Fall Creek Monitoring Station: Highly Resolved Temporal Sampling to Prioritize the Identification of Nontarget Micropollutants in a Small Stream

Long-term impact of Hurricane Maria on point-of-use drinking water quality in Puerto Rico and associated potential adverse health effects

Drinking water security in Puerto Rico (PR) is increasingly challenged by both regulated and emerging anthropogenic contaminants, which was exacerbated by the Hurricane Maria (HM) due to impaired regional water cycle and damaged water infrastructure. Leveraging the NIEHS PROTECT (Puerto Rico Testsite for Exploring Contamination Threats) cohort, this study assessed the long-term tap water (TW) quality changes from March 2018 to November 2018 after HM in PR, by innovatively integrating two different effect-based quantitative toxicity assays with a targeted analysis of 200 organic and 22 inorganic pollutants. Post-hurricane PR TW quality showed recovery after >6-month period as indicated by the decreased number of contaminants showing elevated average concentrations relative to pre-hurricane samples, with significant difference of both chemical and toxicity levels between northern and southern PR. Molecular toxicity profiling and correlation revealed that the HM-accelerated releases of certain pesticides and PPCPs could exert increased cellular oxidative and/or AhR (aryl hydrocarbon receptor)-mediated activities that may persist for more than six months after HM. Maximum cumulative ratio and adverse outcome pathway (AOP) assessment identified the top ranked detected TW contaminants (Cu, Sr, V, perfluorooctanoic acid) that potentially associated with different adverse health effects such as inflammation, impaired reproductive systems, cancers/tumors, and/or organ toxicity. These insights can be incorporated into the regulatory framework for post-disaster risk assessment, guiding water quality control and management for public health protection.

Emerging contaminants: A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

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.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

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.

Animal production predominantly contributes to antibiotic profiles in the Yangtze River

Human-induced antibiotic pollution in the world's large rivers poses significant risk to riverine ecosystems, water quality, and human health. This study identified geophysical and socioeconomic factors driving antibiotic pollution in the Yangtze River by quantifying 83 target antibiotics in water and sediment samples collected in its 6300-km-long reach, followed by source apportionment and statistical modeling. Total antibiotic concentrations ranged between 2.05-111 ng/L in water samples and 0.57-57.9 ng/g in sediment samples, contributed predominantly by veterinary antibiotics, sulfonamides and tetracyclines, respectively. Antibiotic compositions were clustered according to three landform regions (plateau, mountain-basin-foothill, and plains), resulting from varying animal production practices (cattle, sheep, pig, poultry, and aquaculture) in the sub-basins. Population density, animal production, total nitrogen concentration, and river water temperature are directly associated with antibiotic concentrations in the water samples. This study revealed that the species and production of food animals are key determinants of the geographic distribution pattern of antibiotics in the Yangtze River. Therefore, effective strategies to mitigate antibiotic pollution in the Yangtze River should include proper management of antibiotic use and waste treatment in animal production.

Organophosphate esters (OPEs) in plastic food packaging: non-target recognition, and migration behavior assessment

Organophosphate esters (OPEs) are widely used as plasticizers in plastic food packaging; however, the migration of OPEs from plastic to food is largely unstudied. We do not even know the specific number of OPEs that exist in the plastic food packaging. Herein, an integrated target, suspect, and nontarget strategy for screening OPEs was optimized using ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The strategy was used to analyze 106 samples of plastic food packaging collected in Nanjing city, China, in 2020. HRMS allowed full or tentative identification of 42 OPEs, of which seven were reported for the first time. Further, oxidation products of bis(2,4-di-tert-butylphenyl) pentaerythritol diphosphite (AO626) in plastics were identified, implying that the oxidation of organophosphite antioxidants (OPAs) could be an important indirect source of OPEs in plastics. The migration of OPEs was examined with four simulated foods. Twenty-six out of 42 OPEs were detected in at least one of the four simulants, particularly isooctane, in which diverse OPEs were detected at elevated concentrations. Overall, the study supplements the list of OPEs that humans could ingest as well as provides essential information regarding the migration of OPEs from plastic food packaging to food.

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.

