A measurement-driven cross-sectional method to assess the dynamics and flux of dust transport

Abstract

During dust storms, dust particles are transported within a few kilometers above the ground. However, the dynamics and flux of dust transport at these altitudes are not fully understood. In this study, we employed a synergistic wind-particle profiling system in southern China to gather detailed vertical data on both aerosol characteristics and atmospheric dynamics during an unusually severe dust storm that transported from northern to southern China in April 2025. A measurement-driven cross-sectional method was developed to investigate the vertical distribution characteristics of transport dynamics and southward flux of dust, and to assess total amount of dust transported across the country. An intense spring cold front, combined with high dust emissions from the Gobi Desert, drove this southward transport. During the lower-level transport period, dust was primarily transported below 1.5 km, with a maximum southward flux density of 4.9 ± 2.9 g/m² at 775 m. The higher-level transport period exhibited a bimodal flux pattern. Additionally, the west-east distribution of dust concentration followed a Gaussian distribution. The total dust flux transported to southern China during the entire event was approximately 2.48 × 10⁵ tons. Our cross-sectional approach supports quantitative investigations of the transport dynamics of various atmospheric components above the ground.

Introduction

Dust storms arise when strong winds carry sand and dust from dry soil into the atmosphere in arid regions¹. Approximately 2 billion tons of dust are emitted into the atmosphere annually, with Asia contributing approximately 30% of those emissions^2,3. They can influence climate and weather patterns, disrupt ecosystems, and pose risks to human health, agriculture, transportation, and solar energy production^4,5. Human activities, such as construction, farming, and inadequate land management, worsen the situation by removing vegetation and exposing soil to wind erosion⁶. Furthermore, the rising frequency of dust storms is alarming people, as climate change leads to higher temperatures and extended periods of drought, resulting in further soil and vegetation degradation⁷. In recognition of the global challenges posed by dust storms, the United Nations General Assembly designated the period from 2025 to 2034 as the United Nations Decade on Combating Sand and Dust Storms⁸.

The effects of dust storms can extend well beyond their origin. Strong winds can lift dust particles high into the atmosphere, transporting them thousands of kilometers across continents and oceans⁹. Numerous studies have been conducted to assess the impact of dust storms on air quality in affected regions¹⁰. For instance, Kaskaoutis¹¹ employed satellite and ground-based data to examine the effects of severe dust storms over the eastern Mediterranean in March 2018, and revealed that concentration of respirable particulate matter (PM₁₀) in Athens reached 500 μg/m³. Similarly, Idrissa¹² conducted a chemical transport modeling study to investigate the long-distance transport of a severe dust storm that occurred from March 15 to 18, 2021, originating from the Gobi Desert in Mongolia. This study found that daily PM₁₀ levels exceeded 1000 μg/m³ in the North China Plain. Most existing research relies primarily on ground-level data to evaluate air quality impacts. However, it is important to note that dust particles are transported within a few kilometers above the ground¹³. The dynamics and flux of dust transport at these altitudes are not fully understood.

Investigating the dust transport necessitates vertical monitoring of wind patterns and aerosol properties. Recent advancements in remote sensing technology allow for high-resolution vertical profiling of the atmosphere¹⁴. Wind profiling radar can determine the vertical distribution of wind speed and direction by detecting the Doppler shift of electromagnetic waves¹⁵. For instance, a wind profiling system was used to investigated ozone transport flux within the boundary layer in Hong Kong¹⁶. Additionally, particle profiling systems offer significant benefits for monitoring the vertical structure of aerosols¹⁷. Notably, polarization particle lidar has shown great potential in differentiating between non-spherical dust and spherical urban aerosols¹⁸. Laboratory and field studies have revealed that the depolarization ratios (DPR) of non-dust aerosols range from 0.02 to 0.15, while typical DPR values of fine and coarse dust are estimated at 0.16 and 0.37, respectively^19,20. For example, Yadav et al.²¹ used a polarization particle lidar to track the intrusion of a Saharan dust plume in Hatfield, detecting a DPR of 0.27, which confirmed the presence of dust particles.

