June 2003 Gerhard Dahlmann Computerized Oil Spill Identification Contradicts : “Cosi fan tutte” (Mozart) (“They`re all like that“)

June 2003 Gerhard Dahlmann COSI Basics Computerized Oil Spill Identification In forensic oil spill identification, the very complex chemical composition of oil is used for finding oil pollution sources. Generally, two common analytical methods are used for comparing oil samples: Gaschromatography (GC) for sample screening and Gaschromatography-Mass spectrometry coupling (GC/MS) for detailed investigations of the compound classes. may highly support analysts in GC and GC/MS result evaluation. Computerized Oil Spill Identification

June 2003 Gerhard Dahlmann COSI Basics, GC-screening Computerized Oil Spill Identification Generally, gaschromatograms of two oil samples are compared by comparing the shapes of the envelops of the n-alkanes, the unresolved backgrounds and individual peak intensities.

June 2003 Gerhard Dahlmann COSI Basics, GC/MS evaluation Computerized Oil Spill Identification By means of GC/MS, a big number of compound classes of oils may be separately detected and compared. The patterns of the “Biomarkers”, for example, such as the hopanes and the steranes below, have shown to be especially suitable for oil sample comparison.

June 2003 Gerhard Dahlmann COSI Computerized Oil Spill Identification one can zoom into sections of chromatograms for a more detailed comparison, produce overlays, or even subtract chromatograms to pronounce differences. Computerized Oil Spill Identification Basics A lot of oilfeatures have thus to be compared in oil spill identification. has been developed to simplify matters: But one of its main advantages is that all analytical results of all measured oil samples are centrally available on a single personal computer.

June 2003 Gerhard Dahlmann COSI Huge database of oil analyses at hand, continuously growing Computerized Oil Spill Identification Recognition of oil-types Pick up of raw GC/GC-MS-data via network Flexible, automatic parameter calculation Automatic peak detection Scheme Computerized Oil Spill Identification Computerized Oil Spill Identification

June 2003 Gerhard Dahlmann COSI Gas-chromatograms and mass-fragmentograms are rapidly produced from raw GC- and GC/MS-data for comparing an unknown oil sample with ANY oil sample stored in the database. Switches for magnification may allow a more detailed comparison Comparisonsample Oil spill sample Visualisation Computerized Oil Spill Identification Computerized Oil Spill Identification But includes much greater features:

June 2003 Gerhard Dahlmann COSI As soon as the raw data of an oil sample are picked up, chromatographic peaks are automatically detected and parameters (peak ratios) are calculated and stored. In addition, these parameters may also be used for finding oils in the database, which are similar to the spill sample. Example.... These parameters allow a more objective provable and defensible result evaluation than the mere visual comparison of the chromatograms. Computer calculations Computerized Oil Spill Identification Parameters of a spill sample and a comparison sample and their percentage difference (below analytical error marked green, above marked red).

June 2003 Gerhard Dahlmann Clicking on sample No S , Iranian light taken in reveals Iranian oils. (Even a sample taken in 1974 is shown among the first best matching samples.) Only 6 compound ratios out of the cluster of the hopanes have been used here for the classification of 260 crude oils from all over the world. Automatic Oil Identification

June 2003 Gerhard Dahlmann Switches for choosing parameters to be used in the correlation. Description of the comparison form

June 2003 Gerhard Dahlmann Description of the comparison form Calculation of the correlation matrix by using all, preselected or selected parameters on all samples or on sample selections

June 2003 Gerhard Dahlmann Clicking on sample No S , an Arabian crude oil taken in reveals Arabian oils, as fast as these letters appeared (4-5 sec.). Automatic Oil Identification

June 2003 Gerhard Dahlmann Clicking on sample No S , a Nigerian crude oil taken in reveals Nigerian oils. (A sample of „Bonny Light“ taken in 1975 is still recognized as "Nigerian crude oil”.) Automatic Oil Identification

June 2003 Gerhard Dahlmann Automatic detection of GC-peaks and parameter calculation Boiling ranges are calculated for product characterization (see following slides)

June 2003 Gerhard Dahlmann Automatic detection of GC/MS-peaks and parameter calculation

June 2003 Gerhard Dahlmann Similar oils of known types are found Clicking on a sample reveals a proposal for its type

June 2003 Gerhard Dahlmann COSI One detail Computerized Oil Spill Identification The main boiling range is determined and two pointers test the chromatographic structure.

