48 citations as recorded by crossref.

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9. Impact of the strong wintertime East Asian trough on the concurrent PM2.5 and surface O3 in eastern China X. An et al. 10.1016/j.atmosenv.2023.119846
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11. O3–precursor relationship over multiple patterns of timescale: a case study in Zibo, Shandong Province, China Z. Zheng et al. 10.5194/acp-23-2649-2023
12. Impact of weather patterns and meteorological factors on PM2.5 and O3 responses to the COVID-19 lockdown in China F. Shen et al. 10.5194/acp-24-6539-2024
13. Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment W. Lingxia et al. 10.1016/j.apr.2023.101916
14. Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion X. Zheng et al. 10.1016/j.scitotenv.2024.175073
15. Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022 X. Ji et al. 10.1016/j.scitotenv.2024.170035
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21. Ozone response to precursors changes in the Chengdu-Chongqing economic circle, China, from satellite and ground-based observations J. Ren et al. 10.1016/j.scitotenv.2024.176037
22. Evidence for Coordinated Control of PM2.5 and O3: Long-Term Observational Study in a Typical City of Central Plains Urban Agglomeration C. Jia et al. 10.3390/toxics13050330
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24. Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability Z. Mai et al. 10.1021/acs.est.3c07907
25. Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China J. Yang et al. 10.1016/j.apr.2025.102461
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27. Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM2.5 heavy pollution over Twain-Hu Basin, central China J. Yao et al. 10.1016/j.jes.2024.06.007
28. Effects of heat waves on ozone pollution in a coastal industrial city: Meteorological impacts and photochemical mechanisms D. Liao et al. 10.1016/j.apr.2024.102280
29. Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data H. Li et al. 10.5194/acp-23-1131-2023
30. Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al. 10.1017/eds.2023.37
31. Spatiotemporal change of PM2.5 concentration in Beijing-Tianjin-Hebei and its prediction based on machine learning N. Liu et al. 10.1016/j.uclim.2024.102167
32. Opposing trends in the peak and low ozone concentrations in eastern China: anthropogenic and meteorological influences Z. Wang et al. 10.5194/acp-25-347-2025
33. Shifts of Formation Regimes and Increases of Atmospheric Oxidation Led to Ozone Increase in North China Plain and Yangtze River Delta From 2016 to 2019 S. Zhu et al. 10.1029/2022JD038373
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37. Prediction of respiratory diseases based on random forest model X. Yang et al. 10.3389/fpubh.2025.1537238
38. Evaluating the impact of control measures on sulfate-nitrate-ammonium aerosol variations and their formation mechanism in northern China during 2022 Winter Olympic Games Z. Gui et al. 10.1016/j.atmosres.2024.107579
39. Factors driving changes in surface ozone in 44 coastal cities in China X. Liu et al. 10.1007/s11869-023-01446-6
40. Constructing the 3D Spatial Distribution of the HCHO/NO2 Ratio via Satellite Observation and Machine Learning Model Z. Jiang et al. 10.1021/acs.est.4c12362
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42. Uncovering the evolution of ozone pollution in China: A spatiotemporal characteristics reconstruction from 1980 to 2021 S. Ding et al. 10.1016/j.atmosres.2024.107472
43. Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China M. Cheng et al. 10.1016/j.scitotenv.2023.163146
44. The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al. 10.1016/j.jenvman.2023.118645
45. Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions Y. Ni et al. 10.1016/j.scitotenv.2023.168272
46. 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
47. 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
48. Anomalous high ozone in the Pearl River Delta, China in 2019: A cause attribution analysis Y. Wu et al. 10.1016/j.atmosenv.2023.120278