Screening-Level Estimates of Environmental Release Rates, Predicted Exposures, and Toxic Pressures of Currently Used Chemicals

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries

Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.

A prospective ecological risk assessment of high-efficiency III-V/silicon tandem solar cells

III-V/Silicon tandem solar cells offer one of the most promising avenues for high-efficiency, high-stability photovoltaics. However, a key concern is the potential environmental release of group III-V elements, especially arsenic. To inform long-term policies on the energy transition and energy security, we develop and implement a framework that fully integrates future PV demand scenarios with dynamic stock, emission, and fate models in a probabilistic ecological risk assessment. We examine three geographical scales: local (including a floating utility-scale PV and waste treatment), regional (city-wide), and continental (Europe). Our probabilistic assessment considers a wide range of possible values for over one hundred uncertain technical, environmental, and regulatory parameters. We find that III-V/silicon PV integration in energy grids at all scales presents low-to-negligible risks to soil and freshwater organisms. Risks are further abated if recycling of III-V materials is considered at the panels' end-of-life.

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

Assessing city-wide pharmaceutical emissions to wastewater via modelling and passive sampling

With increasing numbers of chemicals used in modern society, assessing human and environmental exposure to them is becoming increasingly difficult. Recent advances in wastewater-based epidemiology enable valuable insights into public exposure to data-poor compounds. However, measuring all >26,000 chemicals registered under REACH is not just technically unfeasible but would also be incredibly expensive. In this paper, we argue that estimating emissions of chemicals based on usage data could offer a more comprehensive, systematic and efficient approach than repeated monitoring. Emissions of 29 active pharmaceutical ingredients (APIs) to wastewater were estimated for a medium-sized city in the Netherlands. Usage data was collected both on national and local scale and included prescription data, usage in health-care institutions and over-the-counter sales. Different routes of administration were considered as well as the excretion and subsequent in-sewer back-transformation of conjugates into respective parent compounds. Results suggest model-based emission estimation on a city-level is feasible and in good agreement with wastewater measurements obtained via passive sampling. Results highlight the need to include excretion fractions in the conceptual framework of emission estimation but suggest that the choice of an appropriate excretion fraction has a substantial impact on the resulting model performance.

Chemical Mixtures and Multiple Stressors: Same but Different?

Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Risk assessment of chemicals and their mixtures are hindered by scarcity and inconsistencies between different environmental exposure limits

In chemical risk assessment, measured or modelled environmental concentrations are compared to environmental exposure limits (EELs), such as Predicted No Effect Concentrations (PNECs) or hazardous concentrations for 5% of species (HC05s) derived from species sensitivity distributions (SSDs). However, for many chemicals the EELs include large uncertainties or, in the worst case, the necessary data for their estimation are completely missing. This makes the assessment of chemical risks and any subsequent implementation of management strategies challenging. In this study we analyzed the uncertainty of EELs and its impact on chemical risk assessment. First, we compared three individual EEL datasets, two primarily based on experimental data and one based on computational predictions. The comparison demonstrates large disagreements between EEL data sources, with experimentally derived EELs differing by more than seven orders of magnitude. In a case-study, based on the predicted emissions of 2005 chemicals, we showed that these uncertainties lead to significantly different risk assessment outcomes, including large differences in the magnitude of the total risk, risk driver identification, and the ranking of use categories as risk contributors. We also show that the large data-gaps in EEL datasets cannot be covered by commonly used computational approaches (QSARs). We conclude that an expanded framework for interpreting risk characterization outcomes is needed. We also argue that the large data-gaps present in ecotoxicological data need to be addressed in order to achieve the European zero pollution vision as the growing emphasis on ambient exposures will further increase the demand for accurate and well-established EELs.

