Nontarget Screening Reveals Time Trends of Polar Micropollutants in a Riverbank Filtration System

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Broad Microbial Community Functions in a Conventional Activated Sludge System Exhibit Temporal Stability

Wastewater microbial communities within conventional activated sludge (CAS) systems can perform hundreds of biotransformations whose relative importance, frequency, and temporal stability remain largely unexplored. To improve our understanding of biotransformations in CAS systems, we collected 24 h composite samples from the influent and effluent of a CAS system over 14 days, analyzed samples using high-resolution mass spectrometry (HRMS), and conducted a nontarget analysis of our HRMS acquisitions. We found that over 50% of the chemical features in the influent were completely removed, and the daily number of detected features exhibited low variability with a coefficient of variation of 0.07. Additionally, we found 352 Core chemical features present in every sample at both locations. We used chemical features to search for evidence of 19 potential biotransformations and detected 9 of these biotransformations at a frequency of over 80 times per day, where evidence for dehydrogenations, hydroxylations, and acetylations was most frequently detected. The daily number of detections for the 9 biotransformations exhibited coefficients of variation ranging from 0.13-0.20, revealing the broad temporal stability for these wastewater microbial community functions. This stability contrasts with the previously observed temporal variability for micropollutant biotransformations, suggesting that micropollutant biotransformations are linked to specialized microbial community functions.

Global Trends and Hotspots in Non-Targeted Screening of Water Pollution Research: Bibliometric and Visual Analysis

Non-targeted screening (NTS) technology has been showing significant potential in identifying contaminants of emerging concern (CECs) in water and has attracted great attention in academia in recent years. It is a method that analyzes samples without pre-selecting substances, enabling the detection and identification of unknown compounds, which is crucial for environmental health and public protection. This study uses the Bibliometrix package in R 4.4.1 and CiteSpace 6.3.R1 software to statistically analyze 589 relevant publications from the Web of Science Core Collection from 2007 to 2024. Our work concentrates on NTS of water bodies; thus, articles that only analyze water sediments without analyzing the water were not considered for inclusion. By conducting a quantitative analysis and visualizing the publication trends, countries, authors, journals, and keywords, the present study identifies research hotspots, compositions, and paradigms within this field, trying to analyze the horizontal and vertical development trends and structural evolution of the research area. The research found that the application of NTS in water pollution studies has progressed through three phases: theoretical exploration, rapid development, and steady progress. From the national level, China leads with the highest number of publications (131), followed by Germany (105), Spain (50), and the United States (39). The top three authors by publication volume are J. Hollender, Nikolaos S. Thomaidis, and Emma L. Schymanski, while the top three by citation count are J. Hollender, Emma L. Schymanski, and M. Krauss. However, international collaboration between countries and researchers still remains an area for improvement. Science of the Total Environment is the journal with the highest number of publications (81), and Environmental Science & Technology holds the highest number of citations. Research on NTS methodologies, suspect screening, and health risk assessments are hot topics in the academic community. Future research is expected to be multidisciplinary, with emerging hotspots likely to focus on including the identification of novel pollutants through NTS, toxicity assessments of biotransformed compounds, and the health impacts and mechanisms of related compounds.

An integrated approach to evaluating water contaminants and evaporation in agricultural water distribution systems

This study presents an innovative approach for assessing water quality in agricultural irrigation networks, integrating stable isotope analysis, in vivo zebrafish screening, and comprehensive chemical profiling to investigate the occurrence, transformation, and potential toxicity of organic contaminants. Stable isotope analysis was used to measure evaporation as a proxy for water residence time in the canal, while liquid chromatography-high resolution mass spectrometry (LC-HRMS) identified a range of organic compounds in water samples collected from both the irrigation canal and its source river. Results indicated a reduction in contaminant levels in the canal compared to the river, with the most significant evaporation and concentration changes occurring at a holding reservoir, suggesting that managing residence time could help reduce water loss in arid irrigation networks. The data also highlighted how evaporation, particularly during the dry, hot season, influences contaminant dynamics. Hierarchical clustering of LC-HRMS results showed notable differences between the chemical profiles of canal and river samples, indicating that irrigation systems may contribute to the degradation or removal of certain compounds. Over 60 % of detected compounds were naturally derived, with anthropogenic contaminants like pesticides and personal care products further highlighting human impacts. Priority contaminants, including DEET and 2-naphthalene sulfonic acid, likely originated from urban activities upstream. Initial screening using zebrafish embryos showed bioactivity across sites, confirming the presence of contaminants needing further examination. Correlation analysis linked natural compounds to evaporation rates, suggesting that flora and fauna play significant roles in the chemical makeup of canal water. Overall, this approach provides a comprehensive framework for monitoring irrigation water, offering insights into contaminant behavior and supporting the development of standardized methods for assessing chemical fate and ecological risks in agricultural irrigation systems.

Riverine seasonal rainfall event tracing of organic pollution sources using fluorescence fingerprint difference spectrum

Elucidating the dynamics of dissolved organic matter (DOM) transport and transformation under seasonal rainfall events is essential for the conservation of riverine ecosystems, for mitigating the effects of climate change, and for crafting informed water management strategies. Therefore, this study aimed to investigate the evolutionary characteristics of organic pollution sources during consecutive rainfall events in early spring and to quantify their relative contributions to the process of surface water pollution. The results showed seasonal rainfall induces water quality exceedances in rivers due to the combined impacts of terrestrial inputs and endogenous releases. Humic acid (HA) (region V) and fulvic acid (FA) (region III) emerged as the predominant organic matter in the water column, with their fluorescence intensity altering as rainwater flushed the riverbed. Sources of pollution include agricultural and urban domestic sources (AS + DS) (72.29 %), industrial and urban domestic and microbial sources (IS + DS + MS) (37.71 %), and agricultural and industrial sources (AS + IS) (63.32 %), indicating that agricultural surface pollution discharges contribute significantly. The gas-chromatography-mass spectrometry (GC-MS) further confirmed that exogenous inputs were predominantly comprised of particulate pollutants. This study underscores the efficacy of fluorescence difference spectrometry in delineating the migration and transformation of river pollution sources during seasonal rainfall and facilitating the implementation of targeted management strategies for river ecosystems.

