Studying and Verifying the Use of Chemical

Biomarkers for Identifying and Quantitating

Oil Residues in the Environment

U.S. Deoartment of the Interior

Minerals )Aanagement Service Gulf of Mexico OCS Region

30

Cooperative Agreement Coastal Marine Institute

Louisiana State University

OCS Study

MMS 2000-086

Coastal Marine Institute
Coastal Marine Institute

Studying and Verifying the Use of Chemical

Biomarkers for Identifying and Quantitating

Oil Residues in the Environment

Authors

Buffy M. Ashton

Rebecca S. East

Maud M. Walsh

M. Scott Miles

Edward B. Overton

Institute for Environmental Sciences

Louisiana State University

Baton Rouge, Louisiana

December 2000

Prepared under MMS Contract

14-35-0001-30660-19933

by

Coastal Marine Institute

Louisiana State University

Room 318 Howe-Russell

Baton Rouge, Louisiana 70801

Published by

U.S. Department of the Interior Minerals Management Service Gulf of Mexico OCS Region

OCS Study

MMS 2000-086

Cooperative Agreement Coastal Marine Institute

Louisiana State University
DISCLAIMER

This report was prepared under contract between the Minerals Management Service

(MMS) and Louisiana State University, Institute for Environmental Studies. This report

has been reviewed by the MMS and approved for publication. Approval does not signify

that the contents necessarily reflect the views and policies of the Service, nor does

mention of trade names or commercial products constitute endorsement or

recommendation for use. It is, however, exempt from review and compliance with MMS

editorial standards.

REPORT AVAILABILITY

Extra copies of the report may be obtained from the Public Information Unit (Mail Stop

5034) at the following address

U.S. Department of the Interior

Minerals Management Service

Gulf of Mexico OCS Regional Office

Attention: Public Information Unit (MS 5034)

1201 Elmwood Park Boulevard

New Orleans, Louisiana 70123-2394

Telephone Number: (504) 736-2519 or

1 -800-200-GULF

CITATION

Suggested citation:

Ashton, B.M., R.S. East, M..M. Walsh, M.S. Miles, and E.B. Overton. 2000. Studying

and Verif'ing the Use of Chemical Biomarkers for Identi'ing and Quantitating

Oil Residues in the Environment. U.S. Dept. of the Interior, Minerals

Management Service, Gulf of Mexico OCS Region, New Orleans, LA.

OCS Study MMS 2000-086. 70 pp.

ACKNOWLEDGEMENTS

The authors would like to acknowledge and thank the many people who participated in

the funding, collection, and analytical phases of this research. Extra special thanks are

extended to Charlie Henry and Paulene Roberts who began the preliminary stages of the

experimental design and assisted in the collection and preliminary data interpretations.

Their knowledge, guidance, and suggestions were invaluable. Shane Bourke assisted in

sample collection. Ron LeBlanc helped with the sample preparation and analysis. Thank

you James Welch, Donny Blanchard, David Brown, Jermifer Betbeze, and Barrett Ortego

for your time and help with the data processing. 111
ABSTRACT

Analytical chemistiy and instrumentation provides environmental scientists with the

ability to identify and track the fate of spilled oil residues in the marine environment

Compounds commonly used for the identification of spilled oil to a source are called

biomarkers. Biomarker compounds are universal in crude oils and petroleum products

and are generally more resistant to environmental weathering than most other oil

constituents. The distribution of biomarker compounds is unique for each oil and

different sources of petroleum exhibit different oil fingerprints. Self-normalizing

fingerprint indexes (SF1) calculated from the oil fingerprints provide a stable and useful

tool for determining a match or nonmatch for different oil residues present in some

environmental samples. A combination high-resolution gas chromatography and mass

spectrometry, visual comparison and self-normalizing fingerprint indexes (SF1) were

utilized to establish eight petroleum biomarkers for oil spill identification and assessment.

The eight petroleum biomarkers chosen for detection and analysis were determined

through a literature search and previous research. SF1 calculated from GC/MS analysis

of tarballs and an oil degradation study validated the use of the eight biomarkers chosen.