Temporal scales of pesticide exposure and risks in German small streams

Following agricultural application, pesticides can enter streams through runoff during rain events. However, little information is available on the temporal dynamics of pesticide toxicity during the main application period. We investigated pesticide application and large scale in-stream monitoring data from 101 agricultural catchments obtained from a Germany-wide monitoring from April to July in 2018 and 2019. We analysed temporal patterns of pesticide application, in-stream toxicity and exceedances of regulatory acceptable concentrations (RAC) for over 70 pesticides. On a monthly scale from April to July, toxicity to invertebrates and algae/aquatic plants (algae) obtained with event-driven samples (EDS) was highest in May/June. The peak of toxicity towards invertebrates and algae coincided with the peaks of insecticide and herbicide application. Future monitoring, i.e. related to the Water Framework Directive, could be limited to time periods of highest pesticide applications on a seasonal scale. On a daily scale, toxicity to invertebrates from EDS exceeded those of grab samples collected within one day after rainfall by a factor of 3.7. Within two to three days, toxicity in grab samples declined compared to EDS by a factor of ten for invertebrates, and a factor of 1.6 for algae. Thus, toxicity to invertebrates declined rapidly within 1 day after a rainfall event, whereas toxicity to algae remained elevated for up to 4 days. For six pesticides, RAC exceedances could only be detected in EDS. The exceedances of RACs coincided with the peaks in pesticide application. Based on EDS, we estimated that pesticide exposure would need a 37-fold reduction of all analysed pesticides, to meet the German environmental target to keep RAC exceedances below 1 % of EDS. Overall, our study shows a high temporal variability of exposure on a monthly but also daily scale to individual pesticides that can be linked to their period of application and related rain events.

Transformation Products of Tire Rubber Antioxidant 6PPD in Heterogeneous Gas-Phase Ozonation: Identification and Environmental Occurrence

6PPD, a tire rubber antioxidant, poses substantial ecological risks because it can form a highly toxic quinone transformation product (TP), 6PPD-quinone (6PPDQ), during exposure to gas-phase ozone. Important data gaps exist regarding the structures, reaction mechanisms, and environmental occurrence of TPs from 6PPD ozonation. To address these data gaps, gas-phase ozonation of 6PPD was conducted over 24-168 h and ozonation TPs were characterized using high-resolution mass spectrometry. The probable structures were proposed for 23 TPs with 5 subsequently standard-verified. Consistent with prior findings, 6PPDQ (C18H22N2O2) was one of the major TPs in 6PPD ozonation (∼1 to 19% yield). Notably, 6PPDQ was not observed during ozonation of 6QDI (N-(1,3-dimethylbutyl)-N'-phenyl-p-quinonediimine), indicating that 6PPDQ formation does not proceed through 6QDI or associated 6QDI TPs. Other major 6PPD TPs included multiple C18H22N2O and C18H22N2O2 isomers, with presumptive N-oxide, N,N'-dioxide, and orthoquinone structures. Standard-verified TPs were quantified in roadway-impacted environmental samples, with total concentrations of 130 ± 3.2 μg/g in methanol extracts of tire tread wear particles (TWPs), 34 ± 4 μg/g-TWP in aqueous TWP leachates, 2700 ± 1500 ng/L in roadway runoff, and 1900 ± 1200 ng/L in roadway-impacted creeks. These data demonstrate that 6PPD TPs are likely an important and ubiquitous class of contaminants in roadway-impacted environments.

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.

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

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

Promotion of Bladder Cancer Cell Metastasis by 2-Mercaptobenzothiazole via Its Activation of Aryl Hydrocarbon Receptor Transcription: Molecular Dynamics Simulations, Cell-Based Assays, and Machine Learning-Driven Prediction

2-Mercaptobenzothiazole (MBT) is an industrial chemical widely used for rubber products, corrosion inhibitors, and polymer materials with multiple environmental and exposure pathways. A growing body of evidence suggests its potential bladder cancer (BC) risk as a public health concern; however, the molecular mechanism remains poorly understood. Herein, we demonstrate the activation of the aryl hydrocarbon receptor (AhR) by MBT and reveal key events in carcinogenesis associated with BC. MBT alters conformational changes of AhR ligand binding domain (LBD) as revealed by 500 ns molecular dynamics simulations and activates AhR transcription with upregulation of AhR-target genes CYP1A1 and CYP1B1 to approximately 1.5-fold. MBT upregulates the expression of MMP1, the cancer cell metastasis biomarker, to 3.2-fold and promotes BC cell invasion through an AhR-mediated manner. MBT is further revealed to induce differentially expressed genes (DEGs) most enriched in cancer pathways by transcriptome profiling. The exposure of MBT at environmentally relevant concentrations induces BC risk via AhR signaling disruption, transcriptome aberration, and malignant cell metastasis. A machine learning-based model with an AUC value of 0.881 is constructed to successfully predict 31 MBT analogues. Overall, we provide molecular insight into the BC risk of MBT and develop an effective tool for rapid screening of AhR agonists.

