11 citations as recorded by crossref.

1. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. 10.1016/j.scitotenv.2023.167763
2. Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis T. He et al. 10.5194/acp-22-14059-2022
3. Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. 10.5194/gmd-16-6337-2023
4. Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States X. Chen et al. 10.1029/2022JD038228
5. Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022 Y. Xie et al. 10.1016/j.atmosenv.2025.121163
6. Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020 R. Zhu et al. 10.5194/acp-23-9745-2023
7. Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region N. Ding et al. 10.1016/j.atmosenv.2024.120483
8. A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020 J. Wang 10.1016/j.eiar.2024.107630
9. 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
10. Discrepancy in assimilated atmospheric CO over East Asia in 2015–2020 by assimilating satellite and surface CO measurements Z. Tang et al. 10.5194/acp-22-7815-2022
11. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations J. Wei et al. 10.5194/acp-23-1511-2023