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ASSESSING A COMPUTER MODEL FOR PREDICTING HUMAN EXPOSURE TO PM2.5
Citation:
McBride, S., R. L. Smith, J. Videk, AND R W. Williams. ASSESSING A COMPUTER MODEL FOR PREDICTING HUMAN EXPOSURE TO PM2.5. Presented at International Society of Exposure Analysis, Stresa, Italy, September 21-25, 2003.
Impact/Purpose:
The primary study objectives are:
1.To quantify personal exposures and indoor air concentrations for PM/gases for potentially sensitive individuals (cross sectional, inter- and intrapersonal).
2.To describe (magnitude and variability) the relationships between personal exposure, and indoor, outdoor and ambient air concentrations for PM/gases for different sensitive cohorts. These cohorts represent subjects of opportunity and relationships established will not be used to extrapolate to the general population.
3.To examine the inter- and intrapersonal variability in the relationship between personal exposures, and indoor, outdoor, and ambient air concentrations for PM/gases for sensitive individuals.
4.To identify and model the factors that contribute to the inter- and intrapersonal variability in the relationships between personal exposures and indoor, outdoor, and ambient air concentrations for PM/gases.
5.To determine the contribution of ambient concentrations to indoor air/personal exposures for PM/gases.
6.To examine the effects of air shed (location, season), population demographics, and residential setting (apartment vs stand-alone homes) on the relationship between personal exposure and indoor, outdoor, and ambient air concentrations for PM/gases.
Description:
This paper compares outputs of a model for predicting PM2.5 exposure with experimental data obtained from exposure studies of selected subpopulations. The exposure model is built on a WWW platform called pCNEM, "A PC Version of pNEM." Exposure models created by pCNEM are similar in spirit to the US Environmental Protection Agency's Fortran-based pNEM ("A probabilistic Version of the NAAQS Exposure Model") which was designed to enable regulators to assess the impact of proposed mitigation strategies for CO and O3 on human exposure. The pCNEM platform enables users to develop exposure models online for any environmental hazard for which requisite pollutant, meteorological and source information is available. Pollutant source parameters can be input as random variables to reflect any uncertainty in their values. Exposure models built using pCNEM account for uncertainty in predicted individual exposures by sampling from 24-hour recall time-activity databases for individuals of particular subpopulations. In this way, the exposure model accounts for daily variability in behavior due to time spent in microenviroments as well as meteorological conditions. To assess model performance, estimated personal exposures from pCNEM are compared to measured exposures for residents from USEPA's Research Triangle Park particulate matter panel study as well as the USEPA 1998 Baltimore Particulate Matter Epidemiology-Exposure Study. Overall, the pCNEM model provides more accurate assessments of personal exposures than measurements from ambient monitors or from nearby outdoor monitors. The sensitivity of model predictions to assumptions about microenvironmental parameters, such as air exchange rates, is also examined. This analysis also demonstrates the utility of the freely available pCNEM platform for application in other contexts by the exposure research community.
This work has been partially funded by the United States Environmental Protection Agency under contract 3D-5925-WATX. It has been subjected to Agency review and approved for publication.