Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

Authors

Priyanka N. deSouza, Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA; CU Population Center, University of Colorado Boulder, CO, USA; Senseable City Lab, Massachusetts Institute of Technology, USA. Electronic address: priyanka.desouza@ucdenver.edu.
Susan Anenberg, Milken Institute School of Public Health, George Washington University, Washington D.C., USA.
Neal Fann, U.S. Environmental Protection Agency, USA.
Lisa M. McKenzie, Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz, Aurora, CO, USA.
Elizabeth Chan, U.S. Environmental Protection Agency, USA.
Ananya Roy, Environmental Defense Fund, NY, USA.
Jose L. Jimenez, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA; Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA.
William Raich, Industrial Economics, Boston, MA, USA.
Henry Roman, Industrial Economics, Boston, MA, USA.
Patrick L. Kinney, Boston University School of Public Health, MA, USA.

Document Type

Journal Article

Publication Date

3-1-2024

Journal

Environment international

Volume

185

DOI

10.1016/j.envint.2024.108416

Abstract

We evaluated the sensitivity of estimated PM and NO health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO, but not PM, attributable deaths. Using county-level predictions instead of 1 km predictions of NO resulted in a lower estimate of mortality attributable to NO by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO for all of Colorado by 2 % each year and 0.3 % for PM. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

APA Citation

deSouza, Priyanka N.; Anenberg, Susan; Fann, Neal; McKenzie, Lisa M.; Chan, Elizabeth; Roy, Ananya; Jimenez, Jose L.; Raich, William; Roman, Henry; and Kinney, Patrick L., "Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado" (2024). GW Authored Works. Paper 4596.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4596

Department

Environmental and Occupational Health