Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis

This paper presents a time-series analysis of SO(2) air concentration and the effects of particulates (either PM(2.5) and PM(10)) concentrations and meteorological conditions (relative humidity and wind speed) on SO(2) trend in Tehran for the period from 2011 to 2020. The source data were obtained from 21 monitoring stations of Air Quality Control Company and meteorological stations in Tehran. To predict the status of future concentration of SO(2), PM(2.5) and PM(10), a Box–Jenkins ARIMA approach was used to model the monthly time series. Considering the whole period of ten years, a somewhat downward trend was noted for SO(2) air concentration, even though a slight rising trend was observed in 2020 year. Monthly sulfur dioxide concentrations showed the lowest value in June and the highest value in January. Seasonal concentrations were lowest in spring and highest in winter. Then, in the ArcGIS software, the IDW method was used to obtain air pollution zoning maps. As a result, the highest average concentration of SO(2) occurred in the north and southwest of Tehran. In the last step, Relations between the SO(2) concentration and particulate matters and relative humidity and wind speed were calculated statistically using the daily average data. We finally concluded that the combined effect of particulate matters and relative humidity with the increasing role of Sulfur dioxide overcomes the decreasing role of wind speed. This study can contribute to a better understanding of the SO(2) air pollution in Tehran affected by meteorological conditions and the rapid urbanization and industrialization, followed by the possible combustion of fuel oil in power plants and health problems.