Sources Of Dust Storms Research Articles

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring

Dust storms are common in Mongolia and northern China, this is a serious threat to the ecological security and socioeconomic development of both countries and the surrounding areas. However, a complete quantitative study of the source area, affected area, and moving path of dust storm events (DSEs) in Mongolia and China is still lacking. In this study, we monitored and analyzed the spatiotemporal characteristics of the source area and affected areas of DSEs in Mongolia and China using the high-spatiotemporal-resolution images taken by the Himawari-8 satellite from March to June 2016–2020. In addition, we calculated the moving path of dusty weather using the HYSPLIT model. The results show that (1) temporality, a total of 605 DSEs occurred in the study area, with most of them occurring in April (232 DSEs), followed by May (173 DSEs). Spatially, the dust storm sources were concentrated in the arid inland areas such as the Taklimakan Desert (TK, 138 DSEs) and Badain Jaran Desert (BJ, 87 DSEs) in the western, and the Mongolian Gobi Desert (GD, 69 DSEs) in the central parts of the study area. (2) From the affected areas of the DSEs, about 60% of the DSEs in Mongolia started locally and then affected downwind China, as approximately 55% of the DSEs in the Inner Mongolia Desert Steppe and Hunshandake Sandy Land came from Mongolia. However, the DSEs in the TK located in the Tarim Basin of northwest China affected the entire study area, with only 31.3% belonging to the local dust. (3) From the moving path of the dusty weather, the dusty weather at the three meteorological stations (Dalanzadgad, Erlian, and Beijing), all located on the main transmission path of DSEs, was mainly transported from the windward area in the northwest, accounting for about 65.5% of the total path. This study provides a reliable scientific basis for disaster prevention and control, and has practical significance for protecting and improving human settlements.

Identifying sand and dust storm sources using spatial-temporal analysis of remote sensing data in Central Iran

Sand and dust storms (SDS) are common meteorological phenomena in arid and semi-arid regions caused by natural or anthropogenic factors. Central Iran, covers a large area on the Iranian plateau, and SDS has been known as a prevalent phenomenon in certain parts of the region since ancient times. The frequency and severity of SDS have increased over the last two decades due to population growth and mismanagement of natural resources. Identifying SDS sources is the first step to combating this phenomenon and reducing its destructive impacts. Accordingly, this study employed a remote sensing approach based on the modeling of environmental parameters to identify high potential SDS sources in Central Iran. The proposed model was implemented through a multi-step masking procedure using 20-year time-series datasets of MODIS and TerraClimate products. According to the results, 5.3% of Central Iran is identified as high potential SDS sources. Among these, sandy sources have the largest share in terms of area (60.9%) and frequency of SDS occurrence (>50%). The highest seasonal frequency of SDS (76%) was in spring and summer. The highest yearly frequency of SDS was observed in 2008, which was 120% higher than the 20-year average (2000−2020). In sandy and salt plain sources, SDS formation is predominantly associated with natural factors. However, in lakes and alluvial sources, anthropogenic activities have been directly linked with variations in SDS frequency and extent. The occurrence of severe droughts has intensified the frequency of SDS emerging from all types of high-potential sources in Central Iran.

Variations in Aerosol Optical Properties over East Asian Dust Storm Source Regions and Their Climatic Factors during 2000–2021

