Pixel-based analysis of comprehensive two-dimensional gas chromatograms (color plots) of petroleum: a tutorial.

We demonstrate how to process comprehensive two-dimensional gas chromatograms (GC × GC chromatograms) to remove nonsample information (artifacts), including background and retention time shifts. We also demonstrate how this, combined with further reduction of the influence of irrelevant information, allows for data analysis without integration or peak deconvolution (pixel-based analysis).

Metals and organotins in multiple bivalve species in a one-off global survey.

The Galathea 3 expedition circumnavigated the globe in 2006-2007 and collected marine samples from six continents. Bivalves were collected from harbours, other impacted locations and reference sites, and samples from 57 sites were analyzed for metals and 47 for organotins, to assess current contamination levels on a global scale. Metal concentrations in nine bivalve species were normalised to the Mytilidae family using conversion factors based on cosampled species and literature bioconcentration factors. The lowest metal and tributyltin concentrations were below background assessment concentrations (BACs) agreed in the Oslo-Paris convention (OSPAR) for the North Sea, and at most harbours the concentrations were orders of magnitude above BACs. The lowest concentrations of Cd and Pb measured here suggest that the BACs should be lower in a worldwide context. The sources of metals were classified according to human impact using principal component analysis. High relative concentrations of Hg, Pb and P were source indicators for industrial activity and land use; Zn, organotins, Cd and Cu for shipping activities, and V for oil spills. Generally the concentration levels at reference sites were low, but not always lower than expected impacted areas. The most contaminated areas were harbours, where especially Copenhagen, St Croix and Sydney, can be considered hotspots of tributyltin as well as a number of metals.

Polychlorinated biphenyls, organochlorine pesticides and polycyclic aromatic hydrocarbons in a one-off global survey of bivalves.

During the Danish Galathea 3 expedition, bivalve samples were collected at the Faroe Islands, Greenland, Ghana, South Africa, Australia, Solomon Islands, New Zealand, Chile, US Virgin Islands, Boston, Newfoundland and Shetland Islands and analysed for organochlorines and PAHs. Concentration differences of up to three orders of magnitude were observed, with the highest concentrations at Boston harbour (SPCB 338 ng g(-1) dw, ΣSPAH 5966 ng g(-1) dw) and the Sydney estuary (ΣSPCB 282 ng g(-1) dw, SPAH 1453 ng g(-1) dw). Local impacts were also found for the Greenland capital Nuuk in terms of PCB and PAH levels, while other Greenland samples came closest to representing PAH background levels. Several locations had undetectable organochlorine levels, including Hobart and Chile, which had the lowest SPAH concentrations (<200 ng g(-1) dw). It was possible to group the stations according to their pyrogenic/petrogenic influence using Principal Component Analyses, and indications of petroleum sources were found at Nuuk.

Assessment of oil weathering by gas chromatography-mass spectrometry, time warping and principal component analysis.

Detailed characterization and understanding of oil weathering at the molecular level is an essential part of tiered approaches for forensic oil spill identification, for risk assessment of terrestrial and marine oil spills, and for evaluating effects of bioremediation initiatives. Here, a chemometric-based method is applied to data from two in vitro experiments in order to distinguish the effects of evaporation and dissolution processes on oil composition. The potential of the method for obtaining detailed chemical information of the effects from evaporation and dissolution processes, to determine weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography-mass spectrometry chromatograms containing homologue series of n-alkanes (m/z 85) and alkyltoluenes (m/z 105). The PCA model based solely on in vitro samples and validated by samples from an authentic marine oil spill gives a detailed description of the temporal changes in n-alkane and alkyltoluene compositions. The PCA model is able to distinguish the two physical weathering processes both with respect to removal rate and relative changes. The model shows that evaporation has a large impact on both the alkyltoluenes and on the n-alkanes (e.g., nC-18 is completely removed after 192 days of in vitro evaporation). Dissolution, however, is shown to be a much slower process for weathering of heavy fuel oils with only limited impact on the alkyltoluenes, and no effects on the n-alkane distribution.

Characterization, weathering, and application of sesquiterpanes to source identification of spilled lighter petroleum products.

Biomarkers have become increasingly important for identifying the source of spilled oil, due to their specificity and high resistance to biodegradation. The biomarkers most commonly used in forensic investigations are the high molecular weight (MW) tri- and pentacyclic terpanes and steranes. For lighter petroleum products such as jet fuels and diesels, the refining processes remove most high MW biomarkers from the original crude oil feedstock. The smaller bicyclic sesquiterpanes, however, are concentrated in these products. Sesquiterpanes are ubiquitous components of crude oils and ancient sediments. Examination of GC-MS chromatograms of these bicyclic biomarkers using their characteristic fragment ions (m/z 123, 179, 193, and 207) provides a highly diagnostic means for identifying spilled oil, particularly for lighter refined product samples that are difficult to identify by current techniques. In this work, sesquiterpanes in crude oils and petroleum products are identified and characterized, distributions of sesquiterpanes in oils and refined products are compared, the effects of evaporative weathering on sesquiterpane distributions are examined, and a methodology using diagnostic indices of sesquiterpanes is developed for oil correlation and differentiation. Finally, two case studies are presented to illustrate the unique utility of sesquiterpanes for fingerprinting and identifying unknown diesel spills.

Multivariate statistical methods for evaluating biodegradation of mineral oil.

Two methods were developed for evaluating natural attenuation and bioremediation of mineral oil after environmental spills and during in vitro experiments. Gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) mode was used to obtain compound-specific data. The chromatographic data were then preprocessed either by calculating the first derivative, retention time alignment and normalization or by peak identification, quantification and calculation of diagnostic ratios within homologue series of polycyclic aromatic compounds (PACs). Finally, principal component analysis (PCA) was applied to the preprocessed chromatograms or diagnostic ratios to study the fate of the oil. The methods were applied to data from an in vitro biodegradation experiment with a North Sea crude oil exposed to three mixtures of bacterial strains: R (alkane degraders and surfactant producers), U (PAC degraders) and M (mixture of R- and U-strains) over a 1-year-period with five sampling times. Assessment of variation in degradability within isomer groups of methylfluorenes (m/z 180), methylphenanthrenes (m/z 192) and methyldibenzothiophenes (m/z 198) was used to evaluate the effects of microbial degradation on the composition of the oil. The two evaluation methods gave comparable results. In the objective pattern matching approach, principal component 1 (PC1) described the general changes in the isomer abundances, whereas M samples were separated from U and R samples along PC2. Furthermore, in the diagnostic ratio approach, a third component (PC3) could be extracted; although minor, it separated R samples from U and M samples. These results demonstrated that the two methods were able to differentiate between the effects due to the different bacterial activities, and that bacterial strain mixtures affected the PAC isomer patterns in different ways in accordance with their different metabolic capabilities.

Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis.

A novel approach for matching oil samples by fluorescence spectroscopy combined with three-way decomposition of spectra is presented. It offers an objective fingerprinting based on the relative composition of polycyclic aromatic compounds (PACs) in oils. The method is complementary to GC-FID for initial screening of oil samples but can also be used for prescreening in the field, onboard ships, using a portable fluorescence spectrometer. Parallel factor analysis (PARAFAC) was applied to fluorescence excitation-emission matrixes (EEMs) of heavy fuel oils (HFOs), light fuel oils, lubricating oils, crude oils, unknown oils, and a sample collected in the spill area two weeks after the Baltic Carrier oil spill (Denmark, 2001). A total of 112 EEMs were decomposed into a five-factor PARAFAC model using excitation wavelengths from 245 to 400 nm and emission wavelengths from 280 to 550 nm. The PARAFAC factors were compared to EEMs of PAC standards with two to five rings, and the comparisons indicate that each of the factors can be related to a mixture of PACs with similar fluorescence characteristics: a mixture of naphthalenes and dibenzothiophenes, fluorenes, phenanthrenes, chrysenes, and five-ring PACs, respectively. Oils were grouped in score plots according to oil type. Except for HFOs and crude oils, the method easily discriminated between the four oil types. Minor overlaps of HFOs and crude oils were observed along all five PARAFAC factors, and the variability of crude oils was large along factor 2 due to a varying content of five-ring PACs. The spill sample was correctly assigned as a HFO with similar PAC pattern as oil from the cargo tank of the Baltic Carrier by comparing the correlation coefficient of scores for the oil spill sample and possible source oils (i.e., oils in the database).

Chromatographic preprocessing of GC-MS data for analysis of complex chemical mixtures.

Hyphenated analytical techniques such as gas chromatography-mass spectrometry (GC-MS) can provide extensive amounts of analytical data when applied to environmental samples. Quantitative analyses of complex contaminant mixtures by commercial preprocessing software are time-consuming, and baseline distortion and incomplete peak resolution increase the uncertainty and subjectivity of peak quantification. Here, we present a semi-automatic method developed specific for processing complex first-order chromatographic data (e.g. selected ion monitoring in GC-MS) prior to chemometric data analysis. Chromatograms are converted into semi-quantitative variables (e.g. diagnostic ratios (DRs)) that can be exported directly to appropriate softwares. The method is based on automatic peak matching, initial parameterization, alternating background noise reduction and peak estimation using mathematical functions (Gaussian and exponential-Gaussian hybrid) with few (i.e. three to four) parameters. It is capable of resolving convoluted peaks, and the exponential-Gaussian hybrid improves the description of asymmetric peaks (i.e. fronting and tailing). The optimal data preprocessing suggested in this article consists of estimation of Gaussian peak parameters and subsequent calculation of diagnostic ratios from peak heights. We tested the method on chromatographic data from 20 replicate oil samples and found it to be less time-consuming and subjective than commercial software, and with comparable data quality.

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.