Petroleum Hydrocarbon Fingerprinting - Numerical
Interpretation Developments
John W. Wigger, P.E. Environmental Liability Management, Inc., Tulsa, Oklahoma
Bruce E. Torkelson, Torkelson Geochemistry, Inc., Tulsa, Oklahoma
ABSTRACT
A feasibility study was conducted to assess the use of mathematical algorithms as aids for interpreting
hydrocarbon fingerprint data. The first algorithm developed applies a correlation routine to determine the
degree of similarity among different hydrocarbon samples. The second algorithm models the evaporation
portion of the weathering process in gasoline. Controlled evaporation experiments of different grades of
gasoline samples were used to create a matrix of numerical multipliers that describes individual component
volatilization. This matrix can be used to: 1) estimate the composition of gasoline after a release, and
inversely 2) estimate the original composition of a gasoline obtained subsequent to a release (for example,
from a monitoring well).
Algorithms like these will probably never fully replace the visual process that is now used to interpret
hydrocarbon fingerprints, however, they have the potential to add a more objective and quantitative
perspective to the process.
INTRODUCTION
Hydrocarbon fuels and derivative products discovered in soils and groundwater at environmental release
sites are often characterized by use of a laboratory technique known as capillary column gas
chromatography (also referred as hydrocarbon fingerprinting, gas chromatography or GC fingerprinting).
GC fingerprinting is an extremely useful tool in the investigation of subsurface contamination of soil and
groundwater. (Bruce and Schmidt) GC fingerprints are used to obtain information from liquid hydrocarbon
samples (free product) by determining the composition of the hydrocarbons present. The identification and
interpretation of GC fingerprints, however, is largely a qualitative practice and dependent upon the skill and
experience of the individuals(s) involved.
BACKGROUND
Petroleum Hydrocarbon Chemistry
Petroleum hydrocarbons consist of a very large number of compounds that, by definition, are found in crude
oil, as well as other sources of petroleum such as natural gas, coal, and peat. Petroleum hydrocarbons
consist of three major groups of compounds. These are alkanes (paraffins), alkenes (olefins), and aromatics.
Paraffins, are one of the major constituents of crude oil and are found in refined petroleum products such as
gasoline, kerosene, diesel fuel, heating oil, etc. There are three major classes of paraffins; these are linear
alkanes, branched alkanes, and naphthenes. The linear alkanes have carbon atoms arranged in a line and
there are only two ends to these molecules. Linear alkanes are also referred to in the literature as n-alkanes.
Branched alkanes have the carbon atoms arranged similar to the n-alkanes, however, some of the carbon
atoms are branched, thus creating many differing configurations. Naphthenes are molecules in which the
carbon atoms are arranged in one or more rings.
Olefins are formed during the refining process of creating petroleum products from crude oil. These
molecules have a double bond and two less hydrogen atoms than their corresponding alkane.
Aromatics contain one or more 6 carbon rings with 3 of the carbons containing double bonds. Examples of
1 ring (or mononuclear) aromatics are Benzene, Toluene, Ethylbenzene, and Xylene (BTEX). Multiple ring
aromatics (polynuclear) are aromatic compounds with multiple 6 carbon ring molecules. Examples of these
are naphthalene, anthracene, pyrene, and many more.
Hydrocarbon products such as gasoline, diesel fuel, and asphalts are all created from crude oil by a variety
of refining and distillation processes. Each product is produced by the combination of multiple individual
hydrocarbon compounds all of which have slightly different vaporization and boiling temperatures. For
example, gasoline is the combination of many lower boiling range compounds including C4 to C12 alkanes,
C4 to C7 alkenes, and aromatics BTEX. The middle boiling range compounds are used in differing
proportions to create products such as kerosene, diesel, and heating oil. These products predominantly
contain C10 to C24 alkanes, and polynuclear aromatics with little to no olefins. (Zemo, Graff, and Bruya)
Hydrocarbon Fingerprinting
GC fingerprints are created by injecting a small portion of the sample into a gas chromatograph. Once
injected, the product is heated and vaporized and carried into a capillary column by a flow of inert gas.
