A U.S. Lead Exposure Hotspots Analysis.

To identify U.S. lead exposure risk hotspots, we expanded upon geospatial statistical methods from a published Michigan case study. The evaluation of identified hotspots using five lead indices, based on housing age and sociodemographic data, showed moderate-to-substantial agreement with state-identified higher-risk locations from nine public health department reports (45-78%) and with hotspots of children's blood lead data from Michigan and Ohio (e.g., Cohen's kappa scores of 0.49-0.63). Applying geospatial cluster analysis and 80th-100th percentile methods to the lead indices, the number of U.S. census tracts ranged from ∼8% (intersection of indices) to ∼41% (combination of indices). Analyses of the number of children <6 years old living in those census tracts revealed the states (e.g., Illinois, Michigan, New Jersey, New York, Ohio, Pennsylvania, Massachusetts, California, Texas) and counties with highest potential lead exposure risk. Results support use of available lead indices as surrogates to identify locations in the absence of consistent, complete blood lead level (BLL) data across the United States. Ground-truthing with local knowledge, additional BLL data, and environmental data is needed to improve identification and analysis of lead exposure and BLL hotspots for interventions. While the science evolves, these screening results can inform "deeper dive" analyses for targeting lead actions.

Modeled Impacts of Drinking Water Pb Reduction Scenarios on Children's Exposures and Blood Lead Levels.

In recent years, environmental lead (Pb) exposure through drinking water has resulted in community public health concerns. To understand potential impacts on blood Pb levels (BLLs) from drinking water Pb reduction actions (i.e., combinations of lead service lines [LSL] and corrosion control treatment [CCT] scenarios), EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia/Integrated Exposure Uptake and Biokinetic (IEUBK) model was applied for U.S. children aged 0 to <6 years. The results utilizing a large drinking water sequential sampling data set from 15 cities to estimate model input concentration distributions demonstrated lowest predicted BLLs for the "no LSLs" with "combined CCT" scenario and highest predicted BLLs for the "yes LSLs" and "no CCT" scenario. Modeled contribution to BLLs from ingestion of residential drinking water ranged from ∼10 to 80%, with the highest estimated for formula-fed infants (age 0 to <1 year). Further analysis using a "bounding" data set spanning a range of realistic water Pb concentrations and variabilities showed BLL predictions consistent with the sequential sampling-derived inputs. Our study illustrates (1) effectiveness of LSL replacement coupled with CCT for reducing Pb in drinking water and children's BLLs, and (2) in some age groups, under realistic local and residential water use conditions, drinking water can be the dominant exposure pathway.

Identification of high lead exposure locations in Ohio at the census tract scale using a generalizable geospatial hotspot approach.

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.

The Environmental Protection Agency's Community-Focused Exposure and Risk Screening Tool (C-FERST) and its potential use for environmental justice efforts.

OBJECTIVES: Our primary objective was to provide higher quality, more accessible science to address challenges of characterizing local-scale exposures and risks for enhanced community-based assessments and environmental decision-making. METHODS: After identifying community needs, priority environmental issues, and current tools, we designed and populated the Community-Focused Exposure and Risk Screening Tool (C-FERST) in collaboration with stakeholders, following a set of defined principles, and considered it in the context of environmental justice. RESULTS: C-FERST is a geographic information system and resource access Web tool under development for supporting multimedia community assessments. Community-level exposure and risk research is being conducted to address specific local issues through case studies. CONCLUSIONS: C-FERST can be applied to support environmental justice efforts. It incorporates research to develop community-level data and modeled estimates for priority environmental issues, and other relevant information identified by communities. Initial case studies are under way to refine and test the tool to expand its applicability and transferability. Opportunities exist for scientists to address the many research needs in characterizing local cumulative exposures and risks and for community partners to apply and refine C-FERST.

Modeled estimates of soil and dust ingestion rates for children.

Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust ingestion via hand and object mouthing of children, using EPA's SHEDS model. Results for children 3 to <6 years old show that mean and 95th percentile total ingestion of soil and dust values are 68 and 224 mg/day, respectively; mean from soil ingestion, hand-to-mouth dust ingestion, and object-to-mouth dust ingestion are 41 mg/day, 20 mg/day, and 7 mg/day, respectively. In general, hand-to-mouth soil ingestion was the most important pathway, followed by hand-to-mouth dust ingestion, then object-to-mouth dust ingestion. The variability results are most sensitive to inputs on surface loadings, soil-skin adherence, hand mouthing frequency, and hand washing frequency. The predicted total soil and dust ingestion fits a lognormal distribution with geometric mean = 35.7 and geometric standard deviation = 3.3. There are two uncertainty distributions, one below the 20th percentile and the other above. Modeled uncertainties ranged within a factor of 3-30. Mean modeled estimates for soil and dust ingestion are consistent with past information but lower than the central values recommended in the 2008 EPA Child-Specific Exposure Factors Handbook. This new modeling approach, which predicts soil and dust ingestion by pathway, source type, population group, geographic location, and other factors, offers a better characterization of exposures relevant to health risk assessments as compared to using a single value.

Rethinking Environmental Protection: Meeting the Challenges of a Changing World.

SUMMARY: From climate change to hydraulic fracturing, and from drinking water safety to wildfires, environmental challenges are changing. The United States has made substantial environmental protection progress based on media-specific and single pollutant risk-based frameworks. However, today's environmental problems are increasingly complex and new scientific approaches and tools are needed to achieve sustainable solutions to protect the environment and public health. In this article, we present examples of today's environmental challenges and offer an integrated systems approach to address them. We provide a strategic framework and recommendations for advancing the application of science for protecting the environment and public health. We posit that addressing 21st century challenges requires transdisciplinary and systems approaches, new data sources, and stakeholder partnerships. To address these challenges, we outline a process driven by problem formulation with the following steps: a) formulate the problem holistically, b) gather and synthesize diverse information, c) develop and assess options, and d) implement sustainable solutions. This process will require new skills and education in systems science, with an emphasis on science translation. A systems-based approach can transcend media- and receptor-specific bounds, integrate diverse information, and recognize the inextricable link between ecology and human health.

Analysis of NHANES measured blood PCBs in the general US population and application of SHEDS model to identify key exposure factors.

Studies have shown that the US population continues to be exposed to polychlorinated biphenyls (PCBs), despite their ban more than three decades ago, but the reasons are not fully understood. The objectives of this paper are to characterize patterns of PCBs in blood by age, gender, and ethnicity, and identify major exposure factors. EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-dietary exposure model was applied, combining fish tissue PCB levels from a NYC Asian Market survey with National Health and Nutrition Examination Survey (NHANES) dietary consumption data, and then linked with blood biomarkers for the same NHANES study subjects. Results reveal that the mean concentration of total PCBs in blood was higher with increasing age; however, for the same age, gender, and ethnicity, the blood PCB concentrations measured in the later NHANES survey were significantly lower than those in the earlier one. The decrease within an age group between the two survey periods lessened with increasing age. Blood PCBs among different ethnicities ranked differently between the older and the younger age groups within each survey. Non-Hispanic Blacks had significantly higher blood PCBs for the >30 year age group. For the 12 to ≤30 year age group, the "Asian, Pacific Islander, Native American or multiracial" group had the highest values, with patterns fairly consistent with fish consumption and modeled PCB exposure patterns. We conclude that for younger people, patterns correspond to reduced environmental contamination over time, and are strongly associated with fish consumption and dietary exposures. Higher PCB concentrations in blood of the older population may partially reflect past exposures to higher environmental PCB concentrations, particularly before the ban.

Methyl mercury exposure from fish consumption in vulnerable racial/ethnic populations: probabilistic SHEDS-Dietary model analyses using 1999-2006 NHANES and 1990-2002 TDS data.

