C20H28O2 C17H8Cl2F8N2O3 C10H16O C13H11ClO C17H16F6N2O C20H28O2 C26H26F2N2 C25H38O5 C28H41N3O3 C22H27F3O4S C12H8Cl2O2 C18H32O2 C13H11ClO C26H28Cl2N4O4 C18H30O2 C14H10F3NO2 C16H19ClN2 C13H8Br2Cl2O2 C13H8Br2Cl2O2 C22H32O2 C22H32O2 C20H30O2 C24H48O2 70630-17-0 76-22-2 80969-37-5 1195-79-5 15435-29-7 183321-74-6 4759-48-2 79902-63-09 120-32-1 80969-37-5 120-32-1 CAS# 40218-96-0 3380-30-1 126-27-2 506-26-3 49752-90-1 90566-53-3 77-38-3 103055-07-8 6217-54-5 530-78-9 6217-54-5 65277-41-1 10417-94-4 5435-29-7 15435-29-7 59130-697 Pesticide Pharmaceutical Class Pharmaceutical Pharmaceutical/Biocide Pharmaceutical Pharamceutical/Biocide Pharmaceutical Pharmaceutical/Biocide Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Biocide Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Pharmaceutical Pesticide Pharmaceutical Pharmaceutical Pharmaceutical/Biocide Pharmaceutical/Biocide Pharmaceutical 23 1.22 ± o K log 2.54 ± 4.35 ± 2.31 ± 6.90 ± 5.89 ± 2.85 ± 3.54 ± 6.90 ± 6.47 ± 6.52 ± 4.80 ± 5.71 ± 3.53 ± 3.76 ± 2.51 ± 4.74 ± 4.20 ± 6.96 ± 5.89 ± 6.96 ± 4.20 ± 4.12 ± 6.47 ± 9.70 ± 5.95 ± 5.95 ± ow 0.68 0.47 0.23 0.65 0.55 1.49 0.19 0.14 0.55 0.63 0.81 0.28 0.34 0.29 0.48 0.88 0.32 0.21 1.22 1.49 1.22 0.21 0.31 1.03 0.61 0.38 0.38 Ionization Negative Positive Negative Positive Positive Positive Negative Positive Positive Negative Negative Negative Positive Negative Negative Negative Negative Negative Negative Positive Positive Negative Negative Negative Positive Negative Negative Lake Greifensee Greifensee Lugano Greifensee Greifensee Lugano Lugano Greifensee Greifensee Greifensee Lugano Greifensee Greifensee Greifensee Lugano Lugano Lugano Lugano Lugano Lugano Greifensee Greifensee Lugano Greifensee Greifensee Lugano Greifensee Δ 0.5 2.3 4.2 2.6 4.6 3.7 0.3 2.5 0.3 2 0.1 0.1 0.4 0.9 2.6 2 0.8 1.6 0.8 1.8 4 2.4 0 0.2 0.8 3.8 1.6 (ppm) m (min) rdce RT Predicted 7.6 11.6 6.5 18.2 11.2 9.9 16.8 14 9 16.8 12.9 10.7 6.7 16.4 14.5 18.2 12.9 19.3 10.7 10.3 11.7 10.3 15 17.3 14.5 21.9 14.5 (min) RT Detected 6.6 9.4 13.5 14.2 11.1 13 15.8 9.4 19.5 13.6 14.7 9.8 9.1 18.7 16 14.4 14.1 12.8 18.6 8.2 14.5 18.6 13.4 18.6 15.9 21.1 16.6 Intensity 1E+06 2E+06 2E+06 2E+06 4E+08 2E+06 1E+07 1E+07 3E+06 1E+06 2E+06 2E+04 1E+06 2E+06 3E+04 2E+06 4E+08 1E+06 4E+06 6E+06 9E+05 9E+07 2E+05 1E+07 2E+06 2E+06 2E+04 3.3. Results and Discussion 89

Figure 3.11: Chromatogram, isotopic pattern and MS/MS fragmentation used for the identiﬁcation of the disinfectant hexachlorophene. The 4 fragments shown were identi- ﬁed successfully by MOLGEN-MSMS, while MetFrag could identify fragments in green. 90 Chapter 3. Suspect and Non-target Screening

Figure 3.12: Chromatogram, isotopic pattern and MS/MS fragmentation used for the identification of the mothproofing agent flucofuron. 3.3. Results and Discussion 91

Acknowledgements

The authors thank Birgit Beck for her help in the sediment analysis, Heinz Singer and Philipp Longree´ for their advice on HRMS/MS, Lee Ferguson for his input in suspect and non-target screening, Markus Moest, Piet Spaak and Flavio Anselmetti for their help in the collection of sediment cores. Funding by the Swiss National Science Foundation (SNF CR32I3 125211) is gratefully acknowledged.

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97 98 Supporting Information to Chapter 3

Liquid Chromatography Tandem High Resolution Mass Spectrometric Detection

Calibration standards (n=9) were made in methanol with concentrations ranging from 0.5 ng to 400 ng of standard mix solution (corresponding to nominal final concentration of 1 μg/L to 800 μg/L in vial). Full scan precursors were used for quantification and product ions were used for identification. Accurate masses for the detection, quantification and internal standards used as well as detector specifications and analysis settings have been reported elsewhere. 1 Quality control accounted for more than 30% of the samples which included check standards and blank samples. Sample duplicates accounted for 20% of the total samples and were run randomly in each sequence and during different days. Data analysis was done by Xcalibur software (Thermo Scien- tific Corp, USA) using Quan Browser for the quantification of analytes with reference standards. Table 3.2: List of compounds used for the suspect screening analysis base in consumption data.

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) (d)-Limonene 5989-27-5 C10H16 3.2 9.7 137.1325 135.1179 Biocide (E)-Octadec-2-enal 51534-37- C18H34O 7.0 15.9 267.2682 265.2537 Biocide 3 (E,Z)-Octadecadi-2,13- 99577-57- C18H32O 6.6 15.3 265.2526 263.2380 Biocide enal 8 (Z,E)-Tetradeca-9,12- 30507-70- C16H28O2 5.0 12.6 253.2162 251.2017 Biocide dienylacetat 1 1-(3,5-dichlorophenyl)-5- C11H13Cl2N3O2 2.7 8.9 290.0458 288.0312 Insecticide/Fungicide isopropyl biuret 1-(6-ﬂuoro-2-benzothiazol- C9H8FNOS 2.1 7.9 198.0383 196.0238 Insecticide/Fungicide 2-yl)ethanol 1-(6-ﬂuoro-2- C9H9FN2S 2.0 7.7 197.0543 195.0398 Insecticide/Fungicide benzothiazolyl)ethylamine 1,3-Dichlor-5-ethyl-5- 89415-87- C6H8Cl2N2O2 1.6 7.0 211.0036 208.9890 Biocide methylimidazolidin-2,4- 2 dion 1,4-Dichlorbenzol 106-46-7 C6H4Cl2 3.2 9.6 146.9763 144.9617 Biocide 1-[(6-chloro-3- C9H8ClN5O2 1.5 6.8 254.0439 252.0294 Insecticide/Fungicide charge pyridinyl)methyl]n-nitro- 1h-imidazol-2-amine 1-[2-[2-chloro-4-(4-chloro- C16H13Cl2N3O3 3.2 9.7 366.0407 364.0261 Insecticide/Fungicide phenoxy)-phenyl]-2-1h- [1,2,4]triazol-yl]-ethanol 1-Decanol 112-30-1 C10H22O 4.1 11.1 159.1743 157.1598 Biocide 2-(4-chlorophenyl)-2- C20H20ClNO4 2.8 9.1 374.1154 372.1008 Insecticide/Fungicide hydroxy-n-[2-(3-methoxy- 4-prop-2-ynyloxy-phenyl)- ethyl]-acetamide 2,2’-Dithiobis[N- 2527-58-4 C16H16N2O2S2 2.7 8.9 333.0726 331.0580 Biocide methylbenzamid] 2,3.dihydro-2,2-diemethyl- C10H12O2 2.2 8.1 165.0910 163.0765 Insecticide/Fungicide 7-benzofuranol 2,4-dichlorobenzyl alcohol 1777-82-8 C7H6Cl2O 2.4 8.3 176.9868 174.9723 Pharma 2-[6-(2- C18H11N3O4 3.8 10.6 334.0822 332.0677 Insecticide/Fungicide cyanophenoxy)pyrimidin- 4-yloxy]benzoic acid 2-aminobenzimidazole 934-32-7 C7H6N3 1.3 6.6 133.0634 131.0489 Insecticide/Fungicide Supporting Information to Chapter 3 99

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) 2-Butyl-benzo[d]isothiazol- 4299-07-4 C11H13NOS 2.9 9.2 208.0791 206.0645 Biocide 3-on 2-dimethylamino-5,6- 40778-16- C8H13N3O 1.8 7.3 168.1131 166.0986 Insecticide/Fungicide dimethylpyrimidin-4-ol 3 2-methyl-2-(4-(2-methyl- C13H17F3N4O4 4.4 11.7 351.1275 349.1129 Insecticide/Fungicide 3-piperidin-1-yl-propyl)- phenyl)-propionic acid 2-phenoxyethanol 122-99-6 C8H10O2 1.1 6.2 139.0754 137.0608 Pharma 3-(2-((1h-1,2,4-triazol- C15H17Cl2N3O3 2.9 9.1 358.0720 356.0574 Insecticide/Fungicide 1-yl)methyl)-2-(2,4- dichlorophenyl)-1,3- dioxolan-4-yl)propan-1-ol 3-(4-cyclopropyl-6- C14H15N3O 2.9 9.2 242.1288 240.1142 Insecticide/Fungicide methylpyrimidin-2- ylamino)phenol 3,4,5,6-tetrabromo-o- 576-55-6 C7H4Br4O 5.6 13.6 420.7068 418.6923 Pharma cresol 3,5,6-trichloro-2-pyridinol 6515-38-4 C5H2Cl3NO 3.1 9.4 197.9275 195.9129 Insecticide/Fungicide 3,5-dichloro-2,4- 83121-15- C6H3Cl2F2N 2.6 8.7 197.9683 195.9538 Insecticide/Fungicide diﬂuoroaniline 7 3-hydroxycarbofuran 16655-82- C12H15NO4 1.1 6.2 238.1074 236.0928 Insecticide/Fungicide 6 3-ketocarbofuran 16709-30- C12H13NO4 1.6 7.0 236.0917 234.0772 Insecticide/Fungicide 1 4-(2-cyanophenoxy)-6- C11H7N3O2 2.3 8.1 214.0611 212.0465 Insecticide/Fungicide hydroxypyrimidine 4-(n’-(3,5- C22H26N2O4 4.0 11.0 383.1965 381.1820 Insecticide/Fungicide dimethylbenzoyl-n-(1,1- dimethylethyl)hydrazino- carbonyl)phenyl acetic acid 4-bromo-2,6- 58170-30- C8H9BrO 3.5 10.1 200.9910 198.9764 Pharma dimethylphenol 2 4-hydroxy-2,5,6- C8HCl3N2O 3.2 9.6 246.9227 244.9082 Insecticide/Fungicide trichloroisophtalonitrile 5,6-dimethyl-2- C8H10O 2.7 8.8 123.0804 121.0659 Insecticide/Fungicide (methylamino)pyrimidin-4- ol 6- 128275- C14H15NO5 1.7 7.2 278.1023 276.0877 Biocide (Phthalimid)peroxyhexan- 31-0 saure¨ 6-chloronicotinic acid 5326-23-8 C6H4ClNO2 1.2 6.4 158.0003 155.9858 Insecticide/Fungicide Abacavir 136470- C14H18N6O 1.6 7.0 287.1615 285.1469 Pharma 78-5 Abamectin 71751-41- C44H66O14 4.1 11.1 819.4525 817.4380 Insecticide/Fungicide 2 Acebutolol 37517-30- C18H28N2O4 1.2 6.3 337.2122 335.1976 Pharma 9 Aceclofenac 89796-99- C16H13Cl2NO4 3.9 10.8 354.0294 352.0149 Pharma 6 Acemetacin 53164-05- C21H18ClNO6 4.1 11.2 416.0895 414.0750 Pharma 9 Acequinocyl 57960-19- C24H32O4 6.2 14.5 385.2373 383.2228 Insecticide/Fungicide 7 Acetamiprid 135410- C10H11ClN4 1.1 6.2 223.0745 221.0599 Insecticide/Fungicide 20-7 Acetylsalicylic acid 50-78-2 C9H8O4 1.1 6.2 181.0495 179.0350 Pharma Acibenzolar-s-methyl 135158- C7H4N2OS2 2.6 8.6 196.9838 194.9692 Insecticide/Fungicide 54-2 Acitretin 54757-46- C21H26O3 7.2 16.2 327.1955 325.1809 Pharma 9 Aclonifen 74070-46- C12H9ClN2O3 3.5 10.1 265.0374 263.0229 Herbicide charge 5 Adapalene 106685- C28H28O3 8.5 18.4 413.2111 411.1966 Pharma 40-9 Agomelatine 138112- C15H17NO2 2.8 8.9 244.1332 242.1187 Pharma 76-2 Ajmaline 4360-12-7 C20H26N2O2 1.2 6.3 327.2067 325.1922 Pharma Albendazole 54965-21- C12H15N3O2S 3.1 9.5 266.0958 264.0812 Pharma 8 Alfuzosin 81403-80- C19H27N5O4 1.9 7.4 390.2136 388.1990 Pharma 7 100 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Aliskiren 173334- C30H53N3O6 2.8 8.9 552.4007 550.3862 Pharma 57-1 Alitretinoin 302-79-4 C20H28O2 7.9 17.3 301.2162 299.2017 Pharma Alizapride 59338-93- C16H21N5O2 1.8 7.3 316.1768 314.1622 Pharma 1 Almotriptan 154323- C17H25N3O2S 2.1 7.9 336.1740 334.1595 Pharma 57-6 Alprazolam 28981-97- C17H13ClN4 3.9 10.7 309.0902 307.0756 Pharma 7 Amantadine 768-94-5 C10H17N 2.4 8.4 152.1434 150.1288 Pharma Ambroxol 18683-91- C13H18Br2N2O 3.1 9.5 376.9859 374.9713 Pharma 5 Amfepramone 90-84-6 C13H19NO 3.0 9.4 206.1539 204.1394 Pharma Amine, C10-16- 70592-80- C15H33NO 4.4 11.6 244.2635 242.2489 Biocide charge Alkyldimethyl-, N-Oxide 2 Amiodarone 1951-25-3 C25H29I2NO3 8.8 18.9 646.0310 644.0164 Pharma Amisulpride 71675-85- C17H27N3O4S 1.1 6.2 370.1795 368.1650 Pharma 9 Amitraz 33089-61- C19H23N3 5.4 13.3 294.1965 292.1819 Insecticide/Fungicide 1 Amitriptyline 50-48-6 C20H23N 5.0 12.5 278.1903 276.1758 Pharma Amitriptylinoxide 4290-60-2 C20H23NO 4.2 11.3 294.1852 292.1707 Pharma charge Amlodipine 88150-42- C20H25ClN2O5 2.1 7.8 409.1525 407.1379 Pharma 9 Amorolﬁne 78613-35- C21H35NO 6.0 14.3 318.2791 316.2646 Pharma 1 Amphetamine 300-62-9 C9H13N 1.8 7.3 136.1121 134.0975 Pharma Ampicillin 69-53-4 C16H19N3O4S 1.5 6.8 350.1169 348.1024 Pharma Amylmetacresol 1300-94-3 C12H18O 4.6 11.9 179.1430 177.1285 Pharma Anastrozole 120511- C17H19N5 2.4 8.3 294.1713 292.1568 Pharma 73-1 Anethole 104-46-1 C10H12O 3.4 10.0 149.0961 147.0815 Pharma Apomorphine 58-00-4 C17H17NO2 2.8 8.9 268.1332 266.1187 Pharma Aprepitant 170729- C23H21F7N4O3 3.0 9.3 535.1575 533.1429 Pharma 80-3 Argatroban 74863-84- C23H36N6O5S 1.4 6.6 509.2541 507.2395 Pharma 6 Aripiprazole 24-29-3 C23H27Cl2N3O2 5.3 13.1 448.1553 446.1408 Pharma Articaine 23964-58- C13H20N2O3S 2.1 7.8 285.1267 283.1122 Pharma 1 Atazanavir 198904- C38H52N6O7 2.9 9.1 705.3970 703.3825 Pharma 31-3 Atomoxetine 83015-26- C17H21NO 4.2 11.3 256.1696 254.1550 Pharma 3 Atorvastatin 134523- C33H35FN2O5 6.4 14.9 559.2603 557.2457 Pharma 03-8 Atracurium besilate 64228-79- C53H72N2O12 9.8 20.5 929.5158 927.5012 Pharma charge 1 Atropine 101-31-5 C17H23NO3 1.9 7.5 290.1751 288.1605 Pharma Azaconazole 60207-31- C12H11Cl2N3O2 2.9 9.2 300.0301 298.0156 Insecticide/Fungicide 0 Azelaic acid 123-99-9 C9H16O4 1.7 7.2 189.1121 187.0976 Pharma Azelastine 58581-89- C22H24ClN3O 5.7 13.8 382.1681 380.1535 Pharma 8 Azoxystrobin free acid (e)- C21H15N3O5 3.8 10.7 390.1084 388.0939 Insecticide/Fungicide 2-(2-[6-cyanophenoxy)- pyrimidin-4-yloxyl]-phenyl- 3-methoxyacrylic acid Bambuterol 81732-65- C18H29N3O5 1.5 6.8 368.2180 366.2034 Pharma 2 Bamipine 4945-47-5 C19H24N2 4.1 11.1 281.2012 279.1867 Pharma Beclometasone 4419-39-0 C22H29ClO5 2.0 7.7 409.1776 407.1631 Pharma Beﬂubutamid 113614- C18H17F4NO2 4.5 11.7 356.1268 354.1123 Herbicide 08-7 Bemetizide 1824-52-8 C15H16ClN3O4S2 1.9 7.5 402.0344 400.0198 Pharma Benalaxyl 71626-11- C20H23NO3 4.1 11.2 326.1751 324.1605 Insecticide/Fungicide 4 Benazepril 86541-75- C24H28N2O5 3.5 10.1 425.2071 423.1925 Pharma 5 Bendiocarb 22781-23- C11H13NO4 1.7 7.2 224.0917 222.0772 Biocide 3 Benfotiamine 22457-89- C19H23N4O6PS 1.1 6.2 467.1149 465.1003 Pharma 2 Supporting Information to Chapter 3 101

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Benperidol 2062-84-2 C22H24FN3O2 3.6 10.2 382.1925 380.1780 Pharma Benproperine 2156-27-6 C21H27NO 5.6 13.6 310.2165 308.2020 Pharma Benthiavalicarb 413615- C18H24FN3O3S 3.6 10.3 382.1595 380.1450 Insecticide/Fungicide 35-7 Benzalkonium chloride 10328-35- C21H38N 4.9 12.5 305.3077 303.2931 Pharma charge 5 Benzatropine 86-13-5 C21H25NO 4.3 11.4 308.2009 306.1863 Pharma Benzbromarone 3562-84-3 C17H12Br2O3 6.0 14.3 422.9226 420.9080 Pharma Benzocaine 23239-88- C9H11NO2 1.8 7.3 166.0863 164.0717 Pharma 5 Benzoic acid 65-85-0 C7H6O2 1.6 7.1 123.0441 121.0295 Insecticide/Fungicide Benzoic acid 65-85-0 C21H24ClNO2S 1.9 7.4 390.1289 388.1144 Pharma Benzothiazol-2-thiol 149-30-4 C7H5NS2 2.9 9.1 167.9936 165.9791 Biocide Benzoxoniumchlorid 19379-90- C23H42NO2 1.3 6.4 365.3288 363.3143 Biocide charge 9 Benzoyl peroxide 94-36-0 C14H10O4 3.4 10.0 243.0652 241.0506 Pharma Benzydamine 642-72-8 C19H23N3O 4.2 11.3 310.1914 308.1768 Pharma Benzyl-C8-18- 63449-41- C19H34N 1.7 7.2 277.2764 275.2618 Biocide charge alkyldimethyl-, Chloride 2 Beta-acetyldigoxin 5355-48-6 C43H66O15 1.5 6.8 823.4474 821.4329 Pharma Betacarotene 7235-40-7 C40H56 17.6 33.4 537.4455 535.4309 Pharma Betamethasone 378-44-9 C22H29FO5 1.7 7.2 393.2072 391.1926 Pharma Beta-sitosterol 83-46-5 C29H50O 9.7 20.3 415.3934 413.3789 Pharma Betaxolol 337376- C18H29NO3 3.0 9.3 308.2220 306.2075 Pharma 15-5 Bethoxazin 163269- C11H9NO2S2 2.0 7.7 252.0147 250.0002 Biocide 30-5 Bexarotene 153559- C24H28O2 8.0 17.5 349.2162 347.2017 Pharma 49-0 Bicalutamide 90357-06- C18H14F4N2O4S 2.3 8.2 431.0683 429.0538 Pharma 5 Bifenazate 149877- C17H20N2O3 4.0 10.9 301.1547 299.1401 Insecticide/Fungicide 41-8 Bifenox 42576-02- C14H9Cl2NO5 4.9 12.4 341.9931 339.9785 Herbicide charge 3 Bifonazole 60628-96- C22H18N2 5.7 13.8 311.1543 309.1397 Pharma 8 Biperiden 514-65-8 C21H29NO 5.0 12.5 312.2322 310.2176 Pharma Bisacodyl 603-50-9 C22H19NO4 3.4 9.9 362.1387 360.1241 Pharma Bisoprolol 66722-44- C18H31NO4 1.8 7.4 326.2326 324.2180 Pharma 9 Bornaprine 20448-86- C21H31NO2 5.4 13.2 330.2428 328.2282 Pharma 6 Borneol 10385-78- C10H18O 2.9 9.1 155.1430 153.1285 Pharma 1 Boscalid 188425- C18H12Cl2N2O 4.9 12.5 343.0399 341.0254 Insecticide/Fungicide 85-6 Bosentan 147536- C27H29N5O6S 3.1 9.4 552.1911 550.1766 Pharma 97-8 Brodifacoum 56073-10- C31H23BrO3 8.5 18.4 523.0903 521.0758 Biocide 0 Bromadiolone 28772-56- C30H23BrO4 4.1 11.1 527.0852 525.0707 Biocide 7 Bromazepam 1812-30-2 C14H10BrN3O 1.9 7.5 316.0080 313.9934 Pharma Bromchlorophene 15435-29- C13H8Br2Cl2O2 6.1 14.5 424.8341 422.8195 Pharma 7 Bromhexine 3572-43-8 C14H20Br2N2 4.9 12.4 375.0066 372.9920 Pharma Bromochloroquinolinol 857762- C9H5BrClNO 3.2 9.6 257.9316 255.9170 Pharma 32-4 Bromocriptine 25614-03- C32H40BrN5O5 4.0 11.0 654.2286 652.2140 Pharma 3 Bromopropylate 18181-80- C17H16Br2O3 5.0 12.6 426.9539 424.9393 Insecticide/Fungicide 1 Bromothalonil 35691-65- C6H6Br2N2 1.8 7.3 264.8970 262.8825 Biocide 7 Budesonide 51333-22- C25H34O6 2.6 8.7 431.2428 429.2283 Pharma 3 Budipine 57982-78- C21H27N 5.1 12.8 294.2216 292.2071 Pharma 2 Bufexamac 2438-72-4 C12H17NO3 2.0 7.6 224.1281 222.1136 Pharma Buﬂomedil 55837-25- C17H25NO4 3.0 9.3 308.1856 306.1711 Pharma 7 102 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Bumetanide 28395-03- C17H20N2O5S 2.6 8.6 365.1166 363.1020 Pharma 1 Bunazosin 80755-51- C19H27N5O3 2.4 8.4 374.2187 372.2041 Pharma 7 Bupirimate 41483-43- C13H24N4O3S 2.2 8.0 317.1642 315.1496 Insecticide/Fungicide 6 Bupivacaine 38396-39- C18H28N2O 3.4 10.0 289.2274 287.2129 Pharma 3 Buprenorphine 52485-79- C29H41NO4 4.9 12.4 468.3108 466.2963 Pharma 7 Buprofezin 69327-76- C16H23N3OS 3.9 10.7 306.1635 304.1489 Insecticide/Fungicide 0 Bupropion 34911-55- C13H18ClNO 3.9 10.7 240.1150 238.1004 Pharma 2 Buspirone 36505-84- C21H31N5O2 2.3 8.2 386.2551 384.2405 Pharma 7 Butalbital 23554-70- C11H16N2O3 1.9 7.4 225.1234 223.1088 Pharma 3 Butorphanol 42408-82- C21H29NO2 3.7 10.4 328.2271 326.2126 Pharma 2 Butralin 33629-47- C14H21N3O4 4.7 12.0 296.1605 294.1459 Herbicide charge 9 C12-14- 85409-23- C23H42N 4.6 11.9 333.3390 331.3244 Biocide charge Alkyl[(ethylphenyl)methyl]- 1 dimethyl-, Chloride Cabergoline 81409-90- C26H37N5O2 2.8 9.0 452.3020 450.2874 Pharma 7 Calcipotriene 112965- C27H40O3 7.2 16.3 413.3050 411.2905 Pharma 21-6 Calcitriol 32222-06- C27H44O3 7.6 16.9 417.3363 415.3218 Pharma 3 Camphor 76-22-2 C10H16O 3.0 9.4 153.1274 151.1128 Pharma Candesartan cilexetil 145040- C33H34N6O6 6.8 15.6 611.2613 609.2467 Pharma 37-5 Capsaicin 404-86-4 C18H27NO3 4.0 11.0 306.2064 304.1918 Pharma Captan 133-06-2 C9H8Cl3NO2S 3.2 9.7 299.9414 297.9269 Insecticide/Fungicide Carbendazim 10605-21- C9H9N3O2 1.8 7.3 192.0768 190.0622 Insecticide/Fungicide/- 7 Biocide Carbofuran 1563-66-2 C12H15NO3 2.1 7.7 222.1125 220.0979 Insecticide/Fungicide Carbosulfan 55285-14- C20H32N2O3S 5.2 12.9 381.2206 379.2061 Insecticide/Fungicide 8 Carfentrazone-ethyl 128639- C15H14Cl2F3N3O3 3.9 10.8 412.0437 410.0292 Herbicide 02-1 Carisoprodol 78-44-4 C12H24N2O4 2.4 8.3 261.1809 259.1663 Pharma Carvedilol 72956-09- C24H26N2O4 3.1 9.5 407.1965 405.1820 Pharma 3 Cefdinir 91832-40- C14H13N5O5S2 1.5 6.8 396.0431 394.0285 Pharma 5 Celecoxib 184007- C17H14F3N3O2S 3.5 10.1 382.0832 380.0686 Pharma 95-2 Celiprolol 56980-93- C20H33N3O4 1.9 7.5 380.2544 378.2398 Pharma 9 Cga 355190 C8H10ClN3O2S 1.0 5.9 248.0255 246.0109 Insecticide/Fungicide Chlordiazepoxide 58-25-3 C16H14ClN3O 2.4 8.4 300.0898 298.0753 Pharma charge Chlorfenapyr 122453- C15H11BrClF3N2O 4.8 12.3 406.9768 404.9623 Biocide 73-0 Chlorfenvinphos 18708-87- C12H14Cl3O4P 4.3 11.5 358.9768 356.9623 Insecticide/Fungicide 7 Chlorhexidine 55-56-1 C22H30Cl2N10 4.4 11.6 505.2105 503.1959 Pharma/Biocide Chlorhexidine digluconate 18472-51- C34H54Cl2N10O14 3.5 10.2 897.3271 895.3125 Biocide 0 Chlormadinone 8065-91-6 C23H29ClO4 4.0 10.9 405.1827 403.1682 Pharma Chlorocarvacrol 5665-94-1 C10H13ClO 4.2 11.2 185.0728 183.0582 Pharma Chlorocresol 59-50-7 C7H7ClO 2.7 8.8 143.0258 141.0113 Pharma/Biocide Chlorophacinon 3691-35-8 C23H15ClO3 2.0 7.6 375.0782 373.0637 Biocide Chlorophene 120-32-1 C13H11ClO 3.6 10.3 219.0571 217.0426 Biocide/Pharma Chloroquine 58175-87- C18H26ClN3 4.5 11.8 320.1888 318.1742 Pharma 4 Chlorothalonil (r611968) C8H3Cl3N2O2 2.8 9.0 264.9333 262.9187 Insecticide/Fungicide Chlorphenamine 132-22-9 C16H19ClN2 3.8 10.7 275.1310 273.1164 Pharma Chlorphenoxamine 77-38-3 C18H22ClNO 4.2 11.3 304.1463 302.1317 Pharma Chlorprothixene 113-59-7 C18H18ClNS 5.1 12.8 316.0921 314.0776 Pharma Chlorpyrifos-methyl 5598-13-0 C7H7Cl3NO3PS 4.1 11.1 321.9023 319.8877 Insecticide/Fungicide Supporting Information to Chapter 3 103

