Tracking complex mixtures of chemicals in our changing environment

Sustainable and Reagentless Fenton Treatment of Complex Wastewater

Conventional Fenton treatment is fundamentally impractical for large-scale applications, as the consumption of Fe(II), H₂O₂, and pH regulators and the accumulation of iron hydroxide sludge are very costly. This paper describes a new method for Fenton treatment of complex wastewater without additional dosing of Fe(II) and H₂O₂, without iron-sludge accumulation, and with less consumption of pH regulators, using a novel bioelectrode system. Our new system includes a novel three-chamber microbial electrolysis unit and Fenton reaction unit, where Fenton reagents are generated by biotic and abiotic cathodes, while the bioanode simultaneously degrades biodegradable organics from the wastewater. The system's self-alkalinity buffering also waives the need for pH regulators. Dissolved organic carbon and 22 specific recalcitrant organics were removed by 99% and between 78 and 100%, respectively. The bioelectrode system generated 13 ± 3 mg/L dissolved Fe(II) and 5 ± 0.4 mg/L H₂O₂ for the Fenton reaction unit. The closed iron cycle avoided iron loss and iron sludge accumulation during operation. The pH regulator dosage and operating costs were just 9.7 and 1.4%, respectively, of what is required by classic Fenton. The low operating cost and reduction in chemical usage make it an efficient, sustainable alternative to the conventional treatment processes currently used for complex wastewater.

Three perspectives on the prediction of chemical effects in ecosystems

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures

Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.

Dosing Methods to Enable Cell-Based In Vitro Testing of Complex Substances: A Case Study with a PAH Mixture

Cell-based testing of multi-constituent substances and mixtures for their potential adverse health effects is difficult due to their complex composition and physical-chemical characteristics. Various extraction methods are typically used to enable studies in vitro; however, a limited number of solvents are biocompatible with in vitro studies and the extracts may not fully represent the original test article's composition. While the methods for dosing with "difficult-to-test" substances in aquatic toxicity studies are well defined and widely used, they are largely unsuited for small-volume (100 microliters or less) in vitro studies with mammalian cells. Therefore, we aimed to evaluate suitability of various scaled-down dosing methods for high-throughput in vitro testing by using a mixture of polycyclic aromatic hydrocarbons (PAH). Specifically, we compared passive dosing via silicone micro-O-rings, cell culture media-accommodated fraction, and traditional solvent (dimethyl sulfoxide) extraction procedures. Gas chromatography-tandem mass spectrometry (GC-MS/MS) was used to evaluate kinetics of PAH absorption to micro-O-rings, as well as recovery of PAH and the extent of protein binding in cell culture media with and without cells for each dosing method. Bioavailability of the mixture from different dosing methods was also evaluated by characterizing in vitro cytotoxicity of the PAH mixture using EA.hy926 and HepG2 human cell lines. Of the tested dosing methods, media accommodated fraction (MAF) was determined to be the most appropriate method for cell-based studies of PAH-containing complex substances and mixtures. This conclusion is based on the observation that the highest fraction of the starting materials can be delivered using media accommodated fraction approach into cell culture media and thus enable concentration-response in vitro testing.

Spatial and temporal variations in anti-androgenic activity and environmental risk in a small river

The biological effects of multiple compounds have been widely investigated in aquatic environments. However, investigations of spatial and temporal variations in biological effects are rarely performed because they are time-consuming and labor-intensive. In this study, the variability of the anti-androgen, receptor-mediated activity of surface water samples was observed over 3 years using in vitro bioassays. Large-volume water samples were collected at one site upstream (Wer site) and two sites downstream (Sil and Nien sites) of a wastewater treatment plant (WWTP) outfall in the Holtemme River. Anti-AR activity was persistently present in all surface water samples over the three years. Large spatial variations in anti-androgenic activity were observed, with the lowest activity at the Wer site (mean concentration of 9.5 ± 7.2 μg flutamide equivalents/L) and the highest activity at the Sil site (mean concentration of 31.1 ± 12.0 μg flutamide equivalents/L) directly influenced by WWTP effluents. On the temporal scale, no distinct trend for anti-AR activity was observed among the seasons in all three years. The anti-androgenic activity at the upstream Wer site showed a decreasing trend from 2014 to 2016, indicating improved water quality. A novel bioanalytical-equivalent-based risk assessment method considering the frequency of risk occurrence was developed and then utilized to assess the environmental risk of anti-androgenic activity in the Holtemme River. The results revealed that the highest risk was present at the Sil site, while the risk was considerably reduced at the Nien site. The risk at the upstream Wer site was the lowest.

Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses

Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.

Multiclass target analysis of contaminants of emerging concern including transformation products, soil bioavailability assessment and retrospective screening as tools to evaluate risks associated with reclaimed water reuse

The occurrence of 200 multiclass contaminants of emerging concern (CECs) encompassing 168 medicinal products and transformation products (TPs), 5 artificial sweeteners, 12 industrial chemicals, and 15 other compounds was investigated in influent and effluent wastewater samples collected during 7 consecutive days from 5 wastewater treatment plants (WWTPs) located in Cyprus. The methodology included a generic solid-phase extraction protocol using mixed-bed cartridges followed by Ultra-High Performance Liquid Chromatography coupled with Quadrupole-Time of Flight Mass Spectrometry (UHPLC-QTOF-MS) analysis. A total of 63 CECs were detected at least in one sample, with 52 and 55 out of the 200 compounds detected in influents and effluents, respectively. Ten (10) out of the 24 families of parent compounds and associated TPs were found in the wastewater samples (influent or effluent). 1-H-benzotriazole, carbamazepine, citalopram, lamotrigine, sucralose, tramadol, and venlafaxine (>80 % frequency of appearance in effluents) were assessed with respect to their bioavailability in soil as part of different scenarios of irrigation with reclaimed water following a qualitative approach. A high score of 12 (high probability) was predicted for 2 scenarios, a low score of 3 (rare occasions) for 2 scenarios, while the rest 28 scenarios had scores 5-8 (unlikely or limited possibility) and 9-11 (possibly). Retrospective screening was performed with the use of a target database of 2466 compounds and led to the detection of 158 additional compounds (medicinal products (65), medicinal products TPs (15), illicit drugs (7), illicit drugs TPs (3), industrial chemicals (11), plant protection products (25), plant protection products TPs (10), and various other compounds (22). This work aspires to showcase how the presence of CECs in wastewater could be investigated and assessed at WWTP level, including an expert-based methodology for assessing the soil bioavailability of CECs, with the aim to develop sustainable practices and enhance reclaimed water reuse.

