(333d) An Improved Hybrid Modeling Framework for Estimation of Human Exposure to Near Roadway Air Pollution

The rapid growth of the world’s motor-vehicle fleet due to population growth and economic improvement causes a significant negative impact on public health. The currently available air quality modeling approaches can compute the source specific pollutant fate on either a regional or a local scale but still lack effective ways to estimate the combined regional and local source contributions to exposure. Temporal variabilities in human activities and differences in pollutant dispersion pattern in stable and unstable atmospheric conditions greatly influence the exposure. Estimating air pollution exposure from local sources such as motor vehicles while considering all the variables impacting the dispersion make the process computationally intensive.

In this study, we employ a hybrid modeling framework combining a 3-D Eulerian chemical transport model and a pseudo steady state dispersion model to provide improved estimation of near road pollutant concentrations. We use the Comprehensive Air Quality Model with Extensions (CAMx), one of the two major air quality models used by Environment Protection Agency (EPA). For local dispersion modeling, we use R-LINE, a line source dispersion model for near surface releases. We employ this modeling framework and estimate human exposure for a wide variety of primary and secondary pollutants. Exposure to roadway emissions mostly depends on population density and temporal activities. In our study, we quantify exposure considering census tract population density and temporal and spatial variability in concentrations. Our approach using a dispersion model is unique as it uses the mass fraction of the total dispersed pollutant at different receptor points and hence is not dependent on either roadway emissions data or extensive model runs. This approach helps overcome the limitation associated with computational burden of regional models.