Advertisement

Spatial analysis of occurrence probability of dusty days in west and southwest of Iran

  • Khadijeh JavanEmail author
  • Maryam Teimouri
Original Paper
  • 26 Downloads

Abstract

In Iran, one of the environmental issues is dust storms, especially in the west and the southwest. These storms are important factors in soil erosion, economic damage to industry, agriculture, transportation sectors, and human life. Therefore, the recognition of the frequency, occurrence probability, and the return period of these storms can be instrumental in reducing damages. The purpose of this study was the spatial analysis of the occurrence probability of dusty days in the west and southwest of Iran in April, May, June, and July, using the Markov chain. The daily dust data was used in 14 synoptic stations during 24 years (1990–2013). In this study, to detect dust storms, a horizontal visibility factor of ≤ 1000 m was used for all meteorological codes. At first, the days were divided into two groups of normal days and dusty days and then the frequency of matrices, probability of transmission, and the stable matrix were calculated. Finally, the spatial distribution of the occurrence probability and the return period of the dust within 2 to 5 days were depicted. The results showed that the average occurrence probability of 2-day dusts was 15% in April and May, 22% in June, and 24% in July. Also, the occurrence probability of 3-day dusts decreased to 4%, 8%, and 9%, respectively. The return period of 1-day dust in all stations of the area and all months was 1.25 day on average; however, due to the increase in the duration of the dust period, its return period increased exponentially. Spatial distribution of stable matrix also revealed that the occurrence probability of dust in the western and southeastern parts of the studied area was more than those of the others.

Keywords

Dust day Markov chain Probability of occurrence Return period West and southwest of Iran 

