108 citations as recorded by crossref.

1. Impact of urban heat island on meteorology and air quality at microenvironments G. Swamy et al.
2. An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al.
3. Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India N. Lakshmi et al.
4. Has the Three-Year Action Plan improved the air quality in the Fenwei Plain of China? Assessment based on a machine learning technique X. Dai et al.
5. Atmospheric carbonyls in urban Beijing in the summertime under the continuous implementation of clean air actions: Decreasing trends, source apportionments and new insights for control strategies X. Zhang et al.
6. Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al.
7. Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison M. Xu et al.
8. A Multi‐Dimensional Decomposition Method of the Meteorology‐Driven and Emission‐Driven Effects on Year‐to‐Year Air Quality Variations Y. Song et al.
9. Application of Kolmogorov-Zurbenko filter to quantify the long-term meteorological and emission impacts on air quality I. Sezen et al.
10. Ground ozone variations at an urban and a rural station in Beijing from 2006 to 2017: Trend, meteorological influences and formation regimes N. Cheng et al.
11. A comprehensive attribution analysis of PM2.5 in typical industrial cities during the winter of 2016–2018: Effect of meteorology and emission reduction A. Huang et al.
12. Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences K. Li et al.
13. 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.
14. Spatial Disparity of Meteorological Impacts on Carbon Monoxide Pollution in China during the COVID-19 Lockdown Period J. Lin et al.
15. An improved decomposition method to differentiate meteorological and anthropogenic effects on air pollution: A national study in China during the COVID-19 lockdown period Y. Song et al.
16. The role of temporal scales in extracting dominant meteorological drivers of major airborne pollutants M. Xu et al.
17. Analysis of the meteorological impact on PM2.5 pollution in Changchun based on KZ filter and WRF-CMAQ C. Fang et al.
18. PM2.5 Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions Z. Li et al.
19. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions M. Qiu et al.
20. Characteristics and Source Apportionment of PM2.5 and O3 during Winter of 2013 and 2018 in Beijing Y. Zhong et al.
21. Unraveling the Drivers of Continuous Summer Ozone Pollution Episodes in Bozhou, China: Toward Targeted Control Strategies K. Wu et al.
22. Spatiotemporal trends and impact factors of PM<sub>2.5</sub> and O<sub>3</sub> pollution in major cities in China during 2015–2020 Y. Zhang et al.
23. Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014–2019: A study based on multiple linear regression and WRF-Chem Z. He et al.
24. Impact of meteorology and aerosol sources on PM2.5 and oxidative potential variability and levels in China J. Liu et al.
25. Changes in urban residential and agricultural sources induce a decrease in PM10 levels in cold clean area: A thirteen-year monitoring at the Longfengshan WMO/GAW regional background station in Northeast China L. Guo et al.
26. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al.
27. Black carbon aerosols in China: spatial-temporal variations and lessons from long-term atmospheric observations H. Zheng et al.
28. Wavelet periodic and compositional characteristics of atmospheric PM2.5 in a typical air pollution event at Jinzhong city, China Y. Dong et al.
29. Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective Z. Ma et al.
30. Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China Z. Qian et al.
31. Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning Y. Lv et al.
32. Links between the optical properties and chemical compositions of brown carbon chromophores in different environments: Contributions and formation of functionalized aromatic compounds X. Li et al.
33. Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021 Y. Lyu et al.
34. Identifying the spatiotemporal variations in ozone formation regimes across China from 2005 to 2019 based on polynomial simulation and causality analysis R. Li et al.
35. Seasonal Characteristics of Forecasting Uncertainties in Surface PM2.5 Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region Q. Du et al.
36. Separation of meteorological and anthropogenic contributions to major ambient pollutants using an integrated explainable machine learning framework: RMT-PAS H. Ke et al.
37. Meteorological influences on PM2.5 and O3 trends and associated health burden since China's clean air actions L. Chen et al.
38. Revealing the driving effect of emissions and meteorology on PM2.5 and O3 trends through a new algorithmic model D. Wang et al.
39. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al.
40. Characteristics and source apportionments of WSIIs under different pollution conditions in Beijing from 2014 to 2017 F. Zuo et al.
41. Contributions of extremely unfavorable meteorology and coal-heating boiler control to air quality in December 2019 over Harbin, China D. Fu et al.
42. Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19 C. Dong et al.
43. Changes in apparent temperature and PM2.5 around the Beijing–Tianjin megalopolis under greenhouse gas and stratospheric aerosol intervention scenarios J. Wang et al.
44. Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China Y. Xue et al.
45. Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model S. Dai et al.
46. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al.
47. Differential response of various pollutant-meteorology factors on O3 in key regions of China: Based on multiple methods and datasets X. Wang et al.
48. Background concentration of atmospheric PM2.5 in the Beijing–Tianjin–Hebei urban agglomeration: Levels, variation trends, and influences of meteorology and emission S. Gao et al.
49. Aggravation effect of regional transport on wintertime PM2.5 over the middle reaches of the Yangtze River under China's air pollutant emission reduction process Y. Bai et al.
50. Evaluation of continuous emission reduction effect on PM2.5 pollution improvement through 2013–2018 in Beijing X. Wang et al.
51. Assessment of the emission mitigation effect on the wintertime air quality in the Guanzhong Basin, China from 2013 to 2017 J. Liu et al.
52. Does place-based green policy improve air pollution? Evidence from China’s National Eco-Industrial Demonstration Park Policy S. Li et al.
