Application of parallel factor analysis model to decompose excitation-emission matrix fluorescence spectra for characterizing sources of water-soluble brown carbon in PM2.5. (15th February 2020)

• Record Type: Journal Article
• Authors: Wang, Huanbo; Zhang, Leiming; Huo, Tingting; Wang, Bin; Yang, Fumo; Chen, Yang; Tian, Mi; Qiao, Baoqing; Peng, Chao
• Is Part Of: Atmospheric environment. Volume 223(2020)
• Publication Date: 2020-02-15
• Subjects: Water-soluble brown carbon -- PM2.5 -- Excitation-emission matrix fluorescence spectroscopy -- PARAFAC model; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2019.117192
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The applicability of parallel factor analysis (PARAFAC) model for identifying potential sources of water-soluble brown carbon (BrC) in fine particulate matter (PM2.5 ) using seasonal and annual excitation-emission matrix (EEM) fluorescence spectra data was investigated. The uncertainties related to the application of PARAFAC model to water-soluble BrC analysis were evaluated and the physicochemical meanings of PARAFAC-derived components were clearly interpreted. EEM spectra were obtained from water-soluble extractions of PM2.5 samples, which were collected at an urban and a suburban site in Chongqing, southwest of China during four one-month periods, each representing a different season in 2015. The measured EEM spectra were decomposed into three individual fluorescence components using PARAFAC algorithm, and the potential sources of BrC were identified based on the fingerprinting characteristics of PARAFAC-derived components. Each of the individual component exhibited similar spectral profiles in different seasons except in summer at the urban site; however, the relative intensities between the components varied with season, suggesting seasonal dependent source intensity of BrC. The relative contributions of the individual fluorescence components to the total fluorescence intensity varied largely from 0 to 89.2% at different excitation and emission wavelengths. Therefore, the relative abundance of each individual component based on the maximum fluorescence intensity (Fmax ) should be used carefully for source apportionment analysis of BrC. Highlights: Sources of BrC were identified through fingerprinting fluorescence characteristics. Uncertainties related to the application of PARAFAC model to BrC were estimated. Relative abundances of individual components varied largely depending on λ ex / λ em .