The use of diagnostic ratios, biomarkers and 3-way Kohonen neural networks to monitor the temporal evolution of oil spills.

Oil spill identification relies usually on a wealth of chromatographic data which requires advanced data treatment (chemometrics). A simple approach based on Kohonen neural networks to handle three-dimensional arrays is presented. A suite of 28 diagnostic ratios was considered to monitor six oils along four months. It was found that some traditional diagnostic ratios were not stable enough. In particular, alkylated PAHs (e.g. 1-methyldibenzothiophene, 4-methylpyrene, 27bbSTER and the TA21 and TA26 triaromatic steroids) seemed less resistant to medium-weathering than biomarkers. One (or two) ratios were found to differentiate each product: 30O, 28ab (and 25nor30ab), C3-dbt/C3-phe, 27Ts, TA26 and 29Ts characterized Ashtart, Brent, Maya, Sahara, IFO and Prestige oils, respectively.

An untargeted gas chromatography mass spectrometry metabolomics platform for marine polychaetes.

The development of an appropriate extraction method for untargeted environmental metabolomic analysis of marine polychaetes could promote their use for environmental monitoring purposes. To this end, we compared four extraction methods on the marine polychaete Nereis virens both exposed to crude oil and non-exposed. XCMS was used for feature detection and preprocessing; different normalization and scaling approaches were tested; and principal component analysis (PCA) was used together with basic statistical tests to ascertain common metabolic patterns and determine the most suitable extraction method. We conclude that a two-step extraction procedure with 80:20 (v/v) methanol:water on freeze dried polychaete tissue provides the best trade-off between analysis time, and extraction efficiency and intermediate reproducibility. No definitive conclusions could be drawn about the ability of the method to discriminate controls and crude oils in actual biological replicates because the experiment was carried out by design on analytical replicates only. We show that the normalization to the sum of all the common features, and the use of a weighted least squares criterion to fit the PCA by means of scaling to the median absolute deviation (MAD) of the pooled quality control samples significantly improved the clustering of controls and crude oil exposed samples. The scaling alone led to an increase of 19% in explained variance compared to ordinary PCA.

Objective chemical fingerprinting of oil spills by partial least-squares discriminant analysis.

An objective method based on partial least-squares discriminant analysis (PLS-DA) was used to assign an oil lump collected on the coastline to a suspected source. The approach is an add-on to current US and European oil fingerprinting standard procedures that are based on lengthy and rather subjective visual comparison of chromatograms. The procedure required an initial variable selection step using the selectivity ratio index (SRI) followed by a PLS-DA model. From the model, a "matching decision diagram" was established that yielded the four possible decisions that may arise from standard procedures (i.e., match, non-match, probable match, and inconclusive). The decision diagram included two limits, one derived from the Q-residuals of the samples of the target class and the other derived from the predicted y of the PLS model. The method was used classify 45 oil lumps collected on the Galician coast after the Prestige wreckage. The results compared satisfactorily with those from the standard methods.

Selecting a reduced suite of diagnostic ratios calculated between petroleum biomarkers and polycyclic aromatic hydrocarbons to characterize a set of crude oils.

A set of 34 crude oils was analysed by GC-MS (SIM mode) and a suite of 28 diagnostic ratios (DR) calculated. They involved 18 ratios between biomarker molecules (hopanes, steranes, diasteranes and triaromatic steroids) and 10 quotients between polycyclic aromatic hydrocarbons. Three unsupervised pattern recognition techniques (i.e., principal components analysis, heatmap hierarchical cluster analysis and Kohonen neural networks) were employed to evaluate the final dataset and, thus, ascertain whether the crude oils grouped as a function of their geographical origin. In addition, an objective variable selection procedure based on Procrustes Rotation was undertaken to select a reduced set of DR that comprised for most of the information in the original data without loosing relevant information. A reduced set of four DR (namely; TA21, D2/P2, D3/P3 and B(a)F/4-Mpy) demonstrated to be sufficient to characterize the crude oils and the groups they formed.

Comparing the weathering patterns of six oils using 3-way generalized Procrustes rotation and matrix-augmentation principal components.

