Alleviating the reference standard dilemma using a systematic exact mass suspect screening approach with liquid chromatography-high resolution mass spectrometry

Nontarget analysis and characterization of p-phenylenediamine-quinones and -phenols in tire rubbers by LC-HRMS and chemical species-specific algorithm

BACKGROUND

N,N'-disubstituted p-phenylenediamine-quinones (PPDQs) are oxidization derivatives of p-phenylenediamines (PPDs) and have raised extensive concerns recently, due to their toxicities and prevalence in the environment, particularly in water environment. PPDQs are derived from tire rubbers, in which other PPD oxidization products besides reported PPDQs may also exist, e.g., unknown PPDQs and PPD-phenols (PPDPs).

RESULTS

This study implemented nontarget analysis and profiling for PPDQ/Ps in aged tire rubbers using liquid chromatography-high-resolution mass spectrometry and a species-specific algorithm. The algorithm took into account the ionization behaviors of PPDQ/Ps in both positive and negative electrospray ionization, and their specific carbon isotopologue distributions. A total of 47 formulas of PPDQ/Ps were found and elucidated with tentative or accurate structures, including 25 PPDQs, 18 PPDPs and 4 PPD-hydroxy-quinones (PPDHQs). The semiquantified total concentrations of PPDQ/Ps were 14.08-30.62 μg/g, and the concentrations followed the order as: PPDPs (6.48-17.39) > PPDQs (5.86-12.14) > PPDHQs (0.16-1.35 μg/g).

SIGNIFICANCE

The high concentrations and potential toxicities indicate that these PPDQ/Ps could seriously threaten the eco-environment, as they may finally enter the environment, accordingly requiring further investigation. The analysis strategy and data-processing algorithm can be extended to nontarget analysis for other zwitterionic pollutants, and the analysis results provide new understandings on the environmental occurrence of PPDQ/Ps from source and overall perspectives.

Pesticides in wastewater treatment plant effluents in the Yeongsan River Basin, Korea: Occurrence and environmental risk assessment

Pesticides are among the main drivers posing risks to aquatic environments, with effluents from wastewater treatment plants (WWTPs) serving as a major source. This study aimed to identify the primary pesticides for which there was a risk of release into aquatic environments through WWTP effluents, thereby enabling more effective contamination management in public water bodies. In this study, monitoring, risk assessment, and risk-based prioritization of 87 pesticides in effluents from three WWTPs in the Yeongsan River Basin, Korea, were conducted. A total of 59 pesticides were detected at concentrations from 0.852 ng/L to 82.044 μg/L and exhibited variable patterns across different WWTP locations. An environmental risk assessment based on the risk quotient (RQ) of individual pesticides identified 13 substances implicated in significant ecotoxicological risks, as they exceeded RQ values of 1 at least once. An optimized risk (RQf)-based prioritization, considering the frequency of the measured environmental concentration (MEC) exceeding the predicted environmental concentration (PNEC), was conducted to identify pesticides that potentially posed risks and thus should be managed as a priority. Four pesticides had an RQf value >1; metribuzin exhibited the highest RQf value of 4.951, followed by 3-phenoxybenzoic acid, atrazin-2-hydroxy, and atrazine. Additionally, five pesticides (terbuthylazine, methabenzthiazuron, diuron, thiacloprid, and fipronil) and another four pesticides (propazine, imidacloprid, hexaconazole, and hexazione) had RQf values >0.1 and > 0.01, respectively. By calculating the contributions of individual pesticides to the RQf of these mixtures (RQf, mix) based on the concentration addition model, it was determined that >95 % of the sum of RQf, mix was driven by the top seven pesticides. These findings highlight the importance of prioritizing pesticides for effective management of contamination sources.

Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

How Wastewater Reflects Human Metabolism─Suspect Screening of Pharmaceutical Metabolites in Wastewater Influent

Pharmaceuticals and their human metabolites are contaminants of emerging concern in the aquatic environment. Most monitoring studies focus on a limited set of parent compounds and even fewer metabolites. However, more than 50% of the most consumed pharmaceuticals are excreted in higher amounts as metabolites than as parents, as confirmed by a literature analysis within this study. Hence, we applied a wide-scope suspect screening approach to identify human pharmaceutical metabolites in wastewater influent from three Swiss treatment plants. Based on consumption amounts and human metabolism data, a suspect list comprising 268 parent compounds and over 1500 metabolites was compiled. Online solid phase extraction combined with liquid chromatography coupled to high-resolution tandem mass spectrometry was used to analyze the samples. Data processing, annotation, and structure elucidation were achieved with various tools, including molecular networking as well as SIRIUS/CSI:FingerID and MetFrag for MS2 spectra rationalization. We confirmed 37 metabolites with reference standards and 16 by human liver S9 incubation experiments. More than 25 metabolites were detected for the first time in influent wastewater. Semiquantification with MS2Quant showed that metabolite to parent concentration ratios were generally lower compared to literature expectations, probably due to further metabolite transformation in the sewer system or limitations in the metabolite detection. Nonetheless, metabolites pose a large fraction to the total pharmaceutical contribution in wastewater, highlighting the need for metabolite inclusion in chemical risk assessment.

Occurrence and risk assessment of pesticides and their transformation products related to olive groves in surface waters of the Guadalquivir river basin

Pesticides are considered one of the main sources of contamination of surface waters, especially in rural areas highly influenced by traditional agricultural practices. The objective of this work was to evaluate the impact caused by pesticides and their transformation products (TPs) related to olive groves in surface waters with strong agricultural pressure. 11 streams were monitored during four sampling campaigns over 2 years. A solid-phase extraction, followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) analysis was used in the quantitative target approach, with more than 70 validated compounds. Target method was combined with a suspect screening strategy involving more than 500 pesticides and TPs, using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) to identify additional pesticides and TPs out of the scope of analysis. A total of 43 different compounds were detected with the target method. The herbicide MCPA was present in all samples and at the highest concentration (1260 ng L-1), followed by the fungicide carbendazim (1110 ng L-1), and the herbicide chlorotoluron (706 ng L-1). The suspect screening strategy revealed the presence of 7 compounds out of the target analysis (1 pesticide and 6 TPs). 6 analytes were confirmed with the analytical standards. Semi-quantification results revealed that TPs exhibited higher concentrations than their corresponding parent compounds, indicating higher persistency. Some small streams showed a comparable number of pesticides and concentrations to the most polluted large river. The determined pesticide and TPs concentrations represented an estimated environmental hazard in almost all sampling sites under study. This work underscores the importance of including pesticide TPs and small streams impacted by extensive agricultural activities in water quality monitoring programs.

