Dust monitoring and investigation of its relationship with topographical, climatic and vegetation factors

Document Type : Original Article

Authors

1 Remote sensing

2 remote sensing/ earth scinse/ chamran ahvaz

‎10.22052/deej.2024.253713.1025

Abstract

Introduction: Airborne particles play an important role in the balance of atmospheric radiation and climate change. The relationship between aerosols and climate systems has received increasing attention as our understanding of these issues increases. A dust storm is a complex process that is influenced by the interactions of atmospheric systems and is basically caused by conditions such as high wind speed, bare soil and dry air.
 
Materials and methods: Aerosol optical depth (AOD) is one of the most important parameters in the field of dust related research. Aerosol optical depth actually refers to the distribution of dust aerosols in the atmosphere. In this research, the dust condition was investigated using the AOD parameter of MODIS product. In order to investigate the relationship between environmental factors, NDVI was used. For each year, based on the total images taken, which is about 24 images per year, the average vegetation cover index was calculated in the Google Earth Engine system as a time series. In this study, among the climatic factors, annual rainfall data from synoptic stations were used to investigate the effect of rainfall on the amount of dust changes in the period from 2013 to 2022. Also, a 90-meter DEM was prepared from the SRTM sensor in order to prepare layers of slope percentage, slope direction and elevation classes.
 
Results: In the research, firstly, to evaluate the dust situation, the aerosol optical depth product was obtained from the MODIS sensor. In order to highlight and monitor as much as possible, the dust phenomenon of the months of June to September, corresponding to the late spring and summer season of each year, when the majority of dust storms are concentrated in these months, was chosen. After downloading the images, the 9-year statistical period (2013-2022) was considered as the studied statistical period in Khuzestan province. The average optical depth of dust was obtained each year based on the images taken in the same year.
The results show that most of the dust was spread in the years 2015, 2016, 2017 and 2018, so that a large part of the province faced the dust phenomenon in these years. The investigation of the dust situation in 2013 and 2014 showed that although most of Khuzestan province is facing a relatively low average AOD, the maximum AOD value is related to classes with values of 0.71 to 1.5 and more than 1.5 located in the southwest. is the province; The maximum value of AOD in these years was 2.82 and considering that AOD is between zero and 5 in the mentioned years, the southern regions have faced a high average dust phenomenon. The purpose of this study was to monitor dust and investigate its relationship with various factors of topography, vegetation and climate. The results of dust relationship with the mentioned factors showed that among all the factors, height and slope direction have the highest correlation (83 percent) and (72 percent) respectively with the phenomenon of dust in the opposite direction, so that an increase in the amount of height causes a decrease in dust and south-facing directions. And smooth areas will have an increase in the amount of this phenomenon. Therefore, by using remote sensing techniques, it is possible to identify dust centers as a valuable tool to combat the expression factor.
 
Discussion and Conclusion: Desertification is one of the most obvious ecological and environmental problems in arid and semi-arid regions. n this study, to identify dust spots, changes in the average occurrence of dust storms were obtained using MODIS sensor data from 2013 to 2022, and then its relationship with climatic factors, sun cover with NDVI index, and topography were evaluated. A decrease in the amount of precipitation that can lead to a decrease in soil moisture, vegetation cover and an increase in drought; Also, the changes in land use in recent years are all among the factors that have led to an increase in the phenomenon of dust, especially in Khuzestan province. Examining the influencing factors such as topography, vegetation and climatic factors is of great importance in identifying and prioritizing the control of dust centers, in addition, the MODIS optical depth sensor product can be of great help as a valuable tool for monitoring and managing this event.

