Next Article in Journal
New 3-D Fluorescence Spectral Indices for Multiple Pigment Inversions of Plant Leaves via 3-D Fluorescence Spectra
Previous Article in Journal
An Optical Detection Model for Stratospheric Airships
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 16
Issue 11
10.3390/rs16111883
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Technical Note

Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023

by
Yundan Li
1 and
Wencai Wang
1,2,*
1
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
2
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(11), 1883; https://doi.org/10.3390/rs16111883
Submission received: 23 March 2024 / Revised: 29 April 2024 / Accepted: 21 May 2024 / Published: 24 May 2024
Browse Figures
Versions Notes

Abstract

:
Dust aerosols serve as a crucial nutrient source to the oceans and profoundly influence marine ecosystems. This study used satellite and ground observations to explore a strong dust event on 9–13 April 2023, emanating from the Gobi Desert, shared by Mongolia and China’s Inner Mongolia region. We investigated the deposition of dust particles and their effects on marine phytoplankton communities. Our findings revealed that the dust event was intense, enduring, and expansive, illustrated by hourly PM10 concentrations peaking at 5055 µg/m3 near the source and consistently exceeding 1000 µg/m3, even at considerable distances. The dust traveled along two different trajectories and was deposited in the same area of the Northwest Pacific. Total dust deposition in the study area (37°N–42°N, 145°E–165°E) was 79.88 mg/m2 from 13 to 18 April, much higher than the 2019–2022 average deposition of 33.03 mg/m2 for the same period. With dust deposition, the observed mean chlorophyll-a concentrations in the area increased to 2.78 mg/m3 on 14 April, an extraordinary 692% increase above the long-term average. These results highlight the profound impact of dust on the productivity of marine phytoplankton communities by inputting more nutrients into the ocean through different pathways.

Graphical Abstract

1. Introduction

Dust aerosols have a significant impact on the radiative energy balance of the Earth–atmosphere system by scattering and absorbing solar and infrared radiation [1,2,3,4], and can also indirectly affect the global climate system by affecting the optical properties of clouds [5,6,7]. Dust aerosols can reduce air quality and visibility [8,9,10], with serious implications for human health [11,12]. In addition to affecting local air quality, trace elements (such as Fe) and nutrients in dust aerosols also affect marine biogeochemical cycles through long-range transport and deposition, enhancing carbon uptake by the ocean [13,14,15,16].
Asian dust is an important component of global dust, accounting for about 40% (8–13 Tg) of global dust, and its effects are widespread [17]. In East Asia, dust emissions are highest in spring [18]. Dust transport and deposition are related to the synoptic situation [19]. The main synoptic-scale systems associated with Asian dust outbreaks are Mongolian cyclones, cold fronts, and Mongolian cold high-pressures [20,21]. Strong pressure gradients between Mongolian cyclones and the high-pressure systems behind them cause strong winds, and wind speed and direction are key factors affecting dust transport [22,23]. Under the influence of long-range transport, it can reach not only the vast areas of northern China but also the Korean peninsula, the Japanese archipelago, the Pacific, and even North America [24,25,26,27].
The deserts of Mongolia and western and northern China (mainly the Taklimakan and Badain Juran, respectively) account for about 70% of total dust emissions in Asia, with Chinese sources accounting for 30% and non-Chinese sources accounting for 40% [28]. In the past decade, large-scale ecological construction projects in the desert areas of northern China have curbed local desertification to some extent, effectively trapping part of the ground dust. However, 78% of Mongolia’s land area was affected by desertification, and 23% was severely or extremely eroded by wind, leading to an increase in the supply of dust sources and soil [29]. Zhang and Gao [30] analyzed 42 Asian dust storms from 2000 to 2002 and found that about 70% of the dust storms affecting China originated from Mongolia and were intensified during their movement over the desert areas of China. Due to the transport of dust, the concentration of PM10 in the atmosphere increased significantly, and near-surface visibility and air quality were both reduced [8,31]. The 15–19 March 2021 process was the strongest sandstorm event in the past 10 years in China, with 75% of the sandstorms originating from Mongolia on 14 March [29]. This dust process affected an area of more than 2.9 million km2 [32] in the northern provinces of China, and 36.3% of the monitoring stations in northern China reached a severe or higher pollution level [33]. Therefore, as a source of dust, Mongolia has increased its influence on the occurrence of dust.
Marine phytoplankton growth is influenced by dissolved iron in seawater [34,35,36], and mineral dust and combustion source aerosols are the main external sources of biologically available iron to open ocean surfaces [37,38]. Wang et al. [39] found that the mixing of dust with anthropogenic pollutants during transport was common. Dust concentrations decreased with increasing transport distance, but the proportion of soluble iron increased from 1% in the source region to 10–40% in the remote region, leading to a significant increase in soluble iron deposition in remote marine regions [34]; source areas and transport pathways were also important factors affecting the chemical composition of dust aerosols [40,41,42]. Onish et al. [43] found differences in the amount of non-mineral dust particles in Asian dust events with different transport routes, suggesting that atmospheric transport routes determine the composition of Asian dust.
As for 30 April 2023, China had the highest frequency of dust storms in a decade during the same period, with Mongolia contributing more than 42% of the dust [44]. The dust process of 9–13 April 2023 originated from the Gobi Desert bordering Mongolia and Inner Mongolia in China. Some studies have been carried out to monitor the intensity and extent of dust effects on land and have shown that the dust was strong, affecting an area of more than 4.5 million km2 and causing severe adverse impacts on urban air quality, people’s lives, and transport [45,46]. However, limited literature exists regarding whether strong dust processes, which have serious impacts on air quality and visibility, also have significant impacts on the marine environment and the exact extent of the marine response. In addition, atmospheric transport routes determine the composition of dust in Asia. This study combines satellite and ground observations for a detailed analysis of the dust transport pathways. Therefore, improving the understanding of the environmental impacts of dust events and identifying dust sources and their transport and deposition is crucial for studying the evolution of near-surface air quality and marine ecosystems.
This paper used multivariate data, including satellite and ground station observations, to analyze the transport path of dust from the source to the ocean. In addition, the impacts of specific dust deposition levels on marine ecosystems were investigated using CAMS (Copernicus Atmosphere Monitoring Service) data. The following is the organization of the paper: Section 2 describes the data used; Section 3 focuses on the transport of dust and the response of the ocean to dust deposition; finally, our findings are summarized and discussed in Section 4.

