Retrospective Risk Assessment of Chemical Mixtures in the Big Data Era: An Alternative Classification Strategy to Integrate Chemical and Toxicological Data

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Prioritizing Organic Pollutants for Shale Gas Exploitation: Life Cycle Environmental Risk Assessments in China and the US

Environmental impacts associated with shale gas exploitation have been historically underestimated due to neglecting to account for the production or the release of end-of-pipe organic pollutants. Here, we assessed the environmental impacts of shale gas production in China and the United States using life cycle assessment. Through data mining, we compiled literature information on organic pollutants in flowback and produced water (FPW), followed by assessments using USEtox to evaluate end-of-pipe risks. Results were incorporated to reveal the life cycle risks associated with shale gas exploitation in both countries. China exhibited higher environmental impacts than the US during the production phase. Substantially different types of organic compounds were observed in the FPW between two countries. Human carcinogenic and ecological toxicity attributed to organics in FPW was 3 orders of magnitude higher than that during the production phase in the US. Conversely, in China, end-of-pipe organics accounted for approximately 52%, 1%, and 47% of the overall human carcinogenic, noncarcinogenic, and ecological impacts, respectively. This may be partially limited by the quantitative data available. While uncertainties exist associated with data availability, our study highlights the significance of integrating impacts from shale gas production to end-of-pipe pollution for comprehensive environmental risk assessments.

Modeling mixtures interactions in environmental toxicology

In the environment, organisms are exposed to mixtures of different toxicants, which may interact in ways that are difficult to predict when only considering each component individually. Adapting and expanding tools from pharmacology, the toxicology field uses analytical, graphical, and computational methods to identify and quantify interactions in multi-component mixtures. The two general frameworks are concentration addition, where components have similar modes of action and their effects sum together, or independent action, where components have dissimilar modes of action and do not interact. Other interaction behaviors include synergism and antagonism, where the combined effects are more or less than the additive sum of individual effects. This review covers foundational theory, methods, an in-depth survey of original research from the past 20 years, current trends, and future directions. As humans and ecosystems are exposed to increasingly complex mixtures of environmental contaminants, analyzing mixtures interactions will continue to become a more critical aspect of toxicological research.

Suspect screening and effect evaluation for small-molecule agonists of the antioxidant response element pathway in fine particulate matter

Oxidative stress is an important mechanism by which fine particulate matter (PM2.5) generates toxicity. However, few inducers have been identified, and the combined effects of the contributing chemicals have rarely been examined. In this study, the occurrence of small-molecule agonists of the antioxidant response element (ARE) pathway was explored in 59 PM2.5 samples from urban Beijing over a one-year period via target and suspect screening analysis. In total, 31 chemicals with diverse structures and use categories were identified and quantified, among which polycyclic aromatic hydrocarbons (PAHs), pesticides, and phytochemicals were the most abundant chemical groups in terms of cumulative concentrations. PAHs and organonitrogen pesticides were also prioritized as the predominant contributors to the cumulative effect of ARE pathway activation, accounting for 55 % and 37 %, respectively. A combination of the prioritized chemicals (i.e., benzo[b]fluoranthene, benzo[k]fluoranthene, pyridaben, and acetochlor) was mixed at a fixed dosing ratio according to the measured average concentrations in samples, and synergism was revealed as the mode of mixture interaction. These findings highlight the importance of high-throughput chemical screening for identifying hazardous components in complex environmental samples, and also expand the knowledge regarding the components contributing to PM2.5-induced oxidative stress.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Toxicity identification evaluation for hydraulic fracturing flowback and produced water during shale gas exploitation in China: Evidence from tissue residues and gene expression

Hydraulic fracturing flowback and produced water (HF-FPW) from shale gas extraction processes is a highly complex medium with potential threats to the environment. Current research on ecological risks of FPW in China is limited, and the link between major components of FPW and their toxicological effects on freshwater organisms is largely unknown. By integrating chemical and biological analyses, toxicity identification evaluation (TIE) was used to reveal causality between toxicity and contaminants, potentially disentangling the complex toxicological nature of FPW. Here, FPW from different shale gas wells, treated FPW effluent, and a leachate from HF sludge were collected from southwest China, and TIE was applied to obtain a comprehensive toxicity evaluation in freshwater organisms. Our results showed that FPW from the same geographic zone could cause significantly different toxicity. Salinity, solid phase particulates, and organic contaminants were identified as the main contributors to the toxicity of FPW. In addition to water chemistry, internal alkanes, PAHs, and HF additives (e.g., biocides and surfactants) were quantified in exposed embryonic fish by target and non-target tissue analyses. The treated FPW failed to mitigate the toxicity associated with organic contaminants. Transcriptomic results illustrated that organic compounds induced toxicity pathways in FPW-exposed embryonic zebrafish. Similar zebrafish gene ontologies were affected between treated and untreated FPW, again confirming that sewage treatment did not effectively remove organic chemicals from FPW. Thus, zebrafish transcriptome analyses revealed organic toxicant-induced adverse outcome pathways and served as evidence for TIE confirmation in complex mixtures under data-poor scenarios.

