A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change

• Type of Material: Article
• Authors: Howard Chang, Emory University; Hua Hao, Emory University; Stefanie Sarnat, Emory University
• Language: English
• Date: 2014-06-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Ltd.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.atmosenv.2014.02.037
• Title of Journal or Parent Work: Atmospheric Environment
• ISSN: 1352-2310
• Volume: 89
• Start Page: 290
• End Page: 297
• Grant/Funding Information: This study was partially supported by USEPA grant R834799 and NIH grant 1R21ES023763.; NARCCAP is funded by the National Science Foundation (NSF), the U.S. Department of Energy (DoE), the National Oceanic and Atmospheric Administration (NOAA), and the USEPA Office of Research and Development.
• Keywords: Meteorology & Atmospheric Sciences; FINE PARTICULATE MATTER; REGIONAL CLIMATE; SCENARIOS; Life Sciences & Biomedicine; Environmental Sciences & Ecology; OUTPUTS; Statistical model; ASTHMA; Physical Sciences; Science & Technology; Air pollution; Uncertainty quantification; Emergency department visit; EMISSIONS; SENSITIVITIES; AIR-POLLUTION; Ozone; Environmental Sciences; MORTALITY; QUALITY; Health impact; Climate change
• Research Categories: Biology, Ecology; Environmental Sciences; Geophysics

Abstract

The adverse health effects of ambient ozone are well established. Given the high sensitivity of ambient ozone concentrations to meteorological conditions, the impacts of future climate change on ozone concentrations and its associated health effects are of concern. We describe a statistical modeling framework for projecting future ozone levels and its health impacts under a changing climate. This is motivated by the continual effort to evaluate projection uncertainties to inform public health risk assessment. The proposed approach was applied to the 20-county Atlanta metropolitan area using regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program. Future ozone levels and ozone-related excesses in asthma emergency department (ED) visits were examined for the period 2041-2070. The computationally efficient approach allowed us to consider 8 sets of climate model outputs based on different combinations of 4 RCMs and 4 general circulation models. Compared to the historical period of 1999-2004, we found consistent projections across climate models of an average 11.5% higher ozone levels (range: 4.8%, 16.2%), and an average 8.3% (range:-7%-24%) higher number of ozone exceedance days. Assuming no change in the at-risk population, this corresponds to excess ozone-related ED visits ranging from 267 to 466 visits per year. Health impact projection uncertainty was driven predominantly by uncertainty in the health effect association and climate model variability. Calibrating climate simulations with historical observations reduced differences in projections across climate models.