Dust storms frequently affect China, primarily due to dust emissions from the Gobi and Taklimakan Deserts. Research indicates a distinct seasonal pattern in dust storm occurrences, with more than 70% of annual events occurring in the spring²². Northern China, due to its proximity to active dust sources and prevailing westerly winds, is recognized as one of the most active regions for dust storms globally²³. A recent letter in Science underscored the significant threat posed by Asian dust to air pollution control efforts in China²⁴. However, significant southward transport of dust storms that adversely impacts air quality in southern China has been less documented. Using ground-level PM₁₀ and fine particulate matter (PM_2.5) concentration data from Guangzhou, a major city in southern China, we identified days significantly impacted by dust from 2016 to 2025 (Table 1). Dust days were defined as those when the daily average PM₁₀ concentration exceeded 100 μg/m³ and the fine mode fraction (FMF, ratio of PM_2.5 to PM₁₀) fell below 0.4. Notably, in April 2025, southern China experienced an exceptionally severe dust storm, with daily PM₁₀ concentrations on April 13 surpassing 400 μg/m³, a level remarkably higher than those observed during previous dust events over the past decade (maximum daily PM₁₀ lower than 140 μg/m³). At the same time, the FMF dropped to a low level of 0.22. The combination of high PM₁₀ levels and low FMF values indicates that this southward dust storm event was unusually intense.

Table 1 Dust storm events. Days that were significantly affected by dust at the Guangzhou city monitoring station from 2016 to 2025

To better understand the transport dynamics and flux of dust above the ground, we employed a synergistic wind-particle profiling system to monitor the unusually severe southward dust storm that occurred in April 2025 in China. This multi-profiling system enables us to gather detailed vertical data on both the aerosol characteristics and the dynamics of dust transport. Additionally, we developed a measurement-driven cross-sectional method to investigate the transport dynamics and southward flux of dust within a few kilometers above the ground in southern China during this significant dust event. Both the vertical and temporal variations of dust flux characteristics were explored. The total amount of dust transported across the country was then investigated using the cross-sectional method. Our measurement-driven cross-sectional method provides vertical information that was limited in previous studies, which primarily relied on ground-level measurements. Additionally, it exemplifies comprehensive analyses that quantitatively examine the long-distance transport characteristics of various anthropogenic and natural air pollutants, including dust, at different altitudes above the ground.

Results

Southward transported dust storm

The weather charts in East Asia during the dust storm event are shown in Fig. S3. On April 11th, a cold air mass in Inner Mongolia in northern China intensified, leading to increased temperature and air pressure gradients. This caused enhanced wind speeds and the formation of northerly winds. The cold air mass then moved southward, creating a cold front that reached southern China on the evening of April 12th. The occurrence of intense cold fronts in spring is infrequent, as they typically occur only in winter. After April 15th, as the land heated up and created low pressure, the wind direction in southern China shifted to southerly winds.

The enhanced winds in arid Inner Mongolia, northern China, can lift dust. Panel (a) in Fig. 1 shows the AI values measured by the OMPS aboard the Suomi NPP satellite on April 11, 2025. High AI values indicate the presence of ultraviolet-absorbing particles, such as dust. It was found that a large amount of dust was present in Inner Mongolia on April 11, with these high values not detected on previous days. Panel (b) displays the time series of wind speed and wind direction at the meteorological station, as well as the average PM₁₀ concentration at four air quality monitoring stations, in Wuhai city of Inner Mongolia from April 9 to 13, 2025. On April 11, the dominant northwesterly wind speed increased to 18 m/s. The strong winds lifted the dust, causing a rapid rise in PM₁₀ concentration. The recorded PM₁₀ concentration exceeded 800 µg/m³. It is noted that some measurements were missing during the dust event. Considering the missing data that may be related to the detection issues and equipment capability (e.g., the upper limit of the measurement), the maximum concentration may exceed 1000 µg/m³.

Fig. 1: Dust storm in northern China.

a Satellite measurement of AI values over northern China on April 11, 2025. b Time series of wind speed and wind direction at the meteorological station, as well as the average PM₁₀ concentration at four air quality monitoring stations, in Wuhai city of Inner Mongolia from April 9 to 14, 2025. Error bars represent the standard deviation of PM₁₀ concentration.

