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

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, O3, 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.
Source: Journal of Environmental Health Science and Engineering - Category: Environmental Health Source Type: research