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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/300

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Title: Comparative testing of PMF and CFA models
Authors: Qin, Y.
Oduyemi, Kehinde O. K.
Chan, L. Y.
Affiliation: University of Abertay Dundee. School of Contemporary Sciences
Keywords: Positive matrix factorization
Convenient factor analysis
Respirable suspended particulate
Source identification
Issue Date: Feb-2002
Publisher: Elsevier
Type: Journal Article
Refereed: peer-reviewed
Rights: Published version (c)Elsevier, available from DOI: 10.1016/S0169-7439(01)00175-7
Citation: Qin, Y., Oduyemi, K. and Chan, L. Y. 2002. Comparative testing of PMF and CFA models. Chemometrics and Intelligent Laboratory Systems. 61(1-2): pp.75–87. [Online] Available from: DOI: 10.1016/S0169-7439(01)00175-7
Abstract: Positive matrix factorization (PMF) and convenient factor analysis (CFA) models have been tested using a large aerosol database measured in Hong Kong. As many as possible chemical components (good elements or so-called weak elements) [Atmos. Environ. 33 (1999) 2169] were selected to compose as large as possible a database. Error estimates and enforced rotation techniques were used in the PMF model trial. These important aspects were not included in a recently published work [Atmos. Environ. 33 (1999) 2169]. The test results of the two models mentioned above were assessed qualitatively by analyzing factor characteristics, and quantitatively by comparing factor mass profiles. CFA model has been shown to be a convenient tool for aerosol source identification and can qualitatively treat the elements that can serve as source tracers as well as PMF model does. PMF model provides expert tool for the identification of aerosol sources and source contribution estimation. It can treat the chemical components from various sources by apportioning these chemical components among the factors more reasonably than CFA model can. Quantitatively, the factor mass profiles produced by a PMF model are better at describing the source structure than those derived by a CFA model.
URI: http://hdl.handle.net/10373/300
ISSN: 0169-7439
Appears in Collections:Science Engineering & Technology Collection

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