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

• Published in: Advances in Meteorology
• Main Authors: Wenhao Zhang, Hui Xu, Fengjie Zheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: Advances in Meteorology 2017
• Subjects: Aerosol Robotic Network
• Online Access: https://doi.org/10.1155/2017/4197652

Classification of Beijing aerosol is carried out based on clustering optical properties obtained from three Aerosol Robotic Network (AERONET) sites. The fuzzy c-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.