A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources

ExpoPath: A method for identifying and annotating exposure pathways from chemical co-occurrence networks

Improving risk evaluation for environmental and human health is of paramount concern for the U.S. Environmental Protection Agency (EPA). This includes the identification and assessment of chemical transport from commercial and industrial sources to environmental and ecological media, where repeated patterns are often categorized as exposure pathways. Utilizing network analysis techniques paired with graph machine learning tools allows for the construction and analysis of a global chemical co-occurrence network with which to identify sets of overlapping or distinct communities that represent likely exposure pathways. Data from several chemical source databases were aggregated and used to generate a chemical co-occurrence network that encoded linkages between source categories and environmental and receptor categories within the EPA's Multimedia Monitoring Database (MMDB). Multiple algorithms were used to detect communities of chemicals within this network, while enrichment of the resulting communities based on presence-in-media information, physicochemical properties, and functional use information helped to annotate likely exposure pathways. This research identified communities of chemicals associated with various pharmaceutical, consumer, pesticide, and persistent chemical pathways. This novel approach to the study of chemical co-occurrence demonstrates the applicability of network analyses and graph machine learning methods for identifying empirical patterns of connectivity within the domain of exposure science. SYNOPSIS: Network analysis and community detection algorithms help reveal linkages among environmental monitoring data and chemical sources while providing supporting evidence for empirically derived exposure pathways.

Which Pollutants Should Be Prioritized for Control in Multipollutant Complex Contaminated Groundwater of Chemical Industrial Parks?

Increasing chemical pollutants in groundwater within chemical industrial parks pose a critical environmental challenge, necessitating innovative strategies to address contaminants with the highest risks to environmental health and ensure sustainable management. Herein, we investigated 277 chemical pollutants from 367 sampling points across 10 rounds, totaling 1,016,590 measured data points. An environmental health prioritization index (EHPI) was proposed and applied to integrate multiple criteria: occurrence, migration, persistence, bioaccumulation, acute and chronic toxicity, and health effects to rank the target pollutants for priority control. Thirty pollutants were classified as the top-priority group and 81 as high-priority, with metals, polycyclic aromatic hydrocarbons, and haloalkanes ranking highest, while emerging contaminants of concern ranked lower. The top 6 pollutants were beryllium, benzo(g,h,i)perylene, nickel, benzo(a)pyrene, chrysene, and arsenic. The EHPI method was compared against five other weighting schemes, including AHP (analytic hierarchy process), entropy, AHP-entropy, AHP-TOPSIS (technique for order preference by similarity to ideal solution), and entropy-TOPSIS. EHPI effectively captured and integrated the results from more simplistic prioritization schemes. Overall, 38 pollutants are recommended for inclusion in the priority control list, focusing on the top-priority group and high detection and exceedance categories. This framework provides critical guidance for focused monitoring, assessment, and control of the highest-risk groundwater pollutants, supporting more effective environmental and human health protection.

Quantifying EDC Emissions from Consumer Products: A Novel Rapid Method and Its Application for Systematic Evaluation of Health Impacts

Endocrine-disrupting chemicals (EDCs) are widely used in consumer products and have been associated with adverse public health outcomes and significant economic costs. We developed a rapid chamber method for measuring EDC emissions from consumer products, significantly reducing the time to reach steady state from weeks or months to minutes or hours. Using this method, we quantified EDC emissions from a wide range of products, determined the emission-control parameters, and established their relationship with the EDC content (Wf) and physicochemical properties. By incorporating Wf data from consumer product databases and applying stochastic models, we systematically estimated emissions for 400 EDC-product combinations and assessed the associated exposure and disease burden for the U.S. population. Our results suggest that more than 60% of these combinations could result in carcinogenic disability-adjusted life years (DALYs) above the acceptable threshold. The overall disease burden caused by EDCs in consumer products can be substantial, with DALYs exceeding those associated with other pollutants, such as particulate matter, in a worst-case scenario. This study provides a valuable tool for prioritizing hazardous EDCs in consumer products, evaluating safer alternatives, and formulating effective intervention strategies, thereby supporting policymakers and manufacturers in making informed, sustainable decisions.

