june 2003 gerhard dahlmann computerized oil spill identification contradicts : “cosi fan tutte”...

June 2003 Gerhard Dahlmann

Computerized Oil SpillIdentification

Contradicts :“Cosi fan tutte”

(Mozart)(“They`re all like

that“)


COSI

Basics

ComputerizedOil 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 SpillIdentification


COSI

Basics, GC-screening


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.


COSI

Basics, GC/MS evaluation


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.


COSIComputerized

Oil SpillIdentification

one can zoom into sections of chromatograms fora more detailed comparison, produce overlays, or

even subtract chromatograms to pronouncedifferences.


Identification

Basics

A lot of oilfeatures have thus to be compared inoil 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.


COSI

Huge database of oil analyses at hand,continuously growing


Identification

Recognition ofoil-types

Pick up of rawGC/GC-MS-data

via network

Flexible, automatic parameter calculation

Automaticpeak detection

Scheme


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Identification


COSIGas-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 magnificationmay allow a more detailed

comparisonComparisonsample

Comparison sample

Oil spill sample

Visualisation


Identification


IdentificationBut

includes much greaterfeatures:


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 usedfor 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


Identification

Parameters of a spill sample and a comparison sampleand their percentage difference (below analytical error

marked green, above marked red).


Clicking on sample No S141.239,Iranian light

taken in 2001...........

.......reveals Iranian oils.

(Even a sample taken in 1974is 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.

AutomaticOil Identification


Switches for choosingparameters

to be used in the correlation.

Description of thecomparison form


Description of thecomparison form

Calculation of the correlation matrixby using

•all,•preselected or

•selected parameters on

•all samples or on

•sample selections


Clicking on sample No S141.256,an Arabian crude oiltaken in 2002...........

.......reveals Arabian oils,

as fast as these letters appeared (4-5 sec.).



Clicking on sample No S141.187,a Nigerian crude oiltaken in 2002...........

.......reveals Nigerian oils.

(A sample of „Bonny Light“taken in 1975

is still recognized as "Nigerian crude oil”.)



Automatic detection of GC-peaks and parameter calculation

Boiling ranges are calculated for product characterization (see following slides)


Automatic detection of GC/MS-peaks and parameter calculation


Similar oils of known types are found

Clicking on a sample.... ....reveals a proposal for its type


COSI

One detail


Identification

The main boiling range is determined and two pointers test the chromatographic structure.


Clicking on the spill sample 2 of case No 984,where samples from an oil spill had to be

compared with several samples from asuspected ship,........

....shows one of the comparison samples of case 984“on top“ of all other samples.

Obviously, a more similar hopane-cluster is notfound among the about 850 crude oil-, oil-product- and

waste oil samples, which contained hopanes.

Still only the 6 hopane-ratios have been usedfor correlation.

case984

Nevertheless, all parameters of two oil samples mustbe identical, when a match is concluded........


......Although the hopane-cluster in case 984shows “uniqueness“, significant differences, i.e. those above the analytical error (marked red),

have to be “explained“,when a “match“ is concluded.

case984

Obviously there is a lower amount of bunker oil in the spill sample than in thecomparison 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.


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.

case958


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.

case930


COSI FAQs (Frequently asked questions)

Does a high correlation coefficient (e.g. 0.999 and above) not necessarily mean that two oil samplesare identical?

No The absolute value of the CC of course also depends on the number of variables usedin 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 = 0.9999 (Best Match) CC=0.9996 (2nd position) CC = 0.9992 (3rd position)


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-parametersfirst -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.


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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


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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 simultaneouslya much stronger connection between a distinct oil spill and its actual

source may be established than before.


Identification


Identification

june 2003 gerhard dahlmann computerized oil spill identification contradicts : “cosi fan tutte”...

Documents

oil sample comparison

unknown oil sample

measured oil samples

oil pollution sources

comparison sample

sample screening

gcms result evaluation

gaschromatography gc