بررسی رخدادگردو غبار درکشور با نسبت شاخص غلظت PM2.5 به PM10
محورهای موضوعی : منابع طبیعی و مخاطرات محیطی
مهسا تمجیدی
1
,
مجید عباسپور طهرانی
2
*
,
یوسف رشیدی
3
,
علیرضا میرزاحسینی
4
1 - سازمان حفاظت محیط زیست
2 - شریف
3 - هیئت علمی دانشگاه بهشتی تهران
4 - دانشکده منابع طبیعی و محیط زیست دانشگاه آزاد اسلامی،واحد علوم و تحقیقات، تهران، ایران
کلید واژه: گرد و غبار , غلظت PM10و PM 2.5 , غلظت آلودگی,
چکیده مقاله :
مقدمه: گرد و غبار بهعنوان یکی از بحرانهای مهم زیستمحیطی در خاورمیانه، تأثیرات گستردهای بر کیفیت زندگی، سالمت انسان، حاصلخیزی خاک، دید، اقتصاد و جنبههای اجتماعی دارد. این پدیده در مناطق خشک و بیابانی، بهویژه در ایران که بخش بزرگی از آن در مناطق خشک و نیمهخشک قرار دارد، رایج است. با توجه به اهمیت این موضوع، شناسایی منابع و عوامل تشدیدکننده آن ضروری است. گرد و غبار شامل ذرات معلق با قطر کمتر از 10 میکرومتر (PM10 (است. این مطالعه به بررسی نسبت غلظت ذرات PM10/5.PM2 بهعنوان شاخصی برای تعیین زمان وقوع گرد و غبار در سامانه پایش محیطزیست کشور میپردازد. مواد و روشها: در این مطالعه یک روش ساده و کاربردی برای تعیین روزهای گرد و غبار ارائه شده است. بر اساس دادههای دریافتی وقوع رخداد گرد و غبار از مراجع معتبر، نسبت PM10/5.PM2 در روزهایی مذکور محاسبه و بر اساس نتایج محاسبات صورت گرفته، شاخص عددی احتمال وقوع گرد و غبار ارائه شده است . بدین منظور ابتدا یک رویداد گرد و غبار در تاریخ مشخص) 25 تیر 1398 ) در استان خوزستان ) شهر اهواز( مورد بررسی قرار گرفت و بر این اساس نسبت دادههای PM10/5.PM2 در استانهای خوزستان، کرمان، یزد، بوشهر، قم، زنجان، مرکزی، لرستان، کرمانشاه، کردستان، گیالن و تهران محاسبه شد. واژههای کلیدی: گردو غبار ، غلظت ، PM 2.5 وPM10 غلظت آلودگی نتایج و بحث: براساس وقوع طوفانهای گرد و غبار در استانهای خوزستان، کرمان، یزد، بوشهر، قم، زنجان، مرکزی، لرستان، کرمانشاه ، کردستان ، گیالن و تهران در بازه زمانی سال 1398 و محاسبه میانگین ساعتی نسبت PM10/5.PM2 عدد شاخص محاسبه شد. نتایج نشان میدهد، زمانی که نسبت PM10/5.PM2 کمتر 1.0 باشد، رخداد گرد و غبار وجود دارد. بر همین اساس روزهای وقوع رویداد به سه دسته روزهای وقوع گرد و غبار 1.0=PM10/5.PM2 ، روزهای سبک گرد و غبار 5.0=PM10/5.PM2 و روزهای متوسط گرد و غبار .شد تقسیم PM2.5/PM10= 0.1-0.5 نتیجهگیری: نتایج نشان داد در بین استانهای بررسی شده، میانگین غلظت PM10 و 5.PM2 در ایستگاه اهواز به ترتیب عدد 355 میکروگرم برمترمکعب و 15.27 میکروگرم برمترمکعب میباشد و هر دو غلظت در محدوده خطرناک میباشند. همچنین با توجه به نتایج بدست آمده، نسبت PM10/5.PM2 و ارتباط موضوعی آن با شاخص کیفیت هوا مورد تایید قرار گرفت و نتایج حاصل میتواند توسط سیاست گزاران برای طراحی و اجرای اقدامات کنترلی موثر، در هنگام وقوع رخداد گرد و غبار مورد استفاده قرار گیرد. نتایج این تحقیق نشان میدهد نسبت 5.PM2به PM10 میتواند به عنوان شاخصی در خصوص زمان وقوع رخداد گرد و غبار مورد توجه قرار گیرد.