Linking chemical surface water monitoring and pesticide regulation in selected European countries

The progress in chemical analytics and understanding of pesticide dynamics in surface waters allows establishing robust data on compounds with frequent exceedances of quality standards. The current chemical, temporal, and spatial coverage of the pesticide monitoring campaigns differs strongly between European countries. A questionnaire revealed differences in monitoring strategies in seven selected European countries; Nordic countries prioritize temporal coverage, while others focus on spatial coverage. Chemical coverage has increased, especially for non-polar classes like synthetic pyrethroids. Sweden combines monitoring data with agricultural practices for derived quantities, while the Netherlands emphasizes spatial coverage to trace contamination sources. None of the EU member states currently has established a process for linking chemical surface water monitoring data with regulatory risk assessment, while Switzerland has recently established a legally defined feedback loop. Due to their design and objectives, most strategies do not capture concentration peaks, especially 2-week composite samples, but also grab samples. Nevertheless, for substances that appear problematic in many data sets, the need for action is evident even without harmonization of monitoring programs. Harmonization would be beneficial, however, for cross-national assessment including risk reduction measures.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Non-target analysis for water characterization: wastewater treatment impact and selection of relevant features

Non-target analyses were conducted to characterize and compare the molecular profiles (UHPLC-HRMS fingerprint) of water samples from a wastewater treatment plant (WWTP). Inlet and outlet samples were collected from three campaigns spaced 6 months apart in order to highlight common trends. A significant impact of the treatment on the sample fingerprints was shown, with a 65-70% abatement of the number of features detected in the effluent, and more polar, smaller and less intense molecules found overall compared to those in WWTP influent waters. Multivariate analysis (PCA) associated with variations of the features between inlets and outlets showed that features appearing or increasing were correlated with effluents while those disappearing or decreasing were correlated with influents. Finally, effluent features considered as relevant to a potentially adverse effect on aqueous media (i.e. those which appeared or increased or slightly varied from the influent) were highlighted. Three hundred seventy-five features common with the 3 campaigns were thus selected and further characterized. For most of them, elementary composition was found to be C, H, N, O (42%) and C, H, N, O, P (18%). Considering the MS2 spectra and several reference MS2 databases, annotations were proposed for 35 of these relevant features. They include synthetic products, pharmaceuticals and metabolites.

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Nontarget Analysis and Comprehensive Characterization of Iodinated Polyfluoroalkyl Acids in Wastewater and River Water by LC-HRMS with Cascade Precursor-Ion Exclusions and Algorithmic Approach

Poly- and perfluoroalkyl acids (PFAAs) are a large family of widespread contaminants of worldwide concern and well-known as "forever chemicals". Direct emission of PFAAs from the fluorochemical industry is a crucial source of PFAA pollutants in the environment. This study implemented nontarget analysis and comprehensive characterization for a category of new PFAA contaminants, i.e., iodinated PFAAs (IPFAAs), in fluorochemical industry wastewater and relevant contaminated river water by liquid chromatography-high-resolution mass spectrometry with a cascade precursor ion exclusion (PIE) strategy and in-house developed data extraction and processing algorithms. A total of 26 IPFAAs (including 2 isomers of an IPFAA) were found and identified with tentative molecular structures. Semiquantification of the IPFAAs was implemented, and the total concentrations of IPFAAs were 0.16-285.52 and 0.15-0.17 μg/L in wastewater and river water, respectively. The high concentrations in association with the predicted ecotoxicities and environmental behaviors demonstrate that these IPFAAs are worthy of more concern and further in-depth research. The cascade PIE strategy along with the data extraction and processing algorithms can be extended to nontarget analysis for other pollutants beyond IPFAAs. The nontarget identification and characterization outcomes provide new understanding on the environmental occurrence and pollution status of IPFAAs from a comprehensive perspective.

Nontarget Discovery of Antimicrobial Transformation Products in Wastewater Based on Molecular Networks

Antimicrobial transformation products (ATPs) in the environment have raised extensive concerns in recent years due to their potential health risks. However, only a few ATPs have been investigated, and most of the transformation pathways of antimicrobials have not been completely elucidated. In this study, we developed a nontarget screening strategy based on molecular networks to detect and identify ATPs in pharmaceutical wastewater. We identified 52 antimicrobials and 49 transformation products (TPs) with a confidence level of three or above. Thirty of the TPs had not been previously reported in the environment. We assessed whether TPs could be classified as persistent, mobile, and toxic (PMT) substances based on recent European criteria for industrial substances. Owing to poor experimental data, definitive PMT classifications could not be established for novel ATPs. PMT assessment based on structurally predictive physicochemical properties revealed that 47 TPs were potential PMT substances. These results provide evidence that novel ATPs should be the focus of future research.

Untargeted metabolomic analysis to explore the impact of soil amendments in a non-conventional wastewater treatment

As non-conventional wastewater treatment, vegetation filters make the most of the natural attenuation processes that occur in soil to remove contaminants, while providing several environmental benefits. However, this practice may introduce contaminants of emerging concern (CECs) and their transformation products (TPs) into the environment. A potential improvement to the system was tested using column experiments containing soil (S) and soil amended with woodchips (SW) or biochar (SB) irrigated with synthetic wastewater that included 11 selected CECs. This study evaluated: i) known CECs attenuation and ii) unknown metabolites formation. Known CECs attenuation was assessed by total mass balance by considering both water and soil media. An untargeted metabolomic strategy was developed to assess the formation of unknown metabolites and to identify them in water samples. The results indicated that SB enhanced CECs attenuation and led to the formation of fewer metabolites. Sorption and biodegradation processes were favored by the bigger surface area of particles in SB column, especially for compounds with negative charges. Incorporating woodchips into soil shortened retention times in the column, which reduced attenuation phenomena and resulted in the formation of significantly more metabolites. Incomplete biodegradation reactions, fostered by shorter retention times in SW column could mainly explain these results.