Keywords

Main Subjects


  1. Adib, A., Oulapour, M., & Chatroze, A., 2018. Effects of wind velocity and soil characteristics on dust storm generation in Hawr-al-Azim Wetland, Southwest Iran. Caspian J. Environ. Sci., 16(4), 333-374.
  2. Almazroui, M., 2019. A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia. Atmospheric Research, 225, 88-95.
  3. Araghizade, M., & Masoodian, S. A., 2021. Climate analysis and study of dust storms in Khorasan Razavi. Physical Geography Research Quarterly, 53(3), 305-318. doi: 10.22059/jphgr.2021.301969.1007515
  4. Arjmand, M., Rashki, A., & 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.
  5. Badarinath, K.V.S., Goto, D., Kharol, S.K., Mahalakshmi, D.V., Sharma, A.R., Nakajima, T., Hashimoto, M., & Takemura, T., 2011. Influence of natural and anthropogenic emissions on aerosol optical properties over a tropical urban site: a study using sky radiometer and satellite data. Atmospheric Research 100(1):111–120 DOI 10.1016/j.atmosres.2011.01.003.
  6. Biancofiore, , Verdecchia, M.C., Piero,T., Barbara, A., Eleonora, B., Marcella, B., Sebastiano, D., Tommaso, S., & Colangeli, C., 2015. Analysis of Surface Ozone Using a Recurrent Neural Network ed. Edward A Keller. Science of the Total Environment 514(4): 379–87.
  7. Boroughani, M., pourhashemi, S., Zarei, M., & Aliabadi, K., 2019. Spatial modeling of the sensitivity of dust centers to its emission in eastern Iran using the enhanced BRT regression tree model. Journal of Arid Regions Geographic Studies, 10(35), 14-28.
  8. Chawla, A., Rajkumar, S., Singh, K. N., Brij Lal, R. D. S. & Thukral, A. K., 2008. Plant species diversity along an altitudinal gradient of Bhabha Valley in Western Himalaya. Journal of Mountain Science 5:
    157-177.
  9. Chen, X., Ding, J., Liu, J., Wang, J., Ge, X., Wang, R., & Zuo, H., 2021. Validation and comparison of high-resolution MAIAC aerosol products over Central Asia. Atmospheric Environment, 251, 118273.
  10. Darvand, S., Khosravi, H., Keshtkar, H., Zehtabian, G., & Rahmati, O., 2021. Comparison of machine learning models to prioritize susceptible areas to dust production. Journal of Range and Watershed Managment, 74(1), 53-68. doi: 10.22059/jrwm.2021.321033.1580
  11. Engelstaedter, S., Kohfeld, K. E., Tegen, I., & Harrison, S. P., 2003. Controls of dust emissions by vegetation and topographic depressions: An evaluation using dust storm frequency data. Geophysical Research Letters, 30(6).
  12. Evans, S., Ginoux, P., Malyshev, S., & Shevliakova, E., 2016. Climate‐vegetation interaction and amplification of Australian dust variability. Geophysical Research Letters, 43(22), 11-823.
  13. Fan, B., Li Guo, N, Li, J., Chen, H., Lin, X., Zhang, M., Shen, Y., Rao, C. W., & Lei, M., 2014. "Earlier vegetation green-up has reduced spring dust storms". Scientific reports 4, no. 1 (2014): 6749.
  14. Faryabi, A., Matinfar, H. R., Alavi Panah, S. K., & Norouzi, A. A., 2019. Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data. Environmental Sciences, 17(3), 151-162. doi: 10.29252/envs.17.3.151.
  15. Ge, J. M., Su, J., Fu, Q., Ackerman, T. P., & Huang, J. P., 2011. Dust aerosol forward scattering effects on ground-based aerosol optical depth retrievals. Journal of Quantitative Spectroscopy and Radiative Transfer112(2), 310-319.
  16. Gholami, A, Rashki, A., & Azari, M., 2021. Investigating the trend of changes in dust and its relationship with precipitation and vegetation in Razavi province. The first national conference of new technologies in the environment and sustainable development with the approach of Corona and the environment, 26 Shahrivar. Iran.
  17. Goudie, A. S., 2009. Dust storms: Recent developments, Journal of Environmental Management, 90, 89-94.
  18. IPCC: Climate Change 2013: The Physical Basis. Contribution of Working Group I to the 25 Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley P. M., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 1535 pp., 2013
  19. Jafari, R., & Alidadi, S., 2022. Desert Dust Mapping and Identification Using MODIS Level 1 and AOD- AI Indices in South West of Iran. Desert Ecosystem Engineering, 10(33), 53-64. doi: ‎22052/deej.2021.10.33.39
  20. jannatrostami, M., Rahimi, M., & kaboli, H., 2021. Evaluation of vegetation role and climatic factors of southern plains of alborz in spatial and temporal distribution of dust phenomenon. Journal of Range and Watershed Managment, 74(2), 339-358. doi: 10.22059/jrwm.2021.318086.1564
  21. Jiang, Y., Kang, M., Zhu, Y. & Ku, G., 2007. Plant biodiversity patterns on Helan Mountain, China. Acta Oecologica 32: 125-
  22. Kang, S., Lee, S. H., Cho, N., Aggossou, C., & Chun, J., 2021. Dust and sandstorm: ecosystem perspectives on dryland hazards in Northeast Asia: a review. Journal of Ecology and Environment, 45(1), 1-9.
  23. Kaskaoutis, D. G., Kahn, R. A., Gupta, P., Jayaraman, A. & Bartzokas, A., 2012. Desert
    Dust Properties, Modelling, and Monitoring. Advances in Meteorology.
