90 citations as recorded by crossref.

1. On the Relationship Between Aerosol and Boundary Layer Height in Summer in China Under Different Thermodynamic Conditions M. Lou et al. 10.1029/2019EA000620
2. Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. 10.5194/acp-21-4471-2021
3. Analysis of fog at Xianyang Airport based on multi-source ground-based detection data H. Ming et al. 10.1016/j.atmosres.2019.01.012
4. Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014 Y. Si et al. 10.1016/j.scitotenv.2019.02.180
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
6. Secondary inorganic aerosol chemistry and its impact on atmospheric visibility over an ammonia-rich urban area in Central Taiwan L. Young et al. 10.1016/j.envpol.2022.119951
7. Air Pollution Zone Migrates South Driven by East Asian Winter Monsoon and Climate Change S. Zhao et al. 10.1029/2021GL092672
8. 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
9. The influence of terrestrial transport on visibility and aerosol properties over the coastal East China Sea B. Wang et al. 10.1016/j.scitotenv.2018.08.312
10. Comparison of the performances between the WRF and WRF-LES models in radiation fog – A case study C. Cui et al. 10.1016/j.atmosres.2019.04.003
11. Saving the world from your couch: the heterogeneous medium-run benefits of COVID-19 lockdowns on air pollution J. Bonardi et al. 10.1088/1748-9326/abee4d
12. Molecular characterization of organic aerosol in winter from Beijing using UHPLC-Orbitrap MS Z. Wang et al. 10.1016/j.scitotenv.2021.151507
13. A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea S. Park et al. 10.3390/atmos15030320
14. Visibility Prediction over South Korea Based on Random Forest B. Kim et al. 10.3390/atmos12050552
15. A new approach of the normalization relationship between PM2.5 and visibility and the theoretical threshold, a case in north China X. Wu et al. 10.1016/j.atmosres.2020.105054
16. Synoptic circulation pattern and boundary layer structure associated with PM2.5 during wintertime haze pollution episodes in Shanghai N. Liu et al. 10.1016/j.atmosres.2019.06.001
17. Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model H. Li et al. 10.1016/j.jes.2024.03.037
18. Haze pollution under a high atmospheric oxidization capacity in summer in Beijing: insights into formation mechanism of atmospheric physicochemical processes D. Zhao et al. 10.5194/acp-20-4575-2020
19. Relationship between meteorological conditions and atmospheric PM2.5 in uranium mining areas and source analysis P. Wei et al. 10.1007/s10967-024-09380-2
20. The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city R. Yadav et al. 10.1039/D2RA00424K
21. The air quality of Palangka Raya, Central Kalimantan, Indonesia: The impacts of forest fires on visibility M. Santoso et al. 10.1080/10962247.2022.2077474
22. Long-term measurements of planetary boundary layer height and interactions with PM2.5 in Shanghai, China L. Pan et al. 10.1016/j.apr.2019.01.007
23. Health risks of PM2.5-bound metals at a street canyon: Implication for traffic non-exhaust emissions Z. Dong et al. 10.1016/j.apr.2024.102317
24. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism Z. Chen et al. 10.1016/j.envint.2020.105558
25. Below-Cloud Aerosol Scavenging by Different-Intensity Rains in Beijing City T. Luan et al. 10.1007/s13351-019-8079-0
26. Characteristics of urban boundary layer in heavy haze process based on Beijing 325m tower data Y. SHI et al. 10.1080/16742834.2019.1547084
27. Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data K. Bai et al. 10.1016/j.envpol.2019.04.104
28. 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
29. Decreasing Dust Over the Middle East Partly Caused by Irrigation Expansion W. Xia et al. 10.1029/2021EF002252
30. Using a coupled large-eddy simulation–aerosol radiation model to investigate urban haze: sensitivity to aerosol loading and meteorological conditions J. Slater et al. 10.5194/acp-20-11893-2020
31. 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
32. 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
33. 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
34. Impact of urbanized atmosphere-land processing to the near-ground distribution of air pollution over Central Liaoning Urban Agglomeration W. Zhang et al. 10.1016/j.atmosenv.2024.120866
35. Aerosol hygroscopicity: Hygroscopic growth proxy based on visibility for low-cost PM monitoring A. Molnár et al. 10.1016/j.atmosres.2019.104815
36. Indoor particulate matter exposure and correlation of PM2.5 with lung efficacy and SpO2 level of Dhaka City Dwellers S. Nahian et al. 10.1007/s11869-024-01586-3
37. Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method L. Chen et al. 10.1016/j.scitotenv.2023.169516
38. 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
39. A homogenized daily in situ PM2.5 concentration dataset from the national air quality monitoring network in China K. Bai et al. 10.5194/essd-12-3067-2020
40. 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
41. 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
42. Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data F. Wang et al. 10.1016/j.apr.2024.102270
43. Vertical observations of the atmospheric boundary layer structure over Beijing urban area during air pollution episodes L. Wang et al. 10.5194/acp-19-6949-2019
44. Simulated impacts of vertical distributions of black carbon aerosol on meteorology and PM2.5 concentrations in Beijing during severe haze events D. Chen et al. 10.5194/acp-22-1825-2022
45. 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
46. 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
47. 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
48. 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
49. Long-term meteorology-adjusted and unadjusted trends of PM2.5 using the AirGAM model over Delhi, 2007–2022 . Chetna et al. 10.1016/j.aeaoa.2024.100255
50. Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al. 10.1186/s12302-025-01068-y
51. Study of Air Quality over Delhi during COVID-19 Lockdown Based on Remote Sensing Observations S. Budakoti 10.1134/S1024856024701215
52. 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
53. Analyzing the influence of the planetary boundary layer height, ventilation coefficient, thermal inversions, and aerosol optical Depth on the concentration of PM2.5 in the city of São Paulo: A long-term study G. Moreira et al. 10.1016/j.apr.2024.102179
54. 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
55. Quantifying the impact of terrain–wind–governed close-effect on atmospheric polluted concentrations L. Wu et al. 10.1016/j.jclepro.2022.132995
56. Impact of the National Clean Air Programme (NCAP) on the particulate matter pollution and associated reduction in human mortalities in Indian cities G. Gopikrishnan & J. Kuttippurath 10.1016/j.scitotenv.2025.178787
57. A Comprehensive Study on Winter PM2.5 Variation in the Yangtze River Delta: Unveiling Causes and Pollution Transport Pathways Y. Pan et al. 10.3390/atmos15091037
58. Roadside atmospheric pollution: still a serious environmental problem in Beijing, China W. Chen et al. 10.1007/s11869-018-0620-2
59. 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
60. 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
61. Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements T. Zhang et al. 10.3390/rs16193713
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. Isotopic imprints of aerosol ammonium over the north China plain A. Mgelwa et al. 10.1016/j.envpol.2022.120376
71. 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
72. The impacts of the atmospheric boundary layer on regional haze in North China Q. Li et al. 10.1038/s41612-021-00165-y
73. 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
74. Distribution of Fine Particulate Matter Pollution in Winter over Eastern China Affected by Synoptic Conditions X. Liu et al. 10.3390/atmos15070821
75. 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
76. 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
77. Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the “2+26” cities of northern China P. Liu et al. 10.1016/j.chemosphere.2024.141439
78. 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
79. 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
80. AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
81. 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
82. Size-Segregated Particulate Mass and Carbonaceous Components in Roadside and Riverside Environments M. Amin et al. 10.3390/app112110214
83. 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
84. Carbonaceous component of Total Suspended Particulate (TSP) in Makassar City, Sulawesi Island, Indonesia M. Amin et al. 10.1088/1755-1315/1199/1/012021
85. 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
86. Exploring the Sensitivity of Visibility to PM2.5 Mass Concentration and Relative Humidity for Different Aerosol Types J. Wang et al. 10.3390/atmos13030471
87. 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
88. 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
89. Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China L. Li et al. 10.3390/atmos11101070
90. Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. 10.1038/s41612-023-00393-4