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INTERPOLATING VANCOUVER'S DAILY AMBIENT PM 10 FIELD
Citation:
Sun, L., J. Zidek, N. D. Le, AND A H. Ozkaynak. INTERPOLATING VANCOUVER'S DAILY AMBIENT PM 10 FIELD. ENVIRONMETRICS 11(6):651-663, (2000).
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:
In this article we develop a spatial predictive distribution for the ambient space- time response field of daily ambient PM10 in Vancouver, Canada. Observed responses have a consistent temporal pattern from one monitoring site to the next. We exploit this feature of the field by adopting a response model with two components, a common deterministic trend across all sites plus a stochastic residual. We are thereby able to whiten the temporal residuals without losing much of the spatial correlation in the original log transformed series. This in turn enables us to develop an effective spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales we can impute Vancouver's daily PM10 field for purposes such as human exposure and health impacts analysis.
The U.S. Environmental Protection Agency through its Office of Research and Development partially funded the research described here under a Cooperative Agreement #CR825267-01 to Harvard University School of Public Health. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.