We combine two major approaches currently used in human air pollution exposure assessment, the direct approach and the indirect approach. The direct approach measures exposures directly using personal monitoring. Despite its simplicity, this approach is costly and is also vulnerable to sample selection bias because it usually imposes a substantial burden on the respondents, making it difficult to recruit a representative sample of respondents. The indirect approach predicts exposures using the activity pattern model to combine activity pattern data with microenvironmental concentrations data. This approach is lower in cost and imposes less respondent burden, thus is less vulnerable to sample selection bias. However, it is vulnerable to systematic measurement error in the predicted exposures because the microenvironmental concentration 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 exposures based on personal monitoring and the indirect estimates based on the 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 dual sample and the indirect-only sample are representative, the indirect estimates from the indirect-only sample is used to improve the precision for the overall estimates. If the dual sample is vulnerable to sample selection bias, the indirect-only sample is used to correct the sample selection bias. We discuss the allocation of the resources between the two subsamples and provide algorithms which can be used to determine the optimal sample allocation. The theory is illustrated with applications to the empirical data obtained from the Washington, DC, Carbon Monoxide (CO) Study.