Forensic Investigations of Diesel Oil Spills in the Environment Using Comprehensive Two-Dimensional Gas Chromatography-High Resolution Mass Spectrometry and Chemometrics: New Perspectives in the Absence of Recalcitrant Biomarkers.

Forensic investigations of oil spills aim to find the responsible source(s) of the spill. Oil weathering processes change the chemical composition of the spilled oil and make the matching of oil spill samples to potential sources difficult. Diesel oil spill cases are more challenging, because biomarkers recalcitrant to long-term weathering are absent. We developed and tested a new method for the analysis and matching of diesel oil spills using two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC - HRMS) and 2D-CHEMSIC (2-Dimensional CHEMometric analysis of Selected Ion Chromatograms), an extension of the CHEMSIC method to GC × GC data. The 2D-CHEMSIC performs pixel-based analysis using chemometrics on concatenated sections of 2D extracted ion chromatograms to assess the overall chemical variability of the samples, with potential applications for matching spill-source pairs in forensic investigations. The method was tested on samples from a number of diesel oil spill cases, (i) distinguishing chemically similar source diesels, (ii) investigating weathering effects on spill samples to determine type and degree of weathering, and (iii) improving the matching of diesel oil spills affected by weathering. Positive matches for spill-source pairs were identified after excluding the signals from the hydrocarbons most susceptible to evaporation, and photo-oxidized spills were also matched due to the presence of unaffected hydrocarbons. Forensic diagnostics obtained by the 2D-CHEMSIC were validated by the conventional CEN-Tr method.

icoshift: An effective tool for the alignment of chromatographic data.

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.

A Tucker model based approach for analysis of complex oil biodegradation data.

A novel method based on gas chromatography-mass spectrometry in selected ion monitoring mode (GC-MS/SIM) and Tucker models is developed to evaluate the effects of oil type, microbial treatments and incubation time on the biodegradation of petroleum hydrocarbons. The data set consists of sections of the m/z 180, 192 and 198 GC-MS/SIM chromatograms of oil extracts from a biodegradation experiment where four oil types were exposed to four microbial treatments over a period of one year. The chosen sections, which are specific to methylfluorenes, phenanthrenes and dibenzothiophenes, were combined in a 4-way array (incubation timexoil typextreatmentxcombined chromatographic retention times) that was analyzed using both principal component analysis and the Tucker model. Several conclusions could be reached: the light fuel oil was the least degradable of those tested, 2- and 3-methyl isomers were more easily degraded compared to the 4-methyl isomers, the mixture of surfactant producers and PAC degraders provided the most effective degradation and the largest part of the degradation occurred between 54 and 132 days.

Practical aspects of chemometrics for oil spill fingerprinting.

Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration and classification that can increase the speed and the objectivity of the analysis and allow for more extensive use of the available data in this field. However, although the chemometric literature is extensive, it does not focus on practical issues that are relevant to oil spill fingerprinting. The aim of this review is to provide a framework for the use of chemometric approaches in tiered oil spill fingerprinting and to provide clear-cut practical details and experiences that can be used by the forensic chemist. The framework is based on methods for initial screening, which include classification of samples into oil type, detection of non matches and of weathering state, and detailed oil spill fingerprinting, in which a more rigorous matching of an oil spill sample to suspected source oils is obtained. This review is intended as a tutorial, and is based on two examples of initial screening using respectively gas chromatography with flame ionization detection and fluorescence spectroscopy; and two of detailed oil spill fingerprinting where gas chromatography-mass spectrometry data are analyzed according to two approaches: The first relying on sections of processed chromatograms and the second on diagnostic ratios.

Chemical fingerprinting of petroleum biomarkers using time warping and PCA.

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows: boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.

Integrated methodology for forensic oil spill identification.

A new integrated methodology for forensic oil spill identification is presented. It consists of GC-MS analysis, chromatographic data processing, variable-outlier detection, multivariate data analysis, estimation of uncertainties, and statistical evaluation. The methodology was tested on four groups of diagnostic ratios composed of petroleum biomarkers and ratios within homologous PAH categories. Principal component analysis (PCA) was employed and enabled the simultaneous analysis of many diagnostic ratios. Weathering was taken into account by considering the sampling uncertainties estimated from replicate spill samples. Statistical evaluation ensured an objective matching of oil spill samples with suspected source oils as well as classification into positive match, probable match, and nonmatch. The data analysis is further refined if two or more source oils are classified as probable match by using weighted least squares fitting of the principal components, local PCA models, and additional information relevant to the spill case. The methodology correctly identified the source of two spill samples (i.e., crude oils from Oseberg East and Oseberg Field Centre) and distinguished them from closely related source oils.