After the dust particles were lifted in arid northern China, they were transported to southern China along with the cold front. Figure S4 shows the spatial distributions of PM₁₀ concentration in China from April 11 to 14, 2025. On April 11, the PM₁₀ concentration in Inner Mongolia and its surrounding areas exceeded 500 μg/m³. Under the influence of northerly winds, the dust particles were transported southward, reaching the Yangtze River basin on April 12 and South China on April 13. The blue line in Fig. 2 shows the time series of PM₁₀ concentration at the Guangzhou University Town station from April 12 to 17, 2025. The PM₁₀ concentration rapidly increased from about 32 μg/m³ on the evening of April 12, peaking at 366 μg/m³ at 4:00 PM on April 13. Assuming the urban background concentration is 32 μg/m³, the proportion of dust in particulate matter reached a maximum of 91.3% at 4:00 p.m. on the 13th (as indicated by the red line). The dust proportion then gradually declined over the next few days.

Fig. 2: Dust proportion in southern China.

Time series of PM₁₀ concentration (blue line) and proportion of dust in PM₁₀ (red line) at Guangzhou University Town station from April 12 to 17, 2025.

Vertical dynamics and flux of dust

The variation in wind patterns greatly influenced the transport and dissipation (DP) of dust events. Wind profiling radar measurements were used to assess the vertical profile of wind in Guangzhou, southern China. As shown in Panel (a) of Fig. 3, we extracted the V wind speed from the radar measurements. Blue colors represent northerly winds, while red colors represent southerly winds. It was found that during the dust transport period from April 13 to the afternoon of April 15, the dominant wind was northerly. From the late afternoon of April 15, the dominant wind shifted to southerly.

Fig. 3: Vertical profiling measurements.

Temporal variations of the vertical profiles of (a) V wind speed, (b) lidar DPR, and (c) PM₁₀ concentration from vertical profiling system in Guangzhou, southern China. The dust transport period was divided into three periods: the lower-level transport (LT) period, the higher-level transport (HT) period, and the dissipation (DP) period.

Next, polarization particle lidar measurements in Guangzhou were used to assess the vertical profile of dust characteristics. The lidar DPR is used to distinguish dust from urban particles. Panel (b) of Fig. 3 shows the temporal variations of the vertical profile of DPR in Guangzhou, southern China. Previous laboratory and field investigations have shown that the DPR for non-dust aerosols is below 0.15, while the typical DPR values for dust are above 0.15^19,20. In this study, we used 0.15 as the threshold for identifying dust. Generally, the dust transport can be divided into three periods: the lower-level transport (LT) period before 4:00 p.m. on April 14, the higher-level transport (HT) period between 5:00 PM on April 14 and 11:00 a.m. on April 15, and the DP period after 12:00 p.m. on April 15. During the LT period, dust was primarily transported within 1.5 km near the ground. During the HT period, the dust layer reached up to 3 km. Temporal variations of the vertical profile of PM₁₀ concentration are shown in Panel (c) of Fig. 3. During the LT period, PM₁₀ concentrations were extremely high within 1 km near the ground, with maximum concentrations exceeding 400 μg/m³. During the HT period, PM₁₀ concentration decreased in the lower layer but increased in the higher layer. During the DP period, the dust concentration significantly decreased.

We further quantitatively compare the vertical profiles of wind and PM₁₀ concentration among the three periods. Panel (a) of Fig. 4 compares the vertical profiles of V wind speed. During the LT period, the northerly wind speed reached a maximum of 6.6 ± 3.8 m/s at 1.45 km, and reduced as height increased above 1.5 km. During the HT period, the wind speed near the ground was weak, which enhanced pollutant accumulation. Meanwhile, the northerly wind speed generally increased with height, reaching around 8–9 m/s between 2 and 3 km. The wind patterns significantly influenced the vertical profiles of dust concentration during the three periods. As shown in Panel (b), during the LT period, high dust concentrations were mainly detected below 1.5 km, with a peak of 334 ± 82 μg/m³ observed at 450 m. During the HT period, dust concentration near the ground remained very high due to the low wind speed. Meanwhile, enhanced dust concentrations were detected in high layers, with a small peak of 101 ± 28 μg/m³ observed at 1.6 km.