2. Materials and Methods

2.1. EOSDIS Worldview Images

The satellite cloud images provided were sourced from the EOSDIS Worldview true color map (https://worldview.earthdata.nasa.gov/, accessed on 5 December 2023), with an overlay of dust score for enhanced analysis. The true-color layer data was obtained from the MODIS sensor on the Aqua satellite and could be accessed for visualization through Worldview and Global Image Browsing Services (GIBS). These images have a resolution of 250 m and are updated on a daily basis.
AIRS, the Atmospheric Infrared Sounder on NASA’s Aqua satellite, is mainly used to observe the temperature and humidity distribution of the Earth’s atmosphere, as well as the content and changes of other atmospheric components [47]. Dust score was determined by comparing radiances of some spectral channels of AIRS. A higher dust score indicates a greater certainty of dust presence, and dust is probable when the score is above 380. The dataset used in our paper includes both daytime and nighttime observations.

2.2. AD-Net Data

AD-Net, a component of the WMO’s Global Atmospheric Observing Programme (GAP), which is also part of the Global Atmospheric Observing Aerosol Lidar Observation Network (GALION), serves a pivotal role in the continuous monitoring of the vertical stratification of dust and assorted aerosols across East Asia. The network uses a dual-wavelength (532 nm and 1064 nm) backscatter lidar, complete with a 532 nm polarization-sensitive receiver [48].
For this study, we tapped into the depolarization ratios and dust extinction coefficients measured at the Tokyo (139.71°E, 35.69°N) and Sapporo (141.34°E, 43.07°N) sites at 532 nm. The depolarization ratio serves as a quantifier of particulate shape asymmetry; typically, aerosol depolarization ratios are close to zero, with higher values indicating greater irregularities. Dust particles, inherently irregular with elevated coarse particle counts, register higher depolarization ratios [3,6]. The recorded spectrum of these ratios spans from 0.06 to 0.35; specifically, a ratio exceeding 0.1 signifies the existence of dust [49,50]. Meanwhile, the dust aerosol extinction coefficient reflects the scattering and absorption effects of dust particles in the atmosphere, with elevated coefficients indicating diminished visibility and intensified pollution levels. The vertical resolution of the lidar data used in our analysis is 30 m, and the temporal resolution is 15 min.

2.3. Himawari-8/9 Satellite Data

Himawari-8 has been in operation by the Japan Meteorological Agency (JMA) since 7 July 2015. Equipped with the Advanced Himawari Imager (AHI), which captures full-disk images every 10 min and images in the vicinity of Japan every 2.5 min. The AHI’s observational purview extends from 60°S to 60°N and 80°E to 160°W, providing multispectral images across 16 different bands ranging from the visible to the infrared spectrum [51]. On 13 December 2022, the JMA transferred the Himawari-8 satellite operations to the Himawari-9 satellite. This study used the daily average chlorophyll-a (Chl-a) concentration data from the Himawari-8/9 satellite from 2016 to 2023, with Himawari-8 data used for the years 2015 to 2022 and Himawari-9 data used for the year 2023.
In this research, we analyzed chlorophyll-a (Chl-a) concentrations derived from the L3CHLDaily (Level 3 Chl-a concentration daily data) to assess the effect of dust deposition on the growth of marine phytoplankton. The spatial resolution of the above data is 0.05° × 0.05°.

2.4. CAMS Global Atmospheric Composition Forecasts Data

The atmospheric composition reanalysis data from the CAMS is primarily utilized for the monitoring and forecasting of global pollutants, aerosols, and greenhouse gases [52]. The European Centre for Medium-Range Weather Forecasts (ECMWF) operates and develops this service as part of the Integrated Forecasting System (IFS). The IFS incorporates cutting-edge meteorological and atmospheric component modeling alongside satellite product data assimilation within the CAMS framework [53]. It accounts for over 50 chemical substances, including ozone, nitrogen dioxide, and carbon dioxide, as well as seven different types of aerosols: desert dust, sea salt, organic matter, black carbon, sulfate, nitrate, and ammonium [54]. The dataset provides global coverage with a spatial resolution of 0.4° × 0.4°, and the temporal resolution is hourly. Dust deposition data from CAMS is only available from 26 June 2018; therefore, this study analyzed the dry and wet deposition of dust aerosols from 2019 to 2023 in April.

2.5. ERA5 Data

ERA5 represents the fifth-generation reanalysis by the ECMWF, offering a comprehensive recounting of global climate and weather conditions. It provides a detailed introduction to the records of the atmosphere, land surface, and waves since 1950, mainly providing daily and monthly average data [55]. This research utilized meteorological variables such as the 500 hPa geopotential height, sea-level pressure fields, and 700 hPa wind fields to decipher the atmospheric conditions influencing dust transport processes. The ERA5 dataset used in this study features a spatial resolution of 0.25° × 0.25° and a temporal resolution of one hour.

2.6. PM10 Concentration Data

Atmospheric particulate matter (PM) is a mixture of inorganic ions, elements, organic components, mineral dust, and water [56]. According to different particle sizes, PM can be divided into PM10 and PM2.5. To assess the impact of dust events on air quality, we utilized hourly PM10 concentration data from the China National Environmental Monitoring Centre. In this study, we selected four representative stations in different regions (North China, Northeast China, Shandong Peninsula, and East China) based on the transmission paths and impact areas of the dust storm observed by satellites to monitor PM10 concentrations. These four sites are Bayan Nur (107.59°E, 40.91°N), Baicheng (122.82°E, 45.61°N), Linyi (118.29°E, 35.06°N), and Hefei (117.25°E, 31.85°N).

3. Results

3.1. Analysis of Synoptic Situation and Long-Range Transport of Dust

As of 30 April 2023, China has experienced 12 dust events this year, which is the highest frequency of dust events for the same period in the past decade. On 9–13 April 2023, a dust storm event originating in the Gobi Desert, on the border of Mongolia and Inner Mongolia, entered China. During this dust storm, the daily average PM10 concentration in North China exceeded the national secondary standard limit of 150 μg/m3, and the surrounding areas of Bohai Sea suffered from continuous air pollution for 7 days (9–15 April 2023), affecting an area of 4.5 million km2 [44,46]. Previous studies have shown that large-scale atmospheric circulation is a key driving factor for annual variations of dust storms, with strong winds being the main driving force for sand lifting and long-distance transport [57]; the Mongolian cyclone was the main synoptic-scale system that triggered this sandstorm process [46]. Figure 1a–e shows the spatio-temporal distribution of the 500 hPa height field, the sea level pressure field, and the 700 hPa wind field during the dust transport process. Figure 1a′–e′ shows the corresponding distribution of the dust score, which reflects the spatio-temporal distribution of the dust transport. When the dust score is above 380, dust is probable.
On 9 April, the dust source region was located ahead of a trough at 500 hPa, close to the Mongolian cyclone. Tsai et al. [58] found that for the source region ahead of the trough, dust particles could be lifted into the free troposphere. If the rising dust particles move into the ascending region of the trough or rise due to other processes, then the dust particles can travel further distances. As a result, some dust particles were transported from the source region to northeastern China by the westerly airflow (Figure 1a′). On 10 and 11 April, the 500 hPa trough and surface cyclone moved eastward, causing an increase in the pressure gradient and strong winds. The impact area of the dust continued to expand. As shown in Figure 1b′, the dust transported to the northeastern region on 9 April continued to move eastward, with some dust particles already transported across Japan and reaching the northwestern Pacific. In addition, under the influence of the northwesterly wind, some dust particles were also transported to eastern China and the Shandong Peninsula (Figure 1c′). On 12 April, dust was again transported from eastern China towards Japan (Figure 1d′), and on 13 April, it spread further into the northwestern Pacific, as shown in Figure 1e′.
Based on the synoptic situation and the distribution of the dust score in Figure 1, the dust originated from the Gobi Desert on the border between Mongolia and Inner Mongolia. Under the influence of high-level wind fields, it was mainly transported over long distances along two paths. The first path was from 9 to 10 April, when it was transported from the source through northeast China to Japan and the northwest Pacific. The second path was from 10 to 13 April, when it was transported from the source through the Shandong Peninsula to Japan and the northwest Pacific. To further investigate the dust transport path and intensity, we needed to combine ground-based observations for analysis.

