A population exposure model for particulate matter ( PM ) , called the Stochastic Human Exposure and Dose Simulation ( SHEDS -PM ) model, has been developed and applied in a case study of daily PM 2.5 exposures for the population living in Philadelphia, PA. SHEDS -PM is a probabilistic model that estimates the population distribution of total PM exposures by randomly sampling from various input distributions. A mass balance equation is used to calculate indoor PM concentrations for the residential microenvironment from ambient outdoor PM concentrations and physical factor data ( e.g., air exchange, penetration, deposition ) , as well as emission strengths for indoor PM sources ( e.g., smoking, cooking ) . PM concentrations in nonresidential microenvironments are calculated using equations developed from regression analysis of available indoor and outdoor measurement data for vehicles, offices, schools, stores, and restaurants / bars. Additional model inputs include demographic data for the population being modeled and human activity pattern data from EPA's Consolidated Human Activity Database ( CHAD ) . Model outputs include distributions of daily total PM exposures in various microenvironments ( indoors, in vehicles, outdoors ) , and the contribution from PM of ambient origin to daily total PM exposures in these microenvironments. SHEDS -PM has been applied to the population of Philadelphia using spatially and temporally interpolated ambient PM 2.5 measurements from 1992 -1993 and 1990 US Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM 2.5 exposures for the population of Philadelphia ( median = 20 g / m 3 ; 90th percentile = 59 g / m 3 ) . Variability in human activities, and the presence of indoor -residential sources in particular, contributed to the observed variability in total PM 2.5 exposures. The uncertainty in the estimated population distribution for total PM 2.5 exposures was highest at the upper end of the distribution and revealed the importance of including estimates of input uncertainty in population exposure models. The distributions of daily microenvironmental PM 2.5 exposures ( exposures due to time spent in various microenvironments ) indicated that indoor -residential PM 2.5 exposures ( median = 13 g / m 3 ) had the greatest influence on total PM 2.5 exposures compared to the other microenvironments. The distribution of daily exposures to PM 2.5 of ambient origin was less variable across the population than the distribution of daily total PM 2.5 exposures ( median = 7 g / m 3 ; 90th percentile =18 g / m 3 ) and similar to the distribution of ambient outdoor PM 2.5 concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM 2.5 exposures as was observed for exposure to other PM 2.5 sources. For most of the simulated population, exposure to PM 2.5 of ambient origin contributed a significant percent of the daily total PM 2.5 exposures ( median = 37.5% ) , espec...

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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.

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