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

McGregor, L.A., Gauchotte-Lindsay, C., Nic Daéid, N., Thomas, R. and Kalin, R.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. (doi:10.1021/es203708w)

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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:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gauchotte-Lindsay, Dr Caroline
Authors: McGregor, L.A., Gauchotte-Lindsay, C., Nic Daéid, N., Thomas, R., and Kalin, R.M.
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
Journal Name:Environmental Science and Technology
Journal Abbr.:EST
Publisher:American Chemical Society
ISSN:0013-936X
ISSN (Online):1520-5851
Published Online:15 February 2012

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