Identification of LC-HRMS nontarget signals in groundwater after source related prioritization

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC-MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC-MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Assessment of environmental forensic indicator for anthropogenic groundwater contamination via target/suspect/nontarget analysis using HRMS techniques

This study compared target/suspect/nontarget analysis via liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with traditional environmental forensic methods, specifically nitrate and its stable N isotope, in assessing groundwater pollution from livestock manure and agriculture. Using an in-house database of 1471 target and suspects, 35 contaminants (pesticides, veterinary drugs, surfactants) were identified, some uniquely linked to specific pollution sources, such as sulfamethazine and 4-formylaminoantipyrine in manure-affected areas. Pesticides were widespread, typically showing higher intensity in agricultural zones. On the other hand, the results of stable N isotope analysis (δ15N-NO3: 4.8 to 16.4‰) indicated the influence of human activities such as fertilizers, sewage, and manure in all sampling sites, including the control site far from the pollution sources and cannot differentiate the specific sources. The study underscores LC-HRMS's efficacy in different pollution sources, surpassing the limitations of stable N isotope analysis, and provides valuable insights for polluted groundwater source tracking strategies.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

New PFASs Identified in AFFF Impacted Groundwater by Passive Sampling and Nontarget Analysis

Monitoring contamination from per- and polyfluoroalkyl substances (PFASs) in water systems impacted by aqueous film-forming foams (AFFFs) typically addresses a few known PFAS groups. Given the diversity of PFASs present in AFFFs, current analytical approaches do not comprehensively address the range of PFASs present in these systems. A suspect-screening and nontarget analysis (NTA) approach was developed and applied to identify novel PFASs in groundwater samples contaminated from historic AFFF use. A total of 88 PFASs were identified in both passive samplers and grab samples, and these were dominated by sulfonate derivatives and sulfonamide-derived precursors. Several ultrashort-chain (USC) PFASs (≤C3) were detected, 11 reported for the first time in Australian groundwater. Several transformation products were identified, including perfluoroalkane sulfonamides (FASAs) and perfluoroalkane sulfinates (PFASis). Two new PFASs were reported (((perfluorohexyl)sulfonyl)sulfamic acid; m/z 477.9068 and (E)-1,1,2,2,3,3,4,5,6,7,8,8,8-tridecafluorooct-6-ene-1-sulfonic acid; m/z 424.9482). This study highlights that several PFASs are overlooked using standard target analysis, and therefore, the potential risk from all PFASs present is likely to be underestimated.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Identification of organic micro-pollutants in surface water using MS-based infrared ion spectroscopy

Comprehensive monitoring of organic micro-pollutants (OMPs) in drinking water sources relies on non-target screening (NTS) using liquid-chromatography and high-resolution mass spectrometry (LC-HRMS). Identification of OMPs is typically based on accurate mass and tandem mass spectrometry (MS/MS) data by matching against entries in compound databases and MS/MS spectral libraries. MS/MS spectra are, however, not always diagnostic for the full molecular structure and, moreover, emerging OMPs or OMP transformation products may not be present in libraries. Here we demonstrate how infrared ion spectroscopy (IRIS), an emerging MS-based method for structural elucidation, can aid in the identification of OMPs. IRIS measures the IR spectrum of an m/z-isolated ion in a mass spectrometer, providing an orthogonal diagnostic for molecular identification. Here, we demonstrate the workflow for identification of OMPs in river water and show how quantum-chemically predicted IR spectra can be used to screen potential candidates and suggest structural assignments. A crucial step herein is to define a set of candidate structures, presumably including the actual OMP, for which we present several strategies based on domain knowledge, the IR spectrum and MS/MS spectrum.

Dynamic Source Distribution and Emission Inventory of a Persistent, Mobile, and Toxic (PMT) Substance, Melamine, in China

Persistent, mobile, and toxic (PMT) substances are affecting the safety of drinking water and are threatening the environment and human health. Many PMT substances are used in industrial processing or consumer products, but their sources and emissions mostly remain unclear. This study presents a long-term source distribution and emission estimation of melamine, a high-production-volume PMT substance of emerging global concern. The results indicate that in China, approximately 1858.7 kilotonnes (kt) of melamine were released into the water (∼58.9%), air (∼27.0%), and soil systems (∼14.1%) between 1995 and 2020, mainly from its production and use in the decorative panels, textiles, and paper industries. The textile and paper industries have the highest emission-to-consumption ratios, with more than 90% emissions per unit consumption. Sewage treatment plants are the largest source of melamine in the environment for the time being, but in-use products and their wastes will serve as significant melamine sources in the future. The study prompts priority action to control the risk of PMT substances internationally.

Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure

Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile (vPvM) substances is essential to prevent or reduce drinking water contamination from them. This study developed machine learning models based on different molecular descriptors (MDs) and defined applicability domains for the screening of PMT/vPvM substances. The models were trained with 3111 substances with expert weight-of-evidence based PMT/vPvM hazard classifications that considered the highest quality data available. The model was based on the hypothesis that PMT/vPvM substances contain similar MDs, representative of chemical structures resistant to degradation, be associated with low sorption (or high-water solubility) and in some cases be associated with known toxic mechanisms. All possible model combinations were tested by integrating different molecular description methods, data balancing strategies and machine learning algorithms. Our model allows one-step prediction of candidate PMT/vPvM substances, and our method was compared with the approach predicting P, M and T separately (i.e. three-step prediction). The results showed that the one-step model achieved a higher accuracy of 92% for PMT/vPvM identification (i.e. positive samples) for an internal test set, and also resulted in a higher accuracy of 90% for an external test set of chemical pollutants detected in Taihu Lake, China. Furthermore, prediction mechanism of the model was interpreted by Shapley additive explanations (SHAP). This work presents an advance of big data in silico screening models for the identification of substances that potentially meet the PMT/vPvM criteria.

Fitness assessment of Mytilus galloprovincialis Lamarck, 1819 after exposure to herbicide metabolite propachlor ESA

The lack of data on the chronic effects of chloroacetanilide herbicide metabolites on non-target aquatic organisms creates a gap in knowledge about the comprehensive impacts of excessive and repeated pesticide use. Therefore, this study evaluates the long-term effects of propachlor ethanolic sulfonic acid (PROP-ESA) after 10 (T1) and 20 (T2) days at the environmental level of 3.5 μg.L^-1 (E1) and its 10x fold multiply 35 μg.L^-1 (E2) on a model organism Mytilus galloprovincialis. To this end, the effects of PROP-ESA usually showed a time- and dose-dependent trend, especially in its amount in soft mussel tissue. The bioconcentration factor increased from T1 to T2 in both exposure groups - from 2.12 to 5.30 in E1 and 2.32 to 5.48 in E2. Biochemical haemolymph profile and haemocyte viability were not affected by PROP-ESA exposure. In addition, the viability of digestive gland (DG) cells decreased only in E2 compared to control and E1 after T1. Moreover, malondialdehyde levels increased in E2 after T1 in gills, and DG, superoxidase dismutase activity and oxidatively modified proteins were not affected by PROP-ESA. Histopathological observation showed several damages to gills (e.g., increased vacuolation, over-production of mucus, loss of cilia) and DG (e.g., growing haemocyte trend infiltrations, alterations of tubules). This study revealed a potential risk of chloroacetanilide herbicide, propachlor, via its primary metabolite in the Bivalve bioindicator species M. galloprovincialis. Furthermore, considering the possibility of the biomagnification effect, the most prominent threat poses the ability of PROP-ESA to be accumulated in edible mussel tissues. Therefore, future research about the toxicity of pesticide metabolites alone or their mixtures is needed to gain comprehensive results about their impacts on living non-target organisms.

Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning

Urban rainfall events can lead to the runoff of pollutants, including industrial, pesticide, and pharmaceutical chemicals. Transporting micropollutants (MPs) into water systems can harm both human health and aquatic species. Therefore, it is necessary to investigate the dynamics of MPs during rainfall events. However, few studies have examined MPs during rainfall events due to the high analytical expenses and extensive spatiotemporal variability. Few studies have investigated the occurrence patterns of MPs and factors that influence their transport, such as rainfall duration, antecedent dry periods, and variations in streamflow. Moreover, while there have been many analyses of nutrients, suspended solids, and heavy metals during the first flush effect (FFE), studies on the transport of MPs during FFE are insufficient. This study aimed to identify the dynamics of MPs and FFE in an urban catchment, using high-resolution monitoring and machine learning methods. Hierarchical clustering analysis and partial least squares regression (PLSR) were implemented to estimate the similarity between each MP and identify the factors influencing their transport during rainfall events. Eleven dominant MPs comprised 75% of the total MP concentration and had a 100% detection frequency. During rainfall events, pesticides and pharmaceutical MPs showed a higher FFE than industrial MPs. Moreover, the initial 30% of the runoff volume contained 78.0% of pesticide and 50.1% of pharmaceutical substances for events W1 (July 5 to July 6, 2021) and W6 (August 31 to September 1, 2021), respectively. The PLSR model suggested that stormflow (m³/s) and the duration of antecedent dry hours (h) significantly influenced MP dynamics, yielding the variable importance on projection scores greater than 1.0. Hence, our findings indicate that MPs in urban waters should be managed by considering FFE.

Biogenic Solution Map Based on the Definition of the Metabolic Correlation Distance between 4-Dimensional Fingerprints

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".

Persistence and Mobility (defined as organic‑carbon partitioning) do not correlate to the detection of substances found in surface and groundwater: Criticism of the regulatory concept of Persistent and mobile substances

The Chemical Strategy for Sustainability (CSS) includes actions to ensure the protection of drinking water resources from chemical pollution. To proactively identify potential pollutants, the German Environment Agency (UBA) proposed the Persistent and Mobile (PM) concept according to which Persistence (criteria of REACH Annex XIII) and Mobility (log Koc < 4) would be proxies for a substance's degradation potential and transport velocity, two processes believed to drive the potential for contamination of surface and groundwater as drinking water sources. Two studies identified hundreds of PM substances while three subsequent studies have selected some of these substances for monitoring in surface, ground- and/or drinking water to support the concept. In the present work, the Persistence of the aforementioned substances was reassessed based on all experimental data publicly available. Depending on the exact study examined, it was found that 15 % to 40 % of the substances were erroneously concluded as P. The reinterpretation of the data indicates that a PM substance does not have a higher likelihood to be detected in surface or groundwater than a non-PM substance. In addition, the PM properties do not have any influence on the level of contamination. Twenty-six to 75 % of the substances selected because they were identified as PM were not found in surface or ground water despite being selected for their high emission pattern. Regulations based primarily on the PM concept, like the CLP and possibly REACH and UN-GHS, are unlikely to appropriately identify substances of concern for drinking water sources. It is more likely that chemical presence in surface and groundwater is driven by emission patterns or local factors. The development of specific exposure models would better contribute to the protection of drinking water resources and consumers.

Aromatic amines leachate from cigarette butts into aquatic environments: Is there risk for water organisms?

There are many toxics, such as aromatic amines (AAs), in cigarette butts (CBs). As CBs are the most abundant litter worldwide, these chemicals may leach into water bodies. In the present work, for the first time, the levels of AAs leachates from CBs in distilled water (DW) and river water (RW) samples were evaluated at different exposure times ranging from 15 min to 30 days. The mean leachate levels of AAs in DW and RW samples were in the range of 0.2-566 and 0.2-596 ng L^-1, respectively, with overall mean values of 569 and 556 ng L^-1. There was no significant difference (p > 0.05) between the total AAs levels as well as the level of each examined AA in DW and RW samples. Aniline (ANL) had the highest leaching rate from CBs into water. The mean leachates of AAs from CBs into water were ranked as: ANL> 1-naphthylamine (1-NA)> 2-naphthylamine (2-NA) > 2,6-dimethylaniline (2, 6-DMA)> ∑toluidine (∑TOL)> o-anisidine (o-ASD)> ∑aminobiphenyl (∑ABP). Ecological risk assessment showed that ∑7AAs, ANL, p-TOL, o-TOL, 2-NA, and ∑ABP had medium risks to sensitive crustaceans and fish. As AAs are not the only hazardous chemicals which may leach from CBs into aquatic environments, restrictions on littering CBs into the environment are required due to the release of different toxics ultimately causing adverse effects on aquatic organisms.

Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources

Persistent and mobile organic substances are those with the highest propensity to be widely distributed in groundwater and thereby, when emitted at low-levels, to contaminate drinking water extraction points and freshwater environments. To prevent such contamination, the European Commission is in the process of introducing new hazard classes for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances within its key chemical regulations CLP and REACH. The assessment of persistence in these regulations will likely be based on simulated half-life, t _1/2, thresholds; the assessment of mobility will likely be based on organic carbon-water distribution coefficient, K _OC, thresholds. This study reviews the use of t _1/2 and K _OC to describe persistence and mobility, considering the theory, history, suitability, data limitations, estimation methods, and alternative parameters. For this purpose, t _1/2, K _OC, and alternative parameters were compiled for substances registered under REACH, known transformation products, and substances detected in wastewater treatment plant effluent, surface water, bank filtrate, groundwater, raw water, and drinking water. Experimental t _1/2 values were rare and only available for 2.2% of the 14 203 unique chemicals identified. K _OC data were only available for a fifth of the substances. Therefore, the usage of alternative screening parameters was investigated to predict t _1/2 and K _OC values, to assist weight-of-evidence based PMT/vPvM hazard assessments. Even when considering screening parameters, for 41% of substances, PMT/vPvM assessments could not be made due to data gaps; for 23% of substances, PMT/vPvM assessments were ambiguous. Further effort is needed to close these substantial data gaps. However, when data is available, the use of t _1/2 and K _OC is considered fit-for-purpose for defining PMT/vPvM thresholds. Using currently discussed threshold values, between 1.9 and 2.6% of REACH registered substances were identified as PMT/vPvM. Among the REACH registered substances detected in drinking water sources, 24-30% were PMT/vPvM substances.

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.

Global groundwater vulnerability for Pharmaceutical and Personal care products (PPCPs): The scenario of second decade of 21st century

The global production of PPCPs have increased by multiple folds promoting excessive exposure of its metabolites to humans via different aquatic systems. The higher residence time of toxic precursors of these metabolites pose direct human health risk. Among the different aquatic systems, the contamination of groundwater by PPCPs is the most concerning threat. This threat is especially critical considering the lesser oxidizing potential of the groundwater as compared to freshwater/river water. A major challenge also arises due to excessive dependency of the world's population on groundwater, which is exponentially increasing with time. This makes the identification and characterization of spatial contamination hotspots highly probabilistic as compared to other freshwater systems. The situation is more vulnerable in developing countries where there is a reported inadequacy of wastewater treatment facilities, thereby forcing the groundwater to behave as the only available sequestrating sink for all these contaminants. With increased consumption of antibiotics and other pharmaceuticals compounds, these wastes have proven capability in terms of enhancing the resistance among the biotic community of the soil systems, which ultimately can become catastrophic and carcinogenic in near future. Recent studies are supporting the aforementioned concern where compounds like diclofenac (analgesic) have attained a concentration of 1.3 mgL^-1 in the aquifer systems of Delhi, India. The situation is far worse for developed nations where prolonged and indiscriminate usage of antidepressants and antibiotics have life threating consequences. It has been confirmed that certain compounds like ofloxacin (antibiotics) and bis-(2-ethylhexyl)phthalate are present in some of the most sensitive wells/springs of the United States and Mexico. The current trend of the situation has been demonstrated by integrating a comparative approach of the published literatures in last three years. This review provides first-hand information report for formulating a directive policy framework for tackling PPCPs issues in the groundwater system.

Non-target screening of micropollutants and transformation products for assessing AOP-BAC treatment in groundwater

Standard monitoring programs give limited insight into groundwater status, especially transformation products (TPs) formed by natural processes or advanced oxidation processes (AOP), are normally underrepresented. In this study, using suspect and non-target screening, we performed a comprehensive analysis of groundwater before and after AOP by UV/H₂O₂ and consecutively installed biological activated carbon filters (BAC). By non-target screening, up to 413 compounds were detected in the groundwater, with an average 70% removal by AOP. However, a similar number of compounds were formed during the process, shown in groundwater from three waterworks. The most polar compounds were typically the most stable during the AOP. A subsequent BAC filter showed removal of 95% of the TPs, but only 46% removal of the AOP remaining precursors. The BAC removal for polar compounds was highly dependent on the acidic and basic functional groups of the molecules. 49 compounds of a wide polarity range could be identified by supercritical fluid chromatography (SFC) and liquid chromatography (LC) with high resolution mass spectrometry (HRMS); of these, 29 compounds were already present in the groundwater. To the best of our knowledge, five compounds have never been reported before in groundwater (4-chlorobenzenesulfonic acid, dibutylamine, N-phenlybenzenesulfonamide, 2-(methylthio)benzothiazole and benzothiazole-2-sulfonate). A further five rarely reported compounds are reported for the first time in Danish groundwater (2,4,6-trichlorophenol, 2,5-dichlorobenzenesulfonic acid, trifluormethansulfonic acid, pyrimidinol and benzymethylamine). Twenty of the identified compounds were formed by AOP, of which 10 have never been reported before in groundwater. All detected compounds could be related to agricultural and industrial products as well as artificial sweeteners. Whereas dechlorination was a common AOP degradation pathway for chlorophenols, the (ultra-) short chain PFAs showed no removal in our study. We prioritized 11 compounds as of concern, however, the toxicity for many compounds remains unknown, especially for the TPs.

