Spatio-temporal variability of aerosol characteristics in Iran using remotely sensed datasets

Document Type : Original Research Paper

Authors

1 Department of Climatology, Tarbiat Modares University, Tehran, Iran

2 Institute of Physics and Electronics, University of Peshawar, Pakistan

Abstract

The present study is the first attempt to examine temporal and spatial characteristics of aerosol properties and classify their modes over Iran. The data used in this study include the records of Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) from MODerate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Index (AI) from the Ozone Monitoring Instrument (OMI), obtained from 2005 to 2015. The high concentration of AOD and AI values (representing high-high cluster) have been observed in the southwest and east regions, while their low concentrations (representing low-low cluster) have been found in the high mountainous areas. Based on AE values, Iran has been divided into three distinct regions, including fine, mixture, and coarse aerosol modes in each season. Results show that the maximum/minimum area under fine aerosols mode has occurred in the autumn, covering an area of 84.15% and in the spring, covering an area of 40.5%. In the case of coarse mode, the maximum/minimum area has been found in the spring, covered area=53.5% / in the Autumn covered area=12. 5%. The different aerosol modes regions strongly coincide with the topographical structure. To analyze the relation between aerosol properties and topography, Aerosol Properties Index (API) has been developed by combining OMI and MODIS datasets. API is a simple indicator, capable of showing the degree of aerosol coarseness in each pixel. There is a negative correlation between API and topography over the studied region, meaning that aerosol concentrations are high in the lowlands, but low in the highlands. However, this relation differs in various geographic regions, as Geographically Weighted Regression (GWR) model shows a higher determination coefficient in all seasons, in comparison to Ordinary Least Squares (OLS). 

Keywords

References

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Pollution
Volume 4, Issue 1
January 2018
Pages 53-67

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