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Polar organic contaminants in natural drinking water sources and their removal by reverseosmosisA high-resolution mass spectrometry studyAlbergamo, V.

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Citation for published version (APA):Albergamo, V. (2019). Polar organic contaminants in natural drinking water sources and their removal by reverseosmosis: A high-resolution mass spectrometry study.

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Download date: 10 Nov 2020

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Chapter 5. Non-target screening of a riverbank filtration site

Published work

V. Albergamo, J. E. Schollée, E. L. Schymanski, R. Helmus, H. Timmer, J. Hollender, P. de Voogt (2019) Non-target screening reveals time trends of polar micropollutants in a riverbank filtration system, Environmental Science & Technology, 53, 13, 7584–7594. DOI: 10.1021/acs.est.9b01750

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Abstract The historic emissions of polar micropollutants in a natural drinking water source were investigated by non-target screening with high-resolution mass spectrometry and open cheminformatics tools. The study area consisted of a riverbank filtration transect fed by the river Lek, a branch of the lower Rhine, and exhibiting up to 60-year travel time. More than 18,000 profiles were detected. Hierarchical clustering revealed that 43% of the 15 most populated clusters were characterised by intensity trends with maxima in the 1990s, reflecting intensified human activities, wastewater treatment plant upgrades and regulation in the Rhine riparian countries. Tentative structure annotation was performed using automated in silico fragmentation. Candidate structures retrieved from ChemSpider were scored based on the fit of the in silico fragments to the experimental tandem mass spectra, similarity to openly accessible accurate mass spectra, associated metadata and presence in a suspect list. 67 unique structures (72 over both ionisation modes) were tentatively identified, 25 of which were confirmed and included contaminants so far unknown to occur in bank filtrate or in natural waters at all, such as tetramethylsulfamide. This study demonstrates that many classes of hydrophilic organics enter riverbank filtration systems, persisting and migrating for decades if biogeochemical conditions are stable.

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5.1 INTRODUCTION

Thousands of anthropogenic chemicals are released into the aquatic environment via wastewater treatment plant (WWTP) effluents, runoffs and accidental spills (Hermes et al., 2018; Loos et al., 2010, 2009; Luo et al., 2014; Schwarzenbach et al., 2006). Transformation products (TPs) formed during water treatment and under environmental conditions increase the complexity of the chemical mixtures that occur in the environment (Bertelkamp et al., 2014; Hollender et al., 2014; Krauss and Hollender, 2008). Freshwater systems are particularly vulnerable to contamination by polar organic micropollutants (MPs) exhibiting low or negative pH-adjusted log distribution coefficients (log D) as they preferentially partition into the water phase. When persistent, polar MPs can migrate indefinitely throughout the water cycle and reach drinking water sources (Eschauzier et al., 2010; Loos et al., 2009; Reemtsma et al., 2016).

In Europe, riverbank filtration (RBF) is a common drinking water pre-treatment with potential to remove dissolved MPs mainly by sorption and biodegradation as surface water infiltrates through the hyporheic zone (Ascott et al., 2016; Benotti et al., 2012; Henzler et al., 2014; Hiscock and Grischek, 2002; Hollender et al., 2018; Hoppe-Jones et al., 2010; Huntscha et al., 2013; Tufenkji et al., 2002; Verstraeten et al., 2003). Sorption to organic matter can delay the transport of neutral and moderately hydrophobic MPs (logD>3) in RBF systems (Bertelkamp et al., 2014). Ion-exchange capacity of soils can result in the retention of cationic MPs, but it is not effective on anionic MPs. Biodegradation is favoured by a redox potential gradient and long travel time, as they result in greater biodiversity of microbial communities and longer time for adaptation (Bertelkamp et al., 2016, 2014; Ghattas et al., 2017; Henzler et al., 2014; Huntscha et al., 2013; Liu et al., 2016; Schaper et al., 2018).

Liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) is the preferred system to analyse most polar MPs in aqueous matrices. The capability of recent mass analysers to achieve sensitive detection with high resolving power (>20,000) and high mass accuracy (<5 ppm) is pivotal to tentatively identify unknown ions via accurate mass spectra without the use of reference standards (Aceña et al., 2015; Hernández et al., 2012; Hollender et al., 2017; Krauss et al., 2010; Schymanski et al., 2014b). In environmental research, these approaches are known as suspect screening and non-target screening (NTS). Suspect screening and NTS are increasingly being applied to environmental samples and are gradually becoming harmonised (Aceña et al., 2015; Hernández et al., 2012; Hollender et al., 2017; Krauss et al., 2010; Schymanski et al.,

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2014b). Suspect screening aims at identifying pollutants expected in a sample. Commonly, HRMS1 data (mass-to-charge ratios of ionised analytes) is searched for masses of substances of interest suspected to occur in the sample (e.g., for study-specific reasons), typically included in a suspect list. Then, accompanying isotopic (and adduct) peaks, HRMS2 spectra (fragment ions) and retention time (tR) are used to support the identification and confirmation of suspect hits (Hernández et al., 2012; Hollender et al., 2017; Krauss et al., 2010; Schymanski et al., 2014a). Initiatives to improve screening efforts include platforms to share suspect lists, e.g., the NORMAN Suspect List Exchange and the U.S. Environmental Protection Agency (EPA) CompTox Dashboard, and openly accessible accurate mass spectral libraries, e.g., MassBank and MassBank of North America (MoNA). In contrast to suspect screening, NTS aims at identifying compounds without looking for certain masses/substances of interest up front, but rather letting the measured data reveal the masses of interest. Since thousands of ions are acquired indiscriminately in HRMS1 full-scans, a prioritisation strategy is required to select masses of interest. Tentative structures are generally assigned based on candidate searching in chemical databases, e.g. PubChem and ChemSpider (Gindulyte et al., 2015; Pence and Williams, 2010), often using HRMS2 spectra (Hollender et al., 2017; Schymanski et al., 2014b). State-of-the-art NTS benefits from the increased availability of computational tools for prioritisation, e.g., statistical analysis methods (Chiaia-Hernandez et al., 2017; Peter et al., 2018; Schollée et al., 2018, 2015), and cheminformatics tools for high-throughput structure annotation, e.g., in silico fragmenters querying openly accessible chemical databases and accurate mass spectral libraries (Ruttkies et al., 2016). An overview on state-of-the-art cheminformatics tools for structure annotation can be found in the literature (Blaženović et al., 2018; Schymanski et al., 2017, 2015).

In this study, we investigated a natural drinking water source consisting of a riverbank filtrate originated from the Lek, a branch of the river Rhine in The Netherlands. Bank filtration at this site exhibits up to 60-year travel time from riverbank to the furthest of a series of wells built by a drinking water utility. This site can be regarded as a hydrogeological archive, where persistent anthropogenic chemicals from the “post-1950s acceleration” to the present are preserved (Steffen et al., 2015). Our goal was to detect major pollution trends across the bank filtration transect and characterise the identities of mobile MPs by applying state-of-the art NTS. To the best of our knowledge, no previous studies have attempted to investigate time series of non-target polar contaminants in a natural bank filtrate with such an extended travel time. Exposure to over half-century of anthropogenic emissions from intensified industrial and agricultural activities followed by mitigation measures such as wastewater treatment upgrades in the 1990s make this

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RBF system a unique location to detect contamination time series and investigate persistent and mobile MPs in the aquatic environment using non-targeted analytical approaches. The occurrence of these chemicals is rationalised and their emission sources discussed. Compounds not previously known to occur in bank filtrate were identified, including chemicals that were not known to occur in the water cycle at all.

5.2. MATERIALS AND METHODS

5.2.1 Standards and Reagents

Detailed information on the analytical standards and reagents used for this study are included in the Appendix D (D-1).

5.2.2 Sampling site and sample collection

Anaerobic bank filtrate with residence times from 1 to 60 years was abstracted from a series of wells located in two adjacent production fields, named Schuwacht and Tiendweg, fed by the river Lek, a branch of the Rhine (Figure 5.1). The traveling time at each well was previously investigated by the drinking water utility Oasen (Gouda, The Netherlands) by means of isotopic age dating and hydrogeological modelling (Timmer, 2006). The well fields are located in the municipality of Krimpen aan de Lek, South Holland, The Netherlands, and provide raw water for approximately 7,000 m3 of drinking water per day. Further details on the RBF site including a map of the groundwater flow lines with modelled travel time are given in the Appendix D (D-2). Bank filtrate samples from nine wells of increasing travel time (n=3) were collected in 5L polypropylene bottles from sampling faucets built on each well and immediately transported to the University of Amsterdam (UvA), where they were kept in the dark at 2 °C and extracted next day.

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Figure 5.1. Map of The Netherlands showing the location of the abstraction wells screened in the present study with well code and travel time in parenthesis.

5.2.3 Solid-phase extraction

The samples were allowed to reach room temperature, then 100 mL was transferred to 250 mL polypropylene bottles and spiked with 100 µL of a mix of 128 isotope-labelled internal standards (IS) available at Eawag at concentration of 1 ng/µl. This resulted in IS concentrations of 1 µg/L in the samples before enrichment by solid-phase extraction (SPE). An offline extraction protocol relying on hydrophilic-lipophilic balance (HLB) sorbent with Oasis cartridges by Waters (Etten-Leur, The Netherlands) was adapted from a procedure described elsewhere (Albergamo et al., 2018) for the enrichment of moderately hydrophobic and polar organics. The adjustments to the extraction protocol were the spike volume and concentrations of IS and the final concentration step, which in the present study resulted in an enrichment factor of 200 as the extracts were diluted 5-fold with deionised water prior LC-HRMS analysis.

5.2.4 LC-HRMS analysis

The samples were analysed at Eawag using LC-HRMS. A high-performance liquid chromatography system (HPLC) consisting of a PAL Autosampler

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(CTC Analytics, Zwingen, Switzerland), an Accela 1250 mixing pump (Thermo Fisher Scientific, San Jose, United States) and a Waters Xbridge C18 column (2.1 × 100 mm, 3.5 μm) was used. The mobile phase consisted of water (A) and methanol (B), both acidified with 0.1% formic acid. The gradient program expressed as A:B was 90:10 at 0 min, 50:50 at 4 min, 5:95 from 17 to 25 min and 90:10 from 25.1 to 29 min. The flow rate was 200 μL/min and the column temperature was 30 °C. The sample injection volume was 20 µL. This LC method was adopted from a previous application to biologically-treated wastewater (Schollée et al., 2015), where its effectiveness for a wide range of polar and moderately polar MPs was shown. Highly polar MPs may not be sufficiently retained and would require dedicated (extraction and) chromatographic methods. HRMS detection was achieved with a Q-Exactive hybrid quadrupole-Orbitrap (Thermo Fisher) equipped with an electrospray ionisation source (ESI). HRMS1 spectra were acquired for masses ranging from m/z 100 to 1,000, with a resolving power of 140,000 at m/z 200 and a mass error below 5 ppm. HRMS2 spectra were recorded in data-dependent mode with a resolving power of 17,500 (more details in the Appendix D, section D-3.1). Separate analysis runs were conducted for positive and negative ionisation modes with a spray voltage of +4 kV and −4 kV, respectively, and a capillary temperature of 300 °C. Confirmation of the prioritised structures was conducted at the University of Amsterdam. Reference standard materials and sample extracts were analysed with an ultrahigh-performance liquid chromatography (UHPLC) system (Nexera, Shimadzu, Den Bosch, The Netherlands) coupled to a maXis 4G q-ToF/HRMS equipped with an ESI source (Bruker Daltonics, Wormer, The Netherlands). Further details on this system are provided in the Appendix D (D-3.2).

5.2.5 Non-target screening workflow

The NTS workflow consisted of three steps dealing with (i) HRMS1 data pre-processing, (ii) prioritisation and (iii) structure elucidation. Unless stated otherwise, the steps were automated and computed within R (version 3.3.2) (R Core Team, 2017). For data pre-processing, the analyses raw files were converted to centroided mzXML format with ProteoWizard (version 3.0) (Chambers et al., 2012) and imported into enviMass (version 3.4) (Loos, 2016). The enviMass settings used for this study are given in the Appendix D (D-4). Separate projects were created for positive and negative ESI data. Peak picking was performed to determine the non-target features, i.e., unique m/z and retention time (tR) pairs. The 128 isotope-labelled IS were used for mass recalibration, tR alignment and intensity normalisation, i.e. for each measurement the intensities of the picked peaks were normalized using the median deviation of all IS from their individual median profile intensity

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(Loos, 2015). The features were profiled, i.e., unique IDs were assigned to m/z and tR pairs detected across different samples. Features detected in analysis blanks, procedural blanks and deionised water samples spiked with isotope-labelled IS (termed blind samples) were profiled and subtracted. Profiled features that were not detected in all three replicates or whose tR was <2 min and >24 min were filtered out. The tR filter ensured that highly polar (tR < 2 min) and low polar organics (tR > 24 min) were excluded from the data processing, as data of substances eluting either early or late are known to be of lesser quality with the existing chromatographic method, yielding confident assignment of (quasi-)isobaric substances challenging. For positive data, grouping of the most common single-charge ESI adducts, i.e. [M+H]+, [M+Na]+, [M+NH4]+ and [M+K]+, was additionally performed to define components (Loos, 2015). Other positive adducts, negative adducts and isotopic peaks that were not included here may increase the final number of profiled features.

The profiled features were prioritised by hierarchical cluster analysis (HCA) based on successful applications to lake sediments (Chiaia-Hernandez et al., 2017) and to ozonation in a WWTP (Schollée et al., 2018). Briefly, profile intensities were normalised to the maximum value detected in the whole dataset and dissimilarities expressed as Euclidean distance were calculated with the “stats” package (daisy function). The hierarchical classification of the profiled features was computed with the “cluster” package (hclust function). More details on the application of HCA to profile prioritisation can be found elsewhere (Chiaia-Hernandez et al., 2017; Schollée et al., 2018). The optimal number of clusters (k) was investigated by silhouette analysis computed with the “cluster” package (silhouette function). The average silhouette width, i.e., a dimensionless value indicating whether an object truly belongs to the cluster it was assigned to, was calculated at different k values (Ng and Han, 1994). Once the optimal number of clusters was defined, the 15 most populated clusters were considered for further prioritisation. In order to obtain good quality spectra for structure annotation, 50% of the most intense ions in these clusters were prioritised and their experimental HRMS1 and HRMS2 data extracted using the “RMassBank” package (Stravs et al., 2013).

Tentative structure elucidation was performed with MetFrag command line version 2.3 in batch mode (Ruttkies et al., 2016). Neutral monoisotopic masses of the prioritised features were used to retrieve structures within 5 ppm mass accuracy from ChemSpider (Pence and Williams, 2010). The maximum number of candidates per feature was set to 5,000. The maximum tree depth (MSn) was set to 2. The candidate structures retrieved by MetFrag are fragmented in silico in a combinatorial manner and the fragments matched to the experimental HRMS2 spectra. Additionally, spectral

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similarities with records in the MoNA spectral library built into MetFrag were calculated using the MetFusion approach (Gerlich and Neumann, 2013). All suspect lists available on the NORMAN Suspect List Exchange as of November 2016 were merged into one large list of 11,922 unique InChIKey codes. The candidate structures were scored based on seven terms: FragScore (in silico fragmentation score); MetFusionOffline (MoNA spectral similarities using the MetFusion approach (Gerlich and Neumann, 2013); CSRefsScores (4 scoring terms: reference count on ChemSpider, reference count on PubMed, reference count on Royal Society of Chemistry and ChemSpider data sources); Suspects (hit in suspect list). The seven scoring terms were normalised by the highest value found among the proposed candidates and equal weighting of 1 was used to calculate the MetFrag Combined Score. An in-house script was used to check for agreement of formulas as calculated by MetFrag and GenFormR (Meringer, 2017). Tentatively annotated spectra and extracted ion chromatograms (EICs) of the non-target features were plotted with the packages “ReSOLUTION” (Schymanski, 2018) and “patRoon” (Helmus, 2018), respectively, for quality control. Finally, identification confidence levels were assigned to all the tentatively annotated features (Schymanski et al., 2014a).

5.3 RESULTS AND DISCUSSION

5.3.1 Data pre-processing, prioritisation and structure annotation

An overview on detection, mass deviation and intensity ranges of the labelled standards is given in the Appendix D (D-5). Pre-processing of HRMS1 spectra with enviMass resulted in 10,850 positive and 7,412 negative profiled features across the transect. The HCA results were visualised by plotting heat maps and dendrograms, shown in Figure 5.2. The HCA classified intensity profiles based on similarities between the detected trends. The results revealed that the data in both polarities were characterised by a series of dynamic trends (clustered at the top of the heat maps) with a high-intensity region between wells LT-P09 and LT-P11, thus in water that originated from the river Lek throughout the 1990s. Additionally, each well seemed to display a set of unique features, visible on the heat maps as high-intensity spots. In the positive data the spots displayed overall increasing dissimilarity with increasing RBF travel time (Figure 5.2a), whereas this behaviour was not observed in the negative data (Figure 5.2b). While it could not be excluded that some compounds would occur only in one well, these profiles might result from detection above peak-picking threshold value exclusively in one well or from lack of detection in all three replicates from adjacent wells.

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Figure 5.2. Heat maps of the clustered profiles intensities across the riverbank filtration transect in positive (panel a) and negative (panel b) ESI data. Well codes are shown on the x-axes and represent the time line from 1 year (LS-P12) to 60-year (LT-P18) old water. Dendrograms are shown on the y-axes, where the prioritised clusters are marked in red. The colour scale used for profile intensities is illustrated in the legend (upper left).

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The optimal number of clusters (k) was determined by silhouette analysis. The average silhouette width values for k= 80 were 0.690 and 0.687 for positive and negative data, respectively, indicating reasonable separation of the data (Ng and Han, 1994). The clusters were sorted by the number of profiled features and the 15 most populated clusters were inspected. Inspection of the prioritised trends is discussed in the following section (see “5.3.2 Interpretation of clustered trends”).

The prioritised clusters were populated with 7525 positive and 5123 negative profiled features, of which 3764 and 3845 were prioritised based on their intensity. The “RMassBank” package was used to extract experimental HRMS1 and HRMS2 information, resulting in 1348 and 983 positive and negative profiled features, respectively, with associated fragmentation data. It should be noted that a higher percentage fraction of negative features was prioritised from the 15 most populated clusters on the basis of their intensities, as only about 25% of these ions triggered an HRMS2 acquisition. An overview of the population of each prioritised cluster, the number of features prioritised from these clusters and how many ions triggered HRMS2 acquisitions is shown in the Appendix D (D-6). Candidate structures were assigned to 884 and 550 positive and negative features, respectively, using MetFrag, whereas 369 positive and 345 negative features were excluded from further identification efforts as the maximum number of candidate structures was exceeded (set to 5,000 to reduce runtime issues and eliminate cases with poor likelihood of success, based on experience). The MetFrag results were initially reduced by filtering out candidates with combined score lower than 4 (out of 7) and without any explained HRMS2 peaks. The minimum score filtering criterion proved too strict for negative data, which displayed lower scores overall, mostly due to lack of metadata, poor fit of the in silico fragments to the experimental data or absence from the suspect list. Consequently, for negative data the minimum score was set to 3, including at least one explained HRMS2 peak. The MetFrag Scores plots were visualised for all tentatively annotated features to ease the assessment of the MetFrag results. An example of MetFrag Scores plot for the positive feature m/z 116.0165 detected at tR 2.5 min is provided in Figure 5.3a.

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Figure 5.3. MetFrag Scores plot of candidate structures to elucidate feature m/z 116.0165 at tR 2.5 min detected in the positive ESI data (panel a). CombScore: Combined Score; NoExplPeaks: number of explained peaks; FragScore: in silico fragmentation score; MetFusionOffline: score for spectral similarities against MassBank of North America (MoNA); AVGCSRefs: average ChemSpider references score (only averaged in the plots); Suspects: hit in suspect list. All plotted scores were normalised to 1 except the MetFusionOffline score (which was normalised during score calculation, but not during plotting, for diagnostic purposes). HRMS2 spectrum of methylisothiazolinone tentatively annotated by MetFrag to elucidate m/z 116.0164 [M+H]+ (panel b). Experimental HRMS2 (MSMS, black line); explained MSMS (ExplMSMS, red dashed line); HRMS1 (MS1, green dashed line).