Non-target, suspect and target screening of chemicals of emerging concern in landfill leachates and groundwater in Guangzhou, South China

Landfill sites have been regarded as a significant source of chemicals of emerging concern (CECs) in groundwater. However, our understanding about the compositions of CECs in landfill leachate and adjacent groundwater is still very limited. Here we investigated the CECs in landfill leachates and groundwater of Guangzhou in South China by target, suspect and non-target analysis using high-resolution mass spectrometry (HRMS). A variety of CECs (n = 242), including pharmaceuticals (n = 64), pharmaceutical intermediates (n = 18), personal care products (n = 9), food additives (n = 18), industrial chemicals (n = 82, e.g., flame retardants, plasticizers, antioxidants and catalysts), pesticides (n = 26), transformation products (n = 8) and other organic compounds (n = 17) were (tentatively) identified by non-target and suspect screening. 142 CECs were quantitated with target analysis, and among them 37, 24 and 27 CECs were detected respectively in the raw leachate (272-1780 μg/L), treated leachate (0.25-0.81 μg/L) and groundwater (0.10-53.7 μg/L). The CECs in the raw leachates were efficiently removed with the removal efficiencies greater than 88.7%. Acesulfame, bisphenol F and ketoprofen were the most abundant compounds in both treated leachate and groundwater. The CECs in groundwater was found most likely to be originated from the landfill sites. Our results highlight the importance of non-target screening in identifying CECs, and reveal the contamination risk of groundwater by landfill leachate.

Pesticide fate during drinking water treatment determined through passive sampling combined with suspect screening and multivariate statistical analysis

Emerging contaminants such as polar pesticides pose a potential risk to human health due to their presence in drinking water. However, their occurrence and fate in drinking water treatment plants is poorly understood. In this study we use passive sampling coupled to suspect screening and multivariate analysis to describe pesticide fate throughout the treatment stream of an operational drinking water treatment plant. Chemcatcher^Ò passive sampling devices were deployed at sites (n = 6) positioned at all stages of the treatment stream during consecutive deployments (n = 20) over a twelve-month period. Sample extracts (n = 120) were analysed using high-resolution liquid chromatography-quadrupole-time-of-flight mass spectrometry and compounds identified against a commercially available database. A total of 58 pesticides and transformation products from different classes were detected. Statistical analysis of the qualitative screening data was performed to identify clusters of pesticides with similar fate during ozonation, granular activated carbon (GAC) filtration, and chlorination. The performance of each treatment process was investigated. Adsorption to GAC media was found to account for the greatest proportion of pesticide attenuation (average removal of 70% based on detection frequency), however, operational performance varied for certain pesticides during periods of episodic and sustained pollution. GAC breakthrough occurred for 21 compounds detected in the GAC filtrate. Eleven pesticides were found to occur in potable water following treatment. We developed a management plan containing controls, triggers, and responses, for five pesticides and a metabolite (atrazine, atrazine desethyl, DEET, dichlorobenzamide, metazachlor, and propyzamide) prioritised based on their current and future risk to treated water quality.

