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Volume 37, Issue 3 (2022)
GeoRes 2022, 37(3): 313-326 |
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Received: 2022/05/26 | Accepted: 2022/07/27 | Published: 2022/07/1
Received: 2022/05/26 | Accepted: 2022/07/27 | Published: 2022/07/1
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Sheikhghaderi S, Alizadeh T, Rezaei Banafsheh M. Dust Storm Detection using Weather Rcscarch Forcast-CHEM Model and Aerosol Optical Depth Product Moderate Resolution Imaging Spectro Radiometer Sensor (Case Study: Kermanshah). GeoRes 2022; 37 (3) :313-326
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1- Department of Remote Sensing and Geographic Information System, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
2- Department of Meteorology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
2- Department of Meteorology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
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Introduction
Dust particles refer to solid or liquid particles suspended in the atmosphere. By scattering and absorbing solar radiation, these particles influence atmospheric optical processes and climatic characteristics. Temperature variations induced by the scattering and absorption of sunlight by atmospheric particulates, regardless of whether these variations are cooling or warming in nature, are significantly dependent on the size and concentration of these particles [Nikfal, 2014]. Therefore, any investigation into the physical, chemical, and optical properties of aerosols is of great importance for enhancing our understanding of the region’s atmospheric aerosols and their effects on natural and human environments.
Identifying the particle size of atmospheric aerosols is typically achieved through laboratory measurements and analyses, which require substantial financial and time resources. Over recent decades, the detection of dust characteristics has been increasingly conducted using numerical modeling capabilities and the unique spectral behavior of dust particles across visible to thermal infrared wavelengths, facilitated by the operation of meteorological and environmental satellites [Miri et al., 2021]. Modeling approaches, particularly remote sensing techniques and satellite imagery have become among the most effective tools for examining dust characteristics and tracing dust transport pathways [Myhre et al., 2005]. Satellite images cover wide spatial extents, overcome the limitations of surface stations, and serve as valuable complementary sources to ground-based observations [Grell et al., 2005]. Their repeatability also makes them essential instruments for monitoring dust and its transport [Wang & Sunder, 2003].
Given the advantages of numerical modeling and remote sensing data, numerical simulations and remote sensing methods have increasingly been recognized as suitable tools for improving understanding of dust phenomena. Numerous global studies have been conducted to determine particle concentrations and identify dust emission sources using these methods. The Aerosol Optical Depth (AOD) product of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is provided daily at a spatial resolution of 1 km. AOD represents the amount and concentration of atmospheric aerosols. By definition, optical depth is a measure of the attenuation of solar radiation by suspended particles and dust, determined through absorption and scattering mechanisms. This dimensionless parameter also provides an indication of atmospheric turbidity [Qi et al., 2013].
In any scientific field, the existence of prior research and familiarity with related studies contributes significantly to advancing the discipline and obtaining logical new results. Accordingly, several national and international studies related to the present research topic are reviewed. Liu et al. (2011), through simulating MODIS aerosol optical depth using the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) and a Geographic Information System (GIS) in China, have concluded that dust simulation through numerical modeling enhances air-quality analysis and forecasting. Eltahan et al. (2018), in their simulation of severe dust events in Egypt using WRF-CHEM schemes and AOD indices, have demonstrated that model results can be brought closer to satellite-derived and Aerosol Robotic Network (AERONET) observations by applying event-specific coefficients. Asadilotfi et al. (2018) have reviewed dust forecasting models and data collection techniques, summarizing information gathering approaches, required datasets, and dust prediction models for temporal and spatial dust assessments.
Iran, due to its particular climatic and geological characteristics, is highly affected by issues such as sandstorms and elevated dust concentrations [Rezazadeh et al., 2011]. In recent years, intensified desertification in Iraq, Saudi Arabia, and Syria has increasingly influenced various aspects of life in Kermanshah Province, located near major dust-source regions. Considering the adverse impacts and increasing frequency of dust storms, the identification, monitoring, and spatiotemporal analysis of dust characteristics are crucial for crisis management and mitigating their detrimental effects. Over recent decades, dust phenomena in Iran have been examined from various statistical [Lashkari & Kaykhosravi, 2008; Miri et al., 2011], synoptic [Miri et al., 2014; Toulabi Nejad et al., 2018; Alizadeh et al., 2020], and modeling perspectives [Rezazadeh et al., 2013; Nikfal, 2014; Nikfal et al., 2017].
A review of previous studies indicates that less attention has been paid to the characteristics of dust, particularly the particle size of dust entering the country. Therefore, the present study aims to investigate the size and nature of aerosol particles associated with a widespread dust event and to identify their emission sources using an integrated approach combining remote sensing and numerical modeling.
Methodology
In the present study, a combination of statistical, modeling, and remote sensing methods was employed to detect and analyze dust storm events. First, present-weather code data for the study area were obtained from the Iran Meteorological Organization. Dust-related weather codes were then classified and extracted. Although the number of dusty days over the long-term record is considerable, the primary criterion for selecting days for numerical modeling and remote sensing analysis was the spatial extent of dust coverage across the entire study area. Accordingly, three events, 17 June 2016, 2 November 2017, and 27 October 2018, were selected based on their substantial spatial coverage to investigate the characteristics of incoming dust particles using satellite imagery and numerical modeling.
