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POPULATION EXPOSURES TO PARTICULATE MATTER: A COMPARISON OF EXPOSURE MODEL PREDICTIONS AND MEASUREMENT DATA
Citation:
Burke, J M., J. Xue, AND A H. Ozkaynak. POPULATION EXPOSURES TO PARTICULATE MATTER: A COMPARISON OF EXPOSURE MODEL PREDICTIONS AND MEASUREMENT DATA. Presented at 11th Annual Meeting of the International Society of Exposure Analysis, Charleston, SC, November 4-8, 2001.
Impact/Purpose:
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
Description:
The US EPA National Exposure Research Laboratory (NERL) is currently developing an integrated human exposure source-to-dose modeling system (HES2D). This modeling system will incorporate models that use a probabilistic approach to predict population exposures to environmental pollutants, including ambient particulate matter (PM). A population exposure model for PM, called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, has been developed and applied using a case study of PM2.5 in Philadelphia, PA.
SHEDS-PM estimates the population distribution of PM exposures by randomly sampling from various input distributions, including both ambient PM concentrations and emission strengths for indoor sources of PM (e.g., cigarette smoking, cooking). A steady-state mass-balance equation is used to calculate indoor PM concentrations for the residential microenvironment using ambient PM concentrations and distributions of available physical factor data (e.g., air exchange, penetration, deposition). PM concentrations in non-residential microenvironments are calculated based on distributions of the effective penetration of ambient PM, which were produced using regression analysis of available measurement data for vehicles, offices, restaurants/bars, schools and stores. Additional model inputs include demographic data for the population being modeled and human activity pattern data from NERL's Consolidated Human Activity Database (CHAD). Model outputs include distributions of PM exposures in various microenvironments (indoors, in vehicles, outdoors) for the population, and the contributions from both PM of ambient origin and indoor sources of PM in these microenvironments.
The PM2.5 population exposure and microenvironmental concentration distributions predicted by the SHEDS model were compared against measurement data available from a variety of sources including recent EPA-sponsored panel studies. While the data and model predictions were within the same order of magnitude, the comparison led to improvements in the model inputs and algorithms. In addition, the limitations of the measurement data currently available for evaluating population exposure models for PM2.5 were also identified. This analysis revealed the need for additional comprehensive exposure studies that include measurements of the temporal variability of microenvironmental PM2.5 concentrations and the factors that govern microenvironmental PM2.5 exposures in order to fully evaluate these types of population exposure models.
This abstract has been reviewed in accordance with the U.S. Environmental Protection Agency's peer and administrative review policies and approved for presentation and publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.