Integrating Satellite Data and Artificial Intelligence for Air Quality Prediction and Mitigation Strategies: A Case Study of Tehran

Document Type : Original Research Paper

Authors

1 Department of Civil and Environmental Engineering, K.N. Toosi University of Technology, Tehran, Iran

2 Department of Civil and Environmental Engineering, Sharif University of Technology, Tehran, Iran

3 Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada

4 Director of Environmental Studies, Tehran Urban Research and Planning Centre (TURPC), Tehran 1964635611, Iran

5 Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

10.22059/poll.2025.393453.2885

Abstract

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.

Keywords

Main Subjects

References

Akima, H. (1970) A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures. Journal of the ACM (JACM). 17 (4). doi:10.1145/321607.321609.
Ali-Taleshi, M.S., Bakhtiari, A.R., Liu, N. & Hopke, P.K. (2025) Characterization and Transport Pathways of High PM2.5 Pollution Episodes During 2015–2021 in Tehran, Iran. Aerosol and Air Quality Research. 25 (5), 1–18. doi:10.1007/S44408-025-00016-Y/METRICS.
Alotaibi, E. & Nassif, N. (2024) Artificial intelligence in environmental monitoring: in-depth analysis. Discover Artificial Intelligence. 4 (1). doi:10.1007/S44163-024-00198-1.
Amiri, Z. & Zare Shahne, M. (2025) Modeling PM2.5 concentration in tehran using satellite-based Aerosol optical depth (AOD) and machine learning: Assessing input contributions and prediction accuracy. Remote Sensing Applications: Society and Environment. 38, 101549. doi:10.1016/J.RSASE.2025.101549.
Anggraini, T.S., Irie, H., Sakti, A.D. & Wikantika, K. (2024) Machine learning-based global air quality index development using remote sensing and ground-based stations. Environmental Advances. 15, 100456. doi:10.1016/J.ENVADV.2023.100456.
Arhami, M., Hosseini, V., Zare Shahne, M., Bigdeli, M., Lai, A. & Schauer, J.J. (2017) Seasonal trends, chemical speciation and source apportionment of fine PM in Tehran. Atmospheric Environment. 153. doi:10.1016/j.atmosenv.2016.12.046.
Arhami, M., Shahne, M.Z., Hosseini, V., Roufigar Haghighat, N., Lai, A.M. & Schauer, J.J. (2018) Seasonal trends in the composition and sources of PM2.5 and carbonaceous aerosol in Tehran, Iran. Environmental Pollution. 239. doi:10.1016/j.envpol.2018.03.111.
Artiola, J.F., Pepper, I.L. & Brusseau, M.L. (2004) Environmental Monitoring and Characterization. doi:10.1016/B978-0-12-064477-3.X5000-0.
Austin, P.C., White, I.R., Lee, D.S. & van Buuren, S. (2021) Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Canadian Journal of Cardiology.37 (9). doi:10.1016/j.cjca.2020.11.010.
Bagheri, H. (2022) A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data. Advances in Space Research. 69 (9). doi:10.1016/j.asr.2022.02.032.
Bourbour, S., Zare Shahne, M. & Safarkhanloo, H. (2025) Non-Gaussian Misalignment Regression Model Development Based on On-Site and Satellite Meteorological and Air Pollutant Data in Tehran, Iran. International Journal of Environmental Research. 19 (4), 1–22. doi:10.1007/S41742-025-00797-5/METRICS.
Ceccato, P., Fernandes, K., Ruiz, D. & Allis, E. (2014) Climate and environmental monitoring for decision making. Earth Perspectives. 1 (1). doi:10.1186/2194-6434-1-16.
Chadalavada, S., Faust, O., Salvi, M., Seoni, S., Raj, N., Raghavendra, U., Gudigar, A., Barua, P.D., Molinari, F. & Acharya, R. (2025) Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review. Environmental Modelling & Software. 185, 106312. doi:10.1016/J.ENVSOFT.2024.106312.
Cordier, T., Alonso-Sáez, L., Apothéloz-Perret-Gentil, L., Aylagas, E., Bohan, D.A., Bouchez, A., Chariton, A., Creer, S., Frühe, L., Keck, F., Keeley, N., Laroche, O., Leese, F., Pochon, X., Stoeck, T., Pawlowski, J. & Lanzén, A. (2021) Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. In: Molecular Ecology. 2021 p. doi:10.1111/mec.15472.
Delbari, S.H., Zare Shahne, M. & Hosseini, V. (2024) An Analysis of Primary Contributing Sources to the PM2.5 Composition in a Port City in Canada Influenced by Traffic, Marine, and Wildfire Emissions. Atmospheric Environment. 334, 120712. doi:10.1016/J.ATMOSENV.2024.120712.
