How Many Chemicals in Commerce Have Been Analyzed in Environmental Media? A 50 Year Bibliometric Analysis

Wastewater treatment plant effluents as an obstacle to the full recovery of restored river sections

Freshwater ecosystems are impacted by multiple human activities, with chemical pollution emerging as a significant concern alongside well-documented stressors like climate change. While river restorations have been conducted to improve the structural condition of rivers, their ecological efficacy proved to be limited and potentially masked by chemical pollution. Conventional wastewater treatment plants (WWTPs) are unable to fully eliminate pollutants, resulting in insufficient water quality in aquatic systems, particularly in small, sensitive rivers with low dilution capacities, posing growing risks to biodiversity. This study investigated the ecotoxicological impact of WWTP effluents on downstream restored river sections. Despite the implementation of restoration measures, these river sections remained in a degraded ecological condition, likely due to the presence of chemical pollution originating from upstream WWTPs. In vitro bioassays were used to assess baseline toxicity, estrogenicity, and dioxin-like activity in water and sediment samples. Due to the complex pollution pattern, it was not possible to attribute the observed effects to the WWTPs alone. However, the results indicate that WWTP discharges act as a stressor on a larger spatial scale and may hinder ecological recovery in restored sections by prevailing toxicity levels. WWTP effluents, along with pollution from e.g. agricultural runoff, are likely obstacles to improving aquatic biodiversity. To support biodiversity recovery, integrated management approaches are necessary, focusing on reducing the impact of WWTP discharges through advanced treatment technologies alongside hydromorphological restoration efforts.

NMR as a Discovery Tool: Exploration of Industrial Effluents Discharged Into the Environment

NMR provides unprecedented molecular information, urgently needed by environmental researchers and policy makers. However, NMR is underutilized in environmental sciences due to the lack of available technologies, limited environmental-specific training opportunities, and easy-to-use workflows. NMR has considerable potential as a discovery tool for novel pollutants, and by-products, exemplified by the recent discovery of the degradation by-product of a rubber additive, 6PPD-quinone, now considered one of the most toxic compounds presently known. This work represents a proof-of-concept case study highlighting the use of NMR to profile effluents from 38 industries across Ontario, Canada. Wastewater effluents from various industrial sectors were analyzed using several 1D and 2D 1H/13C NMR and 19F experiments and were screened both unconcentrated and after lyophilization. Common species could be identified using human metabolic NMR databases, but environmental-specific NMR databases desperately need further development. An example of manually identifying unusual NMR signatures is included; these resulted from phosphinic and phosphonic acids originating from the electroplating industry, for which the environmental impacts are not well understood. Basic 1H NMR quantification is performed using ERETIC, while an optimized approach combining relaxation agents and steady-state-free-precession 19F NMR, to reduce detection limits (at 500 MHz) to sub-ppb (< 1 μg/L) in under 15 min, is demonstrated. The future potential of benchtop NMR (80 MHz) is also considered. This paper represents a guide to others interested in applying NMR spectroscopy to environmental media and demonstrates the potential of NMR as a complementary tool to assist MS in environmental pollutant and by-product discovery.

Predicting sorption of diverse organic compounds in soil-water systems: Meta-analysis, machine learning modeling, and global soil mapping

In recent decades, the environmental detection of various organic compounds (OCs) has highlighted the limitations of conventional soil-water sorption models, which simplify complex experimental conditions and often overlook OCs with polyfunctional and ionizable structures. To address these shortcomings, we compiled a comprehensive soil-water sorption dataset encompassing 20,945 data points for 419 OCs with various functional groups and 1037 different soils. Meta-analysis of the dataset revealed the trends of soil sorption associated with OC substructures, soil properties, and solution conditions. Machine learning models employing the XGBoost algorithm, in conjunction with MACCS fingerprints and experimental conditions, were developed to cover the entire spectrum of speciation for cationic, neutral, and anionic species. Among these, the individual models tailored to each speciation achieved an overall root-mean-square-error value of 0.32 for log Kd. Model interpretation revealed that the models correctly understood the contributions of various substructures, such as multiple aromatic rings and nitrogen or oxygen atoms, to sorption. The models were also found to accurately capture isotherm nonlinearity and the pH effect on the sorption of ionizable OCs. Finally, utilizing soil properties from the Harmonized World Soil Database, the models predicted the sorption of diverse OCs based on global soil properties under simulated environmental scenarios.