Quantification Approaches in Non-Target LC/ESI/HRMS Analysis: An Interlaboratory Comparison

Nontargeted screening (NTS) utilizing liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is increasingly used to identify environmental contaminants. Major differences in the ionization efficiency of compounds in ESI/HRMS result in widely varying responses and complicate quantitative analysis. Despite an increasing number of methods for quantification without authentic standards in NTS, the approaches are evaluated on limited and diverse data sets with varying chemical coverage collected on different instruments, complicating an unbiased comparison. In this interlaboratory comparison, organized by the NORMAN Network, we evaluated the accuracy and performance variability of five quantification approaches across 41 NTS methods from 37 laboratories. Three approaches are based on surrogate standard quantification (parent-transformation product, structurally similar or close eluting) and two on predicted ionization efficiencies (RandFor-IE and MLR-IE). Shortly, HPLC grade water, tap water, and surface water spiked with 45 compounds at 2 concentration levels were analyzed together with 41 calibrants at 6 known concentrations by the laboratories using in-house NTS workflows. The accuracy of the approaches was evaluated by comparing the estimated and spiked concentrations across quantification approaches, instrumentation, and laboratories. The RandFor-IE approach performed best with a reported mean prediction error of 15× and over 83% of compounds quantified within 10× error. Despite different instrumentation and workflows, the performance was stable across laboratories and did not depend on the complexity of water matrices.

Fingerprinting of emerging contaminants in L'Albufera natural park (Valencia, Spain): Implications for wetland ecosystem health

Wetlands are crucial ecosystems that are increasingly threatened by anthropogenic activities. L'Albufera Natural Park, the second-largest coastal wetland in Spain, faces significant pressures from surrounding agricultural lands, industrial activities, human settlements, and associated infrastructures, including treated wastewater inputs. This study aimed at (i) establishing pathways of emerging pollutants entering the natural wetland using both target and non-target screening (NTS) for management purposes, (ii) distinguishing specific contamination hotspots through Geographic Information System (GIS) and (iii) performing basic ecological risk assessment to evaluate ecosystem health. Two sampling campaigns were conducted in the spring and summer of 2019, coinciding with the start and end of the rice cultivation season, the region's primary agricultural activity. Each campaign involved the collection of 51 samples. High-resolution mass spectrometry (HRMS) was employed, using a simultaneous NTS approach with optimized gradients for pesticides and moderately polar compounds, along with complementary NTS methods for polar compounds, to identify additional contaminants of emerging concern (CECs). Quantitative analysis revealed that fungicides comprised a substantial portion of detected CECs, constituting approximately 50% of the total quantified pesticides. Tebuconazole emerged as the predominant fungicide, with the highest mean concentration (>16.9 μg L-1), followed by azoxystrobin and tricyclazole. NTS tentatively identified 16 pesticides, 43 pharmaceuticals and personal care products (PPCPs), 24 industrial compounds, and 12 other CECs with high confidence levels. Spatial distribution analysis demonstrated significant contamination predominantly in the southwestern region of the park, gradually diminishing towards the north-eastern outlet. The composition of contaminants varied between water and sediment samples, with pharmaceuticals predominating in water and industrial compounds in sediments. Risk assessment, evaluated through risk quotient calculations based on parent compound concentrations, revealed a decreasing trend towards the outlet, suggesting wetland degradation capacity. However, significant risk levels persist throughout much of the Natural Park, highlighting the urgent need for mitigation measures to safeguard the integrity of this vital ecosystem.

Managed aquifer recharge as a potential pathway of contaminants of emerging concern into groundwater systems - A systematic review

Groundwater is an often-overlooked resource, while its declining quantity and quality is of global concern. To protect and ensure stable quantity and quality of groundwater systems used as drinking water supplies, a common method is to artificially recharge these groundwater supplies with surface water, a process called managed aquifer recharge (MAR), that has been used globally for decades. However, surface waters used for MAR often contain elevated concentrations of anthropogenic chemicals of emerging concern (CECs), such as plastics, pesticides, pharmaceuticals and personal care products (PPCPs), or per- and polyfluoroalkyl substances (PFAS). When infiltrating this surface water, MAR can thus act as a shortcut for CECs into groundwater systems and eventually drinking water supplies. Especially PFAS are an example of very persistent contaminants showing atypical transport patterns during MAR and thus posing a risk for ground- and drinking water contamination. This systematic review addresses the transport process of CECs through MAR systems by looking at (1) common CEC concentrations in surface waters, (2) factors affecting CEC transport and possible retention during MAR, such as sorption and other physio-chemical mechanisms of CECs, biological and chemical decomposition, or hydrogeological properties of the MAR system, and (3) key contaminants leaching through the MAR systems as well as possible treatment options to improve the retention of CECs during MAR. Since we are facing increasing needs for high quality drinking water, lower CEC drinking water guidelines as well as an increasing number of identified CECs in surface waters, we conclude with a series of recommendations and future research directions to address these issues. Those include the need for regular monitoring programs specifically addressing CECs and especially not yet regulated, (very) persistent and (very) mobile contaminants, such as PFAS, as well as redesigned MAR systems to ensure stable ground- and drinking water quantity and quality.

Assessment of dynamics and variability of organic substances in river bank filtration for prioritisation in analytical workflows

Bank filtration supports the growing global demand for drinking water amidst concerns over organic micropollutants (OMPs). Efforts to investigate, regulate and manage OMPs have intensified due to their documented impacts on ecosystems and human health. Non-targeted analysis (NTA) is critical for addressing the challenge of numerous OMPs. While identification in NTA typically prioritises compounds based on properties like toxicity, considering substance quantity, occurrence frequency and exposure duration is essential for comprehensive risk management. A prioritisation scheme, drawing from intensive sampling and NTA of bank filtrate, is presented and reveals significant variability in OMP occurrence. Quasi-omnipresent substances, though only 7% of compounds, accounted for 44% of cumulative detections. Moderately common substances, constituting 31% of compounds, accounted for 50% of cumulative detections. Rare compounds, comprising 61%, contributed only 6% to cumulative detections. The application of suspect screening for 31 substances to the dataset yielded results akin to NTA, underscoring NTA's value. Correlation between both methods demonstrates the efficacy of high-resolution mass spectrometry-based NTA in assessing temporal and quantitative OMP dynamics.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Tracking Dynamic Chemical Reactivity Networks with High-Resolution Mass Spectrometry: A Case of Microplastic-Derived Dissolved Organic Carbon

