10 citations as recorded by crossref.

1. Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al. https://doi.org/10.1007/s40726-026-00402-z
2. Distinct phytoplankton responses to dust in the Chinese marginal seas: role of synoptic circulation and air–sea heat exchange J. Hu et al. https://doi.org/10.5194/bg-23-5697-2026
3. Roof dust accumulation characteristics and influencing factors in typical urban areas of Beijing C. Su et al. https://doi.org/10.1038/s41598-025-27439-2
4. Local versus external dust source contributions to northern China and selected downwind cities: Evidence from representative dust events in 2025 Y. Fan et al. https://doi.org/10.1016/j.envpol.2026.128369
5. Land cover changes reduce dust aerosol concentrations in Northern China (2000–2020) X. Liu et al. https://doi.org/10.1016/j.envres.2025.120823
6. Air Quality Prediction Affected by Different Activation Functions and Hidden Layer Nodes in Artificial Neural Network Models S. Choi https://doi.org/10.3390/app152412863
7. Modeling CMAQ dry deposition treatment over the western Pacific: a distinct characteristic of mineral dust and anthropogenic aerosols S. Kong et al. https://doi.org/10.5194/acp-25-7245-2025
8. A measurement-driven cross-sectional method to assess the dynamics and flux of dust transport C. Lin et al. https://doi.org/10.1038/s44304-026-00166-y
9. Tracking spatiotemporal dynamics of dust aerosols in China: Emissions, transport, and mitigation insights Y. Che et al. https://doi.org/10.1016/j.jhazmat.2025.140576
10. From source activation to atmospheric loading: a process-based review of dust storm initiation, persistence, and intensity W. Huo et al. https://doi.org/10.1016/j.aeolia.2026.101093