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Characterization and prediction of SO2 concentrations in the atmosphere of Santiago, Chile

Emissions of sulfur dioxide in the city of Santiago are due mainly to industries and construction. Analyzing the hourly averages of this pollutant measured at a station located in Av. La Paz ( three kilometers north of downtown) we observe that most of the days (including Saturdays and Sundays) maximum concentrations occur between 10 A.M. and noon. We have investigated the possibility to build a model for SO 2 forecasting based on values of concentrations of the pollutant on the previous day and forecasted values of temperature, relative humidity and wind speed. We have compared the results obtained with three different models, using different combinations of input variables: persistence, linear regression and a three layer neural network (which is equivalent t a non linear regression). We have found that that best fit is obtained with a neural network that has SO 2 concentrations every six hours on the previous day plus the forecasted meteorological variables as input. Our findings can be used to do practical forecasts of SO 2 the following years.