Target and suspect screening of pesticide residues in soil samples from peach orchards using liquid chromatography quadrupole time-of-flight mass spectrometry

Agricultural soil contamination by pesticide residues has become a serious issue of increasing concern due to their high persistence and toxicity to non-target species. However, as the world's largest peach producer, national scale surveys on pesticide residues in peach orchard soils are scarce in China. In this study, a target and suspect screening method covering over 200 pesticides commonly used in peach orchards was developed using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry in MS^E. An identification strategy using different data processing parameters was developed to identify the pesticide occurrence in soil. The method was applied to soil samples from typical peach orchards in 12 regions across China. The present work also discusses in detail the frequency of occurrence, concentration of pesticides, spatial distribution of multiresidues, and relationship between pesticide occurrence and soil properties. In the tested soil samples, 21 herbicides (level 1), 31 fungicides (level 2a), 24 insecticides (level 2a), and 3 growth regulators (level 2a) were identified. The total concentrations of quantifiable herbicides in the soil samples ranged from 1.05 to 327 ng/g. Only in 5.4% of the soil samples, no pesticide residues were present. By contrast, more than 86% of the total contained multiple residues. This study represents the first large-scale survey of pesticides in soil from peach orchards and provides comprehensive and accurate information on the pesticide residue status for risk assessment.

Spectral change of dissolved organic matter after extracted by solid-phase extraction and its feasibility in predicting the acute toxicity of polar organic pollutants in textile wastewater

Spectroscopic parameters can be used as proxies to effectively trace the occurrence of organic trace contaminants, but their suitability for predicting the toxicity of discharged industrial wastewater with similar spectra is still unknown. In this study, the organic contaminants in treated textile wastewater were subdivided and extracted by four commonly-used solid-phase extraction (SPE) cartridges, and the resulting spectral change and toxicity of textile effluent were analyzed and compared. After SPE, the spectra of the percolates from the four cartridges showed obvious differences with respect to the substances causing the spectral changes and being more readily adsorbed by the WAX cartridges. Non-target screening results showed source differences in organic micropollutants, which were one of the main contributors leading to their spectral properties and spectral variations after SPE in the effluents. Two fluorescence parameters (C1 and humic-like) identified by the excitation emission matrix-parallel factor analysis (EEM-PARAFAC) were closely correlated to the toxicity endpoints for Scenedesmus obliquus (inhibition ratios of cell growth and Chlorophyll-a synthesis), which can be applied to quantitatively predict the change of toxicity effect caused by polar organic pollutants. The results would provide novel insights into the spectral feature analysis and toxicity prediction of the residual DOM in industrial wastewater.

First Discovery of Iodinated Polyfluoroalkyl Acids by Nontarget Mass-Spectrometric Analysis and Iodine-Specific Screening Algorithm

Per- and polyfluoroalkyl acids (PFAAs) including polyfluoroalkyl carboxylic acids and polyfluoroalkyl sulfonic acids are a large category of crucial environmental pollutants of global concern. Besides known PFAAs, numerous unknown species may exist in the environment, urgently needing discovery and characterization. This study implemented nontarget analysis for a group of novel PFAA pollutants, viz., iodinated PFAAs (I-PFAAs) in wastewater from a fluorochemical manufacturing park by liquid chromatography-high-resolution mass spectrometry in combination with an iodine-specific data-processing algorithm. The algorithm took into account the diagnostic fragment iodine ion (I-) together with carbon and sulfur isotopologue distributions. In total, 18 I-PFAA formulas involving 21 congeners were identified. Semiquantification was conducted, and the total concentrations of I-PFAAs were 1.9-274.7 μg/L, indicating severe pollution of I-PFAAs in the wastewater. The determined concentrations along with predicted environmental behaviors and toxicities demonstrate that I-PFAAs merit further in-depth investigation. The analytical method including the instrumental analysis and data-processing algorithm can be extended to screening and identification of I-PFAAs in other matrices. Furthermore, the analysis results for the first time provide recognition on the occurrence, distribution features, and pollution status of I-PFAAs in the environment.

Pesticides monitoring in surface water of a subsistence agricultural catchment in Uganda using passive samplers

Pesticides are intensely used in the agricultural sector worldwide including smallholder farming. Poor pesticide use practices in this agronomic setting are well documented and may impair the quality of water resources. However, empirical data on pesticide occurrence in water bodies of tropical smallholder agriculture is scarce. Many available data are focusing on apolar organochlorine compounds which are globally banned. We address this gap by studying the occurrence of a broad range of more modern pesticides in an agricultural watershed in Uganda. During 2.5 months of the rainy season in 2017, three passive sampler systems were deployed at five locations in River Mayanja to collect 14 days of composite samples. Grab samples were taken from drinking water resources. In these samples, 27 compounds out of 265 organic pesticides including 60 transformation products were detected. In the drinking water resources, we detected eight pesticides and two insecticide transformation products in low concentrations between 1 and 50 ng/L. Also, in the small streams and open fetch ponds, detected concentrations were generally low with a few exceptions for the herbicide 2,4-D and the fungicide carbendazim exceeding 1 ug/L. The widespread occurrence of chlorpyrifos posed the largest risk for macroinvertebrates. The extensive detection of this compound and its transformation product 3,4,5-trichloro-2-pyridinol was unexpected and called for a better understanding of the use and fate of this pesticide.