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: A systematic scoping review

Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

When Does Multiphase Chemistry Influence Indoor Chemical Fate?

Human chemical exposure often occurs indoors, where large variability in contaminant concentrations and indoor chemical dynamics make assessments of these exposures challenging. A major source of uncertainty lies in the rates of chemical transformations which, due to high surface-to-volume ratios and rapid air change rates relative to rates of gas-phase reactions indoors, are largely gas-surface multiphase processes. It remains unclear how important such chemistry is in controlling indoor chemical lifetimes and, therefore, human exposure to both parent compounds and transformation products. We present a multimedia steady-state fugacity-based model to assess the importance of multiphase chemistry relative to cleaning and mass transfer losses, examine how the physicochemical properties of compounds and features of the indoor environment affect these processes, and investigate uncertainties pertaining to indoor multiphase chemistry and chemical lifetimes. We find that multiphase reactions can play an important role in chemical fate indoors for reactive compounds with low volatility, i.e., octanol-air equilibrium partitioning ratios (Koa) above 108, with the impact of this chemistry dependent on chemical identity, oxidant type and concentration, and other parameters. This work highlights the need for further research into indoor chemical dynamics and multiphase chemistry to constrain human exposure to chemicals in the built environment.

Formation kinetics of SVOC organic films and their impact on child exposure in indoor environments

We conducted an SVOC mass transfer and child-exposure modeling analysis considering the combined sorption of multiple SVOCs containing DnBP, BBP, DEHP, DINP and DINCH in indoor environments. A mechanistic model was applied to describe the organic film formation, and a partition-coefficient-prediction model was originally developed for the realistic organic films. The characteristics of film formation on impermeable surfaces were examined based on three different assumptions: the widely-used constant Kns,im assumption, Koa assumption, and the proposed Kom assumption (predicted specifically for the realistic organic films in this study). After long-term SVOC sorption, the organic film reached increasing equilibrium gradually under constant Kns,im assumption. While under Koa and Kom assumption, organic films exhibited nearly linear increases on surfaces, the trends of which agreed well with field studies. However, the film thicknesses calculated under Kom assumption with larger film partition coefficients were approximately twice larger than those under Koa assumption. Meanwhile, Horizontal surfaces with higher deposition rates of particle-phase SVOCs exhibited larger velocities of film growth compared to vertical surfaces. Under the Kom assumption, exposures of hazardous SVOCs for a 3-year-old child increased by 87.5 %-198.7 % even with the weekly cleaning of indoor impermeable surfaces, carpet and cloth. This study is anticipated to provide valuable insights into the film-forming characteristics of multiple SVOCs and the accompanying significant health risks to human beings in indoor environments.

A level IV fugacity-based multimedia model based on steady-state particle/gas partitioning theory and its application to study the spatio-temporal trends of PBDEs in atmosphere of northeast China

Particle/gas (P/G) partitioning can significantly affect the environmental behavior of atmospheric pollutants. In this study, we established a large-scale level IV fugacity-based multimedia model (the S-L4MF Model) based on the steady-state P/G partitioning theory. The spatial and temporal trends with the atmospheric contamination of polybrominated diphenyl ethers (PBDEs) in northeastern China under various climate conditions were simulated by the model. There is a reasonable agreement between the simulated and measured gaseous and particulate concentrations of 3 selected PBDE congeners (BDE-47, -99 and -209). For BDE-47, -99 and -209, 91.9 %, 94.8 % and 86.2 % of data points in the evaluation of the spatial trend, whereas 97.4 %, 98.2 % and 91.6 % of data points in the evaluation of the temporal trend, exhibit discrepancies between the modeled and measured data within 1 order of magnitude. The S-L4MF Model performed better than the other model with the same configuration but an equilibrium-state P/G partitioning assumption. The sensitivity and uncertainty analysis indicated that the air temperature and hexadecane-air partition coefficient were the dominant influencing factors on atmospheric concentrations. In addition, the model was successfully applied to study the inter-annual and seasonal variations of gaseous and particulate concentrations of the three PBDEs during 1971-2020 in Harbin, a northeastern Chinese city. Finally, we illustrated the potential to use the model to understand P/G partitioning behavior and the effects of snow and ice on atmospheric concentrations. In summary, the S-L4MF Model provided a powerful and effective tool for studying the environmental behavior of atmospheric organic pollutants, especially in cold regions.