Given that Kermanshah Province is located in western Iran, adjacent to major subtropical desert regions such as Iraq, Syria, and Saudi Arabia, and is frequently affected by dust storms originating from these areas, it was chosen as the study region. Geographically, Kermanshah lies between 47°04′ E longitude and 34°19′ N latitude. The county covers an area of approximately 24,640 km², and its meteorological station is situated at an elevation of 1,420 meters above sea level. The region features a dry to semi-arid climate influenced by Mediterranean humid air masses. Its average annual precipitation ranges between 400 and 500 mm, and its mean annual temperature is about 16°C. The prevailing wind direction throughout the year is from the west, with occasional shifts toward the northwest and southwest.
WRF-CHEM Simulation Model
In this study, numerical modeling was employed to assess air quality and simulate PM₁₀ concentrations in Kermanshah. Because dust concentration modeling is part of a complex atmospheric quality assessment process encompassing meteorological parameters such as wind speed and direction, atmospheric turbulence, radiation, cloud formation, precipitation, and chemical processes such as aerosol emission, deposition, and transport a robust and comprehensive model was required. The WRF-CHEM model used in this research is designed for such applications, offering dual dynamical cores, three-dimensional parameter datasets, simulation systems, and software architecture that support parallel computing and system expansion. It includes both the Non-Hydrostatic Mesoscale Model (NMM) and the Advanced Research WRF dynamical core, making it suitable for a wide range of spatial scales from a few meters to several thousand meters, and even for planetary-scale simulations [Stockwell, 1997].
WRF-CHEM incorporates several aerosol and atmospheric chemistry modules, including RADM2, MADE/SORGAM, GOCART, and the Madronich photolysis scheme, which are used for parameterizing different aerosol species and atmospheric pollutants [Goudie, 2001]. In this study, the advanced MADE-SORGAM scheme was applied as the aerosol emission scheme [Ackermann, 1998]. This scheme, originally developed for modeling dust particles in Europe [Binkowski & Shankar, 1995], represents atmospheric aerosols in three modal size ranges, particles smaller than 0.1 µm, accumulation-mode particles between 0.1 and 2 µm, and coarse particles larger than 2 µm, each following a log-normal distribution. Aerosol particles with diameters smaller than 10 µm also follow a log-normal distribution, which forms a fundamental basis for aerosol parameterizations.
The simulation domain consisted of a grid of 171×146 points at a spatial resolution of 10 km, covering parts of the Middle East and nearly the entirety of Iran, with western Iran and the city of Kermanshah positioned at the center of the domain. Meteorological fields were simulated using initial and boundary conditions derived from FNL reanalysis data, obtained from the National Centers for Environmental Prediction (NCEP) at 6-hour intervals and 1°×1° spatial resolution [Nikfal et al., 2017]. Using WRF-CHEM version 3.6 and the MADE-SORGAM scheme, PM₁₀ concentrations were estimated through online simulation. Post-processing of the PM₁₀ concentration fields was performed, and regions with maximum PM₁₀ levels were identified as the primary dust emission sources.
Use of Satellite Imagery and Dust Enhancement Methods
MODIS (Moderate Resolution Imaging Spectroradiometer) data are provided as ready-to-use thematic products, one of the most important being aerosol optical depth (AOD). Higher AOD values indicate higher aerosol content, making this product widely used in dust-related research [Drury et al., 2008]. The MCD19A2 Version 6 MODIS product offers atmospherically corrected, multi-angle AOD retrievals from the Aqua and Terra satellites, with a daily spatial resolution of 1 km. AOD represents the optical thickness of atmospheric aerosols, indicating the extent to which airborne particles prevent sunlight from passing through the atmosphere. Aerosols scatter and absorb incoming solar radiation, reducing visibility and increasing optical depth.
From a ground-based observer perspective, an AOD below 0.1 corresponds to a bright, clear sky with high visibility; as AOD increases from 0.1 to 0.5, and particularly above 0.3, dust loading becomes substantial enough that the sun may no longer be visible. The MCD19A2 AOD dataset includes blue-band AOD at 0.47 µm and green-band AOD at 0.55 µm. In addition to monitoring dust storms, this product is also used for detecting volcanic ash and assessing air pollution [Munchak et al., 2014].
Findings
Simulation of PM₁₀ Concentration Using the WRF-CHEM Model
The simulation results showed that on 16 June 2016, the initial dust plume developed over central Iraq under the influence of westerly winds. As time progressed, the concentration of suspended particles increased and the dust mass was transported toward the western regions of Iran by strengthening westerly and northwesterly flows. Analysis of PM₁₀ concentration distribution maps indicated that the dust was initially generated over the deserts of Syria and Jordan and subsequently expanded across the central and western deserts of Iraq. The development and intensification of dust storms were strongly affected by local geographical conditions, particularly topography.
On 17 June 2016, the concentration of dust particles increased significantly across the western parts of Iran. The pressure pattern, consisting of an Arabian high-pressure system to the south and a low-pressure system to the north of the study region, generated northwesterly–southeasterly wind flows, which guided the dust plume into the study area. Dust concentrations exceeded 3000 μg m⁻³ on this day. On 18 June 2016, the dust load gradually decreased.