Ditria, E.M., Buelow, C.A., Gonzalez-Rivero, M. & Connolly, R.M. (2022) Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science.9. doi:10.3389/fmars.2022.918104.
Dressing, S.A. & Meals, D.W. (2010) Monitoring and evaluating nonpoint source watershed projects. United States Environmental Protection Agency Office of Water. (May).
Duan, Y., Edwards, J.S. & Dwivedi, Y.K. (2019) Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management. 48. doi:10.1016/j.ijinfomgt.2019.01.021.
Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D. & Alsdorf, D.E. (2007) The shuttle radar topography mission. Reviews of Geophysics. 45 (2). doi:10.1029/2005RG000183.
Hameed, M., Sharqi, S.S., Yaseen, Z.M., Afan, H.A., Hussain, A. & Elshafie, A. (2017) Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Computing and Applications. 28. doi:10.1007/s00521-016-2404-7.
Heger, M., Sarraf, M. & Heger, M.P. (2018) Air Pollution in Tehran: Health Costs, Sources, and Policies. Air Pollution in Tehran. doi:10.1596/29909.
Ibrahim, A., Wayayok, A., Shafri, H.Z.M. & Toridi, N.M. (2024) Remote Sensing Technologies for Unlocking New Groundwater Insights: A Comprehensive Review. Journal of Hydrology X. 23, 100175. doi:10.1016/J.HYDROA.2024.100175.
Järvi, L., Kurppa, M., Kuuluvainen, H., Rönkkö, T., Karttunen, S., Balling, A., Timonen, H., Niemi, J. V. & Pirjola, L. (2023) Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone. Science of the Total Environment. 856. doi:10.1016/j.scitotenv.2022.158974.
Javadi, M.A., Shahne, M.Z. & Amiri, Z. (2025) Integration of Sentinel-5P Satellite Data and Machine Learning for Spatiotemporal Prediction of NO2 in Delhi: Impacts of COVID-19 Lockdown. Atmospheric Pollution Research. 102702. doi:10.1016/J.APR.2025.102702.
Javan, F.D., Samadzadegan, F. & Toosi, A. (2025) Air pollution observation—bridging spaceborne to unmanned airborne remote sensing: a systematic review and meta-analysis. Air Quality, Atmosphere & Health 2025. 1–69. doi:10.1007/S11869-025-01771-Y.
Jollife, I.T. & Cadima, J. (2016) Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.374 (2065). doi:10.1098/rsta.2015.0202.
Kafi, A.M., Hosseinipoor, M., Zare Shahne, M., Jamaat, A., Kafi, A.M., Hosseinipoor, M., Zare Shahne, M. & Jamaat, A. (2024) Integrating Sentinel-5P Satellite Data and Machine Learning Algorithms for Air Quality Index Prediction in Tehran: A Comprehensive Study on Factors Influencing Air Quality. EGUGA. 4506. doi:10.5194/EGUSPHERE-EGU24-4506.
Kamali, N., Zare Shahne, M. & Arhami, M. (2015) Implementing spectral decomposition of time series data in artificial neural networks to predict air pollutant concentrations. Environmental Engineering Science. 32 (5). doi:10.1089/ees.2014.0350.
Kim, M. kyu, Chang, J.W., Park, K. & Yang, D.R. (2022) Comprehensive assessment of the effects of operating conditions on membrane intrinsic parameters of forward osmosis (FO) based on principal component analysis (PCA). Journal of Membrane Science. 641. doi:10.1016/j.memsci.2021.119909.
Koçak, E. (2025) Comprehensive evaluation of machine learning models for real-world air quality prediction and health risk assessment by AirQ+. Earth Science Informatics. 18 (3), 1–17. doi:10.1007/S12145-025-01941-7/FIGURES/4.
Laña, I., Olabarrieta, I. (Iñaki), Vélez, M. & Del Ser, J. (2018) On the imputation of missing data for road traffic forecasting: New insights and novel techniques. Transportation Research Part C: Emerging Technologies. 90. doi:10.1016/j.trc.2018.02.021.
Lever, J., Krzywinski, M. & Altman, N. (2017) Points of Significance: Principal component analysis. Nature Methods.14 (7). doi:10.1038/nmeth.4346.
Ma, J., Cheng, J.C.P., Jiang, F., Chen, W., Wang, M. & Zhai, C. (2020a) A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. Energy and Buildings. 216. doi:10.1016/j.enbuild.2020.109941.
Ma, J., Li, Z., Cheng, J.C.P., Ding, Y., Lin, C. & Xu, Z. (2020b) Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network. Science of the Total Environment. 705. doi:10.1016/j.scitotenv.2019.135771.
Maryam Zare, S., Amir, S. & Fatemeh, N. (2022) A hybrid deep learning model to forecast air quality data based on COVID-19 outbreak in Mashhad, Iran. Annals of Civil and Environmental Engineering. 6 (1). doi:10.29328/journal.acee.1001035.
