The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and HYSPLIT Model
Atmospheric aerosols have constituted a crucial environmental and climate issue. There is lack of studies dealing with monitoring of aerosol patterns over Bangladesh. This review attempts to analyze the seasonal variations in AOD over Bangladesh during the period 2002-2011, using MODerate resolution Imaging Spectroradiometer (MODIS) Level 3 remote sensing data. A Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to generate a backward trajectory in order to identify the origins of air masses, with the aim of understanding the spatio-temporal variability in aerosol concentration. Seasonal variations during the last decade show maximum AOD values during pre-monsoon season; while minimum AOD during post-monsoon. An evidence of decadal decreasing trend in AOD is found during monsoon season while all other seasons show increasing trends. High spatio-temporal variations of AOD are observed during different seasons in 2010. Back trajectory analysis indicates that Bangladesh is mainly affected by the pollutants and desert dust of India combining with sea salt particles blown from the Arabian Sea. The air masses are arriving at lower altitudes (500m, 1500m) mainly from western India and Indian subcontinent but at higher altitude (2500m) especially in winter season it comes from far western regions, such as Europe and various sub-Saharan regions of Africa. Different flow patterns of air masses during post-monsoon are observed that the air masses are arriving from southeast in the direction; in case of Sylhet division the sources of air masses are in the coastal regions of Thailand, border regions of Myanmar and China. These studies become helpful to understand the nature and influence of aerosols on seasonal dynamics over Bangladesh.
Mainul Islam Mamun,
Pallab Kumar Mondol,
The Seasonal Variability of Aerosol Optical Depth over Bangladesh Based on Satellite Data and HYSPLIT Model, American Journal of Remote Sensing.
Vol. 2, No. 4,
2014, pp. 20-29.
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