|
Introduction: Dust storms, as one of the major environmental crises in the Middle East, have widespread impacts on the quality of life, human health, soil fertility, visibility, the economy, and social aspects. This phenomenon is common in arid and desert regions, especially in Iran, where a significant portion of the country is located in dry and semi-arid areas. Given the importance of this issue, identifying the sources and aggravating factors is essential. Dust storms consist of particulate matter with a diameter of less than 10 micrometers (PM10). This study examines the PM2.5/PM10 concentration ratio as an indicator for determining the occurrence of dust storms within the country's environmental monitoring system. Materials and Methods: In this study, a simple and practical method for determining dust days is presented. Based on the received data of dust occurrence from reliable authorities, the ratio of PM2.5/PM10 was calculated on the mentioned days and based on the results of the calculations, a numerical index of the probability of dust occurrence was presented. For this purpose, first a dust event on a specific date (July 25, 2018) was investigated in Khuzestan province (Ahvaz city) and based on this, the ratio of PM2.5/PM10 data in the provinces of Khuzestan, Kerman, Yazd, Bushehr, Qom, Zanjan, Markazi, Lorestan, Kermanshah, Kurdistan, Gilan and Tehran were calculated. |
|
|
Results and Discussion: Based on the occurrence of dust storms in the provinces of Khuzestan, Kerman, Yazd, Bushehr, Qom, Zanjan, Markazi, Lorestan, Kermanshah, Kurdistan, Gilan and Tehran in the period of 2018 and calculating the hourly average ratio of PM2.5/PM10 index number Calculated. The results show that when the PM2.5/PM10 ratio is less than 0.1, there is dust occurrence. Based on this, the days of the event are divided into three categories: PM2.5/PM10 =0.1 dust days, PM2.5/PM10 =0.5 light dust days, and PM2.5/PM10 =0.1-0.5 medium dust days. |
|
|
Conclusion: The results showed that among the investigated provinces, the average concentration of PM10 and PM2.5 in Ahvaz station is 355 micrograms per cubic meter and 27.15 micrograms per cubic meter, respectively, and both concentrations are in the dangerous range. Also, according to the obtained results, the PM2.5/PM10 ratio and its relation with the air quality index were confirmed, and the results can be used by policymakers to design and implement effective control measures during dust incidents. be used The results of this research show that the ratio of PM2.5 to PM10 can be considered as an indicator regarding the time of dust occurrence. |
Reference: 1. Ahrari A. Atmospheric correction of satellitimages. 2019; https://girs.ir/remote-sensing-atmospheric-corrections. ]In Persian[Asrari A and Padayar M. Investigating changes in the concentration of suspended particles in Mashhad using meteorological data in the years 2010-2015. Journal of Environmental Health Research, 2018 (1): 86-91. https://www.sid.ir/paper/261394/fa [InPersian]
2. Barmpadimos, I., Keller, J., Oderbolz, D., Hueglin, C., & Prevot, A. S. H. (2012). One decade of parallel fine (PM2.5) and coarse (PM10-PM2.5) particulate matter measurements in Europe: Trends and variability. Atmospheric Chemistry and Physics, 12, 3189–320. www.atmos-chem-phys.net/12/3189/2012/3.doi:10.5194/acp-12-3189-2012
. 3. Baghbanan P. Ghavidel Y. Farajzadeh M. Temporal long-term variations in the occurrence of dust storm days in Iran. Meteorol Atmos Phys. 2020; 132: 885–898. https://Doi.org/10.1007/s00703- 020-00728-3. [IN Persian]
