DOI: 10.1016/j.atmosenv.2014.02.037
Scopus记录号: 2-s2.0-84896824110
论文题名: A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change
作者: Chang H ; H ; , Hao H ; , Sarnat S ; E
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 89 起始页码: 290
结束页码: 297
语种: 英语
英文关键词: Air pollution
; Climate change
; Emergency department visit
; Health impact
; Ozone
; Statistical model
; Uncertainty quantification
Scopus关键词: Ambient ozone concentration
; Computationally efficient
; Emergency departments
; General circulation model
; Health impact
; Regional climate modeling (RCM)
; Statistical modeling
; Uncertainty quantifications
; Air pollution
; Climate change
; Climate models
; Emergency rooms
; Health
; Risk assessment
; Statistical methods
; Ozone
; ozone
; ambient air
; atmospheric pollution
; climate change
; concentration (composition)
; future prospect
; health impact
; health risk
; metropolitan area
; numerical model
; ozone
; public health
; regional climate
; temporal analysis
; uncertainty analysis
; article
; asthma
; climate change
; emergency ward
; health impact assessment
; historical period
; human
; major clinical study
; priority journal
; public health
; risk assessment
; simulation
; Atlanta
; Georgia
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: 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. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80648
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Rd NE, Mailstop 1518-002-3AA, Atlanta, GA 30322, United States; Department of Environmental Health, Emory University, United States
Recommended Citation:
Chang H,H,, Hao H,et al. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change[J]. Atmospheric Environment,2014-01-01,89