Smoothing exponential techniques applied for pollutant concentration prediction

In recent years, the city of Salamanca has been catalogued as one of the most polluted cities in Mexico. Sulphur Dioxide (SO₂) and Particulate Matter less than 10 microns in diameter (PM₁₀) are considered the most important atmospheric pollutants in this zone. In this work we apply smoothing exponential techniques to predict the air pollution concentrations in Salamanca. A database used to find the best smoothing and trend parameters (¿, ¿) corresponds to historical time series. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).