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Multivariate statistical methods for the environmental forensic classification of coal tars from former manufactured gas plants

McGregor, Laura A. and Gauchotte-Lindsay, Caroline and Daeid, Niamh Nic and Thomas, Russell and Kalin, Robert M. (2012) Multivariate statistical methods for the environmental forensic classification of coal tars from former manufactured gas plants. Environmental Science and Technology, 46 (7). pp. 3744-3752.

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Abstract

Compositional disparity within a set of 23 coal tar samples (obtained from 15 different former manufactured gas plants) was compared and related to differences between historical on-site manufacturing processes. Samples were prepared using accelerated solvent extraction prior to analysis by two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A suite of statistical techniques, including univariate analysis, hierarchical cluster analysis, two-dimensional cluster analysis, and principal component analysis (PCA), were investigated to determine the optimal method for source identification of coal tars. The results revealed that multivariate statistical analysis (namely, PCA of normalized, preprocessed data) has the greatest potential for environmental forensic source identification of coal tars, including the ability to predict the processes used to create unknown samples.

Item type: Article
ID code: 40050
Keywords: multivariate statistical analysis , cluster analysis, solvent extraction, environmental forensic identification, coal tar, Engineering (General). Civil engineering (General)
Subjects: Technology > Engineering (General). Civil engineering (General)
Department: Faculty of Engineering > Civil and Environmental Engineering
Faculty of Science > Pure and Applied Chemistry
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 14 Jun 2012 11:56
    Last modified: 27 Mar 2013 16:25
    URI: http://strathprints.strath.ac.uk/id/eprint/40050

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