  24. Kassa, A., 1990. Drought risk monitoring for Sudan using NDVI, 1982-1993. A Dissertation submitted to the University College London. (16) Kogan, F.N., 1993. United States droughts of late 1980's as seen by NOAA polar orbiting satellites. International Geoscience and Remote Sensing Symposium, 1: 197-199
  25. Kurosaki, Y., & Mikami, M., 2005. Regional difference in the characteristic of dust event in East Asia: relationship among dust outbreak, surface wind and land surface condition. Journal of the Meteorological Society of Japan. 83(1), 1-18
  26. Lin, C. A., Zhang, Y., Heath, G., Henze, D. K., Sengupta, M., & Lu, C. H., 2023. Improvement of aerosol optical depth data for localized solar resource assessment. Solar Energy, 249, 457-466.
  27. Maleki, S., Mir, M., & Rhdari, V. (2022). Investigating the change in the Sand and Dust Stormsâ intensity in affected areas in Sistan Plain. Desert Ecosystem Engineering10(30), 111-125. doi: 10.22052/deej.2021.10.30.59
  28. Mao, K.B., Ma, Y., Xia, L., Chen, W. Y., Shen, X. Y., He, T.J., & Xu, T.R., 2014. Global aerosol change in the last decade: An analysis based on MODIS data, Atmospheric Environment 94: 680-686.
  29. Mei, D., Xiushan, L., Lin, S., & Ping, W. 2008. A duststorm process dynamic monitoring with multitemporal MODIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., 37, 965-970
  30. Miyajima, Y. & Takahashi, K., 2007. Changes with altitude of the stand structure of temperate forests on Mount Norikura, Central Japan. Journal of Forest Resources 12: 187-192.
  31. Mohammadi, A., Ghazavi, R., Mirzaei, R., & Naseri, H., 2019. Investigation of the vegetation Cover Pattern Change Using MODIS Images and its Relation with Rainfall distribution. Journal of Range and Watershed Managment, 72(3), 843-852. doi: 10.22059/jrwm.2019.280679.1381
  32. Mohammadpour, K., Saligheh, M., Darvishi Bloorani, A., & Raziei, T., 2020. Analysis and Comparing Satellite Products and Simulated of AOD in West Iran (2000-2018). Journal of Spatial Analysis Environmental Hazards; 7 (1) :15-32
  33. Naeimi, M., Yousefi, M. J., Khosroshahi, M., Zandifar, S., & Ebrahimikhusfi, Z., 2020. Climatic factors affecting dune mobility in the west of Khorasan Razavi Province, Iran. The Journal of Geographical Research on Desert Areas7(2), 25-45.
  34. Pan, L., Che, H., Geng, F., Xia, X., Wang, Y., Zhu, C., Chen, M., GAO W. & Guo, L. 2010. Aerosol optical properties based on ground measurements over the Chinese Yangtze Delta Atmos Environ, 44: pp. 2587– 2596.
  35. Qin, W., Fang, H., Wang, L., Wei, J., Zhang, M., Su, X., & Liang, X., 2021. MODIS high-resolution MAIAC aerosol product: Global validation and analysis. Atmospheric Environment, 264, 118684.
  36. Rangzan, K., Zarasvandi, A., Abdolkhani, A., & Mojaradi, B., 2014. Modeling of Air Pollution using MODIS Data: Khouzestan Dust storm. Advanced Applied Geology, 4(4), 38-45.
  37. Rangzan , K., Zarasvandi, A., kabolizadeh, M., mohammadi, S., & mayahi, J., 2022. Spatiotemporal evaluation of PM2.5 concentration in Khuzestan province and examining the factors affecting it. Environmental Sciences, 20(2), 199-222. doi: 10.52547/envs.2022.33613
  38. Sang, W., 2009. Plant diversity patterns and their relationships with soil and climatic factors along an altitudinal gradient in the middle Tianshan Mountain area, Xinjiang, China. Ecological research 24: 303-314.
  39. Sedaghat, M., & Nazaripour, H., 2020. Monitoring variability of soil moisture in Hour-al-Azim Wetland and its relation to dust storms in southwest Iran. Scientific- Research Quarterly of Geographical Data (SEPEHR)29(114), 133-145. doi: 10.22131/sepehr.2020.44598
  40. Shahsavani, A., nadafi, K., yarahmadi, M., kermani, M., & yarahmadi, E., 2012. Investigation patterns, formation mechanisms and impacts of dust haze. Nivar37(80-81), 65-82.
  41. Sharma, V., Ghosh, S., Kumari, M., Taloor, A. K., Singh, S., Arola, A., & Devara, P. C., 2022. Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India. Journal of Landscape Ecology, 15(3), 82-101.
  42. Tao, M., Wang, J., Li, R., Wang, L., Wang, L., Wang, Z., & Chen, L., 2019. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation. Atmospheric environment, 213, 159-169.
  43. Urban, F. E., Reynolds, R. L., & Fulton, R., 2009. The dynamic interaction of climate, vegetation, and dust emission, Mojave Desert, USA. Arid environments and wind erosion, 243-267.
  44. Yar Ahmadi, , Nasiri, B., Khushkish, A., & Nikbakht, H., 2022. The effect of weather fluctuations on the occurrence of dust (a case study of dust in the west and southwest of Iran). Desert Ecosystem Engineering, 3(5), 19-28
  45. Zhou, Y., Gao, X., Meng, X., Lei, J., & Halik, Ü., 2022. Characteristics of the Spatio-Temporal Dynamics of Aerosols in Central Asia and Their Influencing Factors. Remote Sensing, 14(11), 2684.
  46. Zong, X., Xia, X., & Che, H., 2015. Validation of aerosol optical depth and climatology of aerosol vertical distribution in the Taklimakan Desert. Atmospheric Pollution Research6(2), 239-244.