Investigating the Best Representative Dust Activities Index, Its Spatial-Temporal Changes, and Its Relationship with Environmental Factors in Iranian Dry Areas

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran

2 Department of Geography, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran.

3 Postdoctoral Researcher, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

چکیده

The current study primarily sought to select the best index for elaborating on dust activities, analyze the temporal and spatial changes of the index’s trend, and examine the relationship of the index with environmental factors in Iranian dry regions. To this end, the study examined the data collected on dust concentration, dust storm index, the number of dusty days (NDD), the pollution caused by dust storms, and the frequency of all dust events over a period of 18 years (2001-2018) using the MODIS-aerosols optical depth (AOD) product. Moreover, Pearson's correlation coefficient was used to analyze the correlation between the indices and AOD data sets. On the other hand, the trend of the best index annual changes in twenty-eight Iranian urban areas was analyzed using the Mann-Kendall method. Also, the most important environmental factors controlling dust activities in high-risk areas were identified using the random forest model. The results of the study indicated a strong correlation between NDD and AOD in Iranian dry regions (r= 0.7; p-value= 0.001). It was also found that the trend of NDD’s annual changes significantly increased in Torbat Heydarieh, Nehbandan, and Anar (Z>+1.96). However, the trend significantly decreased in Chabahar and Iranshahr (Z>│-1.96│. Generally, the results indicated an insignificant decreasing trend of annual NDD changes across the entire Iranian arid regions from 2001 to 2018 (Z= -0.45). on the other hand, the random forest model suggested that air pressure and wind speed exerted the greatest influence on dust activities that occurred in Iran’s high-risk area throughout the study period. Therefore, it could be argued that the findings of this study can help better monitor dust events and reduce their environmental risks in Iranian dry areas.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the Best Representative Dust Activities Index, Its Spatial-Temporal Changes, and Its Relationship with Environmental Factors in Iranian Dry Areas

نویسندگان [English]

  • Zohre Ebrahimi khusfi 1
  • Mohsen Ebrahimi-Khusfi 2
  • Maryam Mirakbari 3
1 Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran
2 Department of Geography, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran.
3 Postdoctoral Researcher, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
چکیده [English]

The current study primarily sought to select the best index for elaborating on dust activities, analyze the temporal and spatial changes of the index’s trend, and examine the relationship of the index with environmental factors in Iranian dry regions. To this end, the study examined the data collected on dust concentration, dust storm index, the number of dusty days (NDD), the pollution caused by dust storms, and the frequency of all dust events over a period of 18 years (2001-2018) using the MODIS-aerosols optical depth (AOD) product. Moreover, Pearson's correlation coefficient was used to analyze the correlation between the indices and AOD data sets. On the other hand, the trend of the best index annual changes in twenty-eight Iranian urban areas was analyzed using the Mann-Kendall method. Also, the most important environmental factors controlling dust activities in high-risk areas were identified using the random forest model. The results of the study indicated a strong correlation between NDD and AOD in Iranian dry regions (r= 0.7; p-value= 0.001). It was also found that the trend of NDD’s annual changes significantly increased in Torbat Heydarieh, Nehbandan, and Anar (Z>+1.96). However, the trend significantly decreased in Chabahar and Iranshahr (Z>│-1.96│. Generally, the results indicated an insignificant decreasing trend of annual NDD changes across the entire Iranian arid regions from 2001 to 2018 (Z= -0.45). on the other hand, the random forest model suggested that air pressure and wind speed exerted the greatest influence on dust activities that occurred in Iran’s high-risk area throughout the study period. Therefore, it could be argued that the findings of this study can help better monitor dust events and reduce their environmental risks in Iranian dry areas.

کلیدواژه‌ها [English]

  • Dust Events
  • Number of Dusty Days (NDD)
  • Mann-Kendall Test
  • Environmental Factors
  • Machine Learning