Identifying Potential Chemicals of Concern in Children's Products in a Regulatory Context: A Systematic Evidence Mapping Approach

BACKGROUND

Children's vulnerability to chemical toxicant exposures demands strong consideration of the chemical composition of products designed for and marketed toward them. Inadequacies in health-protective legislation and lack of mandatory ingredient disclosure in most children's products have created significant gaps in protection and oversight. Scientific literature can provide insight into the chemical constituency of children's products that may be useful for prioritizing future regulatory efforts.

OBJECTIVE

We aimed to present a proof of concept for applying systematic evidence mapping methodology to identify which chemicals of potential concern have been reported in the scientific literature to be present in products marketed toward children, compile a compendium of data to inform future regulatory efforts, and identify research needs.

METHODS

We conducted a broad, all-encompassing survey of the available literature from four databases to identify chemicals present in children's products. Using systematic evidence mapping methodologies, we constructed a database of children's products and their chemical constituents (termed "product-chemical combinations") based on a broad survey of current and relevant environmental health literature. Our study focused on chemicals listed on the California Safer Consumer Products Program's Candidate Chemicals List, which includes chemicals with one or more known hazard traits. We then conducted an exploratory data analysis of product category and product-chemical combination frequencies to identify common chemicals in specific products.

RESULTS

Our systematic evidence mapping identified 206 potentially hazardous chemicals in children's products, 170 of which were found in toys. In total, we found 1,528 distinct product-chemical combinations; 582 product-chemical combinations included chemicals known to be hazardous or potentially hazardous. Ortho-phthalates in plastic toys, parabens in children's creams and lotions, and bisphenols in both baby bottles and teethers were the most frequently encountered product-chemical combinations of potential concern.

DISCUSSION

The frequently reported presence of endocrine-disrupting chemicals in multiple types of children's products raises concerns for aggregate exposures and reveals gaps in regulatory protections for this sensitive subpopulation. Our reproducible and systematic evidence-based approach serves as a case study that can guide other prioritization efforts for transparent regulatory action aimed at improving the safety of chemicals in consumer products. https://doi.org/10.1289/EHP15394.

Suspect and Nontarget Screening of Organic Micropollutants in Swiss Sewage Sludge: A Nationwide Survey

The increasing amount of sewage sludge generated during wastewater treatment poses both growing management challenge and environmental issues. Sludge with many co-occurring contaminants is often destined to land application which raises concern regarding human and environmental health. It is also a good integrator in time and space and can provide valuable information on consumption pattern and change over time. Here, we have conducted suspect and nontarget screening (SNTS) in sludge from 29 wastewater treatment plants (WWTPs) covering 30% of the Swiss population. Over 500 contaminants were identified and up to 382 quantified, with concentrations ranging from a few ng/g to several thousand ng/g, which translated into total annual loads of approximately 5 g of micropollutants per Swiss citizen. The distribution of detected substances was dominated by pharmaceuticals in terms of number of compounds (>250) and personal care products in terms of concentration (e.g., 75 μg/g for linoleic acid). Homologous series analysis revealed the presence of multiple classes of surfactants among those compounds with the highest signal intensities in sludge. Principal component analysis and hierarchical clustering showed that spatial distribution of contaminants across Switzerland was not homogeneous, while Pearson correlation indicated that changes can be attributed to different anaerobic digestion times in WWTPs.

Multidimensional-Constrained Suspect Screening of Hydrophobic Contaminants Using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Ion Mobility-Mass Spectrometry

Suspect screening strives to rapidly monitor a large number of substances in a sample using mass spectral libraries. For hydrophobic organic contaminants (HOCs), these libraries are traditionally based on electron ionization mass spectra. However, with the growing use of state-of-the-art mass spectrometers, which often use alternative ionization approaches and separation techniques, new suspect screening workflows and libraries are urgently needed. This study established a new suspect screening library for 1,590 HOCs, including exact mass and a combination of measured and model-predicted values for retention time (RT) and collision cross section (CCS). The accuracy of in silico predictions was assessed using standards for 102 HOCs. Thereafter, using gas chromatography-atmospheric pressure chemical ionization-ion mobility-mass spectrometry, a suspect screening workflow constrained by the full scan mass spectrum of (quasi-)molecular ions (including isotope patterns), RT, CCS, and fragmentation mass spectra, together with a continuous scoring system, was established to reduce false positives and improve identification confidence. Application of the method to fortified and standard reference sediment samples demonstrated true positive rates of 79% and 64%, respectively, with all false positives attributed to suspect isomers. This study offers a new workflow for improved suspect screening of HOCs using multidimensional information and highlights the need to enrich mass spectral databases and extend the applicable chemical space of current in silico tools to hydrophobic substances.

