ARTIFICIAL OZONE

In this paper a short-term ambient ozone model based on artificial neural networks is presented. The method has been successfully applied in the city of Seville (Spain) to forecast ozone emission concentration typically one day in advance. The applied methodology is based on the following steps: l. The development of an exhaustive measurement campaign in order to evaluate ozone pollution in the city during 1999 summer, autumn and winter (partially) providing a consistent data series of ozone and meteorological variables. 2. A screening of the most relevant variables affecting ozone concentration to obtain accurate forecasting capabilities based on a reduced number of easy-to-measure parameters: traffic, meteorology and features of the area (street width, building height, etc.).