Statistical models for quantifying the spatial distribution of seasonally derived ozone standards

Abstract

The U.S. Environmental Protection Agency's (EPA) National Ambient Air Quality Standard (NAAQS) for ground-level ozone is now based on the fourth-highest daily maximum 8-hour average ozone level (FHDA). Standard geostatistical models may not be appropriate for interpolating such a statistic off of a network of monitoring sites. The performance of different statistical models in predicting this standard at locations where monitors are not located is compared. Special attention is given to two models: a daily model that uses a spatial autoregression to account for spatial and temporal dependence, and a seasonal model that assumes the FHDA field is Gaussian and employs spatial statistical techniques. Based on five seasons of ozone data collected in and around North Carolina, cross-validation shows a preference to the daily model over the seasonal model. In addition to the above models, a spatial extreme value model is also compared to the daily model. Results show that the two vastly different methods give remarkably similar results.