Nonlinear models for ground-level ozone forecasting.

One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop statistical models giving short-term prediction of future ground-level ozone concentrations. Since there are few physical insights about the dynamic relationship between ozone, precursor emissions and/or meteorological factors, a nonparametric and nonlinear approach sems promising in order to specify the prediction models. First, we apply four nonparametric procedures to forecast daily maximum 1-hour and maximum 8-hours averages of ozone concentrations in an urban area. Then, in order to improve the prediction performances, we combine the time series of the forecasts. This idea seems to give promising results.