14 citations as recorded by crossref.

1. TransNet: a transport-informed graph neural network for forecasting PM2.5 concentrations across South Korea R. Dimri et al. https://doi.org/10.1038/s44407-026-00052-x
2. Quantification of life expectancy loss from air pollution in Slovakia I. Berta et al. https://doi.org/10.1038/s44407-026-00085-2
3. Assessing Performance from Space N. Huhe & J. Chen https://doi.org/10.1525/as.2025.2663626
4. Barriers to helicopter emergency medical services in a Haze-Prone, mountainous region of Northern Thailand K. Sutham et al. https://doi.org/10.1186/s13049-025-01498-w
5. First Land Use and Air Quality Study in Greater Rosario, Argentina: A Ground-Satellite Assessment of PM2.5 G. Piñol et al. https://doi.org/10.3390/atmos17070638
6. PM2.5 Episodes in Central Taiwan Under Weak Southwesterly Winds in Spring M. Chuang et al. https://doi.org/10.1007/s44408-025-00063-5
7. Source-specific air pollution in areas and periods with limited data: application to 1940 in the United States X. Shan et al. https://doi.org/10.1088/2752-5309/ae1bc7
8. PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022 H. Hao et al. https://doi.org/10.5194/essd-16-4051-2024
9. COVID lockdowns significantly affect statewide atmospheric fine aerosols in India after excluding long-term pollution patterns and time-lag effect T. Etchie et al. https://doi.org/10.1016/j.atmosenv.2024.121013
10. Factors influencing the relationships between hourly visibility and aerosol optical depth in China—implications for hourly aerosol optical depth retrievals using automatic visibility observations N. Shang et al. https://doi.org/10.1016/j.atmosenv.2025.121353
11. Decoding cold-season PM2.5 and its chemical compositions in China's economic powerhouses after the Chinese COVID-19 pandemic lockdown: A national-scale analysis X. Dao et al. https://doi.org/10.1016/j.jclepro.2025.146902
12. Long-term retrieval and analysis of aerosol components and optical properties in Tianjin, China: Insights from GRASP/component approach and sun-photometer observations (2014–2024) X. Zhang et al. https://doi.org/10.1016/j.atmosres.2025.108695
13. Rising PM_2.5 variability fuels mortality from pollution extremes J. Deng et al. https://doi.org/10.59717/j.xinn-med.2026.100238
14. Artificial intelligence model-based high-accuracy estimation of global PM2.5 concentrations using gap-filled aerosol optical depth Z. Wang et al. https://doi.org/10.1016/j.jclepro.2026.148788