Chemical degradation testing often involves monitoring the loss of a chemical or the evolution of a single diagnostic product through time. Here, we demonstrate a novel approach to tracing complex degradation networks using mass-spectrometry-based methods and open cheminformatics tools. Ester- and ether-based thermoplastic polyurethane (TPU_Ester and TPU_Ether) microplastics (350 μm) and microplastics-derived dissolved organic carbon (MP-DOC) were photoweathered in a simulated marine environment and subsequently analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We formula-annotated 1342 and 2344 unique features in the MP-DOC of TPU_Ester and TPU_Ether, respectively. From these, we extracted 199 and 568 plausible parent-transformation product pairs via matching of features (a) with complementary increasing and decreasing trends (Spearman's correlation coefficient between normalized intensity and time), (b) spectral similarities of at least three accurate mass MS2 fragments, and (c) at least 3 ppm agreement between the theoretical and measured change in m/z between the parent-transformation product formula. Molecular network analysis revealed that both chain scission and cross-linking reactions occur dynamically rather than degradation proceeding in a monotonic progression to smaller or more oxygenated structures. Network nodes with the highest degree of centrality were tentatively identified using in silico fragmentation and can be prioritized for toxicity screening or other physicochemical properties of interest. This work has important implications for chemical transformation tracking in complex mixtures and may someday enable improved elucidation of environmental transformation rules (i.e., structure-reactivity relationships) and fate modeling.

Using machine learning to trace the pollution sources of disinfection by-products precursors compared to receptor models

To increase the efficiency of managing backup water resources, it is critical to identify and allocate pollution sources. Source apportionment of dissolved organic matter (DOM) was investigated in our work. Parallel factor analysis (PARAFAC) and the Spearman correlation analysis were used for source identification. After that, a newly hybrid model applying the fuzzy c-means and support vector regression (FCM-SVR) was employed for source apportionment compared to receptor models. The results demonstrated that the FCM-SVR model exhibited excellent generalization, and only required standardization and normalization as pre-processing steps for dataset. According to the results, microbial sources played a key role (28.1 %) in the formation potential of disinfection byproducts (DBPFPs). Additionally, shipping marine sources exhibited a substantial contribution (21.2 %) to DBPFPs. The prediction accuracy of DBPFPs was matched or exceeded receptor models, and the R2 of DOC (0.884) was significantly high. Therefore, we recommend the FCM-SVR model combined with PARAFAC to trace the source of DBPFPs as its significant effectiveness in source identification, source apportionment, and prediction accuracy, possessing the potential for further applicability in tracking more organic compounds. ENVIRONMENTAL IMPLICATION: The disinfection byproducts precursors in water sources, which were thought to be hazardous materials in this study, are proved to be chlorinated into carcinogenic disinfection byproducts (DBPs) during drinking water treatment, However, the source apportionment methods of DBPs are not well developed compared to other inorganic matter, e.g., heavy metals and ammonia nitrogen. We proposed a new FCM-SVR model to trace the source of DBPs, which required easier pre-treatment and resulted a better source apportionment and prediction accuracy. As a result, it could provide a different prospect and useful management advices to trace the source of DBPs.

Combination of multivariate data analysis and mixing modelling to assess tracer potential of contaminants of emerging concern in aquifers

Collected evidence has shown that contaminants of emerging concern (CECs) in conjunction with more conventional tracers (major ions, nutrients, isotopes etc.) can be used to trace pollution origin in aquatic systems. However, in highly mixed aquifer systems signals obtained from conventional tracers overlap diminishing their potential to be used as tracers. In this study, we present an approach that incorporates multivariate statistical analysis (principal component analysis (PCA) and Kohonen's Self-Organizing Map method (SOM)) and mixing modelling to identify the most suitable CECs to be employed as anthropogenic tracers. The study area is located in the Besòs River Delta (Barcelona, NE Spain) and represents the highly mixed aquifer system. A one-year monthly based monitoring campaign was performed to collect the information about the concentrations of 105 CECs as well as major and minor ions in the river and along the groundwater flow. The dimensionality of the obtained dataset was reduced to 25 CECs, based on their estimated health risk effects, for multivariate data analysis. The obtained results showed the overlap of conventional tracers' signals obtained from PCA. In case of CECs, PCA revealed differences in their distributions allowing the differentiation of the roles of natural attenuation processes, local and regional flows on their occurrence in different parts of the aquifer. This was not possible to do using solely CECs' distribution profiles. SOMs provided the lacking information about the modality of the distribution of each CECs, revealing their ability to represent factors controlling the groundwater hydrochemistry, which assist in defining their tracer potential. Based on the obtained results four identified persistent CECs, two with unimodal (lamotrigine and 5-Desamino-5-oxo-lamotrigine) and two with bimodal (carbamazepine and diazepam (higher modality was not revealed)) distributions, were selected to run a mixing model to compare their tracer performance.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

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.

PBT assessment of chemicals detected in effluent of wastewater treatment plants by suspected screening analysis

Wastewater treatment plants (WWTPs) are the major sources of contaminants discharged into downstream water bodies. Profiling the contaminants in effluent of WWTPs is crucial to assess the potential eco-risks toward downstream organisms. To this end, this study investigated the contaminants in effluent of 10 WWTPs locating in 10 cities of Yangtze River delta region of China by suspected screening analysis. Further, the persistence, bioaccumulation, toxicity (PBT) and the characteristics sub-structures of PBT-like chemicals were analyzed. Totally, 704 chemicals including 155 chemical products, 31 food additives, 52 natural substances, 112 personal care products, 123 pesticides, 192 pharmaceuticals, 17 hormones and 22 others were found. The results of PBT analysis suggested that 42 chemicals (5.97% among the detected chemicals in WWTPs) were with PBT property. Among them, 31 contaminants were not reported previously. 9 characteristics sub-structures (N-methyleneisobutylamine, 1-naphthaldehyde, 2,3,3-trimethylcyclohexene, cyclohexanol, N-sec-butyl-n-propylamine, (5E)-2,6-dimethylocta-1,5-diene, 2-ethylphenol, pentadecane and 6-methoxyhexane) were found for PBT-like chemicals. The sub-structures of highly linear alkyl partially explained the significantly higher PBT score for personal care products. Present study provides fundamental information on PBT properties of contaminants in effluent of WWTPs, which will benefit to prioritize contaminants with high concerns in effluent of WWTPs.