Prioritizing molecular formulae identified by non-target analysis through high-throughput modelling: application to identify compounds with high human accumulation potential from house dust

Because it is typically not possible to pursue compound identification efforts for all chemical features detected during non-target analysis (NTA), the need for prioritization arises. Here we propose a strategy that ranks chemical features detected in environmental samples based on a model-derived metric that quantifies a feature's attribute that makes it desirable to elucidate its structure, e.g., a high potential for bioaccumulation in humans or wildlife. The procedure involves the identification of isomers that could plausibly represent the molecular formulae assigned to NTA-detected chemical features. For each isomer, the prioritization metric is calculated using properties predicted with high-throughput methods. After the molecular formulae are ranked based on the average values of the prioritization metric calculated for all isomers assigned to a formula, the highest ranked molecular formulae are prioritized for structure elucidation. We applied this workflow to features identified in house dust, using the ratio of chemical intake through dust ingestion to chemical concentration in blood (dose-to-concentration ratio, DCR) as the prioritization metric. Collections of isomers for the molecular formulae were assembled from the PubChem database and DCR was estimated using partitioning and biotransformation properties predicted for each isomer using quantitative structure property relationships. The ten top-ranked molecular formulae with notably lower average DCR-values represented mostly compounds already known to be indoor pollutants of concern, such as two polybrominated diphenyl ethers, bis(2-ethylhexyl) tetrabromophthalate, tetrabromobisphenol A, tris(1,3-dichloroisopropyl)phosphate and the azo dye disperse blue 373.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Explore the Dosimetric Relationship between the Intake of Chemical Contaminants and Their Occurrence in Blood and Urine

The dosimetric relationship between the human intake dose of a chemical contaminant (an "external dose") and its concentrations in bodily fluids such as blood and urine (related to an "internal dose"), often characterized by a dose-to-concentration ratio, has critical applications in exposure science, toxicology, and risk assessment, especially in the "new approach methods" era. However, there is a lack of a mechanistic, systematic understanding of how such a dosimetric relationship depends on fundamental chemical properties, such as partition coefficients and biotransformation half-lives. Here, we investigate this issue using a well-evaluated toxicokinetic model, which links external and internal doses by quantifying the absorption and elimination of chemicals. Results are visualized in a series of chemical partitioning space plots, whereby a chemical's dose-to-concentration ratio can be approximately predicted based on its partitioning between air, water, and octanol phases. Our results indicate that when taken in equal doses, chemicals with low volatility and moderate to high hydrophobicity exhibit the highest concentrations in the blood, and chemicals undergoing significant biotransformation tend to exhibit lower concentrations in comparison to their counterparts undergoing negligible biotransformation but possessing similar partitioning properties. Chemicals with high hydrophilicity have the highest concentrations in urine. Such revealed property dependence is similar for both adults and children and for individuals with normal body weights and with obesity. Overall, insights gained from this study are important in predicting blood and urinary concentrations from exposure information and in determining the exposure rate that produces the blood or urinary concentrations observed in biomonitoring studies.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization

BACKGROUND

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans.

OBJECTIVES

Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern.

METHODS

We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances.

RESULTS

We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07μM, the mean absolute error (MAE) values of 1.28 and 1.56μM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 μM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants.

DISCUSSION

We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

Bayesian inference of chemical exposures from NHANES urine biomonitoring data

BACKGROUND

Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data.

OBJECTIVE

Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty.

METHODS

Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts.

RESULTS

Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort.

