Dust storm simulation over Iran using HYSPLIT
© Ashrafi et al.; licensee BioMed Central Ltd. 2014
Received: 9 August 2012
Accepted: 15 October 2013
Published: 7 January 2014
Particulate matters have detrimental effects on human health, environment and economic. This pollutant may emit from anthropogenic or natural sources. On global scale, main proportion of natural particulate matter release to the atmosphere because of wind erosion from arid and semi-arid regions. Recently, the amount of dust coming from Arabian countries has dramatically increased, especially dust storms that are affecting western and even central parts of Iran. This phenomenon has caused a lot of environmental problems. Dust source identification and trajectory simulation using numerical techniques are the main aims of this study. HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model dust module and trajectory simulation are utilized in this research and two case studies are investigated (in May and June 2010). The base of the HYSPLIT dust module is the PM10 dust storm emission algorithm for desert land use. This methodology is applied to estimate hotspots and trajectories. Due to the results, dust storms started on May 17th and June 7th because of high wind shear (>8.5 m/s) from the western Syrian Desert. The source region limited to 32.50 °N to 33.80 °N and 38.00 °E to 38.80 °E coordinates. Dust plumes lifted and dispersed towards the east and southeast of the sources and reached Ahvaz on May 18th and June 8th. The average of PM10 concentration in these dates reached 625 and 494 on Ahvaz monitoring stations, respectively. Moreover, the results gained from the model for dust motion simulation are similar to the MODIS satellite images.
KeywordsDust storms Dust sources Trajectory PM10 HYSPLIT MODIS satellite images
There are many drawbacks with the dust phenomenon such as environmental, socio-economic, human health, climate and microclimate problems . Some of these issues are discussed as follows.
Wind-blown dust is an effective factor for the transport of pathogens and pollutants [7, 8] and also can influence air quality downwind of dust source regions by reducing visibility, soiling property and causing illnesses [9, 10]. Inhalation of dust particles can cause heart beat irregularities, heart attacks and respiratory problems, severe and chronic headaches, severe allergies and skin diseases .
Particles such as mineral dust, by absorbing ultraviolet radiation can inhibit smog production, having profound implications in the control of air pollution in urban areas . Furthermore, the interactions between wind-blown dust and anthropogenic pollutants aggravate the generation of secondary aerosols .
Dust particles have a significant effect on climate, acting both directly (by scattering and absorbing radiation) and indirectly (by changing the optical properties of clouds) on the Earth’s radiation balance . Absorption and scattering of solar radiation caused by dust events may affect air temperatures . In another way Dust fertilization (including iron and phosphorus) of poor nutrient marine environments can increase formation of phytoplankton and can influence the global cycle of carbon .
Different Techniques have been developed to identify dust hotspots and pathways. Numerical modeling, trajectory analysis, Remote sensing and satellite imagery, dust observations and metrological data analysis, mineral tracers and geological models can be applied as the principal tools used to research dust events [4, 6, 17–20].
In Iran a few studies have been carried out to determine dust sources, trajectories, contribution of dust to urban PM10 concentrations and temporal and spatial coverage of dust by using modeling techniques . It should be mentioned that most of the conducted studies on these issues used satellite images and meteorological data analysis [22, 23]. Most of the Dust storms in Iran are coming from the western and southern neighboring countries and they affect western and central regions of Iran .
In this research, the most high risk city of Iran namely Ahvaz, is chosen as the case study. According to WHO database , Ahvaz with 372 annual mean of concentration of particulate matter is the first polluted city in the world. Finding the dust sources and the trajectories which cause the dust in Iran (specially the cities of Ahvaz and Tehran) has a significant importance. Therefore, this research uses numerical modeling techniques to study meteorological parameters, sources and trajectories of suspended particles of dust storms from wind erosions events. Surly, the results aim to control and reduce the amount of pollutions.
Materials and methods
HYSPLIT model description
The HYSPLIT model uses puff or particle approaches to compute trajectories, complex dispersion and deposition. The model computation method is a combination of Eulerian (concentrations are calculated for each grid cell using integration of pollutant fluxes at every grid cell interface due to advection and diffusion) and Lagrangian (concentrations are computed by summing the contribution of each pollutant “puff” that is advected through the grid cell as represented by its trajectory) approaches.
