Determination of the most effective control methods of SO2 Pollution in Tehran based on adaptive neuro-fuzzy inference system

Abstract

Air pollution in metropolises is one of the serious problems of human life. Tehran is one of the cities facing air pollution problem. Urban managers concern about choosing different management methods to control air pollution. In this study, a combination of fuzzy systems and neural networks has been used to select the most suitable scenario for controlling SO2 pollution. According to the method presented in this paper, 8 input data categories such as wind speed, precipitation, temperature, pressure, humidity, gas oil consumption, gasoline consumption and urban green space levels have been used as independent parameters and SO2 pollutant concentration has been considered as the dependent parameter. The contribution of each meteorological station to the meteorological data was determined by Thiessen Polygon Method. Then, using adaptive neural fuzzy inference systems, modeling was done in Sugeno Method and the least root mean square error (3.19) was determined for the model. Then, by changing each of the independent parameters, the effect of each of these independent parameters on SO2 pollutant was measured. The results showed that the parameters of pressure, urban green space, gasoline consumption, gas oil consumption, temperature, wind speed and humidity, respectively, had the greatest effect on reducing the SO2 concentration. Since the parameters of gasoline and gas oil consumption as well as the area of green space are changeable by different policies and by human decisions, the concentration of SO2 pollutant can be controlled by reducing the consumption of gasoline and gas oil and increasing the green space in Tehran.