the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inversion Algorithm of Black Carbon Mixing State Based on Machine Learning
Abstract. Black carbon (BC) radiative impact is significantly influenced by its mixing state. Single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records individual particle signals, it requires complex data processing to convert raw signals into particle size and mixing states. Besides, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs a light gradient boosting machine (LightGBM) to establish an inversion model which directly correlates SP2 signals with the mixing state of BC-containing particles. Our model achieves high accuracy for both particle size inversion and optical cross-section inversion of BC-containing particles, with R2 higher than 0.98. Further, we employed the Shapley Additive exPlanation (SHAP) method to analyze the importance of input features from SP2 signals in the inversion model of the entire particle diameter (Dp) and explored their underlying physical significance. Compared to the widely used Leading-Edge-Only (LEO) fitting method, the machine learning (ML) method utilizes a larger coverage of signals encompassing the peak of scattering signal rather than the leading-edge data. This allows for more accurate capture of the diverse characteristics of particles. Moreover, the ML method uses signals with a high signal-to-noise ratio, providing better noise resistance. Our model is capable of accurately and efficiently acquiring the single-particle information and statistical results of the BC mixing state, which provides essential data for BC aging mechanism investigation and further BC radiative effects assessment.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-2496', Anonymous Referee #2, 03 Dec 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2496/egusphere-2024-2496-RC1-supplement.pdf
- AC2: 'Reply on RC1', Jiandong Wang, 15 Dec 2024
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CC1: 'Comment on egusphere-2024-2496', Xiaolong Fan, 06 Dec 2024
The Single-particle soot photometer (SP2) is a widely recognized instrument for quantifying the mixing state of black carbon (BC). However, deriving BC mixing state from SP2 measurements remains challenging. This study introduces a user-friendly SP2 inversion method based on machine learning. Notably, the machine learning approach does not merely replicate the results of physical inversion methods but also utilizes previously unexploited signals. It overcomes the low signal-to-noise ratio issue in input signal prevalent in conventional methods. This advancement will benefit the development of BC mixing state observations and radiative effect assessments. Overall, the manuscript is well-organized, and I recommend its publication after minor revisions.
- There appears to be a correlation between the deviation of predicted values from the true values and particle size, as observed in Figure 3c. It would be beneficial to further characterize the relationship between prediction accuracy and particle diameter (Dp). This analysis could provide valuable insights into the model's performance across different particle size ranges and potentially identify any systematic biases or limitations in the prediction methodology.
- Does the deviation between the predicted values and the true values refer to the test set, or does it also occur in the training set? What could be the underlying reasons for this? Please clarify.
- Does the inversion of Dc and Dp in BC-containing particles utilize multiple outputs from the same trained model, or from different models? Additionally, does Dc influence the inversion of Dp?
- Could you elaborate on the rationale behind selecting LightGBM over alternative models?Â
Citation: https://doi.org/10.5194/egusphere-2024-2496-CC1 - AC3: 'Reply on CC1', Jiandong Wang, 15 Dec 2024
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RC2: 'Comment on egusphere-2024-2496', Anonymous Referee #1, 13 Dec 2024
Please find my comments in the attachment.
- AC1: 'Reply on RC2', Jiandong Wang, 15 Dec 2024
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