An integrated non-targeted and targeted analysis approach for identification of semi-volatile organic compounds in indoor dust

Household dust contains a wide variety of semi-volatile organic compounds (SVOCs) that may pose health risks. We developed a method integrating non-targeted analysis (NTA) and targeted analysis (TA) to identify SVOCs in indoor dust. Based on a combined use of gas and liquid chromatography with high-resolution mass spectrometry, an automated, time-efficient NTA workflow was developed, and high accuracy was observed. A total of 128 compounds were identified at confidence level 1 or 2 in NIST standard reference material dust (SRM 2585). Among them, 113 compounds had not been reported previously, and this suggested the value of NTA in characterizing contaminants in dust. Additionally, TA was done to avoid the loss of trace compounds. By integrating data obtained from the NTA and TA approaches, SVOCs in SRM 2585 were prioritized based on exposure and chemical toxicity. Six of the top 20 compounds have never been reported in SRM 2585, including melamine, dinonyl phthalate, oxybenzone, diheptyl phthalate, drometrizole, and 2-phenylphenol. Additionally, significant influences of analytical instruments and sample preparation on NTA results were observed. Overall, the developed method provided a powerful tool for identifying SVOCs in indoor dust, which is necessary to obtain a more complete understanding of chemical exposures and risks in indoor environments.

Nontarget Identification of Novel Organophosphorus Flame Retardants and Plasticizers in Rainfall Runoffs and Agricultural Soils around a Plastic Recycling Industrial Park

Plastic recycling and reprocessing activities may release organophosphate ester (OPE) flame retardants and plasticizers into the surrounding environment. However, the relevant contamination profiles and impacts remain not well studied. This study investigated the occurrence of 28 OPEs and their metabolites (mOPEs) in rainfall runoffs and agricultural soils around one of the largest plastic recycling industrial parks in North China and identified novel organophosphorus compounds (NOPs) using high-resolution mass spectrometry-based nontarget analysis. Twenty and twenty-seven OPEs were detected in runoff water and soil samples, with total concentrations of 86.0-2491 ng/L and 2.53-199 ng/g dw, respectively. Thirteen NOPs were identified, of which eight were reported in the environment for the first time, including a chlorine-containing OPE, an organophosphorus heterocycle, a phosphite, three novel OPE metabolites, and two oligomers. Triphenylphosphine oxide and diphenylphosphinic acid occurred ubiquitously in runoffs and soils, with concentrations up to 390 ng/L and 40.2 ng/g dw, respectively. The downwind areas of the industrial park showed elevated levels of OPEs and NOPs. The contribution of hydroxylated mOPEs was higher in soils than in runoffs. These findings suggest that plastic recycling and reprocessing activities are significant sources of OPEs and NOPs and that biotransformation may further increase the ecological and human exposure risk.

Supercritical Fluid Chromatography Coupled to High-Resolution Mass Spectrometry Reveals Persistent Mobile Organic Compounds with Unknown Toxicity in Wastewater Effluents

Broad screening approaches for monitoring wastewater are normally based on reversed-phase liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS). This method is not sufficient for the very polar micropollutants, neglected in the past due to a lack of suitable analytical methods. In this study, we used supercritical fluid chromatography (SFC) to detect very polar and yet-undetected micropollutants in wastewater effluents. We tentatively identified 85 compounds, whereas 18 have only rarely been detected and 11 have not previously been detected in wastewater effluents such as 17α-hydroxypregnenolone, a likely transformation product (TP) of steroids, and 1H-indole-3-carboxamide, a likely TP from new synthetic cannabinoids. Suspect screening of 25 effluent wastewater samples from 8 wastewater treatment plants revealed several distinct potential pollution sources such as a pharmaceutical company and a golf court. The analysis of the same samples with LC-HRMS showed clearly how SFC increases the ionization efficiency for low-molecular-weight micropollutants (m/z < 300 Da) by a factor 2 to 87 times, which significantly improved the mass spectra for identifying very polar compounds. In order to assess which micropollutants might be of environmental concern, literature and toxicological databases were screened. There was a lack of available hazard and bio-activity data for regulatory-relevant in vitro and in vivo assays for >50% of the micropollutants. Especially, 70% of the data were lacking for the whole organism (in vivo) tests.

InSpectra - A platform for identifying emerging chemical threats

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

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.

Photochemical weathering of polyurethane microplastics produced complex and dynamic mixtures of dissolved organic chemicals

Sunlight exposure can naturally mitigate microplastics pollution in the surface ocean, however it results in emissions of dissolved organic carbon (DOC) whose characteristics and fate remain largely unknown. In this work, we investigated the effects of solar radiation on polyether (TPU_Ether) and polyester (TPU_Ester) thermoplastic polyurethane, and on a thermoset polyurethane (PU_Hardened). The microplastics were irradiated with simulated solar light with a UV dose of 350 MJ m^-2, which corresponds to roughly 15 months outdoor exposure at 31° N latitude. The particles were characterized using ATR-FTIR and elemental analysis. The DOC released to the aqueous phase was quantified by total organic carbon analysis and characterized by nontarget liquid chromatography coupled to high-resolution mass spectrometry. Polyurethane microplastics were degraded following mechanisms reconcilable with UV photo-oxidation. The carbon mass fraction released to the aqueous phase was 8.5 ± 0.5%, 3.7 ± 0.2%, and 2.8 ± 0.2% for TPU_Ether, TPU_Ester, and PU_Hardened, respectively. The corresponding DOC release rates, expressed as mg carbon per UV dose were 0.023, 0.013, and 0.010 mg MJ^-1 for TPU_Ether, TPU_Ester and PU_Hardened, respectively. Roughly three thousand unique by-products were released from photo-weathered TPUs, whereas 540 were detected in the DOC of PU_Hardened. This carbon pool was highly complex and dynamic in terms of physicochemical properties and susceptibility to further photodegradation after dissolution from the particles. Our results show that plastics photodegradation in the ocean requires chemical assessment of the DOC emissions in addition to the analysis of aged microplastics and that polymer chemistry influences the chain scission products.

New insight into fate and transport of organic compounds from pollution sources to aquatic environment using non-targeted screening: A wastewater treatment plant case study

A variety of chemicals discharged into the aquatic environment by the wastewater treatment plant (WWTP), which is a potential source of hazard to the ecological environment and human health. This study established a novel analytical method for all compounds using non-targeted screening to comprehensively explore the fate and transport of organic compounds from WWTP to aquatic environment. 3967 and 3636 features were detected in WWTP samples and river samples, respectively. Multi-level classification was applied to all identified compounds, and results showed that aliphatics were dominant in both abundance and response, accounting for an average of 35.49 % and 74.10 %, respectively. A total of 88 Emerging Contaminants (ECs), including 22 endocrine disrupting chemicals (EDCs), 12 pharmaceuticals and personal care products (PPCPs), 12 pesticides, 10 volatile organic compounds (VOCs), 5 persistent organic pollutants (POPs) and 27 chemicals with other uses, were identified from all compounds, and their traceability analysis was performed. Furthermore, the contribution rate of organic compounds from WWTP effluent to river was calculated to be 33.60 % by the analysis of source-sink relationship. An in-depth and comprehensive exploration of the fate and transport of all organic compounds will help to provide guidelines for the treatment technologies and achieve the traceability of pollutants.

