Investigating the Ratio of Pm2.5 to Pm10 Particle Concentration in Tehran City and Compiling an Index for the Occurrence of Dust

Document Type : Article

Authors

1 Environmental Research Sciences Institute, Shahid Beheshti University, Iran.

2 Environmental Research Sciences Institute, Shahid Beheshti University, Iran

Abstract

Suspended particles are known as one of the most important and common pollutants worldwide. Since dust storms are related to suspended particles, the current research examines the ratio of average concentrations of PM2.5 and PM10 suspended particles and determines an index for dust occurrence. In this study, PM2.5 and PM10 particle concentration data at 3 monitoring stations of the Tehran Municipality Air Quality Control Organization (Tarbiat Modares, Piroozi and Sharif stations) in 1396-1402 (2018-2023) were used, and by extracting AOD data from the Giovanni site and determining dust days, and finally comparing it with data from the Barcelona Regional Dust Center, the accuracy of dust days was ensured. The results show that the average concentrations of PM2.5 and PM10 and the PM2.5/PM10 ratio on dusty days are higher than this value on dust-free days. The daily changes in the average concentration of particulate matter at the selected stations show that in most cases, the changes in the concentration of particulate matter are similar and both increase and decrease together, but the rate of change is not the same. Depending on the origin of particulate matter production, their ratio can be used to determine the dust index. On most days, the average PM10 concentration of the station is higher than the dust level obtained from the data of the Barcelona Regional Dust Center, because the air quality measurement stations measure the amount of particulate matter emitted from natural and artificial sources together, but the data of the Barcelona Regional Dust Center shows the amount of dust. From the PM2.5/PM10 ratio, it can be concluded that dust is present if this ratio is less than 0.4. According to the results of the research and the days with dust in 1396 (2018) in the month of Ordibehesht, 1397 (2019) in the months of Ordibehesht and Mehr, 1398 (2020) in the month of Mehr, 1399 (2021) in the months of Esfand and Farvardin, 1400 (2022) in the months of Ordibehesht, Mordad and Azar, and 1401 (2023) in the month of Tir; therefore, it can be concluded that dust often occurs in spring and autumn, then in summer, and finally in winter.

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1. Haqbayan, and Tashayo, B., 2020. Integrating ground-based air quality monitoring stations with mobile sensor units to improve the accuracy of PM2.5 concentration modeling. J. Sephr Geographical Information Research Quarterly,29 (116), pp.45-58 [In Persian] https://doi.org/10.22131/sepehr.2021.242859
3.Anselin, L. and Syabri, I. & Kho, Y., 2005. GEODA: An introduction to spatial data analysis.
Geographical Analysis,38(1), pp.5–22 https://doi.org/10.1111/j.0016-7363.2005.00671.x
4. Goudie, A. & Middleton, N. 2001. Saharan dust storms: nature and consequences.
Earth-Science Reviews,56(1–4), pp.179–204.D‌O‌I:10.1016/s0012-8252(01)00067-8.
5. world Urbanization Prospects 2018. United Nations. New York. 2018
11. Fan, H. and Zhao, C. and Yang, Y. & Yang, X., 2021. Spatio-Temporal variations of the PM2.5/PM10 ratios and its application to air pollution type classification in China. Frontiers In Environmental Science, 9
12. Munir, Said., 2016. Analysing temporal trends in the ratios of PM5/PM10 in the UK.
Aerosol And Air Quality Research,17(1), pp.34-48.D‌O‌I:10.4209/aaqr.2016.02.0081
13. Wang, H. and Zhuang, Y. & Wang, Y. & Sun, Y. & Yuan, H. & Zhuang, G. & Hao, Z., 2008. Long-term monitoring and source apportionment of PM2.5/PM10 in Beijing, China. Environmental Sciences, 20(11), pp.1323–1327.D‌O‌I:10.1016/s1001-0742(08)62228-7
14. Davtalab, M. and Byčenkienė, S. & Bimbaitė, V. 2023. Long-term spatial and temporal evaluation of the PM2.5 and PM10 mass concentrations in Lithuania. Atmospheric Pollution Research, 14(12), pp. 101951. https://doi.org/10.1016/j.apr.2023.101951
15. Baggam, S. & Rao, S. & Upadhya, A. & Kulkarni, P. & Sreekanth, V. 2021. PM2.5/PM10 ratio characteristics over urban sites of India. J.Advances In Space Research. 67(10), pp. 3134-3146.https://doi.org/10.1016/j.asr.2021.02.008
Zhang, X. and Sharratt, B. & Liu, L. & Wang, Z. & Pan, X. & Lei, J. & Wu, S. & Huang, S. & Guo, Y. & Li, J. & Tang, X. & Yang, T. & Tian, Y. & Chen, X. & Hao, J. & Zheng, H. & Yang, Y. & Lyu, Y. 2018. East Asian dust storm in May 2017: observations, modelling, and its influence on the Asia-Pacific region. Atmospheric Chemistry And Physics, 18(11), pp. 8353–8371. https://doi.org/10.5194/acp-18-8353-2018.
16. Rashki, A. and Middleton, N. & Goudie, A. 2021. Dust storms in Iran – Distribution, causes, frequencies and impacts.
Aeolian Research, 48, pp. 100655. D‌O‌I:10.1016/j.aeolia.2020.100655