Extended Virtual Screening Strategies To Link Antiandrogenic Activities and Detected Organic Contaminants in Soils

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.

Novel Approach to Screen Endocrine-Disrupting Chemicals via Endocrine-Enhanced Reduced Human Transcriptome

Endocrine-disrupting chemicals (EDCs) can interfere with multiple pathways and trigger different modes of action. Thus, the traditional EDC in vitro screening processes often require a battery of bioassays to cover multiple target pathways. Here we developed an endocrine-enhanced reduced human transcriptome (ERHT) focused on hormone receptor signaling induced by the EDCs regulating specific genes. ERHT was developed based on 1200 prioritized genes covering 110 endocrine-related biological pathways across eight potential adverse outcomes. The ability of this approach to identify EDCs was derived from machine learning of 1068 dose-dependent transcriptome profiles and enhanced by quantifying chemical-induced critical pathway responses, and thus, it demonstrated excellent classification performance (AUC = 0.84 ± 0.03) in internal cross-validation. We ultimately applied this approach to known EDCs and inactive substances to validate the reliability of this approach. Through external validation on 210 chemicals, the extrapolation accuracy exceeded 80%, demonstrating the outstanding practical performance of this approach. Meanwhile, the pathway responses induced by the same chemical were consistent with the experimental results reported by multiple sequencing platforms, highlighting the robustness of this approach. The above results demonstrate that this approach can provide novel insights for EDCs' high-throughput screening and comprehensive toxic mechanisms through biological pathways.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

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.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Occurrence and Prioritization of Human Androgen Receptor Disruptors in Sewage Sludges Across China

The global practice of reusing sewage sludge in agriculture and its landfill disposal reintroduces environmental contaminants, posing risks to human and ecological health. This study screened sewage sludge from 30 Chinese cities for androgen receptor (AR) disruptors, utilizing a disruptor list from the Toxicology in the 21st Century program (Tox21), and identified 25 agonists and 33 antagonists across diverse use categories. Predominantly, natural products 5α-dihydrotestosterone and thymidine emerged as agonists, whereas the industrial intermediate caprolactam was the principal antagonist. In-house bioassays for identified disruptors displayed good alignment with Tox21 potency data, validating employing Tox21 toxicity data for theoretical toxicity estimations. Potency calculations revealed 5α-dihydrotestosterone and two pharmaceuticals (17β-trenbolone and testosterone isocaproate) as the most potent AR agonists and three dyes (rhodamine 6G, Victoria blue BO, and gentian violet) as antagonists. Theoretical effect contribution evaluations prioritized 5α-dihydrotestosterone and testosterone isocaproate as high-risk AR agonists and caprolactam, rhodamine 6G, and 8-hydroxyquinoline (as a biocide and a preservative) as key antagonists. Notably, 16 agonists and 20 antagonists were newly reported in the sludge, many exhibiting significant detection frequencies, concentrations, and/or toxicities, demanding future scrutiny. Our study presents an efficient strategy for estimating environmental sample toxicity and identifying key toxicants, thereby supporting the development of appropriate sludge management strategies.

Effect-directed analysis of androgenic compounds from sewage sludges in China

The safety of municipal sewage sludge has raised great concerns because of the accumulation of large-scale endocrine disrupting chemicals in the sludge during wastewater treatment. The presence of contaminants in sludge can cause secondary pollution owing to inappropriate disposal mechanisms, posing potential risks to the environment and human health. Effect-directed analysis (EDA), involving an androgen receptor (AR) reporter gene bioassay, fractionation, and suspect and nontarget chemical analysis, were applied to identify causal AR agonists in sludge; 20 of the 30 sludge extracts exhibited significant androgenic activity. Among these, the extracts from Yinchuan, Kunming, and Shijiazhuang, which held the most polluted AR agonistic activities were prepared for extensive EDA, with the dihydrotestosterone (DHT)-equivalency of 2.5 - 4.5 ng DHT/g of sludge. Seven androgens, namely boldione, androstenedione, testosterone, megestrol, progesterone, and testosterone isocaproate, were identified in these strongest sludges together, along with testosterone cypionate, first reported in sludge media. These identified androgens together accounted for 55 %, 87 %, and 52 % of the effects on the sludge from Yinchuan, Shijiazhuang, and Kunming, respectively. This study elucidates the causative androgenic compounds in sewage sludge and provides a valuable reference for monitoring and managing androgens in wastewater treatment.