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by major European rivers. This study investigated the spatial distribution of chemicals in the Ukrainian shelf (the northwestern part of the Black Sea) and their main sources. Chemical occurrence data used in the study was generated within the Joint Black Sea Surveys (JBSS), which took place in 2016 and 2017 as a part of the EU/UNDP EMBLAS II project (www.emblasproject.org). During the JBSS, seawater samples were analyzed by a non-target screening workflow using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Open-source algorithms were applied to generate a combined dataset of 30,489 detected chemical signals and their intensities. Out of these, 35 compounds were tentatively identified by the application of a non-target screening identification workflow based on automated matching of their mass spectra against those in available mass spectral libraries. The dataset was used to generate images, representing spatial distribution of each of the signals. These images were then used as an input to a deep learning convolutional neural network classification model. The study resulted in the development of an open-source end-to-end workflow for the estimation of the pollution load by chemicals contributed by the two major inflowing rivers (Danube and Dnieper) and other, so far unidentified, sources. A dedicated dashboard was built to facilitate data visualization per detected signal/compound. The presented model proved to be especially useful at the prioritization of signals of unknown compounds, which is of key importance for the follow up structure elucidation efforts of bulky non-target screening data. The deep learning approach for peak prioritization of unknown chemicals in the environment has been used for the first time.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Machine learning for identification of silylated derivatives from mass spectra

Motivation

Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).

Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.

Results

This study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Identifying congeners and transformation products of organic contaminants within complex chemical mixtures in impacted surface waters with a top-down non-targeted screening workflow

Over 350,000 compounds are registered for production and use including a high number of congeners found in complex chemical mixtures (CCMs). With such a high number of chemicals being released in the environment and degraded into transformation products (TPs), the challenge of identifying contaminants by non-targeted screening (NTS) is massive. "Bottom-up" studies, where compounds are subjected to conditions simulating environmental degradation to identify new TPs, are time consuming and cannot be relied upon to study the TPs of hundreds of thousands of compounds. Therefore, the development of "top-down" workflows, where the structural elucidation of unknown compounds is carried directly on the sample, is of interest. In this study, a top-down NTS workflow was developed using molecular networking and clustering (MNC). A total of 438 compounds were identified including 176 congeners of consumer product additives and 106 TPs. Reference standards were used to confirm the identification of 53 contaminants among them lesser-known pharmaceuticals (aliskiren, sitagliptin) and consumer product additives (lauramidopropyl betaine, 2,2,4-trimethyl-1,2-dihydroquinoline). The MNC tools allowed to group similar TPs and congeners together. As such, several previously unknown TPs of pesticides (metolachlor) and pharmaceuticals (gliclazide, irbesartan) were identified as tentative candidates or probable structures. Moreover, some congeners that had no entry on global repositories (PubChem, ChemSpider) were identified as probable structures. The workflow worked efficiently with oligomers containing ethylene oxide moieties, and with TPs structurally related to their parent compounds. The top-down approach shown in this study addresses several issues with the identification of congeners of industrial compounds from CCMs. Furthermore, it allows elucidating the structure of TPs directly from samples without relying on bottom-up studies under conditions discussed herein. The top-down workflow and the MNC tools show great potential for data mining and retrospective analysis of previous NTS studies.

Suspecting screening "known unknown" pesticides and transformation products in soil at pesticide manufacturing sites

The occurrence and risks of pesticides and their transformation products in soil at the manufacturing sites are "known unknowns." In this study, pesticides and their transformation products were screened in soil at 6 pesticide manufacturing sites across China using liquid and gas chromatography coupled with quadrupole time-of-flight mass spectrometry. The screening strategy can correctly identify 75% of 209 pesticides spiked at 50 ng g^-1. A total of 212 pesticides were identified; 23.1% of pesticides detected were above 200 ng g^-1, and the maximum concentration was 1.5 × 10⁵ ng g^-1. The risk quotients of 20% pesticides were greater than 1, and the maximum risk quotient of imidacloprid reached 6.3 × 10⁴. The most recent site showed a larger number of pesticides with higher diversity, whereas older sites were dominated by organochlorine insecticides. The extended screen identified 163 transformation products with concentrations up to 6.6 × 10⁴ ng g^-1. Half of the transformation products had higher concentrations than their parent compounds, and metabolic ratios up to 371 were observed. The results of this study validate the prevalence of pesticides and their transformation products in soil at pesticide manufacturing sites. The results also highlight the importance of comprehensive screening at industrial sites and call for improved management and regulation of pesticide manufacturing, particularly for in-service facilities.

Discovering pesticides and their TPs in Luxembourg waters using open cheminformatics approaches

The diversity of hundreds of thousands of potential organic pollutants and the lack of (publicly available) information about many of them is a huge challenge for environmental sciences, engineering, and regulation. Suspect screening based on high-resolution liquid chromatography-mass spectrometry (LC-HRMS) has enormous potential to help characterize the presence of these chemicals in our environment, enabling the detection of known and newly emerging pollutants, as well as their potential transformation products (TPs). Here, suspect list creation (focusing on pesticides relevant for Luxembourg, incorporating data sources in 4 languages) was coupled to an automated retrieval of related TPs from PubChem based on high confidence suspect hits, to screen for pesticides and their TPs in Luxembourgish river samples. A computational workflow was established to combine LC-HRMS analysis and pre-screening of the suspects (including automated quality control steps), with spectral annotation to determine which pesticides and, in a second step, their related TPs may be present in the samples. The data analysis with Shinyscreen (https://gitlab.lcsb.uni.lu/eci/shinyscreen/), an open source software developed in house, coupled with custom-made scripts, revealed the presence of 162 potential pesticide masses and 96 potential TP masses in the samples. Further identification of these mass matches was performed using the open source approach MetFrag (https://msbi.ipb-halle.de/MetFrag/). Eventual target analysis of 36 suspects resulted in 31 pesticides and TPs confirmed at Level-1 (highest confidence), and five pesticides and TPs not confirmed due to different retention times. Spatio-temporal analysis of the results showed that TPs and pesticides followed similar trends, with a maximum number of potential detections in July. The highest detections were in the rivers Alzette and Mess and the lowest in the Sûre and Eisch. This study (a) added pesticides, classification information and related TPs into the open domain, (b) developed automated open source retrieval methods - both enhancing FAIRness (Findability, Accessibility, Interoperability and Reusability) of the data and methods; and (c) will directly support "L'Administration de la Gestion de l'Eau" on further monitoring steps in Luxembourg.

