Development of a separation framework for effects-based targeted and non-targeted toxicological screening of water and wastewater

Recent advances in bioassays for assessing the toxicity of environmental contaminants in effect-directed analysis

Chemical cocktails in the environment can cause adverse impacts on ecosystems and human health even at low concentrations. Effect-directed analysis (EDA) has proven to be very valuable in identifying key toxic substances in environmental mixtures. For this, it is important to carefully select accurate bioassays from a wide range of tests for EDA when applying it to actual environmental samples. This article reviews studies published from 2014 to 2023 that have applied EDA and summarizes the bioassays and their corresponding biological effects. A total of 127 studies were selected from 591 publications evaluating the toxic effects of environmental samples, including wastewater, surface water, and sediments. Here, bioassays used in EDA are summarized, including the assays that measure specific receptor-mediated modes of action (MOA), induction of xenobiotic metabolism pathways, and induction of adaptive stress response pathways using either in vitro or in vivo bioassays. Also, the identified substances using EDA are discussed based on their MOA. The importance of EDA in establishing a comprehensive approach for the detection of environmental contaminants using bioanalytical methods is emphasized. The current limitations and benefits of using EDA in practical applications are outlined and strategies for moving forward are proposed.

FT-GNN Tool for Bridging HRMS Features and Bioactivity: Uncovering Unidentified Estrogen Receptor Agonists in Sewage

Identifying primary estrogen receptor (ER) agonists in municipal sewage is essential for ensuring the health of aquatic environments. Given the complex and variable chemical composition of sewage, the predominant ER agonists remain unclear. High-resolution mass spectrometry (HRMS)-based models have been developed to predict compound bioactivity in complex matrices, but further optimization is needed to effectively bridge HRMS features with ER agonists. To address this challenge, an FT-GNN (fragmentation tree-based graph neural network) model was proposed. Given limited data and class imbalance, data augmentation was performed using model predictions within the applicability domain (AD) and oversampling technique (OTE). Model development results demonstrated that integrating the FT-GNN with data augmentation improved the balanced accuracy (bACC) value by 6%-31%. The developed model, with a high bACC to identify more true ER agonists, efficiently classified tens of thousands of unidentified HRMS features in sewage, reducing postprocessing workload in nontargeted screening. Analysis of ER agonist transformation during sewage treatment revealed the anaerobic stage as key to both their removal and formation. Estrogenic effect balance analysis suggests that α-E2 and 9,11-didehydroestriol may be two previously overlooked key ER agonists. Collectively, the development and application of the FT-GNN model are crucial advancements toward credible tracking and efficient control of estrogenic risks in water.

Comprehensive Nontargeted Analysis of Drinking Water Supplies to Identify Chemicals Associated with Estrogen Receptor Agonism or Present in Regions of Elevated Breast Cancer Occurrence

To explore the hypothesis that differential exposures to estrogen active chemicals may contribute to regional disparities in cancer incidence, a comprehensive targeted and nontargeted analysis was conducted over two seasons (2020) for drinking water samples from 120 households served by 8 public water systems (4 with historically elevated breast cancer incidence) and from 15 brands of retail water. All samples were analyzed using gas and liquid chromatography with high-resolution mass spectrometry and a bioassay for estrogen receptor agonism. Target compounds included disinfection byproducts, per- and polyfluoroalkyl substances (PFAS), trace elements, and compounds selected for their possible relation to breast cancer. Over 7500 GC and LC nontargeted molecular features passed all quality control filters in each sampling season and were prioritized for identification if they were related to measured estrogen receptor agonism or were present at higher levels in areas with high breast cancer incidence (n = 1036). Benzothiazole-2-sulfonic acid, acetyl tributyl citrate, and diphenyl sulfone were among the prioritized and confirmed nontarget compounds. Nine polycyclic aromatic hydrocarbons and two ketone derivatives displayed significant negative correlations with estrogen receptor agonism. Many prioritized compounds remained unidentified, as 84.4% of the LC features and 77.5% of the GC features could not be annotated with high confidence.