AOD analysis shows that on 16 June 2016, optical depth values were relatively high due to the vertical expansion of the dust plume into the upper atmospheric layers. On 17 June, the formation of a severe dust storm and the uplift of dust to lower atmospheric layers increased optical depth to about 1.0, indicating high particle density. On 18 June, the dominance of westerly and northwesterly winds led to a reduction in optical depth, reflecting improved atmospheric clarity although visibility remained affected.
On 1 November 2017, a major dust plume formed over Syria, Jordan, northern Saudi Arabia, and the central and western deserts of Iraq. Dust concentrations increased further due to westerly and northwesterly winds. On 2 November, the presence of a low-pressure system in the mid-latitudes enhanced dust transport across the region. The combined influence of the Arabian high-pressure system and a low-pressure system to the north directed the dust plume toward Iran. Additionally, another dust system originating from the Arabian Peninsula entered the southwestern and western parts of Iran, causing simultaneous dust transport from multiple source regions. Dust concentrations exceeded 1500 μg m⁻³. On 3 November, with the establishment of northerly winds and anticyclonic circulation, the amount of suspended particles in the atmosphere decreased sharply.
AOD data confirm these findings, showing moderate optical depth values at the onset of the event, followed by a notable increase during the peak of the storm, and a subsequent decline as westerly and northwesterly winds prevailed.
On 26 October 2018, dust plume formation occurred over northern Saudi Arabia and southern and central Iraq. The presence of a high-pressure system over the Arabian Peninsula and a thermal low to the northwest of the study region enhanced wind convergence and intensified wind speeds. Cyclonic circulation within this pressure configuration drew dust-laden air from the deserts of Iraq, Kuwait, and northern Saudi Arabia toward the Persian Gulf and western Iran, establishing favorable conditions for the development of a dust storm. During the following hours, dust spread over Kermanshah, western Khuzestan, Ilam, and parts of Lorestan.
On 27 October, another dust core formed over northern Iraq, eastern Syria, and Jordan under a low-pressure system with counter-clockwise circulation. The interaction between a polar trough and a subtropical ridge strengthened wind flows from northwest to southeast, further transporting dust into the study region. A jet streak between these two systems elevated wind speeds in the mid-troposphere and intensified dust transport. As a result, PM₁₀ concentrations increased significantly and exceeded 3000 μg m⁻³. On 28 October, dust concentrations declined following a shift in the atmospheric circulation pattern.
AOD measurements for this event indicate moderate optical depth during the early stages, followed by a sharp increase during peak dust activity, and a subsequent decrease as northwesterly winds became dominant.
Discussion
In this study, a combination of remote sensing techniques and numerical modeling was employed to detect, track, and evaluate the concentration of dust particles transported into Kermanshah during several major dust events over a three-year period. Iran’s location within the arid and semi-arid belt of the Northern Hemisphere, together with its proximity to subtropical desert regions such as Iraq, Syria, and Saudi Arabia, has resulted in the frequent occurrence of dust-related hazards across its western half. Using the WRF-CHEM model and MODIS AOD products, this study investigated the characteristics of dust storms affecting the city of Kermanshah.
Online air-quality modeling with the simultaneous incorporation of meteorological and atmospheric chemistry parameters plays a crucial role in accurately simulating dust concentration, particularly when predicting PM₁₀ levels. The model outputs showed that the PM₁₀ time series for Kermanshah exhibited a sharp increase at the onset of each storm, indicating that the model successfully reproduced the temporal evolution and intensity of the events. Moreover, the spatial distribution patterns of PM₁₀ demonstrated that the model accurately identified the major dust-emission sources. Therefore, the performance of the advanced MADE-SORGAM aerosol scheme in estimating PM₁₀ concentrations, particularly regarding spatial variability and storm detection was found to be satisfactory and reliable.
According to the WRF-CHEM simulations, the main driver of the June 2016 dust outbreak was not only the instability generated by surface thermal lows but also the influence of the Arabian high-pressure system, which enhanced wind convergence and produced northwesterly–southeasterly flows toward western Iran. Despite mid-tropospheric instability, the lack of sufficient moisture within the advected air masses, the passage of airflows over the deserts of Iraq and Syria, and the dry surface and soil conditions across both Iran and its neighboring countries facilitated the formation of dust sources and their subsequent transport toward the study area.
In November 2017, the presence of a strong Arabian high-pressure system and a low-pressure system north of the study region generated northwesterly–southeasterly winds that transported dust into Kermanshah. Eastward movement of the low-pressure system over Iraq and Syria further intensified dust transport into western Iran. Additionally, a simultaneous dust storm originating from the Arabian Peninsula affected the southwestern and western parts of the country. Thus, during this event, dust was transported from multiple source regions through two distinct atmospheric systems.
In October 2018, WRF-CHEM outputs indicated the formation of dust cores over the northern deserts of Saudi Arabia and the southern and central deserts of Iraq. The combined presence of a high-pressure system over the Arabian Peninsula and a thermal low to the northwest enhanced flow convergence and increased wind speeds. Cyclonic circulation within this system drew air masses laden with dust from Iraq, Kuwait, and northern Saudi Arabia toward the Persian Gulf and western Iran, creating favorable conditions for intense dust generation and transport. Across all investigated events, June 2016, November 2017, and October 2018, the model results confirmed that the incoming dust plumes followed a northwesterly–southeasterly trajectory and primarily originated from northwestern Iraq (the Iraq–Syria border region), central Iraq, and the desert regions of Saudi Arabia.