Importance of hydraulic travel time for the evaluation of organic compounds removal in bank filtration

The growing global demand for drinking water is driving both the diversification of water supply sources and their sustainability. River bank filtration (RBF) is an excellent option since it strongly reduces the extent of treatment steps compared to direct usage of surface water. Organic micropollutants (e.g. pharmaceuticals) are widely recognized as a hazard in drinking water production from surface water. Due to their potentially high mobility, stability, bioaccumulation and persistency, these substances can pass through RBF-systems. Scientific studies on compound removal and attenuation efficiency of RBF rely on the knowledge of travel time to compare concentrations in the river to the ones in the bank filtrate since water quality in rivers can change rapidly. However, bank filtrate samples represent a mixture of water with different travel times as the flow paths vary. This has not yet been considered in studies of bank filtration removal efficiency for organic micro pollutants. Here we present a method that considers the residence-time distribution of the bank filtrate sample obtained by groundwater modelling to evaluate the removal efficiency of RBF for organic micropollutants. The method was tested in a comprehensive study with 50 samples taken over a one-year-period at a river bank filtration site in Vienna (Austria). Our findings revealed that better coverage of varying river water quality (higher sampling frequency during the period of infiltration) resulted not only in a higher number of compounds considered as removed but also significantly reduced the number of compounds considered to have formed during the RBF process. The application of the presented method indicated that RBF is very effective in removing organic micropollutants. Considering different travel times will provide better models and a better understanding of the potential of RBF for pollutant removal and thus supports its safe application as a solution to the growing demand for drinking water.

Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.

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.

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.

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.

Artificial infiltration in drinking water production: Addressing chemical hazards using effect-based methods

Artificial infiltration is an established managed aquifer recharge method that is commonly incorporated into drinking water processes. However, groundwater sourced from this type of purification method is prone to contamination with chemical hazards. Such an instance was previously shown at a Swedish DWTP where the river water was contaminated by hazardous chemicals during artificial infiltration. Further, there remains a paucity of research studying the quality of drinking water following this type of treatment from an effect-based bioanalytical perspective. In the current study, an effect-based assessment for chemical hazards was conducted for a Swedish drinking water system comprised of two DWTPs fed artificially-infiltrated river water. In this system, artificial infiltration of the river water takes approximately six to eight months. A sampling event was conducted in the autumn season and the samples were enriched by solid phase extraction. A panel of cell-based reporter gene assays representing several toxicity pathways was selected: oxidative stress response (Nrf2 activity), aryl hydrocarbon receptor (AhR) activation, and hormone receptor-mediated effects (estrogen receptor [ER], androgen receptor [AR]). AhR and ER bioactivities were detected in samples collected from the river intake and in the open-air infiltration basins prior to artificial infiltration. However, the AhR activity decreased and ER activity was effectively removed following artificial infiltration. In the Nrf2 and AR assays, no bioactivities above cut-off levels were detected in any samples collected along the entire treatment process of the drinking water production from source to tap. Using a suite of bioassays, the current study highlighted the effectiveness of artificial infiltration in reducing bioactive compounds in this raw river water. Although artificial infiltration is a common purification method in drinking water production, the limited number of effect-based studies evaluating the effectiveness of this method emphasizes the need for further research to better understand the risks and benefits of this water treatment process.

Occurrence and distribution of Pharmaceuticals and Personal Care Products (PPCPs) in wastewater related riverbank groundwater

Pharmaceutical and personal care products (PPCPs) are among the most frequently reported groups of emerging contaminants in groundwater worldwide. PPCPs in rivers may infiltrate into groundwater through hydraulic exchange and potentially threaten drinking water safety and human health. In the present study, the occurrence and distribution of nine PPCPs in riverbank groundwater and adjacent rivers (distance up to 113 m) were investigated at four sites with different lithological features and permeabilities of aquifers in a city in North China. Seven of nine PPCPs were detectable in groundwater, ranging from <LOQ (limit of quantification) to 128 ng/L. N,N-diethyl-meta-toluamide (DEET), carbamazepine, and caffeine had the highest detection frequencies (>90%). The concentrations and major compounds in river water varied with the sampling location and water system distribution, resulting in distinct compositions of PPCPs in the groundwater at each site along with different lithology and hydrological conditions. The spatial distribution of PPCPs in riverbank groundwater was affected by the hydraulic connection between the groundwater and river and the lithology of aquifers. Direct hydraulic connection of a fine sand aquifer to the adjacent river caused a decrease in PPCPs with increasing distance. The results also suggested that sandy gravel aquifers had a lower capacity to attenuate PPCPs compared to that of fine sand. Significant correlations between PPCP concentrations and thirteen physicochemical factors of groundwater were discovered, including nitrate, potassium, and manganese. Overall, this study provides important evidence on the role of lithology and hydrological conditions on the composition, distribution, and influential physicochemical factors of PPCPs in riverbank groundwater.

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.

Suspect, non-target and target screening of pharmaceuticals and personal care products (PPCPs) in a drinking water system

Drinking water quality and safety are very important in protecting human health. Chemical contaminants in drinking water system have become an increasing concern. Our knowledge about what chemicals are present in drinking water is still limited. Here we screened chemicals of emerging concern in a conventional drinking water system based on suspect, non-target screening and target analysis, and assessed their variations in different seasons and different treatment units. Overall, 720 chemicals were identified with HRMS databases from the suspect and non-target screening and 48 chemicals in five categories were further confirmed with the high confidence level, with predominance of pharmaceuticals and personal care products (PPCPs) and pesticides. Four compounds are newly found in aquatic environment with no literature or chemical occurrence data record. Temporal variations and variable removals were observed for these chemicals in the system. Target analysis of 110 PPCPs showed detection of 21, 19 and 22 compounds in the drinking water treatment plant with a concentration range of 0.11-844 ng/L in the three seasons, but only 8, 9 and 15 compounds detected in tap water (0.16-32.5 ng/L). The variations of the detected chemicals were less obvious in tap water, with most having concentrations below 2 ng/L. The results indicated efficient removal for most PPCPs in the drinking water system. The findings from this study demonstrated the strong capability of combined non-target screening and target analysis in identifying and assessing various organic chemicals in drinking water system.

