  Atmospheric pollutants concentration forecasting is an important issue in air
quality monitoring. Qualitair Corse, the organization responsible for
monitoring air quality in Corsica (France) region, needs to develop a
short-term prediction model to lead its mission of information towards the
public. Various deterministic models exist for meso-scale or local forecasting,
but need powerful large variable sets, a good knowledge of atmospheric
processes, and can be inaccurate because of local climatical or geographical
particularities, as observed in Corsica, a mountainous island located in a
Mediterranean Sea. As a result, we focus in this study on statistical models,
and particularly Artificial Neural Networks (ANN) that have shown good results
in the prediction of ozone concentration at horizon h+1 with data measured
locally. The purpose of this study is to build a predictor to realize
predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to
anticipate pollution peak formation and to take appropriated prevention
measures. Specific meteorological conditions are known to lead to particular
pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN
models will be used, for meteorological conditions clustering and for
operational forecasting.
