forecasting ambient air pollutants by box-jenkins stochastic models in tehran

This paper studies the behavior of six air pollutants (including pmlo, pm₂.₅, o₃, so₂, no₂, and co) in tehran over a 6-year time span. 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, factor analysis is conducted to investigate the relationships between the air pollutants where the results show that four factors account for 93.2704% of the total variance. in this regard, the factor containing pmlo and pm2.5 and the factor containing co and no2 are, respectively, the most and the second most affecting factors with the proportion of 43.2594% and 21.6500% of the total variability. since both of these factors stem from the large-scale use of fossil-fuel vehicles, reducing the number of vehicles or improving the quality of fossil fuels, may increase air quality by 60%.