93 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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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
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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
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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
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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. 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. 10.3390/urbansci9050138
47. 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
48. 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
49. 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
50. 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
51. Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset A. Salcedo-Bosch et al. 10.1186/s12302-025-01068-y
52. Study of Air Quality over Delhi during COVID-19 Lockdown Based on Remote Sensing Observations S. Budakoti 10.1134/S1024856024701215
53. 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
54. 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
55. 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
56. Quantifying the impact of terrain–wind–governed close-effect on atmospheric polluted concentrations L. Wu et al. 10.1016/j.jclepro.2022.132995
57. 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
58. 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
59. Roadside atmospheric pollution: still a serious environmental problem in Beijing, China W. Chen et al. 10.1007/s11869-018-0620-2
60. 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
61. 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
62. Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements T. Zhang et al. 10.3390/rs16193713
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
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74. 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
75. Distribution of Fine Particulate Matter Pollution in Winter over Eastern China Affected by Synoptic Conditions X. Liu et al. 10.3390/atmos15070821
76. 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
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79. 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
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