NUMERICAL MODELING OF THE DYNAMICS OF POLLUTANT PARTICLES IN THE ATMOSPHERE OF UST-KAMENOGORSK

The article presents a numerical modeling study of the dynamics of pollutant particles in the atmosphere of Ust-Kamenogorsk, with a focus on PM2.5 emissions from the Ust-Kamenogorsk Metallurgical Plant (UKMP). The modeling employs advanced techniques that account for meteorological conditions, topographical features, and anthropogenic pollution sources. Special attention is given to estimating PM2.5 concentrations across different seasons and analyzing their impact on air quality and public health. The modeling results aid in identifying key pollution hotspots and evaluating the effectiveness of emission reduction measures. Comparison with data from other months reveals seasonal variations in PM2.5 levels, enabling more accurate forecasts of pollutant impacts depending on the time of year.

1. Assanov, D., et al. Spatiotemporal Patterns of Air Pollution in an Industrialised City—A Case Study of Ust-Kamenogorsk, Kazakhstan / D. Assanov, M. K. Kozhakhmetov, A. R. Kasyma, et al. // Atmosphere. – 2022. – Vol. 13, Article 1956. – [MDPI].

2. Temirbekov, N., et al. Atmospheric Modelling of Photochemical Transformations of Pollutants: Impact of Weather Conditions and Diurnal Cycle (Case Study: Ust-Kamenogorsk, Kazakhstan) / N. Temirbekov, Y. Malgazhdarov, D. Tamabay, A. Temirbekov // Mathematical Modelling of Engineering Problems. – 2023. – Vol. 10, Issue 5, P. 1699–1705.

3. Lushi E. An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources / E. Lushi, J. M. Stockie // Atmospheric Environment. – 2010. – Vol. 44, No. 8. – P. 1097–1107. https://doi.org/10.1016/j.atmosenv.2009.12.024

4. Xu X. Multilayer temperature inversion structures and their potential impact on air quality / X. Xu, Q. Li, C. Li, Y. Zhang // Science of the Total Environment. – 2020. – Vol. 730. – Art. 139065. https://doi.org/10.1016/j.scitotenv.2020.139065

5. Feng X. Temperature inversions in the atmospheric boundary layer and their impact on air pollution in the Sichuan Basin / X. Feng, S. Wei, S. Wang // Science of the Total Environment. – 2020. – Vol. 730. – Art. 139065. https://doi.org/10.1016/j.scitotenv.2020.139065

6. Han J. Machine Learning for Urban Air Quality Analytics: A Survey / J. Han, W. Zhang, H. Liu, H. Xiong // arXiv preprint arXiv:2310.09620. – 2023. – P. 44. https://doi.org/10.48550/arXiv.2310.09620

7. Cho E. Impact of Meteorological Factors on PM2.5 Concentrations in Urban Environments / E. Cho, S. Kim // International Journal of Environmental Research and Public Health. – 2021. – Vol. 18, No. 14. – Art. 7504. https://doi.org/10.3390/ijerph18147504

8. Lin H. Fine particulate air pollution and hypertension / H. Lin et al. // Hypertension. – 2017. – Vol. 69, No. 5. – P. 806–812. https://doi.org/10.1161/hypertensionaha.116.08834

9. Lelieveld J. Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions / J. Lelieveld et al. // Nature. – 2015. – Vol. 525, No. 7569. – P. 367–371. https://doi.org/10.1038/nature15371

10. Mukhopadhyay A. Sustainable strategies for particulate pollution mitigation in industrial regions / A. Mukhopadhyay et al. // Environmental Development and Sustainability. – 2014. – Vol. 16, No. 1. – P. 35–48. https://doi.org/10.1007/s10668-013-9477-y