Toward Assessing Absolute Environmental Sustainability of Chemical Pollution

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

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

Plant-derived artificial miRNA effectively reduced the proliferation of aphid (Aphidoidea) through spray-induced gene silencing

BACKGROUND

Aphids (Hemiptera: Aphididae) are notorious sap-sucking insects that rampantly threaten agricultural production worldwide. Current management against aphids in the field heavily relies on chemical pesticides, which makes economical and eco-friendly methods urgently needed. Spray-induced gene silencing (SIGS) offers a powerful and precise approach to pest management. However, the high costs and instability of double-stranded RNA (dsRNA) regulators applied for downstream RNA interference (RNAi) still limit this strategy. It remains uncertain if RNAi regulators applied in SIGS could extend to small RNA (sRNA), especially miRNA.

RESULTS

We chose two sRNA sequences, miR-9b and miR-VgR, whose corresponding targets ABCG4 and VgR are both essential for aphid growth and development. The efficacy of these sequences was initially verified by chemically synthetic single-stranded RNA (syn-ssRNA). Through spray treatment, we observed a significantly decreased survival number and increased abnormality rate of green peach aphids fed on the host under laboratory conditions. Based on our previous study, we generated transgenic plants expressing artificial miR-9b (amiR-9b) and miR-VgR (amiR-VgR). Remarkably, plant-derived amiRNA exerted potent and long-lasting inhibitory efficacy with merely one percent concentration of chemical synthetics. Notably, the simultaneous application of amiR-9b and amiR-VgR exhibited superior inhibitory efficacy.

CONCLUSION

We explored the potential use of sRNA-based biopesticide through SIGS while investigating the dosage requirements. To optimize this strategy, the utilization of plant-derived amiRNA was proposed. The results suggested that attributed to stability and durability, deploying amiRNA in pest management is a potential and promising solution for the field application. © 2024 Society of Chemical Industry.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

BACKGROUND

Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

OBJECTIVES

We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

METHODS

We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there.

RESULTS

We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states.

DISCUSSION

Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

Space-specific flux estimation of atmospheric chemicals from point sources to soil

Predicting chemical flux to soil from industrial point sources accurately at a regional scale has been a significant challenge due to high uncertainty in spatial heterogeneity and quantification. To address this challenge, we developed an innovative approach by combining California Air Resources Board Puff (CALPUFF) and mass balance models, leveraging their complementary strengths in quantitative accuracy and spatial precision. Specifically, CALPUFF was used to predict the polycyclic aromatic hydrocarbons (PAHs) flux to soil due to industrial sources. Additionally, the spatial distribution coefficient of PAHs flux (e.g., si for spatial unit i) was calculated by neural network and combined with the mass balance model to obtain the results of total PAHs fluxes, which were then combined with the results predicted by CALPUFF to effectively estimate the contribution of industrial sources to soil PAHs flux. Taking a petrochemical industry region located in Zhejiang province, China as a case study, results showed the input Phenanthrene (Phe) and Benzo(a)pyrene (BaP) fluxes predicted by CALPUFF were generally lower than those by the mass balance model, with slightly different distribution patterns. CALPUFF results, based on 36 industrial sources, partially represent those of the mass balance model, which includes all sources and pathways. It was suggested that industrial sources contributed 49%-89% and 65%-100% of soil Phe and BaP, respectively across the study area. The average Phe flux from point sources by deposition averaged 2.68 mg m-2∙a-1 in 2021, accounting for approximately 60% of the total Phe flux to soil. The average BaP flux from point sources by deposition averaged 0.0755 mg m-2∙a-1, accounting for only 0.1%-3.65% of the total BaP flux to soil. Thereby, our approach fills up a gap between the relevance to point sources and the accuracy of deposition quantification in estimating chemical flux from specific point sources to soil at a regional scale.

