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18. COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas S. Grange et al. 10.5194/acp-21-4169-2021
19. The air quality impacts of pre-operational hydraulic fracturing activities S. Wilde et al. 10.1016/j.scitotenv.2022.159702
20. 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
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24. 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
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26. Quantitative assessment of the impact of biomass burning episodes on surface solar radiation using machine learning technology: A case study of a pollution event in Beijing Z. Li et al. 10.1016/j.jastp.2023.106022
27. The AirGAM 2022r1 air quality trend and prediction model S. Walker et al. 10.5194/gmd-16-573-2023
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29. Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels S. Ceballos-Santos et al. 10.3390/ijerph182413347
30. Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions S. Grange et al. 10.1016/j.aeaoa.2021.100145
31. Nitrogen‐Containing Functional Groups Dominate the Molecular Absorption of Water‐Soluble Humic‐Like Substances in Air From Nanjing, China Revealed by the Machine Learning Combined FT‐ICR‐MS Technique Y. Hong et al. 10.1029/2023JD039459
32. Machine learning revealing key factors influencing HONO chemistry in Beijing during heating and non-heating periods W. Zhang et al. 10.1016/j.atmosres.2023.107130
33. Reviewing the Crop Residual Burning and Aerosol Variations during the COVID-19 Pandemic Hit Year 2020 over North India M. Hari et al. 10.3390/pollutants1030011
34. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 J. Nicely et al. 10.5194/acp-20-1341-2020
35. Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions Q. Dai et al. 10.1016/j.envpol.2023.121344
36. Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models J. Barré et al. 10.5194/acp-21-7373-2021
37. Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities Q. Dai et al. 10.1029/2021GL093403
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39. Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions Y. Wong et al. 10.1016/j.jclepro.2022.132893
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43. Comparison of daily streamflow forecasts using extreme learning machines and the random forest method X. Li et al. 10.1080/02626667.2019.1680846
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47. A quantitative analysis of causes for increasing ozone pollution in Shanghai during the 2022 lockdown and implications for control policy Y. Zhang et al. 10.1016/j.atmosenv.2024.120469
48. Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021 Q. Dai et al. 10.1007/s11430-022-1128-1
49. Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
50. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
51. The role of sources and meteorology in driving PM2.5-bound chlorine X. Wang et al. 10.1016/j.jhazmat.2022.129910
52. 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
53. Air quality and health impact of 2019–20 Black Summer megafires and COVID-19 lockdown in Melbourne and Sydney, Australia R. Ryan et al. 10.1016/j.envpol.2021.116498
54. Linking Switzerland's PM<sub>10</sub> and PM<sub>2.5</sub> oxidative potential (OP) with emission sources S. Grange et al. 10.5194/acp-22-7029-2022
55. 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
56. 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
57. Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone C. Keller et al. 10.5194/acp-21-3555-2021
58. Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network G. Lin et al. 10.1016/j.ecoinf.2022.101674
59. Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies A. Santos & S. Mendonça 10.1007/s11192-022-04327-4
60. 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
61. Assessing the Nonlinear Effect of Atmospheric Variables on Primary and Oxygenated Organic Aerosol Concentration Using Machine Learning Y. Qin et al. 10.1021/acsearthspacechem.1c00443
62. An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al. 10.1038/s41612-023-00536-7
63. Different approaches to explore the impact of COVID-19 lockdowns on carbonaceous aerosols at a European rural background site S. Mbengue et al. 10.1016/j.scitotenv.2023.164527
64. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique T. Vu et al. 10.5194/acp-19-11303-2019
65. A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al. 10.5194/acp-22-8385-2022
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. Spatial-temporal characterization of air pollutants using a hybrid deep learning/Kriging model incorporated with a weather normalization technique G. Lin et al. 10.1016/j.atmosenv.2022.119304
68. 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
69. 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
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73. Determining the impacts of the incineration of sacrificial offerings on PM2.5 pollution in Lanzhou, Northwest China X. Liu et al. 10.1016/j.atmosenv.2022.119155
74. True Reduction in the Air Pollution Levels in the Community of Madrid During the COVID-19 Lockdown J. Cordero et al. 10.3389/frsc.2022.869000
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76. Marine fuel restrictions and air pollution: A study on Chinese ports considering transboundary spillovers X. Chen et al. 10.1016/j.marpol.2024.106136
77. 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
78. Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools Y. Lin et al. 10.5194/acp-22-16073-2022
79. Molecular-scale investigation on the composition, sources and secondary generation of organic aerosols in polluted central China Z. Dong et al. 10.1016/j.jclepro.2023.139830
80. Growth of nitrate contribution to aerosol pollution during wintertime in Xi'an, northwest China: Formation mechanism and effects of NH3 H. Su et al. 10.1016/j.partic.2023.09.014
81. Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods Q. Wu et al. 10.1016/j.atmosenv.2022.119083
82. The effect of national protest in Ecuador on PM pollution R. Zalakeviciute et al. 10.1038/s41598-021-96868-6
83. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. 10.3390/app11167326
84. Long-term trends in particulate matter from wood burning in the United Kingdom: Dependence on weather and social factors A. Font et al. 10.1016/j.envpol.2022.120105
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88. How much urban air quality is affected by local emissions: A unique case study from a megacity in the Pearl River Delta, China M. Tang et al. 10.1016/j.atmosenv.2023.119666
89. Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018 S. Grange et al. 10.5194/amt-13-1867-2020
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100. Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city X. Tian et al. 10.1016/j.scitotenv.2022.156657
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