Passive Sampling: A Greener Technique for the 'Dual Carbon' Goal While Implementing the Action Plan for Controlling Emerging Pollutants

In response to global climate change, China recently committed to achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Carbon emission reduction should be considered in every sector of society including environmental monitoring. As an energy-saving technique in environmental monitoring, passive sampling has many advantages, such as in-situ sampling and a reduction in time/labour requirements. This perspective illustrates the "greenness" of passive sampling techniques, by comparison with traditional sampling methods, and its potential contribution to the 'dual carbon' goal. At the same time passive sampling can provide key support for the action plan for controlling emerging pollutants in China.

Energy Barriers for Steroid Hormone Transport in Nanofiltration

Nanofiltration (NF) membranes can retain micropollutants (MPs) to a large extent, even though adsorption into the membrane and gradual permeation result in breakthrough and incomplete removal. The permeation of MPs is investigated by examining the energy barriers (determined using the Arrhenius concept) for adsorption, intrapore diffusion, and permeation encountered by four different steroid hormones in tight and loose NF membranes. Results show that the energy barriers for steroid hormone transport in tight membrane are entropically dominated and underestimated because of the high steric exclusion at the pore entrance. In contrast, the loose NF membrane enables steroid hormones partitioning at the pore entrance, with a permeation energy barrier (from feed toward the permeate side) ranging between 96 and 116 kJ/mol. The contribution of adsorption and intrapore diffusion to the energy barrier for steroid hormone permeation reveals a significant role of intrapore diffusive transport on the obtained permeation energy barrier. Overall, the breakthrough phenomenon observed during the NF of MPs is facilitated by the low energy barrier for adsorption. Experimental evidence of such principles is relevant for understanding mechanisms and ultimately improving the selectivity of NF.

Dietary Intake Contributed the Most to Chlorinated Paraffin Body Burden in a Norwegian Cohort

Determining the major human exposure pathways is a prerequisite for the development of effective management strategies for environmental pollutants such as chlorinated paraffins (CPs). As a first step, the internal and external exposure to CPs were quantified for a well-defined human cohort. CPs in participants' plasma and diet samples were analyzed in the present study, and previous results on paired air, dust, and hand wipe samples were used for the total exposure assessment. Both one compartment pharmacokinetic modeling and forensic fingerprinting indicate that dietary intake contributed the most to body burden of CPs in this cohort, contributing a median of 60-88% of the total daily intakes. The contribution from dust ingestion and dermal exposure was greater for the intake of long-chain CPs (LCCPs) than short-chain CPs (SCCPs), while the contribution from inhalation was greater for the intake of SCCPs than medium-chain CPs (MCCPs) and LCCPs. Significantly higher concentrations of SCCPs and MCCPs were observed in diets containing butter and eggs, respectively (p < 0.05). Additionally, other exposure sources were correlated to plasma levels of CPs, including residence construction parameters such as the construction year (p < 0.05). This human exposure to CPs is not a local case. From a global perspective, there are major knowledge gaps in biomonitoring and exposure data for CPs from regions other than China and European countries.

Combining Passive Sampling with Suspect and Nontarget Screening to Characterize Organic Micropollutants in Streams Draining Mixed-Use Watersheds

Organic micropollutants (OMPs) represent an anthropogenic stressor on stream ecosystems. In this work, we combined passive sampling with suspect and nontarget screening enabled by liquid chromatography-high-resolution mass spectrometry to characterize complex mixtures of OMPs in streams draining mixed-use watersheds. Suspect screening identified 122 unique OMPs for target quantification in polar organic chemical integrative samplers (POCIS) and grab samples collected from 20 stream sites in upstate New York over two sampling seasons. Hierarchical clustering established the co-occurrence profiles of OMPs in connection with watershed attributes indicative of anthropogenic influences. Nontarget screening leveraging the time-integrative nature of POCIS and the cross-site variability in watershed attributes prioritized and confirmed 11 additional compounds that were ubiquitously present in monitored streams. Field sampling rates for 37 OMPs that simultaneously occurred in POCIS and grab samples spanned the range of 0.02 to 0.22 L/d with a median value of 0.07 L/d. Comparative analyses of the daily average loads, cumulative exposure-activity ratios, and multi-substance potentially affected fractions supported the feasibility of complementing grab sampling with POCIS for OMP load estimation and screening-level risk assessments. Overall, this work demonstrated a multi-watershed sampling and screening approach that can be adapted to assess OMP contamination in streams across landscapes.

Occurrence and Temporal Trends of Benzotriazole UV Stabilizers in Mollusks (2010-2018) from the Chinese Bohai Sea Revealed by Target, Suspect, and Nontarget Screening Analysis

Benzotriazole UV stabilizers (BZT-UVs), including 2-(3,5-di-tert-amyl-2-hydroxyphenyl)benzotriazole (UV-328) that is currently under consideration for listing under the Stockholm Convention, are applied in many commodities and industrial products. However, limited information is available on the interannual variation of their environmental occurrence. In this study, an all-in-one strategy combining target, suspect, and nontarget screening analysis was established to comprehensively explore the temporal trends of BZT-UVs in mollusks collected from the Chinese Bohai Sea between 2010 and 2018. Significant residue levels of the target analytes were determined with a maximum total concentration of 6.4 × 10³ ng/g dry weight. 2-(2-Hydroxy-3-tert-butyl-5-methyl-phenyl)-5-chloro-benzotriazole (UV-326), 5-chloro-2-(3,5-di-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-327), and 2-(2-hydroxy-5-methylphenyl) benzotriazole (UV-P) were the predominant analogues, and UV-328 was the most frequently detected BZT-UV with a detection frequency (DF) of 87%. Whereas five biotransformation products and six impurity-like BZT-UVs were tentatively identified, their low DFs and semi-quantified concentrations suggest that the targeted analytes were the predominant BZT-UVs in the investigated area. A gradual decrease in the total concentrations of BZT-UVs was observed, accompanied by downward trends of the abundant compounds (e.g., UV-326 and UV-P). Consequently, the relative abundance of UV-327 increased because of its consistent environmental presence. These results suggest that continuous monitoring and risk assessment of BZT-UVs other than UV-328 are of importance in China.

Challenges on solar oxidation as post-treatment of municipal wastewater from UASB systems: Treatment efficiency, disinfection and toxicity

The application of solar photo-Fenton as post-treatment of municipal secondary effluents (MSE) in developing tropical countries is the main topic of this review. Alternative technologies such as stabilization ponds and upflow anaerobic sludge blanket (UASB) are vastly applied in these countries. However, data related to the application of solar photo-Fenton to improve the quality of effluents from UASB systems are scarce. This review gathered main achievements and limitations associated to the application of solar photo-Fenton at neutral pH and at pilot scale to analyze possible challenges associated to its application as post-treatment of MSE generated by alternative treatments. To this end, the literature review considered studies published in the last decade focusing on CECs removal, toxicity reduction and disinfection via solar photo-Fenton. Physicochemical characteristics of effluents originated after UASB systems alone and followed by a biological post-treatment show significant difference when compared with effluents from conventional activated sludge (CAS) systems. Results obtained for solar photo-Fenton as post-treatment of MSE in developed countries indicate that remaining organic matter and alkalinity present in UASB effluents may pose challenges to the performance of solar advanced oxidation processes (AOPs). This drawback could result in a more toxic effluent. The use of chelating agents such as Fe³⁺-EDDS to perform solar photo-Fenton at neutral pH was compared to the application of intermittent additions of Fe²⁺ and both of these strategies were reported as effective to remove CECs from MSE. The latter strategy may be of greater interest in developing countries due to costs associated to complexing agents. In addition, more studies are needed to confirm the efficiency of solar photo-Fenton on the disinfection of effluent from UASB systems to verify reuse possibilities. Finally, future research urges to evaluate the efficiency of solar photo-Fenton at natural pH for the treatment of effluents from UASB systems.