"PAW" a smart analytical process assessing lipophilicity of solutes in mixtures

BACKGROUND

The analysis of mixtures of contaminants remains a challenging task in many fields, including water quality and waste management. For example, the degradation of industrial waste such as plastics, leads to complex mixtures with hundreds of organic contaminants and often non-referenced analytes. In such cases, non-targeted or effects-based analyses provide complementary information to classical targeted-analyses, regarding contaminants nature or properties (molecular mass, lability, toxicity). In this study, a novel analytical method is proposed to characterise mixtures of unknown organic contaminants, with a focus on the lipophilicity of solutes.

RESULTS

The proposed process, named "PAW" (Partition of Aqueous Waste), aims at the quantification of octanol-water partition coefficients (POW) of mixed organic analytes. The process is based on sequential liquid-liquid partition equilibria. The output result is a lipophilicity histogram of the solutes, screened according to the chosen detection method. The process quantifies the distribution of analytes as a function of their octanol-water partition coefficients, without requiring any identification or prior knowledge. The PAW process is applicable with various detectors (UV-Visible, total carbon, liquid scintillation, etc.) allowing to focus on specific families of contaminants (e.g. organic solutes, colloids, 14C-bearing, etc.). Experimental proofs of concept are proposed, illustrating process implementation and possible fields of application. The first example deals with purity analysis of synthetic radiolabeled compounds. The second example aims the monitoring of cellulose degradation and quantification of the lipophilicity of degradation products.

SIGNIFICANCE

The PAW analytical process seems especially useful for characterisation of mixtures containing both hydrophilic and lipophilic compounds, e.g. neutral and ionizable organic contaminants, hardly characterisable simultaneously by chromatographic methods. It could be complementary to more detailed targeted or screening analysis of samples and effluents. For example it may help assessing the composition and environmental fate of mixtures of unknown analytes, thus facilitating waste management or mitigation strategies.

High-efficiency effect-directed analysis (EDA) advancing toxicant identification in aquatic environments: Latest progress and application status

Facing the great threats to ecosystems and human health posed by the continuous release of chemicals into aquatic environments, effect-directed analysis (EDA) has emerged as a powerful tool for identifying causative toxicants. However, traditional EDA shows problems of low-coverage, labor-intensive and low-efficiency. Currently, a number of high-efficiency techniques have been integrated into EDA to improve toxicant identification. In this review, the latest progress and current limitations of high-efficiency EDA, comprising high-coverage effect evaluation, high-resolution fractionation, high-coverage chemical analysis, high-automation causative peak extraction and high-efficiency structure elucidation, are summarized. Specifically, high-resolution fractionation, high-automation data processing algorithms and in silico structure elucidation techniques have been well developed to enhance EDA. While high-coverage effect evaluation and chemical analysis should be further emphasized, especially omics tools and data-independent mass acquisition. For the application status in aquatic environments, high-efficiency EDA is widely applied in surface water and wastewater. Estrogenic, androgenic and aryl hydrocarbon receptor-mediated activities are the most concerning, with causative toxicants showing the typical structural features of steroids and benzenoids. A better understanding of the latest progress and application status of EDA would be beneficial to further advance in the field and greatly support aquatic environment monitoring.