Fig. 4: Vertical profiles among three periods.

Vertical profiles of a V wind speed, b PM₁₀ concentration with the DPR values exceeding 0.15, and c southward dust flux density during the LT (blue lines), HT (green lines), and DP (red lines) periods.

Observations of wind and particle profiles were combined to assess the southward dust flux density at the vertical profiling station. Panel (c) of Fig. 4 compares the vertical profile of southward dust flux density across the three periods. During the LT period, high southward dust flux density values were mainly detected below 1.5 km, with a maximum of 4.9 ± 2.9 g/m² at 775 m. During the HT period, the southward dust flux density exhibited a bimodal pattern, reaching maximum values of 2.6 ± 0.6 g/m² at 875 m and 2.3 ± 0.6 g/m² at 1.83 km. During the DP period, the southward dust flux density was much lower due to the dominance of southerly winds. These results indicate the significant impact of wind profiles on dust concentration and the transport flux at different altitudes.

Temporal variation of dust flux

Hourly southward dust flux for the 1-km wide cross section at the profiling station in Guangzhou was estimated. Panel (a) of Fig. 5 shows the hourly variation of the southward dust flux across the 1-km wide cross section. Since measurements of PM₁₀ concentration include both dust and urban particles, the estimates were adjusted to exclude urban aerosols based on the proportion of dust in PM₁₀ (Fig. 2). The adjusted southward dust flux across the 1-km wide cross section peaked at 10.3 tons per hour at 8:00 a.m. on April 13.

Fig. 5: Dust flux in southern China.

a Temporal variation of the southward dust flux across the 1-km wide cross section at the profiling station in Guangzhou before (blue bars) and after (red bars) adjusting for the proportion of dust in the particles. b Temporal variations of the southward dust flux in southern China.

The west-east distribution of dust transported to southern China is complex. Figure 6 illustrates the west-east distribution of dust concentration at 23°N in southern China during the transport period. After excluding the contribution of urban aerosols, the dust concentration generally follows a Gaussian distribution with respect to longitude:

$$c=275\times {e}^{-\frac{({x-112)}^{2}}{2{\times 4.1}^{2}}}$$

(1)

Fig. 6: West-east distribution.

West-east distribution of dust concentration at 23°N in southern China during the transport period. The dust concentration followed a Gaussian distribution with respect to longitude.

Along the Gaussian curve, the total amount of dust between 100°E and 125°E (i.e., the area under the entire Gaussian curve) is 1,086 times the value in the 0.01° bin between 113.39°E and 113.40°E (which corresponds to a 1-km wide cross section) at the lidar location. As a result, the southward dust flux for southern China is approximately 1086 times that of the 1-km wide cross section at the vertical profiling station. By applying a factor of 1086, we obtained temporal variation of the southward dust flux in southern China, as shown in Panel (b) of Fig. 5. The maximum southward dust flux in southern China reached up to 1.12 × 10⁴ tons per hour at 8:00 a.m. on April 13. The total southward dust flux in southern China was estimated to be 1.77 × 10⁵ tons and 0.70 × 10⁵ tons during the LT and HT periods, respectively. During the entire dust transport period, the total southward dust flux in southern China was 2.48 × 10⁵ tons (or 248 Gg). This amount of long-distance transport is reasonable compared to the dust emissions and depositions observed during other events. For example, dust emissions and depositions during 1- to 5-day dust events in Iran were estimated to be approximately 740–1400 and 50–90 Gg, respectively²⁵. Additionally, during the 22-day severe dust storm period in March 2021 in East Asia, it was estimated that 12.87 Tg of dust was either suspended in the atmosphere or carried to other locations²⁶.