Quantifying pesticide emissions for drift deposition in comparative risk and impact assessment

Estimating emissions of chemical pesticides used in agriculture is an essential component in evaluating the potential toxicity-related impacts on humans and ecosystems in various comparative risk and impact assessment frameworks, such as life cycle assessment, environmental footprinting, absolute environmental sustainability assessment, chemical substitution, and risk prioritization. Emissions related to drift deposition-usually derived from drift experiments-can reach non-target areas, and vary as a function of crop characteristics and application technique. We derive cumulative drift deposition fractions for a wide range of experimental drift functions for use in comparative and mass-balanced approaches. We clarify that cumulative drift deposition fractions require to integrate the underlying drift functions over the relevant deposition area and to correct for the ratio of deposition area to treated field area to arrive at overall mass deposited per unit mass of applied pesticide. Our results show that for most crops, drift deposition fractions from pesticide application are below 0.03 (i.e. 3% of applied mass), except for grapes and fruit trees, where drift fractions can reach 5% when using canon or air blast sprayers. Notably, aerial applications on soybeans can result in significantly higher drift deposition fractions, ranging from 20% to 60%. Additionally, varying the nozzle position can lead to a factor of five differences in pesticide deposition, and establishing buffer zones can effectively reduce drift deposition. To address remaining limitations in deriving cumulative drift deposition fractions, we discuss possible alternative modelling approaches. Our proposed approach can be implemented in different quantitative and comparative assessment frameworks that require emission estimates of agricultural pesticides, in support of reducing chemical pollution and related impacts on human health and the environment.

Mapping chemical footprints of organic micropollutants in European streams

There is increasing awareness that chemical pollution of freshwater systems with complex mixtures of chemicals from domestic sources, agriculture and industry may cause a substantial chemical footprint on water organisms, pushing aquatic ecosystems outside the safe operating space. The present study defines chemical footprints as the risk that chemicals or chemical mixtures will have adverse effects on a specific group of organisms. The aim is to characterise these chemical footprints in European streams based on a unique and uniform screening of more than 600 chemicals in 445 surface water samples, and to derive site- and compound-specific information for management prioritisation purposes. In total, 504 pesticides, biocides, pharmaceuticals and other compounds have been detected, including frequently occurring and site-specific compounds with concentrations up to 74 µg/L. Key finding is that three-quarter of the investigated sites in 22 European river basins exceed established thresholds for chemical footprints in freshwater, leading to expected acute or chronic impacts on aquatic organisms. The largest footprints were recorded on invertebrates, followed by algae and fish. More than 70 chemicals exceed thresholds of chronic impacts on invertebrates. For all organism groups, pesticides and biocides were the main drivers of chemical footprints, while mixture impacts were particularly relevant for invertebrates. No clear significant correlation was found between chemical footprints and the urban discharge fractions, suggesting that effluent-specific quality rather than the total load of treated wastewater in the aquatic environment and the contribution of diffuse sources, e.g. from agriculture, determine chemical footprints.

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8-46% of marketed chemicals based on 1-10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

PBT assessment of chemicals detected in effluent of wastewater treatment plants by suspected screening analysis

Wastewater treatment plants (WWTPs) are the major sources of contaminants discharged into downstream water bodies. Profiling the contaminants in effluent of WWTPs is crucial to assess the potential eco-risks toward downstream organisms. To this end, this study investigated the contaminants in effluent of 10 WWTPs locating in 10 cities of Yangtze River delta region of China by suspected screening analysis. Further, the persistence, bioaccumulation, toxicity (PBT) and the characteristics sub-structures of PBT-like chemicals were analyzed. Totally, 704 chemicals including 155 chemical products, 31 food additives, 52 natural substances, 112 personal care products, 123 pesticides, 192 pharmaceuticals, 17 hormones and 22 others were found. The results of PBT analysis suggested that 42 chemicals (5.97% among the detected chemicals in WWTPs) were with PBT property. Among them, 31 contaminants were not reported previously. 9 characteristics sub-structures (N-methyleneisobutylamine, 1-naphthaldehyde, 2,3,3-trimethylcyclohexene, cyclohexanol, N-sec-butyl-n-propylamine, (5E)-2,6-dimethylocta-1,5-diene, 2-ethylphenol, pentadecane and 6-methoxyhexane) were found for PBT-like chemicals. The sub-structures of highly linear alkyl partially explained the significantly higher PBT score for personal care products. Present study provides fundamental information on PBT properties of contaminants in effluent of WWTPs, which will benefit to prioritize contaminants with high concerns in effluent of WWTPs.

