A Comprehensive Platform for Air Pollution Control System Operation in Smart Cities of Developing Countries: A Case Study of Tehran

Abstract

Controlling air pollution in megacities is crucial from both a health and environmental perspective. Air pollution is a pervasive problem in these densely populated cities and must be effectively managed to promote sustainability. This study presents a framework for the intelligent management of air pollution control systems in developing nations. The framework was developed through the use of an expert agreement model involving five managers from Tehran. The results indicate that the Decision Support System (DSS) is equipped with calibrated sensors for monitoring air quality. Additionally, the concentration of air pollutants can be determined through the application of machine learning algorithms and the analysis of historical data. Rapid response methods are then applied to mitigate the acute and chronic effects of air pollution. The DSS also incorporates citizens' feedback to evaluate the effectiveness of air pollution control measures. Implementing this model in developing nations' smart cities can help achieve several of the United Nations' Sustainable Development Goals (SDGs), such as good health and well-being, sustainable cities and communities, climate action, and life on land.