Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

Assessment of persistent organic pollutants in killer whales (Orcinus orca) of the Canadian Arctic: Implications for subsistence consumption and conservation strategies.

Killer whales (Orcinus orca) historically restricted to certain Arctic regions due to extensive sea ice have recently been documented farther north and for longer durations in the Canadian Arctic. These apex predators accumulate high levels of persistent organic pollutants (POPs). The objective of this study was to evaluate the concentrations and profiles of POPs in killer whales of the Canadian Arctic, thus determining potential risks for Inuit communities if consumed. Biopsies were collected from 33 killer whales across areas of the Canadian Arctic between 2009 and 2021. Significant variability in POP concentrations was observed among whales. The cumulative POP concentrations ranged from 12 to >2270 mg/kg lw, representing ∼200-fold increase from the least to the most contaminated individual. The rank order of concentrations of the top five contaminant classes was ∑DDT, ∑PCB, ∑CHL, ∑Toxaphene, and Dieldrin. Several emerging Arctic contaminants were detected, including chlorpyrifos, endosulfan, pentachloroanisole, and polychlorinated naphthalenes, although at relatively lower concentrations than legacy POPs. Considering the elevated blubber POP levels in killer whales, recommended daily consumption thresholds, established based on human tolerable daily intake (TDI) values, were notably restricted for ∑PCB (<0.14 g), ∑DDT (<6.9 g), ∑CHL (<13 g), dieldrin (<8 g) and heptachlor epoxide (<5 g). Killer whales in the Canadian Arctic exhibited higher POP concentrations than other commonly hunted species such as polar bears, ringed seals, and Arctic char. We acknowledge that a more holistic risk assessment of diet is required to assess the cumulative impacts of contaminant mixtures as well as nutritional quality of tissues commonly consumed by northern communities.

The role of trust in the use of artificial intelligence for chemical risk assessment.

Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Machine Learning (ML) models. However, implementation of AI/ML models in risk assessment is lagging behind. Most AI/ML models are considered 'black boxes' that lack mechanistical explainability, causing risk assessors to have insufficient trust in their predictions. Here, we explore 'trust' as an essential factor towards regulatory acceptance of AI/ML models. We provide an overview of the elements of trust, including technical and beyond-technical aspects, and highlight elements that are considered most important to build trust by risk assessors. The results provide recommendations for risk assessors and computational modelers for future development of AI/ML models, including: 1) Keep models simple and interpretable; 2) Offer transparency in the data and data curation; 3) Clearly define and communicate the scope/intended purpose; 4) Define adoption criteria; 5) Make models accessible and user-friendly; 6) Demonstrate the added value in practical settings; and 7) Engage in interdisciplinary settings. These recommendations should ideally be acknowledged in future developments to stimulate trust and acceptance of AI/ML models for regulatory purposes.

Intuitive toxicology in the 21st century-Bridging the perspectives of the public and risk assessors in Europe.

Three decades ago, several articles on the subjectivity in chemical risk judgments (i.e., labeled "intuitive toxicology") measured the divide between the public and toxicologists with different backgrounds regarding the validity of predicting health effects based on in vivo studies. Similar divides with impacts on societal discourse and chemical risk assessment practices might exist concerning alternative toxicity testing methods (i.e., in vitro and in silico). However, studies to date have focused either on the public's views of in vivo or stem cell testing or on experts' views of in vivo testing and potential alternatives (i.e., toxicologists and medical students), which do not allow for a direct investigation of potential divides. To fill this knowledge gap, we conducted two online surveys, involving members of the German-speaking public in Switzerland and European human health risk assessors, respectively. This article presents the results of these two surveys regarding the divide in the public's and risk assessors' perspectives on risk assessment based on in vivo, in vitro, and in silico testing. Particularly, the survey with the risk assessors highlights that, beyond scientific and regulatory barriers, alternatives to in vivo testing may encounter individual hurdles, such as higher uncertainty associated with them. Understanding and addressing these hurdles will be crucial to facilitate the integration of new approach methodologies into chemical risk assessment practices as well as a successful transition toward next-generation risk assessment, bringing us closer to a fit-for-purpose and more efficient regulatory landscape.

