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Background: Lead is a persistent, ubiquitous pollutant whose historical sources have been largely addressed through regulation and voluntary actions. The United States (U.S.) has achieved significant decreases in children's blood lead levels (BLL) over the past 40 years; however, there is no known safe level of Pb exposure. Some communities continue to be disproportionately impacted by exposure to Pb, including Black children and families living in older homes., Objective: To identify Ohio (OH) census tracts with children exposed to Pb and evaluate potential exposure determinants., Methods: We obtained individual children's blood Pb data from 2005-2018 in OH. The percent of children with elevated BLL (EBLL) was calculated for OH census tracts using three blood Pb reference values (3.5, 5, and 10 µg/dL). Getis-Ord Gi* geospatial hotspot or top 20th percentile methodologies were then applied to identify "hotspots." Findings across multiple time periods and blood Pb reference values were evaluated and compared with existing Pb exposure indices and models., Results: Consistency was observed across different blood Pb reference values, with the main hotspots identified at 3.5 µg/dL, also identified at 5 and 10 µg/dL. Substantial gains in public health were demonstrated, with the biggest decreases in the number of census tracts with EBLL observed between 2008-2010 and 2011-2013. Across OH, 355 census tracts (of 2850) were identified as hotspots across 17 locations, with the majority in the most populated cites. Generally, old housing and sociodemographic factors were indicators of these EBLL hotspots. A smaller number of hotspots were not associated with these exposure determinants. Variables of race, income, and education level were all strong predictors of hotspots., Impact Statement: The Getis-Ord Gi* geospatial hotspot analysis can inform local investigations into potential Pb exposures for children living in OH. The successful application of a generalizable childhood blood Pb methodology at the census tract scale provides results that are more readily actionable. The moderate agreement of the measured blood Pb results with public Pb indices provide confidence that these indices can be used in the absence of available blood Pb surveillance data. While not a replacement for universal blood Pb testing, a consistent approach can be applied to identify areas where Pb exposure may be problematic., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (© 2020 Society for Risk Analysis.)

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.

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.

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quantify associations between environmental and social stressors. Socio-demographic variables were generated based on U.S. Census tract-level income, race/ethnicity population percentage, education level, and age information from the 2010-2014, 5-Year Summary files in the American Community Survey (ACS) database, and chemical variables were generated by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) census tract-level air pollutant exposure concentration data. Six mobile- and industrial-source pollutants were chosen for analysis, including acetaldehyde, benzene, cyanide, particulate matter components of diesel engine emissions (namely, diesel PM), toluene, and 1,3-butadiene. ARM was then applied to quantify and visualize the associations between the chemical and socio-demographic variables. Census tracts with a high percentage of racial/ethnic minorities and populations with low income tended to have higher estimated chemical exposure concentrations (fourth quartile), especially for diesel PM, 1,3-butadiene, and toluene. In contrast, census tracts with an average population age of 40-50 years, a low percentage of racial/ethnic minorities, and moderate-income levels were more likely to have lower estimated chemical exposure concentrations (first quartile). Unsupervised data mining methods can be used to evaluate potential associations between environmental inequalities and social disparities, while providing support in public health decision-making contexts.

Background: Drinking water and other sources for lead are the subject of public health concerns around the Flint, Michigan, drinking water and East Chicago, Indiana, lead in soil crises. In 2015, the U.S. Environmental Protection Agency (EPA)'s National Drinking Water Advisory Council (NDWAC) recommended establishment of a "health-based, household action level" for lead in drinking water based on children's exposure., Objectives: The primary objective was to develop a coupled exposure-dose modeling approach that can be used to determine what drinking water lead concentrations keep children's blood lead levels (BLLs) below specified values, considering exposures from water, soil, dust, food, and air. Related objectives were to evaluate the coupled model estimates using real-world blood lead data, to quantify relative contributions by the various media, and to identify key model inputs., Methods: A modeling approach using the EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia and Integrated Exposure Uptake and Biokinetic (IEUBK) models was developed using available data. This analysis for the U.S. population of young children probabilistically simulated multimedia exposures and estimated relative contributions of media to BLLs across all population percentiles for several age groups., Results: Modeled BLLs compared well with nationally representative BLLs (0-23% relative error). Analyses revealed relative importance of soil and dust ingestion exposure pathways and associated Pb intake rates; water ingestion was also a main pathway, especially for infants., Conclusions: This methodology advances scientific understanding of the relationship between lead concentrations in drinking water and BLLs in children. It can guide national health-based benchmarks for lead and related community public health decisions. https://doi.org/10.1289/EHP1605.

