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C omputerized O il S pill I dentification

C O S I. C omputerized O il S pill I dentification. Contradicts : “ Cosi fan tutte ” (Mozart) (“They`re all like that“). C omputerized O il S pill I dentification. COSI. Basics. In forensic oil spill identification, the very complex chemical composition of oil is used

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C omputerized O il S pill I dentification

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  1. C O S I Computerized Oil SpillIdentification Contradicts : “Cosi fan tutte” (Mozart) (“They`re all like that“) June 2003 Gerhard Dahlmann

  2. Computerized Oil Spill Identification COSI Basics 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. Computerized Oil Spill Identification may highly support analysts in GC and GC/MS result evaluation. June 2003 Gerhard Dahlmann

  3. Computerized Oil Spill Identification COSI Basics, GC-screening 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

  4. Computerized Oil Spill Identification COSI Basics, GC/MS evaluation 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

  5. Computerized Oil Spill Identification COSI Basics A lot of oilfeatures have thus to be compared in oil spill identification. Computerized Oil Spill Identification has been developed to simplify matters: one can zoom into sections of chromatograms for a more detailed comparison, produce overlays, or even subtract chromatograms to pronounce differences. 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

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

  7. Switches for magnification may allow a more detailed comparison Comparison sample Computerized Oil Spill Identification But includes much greater features: Computerized Oil Spill Identification COSI Visualisation 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. Oil spill sample Comparisonsample June 2003 Gerhard Dahlmann

  8. Computerized Oil Spill Identification COSI Computer calculations • 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. • These parameters allow a • more objective • provable and • defensible result evaluation • than the mere visual comparison of the chromatograms. In addition, these parameters may also be used for finding oils in the database, which are similar to the spill sample. Example.... 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

  9. Automatic Oil Identification Clicking on sample No S141.239, Iranian light taken in 2001........... .......reveals Iranian oils. 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. (Even a sample taken in 1974 is shown among the first best matching samples.) June 2003 Gerhard Dahlmann

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

  11. 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

  12. Automatic Oil Identification Clicking on sample No S141.256, an Arabian crude oil taken in 2002........... .......reveals Arabian oils, as fast as these letters appeared (4-5 sec.). June 2003 Gerhard Dahlmann

  13. Automatic Oil Identification Clicking on sample No S141.187, a Nigerian crude oil taken in 2002........... .......reveals Nigerian oils. (A sample of „Bonny Light“ taken in 1975 is still recognized as "Nigerian crude oil”.) June 2003 Gerhard Dahlmann

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

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

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

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

  18. 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. case 984 Still only the 6 hopane-ratios have been used for correlation. Obviously, a more similar hopane-cluster is not found among the about 850 crude oil-, oil-product- and waste oil samples, which contained hopanes. Nevertheless, all parameters of two oil samples must be identical, when a match is concluded........ June 2003 Gerhard Dahlmann

  19. ......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. 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. case 984 June 2003 Gerhard Dahlmann

  20. case 958 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. June 2003 Gerhard Dahlmann

  21. case 930 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. June 2003 Gerhard Dahlmann

  22. Computerized Oil Spill Identification COSI FAQs (Frequently asked questions) Does a high correlation coefficient (e.g. 0.999 and above) not necessarily mean that two oil samples are identical? No The 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). CC = 0.9999 (Best Match) CC=0.9996 (2nd position) CC = 0.9992 (3rd position) Example (case 984) June 2003 Gerhard Dahlmann

  23. Computerized Oil Spill Identification 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: • 722500. June 2003 Gerhard Dahlmann

  24. Computerized Oil Spill Identification COSI Advantages not demonstrated here • 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 June 2003 Gerhard Dahlmann

  25. Computerized Oil Spill Identification Conclusions 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. 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 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. June 2003 Gerhard Dahlmann

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