The East Asian dust storms occur in western and northern China, and southern Mongolia every year, particularly in spring. In this study, we use satellite aerosol products to demonstrate the spatial and temporal variation in aerosol optical depth (AOD) from MODIS, and the absorbing aerosol index (AAI) from TOMS and OMI, over the main dust storm source regions (MDSR), and to investigate their relationship to vegetation coverage (NDVI), soil properties (surface soil moisture content and soil temperature 0–10 cm underground), and climatic factors (surface wind speed, air temperature at 2 m above the ground, and precipitation) in spring for the period of 2000–2021. Compared with dust storm occurrence frequency (DSF) observed at surface stations, MODIS AOD, TOMS AAI, and OMI AAI showed consistent spatial distributions and seasonal variations with DSF in the MDSR, with correlation coefficients of 0.88, 0.55, and 0.88, respectively. The results showed that AOD and AAI over the MDSR decreased during 2000–2005, 2006–2017, and 2000–2021, but increased during 2017–2021.The improvements in vegetation coverage and soil moisture together with favorable climatic factors (the increase in temperature and precipitation and the decrease in surface wind speed) resulted in the decreasing trend of AOD and AAI during 2000–2005, 2006–2017, and the entire period of 2000–2021. Conversely, the increase in surface wind speed, the decrease in temperature and the low soil moisture in 2018 and 2020 were the reasons for the increases in AOD and AAI over the MDSR during 2017–2021. The combination effects of surface wind, temperature, soil moisture, and vegetation coverage would determine DSF, AOD, and AAI, in the end, under global climate change.

Desiccation of the Transboundary Hamun Lakes between Iran and Afghanistan in Response to Hydro-climatic Droughts and Anthropogenic Activities

This paper investigates the hydro-climatic reasons behind the desiccation of the Hamun Lakes in the Iran-Afghanistan border. We analyzed changes in the flow of the Hirmand River (90 percent of the total inflow to the lakes) at the international border, and precipitation over this river’s sub-basin during 1960–2016 by calculating standardized indices for precipitation (SPI) and discharge (SDI). We applied Normalized Difference Spectral Indices using satellite images from 1987 to 2021 to observe monthly areal change of the lakes. The results show that the major cause of desiccation is upstream water regulation which severely reduced the Hirmand River inflow delivery to the lakes. Constructed reservoirs near the lakes by Iran in 2008, compounded the effect of Afghanistan water regulation to aggravate the situation. There is a discernible shift in the relation between the Hirmand River flow at the border and upstream precipitation before and after 2004. In 1960–2003, high Hirmand River inflows were expected due to high precipitation, while the flow declined after 2004 despite large amounts of upstream precipitation. Although a long period of drought from 1998 to 2004 decreased the lakes’ area, the lake system is primarily falling victim to anthropogenic flow reduction recently. Increased regulation of flows and use of water for irrigation in Afghanistan and Iran underscores the necessity of bilateral dialogues between the two countries to consider environmental flow for the lakes. The lakes’ shrinkage places socio-economic stress on an already-vulnerable region with public health implications as the exposed lake beds turn into major sources of dust storms.

Automatic Identification of Sand and Dust Storm Sources Based on Wind Vector and Google Earth Engine

Sand and dust storms (SDS) are both symptoms and causes of desertification. As one of the essential parts of desertification control, SDS source identification can be readily carried out using remote sensing data. This letter proposes an automatic SDS source identification method based on ERA5 surface wind direction and MODIS daily surface reflectance. This is also the first time Google Earth Engine (GEE) has been used to fully automate SDS source mapping, from spatial data processing to results visualization. In this study, we use the zero-crossing edge detection algorithm to extract the dust plume edge based on the Enhanced Dust index (EDI), and then trace the point sources through the upwind direction. We evaluate the model performance based on 12 SDS events in Arid Central Asia (ACA) and validate against manually labeled SDS point sources. The results showed that the mean accuracy of SDS sources estimation was greater than 67%. To demonstrate the geographic scalability of the method, we also investigate the spatial pattern of SDS point sources in ACA from 2000 to 2021. Last, we adopt land cover data to discuss its applicability in time-series studies. Experimental results have demonstrated that our proposed method has the ability to identify SDS source points accurately and efficiently. We anticipate that this method will play an essential role in dust risk assessment and desertification control.