Evaluation of Sample Preparation Methods for Non-Target Screening of Organic Micropollutants in Urban Waters Using High-Resolution Mass Spectrometry

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms of the amount and relevance of the information obtained by subsequent NTS analysis. The goal of this work was to compare a large number of sample extraction methods for the unknown screening of urban waters. Various phases were tested for the solid-phase extraction of micropollutants from these waters. The evaluation of the different phases was assessed by statistical analysis based on the number of detected molecules, their range, and physicochemical properties (molecular weight, standard recoveries, polarity, and optical properties). Though each cartridge provided its own advantages, a multilayer cartridge combining several phases gathered more information in one single extraction by benefiting from the specificity of each one of its layers.

Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake-Three Rivers System

Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake-river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspect and nontarget screening with mass balance modeling to investigate OMP contamination in the Onondaga Lake-Three Rivers system of New York. Suspect and nontarget screening enabled by liquid chromatography-high-resolution mass spectrometry led to the confirmation and quantification of 105 OMPs in water samples collected throughout the lake-river system, which were grouped by their concentration patterns into wastewater-derived and mixed-source clusters via hierarchical cluster analysis. Four of these OMPs (i.e., galaxolidone, diphenylphosphinic acid, N-butylbenzenesulfonamide, and triisopropanolamine) were prioritized and identified by nontarget screening based on their characteristic vertical distribution patterns during thermal stratification in Onondaga Lake. Mass balance modeling performed using the concentration and discharge data highlighted the export of OMPs from Onondaga Lake to the Three Rivers as a major contributor to the OMP budget in this lake-river system. Overall, this work demonstrated the utility of an integrated screening and modeling framework that can be adapted for OMP characterization, fate assessment, and load apportionment in similar surface water systems.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Evaluation, optimization, and application of three independent suspect screening workflows for the characterization of PFASs in water

Suspect screening is a valuable tool for characterizing per- and polyfluoroalkyl substances (PFASs) in environmental media. Although a variety of data mining tools have been developed and applied for suspect screening of PFAS, few suspect screening workflows have undergone a comprehensive performance evaluation or optimization. The goals of this research were to: (1) evaluate and optimize three independent suspect screening workflows for the detection of PFASs in water samples; and (2) apply the optimized suspect screening workflows to an environmental sample to determine the extent to which suspect screening results converge. We evaluated and optimized suspect screening workflows using Compound Discoverer v3.2, enviMass v4.2, and FluoroMatch v2.4 using test samples containing 33 target PFASs. The average sensitivity (Sen) and selectivity (Sel) for each workflow across the test samples was: Compound Discoverer Sen = 71%, Sel = 85%; enviMass Sen = 89%, Sel = 80%; FluoroMatch Sen = 51%, Sel = 82%. We then applied the optimized workflows to a contaminated groundwater sample containing an unknown number of PFASs. Each workflow managed to annotate unique PFASs that were not annotated by the other workflows including 2 by Compound Discoverer and 19 each by enviMass and FluoroMatch. Thirty-two enviMass hits and 28 of the Compound Discoverer and FluoroMatch hits were annotated by at least one of the other workflows. Sixteen PFASs were annotated by all three of the optimized workflows. This work provides a basis for conducting suspect screening for PFASs that will lead to more consistent reporting of suspect screening data.

Suspect screening of pharmaceuticals in fish livers based on QuEChERS extraction coupled with high resolution mass spectrometry

The presence of pharmaceuticals and personal care products (PPCPs) in aquatic environments is of increasing concern due to the presence of residues in fish and aquatic organisms, and emerging antibiotic resistance. Wastewater release is an important contributor to the presence of these compounds in aquatic ecosystems, where they may accumulate in food webs. The traditional environmental surveillance approach relies on the targeted analysis of specific compounds, but more suspect screening methods have been developed recently to allow for the identification of a variety of contaminants. In this study, a method based on QuEChERS extraction - using acetonitrile/water mixture as solvent and PSA/C₁₈ for sample clean-up - was applied to identify pharmaceuticals and their metabolites in fish livers. Both target and suspect screening workflows were used and fish were sampled upstream and downstream of wastewater treatment plants from the Scioto River, Ohio (USA). The method performed well in terms of extraction of some target PPCPs, with recoveries generally above 90%, good repeatability (<20%), and linearity. Based on target analysis, lincomycin and sulfamethoxazole were two antibiotics identified in fish livers at average concentrations of 30.3 and 25.6 ng g^-1 fresh weight, respectively. Using suspect screening, another antibiotic, azithromycin and an antidepressant metabolite, erythrohydrobupropion were identified (average concentrations: 27.8 and 13.8 ng g^-1, respectively). The latter, reported, to the best of our knowledge, for the first time in fish livers, was also found at higher concentrations in fish livers sampled downstream vs. upstream. The higher frequency of detection for azithromycin in benthic feeding fish species (63%) as well as clusters identified between different foraging groups suggest that foraging behavior may be an important mechanism in the bioaccumulation of PPCPs. This study shows how suspect screening is effective in identifying new contaminants in fish livers, notably using differential analysis among different spatially distributed samples.

Identification of Pesticide Transformation Products in Surface Water Using Suspect Screening Combined with National Monitoring Data

Pesticides are widespread anthropogenic chemicals and well-known environmental contaminants of concern. Much less is known about transformation products (TPs) of pesticides and their presence in the environment. We developed a novel suspect screening approach for not well-explored pesticides (n = 16) and pesticide TPs (n = 242) by integrating knowledge from national monitoring with high-resolution mass spectrometry data. Weekly time-integrated samples were collected in two Swedish agricultural streams using the novel Time-Integrating, MicroFlow, In-line Extraction (TIMFIE) sampler. The integration of national monitoring data in the screening approach increased the number of prioritized compounds approximately twofold (from 23 to 42). Ultimately, 11 pesticide TPs were confirmed by reference standards and 12 TPs were considered tentatively identified with varying levels of confidence. Semiquantification of the newly confirmed TPs indicated higher concentrations than their corresponding parent pesticides in some cases, which highlights concerns related to (unknown) pesticide TPs in the environment. Some TPs were present in the environment without co-occurrence of their corresponding parent compounds, indicating higher persistency or mobility of the identified TPs. This study showcased the benefits of integrating monitoring knowledge in this type of studies, with advantages for suspect screening performance and the possibility to increase relevance of future monitoring programs.