3.2. Analysis of Ground Station Observations

Although satellite-based monitoring can provide dust distribution with higher spatial resolution and continuous spatial coverage, it cannot directly measure accurate ground-level PM10 concentration to monitor air quality. Therefore, the combined use of ground-based and satellite data can provide more reliable information for dust storm studies. Based on the satellite analysis of the dust transport pathways in Figure 1, four air quality monitoring stations in China (color in Figure 2a) were selected for further analysis of dust transport and intensity: Bayan Nur (107.59°E, 40.91°N), Baicheng (122.82°E, 45.61°N), Linyi (118.29°E, 35.06°N), and Hefei (117.25°E, 31.85°N). In addition, since the PM10 data used in this paper was only for the region within China, we selected two lidar stations in Japan (black), Sapporo (141.34°E, 43.07°N), and Tokyo (139.71°E, 35.69°N), for a complementary ground-based study.
PM10 concentrations at the Bayan Nur station in the dust source region increased on 9 April and peaked at 5055 μg/m3 on 10 April. On 10 April, PM10 concentrations at the Baicheng station in northeast China began to rise, peaking at over 2500 μg/m3. Combined with the satellite observations in Figure 1, on 9 and 10 April, the dust was transported from the source to Japan along the first path across northeastern China. As a result, the depolarization ratio and the dust extinction coefficient at the Sapporo site increased significantly from the early hours of 10 April, with the values concentrated in the range of 0.2–0.3 and the heights concentrated in the range of 2–5 km, indicating that some dust had been transported to the northern region of Japan. From 12 April, the dust settled and concentrated near the ground (within 3 km), weakening with time and gradually clearing by the early morning of 15 April (Figure 3).
From 10 April, some dust particles were transported from the source region across the Shandong Peninsula to the Northwest Pacific via the second path, which lagged behind the first path. At 9:00 on 11 April, the PM10 concentration at the Linyi station increased rapidly and peaked at 2397 µg/m3. On 12 April, the impact of the dust spread southwards to the Hefei station, where the PM10 concentration rose rapidly to 1415 µg/m3. At the same time, the dust continued to be transported eastwards towards Japan. On 11 April, dust was observed at Tokyo station. However, from the afternoon of 11 to 12 April, the depolarization ratio and the dust extinction coefficient decreased rapidly. This may be due to dust from the first track being transported eastward by the wind field, causing local pollution to decrease. On 13 April, the extinction coefficient of the dust near the surface of the Tokyo Station was very high, and the air mass consisted mainly of irregular particles, indicating that the dust transported by the second path had arrived in Japan and started to settle. On 14 and 15 April, there was a slight increase in the height of the dust particles, an indication that there was still long-range transport of dust from external sources, causing continuous pollution at this station.
To understand the intensity of this dust event, the high PM10 concentration of this dust process was compared with the PM10 concentration of previous processes. During the dust process of 3–8 May 2017, hourly PM10 concentrations at sites in Shandong Province exceeded 500 µg/m3 and were below 400 µg/m3 during the second process of 11–14 May [59]. The PM10 concentration at Bayan Nur increased to 4171 µg/m3 at 00:00 on 3 May 2020, while the dust event disappeared in the early morning of 4 May with a strong but short duration [20]. Filonchyk [60] found that on 15 March 2021 at 03:00, the PM10 concentration at Erdos station reached 9811 µg/m3, and the hourly PM10 concentration in Beijing exceeded 6400 µg/m3, which was the largest and most severe to affect the region in the last decade. In summary, the impact of this dust process on air quality from 9 to 13 April 2023 was characterized by its remarkable intensity, extensive spatial coverage, and prolonged duration compared to previous dust events.
According to the ground observation results, the large-scale transport of this dust event was mainly divided into two paths. The first transport path was from Bayan Nur to Baicheng and then to northern Japan, and the second transport path was from Bayan Nur to Linyi and Hefei and then to Tokyo, which also corresponded with the wind field and satellite observation results in Figure 1.

3.3. The Response of Ocean to Dust Deposition

Nutrients are one of the necessary conditions for the growth of marine phytoplankton. The main sources of nutrients in the oceans are terrestrial runoff inputs, benthic nutrient upwellings, and atmospheric depositions [61,62,63,64]. Previous studies showed that the dust concentration decreased from the source to the downstream, but dust interacted with anthropogenic acid gas emissions during transport, resulting in more soluble iron transport to the ocean [34,65]. Therefore, as shown in Table 1, Chl-a concentrations in the oceans increased to varying degrees after dust transport. The dust process on 9–13 April 2023 came from both Chinese source areas (Inner Mongolia) and non-Chinese source areas (Mongolia). After long-distance transport through the two different pathways, how the deposition to the ocean affected marine phytoplankton needed further discussion. Therefore, we selected an area in the open ocean in a study to reduce the effect of terrestrial runoff on marine Chl-a concentrations.
Figure 4 shows the distribution of the difference between the Chl-a concentrations in 2023 and the average of the previous years (2016–2022) from 10 to 18 April. From 11 April, the Chl-a concentrations in the 37°N–42°N, 145°E–165°E region showed a significant increase compared to previous years, and the strongest increase was reached on 14 April, with the maximum difference in Chl-a concentrations exceeding 80 mg/m3, indicating that this dust process had an important impact on the growth of marine phytoplankton. To investigate the relationship between the obvious increase in Chl-a and dust events, we selected 37°N–42°N and 145°E–165°E (red box in Figure 4) as the study area and analyzed the trends of dust deposition and Chl-a concentrations within this area.
As shown in Figure 5a, due to the long-range transport of dust, there was a significant increase in dust deposition in the study area from 13 April onwards, with total deposition peaking at 29.14 mg/m2 on 14 April. The dust deposition lasted for a long period of time due to the continuous transport through the two transport paths, and the total deposition reached 79.88 mg/m2 from 13 to 18 April, with an average daily deposition of 13.31 mg/m2, which was much higher than the total average deposition of 33.03 mg/m2 for the same period in 2019–2022. Moreover, the dust was deposited into the same marine area via two different pathways, and both pathways passed through the industry-developed areas in China, which may take more nutrients entering the ocean via different pathways. Guo et al. [67] found that dry deposition played a dominant role in the East Asian dust source area, while wet deposition was more important in northeastern China, Japan, and the Pacific Ocean, which were far from the source area. Therefore, this dust deposition process was dominated by wet deposition in the study area. Previous studies have shown that iron was more soluble under wet deposition; therefore, wet deposition was associated with increased bioavailable Fe, which can more effectively increase Chl-a concentrations [67,68,69]. As a result, Chl-a concentrations in the study area showed an obvious increase from 12 April (Figure 5b). It reached a peak of 2.78 mg/m3 on 14 April, a high increase of 692% compared to the multi-year mean (0.35 mg/m3), and maintained an increase on 13–18 April. Therefore, the response of marine Chl-a concentrations to this dust event was strong and long-lasting and also reached a high level compared with previous studies (Table 1).

