Prediction of the results of implementation of air pollution control strategies using the Geo-Artificial Neural Network for Tehran metropolis

Subject Areas : Air Pollution

Mehran  Ghoddousi ¹ (PhD in Environmental Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Farideh  Atabi ² (Associate Professor, Department of Environmental Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. *(Corresponding Author))
Jafar  Nouri ³ (Professor, Faculty of Health and Medical Sciences, University of Tehran, Tehran, Iran.)
Alireza  Gharagozlu ⁴ (Associate Professor, Department of GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran.)

Received: 2014-03-25 Accepted : 2016-12-25 Published : 2019-06-22

Keywords: Decision support system, Air pollution, Air pollution control strategy, Scenario, Geo–Artificial Neural Network models,

Abstract :

Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the result of urban air pollution controlling strategies in Tehran metropolis using a multi-dimensional decision support system. Method: First, the most appropriate air pollution control strategies were selected based on existing conditions and structures in each zone of the city and then weighed according to selected criteria. Based on the spatial monitoring of air pollution formation patterns in the past and present time, as well as the analysis of their effects, the results of implementing air pollution control strategies were simulated using Geo-Artificial Neural Network models. In the next step, variables of time series and uncertainty variables were simulated for predicting the potential future air pollution patterns and finally, the results of the defined control strategies were evaluated based on spatial thematic layers. Findings: Definition of final clusters of air quality control strategies, weighting and ranking of the selected policies based on defined criteria have been the first findings of this research. Also, extraction of time series zoning based on the data collected during a four-year period, as well as simulation of the baseline scenario models and spatial data layers of their output were among the achievements of this study. Finally, the modeling of the predictive variables, design of the air quality control software and the prediction of the results of the the implementation of air pollution control strategies were presented. The results showed that by applying the Geo-Artificial Neural Network models (GANN), the urban managers could effectively predict the results of implementing the air pollution control strategies. Discussion and Conclusion: The results of this study showed that the spatio-temporal analysis supports the process of evaluation and prediction of the effects of pollution and can be used to determine the best pollution control strategies for the zones affected by air pollution. The final results of GANN models indicate that if the selected strategies are implemented based on the scenarios defined, in the "optimistic scenario", air quality in all areas of Tehran is completely stable and remains healthy, while in the "ordinary scenario" will reduce the level of air pollution up to 70 percent in the autumn and winter season if the selected strategies are implemented compared to the lack of implementation of control plans. The final model of the verification process model also confirmed that the pattern of pollution predicted by the model in each of the urban areas had a proper trend and adaptation compared to the pattern of contamination obtained from the actual results of the field data.  

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