Abstract View
Source-Resolved Variability of Fine Particulate Matter and Human Exposure in an Urban Area
BRIAN DINKELACKER, Pablo Garcia, Iannis Kioutsioukis, Peter Adams, Spyros Pandis, Carnegie Mellon University
Abstract Number: 214
Working Group: Aerosol Exposure
Abstract
Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx was used to assess the impact of increasing model resolution on the ability to predict the population exposure to PM2.5 at 36 x 36, 12 x 12, 4 x 4, and 1 x 1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences in predicted PM2.5 concentration can be observed, while increasing the resolution to 12 x 12 km resolves urban-rural gradients. Increasing resolution to 4 x 4 km resolves large stationary sources such as power plants and the 1 x 1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Despite improvements in the ability of the model to capture localized concentration gradients, the average population weighted PM2.5 concentration does not change significantly with resolution. This suggests that even medium resolution PM2.5 predictions may be sufficient to characterize average PM2.5 exposure. Individual source contributions to population exposure were assessed at the 1 x 1 km scale. During the winter simulation period, residential wood combustion had the largest contribution to total PM2.5. During the summer period, the contribution from residential wood combustion is negligible while power generation had the largest contribution to total PM2.5. The contributions of local sources such as on-road traffic, residential wood combustion, cooking, and miscellaneous area emissions to human exposure exceed their contributions to average PM2.5 in the area.