4. Discussion

Dust events not only have serious impacts on air quality and visibility, but their deposition processes also play an important role in marine ecosystems, which in turn affects the global carbon cycle. In recent years, Mongolia, as a source of dust, has increased its influence on the occurrence of dust. According to weather conditions and satellite observations, Mongolian cyclones and strong winds were the main factors driving the long-range dust drift. Combining multiple observational data and CAMS data, the intensity, transmission pathways, deposition, and ocean response of the dust were investigated. We found that the arrival of the dust storm led to a rapid increase in PM10 concentrations, with hourly PM10 concentrations reaching up to 5055 µg/m3 at the Bayan Nur station. Air quality was also severely affected in Northeast China, North China, and the eastern coastal areas of China, where hourly concentrations exceeded 1000 µg/m3. Under the continuous transportation through two paths, the cumulative dust deposition from 13 to 18 April was 79.88 mg/m2, which was much higher than the average deposition of 33.03 mg/m2 over the same period in previous years. In addition, although a larger study area was selected in this paper, the response of ocean Chl-a concentrations was stronger, increasing by 692%, which is the result of several factors.
First, the dust process was characterized by its remarkable intensity, extensive spatial coverage, and prolonged duration compared to previous dust events [20,59,60], creating conditions for the transport of more nutrients to the ocean. Second, during the transport process, the dust may mix with anthropogenic pollutants, increasing the deposition of soluble iron and further enhancing the ocean response [34,66,70,71]. Moreover, this dust process was mainly transported to the ocean via two pathways, passing through the industry-developed areas of China, which may have mixed more with anthropogenic aerosols, thereby increasing the bioavailable iron input to the ocean. Finally, differences in the source area may also lead to differences in the properties of dust particles [40,42,72]. This dust source was from the Gobi Desert on the China–Mongolia border, which differs from some previous studies [13,14,66]. Whether the dust source was also one of the factors causing this strong ocean response needs further discussion in the future. In addition, this paper focused on the transport pathways of dust and the response of the ocean to dust deposition. However, variations in the chemical properties of dust aerosols during transport, as well as the composition and amounts of nutrients deposited in the ocean, need to be investigated on the basis of further observational analyses.

Author Contributions

Conceptualization, Y.L. and W.W.; methodology, Y.L. and W.W.; software, Y.L.; validation, Y.L. and W.W.; data curation, Y.L. formal analysis, Y.L. and W.W.; investigation, Y.L. and W.W.; resources, Y.L. and W.W.; writing—original draft preparation, Y.L.; writing—review and editing, W.W.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 41875174.

Data Availability Statement

The software used in this study is MATLAB (Version R2019b). The data we used here are available. The EOSDIS worldview data is available at https://worldview.earthdata.nasa.gov/, accessed on 5 December 2023). The AD-Net data products are available at www-lidar.nies.go.jp/AD-Net/ncdf/, accessed on 7 December 2023. The Himawari-8/9 satellite data are obtained from ftp.ptree.jaxa.jp. The CAMS aerosol reanalysis and deposition data are available at CAMS global atmospheric composition forecasts (copernicus.eu). The ERA5 Reanalysis data is available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, accessed on 17 November 2023. The PM10 concentrations are collected from the China National Environmental Monitoring Centre http://www.cnemc.cn/, accessed on 20 December 2023.