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.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line.

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 µM to 50 µM; rotenone: from 0.04096 to 10 µM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

OECD harmonised template 201: Structuring and reporting mechanistic information to foster the integration of new approach methodologies for hazard and risk assessment of chemicals.

In the European Union, the Chemicals Strategy for Sustainability (CSS) highlights the need to enhance the identification and assessment of substances of concern while reducing animal testing, thus fostering the development and use of New Approach Methodologies (NAMs) such as in silico, in vitro and in chemico. In the United States, the Tox21 strategy aims at shifting toxicological assessments away from traditional animal studies towards target-specific, mechanism-based and biological observations mainly obtained by using NAMs. Many other jurisdictions around the world are also increasing the use of NAMs. Hence, the provision of dedicated non-animal toxicological data and reporting formats as a basis for chemical risk assessment is necessary. Harmonising data reporting is crucial when aiming at re-using and sharing data for chemical risk assessment across jurisdictions. The OECD has developed a series of OECD Harmonised Templates (OHT), which are standard data formats designed for reporting information used for the risk assessment of chemicals relevant to their intrinsic properties, including effects on human health (e.g., toxicokinetics, skin sensitisation, repeated dose toxicity) and the environment (e.g., toxicity to test species and wildlife, biodegradation in soil, metabolism of residues in crops). The objective of this paper is to demonstrate the applicability of the OHT standard format for reporting information under various chemical risk assessment regimes, and to provide users with practical guidance on the use of OHT 201, in particular to report test results on intermediate effects and mechanistic information.

Quantitative analyses of misclassification rates in the hazard classification of environmental chemicals: Evaluation of procedures for deriving predicted no-effect concentrations in the Chemical Substance Control Law in Japan.

The quality of chemical management depends more or less on practical procedures used to assess chemicals. This study quantitatively assessed the efficacy of a derivation procedure for calculating no-effect concentrations for screening assessment of environmental hazards under the Chemical Substance Control Law in Japan. We first evaluated the derivation procedure by applying a series of test ecotoxicity datasets to the procedure and calculating the resulting misclassification rates of the hazardous class of chemicals. In this study, a chemical was deemed to have been misclassified if its classification differed from its classification based on the full dataset (chronic toxicity data for three trophic levels), which was defined as the correct assignment. We also calculated the effects of additional uncertainty factors to decrease the variance (i.e., to improve the consistency) of the misclassification rates among cases with different data availability in the derivation procedure. The results showed that the derivation procedure resulted in very high rates of misclassification when only particular sets of ecotoxicity data were available (e.g., only chronic toxicity data of algae were available). Our analyses also showed that the use of additional uncertainty factors improved the consistency of the misclassification rates within the derivation procedure. Our study presents a broadly applicable calculation framework for quantifying error rates in assessment procedures and serves as a case study for future development and reforms of chemical assessment processes and policies, while additional analyses using more extensive ecotoxicity data with various modes of actions are needed in the future.

Background radiation and cancer risks: A major intellectual confrontation within the domain of radiation genetics with multiple converging biological disciplines.

This paper assesses the judgments of leading radiation geneticists and cancer risk assessment scientists from the mid-1950s to mid-1970s that background radiation has a significant effect on human genetic disease and cancer incidence. This assumption was adopted by the National Academy of Sciences (NAS) Biological Effects of Atomic Radiation (BEAR) I Genetics Panel for genetic diseases and subsequently applied to cancer risk assessment by other leading individuals/advisory groups (e.g., International Commission on Radiation Protection-ICRP). These recommendations assumed that a sizeable proportion of human mutations originated from background radiation due to cumulative exposure over prolonged reproductive periods and the linear nature of the dose-response. This paper shows that the assumption that background radiation is a significant cause of spontaneous mutation, genetic diseases, and cancer incidence is not supported by experimental and epidemiological findings, and discredits erroneous risk assessments that improperly influenced the recommendations of national and international advisory committees, risk assessment policies, and beliefs worldwide.

