Emission, Transport, and Deposition Mechanisms for a Severe Summer Dust Storm Originating in Southern Mongolia

Abstract

This study investigated an intense and unusual summer transboundary dust storm event that occurred between 21 and 23 June 2024. By integrating remote sensing observations, reanalysis data, WRF-Chem simulations, and LAGRANTO trajectory tracking, we systematically revealed the dust emission, transport, deposition, and formation mechanisms of this event. The dust primarily originated from the Gobi region of southern Mongolia, where concentrations exceeded 10,000 µg m⁻³, and decayed exponentially as the Mongolian cyclone moved southeastward. Post border-crossing into China, the event transitioned to blowing and floating dust, with concentrations decreasing significantly. During transport, dry deposition dominated the source area and the frontal part of the transport path in the early stages, while wet deposition was associated with the precipitation system of the Mongolian cyclone and concentrated north and east of the cyclone’s track. On 21 June 2024, the average wind speed in the source region reached 11.35 ms⁻¹, the highest recorded in the past 45 years. This was attributed to surface anomalies, including reduced soil moisture, poor vegetation cover, higher temperatures, and decreased precipitation relative to the multi-year average. The comprehensive application of multi-source data and models in this work elucidates the full lifecycle of this rare summer dust event, providing scientific insights into the atmospheric processes governing extreme dust events and their transboundary impacts.

1. Introduction

Dust storms are a global environmental issue that not only degrade air quality but also pose risks to human health, hinder crop growth, and bring traffic to a complete standstill. More importantly, dust storms can interfere with regional and global climate systems by altering the surface radiation balance and cloud microphysical processes [1,2,3]. In recent years, the implementation of ecological projects in China, such as the Three-North Shelter Forest Program and the return of farmland to forests and grasslands, has resulted in a significant increase in vegetation cover and a reduction in soil desertification, while climate change has also synergistically contributed to this decline. Consequently, the frequency and intensity of dust storm events have exhibited a marked decreasing trend [4,5]. However, since 2021, northern China has experienced a resurgence in both the frequency and intensity of dust storms. According to Borjigin et al. [6], this phenomenon may be related to an increase in cross-border dust input. In fact, in the spring of 2021 and 2023, several large-scale severe dust-storm events occurred in China [7,8]. The Summer Solstice dust event on 21 June 2024, represents a significant change, breaking the conventional spring-dominated dust pattern in East Asia. Dust storms occurring in late June are rare; therefore, this abnormal phenomenon attracted considerable attention. Analysis revealed that this particular dust storm event also showed clear signs of cross-border dust transport and exhibited distinctive changes in concentration characteristics after crossing the border. Given that cross-border transport has a significant impact on dust storm events in China [9], research on dust concentration changes during cross-border transport shows a substantial gap. Dust storms originating in Mongolia not only affect China but also pose potential threats to countries such as Japan and South Korea, with far-reaching implications globally [10,11]. Elucidating the patterns of dust concentration variation during cross-border transport is critical for quantitatively assessing the contribution of external dust sources to domestic air quality degradation and for providing a scientific foundation for formulating targeted and effective dust prevention and control strategies at national and regional levels.

Remote sensing, tracking models, and numerical simulations are commonly used to investigate the emissions, transport, and deposition of dust [12,13]. Remote sensing utilizes satellites to obtain information from a distance, enabling observations of the macroscopic distribution of dust [14,15]. For example, Minamoto et al. [16] used Himawari-8 satellite images to identify the source area and transport process of the East Asian dust storms that occurred in May 2017. Tracking models can be used to further determine the origin and transport pathway of dust through specific algorithms. Compared with remote sensing, such models can more accurately determine dust trajectories [17]. For example, Ye et al. [18] used the HYSPLIT trajectory model and the three-dimensional concentration-weighted trajectory model to study the distribution, transport, and impacts on urban air quality of two extreme and typical dust events in 2021, thereby revealing the dynamic processes of dust in the atmosphere. Numerical simulation is a more detailed method that uses mathematical models to represent the dynamic processes of atmospheric dust [13,19]. For example, Zhao et al. [20] simulated the northwest China dust event in May 2018 using the WRF-Chem model, identifying the dust sources, transport characteristics, and impact areas, thereby demonstrating the value of this model in dust research.

