Spatio-temporal variation and change point of Iran Aerosol absorption index (AAI) based on the output of TOMS and OMI sensors

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. Since the effect of aerosols on airborne processes is more intense in the ultraviolet group, the Aerosol Index (AI) is a useful and accurate method for detecting ultraviolet absorbing humus, such as soot and dust. The positive AI indicator represents aerosols, clouds are close to zero, and negative values are the absence of humus or due to the presence of non-absorbent particles in the UV group. The aim of this study was to investigate the spatio-temporal retrieval of the TOMS and OMI sensors in Iran. The results of this research can be useful in identifying seasonal sources of dust, critical areas and ultimately its feedback in the climate system for future studies.
TOMS Nimbus 7 (TOMSN7L3 v008) data was used during the period 1979 to 1992; TOMS EP (TOMSEPL3 v008); 1996 to 2005; and OMI (OMTO3d v003) for the period of 2005 to 2015. In this research, the change of AAI index was studied using the Buishand test. After decoding the necessary data and calculations, the maps of each in the ARCGIS environment were mapped using the inverse distance-weighted method (IDW) with the least amount of Root Mean Square Error (RMSE).
For each of the three satellites data, spring and summer, the highest amount of statistical data is the mean and maximum. The range of changes also increases naturally in the seasons with the mean peak, which is the maximum range of spring and summer changes. Seasonal changes of the amount of AI in Iran is due to a synergy in terms of emissions and atmospheric conditions. Winter AI has its non-alternating pattern synoptic systems that affect Iran during the cold season, especially winter is mainly, due to significant rainfall and humidity that, reduce AI. This is the most important factor that has caused the regions of central Iran to have fewer highs than those of the southeast to southwest of Iran. Therefore, the height of the boundar layer in very dry areas and the central regions of the desert naturally is higher than wetland areas of the desert, resulting in a higher value of Aerosol index (AI). So, in none of the three satellites data set studied after the average change point was less than before the change point, which indicates a significant increase in the aerosol in Iran's airspace.
The results of the study have shown that in each of the three satellites data set studied, the warm- period has the maximum Aerosol index (AI). Seasonal variation of the aerosol index in Iran is due to the mixing of airborne aerosol the spring it has been shown  the activation of the regional dust sources. The minimum amount of Aerosol Index (AI) occurred in winter, possibly due to increased precipitation in the studied area. Rainfall has two important control effects on dust storm activity. 1-From soil moisture and 2-vegetation. Dust loads in the vast area of Iran can be classified into three general categories: 1-Urban/industrial activities and biomass burning (mainly in Isfahan, Tabriz, Tehran and Khuzestan), 2. Dust transport of desert areas and semi-arid (mainly arid and semiarid areas of Iraq, Syria, Saudi Arabia and North Africa) and 3) marine environments. The regional role of the atmosphere in the release and transport of aerosols to Iran plays a significant role, in the middle of spring until late summer, a small area of thermal low is formed inside Iran and Saudi Arabia, and this leads to strong pressure gradient responsible for the creation of northwesterly winds. The point of change in the annual Aerosol Absorption Index (AAI) by the Buishand test has shown that the Aerosol Index has been upgraded based on three satellite data and two sensors, so that the mean Aerosol Index (AI) after the change point in every three  satellite and the two sensors data surveyed that have more the change point than before.