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TOWARDS RELIABLE AND COST-EFFECTIVE OZONE EXPOSURE ASSESSMENT: PARAMETER EVALUATION AND MODEL VALIDATION USING THE HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA
Citation:
Xue, J, S V. Liu, A H. Ozkaynak, AND J. D. Spengler. TOWARDS RELIABLE AND COST-EFFECTIVE OZONE EXPOSURE ASSESSMENT: PARAMETER EVALUATION AND MODEL VALIDATION USING THE HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA. Presented at International Society of Exposure Analysis, Stresa, Italy, September 21-25, 2003.
Impact/Purpose:
The primary objective of this research is to produce a documented version of the aggregate SHEDS-Pesticides model for conducting reliable probabilistic population assessments of human exposure and dose to environmental pollutants. SHEDS is being developed to help answer the following questions:
(1) What is the population distribution of exposure for a given cohort for existing scenarios or for proposed exposure reduction scenarios?
(2) What is the intensity, duration, frequency, and timing of exposures from different routes?
(3) What are the most critical media, routes, pathways, and factors contributing to exposures?
(4) What is the uncertainty associated with predictions of exposure for a population?
(5) How do modeled estimates compare to real-world data?
(6) What additional human exposure measurements are needed to reduce uncertainty in population estimates?
Description:
Accurate assessment of chronic human exposure to atmospheric criteria pollutants, such as ozone, is critical for understanding human health risks associated with living in environments with elevated ambient pollutant concentrations. In this study, we analyzed a data set from a one-year ozone monitoring study of 196 children age 6 to 12 years living in Southern California. For each study subject, detailed questionnaire information on time activity and housing characteristics, and measurements of personal, indoor, and outdoor ozone concentrations were collected. Statistical mixed models with various variance-covariance structures were applied to these data in order to determine important predictors of longitudinal personal ozone exposures. Using an empirical best linear unbiased prediction technique, model predictions were then validated against the field measurements by splitting the full data set randomly into two parts, one for estimating an optimum model and the other for testing its performance. The results of model fitting showed that the most important predictors for personal ozone exposure were: indoor ozone concentrations, central ambient ozone concentrations, outdoor ozone concentrations at subject home, season, gender, time spent outdoors, house fan usage and the presence of gas range in the house. Hierarchal models of personal ozone concentrations, involving a set of predictive variables, based on questionnaire-based data and ozone concentration measurements, indicated the following levels of explanatory power for each of the predictive models of personal ozone exposure: questionnaire data on time activity and housing characteristics alone (rho=0.67); questionnaire variables plus routinely available central ambient ozone concentration measurements (rho=0.76); questionnaire variables plus central site and indoor ozone concentration measurements (rho=0.86). These results provide important information on key predictors of chronic human exposures to ambient ozone for children and offer insights into how to best reliably and cost-effectively predict personal ozone exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost-effective exposure study designs and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.
This work has been funded in part by the United States Environmental Protection Agency. It has been subjected to Agency review and approved for publication.