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The MetFrag Scores plot in Figure 5.3a shows individual and combined scores assigned to 59 candidates retrieved from ChemSpider and tentatively annotated to elucidate the non-target feature of interest, in this example positive feature m/z 116.0165 (tR=2.5 min). The top 3 ranking structures were methylisothiazolinone, thiazole-5-methanol and 2-methoxy thiazole, which displayed a rounded score of 5.4, 4.4 and 3.8, respectively (CombScore, black line). The structure of methylisothiazolinone could explain 7/16 peaks, whereas the 2nd and 3rd candidates could explain 4/16 peaks (NoExplPeaks, red dashed line). The higher number of matching fragments resulted in a higher in silico fragmentation score for methylisothiazolinone (FragScore, red line). The top 3 candidates did not differ substantially in terms of spectral similarity scores (MetFusionOffline, blue dotted line), which was low overall and indicated lack of similar or matching HRMS2 spectra in the library. The reference scores of the top 3 candidates were comparable, although higher for the first candidate (AvgCSRefs, green dot-dash line). The top 2 candidates also had a suspect hit (Suspects, purple dot). Further processing involved generating a tentatively annotated HRMS2 spectrum from the output of MetFrag and GenFormR. MetFrag retrieved candidate structures from ChemSpider, generated fragments in silico, back-calculated their (de)protonated monoisotopic masses and fitted them to the experimental HRMS2 data. GenFormR instead performed an algebraic calculation on spectral data to find the best formulas fitting the precursor and product ions. As these approaches are complementary, GenFormR was used to gain additional information to MetFrag to enhance the interpretation of the spectra. An example of tentatively annotated spectrum of the highest ranking candidate to elucidate the structure of positive feature m/z 116.0164 is shown in Figure 5.3b. The structure of methylisothiazolinone was eventually confirmed with a reference standard.

Applying the NTS workflow resulted in the tentative annotation of 72 non-target features (all Level 3), 45 from positive clusters and 27 from negative clusters. The full lists of tentatively annotated features, including identification confidence levels, is given in the Appendix D (D-7). Data reduction at the different steps of the workflow is shown in Fig. 5.4.

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Figure 5.4. Data reduction charts for posit ive ESI (left) and negative ESI (right) data.

Reference standards of 42 compounds were obtained based on availability and price. All chemicals were highest ranking candidates of their respective non-target features, with the exception of atrazine-desethyl-2-hydroxy and metamitron-desamino, which were the second-highest ranking candidates, selected based on expert knowledge and following inspection of the MetFrag scores. Atrazine-desethyl-2-hydroxy was chosen for its higher in silico fragmentation score and spectral similarity score compared to the top candidate. The identification of metamitron-desamino represented an interesting case to demonstrate the importance of analyst judgement. This compound was the second-top ranking structure to explain positive ESI feature m/z 188.0817 at tR 6.55 min with a MetFrag Combined Score of 3.25 and 11/17 explained HRMS2 peaks. The highest ranking structure for this feature was the phosphodiesterase 3 inhibitor amrinone, with a score of 5.74 and 10/17 annotated HRMS2 peaks. The most pronounced differences in the MetFrag scores of these two structures were found in the number of references. Since one of the scoring criteria was the number of PubMed references, it was not surprising that the pharmaceutical amrinone had a higher score than a pesticide TP. However, since nearly all confirmed chemicals originated from either industrial or agricultural activities, metamitron-desamino was thought to be the more likely structure. Reference standards were obtained for both compounds, leading to confirmation of the pesticide metabolite. It is noteworthy that metamitron-desamino would have been missed without amrinone being the first candidate as its MetFrag score was below the cut-off value used for prioritisation of the MetFrag results of the positive ESI data.

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The candidate structures of 25 out of 42 non-target features were confirmed, resulting in a success rate of 60% against the confirmation subset. An extensive evaluation of the performance of MetFrag can be found in the literature (Ruttkies et al., 2016; Schymanski et al., 2017). Details of the successfully confirmed compounds are shown in Table 5.1. Of the 17 compounds that were not confirmed, 4 could not be detected by the UHPLC-ESI-q-TOF/HRMS system and 13 eluted at a different tR.

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Table 5.1. List of non-target contaminants confirmed with reference standards

Compound Formula Neutral

mass Adduct

tR

sample tR

standard ChemSpider ID (hyperlinked)

Methylisothiazolinone C4H5NOS 115.0092 [M+H]+ 2.5 2.5 36393

1,3-Benzothiazole C7H5NS 135.0143 [M+H]+ 7.5 7.5 6952

Tetramethylsulfamide * C4H12N2O2S 152.0620 [M+H]+ 4.8 4.8 121689

Atrazine-desethyl-2-hydroxy * C6H11N5O 169.0964 [M+H]+ 3.1 3.0 96906

4-Toluenesulfonamide * C7H9NO2S 171.0354 [M+H]+ 5.6 5.6 6033

Simazine-2-hydroxy C7H13N5O 183.1120 [M+H]+ 4.6 4.5 16505

Metamitron-desamino * C10H9N3O 187.0746 [M+H]+ 6.0 6.0 157884

Benzoguanamine C9H9N5 187.0858 [M+H]+ 5.0 4.9 6797

2,6-dichlorobenzamide * C7H5Cl2NO 188.9748 [M+H]+ 5.0 5.0 15359

Carbendazim * C9H9N3O2 191.0695 [M+H]+ 4.8 4.7 23741

Chlortoluron C10H13ClN2O 212.0716 [M+H]+ 9.1 9.1 25472

Diuron-desmethyl C8H8Cl2N2O 218.0014 [M+H]+ 9.8 9.8 18040

Diphenylphosphinic acid * C12H11O2P 218.0497 [M+H]+ & [M-H]- 7.7 7.7 14810

5-Amino-2-chlorotoluene-4-sulfonic acid * C7H8ClNO3S 220.9913 [M+H]+ & [M-H]- 5.4 5.4 6670

Chloridazon C10H8ClN3O 221.0356 [M+H]+ 6.0 6.0 14790

Naphthionic acid * C10H9NO3S 223.0303 [M+H]+ & [M-H]- 2.0 2.0 6532

Lamotrigine C9H7Cl2N5 255.0079 [M+H]+ 6.7 6.6 3741

Tributyl phosphate * C12H27O4P 266.1647 [M+H]+ & [M-H]- 14.9 15.0 29090

Acesulfame C4H5NO4S 162.9939 [M-H]- 2.5 2.5 33607

O,O-Diethyl thiophosphate * C4H11O3PS 170.0166 [M-H]- 3.6 3.6 635

p-Toluidine-m-sulfonic acid * C7H9NO3S 187.0303 [M-H]- 3.3 3.3 60405 Dibutyl phosphate * C8H19O4P 210.1020 [M-H]- 8.8 8.8 7593 Camphorsulfonic acid * C10H16O4S 232.0769 [M-H]- 5.0 5.0 17438 4-Amino-2,5-dichlorobenzenesulfonic acid * C6H5Cl2NO3S 240.9367 [M-H]- 2.6 2.6 59986 4-Dodecylbenzenesulfonic acid C18H30O3S 326.1915 [M-H]- 17.3 17.3 8172

* Identified in cluster with intensity maxima in the 1990s

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5.3.2 Interpretation of clustered time trends

Several prioritised clusters displayed trends with intensity maxima in the 1990s (wells LT-P09 and LT-P11), there were seven in the positive ESI data and six in the ESI negative data. The presence of these clusters could be rationalised by the history of anthropogenic emissions and restoration measures in the Rhine basin from the 1950s onwards. River protection programmes coordinated by the International Commission for the Protection of the Rhine (ICPR) resulted in domestic and industrial WWTPs being built between 1970 and 1990 in the Rhine riparian countries (Wieriks and Schulte-Wülwer-Leidig, 2009). A turning point in international river basin management was the 1986 Sandoz accident when 15,000 m3 of water mixed with organophosphorus and organochlorine compounds including pesticides, dyes, solvents and intermediates accidentally entered the upper Rhine in Switzerland, resulting in widespread mortality of fish, macroinvertebrate and plankton communities in the riparian countries downstream (Capel et al., 1988; Verweij, 2017; Wieriks and Schulte-Wülwer-Leidig, 2009). Following the Sandoz accident, the ICPR established the Rhine Action Programme (RAP) to coordinate and implement measures to lower the discharge of hazardous substances. Their goals were achieved in the mid-1990s when substantial reduction of organic emissions, improved oxygen content and biodiversity were reported (Wieriks and Schulte-Wülwer-Leidig, 2009).

Given the high number of (tentatively) identified substances, the discussion of the results is limited to the most significant findings from the confirmed identities, whose profiles are highlighted in Figure 5.5. The cluster shown in Figure 5.5a indicated a net increase of emissions from the 1950s to the 1990s, followed by a moderate but constant decrease in the 2000s. In this cluster a negligible number of profiles displayed intensity maxima in the early 2000s, however these are not discussed further as no candidate structures could be associated to such trends. Based on the known sources and environmental fate of 2,6-dichlorbenzamide (Björklund et al., 2011) (Fig. 5.5a – green line), nonpoint source emissions from agricultural applications of

biocides or sewage farming could explain some profiles.

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Figure 5.5. Examples of prioritised clusters with identified non-target compounds. On the x-axis the well codes are shown. The bank filtrate travel time was: ≈ 1 year (LS-P12), 6–11 years (LT-P01), 8–11 years (LT-P03), 8–13 years (LT-P05), 10–16 years (LT-P07), 15–20 years (LT-P09), 19–25 (LT-P11), ≈ 40 years (LT-P14), 50–60 years (LT-P18). Fig. 5.5a: Positive ESI cluster with maxima in the 1990s accounting for 231 profiles. The profiles of tetramethylsulfamide (blue), 4-toluenesulfonamide (red), 2,6-dichlorobenzamide (green), and tributyl phosphate (grey) are shown in colour. Fig. 5.5b: Negative ESI cluster with maxima in the 1990s accounting for 286 profiles. The profiles of 4−amino−2,5−dichlorobenzenesulfonic acid (green), camphorsulfonic acid (blue), O,O-diethyl thiophosphate (red), and 5-amino−2−chlorotoluene−4−sulfonic acid (grey) are shown in colour. Fig. 5.5c: Positive ESI cluster with gradual increase from the 1970s and displaying stable intensities throughout the 1990s and early 2000s accounting for 238 profiles. The profiles of metamitron-desamino (yellow), diphenylphosphinic acid (green) and atrazine-desethyl-2-hydroxy (blue) are shown in colour. Fig. 5.5d: Positive ESI cluster with maxima in bank filtrate with 1-year travel time (late 2015) accounting for 834 profiles. All trends significantly overlapped and no plots were manipulated for display purposes. The profiles of lamotrigine (blue) and simazine-2-hydroxy (red) are shown in colour.

