Articles | Volume 25, issue 12
https://doi.org/10.5194/acp-25-6161-2025
© 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-25-6161-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sources and trends of black carbon aerosol in the megacity of Nanjing, eastern China, after the China Clean Action Plan and Three-Year Action Plan
Abudurexiati Abulimiti
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yanlin Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Mingyuan Yu
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yihang Hong
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yu-Chi Lin
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Chaman Gul
Reading Academy, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
Fang Cao
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Cited
8 citations as recorded by crossref.
- Attribution of Black Carbon Variability in China (2000–2019) from a Perspective of Machine Learning R. Fan et al. https://doi.org/10.3390/atmos16121378
- Insight into the Formation of Winter Black Carbon and Brown Carbon over Xi’an in Northwestern China D. Li et al. https://doi.org/10.3390/toxics14010093
- Black carbon aerosols in China: spatial-temporal variations and lessons from long-term atmospheric observations H. Zheng et al. https://doi.org/10.5194/acp-25-16363-2025
- PM2.5 Composition, Sources, and Health Risks in Madinah, Saudi Arabia: A Pre-Vision 2030 Baseline Y. Alsufayan et al. https://doi.org/10.3390/environments13090502
- Source-specific black carbon responses to short-term interventions and cross-year emission changes in coastal Qingdao J. Hao et al. https://doi.org/10.1016/j.atmosenv.2026.122299
- Regional diversity in black carbon emissions, size, and mixing state across Asia: Implications for climate and public health S. Mishra et al. https://doi.org/10.1016/j.earscirev.2026.105578
- Divergent Responses to Wet Scavenging: Fossil Fuel Black Carbon Exhibits Longer Lifetime and Stronger Light Absorption than Biomass Burning BC M. Yu et al. https://doi.org/10.1021/acs.estlett.6c00058
- Understanding the spatial heterogeneity of black carbon variation drivers in China: Views from explainable machine learning H. Zheng et al. https://doi.org/10.1016/j.atmosres.2026.108749
8 citations as recorded by crossref.
- Attribution of Black Carbon Variability in China (2000–2019) from a Perspective of Machine Learning R. Fan et al. https://doi.org/10.3390/atmos16121378
- Insight into the Formation of Winter Black Carbon and Brown Carbon over Xi’an in Northwestern China D. Li et al. https://doi.org/10.3390/toxics14010093
- Black carbon aerosols in China: spatial-temporal variations and lessons from long-term atmospheric observations H. Zheng et al. https://doi.org/10.5194/acp-25-16363-2025
- PM2.5 Composition, Sources, and Health Risks in Madinah, Saudi Arabia: A Pre-Vision 2030 Baseline Y. Alsufayan et al. https://doi.org/10.3390/environments13090502
- Source-specific black carbon responses to short-term interventions and cross-year emission changes in coastal Qingdao J. Hao et al. https://doi.org/10.1016/j.atmosenv.2026.122299
- Regional diversity in black carbon emissions, size, and mixing state across Asia: Implications for climate and public health S. Mishra et al. https://doi.org/10.1016/j.earscirev.2026.105578
- Divergent Responses to Wet Scavenging: Fossil Fuel Black Carbon Exhibits Longer Lifetime and Stronger Light Absorption than Biomass Burning BC M. Yu et al. https://doi.org/10.1021/acs.estlett.6c00058
- Understanding the spatial heterogeneity of black carbon variation drivers in China: Views from explainable machine learning H. Zheng et al. https://doi.org/10.1016/j.atmosres.2026.108749
Saved (final revised paper)
Latest update: 19 Sep 2026
Short summary
To improve air quality, the Chinese government has implemented strict clean-air measures. We explored how black carbon (BC) responded to these measures and found that a reduction in liquid fuel use was the main factor driving a decrease in BC levels. Additionally, meteorological factors also played a significant role in the long-term trends of BC. These factors should be considered in future emission reduction policies to further enhance air quality improvements.
To improve air quality, the Chinese government has implemented strict clean-air measures. We...
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