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Chlortalidone 82410-32- C14H11ClN2O4S 1.0 6.0 339.0201 337.0055 Pharma 0 Ciclopirox 29342-05- C12H17NO2 2.7 8.9 208.1332 206.1187 Pharma 0 Cilazapril 88768-40- C22H31N3O5 2.3 8.1 418.2336 416.2191 Pharma 5 Cilostazol 73963-72- C20H27N5O2 4.0 11.0 370.2238 368.2092 Pharma 1 Cinacalcet 226256- C22H22F3N 6.8 15.5 358.1777 356.1632 Pharma 56-0 Cinchocaine 85-79-0 C20H29N3O2 4.0 11.0 344.2333 342.2187 Pharma Cineole 470-82-6 C10H18O 3.1 9.5 155.1430 153.1285 Pharma Cinidon-ethyl 142891- C19H17Cl2NO4 4.2 11.3 394.0607 392.0462 Herbicide 20-1 Cinnarizine 298-57-7 C26H28N2 5.4 13.3 369.2325 367.2180 Pharma Cisatracurium besilate 64228-81- C53H72N2O12 9.8 20.5 929.5158 927.5012 Pharma charge 5 Citalopram 59729-33- C20H21FN2O 3.7 10.5 325.1711 323.1565 Pharma 8 Clemastine 14976-57- C21H26ClNO 5.5 13.4 344.1776 342.1630 Pharma 9 Clioquinol 130-26-7 C9H5ClINO 3.5 10.1 305.9177 303.9032 Pharma Clobazam 22316-47- C16H13ClN2O2 1.8 7.4 301.0738 299.0593 Pharma 8 Clobetasol 25122-41- C22H28ClFO4 2.3 8.2 411.1733 409.1587 Pharma 2 Clodinafop-propargyl 105512- C17H13ClFNO4 3.8 10.6 350.0590 348.0444 Herbicide 06-9 Clofentezine 74115-24- C14H8Cl2N4 4.2 11.2 303.0199 301.0053 Insecticide/Fungicide 5 Clomethiazole 533-45-9 C6H8ClNS 2.8 9.0 162.0139 159.9993 Pharma Clomifene 911-45-5 C26H28ClNO 6.7 15.5 406.1932 404.1787 Pharma Clomipramine 303-49-1 C19H23ClN2 5.7 13.7 315.1623 313.1477 Pharma Clonazepam 1622-61-3 C15H10ClN3O3 2.5 8.5 316.0483 314.0338 Pharma charge Clonidine 4205-90-7 C9H9Cl2N3 1.5 6.8 230.0246 228.0101 Pharma Clopamide 636-54-4 C14H20ClN3O3S 1.9 7.4 346.0987 344.0841 Pharma Clopidogrel 113665- C16H16ClNO2S 3.8 10.7 322.0663 320.0518 Pharma 84-2 Cloprednol 5251-34-3 C21H25ClO5 1.7 7.1 393.1463 391.1318 Pharma Cloquintocet-mexyl 99607-70- C18H22ClNO3 5.0 12.6 336.1361 334.1215 Herbicide 2 Clorazepic acid 23887-31- C16H11ClN2O3 2.1 7.7 315.0531 313.0385 Pharma 2 Clothianidin 205510- C6H8ClN5O2S 1.1 6.2 250.0160 248.0014 Insecticide/Fungicide charge 53-8 Clozapine 5786-21-0 C18H19ClN4 2.8 9.0 327.1371 325.1225 Pharma Codeine 6059-47-8 C18H21NO3 1.3 6.5 300.1594 298.1449 Pharma Coumarin 91-64-5 C9H6O2 1.5 6.9 147.0441 145.0295 Pharma Coumatetralyl 5836-29-3 C19H16O3 3.5 10.1 293.1172 291.1027 Biocide Cromoglicic acid 16110-51- C23H16O11 1.6 6.9 469.0765 467.0620 Pharma 3 Crotamiton 483-63-6 C13H17NO 2.7 8.9 204.1383 202.1237 Pharma Cyazofamid cyazofamid C13H13ClN4O2S 1.8 7.3 325.0521 323.0375 Insecticide/Fungicide Cyclobenzaprine 303-53-7 C20H21N 4.7 12.2 276.1747 274.1601 Pharma Cyclopentolate 512-15-2 C17H25NO3 2.5 8.4 292.1907 290.1762 Pharma Cycluron 2163-69-1 C11H22N2O 2.2 8.0 199.1805 197.1659 Herbicide Cyﬂufenamid 180409- C20H17F5N2O2 5.0 12.6 413.1283 411.1137 Insecticide/Fungicide 60-3 Cyhexatin 13121-70- C18H34OSn 4.4 11.6 387.1704 385.1559 Insecticide/Fungicide 5 Cyphenothrin 39515-40- C24H25NO3 6.6 15.3 376.1907 374.1762 Biocide 7 Cyproterone 2098-66-0 C22H27ClO3 3.6 10.3 375.1721 373.1576 Pharma Dabigatran etexilate 211915- C34H41N7O5 5.6 13.6 628.3242 626.3096 Pharma 06-9 Dantrolene 7261-97-4 C14H10N4O5 1.6 7.0 315.0724 313.0578 Pharma charge Darifenacin 133099- C28H30N2O2 5.1 12.8 427.2380 425.2235 Pharma 04-4 Darunavir 206361- C27H37N3O7S 1.9 7.5 548.2425 546.2279 Pharma 99-1 Dasatinib 302962- C22H26ClN7O2S 1.1 6.2 488.1630 486.1484 Pharma 49-8 Dazomet 533-74-4 C5H10N2S2 1.3 6.5 163.0358 161.0213 Insecticide/Fungicide 104 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Dectaflur 1838-19-3 C18H38N 4.3 11.5 269.3077 267.2931 Pharma charge Deferasirox 201530- C21H15N3O4 3.5 10.1 374.1135 372.0990 Pharma 41-8 Delapril 83435-66- C26H32N2O5 4.2 11.3 453.2384 451.2238 Pharma 9 Dequalinium 6707-58-0 C30H38N4 6.4 15.0 455.3169 453.3024 Pharma charge Desflurane 57041-67- C3H2F6O 1.2 6.3 169.0083 166.9937 Pharma 5 Desipramine 300-87-8 C18H22N2 4.8 12.3 267.1856 265.1710 Pharma Desloratadine 100643- C19H19ClN2 4.6 12.0 311.1310 309.1164 Pharma 71-8 Desogestrel 54024-22- C22H30O 5.7 13.7 311.2369 309.2224 Pharma 5 Desoximetasone 382-67-2 C22H29FO4 2.1 7.8 377.2123 375.1977 Pharma Dexketoprofen trometamol 156604- C16H14O3 3.0 9.3 255.1016 253.0870 Pharma 79-4 Dextromethorphan 125-70-2 C18H25NO 4.0 10.9 272.2009 270.1863 Pharma D-fenchone 126-21-6 C10H16O 3.0 9.4 153.1274 151.1128 Pharma Diafenthiuron 80060-09- C23H32N2OS 7.0 16.0 385.2308 383.2163 Insecticide/Fungicide 9 Diatrizoic acid 117-96-4 C11H9I3N2O4 1.4 6.6 614.7769 612.7624 Pharma Diazoxide 364-98-7 C8H7ClN2O2S 1.0 6.1 230.9990 228.9844 Pharma Dichlofluanid 1085-98-9 C9H11Cl2FN2O2S2 3.7 10.5 332.9696 330.9550 Biocide Dichlorvos 62-73-7 C4H7Cl2O4P 1.4 6.6 220.9532 218.9386 Insecticide/Fungicide/- Biocide Dicloxacillin 3116-76-5 C19H17Cl2N3O5S 3.9 10.7 470.0339 468.0193 Pharma Dicyclomine hydrochloride 77-19-0 C19H35NO2 6.1 14.3 310.2741 308.2595 Pharma Dienogest 65928-58- C20H25NO2 2.3 8.2 312.1958 310.1813 Pharma 7 Diethofencarb 87130-20- C14H21NO4 3.0 9.3 268.1543 266.1398 Insecticide/Fungicide 9 Difenacoum 56073-07- C31H24O3 7.6 16.9 445.1798 443.1653 Biocide 5 Difenoconazole 119446- C19H17Cl2N3O3 4.9 12.4 406.0720 404.0574 Insecticide/Fungicide 68-3 Difethialon 104653- C31H23BrO2S 6.3 14.7 539.0675 537.0529 Biocide 34-1 Diflubenzuron 35367-38- C14H9ClF2N2O2 3.6 10.3 311.0393 309.0248 Insecticide/Fungicide/- 5 Biocide Digitoxin 71-63-6 C41H64O13 2.0 7.7 765.4420 763.4274 Pharma Dihydrocodeine 125-28-0 C18H23NO3 1.5 6.8 302.1751 300.1605 Pharma Dihydroergocryptine 25447-66- C32H43N5O5 3.0 9.3 578.3337 576.3191 Pharma 9 Dihydroergotamine 511-12-6 C33H37N5O5 2.4 8.3 584.2867 582.2722 Pharma Diltiazem 42399-41- C22H26N2O4S 2.8 9.0 415.1686 413.1541 Pharma 7 Dimer (ding 2011) C18H20Cl2N8O2 2.5 8.4 451.1159 449.1014 Insecticide/Fungicide Dimethicone 9006-65-9 C8H24O2Si3 6.7 15.4 237.1157 235.1011 Pharma Dimethomorph 110488- C21H22ClNO4 3.3 9.8 388.1310 386.1165 Insecticide/Fungicide 70-5 Dimethyldioctylammonium- 5538-94-3 C18H40N 2.2 8.0 271.3234 269.3088 Biocide charge chlorid Dimethyltetradecyl [3- 41591-87- C22H50NO3Si 1.2 6.4 405.3633 403.3487 Biocide charge (trimethoxysilyl)propyl]am- 1 moniumchlorid Dimetindene 5636-83-9 C20H24N2 5.0 12.6 293.2012 291.1867 Pharma Dinoprostone 363-24-6 C20H32O5 3.5 10.2 353.2323 351.2177 Pharma Diphenhydramine 58-73-1 C17H21NO 3.1 9.5 256.1696 254.1550 Pharma Diphenoxylate 915-30-0 C30H32N2O2 6.3 14.8 453.2537 451.2391 Pharma Diphenyl 81846-81- C12H10 3.8 10.6 155.0855 153.0710 Pharma 3 Dipyridamole 58-32-2 C24H40N8O4 2.7 8.9 505.3245 503.3100 Pharma Distigmine 15876-67- C22H32N4O4 1.7 7.2 417.2496 415.2351 Pharma charge 2 Disulfiram 97-77-8 C10H20N2S4 3.7 10.4 297.0582 295.0437 Pharma Dithianon 3347-22-6 C14H4N2O2S2 2.1 7.8 296.9787 294.9641 Insecticide/Fungicide Dithranol 480-22-8 C14H10O3 2.9 9.1 227.0703 225.0557 Pharma Dobutamine 74753-15- C18H23NO3 3.5 10.1 302.1751 300.1605 Pharma 4 Docetaxel 114977- C43H53NO14 2.8 9.0 808.3539 806.3393 Pharma 28-5 Docosahexanoic acid 6217-54-5 C22H32O2 8.6 18.6 329.2475 327.2330 Pharma Docusate 577-11-7 C20H37O7S 6.1 14.4 422.2333 420.2187 Pharma charge Supporting Information to Chapter 3 105

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Dodecyl benzene sulfonic 27176-87- C18H30O3S 4.8 12.2 327.1988 325.1843 Pharma acid 0 Dodecylguanidin Monohy- 13590-97- C13H29N3 3.9 10.7 228.2434 226.2289 Biocide drochlorid 1 Dodine 2439-10-3 C13H29N3 3.9 10.7 228.2434 226.2289 Insecticide/Fungicide Dolasetron mesylate 115956- C19H20N2O3 2.6 8.7 325.1547 323.1401 Pharma 12-2 Domperidone 57808-66- C22H24ClN5O2 3.4 9.9 426.1691 424.1546 Pharma 9 Donepezil 120014- C24H29NO3 4.9 12.4 380.2220 378.2075 Pharma 06-4 Doxazosin 74191-85- C23H25N5O5 2.1 7.8 452.1928 450.1783 Pharma 8 Doxepin 1668-19-5 C19H21NO 4.0 10.9 280.1696 278.1550 Pharma Doxylamine 469-21-6 C17H22N2O 2.4 8.3 271.1805 269.1659 Pharma d-Phenothrin 188023- C23H26O3 6.0 14.3 351.1955 349.1809 Biocide 86-1 Drospirenone 67392-87- C24H30O3 4.0 11.0 367.2268 365.2122 Pharma 4 d-trans-Tetramethrin 1166-46-7 C22H49NO4S 3.1 9.5 424.3455 422.3310 Biocide Duloxetine 116539- C18H19NOS 4.7 12.1 298.1260 296.1115 Pharma 59-4 Dydrogesterone 152-62-5 C21H28O2 3.5 10.1 313.2162 311.2017 Pharma Ebastine 90729-43- C32H39NO2 7.6 17.0 470.3054 468.2908 Pharma 4 Econazole 68797-30- C18H15Cl3N2O 6.9 15.7 381.0323 379.0177 Pharma 8 Efavirenz 154598- C14H9ClF3NO2 4.7 12.1 316.0347 314.0201 Pharma 52-4 Eicosapentaenoic acid 10417-94- C20H30O2 7.9 17.3 303.2319 301.2173 Pharma 4 Eletriptan 143322- C22H26N2O2S 3.5 10.2 383.1788 381.1642 Pharma 58-1 Enalapril 75847-73- C20H28N2O5 2.5 8.4 377.2071 375.1925 Pharma 3 Enbucrilate 82410-32- C8H11NO2 2.4 8.3 154.0863 152.0717 Pharma 0 Endosulfan 115-29-7 C9H6Cl6O3S 2.6 8.6 404.8242 402.8096 Insecticide/Fungicide Entacapone 130929- C14H15N3O5 2.2 8.0 306.1084 304.0939 Pharma charge 57-6 Epirubicin 56420-45- C27H29NO11 1.9 7.4 544.1813 542.1668 Pharma 2 Eplerenone 107724- C24H30O6 1.5 6.9 415.2115 413.1970 Pharma 20-9 Erlotinib 183321- C22H23N3O4 2.8 9.0 394.1761 392.1616 Pharma 74-6 Erythromycin 82343-12- C37H67NO13 2.5 8.5 734.4685 732.4540 Pharma 2 Esketamine 33643-46- C13H16ClNO 3.1 9.5 238.0993 236.0848 Pharma 8 Esmolol 81147-92- C16H25NO4 2.0 7.7 296.1856 294.1711 Pharma 4 Esomeprazole 73590-58- C17H19N3O3S 3.4 10.0 346.1220 344.1074 Pharma 6 Estradiol 5864-38-0 C18H24O2 3.9 10.9 273.1849 271.1704 Pharma Estramustine 2998-57-4 C23H31Cl2NO3 5.5 13.4 440.1754 438.1608 Pharma Estriol 1228-72-4 C18H24O3 2.8 9.0 289.1798 287.1653 Pharma Ethacridine 442-16-0 C15H15N3O 2.1 7.8 254.1288 252.1142 Pharma Ethinylestradiol 57-63-6 C20H24O2 4.1 11.2 297.1849 295.1704 Pharma Ethyl hydrogen fumarate 2459-05-4 C6H8O4 1.4 6.7 145.0495 143.0350 Pharma Ethylene bisisothiocyanate C4H4N2S3 2.3 8.1 176.9609 174.9464 Insecticide/Fungicide sulphide Etodolac 41340-25- C17H21NO3 3.9 10.8 288.1594 286.1449 Pharma 4 Etofenamate 30544-47- C18H18F3NO4 4.2 11.3 370.1261 368.1115 Pharma 9 Etofenprox 80844-07- C25H28O3 6.3 14.8 377.2111 375.1966 Insecticide/Fungicide/- 1 Biocide Etoﬁbrate 31637-97- C18H18ClNO5 3.4 10.0 364.0946 362.0801 Pharma 5 Etomidate 15301-65- C14H16N2O2 3.1 9.4 245.1285 243.1139 Pharma 2 106 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Etonogestrel 54048-10- C22H28O2 3.9 10.8 325.2162 323.2017 Pharma 1 Etoricoxib 202409- C18H15ClN2O2S 2.9 9.2 359.0616 357.0470 Pharma 33-4 Etoxazole 153233- C21H23F2NO2 5.6 13.6 360.1770 358.1624 Insecticide/Fungicide 91-1 Etravirine 269055- C20H15BrN6O 4.0 11.0 435.0563 433.0418 Pharma 15-4 Eugenol 97-53-0 C10H12O2 2.7 8.9 165.0910 163.0765 Pharma Ezetimibe 163222- C24H21F2NO3 3.9 10.9 410.1562 408.1417 Pharma 33-1 Famoxadone 131807- C22H18N2O4 5.3 13.0 375.1339 373.1194 Insecticide/Fungicide 57-3 Felbinac 5728-52-9 C14H12O2 3.2 9.6 213.0910 211.0765 Pharma Felodipine 72509-76- C18H19Cl2NO4 4.5 11.7 384.0764 382.0618 Pharma 3 Fenamidone 161326- C17H17N3OS 4.7 12.2 312.1165 310.1020 Insecticide/Fungicide 34-7 Fenazaquin 120928- C20H22N2O 5.4 13.3 307.1805 305.1659 Insecticide/Fungicide 09-8 Fenbuconazole 119611- C19H17ClN4 4.3 11.5 337.1215 335.1069 Insecticide/Fungicide 00-6 Fenhexamid 126833- C14H17Cl2NO2 4.8 12.3 302.0709 300.0564 Insecticide/Fungicide 17-8 Fenitrothion 122-14-5 C9H12NO5PS 3.3 9.8 278.0247 276.0101 Biocide charge Fenoldopam mesylate 67227-57- C16H16ClNO3 2.6 8.6 306.0891 304.0746 Pharma 0 Fenoterol 13392-18- C17H21NO4 1.2 6.4 304.1543 302.1398 Pharma 2 Fenoxaprop-P-ethyl 71283-80- C18H16ClNO5 4.4 11.6 362.0790 360.0644 Herbicide 2 Fenoxycarb 79127-80- C17H19NO4 3.3 9.8 302.1387 300.1241 Insecticide/Fungicide 3 Fenpropidin 67306-00- C19H31N 5.4 13.3 274.2529 272.2384 Insecticide/Fungicide 7 Fenpropimorph carboxylic C20H31NO3 1.6 7.1 334.2377 332.2231 Insecticide/Fungicide acid Fenpyroximate 111812- C24H27N3O4 5.0 12.6 422.2074 420.1929 Insecticide/Fungicide 58-9 Fentanyl 437-38-7 C22H28N2O 3.9 10.8 337.2274 335.2129 Pharma Fenticonazole 72479-26- C24H20Cl2N2OS 7.3 16.3 455.0746 453.0601 Pharma 6 Fesoterodine 286930- C26H37NO3 5.3 13.0 412.2846 410.2701 Pharma 03-8 Fexofenadine 83799-24- C32H39NO4 2.8 9.0 502.2952 500.2806 Pharma 0 Finasteride 98319-26- C23H36N2O2 3.2 9.6 373.2850 371.2704 Pharma 7 Fipronil amide C12H6Cl2F6N4O2S 3.5 10.1 454.9565 452.9420 Insecticide/Fungicide Flavoxate 15301-69- C24H25NO4 5.0 12.5 392.1856 390.1711 Pharma 6 Flecainide 54143-55- C17H20F6N2O3 4.0 10.9 415.1451 413.1305 Pharma 4 Flocoumafen 90035-08- C33H25F3O4 4.7 12.1 543.1778 541.1632 Biocide 8 Fluazifop-P-butyl 79241-46- C19H20F3NO4 4.3 11.5 384.1417 382.1272 Herbicide 6 Fluazinam 79622-59- C13H4Cl2F6N4O4 6.9 15.8 464.9587 462.9441 Insecticide/Fungicide charge 6 Flucloxacillin 5250-39-5 C19H17ClFN3O5S 3.4 10.0 454.0634 452.0489 Pharma Flufenamic acid 530-78-9 C14H10F3NO2 5.2 12.9 282.0736 280.0591 Pharma Flufenoxuron 101463- C21H11ClF6N2O3 5.1 12.8 489.0435 487.0290 Biocide 69-8 Flumioxazin 103361- C19H15FN2O4 1.9 7.4 355.1089 353.0943 Herbicide 09-7 Flunarizine 40218-96- C26H26F2N2 5.8 14.0 405.2137 403.1991 Pharma 0 Flunitrazepam 1622-62-4 C16H12FN3O3 1.9 7.5 314.0935 312.0790 Pharma charge Fluocinonide 356-12-7 C26H32F2O7 2.8 8.9 495.2189 493.2043 Pharma Fluocortolone 152-97-6 C22H29FO4 2.1 7.8 377.2123 375.1977 Pharma Fluorescein 2321-07-5 C20H12O5 2.2 8.1 333.0758 331.0612 Pharma Fluoxastrobin 193740- C21H16ClFN4O5 5.2 13.0 459.0866 457.0720 Insecticide/Fungicide 76-0 Supporting Information to Chapter 3 107

