Stochastic Microenvironment Models for Air Pollution Exposure
Exposure assessment is a crucial link in air pollution risk assessment and management. With the recent advances in instrumentation, it has become possible to measure air pollution exposure in the vicinity of the individual human subjects, using either personal monitoring or microenvironment monitoring. For many important pollutants such as carbon monoxide, nitrogen dioxide, and volatile organic compounds, the air pollution exposure depends crucially on the location and activity of the individual: indoor vs. outdoor, smoking vs. not smoking, etc. The stochastic microenvironment models were developed to relate air pollution exposure to the location and activity. This Note reviews the two major existing models, the Cartesianization method and SHAPE (simulation of human activity and pollutant exposure), and compares their assumptions and implications. The author also proposes a new model, the variance components model, which includes both Cartesianization and SHAPE as special cases. The variance components model considers both long-term average concentrations and short-term fluctuations. The Cartesianization focuses on long-term averages, while SHAPE focuses on short-term fluctuations. The author proposes to choose among the three models by examining the variance function that relates variability to averaging time.