Occurrence, Distribution, and Environmental Behavior of Persistent, Mobile, and Toxic (PMT) and Very Persistent and Very Mobile (vPvM) Substances in the Sources of German Drinking Water

Persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances have been recognized as a threat to both the aquatic environment and to drinking water resources. These substances are currently prioritized for regulatory action by the European Commission, whereby a proposal for the inclusion of hazard classes for PMT and vPvM substances has been put forward. Comprehensive monitoring data for many PMT/vPvM substances in drinking water sources are scarce. Herein, we analyze 34 PMT/vPvM substances in 46 surface water, groundwater, bank filtrate, and raw water samples taken throughout Germany. Results of the sampling campaign demonstrated that known PMT/vPvM substances such as 1H-benzotriazole, melamine, cyanuric acid, and 1,4-dioxane are responsible for substantial contamination in the sources of German drinking water. In addition, the results revealed the widespread presence of the emerging substances 2-acrylamido-2-methylpropanesulfonic acid (AMPS) and diphenylguanidine (DPG). A correlation analysis showed a pronounced co-occurrence of PMT/vPvM substances associated predominantly with consumer or professional uses and also demonstrated an inhomogeneous co-occurrence for substances associated mainly with industrial use. These data were used to test the hypothesis that most PMT/vPvM substances pass bank filtration without significant concentration reduction, which is one of the main reasons for introducing PMT/vPvM as a hazard class within Europe.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

Comprehensive screening of polar emerging organic contaminants including PFASs and evaluation of the trophic transfer behavior in a freshwater food web

Bioaccumulation and trophic transfer of persistent legacy contaminants have been intensively characterized, but little is known on the contaminants of emerging concern (CECs) in freshwater food webs. Herein, we comprehensively screened CECs with a focus on polar substances and further evaluated their trophic transfer behavior in selected items from the food web of Lake Templin, Germany. Weselected one plankton, two mussel, and nine fish samples covering three trophic levels. With an effective multi-residue sample preparation method and high-resolution mass spectrometry-based target, suspect, and non-target screening, we characterized 477 targets and further screened unknown features in complex biota matrices. Of the 477 targets, 145 were detected and quantified in at least one species (0.02-3640 ng/g, dry weight). Additionally, the suspect and non-target analysis with experimental mass spectra libraries and in silico techniques (MetFrag and SIRIUS4/CSI:FingerID) enabled further identification of 27 unknown compounds with 19 confirmed by reference standards. Overall, the detected compounds belong to a diverse group of chemicals, including 71 pharmaceuticals, 27 metabolites, 26 pesticides, 16 per- and polyfluoroalkyl substances (PFASs), 4 plasticizers, 3 flame retardants, 11 other industrial chemicals and 14 others. Moreover, we determined the trophic magnification factor (TMF) of 34 polar CECs with >80% detection frequency, among which 6 PFASs including perfluorooctane sulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluorohexane sulfonic acid (PFHxS), perfluorotridecanoic acid (PFTrA), perfluorotetradecanoic acid (PFTeA), and perfluoroundecanoic acid (PFUnA), exhibited biomagnification potential (TMF =1.8 - 4.2, p < 0.05), whereas 5 pharmaceuticals (phenazone, progesterone, venlafaxine, levamisole, and lidocaine) and 1 personal care product metabolite (galaxolidone) showed biodilution potential (TMF = 0.4 - 0.6, p < 0.05).

Combining reverse osmosis and microbial degradation for remediation of drinking water contaminated with recalcitrant pesticide residue

Groundwater contamination by recalcitrant organic micropollutants such as pesticide residues poses a great threat to the quality of drinking water. One way to remediate drinking water containing micropollutants is to bioaugment with specific pollutant degrading bacteria. Previous attempts to augment sand filters with the 2,6-dichlorobenzamide (BAM) degrading bacterium Aminobacter niigataensis MSH1 to remediate BAM-polluted drinking water initially worked well, but the efficiency rapidly decreased due to loss of degrader bacteria. Here, we use pilot-scale augmented sand filters to treat retentate of reverse osmosis treatment, thus increasing residence time in the biofilters and potentially nutrient availability. In a first pilot-scale experiment, BAM and most of the measured nutrients were concentrated 5-10 times in the retentate. This did not adversely affect the abundances of inoculated bacteria and the general prokaryotic community of the sand filter presented only minor differences. On the other hand, the high degradation activity was not prolonged compared to the filter receiving non-concentrated water at the same residence time. Using laboratory columns, it was shown that efficient BAM degradation could be achieved for >100 days by increasing the residence time in the sand filter. A slower flow may have practical implications for the treatment of large volumes of water, however this can be circumvented when treating only the retentate water equalling 10-15% of the volume of inlet water. We therefore conducted a second pilot-scale experiment with two inoculated sand filters receiving membrane retentate operated with different residence times (22 versus 133 min) for 65 days. While the number of MSH1 in the biofilters was not affected, the effect on degradation was significant. In the filter with short residence time, BAM degradation decreased from 86% to a stable level of 10-30% degradation within the first two weeks. The filter with the long residence time initially showed >97% BAM degradation, which only slightly decreased with time (88% at day 65). Our study demonstrates the advantage of combining membrane filtration with bioaugmented filters in cases where flow rate is of high importance.