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Flupentixol 53772-82- C23H25F3N2OS 4.1 11.1 435.1712 433.1567 Pharma 0 Fluphenazine 69-23-8 C22H26F3N3OS 4.1 11.2 438.1821 436.1676 Pharma Flupirtine 56995-20- C15H17FN4O2 2.7 8.7 305.1408 303.1263 Pharma 1 Fluprednidene 2193-87-5 C22H27FO5 1.7 7.2 391.1915 389.1770 Pharma Fluquinconazole 136426- C16H8Cl2FN5O 3.7 10.4 376.0163 374.0017 Insecticide/Fungicide 54-5 Flurazepam 17617-23- C21H23ClFN3O 3.0 9.3 388.1586 386.1441 Pharma 1 Flurbiprofen 51543-39- C15H13FO2 3.8 10.6 245.0972 243.0827 Pharma 6 Flurochloridon 61213-25- C12H10Cl2F3NO 3.3 9.9 312.0164 310.0019 Herbicide 0 Fluroxypyr als Ester 81406-37- 15H21Cl2FN2O3 5.0 12.5 187.0986 185.0840 Herbicide 3 Flutamide 337962- C11H11F3N2O3 3.5 10.2 277.0795 275.0649 Pharma 98-8 Fluticasone 90566-53- C22H27F3O4S 1.4 6.7 445.1655 443.1509 Pharma 3 Fluticasone propionate 90566-53- C25H31F3O5S 2.5 8.5 501.1917 499.1772 Pharma 3 Fluvastatin 93957-54- C24H26FNO4 4.9 12.4 412.1919 410.1773 Pharma 1 Fluvoxamine 54739-18- C15H21F3N2O2 3.1 9.5 319.1628 317.1482 Pharma 3 Folpet 133-07-3 C9H4Cl3NO2S 3.8 10.6 295.9101 293.8956 Insecticide/Fungicide/- Biocide Fosamprenavir 226700- C25H36N3O9PS 2.2 7.9 586.1983 584.1837 Pharma 79-4 Fosinopril 98048-97- C30H46NO7P 6.1 14.4 564.3085 562.2939 Pharma 6 Fosphenytoin sodium 93390-81- C16H15N2O6P 1.4 6.6 363.0740 361.0595 Pharma 9 Fulvestrant 129453- C32H47F5O3S 9.1 19.4 607.3239 605.3093 Pharma 61-8 Furosemide 54-31-9 C12H11ClN2O5S 2.3 8.2 331.0150 329.0004 Pharma Fusidic acid 6990-06-3 C31H48O6 6.8 15.5 517.3524 515.3378 Pharma Galantamine 357-70-0 C17H21NO3 2.3 8.1 288.1594 286.1449 Pharma Gallopamil 16662-47- C28H40N2O5 4.7 12.0 485.3010 483.2864 Pharma 8 Gamolenic acid 506-26-3 C18H30O2 7.3 16.4 279.2319 277.2173 Pharma Gemﬁbrozil 25812-30- C15H22O3 4.8 12.2 251.1642 249.1496 Pharma 0 Gestodene 60282-87- C21H26O2 3.3 9.7 311.2006 309.1860 Pharma 3 Glibenclamide 10238-21- C23H28ClN3O5S 4.8 12.3 494.1511 492.1365 Pharma 8 Gliclazide 21187-98- C15H21N3O3S 2.1 7.9 324.1376 322.1231 Pharma 4 Glimepiride 93479-97- C24H34N4O5S 4.7 12.1 491.2323 489.2177 Pharma 1 Glipizide 29094-61- C21H27N5O4S 3.4 9.9 446.1857 444.1711 Pharma 9 Gliquidone 33342-05- C27H33N3O6S 4.7 12.0 528.2163 526.2017 Pharma 1 Granisetron 109889- C18H24N4O 3.0 9.3 313.2023 311.1877 Pharma 09-0 Griseofulvin 126-07-8 C17H17ClO6 1.9 7.5 353.0786 351.0641 Pharma Haloperidol 52-86-8 C21H23ClFNO2 4.2 11.3 376.1474 374.1329 Pharma Hec-5725-carboxylic acid C19H13ClFN3O5 4.9 12.4 418.0601 416.0455 Insecticide/Fungicide Hec-5725-des- C15H13FN4O5 2.8 9.0 349.0943 347.0797 Insecticide/Fungicide chlorophenyl Hexadecyl(2- 59130-69- C24H48O2 10.6 21.9 369.3727 367.3582 Pharma ethylhexanoate) 7 Hexaﬂumuron 86479-06- C16H8Cl2F6N2O3 5.4 13.2 460.9889 458.9743 Biocide/Insecticide/Fungicide 3 Hexetidine 141-94-6 C21H45N3 5.3 13.0 340.3686 338.3541 Pharma Hexythiazox 78587-05- C17H21ClN2O2S 4.6 11.9 353.1085 351.0939 Insecticide/Fungicide 0 Hydramethylnon 67485-29- C25H24F6N4 2.3 8.2 495.1978 493.1832 Biocide 4 Hydrocodone 125-29-1 C18H21NO3 2.2 7.9 300.1594 298.1449 Pharma 108 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Hydrocortisone 50-23-7 C21H30O5 1.6 7.0 363.2166 361.2020 Pharma Hydromorphone 466-99-9 C17H19NO3 1.6 7.0 286.1438 284.1292 Pharma Hydroxychloroquine 118-42-3 C18H26ClN3O 3.0 9.4 336.1837 334.1692 Pharma Hydroxyprogesterone 68-96-2 C21H30O3 3.1 9.4 331.2268 329.2122 Pharma Hydroxyzine 68-88-2 C21H27ClN2O2 2.4 8.3 375.1834 373.1688 Pharma Hymecromone 90-33-5 C10H8O3 1.6 7.0 177.0546 175.0401 Pharma Ibandronic acid 114084- C9H23NO7P2 1.7 7.1 320.1023 318.0877 Pharma 78-5 Icaridin 119515- C12H23NO3 2.2 8.0 230.1751 228.1605 Biocide 38-7 Idarubicin hydrochloride 58957-92- C26H27NO9 2.1 7.8 498.1759 496.1613 Pharma 9 Imatinib 152459- C29H31N7O 3.0 9.3 494.2663 492.2517 Pharma 95-5 Imazalil 73790-28- C14H14Cl2N2O 3.8 10.6 297.0556 295.0410 Insecticide/Fungicide 0 Imidacloprid 138261- C9H10ClN5O2 1.1 6.2 256.0596 254.0450 Insecticide/Fungicide charge 41-3 Imidacloprid-guanidin- C9H9ClN4 1.1 6.2 209.0589 207.0443 Insecticide/Fungicide olefin (=desnitro-olefin) Imidacloprid-nitroso C8H10ClN5OS 1.0 6.0 260.0367 258.0222 Insecticide/Fungicide Imipramine 50-49-7 C19H24N2 5.0 12.6 281.2012 279.1867 Pharma Imiprothrin 72963-72- C17H22N2O4 2.4 8.4 319.1652 317.1507 Biocide 5 Imiquimod 99011-02- C14H16N4 3.1 9.4 241.1448 239.1302 Pharma 6 Indapamide 26807-65- C16H16ClN3O3S 2.7 8.7 366.0674 364.0528 Pharma 8 Indinavir 150378- C36H47N5O4 1.9 7.5 614.3701 612.3555 Pharma 17-9 Indoxacarb 144171- C22H17ClF3N3O7 5.4 13.3 528.0780 526.0634 Insecticide/Fungicide 61-9 Iodosulfuron 185119- C13H12IN5O6S 1.8 7.4 493.9626 491.9480 Herbicide 76-0 Iotalamic acid 2276-90-6 C11H9I3N2O4 1.5 6.8 614.7769 612.7624 Pharma IPBC 55406-53- C8H12INO2 2.5 8.6 281.9986 279.9840 Biocide 6 Ipratropium bromide 22254-24- C20H30NO3 1.3 6.5 333.2298 331.2153 Pharma charge 6 Iprodione 36734-19- C13H13Cl2N3O3 2.3 8.1 330.0407 328.0261 Insecticide/Fungicide 7 Iprovalicarb 140923- C18H28N2O3 3.6 10.3 321.2173 319.2027 Insecticide/Fungicide 17-7 Irinotecan 100286- C33H38N4O6 3.5 10.2 587.2864 585.2719 Pharma 90-6 Isoconazole 27523-40- C18H14Cl4N2O 6.3 14.7 414.9933 412.9787 Pharma 6 Isoflurane 26675-46- C3H2ClF5O 1.5 6.9 184.9787 182.9642 Pharma 7 Isotretinoin 4759-48-2 C20H28O2 7.9 17.3 301.2162 299.2017 Pharma Isoxadifen-ethyl 163520- C18H17NO3 4.0 11.0 296.1281 294.1136 Herbicide 33-0 Isradipine 75695-93- C19H21N3O5 3.5 10.1 372.1554 370.1408 Pharma 1 Itraconazole 84604-65- C35H38Cl2N8O4 6.2 14.5 705.2466 703.2320 Pharma 9 Ivabradine 155974- C27H36N2O5 3.7 10.5 469.2697 467.2551 Pharma 00-8 Ketamine 6740-88-1 C13H16ClNO 3.1 9.5 238.0993 236.0848 Pharma Ketoconazole 65277-42- C26H28Cl2N4O4 4.5 11.7 531.1560 529.1415 Pharma 1 Ketorolac 74103-06- C15H13NO3 2.3 8.2 256.0968 254.0823 Pharma 3 Labetalol 36894-69- C19H24N2O3 2.4 8.3 329.1860 327.1714 Pharma 6 Lansoprazole 103577- C16H14F3N3O2S 3.7 10.4 370.0832 368.0686 Pharma 45-3 Lapatinib 231277- C29H26ClFN4O4S 5.3 13.1 581.1420 579.1275 Pharma 92-2 Latanoprost 130209- C26H40O5 5.7 13.7 433.2949 431.2803 Pharma 82-4 Laurylamine dipropylene- 2372-82-9 C18H41N3 3.6 10.3 300.3373 298.3228 Biocide/Pharma diamine Supporting Information to Chapter 3 109

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Leﬂunomide 75706-12- C12H9F3N2O2 2.4 8.4 271.0689 269.0543 Pharma 6 Lercanidipine 100427- C36H41N3O6 6.9 15.7 612.3068 610.2923 Pharma charge 26-7 Letrozole 112809- C17H11N5 2.2 8.0 286.1087 284.0942 Pharma 51-5 Levomepromazine 60-99-1 C19H24N2OS 5.1 12.7 329.1682 327.1537 Pharma Levomethadone 125-58-6 C21H27NO 4.2 11.2 310.2165 308.2020 Pharma Levonorgestrel 6533-00-2 C21H28O2 3.5 10.1 313.2162 311.2017 Pharma Levothyroxine sodium 24486-40- C15H11I4NO4 4.1 11.2 777.6940 775.6794 Pharma 6 Linalool 78-70-6 C10H18O 2.6 8.7 155.1430 153.1285 Biocide Linezolid 165800- C16H20FN3O4 1.3 6.4 338.1511 336.1365 Pharma 03-3 Linoleic acid 80969-37- C18H32O2 7.5 16.8 281.2475 279.2330 Pharma 5 Loperamide 53179-11- C29H33ClN2O2 5.2 12.9 477.2303 475.2158 Pharma 6 Lopinavir 192725- C37H48N4O5 5.9 14.2 629.3697 627.3552 Pharma 17-0 Loratadine 79794-75- C22H23ClN2O2 5.7 13.7 383.1521 381.1375 Pharma 5 Lorazepam 846-49-1 C15H10Cl2N2O2 2.4 8.3 321.0192 319.0047 Pharma Lormetazepam 848-75-9 C16H12Cl2N2O2 2.2 8.0 335.0349 333.0203 Pharma Lovastatin 75330-75- C24H36O5 4.7 12.2 405.2636 403.2490 Pharma 5 Lufenuron 103055- C17H8Cl2F8N2O3 6.1 14.5 510.9857 508.9711 Insecticide/Fungicide 07-8 Lumefantrine 82186-77- C30H32Cl3NO 8.9 19.0 528.1622 526.1477 Pharma 4 Malathion 121-75-5 C10H19O6PS2 2.8 8.9 331.0433 329.0288 Biocide Mandipropamid 374726- C23H22ClNO4 3.7 10.5 412.1310 410.1165 Insecticide/Fungicide 62-2 Manidipine 120092- C35H38N4O6 5.1 12.8 611.2864 609.2719 Pharma charge 68-4 Maprotiline 10262-69- C20H23N 4.5 11.8 278.1903 276.1758 Pharma 8 Maraviroc 376348- C29H41F2N5O 5.8 13.9 514.3352 512.3206 Pharma 65-1 Mebendazole 31431-39- C16H13N3O3 2.7 8.8 296.1030 294.0884 Pharma 7 Mebeverine 2753-45-9 C25H35NO5 5.1 12.8 430.2588 428.2442 Pharma Mecetroniumetilsulfat 08.10.3006 C20H44N 3.0 9.4 299.3547 297.3401 Biocide charge Mecetroniumetilsulfat 08.10.3006 C22H49NO4S 3.0 9.4 424.3455 422.3310 Biocide Meclizine hydrochloride 569-65-3 C25H27ClN2 5.9 14.0 391.1936 389.1790 Pharma Medazepam 61-33-6 C16H15ClN2 4.4 11.7 271.0997 269.0851 Pharma Medrogestone 977-79-7 C23H32O2 4.5 11.7 341.2475 339.2330 Pharma Medroxyprogesterone 520-85-4 C22H32O3 3.5 10.1 345.2424 343.2279 Pharma Mefenpyr-Diethyl 135590- C16H18Cl2N2O4 3.9 10.7 373.0716 371.0571 Herbicide 91-9 Meﬂoquine 49752-90- C17H16F6N2O 3.9 10.7 379.1240 377.1094 Pharma 1 Mefruside 7195-27-9 C13H19ClN2O5S2 2.1 7.8 383.0497 381.0351 Pharma Megestrol 3562-63-8 C22H30O3 3.4 10.0 343.2268 341.2122 Pharma Meloxicam 71125-38- C14H13N3O4S2 1.8 7.4 352.0420 350.0275 Pharma 7 Melperone 3575-80-2 C16H22FNO 3.8 10.7 264.1758 262.1613 Pharma Memantine 19982-08- C12H21N 3.3 9.9 180.1747 178.1601 Pharma 2 Menthone 491-07-6 C10H18O 2.9 9.1 155.1430 153.1285 Pharma Mepanipyrim 110235- C14H13N3 3.7 10.4 224.1182 222.1037 Insecticide/Fungicide 47-7 Mepivacaine 34811-66- C15H22N2O 2.0 7.6 247.1805 245.1659 Pharma 0 Mepronil 55814-41- C17H19NO2 4.2 11.3 270.1489 268.1343 Insecticide/Fungicide 0 Meptazinol 54340-58- C15H23NO 3.8 10.6 234.1852 232.1707 Pharma 8 Meptyldinocap 131-72-6 C18H24N2O6 6.3 14.8 365.1707 363.1562 Insecticide/Fungicide charge Mesuximide 77-41-8 C12H13NO2 1.5 6.8 204.1019 202.0874 Pharma Metalaxyl 57837-19- C15H21NO4 2.1 7.9 280.1543 278.1398 Insecticide/Fungicide 1 110 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Metamfepramone 15351-09- C11H15NO 2.1 7.8 178.1226 176.1081 Pharma 4 Metaxalone 1665-48-1 C12H15NO3 2.6 8.7 222.1125 220.0979 Pharma Metconazole 125116- C17H22ClN3O 3.6 10.3 320.1524 318.1379 Insecticide/Fungicide 23-6 Methidathion 950-37-8 C6H11N2O4PS3 2.3 8.1 302.9691 300.9546 Insecticide/Fungicide Methiocarb 2032-65-7 C11H15NO2S 3.1 9.5 226.0896 224.0751 Insecticide/Fungicide Methiocarb sulfone 2179-25-1 C11H15NO4S 1.3 6.6 258.0795 256.0649 Insecticide/Fungicide Methiocarb sulfoxide 2635-10-1 C11H15NO3S 1.2 6.4 242.0845 240.0700 Insecticide/Fungicide Methohexital 151-83-7 C14H18N2O3 2.4 8.2 263.1390 261.1245 Pharma Methoxyfenozide 161050- C22H28N2O3 4.7 12.2 369.2173 367.2027 Insecticide/Fungicide 58-4 Methyl n-(2[1-(4- C18H16ClN3O3 4.7 12.1 358.0953 356.0807 Insecticide/Fungicide chlorophenyl)-1h- pyrazol-3-yl] oxymethyl phenyl)carbamate Methylanthranilat 134-20-3 C8H8NO2 2.0 7.6 151.0628 149.0482 Biocide charge Methylnonylketon 112-12-9 C11H22O 4.2 11.3 171.1743 169.1598 Biocide Methylphenidate 113-45-1 C14H19NO2 2.8 8.9 234.1489 232.1343 Pharma Methylprednisolone ace- 86401-95- C27H36O7 3.3 9.8 473.2534 471.2388 Pharma ponate 8 Methylprednisone 83-43-2 C22H28O5 2.0 7.7 373.2010 371.1864 Pharma Metixene 139755- C20H23NS 5.7 13.7 310.1624 308.1478 Pharma 83-2 Metoclopramide 364-62-5 C14H22ClN3O2 1.7 7.1 300.1473 298.1328 Pharma Metrafenone C19H21BrO5 4.6 11.9 409.0645 407.0500 Insecticide/Fungicide Mexiletine 31828-71- C11H17NO 2.6 8.7 180.1383 178.1237 Pharma 4 Mezlocillin 51481-65- C21H25N5O8S2 2.4 8.3 540.1217 538.1072 Pharma 3 Mianserin 24219-97- C18H20N2 3.4 9.9 265.1699 263.1554 Pharma 4 Miconazole 22916-47- C18H14Cl4N2O 6.3 14.7 414.9933 412.9787 Pharma 8 Midazolam 59467-70- C18H13ClFN3 4.3 11.5 326.0855 324.0709 Pharma 8 Mifepristone 84371-65- C29H35NO2 5.4 13.3 430.2741 428.2595 Pharma 3 Milrinone 78415-72- C12H9N3O 1.2 6.3 212.0818 210.0673 Pharma 2 Minoxidil 38304-91- C9H15N5O 1.4 6.6 210.1349 208.1204 Pharma charge 5 Miristalkoniumchlorid 139-08-2 C23H42ClN 3.5 10.2 368.3079 366.2933 Biocide Mirtazapine 85650-52- C17H19N3 3.0 9.4 266.1652 264.1506 Pharma 8 Misoprostol 59122-46- C22H38O5 4.5 11.7 383.2792 381.2646 Pharma 2 Mitotane 53-19-0 C14H10Cl4 5.9 14.0 318.9609 316.9464 Pharma Mitoxantrone hydrochlo- 65271-80- C22H28N4O6 2.3 8.1 445.2082 443.1936 Pharma ride 9 Mizolastine 108612- C24H25FN6O 5.0 12.7 433.2147 431.2001 Pharma 45-9 Moclobemide 71320-77- C13H17ClN2O2 1.2 6.3 269.1051 267.0906 Pharma 9 Modaﬁnil 68693-11- C15H15NO2S 1.1 6.2 274.0896 272.0751 Pharma 8 Moexipril 103775- C27H34N2O7 3.4 9.9 499.2439 497.2293 Pharma 10-6 Mometasone 105102- C22H28Cl2O4 2.6 8.7 427.1437 425.1292 Pharma 22-5 Monolinuron 1746-81-2 C9H11ClN2O2 2.2 8.0 215.0582 213.0436 Biocide Montelukast 158966- C35H36ClNO3S 9.5 20.1 586.2177 584.2032 Pharma 92-8 Moxaverine 10539-19- C20H21NO2 5.1 12.7 308.1645 306.1500 Pharma 2 Mpa 53-36-1 C24H32O6 2.6 8.6 417.2272 415.2126 Pharma Mupirocin 12650-69- C26H44O9 3.0 9.2 501.3058 499.2913 Pharma 0 Muscalure 27519-02- C23H46 10.3 21.4 323.3672 321.3527 Biocide 4 Myclobutanil 88671-89- C15H17ClN4 3.7 10.4 289.1215 287.1069 Insecticide/Fungicide 0 Supporting Information to Chapter 3 111

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Mycophenolate mofetil 115007- C23H31NO7 3.8 10.6 434.2173 432.2028 Pharma 34-6 Mycophenolic acid 483-60-3 C17H20O6 4.2 11.3 321.1333 319.1187 Pharma Myrtecaine 7712-50-7 C17H31NO 4.7 12.1 266.2478 264.2333 Pharma N-((4-chlorophenyl)- C12H16ClN 3.5 10.1 210.1044 208.0899 Insecticide/Fungicide methyl)-n- cyclopentylamide N-(1,1-dimethyethyl)-n- C22H26N2O3 4.0 10.9 367.2016 365.1871 Insecticide/Fungicide (4-acetylebenzoyl)-3,5- dimethylbenzohydrazine N-(2,6-dimethylphenyl)-n- 70630-17- C14H19NO4 2.0 7.6 266.1387 264.1241 Insecticide/Fungicide (methoxyacetyl)alanine 0 Nabumetone 42924-53- C15H16O2 3.2 9.7 229.1223 227.1078 Pharma 8 Nadifloxacin 124858- C19H21FN2O4 2.7 8.8 361.1558 359.1413 Pharma 35-1 Nadolol 42200-33- C17H27NO4 1.2 6.3 310.2013 308.1867 Pharma 9 Naftidrofuryl 31329-57- C24H33NO3 5.4 13.2 384.2533 382.2388 Pharma 4 Naftifine 65472-88- C21H21N 5.4 13.3 288.1747 286.1601 Pharma 0 Naltrexone 16590-41- C20H23NO4 1.4 6.7 342.1700 340.1554 Pharma 3 Napropamide 15299-99- C17H21NO2 3.4 10.0 272.1645 270.1500 Herbicide 7 Naratriptan 121679- C17H25N3O2S 2.7 8.8 336.1740 334.1595 Pharma 13-8 Nateglinide 105816- C19H27NO3 4.7 12.1 318.2064 316.1918 Pharma 04-4 Natrium 2-biphenylat 132-27-4 C12H9O 3.1 9.5 170.0726 168.0581 Biocide charge Natrium-Chlorocrestol 15733-22- C7H6ClO 2.8 9.0 142.0180 140.0034 Biocide charge 9 Nebivolol 104365- C22H25F2NO4 3.7 10.5 406.1824 404.1679 Pharma 59-5 Nefazodone 83366-66- C25H32ClN5O2 5.0 12.6 470.2317 468.2172 Pharma 9 Nelfinavir 159989- C32H45N3O4S 5.5 13.5 568.3204 566.3058 Pharma 64-7 Nevirapine 129618- C15H14N4O 3.9 10.8 267.1240 265.1095 Pharma 40-2 N-formyl-n’- C13H17Cl3N2O3 3.7 10.5 355.0378 353.0232 Insecticide/Fungicide propyl-n’-2(2,4,6- trichlorophenoxy)ethylurea Nicergoline 27848-84- C24H26BrN3O3 3.6 10.3 484.1230 482.1085 Pharma 6 Niclosamide 50-65-7 C13H8Cl2N2O4 4.6 11.9 326.9934 324.9788 Pharma charge Nicoboxil 1322-29-8 C12H17NO3 1.8 7.4 224.1281 222.1136 Pharma Nicotine 22083-74- C10H14N2 1.0 6.0 163.1230 161.1084 Pharma 5 Nifedipine 21829-25- C17H18N2O6 2.5 8.5 347.1238 345.1092 Pharma charge 4 Nifuratel 4936-47-4 C10H11N3O5S 1.5 6.9 286.0492 284.0347 Pharma charge Nifuroxazide 965-52-6 C12H9N3O5 1.5 6.8 276.0615 274.0469 Pharma charge Nilotinib 641571- C28H22F3N7O 5.0 12.6 530.1911 528.1765 Pharma 10-0 Nilvadipine 75530-68- C19H19N3O6 2.4 8.4 386.1347 384.1201 Pharma charge 6 Nimodipine 66085-59- C21H26N2O7 3.1 9.5 419.1813 417.1667 Pharma charge 4 Nisoldipine 63675-72- C20H24N2O6 3.9 10.8 389.1707 387.1562 Pharma charge 9 Nitrazepam 146-22-5 C15H11N3O3 2.5 8.4 282.0873 280.0728 Pharma charge Nitrendipine 39562-70- C18H20N2O6 3.0 9.3 361.1394 359.1249 Pharma charge 4 Nitroglycerin 55-63-0 C3H5N3O9 1.5 6.9 228.0099 225.9953 Pharma charge Nitroxoline 4008-48-4 C9H6N2O3 2.1 7.8 191.0451 189.0306 Pharma charge Noa 407475 C8H11ClN4OS 1.3 6.5 247.0415 245.0269 Insecticide/Fungicide Nonivamide 2444-46-4 C17H27NO3 3.8 10.6 294.2064 292.1918 Pharma Norethisterone 68-22-4 C20H26O2 3.0 9.3 299.2006 297.1860 Pharma Norgestimate 35189-28- C23H31NO3 5.0 12.6 370.2377 368.2231 Pharma 7 Nortriptyline 72-69-5 C19H21N 4.7 12.2 264.1747 262.1601 Pharma 112 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Noscapine 128-62-1 C22H23NO7 2.0 7.6 414.1547 412.1402 Pharma Novaluron 116714- C17H9ClF8N2O4 6.2 14.7 493.0196 491.0050 Insecticide/Fungicide 46-6 Octenidine 71251-02- C36H62N4 12.9 25.7 551.5047 549.4902 Pharma 0 OIT 26530-20- C11H19NOS 2.5 8.4 214.1260 212.1115 Biocide 1 Olaﬂur 17671-49- C27H58N2O3 6.1 14.4 459.4520 457.4375 Pharma 7 Olanzapine 132539- C17H20N4S 2.6 8.6 313.1481 311.1336 Pharma 06-1 Olmesartan medoxomil 144689- C29H30N6O6 3.3 9.8 559.2300 557.2154 Pharma 63-4 Olsalazine 15722-48- C14H10N2O6 4.6 12.0 303.0612 301.0466 Pharma 2 Ondansetron 99614-02- C18H19N3O 4.0 10.9 294.1601 292.1455 Pharma 5 O-phenylphenol 132-27-4 C12H10O 3.3 9.8 171.0804 169.0659 Pharma Opipramol 315-72-0 C23H29N3O 3.4 10.0 364.2383 362.2238 Pharma Orlistat 96829-58- C29H53NO5 8.2 17.9 496.3997 494.3851 Pharma 2 Orphenadrine 83-98-7 C18H23NO 3.7 10.4 270.1852 268.1707 Pharma Orthophenylphenol 90-43-7 C12H10O 3.3 9.8 171.0804 169.0659 Insecticide/Fungicide/- Biocide Oryzalin 19044-88- C12H18N4O6S 2.2 8.1 347.1020 345.0874 Herbicide charge 3 Oxadiargyl 39807-15- C15H14Cl2N2O3 4.5 11.7 341.0454 339.0309 Herbicide 3 Oxandrolone 53-39-4 C19H30O3 2.6 8.6 307.2268 305.2122 Pharma Oxaprozin 21256-18- C18H15NO3 4.0 11.0 294.1125 292.0979 Pharma 8 Oxetacaine 126-27-2 C28H41N3O3 3.4 9.9 468.3221 466.3075 Pharma Oxprenolol 6452-71-7 C15H23NO3 1.8 7.4 266.1751 264.1605 Pharma Oxybuprocaine 99-43-4 C17H28N2O3 3.5 10.2 309.2173 307.2027 Pharma Oxybutynin 5633-20-5 C22H31NO3 4.0 10.9 358.2377 356.2231 Pharma Oxychinolin 148-24-3 C9H7NO 1.8 7.4 146.0600 144.0455 Insecticide/Fungicide/- Pharma/Biocie Oxyﬂuorfen 42874-03- C15H11ClF3NO4 4.9 12.4 362.0401 360.0256 Herbicide charge 3 Oxymetazoline 1491-59-4 C16H24N2O 4.9 12.4 261.1961 259.1816 Pharma p- 20018-09- C8H8I2O2S 4.9 12.5 422.8407 420.8262 Biocide [(Diiodmethyl)sulfonyl]toluol 1 Paclitaxel 33069-62- C47H51NO14 3.3 9.8 854.3382 852.3237 Pharma 4 Paliperidone 144598- C23H27FN4O3 2.0 7.6 427.2140 425.1994 Pharma 75-4 Parecoxib 198470- C19H18N2O4S 3.1 9.5 371.1060 369.0915 Pharma 84-7 Paricalcitol 131918- C27H44O3 7.4 16.6 417.3363 415.3218 Pharma 61-1 Paroxetine 189380- C19H20FNO3 4.7 12.2 330.1500 328.1354 Pharma 72-1 P-chlorophenol 106-48-9 C6H5ClO 2.2 7.9 129.0102 126.9956 Pharma Penbutolol 38363-40- C18H29NO2 4.2 11.3 292.2271 290.2126 Pharma 5 Penconazole 66246-88- C13H15Cl2N3 4.2 11.3 284.0716 282.0570 Insecticide/Fungicide 6 Pencycuron 66063-05- C19H21ClN2O 5.0 12.6 329.1415 327.1270 Insecticide/Fungicide 6 Pendimethalin 40487-42- C13H19N3O4 4.4 11.6 282.1448 280.1303 Herbicide charge 1 Penicillin 1406-09-3 C16H17N2O4S 1.9 7.4 334.0982 332.0836 Pharma charge Penicillin g 61-33-6 C16H18N2O4S 1.9 7.4 335.1060 333.0915 Pharma Penicillin v 87-08-1 C16H18N2O5S 1.9 7.4 351.1009 349.0864 Pharma Pentaerythrityl tetranitrate 78-11-5 C5H8N4O12 2.4 8.3 317.0211 315.0066 Pharma charge Pentoxyverine 77-23-6 C20H31NO3 4.2 11.3 334.2377 332.2231 Pharma Perazine 84-97-9 C20H25N3S 4.2 11.2 340.1842 338.1696 Pharma Perindopril 82834-16- C19H32N2O5 2.6 8.6 369.2384 367.2238 Pharma 0 Permethrin 52645-53- C21H20Cl2O3 7.4 16.6 391.0862 389.0717 Pharma/Biocide 1 Peroxyoctansaure¨ 33734-57- C8H16O3 2.6 8.7 161.1172 159.1027 Biocide 5 Supporting Information to Chapter 3 113