Including the bioconcentration of pesticide metabolites in plant uptake modeling

Although several models of pesticide uptake into plants are available, there are few modeling studies on the bioconcentration of metabolites in plants. Ignoring metabolites in plant uptake models can result in an underestimation of the parent compound's overall impacts on human health associated with pesticide residues in harvested food crops. To address this limitation, we offer a metabolite-based plant uptake model to predict the bioconcentration of the parent compound and its metabolites in plants. We used the uptake of glyphosate and its major metabolite (aminomethylphosphonic acid, AMPA) into potato as an example. The analysis of variability revealed that soil properties (affecting the soil sorption coefficient), dissipation half-life in soil, and metabolic half-life in the potato had a significant impact on the simulated AMPA concentration in the potato, indicating that regional variability could be generated in the plant bioconcentration process of metabolites. The proposed model was further compared using the non-metabolite model. The findings of the comparison suggested that the non-metabolite model, which is integrated with the AMPA bioconcentration process, can predict the AMPA concentration in the potato similarly to the proposed model. In conclusion, we provide insight into the bioconcentration process of metabolites in tuber plants from a modeling viewpoint, with some crucial model inputs, such as biotransformation and metabolic rate constants, requiring confirmation in future studies. The modeling demonstration emphasizes that it is relevant to consider bioaccumulation of metabolites, which can propagate further into increased overall residues of harmful compounds, especially in cases where metabolites have higher toxicity effect potency than their respective parent compounds.

To Split or Not to Split: Characterizing Chemical Pollution Impacts in Aquatic Ecosystems with Species Sensitivity Distributions for Specific Taxonomic Groups

Bridging applied ecology and ecotoxicology is key to protect ecosystems. These disciplines show a mismatch, especially when evaluating pressures. Contrasting to applied ecology, ecotoxicological impacts are often characterized for whole species assemblages based on Species Sensitivity Distributions (SSDs). SSDs are statistical models describing per chemical across-species sensitivity variation based on laboratory toxicity tests. To assist in the aligning of the disciplines and improve decision-support uses of SSDs, we investigate taxonomic-group-specific SSDs for algae/cyanobacteria/aquatic plants, invertebrates, and vertebrates for 180 chemicals with sufficient test data. We show that splitting improves pollution impact assessments for chemicals with a specific mode of action and, surprisingly, for narcotic chemicals. We provide a framework for splitting SSDs that can be applied to serve in environmental protection, life cycle assessment, and management of freshwater ecosystems. We illustrate that using split SSDs has potentially large implications for the decision-support of SSD-based outputs around the globe.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

A sustainable process for selective recovery of metals from gallium-bearing waste generated from LED industry

With the rapid development of the LED industry, gallium (Ga)-bearing waste generated is regarded as one of the most hazardous as it typically contains heavy metals and combustible organics. Traditional technologies are characterized by long processing routes, complex metal separation processes and significant secondary pollution emission. In this study, we proposed an innovative and green strategy to selectively recovery Ga from Ga-bearing waste by using a quantitative phase-controlling transition process. In the phase-controlling transition process, the gallium nitride (GaN) and indium (In) are converted to alkali-soluble gallium (III) oxide (Ga₂O₃) and alkali-insoluble indium oxides (In₂O₃) by oxidation calcination, while nitrogen is converted into diatomic nitrogen gas instead of ammonia/ammonium (NH₃/NH₄⁺). By selective leaching with NaOH solution, nearly 92.65% of Ga can be recycled with a leaching selectivity of 99.3%, while little emissions of NH₃/NH₄⁺. Ga₂O₃ with a purity of 99.97% was obtained from the leachate which is also economy promising by economic assessment. Therefore, the proposed methodology compared to the conventional acid and alkali leaching methods is potentially greener and more efficient process for extracting valuable metals from nitrogen-bearing solid waste.

