ANTHROPOGENIC FACTORS OF ATMOSPHERIC AIR POLLUTION IN TASHKENT CITY AND ANALYSIS OF POLLUTANTS (A CASE STUDY OF NITROGEN DIOXIDE)

Authors

  • Shakhboz Bozorboyevich Zaripov

    National University of Uzbekistan image/svg+xml

  • Nilufar Ravshanovna Ismatova

    Specialized school named after Mirzo Ulugbek

Keywords: urbanization, air pollution, Geographic Information System (GIS), Inverse Distance Weighting (IDW), nitrogen dioxide (NO₂), air quality monitoring

Abstract

The study investigates the spatial and temporal variations in atmospheric nitrogen dioxide (NO₂) concentrations originating from anthropogenic sources in Tashkent during the summer seasons of 2013-2023. The primary objective of the research was to identify long-term trends in NO₂ concentrations, produce spatial distribution maps, and assess the principal anthropogenic sources contributing to atmospheric pollution. The research was based on data obtained from air quality monitoring stations operated by the Hydrometeorological Service of the Republic of Uzbekistan. Geographic Information System (GIS) technologies were employed to analyze the spatial distribution of nitrogen dioxide, while the Inverse Distance Weighting (IDW) interpolation method implemented in ArcGIS Pro was used to generate spatial distribution maps. Seasonal distribution maps for the summer period were produced at five-year intervals throughout the study period. In addition, descriptive statistical methods and graphical analyses were applied to evaluate temporal changes in pollutant concentrations. The results indicate that the highest nitrogen dioxide (NO₂) concentrations during the summer season were recorded in 2023.

References

1. Alkabbani H., Ramadan A., Zhu Q., & Elkamel A. An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Atmosphere, 13(7), 2022. 1144. https://doi.org/10.3390/atmos13071144

2. Azizov Q., & Beketov, A. Traffic flow characteristics and their impact on air pollution in urban streets: A case study of Tashkent. Engineer International Scientific Journal, 2(4). 2024. ISSN 3030-3893

3. Bell L. The use of ambient air quality modeling to estimate individual and population exposure for human health research: A case study of ozone in the Northern Georgia Region of the United States. Environment International, 32, 586–593. 2006.

4. Bhat T., Jiawen G., & Farzaneh H. (2021). Air Pollution Health Risk Assessment (AP-HRA), principles. International Journal of Environmental Research and Public Health, 18(4), 1935. https://doi.org/10.3390/ijerph18041935

5. Bollen J., & Brink C. Air pollution policy in Europe: Quantifying the interaction with greenhouse gases and climate change policies. Energy Economics, 46, 202–215. 2014.

6. Cheng F., Zheng J., Wei C., Mu Q., Zheng B., Wang B., Gao M., Zhang Q., & He B. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Science Advances, 2. 2016.

7. Chen D., Xu T., Li, Y., Zhou Y., Lang J., Liu X., & Shi H. A hybrid approach to forecast air quality during high-PM concentration pollution period. Aerosol and Air Quality Research, 15, 1325–1337. 2015.

8. Christoph S., David M., Keywan R., Jan M., Elmar, K., Detlef, V., Jessica, J., Carmenza, R., Edgar, H., Massimo, T., Sevastianos, M., Oliver, L., Joyashree, R., Yacob M., Navroz D., Johannes B., Diana V., & Ottmar E. Integrating global climate change mitigation goals with other sustainability objectives: A synthesis. Annual Review of Environment and Resources, 40, 363–394. 2015.

9. Diao Y., Zuo Q., & Ma J. Urbanization, water use level and their coupled coordination in the Yellow River Basin. Journal of Beijing Normal University (Natural Science). 2020.

10. Dobesch H., Dumolard P., & Dyras I. Spatial interpolation for climate data: The use of GIS in climatology and meteorology (2nd ed.). 2013. Wiley. https://books.google.iq/books?isbn=1118614992

11. Derwent R., Jenkin M., Saunders S., Pilling M., Simmonds P., Passant, N., Dollard, G., Dumitrean, P., & Kent, A. (2003). Photochemical ozone formation in northwest Europe and its control. Atmospheric Environment, 37, 1983–1991.

12. Duggan S. Carbon monoxide exposure inside UK road vehicles: A pilot study. Environment International. https://doi.org/10.1016/j.envint.2024.109070

13. Eslami A., & Ghasemi S.M. (2018). Determination of the best interpolation method in estimating the concentration of environmental air pollutants in Tehran city in 2015. Journal of Air Pollution and Health, 3(4), 187–198. https://doi.org/10.18502/japh.v3i4.402

14. Fajar W., Nusrat E., Rabiya N., Waqas A., Muhammad, K., Laila S., Aqil T., Hira A., & Qamar Z. Geo-spatial distribution of air pollutants in urban area and its potential health risk analysis solutions. Urban Climate, 61. https://doi.org/10.1016/j.uclim.2025.102380

15. Gao H., Sun L., Wu H., Chen J., Cheng Y., & Zhang Y. The predictive value of neutrophil-lymphocyte ratio at presentation for delayed neurological sequelae in carbon monoxide poisoning. Inhalation Toxicology, 33(4), 121–127. 2021.https://doi.org/10.1080/08958378.2021.1887410

16. Han S., Bian H., Feng Y., Liu A., Li X., Zeng F., & Zhang X. Analysis of the relationship between O₃, NO and NO₂ in Tianjin, China. Aerosol and Air Quality Research, 11, 128–139. 2011.

17. Jumaah H., Mohammed H., Bahareh K., Mojaddadi, R., & Sarah J. (2019). Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomatics, Natural Hazards and Risk, 10(1), 2185–2199. https://doi.org/10.1080/19475705.2019.1683084

18. Jung M., Yuexiong D., Vincent G., Changqing L., & Zhiwei W. Spatiotemporal prediction of PM₂.₅ concentrations at different time granularities using IDW-BLSTM. IEEE Access Journal, 7. 2019.

19. Kuttler W., & Strassburger A. Air quality measurements in urban green areas: A case study. Atmospheric Environment, 33, 4101–4108. 1999.

20. Khan J., Kakosimos K., Raaschou-Nielsen, O., Brandt J., Jensen S. S., Ellermann T., et al. Development and performance evaluation of new AirGIS–a GIS-based air pollution and human exposure modelling system. Atmospheric Environment, 198, 102–121. 2019.

21. Li R., Cui L., Li J., Zhao A., Fu H., Wu Y., Zhang L., Kong L., & Chen J. Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014–2016. Atmospheric Environment, 161, 235–246. 2017.

22. Li R.., Wang Z., Cui L., Fu H., Zhang Z., Kong L., Chen W., & Ch. J. Air pollution characteristics in China during 2015–2016: Spatiotemporal variations and key meteorological factors. Science of the Total Environment. 2018.

23. Li X., & Xu H. The energy-conservation and emission-reduction paths of industrial sectors: Evidence from China’s 35 industrial sectors. Energy Economics, 86, 104628. 2020.