Articles | Volume 24, issue 8
https://doi.org/10.5194/acp-24-5025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-5025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2024
Research article |
| 29 Apr 2024
| 29 Apr 2024
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data
Xiaoli Wei
Shanghai Meteorological Service, Shanghai, 200030, China
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Qian Cui
Wuhan Meteorological Bureau, Wuhan, 430000, China
Leiming Ma
Shanghai Meteorological Service, Shanghai, 200030, China
Feng Zhang
CORRESPONDING AUTHOR
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China
Wenwen Li
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China
Peng Liu
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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Cited
17 citations as recorded by crossref.
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- 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
- 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
- Collection and characterization of aerosol particles T. Gao et al. https://doi.org/10.1088/2053-1591/adc4c2
- Advanced simulation and measurement of skylight polarization patterns across distinct aerosol type environments S. Li et al. https://doi.org/10.1016/j.scitotenv.2025.178768
- 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
- Enhancing accuracy in identifying absorbing aerosol types and their radiative impacts K. Ansari & S. Ramachandran https://doi.org/10.1038/s41612-025-01167-w
- Long-term validation and error analysis of MODIS and VIIRS dark target aerosol products in Africa X. Xue et al. https://doi.org/10.1016/j.atmosres.2025.108372
- Intensity-resolved classification of forest fire smoke via fused satellite and ground-based optical climatology Y. Ma et al. https://doi.org/10.1016/j.envres.2026.124710
- A machine learning-based model for aerosol scattering hygroscopic growth factor (f(RH)) prediction in Beijing urban area H. Tong et al. https://doi.org/10.1016/j.atmosenv.2025.121699
- 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
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- The evolution of aerosol types and direct radiative forcing in Beijing-Tianjin-Hebei, China: A long-term analysis with a modified Gaussian mixture model J. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109072
- Effects of aerosol and cloud on global gross primary productivity M. Zhang et al. https://doi.org/10.1016/j.jclepro.2025.147385
17 citations as recorded by crossref.
- Development of a hybrid algorithm for the simultaneous retrieval of aerosol optical thickness and fine-mode fraction from multispectral satellite observation combining radiative transfer and transfer learning approaches C. Tang et al. https://doi.org/10.1016/j.rse.2025.114619
- Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements Z. Zhang et al. https://doi.org/10.5194/amt-19-2555-2026
- 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
- 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
- Collection and characterization of aerosol particles T. Gao et al. https://doi.org/10.1088/2053-1591/adc4c2
- Advanced simulation and measurement of skylight polarization patterns across distinct aerosol type environments S. Li et al. https://doi.org/10.1016/j.scitotenv.2025.178768
- 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
- Enhancing accuracy in identifying absorbing aerosol types and their radiative impacts K. Ansari & S. Ramachandran https://doi.org/10.1038/s41612-025-01167-w
- Long-term validation and error analysis of MODIS and VIIRS dark target aerosol products in Africa X. Xue et al. https://doi.org/10.1016/j.atmosres.2025.108372
- Intensity-resolved classification of forest fire smoke via fused satellite and ground-based optical climatology Y. Ma et al. https://doi.org/10.1016/j.envres.2026.124710
- A machine learning-based model for aerosol scattering hygroscopic growth factor (f(RH)) prediction in Beijing urban area H. Tong et al. https://doi.org/10.1016/j.atmosenv.2025.121699
- 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
- In-tandem multi-waveband particulate absorption and size observations yield substantial changes in radiative forcing over industrial Central China L. Guan et al. https://doi.org/10.5194/acp-26-3107-2026
- Simulation and experimental investigation of electret polypropylene fiber preparation via centrifugal melt electrospinning for enhanced air filtration H. Ye et al. https://doi.org/10.1016/j.seppur.2024.130113
- Multi-method aerosol classification over the Eastern Mediterranean based on long-term AERONET observations and machine learning G. Akgül et al. https://doi.org/10.1016/j.atmosenv.2026.122242
- The evolution of aerosol types and direct radiative forcing in Beijing-Tianjin-Hebei, China: A long-term analysis with a modified Gaussian mixture model J. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109072
- Effects of aerosol and cloud on global gross primary productivity M. Zhang et al. https://doi.org/10.1016/j.jclepro.2025.147385
Saved (final revised paper)
Latest update: 30 Jul 2026
Short summary
A new aerosol-type classification algorithm has been proposed. It includes an optical database built by Mie scattering and a complex refractive index working as a baseline to identify different aerosol types. The new algorithm shows high accuracy and efficiency. Hence, a global map of aerosol types was generated to characterize aerosol types across the five continents. It will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.
A new aerosol-type classification algorithm has been proposed. It includes an optical database...
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