A case study is presented in which advanced chemical fingerprinting and data interpretation techniques are used to characterize and compare the weathering processes of six oil spillages made under controlled conditions (including the heavy oil released after the Prestige tanker sunk off the Galician coast-NW Spain on 2002). A tiered analytical approach using gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) was applied along with two different approaches for 3-way analyses; namely, generalized Procrustes rotation, and matrix-augmented principal components analysis. Results showed that the two 3-way chemometric techniques leaded to essentially the same conclusions when analyzing three sets of compounds fingerprinting the spilled hydrocarbons (aliphatic hydrocarbons, polycyclic aromatic hydrocarbons (PAHs) and a set of diagnostic ratios). A steady evolution on the weathering of the oils was observed with both techniques, but for the diagnostic ratios. The variables involved on the weathering were the lightest aliphatic hydrocarbons and a general combination of the PAHs, which differentiated mostly among the light and the heavy products (fuel oils).

Self organizing maps for analysis of polycyclic aromatic hydrocarbons 3-way data from spilled oils.

In this paper, the application of a new method based on self-organizing maps (SOM; termed MOLMAP, molecular map of atom-level properties) to handle 3-way data generated in a monitoring environmental study is presented. The study comprised 50 polycyclic aromatic hydrocarbons (PAHs) analyzed in samples derived from the weathering of six oil products (four crude oils and two fuel oils) spilled under controlled conditions for about 4 months. MOLMAP yielded useful information on each mode of the data cube: weathering samples, spilled oil products, and PAHs. Thus, the different behaviors of the six oils were ascertained, along with their particular evolution on time, and their weathering patterns were studied in terms of the original PAHs. Thus, the two heaviest products (two fuel oils) were characterized by two neurons whose more relevant weights were associated to heavy PAHs, as C(1)-fluoranthene, C(2)-fluoranthene, benzo(ghi)perylene, and dibenz(ah)anthracene. The six spilled products were projected on different regions on both the MOLMAP-SOM and a subsequent principal components analysis (PCA) scatter plot, developed using the so-called MOLMAP-scores. Besides, it was possible to further differentiate between unweathered, or slightly weathered, samples and the most weathered ones. The more relevant PAHs characterizing those samples were assessed studying the weights of the neurons in which the samples got projected.

Identification of petroleum hydrocarbons using a reduced number of PAHs selected by Procrustes rotation.

Identifying petroleum-related products released into the environment is a complex and difficult task. To achieve this, polycyclic aromatic hydrocarbons (PAHs) are of outstanding importance nowadays. Despite traditional quantitative fingerprinting uses straightforward univariate statistical analyses to differentiate among oils and to assess their sources, a multivariate strategy based on Procrustes rotation (PR) was applied in this paper. The aim of PR is to select a reduced subset of PAHs still capable of performing a satisfactory identification of petroleum-related hydrocarbons. PR selected two subsets of three (C(2)-naphthalene, C(2)-dibenzothiophene and C(2)-phenanthrene) and five (C(1)-decahidronaphthalene, naphthalene, C(2)-phenanthrene, C(3)-phenanthrene and C(2)-fluoranthene) PAHs for each of the two datasets studied here. The classification abilities of each subset of PAHs were tested using principal components analysis, hierarchical cluster analysis and Kohonen neural networks and it was demonstrated that they unraveled the same patterns as the overall set of PAHs.

The comparison of two heavy fuel oils in composition and weathering pattern, based on IR, GC-FID and GC-MS analyses: application to the Prestige wreackage.

This paper compares the weathering patterns of two similar fuel oils: a fuel oil spilled after a ship accident (Prestige-Nassau, off the Galician coast -NW Spain-) and a fuel designed to cope with the numerous quests for samples to carry out scientific studies (IFO). Comparative studies were made to evaluate the capability of common fingerprinting analytical techniques to differentiate the fuels, as well as their capabilities to monitor their weathering. The two products were spilled under controlled conditions during ca. four months to assess how they evolved on time. Mid-IR spectrometry and gas chromatography (flame ionization and mass spectrometry detectors) were used. IR indexes related to total aromaticity, type of substituents (branched or linear chains) and degree of aromatic substitution reflected well the differences between the fuels during weathering. Regarding the chromatographic measurements, the n-alkanes became highly reduced for both fuel oils and it was found that the PAHs of the synthetic fuel (IFO) were more resistant to weathering. Regarding biomarkers, the different profiles of the steranes, diasteranes and triaromatic steroids allowed for a simple differentiation amongst the two products. The %D2/P2 ratio differentiated both products whereas the %N3/P2 one ordered the samples according to the extent of their weathering.

