Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6471-2026
https://doi.org/10.5194/acp-26-6471-2026
Research article
 | 
13 May 2026
Research article |  | 13 May 2026

Interpretable machine learning quantifies composition and size influences on aerosol spectral absorption

Wenfang Wang, Pengfei Tian, Shuhua Zeng, Yifei Zhang, Zeren Yu, Chen Cui, Yunfei Wu, Min Chen, and Lei Zhang

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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.
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