Background: Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be formed from the thousands of environmental chemicals is enormous, and testing all of them would be impossible., Objectives: We seek to develop and demonstrate a method for identifying those mixtures that are most prevalent in humans., Methods: We applied frequent itemset mining, a technique traditionally used for market basket analysis, to biomonitoring data from the 2009-2010 cycle of the continuous National Health and Nutrition Examination Survey (NHANES) to identify combinations of chemicals that frequently co-occur in people., Results: We identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the U.S. population, as well as three supercombinations consisting of relatively many chemicals that occur in a small but nonnegligible proportion of the U.S. population., Conclusions: We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. https://doi.org/10.1289/EHP1265.

The quality of exposure assessment is a major determinant of the overall quality of any environmental epidemiology study. The use of biomonitoring as a tool for assessing exposure to ubiquitous chemicals with short physiologic half-lives began relatively recently. These chemicals present several challenges, including their presence in analytical laboratories and sampling equipment, difficulty in establishing temporal order in cross-sectional studies, short- and long-term variability in exposures and biomarker concentrations, and a paucity of information on the number of measurements required for proper exposure classification. To date, the scientific community has not developed a set of systematic guidelines for designing, implementing and interpreting studies of short-lived chemicals that use biomonitoring as the exposure metric or for evaluating the quality of this type of research for WOE assessments or for peer review of grants or publications. We describe key issues that affect epidemiology studies using biomonitoring data on short-lived chemicals and propose a systematic instrument--the Biomonitoring, Environmental Epidemiology, and Short-lived Chemicals (BEES-C) instrument--for evaluating the quality of research proposals and studies that incorporate biomonitoring data on short-lived chemicals. Quality criteria for three areas considered fundamental to the evaluation of epidemiology studies that include biological measurements of short-lived chemicals are described: 1) biomarker selection and measurement, 2) study design and execution, and 3) general epidemiological study design considerations. We recognize that the development of an evaluative tool such as BEES-C is neither simple nor non-controversial. We hope and anticipate that the instrument will initiate further discussion/debate on this topic., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The U.S. EPA's SHEDS-Multimedia model was applied to enhance the understanding of children's exposures and doses to multiple pyrethroid pesticides, including major contributing chemicals and pathways. This paper presents combined dietary and residential exposure estimates and cumulative doses for seven commonly used pyrethroids, and comparisons of model evaluation results with NHANES biomarker data for 3-PBA and DCCA metabolites. Model input distributions were fit to publicly available pesticide usage survey data, NHANES, and other studies, then SHEDS-Multimedia was applied to estimate total pyrethroid exposures and doses for 3-5 year olds for one year variability simulations. For dose estimations we used a pharmacokinetic model and two approaches for simulating dermal absorption. SHEDS-Multimedia predictions compared well to NHANES biomarker data: ratios of 3-PBA observed data to SHEDS-Multimedia modeled results were 0.88, 0.51, 0.54 and 1.02 for mean, median, 95th, and 99th percentiles, respectively; for DCCA, the ratios were 0.82, 0.53, 0.56, and 0.94. Modeled time-averaged cumulative absorbed dose of the seven pyrethroids was 3.1 nmol/day (versus 8.4 nmol/day for adults) in the general population (residential pyrethroid use and non-use homes) and 6.7 nmol/day (versus 10.5 nmol/day for adults) in the simulated residential pyrethroid use population. For the general population, contributions to modeled cumulative dose by chemical were permethrin (60%), cypermethrin (22%), and cyfluthrin (16%); for residential use homes, contributions were cypermethrin (49%), permethrin (29%), and cyfluthrin (17%). The primary exposure route for 3-5 year olds in the simulated residential use population was non-dietary ingestion exposure; whereas for the simulated general population, dietary exposure was the primary exposure route. Below the 95th percentile, the major exposure pathway was dietary for the general population; non-dietary ingestion was the major pathway starting below the 70th percentile for the residential use population. The new dermal absorption methodology considering surface loading had some impact, but did not change the order of key pathways., (Published by Elsevier Ltd.)

