Articles | Volume 22, issue 8
https://doi.org/10.5194/acp-22-5495-2022
© 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-22-5495-2022
© 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 Apr 2022
Research article |
| 26 Apr 2022
| 26 Apr 2022
Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach
Haoran Zhang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Nan Li
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Keqin Tang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Hong Liao
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Chong Shi
National Institute for Environmental Studies, Center for Global
Environmental Research, Tsukuba, Ibaraki, Japan
Institute of Remote Sensing and Digital Earth, Chinese Academy of
Sciences, Beijing, 100094, China
Cheng Huang
State Environmental Protection Key Laboratory of Formation and
Prevention of the Urban Air Pollution Complex, Shanghai Academy of
Environmental Sciences, Shanghai, 200233, China
Hongli Wang
State Environmental Protection Key Laboratory of Formation and
Prevention of the Urban Air Pollution Complex, Shanghai Academy of
Environmental Sciences, Shanghai, 200233, China
College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
Xinlei Ge
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Mindong Chen
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Zhenxin Liu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Department of Atmospheric Science, School of Environmental Studies,
China University of Geosciences, Wuhan, 430074, China
Jianlin Hu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, Nanjing University of Information Science & Technology,
Nanjing, 210044, China
Model code and software
The source codes of the MTEA model H. Zhang and H. Li http: //www.nuistairquality.com/m_tea
Short summary
We developed a new algorithm with low economic/technique costs to identify primary and secondary components of PM2.5. Our model was shown to be reliable by comparison with different observation datasets. We systematically explored the patterns and changes in the secondary PM2.5 pollution in China at large spatial and time scales. We believe that this method is a promising tool for efficiently estimating primary and secondary PM2.5, and has huge potential for future PM mitigation.
We developed a new algorithm with low economic/technique costs to identify primary and secondary...
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