Forecasting Ambient Air Pollutants by Box-Jenkins Stochastic Models in Tehran

This paper presents a study over the behavior of six air pollutants including PM10, PM2.5, O3, SO2, NO2 and CO in Tehran during a 6-year timespan. In this paper, an iterative procedure based on the univariate Box-Jenkins stochastic models is applied to develop the most effective forecasting model for each air pollutant. Applying a number of widely used criteria, the best model for each air pollutant is selected and the results show that, the proposed models perform accurately and satisfactorily for both fitting and predicting where, the fitted and predicted values are so close to the true values of the related data. Finally, a factor analysis is conducted to investigate the relationships between the air pollutants where the results show that four factors accounts for 93.2704% of the total variance. In this regard, the factor containing PM10 and PM2.5 and the factor containing CO and NO2 are, respectively, the most and the second most affecting factors with proportion of 43.2594% and 21.6500% of total variability. While both factors originate from high number of automobiles which use fossil fuels, decreasing the number of automobiles or increasing the quality of fossil fuels may result in up to 60% improvement in air quality.