气候变化领域集成服务门户(CCPortal): A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements

CCPortal

DOI	10.5194/acp-20-2489-2020
	A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements
	Li Z.; D'Ambro E.L.; Schobesberger S.; Gaston C.J.; Lopez-Hilfiker F.D.; Liu J.; Shilling J.E.; Thornton J.A.; Cappa C.D.
发表日期	2020
ISSN	1680-7316
起始页码	2489
结束页码	2512
卷号	20 期号:4
英文摘要	One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral dataset helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed an algorithm for clustering mass spectra, the noise-sorted scanning clustering (NSSC), appropriate for application to thermal desorption measurements of collected OA particles from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO-CIMS). NSSC, which extends the common density-based special clustering of applications with noise (DBSCAN) algorithm, provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for the determination of thermal profiles for compositionally distinct clusters of mass spectra, increasing the accessibility and enhancing the interpretation of FIGAERO data. Applications of NSSC to several laboratory biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of NSSC to distinguish different types of thermal behaviors for the components comprising the particles along with the relative mass contributions and chemical properties (e.g., average molecular formula) of each mass spectral cluster. For each of the systems examined, more than 80% of the total mass is clustered into 9-13 mass spectral clusters. Comparison of the average thermograms of the mass spectral clusters between systems indicates some commonality in terms of the thermal properties of different BSOA, although with some system-specific behavior. Application of NSSC to sets of experiments in which one experimental parameter, such as the concentration of NO, is varied demonstrates the potential for mass spectral clustering to elucidate the chemical factors that drive changes in the thermal properties of OA particles. Further quantitative interpretation of the thermograms of the mass spectral clusters will allow for a more comprehensive understanding of the thermochemical properties of OA particles. © 2020 Author(s).
语种	英语
scopus关键词	aerosol composition; algorithm; cluster analysis; concentration (composition); data interpretation; mass spectrometry; measurement method; thermodynamic property
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/247944
作者单位	Atmospheric Science Graduate Group, University of California, Davis, CA, United States; Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; Department of Chemistry, University of Washington, Seattle, WA, United States; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, United States; Department of Civil and Environmental Engineering, University of California, Davis, CA, United States; School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, Minhang District, China; Oak Ridge Institute for Science and Education, US Environmental Protection Agency, Research Triangle Park, NC, United States; Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, United States; TofWerk AG, Thun, Switzerland; School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang, China
推荐引用方式 GB/T 7714	Li Z.,D'Ambro E.L.,Schobesberger S.,et al. A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements[J],2020,20(4).
APA	Li Z..,D'Ambro E.L..,Schobesberger S..,Gaston C.J..,Lopez-Hilfiker F.D..,...&Cappa C.D..(2020).A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(4).
MLA	Li Z.,et al."A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.4(2020).