Of the eight SF1, four remain stable over an extended period of time and laboratory

simulated weathering. Visual comparison of biomarker fingerprints played an important

role in distinguishing gross, and in some cases subtle, differences between unknown

environmental samples. Double SF1 scatterplots were also utilized as a screening tool for

source matchlnonmatch determinations.

v
TABLE OF CONTENTS

ABSTRACT

LIST OF FIGURES ix

LIST OF TABLES xi

INTRODUCTION 1

Research Objective 1 Background Information 1 Biomarkers 3

Analytical Chemistry and Oil Characterization - 5

Self-Normalizing Fingerprint Indexes 8

Project Goals 9

Preliminary Stages 13

Laboratory-Simulated Weathering and Degradation Study 14

Bioaccumulation 15

Classification and Identification of Tarballs 15

METHODOLOGY 16

General Laboratory Methodology 16

GCIMS Methodology 16

Calculation of SF1 17

Visual Matching 17

Laboratory-Simulated Weathering and Degradation Study 18

Bioaccumulation 19

Classification and Identification of Tarballs 20

RESULTS AND DISCUSSION 23

Laboratory-Simulated Weathering and Degradation Study 23

Bioaccumulation 30

Classification and Identification of Tarballs 32

Morphological Appearance 34

Analytical Chemistry Results 38

Visual Matching 39

SF1 and Scatterplots 47

CONCLUSIONS AND RECOMMENDATIONS 49

LITERATURE CITED 53

APPENDIX. Biomarkers of Petroleum Products in the Marine Environment: A

Bibliography 57

VII
LIST OF FIGURES

Structures of triterpanes and steranes, both common biomarker compounds 4

Other oil biomarker compounds dibenzothiophene; phenanthrene; chrysene;

and pyrene

Extracted ion chromatogram indicating C-17/Pristane and C-18/Phytane SF1 10

Extracted ion chromatogram indicating C-3 DBT (a/b) SF1 10

Extracted ion chromatogram indicating C-3 Phen (a/b) SF1 11

Extracted ion chromatogram indicating C-i Pyr (a/b) SF1 ii

Extracted ion chromatogram indicating C-i Chr (a/b) SF1 i2

Extracted ion chromatogram indicating Norhopane/Hopane SF1 12

Sampling locations along the Louisiana coastline for stranded oil and tar survey,

1998 21

Average TTAH for each site and two controls 24

Total sources utilized by microorganisms from BiologTM results 26

Percent aromatic chemicals utilized by microorganisms from Biologm results 26

Total polycyclic aromatic hydrocarbons (PAHs) and biomarker concentrations in

Fl fractions from mussel tissues 31

Total polycyclic aromatic hydrocarbons (PAHs) and biomarker concentrations in

Fl fractions from clam tissues 31

Surface color distribution of tarballs collected in 1998 survey 35

Inside color distribution for tarballs collected in 1998 survey 35

Percent organic matter distribution for tarballs collected in 1998 survey 36

Percent sand distribution for tarballs collected in 1998 survey 36

Percent shell distribution for tarballs collected in 1998 survey 37

Tarball pliability score for 1998 samples 37

ix
LIST OF FIGURES

(CONTINUED)