Chemical-toxicological insights and process comparison for estrogenic activity mitigation in municipal wastewater treatment plants

Efforts in water ecosystem conservation require an understanding of causative factors and removal efficacies associated with mixture toxicity during wastewater treatment. This study conducts a comprehensive investigation into the interplay between wastewater estrogenic activity and 30 estrogen-like endocrine disrupting chemicals (EEDCs) across 12 municipal wastewater treatment plants (WWTPs) spanning four seasons in China. Results reveal substantial estrogenic activity in all WWTPs and potential endocrine-disrupting risks in over 37.5 % of final effluent samples, with heightened effects during colder seasons. While phthalates are the predominant EEDCs (concentrations ranging from 86.39 %) for both estrogenic activity and major EEDCs (phthalates and estrogens), with the secondary and tertiary treatment segments contributing 88.59 ± 8.12 % and 11.41 ± 8.12 %, respectively. Among various secondary treatment processes, the anaerobic/anoxic/oxic-membrane bioreactor (A/A/O-MBR) excels in removing both estrogenic activity and EEDCs. In tertiary treatment, removal efficiencies increase with the inclusion of components involving physical, chemical, and biological removal principles. Furthermore, correlation and multiple liner regression analysis establish a significant (p < 0.05) positive association between solid retention time (SRT) and removal efficiencies of estrogenic activity and EEDCs within WWTPs. This study provides valuable insights from the perspective of prioritizing key pollutants, the necessity of integrating more efficient secondary and tertiary treatment processes, along with adjustments to operational parameters like SRT, to mitigate estrogenic activity in municipal WWTPs. This contribution aids in managing endocrine-disrupting risks in wastewater as part of ecological conservation efforts.

Screening, identification and control of unknown estrogen-like compounds in Maillard reaction products of glucose-arginine/lysine model systems

It was speculated that estrogen-like compounds may be produced by chemical reactions during food processing, such as Maillard reaction, which would disrupt the endocrine system of organisms. Herein, the Maillard reaction in the process of high temperature for long time was simulated by using model system, and unknown estrogen-like compounds produced during Maillard reaction were screened by colorimetric assay based on dual estrogen receptor (ER)-gold nanoparticles (AuNPs) and enzyme-linked immunosorbent assay (ELISA). Possible structures of estrogen-like compounds were inferred by ultra-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UPLC-QTOF/MS) in combination with a mass database, and finally the structure of estrogen-like compound, 2, 4-dihydroxy-1, 4-benzoxazin-3-one-2-o-β-D-glucopyranoside (DIBOA-glc), was identified by high resolution orbitrap mass spectrometry (Orbitrap HRMS). This is the first study of the screening and identification of unknown estrogen-like compounds produced in Maillard reaction. Additionally, strategy of controlling the formation of DIBOA-glc by adding vitamin B6 in Maillard reaction was proposed, providing effective proposals for the safety control in actual food processing.

Treatment of nanofiltration membrane concentrates integrated magnetic biochar pretreatment with anaerobic digestion

nanofiltration membrane concentrate (NMC) is an emerging type of wastewater with significant environmental concerns. which can be treated efficiently by an integrated method. In this study, magnetic biochar (MBC) pretreatment integrated with anaerobic digestion (AD) (MBC + AD) was used to treat NMC. Results showed that under the optimal MBC + AD conditions, 79%, 69.4%, 52.9%, and 86.5% of COD, total nitrogen (TN), chromaticity, and light absorbing substances were reduced. For heavy metals removal, 18.3%, 70.0%, 96.4%, 43.8% and 97.5% of Cr (VI), Cd, Pb, Cu and Zn were removed, respectively. LC-MS analysis indicated that p-nitrophenol (4-NP) diethyl and phthalate (DEP) were the main organic pollutants in NMC with a removal rate of 60% and 90%. Compared with single AD, in MBC + AD samples, bacterial activity was improved, and genus DMER64 (23.2%) was dominant. The predominant archaea were Methanocorpusculum (53.3%) and Methanosarcina (25.3%), with microbial restructuring and slight methane generation. Additionally, metabolic pathway prediction revealed that both bacterial and archaeal metabolism were significantly enhanced, contributing to the central functional pathways, namely microbial activity metabolism and biodegradation metabolism. In addition, the significantly increased genera Syner-01, Vulcanibacillus, Methanocorpusculum, and Norank_c_Bathyarchaeia were significantly positively related to metabolic function. This finding demonstrated that MBC + AD enhanced contaminant removal, mainly by regulating bacterial diversity and activity. Moreover, the toxicity of NMC decreased after MBC + AD treatment. This study provides a potential biological strategy for the treatment of membrane concentrates and water recovery.

