94 citations as recorded by crossref.

1. Impact of urban heat island on meteorology and air quality at microenvironments G. Swamy et al. 10.1080/10962247.2020.1783390
2. Heavy haze pollution during the COVID-19 lockdown in the Beijing-Tianjin-Hebei region, China X. Zhang et al. 10.1016/j.jes.2021.08.030
3. 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
4. Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India N. Lakshmi et al. 10.1016/j.scitotenv.2022.155215
5. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al. 10.1016/j.scitotenv.2020.140776
6. 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. 10.1016/j.atmosenv.2022.119204
7. 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. 10.1360/SSTe-2022-0271
8. Using highly time-resolved online mass spectrometry to examine biogenic and anthropogenic contributions to organic aerosol in Beijing A. Mehra et al. 10.1039/D0FD00080A
9. 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. 10.1016/j.isprsjprs.2022.05.011
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11. Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison M. Xu et al. 10.1016/j.envpol.2021.117099
12. A Multi‐Dimensional Decomposition Method of the Meteorology‐Driven and Emission‐Driven Effects on Year‐to‐Year Air Quality Variations Y. Song et al. 10.1029/2020EA001424
13. Application of Kolmogorov-Zurbenko filter to quantify the long-term meteorological and emission impacts on air quality I. Sezen et al. 10.1016/j.atmosres.2023.106714
14. 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. 10.1016/j.jclepro.2019.06.204
15. 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. 10.1016/j.atmosres.2023.107181
16. A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
17. Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences K. Li et al. 10.5194/acp-20-11423-2020
18. 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
19. Long-term trends of ambient nitrate (NO<sub>3</sub><sup>−</sup>) concentrations across China based on ensemble machine-learning models R. Li et al. 10.5194/essd-13-2147-2021
20. Spatial Disparity of Meteorological Impacts on Carbon Monoxide Pollution in China during the COVID-19 Lockdown Period J. Lin et al. 10.1021/acsearthspacechem.1c00251
21. Spatiotemporal variations and connections of single and multiple meteorological factors on PM2.5 concentrations in Xi'an, China X. Zhang et al. 10.1016/j.atmosenv.2022.119015
22. Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang C. Fang et al. 10.3390/ijerph20032273
23. 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. 10.1016/j.atmosenv.2021.118270
24. The role of temporal scales in extracting dominant meteorological drivers of major airborne pollutants M. Xu et al. 10.5194/acp-23-14065-2023
25. How human mega-events influence urban airborne PM2.5 pollution: A systematic review and meta-analysis S. Li et al. 10.1016/j.envpol.2021.117009
26. Analysis of the meteorological impact on PM2.5 pollution in Changchun based on KZ filter and WRF-CMAQ C. Fang et al. 10.1016/j.atmosenv.2021.118924
27. Photochemical aging of Beijing urban PM2.5: Production of oxygenated volatile organic compounds H. Xia et al. 10.1016/j.scitotenv.2020.140751
28. 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. 10.5194/acp-21-2999-2021
29. PM2.5 Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions Z. Li et al. 10.3390/atmos13101696
30. Causation inference in complicated atmospheric environment Z. Chen et al. 10.1016/j.envpol.2022.119057
31. 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. 10.1016/j.atmosenv.2022.119072
32. 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
33. Climate Change Policies and the Carbon Tax Effect on Meat and Dairy Industries in Brazil A. Alvim & E. Sanguinet 10.3390/su13169026
34. Characteristics and Source Apportionment of PM2.5 and O3 during Winter of 2013 and 2018 in Beijing Y. Zhong et al. 10.3390/atmos11121324
35. 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. 10.1360/TB-2021-0767
36. 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
37. 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. 10.1016/j.scitotenv.2021.150792
38. 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. 10.1016/j.envpol.2024.125114
39. Impact of meteorology and aerosol sources on PM2.5 and oxidative potential variability and levels in China J. Liu et al. 10.5194/acp-24-10849-2024
40. 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. 10.1016/j.atmosenv.2024.120814
41. 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
42. Dynamic evaluation of China's atmospheric environmental pressure from 2008 to 2017: Trends and drivers A. Huang et al. 10.1016/j.jes.2024.02.017
43. Projection of weather potential for winter haze episodes in Beijing by 1.5 °C and 2.0 °C global warming L. Qiu et al. 10.1016/j.accre.2020.09.002
44. 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. 10.3390/ijerph19031607
45. Contributions of meteorology and anthropogenic emissions to the trends in winter PM2.5 in eastern China 2013–2018 Y. Wu et al. 10.5194/acp-22-11945-2022
46. Wavelet periodic and compositional characteristics of atmospheric PM2.5 in a typical air pollution event at Jinzhong city, China Y. Dong et al. 10.1016/j.apr.2020.09.013
47. Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al. 10.1016/j.atmosenv.2023.120265
48. Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al. 10.1016/j.apgeochem.2023.105614