Although research on dust storms is relatively extensive, few studies have examined dust storm events during low-incidence seasons. Furthermore, the mechanism underlying this type of dust event remains unclear. In particular, the concentration changes during cross-border transport have not received sufficient attention. This study focuses on a dust storm event that occurred on 21 June 2024, and comprises an in-depth investigation into the factors contributing to its occurrence, particularly during a typically low-incidence season. This study examines abnormal atmospheric circulation, surface vegetation cover, and the potential influence of gale-force winds. By considering the entire dust cycle, from its origin through transport to deposition, this study aims to provide a scientific basis for explaining the mechanism of occurrence of this unusual dust storm and to offer references for its prediction and prevention.

2. Materials and Methods

2.1. Data

2.1.1. Himawari Satellite Data

In this study, dust RGB images constructed from the far-infrared channels of the Himawari-9 satellite were used to accurately locate the dust source, transport pathway, and affected areas of the 21 June 2024, dust event. Himawari-9 is a major representative of the Japanese geostationary meteorological satellite series. These satellites can observe the Earth with high temporal and spatial resolutions. Himawari-9 provides full-disk Earth images every 10 min with a spatial resolution of 0.5–1 km in the visible and near-infrared bands and 2 km in the infrared band [21]. Data are available from the Japan Meteorological Agency (JMA) official data distribution platform: https://www.jma.go.jp/jma/indexe.html (accessed on 23 February 2026).

2.1.2. ERA5 Reanalysis Data

ERA5 reanalysis meteorological data was used in this study. ERA5 is a fifth-generation global atmospheric reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF). It integrates a large volume of observational data and employs an advanced numerical weather prediction model for reanalysis. The data have high spatiotemporal resolution. The temporal resolution is hourly, and the horizontal resolution is 0.25° × 0.25°. The data include atmospheric, land-surface, and oceanic variables [22]. Data were obtained from the ECMWF Climate Data Store (CDS): https://cds.climate.copernicus.eu/ (accessed on 23 February 2026).

2.1.3. MERRA-2 Reanalysis Data

To analyze dust deposition, we used Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. The dataset covers the period from 1980 to the present, and has a horizontal resolution of 0.625° × 0.5° (latitude and longitude). It contains assimilated aerosol data, including dry and wet deposition, for various aerosol component species. The daily dry and wet deposition data for all five dust particle-size intervals were selected for this study, providing strong support for dust deposition analysis [23]. Data were accessed via the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC): https://disc.gsfc.nasa.gov/ (accessed on 23 February 2026).

2.1.4. SYNOP Station Data

SYNOP (Surface Weather Observations) is a standard code managed by the World Meteorological Organization (WMO) for reporting weather and climate observations. It is used globally to share data from manual and automatic weather stations for general weather forecasting. Currently, approximately 8600 fixed SYNOP observation locations are available through the WMO’s data feed, with regular reports sent every 3 h (starting from 00:00 UTC). In relevant studies, SYNOP data have been used to distinguish between dust storms, blowing sand, and floating dust. For example, different sand-and-dust-related weather phenomena are categorized by codes or labels such as “dust storm (09, 30−35, 98)”, “blowing dust (07, 08)”, and “floating dust (06)”. Data were sourced from the WMO data feed: https://public.wmo.int/ (accessed on 23 February 2026).

2.2. Models

2.2.1. WRF-Chem Model

The WRF-Chem model is a coupled regional atmospheric dynamics–chemistry model that integrates a chemical module into the WRF model. The simulation domain for this model covered the entire territory of Mongolia and parts of China. The center of the simulation domain was set at 40° N and 105° E. The horizontal grid resolution was 9 km × 9 km, corresponding to 630 × 430 grid points, with 38 vertical layers. The model was run from 00 UTC on 18 June 2024, to 23 UTC on 23 June 2024. Meteorological initial and lateral boundary conditions were provided by the ERA5 reanalysis dataset. To ensure that the simulated atmospheric circulations remained consistent with the large-scale ERA5 fields—which is particularly important for reliable dust emission and transport modeling—the Grid Nudging (analysis nudging) technique was applied throughout the simulation. During the simulation, the GOCART simple aerosol scheme was adopted along with the dust scheme proposed by Shao (2004) at the University of Cologne. Specifically, the chemistry scheme used was the GOCART simple aerosol scheme without anthropogenic emission input (chem_opt = 300). The dust size distribution in the Shao (2004) module is approximated by five dust bins. The main physical parameterization schemes selected for the simulation are detailed as follows: For the microphysics process, the Thompson scheme was adopted; for long-wave radiation and short-wave radiation, the RRTMG scheme was used consistently; the Monin-Obukhov scheme was selected for the surface layer; the Noah land-surface model was employed to simulate land-surface processes; and the YSU scheme was applied for the boundary layer.

2.2.2. LAGRANTO Model

The Lagrangian Analysis Tool (LAGRANTO) is primarily used in atmospheric and oceanic studies to investigate material transport and trajectory tracking. Based on the Lagrangian method, it accurately tracks the movement trajectories of air or fluid parcels to analyze the relevant physical processes. In this study, LAGRANTO was applied to the meteorological fields provided by WRF-Chem and the simulated five dust bins. Forward trajectories were calculated for 48 h to identify the transport pathways of the air parcels. Consequently, dust concentration data along the trajectory of each air parcel were successfully obtained, thereby providing crucial insights into dust movement trajectories and concentration changes [24].

3. Results and Discussion

3.1. Dust Transport Process

3.1.1. Evolution of the Dust Process and Weather Situation

The dust storm examined in this study began in the Gobi region of southern Mongolia from the evening of 20 June to the early morning of 21 June 2024 (Figure 1a,b). At this time, a low-pressure system, the Mongolian cyclone, formed over Mongolia, Inner Mongolia, and China. The central pressure of the cyclone decreased to 990 hPa. Northerly gales generated by the cyclone were sufficiently powerful to lift surface dust into the air. Station data recorded evidence of dust storms occurring in southern Mongolia. The gusts behind the cyclone reached levels of 9–10, driving the dust and weather system eastward and southward. Dust from the Xilingol League in central Inner Mongolia crossed the border into China, after which it decreased in concentration and spread as blowing sand and floating dust (Figure 1c). Subsequently, the dust spread further with the rotation of the Mongolian cyclone. During this process, dust storms persisted in some parts of Mongolia, whereas in China they spread as blowing sand and floating dust (Figure 1d,e). The dust ended during the early morning of 23 June 2024 (Figure 1f).

3.1.2. Dust Transport Trajectories and Concentration Changes

The dust primarily originated from source regions in southern Mongolia and was transported southeastward, affecting multiple regions in China. Dust concentrations peaked near the source areas and gradually decreased with increasing transport distance (Figure 2a). At different altitudes, dust exhibited distinct transport characteristics. Higher trajectories resulted in a broader spatial influence, but lower dust concentrations, whereas lower trajectories were associated with higher dust concentrations (Figure 2b).

To further investigate variations in dust concentration following its intrusion into China, we analyzed PM₁₀ monitoring data. After entering China, dust concentrations remained relatively high. Notably, the PM₁₀ time series at the three stations exhibited a distinct bimodal structure, which can be attributed to the two-stage evolution of the Mongolian cyclone. The first, relatively weak peak corresponds to the initial eastward transport of dust along the leading edge of the developing cyclonic front. In contrast, the second, stronger peak coincides with the mature stage of the cyclone, when a closed vortex over eastern Mongolia drove southward secondary transport of high-concentration dust plumes. The maximum PM₁₀ concentration at Xilinhot Station was found even to exceed 2500 µg m⁻³ (Figure 3a,b).

Subsequently, as the dust was transported southward, its concentration gradually decreased. Along its transport path, it passed through Hunshandake Sandy Land. Upon reaching Zhangjiakou Station, the maximum PM₁₀ value was found to be approximately 1500 µg m⁻³ (Figure 3c,d). As the dust continued to move southward, the maximum PM₁₀ concentration in Beijing dropped to around 600 µg m⁻³ (Figure 3e,f).

To evaluate the reliability of the WRF-Chem simulation in capturing the dust evolution, we compared the simulated PM₁₀ concentrations with the ground-based observations at the three stations (Xilinhot, Zhangjiakou, and Beijing). As shown in Figure 3, the WRF-Chem model successfully reproduced the temporal variation patterns of PM10 during the dust event. The model captured the distinct bimodal structure at Xilinhot and the subsequent southward propagation of the dust plume, accurately reflecting the two-stage evolution of the Mongolian cyclone. However, a systematic overestimation of PM₁₀ concentrations was noted in the simulation. For instance, while the observed peak at Xilinhot exceeded 2500 µg m⁻³, the simulated peak value was higher. This overestimation can be attributed to several factors, including uncertainties in the dust emission scheme and the spatial resolution of surface land-use data. Despite the bias in absolute magnitude, the model’s ability to capture the timing and relative intensity of the concentration peaks demonstrates its robustness in simulating the dynamic transport processes and concentration variations in this transboundary dust event.

3.2. Variations in Dust Deposition

Dust is deposited via both dry and wet deposition. On 21 June 2024, dry deposition was mainly concentrated in the source area and front section of the transport path (Figure 4a), while wet deposition mainly occurred in the precipitation areas of eastern Inner Mongolia and northeastern Mongolia; that is, on the northern side of the Mongolian cyclone (Figure 4d). On 22 June, the extent of dry dust deposition expanded further, reflecting the process of dust diffusion in the atmosphere (Figure 4b). Wet deposition occurred in areas with relatively low precipitation in eastern Mongolia and eastern Inner Mongolia (Figure 4e). On 23 June, dry deposition concentrations decreased. Although the deposition range remained extensive, color intensity decreased, indicating a weakening of the dry deposition process and a gradual dissipation of the dust storm (Figure 4c).

During the early transport phase, although dust was continuously lost via dry deposition, wet deposition also continuously reduced atmospheric dust, resulting in a gradual decrease in the amount of dust that could be transported. Eventually, the concentration of dry dust deposition decreased, and the dust storm gradually dissipated. Throughout this process, deposition was found to be a key factor in reducing dust concentration during transport. Both the gravitational settling effect of dry deposition and the scavenging effect of precipitation in wet deposition significantly altered the dust concentration, thereby affecting the evolution of the dust storm.

3.3. Dust Emission Conditions

3.3.1. Strong Winds

Strong winds are the primary dynamic factor in dust storm formation [5]. On 21 June 2024, extremely high wind speeds, coinciding with the occurrence of dust storms, were recorded in the Gobi region of southern Mongolia. The average daily wind speed reached 11.35 m s⁻¹, and this was the highest average recorded over the past 45 years (Figure 5). Notably, the interannual to decadal variability of Mongolian cyclone activity has increased in recent decades, and this trend is closely associated with global warming and related circulation anomalies. Studies have indicated that the Mongolian cyclone, as the primary driver of strong winds and a key trigger of dust storms in southern Mongolia, has shown a gradual increase in both frequency and intensity since the early 2000s [6]. This broader climate trend has increased the likelihood of extreme gale events in the dust source region, making severe dust storms (such as the June 2024 event) more probable even during typically low-incidence seasons. The wind speed probability density distribution during the period 1980–2024 indicates that most wind speeds were concentrated between 2 and 6 m s⁻¹, presenting a typical unimodal distribution. The wind speed of 11.35 m s⁻¹, recorded on 21 June 2024, was an extreme outlier in the density distribution. Therefore, strong winds were the main factor contributing to the occurrence of this dust storm.

3.3.2. Dust Source Region Conditions

Factors such as vegetation, soil moisture, and climatic conditions in the sand source area and along the dust transport pathway directly and significantly affect the generation and intensification of dust [25]. In June 2024, southeastern Mongolia and parts of northern China experienced notable climatic anomalies that collectively created favorable conditions for dust storm formation. The soil water content in these regions was lower than the 24-year average (Figure 6a), and the Normalized Difference Vegetation Index (NDVI) also fell below the multi-year average (Figure 6b), indicating reduced vegetation cover. Concurrently, the 2 m temperature was higher (Figure 6c), and precipitation was lower than the 24-year mean (Figure 6d). These combined anomalies are driven by long-term warming and drying trends over the past two to three decades, which have degraded surface conditions and increased dust emission susceptibility, and were further amplified by interannual climate variability.

