Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6471-2026
© Author(s) 2026. 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-26-6471-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 May 2026
Research article |
| 13 May 2026
| 13 May 2026
Interpretable machine learning quantifies composition and size influences on aerosol spectral absorption
Wenfang Wang
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
Shuhua Zeng
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yifei Zhang
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Zeren Yu
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Chen Cui
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yunfei Wu
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Min Chen
CORRESPONDING AUTHOR
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Lei Zhang
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
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Short summary
We separated the roles of chemical composition and particle size in influencing absorption Ångström exponent (AAE) using ground and column measurements together with interpretable machine learning. We found that near surface AAE is influenced by higher fine mineral dust and inorganic ions fractions. Fine-mode effective radius has an influence close to black carbon on columnar AAE. Columnar AAE contributes to radiative forcing at the top of the atmosphere comparably to single scattering albedo.
We separated the roles of chemical composition and particle size in influencing absorption...
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