Méndez, M., Merayo, M.G. & Núñez, M. (2023) Machine learning algorithms to forecast air quality: a survey. Artificial Intelligence Review. 56 (9). doi:10.1007/s10462-023-10424-4.
Mishra, D. & Goyal, P. (2015) Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra. Atmospheric Pollution Research. 6 (1). doi:10.5094/APR.2015.012.
Mohammadi, H., Cohen, D., Babazadeh, M. & Rokni, L. (2012) The Effects of Atmospheric Processes on Tehran Smog Forming. Iranian Journal of Public Health. 41 (5), 1. https://pmc.ncbi.nlm.nih.gov/articles/PMC3468992/.
Moshenberg, S., Lerner, U. & Fishbain, B. (2015) Spectral methods for imputation of missing air quality data. Environmental Systems Research. 4 (1). doi:10.1186/s40068-015-0052-z.
Noori, R., Hoshyaripour, G., Ashrafi, K. & Araabi, B.N. (2010) Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment. 44 (4). doi:10.1016/j.atmosenv.2009.11.005.
Pena, M., Ortega, P. & Orellana, M. (2019) A novel imputation method for missing values in air pollutant time series data. In: 2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019. 2019 p. doi:10.1109/LA-CCI47412.2019.9037053.
Ringnér, M. (2008) What is principal component analysis? Nature Biotechnology.26 (3). doi:10.1038/nbt0308-303.
Sarker, I.H. (2021a) Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science.2 (5). doi:10.1007/s42979-021-00765-8.
Sarker, I.H. (2021b) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science.2 (3). doi:10.1007/s42979-021-00592-x.
Savić, M., Mihajlović, I., Arsić, M. & Živković, Ž. (2014) Adaptive-network-based fuzzy inference system (ANFIS) modelbased prediction of the surface ozone concentration. Journal of the Serbian Chemical Society. 79 (10). doi:10.2298/JSC140126039S.
Shahne, M.Z., Arhami, M., Hosseini, V. & Al Haddad, E. (2022a) Particulate emissions of real-world light-duty gasoline vehicle fleet in Iran. Environmental Pollution. 292. doi:10.1016/j.envpol.2021.118303.
Shahne, M.Z., Sezavar, A., Najibi, F. & Khoshand, A. (2022b) Automatic Image-based Air Quality Detection based on Deep Convolutional Neural Networks and Histogram Analysis. 255. doi:10.34894/VQ1DJA.
Shams, S.R., Jahani, A., Kalantary, S., Moeinaddini, M. & Khorasani, N. (2021) Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Scientific Reports. 11 (1). doi:10.1038/s41598-021-81455-6.
Subramaniam, S., Raju, N., Ganesan, A., Rajavel, N., Chenniappan, M., Prakash, C., Pramanik, A., Basak, A.K. & Dixit, S. (2022) Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review. Sustainability (Switzerland).14 (16). doi:10.3390/su14169951.
Thomson, M.C., Connor, S.J., Zebiak, S.E., Jancloes, M. & Mihretie, A. (2011) Africa needs climate data to fight disease. Nature.471 (7339). doi:10.1038/471440a.
United Nations (2022) United Nations Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Summary of Results. Department of Economic and Social Affairs Population Division. 3 (UN DESA/POP/2022/TR/NO. 3).
Waymond, R. (2020) Artificial Intelligence in a Throughput Model. Artificial Intelligence in a Throughput Model. doi:10.1201/9780429266065.
Yeganeh, B., Hewson, M.G., Clifford, S., Tavassoli, A., Knibbs, L.D. & Morawska, L. (2018) Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system. Environmental Modelling and Software. 100. doi:10.1016/j.envsoft.2017.11.031.
Yu, Y., Yu, J.J.Q., Li, V.O.K. & Lam, J.C.K. (2020) A Novel Interpolation-SVT Approach for Recovering Missing Low-Rank Air Quality Data. IEEE Access. 8. doi:10.1109/ACCESS.2020.2988684.
Zare Shahne, M., Haghighat, N.R., Hosseini, V., Uzu, G., Taheri, A., Darfeuil, S., Ginot, P., Besombes, J.L., Pin, M., Jaffrezo, J.L. & Shamloo, A. (2025) Seasonal variation of the chemical content and source identification of PM2.5 in a mixed landuse in Iran. International Journal of Environmental Science and Technology. 22 (6), 4157–4172. doi:10.1007/S13762-024-06138-X/METRICS.
Zhou, X.H. (2020) Challenges and strategies in analysis of missing data. Biostatistics and Epidemiology. 4 (1). doi:10.1080/24709360.2018.1469810.

Pollution
Volume 12, Issue 1
January 2026
Pages 58-70

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