The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.

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.

HighlightsTo assign use-related information to chemicals to help prioritize which will be given more scrutiny relative to human exposure potential.Categorical chemical use and functional information are presented through the Chemical/Product Categories Database (CPCat).CPCat contains information on >43,000 unique chemicals mapped to ∼800 terms categorizing their usage or function.The CPCat database is useful for modeling and prioritizing human chemical exposures.

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.

Structure-based predictions of chemicals' functions in products and reported bioactivities from toxicological assays can identify potentially safer alternatives.

This study examined the spatial, socioeconomic status (SES), and temporal patterns of ambient air pollution in Accra, Ghana. Over 22 months, integrated and continuous rooftop particulate matter (PM) monitors were placed at a total of 11 residential or roadside monitoring sites in four neighborhoods of varying SES and biomass fuel use. PM concentrations were highest in late December and January, due to dust blown from the Sahara. Excluding this period, annual PM(2.5) ranged from 39 to 53 microg/m(3) at roadside sites and 30 to 70 microg/m(3) at residential sites; mean annual PM(10) ranged from 80 to 108 microg/m(3) at roadside sites and 57 to 106 microg/m(3) at residential sites. The low-income and densely populated neighborhood of Jamestown/Ushertown had the single highest residential PM concentration. There was less difference across traffic sites. Daily PM increased at all sites at daybreak, followed by a mid-day peak at some sites, and a more spread-out evening peak at all sites. Average carbon monoxide concentrations at different sites and seasons ranged from 7 to 55 ppm, and were generally lower at residential sites than at traffic sites. The results show that PM in these four neighborhoods is substantially higher than the WHO Air Quality Guidelines and in some cases even higher than the WHO Interim Target 1, with the highest pollution in the poorest neighborhood.

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Many urban households in developing countries use biomass fuels for cooking. The proportion of household biomass use varies among neighborhoods, and is generally higher in low socioeconomic status (SES) communities. Little is known of how household air pollution varies by SES and how it is affected by biomass fuels and traffic sources in developing country cities. In four neighborhoods in Accra, Ghana, we collected and analyzed geo-referenced data on household and community particulate matter (PM) pollution, SES, fuel use for domestic and small-commercial cooking, housing characteristics, and distance to major roads. Cooking area PM was lowest in the high-SES neighborhood, with geometric means of 25 (95% confidence interval, 21-29) and 28 (23-33) μg/m 3 for fine and coarse PM (PM 2.5 and PM 2.5-10 ), respectively; it was highest in two low-SES slums, with geometric means reaching 71 (62-80) and 131 (114-150) μg/m 3 for fine and coarse PM. After adjustment for other factors, living in a community where all households use biomass fuels would be associated with 1.5-to 2.7-times PM levels in models with and without adjustment for ambient PM. Community biomass use had a stronger association with household PM than household's own fuel choice in crude and adjusted estimates. Lack of regular physical access to clean fuels is an obstacle to fuel switching in low-income neighborhoods and should be addressed through equitable energy infrastructure. sustainable development | urbanization | global health | household energy | Africa T he populations of cities in the developing world are growing, with sub-Saharan Africa having the highest urban population growth rate worldwide (1). Some urban environmental health risks in the developing world are similar to those in high-income countries, such as the role of transportation as a determinant of particulate matter (PM) pollution levels and spatial patterns (2-5). Urban environmental health risks in developing countries also have some unique features, including high exposure to multiple risks in low-income "slum" neighborhoods (6, 7). A feature of urban PM pollution that, with few exceptions, is unique to developing countries is the widespread household use of biomass fuels (8, 9). Therefore, PM pollution in urban homes may be because of household or neighborhood biomass use in addition to sources that are also found in high-income countries, such as transportation and industrial pollution.The patterns and sources of indoor air pollution in high-income countries have been studied (10-12). There is also increasing attention to residential indoor air quality in developing countries, including the concentrations of various pollutants, their sources, and the role of ventilation (13-15). However, most current studies of biomass fuels and household air pollution in developing countries have focused on the indoor environment in rural areas, where biomass is the most common or even universal household fuel. There are few studies of household PM in developing country cities, especially in ...