Chemical Leaching from Tire Wear Particles with Various Treadwear Ratings

Physical friction between a tire and the road surface generates tire wear particles (TWPs), which are a source of microplastics and particulate matter. This study investigated the trends of chemical leaching from TWPs depending on the treadwear rating of the tire. A road simulator was used to produce TWPs from tires with various treadwear ratings. Liquid chromatography–tandem mass spectrometry was used to analyze the chemical leaching from TWPs, with a particular focus on benzothiazole and its derivative 2-hydroxy benzothiazole. However, chemical mapping via high-resolution tandem mass spectrometry detected another derivative: 2-mercaptobenzothiazole. The benzothiazole groups were observed to have different leaching tendencies, implying that using benzothiazole as a marker compound may lead to incorrect TWP quantitation. The results of this research also suggest that the ecotoxicological influence of TWPs can vary with the treadwear rating of a tire.

Overarching issues on relevant pesticide transformation products in the aquatic environment: A review

The intensification of agricultural production during the last decades has forced the rapid increase in the use of pesticides that finally end up in the aquatic environment. Albeit well-documented, pesticides continue to raise researchers' attention, because of their potential adverse impacts on the environment and, inevitably, humans. Once entering the aquatic bodies, pesticides undergo biotic and abiotic processes, resulting in transformation products (TPs) that sometimes are even more toxic than the parent compounds. A substantial shift of the scientific interest in the TPs of pesticides has been observed since their environmental fate, occurrence and toxicity is still in its formative stage. In an ongoing effort to expand the existing knowledge on the topic, several interesting works have been performed mostly in European countries, such as France, Germany, Italy, Switzerland, Greece, and Spain that counts the highest number of relevant publications. Pesticide TPs have been also studied to a lesser extent in Asia, North and South America. To this end, the main objective of this review is to delineate the global occurrence, fate, toxicity as well as the analytical challenges related to pesticide TPs in surface, ground, and wastewaters, with the view to contribute to a better understanding of the environmental problems related with TPs formation. The concentration levels of the TPs, ranging from the low ng/L to high μg/L scale and distributed worldwide. Ultimately, an attempt to predict the acute and chronic toxicity of TPs has been carried out with the aid of an in-silico approach based on ECOSAR, revealing increased chronic toxicity for the majority of the identified TPs, despite the change they underwent, while a small portion of them presented serious acute toxicity values.

Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features

Non-target screening of trace organic compounds complements routine monitoring of water bodies. So-called features need to be extracted from the raw data that preferably represent a chemical compound. Relevant features need to be prioritized and further be interpreted, for instance by identifying them. Finally, quantitative data is required to assess the risks of a detected compound. This review presents recent and noteworthy contributions to the processing of non-target screening (NTS) data, prioritization of features as well as (semi-) quantitative methods that do not require analytical standards. The focus lies on environmental water samples measured by liquid chromatography, electrospray ionization and high-resolution mass spectrometry. Examples for fully-integrated data processing workflows are given with options for parameter optimization and choosing between different feature extraction algorithms to increase feature coverage. The regions of interest-multivariate curve resolution method is reviewed which combines a data compression alternative with chemometric feature extraction. Furthermore, prioritization strategies based on a confined chemical space for annotation, guidance by targeted analysis and signal intensity are presented. Exploiting the retention time (RT) as diagnostic evidence for NTS investigations is highlighted by discussing RT indexing and prediction using quantitative structure-retention relationship models. Finally, a seminal technology for quantitative NTS is discussed without the need for analytical standards based on predicting ionization efficiencies.

