4.5 Article

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

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CLIMATIC CHANGE
卷 128, 期 1-2, 页码 71-84

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SPRINGER
DOI: 10.1007/s10584-014-1290-1

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  1. Centers for Disease Control and Prevention (CDC) [5 U01 EH000405]
  2. National Institutes of Health (NIH) [1R21ES020225]
  3. Office of Science of the U.S. Department of Energy [DEAC05-00OR22725]
  4. Office of Science of the U.S. Department of Energy as part of the Regional and Global Climate Modeling Program

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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 (O-3) 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 O-3 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 O-3. 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. O-3-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 O-3-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 O-3 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.

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