16 citations as recorded by crossref.

1. Global Quantification of Black Carbon in Seasonal Snow: A Physically and Observationally Constrained Machine-Learning Framework Y. Chen et al.
2. From accurate to actionable: Interpretable PM2.5 forecasting with feature engineering and SHAP for the Liverpool–Wirral region S. Malakouti
3. Urban PM2.5 concentration monitoring: A review of recent advances in ground-based, satellite, model, and machine learning integration S. Lolli
4. The Role of Machine Learning in Enhancing Particulate Matter Estimation: A Systematic Literature Review A. Alkhodaidi et al.
5. Multimodal PM2.5 Forecasting Using Satellite Imagery and Sensor Data with Semi-supervised Deep Learning T. Srimanee et al.
6. Enhancing spatiotemporal coverage of satellite-derived high-resolution NO2 data with a super-resolution model M. Zhang et al.
7. Enhancing 72-Hour air quality forecasting with an observation-driven deep learning chemistry transport model S. Li & J. Xing
8. Assimilating FY-4B satellite aerosol data to improve PM₂.₅ and surface shortwave radiation prediction F. Hu et al.
9. Enhancing PM 2.5 exposure assessment across China with a novel urban–rural balanced estimation framework Y. Ding et al.
10. Control of wind speed and contact angle on submicron particulate matter sampling B. Sun et al.
11. Ensemble Artificial Intelligence Fusing Satellite, Reanalysis, and Ground Observations for Improved PM2.5 Prediction M. Haseeb et al.
12. Global hourly seamless AOD through measurement-adjusted machine learning fusion of multi-satellite and reanalysis data Y. Ding et al.
13. A general review on the applications of machine learning to PM2.5 air pollution forecasting S. Patel et al.
14. Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al.
15. A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO2 Concentration from Satellite and Ground Monitors J. Xing et al.
16. AI-enhanced subseasonal forecasting of extreme temperature risks J. Xing et al.