Enhancement of airborne dust using MODIS AOD products also revealed the presence of high aerosol loads during these episodes, confirming that the degraded air quality in western Iran, particularly in Kermanshah, was directly linked to transboundary dust storms. Incorporating AOD data improved the accuracy of storm characterization and provided complementary insight into the intensity and spatial extent of dust loading. The strong consistency observed between WRF-CHEM outputs and MODIS AOD data suggests that both tools are highly effective for detecting and evaluating dust events in this region.
Findings from this research align well with those of previous studies that used WRF-CHEM and MODIS AOD. For example, Alizadeh et al. (2020) have employed WRF-CHEM and HYSPLIT to track dust storms and identified the western and central Iraqi deserts, Syria, and northern Saudi Arabia as the primary source regions, consistent with the present results. Similarly, Miri et al. (2021), using WRF-CHEM and MODIS AOD, reported that eastern Syria and eastern Iraq serve as the dominant dust-emission sources affecting western and southwestern Iran, with both datasets showing strong agreement. Other research using WRF-CHEM to simulate PM₁₀ in eastern Iran demonstrated that the highest dust concentrations aligned well with known dust sources in the dried bed of the Hamoun wetlands. Furthermore, studies employing the HTAP scheme within WRF-CHEM to analyze spatiotemporal variations in aerosols, tropospheric ozone, and dust over the Middle East have also confirmed the model’s high capability in identifying dust-source regions and characterizing their properties.
Overall, the strong agreement between WRF-CHEM simulations and MODIS AOD products in this study confirms the robustness and applicability of the WRF-CHEM model for air-quality modeling and dust-storm prediction. This is particularly relevant for aerosols originating from natural emission sources such as erodible landscapes and desert environments.
Conclusion
MODIS AOD imagery, together with WRF-CHEM model simulations, demonstrates satisfactory effectiveness in identifying the characteristics and source regions of severe dust storms affecting the Kermanshah region.
Acknowledgements: We extend our sincere appreciation to all individuals who supported us in conducting this research.
Ethical Approval: This article has not been published in any domestic or international journal.
Conflict of Interest: This article is not derived from any project or thesis, and the authors report no conflicts of interest.
Authors’ Contributions: Sheikhghaderi SH (First Author), Methodologist/Main Researcher (40%); Alizadeh T (Second Author), Assistant Researcher/Statistical Analyst (35%); Rezaei Banafsheh M (Third Author), Introduction Writer/Discussion Writer (25%)
Funding: All expenses required for conducting this article were fully covered by the authors.
Dust particles refer to solid or liquid particles suspended in the atmosphere. By scattering and absorbing solar radiation, these particles influence atmospheric optical processes and climatic characteristics. Temperature variations induced by the scattering and absorption of sunlight by atmospheric particulates, regardless of whether these variations are cooling or warming in nature, are significantly dependent on the size and concentration of these particles [Nikfal, 2014]. Therefore, any investigation into the physical, chemical, and optical properties of aerosols is of great importance for enhancing our understanding of the region’s atmospheric aerosols and their effects on natural and human environments.
Identifying the particle size of atmospheric aerosols is typically achieved through laboratory measurements and analyses, which require substantial financial and time resources. Over recent decades, the detection of dust characteristics has been increasingly conducted using numerical modeling capabilities and the unique spectral behavior of dust particles across visible to thermal infrared wavelengths, facilitated by the operation of meteorological and environmental satellites [Miri et al., 2021]. Modeling approaches, particularly remote sensing techniques and satellite imagery have become among the most effective tools for examining dust characteristics and tracing dust transport pathways [Myhre et al., 2005]. Satellite images cover wide spatial extents, overcome the limitations of surface stations, and serve as valuable complementary sources to ground-based observations [Grell et al., 2005]. Their repeatability also makes them essential instruments for monitoring dust and its transport [Wang & Sunder, 2003].
Given the advantages of numerical modeling and remote sensing data, numerical simulations and remote sensing methods have increasingly been recognized as suitable tools for improving understanding of dust phenomena. Numerous global studies have been conducted to determine particle concentrations and identify dust emission sources using these methods. The Aerosol Optical Depth (AOD) product of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is provided daily at a spatial resolution of 1 km. AOD represents the amount and concentration of atmospheric aerosols. By definition, optical depth is a measure of the attenuation of solar radiation by suspended particles and dust, determined through absorption and scattering mechanisms. This dimensionless parameter also provides an indication of atmospheric turbidity [Qi et al., 2013].
In any scientific field, the existence of prior research and familiarity with related studies contributes significantly to advancing the discipline and obtaining logical new results. Accordingly, several national and international studies related to the present research topic are reviewed. Liu et al. (2011), through simulating MODIS aerosol optical depth using the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) and a Geographic Information System (GIS) in China, have concluded that dust simulation through numerical modeling enhances air-quality analysis and forecasting. Eltahan et al. (2018), in their simulation of severe dust events in Egypt using WRF-CHEM schemes and AOD indices, have demonstrated that model results can be brought closer to satellite-derived and Aerosol Robotic Network (AERONET) observations by applying event-specific coefficients. Asadilotfi et al. (2018) have reviewed dust forecasting models and data collection techniques, summarizing information gathering approaches, required datasets, and dust prediction models for temporal and spatial dust assessments.
