Statistical Models for Quantifying the Spatial Distribution of Seasonally Derived Ozone Standards
Eric Gilleland, formerly GSP graduate student currently with RAL/NCAR
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.