International Journal of Environmental Research

, Volume 13, Issue 1, pp 161–174 | Cite as

Numerical Simulation of a Severe Dust Storm over Ahvaz Using the HYSPLIT Model

  • Reza Khalidy
  • Hesam Salmabadi
  • Mohsen SaeediEmail author
Research Paper


In this paper, numerical simulation of a severe dust storm over Ahvaz on 16–18 June 2016 was performed using the HYSPLIT. To that end, the HYSPLIT model has been configured for different model setups to determine the most desirable suite of parameters to represent the measured concentrations better. Among several model parameters, meteorological dataset (GDAS1.0 and GDAS0.5), friction velocity (P10F) and particle numbers (NumPar) were detected as the most influential parameters on the simulation results. Accordingly, with P10F = 0.6, NumPar = 5000, and GDAS1.0 configurations, the model has reproduced the most precise results, and the correlation coefficient between the measured and the simulated PM10 concentration was as high as 0.9. Since the threshold friction velocity influences the results significantly, it is suggested to calculate the P10F coefficient for each distinct dust storm meticulously. Once the best model parameters have been obtained, the HYSPLIT has been run for 33 possible sources separately to estimate the contribution from each sub-region to the levels of the measured PM10 concentration of Ahvaz during 16–18 June 2016. According to the results, central Iraq, northern Syria, western Iraq, and Al-Hawzieh/Al-Azim wetland account for 71, 19, 6, and 4%, respectively.

Article Highlights

  • A severe dust storm in 2016 over Ahvaz city was simulated quantitatively.

  • Validation of simulation results was accomplished using ground-based data and satellite observations.

  • Determination of the Middle East dust origins contribution in the dust storm was performed.

  • Western and central parts of Iraq were detected as the main sources of the dust storm.


HYSPLIT Source apportionment Middle East dust storms Ahvaz Numerical simulation Satellite observation 



The authors are grateful to the Atmospheric Research Center (ARC) of Iran University of Science and Technology for its support to this research. The authors also acknowledge the NOAA Air Resources Laboratory (ARL) and NCEI for the provision of the HYSPLIT transport and dispersion model, and hourly meteorological observation data, respectively. The MODIS and OMI products were acquired through GIOVANNI online software of NASA. We also would like to express our thanks to Mr. Alireza Azarnia of DOE- Khuzestan branch for providing the hourly PM10 concentration data.


  1. ARL (2018) Global data assimilation system (GDAS1) archive information. Accessed June 2018
  2. Ashrafi K, Shafiepour-Motlagh M, Aslemand A, Ghader S (2014) Dust storm simulation over Iran using HYSPLIT. J Environ Health Sci 12:9CrossRefGoogle Scholar
  3. Beegum SN, Gherboudj I, Chaouch N, Temini M, Ghedira H (2018) Simulation and analysis of synoptic scale dust storms over the Arabian Peninsula. Atmos Res 199:62–81CrossRefGoogle Scholar
  4. Broomandi P, Dabir B, Bonakdarpour B, Rashidi Y (2017) Mineralogical and chemical characterization of suspended atmospheric particles in Ahvaz. Int J Environ Res 11:55–62CrossRefGoogle Scholar
  5. Cao H, Amiraslani F, Liu J, Zhou N (2015a) Identification of dust storm source areas in West Asia using multiple environmental datasets. Sci Total Environ 502:224–235CrossRefGoogle Scholar
  6. Cao H, Liu J, Wang G, Yang G, Luo L (2015b) Identification of sand and dust storm source areas in Iran. J Arid Land 7:567–578CrossRefGoogle Scholar
  7. Choobari OA, Zawar-Reza P, Sturman A (2014) The global distribution of mineral dust and its impacts on the climate system: a review. Atmos Res 138:152–165CrossRefGoogle Scholar
  8. Cuspilici A, Monforte P, Ragusa M (2017) Study of Saharan dust influence on PM10 measures in Sicily from 2013 to 2015. Ecol Indic 76:297–303CrossRefGoogle Scholar
  9. DOE (2016) Department of Environment, Khuzestan Division. Accessed June 2018
  10. Draxler RR, Gillette DA, Kirkpatrick JS, Heller J (2001) Estimating PM10 air concentrations from dust storms in Iraq, Kuwait and Saudi Arabia. Atmos Environ 35:4315–4330CrossRefGoogle Scholar
  11. Draxler RR, Ginoux P, Stein AF (2010) An empirically derived emission algorithm for wind-blown dust. J Geophys Res Atmos 115:D16212. CrossRefGoogle Scholar
  12. Escudero M, Stein A, Draxler R, Querol X, Alastuey A, Castillo S, Avila A (2006) Determination of the contribution of northern Africa dust source areas to PM10 concentrations over the central Iberian Peninsula using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) model. J Geophys Res Atmos 111:D06210. CrossRefGoogle Scholar
  13. Francis DBK, Flamant C, Chaboureau J-P, Banks J, Cuesta J, Brindley H, Oolman L (2017) Dust emission and transport over Iraq associated with the summer Shamal winds. Aeolian Res 24:15–31CrossRefGoogle Scholar
  14. Ge Y, Abuduwaili J, Ma L, Wu N, Liu D (2016) Potential transport pathways of dust emanating from the playa of Ebinur Lake, Xinjiang, in arid northwest China. Atmos Res 178:196–206CrossRefGoogle Scholar
  15. Gebhart KA, Schichtel BA, Barna MG (2005) Directional biases in back trajectories caused by model and input data. J Air Waste Manag 55:1649–1662CrossRefGoogle Scholar
  16. Ginoux P, Prospero JM, Gill TE, Hsu NC, Zhao M (2012) Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev Geophys 50:RG3005. CrossRefGoogle Scholar
  17. Givehchi R, Arhami M, Tajrishy M (2013) Contribution of the Middle Eastern dust source areas to PM10 levels in urban receptors: case study of Tehran, Iran. Atmos Environ 75:287–295CrossRefGoogle Scholar
  18. Goudie AS, Middleton NJ (2006) Desert dust in the global system. Springer, HeidelbergGoogle Scholar
  19. Hamidi M, Kavianpour MR, Shao Y (2017) A quantitative evaluation of the 3–8 July 2009 Shamal dust storm. Aeolian Res 24:133–143CrossRefGoogle Scholar
  20. Hemond HF, Fechner EJ (2014) Chemical fate and transport in the environment. Acaemic, New YorkGoogle Scholar
  21. Hernández-Ceballos M, Skjøth C, García-Mozo H, Bolívar J, Galán C (2014) Improvement in the accuracy of back trajectories using WRF to identify pollen sources in southern Iberian Peninsula. Int J Biometeorol 58:2031–2043CrossRefGoogle Scholar
  22. Hsu NC, Herman JR, Torres O, Holben BN, Tanre D, Eck TF, Smirnov A, Chatenet B, Lavenu F (1999) Comparisons of the TOMS aerosol index with Sun-photometer aerosol optical thickness: results and applications. J Geophys Res Atmos 104(D6):6269–6279CrossRefGoogle Scholar
  23. Huang XX, Wang TJ, Jiang F, Liao JB, Cai YF, Yin CQ, Zhu JL, Han Y (2013) Studies on a severe dust storm in East Asia and its impact on the air quality of Nanjing, China. Aerosol Air Qual Res 13:179–193CrossRefGoogle Scholar
  24. Kaskaoutis DG, Rashki A, Houssos EE, Mofidi A, Goto D, Bartzokas A, Francois P, Legrand M (2015) Meteorological aspects associated with dust storms in the Sistan region, southeastern Iran. Clim Dyn 45:407–424CrossRefGoogle Scholar
  25. Khaefi M, Geravandi S, Hassani G, Yari AR, Soltani F, Dobaradaran S, Moogahi S, Mohammadi MJ, Mahboubi M, Alavi N, Farhadi M (2017) Association of particulate matter impact on prevalence of chronic obstructive pulmonary disease in Ahvaz, southwest Iran during 2009–2013. Aerosol Air Qual Res 17:230–237CrossRefGoogle Scholar
  26. Lee Y, Yang X, Wenig M (2010) Transport of dusts from East Asian and non-East Asian sources to Hong Kong during dust storm related events 1996–2007. Atmos Environ 44:3728–3738CrossRefGoogle Scholar
  27. Maleki H, Sorooshian A, Goudarzi G, Nikfal A, Baneshi MM (2016) Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res 22:135–140CrossRefGoogle Scholar
  28. McGowan H, Clark A (2008) Identification of dust transport pathways from Lake Eyre, Australia using Hysplit. Atmos Environ 42:6915–6925CrossRefGoogle Scholar
  29. Middleton N (2017) Desert dust hazards: a global review. Aeolian Res 24:53–63CrossRefGoogle Scholar
  30. Moridnejad A, Karimi N, Ariya PA (2015a) A new inventory for Middle East dust source points. Environ Monit Assess 187:582CrossRefGoogle Scholar
  31. Moridnejad A, Karimi N, Ariya PA (2015b) Newly desertified regions in Iraq and its surrounding areas: significant novel sources of global dust particles. J Arid Environ 116:1–10CrossRefGoogle Scholar
  32. 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
  33. Notaro M, Alkolibi F, Fadda E, Bakhrjy F (2013) Trajectory analysis of Saudi Arabian dust storms. J Geophys Res Atmos 118:6028–6043CrossRefGoogle Scholar
  34. Prospero JM, Ginoux P, Torres O, Nicholson SE, Gill TE (2002) Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev Geophys 40(1):1002. CrossRefGoogle Scholar
  35. Rashki A, Arjmand M, Kaskaoutis D (2017) Assessment of dust activity and dust-plume pathways over Jazmurian Basin, southeast Iran. Aeolian Res 24:145–160CrossRefGoogle Scholar
  36. Salmabadi H, Saeedi M (2018) Determination of the transport routes of and the areas potentially affected by SO2 emanating from Khatoonabad copper smelter (KCS), Kerman province, Iran using HYSPLIT. Atmos Pollut Res. Google Scholar
  37. Shahsavani A et al (2012) Characterization of ionic composition of TSP and PM 10 during the Middle Eastern Dust (MED) storms in Ahvaz, Iran. Environ Monit Assess 184:6683–6692CrossRefGoogle Scholar
  38. Sotoudeheian S, Salim R, Arhami M (2016) Impact of Middle Eastern dust sources on PM10 in Iran: highlighting the impact of Tigris-Euphrates basin sources and Lake Urmia desiccation. J Geophys Res Atmos 121:14018–14034. CrossRefGoogle Scholar
  39. Stein A, Draxler RR, Rolph GD, Stunder BJ, Cohen M, Ngan F (2015) NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 96:2059–2077CrossRefGoogle Scholar
  40. Su L, Yuan Z, Fung JC, Lau AK (2015) A comparison of HYSPLIT backward trajectories generated from two GDAS datasets. Sci Total Environ 506:527–537CrossRefGoogle Scholar
  41. Xin Y, Wang G, Chen L (2016) Identification of long-range transport pathways and potential sources of PM10 in Tibetan Plateau uplift area: case study of Xining, China in 2014. Aerosol Air Qual Res 16:1044–1054CrossRefGoogle Scholar
  42. Yu Y, Notaro M, Kalashnikova OV, Garay MJ (2016) Climatology of summer Shamal wind in the Middle East. J Geophys Res Atmos 121:289–305CrossRefGoogle Scholar

Copyright information

© University of Tehran 2019

Authors and Affiliations

  1. 1.Environment Research Laboratory, School of Civil EngineeringIran University of Science and TechnologyTehranIran

Personalised recommendations