16 citations as recorded by crossref.

1. Exploring the spatial sensitivity of environmental parameters: emission source location, building layout, and emission intensity in urban microscale CFD modeling C. Zhang et al. https://doi.org/10.1016/j.buildenv.2026.114936
2. Effects of urban canopy on kinematic and boundary layer structures of the landfalling Typhoon Lekima (2019) X. Ao et al. https://doi.org/10.1016/j.uclim.2026.102859
3. Origin and transformation of volatile organic compounds at a regional background site in Hong Kong: Varied photochemical processes from different source regions Q. Yuan et al. https://doi.org/10.1016/j.scitotenv.2023.168316
4. Validation and revision of low latitudes cloud base height from ERA5 D. Li et al. https://doi.org/10.1016/j.atmosres.2024.107595
5. AI-driven approaches for air pollution modelling: A comprehensive systematic review L. Garbagna et al. https://doi.org/10.1016/j.envpol.2025.125937
6. Formation and source apportionment of ozone in Nanjing affected by tropical cyclones with different tracks Y. Liu et al. https://doi.org/10.1016/j.atmosres.2025.108707
7. Exploring the Influence of Tropical Cyclones on Regional Air Quality Using Multimodal Deep Learning Techniques M. Younis et al. https://doi.org/10.3390/s24216983
8. Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model Z. Fang et al. https://doi.org/10.3390/rs16122207
9. Spatiotemporal heterogeneity of ozone pollution and its driving factors across key regions in China H. Zhang et al. https://doi.org/10.1016/j.jhazmat.2025.139811
10. Comparison between Two-Level Machine Learning and Deep Learning for Groundwater Potential Mapping in the Rmel Aquifer (Northwestern Morocco) M. Chahid et al. https://doi.org/10.1007/s41748-025-00857-y
11. Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning J. Mi et al. https://doi.org/10.1117/1.JRS.18.012007
12. Tropical cyclone-related heatwave episodes in the Greater Bay Area, China: Synoptic patterns and urban-rural disparities M. Zhang et al. https://doi.org/10.1016/j.wace.2024.100656
13. Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis X. Yuan et al. https://doi.org/10.1016/j.envpol.2025.126012
14. Acid rain prediction in the Guangdong-Hong Kong-Macao Greater Bay Area using an explainable machine learning framework Z. Huang et al. https://doi.org/10.1016/j.apr.2024.102201
15. Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning Q. He et al. https://doi.org/10.1021/acs.est.4c02926
16. The spatiotemporal variations of PM2.5 concentration and its relationship with meteorological parameters: A multi-scale analysis in Madrid and Valencia, Spain L. Wei & J. Sobrino https://doi.org/10.1016/j.atmosres.2025.108167