The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper, an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results. Implications: This paper develops a statistical approach to both analyzing and forecasting daily meteorologically adjusted tropospheric ozone maxima in the presence of seasonality and trend. The methods are applied to a 10-year follow-up of daily maxima of ozone levels for the metropolitan area of Guadalajara. One-day-lagged and present meteorological variables, 1- and 2-day-lagged tropospheric ozone maxima, seasonality, and a curvilinear trend are important predictors of the daily tropospheric ozone maximum in Guadalajara. The method provides a reliable tool to predict ozone levels exceeding a relevant threshold.
