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Climate change is expected to have many impacts on the environment, including changes in ozone concentrations at the surface level. A key public health concern is the potential increase in ozone-related summertime mortality if surface ozone concentrations rise in response to climate change. Although ozone formation depends partly on summertime weather, which exhibits considerable inter-annual variability, previous health impact studies have not incorporated the variability of ozone into their prediction models. A major source of uncertainty in the health impacts is the variability of the modeled ozone concentrations. We propose a Bayesian model and Monte Carlo estimation method for quantifying health effects of future ozone. An advantage of this approach is that we include the uncertainty in both the health effect association and the modeled ozone concentrations. Using our proposed approach, we quantify the expected change in ozone-related summertime mortality in the contiguous United States between 2000 and 2050 under a changing climate. The mortality estimates show regional patterns in the expected degree of impact. We also illustrate the results when using a common technique in previous work that averages ozone to reduce the size of the data, and contrast these findings with our own. Our analysis yields more realistic inferences, providing clearer interpretation for decision making regarding the impacts of climate change., (© 2015, The International Biometric Society.)

Despite a growing body of evidence demonstrating the importance of cities as sources of many local, regional, and global impacts on the atmosphere, ecosystems, and human populations, most theories on the relationship between society and the environment have focused on the global or national level. A variety of theories exist on human-environment interactions; for example, ecological modernization, urban transitions, and human ecology. However, with the exception of urban transitions, these theories have been mainly concerned with nation states and have ignored the subnational and local (city) levels. This article aims at filling this gap by employing ordinary least squares regression to examine these theories at the city level using the STIRPAT formula. It finds that with the exception of population (which shows an unstable relationship with the impacts indicators applied in the analysis) a remarkable level of variation exists in the importance of drivers across the three exercises. This led us to conclude that urban atmospheric pollutants result from diverse activities (e.g., transportation, industrial), are formed through different processes (vehicle combustion, biomass burning), have a residence time ranging from hours to years, and are the outcome of diverse sets of societal and environmental drivers.