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Perphenazine 58-39-9 C21H26ClN3OS 3.8 10.7 404.1558 402.1412 Pharma Pethidine 57-42-1 C15H21NO2 3.0 9.4 248.1645 246.1500 Pharma Phenazopyridine hy- 94-78-0 C11H11N5 2.8 8.9 214.1087 212.0942 Pharma drochloride Phenobarbital 50-06-6 C12H12N2O3 1.3 6.6 233.0921 231.0775 Pharma Phenoxybenzamine 59-96-1 C18H22ClNO 4.6 12.0 304.1463 302.1317 Pharma Phenprocoumon 435-97-2 C18H16O3 3.7 10.4 281.1172 279.1027 Pharma Phentermine 122-09-8 C10H15N 2.2 8.0 150.1277 148.1132 Pharma Phenylbutazone 50-33-9 C19H20N2O2 3.5 10.2 309.1598 307.1452 Pharma Phenytoin 57-41-0 C15H12N2O2 2.2 7.9 253.0972 251.0826 Pharma Phosalone 2310-17-0 C12H15ClNO4PS2 3.7 10.5 367.9941 365.9796 Insecticide/Fungicide Phoxim 14816-18- C12H15N2O3PS 3.4 9.9 299.0614 297.0468 Biocide 3 Phthalic acid 88-99-3 C8H6O4 1.3 6.5 167.0339 165.0193 Insecticide/Fungicide Phytomenadione 27696-10- C31H46O2 11.7 23.7 451.3571 449.3425 Pharma 2 Picoxystrobin 117428- C18H16F3NO4 4.3 11.5 368.1104 366.0959 Insecticide/Fungicide 22-5 Pimecrolimus 137071- C43H68ClNO11 4.4 11.6 810.4554 808.4408 Pharma 32-0 Pindolol 13523-86- C14H20N2O2 1.5 6.8 249.1598 247.1452 Pharma 9 Pioglitazone 111025- C19H20N2O3S 4.0 10.9 357.1267 355.1122 Pharma 46-8 Pipamperone 1893-33-0 C21H30FN3O2 2.3 8.1 376.2395 374.2249 Pharma Piperacillin 61477-96- C23H27N5O7S 1.8 7.4 518.1704 516.1558 Pharma 1 Piperonyl butoxide 51-03-6 C12H15O3 4.8 12.2 208.1094 206.0948 Biocide charge Pirenzepine 28797-61- C19H21N5O2 1.7 7.1 352.1768 350.1622 Pharma 7 Piretanide 55837-27- C17H18N2O5S 2.5 8.5 363.1009 361.0864 Pharma 9 Piribedil 3605-01-4 C16H18N4O2 2.3 8.2 299.1503 297.1357 Pharma Piritramide 302-41-0 C27H34N4O 3.6 10.3 431.2805 429.2660 Pharma P-methyl-phenethylamine 3261-62-9 C9H13N 1.9 7.5 136.1121 134.0975 Insecticide/Fungicide Policresulen 101418- C23H24O12S3 1.2 6.3 589.0503 587.0357 Pharma 00-2 Polidocanol 3055-99-0 C30H62O10 2.3 8.2 583.4416 581.4270 Pharma Posaconazole 171228- C37H42F2N8O4 4.8 12.2 701.3370 699.3224 Pharma 49-2 Prajmalium 35080-11- C23H33N2O2 1.2 6.3 370.2615 368.2469 Pharma charge 6 Prallethrin 23031-36- C19H24O3 4.5 11.8 301.1798 299.1653 Biocide 9 Pramipexole 104632- C10H17N3S 2.3 8.2 212.1216 210.1070 Pharma 26-0 Prasterone 53-43-0 C19H28O2 3.0 9.3 289.2162 287.2017 Pharma Prasugrel 150322- C20H20FNO3S 3.9 10.8 374.1221 372.1075 Pharma 43-3 Pravastatin 81093-37- C23H36O7 3.1 9.5 425.2534 423.2388 Pharma 0 Prazepam 2955-38-6 C19H17ClN2O 4.0 10.9 325.1102 323.0957 Pharma Praziquantel 57452-31- C19H24N2O2 2.4 8.4 313.1911 311.1765 Pharma 0 Prazosin 19237-84- C19H21N5O4 1.3 6.5 384.1666 382.1521 Pharma 4 Prednicarbate 73771-04- C27H36O8 3.2 9.6 489.2483 487.2337 Pharma 7 Prednisone 53-03-2 C21H26O5 1.6 7.0 359.1853 357.1707 Pharma Pridinol 511-45-5 C20H25NO 4.2 11.3 296.2009 294.1863 Pharma Prilocaine 721-50-6 C13H20N2O 1.9 7.5 221.1648 219.1503 Pharma Probenecid 57-66-9 C13H19NO4S 2.9 9.1 286.1108 284.0962 Pharma Procaine 59-46-1 C13H20N2O2 2.0 7.6 237.1598 235.1452 Pharma Procarbazine 671-16-9 C12H19N3O 1.3 6.5 222.1601 220.1455 Pharma Prochlorperazine 58-38-8 C20H24ClN3S 4.8 12.3 374.1452 372.1307 Pharma Progesterone 57-83-0 C21H30O2 3.7 10.4 315.2319 313.2173 Pharma Proglumetacin 57132-53- C46H58ClN5O8 6.6 15.3 844.4047 842.3901 Pharma 3 Proguanil 500-92-5 C11H16ClN5 2.3 8.1 254.1167 252.1021 Pharma Prolintane 493-92-5 C15H23N 4.3 11.4 218.1903 216.1758 Pharma Promazine 58-40-2 C17H20N2S 4.6 11.9 285.1420 283.1274 Pharma Promethazine 38878-40- C17H20N2S 4.5 11.8 285.1420 283.1274 Pharma 9 114 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Propafenone 107300- C21H27NO3 3.4 9.9 342.2064 340.1918 Pharma 59-4 Propetamphos 31218-83- C10H20NO4PS 1.8 7.3 282.0923 280.0778 Biocide 4 Propicillin 551-27-9 C18H22N2O5S 2.8 8.9 379.1322 377.1177 Pharma Propiverine 60569-19- C23H29NO3 4.4 11.6 368.2220 366.2075 Pharma 9 Propofol 2078-54-8 C12H18O 3.6 10.3 179.1430 177.1285 Pharma Propoxyphene-n 469-62-5 C22H29NO2 5.3 13.1 340.2271 338.2126 Pharma Propranolol 525-66-6 C16H21NO2 2.6 8.7 260.1645 258.1500 Pharma Propyphenazone 479-92-5 C14H18N2O 2.1 7.7 231.1492 229.1346 Pharma Propyzamide 23950-58- C12H11Cl2NO 3.5 10.2 256.0290 254.0145 Herbicide 5 Proquinazid 189278- C14H17IN2O2 4.3 11.4 373.0408 371.0262 Insecticide/Fungicide 12-4 Prosulfocarb 52888-80- C14H21NOS 3.8 10.6 252.1417 250.1271 Herbicide 9 Prothioconazole 178928- C14H15Cl2N3OS 2.8 8.9 344.0386 342.0240 Insecticide/Fungicide 70-6 Prothipendyl 303-69-5 C16H19N3S 4.0 11.0 286.1372 284.1227 Pharma Protionamide 14222-60- C9H12N2S 2.0 7.7 181.0794 179.0648 Pharma 7 Pyraﬂufen-ethyl 129630- C15H13Cl2F3N2O4 4.4 11.7 413.0277 411.0132 Herbicide 19-9 Pyrantel 15686-83- C11H14N2S 3.1 9.5 207.0950 205.0805 Pharma 6 Pyrethrine 8003-34-7 C21H28O3 4.9 12.4 329.2111 327.1966 Biocide Pyrethrum 121-21-1 C21H28O3 4.6 11.9 329.2111 327.1966 Biocide Pyridate 55512-33- C19H23ClN2O2S 6.2 14.6 379.1242 377.1096 Herbicide 9 Pyridin-3-carbaldehyde C7H6O2 1.4 6.6 123.0441 121.0295 Insecticide/Fungicide Pyrifenox 83227-23- C14H12Cl2N2O 3.7 10.5 295.0399 293.0254 Insecticide/Fungicide 0 Pyriproxyfen 95737-68- C20H19NO3 5.4 13.2 322.1438 320.1292 Biocide 1 Pyritinol 1098-97-1 C16H20N2O4S2 1.6 7.0 369.0937 367.0792 Pharma Pyrvinium 7187-62-4 C26H28N3 4.6 12.0 383.2356 381.2210 Pharma Quetiapine 111974- C21H25N3O2S 1.9 7.6 384.1740 382.1595 Pharma 69-7 Quinapril 337376- C25H30N2O5 3.7 10.5 439.2227 437.2082 Pharma 15-5 Quinidine 56-54-2 C20H24N2O2 3.3 9.8 325.1911 323.1765 Pharma Quinisocaine 86-80-6 C17H24N2O 4.4 11.6 273.1961 271.1816 Pharma Quinoclamine 2797-51-5 C10H6ClNO2 1.6 7.1 208.0160 206.0014 Herbicide Quinoxyfen 124495- C15H8Cl2FNO 5.0 12.6 308.0040 305.9894 Insecticide/Fungicide 18-7 Quizalofop-P-ethyl 100646- C18H15ClN2O4 4.3 11.4 359.0793 357.0648 Herbicide 51-3 Rabeprazole 117976- C18H21N3O3S 3.0 9.3 360.1376 358.1231 Pharma 89-3 Racecadotril 81110-73- C21H23NO4S 3.1 9.4 386.1421 384.1275 Pharma 8 Raloxifene 520-27-4 C28H27NO4S 6.1 14.4 474.1734 472.1588 Pharma Ramipril 87333-19- C23H32N2O5 3.3 9.8 417.2384 415.2238 Pharma 5 Reboxetine 98769-81- C19H23NO3 2.8 9.0 314.1751 312.1605 Pharma 4 Repaglinide 135062- C27H36N2O4 6.2 14.6 453.2748 451.2602 Pharma 02-1 Rifabutin 72559-06- C46H62N4O11 7.5 16.7 847.4488 845.4342 Pharma 9 Rifampicin 13292-46- C43H58N4O12 4.2 11.4 823.4124 821.3978 Pharma 1 Rifaximin 80621-81- C43H51N3O11 7.8 17.2 786.3596 784.3451 Pharma 4 Riluzole 1744-22-5 C8H5F3N2OS 3.1 9.4 235.0147 233.0002 Pharma Risperidone 106266- C23H27FN4O2 3.5 10.1 411.2191 409.2045 Pharma 06-2 Rivastigmine 123441- C14H22N2O2 2.2 8.1 251.1754 249.1609 Pharma 03-2 Rizatriptan 145202- C15H19N5 1.4 6.7 270.1713 268.1568 Pharma 66-0 Supporting Information to Chapter 3 115

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Rocuronium bromide 119302- C32H53N2O4 3.2 9.7 530.4078 528.3933 Pharma 91-9 Rofecoxib 162011- C17H14O4S 1.6 7.0 315.0686 313.0540 Pharma 90-7 Ropinirole 91374-21- C16H24N2O 3.0 9.4 261.1961 259.1816 Pharma 9 Ropivacaine 84057-95- C17H26N2O 3.0 9.2 275.2118 273.1972 Pharma 4 Rosiglitazone 122320- C18H19N3O3S 3.2 9.6 358.1220 356.1074 Pharma 73-4 Rosuvastatin 287714- C22H28FN3O6S 2.5 8.5 482.1756 480.1610 Pharma 41-4 Rotenon 83-79-4 C23H22O6 3.3 9.8 395.1489 393.1344 Insecticide/Fungicide Rotigotine 99755-59- C19H25NOS 5.4 13.3 316.1730 314.1584 Pharma 6 Rufinamide 106308- C10H8F2N4O 1.5 6.8 239.0739 237.0593 Pharma 44-5 Rupatadine 158876- C26H26ClN3 5.6 13.5 416.1888 414.1742 Pharma 82-5 Salicylic acid 69-72-7 C7H6O3 2.2 8.1 139.0390 137.0244 Pharma Salmeterol 89365-50- C25H37NO4 4.2 11.2 416.2795 414.2650 Pharma 4 Saquinavir 127779- C38H50N6O5 2.6 8.6 671.3915 669.3770 Pharma 20-8 Selegiline 14611-51- C13H17N 2.6 8.7 188.1434 186.1288 Pharma 9 Sennoside b 517-43-1 C42H38O20 1.5 6.9 863.2029 861.1884 Pharma Sertaconazole 99592-32- C20H15Cl3N2OS 6.6 15.3 437.0043 434.9898 Pharma 2 Sertraline 79617-96- C17H17Cl2N 5.3 13.1 306.0811 304.0665 Pharma 2 Sevoflurane 28523-86- C4H3F7O 1.8 7.2 201.0145 198.9999 Pharma 6 Sibutramine 106650- C17H26ClN 5.7 13.8 280.1827 278.1681 Pharma 56-0 Sildenafil 139755- C22H30N6O4S 2.3 8.2 475.2122 473.1976 Pharma 83-2 Silymarin 22888-70- C25H22O10 1.9 7.6 483.1286 481.1140 Pharma 6 Simvastatin 79902-63- C25H38O5 5.2 12.9 419.2792 417.2646 Pharma 9 Sitagliptin 486460- C16H15F6N5O 1.4 6.7 408.1254 406.1108 Pharma 32-6 Sitaxentan 184036- C18H15ClN2O6S2 4.4 11.6 455.0133 452.9987 Pharma 34-8 S-Methopren 65733-16- C19H34O3 6.0 14.3 311.2581 309.2435 Biocide 6 Solifenacin 242478- C23H26N2O2 4.9 12.5 363.2067 361.1922 Pharma 37-1 Soneclosan 3380-30-1 C12H8Cl2O2 4.4 11.6 254.9974 252.9829 Biocide Sorafenib 284461- C21H16ClF3N4O3 5.3 13.1 465.0936 463.0790 Pharma 73-0 Spiramycin 8025-81-8 C43H74N2O14 1.9 7.4 843.5213 841.5067 Pharma Spirapril 83647-97- C22H30N2O5S2 3.9 10.8 467.1669 465.1523 Pharma 6 Spirodiclofen 148477- C21H24Cl2O4 6.6 15.3 411.1124 409.0979 Insecticide/Fungicide 71-8 Spironolactone 52-01-7 C24H32O4S 2.9 9.1 417.2094 415.1949 Pharma Stearic acid 646-29-7 C18H36O2 7.9 17.5 285.2788 283.2643 Pharma Stiripentol 49763-96- C14H18O3 3.7 10.4 235.1329 233.1183 Pharma 4 Sulindac 38194-50- C20H17FO3S 4.3 11.4 357.0955 355.0810 Pharma 2 Sunitinib 557795- C22H27FN4O2 3.2 9.6 399.2191 397.2045 Pharma 19-4 Tacrolimus 104987- C44H69NO12 3.0 9.4 804.4893 802.4747 Pharma 11-3 Tadalafil 171596- C22H19N3O4 1.4 6.7 390.1448 388.1303 Pharma 29-5 Talinolol 38649-73- C20H33N3O3 3.3 9.8 364.2595 362.2449 Pharma 9 Ta mox ife n 10540-29- C26H29NO 6.3 14.8 372.2322 370.2176 Pharma 1 116 Supporting Information to Chapter 3

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Tamsulosin 66-23-9 C20H28N2O5S 2.5 8.4 409.1792 407.1646 Pharma TCMTB 21564-17- C9H6N2S3 3.3 9.8 238.9766 236.9620 Biocide 0 Tebufenozide 112410- C22H28N2O2 5.4 13.2 353.2224 351.2078 Insecticide/Fungicide 23-8 Tebufenpyrad 119168- C18H24ClN3O 4.1 11.1 334.1681 332.1535 Insecticide/Fungicide 77-3 Teflubenzuron 83121-18- C14H6Cl2F4N2O2 4.5 11.8 380.9815 378.9670 Insecticide/Fungicide 0 Te fl u t h r in 79538-32- C17H14ClF7O2 5.5 13.5 419.0643 417.0498 Insecticide/Fungicide 2 Telithromycin 191114- C43H65N5O10 4.1 11.1 812.4804 810.4659 Pharma 48-4 Telmisartan 144701- C33H30N4O2 8.4 18.3 515.2442 513.2296 Pharma 48-4 Temazepam 846-50-4 C16H13ClN2O2 2.2 7.9 301.0738 299.0593 Pharma Te r a zo s in 63074-08- C19H25N5O4 1.5 6.8 388.1979 386.1834 Pharma 8 Terbinafine 91161-71- C21H25N 5.8 13.9 292.2060 290.1914 Pharma 6 Terbufos 13071-79- C9H21O2PS3 3.7 10.4 289.0514 287.0369 Insecticide/Fungicide 9 Terfenadine 50679-08- C32H41NO2 7.6 16.9 472.3210 470.3065 Pharma 8 Testosterone 571-41-5 C19H28O2 3.3 9.8 289.2162 287.2017 Pharma Tetrabenazine 58-46-8 C19H27NO3 2.7 8.7 318.2064 316.1918 Pharma Tetracaine 94-24-6 C15H24N2O2 3.0 9.3 265.1911 263.1765 Pharma Tetramethrin 7696-12-0 C19H25NO4 4.6 12.0 332.1856 330.1711 Biocide Tetrazepam 10379-14- C16H17ClN2O 3.7 10.4 289.1102 287.0957 Pharma 3 Tetryzoline 84-22-0 C13H16N2 3.7 10.4 201.1386 199.1241 Pharma Thiabendazole 148-79-8 C10H6N3S 2.8 9.0 201.0355 199.0210 Insecticide/Fungicide/- Biocide Thiacloprid 111988- C10H9ClN4S 2.1 7.8 253.0309 251.0164 Insecticide/Fungicide 49-9 Thiacloprid-amide C10H11ClN4OS 1.1 6.1 271.0415 269.0269 Insecticide/Fungicide Thiamethoxam 153719- C8H10ClN5O3S 1.3 6.5 292.0266 290.0120 Insecticide/Fungicide charge 23-4 Thioctic acid 62-46-4 C8H14O2S2 3.4 10.0 207.0508 205.0362 Pharma Thiocyclam hydrogen ox- 31895-22- C5H11NS3 1.3 6.6 182.0126 179.9981 Insecticide/Fungicide alate 4 Thiopental 76-75-5 C11H18N2O2S 2.9 9.1 243.1162 241.1016 Pharma Thiophanate-methyl 23564-05- C12H14N4O4S2 2.8 8.9 343.0529 341.0384 Insecticide/Fungicide 8 Thioridazine 50-52-2 C21H26N2S2 6.5 15.0 371.1610 369.1465 Pharma Thiram 137-26-8 C6H12N2S4 2.7 8.9 240.9956 238.9811 Insecticide/Fungicide/- Biocide Thymol 89-83-8 C10H14O 3.5 10.2 151.1117 149.0972 Pharma Tiaprofenic acid 33005-95- C14H12O3S 2.8 9.0 261.0580 259.0434 Pharma 7 Tibolone 5630-53-5 C21H28O2 3.9 10.8 313.2162 311.2017 Pharma Ticarcillin disodium 34787-01- C15H16N2O6S2 1.0 6.0 385.0523 383.0377 Pharma 4 Ticlopidine 55142-85- C14H14ClNS 4.4 11.5 264.0608 262.0463 Pharma 3 Tilidine 20380-58- C17H23NO2 3.7 10.5 274.1802 272.1656 Pharma 9 Timolol 26839-75- C13H24N4O3S 1.8 7.2 317.1642 315.1496 Pharma 8 Tipranavir 174590- C31H33F3N2O5S 6.7 15.4 603.2135 601.1990 Pharma 27-3 Tizanidine 51322-75- C9H8ClN5S 1.4 6.6 254.0262 252.0116 Pharma 9 Tolcapone 134308- C14H11NO5 3.1 9.5 274.0710 272.0564 Pharma charge 13-7 Tolnaftate 2398-96-1 C19H17NOS 5.8 13.9 308.1104 306.0958 Pharma Tolonium 92-31-9 C15H17N3S 3.5 10.2 272.1216 270.1070 Pharma Tolperisone 728-88-1 C16H23NO 3.7 10.5 246.1852 244.1707 Pharma Tolterodine 124936- C22H31NO 5.7 13.8 326.2478 324.2333 Pharma 74-9 Tolylfluanid 731-27-1 C10H13Cl2FN2O2S2 3.9 10.8 346.9852 344.9707 Biocide To r a s e mid e 56211-40- C16H20N4O3S 2.0 7.6 349.1329 347.1183 Pharma 6 Supporting Information to Chapter 3 117

Name CAS # Formula log Predicted M+H M-H Class Notes Kow Rt (min) (m/z) (m/z) Tosylchloramid-Natrium 127-65-1 C7H7ClNO2S 1.9 7.4 204.9959 202.9813 Biocide charge Tramazoline 1082-57-1 C13H17N3 2.5 8.5 216.1495 214.1350 Pharma Trandolapril 87679-37- C24H34N2O5 3.8 10.6 431.2540 429.2395 Pharma 6 Tranylcypromine 155-09-9 C9H11N 1.6 7.0 134.0964 132.0819 Pharma Trapidil 15421-84- C10H15N5 1.6 7.0 206.1400 204.1255 Pharma 8 Trazodone 19794-93- C19H22ClN5O 3.2 9.7 372.1586 370.1440 Pharma 5 Triadimenol 55219-65- C14H18ClN3O2 3.3 9.8 296.1160 294.1015 Insecticide/Fungicide 3 Triamcinolone acetonide 76-25-5 C24H31FO6 2.7 8.8 435.2177 433.2032 Pharma Tr ia z a ma t e 112143- C13H22N4O3S 2.6 8.7 315.1485 313.1340 Insecticide/Fungicide 82-5 Tribenoside 10310-32- C29H34O6 4.9 12.5 479.2428 477.2283 Pharma 4 Trifloxystrobin 141517- C20H19F3N2O4 4.8 12.3 409.1370 407.1224 Insecticide/Fungicide 21-7 Triflumizole 99387-89- C15H15ClF3N3O 3.6 10.3 346.0929 344.0783 Insecticide/Fungicide 0 Triflumuron 64628-44- C15H10ClF3N2O3 4.9 12.4 359.0405 357.0259 Biocide 0 Trifluralin 1582-09-8 C13H16F3N3O4 4.7 12.2 336.1166 334.1020 Herbicide charge Triforine 26644-46- C10H14Cl6N4O2 1.8 7.3 432.9321 430.9175 Insecticide/Fungicide 2 Trihexyphenidyl 144-11-6 C20H31NO 5.3 13.1 302.2478 300.2333 Pharma Trimipramine 739-71-9 C20H26N2 5.4 13.3 295.2169 293.2023 Pharma Tripelennamine 91-81-6 C16H21N3 2.7 8.9 256.1808 254.1663 Pharma Triprolidine 486-12-4 C19H22N2 3.7 10.5 279.1856 277.1710 Pharma Trofosfamide 72282-84- C9H18Cl3N2O2P 2.3 8.1 323.0244 321.0099 Pharma 9 Tropicamide 1508-75-4 C17H20N2O2 1.2 6.3 285.1598 283.1452 Pharma Trospium 10405-02- C25H30NO3 2.8 8.9 393.2298 391.2153 Pharma 4 Tyloxapol 337376- C14H22O 5.3 13.1 207.1743 205.1598 Pharma 15-5 Urapidil 34661-75- C20H29N5O3 1.2 6.3 388.2343 386.2198 Pharma 1 Ursodeoxycholic acid 128-13-2 C24H40O4 5.1 12.7 393.2999 391.2854 Pharma Valdecoxib 181695- C21H23ClFNO2 2.7 8.8 376.1474 374.1329 Pharma 72-7 Valproic acid 99-66-1 C8H16O2 3.0 9.2 145.1223 143.1078 Pharma Vardenafil 224785- C23H32N6O4S 2.8 9.0 489.2279 487.2133 Pharma 90-4 Vinclozolin C12H9Cl2NO3 3.7 10.5 286.0032 283.9887 Insecticide/Fungicide Vinorelbine tartrate 71486-22- C45H54N4O8 4.8 12.3 779.4014 777.3869 Pharma 1 Voriconazole 137234- C16H14F3N5O 1.6 7.0 350.1223 348.1078 Pharma 62-9 Warfarin 5543-57-7 C19H16O4 2.2 8.0 309.1121 307.0976 Pharma Xipamide 14293-44- C15H15ClN2O4S 2.3 8.1 355.0514 353.0368 Pharma 8 Xylometazoline 526-36-3 C16H24N2 5.4 13.2 245.2012 243.1867 Pharma Yohimbine 65-19-0 C21H26N2O3 2.1 7.8 355.2016 353.1871 Pharma Ziprasidone 146939- C21H21ClN4OS 3.6 10.3 413.1197 411.1052 Pharma 27-7 Zolmitriptan 139264- C16H21N3O2 1.9 7.5 288.1707 286.1561 Pharma 17-8 Zolpidem 82626-48- C19H21N3O 3.9 10.7 308.1757 306.1612 Pharma 0 Zopiclone 43200-80- C17H17ClN6O3 1.5 6.9 389.1123 387.0978 Pharma 2 Zotepine 26615-21- C18H18ClNOS 4.7 12.1 332.0870 330.0725 Pharma 4 Zoxamide 156052- C14H16Cl3NO2 4.6 12.0 336.0319 334.0174 Insecticide/Fungicide 68-5 Zuclopenthixol 982-24-1 C22H25ClN2OS 3.8 10.6 401.1449 399.1303 Pharma 118 Supporting Information to Chapter 3 a l .:Pstv nig ntetre nlsso aeLgn hwn eprlpteno ifrn organic different of pattern temporal a showing Lugano Lake of analysis target the in contaminants. findings Positive 3.3: Table 1915-1908 1925-1915 1954-1925 1965-1954 1975-1965 1984-1975 1994-1984 2004-1994 2012-2004 Year (pg/g benzamid 3-methyl- N-diethyl- N 0 0 0 0 0 0 0 0 620 dw ) (pg/g Pometryn Terbutryn/ 0 0 0 0 0 0 270 320 870 dw ) (pg/g nazole Propico- 0 0 0 0 0 0 0 0 260 dw ) (pg/g Fludioxonil 0 0 0 0 0 0 0 0 350 dw ) (ng/g Tonalide 0 0 0 10 70 20 200 250 200 dw ) (ng/g Benzotriazole 0 0 0 0 0 D D D D dw ) (ng/g Irgarol 0 0 0 0 0 0 2 7 10 dw ) (ng/g Alachlor Acetochlor/ 0 0 0 0 0 0 0 0 D dw ) (ng/g Galaxolidon 0 0 0 0 0 0 10 40 60 dw ) (ng/g Triclosan 0 0 0 0 0 4 20 30 20 dw ) (ng/g Triclocarban 0 0 0 0 2 20 110 30 7 dw ) A 14 (ng/g BAC 0 0 0 0 0 6 20 10 8 dw ) (ng/g C16:C16 DADMAC 0 0 0 0 0 3 142 129 1 dw ) (ng/g C18:C18 DADMAC 0 0 0 0 0 0 44 31 0 dw ) Supporting Information to Chapter 3 119

Figure 3.13: Histogram of mass defect (mDa) vs. frequency for masses up to 1000 Da for more than 850 compounds. 120 Supporting Information to Chapter 3

Figure 3.14: Lake Lugano sediment matrix (black dots) in negative ionization with their mass defect (mDA) plotted in the y-axis against nominal (m/z) masses at the x-axis. Suspect compunds are plotted in red dots. Supporting Information to Chapter 3 121

Figure 3.15: Lake Lugano sediment matrix (black dots) in positive ionization with their mass defect (mDA) plotted in the y-axis against nominal (m/z) masses at the x-axis. Suspect compunds are plotted in red dots.