53. Heavy haze pollution during the COVID-19 lockdown in the Beijing-Tianjin-Hebei region, China X. Zhang et al.
54. An integrated CFD and CNN-GRU approach for accurate prediction of PM2.5 in greenhouse environments of Northeast China S. Zhang et al.
55. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al.
56. 污染减排与气象因素对我国主要城市2015~2021年环境空气质量变化的贡献评估 启. 戴 et al.
57. Using highly time-resolved online mass spectrometry to examine biogenic and anthropogenic contributions to organic aerosol in Beijing A. Mehra et al.
58. The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN) Z. Wang et al.
59. Multi-scale influence of urban green space vegetation on deposition and diffusion of atmospheric particulate matter: Implications for functional design X. LI et al.
60. A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al.
61. Long-term trends of ambient nitrate (NO3−) concentrations across China based on ensemble machine-learning models R. Li et al.
62. Measurement report: Long-term assessment of primary and secondary organic aerosols in the Shanghai megacity throughout China's Clean Air actions since 2010 H. Yu et al.
63. Spatiotemporal variations and connections of single and multiple meteorological factors on PM2.5 concentrations in Xi'an, China X. Zhang et al.
64. Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang C. Fang et al.
65. How human mega-events influence urban airborne PM2.5 pollution: A systematic review and meta-analysis S. Li et al.
66. Photochemical aging of Beijing urban PM2.5: Production of oxygenated volatile organic compounds H. Xia et al.
67. Assessment of meteorology vs. control measures in the China fine particular matter trend from 2013 to 2019 by an environmental meteorology index S. Gong et al.
68. Causation inference in complicated atmospheric environment Z. Chen et al.
69. Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study W. Xiaoqi et al.
70. Sources and trends of black carbon aerosol in the megacity of Nanjing, eastern China, after the China Clean Action Plan and Three-Year Action Plan A. Abulimiti et al.
71. Climate Change Policies and the Carbon Tax Effect on Meat and Dairy Industries in Brazil A. Alvim & E. Sanguinet
72. Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods Q. Wu et al.
73. Inter-annual changes in transboundary air quality from KORUS-AQ 2016 to SIJAQ 2022 campaign periods and assessment of emission reduction strategies in Northeast Asia M. Park et al.
74. Dynamic evaluation of China's atmospheric environmental pressure from 2008 to 2017: Trends and drivers A. Huang et al.
75. Projection of weather potential for winter haze episodes in Beijing by 1.5 °C and 2.0 °C global warming L. Qiu et al.
76. Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing–Tianjin–Hebei Region, China S. Wang et al.
77. Contributions of meteorology and anthropogenic emissions to the trends in winter PM2.5 in eastern China 2013–2018 Y. Wu et al.
78. Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al.
79. Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al.
80. City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis Y. Zhang et al.
81. LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles R. Jiang et al.
82. Effects of atmospheric circulations on the interannual variation in PM2.5 concentrations over the Beijing–Tianjin–Hebei region in 2013–2018 X. Wang & R. Zhang
83. Atmospheric circulation anomalies related to the winter PM2.5 mass concentration rapid decline cases in Beijing, China Y. Ren et al.
84. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017 H. Yu et al.
85. Significant changes in autumn and winter aerosol composition and sources in Beijing from 2012 to 2018: Effects of clean air actions J. Li et al.
86. Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability Z. Wang et al.
87. Increasing volatile organic compounds emission from massive industrial coating consumption require more comprehensive prevention D. Wang et al.
88. Impacts of reductions in anthropogenic emissions from 2015 to 2024 on PM2.5 and meteorological conditions over China D. Li et al.
89. Meteorological influences on PM2.5 variation in China using a hybrid model of machine learning and the Kolmogorov-Zurbenko filter S. Gao et al.
90. Machine learning–based multiscale quantification of long-term emission contributions to PM2.5 variability in China H. Li et al.
91. The WRF-CMAQ Simulation of a Complex Pollution Episode with High-Level O3 and PM2.5 over the North China Plain: Pollution Characteristics and Causes X. Dou et al.
92. Seasonal characterization, sources, and source-specific risks of PM2.5 bound PAHs at different types of urban sites in central China Z. Dong et al.
93. Forecasting of monthly air quality index and understanding the air pollution in the urban city, India based on machine learning models and cross-validation C. Pande et al.
94. Machine learning and causal inference for disentangling air pollution reduction during the Asian Games in megacity Hangzhou F. Zhang et al.
95. Characteristics of PM2.5 Pollution with Comparative Analysis of O3 in Autumn–Winter Seasons of Xingtai, China H. Wang et al.
96. Meteorology impact on PM2.5 change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China X. Sun et al.
97. Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations X. Chen et al.
98. Variations in the characteristics and sources of PM2.5 during the COVID-19 lockdown in Xiangyang, Hubei Province, central China Y. Mao et al.
99. Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al.
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.
101. Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology S. Zhai et al.
102. A Sustainability-driven Integrated model of strategic management for coastal urban projects T. Huynh et al.
103. Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective T. Li et al.
104. Increasing surface ozone and enhanced secondary organic carbon formation at a city junction site: An epitome of the Yangtze River Delta, China (2014–2017) Y. Liu et al.
105. Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling X. Chen et al.
106. Quantifying contribution of weather patterns to PM2.5 concentrations based on spatial effects and health risk assessment J. Liu et al.
107. Estimating the mortality burden attributable to temperature and PM2.5 from the perspective of atmospheric flow L. Han et al.
108. Source profiles and emission factors of organic and inorganic species in fine particles emitted from the ultra-low emission power plant and typical industries X. Zeng et al.