Volume 37, Issue 3 (2022)                   GeoRes 2022, 37(3): 313-326 | Back to browse issues page
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1- Department of Remote Sensing and Geographic Information System, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
2- Department of Meteorology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
* Corresponding Author Address: Faculty of Planning and Environmental Sciences, University of Tabriz, Imam Khomeini street, Tabriz, Iran. Postal Code: 5166616471 (alizadehtoba@yahoo.com)
Abstract   (832 Views)
Aims: Due to its location in the Middle East, Iran is affected by its climatic and geological characteristics. Since one of the main sources of airborne particulate matter is desert, issues such as sandstorms, high concentrations of airborne particles, and reduced visibility are some of the major recent climate and meteorological problems in the country, especially in the western border provinces. The purpose of this study is to simulate the size of atmospheric dust particles to reveal the sources that send them to the western region of the country.
Methodology: In this regard, numerical modeling and remote sensing methods were used for severe dust events in June 2016, November 2017, and October 2018. The data used include the weather codes of the synoptic station, the aerosol optical depth product of the moderate resolution imaging spectro radiometer sensor, and the National Centers for Environmental Prediction/NFL data for the implementation of the weather rcscarch forcast-CHEM  model.
Findings: The results showed that the outputs of the weather rcscarch forcast-CHEM numerical model in all three dust events, the regions located in the northwest of Iraq (border between Iraq and Syria), the regions of northern Saudi Arabia, and the eastern regions of Iraq are the main sources. Dust particles were detected in the study area. Dust originating from these areas in a northwest-southeast direction and with a significant spatial and temporal extent affects more than 3 days and the spatial expansion of western regions of Iran, especially Kermanshah.
Conclusion: Based on the results of this study, aerosol optical depth  images of the Modis sensor and simulation of the weather rcscarch forcast-CHEM model have an acceptable performance to identify the characteristics and sources of heavy dust entering the Kermanshah region.
Article number: 2
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References
1. Ackermann I J, Hass H, Memmesheimer M, Ebel A, Binkowski F S, Shankar U (1998). Modal aerosol dynamics model for Europe: Development and first applications. Atmospheric Environment. 32(17):2981-2999. [Link] [DOI:10.1016/S1352-2310(98)00006-5]
2. Alizadeh T, Rezaei Banafsheh M, Goodarzi G, Sheikh Ghaderi S H (2020). "Numerical tracking and simulation of dust storm in Kermanshah". Journal of Desert Management. 9(2):1-14. [Persian] [Link]
3. Asadilotfi R, Aleshekh A A, Bezadi S (2018). A review prediction models of dust phenomena and data collection techniques. Geospatial Engineering Journal. 9(4):51-66. [Persian] [Link]
4. Binkowski F S, Shankar U (1995). The regional particulate matter model: 1. Mode desription and preliminary results. Journal of Geophysical Research: Atmospheres. 100(D12):26191-26209. [Link] [DOI:10.1029/95JD02093]
5. Drury E, Daniel J, Wang J, Robert J, Spurr D, Kelly C (2008). Improved algorithm for MODIS satellite retrievals of aerosol optical depths over western North America. Journal of Geophysical Research: Atmospheres. 113(D16204):1-11. [Link] [DOI:10.1029/2007JD009573]
6. Eltahan M, Shokr M, Sherif A O (2018). Simulation of severe dust events over Egypt using tuned dust schemes in weather research forecast (WRF-Chem). Atmosphere. 9(7):246-270. [Link] [DOI:10.3390/atmos9070246]
7. Goudie A S, Middleton N J (2001). Saharan dust storms: Nature and consequences.Earth-Science Reviews. 65(1-4):179-204. [Link] [DOI:10.1016/S0012-8252(01)00067-8]