Bibliography

[1] Chiaia-Hernandez, A.C.; Krauss, M.; Hollender, J., Screening of lake sediments for emerging contaminants by liquid chromatography atmospheric pressure photoionization and electrospray ionization coupled to high resolution mass spectrometry. Envi- ron Sci Technol 2012, 47, (2), 976-986.

123 124 BIBLIOGRAPHY Chapter 4

Bioconcentration of Organic Contaminants in Daphnia Resting Eggs

Aurea C. Chiaia-Hernandez, Roman Ashauer, Markus Moest, Tobias Hollingshaus, Junho Jeon, Piet Spaak, and Juliane Hollender Environ. Sci. Technol., 2013, 47(18), pp 10667-10675

125 126 Chapter 4. Bioconcentration

Abstract Organic contaminants detected in sediments from Lake Greifensee and other compounds falling in the log Dow range from 1 to 7 were selected to study the bioconcentration of organic contaminants in sediments in Daphnia resting eggs (ephippia). Our results show that octocrylene, tonalide, triclocarban, and other personal care products, pesticides and biocides can bioaccumulate in ephippia with log BCF values up to 3. Data on the uptake and depuration kinetics show a better fit towards a two compartment organism model over a single compartment model due to the differences in ephippial egg content in the environment. The obtained BCFs correlate with hydrophobicity for neutral compounds. Independence between BCF and hydrophobicity was observed for partially ionized compounds with log Dow values around 1. Internal concentrations in ephippia in the environment were predicted based on sediment concentrations using the equilibrium partitioning model and calculated BCFs. Estimated μ μ internal concentration values ranged between 68,000 g/kglip to 1 g/kglip with triclocarban having the highest internal concentrations followed by tonalide and triclosan. The outcomes indicate that contaminants can be taken up by ephippia from the water column or the pore water in the sediment and might influence fitness and sexual reproduction in the aquatic key species of the genus Daphnia.

4.1 Introduction

During the last century, most European lakes with anthropogenic point sources went through a phase of eutrophication which was accompanied by a shift in species composition and diversity of both pelagic and littoral communities, reduction of water quality, and even occasional fish kills. 1 The installation of sewage treatment plants in the 1980s3 and their continuous upgrade enabled the recovery of many lakes to their original trophic state. Simultaneous to eutrophication, numerous new chemicals have been produced for use in households, agriculture, and industry.4 Studies have revealed that chemical pollutants influence the aquatic food web,5,6 but how this is happening and at which time-scales chemical pollutants are influencing the aquatic food web, as well as the extent of the impact is not well known. Measurements of emerging contaminants such as pharmaceutical and personal care products (PPCPs) started in the 1990s, thus the exposure before is not well studied. In our previous work, we showed that sediments can be integrators in time of polar and medium polar organic compounds providing historical patterns of chemical deposition. 7 The ability of organic contaminants to sorb to sediments constitutes a primary source of exposure for benthic organisms. Benthic organisms can accumulate organic contami- 4.1. Introduction 127

nants from the particulate and interstitial components of sediments as well as from the water column. 8,9 Sediment pore water plays an important role in sediment-water sorption mechanisms and bioavailability, given that it is captured during the sedimentation process and is essentially isolated from the water column. 8,10 One of the most important planktonic grazers in pelagic food webs are species of the genus Daphnia (Crustacea, Anomopoda; water fleas). Daphnia species serve as food for fish and invertebrates, and they feed on algae and bacteria.2 Daphnia normally reproduce clonally (parthenogenetic cycle) but they switch to sexual reproduction (sexual cycle) when environmental conditions are not ideal and the production of males and sexual eggs is then triggered by changes in food level, crowding, and photoperio.11,12 Sexual eggs are diapause stages, enclosed in a structure called ephippium that can sink to the bottom of the lake and remain there until conditions become more favorable. Ephippia can also be transported by wind or aquatic birds to other water bodies and thus colonize new habitats. In the deeper parts of lakes, these diapausing eggs do not get a hatching stimulus and can remain in the sediment providing an unbiased archive of past populations. Ephippia can be extracted from sediment cores and either hatched for experimental purposes or directly analyzed with molecular genetic methods. 13 Anal- ysis of the genetic architecture of ephippia revealed that lake eutrophication was associated with a shift in species composition and the population structure of evolutionary lineages. 2 However, whether the shift in species composition was directly caused by phosphate or if phosphate concentrations are only a proxy for other parameters like chemical contamination is not known. Chemical contaminants can affect natural Daph- nia populations either by direct toxin uptake, or by indirect ingestion of contaminated algae. Pollutants may bioaccumulate in their resting eggs already in the water column or after sedimentation, influencing the fitness and their sexual reproduction, and therefore the evolutionary potential of an aquatic key species. However, there is almost no information about bioconcentration in ephippia in literature. Wyn et al. 14 evaluated the temporal and spatial patterns of metal contamination in resting eggs from sediment cores. Analysis of organic contaminants in ephippia extracted from sediment cores has never been done due to the small number of ephippia available in the sediment extraction and the amount needed for the chemical analysis. Therefore, the objectives of this study were to: (i) clarify to what extent organic contaminants can bioconcentrate in Daphnia resting eggs (ephippia) in uptake and elimination experiments; and (ii) reconstruct their contamination in the past based on sediment concentrations using an equilibrium partitioning model. The information obtained from the characterization of Lake Greifensee from our previous work was used to select a range of relevant organic contaminants which was complemented with further compounds 128 Chapter 4. Bioconcentration

with different hydrophobicity.

4.2 Experimental Section

Details on the sources, preparation, and storage of reference standards and reagents are provided in the Supporting Information (SI).

4.2.1 Sample Collection and Preservation

Two sediment cores were taken from Lake Greifensee, located 11 km to the northeast of Zurich, Switzerland. Sediment cores were collected using a free fall gravity corer and stored vertically in the dark at 4 ◦C until analysis. Dating of the sediment cores was performed by counting yearly laminations as has been described elsewhere. 7 Total phosphorus concentrations for each sediment layer were measured using peroxodisulfate oxidation as described by Ebina et al.15. Ephippia from the Daphnia longispina-galeata species complex were collected with nets from Lake Greifensee during peaks of sexual reproduction in spring and fall 2011. Ephippia were stored in 10 L polyethylene (HD- PE) plastic containers (Hünersdorff GmbH, Germany) and kept at 12 ◦C before clean- ing. Ephippia were cleaned using different sieves (mesh sizes: <1 mm) and rinsed with filtered and double autoclaved lake water to remove any residues of algae or other particles. After cleanup, ephippia were stored in the dark at 4 ◦C in 500 mL Schott bottles (Schott Duran, Germany) containing double autoclaved and filtered lake water. Lake water was collected simultaneously with ephippia, filtered through a glass fiber filter (pore size: 0.7 μm, Sartorius Stedim Biotech, France) and subsequently double autoclaved at 120 ◦C for 30 min each with a Vapoklav 500 (HP Medizintechnik GmbH, Germany). The treated lake water contained none of the studied chemicals in background concentrations above the limits of detection.

4.2.2 Ephippia Exposure: Uptake and Depuration Kinetics

For determination of the uptake kinetics, between 30 to 100 mg of wet weight (ww) clean ephippia were transferred to 250 mL Schott bottles ﬁlled with 200 mL of exposure medium. The medium consisted of treated lake water containing a mixture of 16 analytes with a nominal ﬁnal concentration between 150 to 250 μg/L. The analytes included 4.2. Experimental Section 129

pesticides, corrosion inhibitors, biocides and personal care products. The chemical selection was based on the organic contaminants found in sediments of Lake Greifensee from our previous study. In addition, 7 compounds which include pharmaceuticals and pesticides, were added to the study to have a broader range of analytes with different physical chemical properties (log Dow 1-7, log octanol-water partition coefficient (log Kow) corrected for the dissociation at the pH 8.2 of the medium). The chemicals were spiked as a mixture in ethanol with a final ethanol content of 0.4% (v/v). The complete list of analytes used in the experiment are reported in Table 4.1. The assays with the exposed ephippia were placed on a shaker SM-30 Contra (Edmund Bühler GmbH, Hechingen, Germany) at 12 ◦C and stored in the dark. After a specific time of exposure (up to 120 h), based on an estimation time for accumulation, the assays were sieved (mesh size: <1 mm), rinsed and transferred to 2 mL centrifuge tubes (Eppendorf AG, Germany). The samples were stored at −20 ◦C until extraction. In addition, 1 mL aliquot of each exposure medium were transferred to 5 mL glass vials (Infochroma Ag, Switzerland) and stored at −20 ◦C until analysis. In total, samples were collected at 21 different time points in duplicates. For depuration, previously exposed (for 120 h) ephippia were sieved, weighed, and between 30 to 100 mgww ephippia were transferred to individual 250 mL Schott bottles containing 200 mL of treated lake water (without contaminants). The medium was exchanged every 24 h to avoid re-uptake of chemicals. Samples were collected at 21 different time points in duplicates. Clean up and preparation was carried out as described above. Quality controls were taken during the experiment and include control medium consisting of treated lake water containing 0.4% ethanol (v/v), sample medium containing ephippia in treated lake water and 0.4% ethanol (v/v), and exposure medium. The controls were collected every 24 h and analyzed for possible cross contamination. No contamination was present in the controls or in the exposure medium. For all compounds the concentration in the medium was stable during the entire experiment. The measured concentrations over the complete experiment are illustrated in Figure 1 as well as in Table S3 in SI. In addition, pH (8.2 ± 0.2) and dissolved oxygen (10.1 ± 0.3) (mg/L) were monitored and kept constant during the experiment. An illustration of the complete experiment is provided in Figure 4.5 (SI). ± Lipid content (flip) was measured to be 1.5% 0.3 of the wet weight and was determined gravimetrically according to the method developed by Smedes et al. 16 Water content was determined gravimetrically and accounted for 89% ± 1 of the ephippia weight. 130 Chapter 4. Bioconcentration

4.2.3 Clean-up and Enrichment of Extracts

Frozen ephippia were transferred to individual 2 mL microcentrifuge tubes (Greiner Bio- One Ltd, Germany) containing approximately 200 to 300 mg of 0.5 mm size beads mixture of zirconium and silica beads (BioSpec Products Inc., USA). Subsequently, ephippia were suspended in 440 μL of methanol and 60 μL of an internal standard mix solution with a nominal final concentration in the vial of 240 μg/L were added. Ephippia were homogenized six times for 15 seconds at a speed of 6.0 m/s with a FastPrepR FP120 instrument (Thermo Savant, California, USA). After homogenization, the centrifuge tubes were rinsed with 500 μL of methanol and the two extracts were combined to give a final extraction volume of 1 mL. The extracts were diluted with 19 mL of nano-pure water and enriched by solid phase extraction (SPE) (OasisR HLB cartridges. Waters Corp., USA). Samples were eluted with a mixture of 50:50 methanol and isopropanol (v/v), evaporated to 100 μL using an EZ-2 Personal Evaporator (Genevac, USA) and diluted to 1 mL with a solvent mixture of 50:50 nanop- ure water and methanol (v/v). Control and exposure medium were directly analyzed by transferring 940 μL of medium to 1 mL HPLC vials (BGB Analytics AG, Switzerland) followed by addition of 60 μLof internal standard mix solution. Extraction, enrichment and analysis of sediment samples was performed according to Chiaia-Hernandez et al. 7 Cleanup of sediment extract was performed by adding 5 mL of acetonitrile, followed by 1.6 g of MgSO4 and 0.4 g NH4Cl. The mixture was vortexed and centrifuged. After separation, the acetonitrile phase was transferred to a graduated centrifuge tube, evaporated and brought to a volume of 500 μL by adding methanol. The final extract was filtered into 2 mL autosample vials using 0.2 μm PTFE filters (BGB analytics, Boeckten, Switzerland). Furthermore, sediment samples were sliced, freeze-dried and extracted by means of pressurizing liquid extraction followed by liquid-liquid partitioning. Sediment extracts were analyzed by LC-ESI/APPI-HR-MS as described elsewhere.7

4.2.4 Liquid Chromatography Tandem High Resolution Mass Spec- trometric Detection

Ephippia extracts were separated through an X-bridge C18 column (2.1 × 50 mm with particle size of 3.5 μm) with a ﬂow rate of 200 μL/min and a linear gradient of 28 min starting with 95% of 0.1% FA (v/v) in HPLC water and 5% of 0.1% formic acid(FA) (v/v) in methanol. After 17 min 10% isopropanol were added to improve elution of hydropho- 4.2. Experimental Section 131

bic compounds. For generation of ions, electrospray ionisation (ESI) in the negative and positive mode was used in two separate injections. Detection was performed using a LTQ (Linear Trap Quadrupole) Orbitrap mass spectrometer (Thermos Fisher Scientific Corp. USA). Data dependent high resolution mass spectrometry (HR-MS) with a resolution of 60000 and a mass accuracy of <5 ppm were used for peak detection followed by a product ion spectra (HR-MS/MS) at a resolution of 7500 for peak identification. Data analysis was done with Xcalibur software (Thermo Scientific, USA). Accuracy and precision of the method were determined in independent studies with an overall average method recoveries for ephippia and lake water were 92% and 82% with an average precision of 7% and 8%, respectively. Details and specifications on recoveries, quantification and detection of analytes are reported in SI and include internal standards, exact masses, ionization and retention times (see Table S2 and S3) as well as calibration curves and quality controls used.

4.2.5 Bioconcentration Factor (BCF)

One Compartment Organism Model

The bioconcentration factor (BCFi) is deﬁned as the ratio of the concentration of a given compound i in an organism or organisms compartment (Ciorg)[mol/kgww]to the concentration in the surrounding medium (Cimed), [M] at steady state (Equation 4.1). Ephippia can uptake and eliminate organic contaminants from sediments or from the water column only by passive uptake and depuration mechanisms since ephippia represent a resting stage and metabolic activities are not expected. Assuming ﬁrst order kinetics for uptake and elimination of each chemical, the Ciorg can be described by Equation 4.2 and the BCF [L/kgww] can be expressed by the ratio of the uptake rate −1 constant ku [L/(kgww × h)] and the elimination rate constant ke [h ] (Equation 4.1).

Ciorg ku BCFi = = (4.1) Cimed ke dC iorg k × C t − k × C t dt = u imed( ) e iorg( ) (4.2)

Two Compartment Organism Model

The one compartment organism model (4.2 Equation 2) was used as a ﬁrst attempt to determinate the uptake (ku) and elimination (ke) rates of chemicals in ephippia. When 132 Chapter 4. Bioconcentration

this model was used, we observed that the model ﬁt could not capture the fast uptake and elimination rates probably due to the difference in ephippia egg content. Ephip- pia are made up of chitinous sheets that can be melanized and enclose the dormant embryos. Daphnia from the longispina-galeata complex species produce ephippia containing no eggs (empty), one egg or two eggs (full). In our case, around 80% of ephippia were empty while the remaining 20% were full. The percent values were obtained by taking an ephippia aliquot, opening the ephippia with dissecting needles and examining the number of eggs per ephippium by eye under a stereo microscope. Without destruction, empty and full ephippia cannot be distinguished as illustrated in Figure 4.6 (SI). In order to describe the bioconcentration of this mixture of full and empty ephippia, a two compartment organism model was used, where the total Ciorg(total) is described as slow uptake (kufull) in the full ephippia and fast uptake (kuempty) in the empty ephippia (Equation 4.3 and 4.4). Ciorgfull and Ciorgempty [mol/kgww] are the concentrations of a given compound in the full and empty ephippia whereas Cimed, [M] is the concentration in the surrounding medium. Assuming two well mixed compartments (full and empty) and similar lipid and mass, the Ciorg(total) can be described by Equation 4.5 according to the percentage of full and empty ephippia. The uptake (kufull,kuempty) × −1 [L/(kgww h)] and elimination (kefull,keemty)[h ] kinetics can be used to calculate BCFs (L/kgww) as expressed in Equation 4.6 and 4.7.

dC iorgfull k × C t − k × C t dt = ufull imed( ) efull iorgfull( ) (4.3)

dC iorgfast k × C t − k × C t dt = uempty imed( ) eempty iorgempty( ) (4.4)

Ciorg(total)(t)=0.2 × Ciorgfull(t)+0.8 × Ciorgempty(t) (4.5)

kufull BCFfull = (4.6) kefull

kuempty BCFempty = (4.7) keempty Re-calculating BCFs for different fractions of full and empty ephippia depending on the different ephippia composition in the environment could be performed by changing the fractions (0.20/0.80) of full and empty ephippia in Equation 4.5 and by recalculating BCFs using the same rate constants, since rate constants are independent of the full and empty fraction. Additional lipid normalized BCFs are provided in Table S1. After 4.2. Experimental Section 133

solving the differential Equation 4.2-4.4, the rate constants (k) from the experimental data for the one and the two compartment model were obtained by weighted least square minimization using the NLS function in R, starting values were calculated with Aquasim (Reichert, P., AQUASIM 2.1 Eawag, Switzerland).17–19 For the measured data a 10% standard deviation was assumed.

The time to reach 95% steady state (SS) for the empty (Ciorgempty) and full compartment (Ciorgfull) were calculated using the analytical solution of Equation 4.3 and 4.4. For the combined empty and full compartment (Ciorg(total)),thetimetoreach95% SS was calculated using Equation 4.5 in combination with the function uniroot from the package rootSolve in R.19–21

4.2.6 Predicting Concentrations in Ephippia in the Environment

To evaluate the bioconcentration of hydrophobic organic compounds (HOC) from sediment, the equilibrium partitioning model (EqP) is often used. EqP assumes that organic contaminants are distributed between the lipids of the organisms, the pore water, and the organic carbon of the sediment, and that these compartments are in equilibrium.22,23 Based on this concept, the accumulation of a chemical in aquatic organisms can be estimated by Equation 4.8, where Clip is the lipid normalized steady-state con- μ μ centration in the biota ( g/kglip), Cipw is the concentration in the pore water ( g/L) and BCFlip is the lipid normalized aqueous bioconcentration factor (L/kglip)whichis 23 obtained from our experimental data. Cipw concentrations can be estimated by standard approaches using the organic carbon sorption coefﬁcient values (Koc), the organic contaminant concentrations measured in sediments from Lake Greifensee (Cised), and the fraction of organic carbon (foc) as stated in Equation 4.9.6,23. Values used to calculate Clip) are provided in Table 4.1.

Clip = BCFlip × Cipw (4.8)

Cised Cipw = (4.9) Koc × foc 134 Chapter 4. Bioconcentration

4.3 Results and Discussion

4.3.1 Sediment Analysis

Our earlier analyses of sediments from Lake Greifensee show that biocides, musk fragrances, and other personal care products were the most frequently detected com- 7 pounds with concentrations ranging from pg/gdw to ng/gdw. In Table 4.1 the maximum and minimum concentrations of the organic contaminants found in sediments from Lake Greifensee as well as the physicochemical properties of all analytes used in the study are compiled. In this study, the biocide triclocarban (TCC) was quantiﬁed from sediments of Lake Greifensee as a complementary information to our previous study. TCC concentrations range from 2 to 150 ng/gdw. The highest concentrations were found in the 1970s similar to the input pattern of triclosan (TCS) as illustrated in Figure 4.8 (SI). TCC has been employed since 1957 as antimicrobial agent similar to TCS in a variety of consumer products. Although, TCC is not yet monitored in Switzerland, TCC is approved for use as an antimicrobial in cosmetic products at a maximum use concentration of 0.2% (Annex 2, Swiss Cosmetic Product Regulation).

4.3.2 Uptake and Depuration Kinetics

The two compartment organism model used as an alternative to the one compartment organism model (Equation 4.2), assumes that the uptake (kuempty) and elimination (keempty) of chemicals are faster in the empty ephippia since the dormant embryos are missing and organic contaminants have to pass only through the outer layer shell to enter or exit the ephippium mainly consisting of chitin and only few lipids, while full ephippia containing one or two dormant embryos will have slower uptake (kufull)and elimination (kefull) rates. This assumption was confirmed by comparing the one vs. two compartment organism model as shown in Figure 4.1. In addition, for model selection the Akaike information criterion (AIC) was applied to measure the relative goodness of the fit of the two models. Lower AIC values indicate the model that fits better with respect to the number of parameters included in the model. 24 The AIC test points toward the two compartment model as a preferable model for more than 80% of the analytes studied and therefore, was used for the final BCF calculations. The experimental data obtained for the exposure and elimination of ephippia are shown in Table 4.1. The data indicates very fast uptake and elimination after a couple of hours 4.3. Results and Discussion 135

from the start of the experiment for most of the compounds. Terbuthylazine was the fastest to reach 95% equilibrium within 74 h (see Table 4.1 and Figure 4.9 in the SI). The fast uptake of terbuthylazine could be attributed mainly to its log Dow of 2.5. Irgarol and terbutryn, compounds with log Dow values around 3 and having similar molecular structure reached 95% steady state (SS) after 92 and 200 h of exposure, respectively, which might indicate that interactions other than diffusion driven by hydrophobicity play a role for the elimination of terbutryn. Benzotriazole and valsartan reach 95% SS after 840 and 579 h, results that are not in agreement with their low log Dow of 1, but can be attributed to their high polarity and partially ionized form. Uptake and elimination is not completely reached for clotrimazole, octocrylene, ritonavir, TCC and tonalide. The 95% SS for these compounds is reached between 260 to up to 1290 h which is consistent with their log Dow ≥ 5. For example, TCC has been reported to be quickly eliminated 25 from ﬁsh tissue (t1/2=1h) mainly through metabolism with only 1% TCC remaining. In ephippia, metabolism does not occur and therefore TCC has a very slow elimination which is consistent with our data. The difference in time to reach equilibrium (95% SS) between the full (Ciorgfull) and empty (Ciorgempty) compartment were between 50 to 500 times, and the 95% SS values for the combined compartment (Ciorg (total)) were comparable to the full compartment as reported in Table 4.1 and Table 4.5 and Figure 4.9 in SI. For discussion of the results, log Dow (pH=8.2) values were used instead of log Kow values to correct for pH-dependency. A variability in the uptake phase after 50 h is observed. This can be explained by the increasing growth of fungi with time around the ephippia as illustrated in Figure 4.7 in SI. Ephippia are directly in contact with the environment so bacteria and fungal spores are regularly seen associated with them. 26 In some cases the growth of fungi can be controlled to a certain degree by treating ephippia with bleach. However, treating ephippia from the Daphnia longispina-galeta species complex with bleach damages the ephippial sheets and eggs in an unpredictable way, and therefore we could only limit fungal growth by using ﬁltered and autoclaved medium. As a result, we could not distinguish the fraction of the compounds accumulated in fungi from that in ephippia, likely resulting in overestimated concentrations in ephippia in the later phase of the uptake experiment.

