Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty
- 549 Downloads
The spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5) and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and −2118 deaths/year under RCP4.5 (95 % CI: −3021 to −1216), when allowing for climate change and emissions reduction. The uncertainty of O3-related excess mortality estimates was mainly caused by RCP emissions pathways. Excess mortality estimates attributable to the combined effect of climate and emission changes on O3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.
KeywordsExcess Mortality Population Projection Representative Concentration Pathway West North Central Community Earth System Model Version
This study was supported by the Centers for Disease Control and Prevention (CDC) (Grant No. 5 U01 EH000405) and by the National Institutes of Health (NIH) (Grant No. 1R21ES020225). National Science Foundation through TeraGrid resources provided by National Institute for Computational Sciences (NICS) (TG-ATM110009 and UT-TENN0006) and resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory supported by the Office of Science of the U.S. Department of Energy (DEAC05-00OR22725) were used for the climate and air pollution model simulations. Yang Gao was partly supported by the Office of Science of the U.S. Department of Energy as part of the Regional and Global Climate Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute (DE-AC05-76RL01830).
- Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (2012) Wide-Ranging On Line Data for Epidemiologic Research (WONDER). http://wonder.cdc.gov. Accessed 27 November 2012.
- Preston SH, Heuveline P, Michel Guillot M (2001) Demography: measuring and modeling population processes. Blackwell, New YorkGoogle Scholar
- Seinfeld JH, Pandis SN (2006) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New YorkGoogle Scholar
- U.S. Environmental Protection Agency (USEPA) (2009) Land-Use scenarios: national-scale housing-density scenarios consistent with climate change storylines (final report), EPA/600/R-08/076F. USEPA, WashingtonGoogle Scholar
- U.S. Environmental Protection Agency (USEPA) (2012) BenMap: environmental benefits mapping and analysis program: User’s manual appendices. USEPA, Office of Air Quality Planning and Standards, Research Triangle Park, NCGoogle Scholar