257 citations as recorded by crossref.

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22. 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
23. 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
24. 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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30. 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
31. Assessing Impacts of the 2022 Winter Olympic Games on Meteorology and of Atmospheric Ammonia Emissions in Taiyuan City Using a Random Forest Model M. REN et al. 10.3724/EE.1672-9250.2024.52.015
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37. 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
38. 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
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42. 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
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47. 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
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49. 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
50. 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
51. Global estimates of ambient NO2 concentrations and long-term health effects during 2000–2019 W. Sun et al. 10.1016/j.envpol.2024.124562
52. 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
53. 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
54. 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
55. 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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57. 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
58. How Effective are Air Pollution Control Policies in China? Evidence from 35 Cities Nationwide L. Zhu 10.1142/S1464333222500041
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60. 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
61. 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
62. 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
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71. Variations of Wintertime Ambient Volatile Organic Compounds in Beijing, China, from 2015 to 2019 J. Li et al. 10.1021/acs.estlett.2c00919
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79. 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
80. 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
81. 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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86. A Review of Quantitative Assessment Methods for the Impacts of Emission and Meteorological Changes on PM_2.5 and O₃ Pollution 宇. 严 10.12677/aep.2025.155077
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