Exploiting Molecular Ions for Screening Hydrophobic Contaminants in Sediments Using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Ion Mobility-Mass Spectrometry

Hydrophobic organic contaminants (HOCs) are conventionally screened by matching electron ionization (EI) mass spectra acquired using gas chromatography-mass spectrometry (GC-MS) to reference spectra. However, extensive in-source fragmentation hampers de novo structure elucidation of novel substances that are absent from EI databases. To address this problem, a new method based on GC-atmospheric pressure chemical ionization (APCI) coupled to ion mobility-high resolution mass spectrometry (IM-HRMS) was developed for simultaneous target, suspect, and nontarget screening of HOCs. Of 102 target chemicals, 85.3% produced (quasi-)molecular ions as base peaks, while 71.6% displayed method detection limits lower than those of GC-EI-low resolution MS. The optimized method was applied to standard reference sediment and sediments from the Baltic Sea, an Arctic shelf, and a Norwegian lake. In total, we quantified 56 target chemicals with concentrations ranging from 4.86 pg g-1 to 124 ng g-1 dry weight. Further, using a combination of full scan mass spectrum, retention time, collision cross section (CCS), and fragmentation spectrum, a total of 54 suspects were identified at Confidence Level (CL) 2. Among the remaining features, 169 were prioritized using a halogen-selective CCS cutoff (100 Å2 + 20% mass), leading to annotation of 54 substances (CL ≤ 3). Notably, a suite of fluorotelomer thiols, disulfides, and alkyl sulfones were identified in sediment (CL 1-2) for the first time. Overall, this work demonstrates the potential of GC-APCI-IM-HRMS as a next-generation technique for resolving complex HOC mixtures in environmental samples through exploitation of molecular ions.

Evaluating advancements and opportunities in electro-assisted biodehalogenation of emerging halogenated contaminants

Electro-assisted biodehalogenation (EASB) as a biostimulation strategy can accelerate the slow attenuation of emerging halogenated contaminants (EHCs) in anaerobic aqueous environments. A timely review is urgent to evaluate the knowledge gaps and potential opportunities, further facilitating its design and application. Till now, EASB achieves promising progress in accelerating biohalogenation rates, promoting the detoxification of EHCs to cope with unfavourable environments and mitigating greenhouse gas emissions. However, EASB of EHCs still faces several knowledge gaps. Exploring crucial microbes and deciphering insights into dehalogenase characteristics and extracellular electron transfer (EET) pathways remain the prominent task for EASB of EHCs. Moreover, microbial ecological relationships and intricate environmental factors affecting performances and applications are largely underexplored. The emergence of emerging tools holds promises for sorting the intricate changes and addressing these knowledge gaps. Judicious use of emerging tools will rejuvenate EASB strategy, from EET to scale-up, to purposefully and effectively address cascading EHCs.

Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health

Cabbage is a widely consumed vegetable in the human diet because of its low cost, broad availability and high nutritional value. The rising use of pesticides in food production creates a need to assess vegetable toxicity, which primarily results from residues in food products and environmental exposure. The study aims to offer exploration of vegetable toxicity in cabbage with the help of reliable QSTR and q-RASTR models. All the developed models were robust and predictive enough (Q2LOO = 0.7491-0.8164, Q2F1 = 0.5243-0.6253, Q2F2 = 0.513-0.617, MAEext = 0.495-0.690). Furthermore, the reliability and predictability of models were assessed and confirmed by applicability domain and prediction reliability indicator analysis. Additionally, different machine learning models were developed to making effective predictions and multiple linear regression (MLR) comparison. Consensus approach was advocated data gap filling for USEPA ECOTOX database compounds. The most and least toxic compounds from both MLR model predictions were prioritized and analyzed. Mechanistic interpretation highlighted the structural features or fragments responsible for the agrochemical toxicity and a safe approach for designing green chemicals minimizing the toxicity. This first reported study can be useful for toxicity profiling, data gap filling and designing safer and green agrochemical for minimizing vegetable toxicity, healthy human life and environmental safety.

MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.Scientific Contribution:In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants

Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO•). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the applicability domain (AD) has been significantly limited. To address this limitation, we curated a much larger experimental data set (Primary data set), which contains 2358 kinetic records. We then employed both the group contribution method (GCM) and a semisupervised learning (SSL) strategy to add new data points, aiming to effectively expand the model's AD while improving model performance. The results indicated that GCM improved the model's performance for chemicals outside the AD, while SSL expanded the model's AD. The final model, after incorporating 147,168 new data points, achieved an R2 = 0.77, root-mean-square-error = 0.32, and mean-absolute-error = 0.24 on the test set. Importantly, the AD was expanded by 117% compared to the model developed solely based on the Primary data set, and the final model can be reliably applied to more than 560,000 chemicals from the DSSTox database. Further model interpretation results indicated that the model made predictions based on a correct "understanding" of the impact of key substituents and reactive sites toward HO•. This research provides an effective method for augmenting data sets, which is important in improving ML model performance and expanding AD. The final model has been made widely accessible through a free online predictor.

A Glimpse of Research Trends and Frontiers in the Etiology of Premature Ovarian Insufficiency via Bibliometric Analysis

INTRODUCTION

Premature Ovarian Insufficiency (POI) is the most common reproductive aging disorder in women of reproductive age, which is characterized by decreased ovarian function in women before the age of 40. Etiology research of POI has garnered interest and attention from scholars worldwide over the past decades.

METHODS

However, to the best of our knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the research trends of POI etiology. This article aimed to analyze current scientific findings on the etiology of POI, offering innovative ideas for further research. Research articles on the etiology of POI from 1994 to 2023 were collected from the Web of Science Core Collection. A total of 456 research articles were included, and the total number of publications increased annually. We used VOSviewer and bibliometric.com to analyze the keywords, terms, institution, publication country/region, author name, publication journal, and the sum of times the articles have been cited.

RESULTS

This study has shown that a research hotspot is the genetic etiology of POI; however, there is still a lack of research on the impact of epigenetic alterations, iatrogenic injuries, environmental pollution, social stress, and unhealthy lifestyles on the pathogenesis of POI.

CONCLUSION

The factors illustrated here represent potential future directions for POI etiology research and warrant more attention from researchers.

Occurrence of contaminants of emerging concern in different water samples from the lower part of the Danube River Middle Basin - A review

This study intends to assess the extent of the occurrence of CECs in different water types based on the literature data reported for the countries from a lower part of the Middle Danube Basin, including those belonging to the Western Balkan (WB) region and two upstream neighboring EU Member States, Croatia and Slovenia. These countries share main freshwater courses important for drinking water supply, agriculture, industry, navigation, tourism, etc, but in some of them there are low rate of wastewater treatment, impacting the chemical status of water resources in the region and probably beyond, if downstream countries are considered. The literature survey revealed 38 investigative studies reporting data on CECs in water matrices sampled in the region in the period 2008-2022. Surface water was the most frequently studied water type in WB countries, while wastewater was the dominant water type studied in Slovenia and Croatia. The most often analyzed compounds in the studies dealing with surface water and wastewater were the anti-epileptic drug carbamazepine, some non-steroidal anti-inflammatory drugs, and antibiotics; pharmaceutically active compounds were also the most analyzed CECs in groundwater and drinking water. Additionally, similarities/dissimilarities among the experimental approaches in these studies were discussed in relation to the state-of-the-art research directions for the CECs surveillance in the European Union, resulting in summarized strengths and gaps in capacities for the wide-range surveillance of CECs in the lower part of the Middle Danube Basin. This is the first integral overview of the studies on CECs in waters from the countries belonging to this part of the Danube Basin, representing a valuable baseline for further enhancement of the relevant monitoring efforts and chemical status of the regional water resources, especially in countries with poor wastewater management.