Increased coverage and high confidence in suspect screening of emerging contaminants in global environmental samples

Suspect screening using liquid chromatography with high resolution mass spectrometry provides an opportunity for expanding the detection coverage of emerging contaminants in the environment. Screening workflows may suffer from high frequency of false positives or insufficient confidence in the identification of compounds; however, stringent criteria could lead to high false negatives. A workflow must have a balanced criteria, both selective and sensitive, to be able to identify real features without missing low abundant features traceable to analytes of interest. A highly selective (87%) and sensitive (97%) workflow was developed by characterizing the occurrence of contaminants in wastewater and surface water from Hong Kong, India, Philippines, Sweden, Switzerland, and the U.S. Sixty-eight contaminants were identified and confirmed with reference standards, including pharmaceuticals, pesticides, and industrial chemicals. The antimicrobials metronidazole, clindamycin, linezolid, and rifaximin were detected. Notably, antifungal compounds were detected in samples from six countries, with levels up to 1380 ng/L. Amoxicillin transformation products, penilloic acid (285-8047 ng/L) and penicilloic acid (107 ng/L), were confirmed for the first time with reference standards in wastewater samples from India, Sweden, and U.S. This workflow provides an efficient approach to broad-scale identification of emerging contaminants using publicly-available databases for suspect screening and prioritization.

An annotation database for chemicals of emerging concern in exposome research

BACKGROUND

Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.

OBJECTIVES

This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.

METHODS

The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.

RESULTS

All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag.

DISCUSSION

The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion

The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.

Retrospective HRMS Screening and Dedicated Target Analysis Reveal a Wide Exposure to Pyrrolizidine Alkaloids in Small Streams

Pyrrolizidine alkaloids (PAs) are found to be toxic pollutants emitted into the environment by numerous plant species, resulting in contamination. In this article, we investigate the occurrence of PAs in the aquatic environment of small Swiss streams combining two different approaches. Pyrrolizidine alkaloids (PAs) are toxic secondary metabolites produced by numerous plant species. Although they were classified as persistent and mobile and found to be emitted into the environment, their occurrence in surface waters is largely unknown. Therefore, we performed a retrospective data analysis of two extensive HRMS campaigns each covering five small streams in Switzerland over the growing season. All sites were contaminated with up to 12 individual PAs and temporal detection frequencies between 36 and 87%. Individual PAs were in the low ng/L range, but rain-induced maximal total PA concentrations reached almost 100 ng/L in late spring and summer. Through PA patterns in water and plants, several species were tentatively identified as the source of contamination, with Senecio spp. and Echium vulgare being the most important. Additionally, two streams were monitored, and PAs were quantified with a newly developed, faster, and more sensitive LC-MS/MS method to distinguish different plant-based and indirect human PA sources. A distinctly different PA fingerprint in aqueous plant extracts pointed to invasive Senecio inaequidens as the main source of the surface water contamination at these sites. Results indicate that PA loads may increase if invasive species are sufficiently abundant.

Suspect screening based on market data of polar halogenated micropollutants in river water affected by wastewater

Wastewater treatment plants (WWTPs) are known point sources of contaminants of emerging concern (CECs) to the aquatic environment, but current knowledge is mostly limited to well-known chemical structures. In this study, we sought to identify unknown CECs polluting the aquatic environment through a novel suspect screening approach for organohalogens, i.e. organic halogenated molecules often toxic and resistant to transformation and characterised as persistent organic pollutants (POPs). Surface water samples were collected with passive samplers in the Fyris River catchment (Uppsala, Sweden), analysed using liquid chromatography high-resolution mass spectrometry (LC-HRMS) and screened for organohalogens using a suspect screening approach based on market data obtained from a regulatory authority. Thirteen suspects from very different application areas were confirmed or tentatively identified with high confidence, including seven previously unknown structures (diflufenican, chlorzoxazone, 3-(3,4-dichlorophenyl)-1,1-dimethylurea, 2,4-disulfamyl-5-trifluoromethylaniline, 5-amino-2-chlorotoluene-4-sulfonic acid, perfluoropentane-1-sufonic acid, (2-chlorophenyl)(hydroxy)methanesulfonic acid). Spatiotemporal occurrence patterns were detected, which helped to understand the usage pattern of the chemicals and pinpoint potential pollution sources, e.g. specific WWTPs in the catchment. Several of the newly identified structures had virtually no information publicly available and were detected years after their last registered use, which highlights the knowledge gaps and concerns about POPs.

Organic Micropollutants in New York Lakes: A Statewide Citizen Science Occurrence Study

The widespread occurrence of organic micropollutants (OMPs) is a challenge for aquatic ecosystem management, and closing the gaps in risk assessment of OMPs requires a data-driven approach. One promising tool for increasing the spatiotemporal coverage of OMP data sets is through the active involvement of citizen volunteers to expand the scale of OMP monitoring. Working collaboratively with volunteers from the Citizens Statewide Lake Assessment Program (CSLAP), we conducted the first statewide study on OMP occurrence in surface waters of New York lakes. Samples collected by CSLAP volunteers were analyzed for OMPs by a suspect screening method based on mixed-mode solid-phase extraction and liquid chromatography-high resolution mass spectrometry. Sixty-five OMPs were confirmed and quantified in samples from 111 lakes across New York. Hierarchical clustering of OMP occurrence data revealed the relevance of 11 most frequently detected OMPs for classifying the contamination status of lakes. Partial least squares regression and multiple linear regression analyses prioritized three water quality parameters linked to agricultural and developed land uses (i.e., total dissolved nitrogen, specific conductance, and a wastewater-derived fluorescent organic matter component) as the best combination of predictors that partly explained the interlake variability in OMP occurrence. Lastly, the exposure-activity ratio approach identified the potential for biological effects associated with detected OMPs that warrant further biomonitoring studies. Overall, this work demonstrated the feasibility of incorporating citizen science approaches into the regional impact assessment of OMPs.