NHANES subjects self-identified as "Asian, Pacific Islander, Native American, or multiracial" (A/P/N/M) have higher levels of blood organic mercury than other racial/ethnic groups; however, the reasons for this have been unclear. This research uses exposure modeling to determine the reasons for elevated blood methylmercury (MeHg) levels, and also extends previous analyses of observed NHANES blood levels. The probabilistic SHEDS-Dietary model was applied, using MeHg fish residue data from FDA's Total Diet Study (1990-2002) combined with NHANES/WWEIA (1999-2006) fish consumption data, to generate exposure estimates by race/ethnicity, age group, and fish type. Statistical analyses of blood methylmercury levels in the (6 times larger) 1999-2006 NHANES data were compared against previous published results for 1999-2002 data. The A/P/N/M group has higher fish intake, modeled MeHg exposures, and blood levels than the general population and other racial/ethnic groups. Tuna, other saltwater fish, and other freshwater fish are key food types driving dietary MeHg exposure. The 1-<3 years-old A/P/N/M group has the highest mean dietary MeHg intake per body weight (0.06 µg/kg/day; ~2.3 times higher than the rest of the population). Fish intake and modeled exposure predictions correlate well with NHANES blood biomarker levels. This study, using the SHEDS-Dietary model with national data, reinforces and expands upon previous observations that dietary exposure via fish consumption is an important route for methylmercury intake by the general population, and especially for racial/ethnic groups with higher fish consumption. These probabilistic dietary modeling approaches could be applied for local populations (e.g., tribes) and other chemicals and foods, if data are available.

Comparison of four probabilistic models (CARES(®), Calendex™, ConsExpo, and SHEDS) to estimate aggregate residential exposures to pesticides.

Two deterministic models (US EPA's Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children's Non-Occupational Exposure to Pesticides by all Relevant Pathways (Draft Protocol)) and four probabilistic models (CARES(®), Calendex™, ConsExpo, and SHEDS) were used to estimate aggregate residential exposures to pesticides. The route-specific exposure estimates for young children (2-5 years) generated by each model were compared to evaluate data inputs, algorithms, and underlying assumptions. Three indoor exposure scenarios were considered: crack and crevice, fogger, and flying insect killer. Dermal exposure estimates from the OPP Residential SOPs and the Draft Protocol were 4.75 and 2.37 mg/kg/day (crack and crevice scenario) and 0.73 and 0.36 mg/kg/day (fogger), respectively. The dermal exposure estimates (99th percentile) for the crack and crevice scenario were 16.52, 12.82, 3.57, and 3.30 mg/kg/day for CARES, Calendex, SHEDS, and ConsExpo, respectively. Dermal exposure estimates for the fogger scenario from CARES and Calendex (1.50 and 1.47 mg/kg/day, respectively) were slightly higher than those from SHEDS and ConsExpo (0.74 and 0.55 mg/kg/day, respectively). The ConsExpo derived non-dietary ingestion estimates (99th percentile) under these two scenarios were higher than those from SHEDS, CARES, and Calendex. All models produced extremely low exposure estimates for the flying insect killer scenario. Using similar data inputs, the model estimates by route for these scenarios were consistent and comparable. Most of the models predicted exposures within a factor of 5 at the 50th and 99th percentiles. The differences identified are explained by activity assumptions, input distributions, and exposure algorithms.

Modeling joint exposures and health outcomes for cumulative risk assessment: the case of radon and smoking.

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors.

The EPA's human exposure research program for assessing cumulative risk in communities.