Discussion

The measurement-driven cross-sectional method developed in this study provides vertical information that was limited in previous studies, which primarily relied on ground-level measurements. First, the multi-profiling system allows us to gather detailed vertical data during dust events. The analysis in this study emphasizes the importance of integrating wind and polarization particle profiling data to quantitatively examine the long-distance transport dynamics of dust. Specifically, wind profiling offers insights into the dynamics of dust transport, while polarization particle profiling provides valuable information about the characteristics of the dust itself. Second, this method enables investigations into the transport dynamics and flux of dust within a few kilometers above the ground. By combining the analysis of vertical profiles of wind and aerosol properties, we can gain a clearer understanding of the vertical distribution characteristics of dust transport dynamics and flux. Additionally, the cross-sectional method allows for assessments of the total amount of dust transported across the country.

As the climate continues to warm, already arid regions are becoming increasingly dry, resulting in more frequent and severe dust storms. Research indicates that climate change has led to extended periods when the ground remains bare, dry, and susceptible to wind erosion—conditions that promote the development of dust storms²⁷. These storms not only diminish air quality and visibility but also pose serious health risks to communities and disrupt local ecosystems. Additionally, the altered weather patterns linked to climate change can worsen land degradation, creating a cycle that leads to even more dust storms. Therefore, addressing climate change is essential for mitigating these environmental threats and safeguarding both human health and the fragile ecosystems of our planet.

Due to the prevailing westerlies in spring, northern China is often affected by dust storms originating from the Gobi Desert. Southern China is less impacted by these dust storms. An exceptionally intense southward dust storm occurred in April 2025 and was associated with an intense cold front, which typically occurs only in winter. The northerly winds accompanying the cold front carried dust particles from north to south, significantly affecting air quality in southern China. The occurrence of intense cold front in spring, combined with the dry surface in the Gobi areas, creates unusual conditions for the severe southward transport of dust storms. This indicates the impacts of climate change on atmospheric circulations, which further influence the transport of air pollutants.

Our findings offer valuable assistance in air quality management and public health planning. The ability to accurately assess dust transport dynamics and flux can inform policymakers and environmental agencies about the potential impacts of dust storms in affected regions, particularly in southern China, which is often less studied in this context. The insights gained from this study can aid in developing predictive models for dust storm events, allowing for timely warnings and mitigation strategies to protect human health and the environment. Given the rising concerns about air pollutants and greenhouse gases, our cross-sectional approach provides a valuable framework for conducting quantitative investigations into the transport dynamics of various atmospheric components, such as particulate matter, volatile organic compounds, nitrogen oxides, and carbon dioxide, that contribute to air quality degradation and climate change. Overall, our research provides a foundation for further studies and practical applications aimed at addressing the challenges posed by air pollution and changing climate.

In our study, vertical profiles of PM₁₀ concentration were derived from the lidar-measured aerosol extinction coefficient (σ_a) because of their close relationship^28,29. We acknowledge that aerosol concentration, extinction efficiency, and hygroscopic effects all play a role in the light extinction of aerosols³⁰. Additionally, aerosol size distribution and chemical composition have complex effects on extinction efficiency³¹. Unfortunately, detailed information on the vertical profiles of aerosol physical and chemical characteristics, as well as humidity, is limited. Given this lack of comprehensive data, we adopted a practical approach that directly links PM₁₀ concentration to the aerosol extinction coefficient. Future analyses are needed to enhance the estimation of PM₁₀ concentration from lidar measurements by considering the detailed impacts of aerosol physical and chemical characteristics, as well as the effects of humidity.

Moreover, the coexistence of dust and urban aerosols in a complex mixed state complicates the accurate identification of dust content within particles. In this study, we used a lidar DPR of 0.15 as the threshold for identifying dust, coupled with an assumption regarding the proportion of urban aerosols present in the samples. Assuming that the daily average PM₁₀ concentration was around 350 μg/m³ during the dust storm, the total increase in PM₁₀ concentration was approximately 320 μg/m³ after removing the local background concentration of 32 μg/m³. We acknowledged that pollutants from cities in northern China may have partly contributed to the increase in PM₁₀ levels in Guangzhou. To better understand the impacts of pollutant transport from other northern cities, we evaluated the typical increase in PM₁₀ concentration in Guangzhou during regular (non-dust) years, which is likely associated with pollutant transport from cities in northern China. Based on PM₁₀ data from the same air quality monitoring station during the spring (from March 1 to May 31) in 2024, the average PM₁₀ concentration for the 25% most polluted hours was estimated to be 68.7 ± 18.5 μg/m³. Therefore, we assume that the transport of urban pollutants from cities in northern China contributed to an increase in PM₁₀ concentration of 37 μg/m³ after removing the background concentration. This increase accounts for approximately 11% of the total PM₁₀ increase during the dust storm in April 2025. In this study, we applied a practical method to remove the impacts of urban aerosols from local cities. Without considering the effects of urban pollutant transport from other cities in northern China, the results of dust flux may be overestimated by 10–15%.

Several additional uncertainties arise in the estimation of dust transport flux in this study. One significant factor is that most coarse particles tend to settle during transport, which led us to exclude coarse dust particles with a diameter larger than 10 μm from our analysis. Furthermore, the west-east distribution of dust transported to southern China is intricate, and we approximated that the dust follows Gaussian distribution characteristics with respect to longitude. These assumptions and simplifications indicate the challenges in accurately quantifying dust transport and emphasize the need for further research to refine our understanding of the interactions between dust and urban aerosols, as well as the underlying mechanisms driving dust transport in this region.

In this study, we developed a measurement-driven cross-sectional approach to examine the transport dynamics and flux of dust above the ground during an exceptionally severe southward dust storm in China. The use of a synergistic wind-particle profiling system significantly enhanced our understanding of the vertical distribution characteristics of dust transport across the country. The combination of an intense cold front in spring and the dry conditions in the Gobi region creates unique circumstances for the severe southward movement of dust storms. The assumptions and simplifications made in this research highlight the challenges of accurately quantifying dust transport and underscore the need for additional studies to enhance our estimations. For example, the presence of dust and urban aerosols in a complex mixed state complicates the accurate assessment of dust content within particles. This study is timely, given the increasing frequency of dust storms associated with climate change. Our measurement-driven cross-sectional method provides new insights into the dynamics and flux of dust transport and supports quantitative investigations of various atmospheric components above the ground.

Methods

Polarization particle profiling system

The particle lidar system was produced by the Anhui Institute of Optics and Fine Mechanics, an institute of the Chinese Academy of Sciences. It measures vertical profiles of aerosol extinction coefficient (σ_a) and the DPR. The lidar is situated in Guangzhou University Town (23.0492°N, 113.3962°E), at the heart of the Greater Bay Area in southern China (see Fig. S1). Its laser emitting system generates pulses at a wavelength of 532 nm. The atmospheric backscattered light collected by the telescope is separated by a polarizing prism, filtered for each channel, and then detected using photomultiplier tubes.

The Fernald method was employed to derive σ_a from the raw lidar data³². The backscattering lidar equation can be expressed as:

$$P\left(z\right)=\frac{C{P}_{0}}{{z}^{2}}\left[{\beta }_{a}\left(z\right)+{\beta }_{m}\left(z\right)\right]exp\left\{-2{\int }_{0}^{z}\left[{\sigma }_{a}\left({z}^{{\prime} }\right)+{\sigma }_{m}\left({z}^{{\prime} }\right)\right]d{z}^{{\prime} }\right\}$$

(2)

where P(z) represents the power of received signal at altitude z; C represents the lidar constant; P₀ denotes the initial laser emission power; ${\beta }_{a}\left(z\right)$ and ${\beta }_{m}\left(z\right)$ represent the backscattering coefficients from aerosols and molecules, respectively; and ${\sigma }_{a}\left(z\right)$ and ${\sigma }_{m}\left(z\right)$ denote the extinction coefficients from aerosols and molecules, respectively. Assuming lidar ratios for aerosols and molecules are ${S}_{a}=\frac{{\sigma }_{a}\left(z\right)}{{\beta }_{a}\left(z\right)}=50$ ${Sr}$ and ${S}_{m}=\frac{{\sigma }_{m}\left(z\right)}{{\beta }_{m}\left(z\right)}=\frac{8\pi }{3}$, respectively, the aerosol extinction coefficient ${\sigma }_{a}\left(z\right)$ can be calculated as:

