Abstract
Compound-specific isotope analysis (CSIA) is fast becoming an important tool to provide chemical evidence in a forensic investigation. Attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large dataset is analyzed and the isotopic differences between samples are subtle. Thus, this study intends to demonstrate any linkages between diesel fuels in a large number of datasets where subtlety in the isotopic values is accentuated by the near single-point source of origin. Diesel fuels were obtained from various locations in the South Island of New Zealand. Aliquots of these samples were diluted with n-pentane and subsequently analyzed with gas chromatography-isotope ratio mass spectrometry (GC-IRMS) for carbon and hydrogen isotope values. The data obtained were subjected to principal component analysis (PCA) and hierarchical clustering. A wide range of delta (super 13) C and delta (super 2) H values were determined for the ubiquitous alkane compounds (the greatest values being -4.5 ppm and -40 ppm, respectively). Based on the isotopic character of the alkanes it is suggested that diesel fuels from different locations were distinguishable and that the key components in the differentiation are the delta (super 2) H values of the shorter chain-length alkanes. However, while the stable isotope measurements may provide information to classify a sample at a broad scale, much more detailed information is required on the temporal and spatial variability of diesel compositions. The subtle differences of the stable isotope values within the alkanes of different diesel fuels highlighted the power of CSIA as a means of differentiating petroleum products of different origins, even more so when two or more stable isotopes data are combined. This paper shows that CSIA when used in tandem with multivariate statistical methods can provide suitable tools for source apportionment of hydrocarbons by demonstrating a straightforward approach, thus eliminating lengthy analytical processes.