Modernizing persistence-bioaccumulation-toxicity (PBT) assessment with high throughput animal-free methods

The assessment of persistence (P), bioaccumulation (B), and toxicity (T) of a chemical is a crucial first step at ensuring chemical safety and is a cornerstone of the European Union's chemicals regulation REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals). Existing methods for PBT assessment are overly complex and cumbersome, have produced incorrect conclusions, and rely heavily on animal-intensive testing. We explore how new-approach methodologies (NAMs) can overcome the limitations of current PBT assessment. We propose two innovative hazard indicators, termed cumulative toxicity equivalents (CTE) and persistent toxicity equivalents (PTE). Together they are intended to replace existing PBT indicators and can also accommodate the emerging concept of PMT (where M stands for mobility). The proposed "toxicity equivalents" can be measured with high throughput in vitro bioassays. CTE refers to the toxic effects measured directly in any given sample, including single chemicals, substitution products, or mixtures. PTE is the equivalent measure of cumulative toxicity equivalents measured after simulated environmental degradation of the sample. With an appropriate panel of animal-free or alternative in vitro bioassays, CTE and PTE comprise key environmental and human health hazard indicators. CTE and PTE do not require analytical identification of transformation products and mixture components but instead prompt two key questions: is the chemical or mixture toxic, and is this toxicity persistent or can it be attenuated by environmental degradation? Taken together, the proposed hazard indicators CTE and PTE have the potential to integrate P, B/M and T assessment into one high-throughput experimental workflow that sidesteps the need for analytical measurements and will support the Chemicals Strategy for Sustainability of the European Union.

Advancing water footprint assessments: Combining the impacts of water pollution and scarcity

Several water footprint indicators have been developed to curb freshwater stress. Volumetric footprints support water allocation decisions and strive to increase water productivity in all sectors. In contrast, impact-oriented footprints are used to minimize the impacts of water use on human health, ecosystems, and freshwater resources. Efforts to combine both perspectives in a harmonized framework have been undertaken, but common challenges remain, such as pollution and ecosystems impacts modelling. To address these knowledge gaps, we build upon a water footprint assessment framework proposed at conceptual level to expand and operationalize relevant features. We propose two regionalized indicators, namely the water biodiversity footprint and the water resource footprint, that aggregate all impacts from toxic chemicals, nutrients, and water scarcity. The first impact indicator represents the impacts on freshwater ecosystems. The second one models the competition for freshwater resources and its consequences on freshwater availability. As part of the framework, we complement the two indicators with a sustainability assessment representing the levels above which ecological and human freshwater needs are no longer sustained. We test our approach assessing the sustainability of water use in the European Union in 2010. Water stress hampers 15 % of domestic, agricultural and industrial water demand, mainly due to irrigation and pesticide emissions in southern Europe. Moreover, damage to the freshwater ecosystems is widespread and mostly resulting from chemical emissions from industry. Approximately 5 % of the area is exceeding the regional sustainability limits for ecosystems and human water requirements altogether. Concerted efforts from all sectors are needed to reduce the impacts of emissions and water consumption under the sustainability limits. These advances are considered an important step toward the harmonization of volumetric and impact-oriented approaches to achieve consistent and holistic water footprinting as well as contributing to strengthen the policy relevance of water footprint assessments.