$${\sigma }_{a}\left(z\right)=-\frac{{S}_{a}}{{S}_{m}}{\sigma }_{m}\left(z\right)+\frac{P\left(z\right){z}^{2}exp\left[2\left(\frac{{S}_{a}}{{S}_{m}}-1\right){\int }_{z}^{{z}_{c}}{\sigma }_{m}\left(z\right){dz}\right]}{\frac{P({z}_{c}){z}^{2}}{{\sigma }_{a}\left({z}_{c}\right)+\frac{{S}_{a}}{{S}_{m}}{\sigma }_{m}\left({z}_{c}\right)}+2{\int }_{z}^{{z}_{c}}P(z){z}^{2}exp\left[2\left(\frac{{S}_{a}}{{S}_{m}}-1\right){\int }_{z}^{{z}_{c}}{\sigma }_{m}\left(z\right){dz}\right]{dz}}$$

(3)

where ${z}_{c}$ represents the reference altitude in the high layer, where the amount of aerosols is negligible.

Moreover, the DPR is defined as the ratio of the cross-polarized signal to the parallel-polarized signal, serving as an indicator of particle shape. It is calculated from the ratio of backscattering coefficients for the cross and parallel polarization channels:

$${DPR}(z)=\frac{{\beta }_{\perp }\left(z\right)}{{\beta }_{\Vert }\left(z\right)}$$

(4)

Further details on the polarization particle lidar system can be found from Lv et al. (2017)³³ and Jin et al. (2024)³⁴. The σ_a and DPR data from lidar measurements taken between April 13 and 16 were employed in this study. The vertical resolution of the lidar measurements was 25 m. During data pre-processing, the lidar data were aggregated to an hourly format. The lidar’s blind zone height is approximately 0.1 km. Therefore, the values of σ_a and DPR below 0.1 km were assumed to be consistent with those at 0.1 km. Lidar measurements of σ_a provide insights into the vertical distribution of aerosols. By assuming that σ_a and PM₁₀ concentrations exhibit similar vertical distributions, we derived the temporal variation of the vertical profile of PM₁₀ concentration. Specifically, we calculated the ratio between PM₁₀ concentration and σ_a at ground level, which was then applied to the vertical profile of σ_a to obtain the vertical profile of PM₁₀ concentration.

Wind profiling system

The vertical profile of wind was measured by a wind profiling radar located at the Nansha meteorological station (22.7718°N, 113.6077°E), approximately 30 km from the particle lidar station (see Fig. S1). The radar is operated by the China Meteorological Administration and emits electromagnetic waves at a frequency of 1300 MHz. It functions in Doppler Beam Swinging (DBS) mode, which detects the Doppler shifts (f_D) of radar signals backscattered by atmospheric turbulence. The DBS scan mode directs the beam at five different lines of sight (LOS), which includes one vertical LOS and four LOS with a fixed elevation angle of 75° at four azimuth angles: 0° (north), 90° (east), 180° (south), and 270° (west). For each LOS, the wind radar detects f_D between the emitted and received laser signals. The obtained f_D along the five LOS can be converted to the corresponding radial wind velocity (V_R):

$${V}_{R}(z)=\frac{{f}_{D}\left(z\right)\cdot \lambda }{2}$$

(5)

Based on the five radial wind velocities, a three-dimensional wind vector can be constructed, which includes east-west horizontal wind speed (U), north-south horizontal wind speed (V), and vertical wind speed (W). Measurements of hourly horizontal wind speed and direction were collected from 100 m to 5.5 km above ground level, with a vertical resolution of 60 m. The data were then resampled to a vertical resolution of 25 m to align with the particle lidar measurements. For further information on the wind profile measurements, please refer to Li et al. (2022)³⁵.

Ground air quality and meteorological data

Hourly ground-measured PM_2.5 and PM₁₀ concentrations were obtained from the China National Environmental Monitoring Center (http://www.cnemc.cn) in the Chinese mainland. Locations of 1407 air quality monitoring stations in the study region are shown in Fig. S2. The air quality instruments used to monitor PM_2.5 and PM₁₀ concentrations are properly operated and maintained by the government to ensure data quality and reliability. Meanwhile, ground wind data were sourced from the global telecommunications system of the World Meteorological Organization. As shown in Fig. S1, air quality and wind data at Wuhai city (23.0492°N, 113.3962°E) in Inner Mongolia were employed to understand the dust conditions in the source region. Data from Guangzhou city were used to investigate the impacts of dust transport in southern China. Additionally, weather charts covering East Asia were obtained from the Hong Kong Observatory (https://www.hko.gov.hk/) to help understand the background meteorological conditions during the dust storm event.

