Air Quality Management in Tehran Using a Multi-Dimensional Decision Support System

Abstract

The metropolis of Tehran currently houses more than 15% of the permanent and floating working population of the country, thus exacerbating the problem of imploding centralization caused by the unique prevailing administrative, educational, and commercial structures of the region [1]. The phenomenon of urbanization has been transforming the natural landscape and habitat of Tehran for years, changing its human activity and lifestyle and causing various types of damage to its natural resources Pol. J. Environ. Stud. Vol. 26, No. 2 (2017), 593-603 AbstractManagement of air pollution in Tehran, Iran, has been a significant challenge for urban authorities in recent years owing to the number and complexity of the factors affecting the formation and spread of the pollutions. The present study used an integrated modeling approach involving Spatio, Temporal, Uncertainty Decision Support Systems (STUDSS) using Multi Criteria Decision Analysis (MCDA) and an Artificial Neural Network (ANN) for the virtual simulation and strategy assessment of air pollution. Since sources of air pollution and associated pollution control strategies are dependent on location, time and uncertain variables, Multi-Dimensional Decision Support System (MDDSS) can be efficient tool for urban air quality decision-making process. In order to model and evaluate air pollution, time-series data over a period of four years, screened and classified management strategies as well as other structural and environmental data such as land uses, terrain topography, heights of buildings, climatic conditions, population density and pollution sources were modeled using advanced software packages of MCDA and ANN. They were ultimately simulated and evaluated using the MDDSS. The results obtained from the implementation of the STUDSS showed that this tool could be used to provide sustainable solutions to air quality in metropolises and could respond to social satisfaction and economic development.

The quality of the first and second Vaal Triangle Airshed Priority Area (VTAPA) Air Quality Management Plans

“…There has not been a study that evaluates or assesses air quality management plans in South Africa. Research available internationally focuses on the improvement of air quality through different models to compare the ambient air quality before and after management interventions have been put in place (Berhane et al, 2016;Cheng et al, 2019;D'Elia et al, 2009;Ghodousi et al, 2017;Kim & Lee, 2018;Mardones & Cornejo, 2020;Pisoni et al, 2019;Thunis et al, 2017;Wang et al, 2016;Wang & Hao, 2012). This method has been successfully and widely used in the review of the quality of environmental impact assessments and modified for strategic environmental assessment reports, environmental management programmes as well as health impact assessments similar to this study (Anifowose et al, 2016;Bonde & Cherp, 2000;Chang et al, 2013;Cilliers et al, 2015;Fischer, 2010;Fredsgaard et al, 2009;Hallatt et al, 2015;Sandham & Pretorius, 2008;Sandham et al, 2013b;Swanepoel et al, 2019).…”

Assessment and Predictions of Air Traffic Noise at Mitiga International Airport in Tripoli, Libya

“…Now, it is possible to create a predictive method by utilizing a range of detection devices to gather traffic data and incorporating historical traffic patterns and other significant variables [21]. Since ANN was already used in predicting air pollution [22], and it is also crucial to develop models that accurately predict the noise generated by air or road traffic [20], this was the reason why, in the final part of this paper, ANNs were used to assess the reliability of predicting noise levels generated by air traffic.…”

Air monitoring system of the Krasnoyarsk Science Center SB RAS

“…The above circumstances have necessitated the development of a system for scientific monitoring of the air environment. Systems of this type are now being created in many regions, countries and cities of the world [4][5][6][7][8].…”