June 2003 Gerhard Dahlmann Clicking on the spill sample 2 of case No 984, where samples from an oil spill had to be compared with several samples from a suspected ship, shows one of the comparison samples of case 984 “on top“ of all other samples. Obviously, a more similar hopane-cluster is not found among the about 850 crude oil-, oil-product- and waste oil samples, which contained hopanes. Still only the 6 hopane-ratios have been used for correlation. case 984 Nevertheless, all parameters of two oil samples must be identical, when a match is concluded

June 2003 Gerhard Dahlmann......Although the hopane-cluster in case 984 shows “uniqueness“, significant differences, i.e. those above the analytical error (marked red), have to be “explained“, when a “match“ is concluded. case 984 Obviously there is a lower amount of bunker oil in the spill sample than in the comparison sample caused by weathering and/or inhomogeneous distribution of oil. Especially compound clusters in the overlapping region of the bunker oil - lubricating oil mixture might thus show differences.

June 2003 Gerhard Dahlmann Rarely anything is left from the bunker oil in the spill sample of case 958. But “with about 850 different oils behind”, and only two possible polluters, the results stringently point to one of the suspected ships. case 958

June 2003 Gerhard Dahlmann The spill sample of case 930 is also severely effected by evaporation. Again the corresponding comparison sample is found “on top” of all other samples. case 930

June 2003 Gerhard Dahlmann COSI FAQs (Frequently asked questions) Does a high correlation coefficient (e.g and above) not necessarily mean that two oil samples are identical? NoThe absolute value of the CC of course also depends on the number of variables used in correlation. Higher values may appear when the number of variables is small, and smaller differences of the CC may become more important. When the correlation coefficient is high, the difference between single variables may still be significant (exceed the analytical error). Example (case 984) CC = (Best Match) CC= (2nd position) CC = (3rd position) Computerized Oil Spill Identification

June 2003 Gerhard Dahlmann COSI FAQs (Frequently asked questions) Why are not all variables used from the beginning for correlation? Some of the parameters are susceptible to weathering effects. Inhomogeneous distributions especially of product-mixtures (waste oil) may avoid finding right answers. Experience has shown that it is always a good idea to look at the hopane-parameters first -if hopanes are present. What time does it take to built the correlation-matrix? This time depends on the number of samples number of variables hardware used Examples (P4, 2,6 GHz): 850 samples, 6 variables : about 40 seconds 850 samples, 29 variables: about 120 seconds number of entries into the CC-matrix in these cases: Computerized Oil Spill Identification

June 2003 Gerhard Dahlmann COSI Continuous information about the stages of analyses Easy reporting Recognition and avoiding of errors Flexible parameter setting Checking of parameter diversity Stringent (automatic) Quality Management Advantages not demonstrated here Computerized Oil Spill Identification

June 2003 Gerhard Dahlmann Computerized Oil Spill Identification adds a new dimension to forensic Oil Spill Identification: The system is fast and greatly saves laboratory resources, reliable and comfortable. Conclusions greatly increases the possibilities for finding the sources of oil pollution. Those responsible for producing/transporting and using oil at sea should be aware of this. But not only new media are provided for comparing oil samples. By comparing an oil sample with many hundred oils simultaneously a much stronger connection between a distinct oil spill and its actual source may be established than before. Computerized Oil Spill Identification Computerized Oil Spill Identification