Short-time particulate matter PM10 forecasts using predictive modeling techniques

Two types of predictive modeling techniques - seasonal autoregressive integrated moving average (SARIMA) and a new Generalized PathSeeker (GPS) Regularized Regression method have been used for modeling data related to ambient air quality. The models are built for the measured data for the primary air pollutant - particulate matter PM10 in the town of Shumen, Bulgaria. The time series analysis was carried out based on hourly data with respect to six meteorological variables during a period of one month. The constructed models have been used for short-term four-days-ahead forecasts. The obtained results demonstrate some advantages of the GPS method over seasonal ARIMA stochastic modeling and its applicability. This gives a new perspective for analyzing and preventing the possible pollution problems in urban areas.