Confirming the environmental occurrence of tetramethylsulfamide (TMS) was a novel discovery of the present study (Fig. 5.5a – blue line), highlighting the environmental significance of NTS. No information about production or use of TMS were found on the European Chemical Agency (ECHA) or the

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US EPA websites. TMS was not present in the NORMAN Suspect Lists Exchange or in accurate mass spectral libraries. A search on PubChem returned 50 patents detailing formulations for the synthesis of dyes, flame retardants and pesticides. TMS can be used for the synthesis of sulphur trioxide-dimethylamine complex, a sulphating reagent for dyestuffs (Gilbert, 1962). TMS can also be a by-product of the synthesis of dimethylsulfamyl chloride, a chemical used in a variety of industrial applications (Hargittai and Brunvoll, 1976), or a by-product of the synthesis of sulfur-containing aziridines chemosterilants (Borkovec and Woods, 1963). The exact source of emissions of TMS, although likely industrial, remains so far unknown. The identity of 4-toluenesulfonamide (4-TSA) in a cluster with maxima in the 1990s was confirmed with a reference standard (Fig. 5.5a – red line). 4-TSA is a plasticizer, an intermediate for the synthesis of pesticides and disinfection by-product of the antimicrobial agent N-sodium-N-chloro-p-toluenesulfonamide. 4-TSA was detected in the Berlin area in surface water, groundwater and bank filtrate at concentrations up to 0.9 µg/L, 14.1 µg/L and 0.24 ng/L, respectively (Richter et al., 2008). The authors pointed to untreated WWTP effluents and sewage farms as sources of sulfonamides and concluded that 4-TSA can help identifying bank filtrate originating from polluted surface water, supporting the results of our study.

A more dynamic intensity trend showing increasing emission from the 1950s to the 1990s followed by a substantial decrease in the 2000s is shown in Figure 5.5b. In this cluster, camphorsulfonic acid (CSA) was tentatively identified and later confirmed, along with the metabolite O,O-Diethyl thiophosphate (DETP), the industrial chemicals 5-Amino-2-chlorotoluene-4-sulfonic acid (ACTSA) and 4-Amino-2,5-dichlorobenzenesulfonic acid (ADCBSA). To the best of our knowledge, this is the first time CSA is confirmed in bank filtrate and its environmental persistence demonstrated (Fig. 5.5b – blue line), highlighting the environmental significance of NTS. CSA was not included in the suspect list and only one reference was found reporting its occurrence in a WWTP effluent from a rubber manufacturing site in Spain (Puig et al., 1996). CSA is used as dopant in the synthesis of polyaniline, a conductive polymer (Lee and Yang, 2010; MacDiarmid and Epstein, 1994). Camphor derivatives, such as CSA, are used as UV filters in cosmetic products and eventually reach surface waters via insufficiently treated domestic WWTP effluents (Silvia Díaz-Cruz et al., 2008). The UV filter terephthalydene dicamphorsulfonic acid (TPDCSA) is unstable under photolysis in aqueous media and uncharacterised degradation product(s) with UV absorbance <290 nm were reported (Serpone et al., 2002). CSA has a UV/Vis absorbance of 285 nm (Huang et al., 2003), suggesting that it may originate from TPDCSA in the environment, rather than exclusively from industrial sources.

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ACTSA (Fig. 5.5b – grey line) is an important building block for the synthesis of dyes reported to persist in chemical and biological wastewater treatment (LI et al., 2006). Its 2-amino isomer can be obtained from the cleavage of the azo dye Pigment Red 53 and was included in a priority list of 23 unregulated aromatic amines of toxicological concern (Brüschweiler et al., 2014). Limited literature references were found for this compound and to our knowledge this is the first time it is identified in a riverbank filtrate. The REACH registration dossier of ACTSA was submitted by a dye manufacturer with a production site at the river Main, the longest tributary of the Rhine, approximately 500 km upstream of the RBF site. The identity of another dyestuff synthesis intermediate, ADCBSA, was confirmed in this cluster (Fig. 5.5b – green line). This chemical occurs in liquid waste from manufacturing processes; however, liquid waste containing ADCBSA is usually treated separately before being sent to WWTPs because this compound is toxic to microorganisms and inhibits biological treatment (Bednarik et al., 2007). This is the first time ADCBSA is identified in a riverbank filtrate and its persistence reported. Its decreasing intensities in the 2000s might be explained by upgrades of industrial WWTPs with ion exchange resins and/or by decreased production volumes. DETP is a product of mammal metabolism and biological wastewater treatment, as well as an environmental TP of insecticides, flame retardants, plasticisers and industrial chemicals (Rousis et al., 2016; Vidya Lakshmi et al., 2009). Even if nonpoint sources cannot be excluded, the intensity profile of DETP (Fig. 5.5b – red line) suggests industrial or domestic effluents as possible sources. The decrease in the young bank filtrate might be explained by the upgrading of WWTPs and the implementation of more effective regulation in the 2000s in the riparian Rhine countries. The hypothesis about the point sources of DETP was supported by a recent wastewater-based epidemiological study on the human exposure to pesticides, which reported a detection frequency of 7% in domestic WWTP effluents across Europe in concentrations in the low ng/L range (Rousis et al., 2017). We demonstrate that DETP can enter RBF systems, where it can persist and migrate for at least two decades in the dark anaerobic aqueous environments.

Contaminants that entered the RBF in the mid-1970s and displayed stable intensities along the transect were found among the prioritised clusters. In the cluster shown in Figure 5.5c, the metabolites metamitron-desamino, atrazine-desethyl-2-hydroxy and diphenylphosphinic acid (DPPS) were confirmed with their respective reference standards. Metamitron-desamino is the main biodegradation product of the herbicide metamitron, a mobile chemical known to reach surface waters via polluted runoffs or WWTP effluents and with high potential to leach into groundwater. Concentrations of metamitron-desamino up to 680 ng/L have been reported from rivers

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impacted by urban and agricultural activities (Moschet et al., 2015). Recent research showed that this metabolite can originate from biodegradation in water-sediment systems(Wang et al., 2017). The profile of metamitron-desamino (Fig. 5.5c – yellow line) matched the sales data of its parent compound metamitron, introduced in the European Union in 1975 and displaying a stable sales trend from the mid-1990s onwards (Commission of the European Union, 2002). The lower intensities in the first well might reflect the recent introduction of herbicide formulations with lower concentrations of metamitron combined with other active substances (German Federal Office of Consumer Protection and Food Safety, 2012). DPPS (Fig. 5.5c – green line) is a degradation product of the pharmaceutical precursor triphenylphosphine oxide (TPPO). TPPO was quantified at concentrations below 300 ng/L in bank filtrate with up to 4-year travel time from the same area investigated in our study (Hamann et al., 2016). Literature indicated that DPPS was fully degraded within 30 days in a fixed-bed bioreactor filled with aerobic Rhine water and that a major source of TPPO is located approximately 400 km upstream of the RBF system investigated in the present manuscript (Knepper and Karrenbrock, 2006). DPPS is a highly hydrophilic anionic compound (logDpH7.4 = -1.69; pKa = 2.3), so it is not retarded by bank filtration. For the first time we found that DPPS can be persistent and mobile in the dark anaerobic aqueous environment.

Profiles displaying intensity maxima in well LS-P12 (1-year old water) followed by a sudden decrease in the rest of the transect were assigned to the 2nd and 4th most populated clusters in positive and negative ESI data, respectively (Fig. 5.5d). It could be assumed that such clusters would include profiles of chemicals possibly infiltrated only recently, infiltrated and diluted to below detection level in the older bank filtrate, or formed at the riverbank within 1-year travel time and either degraded further or diluted to undetectable concentrations. In this cluster, lamotrigine, simazine-2-hydroxy, diuron-desmethyl and 1,3-benzothiazole were confirmed with reference standards. The anticonvulsant lamotrigine (Fig. 5.5d – blue line) is known to be persistent to biological wastewater treatment and has been previously detected in surface water (Zonja et al., 2016) and in bank filtrate with short travel time (Huntscha et al., 2013). Detection limited to the first well was not expected, because lamotrigine was first marketed in the EU already in 1993. Literature data on degradation of lamotrigine in anaerobic conditions was not found. Although reductive dehalogenation of aryl halide groups might occur in such conditions (Hartkamp-Commandeur et al., 1996), dechlorinated TPs of lamotrigine were screened for and not detected in the experimental HRMS1 data. A recent study on the fate of pharmaceuticals in soils irrigated with reclaimed wastewater found that lamotrigine (logDpH7.4 = 1.68) displayed the highest sorption affinity to soil compared to carbamazepine (logDpH7.4 =

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2.28) and its metabolites (Paz et al., 2016). The 6- to 11-year travel time between wells LS-P12 and LT-P01 might have maximised adsorptive interactions and restrained lamotrigine mobility in the sub-surface. Ultimately, the contribution of dilution to undetectable levels in the older bank filtrate could not be determined. It is noteworthy how simazine-2-hydroxy was not detected after at least 11-year travel time (Fig. 5.5d – red line), whereas atrazine-desethyl-2-hydroxy showed persistence across the transect (Fig. 5.5c – blue line). It is unclear whether simazine-2-hydroxy was removed during RBF, transformed further or diluted to undetectable concentrations in the older bank filtrate. Previous studies found that simazine occurred in surface water and groundwater at concentrations up to 10 times lower than atrazine (Sabik et al., 2000). Both triazine herbicides that lead to formation of these TPs were banned in the European Union in 2004. Other known metabolites of atrazine and simazine were screened for in HRMS1 data, but were not detected. These TPs might have been either absent from the RBF transect or occurred at undetectable concentrations. The detection of atrazine-desethyl-2-hydroxy at constant levels might be attributed to the release of atrazine (or TPs) from contaminated river sediments prior to infiltration in the RBF system (Guo et al., 2016). It cannot be excluded that the hydroxylated TP prevailed over other more commonly detected dealkylated metabolites (Kolpin et al., 1998), as the aquifer screened in this study differs from others for being both confined and anaerobic. For example, in a recent screening of three aerobic bank filtration sites, atrazine-2-hydroxy and atrazine-desethyl were found both in low ng/L concentration whereas atrazine-desethyl-2-hydroxy and atrazine-desisopropyl were not detected (Hollender et al., 2018).