Effect-based monitoring to integrate the mixture hazards of chemicals into water safety plans

Water safety plans (WSPs) are intended to assure safe drinking water (DW). WSPs involve assessing and managing risks associated with microbial, chemical, physical and radiological hazards from the catchment to the consumer. Currently, chemical hazards in WSPs are assessed by targeted chemical analysis, but this approach fails to account for the mixture effects of the many chemicals potentially present in water supplies and omits the possible effects of non-targeted chemicals. Consequently, effect-based monitoring (EBM) using in vitro bioassays and well plate-based in vivo assays are proposed as a complementary tool to targeted chemical analysis to support risk analysis, risk management and water quality verification within the WSP framework. EBM is frequently applied to DW and surface water and can be utilised in all defined monitoring categories within the WSP framework (including 'system assessment', 'validation', 'operational' and 'verification'). Examples of how EBM can be applied within the different WSP modules are provided, along with guidance on where to apply EBM and how frequently. Since this is a new area, guidance documents, standard operating procedures (SOPs) and decision-making frameworks are required for both bioassay operators and WSP teams to facilitate the integration of EBM into WSPs, with these resources being developed currently.

Endocrine disrupting chemicals entering European rivers: Occurrence and adverse mixture effects in treated wastewater

In the present study on endocrine disrupting chemicals (EDCs) in treated wastewater, we used chemical and effect-based tools to analyse 56 wastewater treatment plant (WWTP) effluents from 15 European countries. The main objectives were (i) to compare three different receptor-based estrogenicity assays (ERα-GeneBLAzer, p-YES, ERα-CALUX®), and (ii) to investigate a combined approach of chemical target analysis and receptor-based testing for estrogenicity, glucocorticogenic activity, androgenicity and progestagenic activity (ERα-, GR-, AR- and PR-GeneBLAzer assays, respectively) in treated wastewater. A total of 56 steroids and phenols were detected at concentrations ranging from 25 pg/L (estriol, E3) up to 2.4 μg/L (cortisone). WWTP effluents, which passed an advanced treatment via ozonation or via activated carbon, were found to be less contaminated, in terms of lower or no detection of steroids and phenols, as well as hormone receptor-mediated effects. This result was confirmed by the effect screening, including the three ERα-bioassays. In the GeneBLAzer assays, ERα-activity was detected in 82 %, and GR-activity in 73 % of the samples, while AR- and PR-activity were only measured in 14 % and 21 % of the samples, respectively. 17β-estradiol was confirmed as the estrogen dominating the observed estrogenic mixture effect and triamcinolone acetonide was the dominant driver of glucocorticogenic activity. The comparison of bioanalytical equivalent concentrations (BEQ) predicted from the detected concentrations and the relative effect potency (BEQ_chem) with measured BEQ (BEQ_bio) demonstrated good correlations of chemical target analysis and receptor-based testing results with deviations mostly within a factor of 10. Bioassay-specific effect-based trigger values (EBTs) from the literature, but also newly calculated EBTs based on previously proposed derivation options, were applied and allowed a preliminary assessment of the water quality of the tested WWTP effluent samples. Overall, this study demonstrates the high potential of linking chemical with effect-based analysis in water quality assessment with regard to EDC contamination.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS²). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC₅₀) values from the CompTox database and validated for 219 chemicals and 420 MS² spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC₅₀ values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC₅₀ values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log K_OW.

In vivo environmental metabolomic profiling via a novel microextraction fiber unravels sublethal effects of environmental norfloxacin in gut bacteria

Emerging contaminants (ECs), especially antibiotics, have significantly polluted the environment and threaten the living circumstance of organisms. Environmental metabolomic has emerged to investigate the sublethal effects of ECs. However, lacking noninvasive and real-time sample pretreatment techniques restricts its development in environmental toxicology. Hence, in this study, a real-time and in vivo untargeted analytical technique towards microbial endogenous metabolites was developed via a novel composite solid-phase microextraction (SPME) fiber of ZIF-67 and polystyrene to realize the high-coverage capture of living gut microbial metabolites. To reveal the exposure risks of typical antibiotic - norfloxacin (NFX) to gut bacteria, four representative bacteria were exposed to NFX at environmentally relevant levels. Using the proposed SPME fiber, 70 metabolites were identified to obtain an apparent metabolic separation feature between control and NFX-treated (10 ng/mL) microbial groups, which revealed that the low environmental relevant concentration of NFX would affect normal metabolism of gut bacteria. Additionally, NFX exhibited species-specific toxic effects on microbial growth, especially Escherichia coli displaying a distinct dose-dependent trend. Antioxidative enzymatic activities results demonstrated that beneficial bacteria maintained the state of oxidative stress while symbiotic bacteria suffered from oxidative stress injury under NFX contamination, further corroborating its impact on human intestinal health. This study highlights the suitability of in vivo SPME in the field of metabolite extraction and simultaneously possesses a brilliant application foreground in the environmental metabolomics.

Biomarker development for neonicotinoid exposure in soil under interaction with the synergist piperonyl butoxide in Folsomia candida

Pesticide toxicity is typically assessed by exposing model organisms to individual compounds and measuring effects on survival and reproduction. These tests are time-consuming, labor-intensive, and do not accurately capture the effect of pesticide mixtures. Moreover, it is unfeasible to screen the nearly infinite combinations of mixtures for synergistic effects on model organisms. Therefore, reliable molecular indicators of pesticide exposure have to be identified, i.e., biomarkers. These biomarkers can form the basis of rapid and economical screening procedures to assess the toxicity of pesticides even under synergistic interaction with other pollutants. In this study, we screened the expression patterns of eight genes for suitability as a biomarker for neonicotinoid exposure in the soil ecotoxicological model Folsomia candida (springtails). Springtails were exposed to the neonicotinoids imidacloprid and thiacloprid either alone or with various levels of piperonyl butoxide (PBO), which inhibits cytochrome P450 enzymes (CYPs): a common point of synergistic interaction between neonicotinoid and other pesticides. First, we confirmed PBO as a potency enhancer for neonicotinoid toxicity to springtail fecundity, and then used it as a tool to confirm biomarker robustness. We identified two genes that are reliably indicative for neonicotinoid exposure even under metabolic inhibition of CYPs by PBO, nicotinic acetylcholine receptor-subunit alpha 1 (nAchR) and sodium-coupled monocarboxylate transporter (SMCT). These results can form the basis for developing high-throughput screening procedures for neonicotinoid exposure in varying mixture compositions.