Joint action of binary mixtures based on parameter k·ECx from concentration-response curves in long-term toxicity assay

A previous acute toxicity study of binary mixtures showed that the combined toxicity can be predicted with the parameter k∙EC_x. To systematically investigate the ability of k∙EC_x to predict the chronic combined toxicity of binary mixtures, the toxicity of six contaminants and five binary mixtures was determined by long-term microplate toxicity analysis (L-MTA) using Aliivibrio fischeri as the test organism. The independent action model (IA) and the relative model deviation ratio (rMDR) were employed to determine the relationship between the Δ(k∙EC_x)% and rMDR_x. The results showed that these two factors conformed to the exponential function in long-term toxicity. Owing to the time-dependence of toxicity, the mixture type of chronic toxicity changes to the relative type of acute toxicity. If the acute toxicity of binary mixtures changes their mode of joint action throughout the concentration range, the chronic toxicity will also change their mode of joint action, and vice versa. This study clarified the change rules of the joint action of binary mixtures in acute and chronic toxicity which can promote research on chronic toxicity of binary mixtures.

S-scheme BiVO4/CQDs/β-FeOOH photocatalyst for efficient degradation of ofloxacin: Reactive oxygen species transformation mechanism insight

Photocatalysis technology exhibited promising application for advanced treatment of wastewater. Nevertheless, the design of efficient photocatalyst and the mechanism of free radicals in pollutant degradation still remained to be further investigated. Herein, BiVO₄/CQDs/β-FeOOH photocatalyst was fabricated by electrostatic self-assembly method, which exhibited the excellent photocatalytic performance. Under visible-light irradiation, the removal rate of ofloxacin by BiVO₄/CQDs/β-FeOOH (0.25 min^-1) was 1.93 times than pristine BiVO₄, and the removal efficiency in 15 min reached 99.21%. The perfect reusability of BiVO₄/CQDs/β-FeOOH was ascribed to the persistent catalytic active centers provided by the renewable surface oxygen vacancies on the β-FeOOH. As electron transfer channels, CQDs facilitated the transfer of BiVO₄ photogeneration electrons. The matched band structure allowed the construction of S-scheme heterojunctions, and the higher conduction band position was retained while the carrier separation was promoted. More importantly, this work firstly reported the phenomenon that the main reactive groups in the photocatalysis process would be directionally transformed with the change of pH conditions. Based on the analysis of capture and electron paramagnetic resonance experiments, ·O₂^- was the main free radicals to photodegrade OFL in neutral and alkaline conditions. However, when the solution pH turned into acidic, the photodegradation of OFL was dominated by ¹O₂. This innovative phenomenon was due to that acidic condition accelerated the reaction kinetics of spontaneous transformation of ·O₂^- to ¹O₂ and inhibited the direct oxidation of pollutants by ·O₂^-. Accordingly, this research could inspire theoretical study of free radical reaction and the design of S-scheme heterojunction photocatalysts.

Point or non-point source: Toxicity evaluation using m-POCIS and zebrafish embryos in municipal sewage treatment plants and urban waterways

Municipal sewage treatment plants (STPs) have been regarded as an important source of organic contaminants in aquatic environment. To assess the impact of STPs on occurrence and toxicity of STP-associated contaminants in receiving waterways, a novel passive sampler modified from polar organic chemical integrative sampler (m-POCIS) was deployed at the inlet and outlet of a STP and several upstream and downstream sites along a river receiving STP effluent in Guangzhou, China. Eighty-seven contaminants were analyzed in m-POCIS extracts, along with toxicity evaluation using zebrafish embryos. Polycyclic musks were the predominant contaminants in both STP and urban waterways, and antibiotics and current-use pesticides (e.g., neonicotinoids, fiproles) were also ubiquitous. The m-POCIS extracts from downstream sites caused significant deformity in embryos, yet the toxicity could not be explained by the measured contaminants, implying the presence of nontarget stressors. Sewage treatment process substantially reduced embryo deformity, chemical oxygen demand, and contamination levels of some contaminants; however, concentrations of neonicotinoids and fiproles increased after STP treatment, possibly due to the release of chemicals from perturbed sludge. Source identification showed that most of the contaminants found in urban waterways were originated from nonpoint runoff, while cosmetics factories and hospitals were likely point sources for musks and antibiotics, respectively. Although the observed embryo toxicity could not be well explained by target contaminants, the present study showed a promising future of using passive samplers to evaluate chemical occurrence and aquatic toxicity concurrently. Zebrafish embryo toxicity significantly decreased after sewage treatment, but higher toxicity was observed for downstream samples, demonstrating that urban runoff may produce detrimental effects to aquatic life, particularly in rainy season. These results highlight the relevance of monitoring nonpoint source pollution along with boosting municipal sewage treatment infrastructure.

Signposts for Aquatic Toxicity Evaluation in China: Text Mining using Event-Driven Taxonomy within and among Regions

Selection of toxicity endpoints affects outcomes of risk assessment. Scientific decisions based on more holistic evidence is preferable for designing bioassay batteries rather than subjective selections, particularly when systems are poorly understood. Here, we propose a novel event-driven taxonomy (EDT)-based text mining tool to prioritize stressors likely to elicit water quality deterioration. The tool integrated automated literature collection, natural language processing using adverse outcome pathway-based toxicological terminologies and machine learning to classify event drivers (EDs). From aquatic toxicity assessments within China over the past decade, we gathered over 14 000 sources of information. With a dictionary that included 1039 toxicological terms, 15 bioassay-related modes of actions were mapped, yet less than half of the bioassays could be elucidated by available adverse outcome pathways. To fill these mechanistic knowledge gaps, we developed a Naïve Bayesian ED-classifier to annotate apical responses. The classifier's 4-fold cross-validation reached 74% accuracy and labeled 85% bioassays as 26 EDs. Narcosis, estrogen receptor-, and aryl hydrogen receptor-mediators were the major EDs in aquatic systems across China, whereas individual regions had distinct ED fingerprints. The EDT-based tool provides a promising diagnostic strategy to inform region-specific bioassay design and selection for water quality assessments in a big data era.