Acknowledgments

We acknowledge the Worldview tool from NASA’s Earth Observing System Data and Information System (EOSDIS) for providing satellite imagery. We are grateful to the Japan Meteorological Agency for providing Himawari-8/9 data. We also appreciate the open access data released by the Asian Dust and Aerosol Lidar Observation Network (AD-Net), China National Environmental Monitoring Centre, European Centre for Medium-Range Weather Forecasts (ECMWF), and Copernicus Atmosphere Monitoring Service (CAMS).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Francis, D.; Chaboureau, J.-P.; Nelli, N.; Cuesta, J.; Alshamsi, N.; Temimi, M.; Pauluis, O.; Xue, L. Summertime Dust Storms over the Arabian Peninsula and Impacts on Radiation, Circulation, Cloud Development and Rain. Atmos. Res. 2021, 250, 105364. [Google Scholar] [CrossRef]
  2. Kok, J.F.; Storelvmo, T.; Karydis, V.A.; Adebiyi, A.A.; Mahowald, N.M.; Evan, A.T.; He, C.; Leung, D.M. Mineral Dust Aerosol Impacts on Global Climate and Climate Change. Nat. Rev. Earth Environ. 2023, 4, 71–86. [Google Scholar] [CrossRef]
  3. Wang, W.; Huang, J.; Zhou, T.; Bi, J.; Lin, L.; Chen, Y.; Huang, Z.; Su, J. Estimation of Radiative Effect of a Heavy Dust Storm over Northwest China Using Fu–Liou Model and Ground Measurements. J. Quant. Spectrosc. Radiat. Transf. 2013, 122, 114–126. [Google Scholar] [CrossRef]
  4. Hu, Z.; Jin, Q.; Ma, Y.; Pu, B.; Ji, Z.; Wang, Y.; Dong, W. Temporal Evolution of Aerosols and Their Extreme Events in Polluted Asian Regions during Terra’s 20-Year Observations. Remote Sens. Environ. 2021, 263, 112541. [Google Scholar] [CrossRef]
  5. Huang, J.; Wang, T.; Wang, W.; Li, Z.; Yan, H. Climate Effects of Dust Aerosols over East Asian Arid and Semiarid Regions. J. Geophys. Res. Atmos. 2014, 119, 11398–11416. [Google Scholar] [CrossRef]
  6. Wang, W.; Huang, J.; Minnis, P.; Hu, Y.; Li, J.; Huang, Z.; Ayers, J.K.; Wang, T. Dusty Cloud Properties and Radiative Forcing over Dust Source and Downwind Regions Derived from A-Train Data during the Pacific Dust Experiment. J. Geophys. Res. Atmos. 2010, 115, D00H35. [Google Scholar] [CrossRef]
  7. Wang, W.; Sheng, L.; Jin, H.; Han, Y. Dust Aerosol Effects on Cirrus and Altocumulus Clouds in Northwest China. J. Meteorol. Res. 2015, 29, 793–805. [Google Scholar] [CrossRef]
  8. Lachatre, M.; Foret, G.; Laurent, B.; Siour, G.; Cuesta, J.; Dufour, G.; Meng, F.; Tang, W.; Zhang, Q.; Beekmann, M. Air Quality Degradation by Mineral Dust over Beijing, Chengdu and Shanghai Chinese Megacities. Atmosphere 2020, 11, 708. [Google Scholar] [CrossRef]
  9. Li, X.; Liu, X.; Yin, Z.-Y. The Impacts of Taklimakan Dust Events on Chinese Urban Air Quality in 2015. Atmosphere 2018, 9, 281. [Google Scholar] [CrossRef]
  10. Zhao, L.; Wang, W.; Hao, T.; Qu, W.; Sheng, L.; Luo, C.; An, X.; Zhou, Y. The Autumn Haze-Fog Episode Enhanced by the Transport of Dust Aerosols in the Tianjin Area. Atmos. Environ. 2020, 237, 117669. [Google Scholar] [CrossRef]
  11. Goudie, A.S. Desert Dust and Human Health Disorders. Environ. Int. 2014, 63, 101–113. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, X.; Zhao, L.; Tong, D.Q.; Wu, G.; Dan, M.; Teng, B. A Systematic Review of Global Desert Dust and Associated Human Health Effects. Atmosphere 2016, 7, 158. [Google Scholar] [CrossRef]
  13. Luo, C.; Wang, W.; Sheng, L.; Zhou, Y.; Hu, Z.; Qu, W.; Li, X.; Hai, S. Influence of Polluted Dust on Chlorophyll-a Concentration and Particulate Organic Carbon in the Subarctic North Pacific Ocean Based on Satellite Observation and the WRF-Chem Simulation. Atmos. Res. 2020, 236, 104812. [Google Scholar] [CrossRef]
  14. Wang, W.; He, Z.; Hai, S.; Sheng, L.; Han, Y.; Zhou, Y. Dust Aerosol’s Deposition and Its Effects on Chlorophyll-A Concentrations Based on Multi-Sensor Satellite Observations and Model Simulations: A Case Study. Front. Environ. Sci. 2022, 10, 875365. [Google Scholar] [CrossRef]
  15. Yoon, J.-E.; Lim, J.-H.; Shim, J.-M.; Kwon, J.-I.; Kim, I.-N. Spring 2018 Asian Dust Events: Sources, Transportation, and Potential Biogeochemical Implications. Atmosphere 2019, 10, 276. [Google Scholar] [CrossRef]
  16. Zhang, T.; Zheng, M.; Sun, X.; Chen, H.; Wang, Y.; Fan, X.; Pan, Y.; Quan, J.; Liu, J.; Wang, Y.; et al. Environmental Impacts of Three Asian Dust Events in the Northern China and the Northwestern Pacific in Spring 2021. Sci. Total Environ. 2023, 859, 160230. [Google Scholar] [CrossRef] [PubMed]
  17. Kok, J.F.; Adebiyi, A.A.; Albani, S.; Balkanski, Y.; Checa-Garcia, R.; Chin, M.; Colarco, P.R.; Hamilton, D.S.; Huang, Y.; Ito, A.; et al. Contribution of the World’s Main Dust Source Regions to the Global Cycle of Desert Dust. Atmos. Chem. Phys. 2021, 21, 8169–8193. [Google Scholar] [CrossRef]
  18. Hu, Z.; Huang, J.; Zhao, C.; Bi, J.; Jin, Q.; Qian, Y.; Leung, L.R.; Feng, T.; Chen, S.; Ma, J. Modeling the Contributions of Northern Hemisphere Dust Sources to Dust Outflow from East Asia. Atmos. Environ. 2019, 202, 234–243. [Google Scholar] [CrossRef]
  19. Wang, W.; Fang, Z. Numerical Simulation and Synoptic Analysis of Dust Emission and Transport in East Asia. Glob. Planet. Change 2006, 52, 57–70. [Google Scholar] [CrossRef]
  20. Yang, L.; Hu, Z.; Huang, Z.; Wang, L.; Han, W.; Yang, Y.; Tao, H.; Wang, J. Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China. Remote Sens. 2021, 13, 1056. [Google Scholar] [CrossRef]
  21. Li, J.; Hao, X.; Liao, H.; Yue, X.; Li, H.; Long, X.; Li, N. Predominant Type of Dust Storms That Influences Air Quality over Northern China and Future Projections. Earths Future 2022, 10, e2022EF002649. [Google Scholar] [CrossRef]
