(47e) High Resolution Modeling of Air Pollution in Pittsburgh, PA

Living in close proximity to major roads is associated with decreased respiratory and cardiovascular health and lower life expectancy. Unfortunately, over 19% of the United States population lives near major roads with low socioeconomic status individuals representing a disproportionately large fraction of that population. In addition, many individuals actively commute and exercise along major roads. While air pollution from roads dissipates with distance, reaching background levels by 500m, this still leaves a significant fraction of the population routinely exposed to traffic-related air pollution (TRAP) levels substantially higher than background concentrations. There is an urgent need for improved estimates of air pollutant concentrations on relevant spatial and temporal scales to facilitate local policy development, urban planning, and epidemiological studies. We have recently developed a hybrid-modeling framework, HYCAMR, to address this need. HYCAMR combines a regional chemical transport model, CAMx, and a road dispersion model, R-LINE, to estimate hourly pollutant concentrations at 40m x 40m resolution. We use HYCAMR to estimate spatially and temporally resolved concentrations of NOx, PM2.5, and elemental carbon (EC) in Pittsburgh in 2011. We compare the HYCAMR estimates against high resolution monitoring carried out by the CACES team at Carnegie Mellon University. We then use the modeled concentrations to estimate air pollution exposures