The model utilizes meshed meteorological data on one of three conformal map projections (Polar, Lambert and Mercator). The dispersion model requires meteorological data fields that can be obtained from archives or from forecast model outputs and the datasets should be formatted for input to HYSPLIT [25, 26].
The accuracy of the model is considerably dependent on the meteorological data resolution , For this study, we used GDASc meteorological data provided by U.S NOAA.
Where z 0ns is the aerodynamic roughness length for non-saltating conditions, z is the wind measurement height and Von Karman’s constant k is assumed to be 0.4 .
In this research, dispersion simulation is done over the study area with HYSPLIT dust module. A horizontal domain of 30° × 30° with resolution of 0.05◦ × 0.05◦ and a vertical level of 100 meters above ground level is considered in dispersion model. Pollutant concentrations are sampled in every time step and are averaged over every 12 h. The turbulence mixing is computed using a diffusivity approach based on vertical stability estimates and the horizontal wind field deformation. The puff dispersion is assumed to be linear function of time. Ground level concentrations are calculated as average of the lowest 100 m within each horizontal grid cell. HYSPLIT dust storm modeling done for 0.25° × 0.25° resolution for desert dust sources and a total of 100,000 particles or puffs are released during one release cycle with a maximum of 50,000 particles permitted to be carried at any time during the simulation. Release mode is sampled with 3-dimensional (3-D) particle horizontal and vertical option.
The trajectory calculation in any Lagrangian model is based on the following the particle or puff. Therefore, once the basic meteorological data (U, V and W) has been processed and interpolated to the model grid. Trajectories can be computed to test the advection components of the model. The advection is computed from the average of the 3-D velocity vectors for the initial-position P(t) and the first-guess position P’(t + Δt). The velocity vectors are linearly interpolated in both space and time [25, 26].
In this study back trajectory simulations were used for determining source of dust storms and motion direction of dust plume over Middle East and Iran. Back trajectories started from Ahvaz (31.24 °N, 48.49 °E) and Tehran (35.42 °N, 51.25 °E) at the time of dust arrival. For HYSPLIT trajectory setting, four trajectory tracking levels including 500, 1000, 2000 and 3000 m are considered and also the top of model assumed to be 10,000 m.
Turbulence, wind fields and mixing depth values are used as inputs for dispersion model.
Results and discussion
First, the meteorological parameters surveyed in desert areas and the results showed that high wind velocity and mixing height caused to inject and lift the dust to the atmosphere, respectively. Second, dust hotspots determined in Syrian deserts and motion of dust simulated over study area using meteorological fields. The results are discussed in details as follows.
PM 10 concentrations in Ahvaz air quality stations ( )
Date of dust event
Environment dept. station
Meteorology office station
Surface metrological parameters for two dust events in source areas
Surface height (m)
Temperature at 2 m (c)
U winddat 10 m (m/s)
V windeat 10 m (m/s)
Dust module modeling results
Dust hotspots resulted by HYSPLIT dust module
May event hotspots
(32.50 °N, 38.00 °E)
(33.80 °N, 38.30 °E)
(32.50 °N, 38.25 °E)
June event hotspots
(33.75 °N, 38.25 °E)
(33.75 °N, 38.80 °E)
In the first case in May, studying arrival altitudes in Tehran and Ahvaz indicates that vertical distribution of dust in Tehran was 1000 and in Ahvaz was about 2000–3000 m. in the second case in June; height of dust plume was up to 2000 m above ground surface.
According to Figure 7, on May 17th, dust appeared in Syria and after passing over Iraq gradually moved to the western and central parts of Iran. Also Figure 8 shows that dust storm on June 7th after covering large regions of Iraq reached Khuzestan province and finally circulated towards the Persian Gulf and Arabian Peninsula.
In this study, MODIS images confirmed the results of HYSPLIT dust modeling and it was detected that dust plumes had a circulating motion while moving towards eastern parts of Middle East.
In this research source identification and trajectory simulation of two dust storms over Iran which was caused by wind erosion are studied. The HYSPLIT model dust module was applied to a western Middle East deserts. However, the results clarified that both of dust events simulated started from Syrian Desert in similar coordinates. Due to the high shear wind speed and mixing height, dust was released from desert land-use and dispersed horizontally and vertically over the study area. In addition, strong winds transported the dust through large areas of Iraq and Kuwait reaching the significant parts of Iran in about 48 hours. Backward trajectory simulation from Tehran and Ahvaz confirmed dust sources derived by dust module. At end, dust motion in MODIS images were compared to the output of HYSPLIT simulation which showed same trends.
aScanning Imaging Absorption Spectro-Meter for Atmospheric Cartography
bHybrid Single-Particle Lagrangian Integrated Trajectory
cGlobal Data Assimilation System
dZonal component of wind
eMeridional component of wind
fModerate Resolution Imaging Spectro radiometer
The authors gratefully acknowledge the technical support provided by Dr. Majid Azadi (Atmospheric Science and Meteorological Research Center) and Mr. Hossein Vahidi (Academic Center for Education, Culture and Research).
- Wen Kuoa H, Yi Shena H: Indoor and outdoor PM2.5 and PM10 concentrations in the air during a dust storm. Build Environ 2010, 3(45):610–614.View ArticleGoogle Scholar
- Shao Y: Physics and Modeling of Wind Erosion. Germany: Springer Press; 2008.Google Scholar
- Prospero M, Ginoux P, Torres O, Nicholson E, Gill E: Environmental characterization of global source of atmospheric soil dust identified with the nimbus 7 total ozone mapping Spectrometer (TOMS) absorbing aerosol product. J Geophys Res 2002, 40(1):1–31.Google Scholar
- Escudero M, Stein A, Draxler R, Querol X, Alastuey A, Castillo S: Determination of the contribution of north 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 2006, (111):D06210. DOI: 10.1029/2005JD006395
- de Graaf M: Remote Sensing of UV-absorbing aerosols using space-borne spectrometers, Ph.D Thesis. Amsterdam: Vrije Universiteit Amsterdam; 2006. 132Google Scholar
- Gerivani H, Lashkaripour G, Ghafoori M, Jalali N: The source of dust storm in Iran: a case study based on geological information and rainfall data. Carpathian J Earth Environ Sci 2003, 6(1):297–308.Google Scholar
- Shinn EA, Smith GW, Prospero JM, Betzer P, Hayes ML, Garrison V, Barber RT: African dust and the demise of Caribbean coral reefs. Geophys Res Lett 2001, 27(19):3029–3032.View ArticleGoogle Scholar
- Wang Y, Zhuang G, Tang A, Zhan W, Sun Y, Wang Z, An Z: The evolution of chemical components of aerosols at five monitoring site of China during dust storms. Atmos Environ 2007, 41: 1091–1106. 10.1016/j.atmosenv.2006.09.015View ArticleGoogle Scholar
- McKendry G, Hacker P, Stull R, Sakiyama S, Mignacca D, Reid K: Long-range transport of Asian dust to the lower Fraser valley, British Columbia, Canada. J Geophys Res 2001, 106: 18,361–18,370. 10.1029/2000JD900359View ArticleGoogle Scholar
- Chan Y, McTainsh G, Leys J, McGowan H, Tews K: Influence of the 23 October 2002 dust storm on the air quality of four Australian cities. Water Air Soil Pollut 2005, 164: 329–348. 10.1007/s11270-005-4009-0View ArticleGoogle Scholar
- Griffin D, Kellogg C: Dust storms and their impact on ocean and human health: dust in Earth’s atmosphere. Ecohealth 2004, 1: 284–295.View ArticleGoogle Scholar
- Dickerson R, Kondragunta S, Stenchikov G, Civerolo K, Doddridge B, Holben B: The impact of aerosols on solar ultraviolet radiation and photochemical smog. Science 1997, 278: 827–830. 10.1126/science.278.5339.827View ArticleGoogle Scholar
- Alastuey A, Querol X, Castillo S, Escudero M, Avila A, Cuevas E, Torres C, Romero PM, Exposito F, Garcia O, Diaz JP, Dingenen RV, Putaud JP: Characterization of TSP and PM2.5 At izanea and Sta. Cruz de Tenerife (canary islands, Spain) during a Saharan dust episode (July 2002). Atmos Environ 2005, 39(26):4715–4728. 10.1016/j.atmosenv.2005.04.018View ArticleGoogle Scholar
- IPCC: Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Working Group I: the Physical Science Basis of Climate Change. Summary for Policymakers. Geneva, Switzerland: Intergovernmental Panel on Climate Change; 2007.Google Scholar
- Miller RL, Tegen I: Climate response to soil dust aerosols. J Clim 1998, 11: 3247–3267. 10.1175/1520-0442(1998)011<3247:CRTSDA>2.0.CO;2View ArticleGoogle Scholar
- Gao Y, Fan S, Sariento JL: Aeolian iron input to the ocean through precipitation scavenging: a modelling perspective and its implications for natural iron fertilization in the ocean. J Geophys Res 2003, 108(7):4221.View ArticleGoogle Scholar
- Draxler R, Gillette A, Kirkpatrick S, Heller J: Estimating PM10 air concentrations from dust storms in Iraq, Kuwait and Saudi Arabia. Atmos Environ 2001, 35: 4315–4330. 10.1016/S1352-2310(01)00159-5View ArticleGoogle Scholar
- Xuan J: Emission inventory of eight elements, Fe, Al, K, Mg, Mn, Na, Ca and Ti, in dust source region of East Asia. Atmos Environ 2005, 39: 813–821. 10.1016/j.atmosenv.2004.10.029View ArticleGoogle Scholar
- Wang W, Fang ZY: Numerical simulation and synoptic analysis of dust emission and transport in East Asia. Glob Planet Chang 2006, 52: 57–70. 10.1016/j.gloplacha.2006.02.004View ArticleGoogle Scholar
- Alam K, Qureshi S, Blaschke T: Monitoring Spatio-temporal aerosol patterns over Pakistan based on MODIS, TOMS and MISR satellite data and a HYSPLIT model. Atmos Environ 2011, 45: 4641–4651. 10.1016/j.atmosenv.2011.05.055View ArticleGoogle Scholar
- Givehchi R, Arhami M, Tajrishy M: Contribution of the middle eastern dust source areas to PM10 levels in urban receptors: case study of Tehran, Iran. Atmos Environ 2013, 75: 287–295.View ArticleGoogle Scholar
- Zolfaghari H, Abedzadeh H: Synoptic analysis of dust sources in west of Iran. Journal of Geography and Development 2005, 3(6):173–188. (in Persian)Google Scholar
- Iranmanesh F, Akram M: Survey on sources areas and characteristics of dust storms dispersion in sistan region using satellite imagery processing. Construction and research journal 2003, 67: 104. (in Persian)Google Scholar
- WHO: Urban Outdoor air Pollution Database. Department of Public Health and Environment. Geneva, Switzerland: World Health Organization; 2011. http://www.who.int/phe/health_topics/outdoorair/databases/OAP_database.xlsGoogle Scholar
- Draxler R, Hess GD: An overview of the HYSPLIT_4 modeling system for trajectories, dispersion and deposition. Aust Meteorol Mag 1998, 47: 295–308.Google Scholar
- Draxler R, Stunder B, Rolph G, Stein A, Taylor A: Hybrid Single-Particle Lagrangian Integrated. United States: NOAA; 2009.Google Scholar
- Challa VS, Indrcanti J, Baham JM, Patrick C, Rabarison MK, Young JH, Hughes R, Swanier SJ, Hardy MG: Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model. Environ Fluid Mech 2008, 8(4):387–367. trajectories 4 user’s guide. NOAA Tech. Memo, ERL-ARLView ArticleGoogle Scholar
- Marticorena B, Bergametti G, Gillette D, Belnap J: Factors controlling threshold friction velocity in semiarid and arid areas of the United States. J Geophys Res 1997, 102: 23,277–23,287. 10.1029/97JD01303View ArticleGoogle Scholar
- Westphal L, Toon OB, Carlson TN: A two-dimensional numerical investigation of the dynamics and microphysics of Saharan dust storms. J Geophys Res 1987, 92(3):3027–3049.View ArticleGoogle Scholar
- IR.DOE data: Ahvaz air Pollution Monitoring Stations Datasets. Tehran, Iran: Department of Environment; 2011.Google Scholar
- MODIS rapid response: Satellite Images. 2010. http://rapidfire.sci.gsfc.nasa.gov/realtime/2010158/Google Scholar
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