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Integrating Satellite Data and Artificial Intelligence for Air Quality Prediction and Mitigation Strategies: A Case Study of Tehran | ||
| Pollution | ||
| دوره 12، شماره 1، فروردین 2026، صفحه 58-70 اصل مقاله (1.41 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/poll.2025.393453.2885 | ||
| نویسندگان | ||
| Mohammad Amin Javadi1؛ Armin Mollaei Rudsary2؛ Zahra Amiri3؛ Maryam Zare Shahne* 4؛ Zahra Barzegar5؛ Naghmeh Mobarghaee Dinan6 | ||
| 1K. N. Toosi University of Technology, Civil and Environmental Engineering Department, Tehran, Iran | ||
| 2Department of Civil and Environmental Engineering, Sharif University of Technology, Tehran, Iran | ||
| 3Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada | ||
| 4Department of Civil and Environmental Engineering, K.N. Toosi University of Technology, Tehran, Iran | ||
| 5Director of Environmental Studies, Tehran Urban Research and Planning Centre (TURPC), Tehran 1964635611, Iran | ||
| 6Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| Air pollution is a critical challenge in urban centers like Tehran, where over 8 million residents are exposed to pollutants from transportation, industry, and energy use. To address this, researchers combine satellite observations (e.g., Sentinel-5P) with AI models to monitor and predict concentrations of pollutants such as PM2.5, PM10, O3 and NO2. By integrating remote sensing data with ground measurements, machine learning methods—including neural networks, decision trees, and regression models—establish links between meteorological conditions and pollution levels. This hybrid approach overcomes the limitations of traditional monitoring systems while benefiting from tools like Google Earth Engine for efficient analysis of Tehran’s air quality (2019–2024). The resulting forecasts provide policymakers with actionable insights for pollution control, urban planning, and public health strategies. | ||
| کلیدواژهها | ||
| Air pollution؛ Remote sensing؛ Google earth engine؛ Machine learning | ||
| مراجع | ||
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