Articles | Volume 18, issue 5
https://doi.org/10.5194/amt-18-1149-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1149-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Mar 2025
Research article |
| 06 Mar 2025
| 06 Mar 2025
Inversion algorithm of black carbon mixing state based on machine learning
Zeyuan Tian
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Jiaping Wang
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China
Chao Liu
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Jia Xing
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA
Jinbo Wang
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China
Zhouyang Zhang
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yuzhi Jin
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Sunan Shen
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Bin Wang
State 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, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China
Xin Huang
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China
Aijun Ding
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, 210023, China
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Latest update: 08 Mar 2025
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.
The radiative effect of black carbon (BC) is substantially modulated by its mixing state, which...
