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Peer-reviewedCC BYAtmospheric chemistry and physics

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

  • Wenfang Wang1
  • Pengfei Tian2
  • Shuhua Zeng1
  • Yifei Zhang1
  • Zeren Yu1
  • Chen Cui1
  • Y N Wu3
  • Min Chen2
  • Lei Zhang4,1
4 affiliations
  1. 1Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
  2. 2Lanzhou University (Lanzhou, China)
  3. 3State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  4. 4Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
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Abstract

The spectral dependence of aerosol absorption, characterized by the absorption Ångström exponent (AAE), strongly influences radiative effects, yet the relative importance of controlling factors remains poorly quantified. We integrate multisource observations with an interpretable machine-learning framework (Shapley Additive Explanations, SHAP) to disentangle the roles of chemical composition and particle size in shaping AAE and to evaluate radiative impacts. Field observation in Beijing reveal that near-surface AAE is predominantly influenced by higher fine mineral dust and water-soluble inorganic ions fractions. Multi-year columnar data identify dust loading as the dominant factor, followed by carbonaceous aerosols. The fine-mode radius accounts for 29 % of size parameters cumulative importance and ranks closely with black carbon. SHAP diagnostics highlight that columnar AAE contributes to radiative forcing at the top of the atmosphere (TOA) comparably to single scattering albedo (SSA), while its impact is clearly weaker at the bottom of the atmosphere and in the atmosphere. These findings help clarify AAE determinants and reduce uncertainties in aerosol radiative effect assessments.

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Cite

BibTeXRIS
BibTeX
@article{wang2026interpretable,
  title = {{Interpretable machine learning quantifies composition and size influences on aerosol spectral absorption}},
  author = {Wenfang Wang and Pengfei Tian and Shuhua Zeng and Yifei Zhang and Zeren Yu and Chen Cui and Y N Wu and Min Chen and Lei Zhang},
  journal = {Atmospheric chemistry and physics},
  year = {2026},
  month = may,
  doi = {10.5194/acp-26-6471-2026},
  url = {https://doi.org/10.5194/acp-26-6471-2026},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Interpretable machine learning quantifies composition and size influences on aerosol spectral absorption
AU  - Wenfang Wang
AU  - Pengfei Tian
AU  - Shuhua Zeng
AU  - Yifei Zhang
AU  - Zeren Yu
AU  - Chen Cui
AU  - Y N Wu
AU  - Min Chen
AU  - Lei Zhang
PY  - 2026
DA  - 2026/05/13
JO  - Atmospheric chemistry and physics
DO  - 10.5194/acp-26-6471-2026
UR  - https://doi.org/10.5194/acp-26-6471-2026
AB  - The spectral dependence of aerosol absorption, characterized by the absorption Ångström exponent (AAE), strongly influences radiative effects, yet the relative importance of controlling factors remains poorly quantified. We integrate multisource observations with an interpretable machine-learning framework (Shapley Additive Explanations, SHAP) to disentangle the roles of chemical composition and particle size in shaping AAE and to evaluate radiative impacts. Field observation in Beijing reveal that near-surface AAE is predominantly influenced by higher fine mineral dust and water-soluble inorganic ions fractions. Multi-year columnar data identify dust loading as the dominant factor, followed by carbonaceous aerosols. The fine-mode radius accounts for 29 % of size parameters cumulative importance and ranks closely with black carbon. SHAP diagnostics highlight that columnar AAE contributes to radiative forcing at the top of the atmosphere (TOA) comparably to single scattering albedo (SSA), while its impact is clearly weaker at the bottom of the atmosphere and in the atmosphere. These findings help clarify AAE determinants and reduce uncertainties in aerosol radiative effect assessments.
SN  - 1680-7316
SN  - 1680-7324
ER  -