Three-dimensional distribution characteristics of multiple pollutants in the soil at a steelworks mega-site based on multi-source information

Soil pollution at steelworks mega-sites has become a severe environmental issue worldwide. However, due to the complex production processes and hydrogeology, the soil pollution distribution at steelworks is still unclear. This study scientifically cognized the distribution characteristics of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals (HMs) at a steelworks mega-site based on multi-source information. Specifically, firstly, 3D distribution and spatial autocorrelation of pollutants were obtained by interpolation model and local indicators of spatial associations (LISA), respectively. Secondly, the characteristics of horizontal distribution, vertical distribution, and spatial autocorrelations of pollutants were identified by combining multi-source information such as production processes, soil layers, and properties of pollutants. Horizontal distribution showed that soil pollution in steelworks mainly occurred in the front end of the steel process chain. Over 47% of PAHs and VOCs pollution area were distributed in coking plants and over 69% of HMs in stockyards. Vertical distribution indicated that HMs, PAHs, and VOCs were enriched in the fill, silt, and clay layers, respectively. Spatial autocorrelation of pollutants was positively correlated with their mobility. This study clarified the soil pollution characteristics at steelworks mega-sites, which can support the investigation and remediation of steelworks mega-sites.

Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals

BACKGROUND

Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals.

OBJECTIVES

Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable.

METHODS

We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (BMDh). We hypothesized that a quantile of the BMDh distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow.

RESULTS

The BMDh distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [R2≥0.78, residual standard error (RSE)≤0.53 log10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived.

DISCUSSION

In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.

Comparative selective pressure potential of antibiotics in the environment

To guide both environmental and public health policy, it is important to assess the degree of antibiotic resistance selection pressure under measured environmental concentrations (MECs), and to compare the efficacy of different mitigation strategies to minimize the spread of resistance. To this end, the resistance selection and enrichment potential due to antibiotic emissions into the environment must be analysed from a life cycle perspective, for a wide range of antibiotics, and considering variations in the underlying fitness costs between different resistance mutations and genes. The aim of this study is to consistently derive fitness cost-dependent minimum selective concentrations (MSCs) from readily available bacterial inhibition data and to build MSC-based species sensitivity distributions (SSDs). These are then used to determine antibiotic-specific resistance selection concentrations predicted to promote resistance in 5% of exposed bacterial species (RSC₅). Using a previously developed competition model, we provide estimated MSC₁₀ endpoints for 2,984 antibiotic and bacterial species combinations; the largest set of modelled MSCs available to date. Based on constructed SSDs, we derive RSC₅ for 128 antibiotics with four orders of magnitude difference in their 'selective pressure potential' in the environment. By comparing our RSC₅ to MECs, we highlight specific environmental compartments (e.g. hospital and wastewater effluents, lakes and rivers), as well as several antibiotics (e.g. ciprofloxacin, norfloxacin, enrofloxacin, and tetracycline), to be scrutinized for their potential role in resistance selection and dissemination. In addition to enabling comparative risk screening of the selective pressure potential of multiple antibiotics, our SSD-derived RSC₅ provide the point of departure for calculating new life cycle-based characterization factors for antibiotics to compare mitigation strategies, thereby contributing towards a 'One-Health' approach to tackling the global antibiotic resistance crisis.

Framework for defining pesticide maximum residue levels in feed: applications to cattle and sheep

BACKGROUND

Pesticide residues in animal feed can endanger animal health and compromise the safety of livestock products for human consumption. Even though policymakers such as the European Union and the World Health Organization have established maximum residue levels (MRLs) for pesticides in both human food and animal feed, there is no systematic management of pesticides in animal feed that considers the entire supply chain. In response, we propose a framework for defining consistent MRLs for pesticides in animal feed that assesses the impact of defined MRLs on upstream (e.g., MRLs in feed crops) and downstream (e.g., MRLs in livestock products) sectors of the livestock-product supply chain.