Novel prioritisation strategies for evaluation of temporal trends in archived white-tailed sea eagle muscle tissue in non-target screening

Environmental monitoring studies based on target analysis capture only a small fraction of contaminants of emerging concern (CECs) and miss pollutants potentially harmful to wildlife. Environmental specimen banks, with their archived samples, provide opportunities to identify new CECs by temporal trend analysis and non-target screening. In this study, archived white-tailed sea eagle (Haliaeetus albicilla) muscle tissue was analysed by non-targeted high-resolution mass spectrometry. Univariate statistical tests (Mann-Kendall and Spearman rank) for temporal trend analysis were applied as prioritisation methods. A workflow for non-target data was developed and validated using an artificial time series spiked at five levels with gradient concentrations of selected CECs (n = 243). Pooled eagle muscle tissues collected 1965-2017 were then investigated with an eight-point time series using the validated screening workflow. Following peak detection, peak alignment, and blank subtraction, 14 409 features were considered for statistical analysis. Prioritisation by time-trend analysis detected 207 features with increasing trends. Following unequivocal molecular formula assignment to prioritised features and further elucidation with MetFrag and EU Massbank, 13 compounds were tentatively identified, of which four were of anthropogenic origin. These results show that it is possible to prioritise new CECs in archived biological samples using univariate statistical approaches.

Persistence and removal of trace organic compounds in centralized and decentralized wastewater treatment systems

The persistence of trace organic chemicals in treated effluent derived from both centralized wastewater treatment plants (WWTPs) and decentralized wastewater treatment systems (DEWATS) is of concern due to their potential impacts on human and ecosystem health. Here, we utilize non-targeted analysis (NTA) with comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry (GC × GC/TOF-MS) to conduct an evaluation of the common persistent and removed compounds found in two centralized WWTPs in the USA and South Africa and one DEWATS in South Africa. Overall, removal efficiencies of chemicals were similar between the treatment plants when they were compared according to the number of chemical features detected in the influents and effluents of each treatment plant. However, the DEWATS treatment train, which has longer solids retention and hydraulic residence times than both of the centralized WWTPs and utilizes primarily anaerobic treatment processes, was able to remove 13 additional compounds and showed a greater decrease in normalized peak areas compared to the centralized WWTPs. Of the 111 common compounds tentatively identified in all three influents, 11 compounds were persistent in all replicates, including 5 compounds not previously reported in effluents of WWTPs or water reuse systems. There were no significant differences among the physico-chemical properties of persistent and removed compounds, but significant differences were observed among some of the molecular descriptors. These results have important implications for the treatment of trace organic chemicals in centralized and decentralized WWTPs and the monitoring of new compounds in WWTP effluent.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

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.

Framework for nontargeted investigation of contaminants released by wildfires into stormwater runoff: Case study in the northern San Francisco Bay area

Wildfires can be extremely destructive to communities and ecosystems. However, the full scope of the ecological damage is often hard to assess, in part due to limited information on the types of chemicals introduced to affected landscapes and waterways. The objective of this study was to establish a sampling, analytical, and interpretive framework to effectively identify and monitor contaminants of emerging concern in environmental water samples impacted by wildfire runoff. A nontargeted analysis consisting of comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC/TOF-MS) was conducted on stormwater samples from watersheds in the City of Santa Rosa and Sonoma and Napa Counties, USA, after the three most destructive fires during the October 2017 Northern California firestorm. Chemicals potentially related to wildfires were selected from the thousands of chromatographic features detected through a screening method that compared samples from fire-impacted sites versus unburned reference sites. This screening led to high confidence identifications of 76 potentially fire-related compounds. Authentic standards were available for 48 of these analytes, and 46 were confirmed by matching mass spectra and GC × GC retention times. Of these 46 compounds, 37 had known commercial and industrial uses as intermediates or ingredients in plastics, personal care products, pesticides, and as food additives. Nine compounds had no known uses or sources and may be oxidation products resulting from burning of natural or anthropogenic materials. Preliminary examination of potential toxicity associated with the 46 compounds, conducted via online databases and literature review, indicated limited data availability. Regional comparison suggested that more structural damage may yield a greater number of unique, potentially wildfire-related compounds. We recommend further study of post-wildfire runoff using the framework described here, which includes hypothesis-driven site selection and nontargeted analysis, to uncover potentially significant stormwater contaminants not routinely monitored after wildfires and inform risk assessment. Integr Environ Assess Manag 2021;17:1179-1193. © 2021 SETAC.

Data analysis strategies for the characterization of chemical contaminant mixtures. Fish as a case study

Thousands of chemicals are potentially contaminating the environment and food resources, covering a wide spectrum of molecular structures, physico-chemical properties, sources, environmental behavior and toxic profiles. Beyond the description of the individual chemicals, characterizing contaminant mixtures in related matrices has become a major challenge in ecological and human health risk assessments. Continuous analytical developments, in the fields of targeted (TA) and non-targeted analysis (NTA), have resulted in ever larger sets of data on associated chemical profiles. More than ever, the implementation of advanced data analysis strategies is essential to elucidate profiles and extract new knowledge from these large data sets. Specifically focusing on the data analysis step, this review summarizes the recent progress in integrating data analysis tools into TA and NTA workflows to address the challenging characterization of chemical mixtures in environmental and food matrices. As fish matrices are relevant in both aquatic pollution and consumer exposure perspectives, fish was chosen as the main theme to illustrate this review, although the present document is equally relevant to other food and environmental matrices. The key features of TA and NTA data sets were reviewed to illustrate the challenges associated with their analysis. Advanced filtering strategies to mine NTA data sets are presented, with a particular focus on chemical filters and discriminant analysis. Further, the applications of supervised and unsupervised multivariate analysis methods to characterize exposure to chemical mixtures, and their associated challenges, is discussed.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Measurement(s)	chemical • drinking water
Technology Type(s)	high resolution mass spectrometry • non-target analysis • Interlaboratory
Factor Type(s)	method
Sample Characteristic - Environment	laboratory environment

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665

Data Analytics for Environmental Science and Engineering Research

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

Characterization of advanced wastewater treatment with ozone and activated carbon using LC-HRMS based non-target screening with automated trend assignment

