Study on the influence of key parameters of sand emission on dust flux based on multi-source data

This study analyzes ground-based observations and multi-source remote sensing data from eight dust storm events in 2024 at two sites: the Tazhong (TZ) station in the Taklamakan Desert interior and the Xiaotang (XT) station on its northern margin, systematically investigates the interrelationships among dust particle size, friction velocity (U*), and dust flux, and evaluates the applicability of remote sensing data in dust monitoring. The results indicate that particle size significantly influences both horizontal fluxes (Q) and vertical dust fluxes (F). Fine particles (d[0.5]) enhance surface dust flux, while coarse particles (D[4,3]-due to their greater gravitational settling-are less capable of sustained suspension, limiting their long-distance transport. A positive correlation exists between friction velocity (U*) and Q, whereas its impact on F is weaker, suggesting that vertical transport is regulated primarily by particle size, gravitational settling, and turbulent structures. Regarding remote sensing data, MODIS Aerosol Optical Depth (AOD) shows strong consistency with ground-based dust flux measurements, especially at the Xiaotang (XT) station, where AOD closely follows the variation trends of both Q and F. This reflects the effectiveness of remote sensing data in capturing changes in dust activity. Additionally, the Aerosol Absorbing Index (AAI) from Sentinel-5P exhibits a highly significant positive correlation with ground-level dust concentrations, effectively reflecting the vertical structure of dust events. This research provides valuable data support and theoretical foundation for dust warning systems and desertification control projects.

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Publication Details

Journal
Scientific Reports
Published
2026-03-26
DOI
https://doi.org/10.1038/s41598-026-45242-5
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
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article

Study on the influence of key parameters of sand emission on dust flux based on multi-source data

YongQiang Liu, Xinghua Yang, Mayibaier Maihamuti, Mierxiatijiang Maihemuti et al.
Scientific Reports
Atmospheric aerosols and clouds
article

Study on the influence of key parameters of sand emission on dust flux based on multi-source data

YongQiang Liu, Xinghua Yang, Mayibaier Maihamuti, Mierxiatijiang Maihemuti, Fan Yang, Wen Huo, Chenglong Zhou, Ali Mamtimin, Yifei Wang
article en

Abstract

This study analyzes ground-based observations and multi-source remote sensing data from eight dust storm events in 2024 at two sites: the Tazhong (TZ) station in the Taklamakan Desert interior and the Xiaotang (XT) station on its northern margin, systematically investigates the interrelationships among dust particle size, friction velocity (U*), and dust flux, and evaluates the applicability of remote sensing data in dust monitoring. The results indicate that particle size significantly influences both horizontal fluxes (Q) and vertical dust fluxes (F). Fine particles (d[0.5]) enhance surface dust flux, while coarse particles (D[4,3]-due to their greater gravitational settling-are less capable of sustained suspension, limiting their long-distance transport. A positive correlation exists between friction velocity (U*) and Q, whereas its impact on F is weaker, suggesting that vertical transport is regulated primarily by particle size, gravitational settling, and turbulent structures. Regarding remote sensing data, MODIS Aerosol Optical Depth (AOD) shows strong consistency with ground-based dust flux measurements, especially at the Xiaotang (XT) station, where AOD closely follows the variation trends of both Q and F. This reflects the effectiveness of remote sensing data in capturing changes in dust activity. Additionally, the Aerosol Absorbing Index (AAI) from Sentinel-5P exhibits a highly significant positive correlation with ground-level dust concentrations, effectively reflecting the vertical structure of dust events. This research provides valuable data support and theoretical foundation for dust warning systems and desertification control projects.

Scientific Reports
China Meteorological Administration (CN), Taiyuan Normal University (CN), Xinjiang Entry-Exit Inspection and Quarantine Bureau (CN), Shanxi Normal University (CN), China Electronics Standardization Institute (CN), Xinjiang University (CN)
Openalex Percentile: Top 5%
Atmospheric aerosols and clouds
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