RESULTS

The MRLs determined for the selected pesticides in the feed of cattle and sheep as case study animals indicate that lipophilic pesticides tend to have lower MRLs than hydrophilic pesticides, primarily due to the relatively high toxicity and biotransfer factors of lipophilic pesticides. In addition, we observe that, primarily for lipophilic pesticides, upstream and downstream regulations are not aligned in terms of defining MRLs in feed using current MRLs in crops with relevance to feed and foods of animal origin.

CONCLUSION

Some of the current pesticide regulations in the livestock-product supply chain need to be re-evaluated to ensure that MRLs in the upstream sector (i.e., crops) do not result in unacceptable residues in the downstream sector (i.e., MRLs in livestock products affecting animal and human health). Finally, we provide recommendations for optimizing the derivation of MRLs in feed, including the evaluation of residue fate during feed and food manufacturing processes. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Linking freshwater ecotoxicity to damage on ecosystem services in life cycle assessment

Freshwater ecosystems provide major benefits to human wellbeing-so-called ecosystem services (ES)-but are currently threatened among others by ecotoxicological pressure from chemicals reaching the environment. There is an increased motivation to incorporate ES in quantification tools that support decision-making, such as life cycle assessment (LCA). However, mechanistic models and frameworks that can systematically translate ecotoxicity effect data from chemical tests into eventual damage on species diversity, functional diversity, and ES in the field are still missing. While current approaches focus on translating predicted ecotoxicity impacts to damage in terms of species loss, no approaches are available in LCA and other comparative assessment frameworks for linking ecotoxicity to damage on ecosystem functioning or ES. To overcome this challenge, we propose a way forward based on evaluating available approaches to characterize damage of chemical pollution on freshwater ES. We first outline an overall framework for linking freshwater ecotoxicity effects to damage on related ES in compliance with the boundary conditions of quantitative, comparative assessments. Second, within the proposed framework, we present possible approaches for stepwise linking ecotoxicity effects to species loss, functional diversity loss, and damage on ES. Finally, we discuss strengths, limitations, and data availability of possible approaches for each step. Although most approaches for directly deriving damage on ES from either species loss or damage to functional diversity have not been operationalized, there are some promising ways forward. The Threshold Indicator Taxa ANalysis (TITAN) seems suitable to translate predicted ecotoxicity effects to a metric of quantitative damage on species diversity. A Trait Probability Density Framework (TPD) approach that incorporates various functional diversity components and functional groups could be adapted to link species loss to functional diversity loss. An Ecological Production Function (EPF) approach seems most promising for further linking functional diversity loss to damage on ES flows for human wellbeing. However, in order to integrate the entire pathway from predicted freshwater ecotoxicity to damage on ES into LCA and other comparative frameworks, the approaches adopted for each step need to be harmonized in terms of assumptions, boundary conditions and consistent interfaces with each other.

Ecotoxicity characterization of chemicals: Global recommendations and implementation in USEtox

Chemicals emitted to the environment affect ecosystem health from local to global scale, and reducing chemical impacts has become an important element of European and global sustainability efforts. The present work advances ecotoxicity characterization of chemicals in life cycle impact assessment by proposing recommendations resulting from international expert workshops and work conducted under the umbrella of the UNEP-SETAC Life Cycle Initiative in the GLAM project (Global guidance on environmental life cycle impact assessment indicators). We include specific recommendations for broadening the assessment scope through proposing to introduce additional environmental compartments beyond freshwater and related ecotoxicity indicators, as well as for adapting the ecotoxicity effect modelling approach to better reflect environmentally relevant exposure levels and including to a larger extent chronic test data. As result, we (1) propose a consistent mathematical framework for calculating freshwater ecotoxicity characterization factors and their underlying fate, exposure and effect parameters; (2) implement the framework into the USEtox scientific consensus model; (3) calculate characterization factors for chemicals reported in an inventory of a life cycle assessment case study on rice production and consumption; and (4) investigate the influence of effect data selection criteria on resulting indicator scores. Our results highlight the need for careful interpretation of life cycle assessment impact scores in light of robustness of underlying species sensitivity distributions. Next steps are to apply the recommended characterization framework in additional case studies, and to adapt it to soil, sediment and the marine environment. Our framework is applicable for evaluating chemicals in life cycle assessment, chemical and environmental footprinting, chemical substitution, risk screening, chemical prioritization, and comparison with environmental sustainability targets.

