4.7 Article

Analysis of aerosol particle number size distribution and source attribution at three megacities in China

期刊

ATMOSPHERIC ENVIRONMENT
卷 279, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2022.119114

关键词

Particle number size distributions; Ultrafine particles; k-means clustering; Traffic emissions

资金

  1. Natural Science Foundation of China [42030606]
  2. National Key R&D Program of China [2017YFC1501702]
  3. Guangdong Basic and Applied Basic Research Fund [2020B1515130003]
  4. Guangzhou Science and Technology Bureau [202206010016]

向作者/读者索取更多资源

Air pollution is a major concern in densely populated megacities in China. A series of experiments conducted in Beijing, Shanghai, and Guangzhou revealed that ultrafine particles constitute a high proportion of aerosol pollutants in these cities. The concentration of these particles increases at nighttime due to traffic emissions.
Air pollution has been a major concern in China, especially in densely populated megacities. Particulate pollutants, generally known as aerosols, have sizes that range widely and complex physical and chemical compositions with high spatiotemporal variabilities. We have conducted a series of experiments using the same comprehensive observation system, measuring various aerosol properties in the three megacities of Beijing, Shanghai, and Guangzhou. Although there are differences in the total particle number concentration and particle number size distribution among the three sites due to differences in emission sources and sampling seasons, a high proportion of ultrafine particles was observed in all cities, i.e., 86.33%, 88.34%, and 81.1%, respectively. The high particle number concentration at the Beijing and Guangzhou sites was caused by an increase in nucleation-mode particles at nighttime. The concentration of nucleation-mode particles at nighttime was significantly higher than during the daytime. We call this phenomenon a nocturnal nucleation event, and combined with analyses of factors involved, attribute this event to traffic emissions. To investigate the pollution sources at each location, we identified and quantified the pollution contributions from different sources by applying the k-means clustering analysis to observational datasets of environmental quantities. Clustering results showed that traffic emissions were the main contributor in Beijing and Guangzhou, with direct and significant impacts ranging from 37% to 45%, respectively.

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