COMBINATION OF DIRECT AND INDIRECT APPROACHES FOR EXPOSURE ASSESSMENT

We combine two major approaches currently used in human air pollution exposure assessment, the direct approach and the indirect approach. Th e direct approach measures exposures directly using personal monitorin g. Despite its simplicity, this approach is costly and is also vulnera ble to sample selection bias because it usually imposes a substantial burden on the respondents, making it difficult to recruit a representa tive sample of respondents. The indirect approach pi-edicts exposures using the activity pattern model to combine activity pattern data with microenvironmental concentrations data. This approach is lower in cos t and imposes less respondent burden, thus is less vulnerable to sampl e selection bias. However, it is vulnerable to systematic measurement error in the predicted exposures because the microenvironmental concen tration data might need to be ''grafted'' from other data sources. The combined approach combines the two approaches to remedy the problems in each. A dual sample provides both the direct measurements of exposu res based on personal monitoring and the indirect estimates based on t he activity pattern model. An indirect-only sample provides additional indirect estimates. The dual sample is used to calibrate the indirect estimates to correct the systematic measurement error. If both the du al sample and the indirect-only sample are representative, the indirec t estimates from the indirect-only sample is used to improve the preci sion for the overall estimates. If the dual sample is vulnerable to sa mple selection bias, the indirect-only sample is used to correct the s ample selection bias. We discuss the allocation of the resources betwe en the two subsamples and provide algorithms which can be used to dete rmine the optimal sample allocation. The theory is illustrated with ap plications to the empirical data obtained from the Washington, DC, Car bon Monoxide (CO) Study.