Erratum to: Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Erratum to: Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Dynamics of dust storm and its response to meteorological conditions and anthropogenic impact in South edge of Taklimakan desert, China

In this study, the varying trends of dust storm frequency in a typical oasis located at the South edge of Taklimakan desert, China were analyzed by using time series analysis and regression models. The LUCC (land use/cover change) data, NDVI (Normalized Difference Vegetation Index) remote sensing data, meteorological data and dust storm frequency data for the period of 2004-2018 were collected from local station and ERDAS (Earth Resources Data Analysis System) software, the multivariate relationships between human activities, natural factor and dust storm frequencies were analyzed by using Principal Component Analysis (PCA). Results indicated that the annual dust storm frequency in the study period increased with fluctuation. The monthly dust storm frequency shows higher values between the months of March and June, which accounts for 72.3% of the annual dust storm frequency. Precipitation and wind speed are two meteorological factors which can impact the dust storm formation and its frequency. The correlation between dust storm frequency and temperature was insignificant. Moreover, human activities indirectly affected the dynamics of dust storms by changing the vegetation cover and direct dust emissions. Furthermore, multivariate analysis highlighted a clear relationship among dust storm frequency, meteorological factors and NDVI. The high loadings of dust storm frequency, precipitation, wind speed and NDVI on a PC indicated that increase in precipitation and NDVI will decline dust storm frequency, whereas higher wind speed will enhance dust storm frequency. The findings of this study could be useful to understand the possible causes of dust storms, which can provide the basis for controlling the dust storm source region and also mitigation of the negative effects dust storm on the environment.

The Response of Airborne Mycobiome to Dust Storms in the Eastern Mediterranean

Airborne microbial communities directly impact the health of humans, animals, plants, and receiving ecosystems. While airborne bacterial and fungal communities have been studied by both cultivation-based methods and metabarcoding surveys targeting specific molecular markers, fewer studies have used shotgun metagenomics to study the airborne mycobiome. We analyzed the diversity and relative abundance of fungi in nine airborne metagenomes collected on clear days (“background”) and during dust storms in the Eastern Mediterranean. The negative correlation between the relative abundance of fungal reads and the concentrations of atmospheric particulate matter having an aerodynamic diameter smaller than 10 μm (PM10) indicate that dust storms lower the proportion of fungi in the airborne microbiome, possibly due to the lower relative abundance of fungi in the dust storm source regions and/or more effective transport of bacteria by the dust. Airborne fungal community composition was altered by the dust storms, particularly those originated from Syria, which was enriched with xerophilic fungi. We reconstructed a high-quality fungal metagenome-assembled genome (MAG) from the order Cladosporiales, which include fungi known to adapt to environmental extremes commonly faced by airborne microbes. The negative correlation between the relative abundance of Cladosporiales MAG and PM10 concentrations indicate that its origin is dominated by local sources and likely includes the indoor environments found in the city.

Impacts of Underlying Surface on the Dusty Weather in Central Inner Mongolian Steppe, China

AbstractDust storms are common in northern China and can adversely affect the environment and human health. The Xilingol grassland is the main source of dust storms in China. However, the impacts of different surface features on dusty weather in the Xilingol League remain unclear. This study analyzed the temporal and spatial variation characteristics of surface features and the corresponding effects on dusty weather using data from 13 meteorological stations, MODIS normalized difference vegetation index (NDVI) data, and soil moisture data collected in Xilingol League from 2005 to 2018. Spatially, the dust outbreak frequency (DOF) of Xilingol League decreased gradually from the western desert steppe to the eastern typical steppe. Temporally, the dust outbreak ratio (DOR) and DOF were highest in spring (March–May), with the highest in April. Dusty weather mostly occur in spring but gradually decreased in this season over the 14‐year period. On the contrary, NDVI and soil moisture increased gradually from the west with less precipitation to the east with more precipitation. And showed fluctuating increasing trends over time. The DOR in Xilingol League was negatively correlated with the NDVI and soil moisture, and with the increase of NDVI and soil moisture from the western desert steppe to the eastern typical steppe and the southern cultivated land, the dust emission threshold also increased. The DOR predicted by Xilingol league in spring was significantly correlated with the actual DOR. Our research can provide specific suggestions for different regions of Xilingol League.

