Two-stage predictor of air pollution adapted to daily fluctuation

The paper proposes a new statistical prediction system of air pollution which is developed by applying the multivariate analyses to the accumulated data. First, the data are classified into some clusters corresponding to the patterns of daily fluctuations and various regression models are prepared. Secondly, based on the above models, the following two-stage predictor is introduced; at the first stage a suitable model for the pattern of the predicted day is selected by the discriminant function with the qualitative and quantitative data, and at the second stage the coefficients of the selected one are determined for the prediction of concentration levels. Finally, results are shown that are obtained by applying this method to the prediction of the SO2 concentration levels one and two hours ahead.