Climate Change, Air Quality, and Health: Bayesian Hierarchical Models for Predicting the Change in Mortality Associated with Future Ozone Exposures

• Activity Number: 87
• Type: Invited
• Date/Time: Sunday, August 3, 2014 : 8:30 PM to 10:30 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Stacey Alexeeff*+ and Douglas Nychka and Gabi Pfister
• Companies: NCAR and NCAR and NCAR
• Keywords: uncertainty ; climate change ; air pollution ; big data

Abstract

Climate change is expected to have many impacts on public health and the environment, including changes in air pollution. Statistical models of climate change, air quality and health need to account for two key sources of uncertainty: the uncertainty in the climate and air pollution projections and the uncertainty in the health effect estimate. We develop a Bayesian hierarchical model and Monte Carlo simulation method to address these issues. The key features of our methodology are (i) the propagation of uncertainty in both the health effect and the ozone projections and (ii) use of the empirical distribution of the ozone projections to naturally account for their variation. We apply this method to a study of future ground-level ozone levels in the US. High-resolution regional climate change projections and air quality models are used to model the future exposures. Current relationships between ozone concentrations and mortality are assessed by meta-analysis. We estimate the change in total summertime mortality in the US attributable to the change in future ground-level ozone levels from the 2000's to the 2050's.