15 citations as recorded by crossref.

1. Future Directions in Precipitation Science F. Tapiador et al. 10.3390/rs13061074
2. Airborne measurements of cloud condensation nuclei (CCN) vertical structures over Southern China X. Xu et al. 10.1016/j.atmosres.2021.106012
3. Quantitative assessment of the impact of biomass burning episodes on surface solar radiation using machine learning technology: A case study of a pollution event in Beijing Z. Li et al. 10.1016/j.jastp.2023.106022
4. Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration N. Wang et al. 10.1038/s41612-025-01181-y
5. Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses A. May & H. Li 10.1080/02786826.2022.2114312
6. The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al. 10.1016/j.ecoenv.2023.114911
7. Prediction of CCN spectra parameters in the North China Plain using a random forest model M. Liang et al. 10.1016/j.atmosenv.2022.119323
8. Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning Z. Li et al. 10.1016/j.atmosres.2022.106406
9. Physics-informed learning of aerosol microphysics P. Harder et al. 10.1017/eds.2022.22
10. Sub-micron aerosol and CCN characteristics in Seoul measured during 2019–2021 and CCN prediction using machine learning P. Seo et al. 10.1016/j.atmosenv.2025.121454
11. Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles A. Nair et al. 10.1029/2021GL094133
12. Identifying Driving Factors of Atmospheric N2O5 with Machine Learning X. Chen et al. 10.1021/acs.est.4c00651
13. Pan-Arctic methanesulfonic acid aerosol: source regions, atmospheric drivers, and future projections J. Pernov et al. 10.1038/s41612-024-00712-3
14. Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations J. Pernov et al. 10.5194/acp-25-6497-2025
15. Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models F. Yu et al. 10.1029/2022GL098551