Consumer products are a primary source of chemical exposures, yet little structured information is available on the chemical ingredients of these products and the concentrations at which ingredients are present. To address this data gap, we created a database of chemicals in consumer products using product Material Safety Data Sheets (MSDSs) publicly provided by a large retailer. The resulting database represents 1797 unique chemicals mapped to 8921 consumer products and a hierarchy of 353 consumer product "use categories" within a total of 15 top-level categories. We examine the utility of this database and discuss ways in which it will support (i) exposure screening and prioritization, (ii) generic or framework formulations for several indoor/consumer product exposure modeling initiatives, (iii) candidate chemical selection for monitoring near field exposure from proximal sources, and (iv) as activity tracers or ubiquitous exposure sources using "chemical space" map analyses. Chemicals present at high concentrations and across multiple consumer products and use categories that hold high exposure potential are identified. Our database is publicly available to serve regulators, retailers, manufacturers, and the public for predictive screening of chemicals in new and existing consumer products on the basis of exposure and risk., (Published by Elsevier Ltd.)

Background: Diabetes is a major threat to public health in the United States and worldwide. Understanding the role of environmental chemicals in the development or progression of diabetes is an emerging issue in environmental health., Objective: We assessed the epidemiologic literature for evidence of associations between persistent organic pollutants (POPs) and type 2 diabetes., Methods: Using a PubMed search and reference lists from relevant studies or review articles, we identified 72 epidemiological studies that investigated associations of persistent organic pollutants (POPs) with diabetes. We evaluated these studies for consistency, strengths and weaknesses of study design (including power and statistical methods), clinical diagnosis, exposure assessment, study population characteristics, and identification of data gaps and areas for future research., Conclusions: Heterogeneity of the studies precluded conducting a meta-analysis, but the overall evidence is sufficient for a positive association of some organochlorine POPs with type 2 diabetes. Collectively, these data are not sufficient to establish causality. Initial data mining revealed that the strongest positive correlation of diabetes with POPs occurred with organochlorine compounds, such as trans-nonachlor, dichlorodiphenyldichloroethylene (DDE), polychlorinated biphenyls (PCBs), and dioxins and dioxin-like chemicals. There is less indication of an association between other nonorganochlorine POPs, such as perfluoroalkyl acids and brominated compounds, and type 2 diabetes. Experimental data are needed to confirm the causality of these POPs, which will shed new light on the pathogenesis of diabetes. This new information should be considered by governmental bodies involved in the regulation of environmental contaminants.

Permethrin is a broad-spectrum pyrethroid insecticide and among the most widely used insecticides in homes and crops. Managing the risks for pesticides such as permethrin depends on the ability to consider diverse exposure scenarios and their relative risks. Physiologically based pharmacokinetic models of delta methrin disposition were modified to describe permethrin kinetics in the rat and human. Unlike formulated deltamethrin which consists of a single stereoisomer, permethrin is formulated as a blend of cis- and trans-diastereomers. We assessed time courses for cis-permethrin and trans-permethrin in several tissues (brain, blood, liver, and fat) in the rat following oral administration of 1 and 10mg/kg permethrin (cis/trans: 40/60). Accurate simulation of permethrin in the rat suggests that a generic model structure is promising for modeling pyrethroids. Human in vitro data and appropriate anatomical information were used to develop a provisional model of permethrin disposition with structures for managing oral, dermal, and inhalation routes of exposure. The human permethrin model was used to evaluate dietary and residential exposures in the U.S. population as estimated by EPA's Stochastic Human Exposure and Dose Simulation model. Simulated cis- and trans-DCCA, metabolites of permethrin, were consistent with measured values in the National Health and Nutrition Examination Survey, indicating that the model holds promise for assessing population exposures and quantifying dose metrics.

Reliable, evaluated human exposure and dose models are important for understanding the health risks from chemicals. A case study focusing on permethrin was conducted because of this insecticide's widespread use and potential health effects. SHEDS-Multimedia was applied to estimate US population permethrin exposures for 3- to 5-year-old children from residential, dietary, and combined exposure routes, using available dietary consumption data, food residue data, residential concentrations, and exposure factors. Sensitivity and uncertainty analyses were conducted to identify key factors, pathways, and research needs. Model evaluation was conducted using duplicate diet data and biomonitoring data from multiple field studies, and comparison to other models. Key exposure variables were consumption of spinach, lettuce, and cabbage; surface-to-skin transfer efficiency; hand mouthing frequency; fraction of hand mouthed; saliva removal efficiency; fraction of house treated; and usage frequency. For children in households using residential permethrin, the non-dietary exposure route was most important, and when all households were included, dietary exposure dominated. SHEDS-Multimedia model estimates compared well to real-world measurements data; this exposure assessment tool can enhance human health risk assessments and inform children's health research. The case study provides insights into children's aggregate exposures to permethrin and lays the foundation for a future cumulative pyrethroid pesticides risk assessment.