4. Limitations and Future Implications

While this study advances our understanding of dust emission mechanisms, concentration variations during transboundary transport, and deposition processes, several limitations remain, including limited data resolution and a lack of quantitative attribution of key influencing factors. For example, PM₁₀ monitoring data for southern Mongolia were unavailable for this study. The sparse distribution of monitoring stations across the study area, whether they are near the Gobi Desert in southern Mongolia or in northern China’s dust-affected frontier regions, limits robust characterization of dust concentration variability. Future research will integrate standardized ground-based and satellite lidar observation networks (such as AD-Net, EARLINET, MPLNET), as well as satellite lidar products (e.g., CALIPSO ceilometer, ground-based micro-pulse lidar) to obtain the vertical distribution of dust aerosols in the cross-border region and comprehensively analyze the changes in dust concentration across horizontal and vertical cross-border transport [26,27].

Additionally, this study did not quantify the factors influencing the variations in dust concentration. Future work will employ WRF-Chem sensitivity experiments to adjust key parameters such as surface vegetation cover, soil moisture, and wind speed, and use multiple linear regression models and attribution analysis methods to rigorously assess the relative contributions of natural surface conditions and meteorological factors to dust emission and concentration variations [28,29]. Addressing these limitations will not only refine predictive capabilities but also deepen the fundamental understanding of aerosol lifecycle and its impacts within the atmospheric system [30,31]. In addition, the cross-border nature of this dust storm event also highlights the limitations of single-country dust prevention and control work: the dust source is located in southern Mongolia, and the transport path covers the border areas of China and Mongolia, thereby implying that the joint prevention and control of cross-border dust storms requires close cooperation between the two countries in terms of dust source area management, meteorological early warning, and data sharing. In summary, these shortcomings highlight critical directions for future research and emphasize the need for improved methodologies to deepen the scientific understanding of dust storm phenomena, as well as the importance of cross-border regional cooperation in dust storm prevention and control.

5. Conclusions

This study, through a detailed case analysis, advances our understanding of the synoptic dynamics and land–atmosphere interactions driving rare summer dust storms, thus contributing to the broader field of atmospheric science. By comprehensively applying multi-source data and models, an in-depth exploration of the causes, transport, and deposition mechanisms of dust storms related to the investigation of dust event of 21–23 June 2024 led to the following key conclusions:

(1)

The dust originated from the southern Gobi region of Mongolia and was transported southeastward. The source concentration exceeded 10,000 µg m⁻³ but decreased with source distance. High-altitude dust was widely transported but at relatively low concentrations, whereas the opposite was true at low altitudes. PM₁₀ concentrations gradually decreased southward post-border crossing, as dust shifted from storm-generated to wind-blown and suspended dust.

(2)

Dust deposition pattern: On 21 June, dry deposition was concentrated in the source area and front section of the transport path, while wet deposition dominated in areas of precipitation. By 23 June, the extent of dry deposition contracted post-expansion as dust concentration decreased, thereby indicating the gradual dissipation of the dust storm.

(3)

Origin and dynamic mechanisms of dust-weather events: The dust storm event in June 2024 was triggered by a Mongolian cycle. Strong winds (11.35 m s⁻¹; extreme, equated to historical data for the same period) generated by the low-pressure center of the Mongolian cyclone were the primary force that directly lifted sand and dust. Surface conditions (such as low soil moisture, poor vegetation, high temperatures, and low precipitation) in the dust source area and along the dust pathway enhanced wind erosion and entrainment, thereby establishing a foundation for sustained dust generation. The integrated use of satellite remote sensing, numerical modeling, and trajectory analysis exemplified in this work underscores the importance of multidisciplinary approaches in addressing contemporary challenges in atmospheric research. This study also provides a scientific basis for the study of rare-season dust-storm events in East Asia and for the formulation of cross-border air-quality joint-management strategies.