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

Laura A. McGregor, Caroline Gauchotte-Lindsay, Niamh Nic Daeid, Russell Thomas, Robert M. Kalin

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35 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)3744-3752
Number of pages9
JournalEnvironmental Science and Technology
Issue number7
Early online date15 Feb 2012
Publication statusPublished - 3 Apr 2012


  • multivariate statistical analysis
  • cluster analysis
  • solvent extraction
  • environmental forensic identification
  • coal tar

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