Dust storm source detection using ANP and WRF models in southwest of Iran

Dust storm source detection using ANP and WRF models in southwest of Iran

Biological crust in sand and dust storm source areas of Asia and its impact on dust emission

Even though the biological crusts are critical to dust emissions, no sand and dust forecast model have considered the impacts of the biological crust in dust emission scheme. This situation mainly comes from two scientific difficulties: there is no large scale regional biological crust data available that can be used in the forecast model; there is no quantification of how biological crusts impact on sand emission. In this way, we studied the distribution of biological soil crust in sand and dust storm source areas of Central and East Asia using Moderate Resolution Imaging Spectroradiometer satellite surface reflectance data collected in 2000–2019 to determine its potential impact on dust emission according to two empirical schemes. We further evaluated the relationships between soil crust coverage, roughness length, and dust emission to study SDS source areas. We found that biological crust is widely distributed in SDS source areas of Central and East Asia, with coverage rates of 19.8% in Central Asian deserts, 23.1% in the Gobi Desert, and 17.3%–32.8% in Chinese deserts (p > 0.05). Cyanobacteria and lichen coverage has increased in Chinese deserts, reflecting the recent impacts of the Project of Returning Farmland to Grassland and Farmland to Forests. However, biological soil crust coverage has not increased in Central Asian deserts or the Gobi Desert, and that in Central Asian deserts continues to decrease, demonstrating the complexity of the combined effects of human activities and climate change on its distribution. Biological soil crust increased the roughness length of Central and East Asian SDS source areas by 0.14–0.62 mm. The suppression of dust emission due to biological soil crust did not change among years during the study period. The horizontal and vertical dust flux inhibition coefficient (DFIC) were 2.0–11.0 and 1.7–2.9 (p > 0.05), respectively, clearly showing a suppressive effect. Improvement of the ecological environment in some deserts can lead to the ability of these crusts to inhibit dust erosion errors that must be considered in the dust emission scheme for areas where crust coverage has improved.

Land degradation modeling of dust storm sources using MODIS and meteorological time series data

Land degradation affects environmental integrity and threatens sustainable development worldwide by contributing to a number of social, economic, and ecological problems. This study uses time series of MODIS products, meteorological data, and ground truth map from dust storm sources to assess the spatial-temporal behavior of land degradation through a Random Forest algorithm in the southwest of Iran. Spatial-temporal variations in land surface biophysical properties of dust sources and surrounding areas were modeled using different time-series data on a 16-days, monthly, seasonal, annual, and 20-year basis from 2000 to 2019. Land degradation was estimated using the Random Forest algorithm. Dust storm sources were used to evaluate land degradation estimates. Furthermore, sensitivity analysis was carried out to identify the most influential biophysical variables. The results revealed that (i) 16-day time series data performs best in assessing land degradation; (ii) Apparent Thermal Inertia (ATI) was identified as the most influential variable for land degradation assessment; and (iii) there is a marked tendency towards land degradation in areas with severe land cover changes, mainly change from wetland to other classes.

Quantitative evaluation of ‘No-harm’ rule in international transboundary water law in the Helmand River basin

The international legal principle of ‘no-harm’, as a customary international law, lies at the heart of numerous international transboundary water conventions, rules, and agreements. The current study identifies and examines the three most common categories of harm, namely health, environmental, and economic, in an integrated framework. Exploitation of the Kamal Khan dam over the Hirmand/Helmand Transboundary River internationally shared between Iran and Afghanistan has been selected as a case study. For this aim, an integrated modeling approach that links Soil and Water Assessment Tool (SWAT), Indicators of Hydrologic Alteration (IHA), dust storm events (DST) and crop production is developed to simulate the indicators and their parameters under historical conditions and future exploitation of the dam. Effects on the three types of harm are classified as being low, moderate or high. The results showed that the effects of the dam diversion for economic harm would be high. For instance, it could reduce the wheat production of the Iranian side of Sistan plain up to 70%. The hydrological alteration of the river is also significant, up to 80% during the winter and spring months. This can result in health and environmental harms. The region’s health and environment are already in a critical situation mainly due to the drying of Hamoun wetlands, which rely on the inflows of the Helmand River and are the major sources of dust storms. Notably, these issues would affect the entire plain in both Iran and Afghanistan. The scope of this study was the principle of ‘no-harm’ but the methodology with some additional modifications could be considered for other principles like “equitable and reasonable utilization” in transboundary river basins.

