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Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China
Author(s) -
Chen Liangduo,
Qi Ximeng,
Nie Wei,
Wang Jiaping,
Xu Zheng,
Wang Tianyi,
Liu Yuliang,
Shen Yicheng,
Xu Zhengning,
Kokkonen Tom V.,
Chi Xuguang,
Aalto Pasi P.,
Paasonen Pauli,
Kerminen VeliMatti,
Petäjä Tuukka,
Kulmala Markku,
Ding Aijun
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd034004
Subject(s) - cluster (spacecraft) , aerosol , environmental science , particle number , atmospheric sciences , delta , mass concentration (chemistry) , air pollution , air quality index , particle (ecology) , pollution , meteorology , geography , physics , chemistry , ecology , plasma , organic chemistry , quantum mechanics , astronomy , biology , computer science , thermodynamics , programming language
Submicron particles in polluted regions have received much attention because of their influences on human health and climate. A k‐means clustering technique was performed on a data set of particle number size distributions (PNSD) that was obtained over more than 3 years in the Yangtze River Delta (YRD) region of East China. With simultaneous measurements of meteorological conditions, trace gases and aerosol compositions, seven clusters were categorized and interpreted. Cluster 1 and cluster 2, which accounted for 9.9% of the total PNSD data, were attributed to new particle formation (NPF) and vehicle exhaust emissions with different intensities; Cluster 3 and Cluster 4, which accounted for 10.5% of the total PNSD data, were related to the growth of nucleation mode particles; Cluster 5, which accounted for 37.9% of the total data, was attributed to the humid YRD background; and Cluster 6 and Cluster 7, which accounted for 41.6% of the total data set, were both pollution‐related clusters with similar mass concentrations but completely different PNSD. Although the PM 2.5 mass concentrations were somewhat similar, the particle number concentrations of the accumulation mode particles could vary by more than one order of magnitude from the urban background cluster to the pollution‐related clusters. The cluster proximity diagram and conversion flow chart of clusters clearly show the influence of NPF and growth on haze, as well as the conversion between background and polluted conditions. This study highlights the importance of PNSD for understanding urban air quality and recommends the clustering technique for analyzing complex PNSD datasets.