Numerical Modeling of Kutaisi City Atmospheric Air Pollution with PM2.5 Particles in Winter During Ground Level Calm and Background Eastern Wind in the Free Atmosphere

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Aleksandre A. Surmava
Vepkhia G. Kukhalashvili
Natia G. Gigauri
Liana N. Intskirveli
Leila V. Gverdtsiteli

Abstract

Spatial distribution and time change of PM2.5 particles dissipated in the atmosphere as a result of motor transport traffic in Kutaisi city has been studied using computer modeling. This modeling has been made through combined integration of equations of time evolution of meso-scale atmospheric processes and admixtures’ transfer-diffusion, using the relevant initial and boundary conditions. Such meteorological situation has been considered, during which wind velocity in surface layer of the atmosphere equal to 0, while there is background eastern wind in the free atmosphere. Atmospheric pollution is caused by microparticles dissipated in the air during motor transport traffic. The wind fields formed in boundary layer of the atmosphere resulting from interaction of terrain and background flows in winter and fields of concentrations of microparticles transferred by wind and dissipated in the air at different heights from the earth ground have been plotted using numerical modeling data. Areas of relatively severe and mild contamination have been determined, peculiarities of vertical distribution of concentrations have been analyzed. It has been showed that the shapes of vertical distribution of concentrations in surface layer of the atmosphere are similar of temperature distributions in to dry thermals.

Keywords:
PM2.5, microparticles, concentration field, atmosphere, numerical modeling, calm.
Published: Jul 21, 2025

Article Details

How to Cite
Surmava, A. A., Kukhalashvili, V. G., Gigauri, N. G., Intskirveli, L. N., & Gverdtsiteli, L. V. (2025). Numerical Modeling of Kutaisi City Atmospheric Air Pollution with PM2.5 Particles in Winter During Ground Level Calm and Background Eastern Wind in the Free Atmosphere . Journals of Georgian Geophysical Society, 28(1). Retrieved from https://ggs.openjournals.ge/index.php/GGS/article/view/9248
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References

Pope C.A., Burnett R.T., Thun M.J., Calle E.E., Krewski D. et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc., 287, 2022, pp. 1132–1141.

World Health Organization. Regional Office for Europe. Review of evidence on health aspects of air–REVIHAAP Project. First result, 2022. https://media.xpair.com › pdf › REVIHAAP

Mortality and burden of disease from ambient air pollution-WHO, 2020. https://www.who.int/gho/phe/outdoor_air_pollution/burden/en/

World's most polluted cities (historical data 2017-2022). https://www.iqair.com/world-most-polluted-cities.

Agrawal G., Mohan D., Rahman H. Ambient air pollution in selected small cities in India: observed trends and future challenges. IATSS Research, 45(1), 2021, pp. 19-30. https://doi.org/10.1016/j.iatssr.2021.03.004

Kobza J., Geremek M., Dul L. Characteristics of air quality and sources affecting high levels of PM10and PM2.5 in Poland in Upper Silesia urban area Environmental Monitoring and Assessment 190, 515, 2018.

Integrated Science forParticulate Matter. EPA. United States Environmental Protector Agency, 2019, p. 1967. EPA/600/R-19/188. www.epa.gov.isa.

Hong H., Choi H., Jeon H., Kim Y., Jae-Bum Lee, Park C.H., Kim H.S. An air pollutants prediction method integrating numerical models and artificial intelligence models targeting the area around Busan port in Korea. 1462, Atmosphere, 13(9), 1462, 2022. https://doi.org/10.3390/atmos13091462.

Udristioiu M., Mghouchi Y., Yildizhan H. Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning. Journal of Cleaner Production, Vol. 421, 2023. https://doi.org/10.1016/j.jclepro.2023.138496Get rights and content.

Draper E., Whyatt J., Taylor R., Metcalfe S. Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment. Atmospheric Environment, Vol. 314, 120107, 2023. https://doi.org/10.1016/j.atmosenv.2023.120107Get rights and content.

Chae S., Shin J., Kwon S., Lee S., Kang S., Lee D. PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network. Scientific Reports, vol. 11, 11952, 2021. https://www.nature.com/articles/s41598-021-91253-9.

Deters J., Zalakeviciute R., Gonzalez M., Rybarczyk Y. Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters. Machine intelligence in signal sensing. Processing and Recognition, 2017.https://doi.org/10.1155/2017/5106045.

World Health Organization. Regional office for Europe. Review of evidence on health aspects of air– REVIHAAP Project. First result, 2022. https://media.xpair.com › pdf › REVIHAAP 7. Mortality and burden of disease from ambient air pollution-WHO, 2020. https://www.who.int/gho/phe/outdoor_air_pollution/burden/en/.

Integrated science for particulate matter. EPA. United States Environmental Protector Agency, 2019, p. 1967. EPA/600/R-19/188. www.epa.gov.isa.

Agrawal G., Mohan D., Rahman H. Ambient air pollution in selected small cities in India: observed trends and future challenges. IATSS Research, 45, Iss. 1, 2021, pp. 19-30. https://doi.org/10.1016/j.iatssr.2021.03.004.

Kobza J., Geremek M., Dul L. Characteristics of air quality and sources affecting high levels of PM10 and PM2.5 in Poland. Upper Silesia urban area environmental monitoring and assessment, 190, Article number: 515, 2018.

Environmental pollution, 2021.https://air.gov.ge/reports_page

World'smostpollutedcities (historicaldata 2017-2022). https://www.iqair.com/world-most-polluted-cities.

Amiranashvili A.G., Kirkitadze D.D., Kekenadze E.N. Pandemic of coronavirus COVID - 19 and air pollution in Tbilisi in spring 2020. Journals of Georgian Geophysical Society, 23(1), 2020. https://doi.org/10.48614/ggs2320202654.

Gigauri N., Surmava A., Kukhalashvili V., Intskirveli L., Beglarashvili N. Investigation of the distribution of PM2.5 and PM10 in the atmosphere of Kutaisi through experimental observations. Collection of Scientific Refereed Works of the Institute of Hydrometeorology of the Tbilisi State University, Vol. 135, 2024, pp. 82-87. Doi.org/10.36073/1512-0902-2024-135-82-87.

Surmava A., Intskirveli L., Kordzakhia G. Numerical modeling of dust propagation in the atmosphere of a city with complex terrain. The case of background eastern light air. Journal of Applied Mathematics and Physics, 38.7, 2020, pp.1222-1228. https://doi.org/10.4236/jamp.2020.87092.

Surmava A., Intskirveli L., Kukhalashvili V. Numerical Modeling of the transborder, regional and local diffusion of the dust in Georgian Atmosphere. Publishing House, Technical University, Tbilisi, Georgia. ISBN 978-9941-28-810-4, 2021, 139 p. http//www.gtu.ge (in Georgian).

Surmava A., Intskirveli L., Gigauri N. PM2.5 and PM10 microaerosols in the atmosphere of Tbilisi. Tbilisi, Publishing House of the Institute of Hydrometeorology, 2021, 94 p.

Gigauri N., Intskirveli L., Surmava A., Kukhalashvili V. The results of Kutaisi city atmospheric air pollution with PM Particles. Bulletin of the Georgian National Academy of Sciences, vol. 18, no. 3, 2024, pp. 90-96.