References

  1. Ahmadi M, DadashiRoudbari A (2017) Regional modeling of dust storm of February 8, 2015 in the southwest of Iran. Arab J Geosci 10(21):459CrossRefGoogle Scholar
  2. Al-Hemoud A, Al-Sudairawi M, Neelamanai S, Naseeb A, Behbehani W (2017) Socioeconomic effect of dust storms in Kuwait. Arab J Geosci 10(1):18CrossRefGoogle Scholar
  3. Azizi G, Shamsipour A, Miri M, Safarrad T (2012) Synoptic and remote sensing analysis of dust events in southwestern Iran. Nat Hazards 64(2):1625–1638CrossRefGoogle Scholar
  4. Bollen J, Hers S, Van der Zwaan B (2010) An integrated assessment of climate change, air pollution, and energy security policy. Energy Policy 38(8):4021–4030CrossRefGoogle Scholar
  5. Boloorani AD, Nabavi SO, Bahrami HA, Mirzapour F, Kavosi M, Abasi E, Azizi R (2014) Investigation of dust storms entering Western Iran using remotely sensed data and synoptic analysis. J Environ Health Sci Eng 12(1):124CrossRefGoogle Scholar
  6. Chattopadhyay S, Acharya N, Chattopadhyay G, Prasad SK, Mohanty UC (2012) Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar, India. Compt Rendus Geosci 344(10):473–482CrossRefGoogle Scholar
  7. Cinlar E (2013) Introduction to stochastic processes. Courier Corporation, MineolaGoogle Scholar
  8. Dasgupta S, De UK (2001) Markov chain models for pre-monsoon thunderstorm in Calcutta, India. Indian J Radio Space Phys 30:138–142Google Scholar
  9. Doronzo DM, Khalaf EA, Dellino P, de Tullio MD, Dioguardi F, Gurioli L, Mele D, Pascazio G, Sulpizio R (2015) Local impact of dust storms around a suburban building in arid and semi-arid regions: numerical simulation examples from Dubai and Riyadh, Arabian Peninsula. Arab J Geosci 8(9):7359–7369CrossRefGoogle Scholar
  10. Gao T, Han J (2010) Evolutionary characteristics of the atmospheric circulations for frequent and infrequent dust storm springs in northern China and the detection of potential future seasonal forecast signals. Meteorol Appl 17(1):76–87Google Scholar
  11. Gao T, Han J, Wang Y, Pei H, Lu S (2012) Impacts of climate abnormality on remarkable dust storm increase of the Hunshdak Sandy Lands in northern China during 2001–2008. Meteorol Appl 19(3):265–278CrossRefGoogle Scholar
  12. Garg VK, Singh JB (2010) Markov chain approach on the behavior of rainfall. International Journal of Agricultural and Statistical Sciences 6(1):157–162Google Scholar
  13. Gibson J (2015) Air pollution, climate change, and health. Lancet Oncol 16(6):e269CrossRefGoogle Scholar
  14. Givehchi R, Arhami M, Tajrishy M (2013) Contribution of the Middle Eastern dust source areas to PM 10 levels in urban receptors: case study of Tehran, Iran. Atmos Environ 75:287–295CrossRefGoogle Scholar
  15. Goudie AS (2009) Dust storms: recent developments. J Environ Manag 90(1):89–94CrossRefGoogle Scholar
  16. Goudie AS, Middleton NJ (2006) Desert dust in the global system. Springer Science & Business Media, BerlinGoogle Scholar
  17. Hossain MM, Anam S (2012) Identifying the dependency pattern of daily rainfall of Dhaka station in Bangladesh using Markov chain and logistic regression model. Agric Sci 3(3):385–391Google Scholar
  18. Huang M, Peng G, Zhang J, Zhang S (2006) Application of artificial neural networks to the prediction of dust storms in Northwest China. Glob Planet Chang 52(1):216–224CrossRefGoogle Scholar
  19. Indoitu R, Orlovsky L, Orlovsky N (2012) Dust storms in Central Asia: spatial and temporal variations. J Arid Environ 85:62–70CrossRefGoogle Scholar
  20. Kantz H, Holstein D, Ragwitz M, Vitanov NK (2004) Markov chain model for turbulent wind speed data. Physica A 342(1):315–321CrossRefGoogle Scholar
  21. Lazri M, Ameur S, Brucker JM, Lahdir M, Sehad M (2015) Analysis of drought areas in northern Algeria using Markov chains. J Earth Syst Sci 124(1):61–70CrossRefGoogle Scholar
  22. Liu S, Wang T, Mouat D (2013) Temporal and spatial characteristics of dust storms in the Xilingol grassland, northern China, during 1954–2007. Reg Environ Chang 13(1):43–52CrossRefGoogle Scholar
  23. Lohani VK, Loganathan GV, Mostaghimi S (1998) Long-term analysis and short-term forecasting of dry spells by Palmer Drought Severity Index. Hydrol Res 29(1):21–40CrossRefGoogle Scholar
  24. Mandal KG, Padhi J, Kumar A, Ghosh S, Panda DK, Mohanty RK, Raychaudhuri M (2015) Analyses of rainfall using probability distribution and Markov chain models for crop planning in Daspalla region in Odisha, India. Theor Appl Climatol 121(3-4):517–528CrossRefGoogle Scholar