  22. Hu, Z.; Ma, Y.; Jin, Q.; Idrissa, N.F.; Huang, J.; Dong, W. Attribution of the March 2021 Exceptional Dust Storm in North China. Bull. Am. Meteorol. Soc 2023, 104, E749–E755. [Google Scholar] [CrossRef]
  23. Wu, D.; Liu, J.; Wang, T.; Niu, X.; Chen, Z.; Wang, D.; Zhang, X.; Ji, M.; Wang, X.; Pu, W. Applying a Dust Index over North China and Evaluating the Contribution of Potential Factors to Its Distribution. Atmos. Res. 2021, 254, 105515. [Google Scholar] [CrossRef]
  24. Chen, J.; Li, G. Geochemical Studies on the Source Region of Asian Dust. Sci. China Earth Sci. 2011, 54, 1279–1301. [Google Scholar] [CrossRef]
  25. Zhang, K.; Chai, F.; Zhang, R.; Xue, Z. Source, Route and Effect of Asian Sand Dust on Environment and the Oceans. Particuology 2010, 8, 319–324. [Google Scholar] [CrossRef]
  26. Hu, Z.; Huang, J.; Zhao, C.; Ma, Y.; Jin, Q.; Qian, Y.; Leung, L.R.; Bi, J.; Ma, J. Trans-Pacific Transport and Evolution of Aerosols: Spatiotemporal Characteristics and Source Contributions. Atmos. Chem. Phys. 2019, 19, 12709–12730. [Google Scholar] [CrossRef]
  27. Hu, Z.; Zhao, C.; Huang, J.; Leung, L.R.; Qian, Y.; Yu, H.; Huang, L.; Kalashnikova, O.V. Trans-Pacific Transport and Evolution of Aerosols: Evaluation of Quasi-Global WRF-Chem Simulation with Multiple Observations. Geosci. Model Dev. 2016, 9, 1725–1746. [Google Scholar] [CrossRef]
  28. Zhang, X.; Gong, S.; Zhao, T.; Arimoto, R.; Wang, Y.; Zhou, Z. Sources of Asian Dust and Role of Climate Change versus Desertification in Asian Dust Emission. Geophys. Res. Lett. 2003, 30, 2272. [Google Scholar] [CrossRef]
  29. Liu, B.; Peng, W.; Liu, S.; Yang, T. Estimation on the Dust Lift Amount and Source Contribution of the Heavy Dust Weather in Mid-March 2021 over Central East Asia. J. Desert Res. 2022, 42, 79–86. [Google Scholar]
  30. Zhang, K.; Gao, H. The Characteristics of Asian-Dust Storms during 2000–2002: From the Source to the Sea. Atmos. Environ. 2007, 41, 9136–9145. [Google Scholar] [CrossRef]
  31. Kim, H.-S.; Chung, Y.-S.; Yoon, M.-B. An Analysis on the Impact of Large-Scale Transports of Dust Pollution on Air Quality in East Asia as Observed in Central Korea in 2014. Air Qual. Atmos. Health 2016, 9, 83–93. [Google Scholar] [CrossRef]
  32. Gao, Z.; Hu, X.; Zhang, X.; Wang, Y. Characteristic Analysis of Three Sand-Dust Storm Process in 2021 Based on FY-4A Satellite Remote Sensing Data. Meteorol. Sci. Technol. 2022, 50, 536–544. [Google Scholar] [CrossRef]
  33. Liu, X.; Zhang, Y.; Yao, H.; Lian, Q.; Xu, J. Analysis of the Severe Dust Process and Its Impact on Air Quality in Northern China. Atmosphere 2023, 14, 1071. [Google Scholar] [CrossRef]
  34. Fan, S.-M.; Moxim, W.J.; Levy II, H. Aeolian Input of Bioavailable Iron to the Ocean. Geophys. Res. Lett. 2006, 33, L07602. [Google Scholar] [CrossRef]
  35. Ito, A.; Myriokefalitakis, S.; Kanakidou, M.; Mahowald, N.M.; Scanza, R.A.; Hamilton, D.S.; Baker, A.R.; Jickells, T.; Sarin, M.; Bikkina, S.; et al. Pyrogenic Iron: The Missing Link to High Iron Solubility in Aerosols. Sci. Adv. 2019, 5, eaau7671. [Google Scholar] [CrossRef] [PubMed]
  36. Sunda, W.G.; Huntsman, S.A. Interrelated Influence of Iron, Light and Cell Size on Marine Phytoplankton Growth. Nature 1997, 390, 389–392. [Google Scholar] [CrossRef]
  37. Schulz, M.; Prospero, J.M.; Baker, A.R.; Dentener, F.; Ickes, L.; Liss, P.S.; Mahowald, N.M.; Nickovic, S.; García-Pando, C.P.; Rodríguez, S.; et al. Atmospheric Transport and Deposition of Mineral Dust to the Ocean: Implications for Research Needs. Environ. Sci. Technol. 2012, 46, 10390–10404. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, W.; Luo, C.; Sheng, L.; Zhao, C.; Zhou, Y.; Chen, Y. Effects of Biomass Burning on Chlorophyll-a Concentration and Particulate Organic Carbon in the Subarctic North Pacific Ocean Based on Satellite Observations and WRF-Chem Model Simulations: A Case Study. Atmos. Res. 2021, 254, 105526. [Google Scholar] [CrossRef]
  39. Wang, Y.; Zhuang, G.; Sun, Y.; An, Z. Water-Soluble Part of the Aerosol in the Dust Storm Season—Evidence of the Mixing between Mineral and Pollution Aerosols. Atmos. Environ. 2005, 39, 7020–7029. [Google Scholar] [CrossRef]
  40. Huang, K.; Zhuang, G.; Li, J.; Wang, Q.; Sun, Y.; Lin, Y.; Fu, J.S. Mixing of Asian Dust with Pollution Aerosol and the Transformation of Aerosol Components during the Dust Storm over China in Spring 2007. J. Geophys. Res. Atmos. 2010, 115, D00K13. [Google Scholar] [CrossRef]
  41. Sun, Y.; Zhuang, G.; Wang, Y.; Zhao, X.; Li, J.; Wang, Z.; An, Z. Chemical Composition of Dust Storms in Beijing and Implications for the Mixing of Mineral Aerosol with Pollution Aerosol on the Pathway. J. Geophys. Res. Atmos. 2005, 110, D24209. [Google Scholar] [CrossRef]
  42. Zhao, X.; Zhuang, G.; Wang, Z.; Sun, Y.; Wang, Y.; Yuan, H. Variation of Sources and Mixing Mechanism of Mineral Dust with Pollution Aerosol—Revealed by the Two Peaks of a Super Dust Storm in Beijing. Atmos. Res. 2007, 84, 265–279. [Google Scholar] [CrossRef]
  43. Onishi, K.; Kurosaki, Y.; Otani, S.; Yoshida, A.; Sugimoto, N.; Kurozawa, Y. Atmospheric Transport Route Determines Components of Asian Dust and Health Effects in Japan. Atmos. Environ. 2012, 49, 94–102. [Google Scholar] [CrossRef]
  44. Chen, S.; Zhao, D.; Huang, J.; He, J.; Chen, Y.; Chen, J.; Bi, H.; Lou, G.; Du, S.; Zhang, Y.; et al. Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023. Adv. Atmos. Sci. 2023, 40, 1549–1557. [Google Scholar] [CrossRef]
  45. Filonchyk, M.; Peterson, M.P.; Zhang, L.; Yan, H. An Analysis of Air Pollution Associated with the 2023 Sand and Dust Storms over China: Aerosol Properties and PM10 Variability. Geosci. Front. 2024, 15, 101762. [Google Scholar] [CrossRef]
