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Advances in Meteorology
Volume 2017 (2017), Article ID 4197652, 18 pages
Research Article

Classifying Aerosols Based on Fuzzy Clustering and Their Optical and Microphysical Properties Study in Beijing, China

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Correspondence should be addressed to Hui Xu

Received 14 March 2017; Revised 27 May 2017; Accepted 4 June 2017; Published 24 July 2017

Academic Editor: Harry D. Kambezidis

Copyright © 2017 Wenhao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Classification of Beijing aerosol is carried out based on clustering optical properties obtained from three Aerosol Robotic Network (AERONET) sites. The fuzzy -mean (FCM) clustering algorithm is used to classify fourteen-year (2001–2014) observations, totally of 6,732 records, into six aerosol types. They are identified as fine particle nonabsorbing, two kinds of fine particle moderately absorbing (fine-MA1 and fine-MA2), fine particle highly absorbing, polluted dust, and desert dust aerosol. These aerosol types exhibit obvious optical characteristics difference. While five of them show similarities with aerosol types identified elsewhere, the polluted dust aerosol has no comparable prototype. Then the membership degree, a significant parameter provided by fuzzy clustering, is used to analyze internal variation of optical properties of each aerosol type. Finally, temporal variations of aerosol types are investigated. The dominant aerosol types are polluted dust and desert dust in spring, fine particle nonabsorbing aerosol in summer, and fine particle highly absorbing aerosol in winter. The fine particle moderately absorbing aerosol occurs during the whole year. Optical properties of the six types can also be used for radiative forcing estimation and satellite aerosol retrieval. Additionally, methodology of this study can be applied to identify aerosol types on a global scale.