Understanding the limitations and application of occupational exposure models in a REACH context.

Exposure modeling plays a significant role for regulatory organizations, companies, and professionals involved in assessing and managing occupational health risks in workplaces. One context in which occupational exposure models are particularly relevant is the REACH Regulation in the European Union (Regulation (EC) No 1907/2006). This commentary describes the models for the occupational inhalation exposure assessment of chemicals within the REACH framework, their theoretical background, applications, and limitations, as well as the latest developments and priorities for model improvement. Summing up the debate, despite its relevance and importance in the context of REACH not being in question, occupational exposure modeling needs to be improved in many respects. There is a need to reach a wide consensus on several key issues (e.g., the theoretical background and the reliability of modeling tools), to consolidate and monitor model performance and regulatory acceptance, and to align practices and policies regarding exposure modeling.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform.

INTRODUCTION: High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing. OBJECTIVE: In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well. METHODS: Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass. RESULTS: The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids. CONCLUSIONS: Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Migration of endocrine-disrupting chemicals into food from plastic packaging materials: an overview of chemical risk assessment, techniques to monitor migration, and international regulations.

Plastic packaging materials (PPMs) protect food from contamination, maintain quality, and ease transportation and distribution. Additives included during the manufacturing and processing of PPMs improve flexibility, durability, barrier properties, and sometimes aid the processing itself. During processing, these additives, even the monomers used to produce the plastics, can produce side products or breakdown products as a result of degradation and various chemical reactions. These starting substances and reaction products include 2,2-bis(4-hydroxyphenyl)propane (bisphenol A), phthalates/phthalic acid esters, alkylphenols, and bis(2-ethylhexyl) adipate, which are considered endocrine-disrupting chemicals (EDCs) that may interfere with the human endocrine system and produce adverse reproductive, neurological, developmental, and immune effects. When in contact with food, EDCs can migrate into food if conditions are appropriate, thereby possibly jeopardizing food safety. Chemical risk assessment and regulatory control were developed to reduce human exposure to harmful migrated EDCs. This article gives an overview of the migration of EDCs from PPMs and control measures to reduce the risk of adverse impacts on human health.

Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling.

Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 µg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.

A framework for chemical safety assessment incorporating new approach methodologies within REACH.

The long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.

Comparison of toxicological effects and exposure levels between triclosan and its structurally similar chemicals using in vitro tests for read-across case study.

Read-across based on structural and biological similarities is expected to be a promising alternative method for assessing systemic toxicity. A concrete strategy for quantitative chemical risk assessment would be to stack read-across case studies and extract key considerations from them. Thus, we developed a read-across case study by comparing the toxicological effects based on adverse outcome pathways and exposure levels of different structurally similar chemicals for a target organ. In this study, we selected the hepatotoxicity of triclosan and its structurally similar chemicals including diclosan and 1-chloro-3-(4-chlorophenoxy)benzene. The results of in vitro toxicogenomics showed that disorders of cholesterol synthesis were commonly detected with both triclosan and diclosan. The decrease in hepatocellular cholesterol levels was similar in the cells treated with triclosan and diclosan. Furthermore, the exposure levels of triclosan and diclosan for the liver were similar. Collectively, these results suggest that triclosan and diclosan show similar toxicological effects and severity of hepatotoxicity. Considering the existing repeated dose toxicity data, our prediction results are reasonable regarding the toxicological effect and its severity. Thus, the present study demonstrated the usability of comparing toxicological effects and exposure levels using read-across for quantitative chemical risk assessment.

Cell-Based Chemical Safety Assessment and Therapeutic Discovery Using Array-Based Sensors.