Satellite measurement

To understand the dust conditions in the source region, we applied the ultraviolet aerosol index (AI) from the Ozone Mapping and Profiler Suite (OMPS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. Elevated AI values signify the presence of ultraviolet-absorbing aerosols such as dust in the atmosphere³⁶. The AI value is calculated based on the difference between observed and modeled ratios of absorbing and non-absorbing spectral radiance:

$${AI}=100[lo{g}_{10}\left(\frac{{I}_{\lambda 2,m}}{{I}_{\lambda 1,m}}\right)-lo{g}_{10}\left(\frac{{I}_{\lambda 2,c}}{{I}_{\lambda 1,c}}\right)]$$

(6)

where ${\lambda }_{1}=340$ ${\rm{nm}}$ and ${\lambda }_{2}=378.5$ ${\rm{nm}}$ are the two wavelengths used by the OMPS; ${I}_{\lambda 1,m}$ and ${I}_{\lambda 2,m}$ represent the radiances measured at ${\lambda }_{1}$ and ${\lambda }_{2}$, respectively, while ${I}_{\lambda 1,c}$ and ${I}_{\lambda 2,c}$ denote the calculated radiances at those wavelengths for a Rayleigh scattering-only atmosphere. The imagery has a spatial resolution of 2 km, with daily temporal updates.

Cross-sectional method

In this study, synergistic measurements from the wind and particle profiling systems were used to assess the southward dust flux of the dust storm that occurred in April 2025. Hourly dust flux density at specific altitude z, denoted as f (unit: g/m²), represents the mass of dust passing through a unit area per hour. It can be estimated from dust concentration c (unit: μg/m³) and the north-south component of wind speed V at this altitude (unit: m/s):

$$f(z,t)=c(z,t)\cdot V(z,t)\cdot 1h$$

(7)

At the vertical profiling location, we created a west-east cross-section that is 1 km wide (approximately from 113.39°E to 113.40°E) and 3 km high. We then integrated the dust flux for the entire dust layer from ground to 3 km to obtain the hourly southward dust flux for the 1-km wide cross-section (unit: tons per hour):

$${F(t)}_{1{km}}=\mathop{\sum }\limits_{z=1}^{H(t)}f\left(z,t\right)\cdot \varDelta S=\mathop{\sum }\limits_{z=1}^{H(t)}f\left(z,t\right)\cdot 1{km}\cdot \varDelta z$$

(8)

Next, to obtain the total southward dust flux in southern China, we need information on the dust concentration distribution with respect to longitude. Given that the latitude of the vertical profiling station is around 23°N and high PM₁₀ concentrations during the dust storm event were mainly detected in regions between 100°E and 125°E, we used PM₁₀ concentrations from 136 ground air quality monitoring stations located within a strip between 22°N and 24°N and between 100°E and 125°E (see Fig. S2) to fit the concentration distribution with respect to longitude. Since the observed distribution appears to be approximately Gaussian (see “Results” section), a Gaussian distribution is used to represent the dust concentration distribution with respect to longitude:

$$c=A{e}^{-\frac{({x-\mu )}^{2}}{2{\delta }^{2}}}$$

(9)

Parameters A, μ, and δ determine the shape of the Gaussian distribution. By integrating the dust flux along the longitudes, the hourly southward dust flux in southern China (unit: tons per hour) can be estimated by:

$$F\left(t\right)=\mathop{\sum }\limits_{x=100}^{125}\mathop{\sum }\limits_{z=1}^{H\left(t\right)}f\left(x,z,t\right)\cdot 1{km}\cdot \varDelta z$$

(10)

Based on the hourly variation of the southward dust flux in southern China, we further estimated the total southward dust flux in southern China during the entire dust storm event (unit: tons).