气候变化领域集成服务门户(CCPortal): Aerosol classification by application of machine learning spectral clustering algorithm

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DOI	10.1016/j.apr.2023.102026
	Aerosol classification by application of machine learning spectral clustering algorithm
	Ningombam, Shantikumar S.; Larson, E. J. L.; Indira, G.; Madhavan, B. L.; Khatri, Pradeep
发表日期	2024
ISSN	1309-1042
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	Precise understanding of aerosol classification is crucial for accurately quantifying the effects of aerosols on the Earth's energy budget, improving remote sensing retrieval algorithms, formulating climate changerelated policies, and more. In this study, we used aerosol measurements from the quality assured AERosol Robotic NETwork (AERONET) and utilized a multivariate spectral clustering algorithm, a machine learning tool, to classify global aerosols. The spectral clustering algorithm is a variant of the clustering algorithm that employs eigenvalues and eigenvectors of the data matrix to project the data into a lower -dimensional space of a similar cluster. To accomplish this, we considered five aerosol optical parameters: fine -mode Aerosol Optical Depth, Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo, and Refractive Index from 150 AERONET sites distributed in six continents (Africa, Asia, Australia, Europe, North and South America) during 1993 to 2022. Using the clustering analysis, we identified four primary aerosol types: dust, urban, biomass burning, and mixed aerosols. Among the continents, the African and Asian sites exhibited the highest contribution of dust aerosols, as the region has significant global dust sources. Conversely, Australia, Europe, North, and South America are predominantly influenced by fine -mode aerosols, given their considerable distance from major dust source regions.
英文关键词	Climate change; Earth's energy budget; Spectral clustering; Machine learning; AERONET
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001152343200001
来源期刊	ATMOSPHERIC POLLUTION RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295694
作者单位	Department of Science & Technology (India); Indian Institute of Astrophysics (IIA); University of Colorado System; University of Colorado Boulder; Department of Space (DoS), Government of India; National Atmospheric Research Laboratory (NARL); Tohoku University
推荐引用方式 GB/T 7714	Ningombam, Shantikumar S.,Larson, E. J. L.,Indira, G.,et al. Aerosol classification by application of machine learning spectral clustering algorithm[J],2024,15(3).
APA	Ningombam, Shantikumar S.,Larson, E. J. L.,Indira, G.,Madhavan, B. L.,&Khatri, Pradeep.(2024).Aerosol classification by application of machine learning spectral clustering algorithm.ATMOSPHERIC POLLUTION RESEARCH,15(3).
MLA	Ningombam, Shantikumar S.,et al."Aerosol classification by application of machine learning spectral clustering algorithm".ATMOSPHERIC POLLUTION RESEARCH 15.3(2024).