5.4 ENVIRONMENTAL IMPLICATIONS

This study contributes to the mounting evidence of environmental persistence of hydrophilic organic compounds and shows that polar substances can be highly mobile in RBF systems with long travel time at stable biogeochemical conditions. More research should be done on this or comparable RBF transects to investigate the fate of the most polar MPs, e.g., with even lower logD values than those identified in this study, which might have been lost during enrichment or insufficiently retained by reversed-phase chromatography. We showed that state-of-the-art NTS relying on open computational tools and performed in a semi-automated manner can be an extremely powerful method to explore water contaminants with HRMS-based methods. Spectra of the substances identified in this study will be uploaded to openly accessible accurate mass spectral libraries to contribute to future screenings. The list of confirmed contaminants has been shared with local drinking water utilities to assess their removal in drinking water

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treatment. The trend analysis presented here can be useful to manage bank filtration systems with long travel times in catchment areas impacted by anthropogenic activities. In these cases, to avoid contamination with many legacy pollutants, which overall displayed higher normalised intensities in the older water and thus likely occurred at higher concentrations, the use of young groundwater to produce potable water is recommended, whereas advanced treatment should be applied to the old groundwater. In the case of drinking water treatment at the Tiendweg well field, reverse osmosis is applied to maximise micropollutants removal.

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ACKNOWLEDGMENTS

The authors thank Martin Loos for his support with enviMass and Aurea Chiaia-Hernández and Michael Stravs for helpful discussions at Eawag. The drinking water utility Oasen (Gouda, The Netherlands) is acknowledged for funding the ECROS project at the Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam. Willem-Jan Knibbe and Hans van Woerden at Oasen are acknowledged for arranging collection of the samples. Funding at Eawag was provided through the EU 7th Framework Programme SOLUTIONS project under Grant Agreement No. 603437. Work at LCSB was supported by the Luxembourg National Research Fund (FNR), grant number 12341006. Three anonymous reviewers are acknowledged for helpful comments.

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APPENDIX D. Supplementary information to Chapter 5

D-1: Standards and chemicals Methanol (≥ 99.9%) was purchased from Fisher Scientific (Wohlen, Switzerland). Ultrapure water was obtained from a Barnstead Nanopure stationary laboratory water system (Barnstead Nanopure Thermo Scientific, San Jose, U.S.). Formic acid (≥ 98 %) used as mobile phase modifier was purchased from Merck (Darmstadt, Germany). Isotope-labelled internal standards (listed in table D-1.1) used for mass recalibration, tR alignment and intensity normalisation and reference standards (listed in table D-1.2) used for confirmation of chemical identities were purchased from CDN Isotopes (Canada), Dr. Ehrenstorfer (Germany), HPC Standards (Germany), LGC Standards (Switzerland), Molcan (Canada), MolPort (Latvia), Monsanto (Belgium), Novartis (Switzerland), Riedel-de-Häen (Germany), Sigma-Aldrich (Switzerland and The Netherlands), or Toronto Research Chemicals (Canada) at purities ≥ 95% (analytical grade).

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Table D-1.1. List of isotope-labelled internal standards, their molecular formula and CAS number.

Name Formula CAS

2,2-Difluoro-2-deoxyuridin-13C,15N2 C8H10F2O5[13]C1[15]N2 1233921-75-9

2,4-D-D3 C8H3D3Cl2O3 202480-67-9

2,6-Dichlorbenzamide-3,4,5-D3 C7H2D3Cl2N1O1 1219804-28-0

5-Metyhl-1H-benzotriazole-D6 C7H1D6N3 1246820-65-4

Alachlor-D13 C14H7D13Cl1N1O2 1015856-63-9

Aldicarb (N-methyl-13C-D3-carbamoyl-13C) [13]C2C5H11D3N2O2S1 1261170-77-7

Amisulpride-D5 C17H22D5N3O4S1 1216626-17-3

Atenolol acid-D5 C14H16N1O4D5 1215404-47-9

Atenolol-D7 C14H15D7N2O3 1202864-50-3

Atomoxetine-D3 C17H18D3N1O1 1217776-38-9

Atorvastatin-D5 C33H30D5F1N2O5 222412-82-0

Atrazine-D5 C8H9D5Cl1N5 163165-75-1

Atrazine-2-Hydroxy-D5 C8H10D5N5O1 1276197-25-1

Atrazine-desisopropyl-D5 C5H3D5Cl1N5 1189961-78-1

Azithromycin-D3 C38H69D3N2O12 163921-65-1

Bentazon-D6 C10H6D6N2O3S1 25057-89-0 (unlabelled)

Benzotriazole-D4 C6H1D4N3 1185072-03-0

Bezafibrat-D4 C19H16D4Cl1N1O4 1189452-53-6

Bicalutamid-D4 C18H10D4F4N2O4S1 1185035-71-5

Caffeine-D9 C8H1N4O2D9 72238-85-8

Candesartan-D5 C24H15D5N6O3 1189650-58-5

Carbamazepine-10,11-epoxide-13C, D2 C14H10D2N2O2[13]C1

36507-30-9 (unlabelled)

Carbamazepin-D8 C15H4D8N2O1 298-46-4 (unlabelled)

Carbendazim-D4 C9H5D4N3O2 291765-95-2

Cetirizin-D8 C21H17D8Cl1N2O3 774596-22-4

Chloridazon-D5 C10H3D5Cl1N3O1 1346818-99-4

Chloridazon-desphenyl-15N2 C4H4Cl1N1[15]N2O1 1189649-21-5

Chloridazon-methyl-desphenyl-D3 C5H3D3Cl1N3O1 17254-80-7 (unlabelled)

Chlorotoluron-D6 C10H7D6Cl1N2O1 1219803-48-1

Chlorpyrifos-D10 C9H1D10Cl3N1O3P1S1 285138-81-0

Chlorpyrifos-methyl D6 C7H1D6Cl3N1O3P1S1 2083629-84-7

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Table D-1.1 (continued). List of isotope-labelled internal standards, their molecular formula and CAS number

Name Formula CAS

Chlothianidin-D3 C6H5D3Cl1N5O2S1 1262776-24-8

Citalopram-D6 C20H15D6F1N2O1 1190003-26-9

Clarithromycin-D3 C38H66D3N1O13 959119-17-6

Climbazol-D4 C15H13D4Cl1N2O2 1185117-79-6

Clofibric acid-D4 C10H7D4Cl1O3 1184991-14-7

Clopidogrel-(+/-)-d4 C15H10D4Cl1N1O2S1 1219274-96-0

Clotrimazol-D5 C22H12D5Cl1N2 1185076-41-8

Clozapine-D8 C18H11D8Cl1N4 1185053-50-2

Codeine-13C,D3 C17H18D3N1O3[13]C1 76-57-3 (unlabelled)

Cyclophosphamid-D4 C7H11Cl2N2O2P1D4 173547-45-0

Cyprodinil-D5 C14H10D5N3 1773496-67-5

Desethylatrazine 15N3 C6H10Cl1N2[15]N3 6190-65-4 (unlabelled)

Diazepam-D5 C16H8Cl1N2O1D5 65854-76-4

Diazinon-D10 C12H11D10N2O3P1S1 100155-47-3

Dicamba-D3 C8H3D3Cl2O3 349553-95-3

Dichlorprop-D6 C9H2D6Cl2O3 120-36-5 (unlabelled)

Diclofenac-D4 C14H7D4Cl2N1O2 153466-65-0)

Diflufenican-D3 C19H8D3F5N2O2 1185009-29-3

Dimethenamid-D3 C12H15D3Cl1N1O2S1 1246816-31-8

Dimethoate-D6 C5H6D6N1O3P1S2 1219794-81-6

Diuron-D6 C9H4D6Cl2N2O1 1007536-67-5

Eprosartan-D3 C23H21D3N2O4S1 1185243-70-2

Erythromycin-13C2 C35H67N1O13[13]C2 114-07-8 (unlabelled)

Fenofibrate-D6 C20H15D6Cl1O4 1092484-56-4

Fluconazol-D4 C13H8F2N6O1D4 1124197-58-5

Fluoxetine-D5 C17H13F3N1O1D5 1173020-43-3

Furosemid-D5 C12H6Cl1N2O5S1D5 1189482-35-6

Gabapentin-D4 C9H13N1O2D4 1185039-20-6

Gemcitabine-13C,15N2 C8H11F2N1O4[13]C1[15]N2 1262897-74-4

Hydrochlorothiazide-C13, D2 C6H6Cl1N3O4S2D2[13]C1 1190006-03-1

Hydromorphone-D3 C17H16D3N1O3 136765-37-2

Ibuprofen-D3 C13H15D3O2 121662-14-4

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Table D-1.1 (continued). List of isotope-labelled internal standards, their molecular formula and CAS number.

Name Formula CAS

Imidacloprid D4 C9H6D4Cl1N5O2 1015855-75-0

Indomethacin-D4 C19H12Cl1N1O4D4 87377-08-0

Irbesartan-D3 C25H25D3N6O1 1185120-76-6

Irgarol-D9 C11H10D9N5S1 1189926-01-9

Isoproturon-D6 C12H12D6N2O1 217487-17-7

Lamotrigine-13C3,D3 C6[13]C3H4D3Cl2N5 1246815-13-3)

Levetiracetam-D3 C8H11D3N2O2 1217851-16-5

Lidocaine-D10 C14H12N2O1D10 851528-09-1

MCPA-D3 C9H6D3Cl1O3 352431-14-2

Mecl0zine-D8 C25H19D8Cl1N2 1246816-06-7

Mecoprop D6 C10H5D6Cl1O3 1705649-54-2

Mefenamic acid-D3 C15H12D3N1O2 1189707-81-0

Mesotrion D3 C14H10D3N1O7S1 104206-82-8 (unlabelled)

Metformin-D6 C4H5N5D6 1185166-01-1

Methiocarb-D3 C11H12D3N1O2S1 1581694-94-1

Methylprednisolon-D3 C22H27O5D3 18462-27-6 (unlabelled)

Metolachlor-D6 C15H16D6Cl1N1O2 1219803-97-0

Metolachlor-ESA D11 C15H12D11N1O5S1 947601-85-6 (unlabelled)

Metoprolol-D7 C15H18D7N1O3 1292906-91-2

Metronidazol-D4 C6H5D4N3O3 1261392-47-5

Metsulfuron-methyl-D3 C14H12D3N5O6S1 74223-64-6

Morphine-D3 C17H16D3N1O3 67293-88-3

N,N-diethyl-3-methylbenzamide-D10 C12H7D10N1O1 1215576-01-4

N4-Acetyl-Sulfamethoxazol-D5 C12H9D4N3O4S1 1215530-54-3

N4-Acetyl-Sulfathiazol-D4 C11H7D4N3O3S2 127-76-4 (unlabelled).