After injection the temperature of the column is slowly raised. As the temperature increases the compounds
begin to move through the column, in general the more volatile and lower boiling compounds start moving
first. A flame ionization detector connected to the end of the column detects the components of the product
as they elute from the column. The time that it takes for individual components to go through the column
depends on the temperature, length of column, column characteristics, and the character of the compound
itself.
Five GC fingerprints of various hydrocarbon products (gasoline, kerosene, diesel, JP-8 jet fuel and crude
oil) are presented in Figures 1 through 5. A few of the peaks have been labeled identifying some of the
compounds in each of the products.
Figure 1. Gasoline I (Regular Unleaded Gasoline)
Figure 2. Kerosene I
Figure 3. Diesel Fuel
Figure 4. JP-8 Jet Fuel
Figure 5. Crude Oil
MATERIALS & METHODS
Gas Chromatography
Hydrocarbon samples were analyzed on a Hewlett Packard 5890 instrument equipped with a split/splitless
injector, J&W 30 meter DB-1 column and an FID detector: All gas flow rates were set to manufacturer
specifications. Injections were made in split mode with a split ratio of 1:100. The column oven was
programmed from -10 o to 350 o C at 10 o C/minute with 4 minute hold at 350o C. The injector temperature is
set at 350o C and the detector temperature is 360o C. Data was acquired and processed with an EZChrom
Chromatography data system.
Gasoline Weathering Simulation
One of the weathering processes that can affect released gasoline is evaporation. To simulate evaporation
under controlled conditions, three different grades of gasoline were obtained from a local retailer and
allowed to evaporate to controlled volumes.
The gasoline components with the lowest boiling points tend to volatilize more rapidly than the components
with higher boiling points. On the GC fingerprint the components that have the shortest retention times (left
side of the GC fingerprint) are the most volatile and will tend to decrease in peak intensity preferentially as
more volatilization takes place. This is clearly illustrated in Figure 6, where GC fingerprints from the same
gasoline are shown under differing levels of volatilization.
Figure 6. Controlled Evaporation Of Regular Grade Of Unleaded Gasoline (Note: Chromatograms Have
Been Normalized To Make The Heights of Naphthalene Peaks Equal)
Evaporation Procedures
The evaporation procedure that was used is described below:

Samples of three grades of gasoline (87, 89, and 93 octane) were acquired at a local service
station.

Equal volumes of each grade of gasoline were placed in four (4) 40 milliliter vials, making a total
of 12 vials.

Three (3) vials of each sample were uncovered (total of 9) and allowed to volatilize at room
temperature.

The uncovered vials were closely monitored and capped when the gasoline was reduced to the
desired volume resulting in one vial each at 75%, 50%, and 25% of original volume for each of the
three grades of gasoline.
Algorithms Described
Correlation Coefficient
The correlation coefficient, denoted by , measures the relationship between two data sets that are scaled
to be independent of the unit of measure and is given by the formula:
Where
and
are values in each corresponding data set.
The value of the correlation coefficient is always between -1 and +1. A value of equal to -1 indicates a
perfect linear relationship between the sample values of x and y, with the value of y decreasing as the value
of x increases. A value of equal to +1 also indicates a perfect linear relationship between the sample
values, but one in which the value of y increases as x increases. Larger values of y are associated with larger
values of x; and smaller values of y are associated with smaller values of x. If there is no linear relationship
between the sample values of x and y, then will have a value near or equal to zero (Hayslett).
The correlation coefficient determines whether two data sets move together; that is, whether large values of
one set are associated with large values of the other (positive correlation), whether small values of one set
are associated with large values of the other (negative correlation), or whether the values in the two sets are
unrelated.
