Predictive Modeling of PM10 Concentrations in Riyadh City Using Geospatial Technologies and Machine Learning Algorithms
Keywords:
Pollution, Air Quality, Fine Particulate Matter (PM10), Spatio-temporal Modeling, Independent and Dependent Data
Abstract
Urban air pollution poses a significant challenge, adversely affecting environmental quality, public health, economic development, and the comprehensive management of urban areas. Among various pollutants, particulate matter (PM₁₀) stands out as one of the most critical air pollutants due to its health risks, particularly in major metropolitan centers such as Riyadh. Given the seasonal fluctuations in PM₁₀ concentrations, there is a pressing need for spatiotemporal modeling and the identification of high-risk areas to support mitigation strategies and minimize health impacts. The study aimed to model the spatiotemporal distribution of PM₁₀ and generate risk maps for Riyadh using four machine learning algorithms within a Python programming environment: Random Forest (RF), Extreme Gradient Boosting (XGBoost), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). Seasonal averages of PM₁₀ concentrations were calculated for winter, spring, summer, and autumn to serve as the dependent variable. A set of independent variables, derived from remote sensing and geographic information systems (GIS) data, was incorporated. These included temperature rates, relative humidity, wind speed, precipitation, dust storm frequency, Soil-Adjusted Vegetation Index (SAVI), Enhanced Built-up and Bare Land Index (EBBI), population density, road network density, and distance to industrial areas. These variables were also aggregated at the seasonal level. The findings further indicated that the highest risk of PM₁₀ exposure, as predicted by both the Random Forest and XGBoost models, occurred during summer and autumn (equally), followed by spring, with winter presenting the lowest risk levels. These results underscore the effectiveness of machine learning models in capturing the seasonal dynamics of pollution and offer valuable tools for air quality management in urban environments.
References
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33. Munir, S., Gabr, S. S., Habeebullah, T. M., & Janajrah, M. A. (2016). Spatiotemporal analysis of fine particulate matter (PM₂.₅) in Saudi Arabia using remote sensing data. The Egyptian Journal of Remote Sensing and Space Science, 19(2), 195–205. https://doi.org/10.1016/j.ejrs.2016.06.001
34. Ehrampoush, M., Jamshidi, S., Sakhvidi, M. Z., & Miri, M. (2017). A comparison on function of kriging and inverse distance weighting models in PM10 zoning in urban area. Journal of Environmental Health and Sustainable Development, 2(2), 379–387.
35. Oliver, M. A., & Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313–332. https://doi.org/10.1080/02693799008941549
36. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed.). Wiley. https://doi.org/10.1002/9780470517277
37. Liao, D., Peuquet, D., Duan, Y., Whitsel, E., Dou, J., Smith, R. L., Lin, H., Chen, J., & Heiss, G. (2006). GIS approaches for the estimation of residential-level ambient PM concentrations. Environmental Health Perspectives, 114(9), 1374–1380. https://doi.org/10.1289/ehp.9169
Hengl, T., Heuvelink, G. B. M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1–2), 75–93. https://doi.org/10.1016/j.geoderma.2003.08.018
38. Karimi, B., & Shokrinezhad, B. (2021). Spatial variation of ambient PM2.5 and PM10 in the industrial city of Arak, Iran: A land-use regression. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2021.101235
Ghaemi, Z., Alimohammadi, A., & Farnaghi, M. (2018). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran. Environmental Monitoring and Assessment, 190, 300. https://doi.org/10.1007/s10661-018-6659-6
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41. Alharbi, B., Shareef, M. M., & Husain, T. (2015). Study of chemical characteristics of particulate matter concentrations in Riyadh, Saudi Arabia. Arabian Journal for Science and Engineering, 40(10), 2851–2863. https://doi.org/10.5094/APR.2015.011
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4. Barrett J. R. (2015). "Exported" deaths and short-term PM10 exposure: factoring the impact of commuting into mortality estimates. Environmental health perspectives, 123(1), A22. https://doi.org/10.1289/ehp.123-A22
5. Alharbi, H. A., Rushdi, A., Bazeyad, A. Y., & Al-Mutlaq, K. (2024). Temporal variations, air quality, heavy metal concentrations, and environmental and health impacts of atmospheric PM2.5 and PM10 in Riyadh City, Saudi Arabia. Atmosphere, 15(12), 1448. https://doi.org/10.3390/atmos15121448
6. Modaihsh, A. , Al-Barakah, F. , Nadeem, M. and Mahjoub, M. (2015) Spatial and Temporal Variations of the Particulate Matter in Riyadh City, Saudi Arabia. Journal of Environmental Protection, 6, 1293-1307. doi: 10.4236/jep.2015.611113.