Target and suspect screening analysis reveals persistent emerging organic contaminants in soils and sediments

An approach to identifying persistent organic contaminants in the environment was developed and executed for Switzerland as an example of an industrialized country. First, samples were screened with an in-house list using liquid chromatography high-resolution mass spectrometry (LC-HRMS/MS) and gas chromatography tandem mass spectrometry (GC-MS/MS) in 13 samples from the Swiss National Soil Monitoring Network and three sediment cores of an urban and agricultural contaminated lake. To capture a broader range of organic contaminants, the analysis was extended with a suspect screening analysis by LC-HRMS/MS of >500 halogenated compounds obtained from a Swiss database that includes industrial and household chemicals identified, by means of fugacity modeling, as persistent substances in the selected matrices. In total, the confirmation of 96 compounds with an overlap of 34 in soil and sediment was achieved. The identified compounds consist generally of esters, tertiary amines, trifluoromethyls, organophosphates, azoles and aromatic azines, with azoles and triazines being the most common groups. Newly identified compounds include transformation products, pharmaceuticals such as the flukicide niclofolan, the antimicrobial cloflucarban, and the fungicide mandipropamid. The results indicate that agricultural and urban soils as well as sediments impacted by agriculture and wastewater treatment plants (WWTPs) are the most contaminated sites. The plausibility of this outcome confirms the combination of chemical inventory, modeling of partitioning and persistence, and HRMS-based screening as a successful approach to shed light on less frequently or not yet investigated environmental contaminants and emphasizes the need for more soil and sediment monitoring in the future.

Technical Overview of Orbitrap High Resolution Mass Spectrometry and Its Application to the Detection of Small Molecules in Food (Update Since 2012)

Food safety and quality issues are becoming increasingly important and attract much attention, requiring the development of better analytical platforms. For example, high-resolution (especially Orbitrap) mass spectrometry simultaneously offers versatile functions such as targeted/non-targeted screening while providing qualitative and quantitative information on an almost unlimited number of analytes to facilitate routine analysis and even allows for official surveillance in the food field. This review covers the current state of Orbitrap mass spectrometry (OMS) usage in food analysis based on research reported in 2012-2019, particularly highlighting the technical aspects of OMS application and the achievement of OMS-based screening and quantitative analysis in the food field. The gained insights enhance our understanding of state-of-the-art high-resolution mass spectrometry and highlight the challenges and directions of future research.

Suspect screening and risk assessment of pollutants in the wastewater from a chemical industry park in China

Owing to the production and use of chemicals in chemical industry parks (CIPs), these areas are considered to be highly polluted. However, the type of pollutants presents in the wastewater from CIPs and the risk posed to the environment due to the release of these pollutants remains unclear. In this study, suspect screening was combined with traceability analysis to determine the type of pollutants present in wastewaters at 9 chemical enterprises and wastewater treatment plants (WWTPs) in the CIPs. Additionally, the distribution of nine pollutants from the WWTPs' effluent stage and the risk they posed to the surrounding river was examined through target analysis. Upon conducting suspect analysis, the presence of 65 and 64 chemicals in the 9 chemical enterprises' wastewaters and WWTPs, respectively, was tentatively identified. Traceability analysis of the compounds screened in the effluent from the WWTPs determined that 41 substances were identified as characteristic pollutants of the chemical enterprises, indicating that the suspect screening strategy enabled relatively more efficient identification of the characteristic pollutants compared to traditional quantitative analysis. Targeting analysis combined with ecological risk assessment showed that metolachlor, carbendazim, atrazine, diuron, and chlorpyrifos posed relatively higher risks to aquatic organisms in the surrounding river. Therefore, the refined management of the wastewater treatment plant in the CIPs is necessary.

Use of aloin-based and rosin-based electrospun nanofibers as natural nanosorbents for the extraction of polycyclic aromatic hydrocarbons and phenoxyacetic acid herbicides by microextraction in packed syringe method prior to GC-FID detection

The synthesis of three kinds of sorbents is described. The first kind was a hydrophobic nanofiber as a specific sorbent for non-polar compounds. The second one was a hydrophilic nanofiber as a specific sorbent for polar compounds and the third one was a generic sorbent synthesized from hydrophilic and hydrophobic compounds. The functional groups were natural compounds extracted from aloin plant and gum of pine tree. The aloin/polyacrylonitrile (PAN), rosin/PAN, and aloin/rosin/PAN electrospun nanofibers were synthesized through electrospinning strategy and then characterized using field emission scanning electron microscopy and Fourier transform infrared spectroscopy. Thereafter, the synthesized sorbents were used in microextraction using the packed syringe (MEPS) method. The determination was conducted using gas chromatography with flame ionization detection (GC-FID). Under the optimum condition, the method using aloin/rosin/PAN nanofibers as a sorbent showed a good linearity in the range 1.0-250 ng mL^-1 for polycyclic aromatic hydrocarbons (PAHs) (as a model for non-polar compounds) and 1.0-200 ng mL^-1 for phenoxyacetic acid herbicides (CAPs) (as a model for polar compounds) with correlation coefficient (R²) higher than 0.997. Limits of detections (LODs) for PAHs and CAPs were in the range 0.1-0.3 ng mL^-1 and 0.3-0.5 ng mL^-1, respectively. The intra-day (n = 3) and inter-day (between 3 days) relative standard deviations (RDSs%) were in the range 6.3-12.3% for a single syringe. Finally, the MEPS-GC-FID method was applied as a simple, facile, and time and cost-effective method to analyze environmental, farm, and industrial water samples. Graphical abstract Herein, aloin/rosin/polyacrylonitrile (PAN) electrospun nanofiber was successfully synthesized and applied as a sorbent for extraction of polycyclic aromatic hydrocarbons (PAHs) as non-polar compounds and phenoxyacetic acid herbicides (CPAs) as polar compounds from aqueous solutions before GC-FID analysis.