Synthetic chemicals are widely used in food, agriculture, and medicine, making chemical safety assessments necessary for environmental exposure. In addition, the rapid determination of chemical drug efficacy and safety is a key step in therapeutic discoveries. Cell-based screening methods are non-invasive as compared with animal studies. Cellular phenotypic changes can also provide more sensitive indicators of chemical effects than conventional cell viability. Array-based cell sensors can be engineered to maximize sensitivity to changes in cell phenotypes, lowering the threshold for detecting cellular responses under external stimuli. Overall, array-based sensing can provide a robust strategy for both cell-based chemical risk assessments and therapeutics discovery.

The design of a matrix linking work situations to chemical health risk at the workplace.

In France, laws require each company to draw up an inventory of the risks that may threaten employees' health in order to prioritize the preventive actions to be implemented. Focusing on chemical risk, databases on hazards or exposures are widely available but they lack information regarding chemical risks resulting from combining the hazards of chemicals with their conditions of use, thus generating exposures. Our objective is to build a matrix of French work situations associated with their chemical risk. Eighty-eight work situations were collected from reports written by professionals from the French public health insurance service. Each work situation is defined by descriptive parameters of the task, the exposure, and the hazard. According to an expert elicitation method (Delphi, n = 21 experts), each work situation was assessed and a chemical risk score defined, taking into account all the descriptive exposure and hazard parameters. Chemical risk scores were expressed as a range of values from 0 to 100, with the size of the range chosen by the experts themselves according to their uncertainty. The experts' assessments were merged to assign one risk score for each work situation, variability, and confidence. The results showed that 50% of the work situations had a risk score between 40 and 60. The average variability and confidence were around 15% and 82%, respectively. This work situation matrix constructed from French data can be used by occupational safety and health managers that have similar work situations in their company (Western European industrial sector). In this context, it may be useful to easily determine the level of risks for similar tasks and prioritize those that are most risky. Moreover, it could be used to compare and define the differences between a risk assessment performed by "expertise" and another defined by a software.

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.

Interindividual Variation in Source-Specific Doses is a Determinant of Health Impacts of Combined Chemical Exposures.

All individuals are exposed to multiple chemicals from multiple sources. These combined exposures are a concern because they may cause adverse effects that would not occur from an exposure recieved from any single source. Studies of combined chemical exposures, however, have found that the risks posed by such combined exposures are almost always driven by exposures from a few chemicals and sources and frequently by a single chemical from a single source. Here, a series of computer simulations of combined exposures are used to investigate when multiple sources of chemicals drive the largest risks in a population and when a single chemical from a single source is responsible for the largest risks. The analysis found that combined exposures drive the largest risks when the interindividual variation of source-specific doses is small, moderate-to-high correlations occur between the source-specific doses, and the number of sources affecting an individual varies across individuals. These findings can be used to identify sources with the greatest potential to cause combined exposures of concern.

How to Adapt Chemical Risk Assessment for Unconventional Hydrocarbon Extraction Related to the Water System.

We identify uncertainties and knowledge gaps of chemical risk assessment related to unconventional drillings and propose adaptations. We discuss how chemical risk assessment in the context of unconventional oil and gas (UO&G) activities differs from conventional chemical risk assessment and the implications for existing legislation. A UO&G suspect list of 1,386 chemicals that might be expected in the UO&G water samples was prepared which can be used for LC-HRMS suspect screening. We actualize information on reported concentrations in UO&G-related water. Most information relates to shale gas operations, followed by coal-bed methane, while only little is available for tight gas and conventional gas. The limited research on conventional oil and gas recovery hampers comparison whether risks related to unconventional activities are in fact higher than those related to conventional activities. No study analyzed the whole cycle from fracturing fluid, flowback and produced water, and surface water and groundwater. Generally target screening has been used, probably missing contaminants of concern. Almost half of the organic compounds analyzed in surface water and groundwater exceed TTC values, so further risk assessment is needed, and risks cannot be waived. No specific exposure scenarios toward groundwater aquifers exist for UO&G-related activities. Human errors in various stages of the life cycle of UO&G production play an important role in the exposure. Neither at the international level nor at the US federal and the EU levels, specific regulations for UO&G-related activities are in place to protect environmental and human health. UO&G activities are mostly regulated through general environmental, spatial planning, and mining legislation.