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 PM2.5 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 non-residential 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 PM2.5 measurements from 1992-93 and 1990 U.S. Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM2.5 exposures for the population of Philadelphia (median=20 ug/m3; 90th percentile=59 ug/m3). Variability in human activities, and the presence of indoor residential sources in particular, contributed to the observed variability in total PM2.5 exposures. The uncertainty in the estimated population distribution for total PM2.5 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 PM2.5 exposures (exposures due to time spent in various microenvironments) indicated that indoor residential PM2.5 exposures (median=13 ug/m3) had the greatest influence on total PM2.5 exposures compared to the other microenvironments.
The distribution of daily exposures to PM2.5 of ambient origin was less variable across the population than the distribution of daily total PM2.5 exposures (median=7 ug/m3; 90th percentile=18 ug/m3) and similar to the distribution of ambient outdoor PM2.5 concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM2.5 exposures as was observed for exposure to other PM2.5 sources. For most of the simulated population, exposure to PM2.5 of ambient origin contributed a significant percent of the daily total PM2.5 exposures (median=37.5%), especially for the segment of the population without exposure to environmental tobacco smoke in the residence (median=46.4%).
Development of the SHEDS-PM model using the Philadelphia PM2.5 case study also provided useful insights into the limitations of currently available data for use in population exposure models. In addition, data needs for improving inputs to the SHEDS-PM model, reducing uncertainty and further refinement of the model structure, were identified.
The primary objective of this research is to improve current PM population exposure models to more accurately predict exposures for the general population and susceptible sub-populations. Through model improvements, a better understanding of the major factors controlling exposure to PM will be achieved. Specific objectives of this research are to:
- predict total personal exposure to PM10 and PM2.5 for the general and for susceptible sub-populations residing in different urban environments
- estimate the contribution of ambient PM to predicted total PM exposures
- determine what factors are of primary importance in determining PM exposures, including an analysis of the effects of time spent in various microenvironments and the importance of spatial variability in ambient PM concentrations
- determine what factors contribute the greatest uncertainty to model predictions and make recommendations for measurement and modeling studies to reduce these uncertainties
- predict daily and annual average exposures using single or multi-day time-activity diaries
- incorporate state-of-the-art dosimetric models of the lung into PM population exposure and dose models
- evaluate models against measured data from PM panel and other exposure measurement studies
- develop exposure and dose metrics applicable to acute and chronic environmental epidemiology studies