The impact of persistent organic pollutants on fertility: exposure to the environmental toxicant 2,3,7,8-tetrachlorodibenzo-p-dioxin alters reproductive tract immune responses

Exposure to environmental contaminants can result in profound effects on the host immune system. One class of environmental toxicants, known as dioxins, are persistent environmental contaminants termed "forever chemicals". The archetype toxicant from this group of chemicals is 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD), an immunotoxicant that activates the aryl-hydrocarbon receptor pathway leading to a variety of changes in immune cell responses. Immune cell functions are crucial to the development and maintenance of healthy reproduction. Immune cells facilitate tolerance between at the maternal-fetal interface between the parent and the semi-allogenic fetus and help defend the gravid reproductive tract from infectious assault. Epidemiological studies reveal that exposure to environmental contaminants (such as TCDD) are linked to adverse reproductive health outcomes including endometriosis, placental inflammation, and preterm birth. However, little is known about the molecular mechanisms that underpin how environmental toxicant exposures impact immune functions at the maternal-fetal interface or within the reproductive tract in general. This review presents the most recent published work that studies interactions between dioxin or TCDD exposure, the host immune system, and reproduction.

Insights into the Chemical Exposome during Pregnancy: A Non-Targeted Analysis of Preterm and Term Births

Human-made chemicals are ubiquitous, leading to chronic exposure to complex mixtures of potentially harmful substances. We investigated chemical exposures in pregnant women in New York City by applying a non-targeted analysis (NTA) workflow to 95 paired prenatal urine and serum samples (35 pairs of preterm birth) collected as part of the New York University Children's Health and Environment Study. We analyzed all samples using liquid chromatography coupled with Orbitrap high-resolution mass spectrometry in both positive and negative electrospray ionization modes, employing full scan and data-dependent MS/MS fragmentation scans. We detected a total of 1524 chemical features for annotation, with 12 chemicals confirmed by authentic standards. Two confirmed chemicals dodecyltrimethylammonium and N,N-dimethyldecylamine N-oxide appear to not have been previously reported in human blood samples. We observed a statistically significant differential enrichment between urine and serum samples, as well as between preterm and term birth (p < 0.0001) in serum samples. When comparing between preterm and term births, an exogenous contaminant, 1,4-cyclohexanedicarboxylic acid (tentative), showed a statistical significance difference (p = 0.003) with more abundance in preterm birth in serum. An example of chemical associations (12 associations in total) observed was between surfactants (tertiary amines) and endogenous metabolites (fatty acid amides).

Comprehensive Characterization of Oxidative Stress-Modulating Chemicals Using GPT-Based Text Mining

The screening of hazardous environmental pollutants is hindered by the limited availability of toxicological databases. Large language model (LLM)-based text mining holds the potential to automatically extract complex toxicological information from the literature. Due to its relevance to diseases and the challenge of comprehensive characterization, oxidative stress serves as a suitable case for research by texting mining. In this study, a robust workflow utilizing a LLM (i.e., GPT-4) was developed to extract information on oxidative stress tests, including data collection, text preprocessing, prompt engineering, and performance evaluation procedures. A total of 17,780 relevant records were extracted from 7166 articles, covering 2558 unique compounds. A rising interest in oxidative stress was observed over the past two decades. A list of known prooxidants (n = 1416) and antioxidants (n = 1102) was established, with the leading chemical categories being pharmaceuticals, pesticides, and metals for prooxidants and pharmaceuticals and flavonoids for antioxidants. Structural alert analysis identified potential prooxidant (e.g., chlorobenzene, nitrobenzene, and tertiary amines) and antioxidant (e.g., flavonoid and thiol) substructures. These findings illustrate the feasibility of building toxicological databases through LLM-based text mining in a cost-efficient manner, and the information obtained from the technique holds significant promise for future applications in environmental and health research.

Proteome-wide reverse molecular docking reveals folic acid receptor as a mediator of PFAS-induced neurodevelopmental toxicity

Per- and polyfluoroalkyl substances (PFAS) are a class of long-lasting chemicals with widespread use and environmental persistence that have been increasingly studied for their detrimental impacts to human and animal health. Several major PFAS species are linked to neurodevelopmental toxicity. For example, epidemiological studies have associated prenatal exposure to perfluorooctanoate (PFOA) and perfluorononanoate (PFNA) with autism risk. However, the neurodevelopmental toxicities of major PFAS species have not been systematically evaluated in an animal model, and the molecular mechanisms underlying these toxicities have remained elusive. Using a high-throughput zebrafish social behavioral model, we screened six major PFAS species currently under regulation by the Environmental Protection Agency (EPA), including PFOA, PFNA, perfluorooctane sulfonate (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorobutane sulfonate (PFBS), and hexafluoropropylene oxide dimer acid ammonium salt (GenX). We found that embryonic exposure to PFNA, PFOA, and PFOS induced social deficits in zebrafish, recapitulating one of the hallmark behavioral deficits in autistic individuals. To uncover protein targets of the six EPA-regulated PFAS, we screened a virtual library containing predicted binding pockets of over 80% of the 3D human proteome through reverse molecular docking. We found that folate receptor beta (FR-β, encoded by the gene FOLR2) interacts strongly with PFNA, PFOA, and PFOS but to a lesser degree with PFHxS, PFBS, and GenX, correlating positively with their in vivo toxicity. Embryonic co-exposure to folic acid rescued social deficits induced by PFAS. The folic acid pathway has been implicated in autism, indicating a novel molecular mechanism for PFAS in autism etiology.

Structure confirmation, reactivity, bacterial mutagenicity and quantification of 2,2,4-tribromo-5-hydroxycyclopent-4-ene-1,3-dione in drinking water

The presence of two new disinfectant by-product (DBP) groups in the UK was recently shown using non-target analysis, halogenated-hydroxycyclopentenediones and halogenated-methanesulfonic acids. In this work, we confirmed the structure of 2,2,4-tribromo-5-hydroxycyclopent-4-ene-1,3-dione (TBHCD), and quantified it together with dibromomethanesulfonic acid at 122 ± 34 and 326 ± 157 ng L-1 on average in London's drinking water, respectively (n = 21). We found TBHCD to be photolabile and unstable in tap water and at alkaline pH. Furthermore, spectral and computational data for TBHCD and three other halogenated-hydroxycyclopentenediones indicated they could act as a source of radicals in water and in the body. Importantly, TBHCD was calculated to have a 14.5 kcal mol-1 lower C-Br bond dissociation enthalpy than the N-Br bond of N-bromosuccinimide, a common radical substitution reagent used in organic synthesis. TBHCD was mutagenic in Salmonella/microsome assays using strains TA98, TA100 and TA102. This work reveals the unique features, activity and toxicity of trihalogenated hydroxycyclopent-4-ene-1,3-diones, prompting a need to more comprehensively assess their risks.

Seasonal hot spots of pollution and risks in Western Kenya: A spatial-temporal analysis of almost 800 organic micropollutants

The release of chemicals into the environment presents a significant threat to aquatic ecosystems dependent on the proximity to emission sources and seasonal dynamics of emission and mobilization. While spatial-temporal information on water pollution in Europe is increasing, there are substantial knowledge gaps on seasonal pollution dynamics in tropical countries. Thus, we took Lake Victoria South Basin in western Kenya as a case study to identify spatial and seasonal hot spots of contamination, quantified toxic risks to different groups of organisms, and identified seasonal risk drivers. For this purpose, we analyzed grab water samples from five rivers with agricultural and wastewater treatment plants in their catchment in four different seasons. We used liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) with a target list of 785 organic micropollutants. A total of 307 compounds were detected with concentrations ranging from 0.3 ng/L to 6.6 μg/L. Using a Toxic Unit (TU) approach based on mixture toxicity to standard test organisms, crustaceans were identified as the most affected group followed by algae and fish. For crustaceans, chronic risk thresholds were exceeded in 96 % of all the samples, while 56 % of all samples are expected to be acutely toxic, with the highest risk in February during the dry season. High toxic unit values for algae and fish were recorded in July dry season and May wet season. Diazinon, imidacloprid, clothianidin and pirimiphos-methyl were the major drivers for crustacean toxicity while triclosan and different herbicide mixtures drive risks to algae in dry and wet seasons, respectively. A total of 18 chemicals were found to exceed acute and chronic environmental risk thresholds. With this study, strong spatial-temporal patterns of pollution, risks and risk drivers could be confirmed informing prioritization of monitoring and abatement to enhance water quality and reduce toxic risks.

