Articles | Volume 18, issue 5
https://doi.org/10.5194/amt-18-1149-2025
https://doi.org/10.5194/amt-18-1149-2025
Research article
 | 
06 Mar 2025
Research article |  | 06 Mar 2025

Inversion algorithm of black carbon mixing state based on machine learning

Zeyuan Tian, Jiandong Wang, Jiaping Wang, Chao Liu, Jia Xing, Jinbo Wang, Zhouyang Zhang, Yuzhi Jin, Sunan Shen, Bin Wang, Wei Nie, Xin Huang, and Aijun Ding

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Cited articles

Ahmad, G. N., Fatima, H., Ullah, S., and Saidi, A. S.: Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV, IEEE Access, 10, 80151–80173, https://doi.org/10.1109/ACCESS.2022.3165792, 2022. 
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Short summary
The radiative effect of black carbon (BC) is substantially modulated by its mixing state, which is challenging to derive physically with a single-particle soot photometer. This study establishes a machine-learning-based inversion model which can accurately and efficiently acquire the BC mixing state. Compared to the widely used leading-edge-only method, our model utilizes a broader scattering signal coverage to more accurately capture diverse particle characteristics.
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