Multivariate Stochastic Models of Sulphur Dioxide Pollution in an Urban Area

Three multivariate stochastic mathematical models of daily SO₂ pollution in an urban area (Milan, Italy) during the heating season (mid-October/end of March) are illustrated in the paper. Each model is characterized by a different number of external inputs. Precisely, the first model has no inputs (it is simply an autoregressive relationship), the second one has a temperature input (roughly accounting for emission), the third one has two inputs (temperature and wind speed). From each model a real-time predictor is derived, namely a recursive relationship which, at the end of each day, allows future pollution levels to be forecast on the basis of current concentration and meteorological measurements. The quality of the forecast is rather satisfactory, even in episode situations. The improvements in forecast performance when turning from a predictor with less external inputs to a predictor with more external inputs (i.e., when exploiting more information about meteorology) are also pointed out in the paper.