Assessing the Cumulative Climate-Related Health Risks in the Eastern U.S

Yang Liu (1) Justin Remais (1) John Drake (2) Joshua Fu (2) Uriel Kitron (3) Ying Zhou (1)

(1) Emory University, Rollins School of Public Health, Atlanta GA (2) University of Tennessee, Department of Civil and Environmental Engineering, Knoxville TN (3) Emory University, Department of Environmental Studies, Atlanta GA

     Abstract Number: 62
     Last modified: October 9, 2009

Abstract
Observations of increases in global average air and ocean temperatures, widespread retreat of ice sheets, and numerous other evidence signal a warming trend of our climate system. Climate change is likely to impose sizeable future health costs even in highly developed countries such as the United States caused by various adverse outcomes including elevated air pollution level. Previous research has focused on the health impact of individual stressors without considering their interactions and confounding factors. Yet a comprehensive approach is needed because environmental and public health authorities and affected communities must address a range of climate-sensitive health outcomes and their causative exposures in developing adaptation and mitigation programs.

We propose to model health risks associated with three groups of climate-related stressors: direct (heat waves), proximal (air pollution including ozone and PM2.5) and distal (Lyme disease vectors as the prototype). These stressors are identified by the Intergovernmental Panel on Climate Change report as high priority areas of health impacts, and they may impose a significant health burden in the Midwest, mid-Atlantic, and the Northeast of the U.S. We define the eastern U.S. as our study domain. We will couple the Community Climate System Model (CCSM3), the Weather Research and Forecasting (WRF) model, and the Community Multiscale Air Quality modeling system (CMAQ) to generate exposure estimates under current condition and various IPCC greenhouse gas emission scenarios. Future emission control measures will also be taken into account when predicting air pollution exposures using CMAQ. We will rely on epidemiological evidence to quantify the dose-response relations of each stressor on the general population and various susceptible subpopulations while controlling for the confounding or effect modification from other stressors. We will apply advanced NASA satellite data, NOAA meteorology as well as EPA observations to verify simulated current exposures, and analyze the impact of each analytical step to the overall uncertainty in risk estimates.

Our analysis will serve as a model cumulative risk assessment characterizing the combined risks of three important climate-related stressors with complex interactions in geographic and demographic space. We will develop spatial representations of both hazard overlap and risk overlap. The spatial distribution of vulnerable populations will be examined with an emphasis on identifying potential response locations using geophysical, climatological, and demographic characteristics. Through current collaborations of our team members with local governments, we will not only advance climate-health science, but also develop relevant tools to guide policy decisions regarding the response and preparedness to climate change.