Chemical Exposomics in Human Plasma by Lipid Removal and Large-Volume Injection Gas Chromatography-High-Resolution Mass Spectrometry

For comprehensive chemical exposomics in blood, analytical workflows are evolving through advances in sample preparation and instrumental methods. We hypothesized that gas chromatography-high-resolution mass spectrometry (GC-HRMS) workflows could be enhanced by minimizing lipid coextractives, thereby enabling larger injection volumes and lower matrix interference for improved target sensitivity and nontarget molecular discovery. A simple protocol was developed for small plasma volumes (100-200 μL) by using isohexane (H) to extract supernatants of acetonitrile-plasma (A-P). The HA-P method was quantitative for a wide range of hydrophobic multiclass target analytes (i.e., log Kow > 3.0), and the extracts were free of major lipids, thereby enabling robust large-volume injections (LVIs; 25 μL) in long sequences (60-70 h, 70-80 injections) to a GC-Orbitrap HRMS. Without lipid removal, LVI was counterproductive because method sensitivity suffered from the abundant matrix signal, resulting in low ion injection times to the Orbitrap. The median method quantification limit was 0.09 ng/mL (range 0.005-4.83 ng/mL), and good accuracy was shown for a certified reference serum. Applying the method to plasma from a Swedish cohort (n = 32; 100 μL), 51 of 103 target analytes were detected. Simultaneous nontarget analysis resulted in 112 structural annotations (12.8% annotation rate), and Level 1 identification was achieved for 7 of 8 substances in follow-up confirmations. The HA-P method is potentially scalable for application in cohort studies and is also compatible with many liquid-chromatography-based exposomics workflows.

Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox

The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a "CombinedConfidence" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.

Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge

Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emerging contaminants. Data-driven machine learning (ML) models serve as promising complements. However, reported quantitative structure-biodegradation relationships or ML models on anaerobic biodegradation are mostly based on small data sets (<100 records) and neglect experimental conditions, usually achieving compromised predictions. This work aimed to develop ML models for predicting the biodegradation half-lives of organic pollutants in anaerobic environments (i.e., sediment/soil and sludge). Focusing on important features of both chemicals and experimental conditions, we first curated two data sets, one for sediment/soil (SED) and the other for sludge (SLD), covering 978 records for 206 chemicals from the literature, and then conducted a meta-analysis. Next, we built a binary classification (half-life of 30 days as the cutoff) model with an accuracy of 81% and a regression model with R2 of 0.56 for SED based on LightGBM (80% and 0.31 for SLD based on Extra tree, respectively). The model interpretations underscored the significance of experimental conditions (e.g., temperature and inoculum dosage), as evidenced by their high feature importance, and the models were found to correctly capture the effects of chemical substructures, for example, branched structures and aromatic rings prolonged half-lives while methyl group and ortho-substitution on rings shortened half-lives. The applicability domains of the models were also defined, resulting in reasonable prediction for the half-lives of 41% (SED) or 67% (SLD) of over 4000 persistent, bioaccumulative, and toxic chemicals. Overall, this study pioneers ML models for predicting the anaerobic degradation half-lives, offering valuable support for future evaluation and implementation of chemical anaerobic biodegradation.

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.

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

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

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.

Global Trends and Hotspots in Research on the Health Risks of Organophosphate Flame Retardants: A Bibliometric and Visual Analysis

BACKGROUND

Organophosphate flame retardants (OPFRs) are compounds with a wide range of industrial and commercial applications and are mainly used as flame retardants and plasticizers. The global consumption of OPFRs has risen rapidly in recent decades, and they have been widely detected in environmental media. Unfortunately, OPFRs have been associated with many adverse health outcomes. The issue of the health risks of OPFRs is attracting increasing attention. Therefore, there is a need to review the current state of research and trends in this field to help researchers and policymakers quickly understand the field, identify new research directions, and allocate appropriate resources for further development of the OPFR health risk research field.

METHODS

This study statistically analyzed 1162 relevant publications included in the Web of Science Core Collection from 2003-2023. The internal and external features of the literature, such as publication trends, countries, authors, journals, and keywords, were quantitatively analyzed and visually presented to identify the research hotspots, compositions, and paradigms of the field and to horizontally and vertically analyze the development trends and structural evolution of the field.

RESULTS

The development of the field can be divided into three stages, and the field entered a period of rapid development in 2016. China (649 papers) is the most prolific country, followed by the United States (188 papers). The authors STAPLETON HM and WANG Y have the highest combined impact. International collaboration between countries and researchers still needs to be strengthened. Science of The Total Environment is the most frequently published journal (162 papers), and Environmental Science and Technology is the most frequently cited journal (5285 citations). Endocrine disruption, developmental toxicity, and neurotoxicity are the health effects of greatest interest.

CONCLUSIONS

Future research is expected to be multidisciplinary, and research hotspots may involve a comprehensive assessment of OPFR exposure in the population, exploration of the mechanisms of endocrine-disrupting effects and in vivo metabolic processes, and examination of the health effects of OPFR metabolites.

