Screening for drinking water contaminants of concern using an automated exposure-focused workflow.

BACKGROUND: The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential. OBJECTIVE: Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project. METHODS: The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH. RESULTS: Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization. SIGNIFICANCE: This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.

The chemical landscape of high-throughput new approach methodologies for exposure.

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics.

BACKGROUND: Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking. OBJECTIVE: This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk. METHODS: The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources. RESULTS: A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators. SIGNIFICANCE: Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk. IMPACT STATEMENT: Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

A harmonized chemical monitoring database for support of exposure assessments.

Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. Monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated for chemical and medium, and harmonized into a sustainable machine-readable data format for support of exposure assessments.

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk.

Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.

Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures.

BACKGROUND: Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge. OBJECTIVES: We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. METHODS: We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products. RESULTS: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. DISCUSSION: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data.

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments.

Establishing a system of consumer product use categories to support rapid modeling of human exposure.

Consumer product categorizations for use in predicting human chemical exposure provide a bridge between product composition data and consumer product use pattern information. Furthermore, the categories reflect other factors relevant to developing consumer product exposure scenarios, such as microenvironment of use (e.g., indoors or outdoors), method of application/form of release (e.g., spray versus liquid), release to various media, removal processes (e.g., rinse-off or wipe-off), and route-specific exposure factors (dermal surface areas of application, fraction of release in respirable form). While challenging, developing harmonized product categories can generalize the factors described above allowing for rapid parameterization of route-specific exposure scenario algorithms for new chemical/product applications and efficient utilization of new data on product use or composition. This can be accomplished via mapping product categories to likewise categorized release and use patterns or exposure factors. Here, hierarchical product use categories (PUCs) for consumer products that provide such mappings are presented and crosswalked with other internationally harmonized product categories for consumer exposure assessment. The PUCs were defined by applying use and exposure scenario information to the products in EPA's Chemical and Products Database (CPDat). This paper demonstrates how these PUCs are being used to rapidly parameterize algorithms for scenario-specific use, fate, and exposure in a probabilistic aggregate model of human exposure to chemicals used in consumer products. The PUCs provide a generic representation of consumer products for use in exposure assessment and provide an efficient framework for flexible and rapid data reporting and consumer exposure model parameterization.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality.

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m3 increase in PM2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R2 < 1%), multivariate models began to explain some of the observed heterogeneity (R2 = 13%).

The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products.

Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on more than 75,000 chemicals and more than 15,000 consumer products. The data's primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.

Household fuel use and biomarkers of inflammation and respiratory illness among rural South African Women.

Though literature suggests a positive association between use of biomass fuel for cooking and inflammation, few studies among women in rural South Africa exist. We included 415 women from the South African Study of Women and Babies (SOWB), recruited from 2010 to 2011. We obtained demographics, general medical history and usual source of cooking fuel (wood, electricity) via baseline questionnaire. A nurse obtained height, weight, blood pressure, and blood samples. We measured plasma concentrations of a suite of inflammatory markers (e.g., interleukins, tumor necrosis factor-α, C-reactive protein). We assessed associations between cooking fuel and biomarkers of inflammation and respiratory symptoms/illness using crude and adjusted linear and logistic regression models. We found little evidence of an association between fuel-use and biomarkers of inflammation, pre-hypertension/hypertension, or respiratory illnesses. Though imprecise, we found 41% (95% confidence interval (CI) = 0.72-2.77) higher odds of self-reported wheezing/chest tightness among wood-users compared with electricity-users. Though studies among other populations report positive findings between biomass fuel use and inflammation, it is possible that women in the present study experience lower exposures to household air pollution given the cleaner burning nature of wood compared with other biomass fuels (e.g., coal, dung).

Consumer product chemical weight fractions from ingredient lists.

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter.

BACKGROUND: Household air pollution from solid fuel burning is a leading contributor to disease burden globally. Fine particulate matter (PM2.5) is thought to be responsible for many of these health impacts. A co-pollutant, carbon monoxide (CO) has been widely used as a surrogate measure of PM2.5 in studies of household air pollution. OBJECTIVE: The goal was to evaluate the validity of exposure to CO as a surrogate of exposure to PM2.5 in studies of household air pollution and the consistency of the PM2.5-CO relationship across different study settings and conditions. METHODS: We conducted a systematic review of studies with exposure and/or cooking area PM2.5 and CO measurements and assembled 2,048 PM2.5 and CO measurements from a subset of studies (18 cooking area studies and 9 personal exposure studies) retained in the systematic review. We conducted pooled multivariate analyses of PM2.5-CO associations, evaluating fuels, urbanicity, season, study, and CO methods as covariates and effect modifiers. RESULTS: We retained 61 of 70 studies for review, representing 27 countries. Reported PM2.5-CO correlations (r) were lower for personal exposure (range: 0.22-0.97; median=0.57) than for cooking areas (range: 0.10-0.96; median=0.71). In the pooled analyses of personal exposure and cooking area concentrations, the variation in ln(CO) explained 13% and 48% of the variation in ln(PM2.5), respectively. CONCLUSIONS: Our results suggest that exposure to CO is not a consistently valid surrogate measure of exposure to PM2.5. Studies measuring CO exposure as a surrogate measure of PM exposure should conduct local validation studies for different stove/fuel types and seasons. https://doi.org/10.1289/EHP767.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone.

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O3) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O3 exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O3 exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O3 exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O3 are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O3 exposure as a result of changes in climate that could impact human health.

High-throughput screening of chemicals as functional substitutes using structure-based classification models.

Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure-use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to screen a library of nearly 6400 chemicals with available structure information for potential functional substitutes. Using our Functional Use database (FUse), we could identify uses for 3121 chemicals; 4412 predicted functional uses had a probability of 80% or greater. We demonstrate the potential application of the models to high-throughput (HT) screening for "candidate alternatives" by merging the valid functional substitute classifications with hazard metrics developed from HT screening assays for bioactivity. A descriptor set could be obtained for 6356 Tox21 chemicals that have undergone a battery of HT in vitro bioactivity screening assays. By applying QSURs, we were able to identify over 1600 candidate chemical alternatives. These QSURs can be rapidly applied to thousands of additional chemicals to generate HT functional use information for combination with complementary HT toxicity information for screening for greener chemical alternatives.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

BACKGROUND: Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. METHODS: ZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. RESULTS: Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NOx or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. CONCLUSIONS: The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals.

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.

Exploring consumer exposure pathways and patterns of use for chemicals in the environment.

Humans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to "use categories" describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment.