Evaluation Seasonal Trend of Iran Aerosol Index (AI) Based on Nimbus 7, Earth Probe and Aura Satellite Data

Document Type : Full length article


1 PhD Student in Urban Climatology, Shahid Beheshti University, Tehran, Iran

2 Associate Professor Climatology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

3 Associate Professor of Remote Sensing and GIS, Shahid Beheshti University, GIS Center and Remote Sensing, Tehran, Iran


Aerosols are solid or liquid particles in the air with a typical radius of 0.001 to 100 μm, which have significant and harmful effects on human health. Aerosols come from both natural and human sources, and in recent years, human activities associated with urbanization and industrialization has led to a steady increase in the amount of these particles in the airborne state.  Atmospheric aerosols play a key role in the energy budget of the Earth’s climate system through aerosol–radiation interactions (direct effect) and aerosol–cloud interactions (indirect effect). On the one hand, by absorbing and scattering solar and terrestrial radiation, aerosols generally cool the Earth’s surface and heat the atmosphere, depending on the absorption level of the aerosols. The southwestern part of the Asian continent (Middle East, Arabian Peninsula and Iran-Pakistan-Afghanistan, IPA) contains several deserts (Syrian-Iraqi desert, Rub-Al-Khali, An-Nafud, Al-Dahna, Karakum, Margo, Registan) and semi-desert areas (Iranian Plateau, Sistan) responsible for large emissions of dust aerosols that are usually accumulated over the Arabian Sea. 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
The study area in this study is Iran based on seasonal period. The climate of Iran ranges from the subtropical dry to extremely dry zone in the eastern half and some central areas, wet to extremely wet zone in the southern coastal plains of the Caspian Sea, relatively wet zone in some areas in the west, and semi arid zones over the rest of the country. 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.
The parametric and non-parametric methods have been used to identify the trend in many researchers, but nonparametric methods have been more considered by researchers due to their ability to monitor the unrelated data and also lack of necessity for the normality of data. Non-parametric Mann-Kendall test was used to evaluate the trend of Aerosol Index in Iran. This method is widely used in the field of environmental science. Like many other nonparametric methods such as Mann-Kendall, this method is based on the evaluation of the difference between time series observations
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, it is not calibrated for the data of this meter. The maximum incremental increase of the Aerosol index (AI) was calculated for OMI and the maximum decreasing trend of the Aerosol index (AI) used for the winter (TOMS satellite satellite EP) 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. Some factors including summer Shamal wind, the dynamic and thermal patterns of West Asia, and the Indus Low Pressure, played the greatest role in increasing the hygiene of Iran.
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. This is mainly 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) were 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 observed in all metropolis of Iran with 500,000 to 1 million and more than one million people. There were no negative trends in any cities. In the autumn, we observe 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 resutled from environmental conditions such as land use, soil moisture and drought. The significant factors in HomoSperm regional circulation systems are 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.


