پژوهش های جغرافیای طبیعی، جلد ۵۲، شماره ۱، صفحات ۵۱-۶۴

عنوان فارسی ارزیابی روند فصلی شاخص هواویز (AI) ایران مبتنی بر داده ‌های ماهواره ‌ای Nimbus ۷، Earth Probe، و Aura
چکیده فارسی مقاله هدف از این پژوهش ارزیابی روند شاخص هواویز (AI) فصلی در ایران است. در این راستا، داده‌های سنجنده TOMS دو ماهواره Nimbus 7 و Earth Probe و سنجنده OMI ماهواره Aura اخذ شد و از آزمون ناپارامتریک من- کندال (MK) برای شناسایی روند شاخص هواویز استفاده شد. نتایج نشان داد داده‌های سنجنده TOMS ماهواره EP برای مطالعه روند مناسب نیست، زیرا از سال 2001 داده‌های این سنجنده کالیبراسیون نمی‌شود. بیشینه و کمینه روند شاخص هواویز ایران به‌ترتیب برای سنجنده OMI و TOMS ماهواره Nimbus 7 محاسبه شد. در فصل بهار به‌دلیل فعال‌شدن چشمه‌های گرد و غبار از مناطقی با روند کاهشی کاسته شد و بر مناطقی با روند افزایشی افزوده شد. بیشینه روند افزایشی معنی‌دار و همچنین بیشینه مقدار متوسط شدت روند شاخص هواویز (AI) براساس سنجنده OMI در فصل پاییز محاسبه شد. روند افزایشی شاخص هواویز (AI) در ایران به‌دلیل شرایط محیطی (خشک‌سالی و تغییرات کاربری اراضی) و آب و هوایی (باد شمال تابستانه، الگوهای دینامیکی و حرارتی غرب آسیا، و کم‌فشار حرارتی سِند) است. مقایسه داده‌های ماهواره‌ای با داده‌های ایستگاه‌های همدید گرد و غبار در پهنه‌های مختلف آب و هوایی نشان از تطابق داده‌های ماهواره‌ای و زمینی دارد.
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عنوان انگلیسی Evaluation Seasonal trend of Iran Aerosol Index (AI) based on Nimbus 7, Earth Probe and Aura satellite data
چکیده انگلیسی مقاله Evaluation Seasonal trend of Iran Aerosol Index (AI) based on Nimbus 7, Earth Probe and Aura satellite data Introduction Aerosols are solid or liquid particles in the air with a typical radius of 0.001 to 100 μm, which have a significant and harmful effect on human health. Aerosols come from both natural and human sources, and in recent years, human activities associated with urbanization and industrialization have led to a steady increase in the amount of these particles in the airborne state. The study of the precise variability of the AI index in the long run can provide useful information on aggregates, their origin, spatial temporal variation, climate induction and its feedback in the climate system. This study the purpose of the seasonal evaluation of the Aerosol Absorption Index (AAI) was based on TOMS and OMI sensor data in Iran. Materials and methods In this study, TOMS data from two Nimbus 7 satellites (1992-1979) and Earth Probe (1996-2005) and OMI (2015-2005) satellite EOS Aura The non-parametric Mann-Kendall test was used to identify the Aerosol Index trend. Results and discussion Significant reduction in the trend The TOMS Satellite (EP) Earth Probe's Aerosol Index (AI) is far from waiting for dusty days and Aura satellite data. TOMS sensor data is not recommended for decomposition of the EP, because since 2001, due to the lack of proper calibration of this sensor, data from this sensor and the satellite provide irrational figures;The results have shown that TOMS sensor data is not suitable for the study of the EP, since since 2001, the data of this meter is not calibrated. The maximum incremental increase of the Aerosol index (AI) for OMI and the maximum decreasing trend of the Aerosol index (AI) for the winter (TOMS satellite satellite EP) was calculated in autumn (TOMS sensor of two Nimbus7 and EP satellites). In spring, the soil moisture content decreased and the activation of dust springs decreased relative to the winter season from areas with decreasing trend and increased areas with increasing trend. In summer, areas with an increasing trend based on Nimbus7 satellite (100%) and Aura (96.74%) of the total pixels are covered. Maximum incremental rate and also the maximum average value of the Aerosol index (AI) trend are obtained based on the OMI Satellite Aura sensor in the fall season. The increase in the Aerosol Index (AI) in Iran is due to environmental and climatic conditions. The summer Shamal wind, the dynamic and thermal patterns of West Asia, and the Indus Low Pressure, have the greatest role in increasing the hygiene of Iran. Conclusion The maximum trend