Communities are faced with challenges in identifying and prioritizing environmental issues, taking actions to reduce their exposures, and determining their effectiveness for reducing human health risks. Additional challenges include determining what scientific tools are available and most relevant, and understanding how to use those tools; given these barriers, community groups tend to rely more on risk perception than science. The U.S. Environmental Protection Agency's Office of Research and Development, National Exposure Research Laboratory (NERL) and collaborators are developing and applying tools (models, data, methods) for enhancing cumulative risk assessments. The NERL's "Cumulative Communities Research Program" focuses on key science questions: (1) How to systematically identify and prioritize key chemical stressors within a given community?; (2) How to develop estimates of exposure to multiple stressors for individuals in epidemiologic studies?; and (3) What tools can be used to assess community-level distributions of exposures for the development and evaluation of the effectiveness of risk reduction strategies? This paper provides community partners and scientific researchers with an understanding of the NERL research program and other efforts to address cumulative community risks; and key research needs and opportunities. Some initial findings include the following: (1) Many useful tools exist for components of risk assessment, but need to be developed collaboratively with end users and made more comprehensive and user-friendly for practical application; (2) Tools for quantifying cumulative risks and impact of community risk reduction activities are also needed; (3) More data are needed to assess community- and individual-level exposures, and to link exposure-related information with health effects; and (4) Additional research is needed to incorporate risk-modifying factors ("non-chemical stressors") into cumulative risk assessments. The products of this research program will advance the science for cumulative risk assessments and empower communities with information so that they can make informed, cost-effective decisions to improve public health.

Tools available to communities for conducting cumulative exposure and risk assessments.

This paper summarizes and assesses over 70 tools that could aid with gathering information and taking action on environmental issues related to community-based cumulative risk assessments (CBCRA). Information on tool use, development and research needs, was gathered from websites, documents, and CBCRA program participants and researchers, including 25 project officers who work directly with community groups. The tools were assessed on the basis of information provided by project officers, community members, CBCRA researchers, and by case study applications. Tables summarize key environmental issues and tool features: (1) a listing of CBCRA-related environmental issues of concern to communities; (2) web-based tools that map environmental information; (3) step-by-step guidance documents; (4) databases of environmental information; and (5) computer models that simulate human exposure to chemical stressors. All tools described here are publicly available, with the focus being on tools developed by the US Environmental Protection Agency. These tables provide sources of information to promote risk identification and prioritization beyond risk perception approaches, and could be used by CBCRA participants and researchers. The purpose of this overview is twofold: (1) To present a comprehensive, though not exhaustive, summary of numerous tools that could aid with performing CBCRAs; and (2) To use this toolset as a sample of the current state of CBCRA tools to critically examine their utility and guide research for the development of new and improved tools.

Biologically based modeling of multimedia, multipathway, multiroute population exposures to arsenic.

This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS--Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways.

A probabilistic arsenic exposure assessment for children who contact CCA-treated playsets and decks, Part 1: Model methodology, variability results, and model evaluation.

Concerns have been raised regarding the safety of young children who may contact arsenic residues while playing on and around chromated copper arsenate (CCA)-treated wood playsets and decks. Although CCA registrants voluntarily canceled the production of treated wood for residential use in 2003, the potential for exposure from existing structures and surrounding soil still poses concerns. The EPA's Office of Research and Development developed and applied the probabilistic Stochastic Human Exposure and Dose Simulation model for wood preservatives (SHEDS-Wood) to estimate children's absorbed dose of arsenic from CCA. Skin contact with, and nondietary ingestion of, arsenic in soil and wood residues were considered for the population of children in the United States who frequently contact CCA-treated wood playsets and decks. Model analyses were conducted to assess the range in population estimates and the impact of potential mitigation strategies such as the use of sealants and hand washing after play events. The results show predicted central values for lifetime annual average daily dose values for arsenic ranging from 10(-6) to 10(-5) mg/kg/day, with predicted 95th percentiles on the order of 10(-5) mg/kg/day. There were several orders of magnitude between lower and upper percentiles. Residue ingestion via hand-to-mouth contact was determined to be the most significant exposure route for most scenarios. Results of several alternative scenarios were similar to baseline results, except for the scenario with greatly reduced residue concentrations through hypothetical wood sealant applications; in this scenario, exposures were lower, and the soil ingestion route dominated. SHEDS-Wood estimates are typically consistent with, or within the range of, other CCA exposure models.

A probabilistic arsenic exposure assessment for children who contact chromated copper arsenate (CCA)-treated playsets and decks, Part 2: Sensitivity and uncertainty analyses.

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two-part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA-treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two-stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue-to-skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA-treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time-activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA-treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.