Modeling variability in air pollution-related health damages from individual airport emissions Stefani L. Penna,⁎, Scott T. Booneb, Brian C. Harveyc, Wendy Heiger-Bernaysa, Yorghos Tripodisd, Sarav Arunachalamb, Jonathan I. Levya a Boston University School of Public Health, Department of Environmental Health, 715 Albany St 4W Boston, MA 02118, United States b University of North Carolina at Chapel Hill, UNC Institute for the Environment, 100 Europa Dr., Chapel Hill, NC 27517, United States c Boston University College of Engineering, Department of Biomedical Engineering, 44 Cummington Mall, Boston, MA 02215, United States d Boston University School of Public Health, Department of Biostatistics, 801 Massachusetts Ave., Boston, MA 02118, United States A R T I C L E I N F O Keywords: Aviation emissions CMAQ modeling Regression modeling Air pollution A B S T R A C T In this study, we modeled concentrations of fine particulate matter (PM2.5) and ozone (O3) attributable to precursor emissions from individual airports in the United States, developing airport-specific health damage functions (deaths per 1000 t of precursor emissions) and physically-interpretable regression models to explain variability in these functions. We applied the Community Multiscale Air Quality model using the Decoupled Direct Method to isolate PM2.5- or O3-related contributions from precursor pollutants emitted by 66 individual airports. We linked airport- and pollutant-specific concentrations with population data and literature-based concentration-response functions to create health damage functions. Deaths per 1000 t of primary PM2.5 emissions ranged from 3 to 160 across airports, with variability explained by population patterns within 500…Open full document
Notes
In this study, we modeled concentrations of fine particulate matter(PM2.5) and ozone(O3) attributable to precursor emissions from individual airports in the United States, developing airport-specific health damage functions (deaths per 1000 tof precursor emissions) and physically-interpretable regression models to explain variability in these functions.