in the general trend of the AI indicator in Iran in winter is the TOMS satellite Earth Probe satellite in the northeast and central parts, which, as said, is due to the lack of calibration of the sensor and the satellite. The maximum incremental trend of the Iranian Aerosol Index (AI) for the OMI and the maximum decrease of the Aerosol Index (AI) was calculated for the fall (Numbus7 Satellite TOMS Sensor). The average trend of Iran's AI index for winter is not significant for any of the two sensors and three satellites. In the spring, the intensity and percentage increase was increased relative to the winter season. The growing trend in most parts of Iran is associated with dusty events originating from dry and desert lands of southwestern Iran, especially in Iraq. In the summer, Nimbus7 and Aura satellite data showed more than 95% of the country's total traffic. Zones with increasing trend of TOMS sensor Nimbus7 satellites are seen in all metropolis of Iran with 500,000 to 1 million and more than one million people, and no negative trend was calculated in any cities. In the autumn, we see the maximum percentage and the intensity of the significant increase of the Aerosol Index (AI based on the OMI satellite satellite Aura in the country. This trend can be correlated with decreasing precipitation and the inhibition of the following particles in the airborne atmosphere due to reduced moisture content. The increase in the trend of Huawei index in Iran is due to environmental conditions such as land use, soil moisture, drought and ... The role of HomoSperm regional circulation systems such as Caspian Sea–Hindu Kush Index (CasHKI), negative phase of Pacific Decadal Oscillation (PDO)) with fluctuations, Shamal winds, short-term phenomena such as frontal systems, low-level jets (LLJs), and low pressure of the document on the transit of these particles are significant. Keywords Absorption Aerosol Index (AAI), TOMS Sensor, OMI Sensor, Mann-Kendal Test (MK), Iran. Wurzler, S.; Reisin, T. G. and Levin, Z. (2000). Modification of mineral dust particles by cloud processing and subsequent effects on drop size distributions. Journal of Geophysical Research: Atmospheres, 105(D4): 4501-4512. Yu, Y.; Notaro, M.; Kalashnikova, O. V. and Garay, M. J. (2016). Climatology of summer Shamal wind in the Middle East. Journal of Geophysical Research: Atmospheres, 121(1): 289-305. Ziemke, J.R.; Oman, L.D.; Strode, S.A.; Douglass, A.R.; Olsen, M.A.; McPeters, R.D.; Bhartia, P.K.; Froidevaux, L.; Labow, G.J.; Witte, J.C. and Thompson, A.M. (2019). Trends in global tropospheric ozone inferred from a composite record of TOMS/OMI/MLS/OMPS satellite measurements and the MERRA-2 GMI simulation. Atmospheric Chemistry and Physics, 19(5): 3257-3269. Miller, S.D.; Kuciauskas, A.P.; Liu, M.; Ji, Q.; Reid, J.S.; Breed, D.W.; Walker, A.L. and Mandoos, A.A., (2008). Haboob dust storms of the southern Arabian Peninsula. Journal of Geophysical Research: Atmospheres, 113(D1). Rashki, A.; Kaskaoutis, D. G.; Eriksson, P. G.; Rautenbach, C. D. W.; Flamant, C. and Vishkaee, F. A. (2014). Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations, Natural hazards, 71(1): 563-585. Rashki, A.; Kaskaoutis, D.G.; Mofidi, A.; Minvielle, F.; Chiapello, I.; Legrand, M.; Dumka, U.C. and Francois, P. (2019). Effects of Monsoon, Shamal and Levar winds on dust accumulation over the Arabian Sea during summer–The July 2016 case. Aeolian Research, 36: 27-44. Shao, Y.; Wyrwoll, K.H.; Chappell, A.; Huang, J.; Lin, Z.; McTainsh, G.H.; Mikami, M.; Tanaka, T.Y.; Wang, X. and Yoon, S. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4): 181-204.
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نویسندگان مقاله عباسعلی داداشی رودباری |
دانشجوی دکتری آب و هواشناسی شهری، دانشکدة علوم زمین، دانشگاه شهید بهشتی، تهران، ایران

محمود احمدی |
دانشیار آب و هواشناسی، دانشکدة علوم زمین، دانشگاه شهید بهشتی، تهران، ایران

علیرضا شکیبا |
دانشیار سنجش‏ از دور و GIS، مرکز GIS و سنجش‏ از دور، دانشگاه شهید بهشتی، تهران، ایران


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