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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.)

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.

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.

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.

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., (© 2010 Society for Risk Analysis.)

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.

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.

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. [ABSTRACT FROM AUTHOR]