Non-targeted analysis for organic components of microplastic leachates

Organic components of microplastic leachates were investigated in an integrated non-targeted analysis study that included statistical analysis on leachates generated under different leaching scenarios. Leaching experiments were undertaken with simulated gastric fluid (SGF), river water, and seawater with common polymer types, including polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polyester fabrics comprising both raw and recycled materials. Totals of 111.0 ± 26.7, 98.5 ± 20.3, and 53.5 ± 4.7 different features were tentatively identified as compounds in SGF, freshwater, and seawater leachates, respectively, of which 5 compounds were confirmed by reference standards. The leaching capacities of the media were compared, and the clusters of structurally related features leached in the same medium were studied. For leachates generated from raw and recycled plastics, volcano plots and Pearson's Chi-squared tests were used to identify characteristic features. More characteristic features (3-20) had an average intensity across all recycled plastics that were significantly higher (p < 0.05) than that (1-3) of raw plastics under different conditions. The results indicate that gastric solution is more likely to leach components from microplastics, and there exists the difference of leachate's organic composition between raw and recycled materials, providing new insights into understanding microplastic environmental effects.

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

Characterizing the Chemical Profile of Biological Decline in Stormwater-Impacted Urban Watersheds

Chemical contamination is an increasingly important conservation issue in urban runoff-impacted watersheds. Regulatory and restoration efforts typically evaluate limited conventional parameters and pollutants. However, complex urban chemical mixtures contain hundreds to thousands of organic contaminants that remain unidentified, unregulated, and poorly understood. This study aimed to develop broadly representative metrics of water quality impairment corresponding to previously documented biological degradation along gradients of human impacts. Stream samples (n = 65, baseflow/rainfall conditions, 2017-2018) were collected from 15 regional watersheds (Puget Sound, WA, USA) across an urbanization gradient defined by landscape characteristics. Surface water chemical composition characterized via non-targeted high-resolution mass spectrometry (7068 detections) was highly correlated with landscape-based urbanization gradient (p < 0.01) and season (p < 0.01). Landscape-scale changes in chemical composition closely aligned with two anchors of biological decline: coho salmon (Oncorhynchus kisutch) mortality risk (p < 0.001) and loss of stream macroinvertebrate diversity and abundance (p < 0.001). We isolated and identified 32 indicators for urban runoff impacts and corresponding receiving water ecological health, including well-known anthropogenic contaminants (e.g., caffeine, organophosphates, vehicle-derived chemicals), two related environmental transformation products, and a novel (methoxymethyl)melamine compound. Outcomes support data-directed selection of next-generation water quality indicators for prioritization and evaluation of watershed management efforts intended to protect aquatic ecosystems.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

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.

Occurrence and Distribution of Pharmaceuticals and Their Transformation Products in Luxembourgish Surface Waters

Pharmaceuticals and their transformation products (TPs) are continuously released into the aquatic environment via anthropogenic activity. To expand knowledge on the presence of pharmaceuticals and their known TPs in Luxembourgish rivers, 92 samples collected during routine monitoring events between 2019 and 2020 were investigated using nontarget analysis. Water samples were concentrated using solid-phase extraction and then analyzed using liquid chromatography coupled to a high-resolution mass spectrometer. Suspect screening was performed using several open source computational tools and resources including Shinyscreen (https://git-r3lab.uni.lu/eci/shinyscreen/), MetFrag (https://msbi.ipb-halle.de/MetFrag/), PubChemLite (https://zenodo.org/record/4432124), and MassBank (https://massbank.eu/MassBank/). A total of 94 pharmaceuticals, 88 confirmed at a level 1 confidence (86 of which could be quantified, two compounds too low to be quantified) and six identified at level 2a, were found to be present in Luxembourg rivers. Pharmaceutical TPs (12) were also found at a level 2a confidence. The pharmaceuticals were present at median concentrations up to 214 ng/L, with caffeine having a median concentration of 1424 ng/L. Antihypertensive drugs (15), psychoactive drugs (15), and antimicrobials (eight) were the most detected groups of pharmaceuticals. A spatiotemporal analysis of the data revealed areas with higher concentrations of the pharmaceuticals, as well as differences in pharmaceutical concentrations between 2019 and 2020. The results of this work will help guide activities for improving water management in the country and set baseline data for continuous monitoring and screening efforts, as well as for further open data and software developments.

