A non linear and non parametric approach for ground level air pollutants forecasting

One of the main concerns in air quality management is to forecast pollutant concentrationboth to satisfy needs of public information, to predict air quality indexes and to prevent excessivepollutants concentration having negative effects on vegetation and human health. In someregions, forecasting high concentrations lead to public warning and emergency traffic restrictionsaimed at reducing pollution emission due to car fuel. In this work we describe a non parametricand nonlinear predictive model for ground level pollutants concentration. This model developsapproximated confidence intervals with heteroskedastic conditional variance taking as inputspast values of the pollutants and, eventually, covariates such as meteorological factors orpollutant precursors. In this work we present the model and results on a real data setaapplication taking the point of view that most of the necessary information for prediction iscontained in the series itself. However, the theory may be extended straightforward to inputvariables like rain and temperature. In the application we consider several series of daily valuesof ground level air pollutants and we perform short term forecasting. The model may be also usedfor long term forecasting by considering, for example, the series of weekly or monthly averages.