Classifying Asian dust aerosols and their columnar optical properties using fuzzy clustering

Abstract

The Gustafson-Kessel fuzzy clustering algorithm is used to classify Asian dust aerosols from Aerosol Robotic Network inversions, and properties within different membership degree intervals are analyzed. Five typical Asian dust sites are selected for source data. Based on dust aerosol physics, seven key parameters are selected as clustering inputs. The two clusters, dust and nondust, are then divided by a membership degree threshold of 0.5 on a scale of 0 to 1. The membership degree denotes the level of confidence that a record belongs to a cluster. The degree of mixing is denoted by diminishing membership values; e.g., the classified dust aerosol associated with higher membership degree denotes the purer dust. The two-dimensional principle component projections, together with the properties of the clusters, show the capability of fuzzy clustering to classify a pure aerosol type based on a membership degree. Then the detailed dust properties within different membership degree intervals are analyzed. The results show that both the optical properties and volume size distributions vary significantly with membership degrees and range diversities can be reduced within each interval. The proposed method can also be used to determine weights in an aerosol fitting model to improve the parameterization. The method is most suited to air masses with multicomponent aerosol mixtures and can easily be extended to other types of aerosols.

The Fundamental Aerosol Models Over China Region: A Cluster Analysis of the Ground‐Based Remote Sensing Measurements of Total Columnar Atmosphere

“…Some scholars preliminarily studied aerosol characterization modeling in China (e.g., Chen et al, 2013;Lee et al, 2010;Wu & Zeng, 2014: Zhang, Xu, & Zheng, 2017. However, all of the aerosol models in these studies are established based on the entire volume size distribution and supposing the fine and coarse modes have the same refractive indices.…”

In situ measurements of angular-dependent light scattering by aerosols over the contiguous United States

“…If the scattering medium is assumed to be macroscopically isotropic and symmetric, the scattering matrix elements F 13 and F 14 do not contribute to the total scattered signal and the resulting pair of image intensities allows for direct measurements of F 11 (θ ) as well as F 12 (θ ), with θ representing the zenith scattering angle (azimuthal symmetry is implied by the assumption of a macroscopically isotopic and symmetric medium). The incorporation of calibration data derived from molecular scatterers (CO 2 and N 2 ) that are well characterized (Anderson et al, 1996;Young, 1980) allows for an angulardependent calibration that produces direct measurements of absolute phase function in known units (e.g., Mm −1 sr −1 ), free from truncation error. Assumptions regarding the relative scattering contribution of the extreme angles can then be used to estimate total integrated scattering (β sca ) from the truncated measurements of absolute phase function.…”

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

“…The basic theory of cluster analysis is records in the same cluster which are more similar to each other than to those in other clusters. In fuzzy clustering, every record has a degree of belonging to each cluster, rather than just completely belonging to one cluster [20]. This degree is represented by a parameter named membership degree (between 0 and 1), which indicates the confidence degree of one record belonging to a cluster.…”

“…As a matter of fact, the differences between records at edge of cluster and those near center are inevitable. Particularly 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].…”

“…Particularly 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]. Compared with aforementioned clustering algorithm, not only center of cluster but also a significant parameter named membership degree (between 0 and 1) is provided by fuzzy clustering.…”