Articles | Volume 25, issue 4
https://doi.org/10.5194/acp-25-2613-2025
© 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/acp-25-2613-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2025
Research article |
| 28 Feb 2025
| 28 Feb 2025
Accounting for the black carbon aging process in a two-way coupled meteorology–air quality model
Yuzhi Jin
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Chao Liu
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
David C. Wong
CORRESPONDING AUTHOR
Atmospheric and Environmental Systems Modeling Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA
Golam Sarwar
Atmospheric and Environmental Systems Modeling Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Kathleen M. Fahey
Atmospheric and Environmental Systems Modeling Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Shang Wu
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, 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 University, Nanjing, 210023, China
Jing Cai
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki, 00014, Finland
Zeyuan Tian
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Zhouyang Zhang
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, 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
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 University, Nanjing, 210023, China
Shuxiao Wang
State Key Laboratory of Regional Environment and Sustainability, Beijing, 100084, China
School of Environment, Tsinghua University, Beijing, 100084, China
Viewed
Total article views: 5,364 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Aug 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,393 | 1,455 | 516 | 5,364 | 134 | 171 | 310 |
- HTML: 3,393
- PDF: 1,455
- XML: 516
- Total: 5,364
- Supplement: 134
- BibTeX: 171
- EndNote: 310
Total article views: 2,266 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Feb 2025)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,553 | 639 | 74 | 2,266 | 134 | 74 | 114 |
- HTML: 1,553
- PDF: 639
- XML: 74
- Total: 2,266
- Supplement: 134
- BibTeX: 74
- EndNote: 114
Total article views: 3,098 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Aug 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,840 | 816 | 442 | 3,098 | 97 | 196 |
- HTML: 1,840
- PDF: 816
- XML: 442
- Total: 3,098
- BibTeX: 97
- EndNote: 196
Viewed (geographical distribution)
Total article views: 5,364 (including HTML, PDF, and XML)
Thereof 5,333 with geography defined
and 31 with unknown origin.
Total article views: 2,266 (including HTML, PDF, and XML)
Thereof 2,237 with geography defined
and 29 with unknown origin.
Total article views: 3,098 (including HTML, PDF, and XML)
Thereof 3,096 with geography defined
and 2 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
4 citations as recorded by crossref.
- Inversion Approach for Inferring Mixing State and Improving Optical Estimation of Coated Black Carbon Using Bulk-Volume Variables and Number-Size Distributions Z. Zhang et al. https://doi.org/10.1021/acs.est.5c10094
- AIDA Arctic transport experiment – Part 1: Simulation of northward transport and aging effect on fundamental black carbon properties M. Zanatta et al. https://doi.org/10.5194/ar-3-477-2025
- Evolution of Black Carbon Aging Degree in China from 2013–2019 and Key Driving Factors P. Li et al. https://doi.org/10.1021/acs.estlett.5c00755
- Mixing State, Morphology, and Chemical Composition of Ship-Emitted Soot Revealed by UAV-Based Plume Observations S. Bie et al. https://doi.org/10.1021/acs.est.6c00632
4 citations as recorded by crossref.
- Inversion Approach for Inferring Mixing State and Improving Optical Estimation of Coated Black Carbon Using Bulk-Volume Variables and Number-Size Distributions Z. Zhang et al. https://doi.org/10.1021/acs.est.5c10094
- AIDA Arctic transport experiment – Part 1: Simulation of northward transport and aging effect on fundamental black carbon properties M. Zanatta et al. https://doi.org/10.5194/ar-3-477-2025
- Evolution of Black Carbon Aging Degree in China from 2013–2019 and Key Driving Factors P. Li et al. https://doi.org/10.1021/acs.estlett.5c00755
- Mixing State, Morphology, and Chemical Composition of Ship-Emitted Soot Revealed by UAV-Based Plume Observations S. Bie et al. https://doi.org/10.1021/acs.est.6c00632
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Black carbon (BC) affects climate and the environment, and its aging process alters its properties. Current models, like WRF-CMAQ, lack full accounting for it. We developed the WRF-CMAQ-BCG model to better represent BC aging by introducing bare and coated BC species and their conversion. The WRF-CMAQ-BCG model introduces the capability to simulate BC mixing states and bare and coated BC wet deposition, and it improves the accuracy of BC mass concentration and aerosol optics.
Black carbon (BC) affects climate and the environment, and its aging process alters its...
Altmetrics
Final-revised paper
Preprint