Integration of leave-one-out method and real-time live cell reporter array system to assess the toxicity of mixtures

The ever-increasing number of chemicals and complex mixtures demands a time-saving and cost-effective platform for environmental risk assessment. However, there is limit promising tool for evaluating the contribution of each component to the total toxicity effects of the mixture. Here, four widely distributed environmental pollutants with different mode-of-actions, i.e., cadmium chloride (Cd), nitrofurazone (NFZ), triclosan (TCS), and tris(2-chloroethyl) phosphate (TCEP), were selected as components of artificial mixture. Integration of leave-one-out method and high-dimensional live cell array system was used to explore relative contribution of each component from the mixture. A quaternary mixture (All_4_chems) and four ternary mixtures (Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP) were investigated by Escherichia coli (E. coli) live cell array system with 90 environmental stress genes modified by green fluorescent protein (GFP) expressing reporter vectors. E. coli cytotoxicity tests demonstrated that TCS has antagonism effect with other three chemicals (Cd, NFZ and TCEP), while it was additive effect in other three binary combinations. A total of 26, 23, 13, 31 and 23 genes were significantly altered with fold-change greater than 2 over the 4 h exposure by All_4_chems, Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP, respectively. Clustering analysis based on time-series gene expression patterns and transcriptional effect level index (TELI) showed that Leave_TCEP has similar profiles with All_4_chems, demonstrating TCEP has the least contribution among four components to the quaternary mixture. Leave_NFZ has the least number of significantly altered genes, implying NFZ has the largest toxicity effect contribution to the quaternary mixture. The relative contribution in different pathways indicated that Cd has the most contribution to the mixture in redox stress, while TCS has the least contribution in DNA stress pathway. Collectively, our results demonstrated the utility of high-dimensional toxicogenomics data and leave-one-out method in prioritizing the relative contribution of each component in mixture.

A Bioavailability Model to Predict the Impact of pH and Dissolved Organic Carbon on Ciprofloxacin Ecotoxicity to the Cyanobacterium Microcystis aeruginosa

Ciprofloxacin (CIP) is a pseudopersistent antibiotic detected in freshwater worldwide. As an ionizable chemical, its fate in freshwater is influenced by water chemistry factors such as pH, hardness, and dissolved organic carbon (DOC) content. We investigated the effect of pH, DOC, and Ca²⁺ levels on the toxicity of CIP to Microcystis aeruginosa and developed a bioavailability model on the basis of these experimental results. We found that the zwitterion (CIP^+/- ) is the most bioavailable species of CIP to M. aeruginosa, whereas DOC is the most dominant factor reducing CIP toxicity, possibly via binding of both CIP^+/- and CIP⁺ to DOC. pH likely also regulates CIP-DOC binding indirectly through its influence on CIP speciation. In addition, higher tolerance to CIP by M. aeruginosa was observed at pH < 7.2, but the underlying mechanism is yet unclear. Calcium was identified as an insignificant factor in CIP bioavailability. When parameterized with the data obtained from toxicity experiments, our bioavailability model is able to provide accurate predictions of CIP toxicity because the observed and predicted total median effective concentrations deviated by <28% from each other. Our model predicts that changes in pH and DOC conditions can affect CIP toxicity by up to 10-fold, suggesting that CIP in many natural environments is likely less toxic than in standard laboratory toxicity experiments. Environ Toxicol Chem 2022;41:2835-2847. © 2022 SETAC.

Contribution of sediment contamination to multi-stress in lowland waters

Water bodies in densely populated lowland areas are often impacted by multiple stressors. At these multi-stressed sites, it remains challenging to quantify the contribution of contaminated sediments. This study, therefore, aimed to elucidate the contribution of sediment contamination in 16 multi-stressed drainage ditches throughout the Netherlands. To this end an adjusted TRIAD framework was applied, where 1) contaminants and other variables in the sediment and the overlying water were measured, 2) whole-sediment laboratory bioassays were performed using larvae of the non-biting midge Chironomus riparius, and 3) the in situ benthic macroinvertebrate community composition was determined. It was hypothesized that the benthic macroinvertebrate community composition would respond to all jointly present stressors in both water and sediment, whereas the whole-sediment bioassays would only respond to the stressors present in the sediment. The benthic macroinvertebrate community composition was indeed related to multiple stressors in both water and sediment. Taxa richness was positively correlated with the presence of PO₄-P in the water, macrophyte cover and some pesticides. Evenness, the number of Trichoptera families and the SPEAR_pesticides were positively correlated to the C:P ratios in the sediment, whilst negative correlations were observed with various contaminants in both the water and sediment. The whole-sediment bioassays with C. riparius positively related to the nutrient content of the sediment, whereas no negative relations to the sediment-associated contaminants were observed, even though the lowered SPEAR_pesticides index indicated contaminant effects in the field. Therefore, it was concluded that sediment contamination was identified as one of the various stressors that potentially drove the benthic macroinvertebrate community composition in the multi-stressed drainage ditches, but that nutrients may have masked the adverse effects caused by low and diverse sediment contaminants on C. riparius in the bioassays.

Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS

The search and identification of organic contaminants in agricultural watersheds has become a crucial effort to better characterize watershed contamination by pesticides. The past decade has brought a more holistic view of watershed contamination via the deployment of powerful analytical strategies such as non-target and suspect screening analysis that can search more contaminants and their transformation products. However, suspect screening analysis remains broadly confined to known molecules, primarily due to the lack of analytical standards and suspect databases for unknowns such as pesticide transformation products. Here we developed a novel workflow by cross-comparing the results of various in silico prediction tools against literature data to create an enhanced database for suspect screening of pesticide transformation products. This workflow was applied on tebuconazole, used here as a model pesticide, and resulted in a suspect screening database counting 291 transformation products. The chromatographic retention times and tandem mass spectra were predicted for each of these compounds using 6 models based on multilinear regression and more complex machine-learning algorithms. This comprehensive approach to the investigation and identification of tebuconazole transformation products was retrospectively applied on environmental samples and found 6 transformation products identified for the first time in river water samples.