Iran, due to its particular climatic and geological characteristics, is highly affected by issues such as sandstorms and elevated dust concentrations [Rezazadeh et al., 2011]. In recent years, intensified desertification in Iraq, Saudi Arabia, and Syria has increasingly influenced various aspects of life in Kermanshah Province, located near major dust-source regions. Considering the adverse impacts and increasing frequency of dust storms, the identification, monitoring, and spatiotemporal analysis of dust characteristics are crucial for crisis management and mitigating their detrimental effects. Over recent decades, dust phenomena in Iran have been examined from various statistical [Lashkari & Kaykhosravi, 2008; Miri et al., 2011], synoptic [Miri et al., 2014; Toulabi Nejad et al., 2018; Alizadeh et al., 2020], and modeling perspectives [Rezazadeh et al., 2013; Nikfal, 2014; Nikfal et al., 2017].
A review of previous studies indicates that less attention has been paid to the characteristics of dust, particularly the particle size of dust entering the country. Therefore, the present study aims to investigate the size and nature of aerosol particles associated with a widespread dust event and to identify their emission sources using an integrated approach combining remote sensing and numerical modeling.
Methodology
In the present study, a combination of statistical, modeling, and remote sensing methods was employed to detect and analyze dust storm events. First, present-weather code data for the study area were obtained from the Iran Meteorological Organization. Dust-related weather codes were then classified and extracted. Although the number of dusty days over the long-term record is considerable, the primary criterion for selecting days for numerical modeling and remote sensing analysis was the spatial extent of dust coverage across the entire study area. Accordingly, three events, 17 June 2016, 2 November 2017, and 27 October 2018, were selected based on their substantial spatial coverage to investigate the characteristics of incoming dust particles using satellite imagery and numerical modeling.
Given that Kermanshah Province is located in western Iran, adjacent to major subtropical desert regions such as Iraq, Syria, and Saudi Arabia, and is frequently affected by dust storms originating from these areas, it was chosen as the study region. Geographically, Kermanshah lies between 47°04′ E longitude and 34°19′ N latitude. The county covers an area of approximately 24,640 km², and its meteorological station is situated at an elevation of 1,420 meters above sea level. The region features a dry to semi-arid climate influenced by Mediterranean humid air masses. Its average annual precipitation ranges between 400 and 500 mm, and its mean annual temperature is about 16°C. The prevailing wind direction throughout the year is from the west, with occasional shifts toward the northwest and southwest.
WRF-CHEM Simulation Model
In this study, numerical modeling was employed to assess air quality and simulate PM₁₀ concentrations in Kermanshah. Because dust concentration modeling is part of a complex atmospheric quality assessment process encompassing meteorological parameters such as wind speed and direction, atmospheric turbulence, radiation, cloud formation, precipitation, and chemical processes such as aerosol emission, deposition, and transport a robust and comprehensive model was required. The WRF-CHEM model used in this research is designed for such applications, offering dual dynamical cores, three-dimensional parameter datasets, simulation systems, and software architecture that support parallel computing and system expansion. It includes both the Non-Hydrostatic Mesoscale Model (NMM) and the Advanced Research WRF dynamical core, making it suitable for a wide range of spatial scales from a few meters to several thousand meters, and even for planetary-scale simulations [Stockwell, 1997].
WRF-CHEM incorporates several aerosol and atmospheric chemistry modules, including RADM2, MADE/SORGAM, GOCART, and the Madronich photolysis scheme, which are used for parameterizing different aerosol species and atmospheric pollutants [Goudie, 2001]. In this study, the advanced MADE-SORGAM scheme was applied as the aerosol emission scheme [Ackermann, 1998]. This scheme, originally developed for modeling dust particles in Europe [Binkowski & Shankar, 1995], represents atmospheric aerosols in three modal size ranges, particles smaller than 0.1 µm, accumulation-mode particles between 0.1 and 2 µm, and coarse particles larger than 2 µm, each following a log-normal distribution. Aerosol particles with diameters smaller than 10 µm also follow a log-normal distribution, which forms a fundamental basis for aerosol parameterizations.
The simulation domain consisted of a grid of 171×146 points at a spatial resolution of 10 km, covering parts of the Middle East and nearly the entirety of Iran, with western Iran and the city of Kermanshah positioned at the center of the domain. Meteorological fields were simulated using initial and boundary conditions derived from FNL reanalysis data, obtained from the National Centers for Environmental Prediction (NCEP) at 6-hour intervals and 1°×1° spatial resolution [Nikfal et al., 2017]. Using WRF-CHEM version 3.6 and the MADE-SORGAM scheme, PM₁₀ concentrations were estimated through online simulation. Post-processing of the PM₁₀ concentration fields was performed, and regions with maximum PM₁₀ levels were identified as the primary dust emission sources.
Use of Satellite Imagery and Dust Enhancement Methods
MODIS (Moderate Resolution Imaging Spectroradiometer) data are provided as ready-to-use thematic products, one of the most important being aerosol optical depth (AOD). Higher AOD values indicate higher aerosol content, making this product widely used in dust-related research [Drury et al., 2008]. The MCD19A2 Version 6 MODIS product offers atmospherically corrected, multi-angle AOD retrievals from the Aqua and Terra satellites, with a daily spatial resolution of 1 km. AOD represents the optical thickness of atmospheric aerosols, indicating the extent to which airborne particles prevent sunlight from passing through the atmosphere. Aerosols scatter and absorb incoming solar radiation, reducing visibility and increasing optical depth.