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.

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

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

Screening priority indicator pollutants in full-scale wastewater treatment plants by non-target analysis

Wastewater treatment plants (WWTPs) are the main sources of emerging contaminants (ECs) in aquatic environment. However, the standards for limiting emerging pollutants in effluent are extremely lacking. We investigated the occurrence and removal of emerging pollutants in 16 WWTPs in China using non-target analysis. 568 substances screened out were divided into 9 kinds including 167 pharmaceuticals, 113 natural substances, 85 pesticides, 86 endogenous substances, 64 chemical raw materials, 14 personal care products, 17 food additives, 6 hormones and 16 others. And they were divided into 5 fates. Pesticides and pharmaceutical compounds seemed to be the most notable categories, the kinds detected in each sample is the largest compared with other compounds. Besides, the average removal rate of pesticides and pharmaceuticals in all WWTPs were the lowest, at 9.54% and 23.77%, respectively. Priority pollutants were screened by considering distribution of pollutants with different fates. Pollutants with the same fate especially "consistent" in different WWTPs had attracted attention. 4 potential priority pollutants including metoprolol, carbamazepine, 10, 11-dihydro-10, 11-dihydroxycarbamazepine and irbesartan were proposed. And it was found that the 4 compounds, "consistent suspects" and "consistent non-targets" had similar rankings of removal rate in 16 WWTPs, which can reflect the performance of different WWTPs.

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.

Suspect and non-target screening: the last frontier in environmental analysis

Suspect and non-target screening (SNTS) techniques are arising as new analytical strategies useful to disentangle the environmental occurrence of the thousands of exogenous chemicals present in our ecosystems. The unbiased discovery of the wide number of substances present over environmental analysis needs to find a consensus with powerful technical and computational requirements, as well as with the time-consuming unequivocal identification of discovered analytes. Within these boundaries, the potential applications of SNTS include the studies of environmental pollution in aquatic, atmospheric, solid and biological samples, the assessment of new compounds, transformation products and metabolites, contaminant prioritization, bioremediation or soil/water treatment evaluation, and retrospective data analysis, among many others. In this review, we evaluate the state of the art of SNTS techniques going over the normalized workflow from sampling and sample treatment to instrumental analysis, data processing and a brief review of the more recent applications of SNTS in environmental occurrence and exposure to xenobiotics. The main issues related to harmonization and knowledge gaps are critically evaluated and the challenges of their implementation are assessed in order to ensure a proper use of these promising techniques in the near future.

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.

Identification of core micropollutants of Ergene River and their categorization based on spatiotemporal distribution

Ergene River is heavily utilized for irrigation of fields to grow the main stocks of rice, wheat, and sunflower of Turkey also exported to Europe; therefore, monitoring the river's water quality is crucial for public health. Although the river quality is routinely monitored, the evaluation of pollution based on micropollutants is limited. In this study, we measured 222 organic micropollutants in 300 samples collected from 75 different locations on the Ergene River between August 2017 and May 2018 using direct injection liquid chromatography-tandem spectrometry with optimized scheduled multiple reaction monitoring. In total, 165 micropollutants were detected at a range of concentrations between 1.90 ng/L and 1824.55 μg/L. Sixty-three chemical substances were recurrent micropollutants that were detected at least one location in all seasons. Among them, 41 chemical substances were identified as the core micropollutants of the Ergene River using data-driven clustering methods. Hexa(methoxymethyl)melamine, benzotriazoles, and benzalkonium chlorides were frequently detected core micropollutants with an industrial origin. Besides, diuron, carbendazim, and cadusafos were common pesticides in the river. Core micropollutants were further categorized based on their type of source and environmental behavior using Kurtosis of concentration and load data obtained for each micropollutant. As a result, the majority of the core micropollutants are recalcitrant chemicals either released from a specific source located upstream of the river or have urban and agricultural sources dispersed on the watershed. In this study, we assessed the current state of pollution in the Ergene River at the micropollutant level with a very high spatial resolution and developed a statistical approach to categorize micropollutants that can be used to monitor the extent of pollution and track pollution sources in the river.

Assessment of emerging polar organic pollutants linked to contaminant pathways within an urban estuary using non-targeted analysis

A comprehensive, non-targeted analysis of polar organic pollutants using high resolution/accurate mass (HR/AM) mass spectrometry approaches has been applied to water samples from San Francisco (SF) Bay, a major urban estuary on the western coast of the United States, to assess occurrence of emerging contaminants and inform future monitoring and management activities. Polar Organic Chemical Integrative Samplers (POCIS) were deployed selectively to evaluate the influence of three contaminant pathways: urban stormwater runoff (San Leandro Bay), wastewater effluent (Coyote Creek, Lower South Bay), and agricultural runoff (Napa River). Grab samples were collected before and after deployment of the passive samplers to provide a quantitative snapshot of contaminants for comparison. Composite samples of wastewater effluent (24 hours) were also collected from several wastewater dischargers. Samples were analyzed using liquid-chromatography coupled to high resolution mass spectrometry. Resulting data were analyzed using a customized workflow designed for high-fidelity detection, prioritization, identification, and semi-quantitation of detected molecular features. Approximately 6350 compounds were detected in the combined data set, with 424 of those compounds tentatively identified through high quality spectral library match scores. Compounds identified included ethoxylated surfactants, pesticide and pharmaceutical transformation products, polymer additives, and rubber vulcanization agents. Compounds identified in samples were reflective of the apparent sources and pathways of organic pollutant inputs, with stormwater-influenced samples dominated by additive chemicals likely derived from plastics and vehicle tires, as well as ethoxylated surfactants.

Nontargeted Screening of Halogenated Organic Compounds in Fish Fillet Tissues from the Great Lakes

Fish have been used for decades as bioindicators for assessing toxic contaminants in the Great Lakes ecosystem. Routine environmental monitoring programs target predetermined compounds that do not reflect the complete exposure of chemicals to biota and do not provide the complete halogenated fingerprint of the biota. In the current work, a nontargeted screening method was developed using a two-dimensional gas chromatograph coupled to a high-resolution time-of-flight mass spectrometer and was applied to 149 edible fish fillets from different species in the Great Lakes to characterize a more robust set of halogenated organic compounds across species and among lakes. Lake Ontario had the largest number of novel halogenated organic compounds (NHOCs). Seven NHOCs were observed in species from all lakes, indicating that this regional signature was not species-dependent. Hierarchical cluster analysis showed identical NHOC profiles between bottom dwelling and pelagic species. The NHOCs were grouped into seven clusters with similar structures and potentially similar environmental behaviors. Seven of the 29 NHOCs likely containing methoxy or ethoxy groups on a benzene or benzene-methanol backbone were clustered into one group with similar retention times. Five NHOCs were clustered with legacy contaminants that likely have similar structures or are their degradation products.

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

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

Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis

The pollution of aquatic ecosystems with complex and largely unknown mixtures of organic micropollutants is not sufficiently addressed with current monitoring strategies based on target screening methods. In this study, we implemented an open-source workflow based on non-target screening to unravel longitudinal pollution patterns of organic micropollutants along a river course. The 47 km long Holtemme River, a tributary of the Bode River (both Saxony-Anhalt, Germany), was used as a case study. Sixteen grab samples were taken along the river and analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We applied a cluster analysis specifically designed for longitudinal data sets to identify spatial pollutant patterns and prioritize peaks for compound identification. Three main pollution patterns were identified representing pollutants entering a) from wastewater treatment plants, b) at the confluence with the Bode River and c) from diffuse and random inputs via small point sources and groundwater input. By further sub-clustering of the main patterns, source-related fingerprints were revealed. The main patterns were characterized by specific isotopologue signatures and the abundance of peaks in homologue series representing the major (pollution) sources. Furthermore, we identified 25 out of 38 representative compounds for the patterns by structure elucidation. The workflow represents an important contribution to the ongoing attempts to understand, monitor, prioritize and manage complex environmental mixtures and may be applied to other settings.

More Than a First Flush: Urban Creek Storm Hydrographs Demonstrate Broad Contaminant Pollutographs

Stormwater runoff clearly impacts water quality and ecological health of urban receiving waters. Subsequent management efforts are often guided by conceptual models of contaminant "first flushes", defined by disproportionate concentrations or mass loads early in the storm hydrograph. However, studies examining the dynamics of contaminant transport and receiving water hydrology have primarily focused on "traditional" stormwater contaminants and point sources, with less evaluation of chemically complex nonpoint pollution sources. Accordingly, we conducted baseflow and storm sampling in Miller Creek, a representative small, urban watershed in the Puget Sound region (WA, USA). We comprehensively characterized organic contaminant profiles and dynamics via targeted quantification of 35 stormwater-derived chemicals, complementary nontarget HRMS analyses, and surrogate chemical metrics of ecological health. For quantified analytes, total daily baseflow loads were 0.8-3.4 g/day and storm event loads were ∼80-320 g/storm (∼48 h interval), with nine contaminants detected during storms at >500 ng/L. Notably, urban creek "pollutographs" were much broader than relatively sharp storm hydrographs and exhibited transport-limited (rather than mass-limited) source dynamics, with immediate water quality degradation during low-intensity precipitation and continued mobilization of contaminant mass across the entire hydrograph. Study outcomes support prioritization of source identification and focused stormwater management efforts to improve water quality and promote ecosystem function in small urban receiving waters.

Assessing accuracy, precision and selectivity using quality controls for non-targeted analysis

The benchmarks to assess reproducibility are not well defined for non-target analysis. Parameters to evaluate analytical performance, such as accuracy, precision and selectivity, are well defined for target analysis, but remain elusive for non-target screening analysis. In this study, quality control (QC) guidelines are proposed to assure reliable data in non-target screening methodologies using a simple set of standards. Workflow reproducibility was assessed using an in-house QC mixture containing selected compounds with a wide range of polarity that can be detected either by electrospray ionization (ESI) in positive or negative mode. The analysis was performed by online solid phase extraction (SPE) liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). Data processing was done by a commercially available software, Compound Discoverer v. 3.0 using an environmental working template, which searched a multitude of databases, including Chemspider, EPA Toxcast, MzCloud among others. We have specifically evaluated method specificity, precision, accuracy and reproducibility in terms of peak area and retention time variability, true positive identification rate, intraday (within days) and interday (consecutive days) variations and the use of QC samples to reduce false positives. The method showed a satisfactory accuracy with an identification rate of ≥70% for most of the QC compounds. Precision estimated based on peak area relative standard deviation (RSD) ranged between 30 and 50% for most of the compounds. Data normalization to a single internal standard did not improve peak area variability. Retention time precision showed great repeatability and reproducibility (RSD ≤ 5%). In addition, a simple model of RT vs log K_ow was designed based on our QC mixtures to efficiently reduced false positives by an average of 49.1%.

Evaluating the effects of water matrix constituents on micropollutant removal by activated carbon and β-cyclodextrin polymer adsorbents

The performance of adsorbents for the removal of organic micropollutants (MPs) from water can be influenced by the presence of water matrix constituents. The objective of this research was to evaluate the influence of water matrix constituents on the performance of coconut-shell activated carbon (CCAC), porous β-cyclodextrin polymer (CDP), and CDP coated on cellulose microcrystal (CDP@CMC) adsorbents. MP removals were measured in batch experiments for a mixture of 90 MP at 1 μg L^-1 and MP breakthrough was measured in rapid small-scale column test (RSSCT) experiments for a mixture of 15 MP at 500 ng L^-1. All experiments were performed first with nanopure water, and subsequently with six different water samples collected from two separate groundwater, surface water, and wastewater effluent sources. The results of batch and RSSCT experiments demonstrate more rapid adsorption kinetics and less adsorption inhibition in the presence of matrix constituents for CDP adsorbents relative to CCAC. Further, the treatment capacity of CDP@CMC in the RSSCT experiments was higher than that of CCAC, particularly in more complex water matrices. Statistical analyses were performed to investigate associations between adsorption inhibition among groups of MPs and the concentrations of specific water matrix constituents. For CCAC, adsorption inhibition was observed for all MPs and was primarily attributed to the presence of dissolved organic matter with molar weight less than 1000 Da. For CDP adsorbents, adsorption inhibition was primarily observed for cationic MPs and was attributed to the screening of the negative surface charge of CDP by inorganic ions in water samples with high ionic strength. These data further demonstrate the value of CDP as an alternative adsorbent to CCAC for the removal of MPs during water and wastewater treatment.