  46. Yin, Z.; Huo, Q.; Ma, X.; Zhang, Y.; Ma, X.; Wang, H. Mechanisms of Dust Source Accumulation and Synoptic Disturbance Triggering the 2023 Spring Sandstorm in Northern China. Trans. Atmos. Sci. 2023, 46, 321–331. [Google Scholar]
  47. Chahine, M.T.; Pagano, T.S.; Aumann, H.H.; Atlas, R.; Barnet, C.; Blaisdell, J.; Chen, L.; Divakarla, M.; Fetzer, E.J.; Goldberg, M.; et al. AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases. Bull. Am. Meteorol. Soc. 2006, 87, 911–926. [Google Scholar] [CrossRef]
  48. Sugimoto, N.; Matsui, I.; Shimizu, A.; Nishizawa, T.; Hara, Y.; Uno, I. Lidar Network Observation of Tropospheric Aerosols. In Proceedings of the Lidar Remote Sensing for Environmental Monitoring XI, Incheon, Republic of Korea, 12–14 October 2010; SPIE: Bellingham, WA, USA; Volume 7860, pp. 87–95. [Google Scholar]
  49. Liu, Z.; Zhang, X.; Zheng, Y.; Liu, J. Spatio-Temporal Distribution and Transport Behavior of a Dust Event Based on the CALIPSO in China. Acta Sci. Circumstantiae 2016, 36, 4315–4327. [Google Scholar]
  50. Yin, L.; He, Q.; Li, J.; Meng, L.; Fu, G.; Wu, C.; Xiao, H.; Fan, X. Observation and Study of a Sand and Dust Pollution Process in Hotan City Based on Ground-Based Lidar. China Environ. Sci. 2023, 12, 6290–6300. [Google Scholar] [CrossRef]
  51. Bessho, K.; Date, K.; Hayashi, M.; Ikeda, A.; Imai, T.; Inoue, H.; Kumagai, Y.; Miyakawa, T.; Murata, H.; Ohno, T.; et al. An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites. J. Meteorol. Soc. Jpn. Ser. II 2016, 94, 151–183. [Google Scholar] [CrossRef]
  52. Inness, A.; Ades, M.; Agustí-Panareda, A.; Barré, J.; Benedictow, A.; Blechschmidt, A.-M.; Dominguez, J.J.; Engelen, R.; Eskes, H.; Flemming, J.; et al. The CAMS Reanalysis of Atmospheric Composition. Atmos. Chem. Phys. 2019, 19, 3515–3556. [Google Scholar] [CrossRef]
  53. Flemming, J.; Huijnen, V.; Arteta, J.; Bechtold, P.; Beljaars, A.; Blechschmidt, A.-M.; Diamantakis, M.; Engelen, R.J.; Gaudel, A.; Inness, A.; et al. Tropospheric Chemistry in the Integrated Forecasting System of ECMWF. Geosci. Model Dev. 2015, 8, 975–1003. [Google Scholar] [CrossRef]
  54. Isaza, A.; Kay, M.; Evans, J.P.; Bremner, S.; Prasad, A. Validation of Australian Atmospheric Aerosols from Reanalysis Data and CMIP6 Simulations. Atmos. Res. 2021, 264, 105856. [Google Scholar] [CrossRef]
  55. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 Global Reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  56. Xue, H.; Liu, G.; Zhang, H.; Hu, R.; Wang, X. Similarities and Differences in PM10 and PM2.5 Concentrations, Chemical Compositions and Sources in Hefei City, China. Chemosphere 2019, 220, 760–765. [Google Scholar] [CrossRef] [PubMed]
  57. Xu, C.; Guan, Q.; Lin, J.; Luo, H.; Yang, L.; Tan, Z.; Wang, Q.; Wang, N.; Tian, J. Spatiotemporal Variations and Driving Factors of Dust Storm Events in Northern China Based on High-Temporal-Resolution Analysis of Meteorological Data (1960–2007). Environ. Pollut. 2020, 260, 114084. [Google Scholar] [CrossRef] [PubMed]
  58. Tsai, F.; Chen, G.T.-J.; Liu, T.-H.; Lin, W.-D.; Tu, J.-Y. Characterizing the Transport Pathways of Asian Dust. J. Geophys. Res. Atmos. 2008, 113, D17311. [Google Scholar] [CrossRef]
  59. Li, W.; Wang, W.; Zhou, Y.; Ma, Y.; Zhang, D.; Sheng, L. Occurrence and Reverse Transport of Severe Dust Storms Associated with Synoptic Weather in East Asia. Atmosphere 2019, 10, 4. [Google Scholar] [CrossRef]
  60. Filonchyk, M. Characteristics of the Severe March 2021 Gobi Desert Dust Storm and Its Impact on Air Pollution in China. Chemosphere 2022, 287, 132219. [Google Scholar] [CrossRef] [PubMed]
  61. Gao, X.; Song, J.; Li, X.; Li, N.; Yuan, H. Dissolved Inorganic Carbon in Surface Waters around the Changjiang Estuary and Hangzhou Bay in Summer. Mar. Sci. 2008, 32, 61–67. [Google Scholar]
  62. Guo, J.; Xu, H.; Liu, L.; Chen, D.; Peng, Y.; Yim, S.H.-L.; Yang, Y.; Li, J.; Zhao, C.; Zhai, P. The Trend Reversal of Dust Aerosol over East Asia and the North Pacific Ocean Attributed to Large-Scale Meteorology, Deposition, and Soil Moisture. J. Geophys. Res. Atmos. 2019, 124, 10450–10466. [Google Scholar] [CrossRef]
  63. Hamilton, D.S.; Perron, M.M.G.; Bond, T.C.; Bowie, A.R.; Buchholz, R.R.; Guieu, C.; Ito, A.; Maenhaut, W.; Myriokefalitakis, S.; Olgun, N.; et al. Earth, Wind, Fire, and Pollution: Aerosol Nutrient Sources and Impacts on Ocean Biogeochemistry. Annu. Rev. Mar. Sci. 2022, 14, 303–330. [Google Scholar] [CrossRef] [PubMed]
  64. Ludwig, W.; Dumont, E.; Meybeck, M.; Heussner, S. River Discharges of Water and Nutrients to the Mediterranean and Black Sea: Major Drivers for Ecosystem Changes during Past and Future Decades? Prog. Oceanogr. 2009, 80, 199–217. [Google Scholar] [CrossRef]
  65. Ito, A.; Shi, Z. Delivery of Anthropogenic Bioavailable Iron from Mineral Dust and Combustion Aerosols to the Ocean. Atmos. Chem. Phys. 2016, 16, 85–99. [Google Scholar] [CrossRef]
  66. Li, Y.; Wang, W.; Han, Y.; Liu, W.; Wang, R.; Zhang, R.; Zhao, Z.; Sheng, L.; Zhou, Y. Impact of COVID-19 Emission Reduction on Dust Aerosols and Marine Chlorophyll-a Concentration. Sci. Total Environ. 2024, 918, 170493. [Google Scholar] [CrossRef] [PubMed]
  67. Guo, J.; Yin, Y.; Wang, Y.; Kang, H.; Xiao, H.; Chen, K.; Hao, J. Numerical Study of the Dust Distribution, Source and Sink, and Transport Features over East Asia. China Environ. Sci. 2017, 37, 801–812. [Google Scholar]
  68. Gao, Y.; Fan, S.-M.; Sarmiento, J.L. Aeolian Iron Input to the Ocean through Precipitation Scavenging: A Modeling Perspective and Its Implication for Natural Iron Fertilization in the Ocean. J. Geophys. Res. Atmos. 2003, 108, 4221. [Google Scholar] [CrossRef]