Advanced treatment is increasingly being applied to improve abatement of micropollutants in wastewater effluent and reduce their load to surface waters. In this study, non-target screening of high-resolution mass spectrometry (HRMS) data, collected at three Swiss wastewater treatment plants (WWTPs), was used to evaluate different advanced wastewater treatment setups, including (1) granular activated carbon (GAC) filtration alone, (2) pre-ozonation followed by GAC filtration, and (3) pre-ozonation followed by powdered activated carbon (PAC) dosed onto a sand filter. Samples were collected at each treatment step of the WWTP and analyzed with reverse-phase liquid chromatography coupled to HRMS. Each WWTP received a portion of industrial wastewater and a prioritization method was applied to select non-target features potentially resulting from industrial activities. Approximately 37,000 non-target features were found in the influents of the WWTPs. A number of non-target features (1207) were prioritized as likely of industrial origin and 54 were identified through database spectral matching. The fates of all detected non-target features were assessed through a novel automated trend assignment method. A trend was assigned to each non-target feature based on the normalized intensity profile for each sampling date. Results showed that 73±4% of influent non-target features and the majority of industrial features (89%) were well-removed (i.e., >80% intensity reduction) during biological treatment in all three WWTPs. Advanced treatment removed, on average, an additional 11% of influent non-target features, with no significant differences observed among the different advanced treatment settings. In contrast, when considering a subset of 66 known micropollutants, advanced treatment was necessary to adequately abate these compounds and higher abatement was observed in fresh GAC (7,000-8,000 bed volumes (BVs)) compared to older GAC (18,000-48,000 BVs) (80% vs 56% of micropollutants were well-removed, respectively). Approximately half of the features detected in the WWTP effluents were features newly formed during the various treatment steps. In ozonation, between 1108-3579 features were classified as potential non-target ozonation transformation products (OTPs). No difference could be observed for their removal in GAC filters at the BVs investigated (70% of OTPs were well-removed on average). Similar amounts (67%) was observed with PAC (7.7-13.6 mg/L) dosed onto a sand filter, demonstrating that a post-treatment with activated carbon is efficient for the removal of OTPs.

The source apportionment of N and P pollution in the surface waters of lowland urban area based on EEM-PARAFAC and PCA-APCS-MLR

Multiple sources contribute to nitrogen(N) and phosphorus (P) pollution in lowland urban rivers, and apportioning the sources of N and P pollution is essential for improving the ecological health of urban environments. Three urban polders in Jiaxing were selected to investigate the temporal variations of N and P pollutants in lowland urban river waters under dry and wet conditions. Moreover, the main potential sources of N and P pollution were identified through the correlations of pollutants and components of dissolved organic matter (DOM) derived from excitation-emission matrix (EEM) and parallel factor analysis (PARAFAC). The results indicate that the main pollution sources identified with PCA method were consistent with the potential sources revealed by DOM's EEM-PARAFAC components. Furthermore, absolute principal components score combined with multivariate linear regression (APCS-MLR) was conducted. The results illustrated that domestic wastewater contributes more than 70% of N pollution and river-bottom sediments contribute more than 50% of P pollution under dry conditions. On the contrary, discharged water from the stormwater outlets contributes more than 41% of P and 75% of N under wet conditions. Specifically, about 48% of them come from domestic wastewater, and about 38% come from urban surface runoff. This study highlights the effectiveness of DOM components derived from EEM-PARAFAC in identifying the sources of N and P pollution and the PCA-APCS-MLR in apportioning the contributions of each potential pollution source in lowland urban rivers.

Ozonation products from trace organic chemicals in municipal wastewater and from metformin: peering through the keyhole with supercritical fluid chromatography-mass spectrometry

Ozonation is an important process to further reduce the trace organic chemicals (TrOCs) in treated municipal wastewater before discharge into surface waters, and is expected to form products that are more oxidized and more polar than their parent compounds. Many of these ozonation products (OPs) are biodegradable and thus removed by post-treatment (e.g., aldehydes). Most studies on OPs of TrOCs in wastewater rely on reversed-phase liquid chromatography- mass spectrometry (RPLC-MS), which is not suited for highly polar analytes. In this study, supercritical fluid chromatography combined with high resolution MS (SFC-HRMS) was applied in comparison to the generic RPLC-HRMS to search for OPs in ozonated wastewater treatment plant effluent at pilot-scale. While comparable results were obtained from these two techniques during suspect screenings for known OPs, a total of 23 OPs were only observed by SFC-HRMS via non-targeted screening. Several SFC-only OPs were proposed as the derivatives of methoxymethylmelamines, phenolic sulfates/sulfonates, and metformin; the latter was confirmed by laboratory-scale ozonation experiments. A complete ozonation pathway of metformin, a widespread and extremely hydrophilic TrOC in aquatic environment, was elaborated based on SFC-HRMS analysis. Five of the 10 metformin OPs are reported for the first time in this study. Three different dual-media filters were compared as post-treatments, and a combination of sand/anthracite and fresh post-granular activated carbon proved most effective in OPs removal due to the additional adsorption capacity. However, six SFC-only OPs, two of which originating from metformin, appeared to be persistent during all post-treatments, raising concerns on their occurrence in drinking water sources impacted by wastewater.

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

Groundwater is a major drinking water resource but its quality with regard to organic micropollutants (MPs) is insufficiently assessed. Therefore, we aimed to investigate Swiss groundwater more comprehensively using liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). First, samples from 60 sites were classified as having high or low urban or agricultural influence based on 498 target compounds associated with either urban or agricultural sources. Second, all LC-HRMS signals were related to their potential origin (urban, urban and agricultural, agricultural, or not classifiable) based on their occurrence and intensity in the classified samples. A considerable fraction of estimated concentrations associated with urban and/or agricultural sources could not be explained by the 139 detected targets. The most intense nontarget signals were automatically annotated with structure proposals using MetFrag and SIRIUS4/CSI:FingerID with a list of >988,000 compounds. Additionally, suspect screening was performed for 1162 compounds with predicted high groundwater mobility from primarily urban sources. Finally, 12 nontargets and 11 suspects were identified unequivocally (Level 1), while 17 further compounds were tentatively identified (Level 2a/3). amongst these were 13 pollutants thus far not reported in groundwater, such as: the industrial chemicals 2,5-dichlorobenzenesulfonic acid (19 detections, up to 100 ng L^-1), phenylphosponic acid (10 detections, up to 50 ng L^-1), triisopropanolamine borate (2 detections, up to 40 ng L^-1), O-des[2-aminoethyl]-O-carboxymethyl dehydroamlodipine, a transformation product (TP) of the blood pressure regulator amlodipine (17 detections), and the TP SYN542490 of the herbicide metolachlor (Level 3, 33 detections, estimated concentrations up to 100-500 ng L^-1). One monitoring site was far more contaminated than other sites based on estimated total concentrations of potential MPs, which was supported by the elucidation of site-specific nontarget signals such as the carcinogen chlorendic acid, and various naphthalenedisulfonic acids. Many compounds remained unknown, but overall, source related prioritisation proved an effective approach to support identification of compounds in groundwater.

Optimized suspect screening approach for a comprehensive assessment of the impact of best management practices in reducing micropollutants transport in the Potomac River watershed

The vast number of chemicals potentially reaching aquatic environment pose a challenge in maintaining the quality of water resources. However, best management practices to improve water quality are typically focused on reducing nutrient transport without assessing how these practices may impact the occurrence of micropollutants. The potential for co-management of nutrients and organic micropollutants exists, but few studies have comprehensively evaluated the suite of contaminants associated with different water quality management practices (riparian zone restoration, stormwater management, etc.). Furthermore, most studies dealing with the determination of micropollutants in environmental samples include only a limited number of target analytes, leaving many contaminants undetected. To address this limitation, there has been a gradual shift in environmental monitoring from using target analysis to either suspect screening analysis (SSA) or non-targeted analysis (NTA), which relies on accurate mass measurements, mass spectral fragmentation patterns, and retention time information obtained using liquid chromatography coupled to high-resolution mass spectrometry. The work presented in this paper focuses on a wide-scope detection of micropollutants in surface water samples from the Potomac River watershed (United States). An in-house database composed of 1039 compounds based on experimental analysis of primary standards was established, and SSA workflow was optimized and applied to determine the presence of micropollutants in surface water. A total of 103 micropollutants were detected in the samples, some of which are contaminants that were not previously monitored and belong to various classes such as pharmaceuticals, personal care products, per-and polyfluoroalkyl substances and other persistent industrial chemicals. The impact of best management practices being implemented for nitrogen and phosphorus reductions were also assessed for their potential to reduce micropollutant transport. This work illustrates the advantages of suspect screening methods to determine a large number of micropollutants in environmental samples and reveals the potential to co-manage a diverse array of micropollutants based on shared transport and transformation mechanisms in watersheds.

Non-targeted tandem mass spectrometry enables the visualization of organic matter chemotype shifts in coastal seawater

Urbanization along coastlines alters marine ecosystems including contributing molecules of anthropogenic origin to the coastal dissolved organic matter (DOM) pool. A broad assessment of the nature and extent of anthropogenic impacts on coastal ecosystems is urgently needed to inform regulatory guidelines and ecosystem management. Recently, non-targeted tandem mass spectrometry approaches are gaining momentum for the analysis of global organic matter composition (chemotypes) including a wide array of natural and anthropogenic compounds. In line with these efforts, we developed a non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) workflow that utilizes advanced data analysis approaches such as feature-based molecular networking and repository-scale spectrum searches. This workflow allows the scalable comparison and mapping of seawater chemotypes from large-scale spatial surveys as well as molecular family level annotation of unknown compounds. As a case study, we visualized organic matter chemotype shifts in coastal environments in northern San Diego, USA, after notable rain fall in winter 2017/2018 and highlight potential anthropogenic impacts. The observed seawater chemotype, consisting of 4384 LC-MS/MS features, shifted significantly after a major rain event. Molecular drivers of this shift could be attributed to multiple anthropogenic compounds, including pesticides (Imazapyr and Isoxaben), cleaning products (Benzyl-tetradecyl-dimethylammonium) and chemical additives (Hexa (methoxymethyl)melamine) and potential degradation products. By expanding the search of identified xenobiotics to other public tandem mass spectrometry datasets, we further contextualized their possible origin and show their importance in other ecosystems. The mass spectrometry and data analysis pipelines applied here offer a scalable framework for future molecular mapping and monitoring of marine ecosystems, which will contribute to a deliberate assessment of how chemical pollution impacts our oceans.

Non-targeted screening of trace organic contaminants in surface waters by a multi-tool approach based on combinatorial analysis of tandem mass spectra and open access databases

Non-targeted screening (NTS) in mass spectrometry (MS) helps alleviate the shortcoming of targeted analysis such as missing the presence of concerning compounds that are not monitored and its lack of retrospective analysis to subsequently look for new contaminants. Most NTS workflows include high resolution tandem mass spectrometry (HRMS²) and structure annotation with libraries which are still limited. However, in silico combinatorial fragmentation tools that simulate MS² spectra are available to help close the gap of missing compounds in empirical libraries. Three NTS tools were combined and used to detect and identify unknown contaminants at ultra-trace levels in surface waters in real samples in this qualitative study. Two of them were based on combinatorial fragmentation databases, MetFrag and the Similar Partition Searching algorithm (SPS), and the third, the Global Natural Products Social Networking (GNPS), was an ensemble of empirical databases. The three NTS tools were applied to the analysis of real samples from a local river. A total of 253 contaminants were identified by combining all three tools: 209 were assigned a probable structure and 44 were confirmed using reference standards. The two major classes of contaminants observed were pharmaceuticals and consumer product additives. Among the confirmed compounds, octylphenol ethoxylates, denatonium, irbesartan and telmisartan are reported for the first time in surface waters in Canada. The workflow presented in this work uses three highly complementary NTS tools and it is a powerful approach to help identify and strategically select contaminants and their transformation products for subsequent targeted analysis and uncover new trends in surface water contamination.

Toxicity Testing of Effluent-Dominated Stream Using Predictive Molecular-Level Toxicity Signatures Based on High-Resolution Mass Spectrometry: A Case Study of the Lubbock Canyon Lake System

Current aquatic toxicity assessments usually focus on targeted analyses coupled with toxicity testing to determine the impacts of complex mixtures on aquatic organisms. However, based on this approach alone, it is sometimes difficult to explain observed toxicity from the selected chemical analytes. Recent analytical advances such as high-resolution mass spectrometry (HRMS) can improve the characterizations of the chemical composition of complex mixtures, but the intensive labor required to produce confident identifications limits its utility in high-throughput screening. In the present study, we evaluated a rapid workflow to predict potential toxicity signatures of complex water samples based on high-throughput, tentative HRMS identifications derived from database matching, followed by identification of chemical-ligand interactions and pathway identification. We tested the workflow with water samples from the effluent-dominated Lubbock Canyon Lake System (LCLS). Results across all sites showed that predicted toxicity signatures had little variation when correcting for HRMS false-positive rates. The most common pathways across sites were gonadotropin-releasing hormone receptor and α-adrenergic receptor signaling. Alterations to the predicted pathways were successfully observed in larval zebrafish exposures to LCLS water samples. These results may allow researchers to better utilize rapid assessments of HRMS data for the assessment of adverse impacts on aquatic organisms.