Local Real-Time Forecasting of Ozone Exposure using Temperature Data

Rigorous and prompt assessment of ambient ozone exposure is important for inform- ing the public about ozone levels that may lead to adverse health effects. In this paper, we make use of hierarchical modeling to forecast 8-hour average ozone exposure. Our contribution is to show how incorporating temperature data in addition to observed ozone can significantly improve forecast accuracy, as measured by predictive mean squared error and empirical coverage. Furthermore, our model meets the objective of forecasting in real-time. These advantages are illustrated through modeling data collected at the Village Green monitoring station in Durham, North Carolina.