11 citations as recorded by crossref.

1. The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al. 10.1029/2023GL104928
2. Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015–2022 using a deep learning model Y. Lou et al. 10.1016/j.jhazmat.2025.137308
3. Multiple sources emission inventory closely integrated with atmospheric environment management: A case study of Guangdong, China M. Li et al. 10.1016/j.apr.2023.101825
4. Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources A. Penenko et al. 10.3390/math12010078
5. Long-term variations and trends of tropospheric and ground-level NO2 over typical coastal areas X. Tian et al. 10.1016/j.ecolind.2024.112163
6. Increased methane emissions from oil and gas following the Soviet Union’s collapse T. He et al. 10.1073/pnas.2314600121
7. TROPOMI NO2 Shows a Fast Recovery of China’s Economy in the First Quarter of 2023 H. Li & B. Zheng 10.1021/acs.estlett.3c00386
8. Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method Y. Zhang et al. 10.1016/j.scitotenv.2024.175177
9. Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation L. Kong et al. 10.5194/acp-23-6217-2023
10. A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al. 10.5194/gmd-16-6161-2023
11. Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead A. Sorooshian et al. 10.1021/acsestair.3c00033