111 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. https://doi.org/10.1029/2019EA000620
2. Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014 Y. Si et al. https://doi.org/10.1016/j.scitotenv.2019.02.180
3. Characteristics of Turbulence and Aerosol Optical and Radiative Properties during Haze–Fog Episodes in Shenyang, Northeast China X. Li et al. https://doi.org/10.3390/atmos12121658
4. Saving the world from your couch: the heterogeneous medium-run benefits of COVID-19 lockdowns on air pollution J. Bonardi et al. https://doi.org/10.1088/1748-9326/abee4d
5. Molecular characterization of organic aerosol in winter from Beijing using UHPLC-Orbitrap MS Z. Wang et al. https://doi.org/10.1016/j.scitotenv.2021.151507
6. A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea S. Park et al. https://doi.org/10.3390/atmos15030320
7. Multi-scale concurrent modeling of air pollutants and greenhouse gases over Greater Boston. Part 2: Chemical evaluation and sensitivity simulations Y. Yang et al. https://doi.org/10.1016/j.uclim.2026.102959
8. Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model H. Li et al. https://doi.org/10.1016/j.jes.2024.03.037
9. Relationship between meteorological conditions and atmospheric PM2.5 in uranium mining areas and source analysis P. Wei et al. https://doi.org/10.1007/s10967-024-09380-2
10. Health risks of PM2.5-bound metals at a street canyon: Implication for traffic non-exhaust emissions Z. Dong et al. https://doi.org/10.1016/j.apr.2024.102317
11. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism Z. Chen et al. https://doi.org/10.1016/j.envint.2020.105558
12. Characteristics of urban boundary layer in heavy haze process based on Beijing 325m tower data Y. SHI et al. https://doi.org/10.1080/16742834.2019.1547084
13. Decreasing Dust Over the Middle East Partly Caused by Irrigation Expansion W. Xia et al. https://doi.org/10.1029/2021EF002252
14. 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. https://doi.org/10.1016/j.apr.2019.11.017
15. Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India N. Lakshmi et al. https://doi.org/10.1016/j.scitotenv.2022.155215
16. Current challenges of improving visibility due to increasing nitrate fraction in PM2.5 during the haze days in Beijing, China S. Hu et al. https://doi.org/10.1016/j.envpol.2021.118032
17. Enhancing Sensor Calibration and Source Apportionment with Size-Resolved Optical PM Measurements in Urban Environments X. Qin et al. https://doi.org/10.1016/j.atmosenv.2026.122235
18. Impact of urbanized atmosphere-land processing to the near-ground distribution of air pollution over Central Liaoning Urban Agglomeration W. Zhang et al. https://doi.org/10.1016/j.atmosenv.2024.120866
19. Aerosol hygroscopicity: Hygroscopic growth proxy based on visibility for low-cost PM monitoring A. Molnár et al. https://doi.org/10.1016/j.atmosres.2019.104815
20. Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method L. Chen et al. https://doi.org/10.1016/j.scitotenv.2023.169516
21. Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China L. Wang et al. https://doi.org/10.1016/j.scitotenv.2019.136443
22. A homogenized daily in situ PM2.5 concentration dataset from the national air quality monitoring network in China K. Bai et al. https://doi.org/10.5194/essd-12-3067-2020
23. 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 https://doi.org/10.1016/j.apr.2020.09.008
24. 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. https://doi.org/10.1016/j.apr.2024.102270
25. Vertical observations of the atmospheric boundary layer structure over Beijing urban area during air pollution episodes L. Wang et al. https://doi.org/10.5194/acp-19-6949-2019
26. 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. https://doi.org/10.1016/j.jenvman.2018.12.071
27. Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms I. Zandi et al. https://doi.org/10.3390/urbansci9050138
28. 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. https://doi.org/10.5194/amt-14-3449-2021
29. Long-term meteorology-adjusted and unadjusted trends of PM2.5 using the AirGAM model over Delhi, 2007–2022 . Chetna et al. https://doi.org/10.1016/j.aeaoa.2024.100255
30. Quantifying the impact of terrain–wind–governed close-effect on atmospheric polluted concentrations L. Wu et al. https://doi.org/10.1016/j.jclepro.2022.132995
31. A Comprehensive Study on Winter PM2.5 Variation in the Yangtze River Delta: Unveiling Causes and Pollution Transport Pathways Y. Pan et al. https://doi.org/10.3390/atmos15091037
32. Different characteristics of individual particles from light-duty diesel vehicle at the launching and idling state by AAC-SPAMS B. Su et al. https://doi.org/10.1016/j.jhazmat.2021.126304
33. Analysis of Changes and Statistical Characteristics in PM-2.5 based on the Seasonal Management Data H. Ahn et al. https://doi.org/10.4491/KSEE.2022.44.8.276
34. Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements T. Zhang et al. https://doi.org/10.3390/rs16193713
35. Hybrid framework for improved PM2.5 prediction based on seasonal-trend decomposition and tailored component processing D. Jia et al. https://doi.org/10.1038/s41598-025-04597-x
36. Aerosol vertical profile variations with seasons, air mass movements and local PM2.5 levels in three large China cities L. Wang et al. https://doi.org/10.1016/j.atmosenv.2020.117329
37. 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. https://doi.org/10.5194/acp-19-1521-2019
38. Assessment of emission and meteorological influences on the ambient volatile organic compounds based on spatially resolved source tracing J. Cao et al. https://doi.org/10.1016/j.atmosenv.2026.121795
39. Chemical Characteristics, Sources, and Transport Pathways of PM2.5 in a Mid-Altitude Industrial City of the Upper Yellow River Basin L. Yi et al. https://doi.org/10.1007/s11270-026-09675-y
40. Radiative effects of clouds and fog on long-lasting heavy fog events in northern China L. Guo et al. https://doi.org/10.1016/j.atmosres.2020.105444
41. 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. https://doi.org/10.1007/s42452-020-03569-2
42. Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China J. Dong et al. https://doi.org/10.1016/j.envpol.2022.120392
43. Boundary layer structure characteristics under objective classification of persistent pollution weather types in the Beijing area Z. Sun et al. https://doi.org/10.5194/acp-21-8863-2021
44. GAT-EGRU: A Deep Learning Prediction Model for PM2.5 Coupled with Empirical Modal Decomposition Algorithm G. Yang et al. https://doi.org/10.1007/s11518-023-5562-z
45. Wintertime aerosol chemistry in Beijing during haze period: Significant contribution from secondary formation and biomass burning emission X. Li et al. https://doi.org/10.1016/j.atmosres.2018.10.010
46. 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. https://doi.org/10.1016/j.chemosphere.2024.141439
47. 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. https://doi.org/10.1016/j.atmosenv.2020.117417
48. FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction H. Kamangir et al. https://doi.org/10.1016/j.mlwa.2021.100038
49. AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. https://doi.org/10.3390/s23239446
50. Impacts of the near-surface urban boundary layer structure on PM2.5 concentrations in Beijing during winter L. Wang et al. https://doi.org/10.1016/j.scitotenv.2019.03.097
51. Spatial characterization of periodic behaviors of ground PM2.5 concentration across the Yangtze River Delta and the North China Plain during 2014–2024: A new insight on driving processes of regional air pollution Y. Liu et al. https://doi.org/10.1016/j.envres.2025.121648
52. 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. https://doi.org/10.1371/journal.pone.0283457
53. Exploring the Sensitivity of Visibility to PM2.5 Mass Concentration and Relative Humidity for Different Aerosol Types J. Wang et al. https://doi.org/10.3390/atmos13030471
54. Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China L. Li et al. https://doi.org/10.3390/atmos11101070
55. Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. https://doi.org/10.5194/acp-21-4471-2021
56. Analysis of fog at Xianyang Airport based on multi-source ground-based detection data H. Ming et al. https://doi.org/10.1016/j.atmosres.2019.01.012
