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5. 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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9. Effect of sub-urban scale lockdown on air pollution in Beijing P. Brimblecombe & Y. Lai 10.1016/j.uclim.2020.100725
10. 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
11. 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
12. 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
13. 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
14. 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
15. Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
16. 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
17. 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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19. 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
20. 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
21. 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
22. 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
23. Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
24. The Impact of Chinese New Year on Air Quality in North China Based on Machine Learning Y. Ren et al. 10.1016/j.atmosenv.2024.120874
25. 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
26. 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
27. Size−resolved source apportionment of particulate matter from a megacity in northern China based on one-year measurement of inorganic and organic components Y. Tian et al. 10.1016/j.envpol.2021.117932
28. 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
29. Assessing Impacts of the 2022 Winter Olympic Games on Meteorology and of Atmospheric Ammonia Emissions in Taiyuan City Using <?A3B2 pi6?>a Random Forest Model M. REN et al. 10.3724/EE.1672-9250.2024.52.015
30. Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities K. Maaloul & L. Brahim 10.37394/23205.2022.21.30
31. 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
32. 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
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34. 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
35. 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
36. 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
37. 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
38. Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization M. Wang et al. 10.1016/j.scitotenv.2022.156740
39. 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
40. 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
41. 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
42. Visual Analysis of Odor Interaction Based on Support Vector Regression Method L. Yan et al. 10.3390/s20061707
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44. Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing D. Srivastava et al. 10.5194/acp-21-14703-2021
45. 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
46. 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
47. 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
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49. 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
50. 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
51. 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
52. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al. 10.1016/j.scitotenv.2020.140776
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54. 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
55. How Effective are Air Pollution Control Policies in China? Evidence from 35 Cities Nationwide L. Zhu 10.1142/S1464333222500041
56. Decade-long trends in chemical component properties of PM2.5 in Beijing, China (2011−2020) J. Wang et al. 10.1016/j.scitotenv.2022.154664
57. 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
58. 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
59. 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
60. Improving the Air Pollution Control Measures More Efficiently and Cost-Effectively: View from the Practice in the 7th Military World Games in Wuhan S. Kong et al. 10.1007/s41810-024-00245-5
61. Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China Y. Li et al. 10.1016/j.ecoenv.2024.116354
62. 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
63. 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
64. 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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73. High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities L. Li et al. 10.1016/j.envint.2023.108387
74. 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
75. Air pollutant emissions from coal-fired power plants in China over the past two decades G. Wang et al. 10.1016/j.scitotenv.2020.140326
76. Source apportionment of fine organic carbon (OC) using receptor modelling at a rural site of Beijing: Insight into seasonal and diurnal variation of source contributions X. Wu et al. 10.1016/j.envpol.2020.115078
77. Identifying the key drivers in retrieving blue sky during rapid urbanization in Shenzhen, China X. Peng et al. 10.1016/j.jclepro.2022.131829
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84. Structural Collapse and Coating Composition Changes of Soot Particles During Long‐Range Transport J. Zhang et al. 10.1029/2023JD038871
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86. Anthropogenic-driven changes in concentrations and sources of winter volatile organic compounds in an urban environment in the Yangtze River Delta of China between 2013 and 2021 Z. Zhang et al. 10.1016/j.scitotenv.2024.173713
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88. Elucidating pollution characteristics, temporal variation and source origins of carbonaceous species in Xinxiang, a heavily polluted city in North China H. Liu et al. 10.1016/j.atmosenv.2023.119626
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