Risk from unintentional environmental mixtures in EU surface waters is dominated by a limited number of substances

Unintentional environmental mixtures happen when multiple chemicals co-occur in the environment. A generic mixture assessment factor (MAF), has been proposed to account for this. The MAF is a number by which safe exposure levels for single chemicals are divided to ensure protection against combined exposures to multiple chemicals. Two key elements to judge the appropriateness of a generic MAF are (1) defining the scope of mixtures that need to be addressed by a MAF (i.e.: simple mixtures vs complex mixtures), and (2) the existence of common risk drivers across large spatial scales. Simple mixtures with one to three risk drivers can easily be addressed by chemical-by-chemical regulatory action. Our work provides evidence on the prevalence and complexity of cumulative risk in EU freshwaters based on chemical monitoring data from one of the largest databases in the EU. With 334 chemicals being monitored, low complexity mixtures (one to 3 three risk drivers) dominated. After excluding metals, only 15 out of 307 chemicals (5 %) were most frequent chemical risk drivers. When these 15 chemicals were excluded from the analysis, 95 % of all monitoring site - year combinations did not pose a concern for cumulative risk. Most of these 15 chemicals are already banned or listed in various priority lists, showing that current regulatory frameworks were effective in identifying drivers of single chemical and cumulative risk. Although the monitoring data do not represent the entirety of environmental mixtures in the EU, the observed patterns of (1) limited prevalence of truly complex mixtures, and (2) limited number of overall risk drivers, argue against the need for implementing a generic MAF as a regulatory tool to address risk from unintentional mixtures in EU freshwaters.

Ibuprofen exposure in Europe; ePiE as an alternative to costly environmental monitoring

The EU Water Framework Directive and Priority Substance Directive provide a framework to identify substances that potentially pose a risk to surface waters and provide a legal basis whereby member states are required to monitor and comply with environmental quality standards (EQSs) set for those substances. The cost and effort to continuously measure and analyse real world concentrations in all water bodies across Europe are high. Establishing the reliability of environmental exposure models to predict concentrations of priority substances is key, both to fill data gaps left by monitoring campaigns, and to predict the outcomes of actions that might be taken to reduce exposure. In this study, we aimed to validate the ePiE model for the pharmaceutical ibuprofen by comparing predictions made using the best possible consumption data with measured river concentrations. The results demonstrate that the ePiE model makes useful, conservative exposure predictions for ibuprofen, typically within a factor of 3 of mean measured values. This exercise was performed across a number of basins within Europe, representative of varying conditions, including consumption rates, population densities and climates. Incorporating specific information pertaining to the basin or country being assessed, such as custom WWTP removal rates, was found to improve the realism and accuracy of predictions. We found that the extrapolation of consumption data between countries should be kept to a minimum when modelling the exposure of pharmaceuticals, with the per capita consumption of ibuprofen varying by nearly a factor of 10.

Characterization of ecotoxicological risks from unintentional mixture exposures calculated from European freshwater monitoring data: Forwarding prospective chemical risk management

Current regulatory chemical safety assessments do not acknowledge that ambient exposures are to multiple chemicals at the same time. As a result, potentially harmful exposures to unintentional mixtures may occur, leading to potential insufficient protection of the environment. The present study describes cumulative environmental risk assessment results for European fresh water ecosystems, based on the NORMAN chemical surface water monitoring database (1998-2016). It aims to characterize the magnitude of the mixture problem and the relative contribution of chemicals to the mixture risk, and evaluates how cumulative risks reduce when the acceptable risk per single chemical is fractionally lowered. Available monitoring data were curated and aggregated to 26,631 place-time combinations with at least two chemicals, of which 376 place-time combinations had at least 25 chemicals identified above the Limit of Detection. Various risk metrics were based on measured environmental concentrations (MECs). Mixture risk characterization ratio's (ΣRCRs) ≥ 1 were found for 39% of the place-time combinations, with few chemicals dominating the ΣRCR. Analyses of mixture toxic pressures, expressed as multi-substance Potentially Affected Fractions of species based on No Observed Effect Concentrations (msPAF_NOEC), showed similar outcomes. Small fractional reductions of the ambient chemical concentrations give a steep increase of the percentage of sufficiently protected water bodies (i.e. ΣRCR < 1 and msPAF_NOEC < 5%). Scientific and regulatory aspects of these results are discussed, especially with reference to the representativeness of the monitoring data for characterizing ambient mixtures, the robustness of the findings, and the possible regulatory implementation of the concept of a Mixture Allocation Factor (MAF) for prospective chemicals risk management. Although the monitoring data do not represent the full spectrum of ambient mixture exposures in Europe, results show the need for adapting policies to reach European Union goals for a toxic-free environment and underpin the utility and possible magnitude of a MAF.