57. Secondary inorganic aerosol chemistry and its impact on atmospheric visibility over an ammonia-rich urban area in Central Taiwan L. Young et al. https://doi.org/10.1016/j.envpol.2022.119951
58. Atmospheric Forcing on Solar Energy in Complex Terrain: A Digital Twin Assessment in an Intermontane Basin in Southern Balkans N. Melita et al. https://doi.org/10.3390/atmos17070688
59. Air Pollution Zone Migrates South Driven by East Asian Winter Monsoon and Climate Change S. Zhao et al. https://doi.org/10.1029/2021GL092672
60. Barriers to helicopter emergency medical services in a Haze-Prone, mountainous region of Northern Thailand K. Sutham et al. https://doi.org/10.1186/s13049-025-01498-w
61. Atmospheric circulation anomalies related to the winter PM2.5 mass concentration rapid decline cases in Beijing, China Y. Ren et al. https://doi.org/10.1016/j.atmosres.2024.107665
62. The influence of terrestrial transport on visibility and aerosol properties over the coastal East China Sea B. Wang et al. https://doi.org/10.1016/j.scitotenv.2018.08.312
63. Comparison of the performances between the WRF and WRF-LES models in radiation fog – A case study C. Cui et al. https://doi.org/10.1016/j.atmosres.2019.04.003
64. Visibility Prediction over South Korea Based on Random Forest B. Kim et al. https://doi.org/10.3390/atmos12050552
65. A gradient boosting-based machine learning framework for improving atmospheric visibility numerical prediction C. Han et al. https://doi.org/10.1016/j.atmosres.2026.109012
66. Attribution-Oriented Atmospheric Visibility: Quantifying Meteorological Constraints and Anthropogenic Cycles Leverage T. Lin et al. https://doi.org/10.1021/acs.estlett.5c00874
67. 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. https://doi.org/10.1016/j.atmosres.2020.105054
68. Synoptic circulation pattern and boundary layer structure associated with PM2.5 during wintertime haze pollution episodes in Shanghai N. Liu et al. https://doi.org/10.1016/j.atmosres.2019.06.001
69. Haze pollution under a high atmospheric oxidization capacity in summer in Beijing: insights into formation mechanism of atmospheric physicochemical processes D. Zhao et al. https://doi.org/10.5194/acp-20-4575-2020
70. A New Image Compensation and Segmentation Algorithm to Retrieve Atmospheric Boundary Layer Height for LiDAR RHI 2-D Scan L. Chen et al. https://doi.org/10.1109/TGRS.2026.3673864
71. The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city R. Yadav et al. https://doi.org/10.1039/D2RA00424K
72. The air quality of Palangka Raya, Central Kalimantan, Indonesia: The impacts of forest fires on visibility M. Santoso et al. https://doi.org/10.1080/10962247.2022.2077474
73. Long-term measurements of planetary boundary layer height and interactions with PM2.5 in Shanghai, China L. Pan et al. https://doi.org/10.1016/j.apr.2019.01.007
74. Below-Cloud Aerosol Scavenging by Different-Intensity Rains in Beijing City T. Luan et al. https://doi.org/10.1007/s13351-019-8079-0
75. Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data K. Bai et al. https://doi.org/10.1016/j.envpol.2019.04.104
76. Atmospheric fine organic (PAHs) aerosols in Dokki urban area at Greater Cairo, Egypt: A comprehensive analysis of seasonal characterization, source apportionment and exposure implications S. Hassan et al. https://doi.org/10.1007/s41748-025-00720-0
77. Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model K. Gui et al. https://doi.org/10.1016/j.envint.2020.105801
78. 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. https://doi.org/10.1029/2020JD032478
79. Using a coupled large-eddy simulation–aerosol radiation model to investigate urban haze: sensitivity to aerosol loading and meteorological conditions J. Slater et al. https://doi.org/10.5194/acp-20-11893-2020
80. Characterizing primary dicarboxylic acid emissions from biomass burning in China: Fuel-Specific signatures and decadal emission trends P. He et al. https://doi.org/10.1016/j.envpol.2026.128236
81. Indoor particulate matter exposure and correlation of PM2.5 with lung efficacy and SpO2 level of Dhaka City Dwellers S. Nahian et al. https://doi.org/10.1007/s11869-024-01586-3