4.3.3 Ephippia Bioconcentration Factor (BCF)

The toxicokinetic parameters are reported in Table 4.1. The BCFs of 16 different chemicals were calculated using the rate constant for uptake (kufull) and elimination (kefull). 136 Chapter 4. Bioconcentration a (http://www.epa.gov/opptintr/exposure/pubs/episuitedl.htm) b C and Greifensee Lake of sediments in ephippia. found in contaminants bioconcentration their organic calculate of to concentration parameters minimal toxicokinetics and Maximum 4.1: Table aus C values, atr 5 SS 95% factor, 004kg (0.034 rdce ocnrtosi pipa C ephippia. in concentrations predicted xeietlywt au f005kg 0.015 of value a with experimentally Valsartan Triclosan Triclocarban Tonalide yn Terbutr Terbuthylazine Ritonavir Propiconazole Prochloraz Octocrylene Irgarol Fenpropimorph Fenoﬁbrate Diazinon Clotrimazole Benzotriazole Name K ised auscluae sn avnkth(http://www.chemaxon.com/) MarvinSketch using calculated Values oc auswr acltduigEtmto rga nefc EISie41 sn h o K log the using 4.1) Suite (EPI Interface Program Estimation using calculated were values stemxmmadmnmmcnetainmaue nsdmnsfo aeGefne,K Greifensee, Lake from sediments in measured concentration minimum and maximum the is oc ipw k)maue rmLk riesebten14-99rpre yZnege al., et Zennegg by reported 1940-1999 between Greifensee Lake from measured /kg) stecnetaini h ae,k water, the in concentration the is total class Compund Biocide PCP Pharmaceutical Pesticide Pharmaceutical Biocide Biocide Pesticide Pharmaceutical Biocide PCP Pharmaceutical inhibitor Corosion Pesticide Biocide Pesticide stetm orah9%sed tt o h lwadfs oprmn obnd C combined, compartment fast and slow the for state steady 95% reach to time the is tpH 8.2 at 1.2 D log 5.0 3.0 4.4 6.8 2.9 4.2 4.9 3.6 4.3 2.5 5.2 4.7 1.1 5.3 5.8 ow a lip (L/kg) K log 6.1E+01 1.9E+04 4.3E+02 8.4E+03 1.1E+05 6.1E+02 - 5.3E+03 3.3E+03 7.5E+02 ------ipw /kg a acltduigtefato fognccro (f carbon organic of fraction the using calculated was ww oc b (pg/g C 1400 - 332000 3100 92000 - 9000 600 - - - - 152000 4000 - 620 isedmax u dw n k and ) (pg/g C 1200 - - - - - 600 - 31000 60 - 9000 10000 3000 620 2400 e isedmin steutk n lmnto aecntns BCF constants, rate elimination and uptake the is dw ) (pg/g C 660 ------210 28 2 510 320 24 35 820 ipwmax dw ) (pg/g C - 570 ------3 41 2 48 24 34 26 13 ipwmin dw ) g (L/(h k 3.08E-05 1.04E-05 5.93E-06 2.17E-04 3.45E-04 4.93E-05 1.37E-03 4.09E-03 6.09E-05 2.33E-05 3.31E-03 2.57E-03 8.25E-05 9.56E-04 9.37E-04 8.23E-03 uslow ww × ow oc approach g (L/(h k 1.23E-02 3.56E-03 9.00E-03 2.86E-03 1.34E-02 5.17E-03 1.25E-02 8.25E-03 1.09E-02 1.40E-02 2.30E-03 9.83E-03 9.93E-03 1.67E-02 1.28E-02 6.62E-03 oc bandb vrgn ifrn f different averaging by obtained ) ufull ww 33 steognccro opincoefﬁcient sorption carbon organic the is n h ii rcin(f fraction lipid the and × 34 iorg (L/kg BCF 2.5 2.9 0.7 76 26 9.5 110 500 5.6 1.7 1440 260 8.3 74 56 1240 full stemnmladmaximum and minimal the is ww full o BCF log 0.4 0.5 -0.2 1.9 1.4 1.0 2.0 2.7 0.7 0.2 3.2 2.4 0.9 1.7 1.9 3.1 stebioconcentration the is lip (L/kg BCF 170 200 5060 1720 44 640 7300 33100 370 110 95900 17400 560 4970 3730 82900 determinate ) full lip oc values SS 95% 138 720 74 455 1007 144 198 328 200 92 1289 204 142 183 266 432 total ( C - - 130 - - - - - 11 23 13 220 130 1560 8900 68200 μ iorgmax g/kg lip ) ( C - - - - 110 - - - 1 5 13 220 98 170 830 1100 μ iorgmin g/kg lip ) 4.3. Results and Discussion 137

BenzotJiazole Clotrimazole Diazinon NC-<0!1L:8$S NC-corro.:tOOll1 AICcneCOft"I).: 8::88 NC_,corro.: 8783 NCtmo COft"l).: 100123 AJCi.oo:in-o..: 8189 ...... ,.., vyyo... ~ _.o.._....., ------· '\\'" JC)I h ·x- :' eo_, l , 1ao_, l "' ... "" ' ,.~i . .,.~t ~ t .! '"'

,., "" ti'ne(hrs"" ) .. "" time(hrs"" ) .. "" time[hrs] .. Fenofibrate Fenpropimorph lrgarol AIC-~918 1 AJC - corro.: St096 AIC"'">corro.:91832 NCi.oo:wrci..:S09U ..... -r-··?r:: 20 ~b,-0-· :~~ A .J...... ' "' . :.J..• .Jl.,J::~ ,., 1eo_, l ,.,~t ' ~i ~ t '"' '"' ' '"'

,., ,., ,., "" ti'ne(hrs) .. "" time(hrs) .. "" time[hrs] .. Octocrylene ... Prochloraz Propiconazole ~==:~.~-~ ~==~ !() ~==::&lfi 20 .,...... _...c: • ..J.-.---XA ,., \. . . i[)~ ,.,~t ' ~ I: )f '"' ' '"'

,., ,., ,., :m "" ti'ne(hrs) .. "" time(hrs) .. "" time[hrs] Ritonavir Telbutllylazine Terbutryn AIC-~sno;? o NC-corro.:s1m AJC -corro.:60529 ,, AIC_,corro.:S1306 ::.x;:· NCtmocorro.: SC9l NCi.oo:wrci..:60 91 ... _y . .-'-i_.-....:)-<' .~ .... .;...... c(':-'-·"() . ···········;:,,. ...-...A.J....x::. - ...... ,).. • .Jl.,.J<:: ,.,}t ' . ..': ,.,~t ' 18Ji t '"' ' '"' ' '"'

100 uo 200 1(1) uo 200 too 1SO 3:0 ti'ne(hrs) time(hrs) time[hrs] Tonalide Triclocartlan Triclosan

18Ji '"'

100 19:1 200 1(1) 19:1 200 too 1SO 3:0 ti'ne(hrs) time(hrs) time[hrs] Valsartan -- Two compartment -- One compartment --- Medium concentration

100 150 200 ti'ne(hrs)

Figure 4.1 : Ephippia uptake and elimination kinetics for 16 analytes. The one and two compartment organism models are illustrated with solid lines in blue and red, respectively. Measured exposure medium concentrations are shown with dashed lines in black color. The Akaike information criterion (AIC) is shown in the upper right corner with lower values indicating the "best" model. 138 Chapter 4. Bioconcentration

Values of BCFempty were lower than BCFfull mostly due to the very fast elimination rates (keempty) as reported in Table 4.6, SI. Only rates and BCFs for full ephippia (kufull=(ku), kefull=ke and BCFfull= BCF) are considered and discussed further since empty ephippia are not relevant for reproduction and are not used for further discussion in this manuscript. Greater BCFs were obtained for compounds with higher log Dow values showing a correlation (r2=0.59, p=0.0005) between log BCF and hydrophobicity as illustrated in Figure 4.2. A better correlation was observed (r2=0.72, p=0.0001) when the two polar and partially ionized compounds benzotriazole and valsartan were excluded from the linear fit. Highest BCFs were obtained for the personal care product octocrylene (1440 L/kg) and the biocide TCC (1240 L/kg) while the lowest BCF was found for the pesticide terbuthylazine. The correlation between BCF and hydrophobicity was further studied by comparing the relationship between ku and ke with hydrophobicity separately as shown in Figure 4.3. The model fit was obtained from the rate constant for adsorption and minimum elimination equations proposed by Hendrinks et al. 27 for accumulation of organic substances related to Kow of the chemical and the weight, lipid content, and trophic level of the species. Parameters and equations used for the model fit are reported in SI. The relationship between ku and ke with hydrophobicity estab- lishes that for compounds with log Dow between 2-5, ku increases with hydrophobicity. When ku reaches maximum values (log Dow around 5), ku becomes independent from hydrophobicity as well as for compounds with log Dowow values lower than 2. For elimination rate constants, the ke reaches a plateau for compounds with log Dow values between 2 to 6 and for higher values becomes inversely proportional to hydrophobicity corresponding to a membrane and a diffusion layer controlled elimination process respectively.28 Therefore an increase in BCF for more hydrophobic compounds is the result of a decreasing ke while ku remains constant, and for less hydrophobic compounds an increase in BCF results in an increase in ku while ke remains constant. A decline of ke with decreasing hydrophobicity is observed as in the case of benzotriazole and valsartan which do not follow the model fit. For hydrophilic compounds for which ke increases with hydrophobicity, the BCF becomes independent of hydrophobicity as has been speculated previously by Gobas et al.28 but not confirmed due to the absence of data. They explained this phenomenon by the fact that concentration partitioning of hydrophilic compounds in the lipid phase in the organism becomes less important as compared with the concentration in non-lipid phases, and therefore hydrophobicity and BCF cannot be extrapolated to extremely low hydrophobic compounds. Even though, the data presented in this study show similar trends, Gobas’ hypothesis cannot be clearly confirmed due to the limited data. Overall, our findings are in agreement with Hendrinks et al. 27 who found similar trends 4.3. Results and Discussion 139

between absorption and elimination rate constants and hydrophobicity for different organic compounds. The obtained log BCF values were compared with values obtained via a quantitative structure-activity relationship (QSAR) based on log Kow for bioconcentration of organic chemicals in Daphnia magna described by Geyer et al. 29 Kretschmann et al. 30 reported a lipid content between 1.5-1.8% for Daphnia magna (5-6 d), lipid content similar to ephippia. The estimated log BCF values are systematically higher than the measured ones (Figure 4.2) indicating that Daphnia is not a good model to predict BCF in their ephippia. To the best of our knowledge this is the ﬁrst study that has obtained experimental BCFs of organic contaminants in ephippia. The linear regression of our data on log BCF versus log Dow for ephippia can be used to predict accumulation of other organic pollutants in ephippia. Alternatively uptake and elimination rate constants can

Figure 4.2: Correlation between log Dow and log BCF in ephippia. Measured values are shown in black dots. Green line is the predicted relationships obtained from Geyer et al. (log BCF= 0.850 × log Dow-1.100). 29 Grey line represents the linear ﬁt for all compounds studied and blue line represents the linear ﬁt for compounds with log Dow >2. Red points correspond to benzotriazole and valsartan with log Dow values below 2. 140 Chapter 4. Bioconcentration

be calculated for untested compounds based on our ﬁt of the Hendriks at al.27 model (Figure 4.3 and SI).

4.3.4 Estimated Concentrations in Ephippia in the Environment

Estimated Cipw and Clip in ephippia were obtained using the calculated BCF (BCFfull) values normalized to lipid content (BCFlip) in combination with the lowest and highest organic contaminant concentrations found in sediments from Lake Greifensee. Results are reported in Table 4.1. The estimated internal concentrations in ephippia (Clip) range μ from 1 to 68,000 g/kglip. Highest values were obtained for TCC, followed by tonalide and TCS, while lower values were obtained for terbutryn, propiconazole and irgarol in accordance with the log Dow. Figure 4.4 shows the time series of estimated Clip for TCS, TCC, and tonalide with similar pattern to the total phosphorus (P) input into the

Figure 4.3: Relationship between ephippia uptake (ku) and elimination (ke) rate constants and hydrophobicity in full ephippia (slow compartment) expressed by log Dow. The model ﬁt was obtained from the rate constant for adsorption and minimum elimination equations reported by Hendriks et al.27 4.3. Results and Discussion 141

lake and the highest concentrations around the 1970s. Anthropogenically increased levels of phosphorus from urban and industrial sewage, erosional runoff, and leaching from agricultural areas has been associated with a shift in Daphnia species composition in Lake Greifensee.2 However, if this shift of species composition was caused by total phosphorus input or by organic contaminants still remains a question and the task to assess this a challenge. Although TCC appears to be equally abundant but more persistent in the environment than TCS, the potential impact of TCC on organisms is 31 almost unknown. Estimated Cipw for TCC range from 13 to up to 820 ng/L. The lowest observed effect concentration (LOEC) for TCC in D. magna has been reported at 4,700 32 ng/L and a half maximal effect concentration (EC50) of 10,000 ng/L. Even though the EC50 values for TCC are more than 12 times higher and for the rest of compounds more than 1,000 times higher when compared to predicted Cipw, more studies are needed to understand the mixture toxicity and co-occurrence of organic contaminants in the environment. Based on our fit of the Hendriks et al.27 model to ephippia data, one can calculate uptake and elimination rate constants for further organic contaminants and use those to predict the time course of concentrations in ephippia. This is possible, even for short or transient exposures that may occur in the water column. For diazinon, irgarol and terbuthylazine, 50% SS is reached in about 20 min, therefore these types of exposures could be significant. The use of the EqP to predict Clip has its limitations because more parameters need to be taken into account such as the stability of the compounds in the water column and in the sediments which was neglected in this study. However, the chemicals addressed in this study are known to be persistent and additionally, degradation of organic contaminants in sediments usually take place at much slower rates than bioconcentration processes. The use of predicted Dow and Koc values add an extra uncertainty to the Clip prediction. Nevertheless, the results obtained in this study give the first insight into the past and present concentrations in Daphnia resting eggs. We have shown to what extent organic contaminants can bioconcentrate in Daphnia resting eggs and how this can be predicted using physical-chemical properties. This is a starting point to understand the potential impact of organic contaminants on aquatic organisms relying on resting eggs during their life cycle and moreover to comprehend and predict the impact of these compounds on ecosystems by their effects on benthic- pelagic coupling and evolvability of species. 142 Chapter 4. Bioconcentration

Figure 4.4: Estimated ephippia lipid normalized internal concentrations (Clip) for triclocarban, triclosan and tonalide in Lake Greifensee. Maximum Clip concentrations are observed in the 1970s with a correlation between the highest Clip of organic contaminants and the highest phosphorus concentrations. Different colors correspond to scale units. 4.3. Results and Discussion 143

Acknowledgements The authors thank Etienne¨ Vermeirssen for his advice in the two compartment model, Birgit Beck for her help in the lab, Martin Frey for his comments and his help in modeling and statistics, and Damian Helbling for his comments and suggestion to the manuscript. Funding by the Swiss National Science Foundation (SNF CR32I3 125211) is gratefully acknowledged.

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[12] Ebert, D., Ecology, epidemiology, and evolution of parasitism in Daphnia [In- ternet] , ed. National Library of Medicine (US), N.C.f.B.I. 2005, Bethesda, MD, http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Books.

[13] Kerfoot, W.C.; Weider, L.J., Experimental paleoecology (resurrection ecology): Chasing Van Valen’s Red Queen hypothesis. Limnology and Oceanography 2004, 49, (4 II), 1300-1316.

[14] Wyn, B.; Sweetman, J.N.; Leavitt, P.R.; Donald, D.B., Historical metal concentrations in lacustrine food webs revealed using fossil ephippia from Daphnia. Ecological Applications 2007, 17, (3), 754-764.

[15] Ebina, J.; Tsutsui, T.; Shirai, T., Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res 1983, 17, (12), 1721-1726.

[16] Smedes, F., Determination of total lipid using non-chlorinated solvents. Analyst 1999, 124, (11), 1711-1718.

[17] Bates, D.M.; Chambers, J.M., Nonlinear models. Chapter 10 of Statistical models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole 1992.

[18] Bates, D.M.; Watts, D.G., Nonlinear regression analysis and its applications, Wiley 1988.

[19] R, Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria 2008.

[20] Soetaert, K., rootSolve: Nonlinear root ﬁnding, equilibrium and steady-state analysis of ordinary differential equations. R-package version 1.6 2009.

[21] Soetaert, K.; Herman, P.M.J., A practical guide to ecological modelling. Using R as a simulation platform. 2009, Springer. p. 372 BIBLIOGRAPHY 147

[22] Di Toro, D.M.; Zarba, C.S.; Hansen, D.J.; Berry, W.J.; Swartz, R.C.; Cowan, C.E.; Pavlou, S.P.; Allen, H.E.; Thomas, N.A.; Paquin, P.R., Technical basis for establishing sediment quality criteria for nonionic organic chemicals using equilibrium partitioning. Environ Toxicol Chem 1991, 10, (12), 1541-1583.

[23] Kraaij, R.; Mayer, P.; Busser, F.J.M.; van het Bolscher, M.; Seinen, W.; Tolls, J.; Bel- froid, A.C., Measured pore-water concentrations make equilibrium partitioning work a data analysis. Environ Sci Technol 2002, 37, (2), 268-274.

[24] Bozdogan, H., Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika 1987, 52, (3), 345-370.

[25] Schebb, N.H.; Flores, I.; Kurobe, T.; Franze, B.; Ranganathan, A.; Hammock, B.D.; Teh, S.J., Bioconcentration, metabolism and excretion of triclocarban in larval Qurt medaka (Oryzias latipes). Aquat Toxicol 2011, 105, (3-4), 448-454.

[26] Schultz, T.W.; Kennedy, J.R., The ﬁne structure of the digestive system of Daphnia pulex (Crustacea: Cladocera). Tissue and Cell 1976, 8, (3), 479-490.

[27] Hendriks, A.J.; van der Linde, A.; Cornelissen, G.; Sijm, D.T.H.M., The power of size. 1. Rate constants and equilibrium ratios for accumulation of organic substances related to octanol-water partition ratio and species weight. Environ Toxicol Chem 2001, 20, (7), 1399-1420.

[28] Gobas, F.A.P.C.; Opperhuizen, A.; Hutzinger, O., Bioconcentration of hydrophobic chemicals in ﬁsh: Relationship with membrane permeation. Environ Toxicol Chem 1986, 5, (7), 637-646.

[29] Geyer, H.J.; Scheunert, I.; Brueggemann, R.; Steinberg, C.; Korte, F.; Kettrup, A., QSAR for organic chemical bioconcentration in Daphnia, algae, and mussels. Sci Total Environ 1991, 109-110, (0), 387-394.

[30] Kretschmann, A.; Ashauer, R.; Preuss, T.G.; Spaak, P.; Escher, B.I.; Hollender, J., Toxicokinetic Model Describing Bioconcentration and Biotransformation of Diazinon in Daphnia magna. Environ Sci Technol 2011, 45, (11), 4995-5002.

[31] Chalew, T.E.A.; Halden, R.U., Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban. JAWRA 2009, 45, (1), 4-13.

[32] EPA, TCC Consortium. High Production Volume (HPV) Chemical Challenge Pro- gram Data Availability and Screening Level Assessment-Triclocarban, Report 201- 14186A; http://www.epa.gov/hpv/pubs/summaries/tricloca/c14186tp.pdf, 2002. 148 BIBLIOGRAPHY

[33] Zennegg, M.; Kohler, M.; Hartmann, P.C.; Sturm, M.; Guier, E.; Schmid, P.; Gerecke, A.C.; Heeb, N.V.; Kohler, H.P.E.; Giger, W., The historical record of PCB and PCDD/F deposition at Greifensee, a lake of the Swiss plateau, between 1848 and 1999. Chemosphere 2007, 67, (9), 1754-1761.

[34] USEPA, Estimation Programs Interface SuiteTM for MicrosoftR Windows, v 4.10. United States Environmental Protection Agency, Washington, DC, USA. 2011. Supporting Information to Chapter 4

149 150 Supporting Information to Chapter 4

Standard and Reagents

Formic acid (98-100%) were purchased from Merck (Darmstadt, Germany). Acetone (99.5% purity), ethanol (99.5%) were purchased by Sigma Aldrich (Steinheim, Ger- many). HPLC water and isopropanol (99.5% purity) were purchase from Acros Organ- ics (New Jersey, USA) and methanol (99.9% purity) was purchased from Fisher Sci- entiﬁc (Wohlen, Switzerland). The reference standards (purity ≥ 97%) were purchased from the following providers: Sigma-Aldrich (Steinheim, Germany), TRC Canada (Toronto, Canada), Ciba (Basel, Switzerland), Dr. Ehrenstorfer (Augsburg Germany), LGC Stan- dards (Wesel Germany), Novartis (Basel, Switzerland), and CDN Isotopes (Augsburg, Germany). Detailed information is provided in Table 4.2 and 4.3.

Table 4.2: Standards for the analysis of organic contaminants in lake sediments.

Name CAS-No Group Company Irgarol 28159-98-0 Biocide Sigma-Aldrich Propiconazole 60207-90-1 Biocide Dr. Ehrenstorfer Terbutr yn 886-50-0 Biocide Dr. Ehrenstorfer Triclocarban 101-20-2 Biocide Sigma-Aldrich Triclosan 3380-34-5 Biocide Ciba Benzotriazole 95-14-7 Corosion Inhibitor Sigma-Aldrich Diuron 330-54-1 Insecticide Dr. Ehrenstorfer Tonalide 21145-77-7 Musk Fragance LGC standards Octocrylene (=2-Ethyl-2-cyano- 6197-30-4 Personal Care Product Sigma-Aldrich 3,3-diphenylacrylate Fenpropimorph 67306-03-0 Pesticide Dr. Ehrenstorfer Prochloraz 67747-09-5 Pesticide Dr. Ehrenstorfer Terbutylazine 5915-41-3 Pesticide Dr. Ehrenstorfer N,N-diethyl-3-methylbenzamide 134-62-3 Pesticide Dr. Ehrenstorfer (DEET) Fenoﬁbrate 49562-28-9 Pharmaceutical Sigma-Aldrich Valsartan 137862-53-4 Pharmaceutical TRC Canada Ritonavir 155213-67-5 Pharmaceutical TRC Canada Clotrimazole 23593-75-1 Pharmaceutical TRC Canada Didecyldimethylammonium 2390-68-3 Quaternary Ammo- Sigma-Aldrich nium

Table 4.3: Internal standards for the quantiﬁcation of organic contaminants in lake sediments.

Intenal Standard Company Benzotriazol-D4 TRC Canada Charithromycin-D6 Dr. Ehrenstorfer DEET-D7 Dr. Ehrenstorfer Diazinon-D10 Dr. Ehrenstorfer Fenoﬁbrate D6 TRC Canada Irgarol-D9 CDN Isotopes Supporting Information to Chapter 4 151

Intenal Standard Company Valsartan 13C5 Novartis Propiconazole-D5 Dr. Ehrenstorfer Ritonavir-D6 TRC Canada Terbutryn-D5 Dr. Ehrenstorfer Terbutylazin-D5 Dr. Ehrenstorfer Tonalide D3 Dr. Ehrenstorfer Triclosan 13C6 Dr. Ehrenstorfer

Liquid Chromatography Tandem High Resolution Mass Spectrometric Detection

Separation was performed on a 2.1 × 10 mm C18 security guard cartridge connected to a2.1× 50 mm × 3.5 μm particle size X-bridge C18 column (Waters Corp., Milford, MA) at 35 ◦C. The mobile phase consisted of HPLC water (A), methanol (B) and isopropanol (C). Formic acid (0.1% v/v) was added to eluents A and B. The gradient starts by holding A (95%) for 1 min and by increasing the flow rate from 200 μL/min to 320 μL/min to allow a fast flow of water into the column and dilute the sample. Then B is increased to 10% in 1 min and the flow rate is decreased back to 200 μL/min. Mobile phase B is further increased to 50% in 3 min followed by a ramp of B to 100% in 14 min, after which 100% B is maintained for 4 min. Furthermore, C is increased to 100% in 0.1 min and is maintained at 100% for 8 min. The gradient is brought to initial conditions (95% A) and is held for 8 min for recalibration of the column, giving a total run time of 38 min. The electrospray, source fragmentation and capillary voltage in positive ion mode were set at 5 kV, 15 V and 25 V respectively, and the capillary temperature to 300 ◦C.Inthe negative ion mode the electrospray, source fragmentation and capillary voltage were set at -4 kV, -15 V and -20 V with a capillary temperature of 350 ◦C. Tube lens was set to 60 V for positive and -70 V in negative analysis. The sheath and auxiliary gas flow were set at 50 and 20 arbitrary units respectively.

Quantiﬁcation and Quality Control

Calibration standards (n=8) were made in methanol and HPLC water 50:50 (w/w) with concentrations ranging from 0.5 ng to 400 ng of standard mix solution (corresponding to nominal ﬁnal concentrations of 1 μg/L to 800 μg/L in vial). Analytes were quantiﬁed from calibration standards based on the normalization of analyte responses to internal standards by linear least-squares regression. Exact molecular masses (m/z), ionization 152 Supporting Information to Chapter 4

behavior and internal standards used for the detection and quantification or analytes are illustrated in Table 4.4. Calibration curves were run at the beginning and at the end of each run. Instrument blanks were run before and after each batch of ten samples as well as two check standards of 125 ng and 225 ng (nominal final concentration of 250 μg/L and 450 μg/L in vial). Method blanks were run randomly with each sequence. Quality control accounted for more than 30% of the sample number and sample duplicates accounted for 20% and were run randomly with each sequence and at different days. Deviations of the quality control standards by more than 30% were rejected and samples were re-analyzed. Data analysis was done by Xcalibur software (Thermo Sci- entific Corp, USA) and Quan Browser was used for the quantification of the analytes.

Table 4.4: List of chemicals with internal standards and exact mass used for quantiﬁca- tion.

Synonym Molecular formula Molecular mass M+H M-H Int. Standard Benzotriazol C6H5N3 119.0478 120.0556 N/A Benzotriazol D4 Clotrimazole C22H17ClN2 344.1075 345.1153 N/A Diuron D6 Diazinon C12H21N2O3PS 304.3460 305.1083 N/A Diazinon D10 Fenofibrate C20H21ClO4 360.1123 361.1201 N/A Fenofibrate D6 Fenpropimorph C20H33NO 303.2557 304.2635 N/A Clarithromycin D3 Irgarol C11H19N5S 253.1356 254.1434 N/A Irgarol D9 Octocrylene C24H27NO2 361.2115 362.2115 N/A Fenofibrate D6 Prochloraz C15H16Cl−3N3O2 375.0303 376.0381 N/A Propiconazole D5 Propiconazole C15H17Cl2N3O2 341.0692 342.0771 N/A Propiconazole D5 Ritonavir C37H48N6O5S2 720.3128 721.3200 N/A Ritonavir D6 Terbuthylazine C9H16ClN5 229.1089 230.1167 N/A Terbuthylazine D5 Terbutr yn C10H19N5S 241.1356 242.1434 N/A Ternutryn D5 Tonalide C18H26O 258.1978 259.2056 N/A Tonalide D6 Triclocarban C13Cl3H9N2O 312.9697 N/A 312.9697 Triclosan 13C6 Triclosan C12H7Cl3O2 287.9506 N/A 286.9428 Triclosan 13C6 Valsartan C24H28N5O3 435.2270 435.2265 N/A Valsartan-15N,13C5 Supporting Information to Chapter 4 153

Exposure of Ephippia: Uptake and Depuration Kinetics.

Figure 4.5: Detailed illustration of the exposure and depuration experiments. For determination of the uptake kinetics (Exposure k1), between 30 to 100 mg of wet weight (ww) clean ephippia were transferred to 250 mL Schott bottles ﬁlled with 200 mL of exposure medium. For depuration (Exposure k2), previously exposed (for 120 h) ephippia were sieved, weighted and between 30 to100 mgww ephippia were transferred to individual 250 mL Schott bottles containing 200 mL of treated lake water (without contaminants). The ephippia used for the depuration experiment (t=120 h) were split in 2L assays. Around 50 mg of ephippia were used for a time point in the exposure experiment while around 1.5g were used for the depuration experiment (Exposure k1 + Exposure/Elimination k2). Assays were collected at 21 different time points in duplicates. 154 Supporting Information to Chapter 4

Ephippia: One vs. Two Compartment Organism

Empty and full ephippia cannot be distinguished under the stereo microscopy without destroying the ephippium outer shell as illustrated in Figure 4.6. Even though ephippia may look as if they would contain eggs, the pigmentation does not allow a correct identiﬁcation for empty, one or two egg ephippium.

Figure 4.6: Empty and full ephippia mix. Water ﬂeas of the Daphnia longispina-galeata species complex can produce ephippia containing no eggs (empty), one egg or two eggs (full) and they cannot be distinguished under the stereo microscope.

Figure 4.7: Ephippia from the Daphnia longispina-galeata species complex with visible fungal growth (red arrow) after 120hrs. Supporting Information to Chapter 4 155

Sediment Analysis

Triclocarban was quantiﬁed in sediments from Lake Greifensee with concentrations ranging from 2-150 ng/gdw with the highest concentration in the 1970s and with the same input pattern as triclosan as illustrated in Figure 4.8.

Figure 4.8: Sediment concentrations of triclocarban and triclosan from Lake Greifensee. 156 Supporting Information to Chapter 4

Uptake and Depuration Kinetics

Table 4.5: 95% steady state(SS) for the empty, full and combined compartment (total) for studied compounds.

Name 95% SSempty 95% SSfull (hrs) 95% SStotal (hrs) (hrs) Benzotriazole 13 840 720 Clotrimazole 3 363 328 Diazinon 1 243 138 Fenoﬁbrate 3 240 198 Fenpropimorph 2 224 144 Irgarol 1 214 92 Octocrylene 10 1302 1289 Prochloraz 3 179 142 Propiconazole 2 302 204 Ritonavir 2 1049 1007 Terbuthylazine 2 333 74 Terbutr yn 2 274 200 Tonalide 4 305 266 Triclocarban 3 452 432 Triclosan 5 233 183 Valsartan 1 579 455 Supporting Information to Chapter 4 157

Figure 4.9: Time to reach 95% steady state(SS) for 16 different compounds. Fast (empty), slow (full) and two compartment(total) organism model shown in blue, red and green dashed lines, respectively. Solid lines represent the model ﬁt for the chemical uptake in the organism with time as described in Equation 4.3, 4.4, 4.5. 158 Supporting Information to Chapter 4

Bioconcentration Factors (BCF) of different compounds for ephippia

Table 4.6: Comparison between calculated BCFempty and BCFfull values for 16 different chemicals. BCFempty values were calculated using the rate constant for uptake (kuempty) and elimination (keempty).

