278 citations as recorded by crossref.

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5. Synergetic effects of NH3 and NOx on the production and optical absorption of secondary organic aerosol formation from toluene photooxidation S. Liu et al. 10.5194/acp-21-17759-2021
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7. Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain W. Liu et al. 10.1016/j.envres.2022.113719
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10. AI-based Bayesian structural time series modeling for assessing PM2.5 air quality improvements during the Beijing 2022 Winter Olympics W. Liang et al. 10.1016/j.atmosenv.2025.121328
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15. 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
16. Heterogeneous reactions significantly contribute to the atmospheric formation of nitrated aromatic compounds during the haze episode in urban Beijing Z. Cheng et al. 10.1016/j.scitotenv.2024.170612
17. Changes of ammonia concentrations in wintertime on the North China Plain from 2018 to 2020 Y. He et al. 10.1016/j.atmosres.2021.105490
18. Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies L. Zhang et al. 10.1016/j.jes.2022.12.009
19. Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine B. Cheng et al. 10.1016/j.atmosenv.2021.118437
20. Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
21. New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing P. Zuo et al. 10.1021/acs.est.1c05383
22. Relative importance of meteorological variables on air quality and role of boundary layer height Y. Huang et al. 10.1016/j.atmosenv.2021.118737
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24. Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning M. Wang et al. 10.5194/acp-23-10313-2023
25. Machine learning elucidates the impact of short-term emission changes on air pollution in Beijing W. Zhou et al. 10.1016/j.atmosenv.2022.119192
26. Meteorological influences on PM2.5 variation in China using a hybrid model of machine learning and the Kolmogorov-Zurbenko filter S. Gao et al. 10.1016/j.apr.2023.101905
27. Yearly variations of water-soluble ions over Xi'an, China: Insight into the importance contribution of nitrate to PM2.5 X. Yang et al. 10.1016/j.apr.2024.102296
28. Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
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30. Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning Q. Dai et al. 10.1016/j.envpol.2021.117252
31. Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model J. Khayyam et al. 10.1016/j.scitotenv.2024.171488
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33. The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach M. Cole et al. 10.1007/s10640-020-00483-4
34. Assessing Impacts of the 2022 Winter Olympic Games on Meteorology and of Atmospheric Ammonia Emissions in Taiyuan City Using a Random Forest Model M. REN et al. 10.3724/EE.1672-9250.2024.52.015
35. Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities K. Maaloul & L. Brahim 10.37394/23205.2022.21.30
36. Forecasting the progression of human civilization on the Kardashev Scale through 2060 with a machine learning approach A. Zhang et al. 10.1038/s41598-023-38351-y
37. Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach A. Casallas et al. 10.1007/s11869-023-01303-6
38. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
39. Response of air pollution to meteorological conditions and socioeconomic activities associated to the COVID-19 outbreak in the Yangtze River Economic Belt J. Si et al. 10.1016/j.atmosenv.2024.120390
40. Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah M. Mallet 10.1016/j.apr.2020.08.001
41. Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway E. Bertazza et al. 10.1002/met.2123
42. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al. 10.1016/j.envint.2023.108361
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44. Causes of the unexpected slowness in reducing winter PM2.5 for 2014–2018 in Henan Province X. Chen et al. 10.1016/j.envpol.2022.120928
45. On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain Y. Zhang et al. 10.1016/j.envres.2021.112672
46. Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography P. Sekula et al. 10.3390/su14063388
47. Visual Analysis of Odor Interaction Based on Support Vector Regression Method L. Yan et al. 10.3390/s20061707
48. Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China C. Zhang et al. 10.1016/j.scitotenv.2024.172544
49. Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing D. Srivastava et al. 10.5194/acp-21-14703-2021
50. Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China S. Jiang et al. 10.1016/j.atmosenv.2023.119827
51. A Visibility-Based Historical PM2.5 Estimation for Four Decades (1981–2022) Using Machine Learning in Thailand: Trends, Meteorological Normalization, and Influencing Factors Using SHAP Analysis N. Aman et al. 10.1007/s44408-025-00007-z
52. Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980–2021) Y. Ouma et al. 10.1016/j.scitotenv.2023.169027
53. Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model M. Tang & X. Li 10.1016/j.atmosenv.2024.120733
54. Global estimates of ambient NO2 concentrations and long-term health effects during 2000–2019 W. Sun et al. 10.1016/j.envpol.2024.124562
55. Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19 H. Zheng et al. 10.1016/j.scitotenv.2020.140000
56. Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. 10.3390/rs13091798
57. Comprehensive detection of nitrated aromatic compounds in fine particulate matter using gas chromatography and tandem mass spectrometry coupled with an electron capture negative ionization source X. Shi et al. 10.1016/j.jhazmat.2020.124794
58. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al. 10.1016/j.scitotenv.2020.140776
59. 污染减排与气象因素对我国主要城市2015~2021年环境空气质量变化的贡献评估 启. 戴 et al. 10.1360/SSTe-2022-0271
60. Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
61. How Effective are Air Pollution Control Policies in China? Evidence from 35 Cities Nationwide L. Zhu 10.1142/S1464333222500041
62. The Struggle Against Air Pollution in African Megacities and the Hidden Problems for the Estimation of the Burden of Disease V. Matthaios et al. 10.1002/gch2.202500108
63. Anthropogenic and natural causes for the interannual variation of PM2.5 in East Asia during summer monsoon periods from 2008 to 2018 D. Ma et al. 10.5194/acp-25-12069-2025
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65. Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning Q. Meng et al. 10.5194/acp-25-7485-2025
66. Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran M. Ali-Taleshi et al. 10.1016/j.uclim.2023.101790
67. A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina M. Diaz Resquin et al. 10.5194/essd-15-189-2023
68. Evaluating emissions and meteorological contributions to air quality trends in northern China based on measurements at a regional background station W. Pu et al. 10.1039/D4EA00070F
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71. Can Building Subway Systems Improve Air Quality? New Evidence from Multiple Cities and Machine Learning L. Xie et al. 10.1007/s10640-024-00852-3
72. The evolution of physical and chemical properties of PM2.5 in the developing stage of pollution events in a coastal megacity, South Korea Y. Park et al. 10.1016/j.atmosenv.2025.121435
73. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions M. Qiu et al. 10.5194/acp-22-10551-2022
74. Vertical distribution of tropospheric ozone and its sources of precursors over Beijing: Results from ∼ 20 years of ozonesonde measurements based on clustering analysis Y. Zeng et al. 10.1016/j.atmosres.2023.106610
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76. Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms P. Verma et al. 10.1016/j.apr.2024.102123
77. Investigating sources of black carbon in Beijing with receptor model and continuous hourly measurements from 2016 to 2019 Q. Zhou et al. 10.1016/j.atmosenv.2025.121408
78. Meteorological normalization of NO2 concentrations in the Province of Bolzano (Italian Alps) M. Falocchi et al. 10.1016/j.atmosenv.2020.118048
79. Variations of Wintertime Ambient Volatile Organic Compounds in Beijing, China, from 2015 to 2019 J. Li et al. 10.1021/acs.estlett.2c00919
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85. Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model Z. Song et al. 10.3390/rs13132525
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87. The promotion effect of nitrous acid on aerosol formation in wintertime in Beijing: the possible contribution of traffic-related emissions Y. Liu et al. 10.5194/acp-20-13023-2020
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