A method to select sites for sand and dust storm source mitigation: case study in the Sistan region of southeast Iran

Sand and dust storms (SDS) are a natural phenomenon dominantly originating in arid and semi-arid regions. The vastness and changing distribution of the SDS hotspots, their inaccessibility, and financial limitations are the greatest challenges to projects combating SDS. To boost project success, a prioritized site-selection method which incorporates the physical and human variables interacting with SDS is applied in this paper. This ensures that potential economic and health impacts of SDS on human populations are also included. Six variables were selected for analysis: within-region sand and dust hotspots, changing distribution of the hotspots, residential areas, vegetation cover, soil texture, and the maximum drought-inundation of the Hamoun wetland. SDS hotspot sites for possible stabilization were identified and prioritized using the multi criteria evaluation (MCE) method. Such prioritization assists the practical management of hotspots under conditions of resource and budgetary constraints which are especially common in developing countries. This paper thus provides a template for site selection and prioritization of SDS hotspots for remediation. It also highlights the importance of variables other than SDS source areas for selection of rehabilitation sites. The proposed method, using the Sistan region as an example, is applicable to other regions of the world as a means of reducing the negative effects of known dust storm hotspots.

Numerical simulations of dust storms originated from dried lakes in central and southwest Asia: The case of Aral Sea and Sistan Basin

Ephemeral and dried lakes in southwest and central Asia, such as the Aral Sea and Hamouns in east Iran, are major sources of dust storms with an increasing tendency during the last decades due to anthropogenic influence and climate change. This study examines two characteristic dust-storm events originated from these areas on 12–15 July 2016 and 27–28 May 2018. Thermal low-pressure systems over topographic-low areas and the Caspian Sea High, along with pressure gradients and intense surface winds over the dust sources facilitated the dust outbreaks and transport. Three models (DREAM-NMME-MACC, CAMS and WRF-Chem) are synergized to simulate the spatial distribution of AOD and surface dust concentrations during the dust events. The results show that DREAM-NMME-MACC and WRF-Chem exhibit the highest discrepancies (R = 0.15–0.58; RMSE = 83%–125%) in representing the spatial and temporal distribution of dust compared to Terra-MODIS AODs, while CAMS reveals the best performance (R = 0.29–0.77; RSME = 67%–124%). All models significantly underestimate the high MODIS AODs, especially near the source areas, due to different dust schemes, soil conditions, meteorology and dynamic processes for dust emissions that they comprise. Furthermore, notable differences between the models are revealed in simulations of the PM 10 concentrations in Zabol, east Iran, as the models fail to reproduce the temporal evolution and high intensity of the dust event. In general, all models represent better the dust storm originated from Sistan (13–14 July 2016) rather than the Aralkum dust storm (27–28 May 2018), indicating an incapability in representing the soil characteristics in a progressively drying terrain.

Origin and Transport Pathway of Dust Storm and Its Contribution to Particulate Air Pollution in Northeast Edge of Taklimakan Desert, China

