71 citations as recorded by crossref.

1. Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model W. Choi et al. https://doi.org/10.3390/rs13132464
2. A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al. https://doi.org/10.1080/20964471.2026.2654984
3. Study of the aerosol optical characteristics over the Romanian Black Sea Coast using AERONET data S. Stefan et al. https://doi.org/10.1016/j.apr.2020.04.007
4. Long-Term Variation Study of Fine-Mode Particle Size and Regional Characteristics Using AERONET Data J. Shin et al. https://doi.org/10.3390/rs14184429
5. Long-term quantification of springtime aerosols over Saudi Arabia using multi-satellite remotely sensed data A. Nadeem et al. https://doi.org/10.1007/s11356-024-33871-0
6. Quantifying turbulent coarse particle transport over drylands of Southeastern Iberia using a stand-alone Doppler lidar methodology J. Abril-Gago et al. https://doi.org/10.1016/j.atmosres.2025.108236
7. Advancing global aerosol classification: Source-compositional typology and variability from 30 years (1993–2024) of AERONET observations A. Krishnaveni et al. https://doi.org/10.1016/j.scitotenv.2025.180918
8. Overview of aerosol optical properties over southern West Africa from DACCIWA aircraft measurements C. Denjean et al. https://doi.org/10.5194/acp-20-4735-2020
9. Simulated polarization as a signature of aerosol type P. Hamill et al. https://doi.org/10.1016/j.atmosenv.2020.117348
10. Classification of aerosol types using AERONET version 3 data over Kuwait City F. Khademi & A. Bayat https://doi.org/10.1016/j.atmosenv.2021.118716
11. POLIPHON conversion factors for retrieving dust-related cloud condensation nuclei and ice-nucleating particle concentration profiles at oceanic sites Y. He et al. https://doi.org/10.5194/amt-16-1951-2023
12. Satellite (GOSAT-2 CAI-2) retrieval and surface (ARFINET) observations of aerosol black carbon over India M. Gogoi et al. https://doi.org/10.5194/acp-23-8059-2023
13. Aerosols optical and radiative properties in Indonesia based on AERONET version 3 S. Kusumaningtyas et al. https://doi.org/10.1016/j.atmosenv.2022.119174
14. Photovoltaic Spectral Responsivity and Efficiency under Different Aerosol Conditions D. Kouklaki et al. https://doi.org/10.3390/en16186644
15. Extended POLIPHON dust conversion factor dataset for lidar-derived cloud condensation nuclei and ice-nucleating particle concentration profiles Y. He et al. https://doi.org/10.5194/amt-18-5669-2025
16. Classification of global aerosol types and its radiative effects using Aerosol Robotic Network (AERONET) data S. Mukhopadhyay et al. https://doi.org/10.1016/j.atmosenv.2025.121530
17. A Novel Framework Using Unsupervised Machine Learning for Aerosol Classification: A Case Study of North Indian AERONET Sites V. Hole et al. https://doi.org/10.1007/s12524-026-02508-9
18. Effect of the Vertical Distribution of Absorbing Aerosols on the Atmospheric Correction for Satellite Ocean Color Remote Sensing Z. Song et al. https://doi.org/10.1109/TGRS.2022.3151219
19. A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119098
20. Assessment of variability in PM2.5 and its impact on human health in a West African country S. Tariq et al. https://doi.org/10.1016/j.chemosphere.2023.140357
21. Long-Term Variation of Black Carbon Absorption Aerosol Optical Depth from AERONET Data over East Asia N. Dehkhoda et al. https://doi.org/10.3390/rs12213551
22. Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019) S. Eom et al. https://doi.org/10.1016/j.atmosres.2022.106457
23. Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing N. Sharma et al. https://doi.org/10.1016/j.jastp.2024.106366
24. Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model W. Choi et al. https://doi.org/10.3390/rs13071268
25. Study on global atmospheric aerosol type identification from combined satellite and ground observations X. Nie et al. https://doi.org/10.1016/j.atmosenv.2025.121100
26. Statistical validation of Aeolus L2A particle backscatter coefficient retrievals over ACTRIS/EARLINET stations on the Iberian Peninsula J. Abril-Gago et al. https://doi.org/10.5194/acp-22-1425-2022
27. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.001
28. Impact of climatic factors on volume aerosol size distribution over Northern India N. Sharma et al. https://doi.org/10.1016/j.jastp.2025.106633
29. Validation of Australian atmospheric aerosols from reanalysis data and CMIP6 simulations A. Isaza et al. https://doi.org/10.1016/j.atmosres.2021.105856
30. A Study on the Variation of Aerosol Lidar Ratio according to Aerosol Types on Korea Based on AERONET Sun/Sky Radiometer Data Y. Noh https://doi.org/10.5572/KOSAE.2020.36.1.084
31. Comparison of Aerosol Properties in the Korean Peninsula Between AERONET Version 2 and 3 Data Set J. Lee et al. https://doi.org/10.1007/s13143-020-00221-2
32. 20 years of columnar aerosol properties at Valencia area (Eastern Spain) by ground-based sun-photometry M. Garcia-Suñer et al. https://doi.org/10.1016/j.atmosres.2023.107198
33. Aerosol climatology characterization over Bangladesh using ground-based and remotely sensed satellite measurements S. Zaman et al. https://doi.org/10.1525/elementa.2021.000063
34. Global classification of aerosols based on ground observations: A comparison between an empirical method and machine learning algorithms M. Awais & L. Wang https://doi.org/10.1016/j.pce.2025.104017
35. 15-Year Analysis of Direct Effects of Total and Dust Aerosols in Solar Radiation/Energy over the Mediterranean Basin K. Papachristopoulou et al. https://doi.org/10.3390/rs14071535
36. Multiyear Typology of Long-Range Transported Aerosols over Europe V. Nicolae et al. https://doi.org/10.3390/atmos10090482
37. Diagnostic evaluation of aerosol classification over the Indo-Gangetic Plain: insights from AE–FMF–SSA relationships S. Wadhwa et al. https://doi.org/10.1007/s11356-026-37773-1
38. Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data X. Zhou et al. https://doi.org/10.3390/rs16050904
39. Regional impacts of black carbon morphologies on shortwave aerosol–radiation interactions: a comparative study between the US and China J. Luo et al. https://doi.org/10.5194/acp-22-7647-2022
40. An automated size and time-resolved aerosol collector platform integrated with environmental sensors to study the vertical profile of aerosols Z. Cheng et al. https://doi.org/10.1039/D2EA00097K
41. Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data X. Wei et al. https://doi.org/10.5194/acp-24-5025-2024
42. Effect of Shape and Sizes of Crystal Particles on Angular Distributions of Transmitted Solar Radiation in Two Sensing Geometries: Results of Numerical Simulation T. Zhuravleva https://doi.org/10.1134/S1024856021010127
43. Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data S. Wang et al. https://doi.org/10.3390/rs12172769
44. Aerosol Characteristics Over an Eastern Indian City Using Sun Photometer Observations and Machine Learning Frameworks J. Panda et al. https://doi.org/10.1007/s41810-025-00320-5
45. Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia S. Tariq et al. https://doi.org/10.1016/j.chemosphere.2024.141119
46. Bridging the Gap: Establishing Spatial Correlations and Type Conversion Relationships among Different Aerosol Idetification Methods Q. Chen et al. https://doi.org/10.1016/j.atmosres.2024.107523
47. Canadian Biomass Burning Aerosol Properties Modification during a Long-Ranged Event on August 2018 C. Papanikolaou et al. https://doi.org/10.3390/s20185442
48. A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation W. Choi et al. https://doi.org/10.3390/rs13040609
49. Investigations of aerosol types classification and PM2.5 concentrations: A case study of two major cities in Pakistan M. Zeeshan & K. Alam https://doi.org/10.1007/s11869-024-01616-0
50. Long-Term Variation of Aerosol Optical Properties Associated with Aerosol Types Over East Asia Using Aeronet and Satellite (Viirs, Omi) Data S. Eom et al. https://doi.org/10.2139/ssrn.4155134
51. Investigations on the Aerosol Type Classification using Machine Learning Techniques over Multiple AERONET Sites in India M. Anitha & L. Kumar https://doi.org/10.1016/j.asr.2025.12.045
52. Remotely sensed BC columns over rapidly changing Western China show significant decreases in mass and inconsistent changes in number, size, and mixing properties due to policy actions Z. Liu et al. https://doi.org/10.1038/s41612-024-00663-9
53. Remote sensing retrieval of aerosol types in China using geostationary satellite X. Chen et al. https://doi.org/10.1016/j.atmosres.2023.107150
54. Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution Q. Chen et al. https://doi.org/10.3390/rs16071210
55. Aerosol size distribution using Thailand ground-based instruments and climate variables W. Kumharn et al. https://doi.org/10.1007/s00704-020-03328-8
56. Long-range transport of La Soufrière volcanic plume to the western North Pacific: Influence on atmospheric mercury and aerosol properties S. Ravindra Babu et al. https://doi.org/10.1016/j.atmosenv.2021.118806
57. A global aerosol model based on the analysis of 30-year ground measurements from AERONET (AEROEX model): Implications for satellite-derived aerosol retrievals M. Mishra et al. https://doi.org/10.1016/j.scitotenv.2025.180301
58. Impact of Chemical Composition on Aerosol Scattering: Insights from the Surface Particulate Matter Network and Aerosol Robotic Network S. Eom et al. https://doi.org/10.1021/acs.est.5c09325
59. Impact of a severe dust storm on aerosol properties and their radiative forcing over the Indian subcontinent during winter J. Victor et al. https://doi.org/10.1016/j.atmosres.2024.107282
60. Study on the Himawari-8 aerosol products and aerosol types under the environmental pollution in selected regions of Asia Q. Mao et al. https://doi.org/10.1016/j.jqsrt.2025.109363
61. Seasonal Tropospheric Aerosol Classification Using AERONET Spectral Absorption Properties in African Locations S. Aladodo et al. https://doi.org/10.1007/s41810-022-00140-x
62. Classification of aerosol types in mega-city of Lahore (Pakistan) using ground-based remote sensing M. Khan et al. https://doi.org/10.1007/s11869-024-01511-8
63. Decadal Aerosol Column-Integrated Lidar Ratio Climatology Over East Asia From Synergistic AD-Net Lidar and Sun/Sky Radiometer Observations M. Kim et al. https://doi.org/10.1109/LGRS.2026.3712279
64. Global Aerosol Classification Based on Aerosol Robotic Network (AERONET) and Satellite Observation J. Lin et al. https://doi.org/10.3390/rs13061114
65. A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements A. Kauppi et al. https://doi.org/10.3390/rs16111945
66. The contribution of different aerosol types to direct radiative forcing over distinct environments of Pakistan inferred from the AERONET data R. Khan et al. https://doi.org/10.1088/1748-9326/aba2a6
67. Uncertainties Assessment of Regional Aerosol Classification Schemes in South America A. Scagliotti et al. https://doi.org/10.1007/s41748-024-00423-y
68. Global aerosol models considering their spatial heterogeneities based on AERONET measurements S. Wang et al. https://doi.org/10.1016/j.atmosres.2024.107521
69. Classification of aerosols over Saudi Arabia from 2004–2016 M. Ali et al. https://doi.org/10.1016/j.atmosenv.2020.117785
70. Spatiotemporal analysis of absorbing aerosols and radiative forcing over environmentally distinct stations in East Africa during 2001–2018 G. Khamala et al. https://doi.org/10.1016/j.scitotenv.2022.161041
71. Improvements on Gaussian mixture model and its application in identifying aerosol types in two major cities in the Yangtze River Delta, China J. Wang et al. https://doi.org/10.1016/j.scitotenv.2024.172743