Identification of fuel samples from the Prestige wreckage by pattern recognition methods.

A set of 34 worldwide crude oils, 12 distilled products (kerosene, gas oils, and fuel oils) and 45 oil samples taken from several Galician beaches (NW Spain) after the wreckage of the Prestige tanker off the Galician coast was studied. Gas chromatography with flame ionization detection was combined with chemometric multivariate pattern recognition methods (principal components analysis, cluster analysis and Kohonen neural networks) to differentiate and characterize the Prestige fuel oil. All multivariate studies differentiated between several groups of crude oils, fuel oils, distilled products, and samples belonging to the Prestige's wreck and samples from other illegal discharges. In addition, a reduced set of 13 n-alkanes out of 36, were statistically selected by Procrustes Rotation to cope with the main patterns in the datasets. These variables retained the most important characteristics of the data set and lead to a fast and cheap analytical screening methodology.

Development of a fast analytical tool to identify oil spillages employing infrared spectral indexes and pattern recognition techniques.

A fast analytical tool based on attenuated total reflectance mid-IR spectrometry is presented to evaluate the origin of spilled hydrocarbons and to monitor their fate on the environment. Ten spectral band ratios are employed in univariate and multivariate studies (principal components analysis, cluster analysis, density functions - potential curves - and Kohonen self organizing maps). Two indexes monitor typical photooxidation processes, five are related to aromatic characteristics and three study aliphatic and branched chains. The case study considered here comprises 45 samples taken on beaches (from 2002 to 2005) after the Prestige carrier accident off the Galician coast and 104 samples corresponding to weathering studies deployed for the Prestige's fuel, four typical crude oils and a fuel oil. The univariate studies yield insightful views on the gross chemical evolution whereas the multivariate studies allow for simple and straightforward elucidations on whether the unknown samples match the Prestige's fuel. Besides, a good differentiation on the weathering patterns of light and heavy products is obtained.

Monitoring photooxidation of the Prestige's oil spill by attenuated total reflectance infrared spectroscopy.

The recent release of ca. 70,000 tonnes of a heavy fuel oil from the Prestige-Nassau carrier along the Spanish northern coast, mainly along Galicia, was monitored using attenuated total reflectance-mid IR spectrometry. The fuel was characterized and differentiated from 10 products commonly transported along the Galician coast (and their series of weathered samples) using factor analysis. The Prestige's fuel was weathered under natural conditions and under infrared radiation to study its evolution on time. A correlation was established using the 1690-1700 cm(-1) carbonyl peak, where from it was deduced that IR radiation weathered the product two times faster than natural conditions. The use of 10 weathering indexes was carried out to confirm the main patterns given by factor analysis and to seek out which main functional groups and structures increased or decreased during weathering. It was found that the carbonyl and sulphoxide indexes varied greatly, as well as the total aromaticity and long chains ones. The substitution-related indexes pointed out that highly substituted aromatic structures increased although the total amount of isolated CH groups in aromatic structures reached a plateau.

Screening the origin and weathering of oil slicks by attenuated total reflectance mid-IR spectrometry.

The combination of attenuated total reflectance-fourier transform mid-infrared spectrometry (ATR-FTMIR) and multivariate pattern recognition is presented as a fast and convenient methodology to ascertain the source product an oil slick comes from and to evaluate the extent of its weathering. Different types of hydrocarbons (including crude oils, several heavy distillates and the Prestige's heavy fuel oil) were spilled on metallic containers designed ad hoc and their fate monitored by ATR-FTMIR. Not only environmental conditions were considered for weathering but artificial IR- and UV-irradiation. Pattern-recognition studies revealed that the different hydrocarbons clustered at different locations on the score plots and that the samples corresponding to each oil became ordered according to the extent of their weathering. Among them, fuel oil samples coming from the recent disaster of the Prestige tanker off the Galician shoreline showed a distinctive behaviour. Comparison of natural-, IR- and UV-weathering of a crude oil showed that IR solar radiation can be important in oil-weathering, in addition to broadly-reported UV degradation.