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
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Cited
11 citations as recorded by crossref.
- Spatiotemporal variations of wintertime secondary PM2.5 and meteorological drivers in a basin region over Central China for 2015–2020 Y. Zhu et al. 10.1016/j.apr.2023.101738
- Electrospun Modified SiO2 Nanofiber Membranes as Superamphiphobic Self-Cleaning Filters with High Heat Stability for Efficient Particle Matter Capture N. Lu et al. 10.1021/acsanm.2c02035
- Variations of Secondary PM2.5 in an Urban Area over Central China during 2015–2020 of Air Pollutant Mitigation D. Liang et al. 10.3390/atmos13121962
- Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats C. Zhu & M. Yao 10.1021/acs.est.3c08629
- Evaluating urban and nonurban PM 2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset B. Liu et al. 10.1080/17538947.2024.2310734
- Secondary PM2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan N. Li et al. 10.1016/j.envint.2022.107725
- Influence of ozone pollution on the mixing state and formation of oxygenated organics containing single particles S. Liu et al. 10.1016/j.scitotenv.2024.171880
- Response of warm season secondary pollutants to emissions and meteorology in the North China Plain region during 2018–2022 Y. Gao et al. 10.1016/j.aosl.2024.100495
- Chemical characteristics and sources of PM2.5 in the urban environment of Seoul, Korea S. Kang et al. 10.1016/j.apr.2022.101568
- Impacts of urban–rural disparities in the trends of PM2.5 and ozone levels in China during 2013–2019 L. Xing et al. 10.1016/j.apr.2022.101590
- Increasing but Variable Trend of Surface Ozone in the Yangtze River Delta Region of China K. Tang et al. 10.3389/fenvs.2022.836191
10 citations as recorded by crossref.
- Spatiotemporal variations of wintertime secondary PM2.5 and meteorological drivers in a basin region over Central China for 2015–2020 Y. Zhu et al. 10.1016/j.apr.2023.101738
- Electrospun Modified SiO2 Nanofiber Membranes as Superamphiphobic Self-Cleaning Filters with High Heat Stability for Efficient Particle Matter Capture N. Lu et al. 10.1021/acsanm.2c02035
- Variations of Secondary PM2.5 in an Urban Area over Central China during 2015–2020 of Air Pollutant Mitigation D. Liang et al. 10.3390/atmos13121962
- Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats C. Zhu & M. Yao 10.1021/acs.est.3c08629
- Evaluating urban and nonurban PM 2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset B. Liu et al. 10.1080/17538947.2024.2310734
- Secondary PM2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan N. Li et al. 10.1016/j.envint.2022.107725
- Influence of ozone pollution on the mixing state and formation of oxygenated organics containing single particles S. Liu et al. 10.1016/j.scitotenv.2024.171880
- Response of warm season secondary pollutants to emissions and meteorology in the North China Plain region during 2018–2022 Y. Gao et al. 10.1016/j.aosl.2024.100495
- Chemical characteristics and sources of PM2.5 in the urban environment of Seoul, Korea S. Kang et al. 10.1016/j.apr.2022.101568
- Impacts of urban–rural disparities in the trends of PM2.5 and ozone levels in China during 2013–2019 L. Xing et al. 10.1016/j.apr.2022.101590
1 citations as recorded by crossref.
Latest update: 25 Apr 2024
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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