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24. COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas S. Grange et al. 10.5194/acp-21-4169-2021
25. The air quality impacts of pre-operational hydraulic fracturing activities S. Wilde et al. 10.1016/j.scitotenv.2022.159702
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30. 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
31. Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning X. Qin et al. 10.5194/acp-22-15851-2022
32. 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
33. The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events Z. Song et al. 10.3390/rs16183363
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39. 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
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44. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 J. Nicely et al. 10.5194/acp-20-1341-2020
45. 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
46. 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
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50. Meteorological normalization of NO2 concentrations in the Province of Bolzano (Italian Alps) M. Falocchi et al. 10.1016/j.atmosenv.2020.118048
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52. Aqpet — An R package for air quality policy evaluation Y. Dai et al. 10.1016/j.envsoft.2024.106052
53. Improved estimation of trends in U.S. ozone concentrations adjusted for interannual variability in meteorological conditions B. Wells et al. 10.1016/j.atmosenv.2021.118234
54. Comparison of daily streamflow forecasts using extreme learning machines and the random forest method X. Li et al. 10.1080/02626667.2019.1680846
55. Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022 X. Ji et al. 10.1016/j.scitotenv.2024.170035
56. 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
57. 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
58. 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
59. 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
60. Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
61. 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
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64. 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
65. 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
66. 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
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75. High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning Y. Yang et al. 10.3390/rs16132498
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77. 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
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80. 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
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83. 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
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86. 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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98. 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
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100. 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
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