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.

Risk assessments of emerging contaminants in various waters and changes of microbial diversity in sediments from Yangtze River chemical contiguous zone, Eastern China

Over recent decades, increasing chemical contamination has greatly affected aquatic life and human health, even though most contaminants are present at low concentrations. The large-scale chemical industrial parks (CIPs) concentrated in the Yangtze River Delta account for over half of the total in China, and Jiangsu Province occupies one fifth of the Yangtze River Delta. Inevitably, the ecosystems could be affected by these CIPs. In this study, we collected 35 water and 12 sediment samples from the Yangtze River (Taizhou section) surrounding waters adjacent to concentrated CIPs and determined their cumulative chemical levels to be 0.2 to 28.4 μg/L and cumulative detections to be 11 to 39 contaminants with a median of 20 contaminants. 61 out of 153 screened chemicals were detected from at least one sampling site, and 6 contaminants, mostly semi-volatile organic compounds, appeared at all sites. Among these detected chemicals, di-n-octyl phthalate and dibutyl phthalate were at the highest levels. Ecological assessment revealed that 4-chloroaniline, phenol and dibutyl phthalate possibly would induce adverse effects on Yangtze River (Taizhou) ecosystems. Further aided with an evaluation of integrated biomarker response (IBR) index, it was found that site W06 (downstream of Binjiang CIP wastewater inlet) was the location in greatest need of urgent action. As a result, the microbial diversity of sediments in the Yangtze River mainstream was significantly higher than that of tributaries, where CIPs wastewater entered.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Occurrence of contaminants of emerging concern in surface waters from Paraopeba River Basin in Brazil: seasonal changes and risk assessment

This study describes the application of gas chromatography coupled to mass spectrometry (GC-MS) to evaluate the occurrence of 12 CECs-contaminants of emerging concern (bisphenol A, diclofenac, 17β-estradiol, estriol, estrone, 17α-ethinylestradiol, gemfibrozil, ibuprofen, naproxen, 4-nonylphenol, 4-octylphenol, and acetaminophen) in surface waters from Paraopeba River Basin, Minas Gerais State, Brazil. The analytical procedure was validated and applied to 60 surface water samples collected across four sampling campaigns along the upper and middle watershed. Methods for CECs determination involved sample filtration, and solid-phase extraction (SPE) with subsequent derivatization of the target compounds prior to their analysis by GC-MS. The LOQ varied from 3.6 to 14.4 ng/L and extraction recoveries ranged from 46.1 to 107.1% for the lowest spiked concentration level (10 ng/L). The results showed a profile of spatial distribution of compounds, as well as the influence of rainfall. Ibuprofen (1683.9 ng/L), bisphenol (1587.7 ng/L), and naproxen (938.4 ng/L) occurred in higher concentrations during the rainy season, whereas during the dry season, the concentrations of bisphenol (1057.7 ng/L), estriol (991.0 ng/L), and estrone (978.4 ng/L) were highlighted. The risk assessment of human exposure shows that for most contaminants, the concentration is well below the estimated thresholds for chronic toxicity from water intake. However, estradiol and 17α-ethinylestradiol showed concentrations in the same order of magnitude as the guide values estimated for babies.