SIGNIFICANCE

The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Thirdhand smoke from tobacco, e-cigarettes, cannabis, methamphetamine and cocaine: Partitioning, reactive fate, and human exposure in indoor environments

A source of chemical exposure to humans, thirdhand smoke (THS) refers to the contamination that persists indoors following the cessation of a smoking event. The composition of thirdhand smoke depends on the type of substance from which it originates. Although past studies have investigated the effects of tobacco THS on indoor air quality and human health, few have focused on the chemical composition and health impacts of other sources and components of THS. Here we review the state of knowledge of the composition and partitioning behavior of various types of indoor THS, with a focus on THS from tobacco, e-cigarettes, cannabis, and illicit substances (methamphetamine and cocaine). The discussion is supplemented by estimates of human exposure to THS components made with a chemical fate and exposure model. The modeling results show that while very volatile THS compounds (i.e., aromatics) are likely to be taken up by inhalation, highly water-soluble compounds tended to be dermally absorbed. Conversely, minimally volatile THS compounds with low solubility are predicted to be ingested through hand-to-mouth and object-to-mouth contact.

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.

Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis

With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.

Textile Washing Conveys SVOCs from Indoors to Outdoors: Application and Evaluation of a Residential Multimedia Model

Indoor environments have elevated concentrations of numerous semivolatile organic compounds (SVOCs). Textiles provide a large surface area for accumulating SVOCs, which can be transported to outdoors through washing. A multimedia model was developed to estimate advective transport rates (fluxes) of 14 SVOCs from indoors to outdoors by textile washing, ventilation, and dust removal/disposal. Most predicted concentrations were within 1 order of magnitude of measurements from a study of 26 Canadian homes. Median fluxes to outdoors [μg·(year·home)^-1] spanned approximately 4 orders of magnitude across compounds, according to the variability in estimated aggregate emissions to indoor air. These fluxes ranged from 2 (2,4,4'-tribromodiphenyl ether, BDE-28) to 30 200 (diethyl phthalate, DEP) for textile washing, 12 (BDE-28) to 123 200 (DEP) for ventilation, and 0.1 (BDE-28) to 4200 (bis(2-ethylhexyl) phthalate, DEHP) for dust removal. Relative contributions of these pathways to the total flux to outdoors strongly depended on physical-chemical properties. Textile washing contributed 20% tris-(2-chloroisopropyl)phosphate (TCPP) to 62% tris(2-butoxyethyl)phosphate (TBOEP) on average. These results suggest that residential textile washing can be an important transport pathway to outdoors for SVOCs emitted to indoor air, with implications for human and ecological exposure. Interventions should try to balance the complex tradeoff of textile washing by minimizing exposures for both human occupants and aquatic ecosystems.

Using Polychlorinated Naphthalene Concentrations in the Soil from a Southeast China E-Waste Recycling Area in a Novel Screening-Level Multipathway Human Cancer Risk Assessment

Polychlorinated naphthalene (PCN) concentrations in the soil at an e-waste recycling area in Guiyu, China, were measured and the associated human cancer risk due to e-waste-related exposures was investigated. We quantified PCNs in the agricultural soil and used these concentrations with predictive equations to calculate theoretical concentrations in outdoor air. We then calculated theoretical concentrations in indoor air using an attenuation factor and in the local diet using previously published models for contaminant uptake in plants and fruits. Potential human cancer risks of PCNs were assessed for multiple exposure pathways, including soil ingestion, inhalation, dermal contact, and dietary ingestion. Our calculations indicated that local residents had a high cancer risk from exposure to PCNs and that the diet was the primary pathway of PCN exposure, followed by dermal contact as the secondary pathway. We next repeated the risk assessment using concentrations for other carcinogenic contaminants reported in the literature at the same site. We found that polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) and PCNs caused the highest potential cancer risks to the residents, followed by polychlorinated biphenyls (PCBs). The relative importance of different exposure pathways depended on the physicochemical properties of specific chemicals.

Treatment of particle/gas partitioning using level III fugacity models in a six-compartment system

In this paper, two level III fugacity models are developed and applied using an environmental system containing six compartments, including air, aerosols, soil, water, suspended particulate matters (SPMs), and sediments, as a "unit world". The first model, assumes equilibrium between air and aerosols and between water and SPMs. These assumptions lead to a four-fugacity model. The second model removes these two assumptions leading to a six-fugacity model. The two models, compared using four PBDE congeners, BDE-28, -99, -153, and -209, with a steady flux of gaseous congeners entering the air, lead to the following conclusions. 1. When the octanol-air partition coefficient (K_OA) is less than 10^11.4, the two models produce similar results; when K_OA > 10^11.4, and especially when K_OA > 10^12.5, the model results diverge significantly. 2. Chemicals are in an imposed equilibrium in the four-fugacity model, but in a steady state and not necessary an equilibrium in the six-fugacity model, between air and aerosols. 3. The results from the six-fugacity model indicate an internally consistent system with chemicals in steady state in all six compartments, whereas the four-fugacity model presents an internally inconsistent system where chemicals are in equilibrium but not a steady state between air and aerosols. 4. Chemicals are mass balanced in air and aerosols predicted by the six-fugacity model but not by the four-fugacity model. If the mass balance in air and aerosols is achieved in the four-fugacity model, the condition of equilibrium between air and aerosols will be no longer valid.

Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework

A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.

Addressing uncertainty in mouthing-mediated ingestion of chemicals on indoor surfaces, objects, and dust

In indoor environments, humans ingest chemicals present as surface residues and bound to settled particles (dust), through mouthing hands (hand-to-mouth transfer) and objects (object-to-mouth transfer). Here, we introduce a novel modeling approach in support of systematic investigation into the mouthing-mediated ingestion of chemicals present in indoor environments. This model explicitly considers the indoor dynamics of dust and chemicals, building on mechanistic links with physicochemical properties of chemicals, features of the indoor environment, and human activity patterns. The evaluation of this model demonstrates that it satisfactorily reproduces chemical hand loadings and exposure data reported in the literature. We then use the evaluated model to investigate the response of mouthing-mediated ingestion to chemical partitioning between the gas phase and solid phases, expressed as the octanol-air partition coefficient (K_OA). Assuming a unit emission rate to the indoor environment, we find that low-volatility chemicals (with a K_OA greater than 10⁹) are more efficiently enriched in hand skin, resulting in higher mouthing-mediated ingestion than other compounds. For individuals living in a room with a typical level of dustiness, more than half of the chemical mass found in their hands comes from dust transfer, whereas more than half of the chemical mass ingested is the fraction present as residues on hands. We also use the new model to explore how the mouthing-mediated ingestion of chemicals is dependent on factors describing the indoor environment and human behavior. The model predicts that less frequent cleaning leads to higher accumulation of dust on indoor surfaces, thereby transferring more chemicals to hands and mouth in each contact. Introducing more dust into the room, but maintaining the same cleanup frequency, increases the dustiness of indoor surfaces, which promotes the transfer of relatively volatile chemicals (with a K_OA lower than 10⁹) to hands and mouth but decreases the transfer of chemicals with low volatility. More frequent hand contact with indoor surfaces increases both the hand loading and mouthing-mediated ingestion of chemicals, but the increases are more remarkable for adults than children because the higher surface contact frequency of children "saturates" hand loadings. An increase in handwashing frequency lowers the hand loading and mouthing-mediated ingestion of chemicals and this mitigating process is more prominent for relatively volatile chemicals. The new evaluated modeling approach can facilitate the prediction of mouthing-mediated ingestion for various age groups and the model predictions can be used to aid future fate and (bio)monitoring studies focusing on indoor contamination.

Metabolomic and Transcriptomic Analysis of MCF-7 Cells Exposed to 23 Chemicals at Human-Relevant Levels: Estimation of Individual Chemical Contribution to Effects

BACKGROUND

Humans are constantly being exposed to various xenobiotics at relatively low concentrations. To date, limited evidence is available to ascertain whether a complex xenobiotic mixture at human-relevant levels causes any health effect. Moreover, there is no effective method to pinpoint the contribution of each chemical toward such an effect.

OBJECTIVES

This study aims to understand the responses of cells to a mixture containing 23 xenobiotics at human-relevant levels and develop a feasible method to decipher the chemical(s) that contribute significantly to the observed effect.

METHODS

We characterized the metabolome and transcriptome of breast cancer cells (MCF-7) before and after exposure to the mixture at human-relevant levels; preexposure levels were derived from existing large-scale biomonitoring data. A high-throughput metabolomics-based "leave-one-out" method was proposed to understand the relative contribution of each component by comparing the metabolome with and without the particular chemical in the mixture.

