Fine Scale Modeling to Estimate Human Exposures to Air Pollution

Traffic related air pollution is considered one of the major challenges for a large number of urban population. 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. As pollutants from roadway emission sources reach background concentration levels within a few hundred meters from the source, it is very challenging to implement a model that captures this behavior. 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. We developed a hybrid modeling framework combining a regional model, CAMx - Comprehensive Air Quality Model with Extensions, and a local scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species from onroad emission sources. We utilized all chemical and physical processes available in CAMx and use the Particulate Matter Source Apportionment Technology, PSAT to quantify the concentrations from onroad and non-road emission sources. We employed R-LINE to estimate pollutant distribution from onroad emission sources at a finer resolution. Combining these two models, we estimated combined concentrations at a finer spatial resolution and at hourly temporal resolution. We have applied this modeling framework to three major cities in Connecticut and quantified human exposure to NOx, PM2.5, and elemental carbon (EC) at census block group resolution. We also estimated health risks on different demographic groups associated with PM2.5 exposures. Our approach of 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 extensive roadway emissions data or extensive model runs. Overall, this modeling approach overcomes two major challenges facing hybrid modeling for near roadway exposures- double counting emissions and a lack of temporal variability in estimating concentrations