To characterize population vulnerability to climate change, there is a tremendous need to better understand and quantify the magnitude, distribution, as well as the uncertainties associated with the health impacts projections. We propose to study the spatial variations in population vulnerability related to climate change in the east coast of U.S. through analyzing the health impacts (i.e., mortality) from air pollution and heat waves exposures at 4km resolution during the decades of 2000, 2030 and 2050 under two greenhouse gas emission scenarios. Our proposed study will take advantage of the air pollution and weather projections data that are currently being generated in a project ('Assessing the Cumulative Climate-Related Health Risks in the Eastern U.S') funded by the Centers for Disease Control and Prevention (CDC). IPCC 5th assessment reports (AR5) scenarios are used to generate this unique dataset and the simulations are based on the enhanced Community Climate System Model (CCSM4), coupled with the Community Multi-scale Air Quality model (CMAQ) and the Weather Research and Forecasting model (WRF). The fine resolution in this dataset allows us to study the spatial heterogeneity in the health impacts projections, particularly important for identifying target locations to facilitate preparedness efforts and effective adaptation strategies. In addition to identifying vulnerable geographical locations with increased health impacts related to climate change, we will also study the magnitude of uncertainties introduced by each analytical step of climate change health impacts modeling. In particular, we will determine the relative importance of four components - greenhouse gas emission scenarios, meteorological and air quality modeling, exposure-response characterization, and future population distribution projections and the age structure. We will combine sensitivity analysis and Monte Carlo simulations to test the hypothesis that there are significant variations in the magnitude of uncertainties introduced by each analytical step of climate change health impacts modeling. The uncertainty analysis will help government agencies develop robust and appropriate responses to health impacts caused by climate change, and allocate resources for future research. The end products of our proposed work will be 1) county-specific mortality projections based on combined health impacts from three stressors, PM2.5, ozone and heat waves, along the east coast of U.S. including their temporal evolution up to the decade of 2050, and a graphical representation of the vulnerable locations, 2) a spatially explicit representation of the uncertainty of these estimates, apportioned to error sources. Our work will allow for improved precision of health outcome projections on a very fine spatial scale, facilitating targeted preparedness in the vulnerable populations.

Public Health Relevance

To assess population vulnerability to climate change, we propose to identify vulnerable geographical locations with increased health impacts (i.e., mortality) due to heat waves and air pollution exposures. We will also study the magnitude of uncertainties introduced by each analytical step of health impacts modeling.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-PSE-D (55))
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Dilworth, Caroline H
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Emory University
Public Health & Prev Medicine
Schools of Public Health
United States
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Kim, Young-Min; Zhou, Ying; Gao, Yang et al. (2015) Spatially resolved estimation of ozone-related mortality in the United States under two Representative Concentration Pathways (RCPs) and their uncertainty. Clim Change 128:71-84
Hodges, Maggie; Belle, Jessica H; Carlton, Elizabeth J et al. (2014) Delays reducing waterborne and water-related infectious diseases in China under climate change. Nat Clim Chang 4:1109-1115
Wu, Jianyong; Zhou, Ying; Gao, Yang et al. (2014) Estimation and uncertainty analysis of impacts of future heat waves on mortality in the eastern United States. Environ Health Perspect 122:10-6