احمدی، م. و داداشی رودباری، ع. (1397). آشکارسازی روند و نقطة تغییر گرد و غبار با استفاده از شاخص جذب هواویز (AAI) در پهنه‏های کلان آب و هوایی ایران مبتنی بر برون‏داد داده‏های سنجش از دور، هفتمین همایش ملی مدیریت آلودگی هوا و صدا، 8 و 9 بهمن 1397 دانشگاه شهید بهشتی، تهران.
ارجمند، م.؛ راشکی، ع. و سرگزی، ح. (1397). پایش زمانی و مکانی پدیدة گرد و غبار با استفاده از داده‏های ماهواره‏ای در جنوب شرق ایران، با تأکید بر منطقة جازموریان، اطلاعات جغرافیایی سپهر، 27(106): 153-168.
براتی، غ.؛ مرادی، م.؛ شامخی، ع. و داداشی رودباری، ع. (1396). تحلیل روابط طوفان‏های غباری جنوب ایران با کم‏فشار سِند، مخاطرات محیط طبیعی، 6(13): 91-108.
داداشی رودباری، ع. و احمدی، م. (1398). وردایی زمانی- مکانی و نقطة تغییر شاخص جذب هواویز (AAI) ایران مبتنی بر برون‏داد سنجنده‏های TOMS و OMI، فیزیک زمین و فضا، 45(3): 609-623.
Ahmadi, M. and Dadashirodbari, A. (1397). Detection of the trend and point of dust change using AAI in Iran's large Climatological zones based on Remote Sensing Data Output, 7th National Conference on Air Pollution and Sound Management, 8th and 9th Bahman 1397 Shahid University Beheshti, Tehran (In Persian).
Anuforom, A.C.; Akeh, L.E.; Okeke, P.N. and Opara, F.E. (2007). Inter-annual variability and long-term trend of UV-absorbing aerosols during Harmattan season in sub-Saharan West Africa, Atmospheric Environment, 41(7): 1550-1559.
Arjmand, M.; Rashki, A. and Sargazi, H. (2018). Monitoring of spatial and temporal variability of desert dust over the Hamoun e Jazmurian, Southeast of Iran based on the Satellite Data. Scientific- Research Quarterly of Geographical Data (SEPEHR), 27(106):153-168 (In Persian).
Babu, S.S.; Manoj, M.R.; Moorthy, K.K.; Gogoi, M.M.; Nair, V.S.; Kompalli, S.K.; Satheesh, S.K.; Niranjan, K.; Ramagopal, K.; Bhuyan, P.K. and Singh, D. (2013). Trends in aerosol optical depth over Indian region: Potential causes and impact indicators, Journal of Geophysical Research: Atmospheres, 118(20).
Barati, G.; Moradi, M.; Shamekhi, A. and dadashirodbari, A. (2017). Analysis of Relations between Dust Storms and Indus Low Pressure over Southern Iran, Natural Environmental Hazards, 6(13): 91-108 (In Persian).
Bollasina, M.; Nigam, S. and Lau, K.M. (2008). Absorbing aerosols and summer monsoon evolution over South Asia: An observational portrayal, Journal of Climate, 21(13): 3221-3239.
Dadashi Roudbari, A. and Ahmadi, M. (2019). Spatio-temporal variation and change point of Iran Aerosol absorption index (AAI) based on the output of TOMS and OMI sensors, Journal of the Earth and Space Physics, 45(3): 609-623 (In Persian).
Duhan, D. and Pandey, A. (2013). Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India, Atmospheric Research, 122: 136-149.
Fallah-Ghalhari, G.; Shakeri, F. and Dadashi-Roudbari, A. (2019). Impacts of climate changes on the maximum and minimum temperature in Iran, Theoretical and Applied Climatology, 138: 1539-1562.
Ghasem, A.; Shamsipour, A.; Miri, M. and Safarrad, T. (2012). Synoptic and remote sensing analysis of dust events in southwestern Iran, Natural hazards, 64(2): 1625-1638.
Godon, N.A. and Todhunter, P.E. (1998). A climatology of airborne dust for the Red River Valley of North Dakota, Atmospheric Environment, 32(9): 1587-1594.
Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media.
Hammer, M.S.; Martin, R.V.; Li, C.; Torres, O.; Manning, M. and Boys, B.L. (2018). Insight into global trends in aerosol composition from 2005 to 2015 inferred from the OMI Ultraviolet Aerosol Index, Atmospheric Chemistry & Physics, 18(11).
Harrison, S.P.; Kohfeld, K.E.; Roelandt, C. and Claquin, T. (2001). The role of dust in climate changes today, at the last glacial maximum and in the future, Earth-Science Reviews, 54(1-3): 43-80.
Herman, J.R. and Celarier, E.A. (1997) Earth surface reflectivity climatology at 340–380 nm from TOMS data, Journal of Geophysical Research: Atmospheres, 102(D23): 28003-28011.
James Gauderman, W.; McConnell, R.O.B.; Gilliland, F.; London, S.; Thomas, D.; Avol, E.; Vora, H.; Berhane, K.; Rappaport, E.B.; Lurmann, F. and Margolis, H.G. (2000). Association between air pollution and lung function growth in southern California children, American journal of respiratory and critical care medicine, 162(4): 1383-1390.
Kaskaoutis, D.G.; Houssos, E.E.; Rashki, A.; Francois, P.; Legrand, M.; Goto, D.; Bartzokas, A.; Kambezidis, H.D. and Takemura, T. (2016). The Caspian Sea–Hindu Kush Index (CasHKI): a regulatory factor for dust activity over southwest Asia, Global and Planetary Change, 137: 10-23.
Kaskaoutis, D.G.; Nastos, P.T.; Kosmopoulos, P.G.; Kambezidis, H.D.; Kharol, S.K. and Badarinath, K. V. S. (2010). The aura–OMI aerosol index distribution over Greece, Atmospheric Research, 98(1): 28-39.
Kaskaoutis, D.G.; Rashki, A.; Houssos, E.E.; Goto, D. and Nastos, P.T. (2014). Extremely high aerosol loading over Arabian Sea during June 2008: The specific role of the atmospheric dynamics and Sistan dust storms, Atmospheric environment, 94: 374-384.
Kendall, M.G. (1955). Rank correlation methods.
Kosmopoulos, P.G.; Kaskaoutis, D.G.; Nastos, P.T. and Kambezidis, H.D. (2008). Seasonal variation of columnar aerosol optical properties over Athens, Greece, based on MODIS data, Remote Sensing of Environment, 112(5): 2354-2366.
Li, J.; Carlson, B.E. and Lacis, A.A. (2009). A study on the temporal and spatial variability of absorbing aerosols using Total Ozone Mapping Spectrometer and Ozone Monitoring Instrument Aerosol Index data, Journal of Geophysical Research: Atmospheres, 114(D9).
Liu, H.; Remer, L.A.; Huang, J.; Huang, H.C.; Kondragunta, S.; Laszlo, I.; Oo, M. and Jackson, J.M. (2014). Preliminary evaluation of S‐NPP VIIRS aerosol optical thickness, Journal of Geophysical Research: Atmospheres, 119(7): 3942-3962.
Maghrabi, A. H. and Alotaibi, R. N. (2018). Long-term variations of AOD from an AERONET station in the central Arabian Peninsula, Theoretical and Applied Climatology, 134(3-4): 1015-1026.
Mann, H.B. (1945). Nonparametric tests against trend, Econometrica: Journal of the Econometric Society, 245-259.
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).
Namdari, S., Valizade, K. K., Rasuly, A. A., & Sarraf, B. S. (2016). Spatio-temporal analysis of MODIS AOD over western part of Iran. Arabian Journal of Geosciences, 9(3): 191.
Pozzer, A., De Meij, A., Yoon, J., Tost, H., Georgoulias, A. K., & Astitha, M. (2015). AOD trends during 2001–2010 from observations and model simulations. Atmos. Chem. Phys, 15(10): 5521-5535.
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.
Sabetghadam, S., Khoshsima, M., and Alizadeh-Choobari, O. (2018). Spatial and temporal variations of satellite-based aerosol optical depth over Iran in Southwest Asia: Identification of a regional aerosol hot spot. Atmospheric Pollution Research, 9(5): 849-856.
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.
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.