From a ground-based observer perspective, an AOD below 0.1 corresponds to a bright, clear sky with high visibility; as AOD increases from 0.1 to 0.5, and particularly above 0.3, dust loading becomes substantial enough that the sun may no longer be visible. The MCD19A2 AOD dataset includes blue-band AOD at 0.47 µm and green-band AOD at 0.55 µm. In addition to monitoring dust storms, this product is also used for detecting volcanic ash and assessing air pollution [Munchak et al., 2014].
Findings
Simulation of PM₁₀ Concentration Using the WRF-CHEM Model
The simulation results showed that on 16 June 2016, the initial dust plume developed over central Iraq under the influence of westerly winds. As time progressed, the concentration of suspended particles increased and the dust mass was transported toward the western regions of Iran by strengthening westerly and northwesterly flows. Analysis of PM₁₀ concentration distribution maps indicated that the dust was initially generated over the deserts of Syria and Jordan and subsequently expanded across the central and western deserts of Iraq. The development and intensification of dust storms were strongly affected by local geographical conditions, particularly topography.
On 17 June 2016, the concentration of dust particles increased significantly across the western parts of Iran. The pressure pattern, consisting of an Arabian high-pressure system to the south and a low-pressure system to the north of the study region, generated northwesterly–southeasterly wind flows, which guided the dust plume into the study area. Dust concentrations exceeded 3000 μg m⁻³ on this day. On 18 June 2016, the dust load gradually decreased.
AOD analysis shows that on 16 June 2016, optical depth values were relatively high due to the vertical expansion of the dust plume into the upper atmospheric layers. On 17 June, the formation of a severe dust storm and the uplift of dust to lower atmospheric layers increased optical depth to about 1.0, indicating high particle density. On 18 June, the dominance of westerly and northwesterly winds led to a reduction in optical depth, reflecting improved atmospheric clarity although visibility remained affected.
On 1 November 2017, a major dust plume formed over Syria, Jordan, northern Saudi Arabia, and the central and western deserts of Iraq. Dust concentrations increased further due to westerly and northwesterly winds. On 2 November, the presence of a low-pressure system in the mid-latitudes enhanced dust transport across the region. The combined influence of the Arabian high-pressure system and a low-pressure system to the north directed the dust plume toward Iran. Additionally, another dust system originating from the Arabian Peninsula entered the southwestern and western parts of Iran, causing simultaneous dust transport from multiple source regions. Dust concentrations exceeded 1500 μg m⁻³. On 3 November, with the establishment of northerly winds and anticyclonic circulation, the amount of suspended particles in the atmosphere decreased sharply.
AOD data confirm these findings, showing moderate optical depth values at the onset of the event, followed by a notable increase during the peak of the storm, and a subsequent decline as westerly and northwesterly winds prevailed.
On 26 October 2018, dust plume formation occurred over northern Saudi Arabia and southern and central Iraq. The presence of a high-pressure system over the Arabian Peninsula and a thermal low to the northwest of the study region enhanced wind convergence and intensified wind speeds. Cyclonic circulation within this pressure configuration drew dust-laden air from the deserts of Iraq, Kuwait, and northern Saudi Arabia toward the Persian Gulf and western Iran, establishing favorable conditions for the development of a dust storm. During the following hours, dust spread over Kermanshah, western Khuzestan, Ilam, and parts of Lorestan.
On 27 October, another dust core formed over northern Iraq, eastern Syria, and Jordan under a low-pressure system with counter-clockwise circulation. The interaction between a polar trough and a subtropical ridge strengthened wind flows from northwest to southeast, further transporting dust into the study region. A jet streak between these two systems elevated wind speeds in the mid-troposphere and intensified dust transport. As a result, PM₁₀ concentrations increased significantly and exceeded 3000 μg m⁻³. On 28 October, dust concentrations declined following a shift in the atmospheric circulation pattern.
AOD measurements for this event indicate moderate optical depth during the early stages, followed by a sharp increase during peak dust activity, and a subsequent decrease as northwesterly winds became dominant.
Discussion
In this study, a combination of remote sensing techniques and numerical modeling was employed to detect, track, and evaluate the concentration of dust particles transported into Kermanshah during several major dust events over a three-year period. Iran’s location within the arid and semi-arid belt of the Northern Hemisphere, together with its proximity to subtropical desert regions such as Iraq, Syria, and Saudi Arabia, has resulted in the frequent occurrence of dust-related hazards across its western half. Using the WRF-CHEM model and MODIS AOD products, this study investigated the characteristics of dust storms affecting the city of Kermanshah.
Online air-quality modeling with the simultaneous incorporation of meteorological and atmospheric chemistry parameters plays a crucial role in accurately simulating dust concentration, particularly when predicting PM₁₀ levels. The model outputs showed that the PM₁₀ time series for Kermanshah exhibited a sharp increase at the onset of each storm, indicating that the model successfully reproduced the temporal evolution and intensity of the events. Moreover, the spatial distribution patterns of PM₁₀ demonstrated that the model accurately identified the major dust-emission sources. Therefore, the performance of the advanced MADE-SORGAM aerosol scheme in estimating PM₁₀ concentrations, particularly regarding spatial variability and storm detection was found to be satisfactory and reliable.