7. Al Zboon, K., Matalqah, W., & Ammary, B. (2021). Effect of cement industry on ambient air quality and potential health risk: A case study from Riyadh, Saudi Arabia. Jordanian Journal of Engineering and Chemical Industries, 4(1), 14–23. https://doi.org/10.48103/jjeci422021
8. Yasin, K. H., Yasin, M. I., Iguala, A. D., Gelete, T. B., Tulu, D., & Kebede, E. (2025). Predictive machine learning and geospatial modeling reveal PM10 hotspots and guide targeted air pollution interventions in Addis Ababa, Ethiopia. Discover Applied Sciences, 7(263). https://doi.org/10.1007/s42452-025-06723-w
9. Almaliki, A., & Khattak, A. (2024). Synergizing TabNet and SHAP for PM10 forecasting: Insights from Makkah, Saudi Arabia. IEEE Access, 12, 195528–195543. https://doi.org/10.1109/ACCESS.2024.3520815
10. Altuwayjiri, A., Pirhadi, M., Kalafy, M., Alharbi, B. H., & Sioutas, C. (2021). Impact of different sources on the oxidative potential of ambient particulate matter PM10 in Riyadh, Saudi Arabia: A focus on dust emissions. The Science of the Total Environment, 150590. https://doi.org/10.1016/j.scitotenv.2021.150590
11. Salman, A., Al-Tayib, M., Hag-Elsafi, S., & Al-Duwarij, N. (2016). Assessment of pollution sources in the southeastern of Riyadh and its impact on the population/Saudi Arabia. Arabian Journal of Geosciences, 9, 1–16. https://doi.org/10.1007/s12517-016-2371-4
12. Bian, Q., Alharbi, B. H., Shareef, M. M., Husain, T., Pasha, M., Atwood, S., & Kreidenweis, S. (2017). Sources of PM₂.₅ carbonaceous aerosol in Riyadh, Saudi Arabia. Atmospheric Chemistry and Physics, 18, 3969–3985. https://doi.org/10.5194/ACP-18-3969-2018
13. Shogrkhodaei, S. Z., Razavi-Termeh, S. V., & Fathnia, A. (2021). Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution, 289, 117859. https://doi.org/10.1016/j.envpol.2021.117859
14. Rojas-Rueda, D., Alsufyani, W., Herbst, C., AlBalawi, S., Alsukait, R., & Alomran, M. (2021). Ambient particulate matter burden of disease in the Kingdom of Saudi Arabia. Environmental Research, 111036. https://doi.org/10.1016/j.envres.2021.111036
15. Brook, R. D., Rajagopalan, S., Pope, C. A., et al. (2010). Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation, 121(21), 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1
16. Pope, C. A., & Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association, 56(6), 709–742. https://doi.org/10.1080/10473289.2006.10464485
17. Cressie, N., & Wikle, C. K. (2011). Statistics for spatio-temporal data. John Wiley & Sons.
18. Li, S., Dragicevic, S., & Veenendaal, B. (2021). GeoAI: Applications and emerging trends. International Journal of Geographical Information Science, 35(3), 365–384. https://doi.org/10.1080/13658816.2020.1797490
19. Rybarczyk, Y., & Zalakeviciute, R. (2018). Machine learning approaches for outdoor air quality modelling: A systematic review. Applied Sciences, 8(12), 2570. https://doi.org/10.3390/app8122570
20. Shogrkhodaei, S. Z., Razavi-Termeh, S. V., & Fathnia, A. (2021). Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution, 289, 117859. https://doi.org/10.1016/j.envpol.2021.117859
21. Ghaemi, Z., Alimohammadi, A., & Farnaghi, M. (2018). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran. Environmental Monitoring and Assessment, 190, 300. https://doi.org/10.1007/s10661-018-6659-6
22. Haghbayan, S., & Tashayo, B. (2021). Integrating ground-based air quality monitoring stations with mobile sensor units to improve the accuracy of PM2.5 concentration modeling. Sepehr. https://doi.org/10.22131/sepehr.2021.242859
23. Christakis, I., Stavrakas, I., Hloupis, G., & Tsakiridis, O. (2020, September). Low cost sensor implementation and evaluation for measuring NO₂ and O₃ pollutants. In 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST) (pp. 1–4). IEEE. https://doi.org/10.1109/MOCAST49295.2020.9200245
24. Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2003). Urban sprawl pattern recognition and modeling using GIS. map India, 2003, 28-31.
25. Mozumder, C., Reddy, K. V., & Pratap, D. (2013). Air pollution modeling from remotely sensed data using regression techniques. Journal of the Indian Society of Remote Sensing, 41(2), 269–277. https://doi.org/10.1007/s12524-012-0235-2
26. Sohrabinia, M., & Khorshiddoust, A. M. (2007). Application of satellite data and GIS in studying air pollutants in Tehran. Habitat International, 31(2), 268–275. https://doi.org/10.1016/j.habitatint.2007.02.003
27. Wijeratne, I. K., & Bijker, W. (2006). Mapping dispersion of urban air pollution with remote sensing. In ISPRS Vienna 2006 Symposium (pp. 125–130). International Society for Photogrammetry and Remote Sensing (ISPRS).
28. Zoran, M. A., & Zoran, L. F. V. (2005). Mapping of dispersion of urban air pollution using remote sensing and in-situ monitoring data. Proceedings of SPIE, 5979, Remote Sensing of Clouds and the Atmosphere X, 597914. https://doi.org/10.1117/12.627775
29. Goheer, M. A., Hassan, S. S., Sheikh, A. S., Malik, Y., Uzair, M., & Satti, T. N. (2025). Assessing smog trends and sources of air pollutants across northeastern districts of Punjab, Pakistan using geospatial techniques. International Journal of Environmental Science and Technology, 22, 3657–3674. https://doi.org/10.1007/s13762-024-05236-9
30. Nguyen, X. T., Le, T. T., Nguyen, K. H., Nguyen, T. T. T., & Luu, T. P. K. (2025). Assessing air pollution and seasonal variations in Dong Nai Province, Vietnam: A geospatial analysis of key air quality parameters (2022−2023). Urban Climate, 45, 102475. https://doi.org/10.1016/j.uclim.2025.102475
31. Tikader, M., Mukhopadhyay, D., & Islam, M. Z. (2023). Role of Remote Sensing and GIS in mitigating Particulate Matters that intensify Air pollution in India: A Review. Bulletin of Environment, Pharmacology and Life Sciences, 12(10), 402–418.
32. Tikader, M., Mukhopadhyay, D., & Dabhadker, K. (2024). Remote sensing and GIS in air pollution mitigation: A bibliometric review of Chhattisgarh, India. International Journal of Environment and Climate Change. https://doi.org/10.9734/ijecc/2024/v14i44106
33. Munir, S., Gabr, S. S., Habeebullah, T. M., & Janajrah, M. A. (2016). Spatiotemporal analysis of fine particulate matter (PM₂.₅) in Saudi Arabia using remote sensing data. The Egyptian Journal of Remote Sensing and Space Science, 19(2), 195–205. https://doi.org/10.1016/j.ejrs.2016.06.001
34. Ehrampoush, M., Jamshidi, S., Sakhvidi, M. Z., & Miri, M. (2017). A comparison on function of kriging and inverse distance weighting models in PM10 zoning in urban area. Journal of Environmental Health and Sustainable Development, 2(2), 379–387.
35. Oliver, M. A., & Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313–332. https://doi.org/10.1080/02693799008941549
36. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed.). Wiley. https://doi.org/10.1002/9780470517277
37. Liao, D., Peuquet, D., Duan, Y., Whitsel, E., Dou, J., Smith, R. L., Lin, H., Chen, J., & Heiss, G. (2006). GIS approaches for the estimation of residential-level ambient PM concentrations. Environmental Health Perspectives, 114(9), 1374–1380. https://doi.org/10.1289/ehp.9169
Hengl, T., Heuvelink, G. B. M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1–2), 75–93. https://doi.org/10.1016/j.geoderma.2003.08.018
38. Karimi, B., & Shokrinezhad, B. (2021). Spatial variation of ambient PM2.5 and PM10 in the industrial city of Arak, Iran: A land-use regression. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2021.101235
Ghaemi, Z., Alimohammadi, A., & Farnaghi, M. (2018). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran. Environmental Monitoring and Assessment, 190, 300. https://doi.org/10.1007/s10661-018-6659-6
39. Ministry of Environment, Water, and Agriculture. (2020). Executive regulations for air quality. Retrieved from https://www.mewa.gov.sa/ar/InformationCenter/DocsCenter/RulesLibrary/Pages/default.aspx.
40. Farahani, V. J., Altuwayjiri, A., Pirhadi, M., Verma, V., Ruprecht, A., Diapouli, E., Eleftheriadis, K., & Sioutas, C. (2022). The oxidative potential of particulate matter (PM) in different regions around the world and its relation to air pollution sources. Environmental Science, 2(5), 1076–1086. https://doi.org/10.1039/d2ea00043a
41. Alharbi, B., Shareef, M. M., & Husain, T. (2015). Study of chemical characteristics of particulate matter concentrations in Riyadh, Saudi Arabia. Arabian Journal for Science and Engineering, 40(10), 2851–2863. https://doi.org/10.5094/APR.2015.011
42. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2.
43. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
44. Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the practice of statistics (7th ed.). W.H. Freeman.
45. الزهراني، س. س. م. (2022). التوزيع المكاني لتركُّز الجسيمات العالقة الدقيقة (PM10) و (PM2.5) وعلاقتهما بعناصر المناخ، وحدود تركُّزهما بمدينة الرياض. مجلة جامعة الجوف للعلوم الإنسانية، 13, 210-243. مسترجع من http://search.mandumah.com/Record/1279910.
Published
2025-08-18
How to Cite
Dhikra Abdul-Jalil Ali Sallam, & Dr. Mohamed El-Sayed Hafez. (2025). Predictive Modeling of PM10 Concentrations in Riyadh City Using Geospatial Technologies and Machine Learning Algorithms. Journal of Arts, Literature, Humanities and Social Sciences, (123), 355-380. https://doi.org/10.33193/JALHSS.123.2025.1490
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