Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Prediction of Air Pollution caused by Urban Transport in Tehran Metropolis using the Combination of GIS with LUR Model and Artificial Neural Network

Document Type : Research Paper

Author
Nahid Sajadian
Associate Professore in geography and urban planning Shahid Chamran University
10.22131/sepehr.2015.15556
Abstract
To date, a number of plans have been implemented to reduce air pollution in the city of Tehran.But the problem is that, along with other shortcomings,these planshave often been a passive and temporaryreaction to the increase of air pollution with adherence to crisis management rather than risk management, and no decision-making support system has been used in management decisions based on these plans.Therefore, due to the importance of the subject, this research was carried out by an analytical-applied method using hourly data, carbon monoxide density of 12 stations from a collection of air pollution measurement stations belonging to the air quality company, as well as meteorological dataof wind speed, wind direction and the temperature at the Mehrabad station, all related to the year 1389, and the number of the cars on the highways and streets of city of Tehran with the aim of predicting the temporal-spatial air pollution caused by the urban transport of Tehran Metropolis in line with the application of the spatial decision- making of the air quality management and with the ultimate goal of optimal management of urban transport of Tehran Metropolis. In this regard, since the ultimate goal of the present study is to use its results in controlling the optimal urban transportation as an important source of air pollutants, the LUR method was used to measure carbon monoxide index in the transportationof Tehran metropolis along with other pollutants. An artificial neural network was then used to predict the time of the possible occurrence of air pollution with emphasis on using risk management, and then, based on time predictions resulted from the artificial neural network, the regions with high possibility of air pollution occurrence were identified using the Kriging index.According to the findings of this research,the results were appropriate, so that this model could be used in the air quality management support system to reach the ultimate goal of optimal urban transport management in Tehran Metropolis.
Keywords
Air pollution
City transport
Artificial neural network
Spatial decision-making support system
GIS
LUR Model
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