Profiling of multiple classes of flame retardants in house dust in China: Pattern analysis and human exposure assessment

Legacy [e.g., brominated- (BFRs)] and alternative [e.g., organophosphate- (OPFRs) and nitrogenous- (NFRs)] flame retardants have a propensity to migrate out of consumer products, and thus are dispersed in indoor microenvironments. In this study, simultaneous presence of 11 BFRs, 18 OPFRs and 11 NFRs were measured in house dust collected from Tianjin, China. OPFRs were found at the highest concentrations, with a median value of 3200 ng/g, followed by NFRs (2600) and BFRs (1600). Tris(2-butoxyethyl) phosphate (median: 1800 ng/g), melamine (1100), and BDE-209 (870) were the top three most abundant chemicals in the respective groups. Location-specific patterns of flame retardant concentrations were found with 30%, 20% and 10% of samples were predominated by OPFRs, NFRs and BFRs, respectively, and the remaining samples contained by two or more of the chemical groups occurring concurrently. Network and cluster analysis results indicated the existence of multiple sources of flame retardants in the indoor microenvironment. Estimated human daily intakes via indoor dust ingestion were approximately several tens of ng/kg bw/day and were below their respective reference dose values. Our results indicate widespread occurrence of multiple flame retardant families in indoor dust and suggest need for continued monitoring and efforts to reduce exposures through dust ingestion.

How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models

The assessment of chemical mixture toxicity is one of the major challenges in ecotoxicology. Chemicals can interact, leading to more or less effects than expected, commonly named synergism and antagonism respectively. The classic ad hoc approach for the assessment of mixture effects is based on dose-response curves at a single time point, and is limited to identifying a mixture interaction but cannot provide predictions for untested exposure durations, nor for scenarios where exposure varies in time. We here propose a new approach using toxicokinetic-toxicodynamic modelling: The General Unified Threshold model of Survival (GUTS) framework, recently extended for mixture toxicity assessment. We designed a dedicated mechanistic interaction module coupled with the GUTS mixture model to i) identify interactions, ii) test hypotheses to identify which chemical is likely responsible for the interaction, and finally iii) simulate and predict the effect of synergistic and antagonistic mixtures. We tested the modelling approach experimentally with two species (Enchytraeus crypticus and Mamestra brassicae) exposed to different potentially synergistic mixtures (composed of: prochloraz, imidacloprid, cypermethrin, azoxystrobin, chlorothalonil, and chlorpyrifos). Furthermore, we also tested the model with previously published experimental data on two other species (Bombus terrestris and Daphnia magna) exposed to pesticide mixtures (clothianidin, propiconazole, dimethoate, imidacloprid and thiacloprid) found to be synergistic or antagonistic with the classic approach. The results showed an accurate simulation of synergistic and antagonistic effects for the different tested species and mixtures. This modelling approach can identify interactions accounting for the entire time of exposure, and not only at one time point as in the classic approach, and provides predictions of the mixture effect for untested mixture exposure scenarios, including those with time-variable mixture composition.

The Effects of Single and Combined Stressors on Daphnids-Enzyme Markers of Physiology and Metabolomics Validate the Impact of Pollution

The continuous global increase in population and consumption of resources due to human activities has had a significant impact on the environment. Therefore, assessment of environmental exposure to toxic chemicals as well as their impact on biological systems is of significant importance. Freshwater systems are currently under threat and monitored; however, current methods for pollution assessment can neither provide mechanistic insight nor predict adverse effects from complex pollution. Using daphnids as a bioindicator, we assessed the impact in acute exposures of eight individual chemicals and specifically two metals, four pharmaceuticals, a pesticide and a stimulant, and their composite mixture combining phenotypic, biochemical and metabolic markers of physiology. Toxicity levels were in the same order of magnitude and significantly enhanced in the composite mixture. Results from individual chemicals showed distinct biochemical responses for key enzyme activities such as phosphatases, lipase, peptidase, β-galactosidase and glutathione-S-transferase. Following this, a more realistic mixture scenario was assessed with the aforementioned enzyme markers and a metabolomic approach. A clear dose-dependent effect for the composite mixture was validated with enzyme markers of physiology, and the metabolomic analysis verified the effects observed, thus providing a sensitive metrics in metabolite perturbations. Our study highlights that sensitive enzyme markers can be used in advance on the design of metabolic and holistic assays to guide the selection of chemicals and the trajectory of the study, while providing mechanistic insight. In the future this could prove to become a useful tool for understanding and predicting freshwater pollution.

Mixture Risk Assessment of Complex Real-Life Mixtures-The PANORAMIX Project

Humans are involuntarily exposed to hundreds of chemicals that either contaminate our environment and food or are added intentionally to our daily products. These complex mixtures of chemicals may pose a risk to human health. One of the goals of the European Union's Green Deal and zero-pollution ambition for a toxic-free environment is to tackle the existent gaps in chemical mixture risk assessment by providing scientific grounds that support the implementation of adequate regulatory measures within the EU. We suggest dealing with this challenge by: (1) characterising 'real-life' chemical mixtures and determining to what extent they are transferred from the environment to humans via food and water, and from the mother to the foetus; (2) establishing a high-throughput whole-mixture-based in vitro strategy for screening of real-life complex mixtures of organic chemicals extracted from humans using integrated chemical profiling (suspect screening) together with effect-directed analysis; (3) evaluating which human blood levels of chemical mixtures might be of concern for children's development; and (4) developing a web-based, ready-to-use interface that integrates hazard and exposure data to enable component-based mixture risk estimation. These concepts form the basis of the Green Deal project PANORAMIX, whose ultimate goal is to progress mixture risk assessment of chemicals.

Tetraphenylphosphonium Chloride-Enhanced Ionization Coupled to Orbitrap Mass Spectrometry for Sensitive and Non-targeted Screening of Polyhalogenated Alkyl Compounds from Limited Serum

Although many types of halogenated compounds are known to bioaccumulate in humans, few are routinely biomonitored and many have remained uncharacterized in human exposome studies due to a lack of high-sensitivity and high-resolution analytical methods. In this study, we discovered tetraphenylphosphonium chloride (Ph₄PCl, 10 μM) as a simple additive to the mobile phase, which enhanced the ionizations of polyhalogenated alkyl compounds (such as organochlorinated pesticides [OCPs], chlorinated paraffins [CPs], dechlorane plus [DPs], and some brominated flame retardants [BFRs]) in the form [M + Cl]^- and boosted mass spectrometry responses by an average of 1-3 orders of magnitude at a resolution of 140,000. Ph₄PCl-enhanced ionization coupled with a halogenation-guided screening process was used to establish a sensitive and non-targeted method that required only single-step sample preparation and identified Cl- and/or bromine-containing alkyl compounds. The method enabled the identification of ∼700 polyhalogenated compounds from 200 μL of human serum, 240 of which were known compounds: 33 short-chain CPs, 52 median-chain CPs, 97 long-chain CPs, 22 very short-chain CPs (vSCCPs), 19 OCPs, 13 DPs, and 4 BFRs. We also identified 325 emerging contaminants (34 unsaturated CPs, 285 chlorinated fatty acid methyl esters [CFAMEs], and 6 chloro-bromo alkenes) and 130 new contaminants (114 oxygen-containing CPs, 2 hexachlorocyclohexane structural analogs, and 11 amino-containing and 3 nitrate-containing chlorinated compounds). The full scan results highlighted the dominance of CPs, CFAMEs, vSCCPs, and dichlorodiphenyltrichloroethanes in the serum samples. Ph₄PCl-enhanced ionization enabled the sensitive and non-targeted identifications of polyhalogenated compounds in small volumes of biological fluid.

