Statistical methods of estimating concentration values for use in human exposure estimates have become increasingly more popular because of the complexities in correlating the temporal and spatial concentration variations within microenvironments with the location of people. The number of variables and their associated uncertainty make deterministic models difficult to use. Monte-Carlo simulations of exposure conducted thus far have made no provision for serial correlation effects, and therefore tend to underestimate the highest exposures and overestimate the lowest exposures. The purpose of the sensitivity study is to quantify the factors affecting serial correlation in the indoor microenvironments. Further, the authors investigate in a very preliminary way use of personal exposure monitoring data to infer the value of variables needed to estimate indoor concentrations such as the rates of air exchange, pollutant removal, and pollutant generation. The authors conclude that the use of personal exposure monitoring data to derive rate constants may be useful for order of magnitude estimates.

"EPA 600/D-91/102." Microfiche.