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

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

doi:10.5194/acp-26-6471-2026

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.

https://doi.org/10.5194/acp-26-6471-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 9

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

Supplement

https://doi.org/10.5194/acp-26-6471-2026-supplement

Data sets

Interpretable Machine Learning Quantifies Composition and Size Controls on Aerosol Spectral Absorption Wenfang Wang et al. https://doi.org/10.5281/ZENODO.17852818

Model code and software

Interpretable Machine Learning Quantifies Composition and Size Controls on Aerosol Spectral Absorption Wenfang Wang et al. Wenfang Wang et al.

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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.