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A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5
Citation:
Ozkaynak, A H., M J. Zufall, J M. Burke, J. Xue, AND J. Zidek. A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5. Presented at ISEE/ISEA '99 Conference, Athens, Greece, September 5-8, 1999.
Description:
A first generation probabilistic population exposure model for Particulate Matter (PM), specifically for predicting PM10, and PM2.5, exposures of an urban, population has been developed. This model is intended to be used to predict exposure (magnitude, frequency, and duration) of the general population and susceptible subpopulations to PM of ambient origin. Two-stage Monte-Carlo simulation techniques are used to characterize uncertainty and variability in the various model parameters and inputs.
The model has been applied to Vancouver, Canada, following the statistical spatial interpolation of the ambient PM10 data to predict the distributions of PM exposures by cohort, age, activity type and microenvironrnent category. Results show wide ranges in the predicted personal exposures of various population cohorts which are influenced by the different human activities and the contributions from indoor sources, such as smoking or cooking. Limitations of available data on indoor and outdoor PM measurements in schools ,commuting environments, and different public places are among some of the important sources of model uncertainties.
Model refinements will incorporate the data from new PM exposure measurement and exposure factors research. This work will be performed in-house by EPA, as well as through the cooperative agreements recently initiated with two different University Consortia; UC Berkeley/LBL, CA and EOHSI, NJ.
This paper 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.