  69. Jo, C.O.; Lee, J.-Y.; Park, K.-A.; Kim, Y.H.; Kim, K.-R. Asian Dust Initiated Early Spring Bloom in the Northern East/Japan Sea. Geophys. Res. Lett. 2007, 34, L05602. [Google Scholar] [CrossRef]
  70. Li, W.; Xu, L.; Liu, X.; Zhang, J.; Lin, Y.; Yao, X.; Gao, H.; Zhang, D.; Chen, J.; Wang, W.; et al. Air Pollution–Aerosol Interactions Produce More Bioavailable Iron for Ocean Ecosystems. Sci. Adv. 2017, 3, e1601749. [Google Scholar] [CrossRef] [PubMed]
  71. Meskhidze, N.; Chameides, W.L.; Nenes, A.; Chen, G. Iron Mobilization in Mineral Dust: Can Anthropogenic SO2 Emissions Affect Ocean Productivity? Geophys. Res. Lett. 2003, 30, 2085. [Google Scholar] [CrossRef]
  72. Zhao, S.; Yu, Y.; Xia, D.; Yin, D.; He, J.; Liu, N.; Li, F. Urban Particle Size Distributions during Two Contrasting Dust Events Originating from Taklimakan and Gobi Deserts. Environ. Pollut. 2015, 207, 107–122. [Google Scholar] [CrossRef]
Remotesensing 16 01883 g001
Figure 1. (a–e) 500 hPa geopotential height field, 700 hPa wind field, and sea level pressure field on 9–13 April 2023; (a′–e′) distribution of dust score during the dust transmission process; dust is probable when the score is above 380.
Figure 1. (a–e) 500 hPa geopotential height field, 700 hPa wind field, and sea level pressure field on 9–13 April 2023; (a′–e′) distribution of dust score during the dust transmission process; dust is probable when the score is above 380.
Remotesensing 16 01883 g001
Remotesensing 16 01883 g002
Figure 2. (a) Distribution of ground observation stations, with colored dots for air quality observation stations in China and black dots for lidar stations in Japan. (b) Hourly PM10 concentration at different stations; the dashed box corresponds to the first significant increase in PM10 concentration at the station.
Figure 2. (a) Distribution of ground observation stations, with colored dots for air quality observation stations in China and black dots for lidar stations in Japan. (b) Hourly PM10 concentration at different stations; the dashed box corresponds to the first significant increase in PM10 concentration at the station.
Remotesensing 16 01883 g002
Remotesensing 16 01883 g003
Figure 3. Depolarization ratio and dust extinction coefficient observed by lidar for 9–15 April 2023 during the dust event. (a) Sapporo station; (b) Tokyo station.
Figure 3. Depolarization ratio and dust extinction coefficient observed by lidar for 9–15 April 2023 during the dust event. (a) Sapporo station; (b) Tokyo station.
Remotesensing 16 01883 g003
Remotesensing 16 01883 g004
Figure 4. Temporal and spatial distribution of ΔChl-a (Chl-a2023–Chl-amean) during the dust process, Chl-amean represents the 2016–2022 mean Chl-a concentration, (a–i) corresponds to the ΔChl-a between 9–18 April 2023 and 2016–2022 mean Chl-a concentration. Red box is for the study area: 37°N–42°N, 145°E–165°E.
Figure 4. Temporal and spatial distribution of ΔChl-a (Chl-a2023–Chl-amean) during the dust process, Chl-amean represents the 2016–2022 mean Chl-a concentration, (a–i) corresponds to the ΔChl-a between 9–18 April 2023 and 2016–2022 mean Chl-a concentration. Red box is for the study area: 37°N–42°N, 145°E–165°E.
Remotesensing 16 01883 g004
Remotesensing 16 01883 g005
Figure 5. (a) Variation of spatial average dust deposition in the study area; (b) Variation of spatial average Chl-a concentrations in the study area. The black solid line in (b) represents the 2016–2022 mean Chl-a concentrations, the green solid line represents the 2023 mean Chl-a concentrations, and the bar represents the proportion of increase in Chl-a concentrations in this dust process relative to the multi-year mean.
Figure 5. (a) Variation of spatial average dust deposition in the study area; (b) Variation of spatial average Chl-a concentrations in the study area. The black solid line in (b) represents the 2016–2022 mean Chl-a concentrations, the green solid line represents the 2023 mean Chl-a concentrations, and the bar represents the proportion of increase in Chl-a concentrations in this dust process relative to the multi-year mean.
Remotesensing 16 01883 g005
Table 1. Response of marine Chl-a concentrations after dust events in previous studies.
Table 1. Response of marine Chl-a concentrations after dust events in previous studies.
CitationsTime Period of Dust EventsStudy AreaChl-a Concentration (Maximum)Maximum Increase Ratio of Chl-a Concentration b
Luo et al. [13]12–17 March 201540°N–50°N, 150°E–155°E0.48 mg/m354.8%
Wang et al. [14]21–25 May 201945°N–50°N, 145°E–150°E1.54 mg/m3256%
Yoon et al. [15]Dust event 1: 26–31 March 2018
Dust event 2: 1–4 April 2018		45°N–46°N, 151°E–152°E3.8 mg/m31166.6%
Zhang et al. [16]27–30 March 202130°N–50°N, 120°E–128°E~1.4 a mg/m369%
Li et al. [66]15–20 March 201940°N–45°N, 157°E–162°E0.28 mg/m385%
Dust event in this study9–13 April 202337°N–42°N, 145°E–165°E2.78 mg/m3692%
a The value is close to 1.4. b The increased ratio of the maximum Chl-a concentrations during this dust event to the multi-year average Chl-a concentrations.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, Y.; Wang, W. Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023. Remote Sens. 2024, 16, 1883. https://doi.org/10.3390/rs16111883

AMA Style

Li Y, Wang W. Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023. Remote Sensing. 2024; 16(11):1883. https://doi.org/10.3390/rs16111883

Chicago/Turabian Style

Li, Yundan, and Wencai Wang. 2024. "Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023" Remote Sensing 16, no. 11: 1883. https://doi.org/10.3390/rs16111883

APA Style

Li, Y., & Wang, W. (2024). Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023. Remote Sensing, 16(11), 1883. https://doi.org/10.3390/rs16111883

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop