SIMULATING URBAN CLIMATE AND AIR POLLUTION IN ALMATY: A NUMERICAL MODELING APPROACH

The aim of this study is to analyze the spatial and temporal distribution of temperature and air pollutant concentration in the urban atmosphere of Almaty using numerical modeling techniques. A two-dimensional advection-diffusion model was developed to simulate the diurnal dynamics across a territory of approximately 80 square kilometers. The model incorporates key physical processes such as wind-driven transport, turbulent diffusion, and localized emission sources that are typical of dense urban environments. Simulation results demonstrate a smoother spatial distribution of temperature, largely driven by solar radiation cycles, in contrast to highly localized peaks in pollutant concentrations associated with anthropogenic activities such as transportation and industry. These contrasting behaviors highlight the need for differentiated mitigation strategies. The findings of the study offer important insights for urban planning and the development of effective air quality management policies. The proposed model provides a practical tool for understanding environmental dynamics and evaluating the potential impact of pollution control measures in complex urban terrains.

1. Ivanov, Voynikova D., Stoimenova M., Gocheva-Ilieva S., Iliyev I. Random forests models of particulate matter PM10: A case study: AIP Conference Proceedings (Jan. 2018). https://doi.org/10.1063/1.5064879.

2. Dzaferovic E., Karaduzovic-Hadziabdic K. Air Quality Prediction Using Machine Learning Methods: A Case Study of Bjelave Neighborhood, Sarajevo, BiH in Lecture Notes in Networks and Systems. – Springer International Publishing, 2020. – P. 423. https://doi.org/10.1007/978-3-030-54765-3_29.

3. Zhu J., Li B., Chen H. AQI multi-point spatiotemporal prediction based on K-mean clustering and RNN-LSTM model // Journal of Physics: Conference Series. – 2021. – Vol. 2006. – No.1. – P. 012022. https://doi.org/10.1088/1742-6596/2006/1/012022.

4. Zaurbekov N.S., Aidosov A., Zaurbekova G., Zaurbekova N. Impurity distribution in foggy and low cloud cover conditions: E3S Web of Conferences. – Jan. 2023. – Vol. 420. – P. 09020. https://doi.org/10.1051/e3sconf/202342009020.

5. Zhang X., Jiang X., Li Y. Prediction of air quality index based on the SSA-BiLSTM-LightGBM model // Scientific Reports. – Apr. 2023. – Vol. 13. – No. 1. https://doi.org/10.1038/s41598-023-32775-2.

6. Tessarotto M., Tessarotto M., Abe T. Modelling of Anthropogenic Pollutant Diffusion in the Atmosphere and Applications to Civil Protection Monitoring: AIP Conference Proceedings. – Jan. 2008. https://doi.org/10.1063/1.3076526.

7. Zhou G., Yu Z., Gu Y., Chang L. Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future: IntechOpen eBooks. – IntechOpen, 2019. https://doi.org/10.5772/intechopen.79304.

8. Malhotra M., Walia S., Lin C., Aulakh I.K., Agarwal S. A systematic scrutiny of artificial intelligencebased air pollution prediction techniques, challenges, and viable solutions // Journal of Big Data. – Oct. 2024. – Vol. 11. – No. 1. https://doi.org/10.1186/s40537-024-01002-8.

9. Saheer L.B., Bhasy A., Maktabdar M., Zarrin J. Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas // Frontiers in Big Data. – Mar. 2022. – Vol. 5. https://doi.org/10.3389/fdata.2022.822573.

10. Han Y., Zhang Q., V. O. K. Li, J. C. K. Lam. Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities // arXiv (Cornell University). – Jan. 2021. https://doi.org/10.48550/arxiv.2103.14587.

11. Song J., Han K. Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing // arXiv (Cornell University). – Jan. 2019. https://doi.org/10.48550/arxiv.1904.12303.

12. Suel E. et al. What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery // Remote Sensing. – Jul. 2022. – Vol. 14. – No. 14. – P. 3429. https://doi.org/10.3390/rs14143429.

13. Bravo M.A., Fuentes M., Zhang Y., Burr M.J., Bell M.L. Comparison of Exposure Estimation Methods for Air Pollutants: Ambient Monitoring Data and Regional Air Quality Simulation // ISEE Conference Abstracts. – Sep. 2011. – Vol. 2011. – No. 1. https://doi.org/10.1289/isee.2011.00073.

14. Cromar K. et al. Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report // Annals of the American Thoracic Society. – Oct. 2019. – Vol. 16. – No. 10. – P. 1207. https://doi.org/10.1513/annalsats.201906-477st.

15. Zarrar H., Dyo V. Drive-by Air Pollution Sensing Systems: Challenges and Future Directions // IEEE Sensors Journal. – Aug. 2023. – Vol. 23. – No. 19. – P. 23692. https://doi.org/10.1109/jsen.2023.3305779.

16. Naizabayeva L., Nurzhanov Ch., Orazbekov Zh., Tleuberdieva G. Corporate Environmental Information System Data Storage Development and Management (Environmental Information System) // Open Computer Science. – 2017. – Vol. 7. – P. 29–35.

17. Kolesnikova K., Naizabayeva L., Myrzabayeva A., Lisnevskyi R. Use the neural networks in prediction of environmental processes: Proc. of the 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). – Almaty, Kazakhstan, 2024. – P. 625–630. https://doi.org/10.1109/SIST61555.2024.10629330.

18. Naizabayeva L., Zaitov D., Seilova N. Integrating smart traffic systems with real-time air quality monitoring to minimize emissions and improve urban health // Procedia Computer Science. – 2024. – Vol. 251. – P. 603–608. https://doi.org/10.1016/j.procs.2024.11.156.

19. Naizabayeva L., Zakirova G. Using data analysis methods for predicting the concentration of toxic elements in soil: Proc. of the 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). – Dortmund, Germany, 2023. – P. 573–579. https://doi.org/10.1109/IDAACS58523.2023.10348723.

20. Khrutba V., Zyuzyun V., Spasichenko O., Bilichenko N., Wojcik W., Tergeusizova A., Mamyrbaev O. Application of system analysis for the investigation of environmental friendliness of urban transport systems: Biomass as Raw Material for the Production of Biofuels and Chemicals. – 2021. – P. 19. https://doi.org/10.1201/9781003177593-19.

21. Rabosh I., Khrutyba V.A., Kobzysta O.P. Features of the study of the environmental situation around the objects of road transport infrastructure // Bulletin of the National Transport University. – 2021. – Vol. 3. – No. 50. – P. 170–179. https://doi.org/10.33744/2308-6645-2021-3-50-170-179.

22. URL: https://www.kazhydromet.kz (accessed: 07.02.2025).