A water quality assessment model involving novel fluorescence technology

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Identify organic contaminants of high-concern based on non-targeted toxicity testing and non-targeted LC-HRMS analysis in tap water and source water along the Yangtze River

Many organic pollutants were detected in tap water (TW) and source water (SW) along the Yangtze River. However, the potential toxic effects and the high-concern organics (HCOs) which drive the effect are still unknown. Here, a non-targeted toxicity testing method based on the concentration-dependent transcriptome and non-targeted LC-HRMS analysis combining tiered filtering were used to reveal the overall biological effects and chemical information. Subsequently, we developed a qualitative pathway-structure relationship (QPSR) model to effectively match the biological and chemical information and successfully identified HCOs in TW and SW along the Yangtze River by potential substructures of HCOs. Non-targeted toxicity testing found that the biological potency of both TW and SW was stronger in the downstream of the Yangtze River, and disruption of the endocrine system and cancer were the main drivers of the effect. In addition, non-targeted LC-HRMS analysis combined with retention time prediction results identified 3220 and 631 high-confidence compound structures in positive and negative ion modes, respectively. Then, QPSR model was further implied and identified a total of 103 HCOs, containing 35 industrial chemicals, 30 PPCPs, 26 pesticides, and 12 hormones in TW and SW, respectively. Among them, the neuroactive and hormonal compounds oxoamide, 8-iso-16-cyclohexyl-tetranor prostaglandin E2, E Keppra, and Tocris-0788 showed the highest frequency of detection, which were identified in more than 1/3 of the samples. The strategy of combining non-targeted toxicity testing and non-targeted LC-HRMS analysis will support comprehensive biological effect assessment, identification of HCOs, and risk control of mixtures.

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.

High-Throughput Effect-Directed Monitoring Platform for Specific Toxicity Quantification of Unknown Waters: Lead-Caused Cell Damage as a Model Using a DNA Hybrid Chain-Reaction-Induced AuNPs@aptamer Self-Assembly Assay

A high-throughput cell-based monitoring platform was fabricated to rapidly measure the specific toxicity of unknown waters, based on AuNPs@aptamer fluorescence bioassays. The aptamer is employed in the platform for capturing the toxicity indicator, wherein hybrid chain-reaction (HCR)-induced DNA functional gold nanoparticle (AuNPs) self-assembly was carried out for signal amplification, which is essential for sensitively measuring the sub-lethal effects caused by target compounds. Moreover, the excellent stability given by the synthesized DNA nanostructure provides mild conditions for the aptamer thus used to bind the analyte. Herein, ATP was treated as a toxicity indicator and verified using lead-caused cell damage as a model. Under optimized conditions, excellent performance for water sample measurement was observed, yielding satisfactory accuracy (recovery rate: 82.69-114.20%; CV, 2.57%-4.65%) and sensitivity (LOD, 0.26 µM) without sample pretreatment other than filtration, indicating the method's simplicity, high efficiency, and reliability. Most importantly, this bioassay could be used as a universal platform to encourage its application in the rapid quantification of specific toxicity in varied sources of samples, ranging from drinking water to highly contaminated wastewater.

Methodology for Effect-Based Identification of Bioconcentratable Endocrine Disrupting Chemicals (EDCs) in Water: Establishment, Validation, and Application

Since the wide occurrence of endocrine disrupting chemicals (EDCs) in water is associated with various adverse effects in aquatic organisms, it is urgent to identify key bioconcentratable EDCs. Currently, bioconcentration is generally ignored during the identification of key EDCs. Thus, a methodology for effect-based identification of bioconcentratable EDCs was established in Microcosm, validated in the field, and applied to typical surface water in Taihu Lake. In Microcosm, an inverted U-shaped relationship between logBCFs and logKows was observed for typical EDCs, with medium hydrophobic EDCs (3 ≤ logKow ≤ 7) exhibiting the greatest bioconcentration potentials. On this basis, enrichment methods for bioconcentratable EDCs were established using POM and LDPE, which better fitted the bioconcentration characteristics and enabled the enrichment of 71 ± 8% and 69 ± 6% bioconcentratable compounds. The enrichment methods were validated in the field, where LDPE exhibited a more significant correlation with the bioconcentration characteristics than POM, with mean correlation coefficients of 0.36 and 0.15, respectively, which was selected for further application. By application of the new methodology in Taihu Lake, 7 EDCs were prioritized from 79 identified EDCs as key bioconcentratable EDCs on consideration of their great abundance, bioconcentration potentials, and anti-androgenic potencies. The established methodology could support the evaluation and identification of bioconcentratable contaminants.

