Articles | Volume 22, issue 16
https://doi.org/10.5194/acp-22-10861-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-10861-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2022
Research article |
| 26 Aug 2022
| 26 Aug 2022
Mixing state of black carbon at different atmospheres in north and southwest China
Gang Zhao
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Tianyi Tan
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Shuya Hu
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Zhuofei Du
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Dongjie Shang
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Zhijun Wu
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
Jing Zheng
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Wenfei Zhu
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Mengren Li
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Limin Zeng
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
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Cited
12 citations as recorded by crossref.
- Radiative forcing bias calculation based on COSMO (Core-Shell Mie model Optimization) and AERONET data P. Tiwari et al. 10.1038/s41612-023-00520-1
- The integrating sphere system plus in-situ absorption monitoring: A new scheme to study absorption enhancement of black carbon in ambient aerosols Z. Li et al. 10.1016/j.scitotenv.2023.164355
- Heterogeneous characteristics and absorption enhancement of refractory black carbon in an urban city of China S. Chen et al. 10.1016/j.scitotenv.2023.162997
- Vertical distribution of black carbon and its mixing state in the urban boundary layer in summer H. Liu et al. 10.5194/acp-23-7225-2023
- Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions X. Chen et al. 10.1016/j.isci.2023.108125
- The density of ambient black carbon retrieved by a new method: implications for cloud condensation nuclei prediction J. Ren et al. 10.5194/acp-23-4327-2023
- Overestimation of black carbon light absorption due to mixing state heterogeneity L. Zeng et al. 10.1038/s41612-023-00535-8
- Evolution of refractory black carbon mixing state in an urban environment S. Kasparoglu et al. 10.1016/j.atmosenv.2024.120651
- Markedly different impacts of primary emissions and secondary aerosol formation on aerosol mixing states revealed by simultaneous measurements of CCNC, H(/V)TDMA, and SP2 J. Tao et al. 10.5194/acp-24-9131-2024
- Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation J. Liu et al. 10.5194/amt-16-4961-2023
- Significant contribution of fractal morphology to aerosol light absorption in polluted environments dominated by black carbon (BC) B. Romshoo et al. 10.1038/s41612-024-00634-0
- Mixing state of black carbon at different atmospheres in north and southwest China G. Zhao et al. 10.5194/acp-22-10861-2022
11 citations as recorded by crossref.
- Radiative forcing bias calculation based on COSMO (Core-Shell Mie model Optimization) and AERONET data P. Tiwari et al. 10.1038/s41612-023-00520-1
- The integrating sphere system plus in-situ absorption monitoring: A new scheme to study absorption enhancement of black carbon in ambient aerosols Z. Li et al. 10.1016/j.scitotenv.2023.164355
- Heterogeneous characteristics and absorption enhancement of refractory black carbon in an urban city of China S. Chen et al. 10.1016/j.scitotenv.2023.162997
- Vertical distribution of black carbon and its mixing state in the urban boundary layer in summer H. Liu et al. 10.5194/acp-23-7225-2023
- Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions X. Chen et al. 10.1016/j.isci.2023.108125
- The density of ambient black carbon retrieved by a new method: implications for cloud condensation nuclei prediction J. Ren et al. 10.5194/acp-23-4327-2023
- Overestimation of black carbon light absorption due to mixing state heterogeneity L. Zeng et al. 10.1038/s41612-023-00535-8
- Evolution of refractory black carbon mixing state in an urban environment S. Kasparoglu et al. 10.1016/j.atmosenv.2024.120651
- Markedly different impacts of primary emissions and secondary aerosol formation on aerosol mixing states revealed by simultaneous measurements of CCNC, H(/V)TDMA, and SP2 J. Tao et al. 10.5194/acp-24-9131-2024
- Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation J. Liu et al. 10.5194/amt-16-4961-2023
- Significant contribution of fractal morphology to aerosol light absorption in polluted environments dominated by black carbon (BC) B. Romshoo et al. 10.1038/s41612-024-00634-0
1 citations as recorded by crossref.
Latest update: 18 Nov 2024
Short summary
Black carbon is the second strongest absorbing component in the atmosphere that exerts warming effects on climate. One critical challenge in quantifying the ambient black carbon's radiative effects is addressing the BC microphysical properties. In this study, the microphysical properties of the aged and fresh BC particles are synthetically analyzed under different atmospheres. The measurement results can be further used in models to help constrain the uncertainties of the BC radiative effects.
Black carbon is the second strongest absorbing component in the atmosphere that exerts warming...
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