4. Al-Hemoud A. Al-Dousari A. Misak R. Al-Sudairawi M. Naseeb A. Al-Dashti H. Al-Dousari N. Economic Impact and Risk Assessment of Sand and Dust Storms (SDS) on the Oil and Gas Industry in Kuwait. Sustainability. 2019; 11(1):1-19. https://Doi.org/10.3390/su1101020
. 5. Bahiraei, H., Ayazi, M. H., Rajaei, M. and Ahmadi, H. 2011. Synoptic Statistical Analysis of Dust Occurrence in Ilam Province. Journal of New Attitudes in Human Geography. 4, 47-67 https://www.sid.ir/paper/177129/fa [InPersian]
6. Broomandi P, Crape B, Jahanbakhshi A, Janatian N, Nikfal A, Tamjidi M, Kim JR, Middleton N, Karaca F.Assessment of the association between dust storms and COVID-19 infection rate in southwest Iran. 2022 May;29(24):36392-36411. doi: 10.1007/s11356-021-18195-7. Epub 2022 Jan 21
. 7. Crosbie, E., Sorooshian, A., Monfared, N.A., Shingler, T. and Esmaili, O., 2014. A multi-year aerosol characterization for the greater tehran area using satellite, surface, and modeling data. Atmosphere. 5(2), 178-197. Atmosphere 2014, 5(2), 178-197; https://doi.org/10.3390/atmos5020178
8. Cao H. Amiraslani F. Liu J. Zhou N. Identification of dust storm source areas in West Asia using multiple environmental datasets. Sci Total Environ. 2015; 502: 224-235. Doi.org/10.1016/j.scitotenv.2014.09.025
. 9. Cao H. Fu C. Zhang W. Liu J. Characterizing Sandand Dust Storms (SDS) Intensity in China Based on Meteorological Data. Sustainability. 2018; 10(7): 2372. https://Doi.org/10.3390/su1007237
10. Gang, X., Limin, J., Boen, Z., & Suli, Z. (2015). Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China. Aerosol and Air Quality Research, 17: 741–751, 2017. 1680-8584 print / 2071-1409 online . doi: 10.4209/aaqr.2016.09.0406
11. Li, C., Tsay, S.-C., Fu, J. S., Dickerson, R. R., Ji, Q., Bell, S. W., et al. (2010). Anthropogenicair pollution observed near dust source regions in northwestern China dur-ing springtime 2008. Journal of Geophysical Research: Atmospheres, 115, D00K22. 13 October 2010. https://Doi.org/10.1029/2009JD013659
. 12.
Nel A. Air pollution-related illness: effects of particles. June 2005. Science308(5723): 804-6. DOI: 10.1126/science.1108752. 13. Nowroozi A. Shoaei Z. Dust production centers in the western and southern half of Iran: satellite data and field information. J Agric Sci Technol. 2019; 2(2): 29-35. Doi: 10.22092/AISTJ.2020.342192.1040.[IN Persia]
. 14. Namdari S. Hajibaglo A. Abazari GH. Analysis of changes in Iran's internal dust centers in the last twenty years. J geogr Plan. 2022; 25(78): 345-361. Doi: 10.22034/GP.2021.42751.2739.[IN Persian]
15. Pan, X., Uno, I., Hara, Y., Kuribayashi, M., Kobayashi, H., Sugimoto, N., et al. (2015). Observation of the simultaneous transport of Asian mineral dust aerosols with anthropogenic pollutants using a POPC during a long-lasting dust event in late spring 2014. Geophysical Research Letters, 42, 1593–1598. 04 February 2015. https://Doi.org/10.1002/2014GL062491
16. Onishi, K., Kurosaki, S., Otani, S., Yoshida, A., Sugimoto, N., & Kurozawa, Y. (2012). Atmospheric transport route determines components of Asian dust and health effects in Japan, https://Doi.org/10.1016/j.atmosenv.2011.12.018
17. Sugimoto, N., Hara, Y., Shimizu, A., Nishizawa, T., Matsui, I., & Nishikawa, M. (2013).Analysis of dust events in 2008 and 2009 using the lidar network, surface obser-vations and the CFORS model. Asia-Pacific Journal of Atmospheric Sciences, 49(1), 27–39. https://Doi.org/ 10.1007/s13143-013-0004-3
18. Nobuo Sugimoto, Atsushi Shimizu, Ichiro Matsui, Masataka Nishikawa. (2015). A method for estimating the fraction of mineral dust in particulate matter using PM2.5-to-PM10 ratios. http://Doi.org/10.1016/j.partic.2015.09.005
