58 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. 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
3. 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
4. Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013–2019 X. Meng et al. 10.1016/j.envpol.2022.118865
5. Lidar- and UAV-Based Vertical Observation of Spring Ozone and Particulate Matter in Nanjing, China Y. Qu et al. 10.3390/rs14133051
6. 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
7. 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
8. The effects of the clean air actions on the Beautiful China initiative: The regional heterogeneity analysis H. Wang et al. 10.1016/j.eiar.2024.107598
9. 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
10. 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
11. 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
12. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
13. 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
14. Evaluating the Spatiotemporal Ozone Characteristics with High-Resolution Predictions in Mainland China, 2013–2019 X. Meng et al. 10.2139/ssrn.3957870
15. 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
16. Sensitivity analysis of PM2.5 and O3 co-pollution in Beijing based on GRAPES-CUACE adjoint model Z. Liu et al. 10.1016/j.jes.2024.11.020
17. The Explainable Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing VOCs’ Environmental Fate L. Jovanovic et al. 10.3390/atmos14010109
18. 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
19. Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days Y. Chen et al. 10.2139/ssrn.4045860
20. Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China X. Zhao et al. 10.3390/atmos15091144
21. Projection of the Near-Future PM2.5 in Northern Peninsular Southeast Asia under RCP8.5 T. Amnuaylojaroen et al. 10.3390/atmos13020305
22. Detecting atmospheric oxidation in the PM2.5 and ozone multilayer complex network N. Ying et al. 10.1088/1748-9326/ad7869
23. 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
24. 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
25. 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
26. 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
27. 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
28. Analysis of spatiotemporal variation and relationship to land use – landscape pattern of PM2.5 and O3 in typical arid zone Z. Chen & Z. Zhang 10.1016/j.scs.2024.105689
29. 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
30. 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
31. Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019 J. Mao et al. 10.3389/fenvs.2022.872249
32. 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
33. Spatiotemporal characteristics of PM2.5 and ozone concentrations in Chinese urban clusters C. Deng et al. 10.1016/j.chemosphere.2022.133813
34. Revisiting the impact of temperature on ground-level ozone: A causal inference approach B. Chen et al. 10.1016/j.scitotenv.2024.176062
35. Impact of Particulate Matter-Centric Clean Air Action Plans on Ozone Concentrations in India M. Gupta et al. 10.1021/acsearthspacechem.2c00397
36. 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
37. Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning L. Wang et al. 10.1016/j.envpol.2022.120798
38. Different contributions of meteorological conditions and emission reductions to the ozone pollution during Shanghai’s COVID-19 lockdowns in winter and spring X. Dou et al. 10.1016/j.apr.2024.102252
39. Implications of 1.5 K climate warming on warm-season ozone exposure and atmospheric oxidation capacity in China Z. Shi et al. 10.1016/j.aosl.2024.100556
40. 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
41. 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
42. 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
43. Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China M. Li et al. 10.1016/j.envpol.2024.124515
44. Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions Y. Zhan et al. 10.1016/j.scitotenv.2024.174365
45. Large-scale synoptic drivers of co-occurring summertime ozone and PM2.5 pollution in eastern China L. Zong et al. 10.5194/acp-21-9105-2021
46. 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
47. 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
48. Vertical distribution and transport characteristics of ozone pollution based on lidar observation network and data assimilation over the Pearl River Delta, China Y. Pan et al. 10.1016/j.atmosres.2024.107643
49. 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
50. 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
51. 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
52. Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model T. Amnuaylojaroen & I. Levy 10.1155/2022/3190484
53. 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
54. 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
55. Seasonal and Diurnal Characteristics of the Vertical Profile of Aerosol Optical Properties in Urban Beijing, 2017–2021 X. Zhang et al. 10.3390/rs15020475
56. 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
57. 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
58. 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