determination of dust using remote sensing techniques and numerical simulation

Document Type : Original Article


1 Islamic Republic of Iran meteorological organization

2 Bushehr meteorology organization

3 Assistant Professor/Atmospheric science and meteorological research center

4 Lorestan meteorology organization


Introduction: Natural hazards such as floods, earthquakes, landslides, etc. frequently occur in Iran and other parts of the world, causing considerable economic, social, and environmental problems. In recent years, dust outbreaks in the west and southwest of Iran have been rising, becoming one of the most important environmental challenges in the region.

Material and methods: In this research, a number of intense dust episodes have been selected out of 15 years (2004-2018) of the statistical period, in the warm and cold seasons over the western and southwestern parts of the country. At the first step, in order to detect and observe the intensity of the dust concentration over the study area, the MODIS AOD product from the Deep blue and Dark target algorithms has been analyzed using the ENVI software, and the generated images have been displayed in the ArcGIS software. In the second step, dust source detection has been carried out by the WRF/Chem simulation of the dust concentration and tracking the dust path using the HYSPLIT Lagrangian model.

Results and discussion: The results of the simultaneous study of dust detection using satellite imagery, the simulation of dust concentration using the WRF-Chem coupled model, and the particle movement pattern with the HYSPLIT model showed that East of Syria and northern Iraq in the case studies of the warm season and the west and center of Iraq in the case studies of the cold season are the main sources of dust episodes in the west and southwest of Iran.
Conclusion: Studying the source and transport of dust with the aid of WRF-Chem simulations and the HYSPLIT Lagrangian model provided a complementary decision making to predict the direction of dust motions and using the results in the air quality prediction and management.


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