Highly sensitive and group-targeting detection of steroid estrogens in water environment using a valid oligonucleotide class-specific editing technique

Steroid environmental estrogens (SEEs) are often coexist in water, require complex analytical techniques for separation and monitoring. However, aptamer-based chemical detection often only recognizes one of them, and the detection of SEEs is still a huge challenge. Herein, a group-targeting aptamer with the ability to recognize SEEs was constructed using efficient oligonucleotide class-specific editing technology, and a photoelectrochemical aptasensor capable of detecting the class of SEEs was established. A quantitative analysis of highly toxic SEEs in the environment and carrying similar core carbon skeleton, including 17β-estradiol, esterone, estriol and ethinylestradiol, was performed. The detection limit was as low as 0.1 nM with a response time of only 15 min. Specifically, this method exhibited high anti-interference with different complex media existing. Combining the theoretical calculations with a variety of spectral experiments, the Π-Π stacking and hydrogen bond synergistic interactions between the photoelectric interface and the three ring structures on SEEs and the hydroxyl group of ring 1 were analyzed in depth. Besides, the conformational changes of loose base helix structure and the free rotation limitation of oligonucleotides after the recognition of SEEs at the molecular level were also elucidated, facilitating the transfer of electrons on the surface of the photoelectrode.

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals' super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

Molecular Characterization of Estrogen Receptor Agonists during Sewage Treatment Processes Using Effect-Directed Analysis Combined with High-Resolution Full-Scan Screening

Endocrine-disrupting potential was evaluated during the sewage treatment process using in vitro bioassays. Aryl hydrocarbon receptor (AhR)-, androgen receptor (AR)-, glucocorticoid receptor (GR)-, and estrogen receptor (ER)-mediated activities were assessed over five steps of the treatment process. Bioassays of organic extracts showed that AhR, AR, and GR potencies tended to decrease through the sewage treatment process, whereas ER potencies did not significantly decrease. Bioassays on reverse-phase high-performance liquid chromatography fractions showed that F5 (log K_OW 2.5-3.0) had great ER potencies. Full-scan screening of these fractions detected two novel ER agonists, arenobufagin and loratadine, which are used pharmaceuticals. These compounds accounted for 3.3-25% of the total ER potencies and 4% of the ER potencies in the final effluent. The well-known ER agonists, estrone and 17β-estradiol, accounted for 60 and 17% of the ER potencies in F5 of the influent and primary treatment, respectively. Fourier transform ion cyclotron resonance mass spectrometry analysis showed that various molecules were generated during the treatment process, especially CHO and CHOS (C: carbon, H: hydrogen, O: oxygen, and S: sulfur). This study documented that widely used pharmaceuticals are introduced into the aquatic environments without being removed during the sewage treatment process.

Exploring the In Situ Formation Mechanism of Polymeric Aluminum Chloride-Silica Gel Composites under Mechanical Grinding Conditions: As a High-Performance Nanocatalyst for the Synthesis of Xanthene and Pyrimidinone Compounds

The use of mechanical ball milling to facilitate the synthesis of organic compounds has attracted intense interest from organic chemists. Herein, we report a new process for the preparation of xanthene and pyrimidinone compounds by a one-pot method using polymeric aluminum chloride (PAC), silica gel, and reaction raw materials under mechanical grinding conditions. During the grinding process, polymeric aluminum chloride and silica gel were reconstituted in situ to obtain a new composite catalyst (PAC-silica gel). This catalyst has good stability (six cycles) and wide applicability (22 substrates). The Al-O-Si active center formed by in situ grinding recombination was revealed to be the key to the effective catalytic performance of the PAC-silica gel composites by the comprehensive analysis of the catalytic materials before and after use. In addition, the mechanism of action of the catalyst was verified using density functional theory, and the synthetic pathway of the xanthene compound was reasonably speculated with the experimental data. Mechanical ball milling serves two purposes in this process: not only to induce the self-assembly of silica and PAC into new composites but also to act as a driving force for the catalytic reaction to take place. From a practical point of view, this "one-pot" catalytic method eliminates the need for a complex preparation process for catalytic materials. This is a successful example of the application of mechanochemistry in materials and organic synthesis, offering unlimited possibilities for the application of inorganic polymer materials in green synthesis and catalysis promoted by mechanochemistry.

Natural Cotton Cellulose-Supported TiO2 Quantum Dots for the Highly Efficient Photocatalytic Degradation of Dyes

The artificial photocatalytic degradation of organic pollutants has emerged as a promising approach to purifying the water environment. The core issue of this ongoing research is to construct efficient but easily recyclable photocatalysts without quadratic harm. Here, we report an eco-friendly photocatalyst with in situ generated TiO₂ quantum dots (TQDs) on natural cotton cellulose (CC) by a simple one-step hydrothermal method. The porous fine structure and abundant hydroxyl groups control the shape growth and improve the stability of nanoparticles, making natural CC suitable for TQDs. The TQDs/CC photocatalyst was synthesized without the chemical modification of the TQDs. FE-SEM and TEM results showed that 5-6 nm TQDs are uniformly decorated on the CC surface. The long-term stability in photocatalytic activity and structure of more than ten cycles directly demonstrates the stability of CC on TQDs. With larger CC sizes, TQDs are easier to recycle. The TQDs/CC photocatalysts show impressive potential in the photocatalytic degradation of anionic methyl orange (MO) dyes and cationic rhodamine B (RhB) dyes.

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

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

Predicting reaction rate constants of ozone with ionic/non-ionic compounds in water

Ozonation is a significant technology for the mitigation of pollutants in water. The second-order reaction rate constant (k_O3) of ozone (O₃) with compounds is essential for measuring their reactivity toward O₃ and understanding their fate during ozonation. However, there is a huge gap between the number of existing chemicals and the available experimental k_O3 values. Moreover, the reactivity of ionizable compounds with different ionization forms toward O₃ may differ greatly. In this study, two quantitative structure activity relationship (QSAR) models for non-ionic and ionic species, are respectively established with partial least squares (PLS) and support vector machine (SVM) methods based on the large datasets (324 non-ionic states and 188 ionic states). These models exhibit good fitting ability (non-ionic model: R²_tr > 0.760; ionic model: R²_tr > 0.780), robustness (Q²_CUM > 0.700), predictive performance (non-ionic model: R²_ext > 0.760; ionic model: R²_ext > 0.810) and wide applicability domain. The molecular parameters in two models are revealed to be significantly different, which may be attributed to the significant difference in molecular structures in two datasets and different reactivities of uncharged and charged states toward O₃. Additionally, the overall k_O3 for compounds at certain pH can be estimated by combining the two single QSAR models. These models and methods can become the effective tools for predicting the conversion rate of pollutants by O₃ in the urban sewage and drinking water treatment.