In this study, 71 hydrocarbon chromatogram peaks, each representing a different hydrocarbon compound,
were used in the analysis. Table 1, presents a list of the compounds. Integrated peak areas were measured
and then tabulated for each of the five hydrocarbon samples in figures 1 through 5. The integrated peak is a
measure of the intensity of the response of the flame ionization detector to each of the individual
compounds measured in millivolt seconds. The library samples includes gasoline, kerosene, diesel, JP-8 jet
fuel, and crude oil. Once the peak area data were collected and tabulated for these hydrocarbon samples,
three additional hydrocarbon samples were also collected. The first sample collected had been identified as
a kerosene from the provider, however, the GC fingerprint clearly illustrated a much broader range of
hydrocarbons than the kerosene run earlier. The second sample was a laboratory standard mixture of
gasoline and diesel fuel. The third sample was a regular grade of unleaded gasoline from a different service
station. Figures 7, 8, and 9 present the GC fingerprints of these three respective samples.
Table 1. 71 Hydrocarbon Compounds Used For Analysis
1
iC4 = Isobutane
37
IP14 = C14 Isoprenoid
2
nC4 = Butane
38
1 M naph = 1 Methylnaphthalene
3
iC5 = Isopentane
39
nC13 = Tridecane
4
nC5 = Pentane
40
IP15 = Farnesane
5
2 M Pent = 2 Methylpentane
41
nC14 = Tetradecane
6
3 M Pent = 3 Methylpentane
42
IP16 = C16 Isoprenoid
7
nC6 = Hexane
43
nC15 Pentadecane
8
C6 Olefin = C6 Olefin
44
nC16 = Hexadecane
9
M Cycl Pent = Methyl cyclopentane
45
IP18 = Norpristane
10
2,4 DMP = 2,4 Dimethylpentane
46
nC17 = Heptadecane
11
Bnz = Benzene
47
Pristane = Pristane
12
Cyclo Hexane = Cyclohexane
48
nC18 = Octadecane
13
2 M Hex = 2 Methylhexane
49
Phytane = Phytane
14
3 M Hex = 3 Methylhexane
50
nC19 = Nonadecane
15
Isooctane = Isooctane or 2,2,4 Trimethylpentane
51
nC20 = Eicosane
16
nC7 = Heptane
52
nC21 = Heneicosane
17
MCHX = Methylcyclohexane
53
nC22 = Docosane
18
Tol = Toluene
54
nC23 = Tricosane
19
nC8 = Octane
55
nC24 = Tetracosane
20
EB = Ethylbenzene
56
nC25 = Pentacosane
21
m/p-xyl = m/p Xylene
57
nC26 = Hexacosane
22
o-xyl = o Xylene
58
nC27 = Heptacosane
23
nC9 = Nonane
59
nC28 = Octacosane
24
propylbenzene = n Propylbenzene
60
nC29 = Nonacosane
25
1M 3E benz = 1 Methyl 3 ethylbenzene
61
nC30 = Triacontane
26
1M 4E benz = 1 Methyl 3 ethylbenzene
62
nC31 = Hentriacontane
27
1,3,5 T M Benz = 1,3,5 Trimethylbenzene
63
nC32 = Dotriacontane
28
3,3,4 T M Hept = 3,3,4 Trimethylheptane
64
nC33 = Tritriacontane
29
1,2,4 T M Benz = 1,2,4 Trimethylbenzene
65
nC34 = Tetratriacontane
30
nC10 = Decane
66
nC35 = Pentatriacontane
31
1,2,3 T M Benz = 1,2,3 Trimethylbenzene
67
nC36 = Hexatriacontane
32
nC11 / 1,2,4,5 TeMB = Undecane and 1,2,4,5
Tetramethlybenzene
33
Naph = Naphthalene
68
69
nC37 = Heptatriacontane
nC38 = Octatriacontane
34
nC12 = Dodecane
70
nC39 = Nonatriacontane
35
IP13 = C13 Isoprenoid
71
nC40 = Tetracontane
36
2 M naph = 2 Methylnaphthalene
Figure 7. Kerosene II
Figure 8. Gasoline Diesel Laboratory Mixture
Figure 9. Gasoline II (Regular Unleaded Gasoline)
Once the peak areas for all of the 71 individual components were established for each of the samples, the
correlation coefficient was calculated between the samples presented in Figures 1 through 5, and those
presented in Figures 7, 8, and 9. Table 2 presents the results of these calculations.
