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.
| Original language | English |
|---|---|
| Pages (from-to) | 3744-3752 |
| Number of pages | 9 |
| Journal | Environmental Science and Technology |
| Volume | 46 |
| Issue number | 7 |
| Early online date | 15 Feb 2012 |
| DOIs | |
| Publication status | Published - 3 Apr 2012 |
Keywords
- multivariate statistical analysis
- cluster analysis
- solvent extraction
- environmental forensic identification
- coal tar
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Dive into the research topics of 'Multivariate statistical methods for the environmental forensic classification of coal tars from former manufactured gas plants'. Together they form a unique fingerprint.Impacts
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Improved apportionment of environmental risk and liability through environmental forensics
Robert Kalin (Participant)
Impact: Impact - for External Portal › Economic and commerce, Environment and sustainability - natural world and built environment