82. A physics-guided machine learning framework for enhancing dust storm visibility prediction in arid and semi-arid regions C. Xu et al. https://doi.org/10.1038/s41598-026-39766-z
83. Chemical investigation of polycyclic aromatic hydrocarbon sources and associated health risks in PM2.5 from Eastern India M. Subair et al. https://doi.org/10.1038/s41598-026-41899-0
84. Performance evaluation of photographic measurement in the machine-learning prediction of ground PM2.5 concentration L. Feng et al. https://doi.org/10.1016/j.atmosenv.2021.118623
85. 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. https://doi.org/10.5194/acp-22-1825-2022
86. Unveiling the causes of winter haze in southern Sichuan via observations and explainable machine learning G. Zhou et al. https://doi.org/10.1016/j.atmosenv.2025.121728
87. 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. https://doi.org/10.1029/2021JD035236
88. 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. https://doi.org/10.1016/j.scitotenv.2021.145479
89. Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al. https://doi.org/10.1186/s12302-025-01068-y
90. Study of Air Quality over Delhi during COVID-19 Lockdown Based on Remote Sensing Observations S. Budakoti https://doi.org/10.1134/S1024856024701215
91. Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system U. Shin et al. https://doi.org/10.1016/j.atmosenv.2020.118141
92. 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. https://doi.org/10.1016/j.apr.2024.102179
93. Long-term variation characteristics and influencing factors of low-visibility events on the coast of China R. Lyu et al. https://doi.org/10.1016/j.atmosres.2021.105583
94. 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 https://doi.org/10.1016/j.scitotenv.2025.178787
95. Roadside atmospheric pollution: still a serious environmental problem in Beijing, China W. Chen et al. https://doi.org/10.1007/s11869-018-0620-2
96. Decoding cold-season PM2.5 and its chemical compositions in China's economic powerhouses after the Chinese COVID-19 pandemic lockdown: A national-scale analysis X. Dao et al. https://doi.org/10.1016/j.jclepro.2025.146902
97. The major chemical constituents of PM2.5 and airborne bacterial community phyla in Beijing, Seoul, and Nagasaki E. Park et al. https://doi.org/10.1016/j.chemosphere.2020.126870
98. Absorbing and scattering aerosols over semi-arid region in India: Temporal variation and possible sources P. Soyam et al. https://doi.org/10.1016/j.jastp.2025.106613
99. 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. https://doi.org/10.1016/j.scitotenv.2020.138135
100. 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. https://doi.org/10.1016/j.apr.2020.07.019
101. Isotopic imprints of aerosol ammonium over the north China plain A. Mgelwa et al. https://doi.org/10.1016/j.envpol.2022.120376
102. 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. https://doi.org/10.3389/fenvs.2022.939147
103. The impacts of the atmospheric boundary layer on regional haze in North China Q. Li et al. https://doi.org/10.1038/s41612-021-00165-y
104. Distribution of Fine Particulate Matter Pollution in Winter over Eastern China Affected by Synoptic Conditions X. Liu et al. https://doi.org/10.3390/atmos15070821
105. Size-Segregated Particulate Mass and Carbonaceous Components in Roadside and Riverside Environments M. Amin et al. https://doi.org/10.3390/app112110214
106. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. https://doi.org/10.1016/j.earscirev.2023.104461
107. Carbonaceous component of Total Suspended Particulate (TSP) in Makassar City, Sulawesi Island, Indonesia M. Amin et al. https://doi.org/10.1088/1755-1315/1199/1/012021
108. Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full-Covered SARA AOD at the Urban Scale Z. Fan et al. https://doi.org/10.3390/rs12203368
109. Observational study of the vertical aerosol and meteorological factor distributions with respect to particulate pollution in Xi'an D. Huige et al. https://doi.org/10.1016/j.atmosenv.2021.118215
110. Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. https://doi.org/10.1038/s41612-023-00393-4
111. Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models H. Xu et al. https://doi.org/10.1016/j.atmosres.2025.108160