Metal contamination, bioaccumulation, ROS generation, and epigenotoxicity influences on zebrafish exposed to river water polluted by mining activities

Epigenetic mechanisms are important for gene expression regulation, which is closely related to human health, and epigenetic effects of polluted water bodies have gained increasing research attention. Le'an River suffers from severe trace metal pollution owing to mining activities. In this study, zebrafish was used as a biological model to study pollution of Le'an River after seven consecutive days of exposure. The results showed that midstream and downstream sections of the river were seriously polluted by trace metals. The liver and gill of zebrafish were enriched with trace metals, and cadmium had the highest bioaccumulation factor. Trace metals caused oxidative stress in zebrafish cells, with increases in reactive oxygen species levels. Significant increase of global DNA methylation in liver of middle and downstream section were observed, with values from 125.67% to 165.45% compared with control. Changes in DNA methylation in the promoter region cause significant increase or decrease of the expression of repair genes and apoptosis genes in liver and gill. In summary, Le'an River water exhibited significant epigenetic effects, and it is necessary to consider epigenetic effects in the evaluation of pollution and health risks of river water.

Nonylphenol ethoxylates biodegradation increases estrogenicity of textile wastewater in biological treatment systems

The formation of estrogenic intermediates, i.e. nonylphenol diethoxylate (NP2EO), nonylphenol monoethoxylate (NP1EO), and nonylphenol (NP), following nonylphenol ethoxylates (NPEOs) biodegradation in textile wastewater raises concerns about its endocrine disruptive activity, but the estrogenicity changes of textile wastewater throughout biological treatment processes remain unknown. In the present study, the estrogenicity of textile wastewater sampled from 10 wastewater treatment plants (WWTPs) were investigated using the reporter gene-based T47D-KBluc bioassay. Results showed that the estrogenicity of the textile wastewater significantly increased after either anaerobic or aerobic treatment in all WWTPs, with an average fold change of 3.21, although traditional pollutants were effectively removed. The estradiol equivalents of the effluent (ranging from 1.50 to 4.12 ng-E2/L) were generally higher than published effect based trigger values, indicating an increased risk for the receiving waters. Removal efficiency was high (84.46%) for NPEOs, but was low for NP2EO and NP1EO in the biological treatment processes. Nevertheless, NP had increased concentrations after the treatment. Bioanalytical equivalent concentration of the textile wastewater and that of NP2EO, NP1EO, and NP showed a good linear correlation, of which NP alone contributed more than 70% to the observed estrogenicity. Extending hydraulic retention time was found effective in reducing the estrogenicity as it allows relatively complete degradation of NP, which was further confirmed by running lab-scale A/O reactors fed with NP10EO. The results may extend our knowledge regarding the estrogenicity of textile wastewater and its reduction technologies used in WWTPs.

Factors Affecting Sampling Strategies for Design of an Effects-Directed Analysis for Endocrine-Active Chemicals

Effects-directed analysis (EDA) is an important tool for identifying unknown bioactive components in a complex mixture. Such an analysis of endocrine-active chemicals (EACs) from water sources has promising regulatory implications but also unique logistical challenges. We propose a conceptual EDA (framework) based on a critical review of EDA literature and concentrations of common EACs in waste and surface waters. Required water volumes for identification of EACs under this EDA framework were estimated based on bioassay performance (in vitro and in vivo bioassays), limits of quantification by mass spectrometry (MS), and EAC water concentrations. Sample volumes for EDA across the EACs showed high variation in the bioassay detectors, with genistein, bisphenol A, and androstenedione requiring very high sample volumes and ethinylestradiol and 17β-trenbolone requiring low sample volumes. Sample volume based on the MS detector was far less variable across the EACs. The EDA framework equation was rearranged to calculate detector "thresholds," and these thresholds were compared with the literature EAC water concentrations to evaluate the feasibility of the EDA framework. In the majority of instances, feasibility of the EDA was limited by the bioassay, not MS detection. Mixed model analysis showed that the volumes required for a successful EDA were affected by the potentially responsible EAC, detection methods, and the water source type, with detection method having the greatest effect on the EDA of estrogens and androgens. The EDA framework, equation, and model we present provide a valuable tool for designing a successful EDA. Environ Toxicol Chem 2020;39:1309-1324. © 2020 SETAC.