Naproxen-D3 C14H11O3D3 1094102-82-5

Nelfinavir-D3 C32H42D3N3O4S1 1217629-70-3

Octilinone-D17 C11H2D17N1O1S1 1185109-79-8

O-Desmethylvenlafaxin-D6 C16H19D6N1O2 1062605-69-9

Oxazepam-D5 C15H6Cl1D5N2O2 65854-78-6

Oxcarbazepine-D4 C15H8D4N2O2 1020719-71-4

Paracetamol-D4 C8H5D4N1O2 64315-36-2

Phenazon-D3 C11H9D3N2O1 342821-66-3

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Table D-1.1 (continued). List of isotope-labelled internal standards, their molecular formula and CAS number.

Name Formula CAS

Pirimicarb-D6 C11H12D6N4O2 1015854-66-6

Pravastatin-D3 C23H33D3O7 1329836-90-9

Primidon-D5 C12H9D5N2O2 73738-06-4

Prochloraz-D7 C15H9D7Cl3N3O2 67747-09-5 (unlabelled)

Propazin-D6 C9H10D6Cl1N5 1655498-05-7

Propiconazol D5 C15H12D5Cl2N3O2 1246818-14-3

Propranolol-D7 C16H14D7N1O2 98897-23-5

Ranitidin-D6 C13H16N4O3S1D6 1185238-09-8

Ritalinic acid-D10 C13H7N1O2D10 19395-41-6 (unlabelled)

Ritonavir-D6 C37H42N6O5S2D6 1217720-20-1

Simazin D5 C7H7D5Cl1N5 220621-41-0

Sotalol-D6 C12H14D6N2O3S1 1246820-85-8

Sulcotrion-D3 C14H10D3Cl1O5S1 99105-77-8

Sulfadiazine-D4 C10H6D4N4O2S1 1020719-78-1

Sulfadimethoxin-D4 C12H10D4N4O4S1 1020719-80-5

Sulfamethazine-13C6 C6[13]C6H14N4O2S1 77643-91-5

Sulfamethoxazol-D4 C10H7D4N3O3S1 1020719-86-1

Sulfapyridin-D4 C11H7D4N3O2S1 1189863-86-2

Sulfathiazol-D4 C9H5D4N3O2S2 1020719-89-4

Tebuconazole D6 C16H16D6Cl1N3O1 107534-96-3 (unlabelled)

Tebutam-D4 C15H19D4N1O1 35256-85-0

Terbutryn-D5 C10H14D5N5S1 1219804-47-3

Terbutylazin-D5 C9H11D5Cl1N5 222986-60-9

Thiamethoxam-D3 C8H7D3Cl1N5O3S1 1294048-82-0

Tramadol-D6 C16H19N1O2D6 1109217-86-8

Triclosan-D3 C12H4D3Cl3O2 1020719-98-5

Trimethoprim-D9 C14H9D9N4O3 1189460-62-5

Valsartan-13C5,15N [13]C5C19H29[15]N1N4O3 137862-53-4

Valsartan-acid-D4 C14H6D4N4O2 164265-78-5

Venlafaxin-D6 C17H21N1O2D6 1062606-12-5

Venlafaxine-N,O-didesmethyl-D3 C15H20D3N1O2 1189468-67-4

Verapamil-D6 C27H32N2O4D6 1329611-24-6

148

Table D-1.2. List of unlabelled reference standards, their molecular formula and CAS number.

Compound Formula CAS

2-Amino-6-methylbenzothiazole C8H8N2S 2536-91-6

1,3-Benzothiazole C7H5NS 95-16-9

1,5-Naphthalenedisulfonic acid C10H8O6S2 81-04-9

1-Naphthol-4-sulfonic acid C10H8O4S 84-87-7

2,3,3,3-Tetrafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)propanoic acid C6HF11O3 13252-13-6

2,6-dichlorobenzamide C7H5Cl2NO 2008-58-4

2,6-Di-tert-butylpyridine C13H21N 585-48-8

2-chloroaniline-5-sulfonic acid C6H6ClNO3S 98-36-2

4-Amino-2,5-dichlorobenzenesulfonic acid C6H5Cl2NO3S 88-50-6

4-Aminoacetanilide C8H10N2O 122-80-5

4-Dimethylaminopyridine C7H10N2 1122-58-3

4-Dodecylbenzenesulfonic acid C18H30O3S 121-65-3

4-Hydroxymetanilamide C6H8N2O3S 98-32-8

4-Toluenesulfonamide C7H9NO2S 70-55-3

5-Amino-2-chlorotoluene-4-sulfonic acid C7H8ClNO3S 88-53-9

Acesulfame C4H5NO4S 55589-62-3

Acetanilide C8H9NO 103-84-4

Amrinone C10H9N3O 60719-84-8

Atrazine-desethyl-2-hydroxy C6H11N5O 19988-24-0

Benzoguanamine C9H9N5 91-76-9

Caffeine C8H10N4O2 58-08-2

Camphorsulfonic acid C10H16O4S 3144-16-9

Carbendazim C9H9N3O2 10605-21-7

Chloridazon C10H8ClN3O 58858-18-7

Chlortoluron C10H13ClN2O 15545-48-9

Dibutyl phosphate C8H19O4P 107-66-4

Diphenylphosphinic acid C12H11O2P 1707-03-5

Diuron-desmethyl C8H8Cl2N2O 3567-62-2

Gestageno C21H30O3 68-96-2

Lamotrigine C9H7Cl2N5 84057-84-1

Metamitron-desamino C10H9N3O 36993-94-9

Methylisothiazolinone C4H5NOS 2682-20-4

149

Table D-1.2 (continued). List of unlabelled reference standards, their molecular formula and CAS number.

Compound Formula CAS

Mexiletine C11H17NO 31828-71-4

Monobenzone C13H12O2 103-16-2

Naphthionic acid C10H9NO3S 130-13-2

O,O-Diethyl thiophosphate C4H11O3PS 5871-17-0

Pargyline C11H13N 306-07-0

p-Toluidine-m-sulfonic acid C7H9NO3S 88-44-8

Simazine-2-hydroxy C7H13N50 2599-113

Tetramethylsulfamide C4H12N2O2S 3768-63-6

Tributyl phosphate C12H27O4P 126-73-8

Zearalenol C18H24O5 36455-72-8

150

D-2: Description of the riverbank filtration site

The studied well fields of the drinking water company Oasen (Gouda, The Netherlands) are situated next to the river Lek, a tributary of the river Rhine in the Netherlands. The wells abstract groundwater from sandy deposits of Pleistocene age at a depth of 10-30 meters below surface level. The aquifer is protected by an overlying aquitard of about 10 meters of peat and clay from the Holocene. The well fields have been operated since 1969. Groundwater abstraction rate from 1969 to 1974 was 1–1.5 million m3/year. From 1975 onward the abstraction rate varied between 1.7–2.3 million m3/year. Various research (1) reveals that, based on the hydro-chemical tracers in this water, the abstracted groundwater originates from the river Lek. Isotope research from 2006 (2) reveals that 93-100% of the abstracted water of well fields Lekkerkerk-Schuwacht and Lekkerkerk-Tiendweg is in fact infiltrated river water from the Rhine. The natural purification processes during the aquifer passage of this river bank improves the water quality considerably. However, the most persistent and mobile pollutants are not fully removed (3,4) which leads to the necessity of advanced purification treatment steps in the drinking water utility like activated carbon or reverse osmosis. The age of the abstracted riverbank filtration groundwater varies with the distance to the river and the hydrogeological conditions and was derived with calibrated hydrogeological groundwater flow models (Figure D-2.1). The Lekkerkerk-Schuwacht wells were built at a distance between 70 and 180 m from the riverbank, allowing abstraction of water with an age between 3 months and 10 years (Mean value: 2 years). As a result, the abstracted river bank groundwater of Lekkerkerk-Schuwacht reflects the relatively recent river water quality. From this well field, the youngest bank filtrate (mean travel time = 1 year) was sampled and considered for this screening study. The Lekkerkerk-Tiendweg wells were built on a transect perpendicular to the riverbank, place between 950 and 1,800 m from the Lek. The age of the water from well field Lekkerkerk-Tiendweg varies between 6 years and > 100 years (Mean value: 20 years), allowing abstraction of groundwater reflecting river water quality from the past decades, when the Lek was much more polluted than nowadays, e.g. from the 1950s to 1980s. Fortunately, thanks to strict regulations and international co-operation, the quality of the river water has improved significantly since 1970 (5). This can be observed from the individual wells of which the samples are taken in well field Lekkerkerk-Tiendweg, that are perpendicularly oriented towards the river. These wells abstract the complete range of historical river water quality from the pre-industrial period (> 100 years old) for the wells that are situated at the greatest distance to the river, to relatively recent river water (at least 6 years old) for the wells closest to the river. The wells in between include the period of maximum river water pollution (30-60 years old).

151

References

1. Stuyfzand, P. J.: KIWA mededeling 89 Drinkwater uit oevergrondwater, Anorganische bestanddelen, 1985;

2. Timmer H., Aandeel oevergrondwater berekend via zuurstof-18 isotopen onderzoek. Internal document Oasen, 2006;

3. Stuyfzand, P.J., 1989. Hydrology and water-quality aspects of Rhine bank groundwater in the Netherlands. J. Hydrol. 106, 341–363;

4. Hamann et al, 2016 The fate of organic micropollutants during long-term/long-distance river bank filtration;

5. http://news.bbc.co.uk/2/hi/europe/1371142.stm (accessed on May 15th 2019) Figure D-2.1. Map of well fields site showing estimated flow lines and modelled travel time.

152

D-3: Additional details on the HRMS systems

D-3.1. Quadrupole-Orbitrap data-dependent acquisition settings Full-scan HRMS1 spectra were acquired at a scan rate of 1Hz for masses ranging from m/z 100 to 1,000 and with a resolving power of 140,000 at m/z 200. HRMS2 spectra were acquired for the five most intense ions detected in each cycle of full-scan HRMS1 for masses ranging from m/z 200 to 2000 and with a resolving power of 17,500. A dynamic exclusion window of 8 seconds was set, i.e. a mass would be temporarily placed on an exclusion list for 8 seconds to ensure that one ion would not dominate all HRMS2 scans. The automatic gain control (AGC) target was set at 200,000 and maximum injection time was 100 milliseconds. For fragmentation, stepped normalised collision energies (NCE) ranging from 15% to 90% were derived from the m/z value of the non-target ions.