The Taklimakan Desert in Northwest China is the major source of dust storms in China. The northeast edge of this desert is a typical arid area which houses a fragile oasis eco-environment. Frequent dust storms cause harmful effects on the oasis ecosystem and negative impacts on agriculture, transportation, and human health. In this study, the major source region, transport pathway, and the potential contribution of dust storms to particulate air pollution were identified by using both trajectory analysis and monitoring data. To assess the source regions of dust storms, 48 h backward trajectories of air masses arriving at the Bugur (Luntai) County, which is located at the northeast edge of Taklimakan Desert, China on the dusty season (spring) and non-dusty month (August, representing non-dusty season) in the period of 1999–2013, were determined using Hybrid Single Particle Lagrangian Integrated Trajectory model version 4 (HYSPLIT 4). The trajectories were categorized by k-means clustering into 5 clusters (1a–5a) in the dusty season and 2 clusters (1b and 2b) in the non-dusty season, which show distinct features in terms of the trajectory origins and the entry direction to the site. Daily levels of three air pollutants measured at a station located in Bugur County were analyzed by using Potential Source Contribution Function (PSCF) for each air mass cluster in dusty season. The results showed that TSP is the major pollutant, with an average concentration of 612 µg/m3, as compared to SO2 (23 µg/m3) and NO2 (32 µg/m3) in the dusty season. All pollutants were increased with the dust weather intensity, i.e., from suspended dust to dust storms. High levels of SO2 and NO2 were mostly associated with cluster 1a and cluster 5a which had trajectories passing over the anthropogenic source regions, while high TSP was mainly observed in cluster 4a, which has a longer pathway over the shifting sand desert area. Thus, on strong dust storm days, not only higher TSP but also higher SO2 and NO2 levels were observed as compared to normal days. The results of this study could be useful to forecast the potential occurrence of dust storms based on meteorological data. Research focusing on this dust-storm-prone region will help to understand the possible causes for the changes in the dust storm frequency and intensity, which can provide the basis for mitigation of the negative effects on human health and the environment.

Modeling the potential of Sand and Dust Storm sources formation using time series of remote sensing data, fuzzy logic and artificial neural network (A Case study of Euphrates basin)

Modeling the potential of Sand and Dust Storm sources formation using time series of remote sensing data, fuzzy logic and artificial neural network (A Case study of Euphrates basin)

Determining the source of dust storms with use of coupling WRF and HYSPLIT models: A case study of Yazd province in central desert of Iran

Dust storm, which has a significant impact on regional air quality, is one of the most hazardous meteorological phenomena in the arid areas. Yazd province is one of the arid areas in Iran that is exposed to dust storms. In this study, two cases of dust storms of Yazd province are studied with the use of coupling numerical models and aerosol optical depth (AOD) from MODIS data. We investigated synoptic condition of such dust storms that were formed downstream of upper level through in the area by focusing on two storms, on May 24 and 25, 2014. For this purpose, a dynamic coupling of this case is done using WRF output the HYSPLIT model. This model was implemented to investigate the sources of the dust storms by calculating the back trajectories from the receptor sites. The trajectories indicated that the first and the second case storms occurred in the northeast and the south of Yazd respectively. These results also showed a good agreement with MODIS aerosol optical depth data and HYSPLIT back trajectories paths.

Assessment of mineralogical and chemical properties of airborne dust in Iraq

The mineral composition and chemical and physical analysis of dust particles are essential to assess the potential impacts of dust on climate, environment, soil and health. The aims of this study are to give an overview of the mineralogical and chemical properties of dust storms over Iraq. Also, this study aims to identify potential sources of dust storms over Iraq. The results of particle size analysis showed that clay and silt particulates form an average of 86.9% of a dust storm, whereas sand particles represent an average of 13.2% of all dust samples. The physical analysis of dust samples revealed that low percentages of sand occur in the north region, but they are high in the western and central regions. The results of XRD analysis suggest that kaolinite, gypsum, albite, quartz and calcite were the major mineral dust components. However, lower amounts of palygorskite, microcline, dolomite, illite, chlorite and halite were detected in some station samples. The XRF analysis suggested that Ba, Sr and Cl were the main trace element components in the airborne dust samples. In addition, the chemical analysis of dust samples revealed that high percentages of Zn and Pb occur in the eastern and central regions. The results of HYSPLIT backward trajectories analysis of air parcels revealed that the potential sources of dust storms were the Syrian Desert, North Africa Desert, An-Nafud Desert in Saudi Arabia, the dry lands in south-western Turkey and southwest Iran, and the alluvial plain, Al Jazeera and Western Deserts in Iraq.