199 citations as recorded by crossref.

1. Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities Y. Lv et al. 10.1016/j.apr.2022.101452
2. Synergetic effects of NH<sub>3</sub> and NO<sub><i>x</i></sub> on the production and optical absorption of secondary organic aerosol formation from toluene photooxidation S. Liu et al. 10.5194/acp-21-17759-2021
3. Evaluating urban and nonurban PM 2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset B. Liu et al. 10.1080/17538947.2024.2310734
4. 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
5. Staggered-peak production is a mixed blessing in the control of particulate matter pollution Y. Wang et al. 10.1038/s41612-022-00322-x
6. Two-Dimensional Silicon Fingerprints Reveal Dramatic Variations in the Sources of Particulate Matter in Beijing during 2013–2017 X. Yang et al. 10.1021/acs.est.0c00984
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8. Effect of sub-urban scale lockdown on air pollution in Beijing P. Brimblecombe & Y. Lai 10.1016/j.uclim.2020.100725
9. 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
10. 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
11. 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
12. 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
13. Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
14. 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
15. 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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17. 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
18. 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
19. 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
20. Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
21. 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
22. 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
23. 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
24. 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
25. Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities K. Maaloul & L. Brahim 10.37394/23205.2022.21.30
26. 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
27. 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
28. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. Visual Analysis of Odor Interaction Based on Support Vector Regression Method L. Yan et al. 10.3390/s20061707
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
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46. 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
47. How Effective are Air Pollution Control Policies in China? Evidence from 35 Cities Nationwide L. Zhu 10.1142/S1464333222500041
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49. 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
50. 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
51. 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
52. 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
53. 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
54. Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms P. Verma et al. 10.1016/j.apr.2024.102123
55. Meteorological normalization of NO2 concentrations in the Province of Bolzano (Italian Alps) M. Falocchi et al. 10.1016/j.atmosenv.2020.118048
56. Variations of Wintertime Ambient Volatile Organic Compounds in Beijing, China, from 2015 to 2019 J. Li et al. 10.1021/acs.estlett.2c00919
57. Dramatic changes in atmospheric pollution source contributions for a coastal megacity in northern China from 2011 to 2020 B. Liu et al. 10.5194/acp-22-8597-2022
58. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al. 10.1021/acs.estlett.1c00865
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61. 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
62. 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
63. 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
64. 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
65. 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
66. Identifying the key drivers in retrieving blue sky during rapid urbanization in Shenzhen, China X. Peng et al. 10.1016/j.jclepro.2022.131829
67. Heterogeneous Changes of Chemical Compositions, Sources and Health Risks of Pm2.5 with the “Clean Heating” Policy at Urban/Suburban/Industrial Sites L. Zhiyong et al. 10.2139/ssrn.4177778
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69. The diminishing effects of winter heating on air quality in northern China J. Wang et al. 10.1016/j.jenvman.2022.116536
70. Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak Q. Dai et al. 10.1021/acs.est.0c02776
71. Structural Collapse and Coating Composition Changes of Soot Particles During Long‐Range Transport J. Zhang et al. 10.1029/2023JD038871
72. Assessment of ambient particulate matter and trace gases in Istanbul: Insights from long-term and multi-monitoring stations Ü. Şahin et al. 10.1016/j.apr.2024.102089
73. 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
74. Field Detection of Highly Oxygenated Organic Molecules in Shanghai by Chemical Ionization–Orbitrap Y. Zhang et al. 10.1021/acs.est.1c08346
75. A review about COVID-19 in the MENA region: environmental concerns and machine learning applications H. Meskher et al. 10.1007/s11356-022-23392-z
76. Drivers and impacts of decreasing concentrations of atmospheric volatile organic compounds (VOCs) in Beijing during 2016–2020 Y. Liu et al. 10.1016/j.scitotenv.2023.167847
77. Green recovery or pollution rebound? Evidence from air pollution of China in the post-COVID-19 era T. Feng et al. 10.1016/j.jenvman.2022.116360
78. The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach B. Ly et al. 10.1016/j.jaerosci.2020.105716
79. Two decades of trends in urban particulate matter concentrations across Australia A. de Jesus et al. 10.1016/j.envres.2020.110021
80. Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China R. Li et al. 10.1016/j.scitotenv.2024.170777
81. Airborne particulate matter R. Harrison 10.1098/rsta.2019.0319
82. Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna’s first COVID-19 lockdown: Significance for air quality management M. Brancher 10.1016/j.envpol.2021.117153
83. Appreciating the role of big data in the modernization of environmental governance M. Liu et al. 10.1007/s42524-021-0185-x
84. 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
85. The complex chemical effects of COVID-19 shutdowns on air quality J. Kroll et al. 10.1038/s41557-020-0535-z
86. Opinion: Gigacity – a source of problems or the new way to sustainable development M. Kulmala et al. 10.5194/acp-21-8313-2021
87. Air Pollution Control and Public Health Risk Perception: Evidence from the Perspectives of Signal and Implementation Effects Y. Fan et al. 10.3390/ijerph19053040
88. Arctic/North Atlantic atmospheric variability causes Severe PM10 events in South Korea J. Kim et al. 10.1016/j.scitotenv.2023.169714
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99. Differential Effects of the COVID-19 Lockdown and Regional Fire on the Air Quality of Medellín, Colombia J. Henao et al. 10.3390/atmos12091137
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