10 citations as recorded by crossref.

1. Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China Y. Yuan et al. https://doi.org/10.1016/j.apr.2025.102694
2. Evaluating the effects of COVID-19 lockdowns on air quality across some African countries Z. Han et al. https://doi.org/10.1039/D5EA00111K
3. Quantifying regional transport contributions to PM2.5-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach N. Chen et al. https://doi.org/10.1016/j.envpol.2025.126448
4. Impacts of meteorology and emission reductions on haze pollution during the lockdown in the North China Plain L. Liu et al. https://doi.org/10.5194/acp-25-1569-2025
5. Developing and evaluating a spatiotemporal-specific emission control strategy: A case study of the 2022 Beijing winter olympics Y. Huang et al. https://doi.org/10.1016/j.atmosenv.2026.122181
6. Different contributions of meteorological conditions and emission reductions to the ozone pollution during Shanghai’s COVID-19 lockdowns in winter and spring X. Dou et al. https://doi.org/10.1016/j.apr.2024.102252
7. Toward healthy air quality under CMIP6 scenarios: insights into the compliance with the new WHO guidelines in China Z. Jia et al. https://doi.org/10.3389/fpubh.2026.1803304
8. Simultaneous retrieval of hourly ground-level PM 2.5 and PM 10 from FY-4A/AGRI observations using multi-task deep learning X. Zhao et al. https://doi.org/10.1080/01431161.2026.2641831
9. Interannual Meteorological Forcing and Spatial Heterogeneity of Winter PM2.5 Regimes in Central China Y. He et al. https://doi.org/10.3390/atmos17050502
10. A novel ensemble learning algorithm integrating WRF-CMAQ and downscaling models for hourly estimation of regional air pollution along with vegetation exposure risk detection P. Zhou et al. https://doi.org/10.1080/17538947.2025.2509825