Developing a model to predict air pollution (case study: Tehran City)

The technology development, population growth, development of metropolises and subsequent pollution are serious threats to the environment and public health. Therefore, monitoring and evaluation of various emissions and their sources, and also providing practical strategies of pollution reduction, are necessary to solve these problems. In this regard, the use of modern methods to predict the concentration of pollutants can improve decision-making and provide appropriate solutions. Tehran has been ranked as one of the most polluted cities in Iran. In this study, the meteorological monthly data were employed to achieve potent models based on a Box-Jenkins method for the modelling of concentration level of five major air pollutants in Tehran such as NO2, PM10, O-3, SO2, CO, and Pollutant Standard Index. The best models were selected using goodness of fit criteria such as Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) and least prediction error. Prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures, such as the reduction of emissions and alerting the affected population. The results indicated that the concentration of pollutants in each period was influenced by their level and shocks they received during previous periods, which is mainly explained by special climatic and geographic conditions of Tehran that accumulates the pollution over time.