Continuous high-frequency pesticide monitoring to observe the unexpected and the overlooked

Synthetic Plant Protection Products (PPPs) are a key element for a large part of today's global food systems. However, the transport of PPPs and their transformation products (TPs) to water bodies has serious negative effects on aquatic ecosystems. Small streams in agricultural catchments may experience pronounced concentration peaks given the proximity to fields and poor dilution capacity. Traditional sampling approaches often prevent a comprehensive understanding of PPPs and TPs concentration patterns being limited by trade-offs between temporal resolution and duration of the observation period. These limitations result in a knowledge gap for accurate ecotoxicological risk assessment and the achievement of optimal monitoring strategies for risk mitigation. We present here high-frequency PPPs and TPs concentration time-series measured with the autonomous MS²Field platform that combines continuous sampling and on-site measurements with a high-resolution mass spectrometer, which allows for overcoming temporal trade-offs. In a small agricultural catchment, we continuously measured 60 compounds at 20 minutes resolution for 41 days during the growing season. This observation period included 8 large and 15 small rain events and provided 2560 concentration values per compound. To identify similarities and differences among the compound-specific concentration time-series, we analysed the entire dataset with positive matrix factorisation. Six factors sufficiently captured the overall complexity in concentration dynamics. While one factor reflected dilution during rainfall, five factors identified PPPs groups that seemed to share a common history of recent applications. The investigation per event of the concentration time-series revealed a surprising complexity of dynamic patterns; physico-chemical properties of the compounds did not influence the (dis)similarity of chemographs. Some PPPs concentration peaks led while others lagged by several hours the water level peaks during large events. During small events, water level peaks always preceded concentration peaks, which were generally only observed when the water levels had almost receded to pre-event levels. Thus, monitoring schemes relying on rainfall or water level as proxies for triggering sampling may lead to systematic biases. The high temporal resolution revealed that the Swiss national monitoring integrating over 3.5 days underestimated critical concentration peaks by a factor of eight to more than 32, captured 3 out of 11 exceedances of legal acute quality standards (the relevant values in the Swiss Water Protection Law) and recorded 1 out of 9 exceedances of regulatory acceptable concentrations (the relevant values for the PPPs registration process). MS²Field allowed for observing unexpected and overlooked pesticide dynamics with consequences for further research but also for monitoring. The large variability in timing of concentration peaks relative to water level calls for more in-depth analyses regarding the respective transport mechanisms. To perform these analyses, spatially distributed sampling and time-series of geo-referenced PPPs application data are needed.

Filling the knowledge gap: A suspect screening study for 1310 potentially persistent and mobile chemicals with SFC- and HILIC-HRMS in two German river systems

Persistent and mobile chemicals (PM chemicals) were searched for in surface waters by hydrophilic interaction liquid chromatography (HILIC) and supercritical fluid chromatography (SFC), both coupled to high resolution mass spectrometry (HRMS). A suspect screening was performed using a newly compiled list of 1310 potential PM chemicals to the data of 11 surface water samples from two river systems. In total, 64 compounds were identified by this approach. The overlap between HILIC- and SFC-HRMS was limited (31 compounds), confirming the complementarity of the two methods used. The identified PM candidates are characterized by a high polarity (median logD -0.4 at pH 7.5), a low molecular weight (median 187 g/mol), are mostly ionic (54 compounds) and contain a large number of heteroatoms (one per four carbons on average). Among the most frequently detected novel or yet scarcely investigated water contaminants were cyanoguanidine (11/11 samples), adamantan-1-amine (10/11), trifluoromethanesulfonate (9/11), 2-acrylamido-2-methylpropanesulfonate (10/11), and the inorganic anions hexafluorophosphate (11/11) and tetrafluoroborate (10/11). 31% of the identified suspects are mainly used in ionic liquids, a chemically diverse group of industrial chemicals with numerous applications that is so far rarely studied for their occurrence in the environment. Prioritization of the findings of PM candidates is hampered by the apparent lack of toxicity data. Hence, precautionary principles and minimization approaches should be applied for the risk assessment and risk management of these substances. The large share of novel water contaminants among these findings of the suspect screening indicates that the universe of PM chemicals present in the environment has so far only scarcely been explored. Dedicated analytical methods and screening lists appear essential to close the analytical gap for PM compounds.