The Influence of Chemistry-Transport Model Scale and Resolution on Population Exposure Due to Aircraft Emissions from Three Airports in the United States

摘要

Understanding the impact of aviation emissions on air quality is becoming more important due to the projected growth in aviation transport and decrease in emissions from other anthropogenic sources. Atmospheric chemistry-transport models are often used to determine the marginal impact of emissions on air quality and public health, but the uncertainties related to modeling assumptions are rarely formally characterized from the perspective of public health impact calculations. In this study, we estimate the incremental contribution of commercial aviation emissions to air quality near three U.S. airports — Atlanta Hartsfield, Chicago O'Hare, and Providence T.F. Green — using the Community Multiscale Air Quality Model (CMAQ), a comprehensive chemistry-transport air quality model. To evaluate the significance of model resolution and geographic scales of influence, we ran a one-atmosphere version of CMAQ (with air toxics) at 36- and 12-km resolutions, and calculated the total population exposure per unit emissions at various distances from each airport. Total population exposure per unit emissions was systematically higher for air toxics with increased model grid resolution, and the distance at which most of the population exposure was estimated varied by compound and airport. A 108 x 108 km domain centered on the airport captured most population exposure for reactive gases (e.g., formaldehyde) at airports with high nearby population density, but more than half of the fine particulate matter (PM_(2.5)) exposure occurred outside of a 324 x 324 km domain centered on the airport, given contributions from secondary formation. Our findings provide insight about the model resolution and spatial scales necessary for population risk assessment from airports and other combustion sources, and demonstrate the robustness of risk-based prioritization across multiple grid resolutions.