Forcasting of pollution due to city transport in great Tehran by using GIS, LUR Model and artificial neural network

It is a Known fact that upto now a number of projects have been executed for mitigation of air pollution in Tehran. Realiy is that,In spite of many short comings,such projects have been executed under crisis management systems rather than risk management and there for have locality and piecemeal in the face of increasing air pollution. Futter more nu use has been made of the decision making support system. Judging from statistics and the statements of the responsible authorities such projects have not attend Their objectives. Therefore the present research has been carried out by an analytics applicable method by utilizing statistics of carbon monoxide density in twelve stations of the air pollution studies network,plus meteorological data such as windspeed and wind direction and temperature at Mehrabad meteorological station relating to 2010,number the cars on highways and streets of the city,for the purpose of forecasing time and place of air poloution resulted from the traffic in Tehran, under spatial management system of air quality. In this connection GIS, LUR model and artificial neural network have been utilized.In this regard, since the ultimate goal of this research taking advantage of its results in the optimal control urban transport as a major source of air pollutants LUR method for the measurement of carbon monoxide typicalityin Transport metropolitan Tehran was used along with other pollutants. Then, the artificial neural network to predict the likelihood of air pollution with an emphasis on risk management were used. And then, based on the forecasts of artificial neural network, Using indicator kriging areas with high risk of air pollution, were identified. According to the findings of the this evaluation pattern that Results were appropriate, So that the pattern in air quality management support system the ultimate goal is to optimize the management of urban transport in metropolitan Tehran can be used.