Retrospective screening of high-resolution mass spectrometry archived digital samples can improve environmental risk assessment of emerging contaminants: A case study on antifungal azoles

Environmental risk assessment associated with aquatic and terrestrial contamination is mostly based on predicted or measured environmental concentrations of a limited list of chemicals in a restricted number of environmental compartments. High resolution mass spectrometry (HRMS) can provide a more comprehensive picture of exposure to harmful chemicals, particularly through the retrospective analysis of digitally stored HRMS data. Using this methodology, our study characterized the contamination of various environmental compartments including 154 surface water, 46 urban effluent, 67 sediment, 15 soil, 34 groundwater, 24 biofilm, 41 gammarid and 49 fish samples at 95 sites widely distributed over the Swiss Plateau. As a proof-of-concept, we focused our investigation on antifungal azoles, a class of chemicals of emerging concern due to their endocrine disrupting effects on aquatic organisms and humans. Our results demonstrated the occurrence of antifungal azoles and some of their (bio)transformation products in all the analyzed compartments (0.1-100 ng/L or ng/g d.w.). Comparison of actual and predicted concentrations showed the partial suitability of level 1 fugacity modelling in predicting the exposure to azoles. Risk quotient calculations additionally revealed risk of exposure especially if some of the investigated rivers and streams are used for drinking water production. The case study clearly shows that the retrospective analysis of HRMS/MS data can improve the current knowledge on exposure and the related risks to chemicals of emerging concern and can be effectively employed in the future for such purposes.

Occurrence and risk assessment of organic micropollutants in freshwater systems within the Lake Victoria South Basin, Kenya

The unintended release of chemicals to the environment has led to global concern on water quality prompting widespread research on the occurrence of these compounds in water. While increasing information on organic micropollutants (OMPs) in European water resources is available, there is still limited information on the occurrence of OMPs in African water systems. In this study, a multi-residue analysis covering 428 chemicals using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) was performed on water samples collected from 48 surface water sites within the Lake Victoria South Basin, Kenya. A total of 75 compounds including pharmaceuticals, personal care products (PPCPs), pesticides, and industrial chemicals were detected and an additional three compounds (nevirapine, lamivudine and adenosine) were identified through suspect screening. Four compounds including diphenhydramine, simazine, triethylphosphate and acetyl-sulfamethoxazole (A-SMX) were detected in >80% of the sites showing their ubiquitous nature in the study area. Individual compound concentrations were detected up to 24 μg L^-1. Concentrations above 1 μg L^-1 were also reported for triethylcitrate, N-ethyl-o-toluenesulfonamide, hexazinone, nevirapine, adenosine and carbendazim. While crustaceans were potentially the taxon at risk for acute toxicity (toxic unit (TU) up to 2) with diazinon driving this risk, lower but substantial acute risk (TU 0.5) was observed for algae. Chronic risks were observed in 11 sites for algae (TU > 0.02) and in 5 sites for fish (TU > 0.01). A total of 16 compounds were prioritized based on frequency and extent of the exceedance of thresholds for acute and chronic risks to algae, crustaceans and fish and another 7 compounds prioritized by applying lowest Predicted No-Effect Concentrations (PNEC). Based on these indicators, this study provides candidate priority compounds for monitoring, assessment and abatement in western Kenya.

Retrospective suspect screening reveals previously ignored antibiotics, antifungal compounds, and metabolites in Bangladesh surface waters

Densely populated countries in Asia, such as Bangladesh, are considered to be major contributors to the increased occurrence of global antimicrobial resistance (AMR). Several factors make low-and middle-income countries vulnerable to increased emergence and spread of AMR in the environment including limited regulations on antimicrobial drug use, high volume of antimicrobials used in human medicine and agricultural production, and poor wastewater management. Previous monitoring campaigns to investigate the presence of antibiotics in the aquatic environment have employed targeted analysis in which selected antibiotics are measured using liquid chromatography with tandem mass spectrometry (LC/MS/MS). However, this approach can miss several important contaminants that can contribute to the selective pressure that promotes maintenance and dissemination of antibiotic resistance genes (ARGs) in the environment. Nontarget analysis by suspect screening and reanalysis of stored digital data of previously ran samples can provide information on analytes that were formerly uncharacterized and may be chemicals of emerging concern (CECs). In this study, surface waters in both urban and rural sites in Bangladesh were collected and analyzed for the presence of antibiotic residues and other pharmaceuticals. Utilizing targeted analysis, the antibiotics with the highest concentrations detected were ciprofloxacin (1407 ng/L) and clarithromycin (909 ng/L). In addition, using high-resolution LC/MS/MS in the first ever application of retrospective analysis in samples from Bangladesh, additional antibiotics clindamycin, lincomycin, linezolid, metronidazole, moxifloxacin, nalidixic acid, and sulfapyridine were detected. Prevalence of amoxicillin transformation products in surface waters was also confirmed. In addition, medicinal and agricultural antifungal compounds were frequently found in Bangladeshi surface waters. This later finding - the near ubiquity of antifungal agents in environmental samples - is of particular concern, as it may be contributing to the alarming rise of multi-drug resistant fungal (e.g. Candida auris) disease recently seen in humans throughout the world.

Simultaneous analysis of current-use pesticides and their transformation products in water using mixture-sorbent solid phase extraction and high-performance liquid chromatography-tandem mass spectrometry

Pesticides have posed significant threats to aquatic ecosystems, yet little is known about their transformation products. The challenge is to simultaneously analyze various pesticides and transformation products in water as they have distinct physicochemical properties. A mix-mode solid phase extraction method was established to simultaneously analyze current-use pesticides and their transformation products using a mixture of hydrophile-lipophile balance, weak anion, and cation exchange resins (2:1:1, w/w/w) in combination with high-performance liquid chromatography and tandem mass spectrometry for chemical quantification. Neutral, acidic, and alkaline methanol were used as the elution solvent. Box-Behnken design was applied to optimize extraction conditions. Optimal conditions were as follows: sorbent mass, 200 mg; volume of elution solvent, 5 mL × 3; pH 4. The method was validated for compounds at concentrations from 20 to 1000 ng/L in different types of water samples, with recovery being from 43.5 ± 3.1 to 141 ± 35%. Low method detection limits (0.02-5.6 ng/L) implied that the developed method was sensitive. Finally, the method was applied to monitor current-use pesticides and their transformation products in natural waters. Frequent detection of transformation products of pesticides indicated that their contribution to aquatic risk should not be ignored.