A new on-line SPE LC-HRMS method for the analysis of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in PM2.5 and its application for screening atmospheric particulates from Dublin and Enniscorthy, Ireland

A sensitive analytical method has been developed and validated for the determination of 16 polyfluorinated alkyl substances (PFAS) in fine airborne particulate matter (PM_2.5) using on-line solid phase extraction (SPE) coupled with liquid chromatography (LC) - negative electrospray ionisation high resolution mass spectrometry (-) ESI-HRMS. On-line SPE allows simultaneous sample clean-up from interfering matrices and lower limits of detection (LODs) by injecting a large volume of sample into the LC system without compromising chromatographic efficiency and resolution. The method provides LODs in the range 0.08-0.5 pg/mL of sample extract allowing detection of selected PFAS in aerosol particles at low fg/m³ level and showed good tolerance to the considered PM matrix. The validated method was applied for analysis of PFAS in ambient PM_2.5 samples collected at two urban locations in Ireland, i.e., Enniscorthy and Dublin. Several PFAS were observed above the detection limit, including perfluorobutyrate (PFBA), perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorobutanesulfonic acid (L-PFBS) and perfluorononanoic acid (PFNA), as well as fluorotelomer sulfonates: 4:2 FTS, 6:2 FTS and 8:2 FTS. The results indicate that some toxic PFAS, such as PFOS and PFOA, are still detected in the environment despite being phased out from production and subject to restricted use in the EU and USA for more than two decades. Observation of fluorotelomer sulfonates (4:2 FTS, 6:2 FTS and 8:2 FTS, which are used as alternatives for legacy PFOA and PFOS) in ambient PM_2.5 samples raises a concern about their persistence in the atmosphere and impact on human health considering emerging evidence that they could have similar health endpoints as PFOA and PFOS. To our knowledge, this is the first study to identify PFAS in ambient PM_2.5 at urban locations in Ireland and also the first study to detect 4:2 and 8:2 fluorotelomer sulfonates in atmospheric aerosol particles.

Monitoring Mixture Effects of Neurotoxicants in Surface Water and Wastewater Treatment Plant Effluents with Neurite Outgrowth Inhibition in SH-SY5Y Cells

Cell-based assays covering environmentally relevant modes of action are widely used for water quality monitoring. However, no high-throughput assays are available for testing developmental neurotoxicity of water samples. We implemented an assay that quantifies neurite outgrowth, which is one of the neurodevelopmental key events, and cell viability in human neuroblastoma SH-SY5Y cells using imaging techniques. We used this assay for testing of extracts of surface water collected in agricultural areas during rain events and effluents from wastewater treatment plants (WWTPs), where more than 200 chemicals had been quantified. Forty-one chemicals were tested individually that were suspected to contribute to the mixture effects among the detected chemicals in environmental samples. Sample sensitivity distributions indicated higher neurotoxicity for surface water samples than for effluents, and the endpoint of neurite outgrowth inhibition was six times more sensitive than cytotoxicity in the surface water samples and only three times more sensitive in the effluent samples. Eight environmental pollutants showed high specificity, and those ranged from pharmaceuticals (mebendazole and verapamil) to pesticides (methiocarb and clomazone), biocides (1,2-benzisothiazolin-3-one), and industrial chemicals (N-methyl-2-pyrrolidone, 7-diethylamino-4-methylcoumarin, and 2-(4-morpholinyl)benzothiazole). Although neurotoxic effects were newly detected for some of our test chemicals, less than 1% of the measured effects were explained by the detected and toxicologically characterized chemicals. The neurotoxicity assay was benchmarked against other bioassays: activations of the aryl hydrocarbon receptor and the peroxisome proliferator-activated receptor were similar in sensitivity, highly sensitive and did not differ much between the two water types, with surface water having slightly higher effects than the WWTP effluent. Oxidative stress response mirrored neurotoxicity quite well but was caused by different chemicals in the two water types. Overall, the new cell-based neurotoxicity assay is a valuable complement to the existing battery of effect-based monitoring tools.

Wastewater treatment plant contaminant profiles affect macroinvertebrate sludge degradation

Disposal of the overwhelming amounts of excess wastewater treatment plant (WWTP) sludge is an increasing financial and environmental problem, and new methods to reduce the amount of excess sludge are therefore required. In the natural environment, interactions between multiple macroinvertebrate detritivores mediate the degradation of organic matter. Macroinvertebrates may thus also be able to degrade WWTP sludge, but may meanwhile be impacted by the associated contaminants. Therefore, the aim of the present study was to examine if WWTPs contaminant concentrations and profiles affect the biotic interactions and macroinvertebrate mediated degradation of sludge. Assessing degradation of sludge from three WWTPs differing in contaminant profile by (combinations of) three macroinvertebrate detritovore taxa, revealed that macroinvertebrate enhanced sludge degradation was WWTP and taxa combination specific. Yet, taxa combinations only had an additional positive effect on sludge degradation when compared to single taxa in sludge with a higher contaminant load. This was confirmed by the results of a Cu-spiked sludge degradation experiment, indicating a possible effect of biotic interactions. It was concluded that macroinvertebrates are a potential tool for the reduction of excess WWTP sludge, and that using multispecies assemblages of detritivorous macroinvertebrates may increase the resilience of this additional treatment step.

Occurrence and Ecological Impact of Chemical Mixtures in a Semiclosed Sea by Suspect Screening Analysis

Stress from mixtures of synthetic chemicals is among the key issues that have significant adverse impacts on the marine ecosystems. A robust screening workflow integrating toxicological-based ranking schemes is still deficient for comprehensive investigation on the main constituents in chemical mixtures that contribute to the ecological risks. In this study, the presence and compositions of a collection of priority pollutants were monitored by suspect screening analysis of seawater and estuarine water samples from the semiclosed Bohai Sea. In total, 108 organic pollutants in nine use categories were identified. Pesticides, intermediates, plastic additives, and per- and polyfluoroalkyl substances were the extensively detected chemical groups. Varied distribution patterns of the pollutants were illustrated intuitively in distinctive sampling areas by hierarchical cluster analysis, which were mainly influenced by run-off inputs, ocean currents, and chemical use history. Ecological risks of chemicals with quantified residue levels were first assessed by the toxicity-weighted concentration ranking scheme, and pentachlorophenol was found as the main contributor in the investigating areas. By optimization of multiple alternative variables (e.g., instrumental response and detection frequency), extended ranking of all the identified pollutants was plausible under the toxicological priority index framework. Similarity in toxicological endpoints of the prioritized pollutants could further been screened by ToxAlerts. Aromatic amine was highlighted as the most frequently detected structural alert (SA) for genotoxic carcinogenicity and mutagenicity. These findings fully demonstrate rationality of the ranking schemes integrated into the suspect screening analysis for profiling contamination characteristics, assessing ecological risk potentials, and prioritizing SAs.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

