12 citations as recorded by crossref.

1. New Perspective on Using Observational Uncertainty to Improve Reliability of NO x Emissions Over Northern China L. Lu et al. https://doi.org/10.1109/TGRS.2025.3620116
2. Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions S. Wang et al. https://doi.org/10.1038/s41612-025-00977-2
3. Technical note: DACNO2 – a multi-constraint deep learning framework for high-resolution 3D NO2 field estimation W. Sun et al. https://doi.org/10.5194/acp-26-7741-2026
4. Reconstructing top-down global black carbon emissions using remote sensing and models S. Wang et al. https://doi.org/10.1016/j.apr.2025.102633
5. Surface-observation-constrained high-frequency coal mine methane emissions in Shanxi, China, reveal more emissions than inventories, consistent with satellite inversion F. Lu et al. https://doi.org/10.5194/acp-25-5837-2025
6. Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al. https://doi.org/10.1016/j.atmosenv.2025.121354
7. 卫星观测港口CO2排放间接估算：以日照港为例 陆. Lu Lingxiao et al. https://doi.org/10.3788/AOS250851
8. A global black carbon dataset of column concentration and microphysical information derived from MISR multi-band observations and Mie scattering simulations Z. Liu et al. https://doi.org/10.5194/essd-18-507-2026
9. In-tandem multi-waveband particulate absorption and size observations yield substantial changes in radiative forcing over industrial Central China L. Guan et al. https://doi.org/10.5194/acp-26-3107-2026
10. OMI-Derived Mass- and Number-Conserved Estimation of Black Carbon Emissions J. Liu et al. https://doi.org/10.1021/acs.estlett.5c00340
11. Long-term drivers of increasing diurnal NO2 difference in representative cities of the Pearl River Delta Z. Liu et al. https://doi.org/10.1016/j.jes.2026.02.023
12. Improving estimation of surface PM2.5 by including satellite observations of gases, aerosols, and radiation in tandem J. Kang et al. https://doi.org/10.1088/1748-9326/ae1e17