How neurobehavior and brain development in alternative whole-organism models can contribute to prediction of developmental neurotoxicity

Developmental neurotoxicity (DNT) is not routinely evaluated in chemical risk assessment because current test paradigms for DNT require the use of mammalian models which are ethically controversial, expensive, and resource demanding. Consequently, efforts have focused on revolutionizing DNT testing through affordable novel alternative methods for risk assessment. The goal is to develop a DNT in vitro test battery amenable to high-throughput screening (HTS). Currently, the DNT in vitro test battery consists primarily of human cell-based assays because of their immediate relevance to human health. However, such cell-based assays alone are unable to capture the complexity of a developing nervous system. Whole organismal systems that qualify as 3 R (Replace, Reduce and Refine) models are urgently needed to complement cell-based DNT testing. These models can provide the necessary organismal context and be used to explore the impact of chemicals on brain function by linking molecular and/or cellular changes to behavioural readouts. The nematode Caenorhabditis elegans, the planarian Dugesia japonica, and embryos of the zebrafish Danio rerio are all suited to low-cost HTS and each has unique strengths for DNT testing. Here, we review the strengths and the complementarity of these organisms in a novel, integrative context and highlight how they can augment current cell-based assays for more comprehensive and robust DNT screening of chemicals. Considering the limitations of all in vitro test systems, we discuss how a smart combinatory use of these systems will contribute to a better human relevant risk assessment of chemicals that considers the complexity of the developing brain.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments

A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

Significant improvement in freshwater invertebrate biodiversity in all types of English rivers over the past 30 years

There remains a persistent concern that freshwater biodiversity is in decline and being threatened by pollution. As the UK, and particularly England, is a densely populated nation with rivers of modest dilution capacity, this location is very suitable to examine how freshwater biodiversity has responded to human pressures over the past 30 years. A long-term dataset of 223,325 freshwater macroinvertebrate records from 1989 to 2018 for England was retrieved and examined. A sub-set of approximately 200 sites per English Region (1515 sites in total with 62,514 samples), with the longest and most consistent records were matched with predicted wastewater exposure, upstream land cover and terrain characteristics (latitude, altitude, slope gradient and flow discharge). To understand changes in macroinvertebrate diversity and sensitivity with respect to these parameters, the biotic indices of (i) overall family richness, (ii) Ephemeroptera, Plecoptera, Trichoptera (EPT) family richness, and (iii) the Biological Monitoring Working Party (BMWP) scores of NTAXA (number of scoring taxa) and (iv) ASPT (average score per taxon) were selected. A review of how close the BMWP scores come to those expected at minimally impacted reference sites was included. For all latitudes, altitudes, channel slope, river size, wastewater exposure levels, and differing proportions of upstream woodland, seminatural, arable and urban land cover, all diversity or sensitivity indices examined improved over this period, although this improvement has slowed in some cases post 2003. Mean overall family richness has increased from 15 to 25 family groups, a 66 % improvement. The improvement in mean EPT family richness (3 to 10 families, >300 % improvement), which are considered to be particularly sensitive to pollution, implies macroinvertebrate diversity has benefited from a national improvement in critical components of water quality.

A Critical Review on the Opportunity to Use Placenta and Innovative Biomonitoring Methods to Characterize the Prenatal Chemical Exposome

Adverse effects associated with chemical exposures during pregnancy include several developmental and reproductive disorders. However, considering the tens of thousands of chemicals present on the market, the effects of chemical mixtures on the developing fetus is still likely underestimated. In this critical review, we discuss the potential to apply innovative biomonitoring methods using high-resolution mass spectrometry (HRMS) on placenta to improve the monitoring of chemical exposure during pregnancy. The physiology of the placenta and its relevance as a matrix for monitoring chemical exposures and their effects on fetal health is first outlined. We then identify several key parameters that require further investigations before placenta can be used for large-scale monitoring in a robust manner. Most critical is the need for standardization of placental sampling. Placenta is a highly heterogeneous organ, and knowledge of the intraplacenta variability of chemical composition is required to ensure unbiased and robust interindividual comparisons. Other important variables include the time of collection, the sex of the fetus, and mode of delivery. Finally, we discuss the first applications of HRMS methods on the placenta to decipher the chemical exposome and describe how the use of placenta can complement biofluids collected on the mother or the fetus.