The European exposure science strategy 2020-2030

Exposure science is an emerging and rapidly growing field dedicated to all aspects concerning the contact between chemical, biological, physical or psycho-social stressors and human and ecological receptors. With that, exposure science plays a central role in protecting human and ecosystem health, and contributes to the global transition towards a green and sustainable society. In Europe, however, exposure science is currently not sufficiently recognised as a scientific field, resulting in inefficient uptake into policies. In response, the wider European exposure science community developed elements and actions under the auspices of the Europe Regional Chapter of the International Society of Exposure Science (ISES Europe), for identified priority areas, namely education, exposure models, exposure data, human biomonitoring, and policy uptake. In the present document, we synthesize these strategic elements into an overarching 'European Exposure Science Strategy 2020-2030', following three strategic objectives that focus on acknowledging exposure science as an independent and interconnected field, harmonizing approaches and tools across regulations, and exploring collaboration, education and funding mechanisms. To operationalise this strategy, we present concrete key actions and propose initiatives and funding options for advancing the underlying science, cultivating broader education and cross-sector exposure knowledge transfer, and fostering effective uptake of exposure information into policy. We aim at anchoring European efforts in the global exposure science context, with a special focus on the interface between scientific advancements, application in decision support, and dissemination and training. This will help to develop exposure science as a strong scientific field with the ultimate goal to successfully assess and manage various stressors across sectors and geographic scales.

Advancing exposure data analytics and repositories as part of the European Exposure Science Strategy 2020-2030

High-quality and comprehensive exposure-related data are critical for different decision contexts, including environmental and human health monitoring, and chemicals risk assessment and management. However, exposure-related data are currently scattered, frequently of unclear quality and structure, not readily accessible, and stored in various-partly overlapping-data repositories, leading to inefficient and ineffective data usage in Europe and globally. We propose strategic guidance for an integrated European exposure data production and management framework for use in science and policy, building on current and future data analysis and digitalization trends. We map the existing exposure data landscape to requirements for data analytics and repositories across European policies and regulations. We further identify needs and ways forward for improving data generation, sharing, and usage, and translate identified needs into an operational action plan for European and global advancement of exposure data for policies and regulations. Identified key areas of action are to develop consistent exposure data standards and terminology for data production and reporting, increase data transparency and availability, enhance data storage and related infrastructure, boost automation in data management, increase data integration, and advance tools for innovative data analysis. Improving and streamlining exposure data generation and uptake into science and policy is crucial for the European Chemicals Strategy for Sustainability and European Digital Strategy, in line with EU Data policies on data management and interoperability.

Framework for developing an exposure science curriculum as part of the European Exposure Science Strategy 2020-2030

BACKGROUND

Evaluating and managing exposures to chemical, physical and biological stressors, which frequently interplay with psychological stressors as well as social and behavioural aspects, is crucial for protecting human and environmental health and transitioning towards a sustainable future. Advances in our understanding of exposure rely on input from well-trained exposure scientists. However, no education programmes in Europe are currently explicitly dedicated to cover the broader range of exposure science approaches, applications, stressors and receptors.

OBJECTIVE

To address this challenge, a curriculum is needed that yields credible, well-defined career pathways in exposure science.

METHODS

Needs and conditions for advancing exposure science education in Europe were identified. As a starting point for a way forward, harmonised learning outcomes for exposure science were defined at each level of the European Qualifications Framework. The course programme coordinators were recruited for three varying courses, with respect to the course level and the proportion of the curriculum dedicated to exposure science. These courses were assessed via our systematic course review procedure. Finally, strategic objectives and actions are proposed to build exposure science education programmes.

RESULTS

The ISES Europe 'Education, Training and Communication' expert working group developed a framework for creating a viable exposure science curriculum. Harmonised learning outcomes were structured under eight learning levels, categorised by knowledge, skills and competence. Illustrative case studies demonstrated how education providers integrated these learning outcomes for their educational context and aligned the overall exposure science curriculum.

CONCLUSIONS

The international recognition and adoption of exposure science education will enable advances in addressing global exposure science challenges for various stressors, from behavioural aspects from individual to population scale, and effective communication between exposure scientists and relevant stakeholders and policy makers, as part of the European Exposure Science Strategy 2020-2030.

Dissipation kinetics, residue modeling and human intake of endosulfan applied to okra (Abelmoschus esculentus)

The non-judicious application of the harmful pesticide endosulfan on okra, one of India's most consumed vegetable crops, has resulted in the frequent detection of residues in food samples. This can lead to resistance and the resurgence of various pests and diseases. In this context, combined dissipation and residue dynamics of different endosulfan components or mixtures (isomers and metabolites) in crop compartments are not yet well understood. To address this research gap, the present study evaluates the dissipation and persistence behavior of different endosulfan isomers (alpha-, beta-isomers) and major metabolite (endosulfan sulfate) on okra during 2017 and 2018. The half-life of endosulfan on okra leaves was found to be between 1.79 and 3.47 days. Half of the endosulfan deposits on okra fruits at the recommended doses were dissipated after 2.39 days compared to 1.99 days at double recommended doses (mean of 2017 and 2018 residue data). Measured endosulfan residues were evaluated against the dynamic plant uptake model dynamiCROP. The better fits were observed between modeled and measured residues for fruits (R² from 0.84 to 0.96 and residual standard error (ER) between 0.6 and 1.47) as compared to leaves (R² from 0.57 to 0.88). We also report fractions of endosulfan components ingested by humans after crop harvest. Intake fractions range from 0.0001-7.2 g_intake/kg of applied pesticide. Our results can evaluate pesticide residues in different crops grown for human consumption, including their isomers and metabolites. They can be combined with dose-response information to evaluate human exposure and/or health risk assessment.

Environmental and human health at risk - Scenarios to achieve the Farm to Fork 50% pesticide reduction goals

The recently released Farm to Fork Strategy of the European Union sets, for the first time, pesticide reduction goals at the EU level: 50% reduction in overall use and risk of chemical pesticides and a 50% use reduction of more hazardous pesticides. However, there is little guidance provided as to how to achieve these targets. In this study, we compiled the characteristics of all 230 EU-approved, synthetic, open-field use active substances (AS) used as herbicides, fungicides and insecticides, and explored the potential of seven Farm to Fork-inspired pesticide use reduction scenarios to achieve the 50% reduction goals. The pesticide reduction scenarios were based on recommended AS application rates, pesticide type, soil persistence, presence on the candidate for substitution list, and hazard to humans and ecosystems. All 230 AS have been found to cause negative effects on humans or ecosystems depending on exposure levels. This is found despite the incomplete hazard profiles of several AS. 'No data available' situations are often observed for the same endpoints and specific organisms. The results of the scenarios indicate that only severe pesticide use restrictions, such as allowing only low-hazard substances, will result in the targeted 50% use and risk reductions. Over half of the 230 AS considered are top use or top hazard substances, however, the reduction actions depend on the still to be defined EC priority areas and action plans, also for other recent and related strategies. Broader scenario implications (on productivity, biodiversity or economy) and the response of farmers to the pesticide use restrictions should be explored in those plans to define effective actions. Our results emphasize the need for a re-evaluation of the approved AS and of their representative uses, and the call for open access to AS, crop and region-specific use data to refine scenarios and assess effective reductions.