According to the WRF-CHEM simulations, the main driver of the June 2016 dust outbreak was not only the instability generated by surface thermal lows but also the influence of the Arabian high-pressure system, which enhanced wind convergence and produced northwesterly–southeasterly flows toward western Iran. Despite mid-tropospheric instability, the lack of sufficient moisture within the advected air masses, the passage of airflows over the deserts of Iraq and Syria, and the dry surface and soil conditions across both Iran and its neighboring countries facilitated the formation of dust sources and their subsequent transport toward the study area.
In November 2017, the presence of a strong Arabian high-pressure system and a low-pressure system north of the study region generated northwesterly–southeasterly winds that transported dust into Kermanshah. Eastward movement of the low-pressure system over Iraq and Syria further intensified dust transport into western Iran. Additionally, a simultaneous dust storm originating from the Arabian Peninsula affected the southwestern and western parts of the country. Thus, during this event, dust was transported from multiple source regions through two distinct atmospheric systems.
In October 2018, WRF-CHEM outputs indicated the formation of dust cores over the northern deserts of Saudi Arabia and the southern and central deserts of Iraq. The combined presence of a high-pressure system over the Arabian Peninsula and a thermal low to the northwest enhanced flow convergence and increased wind speeds. Cyclonic circulation within this system drew air masses laden with dust from Iraq, Kuwait, and northern Saudi Arabia toward the Persian Gulf and western Iran, creating favorable conditions for intense dust generation and transport. Across all investigated events, June 2016, November 2017, and October 2018, the model results confirmed that the incoming dust plumes followed a northwesterly–southeasterly trajectory and primarily originated from northwestern Iraq (the Iraq–Syria border region), central Iraq, and the desert regions of Saudi Arabia.
Enhancement of airborne dust using MODIS AOD products also revealed the presence of high aerosol loads during these episodes, confirming that the degraded air quality in western Iran, particularly in Kermanshah, was directly linked to transboundary dust storms. Incorporating AOD data improved the accuracy of storm characterization and provided complementary insight into the intensity and spatial extent of dust loading. The strong consistency observed between WRF-CHEM outputs and MODIS AOD data suggests that both tools are highly effective for detecting and evaluating dust events in this region.
Findings from this research align well with those of previous studies that used WRF-CHEM and MODIS AOD. For example, Alizadeh et al. (2020) have employed WRF-CHEM and HYSPLIT to track dust storms and identified the western and central Iraqi deserts, Syria, and northern Saudi Arabia as the primary source regions, consistent with the present results. Similarly, Miri et al. (2021), using WRF-CHEM and MODIS AOD, reported that eastern Syria and eastern Iraq serve as the dominant dust-emission sources affecting western and southwestern Iran, with both datasets showing strong agreement. Other research using WRF-CHEM to simulate PM₁₀ in eastern Iran demonstrated that the highest dust concentrations aligned well with known dust sources in the dried bed of the Hamoun wetlands. Furthermore, studies employing the HTAP scheme within WRF-CHEM to analyze spatiotemporal variations in aerosols, tropospheric ozone, and dust over the Middle East have also confirmed the model’s high capability in identifying dust-source regions and characterizing their properties.
Overall, the strong agreement between WRF-CHEM simulations and MODIS AOD products in this study confirms the robustness and applicability of the WRF-CHEM model for air-quality modeling and dust-storm prediction. This is particularly relevant for aerosols originating from natural emission sources such as erodible landscapes and desert environments.
Conclusion
MODIS AOD imagery, together with WRF-CHEM model simulations, demonstrates satisfactory effectiveness in identifying the characteristics and source regions of severe dust storms affecting the Kermanshah region.
Acknowledgements: We extend our sincere appreciation to all individuals who supported us in conducting this research.
Ethical Approval: This article has not been published in any domestic or international journal.
Conflict of Interest: This article is not derived from any project or thesis, and the authors report no conflicts of interest.
Authors’ Contributions: Sheikhghaderi SH (First Author), Methodologist/Main Researcher (40%); Alizadeh T (Second Author), Assistant Researcher/Statistical Analyst (35%); Rezaei Banafsheh M (Third Author), Introduction Writer/Discussion Writer (25%)
Funding: All expenses required for conducting this article were fully covered by the authors.
