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PARAMETER EVALUATION AND MODEL VALIDATION OF OZONE EXPOSURE ASSESSMENT USING HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA
XUE, J., S. V. LIU, H. A. OZKAYNAK, AND J. D. SPENGLER. PARAMETER EVALUATION AND MODEL VALIDATION OF OZONE EXPOSURE ASSESSMENT USING HARVARD SOUTHERN CALIFORNIA CHRONIC OZONE EXPOSURE STUDY DATA. JOURNAL OF AIR AND WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, 55:1508-1514, (2005).
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?
To examine factors influencing long-term ozone exposures by children living in urban communities, we analyzed longitudinal data on personal, indoor, and outdoor ozone concentrations as well as related housing and other questionnaire information collected in the one-year-long Harvard Southern California Chronic Ozone Exposure Study. Out of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of ozone concentrations at least in 6 months out of 12 months of study period. Data for these children was randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance-covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using empirical best linear unbiased prediction technique. The results of model fitting showed that the most important predictors for personal ozone exposure include indoor ozone concentration, central ambient ozone concentration, outdoor ozone concentration, season, gender, outdoor time, house fan usage and the presence of a gas range in the house. Hierarchal models of personal ozone concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor ozone concentrations plus questionnaire variables, central and indoor ozone concentrations plus questionnaire variables, indoor ozone concentrations plus questionnaire variables, central ozone concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient ozone for children and offer insights into how to 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 design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.
The United States Environmental Protection Agency through its Office of Research and Development partially funded the research described here. It has been subjected to Agency review and approved for publication.