75 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. 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
4. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al. 10.1016/j.scitotenv.2020.140776
5. 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
6. 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
7. 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
8. 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
9. Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang C. Fang et al. 10.3390/ijerph20032273
20. 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
21. 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
22. 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
23. Photochemical aging of Beijing urban PM2.5: Production of oxygenated volatile organic compounds H. Xia et al. 10.1016/j.scitotenv.2020.140751
24. 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
25. 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
26. Causation inference in complicated atmospheric environment Z. Chen et al. 10.1016/j.envpol.2022.119057
27. 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
28. 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
29. Climate Change Policies and the Carbon Tax Effect on Meat and Dairy Industries in Brazil A. Alvim & E. Sanguinet 10.3390/su13169026
30. Characteristics and Source Apportionment of PM2.5 and O3 during Winter of 2013 and 2018 in Beijing Y. Zhong et al. 10.3390/atmos11121324
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. Increasing volatile organic compounds emission from massive industrial coating consumption require more comprehensive prevention D. Wang et al. 10.1016/j.jclepro.2023.137459
51. 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
52. 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
53. Characteristics of PM2.5 Pollution with Comparative Analysis of O3 in Autumn–Winter Seasons of Xingtai, China H. Wang et al. 10.3390/atmos12050569
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19 C. Dong et al. 10.1007/s11356-021-17889-2
62. Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China Y. Xue et al. 10.3390/atmos11020189
63. 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
64. 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
65. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al. 10.1021/acs.estlett.1c00865
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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