49. 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. 10.3390/toxics11060481
50. 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. 10.1016/j.scitotenv.2022.159339
51. 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. 10.1016/j.envpol.2021.116523
52. 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. 10.1016/j.scitotenv.2021.147418
53. LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles R. Jiang et al. 10.1016/j.scitotenv.2023.161677
54. Effects of atmospheric circulations on the interannual variation in PM<sub>2.5</sub> concentrations over the Beijing–Tianjin–Hebei region in 2013–2018 X. Wang & R. Zhang 10.5194/acp-20-7667-2020
55. Atmospheric circulation anomalies related to the winter PM2.5 mass concentration rapid decline cases in Beijing, China Y. Ren et al. 10.1016/j.atmosres.2024.107665
56. 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. 10.1016/j.envpol.2022.119420
57. 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. 10.5194/acp-20-139-2020
58. 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. 10.5194/acp-21-15631-2021
59. 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. 10.1016/j.envpol.2020.115855
60. 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. 10.1016/j.isprsjprs.2022.03.002
61. Increasing volatile organic compounds emission from massive industrial coating consumption require more comprehensive prevention D. Wang et al. 10.1016/j.jclepro.2023.137459
62. 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
63. 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. 10.1007/s00376-023-3060-3
64. 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. 10.3390/atmos15020198
65. Meteorological influences on PM2.5 and O3 trends and associated health burden since China's clean air actions L. Chen et al. 10.1016/j.scitotenv.2020.140837
66. 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. 10.1016/j.apr.2023.101666
67. Characteristics of PM2.5 Pollution with Comparative Analysis of O3 in Autumn–Winter Seasons of Xingtai, China H. Wang et al. 10.3390/atmos12050569
68. Meteorology impact on PM<sub>2.5</sub> 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. 10.5194/acp-22-3579-2022
69. Revealing the driving effect of emissions and meteorology on PM2.5 and O3 trends through a new algorithmic model D. Wang et al. 10.1016/j.chemosphere.2022.133756
70. Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations X. Chen et al. 10.1016/j.jes.2022.02.036
71. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. 10.1016/j.isprsjprs.2022.09.001
72. Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. 10.1016/j.scitotenv.2023.163189
73. 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
74. Contributions of extremely unfavorable meteorology and coal-heating boiler control to air quality in December 2019 over Harbin, China D. Fu et al. 10.1016/j.apr.2021.101217
75. Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19 C. Dong et al. 10.1007/s11356-021-17889-2
76. A Sustainability-driven Integrated model of strategic management for coastal urban projects T. Huynh et al. 10.1080/13467581.2023.2270024
77. Changes in apparent temperature and PM2.5 around the Beijing–Tianjin megalopolis under greenhouse gas and stratospheric aerosol intervention scenarios J. Wang et al. 10.5194/esd-14-989-2023
78. Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China Y. Xue et al. 10.3390/atmos11020189
79. 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. 10.1016/j.envpol.2023.121886
80. Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective T. Li et al. 10.1016/j.envint.2023.107861
81. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al. 10.1021/acs.estlett.1c00865
82. Differential response of various pollutant-meteorology factors on O3 in key regions of China: Based on multiple methods and datasets X. Wang et al. 10.1016/j.apr.2024.102086
83. 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. 10.1016/j.envpol.2020.114847
84. 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. 10.1016/j.apr.2022.101583
85. Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling X. Chen et al. 10.1016/j.scitotenv.2024.170485
86. Quantifying contribution of weather patterns to PM2.5 concentrations based on spatial effects and health risk assessment J. Liu et al. 10.1016/j.scs.2022.103980
87. 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. 10.1016/j.apr.2021.101111
88. Estimating the mortality burden attributable to temperature and PM2.5 from the perspective of atmospheric flow L. Han et al. 10.1088/1748-9326/abc8b9
89. Evaluation of continuous emission reduction effect on PM2.5 pollution improvement through 2013–2018 in Beijing X. Wang et al. 10.1016/j.apr.2021.101055
90. 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. 10.1016/j.scitotenv.2021.147966
91. Assessment of the emission mitigation effect on the wintertime air quality in the Guanzhong Basin, China from 2013 to 2017 J. Liu et al. 10.1016/j.apr.2021.101196
92. Does place-based green policy improve air pollution? Evidence from China’s National Eco-Industrial Demonstration Park Policy S. Li et al. 10.1007/s11356-023-31168-2
93. Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology S. Zhai et al. 10.5194/acp-19-11031-2019
94. Effects of air pollution control policies on PM<sub>2.5</sub> pollution improvement in China from 2005 to 2017: a satellite-based perspective Z. Ma et al. 10.5194/acp-19-6861-2019