In this application, we are interested in obtaining predictions of the daily distributions of the departures of recreational anglers along the coasts of the United States, as a function of the type of fishing trip, its location and time of year. In order to reflect the circular nature of the departure times, we model them as projected bivariate normal random variables. We propose a new latent hierarchical Bayesian regression model, which makes it possible to incorporate covariates and allows for spatial prediction and inference. We investigate a number of issues related to model specification, model selection and computational efficiency. The approach is applied to a large dataset collected by the US National Oceanic and Atmospheric Administration.

Smog is a term used to describe air pollution that is a result of the interaction of sunlight with certain chemicals in the atmosphere. One of the primary components of smog is ozone. While ozone in the stratosphere protects earth from harmful UV radiation, ozone on the ground (tropospheric ozone) is hazardous to human health. This tropospheric ozone is one of the six criteria pollutants regulated by the US EPA under the Clear Air Act, and has been linked with several adverse health effects. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this presentation, I introduce statistical methods to study and quantify the impact of climate change on ozone, and the potential implications that may have for air quality regulation. More specifically, we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We propose a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. We apply our model to summer ozone from 1997-2005 in the Eastern US, and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast.