Comparative Analysis of Sulfur Dioxide (SO2) Concentration with Satellite Data Using Machine Learning Algorithms - A Case Study

Subject Areas : Natural resources and environmental management

samin rahimzadeh ¹ , Mehdi Ghanbarzadeh Lak ^{2 *} , arezoo soleimany ³

1 - MSc. Student of Civil and Environmental Engineering, School of Engineering, Civil Engineering Department, Urmia University, Urmia, Iran
2 - School of Engineering, Civil Engineering Department, Urmia University, Urmia, Iran
3 - PhD Graduate in Environmental Sciences - Environmental Pollution, Faculty of Natural Resources and Environmental Sciences, Malayer University, Malayer, Iran

Received: 2024-12-30 Accepted : 2025-05-05 Published : 2025-12-27

Keywords: Ambient air pollution, SO₂, Sentinel-5P, machine learning algorithms, Urmia, meteorological parameters,

Abstract :

Air pollution, as one of the main environmental challenges, has significant adverse effects on public health and quality of life. Sulfur dioxide (SO₂), one of the criteria pollutants of ambient air quality, not only causes harmful impacts on human health but also plays a vital role in global phenomena such as acid rain. This study aims to monitor and analyze SO₂ concentrations in Urmia city using data from the TROPOMI sensor onboard the Sentinel-5P satellite, combined with ground-level data and machine learning algorithms. Data were collected during the period 2019 to 2023, preprocessed, and analyzed using performance indicators such as the coefficient of determination (R²) and root mean square error (RMSE). The results revealed that integrating ground-based and satellite data, alongside meteorological parameters such as air temperature, relative humidity, evapotranspiration, wet-bulb temperature, cloud cover, and atmospheric pressure at station height, significantly improved the performance of the models. The XGBoost algorithm, with R² of 0.89 and RMSE of 0.57, was identified as the best-performing model. Analyses excluding meteorological parameters showed a substantial decrease in model accuracy, underscoring the importance of these variables. Satellite data demonstrated their potential to play a key role in bridging information gaps caused by the limitations of ground-based stations. Moreover, combining modern technologies with environmental data can contribute to more precise pollutant identification, improve air quality, and serve as a model for other cities in Iran and developing countries.

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