Identification of Glucocorticoid Receptor Antagonistic Activities and Responsible Compounds in House Dust: Bioaccessibility Should Not Be Ignored

More and more contaminants in dust have been found to be glucocorticoid receptor (GR) disrupting chemicals. However, little is known about the related potency and responsible toxicants, especially for the main bioaccessible ones in dust. An effect-directed analysis (EDA)-based workflow was developed, including solvent-based exhaustive extraction/tenax-assisted bioaccessible extraction (TBE), high-throughput bioassays, suspect and non-target analysis, as well as in silico candidate selection, for a more realistic identification of responsible contaminants in dust. None of the 39 dust samples from 23 cities in China exhibited GR agonistic activity, while GR antagonistic potencies were detected in 34.8% of samples, being significantly different from the high detection frequency of GR agonistic activities in other environmental media. The GR antagonistic potencies of the dust samples were all reduced after bioaccessible extraction. The mean bioaccessibility of GR antagonistic potency compared with the related exhaustive extracts was 36.8%, and the lowest value was 9%. By using in silico candidate selection, greater than 99% candidate chemical structures which were found by a non-target screening strategy were removed. Di-n-butyl phthalate (DnBP), diisobutyl phthalate (DiBP), and nicotine (NIC) were responsible for the activities of the exhaustive extracts of dust, contributing up to 91% potencies. DiBP and DnBP were also responsible for the bioaccessible activities, contributing up to 79% potencies. However, the contribution from NIC decreased significantly and can be ignored because of its low bioaccessibility. This study suggests that the improved workflow combining extraction, reporter gene bioassays, suspect and non-target analysis, as well as in silico candidate selection is useful for EDA analysis in dust samples. In addition, exhaustive extraction may overestimate the risk of contaminants, while bioaccessibility evaluation based on bioaccessible extraction is essential in both effect evaluation and toxicant identification.

Effect-Directed Analysis Based on the Reduced Human Transcriptome (RHT) to Identify Organic Contaminants in Source and Tap Waters along the Yangtze River

Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on in vitro evaluations. Furthermore, 38 of 100 targets, 39 pathway-related suspects, and 16 estrogenic nontargets were systematically identified. Among them, diethylstilbestrol was the dominant contributor, with the estradiol equivalent (EEQ) significantly correlated with EEQ_water. In addition, zearalenone and niclosamide explained up to 54% of the EEQ_water. The RHT-based EDA method could support the effect evaluation, contributor identification, and risk management of micropolluted waters.

Discovery of contaminants with antagonistic activity against retinoic acid receptor in house dust

Retinoic acid receptors (RARs) control reproduction and development in vertebrates, but little attention has been paid to anthropogenic chemicals exhibiting RAR agoniztic/antagonistic activity. Here we applied a His-RARα pull-down assay combined with high-resolution mass spectrometry to identify chemicals with RARα activity in house dust. After screening, a total of 540 peaks were retained as potential RARα ligands. The mass spectra of 14 chemicals matched with those in the database, of which triphenyl phosphate, galaxolidone, di(2-ethylhexyl) phthalate (DEHP), tris(2-ethylhexyl) phosphate (TEHP), and tris(2-butoxyethyl) phosphate were confirmed by their standards. While one chemical in the sample matched with monophenyl phosphate in the MS/MS database, its retention time was much higher than that of monophenyl phosphate standard, suggesting that it may be an in-source fragment. Its parent ion was finally identified to be m/z 399.2663 using a similarity analysis among chromatographic peaks of hundreds of ions at the same retention time in MS¹ spectrum, and bis(2-ethylhexyl) phenyl phosphate (BEHPP) was identified. BEHPP, DEHP, and TEHP were for the first time identified to be RARα antagonists with IC₅₀ values of 6556, 6600, and 2538 nM, respectively. This study improved structural annotation and filled the knowledge gap regarding widespread environmental contaminants with RAR antagonistic activity.

Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening

There is an increasing need for comparable and harmonized retention times (t_R) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds in high-resolution mass spectrometry (HRMS)-based suspect and nontarget screening investigations. In this study, a rigorously tested, flexible, and less system-dependent unified retention time index (RTI) approach for LC is presented, based on the calibration of the elution pattern. Two sets of 18 calibrants were selected for each of ESI+ and ESI-based on the maximum overlap with the retention times and chemical similarity indices from a total set of 2123 compounds. The resulting calibration set, with RTI set to range between 1 and 1000, was proposed as the most appropriate RTI system after rigorous evaluation, coordinated by the NORMAN network. The validation of the proposed RTI system was done externally on different instrumentation and LC conditions. The RTI can also be used to check the reproducibility and quality of LC conditions. Two quantitative structure-retention relationship (QSRR)-based models were built based on the developed RTI systems, which assist in the removal of false-positive annotations. The applicability domains of the QSRR models allowed completing the identification process with higher confidence for substances within the domain, while indicating those substances for which results should be treated with caution. The proposed RTI system was used to improve confidence in suspect and nontarget screening and increase the comparability between laboratories as demonstrated for two examples. All RTI-related calculations can be performed online at http://rti.chem.uoa.gr/.