Biomonitoring is the process by which biomarkers are measured in human tissues and specimens to evaluate exposures. Given the growing number of population-based biomonitoring surveys, there is now an escalated interest in using biomarker data to reconstruct exposures for supporting risk assessment and risk management. While detection of biomarkers is de facto evidence of exposure and absorption, biomarker data cannot be used to reconstruct exposure unless other information is available to establish the external exposure-biomarker concentration relationship. In this review, the process of using biomarker data and other information to reconstruct human exposures is examined. Information that is essential to the exposure reconstruction process includes (1) the type of biomarker based on its origin (e.g., endogenous vs. exogenous), (2) the purpose/design of the biomonitoring study (e.g., occupational monitoring), (3) exposure information (including product/chemical use scenarios and reasons for expected contact, the physicochemical properties of the chemical and nature of the residues, and likely exposure scenarios), and (4) an understanding of the biological system and mechanisms of clearance. This review also presents the use of exposure modeling, pharmacokinetic modeling, and molecular modeling to assist in integrating these various types of information.

A new generation of scientific tools has emerged to rapidly measure signals from cells, tissues, and organisms following exposure to chemicals. High-visibility efforts to apply these tools for efficient toxicity testing raise important research questions in exposure science. As vast quantities of data from high-throughput screening (HTS) in vitro toxicity assays become available, this new toxicity information must be translated to assess potential risks to human health from environmental exposures. Exposure information is required to link information on potential toxicity of environmental contaminants to real-world health outcomes. In the immediate term, tools are required to characterize and classify thousands of environmental chemicals in a rapid and efficient manner to prioritize testing and assess potential for risk to human health. Rapid risk assessment requires prioritization based on both hazard and exposure dimensions of the problem. To address these immediate needs within the context of longer term objectives for chemical evaluation and risk management, a translation framework is presented for incorporating toxicity and exposure information to inform public health decisions at both the individual and population levels. Examples of required exposure science contributions are presented with a focus on early advances in tools for modeling important links across the source-to-outcome paradigm. ExpoCast, a new U.S. Environmental Protection Agency (EPA) program aimed at developing novel approaches and metrics to screen and evaluate chemicals based on the potential for biologically relevant human exposures is introduced. The goal of ExpoCast is to advance characterization of exposure required to translate findings in computational toxicology to information that can be directly used to support exposure and risk assessment for decision making and improved public health.

Although automobile refueling represents the major source of benzene exposure among the nonsmoking public, few data are available regarding such exposures and the associated uptake of benzene. We repeatedly measured benzene exposure and uptake (via benzene in exhaled breath) among 39 self-service customers using self-administered monitoring, a technique rarely used to obtain measurements from the general public (130 sets of measurements were obtained). Benzene exposures averaged 2.9 mg/m(3) (SD = 5.8 mg/m(3); median duration = 3 min) with a range of < 0.076-36 mg/m(3), and postexposure breath levels averaged 160 microg/m(3) (SD = 260 microg/m(3)) with a range of < 3.2-1,400 microg/m(3). Log-transformed exposures and breath levels were significantly correlated (r = 0.77, p < 0.0001). We used mixed-effects statistical models to gauge the relative influences of environmental and subject-specific factors on benzene exposure and breath levels and to investigate the importance of various covariates obtained by questionnaire. Model fitting yielded three significant predictors of benzene exposure, namely, fuel octane grade (p = 0.0011), duration of exposure (p = 0.0054), and season of the year (p = 0.032). Likewise, another model yielded three significant predictors of benzene concentration in breath, specifically, benzene exposure (p = 0.0001), preexposure breath concentration (p = 0.0008), and duration of exposure (p = 0.038). Variability in benzene concentrations was remarkable, with 95% of the estimated values falling within a 274-fold range, and was comprised entirely of the within-person component of variance (representing exposures of the same subject at different times of refueling). The corresponding range for benzene concentrations in breath was 41-fold and was comprised primarily of the within-person variance component (74% of the total variance). Our results indicate that environmental rather than interindividual differences are primarily responsible for benzene exposure and uptake during automobile refueling. The study also demonstrates that self-administered monitoring can be efficiently used to measure environmental exposures and biomarkers among the general public.