Keywords:
References
1. Ackermann I J, Hass H, Memmesheimer M, Ebel A, Binkowski F S, Shankar U (1998). Modal aerosol dynamics model for Europe: Development and first applications. Atmospheric Environment. 32(17):2981-2999. [Link] [DOI:10.1016/S1352-2310(98)00006-5]
2. Alizadeh T, Rezaei Banafsheh M, Goodarzi G, Sheikh Ghaderi S H (2020). "Numerical tracking and simulation of dust storm in Kermanshah". Journal of Desert Management. 9(2):1-14. [Persian] [Link]
3. Asadilotfi R, Aleshekh A A, Bezadi S (2018). A review prediction models of dust phenomena and data collection techniques. Geospatial Engineering Journal. 9(4):51-66. [Persian] [Link]
4. Binkowski F S, Shankar U (1995). The regional particulate matter model: 1. Mode desription and preliminary results. Journal of Geophysical Research: Atmospheres. 100(D12):26191-26209. [Link] [DOI:10.1029/95JD02093]
5. Drury E, Daniel J, Wang J, Robert J, Spurr D, Kelly C (2008). Improved algorithm for MODIS satellite retrievals of aerosol optical depths over western North America. Journal of Geophysical Research: Atmospheres. 113(D16204):1-11. [Link] [DOI:10.1029/2007JD009573]
6. Eltahan M, Shokr M, Sherif A O (2018). Simulation of severe dust events over Egypt using tuned dust schemes in weather research forecast (WRF-Chem). Atmosphere. 9(7):246-270. [Link] [DOI:10.3390/atmos9070246]
7. Goudie A S, Middleton N J (2001). Saharan dust storms: Nature and consequences.Earth-Science Reviews. 65(1-4):179-204. [Link] [DOI:10.1016/S0012-8252(01)00067-8]
8. Grell G A, Peckham S E, Schmitz R, McKeen S A, Frost G, Skamarock W C (2005). Fully coupled "online" chemistry within the WRF model. Atmospheric Environment. 39(37):6957--6975. [Link] [DOI:10.1016/j.atmosenv.2005.04.027]
9. Lashkari H, Kaykhosravi G (2008). Synoptic statistical analysis of dust storms in Khorasan Razavi province (1993-1995). Physical Geography Research Quarterly. 40(65):17-33. [Persian] [Link]
10. Liu Z, Liu Q, Lin H C, Schwarts C S, Lee Y H (2011). Assimilating MODIS aerosol optical depth using WRF-Chem and GIS: Application to a Chinese dust storm. In: 21th WRF Users Workshop.20-24 June 2011. Boulder: United State of America. [Link]
11. Miri M , Shamsipour A A, Azizi Q, Safar Rad T (2011). Statistical analysis-synoptic of dust phenomenon in the western half of Iran. Journal of Environmental Studies. 38(63):123-134. [Persian] [Link]
12. Miri M, Pilevaran R, Zand M, Norouzi A A (2021). Atmospheric dust detection using WRF-chem model and remote sensing data (Case study: West and southwest of Iran). Journal of Soil and Water Resources Protection. 10(4):81-94. [Persian] [Link]
13. Miri M, Azizi Q, Shamsipour A A (2014). Identity summer and winter patterns on arrival dust to the West Iran. Geography and Environmental Planning. 25(4):203-220. [Persian] [Link]
14. Munchak L, Levy R, Mattoo S, Remer L (2014). MODIS atmosphere team webinar series 5: Overview of the 3 km aerosol product in collection 6 [Internet]. Climate and Radiation Laboratory: NASA Goddard Space Flight Center. Available from:https://aerocenter.gsfc.nasa.gov/ext/c6/materials/MODIS_Atmos_Webinar-2_Munchak_3kmaerosol.pdf [Link]
15. Myhre G, Stordal F, Johnsrud M, Diner D J, Geogdzhayev I V, Haywood J M, et al (2005). Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000. Atmospheric Chemistry and Physics. 5(6):1697-1719. [Link] [DOI:10.5194/acp-5-1697-2005]
16. Nikfal A H (2014). Simulation of PM10 particle concentration by WRF-chem paired model in Iran. In: Iranian Seismological Center, 16th Iranian Geophysical Conference. Tehran, 13 May 2014. Tehran: Iran. [Persian] Available From: https://www.sid.ir/Fa/Seminar/ViewPaper.aspx?ID=41717 [Link]
17. Nikfal A, Ranjbar Saadat Abadi A, Karami S, Sehat Kashani S (2017). Capabilities of WRF-chem numerical model in estimating dust concentration (Case study of Tehran dust storm). Environmental Sciences. 15(1):115-126. [Persian] [LinkVVVV]
18. Qi Y, Ge J, Huang J (2013). Spatial and temporal distribution of MODIS and MISR aerosol optical depth over northern China and comparison with AERONET. Chinese Science Bulletin. 58(20):2497-2506. [Link] [DOI:10.1007/s11434-013-5678-5]
19. Rezazadeh M, Irannejad P, Shao Y (2011). Climatology of the Middle East dust events. Aeolian Research.10:103-109. [Link] [DOI:10.1016/j.aeolia.2013.04.001]
20. Rezazadeh M, Irannejad P, Shao Y (2013). Dust emission simulation with the WRF-Chem model using new surface data in the Middle East region. Journal of Earth and Space Physics. 39(1):191-212. [Persian] [Link]
21. Shahid M Z, Chishtie F, Bilal M, Shahid I (2021). WRF-chem simulation for modeling seasonal variations and distributions of aerosol pollutants over the Middle East. Remote Sens. 13(11):2-17. [Link] [DOI:10.3390/rs13112112]
22. Stockwell W R, Kirchner F, Kuhn M, Seefeld S (1997). A new mechanism for regional atmospheric chemistry modeling. Geophysical Research: Atmospheres. 102(D22):25847-25879 [Link] [DOI:10.1029/97JD00849]
23. Toulabi Nejad M, Hejazizadeh Z, Zarei Chaghablaki Z, Amrayi B (2015). Dust storm monitoring in the western half of Iran: A case study of dust storm June 16-19, 2015. Journal of Spatial Analysis of Environmental Hazards. 5(4):107-124. [Persian] [Link]
24. Wang J, Sundar C A (2003). Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophysical Research Letters. 30(21):1-4. [Link] [DOI:10.1029/2003GL018174]