260 citations as recorded by crossref.

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19. High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia I. Jaya & H. Folmer 10.3390/math12182899
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26. The air quality impacts of pre-operational hydraulic fracturing activities S. Wilde et al. 10.1016/j.scitotenv.2022.159702
27. 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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31. 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
32. 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
33. 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
34. 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
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40. 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
41. 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. 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
45. 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
46. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 J. Nicely et al. 10.5194/acp-20-1341-2020
47. 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
48. Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models J. Barré et al. 10.5194/acp-21-7373-2021
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51. 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
52. 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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54. Aqpet — An R package for air quality policy evaluation Y. Dai et al. 10.1016/j.envsoft.2024.106052
55. 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
56. 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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58. 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
59. 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
60. 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
61. 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
62. Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
63. 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
64. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
65. The role of sources and meteorology in driving PM2.5-bound chlorine X. Wang et al. 10.1016/j.jhazmat.2022.129910
66. 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
67. 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
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70. Linking Switzerland's PM10 and PM2.5 oxidative potential (OP) with emission sources S. Grange et al. 10.5194/acp-22-7029-2022
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75. Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies A. Santos & S. Mendonça 10.1007/s11192-022-04327-4
76. 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
77. High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning Y. Yang et al. 10.3390/rs16132498
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79. 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
80. Trends of peroxyacetyl nitrate and its impact on ozone over 2018–2022 in urban atmosphere Z. Lin et al. 10.1038/s41612-024-00746-7
81. 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
82. 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
83. 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
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85. 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
86. Detecting imbalanced financial markets through time-varying optimization and nonlinear functionals N. James & M. Menzies 10.1016/j.physd.2025.134571
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88. 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
89. 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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100. 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
101. 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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124. Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches X. Yao & L. Zhang 10.5194/acp-24-7773-2024
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