Table 2. Results of Correlation Coefficient Determinations
Gasoline I
Kerosene I
Diesel
JP-8 Jet Fuel
Crude Oil
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 7
-0.156
0.732
0.882
0.932
0.379
Gas/Diesel
Mixture
0.638
0.333
0.528
0.505
0.440
0.894
-0.112
-0.213
-0.065
0.134
Kerosene II
Figure 8
Gasoline II
Figure 9
To evaluate the reproducibility of this process, the regular unleaded gasoline presented in Figure 9, was run
on the GC five separate times, thus creating five GC fingerprints and five slightly differing numerical data
sets. The data collected for all 71 compounds were then compared to each other, thus creating a total of
twenty (20) correlation coefficient comparisons.
Gasoline Weathering
An algorithm was developed to model the volatilization process of gasoline released into the environment.
This was accomplished by using experimental data obtained from the controlled evaporation of the three
different grades of gasoline described earlier. GC fingerprint data were used to create a numerical function
that describes the volatilization process. This numerical function can then be applied to fresh gasoline
samples to predict what the product GC fingerprint would look like if weathered in an environmental
release.
As discussed earlier, the gasoline components with the lowest boiling points tend to volatilize more rapidly
than the rest of the components. The components with the highest boiling points (components at the right
hand side of the GC fingerprints with retention times > 10 minutes) experience little volatilization under the
weathering conditions described above.
With the above in mind, it was assumed that the actual volume of the naphthalene stayed relatively constant
during the weathering simulation and can be used similar to an internal standard. By utilizing this, the GC
data from each stage of the weathering process were normalized to the naphthalene peak. Once each GC
fingerprint was normalized to naphthalene, each component was then evaluated as the total volume of
product decreased. Once this process was completed for all components, then a matrix of volatilization
multipliers was created. This matrix consists of a table of factors ranging from 0.0 to 1.0 describing the
amount of volatilization of each of the 71 components at differing stages of evaporation of the total product.
To demonstrate how the matrix was created, Table 3 presents the integrated peak areas for the first 8 of the
71 components from the premium grade unleaded gasoline used in the experiment. Table 4, presents the
same component integrated peak areas after they have been normalized to naphthalene. And Table 5,
presents each component integrated peak area from table 4 normalized from 0 to 1. Table 5 represents the
matrix of multipliers. Two other similar tables were also produced for the mid-grade and regular grades of
gasoline. The entire matrix of multipliers for each of the grades of gasoline was not presented because of
the size of the tables.
Table 3. Peak Areas For Components At Differing % Volatilization
(Premium Grade Gasoline)
Sample Id
iC4
nC4
iC5
nC5
2 M Pent
3 M Pent
nC6
C6 Olefin
Gasoline
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak Area
Prem-0
5139
53846
129407
21998
34183
18112
14922
2865
Prem-25
0
737
23654
6196
20421
11421
10368
1940
Prem-50
0
0
0
0
1666
2305
2894
547
Prem-75
0
0
0
0
0
0
0
0
% Vol.
Table 4. Peak Areas For Components At Differing % Volatilization After Normalizing With Naphthalene (Premium
Grade Gasoline)
Sample Id
iC4
nC4
iC5
nC5
2 M Pent
3 M Pent
nC6
C6 Olefin
Gasoline
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak Area
Prem-0
5139
53846
129407
21998
34183
18112
14922
2865
Prem-25
0
531
17047
4465
14717
8231
7472
1398
Prem-50
0
0
0
0
1165
1612
2024
383
Prem-75
0
0
0
0
0
0
0
0
% Vol.
Table 5. Matrix Of Multipliers For Individual Components Of Gasoline Under Differing Percentages Of Overall
Product Volatilization (Premium Grade Gasoline)
Sample Id
iC4
nC4
iC5
nC5
2 M Pent
3 M Pent
nC6
C6 Olefin
Gasoline
% Vol.
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak
Area
Peak Area
Prem-0
1
1
1
1
1
1
1
1
Prem-25
0
0.01
0.132
0.203
0.4305
0.4544
0.501
0.488
Prem-50
0
0
0
0
0.0341
0.089
0.136
0.1336
Prem-75
0
0
0
0
0
0
0
0
RESULTS & DISCUSSION
Reproducibility
The reproducibility of the gas chromatography analysis technique was evaluated by analyzing the gasoline
sample presented in Figure 9 a total of 5 times. The 20 correlation coefficients calculated between each of
the 5 analyses and the other four had a minimum of 0.99545, a maximum of 0.999989, an average of
0.997921 and a standard deviation of 0.001704. From this evaluation, truly alike GC chromatograms will
probably have correlation coefficients of 0.99 or better. It is possible that other product types may have
different reproducibilities since their data may include different peaks that come from a different part of the
GC fingerprint.
Correlation
Table 2 presents the results of the correlation coefficients calculated when comparing the samples in
Figures 1 through 5 to those in Figures 7 through 9. Prior to calculating the correlation coefficients it was
expected that similar products, for example gasolines, would show higher correlations among themselves
and less correlation when compared to other product types. The exact numbers, however, could not be
anticipated nor how the correlation coefficients would vary between similar and different product types.
Significant, logical, and reproducible differences and similarities in the correlation coefficient numbers are
crucial for this process to be a useful tool. The correlation coefficient results must also make sense and
compare favorably with visual inspection of the GC fingerprints. From this feasibility study, it appears that
there are meaningful similarities and differences in correlation coefficient numbers calculated using GC
fingerprint data. This study suggest that similar product types such as gasolines could be expected to have
correlation coefficients of about 0.9 or better. Dissimilar product types have a much lower correlation
coefficient of perhaps 0.6 or 0.5 or even less. The correlation coefficients shown here also make sense when
compared to the visual evaluation of the GC fingerprints.
An unexpected and interesting result of the correlations was that the JP-8 jet fuel and the kerosene II sample
had a high correlation coefficient. At first this seemed unusual, but it must be remembered that JP-8 and
kerosene are often times from the same distillation range of the crude oil. Visual comparison of the two GC
chromatograms confirms the rather high degree of similarity of the two products.
Gasoline Weathering
The matrix of multipliers created for the evaporation sequences for the three different grades of gasoline
numerically models how the volatile components tend to evaporate from the sample. The matrix of
multipliers can be used to numerically alter ("evaporate") the data from a fresh sample in an attempt to
estimate the composition of the sample after a weathering process. Once the sample has been artificially
altered, it can then be numerically compared to other controlled weathered samples.
This weathering algorithm can also be used in the inverse. For example, if one had a hydrocarbon sample
from a site but did not know the extent of weathering that has already taken place, the sample could be
correlated with the library of samples of known weathered gasolines. Once a library sample with the highest
correlation has been determined, a matrix of multipliers of the sample with the highest correlation could be
used to reconstruct the composition of the original gasoline sample when fresh. This matrix of multipliers
needed to estimate the original gasoline composition would be constructed by simply using the inverse of
the individual compounds within the matrix. that most closely correlated with the weathered sample. (For
example, if Benzene's multiplier is 0.25, then to reconstruct the original amount of Benzene, one would
multiply the peak area by 1/0.25 = 4.0.)
Future Work
The information and techniques presented in this paper represent some beginning examples of the types of
analysis that are possible with GC fingerprint numerical data. Listed below are a number of additional
numerical techniques and experiments that are under consideration for future work:
1.
Investigate algorithms that numerically weight key compounds,
2.
Develop algorithms that use the presence of unique compounds to indicate specific characteristics.
For example olefins indicate the presence of catalytic cracked hydrocarbons.
3.
Develop algorithms that further refine gasoline compound recognition,
4.
Investigate additional controlled weathering experiments on other types of hydrocarbon products,
5.
Investigate other weathering processes such as water washing, biodegradation, volatilization, and
chemical speciation,
6.
Expand the GC fingerprint library to include other petroleum products and hydrocarbon samples
from the extraction of both soils and groundwater,
7.
Investigate correlation experiments using isolated ranges of hydrocarbons; therefore, allowing
mixtures of products (say gasoline and diesel fuel) to be evaluated separately and then numerically
added together.