72 citations as recorded by crossref.

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5. Characteristics of Turbulence and Aerosol Optical and Radiative Properties during Haze–Fog Episodes in Shenyang, Northeast China X. Li et al. 10.3390/atmos12121658
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23. Vertical Gradient Variations in Radiation Budget and Heat Fluxes in the Urban Boundary Layer: A Comparison Study Between Polluted and Clean Air Episodes in Beijing During Winter L. Wang et al. 10.1029/2020JD032478
24. Decreasing Dust Over the Middle East Partly Caused by Irrigation Expansion W. Xia et al. 10.1029/2021EF002252
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26. Mixing layer height estimated from AMDAR and its relationship with PMs and meteorological parameters in two cities in North China during 2014–2017 Z. Lv et al. 10.1016/j.apr.2019.11.017
27. 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
28. Current challenges of improving visibility due to increasing nitrate fraction in PM2.5 during the haze days in Beijing, China S. Hu et al. 10.1016/j.envpol.2021.118032
29. Aerosol hygroscopicity: Hygroscopic growth proxy based on visibility for low-cost PM monitoring A. Molnár et al. 10.1016/j.atmosres.2019.104815
30. Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China L. Wang et al. 10.1016/j.scitotenv.2019.136443
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32. Performance evaluation of photographic measurement in the machine-learning prediction of ground PM2.5 concentration L. Feng et al. 10.1016/j.atmosenv.2021.118623
33. Long-term temporal analysis of the columnar and surface aerosol relationship with planetary boundary layer height at a southern coastal site of Turkey G. Tuna Tuygun & T. Elbir 10.1016/j.apr.2020.09.008
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35. Simulated impacts of vertical distributions of black carbon aerosol on meteorology and PM<sub>2.5</sub> concentrations in Beijing during severe haze events D. Chen et al. 10.5194/acp-22-1825-2022
36. Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data K. Bai et al. 10.1016/j.jenvman.2018.12.071
37. Aerosol Physical‐Optical Properties and PBLH Under Different Air Pollution Levels From Ground Lidar and Satellite Observations Over Shouxian Area, China Y. Zhang et al. 10.1029/2021JD035236
38. Severe haze events in the Indo-Gangetic Plain during post-monsoon: Synergetic effect of synoptic meteorology and crop residue burning emission S. Kumari et al. 10.1016/j.scitotenv.2021.145479
39. A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading Y. Shi et al. 10.5194/amt-14-3449-2021
40. Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system U. Shin et al. 10.1016/j.atmosenv.2020.118141
41. Long-term variation characteristics and influencing factors of low-visibility events on the coast of China R. Lyu et al. 10.1016/j.atmosres.2021.105583
42. Quantifying the impact of terrain–wind–governed close-effect on atmospheric polluted concentrations L. Wu et al. 10.1016/j.jclepro.2022.132995
43. Roadside atmospheric pollution: still a serious environmental problem in Beijing, China W. Chen et al. 10.1007/s11869-018-0620-2
44. Different characteristics of individual particles from light-duty diesel vehicle at the launching and idling state by AAC-SPAMS B. Su et al. 10.1016/j.jhazmat.2021.126304
45. Analysis of Changes and Statistical Characteristics in PM-2.5 based on the Seasonal Management Data H. Ahn et al. 10.4491/KSEE.2022.44.8.276
46. The major chemical constituents of PM2.5 and airborne bacterial community phyla in Beijing, Seoul, and Nagasaki E. Park et al. 10.1016/j.chemosphere.2020.126870
47. Aerosol vertical profile variations with seasons, air mass movements and local PM2.5 levels in three large China cities L. Wang et al. 10.1016/j.atmosenv.2020.117329
48. Two pathways of how remote SST anomalies drive the interannual variability of autumnal haze days in the Beijing–Tianjin–Hebei region, China J. Wang et al. 10.5194/acp-19-1521-2019
49. The spatially heterogeneous response of aerosol properties to anthropogenic activities and meteorology changes in China during 1980–2018 based on the singular value decomposition method S. Ding et al. 10.1016/j.scitotenv.2020.138135
50. Radiative effects of clouds and fog on long-lasting heavy fog events in northern China L. Guo et al. 10.1016/j.atmosres.2020.105444
51. Comparison of ambient air pollution levels of Amritsar during foggy conditions with that of five major north Indian cities: multivariate analysis and air mass back trajectories R. Yadav et al. 10.1007/s42452-020-03569-2
52. Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China J. Dong et al. 10.1016/j.envpol.2022.120392
53. Temporal and spatial variations of haze and fog and the characteristics of PM2.5 during heavy pollution episodes in China from 2013 to 2018 B. Guo et al. 10.1016/j.apr.2020.07.019
54. Isotopic imprints of aerosol ammonium over the north China plain A. Mgelwa et al. 10.1016/j.envpol.2022.120376
55. Collective influences of boundary layer process and synoptic circulation on particulate pollution: A new study in changsha-zhuzhou-xiangtan urban agglomeration of central china T. Wang et al. 10.3389/fenvs.2022.939147
56. The impacts of the atmospheric boundary layer on regional haze in North China Q. Li et al. 10.1038/s41612-021-00165-y
57. Boundary layer structure characteristics under objective classification of persistent pollution weather types in the Beijing area Z. Sun et al. 10.5194/acp-21-8863-2021
58. GAT-EGRU: A Deep Learning Prediction Model for PM2.5 Coupled with Empirical Modal Decomposition Algorithm G. Yang et al. 10.1007/s11518-023-5562-z
59. Wintertime aerosol chemistry in Beijing during haze period: Significant contribution from secondary formation and biomass burning emission X. Li et al. 10.1016/j.atmosres.2018.10.010
60. Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem Z. Lv et al. 10.1016/j.atmosenv.2020.117417
61. FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction H. Kamangir et al. 10.1016/j.mlwa.2021.100038
62. AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
63. Impacts of the near-surface urban boundary layer structure on PM2.5 concentrations in Beijing during winter L. Wang et al. 10.1016/j.scitotenv.2019.03.097
64. Size-Segregated Particulate Mass and Carbonaceous Components in Roadside and Riverside Environments M. Amin et al. 10.3390/app112110214
65. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
66. Carbonaceous component of Total Suspended Particulate (TSP) in Makassar City, Sulawesi Island, Indonesia M. Amin et al. 10.1088/1755-1315/1199/1/012021
67. The spatial and temporal variation of fine particulate matter pollution in Ethiopia: Data from the Atmospheric Composition Analysis Group (1998–2019) A. Shiferaw et al. 10.1371/journal.pone.0283457
68. Exploring the Sensitivity of Visibility to PM2.5 Mass Concentration and Relative Humidity for Different Aerosol Types J. Wang et al. 10.3390/atmos13030471
69. Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full-Covered SARA AOD at the Urban Scale Z. Fan et al. 10.3390/rs12203368
70. Observational study of the vertical aerosol and meteorological factor distributions with respect to particulate pollution in Xi'an D. Huige et al. 10.1016/j.atmosenv.2021.118215
71. Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China L. Li et al. 10.3390/atmos11101070
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