D-3.2 UHPLC-ESI-q-TOF/HRMS settings The analyses to confirm the identities of the prioritised non-target features were conducted with a UHPLC system (Nexera, Shimadzu, Den Bosch, The Netherlands) coupled to a Bruker Daltonics maXis 4G high resolution q-ToF/MS upgraded with HD collision cell and equipped with an ESI source (Wormer, The Netherlands). The chromatographic conditions were identical to those described in the manuscript (“LC-HRMS analysis” in Chapter 5.2). The MS detector was internally calibrated before starting an analysis batch and additionally prior to any injection. This was achieved by infusing a 50 μM sodium formate solution in H2O:MeOH (1:1, v/v) with a loop injection of 20 μL and a loop rinse of 20 μL. Positive and negative ESI were achieved in separate runs by acquiring HRMS1 spectra for masses ranging from m/z 50 to 1,000 with a resolving power of 30,000–60,000 at full width at half maximum (FWHM) and with a spray voltage of +3.5 kV and −3.5 kV for positive and negative mode, respectively. The capillary temperature was 300 °C. HRMS2 spectra were recorded in data-dependent acquisition mode (AutoMSMS) with a resolving power of at least 20,000 FWHM.

153

D-4: enviMass settings (version 3.4)

Peak Picking* Extraction of ion chromatograms

o Maximum retention time gap in an EIC (sec): 180 o Maximum m/z deviation of centroid data point from its EIC mean

(ppm): 5 Peak picking

o Minimum number of centroid data points per peak … : 5 o … within a given RT window (sec): 20 o Maximum RT gap to length to be interpolated (sec): 10 o Maximum RT width of a single peak (sec): 120 o Minimum log10(intensity) threshold: 5 o Minimum Signal/Noise: 5 o Minimum Signal/Base: 2 o Maximum possible number of peaks within a single EIC: 3 o Peak intensity: use peak area or peak intensoid? - Intensoid (max

int.) o Peak mass definition: Mean

Advanced settings o Upper log10(intensity) safety threshold: 7 o How often can a peak detection fail to end the recursion? – peak

picking: 2 o Weight for assigning centroid data points to a peak - peak picking:

1 o Percentage of low-intense data points to discard: 0

Instrument / Resolution o 180 Q-Exactive, ExactivePlus/R140000@200

Mass recalibration Positive / Negative ionisation

o Include mass recalibration for positive/negative ion. mode files? - Yes

o Reference compounds - Internal standards o Maximum allowable m/z correction (ppm): 10 o Maximum m/z deviation of centroid data point from its EIC mean

(ppm): 5 o RT tolerance (sec): 30

Replicates o m/z tolerance (ppm): 5 o RT tolerance window of peaks caused by the same analyte

across replicate samples (sec): 30 o Absolute log intensity tolerance X: 5

Screening (Internal Standards)

154

o RT tolerance of peaks relative to their expected RT (sec): 30 o RT tolerance of peaks in an isotopic pattern: (sec): 10 o m/z tolerance (ppm): 5 o Intensity tolerance (%): 30 o Lower intensity threshold: 50000 o Restrict screening to latest files? - FALSE o Cut-off score: 0.8 o Exclude matches below cut-off – FALSE

Normalization

o Include normalization for positive/negative ion. mode files? - Yes o Minimum of screened files covered by each IS profile? (%): 60 o Screening threshold: 0.8 o Minimum number of IS profile peaks: 50 o Use subsampling? – Yes o Number of blank/blind profiles subsample: 100 o Number of sample profiles in subsample:100

Profiling o Maximum number of newest samples to be processed per ion

mode: 100 o Peak mass deviation within profiles: 5 ppm o Peak deviation within profiles: RT tolerance (sec): 30 o Minimum number of IS profile peaks: 50

* Detailed description of the peak picking parameters can be found at the following URL: https://www.envibee.ch/eng/enviMass/topics/peakpicking.htm (Accessed on May 13th 2019)

155

D-5: Overview on detection of the isotope-labelled standards

The masses of the 128 isotope-labelled standards (IS) were screened in all samples in both positive and negative ESI mode the enviMass settings indicated in section D-4. In positive ionisation mode, 75 standards were detected in all samples, whereas in negative mode these were 43. Out of 128 IS, 25 could be ionised in both modes and were found in all samples, whereas 21 were not detected in either positive or negative ionisation mode, likely due to insufficient enrichment. In Figures D-5.1 and Figure D-5.2 the mass deviation and intensity distribution of the IS compounds is shown, along with information on the completeness of isotopic peaks detection (cut-off score = 0.8). In the positive ionisation mode data (Figure D-5.1) it can be seen that overall higher mass accuracy and intensities were obtained, compared to negative data (Figure D-5.2).

156

Figure D-5.1. Log-intensity (x-axis) and m/z deviation (ppm) (y-axis) of the isotope-labelled standards screened in positive ESI mode

Figure D-5.2. Log-intensity (x-axis) and m/z deviation (ppm) (y-axis) of the isotope-labelled standards screened in negative ESI mode

157

D-6: Overview on the data from the 15 most populated clusters

Table D-6.1. Feature population of the prioritised clusters in positive ESI mode, number of prioritised features and associated HRMS2 data.

Top populated cluster

Features in cluster

Intensity-prioritised

Triggered HRMS2

1 974 487 158

2 834 417 111

3 804 402 188

4 763 382 83

5 728 364 84

6 682 341 148

7 632 316 77

8 514 257 78

9 466 233 74

10 238 119 98

11 231 116 45

12 190 95 60

13 185 93 60

14 160 80 31

15 124 62 53

158

Table D-6.2. Feature population of the prioritised clusters in negative ESI mode, number of prioritised features and associated HRMS2 data.

Top populated cluster

Features in cluster

Intensity-prioritised

Triggered HRMS2

1 651 488 89

2 640 480 98

3 473 355 82

4 460 345 64

5 454 341 76

6 427 320 73

7 426 320 90

8 415 311 69

9 398 299 71

10 286 215 100

11 129 97 49

12 106 80 46

13 100 75 20

14 96 72 34

15 62 47 22

159

D-7: Lists of (tentatively) identified substances

Table D-7.1. Details of (tentatively) identified substances prioritised from positive data.

Compound Formula Neutral mass InChIKey

Score (rank) a Suspect

Ident. Level b

RT (min) In well

Methylisothiazolinone C4H5NOS 115.0092 BEGLCMHJXHIJLR-UHFFFAOYSA-N 5.4 Yes 1 2.5

LS-P12 to LT-P11

2-Pyridylethylamine C7H10N2 122.0844 XPQIPUZPSLAZDV-UHFFFAOYSA-N 3.5 (7th) No 3 7.8 All

4,6-Dimethylpyrimidin-2-ol C6H8N2O 124.0637 WHEQVHAIRSPYDK-UHFFFAOYSA-N 4.2 Yes 3 4.4

LS-P12 to LT-P14

5,5-Dimethylhydantoin C5H8N2O2 128.0586 YIROYDNZEPTFOL-UHFFFAOYSA-N 5.2 Yes 3 4.3

LT-P5 to LT-P11

3-Methyladenine C6H7N5 149.0701 FSASIHFSFGAIJM-UHFFFAOYSA-N 5 Yes 3 3.2 LS-P12

Pheniprazine C9H14N2 150.1157 VXTWEDPZMSVFEF-UHFFFAOYSA-N 4.6 No 3 6.1 LT-P7

Norephedrine C9H13NO 151.0997 DLNKOYKMWOXYQA-CBAPKCEASA-N 5 Yes 3 5.5 LS-P12

Tetramethylsulfamide C4H12N2O2S 152.062 WIOVVBRSQYYSMV-UHFFFAOYSA-N 4.4 No 1 4.8 All

4-Phenyl-1,2,3,6-tetrahydropyridine C11H13N 159.1048

OMPXTQYWYRWWPH-UHFFFAOYSA-N 4.3 No 3 4.1

LS-P12 to LT-P09

3-Hydroxy-2,2-dimethyl-N-propylpropanamide C8H17NO2 159.1259

PLXAWTRZKLDSEF-UHFFFAOYSA-N 2.6 (5th) Yes 3 9.6

LS-P12 to LT-P11

Methyl cinnamate C10H10O2 162.0681 CCRCUPLGCSFEDV-BQYQJAHWSA-N 5.4 Yes 3 7.5 LT-P09

Atrazine-desethyl-2-hydroxy C6H11N5O 169.0964 GCKLGRUZDXSATG-UHFFFAOYSA-N 4.4 (2nd) Yes 1 3.1

LS-P12 to LT-P11

5,6-Diamino-1,3-Dimethyluracil C6H10N4O2 170.0804 BGQNOPFTJROKJE-UHFFFAOYSA-N 4.5 No 3 4.5

LT-P05 to LT-P14

4-Toluenesulfonamide C7H9NO2S 171.0354 LMYRWZFENFIFIT-UHFFFAOYSA-N 6.2 Yes 1 5.6 All

Ethanal tetramer C8H16O4 176.1049 GKKDCARASOJPNG-UHFFFAOYSA-N 4.8 Yes 3 4.5 All

160

Table D-7.1 (continued). Details of (tentatively) identified substances prioritised from positive data.

Compound Formula Neutral mass InChIKey

Score (rank) a Suspect

Ident. Level b

RT (min) In well

Methylephedrine C11H17NO 179.131 FMCGSUUBYTWNDP-ONGXEEELSA-N 3.5 (2nd) Yes 3 4.5

LS-P12 to LT-P01

Simazine-2-hydroxy C7H13N5O 183.1120 YQIXRXMOJFQVBV-UHFFFAOYSA-N 4.7 Yes 1 4.6 LS-P12

Metamitron-desamino C10H9N3O 187.0746 OUSYWCQYMPDAEO-UHFFFAOYSA-N 3.3 (2nd) Yes 1 6

LS-P12 to LT-P14

4-hydroxymetanilamide C6H8N2O3S 188.0256 AVQFHKYAVVQYQO-UHFFFAOYSA-N 5.5 Yes 3 6.1

LT-P03 to LT-P11

2,6-dichlorobenzamide C7H5Cl2NO 188.9748 JHSPCUHPSIUQRB-UHFFFAOYSA-N 6.6 Yes 1 5 All

Carbendazim C9H9N3O2 191.0695 TWFZGCMQGLPBSX-UHFFFAOYSA-N 6.7 Yes 1 4.8

LS-P12 to LT-P11

N,N-Dipropyl-1,4-benzenediamine C12H20N2 192.1626

UOWYGPTYSRURDP-UHFFFAOYSA-N 2.3 (8th) No 3 4.2 LS-P12

Isophthalohydrazide C8H10N4O2 194.0804 UTTHLMXOSUFZCQ-UHFFFAOYSA-N 2.1 (3rd) No 3 3.1

LS-P12 to LT-P09

Ethyl tosylamide C9H13NO2S 199.0667 OHPZPBNDOVQJMH-UHFFFAOYSA-N 4.5 Yes 3 8.3

LS-P12 to LT-P11

3-phenoxybenzylalcohol C13H12O2 200.0837 KGANAERDZBAECK-UHFFFAOYSA-N 4.6(2nd) Yes 3 8.1

LT-P09 to LT-P14

2,4-Dimethylaniline-6-sulfonic acid C8H11NO3S 201.0460

CFCXQQUQLZIZPI-UHFFFAOYSA-N 4.3 Yes 3 4.4

LT-P01 to LT-P09

Dicyclohexylcarbodiimide C13H22N2 206.1783 QOSSAOTZNIDXMA-UHFFFAOYSA-N 5.7 Yes 3 8.4 LS-P12

Chlortoluron C10H13ClN2O 212.0716 JXCGFZXSOMJFOA-UHFFFAOYSA-N 5.5 Yes 1 9.1

LS-P12 to LT-P07

Mesitylsulfonylhydroxylamine C9H13NO3S 215.0616 CHKQALUEEULCPZ-UHFFFAOYSA-N 4.3 No 3 3.1 LT-P05

Diethyl acetamidomalonate C9H15NO5 217.0950 ISOLMABRZPQKOV-UHFFFAOYSA-N 5.5 No 3 4.3 LT-P09

161

Table D-7.1 (continued). Details of (tentatively) identified substances prioritised from positive data.