  25. Mei D, Xiushan L, Lin S, Ping WANG (2008) A dust-storm process dynamic monitoring with multi-temporal MODIS data. The international archives of the photogrammetry. Remote Sens Spat Inf Sci 37:965–970Google Scholar
  26. Moon SE, Ryoo SB, Kwon JG (1994) A Markov chain model for daily precipitation occurrence in South Korea. Int J Climatol 14(9):1009–1016CrossRefGoogle Scholar
  27. Morelli X, Rieux C, Cyrys J, Forsberg B, Slama R (2016) Air pollution, health and social deprivation: a fine-scale risk assessment. Environ Res 147:59–70CrossRefGoogle Scholar
  28. Moridnejad A, Karimi N, Ariya PA (2015) Newly desertified regions in Iraq and its surrounding areas: significant novel sources of global dust particles. J Arid Environ 116:1–10CrossRefGoogle Scholar
  29. Namdari S, Karimi N, Sorooshian A, Mohammadi G, Sehatkashani S (2018) Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East. Atmos Environ 173:265–276CrossRefGoogle Scholar
  30. Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827CrossRefGoogle Scholar
  31. Poggi P, Notton G, Muselli M, Louche A (2000) Stochastic study of hourly total solar radiation in Corsica using a Markov model. Int J Climatol 20(14):1843–1860CrossRefGoogle Scholar
  32. Rahmat SN, Jayasuriya N, Bhuiyan MA (2016) Short-term droughts forecast using Markov chain model in Victoria, Australia. Theor Appl Climatol 129(1–2):445–457CrossRefGoogle Scholar
  33. Ranjbar-saadatabadi A, Mihanparast M, Noori F (2016) Survey of dust phenomena in the west of Iran from the meteorological viewpoint (long-term and short-term study). Nivar 40(92-93):53–66Google Scholar
  34. Rezazadeh M, Irannejad P, Shao Y (2013) Climatology of the Middle East dust events. Aeolian Res 10:103–109CrossRefGoogle Scholar
  35. Sahin AD, Sen Z (2001) First-order Markov chain approach to wind speed modelling. J Wind Eng Ind Aerodyn 89(3):263–269CrossRefGoogle Scholar
  36. Sehatkashani S, Vazifedoust M, Kamali G, Bidokhti AA (2016) Dust detection and AOT estimation using combined VIR and TIR satellite images in urban areas of Iran. Sci Iran 23(5):1984–1993Google Scholar
  37. Small I, Van der Meer J, Upshur RE (2001) Acting on an environmental health disaster: the case of the Aral Sea. Environ Health Perspect 109(6):547–549CrossRefGoogle Scholar
  38. Song Z, Geng X, Kusiak A, Xu C (2011) Mining Markov chain transition matrix from wind speed time series data. Expert Syst Appl 38(8):10229–10239CrossRefGoogle Scholar
  39. Tan M, Li X, Xin L (2014) Intensity of dust storms in China from 1980 to 2007: a new definition. Atmos Environ 85:215–222CrossRefGoogle Scholar
  40. Varotsos C, Assimakopoulos MN, Efstathiou M (2007) Technical note: long-term memory effect in the atmospheric CO2 concentration at Mauna Loa. Atmos Chem Phys 7(3):629–634CrossRefGoogle Scholar
  41. Wang X, Dong Z, Zhang J, Liu L (2004) Modern dust storms in China: an overview. J Arid Environ 58(4):559–574CrossRefGoogle Scholar
  42. Wang R, Liu B, Li H, Zou X, Wang J, Liu W, Cheng H, Kang L, Zhang C (2017) Variation of strong dust storm events in Northern China during 1978–2007. Atmos Res 183:166–172CrossRefGoogle Scholar
  43. Weiss LL (1964) Sequences of wet or dry days described by a Markov chain probability model. Mon Weather Rev 92(4):169–176CrossRefGoogle Scholar
  44. Wilks DS (2006) Statistical methods in the atmospheric sciences, second edn. Academic Press, USAGoogle Scholar
  45. Yarahmadi D, Nasiri B, Khoshkish A, Nikbakht H (2015) Climate change and dusty days in the west and southwest of Iran. Desert Ecosystem Engineering Journal 3(5):19–28Google Scholar
  46. Yusuf AU, Adamu L, Abdullahi M (2014) Markov chain model and its application to annual rainfall distribution for crop production. Am J Theor Appl Stat 3(2):39–43CrossRefGoogle Scholar
  47. Zeinali B, Asghari S (2016) Mapping and monitoring of dust storms in Iran by fuzzy clustering and remote sensing techniques. Arab J Geosci 9(9):549CrossRefGoogle Scholar
  48. Zolfaghari H, Abedzadeh H (2005) Synoptic analysis of dust systems in the West of Iran. J Geogr Dev (Iran) 6:173–188Google Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of GeographyUrmia UniversityUrmiaIran
  2. 2.University of Mohaghegh ArdabiliArdabilIran

Personalised recommendations