Identification of (anti-)androgenic activities and risks of sludges from industrial and domestic wastewater treatment plants

The annual production of sludges is significant all over the world, and large amounts of sludges have been improperly disposed by random dumping. The contaminants in these sludges may leak into the surrounding soils, surface and groundwater, or be blown into the atmosphere, thereby causing adverse effects to human health. In this study, the (anti-)androgenic activities in organic extracts of sludges produced from both industrial and domestic wastewater treatment plants (WWTPs) were examined using reporter gene assay based on MDA-kb2 cell lines and the potential (anti-)androgenic risks were assessed using hazard index (HI) based on bioassays. Twelve of the 18 samples exhibited androgen receptor (AR) antagonistic activities, with AR antagonistic equivalents ranging from 1.2 × 10² μg flutamide/g sludge to 1.8 × 10⁴ μg flutamide/g sludge; however, no AR agonistic activity was detected in any of the tested samples. These 12 sludges were all from chemical WWTPs; no sludges from domestic WWTPs displayed AR antagonistic activity. Aside from wastewater source, treatment scale and technology could also influence AR antagonistic potencies. The HI values of all the 12 sludges exceeded 1.0, and the highest HI value was above 3.0 × 10³ for children; this indicates that these sludges might cause adverse effects to human health and that children are at a greater risk than adults. The anti-androgenic potencies and risks of the subdivided fractions were also determined, and medium-polar and polar fractions were found to have relatively high detection rates and contribution rates to the AR antagonistic potencies and risks of the raw sample extracts.

Concentrations, spatial distributions, and congener profiles of polychlorinated dibenzo-p-dioxins and dibenzofurans around original plastic solid waste recovery sites in China

The concentrations, profiles, and spatial distributions of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in soil and sediment samples from several typical plastic solid waste (PSW) recovery sites (particularly from areas in which PSW is burned openly) in China were investigated. The results showed that burning PSW directly influenced PCDD/F concentrations immediately around the burning area. All of the samples in which soil contained black burning residue, collected from immediately around burning areas, had PCDD/F concentrations (mean 21708 ng kg^-1) and toxic equivalent (TEQ) concentrations (mean 2140 ng I-TEQ kg^-1 or 1877 ng WHO₂₀₀₆-TEQ kg^-1) more than 100 times higher than the concentrations in samples collected away from burning areas (mean 222 ng kg^-1, 8.75 ng I-TEQ kg^-1, 7.96 ng WHO₂₀₀₆-TEQ kg^-1). Principal component analysis and hierarchical cluster analysis indicated that the PCDD/F concentrations in seven soil samples from near PSW burning areas were influenced by PSW burning but that the PCDD/Fs in these soil samples may have had other or multiple sources. PCDD/F distributions at PSW recovery sites have been investigated in few previous studies. The results presented here indicate that appropriate measures should be taken to decrease the ecological risks posed by PSW recovery and to prevent, control, and remediate PCDD/F and other chemical contamination caused by PSW recovery.

Spatial distribution, risk and potential sources of lead in soils in the vicinity of a historic industrial site

Because of measures taken by local and national government agencies to control releases of metals, former industrial sites in China that are contaminated with lead (Pb) in soils have been abandoned. Compared with historic sites themselves, little attention has been paid to contamination with Pb in areas surrounding these sites. In this study, a method by integrating sequential extraction and isotopic fingerprinting was proposed to reveal the key fractions of Pb contaminants in soils, trace their sources and determine the subject of liability for remediation. Topsoils from near a historic site, where lead oxide was produced, were found to be contaminated. Concentrations of Pb in soils were inversely proportional to distances from the industrial site and depth in soils. The predominant form of Pb was the Fe/Mn-oxide-bound fraction (FM3), which accounted for from 53.39% to 82.07% of total concentrations of Pb. Concentrations of Pb in vegetables produced on contaminated soils exceeded those allowed in food for consumption by humans. An assessment of hazards and risks posed by consumption of vegetables grown on these soils indicated relatively high potential for adverse effects on local residents around the closed plant. By use of isotopic finger printing for Pb, the abandoned factory was determined to be the most likely source of Pb in topsoils, especially fraction FM3. To mitigate exposures of people to Pb via consumption of locally produced food, recommended strategies should target legacy sources of Pb to soils in the vicinity of this historic industrial site.