Compound Formula Neutral mass InChIKey

Score

(rank) a Suspect

Ident. Level b

RT

(min) In well

Diuron-desmethyl C8H8Cl2N2O 218.0014 IDQHRQQSSQDLTR-UHFFFAOYSA-N 6.6 Yes 1 9.8 LS-P12

Diphenylphosphinic acid C12H11O2P 218.0497 BEQVQKJCLJBTKZ-UHFFFAOYSA-N 6.4 Yes 1 7.7

LS-P12 to LT-P14

5-Amino-2-chlorotoluene-4-sulfonic acid C7H8ClNO3S 220.9913

VYZCFAPUHSSYCC-UHFFFAOYSA-N 4 Yes 1 5.4

LT-P01 to LT-P18

Chloridazon C10H8ClN3O 221.0356 WYKYKTKDBLFHCY-UHFFFAOYSA-N 5.9 Yes 1 6

LS-P12 to LT-P09

Naphthionic acid C10H9NO3S 223.0303 NRZRRZAVMCAKEP-UHFFFAOYSA-N 5.7 Yes 1 2 All

1,6-Hexanediyl bisacrylate C12H18O4 226.1205 FIHBHSQYSYVZQE-UHFFFAOYSA-N 4.1 Yes 3 9.2

LT-P03 to LT-P05

Ozagrel C13H12N2O2 228.0899 SHZKQBHERIJWAO-AATRIKPKSA-N 4.4 No 3 8.1 LT-P09

Ethyl 2-sulfamoylbenzoate C9H11NO4S 229.0409 CYFKZTWSLPKROH-UHFFFAOYSA-N 3.9 (2nd) No 3 6.4 LT-P09

Lamotrigine C9H7Cl2N5 255.0079 PYZRQGJRPPTADH-UHFFFAOYSA-N 6.8 Yes 1 6.7 LS-P12

1,3,5-Triazin-2-ol, 4,6-di-4-morpholinyl- C11H17N5O3 267.1331

ITWXPXDYRAKSPM-UHFFFAOYSA-N 3.2 (2nd) Yes 3 5.3 LS-P12

Retinal 2 C20H26O 282.1984 QHNVWXUULMZJKD-OVSJKPMPSA-N 5.3 No 3 13.2

LS-P12 to LT-P11

3'-O-Acetylthymidine C12H16N2O6 284.1008 IRFKBRPHBYCMQU-IVZWLZJFSA-N 4.3 No 3 15.3 LT-P09

2,4,6-Tri(2-pyridinyl)-1,3,5-triazine C18H12N6 312.1123

KMVWNDHKTPHDMT-UHFFFAOYSA-N 5.1 No 3 14 LT-P11

Zearalanone C18H24O5 320.1624 APJDQUGPCJRQRJ-LBPRGKRZSA-N 6.4 Yes 3 8.4

LS-P12 to LT-P11

21-Hydroxypregn-4-ene-3,20-dione C21H30O3 330.2195

ZESRJSPZRDMNHY-UHFFFAOYSA-N 3.9 (3rd) Yes 3 17

LS-P12 to LT-P11

a Candidate structure rank shown in parenthesis if different than “1st”; b Identification Confidence Level proposed by Schymanski et al. (2014a).

162

Table D-7.2. Details of the (tentatively) identified substances prioritised from negative ESI data.

Compound Formula Neutral mass InChIKey

Score (rank) a Suspect

Ident. Level b RT (min) In well

1-Naphthol-4-sulfonic acid C10H8O4S 224.0143 HGWQOFDAUWCQDA-UHFFFAOYSA-N

6.9 Yes 3 6.5 All

1-naphthylamine-5-sulfonic acid

C10H9NO3S 223.0303 DQNAQOYOSRJXFZ-UHFFFAOYSA-N

5.6 (2nd) No 3 5.5 LS-P12 to LT-P05

2,3-Diisobutyl-1-naphthalenesulfonic acid

C18H24O3S 320.1446 KBLAMUYRMZPYLS-UHFFFAOYSA-N

3.5 Yes 3 14.2 All

2-Chloroaniline-5-sulfonic acid

C6H6ClNO3S 206.9757 XJQRCFRVWZHIPN-UHFFFAOYSA-N

5.7 No 3 4.4 All

2-Napthol-6-sulfonic acid C10H8O4S 224.0143 VVPHSMHEYVOVLH-UHFFFAOYSA-N

6.0 (2nd) Yes 3 5.1 All

3-[4-(Methoxycarbonyl)phenyl]-1-propanesulfonic acid

C11H14O5S 258.0562 OFGLQSUWZJEIHY-UHFFFAOYSA-N

2.8 (2nd) No 3 5.3 All

4-Amino-2,5-dichlorobenzenesulfonic acid

C6H5Cl2NO3S 240.9367 SJCTXIKOXTUQHC-UHFFFAOYSA-N

5.9 Yes 1 2.6 All

4-Decylbenzenesulfonic acid

C16H26O3S 298.1603 UASQKKHYUPBQJR-UHFFFAOYSA-N

3.8 No 3 16 All

4-Dodecylbenzenesulfonic acid

C18H30O3S 326.1916 KWXICGTUELOLSQ-UHFFFAOYSA-N

6.7 Yes 1 17.3 All

4-Isopropylbenzenesulfonic acid

C9H12O3S 200.0507 CVLHGLWXLDOELD-UHFFFAOYSA-N

3.4 (3rd) No 3 12.4 LT-P09

5-(2-Pyridinyl)-1H-pyrazole-3-carboxylic acid

C9H7N3O2 189.0538 SJBWHTBPIJXUFP-UHFFFAOYSA-N

3.8 No 3 5.1 All

5-Amino-2-chlorotoluene-4-sulfonic Acid

C7H8ClNO3S 220.9913 VYZCFAPUHSSYCC-UHFFFAOYSA-N

4 Yes 1 5.4 LT-P01 to LT-P18

163

Table D-7.2 (continued). Details of the (tentatively) identified substances prioritised from negative ESI data.

Compound Formula Neutral mass InChIKey

Score (rank) a Suspect

Ident. Level b

RT (min) In well

5-Isopropyl-2-methylbenzenesulfonic acid

C10H14O3S 214.0664 OORBHYFTTSLSRU-UHFFFAOYSA-N

3 No 3 7.5 LT-P09

6,7-Dihydroxy-2-naphthalenesulfonic acid

C10H8O5S 240.0092 DKJVSIITPZVTRO-UHFFFAOYSA-N

3.3 No 3 4.1 LS-P12 to LT-P14

Acesulfame C4H5NO4S 162.9939 YGCFIWIQZPHFLU-UHFFFAOYSA-N

6.8 Yes 1 2.5 LS-P12 to LT-P11

Bayer's Acid C10H8O4S 224.0143 HUYJTJXLNBOVFO-UHFFFAOYSA-N

4.3 (3rd) Yes 3 3.4 All

Camphorsulfonic acid C10H16O4S 232.0769 MIOPJNTWMNEORI-UHFFFAOYSA-N

4.4 No 1 5 LT-P03 to LT-P18

Dibutyl phosphate C8H19O4P 210.1021 JYFHYPJRHGVZDY-UHFFFAOYSA-N

6.7 Yes 1 8.8 LS-P12 to LT-P14

Diphenylphosphinic acid C12H11O2P 218.0497 BEQVQKJCLJBTKZ-UHFFFAOYSA-N

6.4 Yes 1 7.7 LS-P12 to LT-P14

Ebert-Merz a-Acid C10H8O6S2 287.9762 VILFVXYKHXVYAB-UHFFFAOYSA-N

5 (2nd) Yes 3 3.2 LT-P05 to LT-P18

Ethyl 2-sulfamoylbenzoate C9H11NO4S 229.0409 CYFKZTWSLPKROH-UHFFFAOYSA-N

3.9 (2nd) No 3 6.5 LT-P01 to LT-P18

Naphthionic acid C10H9NO3S 223.0303 NRZRRZAVMCAKEP-UHFFFAOYSA-N

6.2 Yes 1 2 All

O,O-Diethyl thiophosphate C4H11O3PS 170.0166 PKUWKAXTAVNIJR-UHFFFAOYSA-N

4.2 No 1 3.6 LT-P01 to LT-P18

p-Toluenesulfonic acid C7H8O3S 172.0194 JOXIMZWYDAKGHI-UHFFFAOYSA-N

5.9 Yes 1 6.4 LT-P01

164

Table D-7.2 (continued). Details of the (tentatively) identified substances prioritised from negative ESI data.

Compound Formula Neutral mass InChIKey

Score (rank) a

Suspect

Ident. Level b

RT (min) In well

p-Toluidine-m-sulfonic acid C7H9NO3S 187.0303 BRKFTWHPLMMNHF-UHFFFAOYSA-N

5.5 Yes 1 3.3 LT-P09

Tributyl phosphate C12H27O4P 266.1647 STCOOQWBFONSKY-UHFFFAOYSA-N

5.7 Yes 1 14.9 All

Zearalanone C18H24O5 320.1624 APJDQUGPCJRQRJ-LBPRGKRZSA-N

6.4 Yes 3 8.4 LS-P12 to LT-P11

a Candidate structure rank shown in parenthesis if different than “1st”; b Identification Confidence Level proposed by Schymanski et al. (2014a).