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

Wenhao Zhang,Fengjie Zheng,Hui Xu

doi:10.1155/2017/4197652

Wenhao Zhang, Fengjie Zheng + Show 1 more

Open Access

https://doi.org/10.1155/2017/4197652

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Abstract

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.

Highlights

Aerosol is one of the largest sources of uncertainty in the radiative forcing and plays a key role in global climate change [1, 2]
The following 22 parameters obtained from Aerosol Robotic Network (AERONET) inversion products are applied in the cluster analysis: (a) Single scattering albedo (SSA) at 440, 676, 869, and 1020 nm (b) Real part of refractive index (REFR) at 440, 676, 869, and 1020 nm (c) Imaginary part of refractive index (REFI) at 440, 676, 869, and 1020 nm (d) Asymmetry parameter (ASYM) at 440, 676, 869, and 1020 nm (e) Parameters of particle size distribution: fine/coarse particle volume concentration (VolConF/VolConC); fine/coarse particle volume median radius (VolMedianRadF/VolMedianRadC); fine/coarse particle standard deviation (StdDevF/StdDevC)
It should be noted that the aerosol optical depth (AOD), fine fraction by volume (FFV), sphericity parameter (SP), and water vapor (WV) are used in later discussions

Summary

Introduction

Aerosol is one of the largest sources of uncertainty in the radiative forcing and plays a key role in global climate change [1, 2]. By using SSA and FMF observed by Aerosol Robotic Network (AERONET), Lee et al (2010) classified global aerosol into four types; they are dust, nonabsorbing, black carbon, and mixture [13] In these methods, characteristics of aerosol types at one location or site are represented by mean values of measurements. For multiparticles mixture aerosol, records are usually on the boundaries between several clusters, and only representing them by the center of each cluster is unreasonable [20] To overcome this limitation, Wu and Zeng (2014) applied Gustafson-Kessel fuzzy clustering algorithm to identify the optical properties of pure dust aerosol type [20].

Data and Methodology

Results and Discussions

Characteristics of Aerosol Types

Conclusions

Conflicts of Interest