Preprints
https://doi.org/10.5194/acp-2021-683
https://doi.org/10.5194/acp-2021-683

  05 Oct 2021

05 Oct 2021

Review status: this preprint is currently under review for the journal ACP.

Estimation of Secondary PM2.5 in China and the United States using a Multi-Tracer Approach

Haoran Zhang1, Nan Li1, Keqin Tang1, Hong Liao1, Chong Shi2,3, Cheng Huang4, Hongli Wang4, Song Guo5, Min Hu5, Xinlei Ge1, Mindong Chen1, Zhenxin Liu1, Huan Yu6, and Jianlin Hu1 Haoran Zhang et al.
  • 1Jiangsu 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
  • 2National Institute for Environmental Studies, Center for Global Environmental Research, Tsukuba, Ibaraki, Japan
  • 3Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China
  • 4State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
  • 5College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
  • 6Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China

Abstract. PM2.5, generated via both direct emissions and secondary formations, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online observations or lab analyses, which are generally high economic cost and labor-intensive. In this study, we develop a new method, named multi-tracer estimation algorithm (MTEA), to identify the primary and secondary components from routine observation of PM2.5. By comparing with the long-term and short-term measurements of aerosol chemical components in China, as well as aerosol composition network in the United States, MTEA is proved to be able to successfully capture the magnitude and variation of the primary PM2.5 (PPM) and secondary PM2.5 (SPM). Applying MTEA to China national air quality network, we find that 1) SPM accounts for 63.5 % of PM2.5 in southern cities of China averaged for 2014–2018, while in the North the proportion drops to 57.1 %, and at the same time the secondary proportion in regional background regions is ~19 % higher than that in populous regions; 2) the summertime secondary PM2.5 proportion presents a slight but consistent increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly because of the recent increase in O3 pollution in China; 3) the secondary PM2.5 proportion in Beijing significantly increases by 34 % during the COVID-19 lockdown, which might be the main reason of the observed unexpected PM pollution in this special period; and at least, 4) SPM and O3 show similar positive correlations in the BTH and YRD regions, but the correlations between total PM2.5 and O3 in these two regions are quite different as PPM levels determines. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for the future PM mitigation.

Haoran Zhang et al.

Status: open (until 16 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Haoran Zhang et al.

Haoran Zhang et al.

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Short summary
We developed a new algorithm to identify primary and secondary components of PM2.5 with low economy- and technique-cost. Our model is proved to be reliable by comparing with different observation datasets. We systematically explored the patterns and changes of the secondary PM2.5 pollution in China on a large space- and time-scale. We believe that this method is a promising tool for efficiently estimating primary and secondary PM, and has huge potential for the future air pollution mitigation.
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