Name kuempty L/(hrs keempty BCFempty BCFempty log BCFempty log BCFfull −1 × gww) (hrs ) L/kgww L/kglip L/kgww L/kgww Benzotriazole 8.8E-05 0.2 0.4 26 -0.4 0.5 Clotrimazole 4.4E-02 1.1 42 2774 1.6 2.7 Diazinon 5.0E-03 3.0 2 111 0.2 0.4 Fenoﬁbrate 2.1E-02 1.1 19 1270 1.3 2.0 Fenpropimorph 1.8E-02 1.4 12 818 1.1 1.4 Irgarol 4.9E-03 2.6 2 126 0.3 0.2 Octocrylene 3.3E-03 0.3 11 729 1.0 3.2 Prochloraz 1.1E-02 0.9 12 813 1.1 1.7 Propiconazole 5.3E-03 1.6 3 228 0.5 0.9 Ritonavir 3.6E-03 1.5 2 160 0.4 1.9 Terbuthylazine 2.9E-03 1.9 2 102 0.2 -0.2 Terbutr yn 2.7E-03 1.6 2 116 0.2 0.7 Tonalide 2.5E-02 0.8 30 2029 1.5 2.4 Triclocarban 4.7E-02 1.0 45 3007 1.7 3.1 Triclosan 1.1E-02 0.6 17 1117 1.2 1.9 Valsartan 5.8E-03 2.7 2 143 0.3 1.0

Hendrinks’ Equation

Model fits between hydrophobicity (log Dow) and excretion rate (log kex) were taken from the equation described in Hendriks et al.1 as illustrated in Equation 4.10 and 4.11 for the rate constant for absorption and minimum elimination. Where w is the weight of organism (kg), k is the rate exponent, PCH2 is the lipid fraction of organism, pH2O is × −k water layer diffusion resistance (d kg ), pCH2 is the lipid layer permeation resistance (d × kg−k), γ is the water absorption-excretion coefficient(kgk /d). The parameter values γ k used were; w= 0.000001 kg, k=0.25 and = 200 kg /d. pH2O and pCH2 were optimized to obtain the best fit values. Nonlinear fits were obtained by weighted least square minimization using the NLS function in R. 2–4

W −k k ux = pCH2,i 1 (4.10) p 2 + + H O Dow y Supporting Information to Chapter 4 159

W −k k 1 × ex = pCH2,i 1 (4.11) p 2 × (D − 1) + 1 p 2 + + CH ,i ow H O Dow y

Bibliography

[1] Hendriks, A.J.; van der Linde, A.; Cornelissen, G.; Sijm, D.T.H.M., The power of size. 1. Rate constants and equilibrium ratios for accumulation of organic substances related to octanol-water partition ratio and species weight. Environ Toxicol Chem 2001, 20, (7), 1399-1420.

[2] Bates, D.M.; Chambers, J.M., Nonlinear models. Chapter 10 of Statistical models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole 1992.

[3] Bates, D.M.; Watts, D.G., Nonlinear regression analysis and its applications, Wiley 1988.

[4] R, Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria 2008.

161 162 BIBLIOGRAPHY Chapter 5

Environmental Organic Contaminants Inﬂuence Hatching from Daphnia Resting Eggs and hatchling survival

Aurea C. Chiaia-Hernandez, Markus Moest, Martin P. Frey, Juliane Hollender, and Piet Spaak

163 164 Chapter 5. Daphnia Resting Eggs

Abstract The occurrence and fate of contaminants in surfaces waters are of great environmental concern and pose a threat to aquatic animals, ecosystems and water safety. Risk assessment of organic contaminants toward aquatic organism is difficult and the study of joint toxicity is even more challenging, since most of the organic contaminants are present in different concentrations and the complex life cycles of many aquatic species within different compartments complicate their study. A mixtures of organic contaminants found in sediments of Lake Greifensee were selected to study their effect on hatching ability and hatchling mortality using resting eggs from a Daph- nia species complex inhabiting this lake. In this work, the effect of mixture toxicity on hatching success of resting eggs and hatchling mortality was assessed, finding a significant increase in hatching success and hatchling mortality for resting eggs exposed to organic contaminants. Increasing hatching success has not yet been reported from ecotoxicological studies and adds a novel aspect that requires consideration in risk assessments. In this paper, mechanistic explanations for our results are discussed, as well as the potential implications on the ecology and evolution of aquatic species that rely on a resting egg bank. Furthermore, our results highlight the need of further studies assessing the effects of organic contaminants on benthic-pelagic coupling and aquatic ecosystems.

Key Words: Mixture toxicity, micropollutants, resting egg bank, diapause, benthic- pelagic coupling, ephippia, water ﬂea

5.1 Introduction

Among the most important planktonic grazers in freshwater pelagic food webs are species of the genus Daphnia (Crustacea: Anomopoda; water fleas). Daphnia species constitute a major food source for fish and invertebrates, are important planktonic grazers, and therefore are attributed as a keystone species in lentic ecosystems. 1 Daphnia spp. have been established as an important model organisms in aquatic toxicology 1,2,34 and have recently been adopted as one of 13 model organisms for biomedical research by the National Institutes of Health5. Most toxicological studies are conducted with the large-bodied D. magna e.g. 6–9 a species known to preferentially inhabit ponds. Less attention so far has been paid to other species such as the members of the D. longispina species complex despite of their significance for large lakes ecosystems and importance for drinking water reservoirs, e.g. Lake Zurich and Lake Constance. 1,10 5.1. Introduction 165

Most Daphnia species reproduce clonally (parthenogenetic cycle) during favorable conditions but switch to sexual reproduction (sexual cycle) when environmental conditions are not ideal, triggered by e.g. changes in food level, crowding and photoperiod.1,11,12 During sexual reproduction, Daphnia produce dormant eggs, enclosed in a protective case called ephippium. Ephippia can float on the lake surface, and may be transported by wind, 13 waterfowl, 14,15 insects16 or human activities 17,18 to other water bodies. A portion of the resting eggs sinks to the bottom of the lake where they contribute to the build-up of a so-called resting egg bank. 19 Resting egg banks are not only known for Daphnia but for many aquatic organisms and play a crucial role for their ecology and evolution for comprehensive reviews on egg banks see19–21. Parts of or even the entire active pelagial population are recruited from the egg bank during each growing season via trans-generational hatching, and the egg bank is in return re-stocked with dormant eggs produced by the active population. This interdependence, known as benthic-pelagic coupling, affects population dynamics and the evolutionary potential of many zooplankton species. 20,22 First of all, the egg bank allows to escape from detrimental biotic e.g.23 or abiotic factors e.g. 24 and reduces the extinction risk of local populations. Furthermore, in fluctuating environments, the egg bank helps to maintain the co-existence of species and genotypes via the storage effect. 25–27 The egg bank allows to preserve and increase genetic diversity28,29 and thereby the evolutionary potential of species and buffers local populations against the establishment of immigrants. 30 In addition to their important role for the biology of many aquatic species, dormant eggs can also be used to reconstruct the impact of human-caused environmental change. 31–35 In deeper parts of lakes, dormant eggs do not receive hatching stimuli and remain in the sediment, providing an unbiased archive of past populations. Dormant eggs extracted from the sediments can be hatched for experimental purpose or directly be analyzed with molecular genetic methods several decades back in time. 36–38 Due to the key role of Daphnia spp. in aquatic food webs, their particular life history features, comprehensive understanding of their distribution and biology, as well as the wealth of toxicological information and genomic tools available3, Daphnia is an ideal model system to understand the impact of abiotic factors on lentic ecosystems. For example,34 and Rellstab, Keller31 analyzed resting eggs of the Daphnia longispina - galeata species complex from five European lakes showing a correlation of an increase of D. galeata abundance, interspecific hybridization, and a decrease of D. longispina abundance over time with human-induced changes in total phosphorus levels. Today, many water bodies in western countries have recovered from anthropogenically 166 Chapter 5. Daphnia Resting Eggs

induced eutrophication. Nevertheless, pollutants, in particular so called micropollutants, are still introduced into these systems, representing future threats to natural populations of aquatic organism.39–41 Organic contaminants can enter natural waters from different sources, e.g., via waste water treatment plants effluents, urban and industrial sewage, surface runoff, spray drift and leaching from agricultural areas. Depending on their physical-chemical properties, they sorb to the sediment and form an excellent archive for former pollution. This was shown for highly lipophilic compounds like PCBs42,43 but recently also for medium polar contaminants such as pesticides, personal care products, biocides and corrosion inhibitors. 44 Such medium polar contaminants are much more bioavailable for freshwater organisms as opposed to highly lipophilic organic contaminants. 45 Organic contaminants from the particulate and interstitial components of sediments as well as from the water column constitute a primary source of exposure for benthic organism and their life cycle stages.46,47 Sediments contaminated with mutagenic substances are known to pose a hazard to indigenous biota including adverse effects such as DNA damage, chromosomal aberrations and cancer. 48 In addition, chemical pollutants in aquatic system have been associated with reproductive impairment,49 limb deformities 50 and declines of non-targeted species. 51–53 Despite of the general importance of egg banks for many planktonic organisms and the known accumulation of contaminants in lake sediments, the number of studies ad- dressing a potential effect of pollutants on the function of egg banks is limited, and in some cases with inconclusive results as summarized by Navis et al.54 Angeler et al.55 detected a negative impact of a commercial fire retardant on the emergence success of D. curvirostris. In addition, Raikow showed an effect of ballast tank treatments SeaKleen (menadione)56 and sodium hypochlorite57 on the hatching of resting eggs of different planktonic organisms, among them D. mendotae.ForArtemia cysts no effect of the pesticide chlorpyrifos on hatching could be shown58 and studies on the effect of cadmium and zinc revealed contradicting results. 59–62 However, a detrimental effect of heavy metal exposure on the hatching success of the marine copepod Acartia pacifica was reported. 63 Marcial et al.64 studied the hatching abilities of the rotifer Brachionus plicatilis when exposed to four different pesticides, reporting that sexual reproduction is a more sensitive parameter than asexual reproduction, and that resting egg hatchability is the most sensitive parameter in detecting effects of the pesticide diazinon when resting eggs are exposed during their development. 65 In a recent study on D. magna, Navis et al.54 found evidence for negative effects of the endocrine disruptor fenoxycarb and the insecticide carbaryl on survival and reproduction of hatchlings, as well as the impact of fenoxycarb on the development of resting eggs and hatching success. 5.2. Material and Methods 167

While egg bank and benthic-pelagic coupling may be adversely affected at different levels of the life cyle,54 in this work, we focused on the direct effects of toxicants resting eggs (i.e. hatching success and hatchling mortality). We followed a new approach as we exposed ephippia of a natural population of the D. longispina species complex, directly collected from the surface of Lake Greifensee (Switzerland) after production, to a mixture of organic contaminants that were detected in the sediments from the same lake. In our previous work we showed that organic contaminants can bioaccumulate in ephippia of these species in uptake and elimination experiments. Additionally, ephippia internal concentrations in the environment were predicted based on the past and recent contamination of lake sediments, using the equilibrium partitioning model (EqP). 66 Therefore, the main objectives of this study were to (i) establish an experimental setup for ecotoxicological tests using D. longispina species complex ephippia and to (ii) test for a potential impact of a mixture of contaminants found in the sediments of Lake Greifensee with a 1000 fold increase in concentration to clarify whether an effect can be observed on hatching abilities of resting eggs, and consequently on the ecology and evolution of a species complex that occupies a key role in the ecosystem of most European large lakes.

5.2 Material and Methods

5.2.1 Ephippia Collection and Preparation

Ephippia produced by the Daphnia longispina species complex were collected with nets pulled after a boat from the surface (i.e. within a few days after their production) of Lake Greifensee (N47.3539500◦/E8.6758917) during peaks of sexual reproduction in spring and fall 2011 and stored in 10 L polyethylene (HD-PE) bottles (Hünersdorff GmbH, Germany). Ephippia were cleaned using different sieves of different mesh sizes, rinsed with filtered and double autoclaved lake water and stored in the dark at 4 ◦C in 500 mL Schott bottles (Schott Duran, Germany) until processing, in order to break diapause. Ephippia were dried in dark at 4 ◦C and subsequently pooled together and mixed before the experiment. The lake water used for storage and exposure experiments was collected from Lake Greifensee, filtered through a glass fiber filter (pore size: 0.45 μm, Sartorius Stedim AG, Switzerland) and double autoclaved at 120 ◦C for 30 min each time with a Vapoklav 500 (HP Medizintechnik GmbH, Germany). 168 Chapter 5. Daphnia Resting Eggs

5.2.2 Standards and Reagents

Reference standards used in the experiment had a purity of 97% or greater. Irgarol, triclocarban, benzotriazole, 5-methylbenzotriazole and octocrylene were purchased from Sigma-Aldrich (Steinheim, Germany). Propiconazole, terbutryn and prochloraz were purchased from Dr. Ehrenstorfer (Augsburg, Germany). Triclosan was provided by Ciba (Basel, Switzerland) and tonalide was purchased from LGC Standards (Wesel, Germany).

5.2.3 Hatching Experiment

For the exposure experiment, dry ephippia were moisturized and aliquots of 0.15 mL were transferred to 15 mL polypropylene centrifuge tubes (VWR, Dietikon, Switzer- land). Assay tubes were randomized and filled with 14 mL of exposure medium or control medium, respectively. For each treatment (exposure and control), 30 tubes were prepared with a total of 60 assays per experiment. The exposure medium consisted of filtered and double autoclaved lake water containing a mixture of 10 analytes with a nominal final concentration between 2 to 800 μg/L. Concentrations were selected according to the predicted pore water maximum concentration (Cpwmax) based on sediment analysis from Lake Greifensee reported by Chiaia-Hernandez et al., 44,66 multiplied by a factor of 1000. The exposure mixture included pesticides, corrosion inhibitors, biocides, and personal care products. The chemicals were spiked as a mixture in ethanol with a final ethanol content of 0.2% (v/v). The complete list of analytes with measured concentrations and physicochemical characteristics are reported in Ta- ble 5.1. The control medium contained filtered and double autoclaved lake water with a final ethanol content of 0.2% (v/v). Ephippia were pre-incubated (day 0) for 4 d at 4 ◦C to allow them to equilibrate with the exposure medium and reach steady state for bioconcentration of contaminants as was determined in previous toxicokinetic experiments. 66 After pre-incubation, ephippia were transferred to an incubator to stimulate hatching at a temperature of 20 ◦C and with a light:dark cycle of 16:8 (Memmert GmbH & Co. KG, Schwabach, Germany). In the incubator, the assay tubes were randomized and placed at a 10 degree angle in order to ensure optimum contact between the ephippia and the medium. Assay tubes were randomized daily and examined on day 4, 7, 10, 13 and 15 under a stereo microscope. Simultaneously, exposure and control media were renewed to avoid sorption and degradation of chemicals in the exposure medium due to the experimental setup. Hatchlings were counted, removed from the assay tubes and mortality was recorded. 5.2. Material and Methods 169 5 6 1 1 1 , 3 4 1 , 4 2 4 50 1 g/L) EC μ 8100 2’600- 239-249 10 390-560 4300 1’000- 2’600- 35’000- 35’000- 75’000 13’000 280’000 280’000 b ( 7’100 7 7 0 86 67 6 6 19 0 0 Sorption to Polyethy- lene tubes t=72hrs (%) C ◦ is the concentration of the 20 Stable 62 Stable 73 Stable Stable Stable Stable Stable Stable t=72hrs (%) Degradation byat light med-L ,C C L 30 100 40 70 ◦ 5 2 13 1 1 − ± ± ± ± 4 ± ± ± ± ± g/L) 5 med μ 350 13 23 73 300 < 16 16 630 700 C ( D 20 20 2 20 20 4 1 1 1 − ± ± ± ± ± ± ± ± ± g/L) 5 med μ 47 360 23 < 120 14 320 15 550 650 C ( 40 100 330 40 60 50 2 1 1 ± ± ± ± ± ± ± ± ± g/L) 5 med μ 29 < 450 500 19 19 430 260 650 740 C ( ) and physicochemical characteristics used in the exposure max ed using MarvinSketch 5.11 (ChemAxon http://www.chemaxon.com) is the average concentration at t=0 before distribution and exchange of the g/L) med ipw μ 2 28 210 510 35 24 320 820 660 510 C X1000 ( is the maximal (max) predicted pore water concentrations based on analysis of med at C ow 44 66 ipwmax log D 6.8 2.9 3 5 3.6 4.3 4.4 4.9 a 1.2 1.4 pH 8.2 GPN Version 2. Corrosion in- PCP Corrosion in- PCP Biocide Biocide Compound Class Pesticide Biocide Biocide Biocide hibitor hibitor R is the average medium concentration during incubation at dark and at med-D 49636-02-4 95-14-7 6197-30-4 886-50-0 21145-77-7 CAS # 28159-98-0 67747-09-5 60207-90-1 101-20-2 3380-34-5 96 88 ∗ 95 Daphnia magna . ∗ ∗ (Octanol-water distribution coefficient) values were predict 50 ∗ -21-day ow 50 ´ oth et al. ECOTOX AQUIRE database: http://cfpub.epa.gov/ecotox/. T Aschauer et al. ErC Name Benzotriazole 5-Methylbenzotriazole Irgarol Octocrylene Prochloraz Propiconazole Terbutr yn Tonalide Triclocarban Triclosan log D Compounds known to have antimicrobial activity, C BASF Safety Data SheetFOOTPRINT Mearlite PPDB database: The Pesticide Properties Database (PPDB) http://sitem.herts.ac.uk/aeru/footprint/index2.htm. 48h-EC medium under light exposure and at room temperature. medium in the assays, C 3 Table 5.1: Analytes with measured concentrations (C ∗ a b 1 2 4 5 experiment. Degradation by light usingstudied glass independently containers under and sorption similar experimental to polyethylene conditions. tubes under dark conditions were 6 sediment from Lake Greifensee previously reported. 170 Chapter 5. Daphnia Resting Eggs

The complete experiment was performed twice during different seasons of the year, i.e. December 2012 and March 2013. Quality controls were taken during the experiment and included freshly prepared control and exposure media, as well as control and exposure media taken from random assay tubes while exchanging the media. The quality controls were collected on day 0, 4, 7, 10, 13 and 15 in duplicates and analyzed for possible cross contamination and stability of the exposure medium. No contamination was found in the quality controls. The stability of the chemicals during the experimental conditions was explored in independent studies. Photodegradation was studied with a chemical mixture reported in Table 5.1 with a nominal final concentration of 200 μg/L using glass jars and under similar experimental conditions as described above. Samples were taken at the beginning of the experiment (0 h) and every 24 h for 6 consecutive days in triplicates. The results show a degradation under light exposure of 73 and 62% for triclosan and tonalide after 72 h. The results are consistent with the reported photolysis of triclosan and tonalide in surface and waste water.67,68 No photolysis was observed for the rest of the compounds as illustrated in Figure S1 and Table 5.1. Additionally, a separate study was performed to study the sorption of chemicals to polypropylene tubes under dark conditions. The results listed in Table 5.1 show that most compounds do not significantly sorb to plastic within 72 h but 67% and 86% of triclosan and tonalide, respectively, are lost and therefore not bioavailable over the whole time range of the experiment. The fivefold exchange of the medium during the experiment accounted for those losses due to photolysis and sorption, more frequent exchange was not feasible as we aimed to keep disturbance of the hatching process at minimum. Control and exposure media were directly analyzed by transferring 940 μL of medium to 1 mL HPLC vials (BGB Analytics AG, Switzerland) followed by addition of 60 μLof internal standard mix solution and analyzed by LC-HRMS as described elsewhere. 66 In addition, pH (8.2 ± 0.2) was monitored during the experiment. Furthermore, dissolved oxygen was measured for a subset of samples to check for potential differences between control and exposure medium, however, no such differences were detected.

5.2.4 Ephippia and Egg Counting

Ephippia were counted after the experiment in order to correct for differences in the number of ephippia per assay tube. Ephippia were arranged on 8 × 11 cm glass slides with a white background and photographed with a digital camera (Panasonic DMC- FZ50 camera, Japan, with a Leica DC-Vario-Elmarit 1:2.8-3.7/7.4-88.8 ASPH zoom 5.2. Material and Methods 171

lens, Switzerland). Subsequently, the photographs were used to automatically count ephippia in batch processing mode using CellC 1.2. software. 69 The implemented cluster division algorithm based on cell shape for light microscopy images was used with cluster division set to a value of one and with a background correction applied prior to processing. Moreover, two different intensity thresholds (0.4 and 0.25) were employed to represent a range of ephippia for which reliable results in previous tests had been obtained. The difference between the two ephippia count datasets was minor with 2.1% on average (s.d. 1.1), a maximum difference of 6.1% and no relevant difference between controls and exposed ephippia. Therefore, if not indicated otherwise, only results obtained for intensity threshold 0.25 are reported which yielded a slightly better ephippia count. In order to calculate the number of eggs for the analysis of hatching success, an average egg content per ephippium was determined by opening a subsample of 100 ephippia and counting the eggs under a stereo microscope. The number of exposed eggs in each assay tube was calculated by multiplying the ephippia count datasets with the average egg content (0.17 eggs/ephippium).

5.2.5 Statistical Analysis

The binomial response variables hatching success and mortality were evaluated with a generalized linear model (GLM). In order to account for overdispersion present in both cases a quasi-binomial model was employed. For hatching success the full model with the fixed factors treatment and experiment and their interaction as explanatory variables was first tested. The interaction term was not significant in a partial F-test comparing the full model against a model without interaction. Therefore, the interaction term was removed and a simpler model containing the fixed factors treatment and experiment was used (Table 5.2). The analysis was performed with datasets based on both ephippia counts and both analyses revealed a similar outcome with no relevant differences. For mortality, the full model with the fixed factors, treatment and experiment; and their interaction as explanatory variables was tested. The interaction term was highly significant and the full model was kept (Table 5.3). One extreme outlier was removed from the mortality dataset to improve the model with no relevant effect on the significance of the terms. 172 Chapter 5. Daphnia Resting Eggs

Table 5.2: Estimates and test statistics for a quasi-binomial generalized linear model examining hatching success using ﬁxed factors treatment and experiment as explanatory variables. (Null deviance: 610.27, df=119; Residual deviance: 526.29, df=117, dispersion parameter: 4.438). p-values are given for individual hypothesis tests and partial F-tests.

Estimate Std. Error t p(> |t|) F p(> |F|) Intercept -2.689 0.069 -38.806 < 0.001 Treatment 0.276 0.076 3.661 < 0.001 13.322 < 0.001 Experiment 0.172 0.075 2.285 0.024 5.166 0.025

Table 5.3: Estimates and test statistics for a quasi-binomial generalized linear model examining the mortality using ﬁxed factors treatment, experiment and their interaction as explanatory variables. (Null deviance: 1044.79, df=118; Residual deviance: 294.41, df=115, dispersion parameter: 2.321). p-values are given for individual hypothesis tests and partial F-tests.

Estimate Std. Error t p(> |t|) F p(> |F|) Intercept 1.111 0.138 8.05 < 0.001 Treatment 0.464 0.192 2.414 0.017 Experiment -1.417 0.174 8.138 < 0.001 Treatment * Ex- -2.977 0.327 9.091 < 0.001 89.95 < 0.001 periment

Residual analyses conducted for all models revealed no violation of model assumptions. All models were evaluated using the function glm and partial F-tests for the signiﬁcance of single terms in the models and were performed using the function drop1 in R version 3.0.1. 70

5.3 Results and Discussion

This study reveals that ecotoxicological assays using resting eggs from the D. longispina complex are suited to investigate the impact of organic contaminants found in sediments of large lakes and drinking water reservoirs, on a key species in aquatic ecosystems. Water bodies with large catchment areas can serve as sink for complex mixtures of organic contaminants from different diffuse and point sources. Therefore, the threat that different contaminants pose to the freshwater ecosystems, is now widely recognized and has raised attention in ecotoxicological research. 39,41,71,72 Despite the complex mixtures of different organic contaminants found in the environ- 5.3. Results and Discussion 173

ment, the study of mixed toxicity has not been established and studies of joint toxicity with existing natural populations are scarce. 73 Additionally, many studies focus either on single substances73 or use contaminant - test organism combinations, that are un- certain or even unlikely to actually occur in nature.74,75 Furthermore, particular life cycle stages and compartments occupied by species during certain phases of their development (e.g. pelagic vs. benthic habitat) are often neglected, especially in cases where their consideration would make experiments more laborious. 74 Also ephippia of the D. longispina species complex represent an experimental challenge: e.g. large amounts of the small ephippia are needed to obtain sufficient material and deal with variation in egg content and hatchability and their hydrophobic surface characteristics make it difficult to work with them in the lab. The above mentioned points are a relevant issue as they introduce a bias into aquatic toxicological research and may lead to unrealistic predictions and risk assessments. In the present work, we overcome these shortcomings by exposing resting eggs of a natural population of the D. longispina complex that build-up the ecologically and evolutionary important egg bank in lake sediments to a complex mix of micropollutants previously detected in the sediments of the same lake. Making use of an automated counting software and an elaborate experimental setup, we demonstrate that even with large numbers of ephippia required for these kind of experiment (a total of 254,950 ephippia), the study was conveniently handled and evidence for a significant impact of micropollutants on hatching success and mortality of hatchlings were found in replicated experiments.

5.3.1 Hatching Success

We found a highly significant treatment effect on hatching success (p < 0.001, Fig- ure 5.1 and 5.2). Surprisingly, hatching success increased in both experiments when ephippia were exposed to a mixture of organic contaminants. While a few studies have reported a negative impact of organic and inorganic contaminants on hatching success,54,55,61–65 this is the first time that an increase of hatching has been observed. Three possible explanations for this finding are discussed. First, one or several compounds used in the experiment may interfere with physiolog- ical pathways that control dormancy. They may directly interact with the cellular ma- chinery that maintains cell cycle arrest or affect the sensitivity of signaling pathways that transmit hatching cues. Unfortunately, practically nothing is known about the physiology of dormancy control and the involved regulatory pathways. However, Navis et 174 Chapter 5. Daphnia Resting Eggs

Figure 5.1: Boxplots of hatching success for control and exposure treatments in the two experiments A and B. Each boxplot is based on 30 observations at four time points. Whiskers indicate 1.5 × interquartile range of the upper and the lower quartile, respectively. Outliers are shown as open circles.

