Inversion algorithm of black carbon mixing state based on machine learning
<p>The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The 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 SP...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-03-01
|
| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/1149/2025/amt-18-1149-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849705919569461248 |
|---|---|
| author | Z. Tian Z. Tian J. Wang J. Wang J. Wang J. Wang C. Liu C. Liu J. Xing J. Wang J. Wang Z. Zhang Z. Zhang Y. Jin Y. Jin S. Shen S. Shen B. Wang B. Wang W. Nie W. Nie X. Huang X. Huang A. Ding A. Ding |
| author_facet | Z. Tian Z. Tian J. Wang J. Wang J. Wang J. Wang C. Liu C. Liu J. Xing J. Wang J. Wang Z. Zhang Z. Zhang Y. Jin Y. Jin S. Shen S. Shen B. Wang B. Wang W. Nie W. Nie X. Huang X. Huang A. Ding A. Ding |
| author_sort | Z. Tian |
| collection | DOAJ |
| description | <p>The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The 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. Furthermore, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs the Light Gradient-Boosting Machine (LightGBM), an advanced tree-based ensemble learning algorithm, to establish an inversion model that 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 a coefficient of determination <span class="inline-formula"><i>R</i><sup>2</sup></span> higher than 0.98. We further employ 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 (<span class="inline-formula"><i>D</i><sub>p</sub></span>) and explore 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 the assessment of further BC radiative effects.</p> |
| format | Article |
| id | doaj-art-9dbefcd7e7a4428983eea2e516bb71fa |
| institution | DOAJ |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-9dbefcd7e7a4428983eea2e516bb71fa2025-08-20T03:16:21ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-03-01181149116210.5194/amt-18-1149-2025Inversion algorithm of black carbon mixing state based on machine learningZ. Tian0Z. Tian1J. Wang2J. Wang3J. Wang4J. Wang5C. Liu6C. Liu7J. Xing8J. Wang9J. Wang10Z. Zhang11Z. Zhang12Y. Jin13Y. Jin14S. Shen15S. Shen16B. Wang17B. Wang18W. Nie19W. Nie20X. Huang21X. Huang22A. Ding23A. Ding24State Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaNational Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USAJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaNational Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaState Key Laboratory of Climate System Prediction and Risk Management/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaNational Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaNational Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaNational Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China<p>The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The 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. Furthermore, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs the Light Gradient-Boosting Machine (LightGBM), an advanced tree-based ensemble learning algorithm, to establish an inversion model that 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 a coefficient of determination <span class="inline-formula"><i>R</i><sup>2</sup></span> higher than 0.98. We further employ 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 (<span class="inline-formula"><i>D</i><sub>p</sub></span>) and explore 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 the assessment of further BC radiative effects.</p>https://amt.copernicus.org/articles/18/1149/2025/amt-18-1149-2025.pdf |
| spellingShingle | Z. Tian Z. Tian J. Wang J. Wang J. Wang J. Wang C. Liu C. Liu J. Xing J. Wang J. Wang Z. Zhang Z. Zhang Y. Jin Y. Jin S. Shen S. Shen B. Wang B. Wang W. Nie W. Nie X. Huang X. Huang A. Ding A. Ding Inversion algorithm of black carbon mixing state based on machine learning Atmospheric Measurement Techniques |
| title | Inversion algorithm of black carbon mixing state based on machine learning |
| title_full | Inversion algorithm of black carbon mixing state based on machine learning |
| title_fullStr | Inversion algorithm of black carbon mixing state based on machine learning |
| title_full_unstemmed | Inversion algorithm of black carbon mixing state based on machine learning |
| title_short | Inversion algorithm of black carbon mixing state based on machine learning |
| title_sort | inversion algorithm of black carbon mixing state based on machine learning |
| url | https://amt.copernicus.org/articles/18/1149/2025/amt-18-1149-2025.pdf |
| work_keys_str_mv | AT ztian inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT ztian inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT cliu inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT cliu inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jxing inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT jwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT zzhang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT zzhang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT yjin inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT yjin inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT sshen inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT sshen inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT bwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT bwang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT wnie inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT wnie inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT xhuang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT xhuang inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT ading inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning AT ading inversionalgorithmofblackcarbonmixingstatebasedonmachinelearning |