19. Ishizuka, M., Mikami, M., Leys, J., Yamada, Y., Heidenreich, S., Shao, Y., et al. (2008).Effects of soil moisture and dried rain droplet crust on saltation and dust emis-sion. Journal of Geophysical Research: Atmospheres, 113, D24212. doi:10.1029/2008JD009955, 2008
. 20. Indoitu R. Orlovsky L. Orlovsky N. Dust storms in Central Asia: spatial and tempora variations. J Arid Environ. 2012; 85: 62-70. https://Doi.org/10.1016/j.jaridenv.2012.03.018
. 21. Furman H.K.H. Dust storms in the Middle East: sources of origin and their temporal characteristics. Indoor Built Environ. 2003; 12(6): 419-426. https://Doi.org/10.1177/1420326X03037110
. 22. Fallah Zozli M. A. Vafainejad M. Khairkhah Z. F. Ahmadi D. Finding the origin of dust in southern Iran and its synoptic analysis using remote sensing and geographic information system. RS GIS Natur Res. 2013; 5(4): 61-78. http://girs.iaubushehr.ac.ir. [IN Persian]
. 23. Faridi S. Rahmani S. Hashemi N. Qobadian S. Zokaei M. Economic effects of dust storms. J health. 2019; 11(5): 699-713. Doi. 10.52547/j.health.11.5.699. [IN Persia]
. 24. Kobayashi, H., Hayashi, M., Shiraishi, K., Nakura, Y., Enomoto, T., Miura, K., et al.(2014). Development of a polarization optical particle counter capable of aerosoltype classification. Atmospheric Environment, 97, 486–492. 1 Nov 2014 .doi: 10.1016/j.atmosenv.2014.05.006
. 25. Khojasteh N. Morad E. Comparison of interpolation methods for wind erosion mapping using the USEPA model. Soil. Water. Sci. Agric. Sci and Tech and Nat Resour. 2021; 24(4): 93-110. Doi. 10.47176/jwss.24.4.2251. [IN Persian]
. 26. Jalali Farahani, V. and Arhami, M., 2019. Contribution of Iraqi and Syrian dust storms on particulate matter concentra. December 2019. Atmospheric Environment 222(24):117163. Doi: 10.1016/j.atmosenv.2019.117163
. 27. Sugimoto, N., Nishizawa, T., Shimizu, A., Matsui, I., & Kobayashi, H. (2015). Detectionof internally mixed Asian dust with air pollution aerosols using a polarizationoptical particle counter and a polarization-sensitive two-wavelength lidar. Journal of Quantitative Spectroscopy & Radiative Transfer, 150, 107–113.Sun, C.-H., Lin, Y.-C., & Wang, C.-S. (2003). Doi: 10.1016/j.jqsrt.2014.08.003. 28. Rashki, A., Middleton, N.J. and Goudie, A.S., 2021. Dust storms in Iran – Distribution, causes, frequencies and impacts. Aeol. Res. 48, 100655, January 2021. Doi: 10.1016/j.aeolia.2020.100655
. 29. Reyadh A. Venkataraman L. Monitoring Dust Storms in Iraq Using Satellite Data. Dep .Eng Sys Environ. 2019; 19(17): 3687. Doi: 10.3390/s19173687
. 30. Rayegani B. Barati S. Khoshnava A. Dust and Sand Source Identification Using Remotely Sensed Data: a comprehensive Approach. Sci Total Environ. 2019; 72(1): 83-105.dhttps://Doi.org/10.22059/jrwm.2019.251015.1223. [IN Persian]
. 31. Turkashund M and Kiani M. Analysis of the state of air pollution caused by the climatic effects of fine dust and dust storms in the southern regions of Hamadan province. Environmental Science and Technology Quarterly. 2017. (4) 15-33. https://www.sid.ir/paper/87471/fa. [InPersian]
32. Wang, Y-C. And Lin, Y-K. 2015. Mortality associated with particulate concentration and Asian dust storms in Metropolitan Taipei. Atmospheric Environment, 117:32-40. September 2015.Doi: 10.1016/j.atmosenv.2015.06.055
. 33. Zolfaghari H. Abedzadeh H. Analysis of the origin of dust in Bushehr and Khuzestan using satellite images. Forest. Q. 2018; 78: 48-51. magiran.com/p514858. [IN Persian]
.