Figure 5.2: Mean cumulative hatching success over timer for control and exposure treatments in the two experiments (A and B). Error bars indicate standard errors of means (SEM). 5.3. Results and Discussion 175

al.54 found evidence that the known endocrine disruptor fenoxycarb has a detrimental effect on hatching showing that endocrine disruptors can affect hatching success. From our selected compounds, triclocarban has been classified as a new type of endocrine disruptor since it amplifies the transcriptional activity of steroid hormones and their receptors. 76 Interestingly, triclocarban has indeed been shown to stimulate embryo production in the freshwater mudsnail Potamopyrgus antipodarum at relevant environmental concentrations. 77 Furthermore, having a similar structure to triclocarban, triclosan has been reported to act as endocrine disruptor in fish78 as well as an in- hibitior of lipid biosynthesis, preventing bacteria from building cell membranes and in- terfering with other vital functions.79 In addition, the musk fragrance tonalide, the UV filter octocrylene and the pesticides terbutryn, prochloraz and propiconazole are also suspected to act as endocrine disruptors. 80 A second possible explanation is environmentally cued hatching. 81 Hatching of Daph- nia resting eggs is known to be triggered by signals indicating favorable conditions for development and reproduction in the pelagial, e.g. photoperiod and temperature. 82 There are, however, also examples in the animal kingdom where early hatching is initi- ated when conditions become unfavorable for the egg (“escape hatching”), e.g. in the presence of egg predators or fungal growth.81 and references therein As a result, resting eggs may, in principal, also be capable of sensing a decrease of their chance of survival in the sediment and escape by terminating diapause and hatching. Interestingly, this possibility has to our knowledge not been considered or tested for Daphnia resting eggs so far. Such an escape hatching response may for example have been triggered via cellular damage through the chemicals in our experiment. Third, an increase in hatching could also be explained by a more indirect effect, i.e. reduction of microbial growth caused by the micropollutants mix. Damaged resting eggs infested with fungi or bacteria are regularly encountered in ephippia collected from lake sediments (M. Most,¨ personal observation), and microbial growth is therefore likely to interfere with the development of hatchlings and hatching success. Furthermore, four of the compounds used in the experiment (propiconazole, prochloraz, triclocarban and triclosan) are known to have antimicrobial activity (see Table 5.1). Although we used double autoclaved lake water and sterile tubes in all our assays and no obvious microbial growth during hatching was observed, microbial growth had been observed in pre-tests after several days of incubation. Therefore, inhibition of microbial growth by biocides remains as a likely explanation for the increase in hatching success. In addition to the effect of organic contaminants, a weak significant effect for the experiment (p-value 0.025) explained by a higher hatching success in the second experiment 176 Chapter 5. Daphnia Resting Eggs

was found (Figure 5.1 and 5.2). One possible explanation for this observation may be seasonality. The ﬁrst experiment was conducted in December, while the second experiment was conducted in March, which is the start of the growing season and onset of hatching in nature. 19,83–85

5.3.2 Hatchling Mortality

Mortality of hatchlings was increased in the experiments with the mix of chemicals, however, the size of the effect size differed between experiments resulting in a signiﬁcant interaction (p < 0.001, Figure 5.3 and 5.4). In total, we found 648 dead and 850 alive hatchlings in the control versus 1712 dead and only 67 alive in the exposure treatment. Our experiment was primarily targeted to investigate effects on hatching success but mortality was also recorded in parallel on days 7, 10, 13 and 15. Therefore, mortality here describes the proportion of dead hatchlings encountered after 2 and 3 days, respectively, and individuals may have hatched at any time during this period. As a result the design of our experiment does not allow to disentangle an effect of the toxicants on the developing embryo in the resting egg from the effect on the hatched individuals. However, hatchlings that had died shortly after or even during the hatching process

Figure 5.3: Boxplots of mortality for control and exposure treatments in the two experiments (A and B). Each boxplot is based on 30 observations at four time points. Whiskers indicate 1.5 × interquartile range of the upper and the lower quartile, respectively. Outliers are shown as open circles. 5.3. Results and Discussion 177

Figure 5.4: Mean mortality over timer for control and exposure treatments in the two experiments (A and B). Error bars indicate standard errors of means (SEM). were frequently found in the exposure treatment, some showing deformations and developmental abnormalities. This observation suggests that the mixture of chemicals had already an effect on the developing embryo in the egg. Furthermore, these results are in agreement with the reduced survival of hatchlings of D. magna after resting egg exposure to fenoxycarb and carbaryl.54 While in both experiments mortality was higher in the exposure medium, the size of the effect differed considerably between the two experiments. This difference is due to an increased mortality in the control treatment of experiment A, that may be attributed to subtle differences in handling or in the timing of peak hatching (i.e. hatchlings that hatched shortly after medium exchange remain in the assay tubes for up to three days without food which increases their risk of mortality) between the two experiments. The observed effect on mortality was not unexpected and can be associated with the exposure concentrations of the selected compounds. The effect concentration (EC50) values for Daphnia magna of the studied compounds are between 20 to 200 times higher, with the exception of tonalide, triclosan and triclocarban, when compared to the measured exposure concentration (see Table 5.1). Tonalide has shown to have negative effects in zebraﬁsh embryos and larvae at concentration higher than 100 μg/L. 86 Daphnia magna EC50 values for tonalide, triclosan and triclocarban are similar or higher 178 Chapter 5. Daphnia Resting Eggs

than the exposure concentrations used. 87,88 The 48-h Daphnia magna EC50 values for survival and reproduction have been reported to be 390 μg/L for triclosan89,90 which is close to the exposure concentration in the experiment.

5.3.3 Implications

This study reveals that organic contaminants present in large lakes have the potential to affect the function of egg banks and thereby benthic-pelagic coupling via two processes - increased hatching on one hand and increased mortality of hatchlings on the other hand. Either case may have severe consequences for the ecology and evolution of species that depend on the egg bank. Individuals hatched from resting eggs differ from parthenogenetically produced individuals with respect to life history traits related to population growth, physiology and their ability to cope with different food levels. 91,92 Changes in the contribution of ex-ephippial hatchlings to the pelagic population will therefore affect abundance and average life history traits of the whole population. Such an impact on the seasonal population dynamics of relevant zooplankton species is not only expected to have an effect on the ecology and survival of this species but also on the whole lake ecosystem through food web interactions. 54 In addition, the egg bank also represents a storage of genetic variation produced by sexual recombination over several generations19,29,93 and reduced hatching from the sediments may have severe implications on the evolutionary potential of species. 19 The ecological and evolutionary consequences of decreased hatching success and hatchling ﬁtness have also been discussed by Navis et al.,54 however, this study reveals a further and so far unobserved outcome of organic contaminants exposure of resting eggs, i.e. an increase in hatching success. Daphnia resting eggs do not all hatch at the same occasion e.g. 94 which allows for trans-generational overlap and reducing the risk of extinction.20,29 Increased hatching may therefore interfere with bet-hedging strategies, lead to a depletion of the egg bank and thereby increase the extinction risk of local populations. 19,20,27,29 Further- more, it may affect competition between species in cases when the reaction of hatching to organic contaminants differs between species. Which of the processes observed in our experiment, i.e. increased hatching and increased mortality, respectively, occurs and predominates in nature, depends on environmental concentrations, the mix of organic contaminants as well as exposure time, and requires further studies. We used 1000 fold increased concentrations for the organic contaminants in our experiment to evaluate whether or not an effect in hatchability can be observed, however, future research needs to focus on environmental realis- 5.3. Results and Discussion 179

tic scenarios. Furthermore, resting eggs can stay in the active resting egg bank for decades and therefore chronic low-dose effects are likely to be relevant. Furthermore, availability and uptake of organic contaminants may be altered by the presence of sediment and the microbial ﬂora therein. Such questions can be addressed with long-term mesocosm experiments in the future. In conclusion, this study reveals the potential of micropollutants found in lake sediments to affect ecology and evolution of a key species in large-lake ecosystems, and highlights the urgent need for further research in this direction to assess the risk that emanates from the ongoing input of organic pollutants into aquatic systems. 180 Chapter 5. Daphnia Resting Eggs

Acknowledgements

We thank Esther Keller for her invaluable help during the experiment, Roman Ashauer for his input in the experimental design and Christoph Tellenbach for his statistical advice. We are grateful to Sabine Navis and Luc De Meester for stimulating discussions. Funding by the Swiss National Science Foundation (SNF CR32I3 125211) is gratefully acknowledged. Bibliography

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191 192 Supporting Information to Chapter 5

Figure 5.5: Stability of chemicals by light at 20 ◦C. Triclosan and tonalide show between 73 and 62% degradation while the rest of the compounds are stable. Chapter 6

Conclusion & Outlook

193 194 Chapter 6. Conclusion & Outlook

6.1 Conclusion

This thesis has provided important information about the occurrence of organic contaminants in lake sediments and the effects and interactions of organic contaminants to biota. Our work has focused on the development of analytical techniques and screening approaches to identify relevant organic contaminants in the environment, which until now have not been studied or their occurrence has not been well known. In addition, we have studied the bio-concentration (BCF) of these contaminants in Daphnia resting eggs (ephippia) in laboratory experiments. Based on the BCFs obtained and sediment concentrations measured present and pass ephippia environmental concentrations could be predicted. Furthermore, we have analyzed the hatchability and fitness of ephippia when exposed to pollutants. In Chapter 2 we have developed a multi-residue method for the target and suspect screening of more than 180 chemicals in sediments. The quantification and identification of target compounds with a broad range of physicochemical properties (log Kow 0-12) has been carried out by liquid chromatography followed by electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) coupled with a high resolution Orbitrap mass spectrometry (HRMS/MS). We have successfully used APPI as an alternative ionization technique to distinguish two isomeric musk fragrances by means of different ionization behavior. This method has been employed on sediment cores from Lake Greifensee (Switzerland). The results have shown that biocides, musk fragrances and other personal care products were the most frequently detected compounds. More- over the concentrations of many urban contaminants originating from wastewater correlate with the highest phosphorus input into the lake as a proxy for treatment efficiency. In addition, the use of HRMS have enabled a retrospective analysis of the full-scan data acquisition allowing the detection of suspected compounds like quaternary ammonium surfactants, the biocide triclocarban, as well as the tentative identification of further compounds without reference standards, such as transformation products of triclosan and triclocarban. In Chapter 3 we have applied the multi-residue method described in Chapter 2 to characterize the fate of organic contaminants in a second Swiss lake, namely, Lake Lugano (Switzerland). The results are consistent with the detection of biocides, musk fragrances and other personal care products found in Lake Greifensee. After the characterization of both lakes with regard to target compounds, we have developed suspect and non-target screening approaches to identify compounds that were not part of our originally target screening list, but are in use in Switzerland. The developed approaches 6.1. Conclusion 195

have enabled the exact mass match of 23 suspect compounds with the co-occurrence of the two biocides chlorophene and bromochlorophene in both lakes. Chlorophene could be tentatively identified using in silico fragmentation for computer assisted identification of mass spectra (MetFrag). Furthermore, the non-target approaches developed have enabled the identification and confirmation with reference standards of the mothproofing agent flucofuron and the disinfectant hexachlorophene using the molecular formula generator MOLGEN-MS/MS and MetFrag. In addition, the results of the target, suspect and non-target screening have aided us to focus on specific compounds with similar characteristics like the ones already identified, i.e., the case of chlorophene and its different chlorine congeners. In Chapter 4 we have employed the information obtained from the characterization of Lake Greifensee and Lake Lugano in Chapter 2 and 3 to select a range of relevant organic contaminants to study the bioaccumulation of organic contaminants in Daph- nia resting eggs (ephippia). Accordingly, we have performed uptake and elimination experiments and have calculated the bioconcentration (BCF) of 16 different chemicals. The results indicate that contaminants can be taken up by ephippia from the water column or the pore water in the sediment and can be predicted using physical-chemical properties. In addition, we have predicted internal concentrations in ephippia in the environment based on sediment concentrations using the equilibrium partitioning model and calculated BCFs. In Chapter 5 we have used ecotoxicological assays to investigate the impact of organic contaminants found in sediments on key species in aquatic ecosystems using resting eggs from the D. longispina complex from Lake Greifensee. The results have shown a highly significant treatment effect on hatching success when ephippia were exposed to a mixture of organic contaminants. The outcomes have shown that organic contaminants present in the water phase as well as in sediments have the potential to affect the function of egg banks by increasing hatching and mortality of hatchlings and, therefore, affecting the ecology and evolution of a key species in large-lake ecosystems. Based on the work presented in this thesis, we conclude that sediments can be integrators in time and space for emerging contaminants providing history of deposition over time and allowing the reconstruction of past environmental concentrations. We have also shown that sediments are archives of not only hydrophobic but also polar and medium-polar organic contaminants. With the aid of innovative analytical tools such as generic extraction methods in combination with different ionization techniques like ESI and APPI, HRMS, and suspect and non-target screening approaches, the fate of many organic contaminants with a wide range of physicochemical characteristics can be stud- 196 Chapter 6. Conclusion & Outlook

ied even in complex matrices such as sediments. Furthermore, we have assessed the effect of these organic contaminants on aquatic organism and have conﬁrmed that organic contaminants in the water column and sediments can bioaccumulate in Daphnia resting eggs (ephippia) and hence affect their hatchability and ﬁtness. Resting egg banks are not only known for Daphnia but for many aquatic organisms and play a crucial role for their evolution and for ecology. Therefore, this research provides a fruitful starting point to (1) understand the potential impact of organic contaminants on aquatic organisms relying on resting eggs during their life cycle and (2) comprehend and predict the impact of these compounds on ecosystems by understanding their effects on benthic-pelagic coupling and evolution of species.

6.2 Outlook

Due to the high number of chemicals produced annually, the need of generic analytical methods for the analysis of a wide range of chemicals in a single study is becoming essential. In this work, the use of ESI and APPI as complementary ionization techniques for the analysis of environmental samples has expanded the detection and quantification of musk fragrances and UV-light stabilizers. However, the use of APPI in the current analytical method has been limited to the analysis of medium-polar organic compounds due to the difficulty of the dopant mixing (i.e., toluene and anisole) with the mobile phase. Therefore, we had to start with a high content of methanol (50%) in the gradient used in this method , which has led to the loss of polar compounds due to the use of the divert valve in the first 4 minutes of the run. The method could be expanded to a broader range of organic contaminants, including polar compounds, by adding the dopant in the gas phase by introducing a tubing tee connector to the auxiliary gas port of the vaporizer. This solution was proposed after some discussion with the instrument manufacturer (Thermo Fisher Scientific Corp. USA), and will be implemented in the future. HRMS has emerged as a very powerful tool to study compounds with and without reference standards. Therefore, HRMS is preferable over mass spectrometers with unit resolution to assess the occurrence of organic contaminants in different environmental compartments due to its high resolving power, high mass accuracy, and sensitivity in full scan mode, which allows a reliable screening of molecular ions and their MS/MS fragments against a complex matrix background. 1,2 In this research we have studied different organic contaminants with different physicochemical characteristics based on the experience of the environmental chemistry department at Eawag (Uchem), how- 6.2. Outlook 197

ever, additional compounds that might be more persistent could be incorporated to the method. Howard and Muir3 compiled a list of 610 chemicals that are potentially persis- tentand and bioaccumulative, focusing on chemicals whose whole structure or part of the structure would be persistent. Therefore, in the future this list of chemicals and its degradation products could be incorporated in our suspect screening analysis to study their occurrence in sediments. In the past years, different approaches for the identification of non-target have been developed. However, the limitations of the studies are similar in that they lack mass spectra libraries for compound identification and a limited number of compounds available in databases. Therefore, innovative approaches like the NORMAN MassBank initiative, which is the first public source of mass spectral data for sharing among scientific research community, are needed for the chemical identification and structure elucidation of chemical compounds. In addition, tools like multivariable statistical approaches like principal component analysis (PCA) should be used to help in the structure, simplifica- tion and visualization of large data sets. One ionization technique that could help improve the detection of unknown compounds is APPI. APPI ionization is complex due to the formation of molecular ions, protonated molecular ions and adducts, however, during direct infusion of different chlorinated compounds, we observed a consistent dechlorination and an addition of a hydroxyl group. This pattern has already been reported in the study of polychlorinated biphenyls (PCBs) by GC-APPI-MS. 4 Therefore, under controlled ionization conditions, these interesting patterns can be studied in environmental samples to identify chlorinated compounds or explore more specific ionization pattern with different functional groups. Mass defect filtering analysis proved to be very useful to visualize the distribution of the matrix against suspect compounds. This approach can be extrapolated to non-target candidates and to focus on specific unknown masses that fall outside the boundaries of the matrix. Mass defect analysis in mass spectrometry is very useful since elemental formulas are defined on their accurate mass. Therefore mass defects can be used as a specific filtering criteria if a target class is known. 5 Mass defect filtering can help to identified homologous series within a sample and potentially identified contaminants based in functional groups as have been described by Sleno.5 In additional, a time series analysis could be employed to identify patterns within a sediment core to recognize compounds of interest, especially compounds that are increasing over time. In this work we have observed that many organic contaminants trends correlate with the phosphorus input in the lake and this pattern is specific to sediments of Lake Greifensee. Brede et al. 6 studied the genetic architecture of Daphnia diapausing eggs 198 Chapter 6. Conclusion & Outlook

(ephippia) and the changes in total phosphorus concentrations in lake sediments (i.e., lake Greifensee), demonstrating that anthropogenically induced temporal alterations of habitats are associated with a shift in species composition and the population structure of evolutionary lineages. However, if this shift of specie composition was caused by total phosphorus input or by organic contaminants, then the question remains unanswered and the task to assess this a challenge. Nevertheless, these type of studies are of great importance to understand how micro-pollutants found in natural ecosystems can affect ecology and evolution of a key species. Although, our work focused on ephippia, many organisms are exposed to the same environmental conditions, and their reaction to contaminants is of great importance and also needs to be assessed. Kerfoot et al.,7 showed that once a sediment is extracted, diapausing eggs up to 40 years of age can be sampled and hatched for experimental purpose or directly analyzed with molecular genetic methods at least from 100-year-old resting eggs. This technology could be expanded to monitor biodiversity within a sediment core. DNA obtained directly from environmental samples (environmental DNA) has been used already to assess the diversity of macro-organism communities.8,9 Genetic analyses of permafrost and temperate sediments reveal that plant and animal DNA can be pre- served for a very long periods of time (i.e., ancient sediments). 8 Conserved genetic information can provide unique insights into many evolutionary and ecological processes. With today’s technology and knowledge of DNA sequencing and using quantitative PCR (qPCR), DNA extracted from sediment cores can provide information in the increase or decrease in biodiversity. Furthermore, the inﬂuence or adaptation of different organism communities within a catchment can perhaps be correlated with the occurrence of chemical contamination of sediment cores. In the last decade, the increase of antimicrobial antibiotic resistance genes (ARGs), which have emerged as new environmental contaminants, presents a major concern. 10 Changes in natural ecosystems, including the release of large amounts of antimicro- bials, might alter the population dynamics of microorganisms, including selection of resistance.11 Environmental compartments directly impacted by urban and agricultural activity have been shown to have signiﬁcant higher concentrations of ARGs than less impacted environments 10. Czekalski et al.12, studied the prevalence of multi-resistant bacteria (MRB) and ARGs in the wastewater stream of Lausanne, Switzerland. The results show that hospital sewage contained the highest load of MRB and antibiotic ARGs. Moreover, wastewater shows evidence for the selection of extremely multiresistant strains and accumulation of resistance genes. In addition, a clear indication of pollution of sediments with ARGs in the vicinity of the wastewater treatment plant outlet was observed. Therefore, the characterization of sediments to study the oc- 6.2. Outlook 199

currence of organic contaminants from different sources (i.e, wastewater, urban and industrial sewage, runoff, and leaching from agricultural areas) can be extrapolated to understand the increase of ARGs and MRB. Contamination and concentration patters in sediments could be potentially link or compare against ARGs and MRB to understand their development and microbial dynamics in different ecosystems.

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[10] Pruden, A.; Pei, R.; Storteboom, H.; Carlson, K.H., Antibiotic Resistance Genes as Emerging Contaminants: Studies in Northern Colorado. Environmental Science & Technology 2006, 40, (23), 7445-7450.

[11] Mart´ınez, J.L., Antibiotics and Antibiotic Resistance Genes in Natural Environ- ments. Science 2008, 321, (5887), 365-367.

[12] Czekalski, N.; Berthold, T.; Caucci, S.; Egli, A.; B¨urgmann, H., Increased Levels of Multiresistant Bacteria and Resistance Genes after Wastewater Treatment and Their Dissemination into Lake Geneva, Switzerland. Frontiers in Microbiology 2012, 3. Acknowledgements

203 204 Acknowledgements

I would like to thank all the members of my committee for their help in the completion of my thesis. Special thanks to Piet Spaak for teaching me everything about ephippia, Lee Ferguson for his input and discussion in suspect and non-target screening, Bernhard Wehrli for his advice during my thesis and Juliane Hollender for her time, knowledge and support for the past 4 years as well as for giving me the opportunity to come to Eawag. I also want to thank my former advisor Jennifer Field for teaching me the beauty of research. I would like to thank Markus Moest for his help in this thesis and the nice collaboration we could achieved between the departments of Aquatic Ecology and Environmental Chemistry. Also many thanks to Esther Keller and Birgit Beck for their help in the lab. I would like to thank my two master students; Joris Buiter and Tobias Hollinghaus for their help in this thesis and Kumar Praveen for his help in the analysis of non-target screening. Many thanks to Heinz, Matze and Phillip for their help with the HR-MS instruments. Special thanks to my ofﬁce mates Luba, Damian and Sckrechi for the awesome times we spend in our ofﬁce (E22). Also I would like to thank the new E22 members, Jen and Andrea, for all their help, support and patients in the chaotic moths before handling my thesis. I would like to thank all the current and former members of the Uchem gruop which offered me always their help and friendship. Thank you Martin Loos, Rebeka, Matze, Tobi D., Reto, Marita, Stephie, Sebi, Sara, Kov, Susan, Anna G, Anne, Philliph, Keisuke, Meline, Connie, Nuri, Aduccia, Juergen, Erick, Bernadette, Michale, Irene W., Irene H., Devon, Annika, Xanat and Greg, Marco, Christoph, Michele, Caroline, Anna B, Halua, and many more than for sure I am missing. Also thank you to the Utox and SURF departments at Eawag for their valuable help and friendship. Many thanks to Louise, Eva L., Damian, Emma, Jen, Heinz, Carl and Max Frey for giving me input during my thesis and manuscripts. Many thanks to Martin Frey for all his help, patients and sharing his knowledge in R and LaTeX. Thank you to all members of the reading club Barbara, Jen, Louise, Eva, Nadja, Marion, Stephie, Luba, and Devon. Also thank you to all the Lacrose, Eawag soccer and running teams for the nice times. Thank you Louise, Eva L., Luba and Xanat for being always there for me. Thank you Damian and Martin Frey for the awesome hiking trips and many adventures. Finally, I would like to thank my family, specially my parents and my sisters Itzel and Rocio for all their help, support and friendship all these years. Thank you Sophie, Alex and Carlitos (the new generation) for giving me energy, great times, motivation and unbelievable moments during my studies. Curriculum vitae

AUREA C. HERNANDEZ R. Born on August 7, 1980 in Oaxaca (Mexico)

EDUCATION

2009 - 2013 Ph. D. candidate in Environmental Systems Science Swiss Federal Institute of Aquatic Science and Technology (Eawag) and ETH Zurich, Switzerland Advisor: Prof. Dr. Juliane Hollender Thesis Title: Temporal Records of Organic Contaminants in Lake Sedi- ments, Their Bioconcentration and Effect on Daphnia Resting Eggs 2006 - 2008 M. S. in Chemistry, June 2008 Oregon State University, Corvallis, OR USA Advisor: Prof. Dr. Jennifer A. Field Thesis Title: Large Volume (1,800 μL) Injection HPLC/MS/MS for the Quantitative Determination of Illicit Drugs and Human Urinary Biomarkers in Municipal Wastewater 2001 - 2006 B. S. in Chemistry, March 2006 Oregon State University, Corvallis, OR USA (Including two years at Long Island University, Southampton, NY USA)

RESEARCH POSITIONS

• March 2009 - December 2013: Graduate Research Assistant: Swiss Federal In- stitute of Aquatic Science and Technology (Eawag)- Department of Environmental Chemistry

• September 2006 - January 2009: Graduate Research Assistant, Oregon State

205 206 Curriculum vitae

University, Department of Chemistry - Department of Environmental and Molecu- lar Toxicology

• March 2005 - September 2006: Technical/Research Collaborator, Oregon State University, Department of Environmental and Molecular Toxicology

• June 2004 - September 2004: International Internship, COOPE Sol I DAR (Co- operative for Social Action and Sustainable Development), San Jose, Costa Rica

• September 2001 - March 2003: Technical Collaborator, Brookhaven National Lab- oratory, Long Island, NY

List of Publications

1. Bioaccumulation of Organic Contaminants in Daphnia Resting Eggs Aurea C. Chiaia-Hernandez, Roman Ashauer, Markus Moest, Tobias Holling- shaus, Junho Jeon, Piet Spaak and Juliane Hollender Environ. Sci. Technol., 2013, 47(18), pp 10667-10675

2. Screening of Lake Sediments for Emerging Contaminants by Liquid Chromatog- raphy Atmospheric Pressure Photoionization and Electrospray Ionization Couple to High Resolution Mass Spectrometry Aurea C. Chiaia-Hernandez, Martin Krauss and Juliane Hollender Environ. Sci. Technol., 2012, 47(2), pp 976-986

3. Interpreting Methamphetamine Levels in a High-Use Community Aurea C. Chiaia-Hernandez, Caleb Banta-Green and Jennifer A. Field Environ. Sci. Poll. Res., 2011, 18 (9), pp 1471-1477

4. The spatial epidemiology of cocaine, methamphetamine and 3,4-methylenedioxy- methamphetamine (MDMA) use: a demonstration using a population measure of community drug load derived from municipal wastewater Caleb J. Banta-Green, Jennifer A. Field, Aurea C. Chiaia, Daniel L. Sudakin, Laura Power, Luc de Montigny Addiction, 2009, 104 (11), pp 1874-1880

5. Eliminating Solid Phase Extraction with Large-Volume Injection LC/MS/MS: Anal- ysis of Illicit and Legal Drugs and Human Urine Indicators in US Wastewaters Aurea C. Chiaia, Caleb Banta Green and Jennifer A. Field Environ. Sci. Technol., 2008, 42 (23), pp 8841-8848 Curriculum vitae 207

6. Occurrence and Mass Flows of Fluorochemicals in the Glatt Valley Watershed, Switzerland Carin A Huset, Aurea C Chiaia, Douglas F Barofsky, Niels Jonkers, Hans-Peter E. Kohler, Christoph Ort, Walter Giger, and Jennifer A Field Environ. Sci. Technol., 2008, 42 (17), pp 6369-6377