Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach
<p>PM<span class="inline-formula"><sub>2.5</sub></span>, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to qu...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/5495/2022/acp-22-5495-2022.pdf |
| _version_ | 1818274966809870336 |
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| author | H. Zhang N. Li K. Tang H. Liao C. Shi C. Shi C. Huang H. Wang S. Guo M. Hu X. Ge M. Chen Z. Liu H. Yu J. Hu |
| author_facet | H. Zhang N. Li K. Tang H. Liao C. Shi C. Shi C. Huang H. Wang S. Guo M. Hu X. Ge M. Chen Z. Liu H. Yu J. Hu |
| author_sort | H. Zhang |
| collection | DOAJ |
| description | <p>PM<span class="inline-formula"><sub>2.5</sub></span>, generated via both direct emission and secondary formation,
can have varying environmental impacts due to different physical and
chemical properties of its components. However, traditional methods to quantify different PM<span class="inline-formula"><sub>2.5</sub></span> components are often based on online or offline observations and numerical models, which are generally high economic cost- or 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 PM<span class="inline-formula"><sub>2.5</sub></span>. By comparing
with long-term and short-term measurements of aerosol chemical
components in China and the United States, it is proven that MTEA can
successfully capture the magnitude and variation of the primary PM<span class="inline-formula"><sub>2.5</sub></span>
(PPM) and secondary PM<span class="inline-formula"><sub>2.5</sub></span> (SPM). Applying MTEA to the China National Air Quality Network, we find
that (1) SPM accounted for 63.5 % of the PM<span class="inline-formula"><sub>2.5</sub></span> in cities in southern China
on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China,
and at the same time the secondary proportion in regional background regions
was <span class="inline-formula">∼</span> 19 % higher than that in populous regions; (2) the
summertime secondary PM<span class="inline-formula"><sub>2.5</sub></span> proportion presented a slight but consistent
increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly
because of the recent increase in O<span class="inline-formula"><sub>3</sub></span> pollution in China; (3) the
secondary PM<span class="inline-formula"><sub>2.5</sub></span> proportion in Beijing significantly increased by 34 %
during the COVID-19 lockdown, which might be the main reason for the observed
unexpected PM pollution in this special period; and finally, (4) SPM and
O<span class="inline-formula"><sub>3</sub></span> showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but
the correlations between total PM<span class="inline-formula"><sub>2.5</sub></span> and O<span class="inline-formula"><sub>3</sub></span> in these two regions, as determined from PPM levels,
were quite different. In general, MTEA is a promising
tool for efficiently estimating PPM and SPM, and has huge potential for
future PM mitigation.</p> |
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| format | Article |
| id | doaj.art-6f24c4cccf894aa1a4979a491fc37295 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-12T22:22:15Z |
| publishDate | 2022-04-01 |
| publisher | Copernicus Publications |
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| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-6f24c4cccf894aa1a4979a491fc372952022-12-22T00:09:53ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242022-04-01225495551410.5194/acp-22-5495-2022Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approachH. Zhang0N. Li1K. Tang2H. Liao3C. Shi4C. Shi5C. Huang6H. Wang7S. Guo8M. Hu9X. Ge10M. Chen11Z. Liu12H. Yu13J. Hu14Jiangsu 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, ChinaJiangsu 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, ChinaJiangsu 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, ChinaJiangsu 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, ChinaNational Institute for Environmental Studies, Center for Global Environmental Research, Tsukuba, Ibaraki, JapanInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, ChinaState Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, ChinaState Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, ChinaCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, ChinaJiangsu 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, ChinaJiangsu 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, ChinaJiangsu 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, ChinaDepartment of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, ChinaJiangsu 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<p>PM<span class="inline-formula"><sub>2.5</sub></span>, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM<span class="inline-formula"><sub>2.5</sub></span> components are often based on online or offline observations and numerical models, which are generally high economic cost- or 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 PM<span class="inline-formula"><sub>2.5</sub></span>. By comparing with long-term and short-term measurements of aerosol chemical components in China and the United States, it is proven that MTEA can successfully capture the magnitude and variation of the primary PM<span class="inline-formula"><sub>2.5</sub></span> (PPM) and secondary PM<span class="inline-formula"><sub>2.5</sub></span> (SPM). Applying MTEA to the China National Air Quality Network, we find that (1) SPM accounted for 63.5 % of the PM<span class="inline-formula"><sub>2.5</sub></span> in cities in southern China on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China, and at the same time the secondary proportion in regional background regions was <span class="inline-formula">∼</span> 19 % higher than that in populous regions; (2) the summertime secondary PM<span class="inline-formula"><sub>2.5</sub></span> proportion presented a slight but consistent increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly because of the recent increase in O<span class="inline-formula"><sub>3</sub></span> pollution in China; (3) the secondary PM<span class="inline-formula"><sub>2.5</sub></span> proportion in Beijing significantly increased by 34 % during the COVID-19 lockdown, which might be the main reason for the observed unexpected PM pollution in this special period; and finally, (4) SPM and O<span class="inline-formula"><sub>3</sub></span> showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but the correlations between total PM<span class="inline-formula"><sub>2.5</sub></span> and O<span class="inline-formula"><sub>3</sub></span> in these two regions, as determined from PPM levels, were quite different. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for future PM mitigation.</p>https://acp.copernicus.org/articles/22/5495/2022/acp-22-5495-2022.pdf |
| spellingShingle | H. Zhang N. Li K. Tang H. Liao C. Shi C. Shi C. Huang H. Wang S. Guo M. Hu X. Ge M. Chen Z. Liu H. Yu J. Hu Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach Atmospheric Chemistry and Physics |
| title | Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach |
| title_full | Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach |
| title_fullStr | Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach |
| title_full_unstemmed | Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach |
| title_short | Estimation of secondary PM<sub>2.5</sub> in China and the United States using a multi-tracer approach |
| title_sort | estimation of secondary pm sub 2 5 sub in china and the united states using a multi tracer approach |
| url | https://acp.copernicus.org/articles/22/5495/2022/acp-22-5495-2022.pdf |
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