47 citations as recorded by crossref.

1. Contrasting chemical environments in summertime for atmospheric ozone across major Chinese industrial regions: the effectiveness of emission control strategies Z. Liu et al. 10.5194/acp-21-10689-2021
2. A comprehensive investigation of PM2.5 in the Huaihe River Basin, China: Separating the contributions from meteorology and emission reductions X. Liu et al. 10.1016/j.apr.2023.101647
3. Future Co‐Occurrences of Hot Days and Ozone‐Polluted Days Over China Under Scenarios of Shared Socioeconomic Pathways Predicted Through a Machine‐Learning Approach C. Gong et al. 10.1029/2022EF002671
4. Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019 J. Mao et al. 10.3389/fenvs.2022.872249
5. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China L. Zhang et al. 10.1016/j.scitotenv.2024.171295
6. Spatiotemporal characteristics of PM2.5 and ozone concentrations in Chinese urban clusters C. Deng et al. 10.1016/j.chemosphere.2022.133813
7. Effect of land surface parameters on meteorology and ozone air quality simulations in the Great Bay Area, China Q. Wang et al. 10.1016/j.atmosenv.2023.120146
8. Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013–2019 X. Meng et al. 10.1016/j.envpol.2022.118865
9. Lidar- and UAV-Based Vertical Observation of Spring Ozone and Particulate Matter in Nanjing, China Y. Qu et al. 10.3390/rs14133051
10. Impact of the planetary boundary layer on air quality simulations over the Yangtze River Delta region, China L. Shi et al. 10.1016/j.atmosenv.2021.118685
11. Impact of Particulate Matter-Centric Clean Air Action Plans on Ozone Concentrations in India M. Gupta et al. 10.1021/acsearthspacechem.2c00397
12. Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China K. Xiong et al. 10.1016/j.envpol.2022.120926
13. Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning L. Wang et al. 10.1016/j.envpol.2022.120798
14. Diagnostic analysis of regional ozone pollution in Yangtze River Delta, China: A case study in summer 2020 L. Li et al. 10.1016/j.scitotenv.2021.151511
15. Unexpected Increases of Severe Haze Pollution During the Post COVID‐19 Period: Effects of Emissions, Meteorology, and Secondary Production W. Zhou et al. 10.1029/2021JD035710
16. Assessing the Impacts of Climate Change on Meteorology and Air Stagnation in China Using a Dynamical Downscaling Method A. Hu et al. 10.3389/fenvs.2022.894887
17. Response surface model based emission source contribution and meteorological pattern analysis in ozone polluted days Y. Chen et al. 10.1016/j.envpol.2022.119459
18. Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution Z. Zhang et al. 10.1016/j.envres.2022.113322
19. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
20. Examining and predicting the influence of climatic and terrestrial factors on the seasonal distribution of ozone column depth over Tehran province using satellite observations F. Borhani et al. 10.1007/s11600-023-01179-1
21. Evaluating the Spatiotemporal Ozone Characteristics with High-Resolution Predictions in Mainland China, 2013–2019 X. Meng et al. 10.2139/ssrn.3957870
22. Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India F. Ye et al. 10.1016/j.envpol.2022.119468
23. Correlation between surface PM2.5 and O3 in eastern China during 2015–2019: Spatiotemporal variations and meteorological impacts L. Wang et al. 10.1016/j.atmosenv.2022.119520
24. Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O3 trends by a model W. Pan et al. 10.1016/j.scitotenv.2022.159592
25. Large-scale synoptic drivers of co-occurring summertime ozone and PM<sub>2.5</sub> pollution in eastern China L. Zong et al. 10.5194/acp-21-9105-2021
26. The Explainable Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing VOCs’ Environmental Fate L. Jovanovic et al. 10.3390/atmos14010109
27. Analysis of coordinated relationship between PM<sub>2.5</sub> and ozone and its affecting factors on different timescales J. Sun et al. 10.1360/TB-2021-0742
28. Insights from ozone and particulate matter pollution control in New York City applied to Beijing J. Zhang et al. 10.1038/s41612-022-00309-8
29. Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days Y. Chen et al. 10.2139/ssrn.4045860
30. Using the COVID-19 lockdown to identify atmospheric processes and meteorology influences on regional PM2.5 pollution episodes in the Beijing-Tianjin-Hebei, China I. Sulaymon et al. 10.1016/j.atmosres.2023.106940
31. Projection of the Near-Future PM2.5 in Northern Peninsular Southeast Asia under RCP8.5 T. Amnuaylojaroen et al. 10.3390/atmos13020305
32. Contrasting Trends of Surface PM2.5, O3, and NO2 and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta M. Lv et al. 10.3390/ijerph182312471
33. Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China X. Wang et al. 10.1016/j.atmosenv.2023.120231
34. Double high pollution events in the Yangtze River Delta from 2015 to 2019: Characteristics, trends, and meteorological situations Y. Qin et al. 10.1016/j.scitotenv.2021.148349
35. Assessment of the effect of meteorological and emission variations on winter PM2.5 over the North China Plain in the three-year action plan against air pollution in 2018–2020 H. Du et al. 10.1016/j.atmosres.2022.106395
36. Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network Q. Wang et al. 10.1016/j.jes.2023.09.001
37. Substantial changes in gaseous pollutants and chemical compositions in fine particles in the North China Plain during the COVID-19 lockdown period: anthropogenic vs. meteorological influences R. Li et al. 10.5194/acp-21-8677-2021
38. Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model T. Amnuaylojaroen & I. Levy 10.1155/2022/3190484
39. Relationship between summer time near-surface ozone concentration and planetary boundary layer height in Beijing C. Zhang et al. 10.1016/j.atmosres.2023.106892
40. Impact of the COVID-19 lockdown on typical ambient air pollutants: Cyclical response to anthropogenic emission reduction H. Yao et al. 10.1016/j.heliyon.2023.e15799
41. Sensitivity of PM2.5 and O3 pollution episodes to meteorological factors over the North China Plain S. Ma et al. 10.1016/j.scitotenv.2021.148474
42. Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing L. Qi et al. 10.3390/rs14122762
43. Seasonal and Diurnal Characteristics of the Vertical Profile of Aerosol Optical Properties in Urban Beijing, 2017–2021 X. Zhang et al. 10.3390/rs15020475
44. Air Quality During COVID-19 Lockdown in the Yangtze River Delta and the Pearl River Delta: Two Different Responsive Mechanisms to Emission Reductions in China N. Wang et al. 10.1021/acs.est.0c08383
45. Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021 J. Ding et al. 10.1016/j.jes.2022.03.010
46. Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning Y. Wen et al. 10.1021/acs.estlett.2c00246
47. Driving Forces of Changes in Air Quality during the COVID-19 Lockdown Period in the Yangtze River Delta Region, China T. Liu et al. 10.1021/acs.estlett.0c00511