8. Grell G A, Peckham S E, Schmitz R, McKeen S A, Frost G, Skamarock W C (2005). Fully coupled "online" chemistry within the WRF model. Atmospheric Environment. 39(37):6957--6975. [Link] [DOI:10.1016/j.atmosenv.2005.04.027]
9. Lashkari H, Kaykhosravi G (2008). Synoptic statistical analysis of dust storms in Khorasan Razavi province (1993-1995). Physical Geography Research Quarterly. 40(65):17-33. [Persian] [Link]
10. Liu Z, Liu Q, Lin H C, Schwarts C S, Lee Y H (2011). Assimilating MODIS aerosol optical depth using WRF-Chem and GIS: Application to a Chinese dust storm. In: 21th WRF Users Workshop.20-24 June 2011. Boulder: United State of America. [Link]
11. Miri M , Shamsipour A A, Azizi Q, Safar Rad T (2011). Statistical analysis-synoptic of dust phenomenon in the western half of Iran. Journal of Environmental Studies. 38(63):123-134. [Persian] [Link]
12. Miri M, Pilevaran R, Zand M, Norouzi A A (2021). Atmospheric dust detection using WRF-chem model and remote sensing data (Case study: West and southwest of Iran). Journal of Soil and Water Resources Protection. 10(4):81-94. [Persian] [Link]
13. Miri M, Azizi Q, Shamsipour A A (2014). Identity summer and winter patterns on arrival dust to the West Iran. Geography and Environmental Planning. 25(4):203-220. [Persian] [Link]
14. Munchak L, Levy R, Mattoo S, Remer L (2014). MODIS atmosphere team webinar series 5: Overview of the 3 km aerosol product in collection 6 [Internet]. Climate and Radiation Laboratory: NASA Goddard Space Flight Center. Available from:https://aerocenter.gsfc.nasa.gov/ext/c6/materials/MODIS_Atmos_Webinar-2_Munchak_3kmaerosol.pdf [Link]
15. Myhre G, Stordal F, Johnsrud M, Diner D J, Geogdzhayev I V, Haywood J M, et al (2005). Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000. Atmospheric Chemistry and Physics. 5(6):1697-1719. [Link] [DOI:10.5194/acp-5-1697-2005]
16. Nikfal A H (2014). Simulation of PM10 particle concentration by WRF-chem paired model in Iran. In: Iranian Seismological Center, 16th Iranian Geophysical Conference. Tehran, 13 May 2014. Tehran: Iran. [Persian] Available From: https://www.sid.ir/Fa/Seminar/ViewPaper.aspx?ID=41717 [Link]
17. Nikfal A, Ranjbar Saadat Abadi A, Karami S, Sehat Kashani S (2017). Capabilities of WRF-chem numerical model in estimating dust concentration (Case study of Tehran dust storm). Environmental Sciences. 15(1):115-126. [Persian] [LinkVVVV]
18. Qi Y, Ge J, Huang J (2013). Spatial and temporal distribution of MODIS and MISR aerosol optical depth over northern China and comparison with AERONET. Chinese Science Bulletin. 58(20):2497-2506. [Link] [DOI:10.1007/s11434-013-5678-5]
19. Rezazadeh M, Irannejad P, Shao Y (2011). Climatology of the Middle East dust events. Aeolian Research.10:103-109. [Link] [DOI:10.1016/j.aeolia.2013.04.001]
20. Rezazadeh M, Irannejad P, Shao Y (2013). Dust emission simulation with the WRF-Chem model using new surface data in the Middle East region. Journal of Earth and Space Physics. 39(1):191-212. [Persian] [Link]
21. Shahid M Z, Chishtie F, Bilal M, Shahid I (2021). WRF-chem simulation for modeling seasonal variations and distributions of aerosol pollutants over the Middle East. Remote Sens. 13(11):2-17. [Link] [DOI:10.3390/rs13112112]
22. Stockwell W R, Kirchner F, Kuhn M, Seefeld S (1997). A new mechanism for regional atmospheric chemistry modeling. Geophysical Research: Atmospheres. 102(D22):25847-25879 [Link] [DOI:10.1029/97JD00849]
23. Toulabi Nejad M, Hejazizadeh Z, Zarei Chaghablaki Z, Amrayi B (2015). Dust storm monitoring in the western half of Iran: A case study of dust storm June 16-19, 2015. Journal of Spatial Analysis of Environmental Hazards. 5(4):107-124. [Persian] [Link]
24. Wang J, Sundar C A (2003). Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophysical Research Letters. 30(21):1-4. [Link] [DOI:10.1029/2003GL018174]