The microenvironmental components of the CO concentration in two cities are compared by subtracting the ambient background concentration from personal exposures measured in Denver, Colorado, and Washington, DC. Two surrogate measures for the ambient background concentration are tested. Both improve the similarity of the means in the two cities, but the Denver standard deviations are higher than those in Washington, DC. Microenvironments containing the internal combustion engine have both higher means and standard deviations in Denver compared with Washington, DC. The Washington, DC, mean concentration for automobiles, for example, was 59% of the Denver mean (2.0 ppm versus 4.9 ppm). Washington, DC, had approximately 57% of the Denver emissions, and the difference in mean CO concentrations is roughly consistent with the lower emissions in Washington, DC, due to lower elevation. A surprising finding is that mean CO exposure levels caused by cooking with gas stoves in Washington, DC, were only 58% of the levels in Denver (1.9 ppm and 3.3 ppm, respectively). This result suggests that elevation may exert an influence on gas stove emissions that is similar to its influence on internal combustion engines. Using an averaging time model, analysis of the autocorrelation of sleeping and office microenvironments suggests that considerable serial dependency exists. The microenvironmental data and findings in this paper have important implications for constructing human exposure-activity pattern models. For future human exposure field studies, the findings emphasize the importance of measuring background values in a location that is extremely close to each microenvironment studied.

Adult↗

PubMedDate not supplied

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. 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.

Air Pollutants↗

PubMedDate not supplied

Commentary: defining exposure and related concepts.

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Environmental Exposure↗