مراجع

  1. Ahmadzai, H. (2023). The impact of sand and dust storms on agriculture in Iraq. Middle East Development Journal, 1-16.
  2. Alizadeh‐Choobari, O., Ghafarian, P., & Owlad, E. (2016). Temporal variations in the frequency and concentration of dust events over Iran based on surface observations. International Journal of Climatology, 36(4), 2050-2062.
  3. Berndt, E., Elmer, N., Junod, R., Fuell, K., Harkema, S., Burke, A., & Feemster, C. (2021). A machine learning approach to objective identification of dust in satellite imagery. Earth and Space Science, 8(6), e2021EA001788.
  4. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  5. Chauhan, P. K., Kumar, A., Pratap, V., Chaubey, S. K., & Singh, A. K. (2023). Dust storm characteristics over Indo-Gangetic basin through satellite remote sensing. In Atmospheric Remote Sensing (pp. 373-392): Elsevier.
  6. Dolatkordestani, M., Nosrati, K., Maddah, S., & Tiefenbacher, J. P. (2022). Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine. Geocarto International(just-accepted), 1-14.
  7. Du, H., Xue, X., Wang, T., Lu, S., Liao, J., Li, S., . . . Liu, X. (2022). Modeling dust emission in alpine regions with low air temperature and low air pressure–A case study on the Qinghai-Tibetan Plateau (QTP). Geoderma, 422, 115930.
  8. Ebrahimi-Khusfi, Z., Mirakbari, M., Ebrahimi-Khusfi, M., & Taghizadeh-Mehrjardi, R. (2020). Impacts of vegetation anomalies and agricultural drought on wind erosion over Iran from 2000 to 2018. Applied Geography, 125, 102330.
  9. Ghafarian, P., Kabiri, K., Delju, A. H., & Fallahi, M. (2022). Spatio-temporal variability of dust events in the northern Persian Gulf from 1991 to 2020. Atmospheric Pollution Research, 13(4), 101357.
  10. Jebali, A., Zare, M., Ekhtesasi, M. R., & Jafari, R. (2021). Detection of areas prone to wind erosion and air pollution using DSI and PDSI indices. Natural Hazards, 108(1), 1221-1235.
  11. Kendall, M. (1975). Rank correlation methods (4th edn.) charles griffin. San Francisco, CA, 8, 875.
  12. Kok, J. F., Storelvmo, T., Karydis, V. A., Adebiyi, A. A., Mahowald, N. M., Evan, A. T., . . . Leung, D. M. (2023). Mineral dust aerosol impacts on global climate and climate change. Nature Reviews Earth & Environment, 1-16.
  13. Lay-Ekuakille, A., Ciaccioli, A., Griffo, G., Visconti, P., & Andria, G. (2018). Effects of dust on photovoltaic measurements: A comparative study. Measurement, 113, 181-188.
  14. Luan, B., Zhou, W., Jiskani, I. M., & Wang, Z. (2023). An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines. International Journal of Environmental Research and Public Health, 20(2), 1353.
  15. Ma, M., Yang, X., He, Q., Zhou, C., Mamtimin, A., Huo, W., & Yang, F. (2020). Characteristics of dust devil and its dust emission in northern margin of the Taklimakan Desert. Aeolian Research, 44, 100579.
  16. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259.
  17. Mesbahzadeh, T., Salajeghe, A., Sardoo, F. S., Zehtabian, G., Ranjbar, A., Krakauer, N. Y., . . . Mirakbari, M. (2020). Climatology of dust days in the Central Plateau of Iran. Natural Hazards, 104(2), 1801-1817.
  18. Midi, H., Sarkar, S. K., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model. Journal of interdisciplinary mathematics, 13(3), 253-267.
  19. Modarres, R., & Sadeghi, S. (2018). Spatial and temporal trends of dust storms across desert regions of Iran. Natural Hazards, 90(1), 101-114.
  20. Mohammadpour, K., Rashki, A., Sciortino, M., Kaskaoutis, D. G., & Boloorani, A. D. (2022). A statistical approach for identification of dust-AOD hotspots climatology and clustering of dust regimes over Southwest Asia and the Arabian Sea. Atmospheric Pollution Research, 13(4), 101395.
  21. Namdari, S., Zghair Alnasrawi, A. I., Ghorbanzadeh, O., Sorooshian, A., Kamran, K. V., & Ghamisi, P. (2022). Time series of remote sensing data for interaction analysis of the vegetation coverage and dust activity in the middle east. Remote Sensing, 14(13), 2963.
  22. Natsagdorj, L., Jugder, D., & Chung, Y. (2003). Analysis of dust storms observed in Mongolia during 1937–1999. Atmospheric Environment, 37(9-10), 1401-1411.
  23. Nordine, S., Ziane, A., Dabou, R., Neçaibia, A., Rouabhia, A., Lachtar, S., . . . Boudjamaa, T. (2023). Technical and economic study of the sand and dust accumulation impact on the energy performance of photovoltaic system in Algerian Sahara. Renewable Energy.
  24. O’Loingsigh, T., McTainsh, G., Tews, E., Strong, C., Leys, J., Shinkfield, P., & Tapper, N. (2014). The Dust Storm Index (DSI): a method for monitoring broadscale wind erosion using meteorological records. Aeolian Research, 12, 29-40.
  25. Omidvar, K., Dehghan, M., & Khosravi, Y. (2022). Assessment of relationship between aerosol optical depth (AOD) index, wind speed, and visibility in dust storms using genetic algorithm in central Iran (case study: Yazd Province). Air Quality, Atmosphere & Health, 15(10), 1745-1753.
  26. Ouedraogo, W. Y. S. B., Tiemounou, S., Djibo, M., Doumounia, A., Sanou, S. R., Sawadogo, M., . . . Zougmore, F. (2022). Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition.
  27. Poordehghan Ardekani, F., Tazeh, M., Kalantari, S., & Ebrahimi Khosfi, Z. (2022). Investigating the relationship between dustiness indices and the aerosols optical depth around the Horulazim wetland. Journal of Arid Biome, 12(1), 141-158.
  28. Sedgwick, P. (2012). Pearson’s correlation coefficient. Bmj, 345.
  29. Shao, Y., & Wang, J. (2003). A climatology of Northeast Asian dust events. Meteorologische Zeitschrift, 12(4), 187-196.
  30. Souri, A. H., & Vajedian, S. (2015). Dust storm detection using random forests and physical-based approaches over the Middle East. Journal of Earth System Science, 124(5), 1127-1141.
  31. Sujitha, P., Santra, P., Bera, A., Verma, M., & Rao, S. (2022). Detecting dust loads in the atmosphere over Thar desert by using MODIS and INSAT-3D data. Aeolian Research, 57, 100814.
  32. Zhao, X., Zhao, C., Yang, Y., Sun, Y., Xia, Y., Yang, X., & Fan, T. (2022). Distinct changes of cloud microphysical properties and height development by dust aerosols from a case study over Inner-Mongolia region. Atmospheric Research, 273, 106175.

 

 

مهندسی اکوسیستم بیابان
دوره 12، شماره 10و 11 انگلیسی
شهریور 1402
صفحه 25-38

فایل ها

هم رسانی

ارجاع به این مقاله

آمار