RESULTS

The metabolomic analysis suggested that the mixture altered metabolites associated with cell proliferation and oxidative stress. For the transcriptomes, gene ontology terms and pathways including "cell cycle," "cell proliferation," and "cell division" were significantly altered after mixture exposure. The mixture altered genes associated with pathways such as "genotoxicity" and "nuclear factor erythroid 2-related factor 2 (Nrf2)." Through joint pathways analysis, metabolites and genes were observed to be well-aligned in pyrimidine and purine metabolisms. The leave-one-out results showed that many chemicals made their contributions to specific metabolic pathways. The overall metabolome pattern of the absence of 2,4-dihyroxybenzophenone (DHB) or bisphenol A (BPA) showed great resemblance to controls, suggesting their higher relative contribution to the observed effect.

DISCUSSION

The omics results showed that exposure to the mixture at human-relevant levels can induce significant in vitro cellular changes. Also, the leave one out method offers an effective approach for deconvoluting the effects of the mixture. https://doi.org/10.1289/EHP6641.

Perspectives on advancing consumer product exposure models

Predictive models are used to estimate exposures from consumer products to support risk management decision-making. These model predictions may be used alone in the absence of measured data or integrated with available exposure data. When different models are used, the resulting estimates of exposure and conclusions of risk may be disparate and the origin of these differences may not be obvious. This Perspectives Paper provides recommendations that could promote more systematic evaluation and a wider range of applicability of consumer product exposure models and their predictions, improve confidence in model predictions, and result in more accurate communication of consumer exposure model estimates. Key insights for the exposure science community to consider include: consistency in product descriptions, exposure routes, and scenarios; consistent and explicit definitions of exposure metrics; situation-dependent benefits from using one or multiple models; distinguishing between model algorithms and exposure factors; and corroboration of model predictions with measured data.

How are Humans Exposed to Organic Chemicals Released to Indoor Air?

Humans are exposed to organic chemicals released to indoor air through near-field exposure routes such as air inhalation and nondietary dust ingestion as well as far-field exposure routes such as consumption of food. Here, we explore the relative importance of near- and far-field exposure routes and its variability between chemicals, age groups, and subpopulations, by modeling aggregate human exposure to indoor-released chemicals with diverse partitioning behavior and degradability. Our model results indicate that if chemicals are assumed to be perfectly persistent, dietary and nondietary ingestion dominates human exposure to hydrophobic chemicals of relatively low volatility (with an octanol-air partition coefficient K_OA > 10^6.5 and an octanol-water partition coefficient K_OW < 10¹¹), whereas inhalation of indoor air dominates human exposure to volatile chemicals. Other exposure routes, for example, dermal absorption and drinking water, make a relatively small contribution to human exposure. Reduced chemical persistence in environmental media and biota lowers the contribution of dietary ingestion. For most chemicals other than those with a K_OA between 10⁹ and 10¹² and a K_OW between 10⁶ and 10⁹ (e.g., polybrominated diphenyl ethers), the relative importance of near- and far-field exposure routes is primarily governed by chemical partitioning and degradability rather than age- and population-dependent human exposure factors.

New Approach Methodologies for Exposure Science

Chemical risk assessment relies on knowledge of hazard, the dose-response relationship, and exposure to characterize potential risks to public health and the environment. A chemical with minimal toxicity might pose a risk if exposures are extensive, repeated, and/or occurring during critical windows across the human life span. Exposure assessment involves understanding human activity, and this activity is confounded by interindividual variability that is both biological and behavioral. Exposures further vary between the general population and susceptible or occupationally exposed populations. Recent computational exposure efforts have tackled these problems through the creation of new tools and predictive models. These tools include machine learning to draw inferences from existing data and computer-enhanced screening analyses to generate new data. Mathematical models provide frameworks describing chemical exposure processes. These models can be statistically evaluated to establish rigorous confidence in their predictions. The computational exposure tools reviewed here are oriented toward 'high-throughput' application, that is, they are suitable for dealing with the thousands of chemicals in commerce with limited sources of chemical exposure information. These new tools and models are moving chemical exposure and risk assessment forward in the 21st century.

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R² of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 μg/kg BW/day for 474572 compounds.