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

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

Tire rubber chemicals reduce juvenile oyster (Crassostrea gigas) filtration and respiration under experimental conditions

Tires can release a large number of chemical compounds that are potentially hazardous for aquatic organisms. An ecophysiological system was used to do high-frequency monitoring of individual clearance, respiration rates, and absorption efficiency of juvenile oysters (8 months old) gradually exposed to four concentrations of tire leachates (equivalent masses: 0, 1, 10, and 100 μg tire mL^-1). Leachates significantly reduced clearance (52 %) and respiration (16 %) rates from 1 μg mL^-1, while no effect was observed on the absorption efficiency. These results suggest that tire leachates affect oyster gills, which are the organ of respiration and food retention as well as the first barrier against contaminants. Calculations of scope for growth suggested a disruption of the energy balance with a significant reduction of 57 %. Because energy balance directs whole-organism functions (e.g., growth, reproductive outputs), the present study calls for an investigation of the long-term consequences of chemicals released by tires.

A risk ranking of pesticides in Irish drinking water considering chronic health effects

This paper presents a novel scoring system which facilitates a relative ranking of pesticide risk to human health arising from contaminated drinking water. This method was developed to identify risky pesticides to better inform monitoring programmes and risk assessments. Potential risk was assessed considering pesticide use, chronic human health effects and environmental fate. Site-specific soil conditions, such as soil erodibility, hydrologic group, soil depth, clay, sand, silt, and organic carbon content of soil, were incorporated to demonstrate how pesticide fate can be influenced by the areas in which they are used. The indices of quantity of use, consequence and likelihood of exposure, hazard score and quantity-weighted hazard score were used to describe the level of concern that should be attributed to a pesticide. Metabolite toxicity and persistence were also considered in a separate scoring to highlight the contribution metabolites make to overall pesticide risk. This study presents two sets of results for 63 pesticides in an Irish case study, (1) risk scores calculated for the parent compounds only and (2) a combined pesticide-metabolite risk score. In both cases the results are assessed for two locations with differing soil and hydrological properties. The method developed in this paper can be adapted by pesticide users to assess and compare pesticide risk at site level using pesticide hazard scores. Farm advisors, water quality monitors, and catchment managers can apply this method to screen pesticides for human health risk at a regional or national level.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

Activation of the xenobiotic metabolism and oxidative stress response by mixtures of organic pollutants extracted with in-tissue passive sampling from liver, kidney, brain and blubber of marine mammals

We used in-tissue passive equilibrium sampling using the silicone polydimethylsiloxane (PDMS) to transfer chemical mixtures present in organs from marine mammals with lipid contents between 2.3 and 99%into in vitro bioassays. Tissues from five harbor porpoises (Phocoena phocoena), one harbor seal (Phoca vitulina) and one orca (Orcinus orca) from the North and Baltic Seas were sampled until thermodynamic equilibrium was reached. Mixture effects were quantified with cellular reporter gene bioassays targeting the activation of the aryl hydrocarbon receptor (AhR-CALUX), the peroxisome proliferator-activated receptor gamma (PPARγ-bla) and the oxidative stress response (AREc32), with parallel cytotoxicity measurements in all assays. After removing co-extracted lipids and other matrix residues with a non-destructive cleanup method (freeze-out of acetonitrile extract followed by a primary secondary amine sorbent extraction), the activation of the PPARγ and AREc32 were reduced by factors of on average 4.3 ± 0.15 (n = 22) and 2.5 ± 0.23 (n = 18), respectively, whereas the activation of the AhR remained largely unaltered: 1.1 ± 0.075 (n = 6). The liver extracts showed the highest activation, followed by the corresponding kidney and brain extracts, and the blubber extracts of the animals were the least active ones. The activation of the PPARγ by the liver extracts was reduced after cleanup by a factor of 11 ± 0.26 (n = 7) and the AREc32 activity by a factor of 1.9 ± 0.32 (n = 4). The blubber extracts did not activate the AhR up to concentrations where cytotoxicity occurred or up to an acceptable lipid volume fraction of 0.27% as derived from earlier work, whereas all liver extracts that had undergone cleanup activated the AhR. The developed in-tissue passive sampling approach allows a direct comparison of the bioassay responses between different tissues without further normalization and serves as a quantitative method suitable for biomonitoring of environmental biota samples.

Assessment of a New Approach Method for Grouped Chemical Hazard Estimation: The Toxicity-Normalized Species Sensitivity Distribution (SSDn)

New approach methods are being developed to address the challenges of reducing animal testing and assessing risks to the diversity of species in aquatic environments for the multitude of chemicals with minimal toxicity data. The toxicity-normalized species sensitivity distribution (SSDn) approach is a novel method for developing compound-specific hazard concentrations using data for toxicologically similar chemicals. This approach first develops an SSDn composed of acute toxicity values for multiple related chemicals that have been normalized by the sensitivity of a common species tested with each compound. A toxicity-normalized hazard concentration (HC5n) is then computed from the fifth percentile of the SSDn. Chemical-specific HC5 values are determined by back-calculating the HC5n using the chemical-specific sensitivity of the normalization species. A comparison of the SSDn approach with the single-chemical SSD method was conducted by using data for nine transition metals to generate and compare HC5 values between the two methods. We identified several guiding principles for this method that, when applied, resulted in accurate HC5 values based on comparisons with results from single-metal SSDs. The SSDn approach shows promise for developing statistically robust hazard concentrations when adequate taxonomic representation is not available for a single chemical.

The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) are over 70 000 "complex" chemical mixtures produced and used at significant levels worldwide. Due to their unknown or variable composition, applying chemical assessments originally developed for individual compounds to UVCBs is challenging, which impedes sound management of these substances. Across the analytical sciences, toxicology, cheminformatics, and regulatory practice, new approaches addressing specific aspects of UVCB assessment are being developed, albeit in a fragmented manner. This review attempts to convey the "big picture" of the state of the art in dealing with UVCBs by holistically examining UVCB characterization and chemical identity representation, as well as hazard, exposure, and risk assessment. Overall, information gaps on chemical identities underpin the fundamental challenges concerning UVCBs, and better reporting and substance characterization efforts are needed to support subsequent chemical assessments. To this end, an information level scheme for improved UVCB data collection and management within databases is proposed. The development of UVCB testing shows early progress, in line with three main methods: whole substance, known constituents, and fraction profiling. For toxicity assessment, one option is a whole-mixture testing approach. If the identities of (many) constituents are known, grouping, read across, and mixture toxicity modeling represent complementary approaches to overcome data gaps in toxicity assessment. This review highlights continued needs for concerted efforts from all stakeholders to ensure proper assessment and sound management of UVCBs.