Comprehensive screening of quaternary ammonium surfactants and ionic liquids in wastewater effluents and lake sediments

Quaternary ammonium compounds (QACs) are widely applied as surfactants and biocides in cleaning and personal-care products. Because of incomplete removal during wastewater treatment, QACs are present in wastewater effluents, with which they are discharged into natural waters, where they accumulate in sediments. To assess the levels of QACs in aquatic environments, a liquid chromatography high-resolution mass spectrometry method using both target and suspect screening was developed. The water and sediment sample preparation, measurement, and data analysis workflow were optimized for 22 target compounds with a wide range of hydrophobicity, including ionic liquids that have potential use as solvents and QACs common in personal-care and sanitizing products. In wastewater effluents, average concentrations of all target and suspect QACs combined ranged from 0.4 μg L-1 to 6.6 μg L-1. Various homologs of benzylalkyldimethylammonium (BAC) and dialkyldimethylammonium (DADMAC) as well as the ionic liquid butylpyridinium and 15 suspect QACs were detected in at least one wastewater effluent sample. A spatial profile of sediment samples in a lake demonstrated potential inputs from both municipal wastewater effluent and agricultural sources for BACs. In sediment cores, two distinct trends of temporal QAC accumulation were observed. In lakes with large watersheds and mixed domestic and industrial wastewater sources (Lake Pepin and Duluth Harbor), peak concentrations of QACs were found at depths corresponding to deposition in the 1980s and decreases after this time are attributed to improved wastewater treatment and source control. In a smaller lake with predominantly domestic wastewater inputs (Lake Winona), concentrations of QACs increased slowly over time until today.

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

Fall Creek Monitoring Station: Using Environmental Covariates To Predict Micropollutant Dynamics and Peak Events in Surface Water Systems

This research aimed to further our understanding of how environmental processes control micropollutant dynamics in surface water systems as a means to predict peak concentration events and inform intermittent sampling strategies. We characterized micropollutant concentrations in daily composite samples from the Fall Creek Monitoring Station over 18 months. These data were compiled alongside environmental covariates, including daily measurements of weather, hydrology, and water quality parameters, to generate a novel data set with high temporal resolution. We evaluated the temporal trends of several representative micropollutants, along with cumulative metrics of overall micropollutant contamination, by means of multivariable analyses to determine which combination of covariates best predicts micropollutant dynamics and peak events. Peak events of agriculture-derived micropollutants were best predicted by positive associations with turbidity and UV₂₅₄ absorbance and negative associations with baseflow index. Peak events of wastewater-derived micropollutants were best predicted by positive associations with alkalinity and negative associations with streamflow rate. We demonstrate that these predictors can be used to inform intermittent sampling strategies aimed at capturing peak events, and we generalize the approach so that it could be applied in other watersheds. Finally, we demonstrate how our approach can be used to contextualize micropollutant data derived from infrequent grab samples.

Surface water and groundwater analysis using aryl hydrocarbon and endocrine receptor biological assays and liquid chromatography-high resolution mass spectrometry in Susquehanna County, PA

The contamination of surface water and ground water by human activities, such as fossil fuel extraction and agriculture, can be difficult to assess due to incomplete knowledge of the chemicals and chemistry involved. This is particularly true for the potential contamination of drinking water by nearby extraction of oil and/or gas from wells completed by hydraulic fracturing. A case that has attracted considerable attention is unconventional natural gas extraction in Susquehanna County, Pennsylvania, particularly around Dimock, Pennsylvania. We analyzed surface water and groundwater samples collected throughout Susquehanna County with complementary biological assays and high-resolution mass spectrometry. We found that Ah receptor activity was associated with proximity to impaired gas wells. We also identified certain chemicals, including disclosed hydraulic fracturing fluid additives, in samples that were either in close proximity to impaired gas wells or that exhibited a biological effect. In addition to correlations with drilling activity, the biological assays and high-resolution mass spectrometry detected substances that arose from other anthropogenic sources. Our complementary approach provides a more comprehensive picture of water quality by considering both biological effects and a broad screening for chemical contaminants.