Estimating the long-term trend in the extreme values of tropospheric ozone using a multivariate approach

This paper deals with the identification and estimation of the long-term trend in the extreme values of tropospheric ozone after allowing for the confounding effects of meteorological conditions. The U. S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standard for ozone is stated in terms of exceedances of a specified threshold level. Therefore, the EPA is concerned with the long-term trend in the probability of an exceedance. A multivariate nonparametric probit regression model estimated within the framework of a hierarchical Bayes model is used to model the probability of an exceedance after allowing for the effects of changing meteorological conditions. There are three advantages to using this model. First, the trends estimated at each site in a region can be separated into a city-wide component that is common to all sites and a site-specific component that is unique to the individual site. Second, the hierarchical Bayes framework allows for combining information across monitoring sites to increase the information available regarding the trend at each individual site. Third, the nonparametric model does not require the a priori specification of the functional forms relating the probability of an exceedance to the meteorological variables. Ozone data from four Houston, Texas monitoring sites for the period 1981-1997 are analyzed. We find that there is a downward trend in the probability of an exceedance in the 1980s followed by a relatively flat trend in the 1990s.