Which Micropollutants in Water Environments Deserve More Attention Globally?

Increasing chemical pollution of aquatic environments is a growing concern with global relevance. A large number of organic chemicals are termed as "micropollutants" due to their low concentrations, and long-term exposure to micropollutants may pose considerable risks to aquatic organisms and human health. In recent decades, numerous treatment methods and technologies have been proposed to remove micropollutants in water, and typically several micropollutants were chosen as target pollutants to evaluate removal efficiencies. However, it is often unclear whether their toxicity and occurrence levels and frequencies enable them to contribute significantly to the overall chemical pollution in global aquatic environments. This review intends to answer an important lingering question: Which micropollutants or class of micropollutants deserve more attention globally and should be removed with higher priority? Different risk-based prioritization approaches were used to address this question. The risk quotient (RQ) method was found to be a feasible approach to prioritize micropollutants in a large scale due to its relatively simple assessment procedure and extensive use. A total of 83 prioritization case studies using the RQ method in the past decade were compiled, and 473 compounds that were selected by screening 3466 compounds of three broad classes (pharmaceuticals and personal care products (PPCPs), pesticides, and industrial chemicals) were found to have risks (RQ > 0.01). To determine the micropollutants of global importance, we propose an overall risk surrogate, that is, the weighted average risk quotient (WARQ). The WARQ integrates the risk intensity and frequency of micropollutants in global aquatic environments to achieve a more comprehensive priority determination. Through metadata analysis, we recommend a ranked list of 53 micropollutants, including 36 PPCPs (e.g., sulfamethoxazole and ibuprofen), seven pesticides (e.g., heptachlor and diazinon), and 10 industrial chemicals (e.g., perfluorooctanesulfonic acid and 4-nonylphenol) for risk management and remediation efforts. One caveat is that the ranked list of global importance does not consider transformation products of micropollutants (including disinfection byproducts) and new forms of pollutants (including antibiotic resistance genes and microplastics), and this list of global importance may not be directly applicable to a specific region or country. Also, it needs mentioning that there might be no best answer toward this question, and hopefully this review can act as a small step toward a better answer.

Estimating the release of chemical substances from consumer products, textiles and pharmaceuticals to wastewater

Chemical emissions from households originate from a wide range of sources and results in highly diverse mixtures. This makes traditional monitoring based on analytical chemistry challenging, especially for compounds that appear in low concentrations. We therefore developed a method for predicting emissions of chemicals from households into wastewater, relying on consumption patterns from multiple data sources. The method was then used to predict the emissions of chemical preparations, chemicals leaching from textiles and prescription pharmaceuticals in Sweden. In total we predicted emissions of 2007 chemicals with a combined emission of 62,659 tonnes per year - or 18 g/person and day. Of the emitted chemicals, 2.0% (w/w) were either classified as hazardous to the environment or were both persistent and mobile. We also show that chemical emissions come from a wide range of uses and that the total emission of any individual chemical is determined primarily by its use pattern, not by the total amount used. This emphasizes the need for continuous updates and additional knowledge generation both on emission factors and excretion rates as well as a need for improved reporting on the intended use of individual chemicals. Finally, we scrutinize the model and its uncertainty and suggest areas that need improvement to increase the accuracy of future emission modelling. We conclude that emission modelling can help guide environmental monitoring and provide input into management strategies aimed at reducing the environmental effect caused by hazardous chemicals.

Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk

BACKGROUND

Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management.

OBJECTIVE

We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals.

METHODS

We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts.

RESULTS

Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks.

CONCLUSION

Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.