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Atmospheric pollutants concentration forecasting is an important issue in airquality monitoring. Qualitair Corse, the organization responsible formonitoring air quality in Corsica (France) region, needs to develop ashort-term prediction model to lead its mission of information towards thepublic. Various deterministic models exist for meso-scale or local forecasting,but need powerful large variable sets, a good knowledge of atmosphericprocesses, and can be inaccurate because of local climatical or geographicalparticularities, as observed in Corsica, a mountainous island located in aMediterranean Sea. As a result, we focus in this study on statistical models,and particularly Artificial Neural Networks (ANN) that have shown good resultsin the prediction of ozone concentration at horizon h+1 with data measuredlocally. The purpose of this study is to build a predictor to realizepredictions of ozone and PM10 at horizon d+1 in Corsica in order to be able toanticipate pollution peak formation and to take appropriated preventionmeasures. Specific meteorological conditions are known to lead to particularpollution event in Corsica (e.g. Saharan dust event). Therefore, several ANNmodels will be used, for meteorological conditions clustering and foroperational forecasting.

Urban ozone concentration forecasting with artificial neural network in Corsica