Normal alkane distribution and Total Ion Chromatogram for a relatively

unweathered, aromatic oil 40

Normal alkane distribution and Total Ion Chromatogram for a relatively

unweathered, paraffinic oil 41

Normal alkane distribution and Total Ion Chromatogram for a relatively

unweathered, bimodal wax 42

Normal alkane distribution and Total Ion Chromatogram for a weathered,

aromatic oil 43

Normal alkane distribution and Total Ion Chromatogram for a weathered,

paraffinic oil 44

Normal alkane distribution and Total Ion Chromatogram for a weathered,

bimodal wax

Normal alkane distribution and Total Ion Chromatogram for a weathered, 11CM

C3 Phenanthrene versus C3 Dibenzothiophene/C3 Phenanthrene area double SF1

scatterplot for tarballs from Martin's beach 48

Norhopane/Hopane versus C3 Dibenzothiophene/C3 Phenanthrene area double

SF1 scatterplot for tarballs from Martin's beach 48

Suggested method for match/nonmatch determinations of unknown environmental

oil samples to a reference oil based on visual comparison and SF1 data 51

x

45

46
LIST OF TABLES

Target Petroleum Biomarkers 4

List of Target Oil Constituents Analyzed for by GC/MS-SIM in the IES/RCAT

Laboratory 7

List of Keywords Utilized in the Literature Search 13

SIM Ion Grouping for Target Crude Oil Compounds 17

Percent of TTAH Reduction for the Nine Water Samples After 28-Day Weathering

Experiment 23

Physical Characteristic Data and % Reduction in TTAH for Water Samples Based

on the Experimental Location Average Temperature 27

SF1 Data and Statistical Results for All Collection Sites, Without Nutrients 27

SF1 Data and Statistical Results for All Collection Sites, With Nutrients 28

SF1 Data and Statistical Results for Daily ETC Reference Oil 28

SF1 Profiles for Fourteen Reference Oils 32

Distribution of All Tarballs into Classifications 39

xi
INTRODUCTION

Research Objective

Analytical chemistry and instrumentation provides environmental scientists with the ability to

identi& and track the fate of spilled oil residues in the marine environment. Since the 1960's

many advances have been made in analytical chemistry techniques and instrumentation;

however, despite these advances, more accurate methods are necessary for identiing and

quantif'ing oil contamination in the environment. Biomarkers, for the purpose of this study,

are petroleum components that remain relatively unchanged in oil residues even after natural

environmental weathering processes. Since these biomarker compounds are relatively

persistent, they are commonly used for the identification of a spilled oil to its source. The

objective of this research is to refine the use of biomarkers as tools for oil spill identification

and assessment, and to veri' the analytical approach utilized by the Louisiana State

University, Institute for Environmental Studies, Response and Chemical Assessment Team

(LSU IES/RCAT) with both laboratory and field evaluation studies. High-resolution gas

chromatography and mass spectrometry (GC/MS) and source-fingerprinting techniques will be

employed to fulfill the research objective. Visual comparison, self-normalizing fingerprint

indexes (SF1) calculated from GCIMS analysis and biomarker normalization methods will

provide further validation for the importance of analyzing biomarkers in oil and oil residues

found in the marine environment.

Background Information

Oil spills into marine environments have been recognized as major environmental insults for

more than 30 years. The 1968 Torrey Canyon spill accentuated the large volumes of oil and

potential environmental threat posed by the then newly introduced "supertanker." In 1969, a

production well blow-out off the California coastal city of Santa Barbara caused another major

spill in the marine environment further highlighting environmental concerns generated by oil

pollution. Since the 1960s, there have been numerous spills around the world, including the

infamous Amoco Cadiz and Exxon Valdez spills, as well as major well blow-outs in the North

Sea, Bay of Campeche, and the Persian Gulf Tn addition to these incidents, intentional oil fires

and marine oil spills which occurred during the 1991 Persian Gulf War have also contributed to

environmental contamination and resulted in considerable media attention. Media attention

often leads to public outrage and in some cases, such as in the United States following the

Exxon Valdez, laws are created and passed to control and give liability to the responsible parties

(i.e. The Oil Pollution Act of 1990).

High profile oil spills are not the only source of oil pollution in marine and other environments.

Chronic oil discharges occur from a variety of small spills, natural seeps of oil from rivers and

oceans, non-point source mn-off, operational discharges from tankers, and development

associated with petroleum exploration. As a result, there is approximately 5 million tons of oil

released into the oceans each year. Approximately 6% of the 5 million tons (300,000) are from

tanicer accidents such as the Exxon Valdez, while the remaining 4.7 million tons are from

natural, chronic and acute events (Henry 1995).

1
Crude oil is a complex mixture of organic compounds derived from the partial decomposition

of animals and plants that were once alive, but have been long since dead. The process of oil

formation occurs slowly and produces "simple" organic molecules (petroleum) from more

complex organic structures (organic biomass). Petroleum-type hydrocarbons may exist as a gas

(natural gas), as a liquid (crude oil), as a solid (tar, bitumen), or any combination. All crude

oils are composed primarily of carbon (80-87%) and hydrogen (10-15%), and to a lesser

degree, sulfur (0-10%), nitrogen (0-1%) and oxygen (0-0.5%). Metals are a minor constituent

in petroleum and contribute little to the toxicity of oil. The physical and chemical properties of

crude oil vary with regions of production and zones within these regions. Since crude oils vary,

no single definition or composition is valid for all oils.

In general, all crude oils tend to be composed of the same individual compounds but the

relative abundance of these compounds may vary significantly between different oils. These

differences are important from the refinery processing and environmental chemistry perspective

because they are useful for predicting how an oil will behave if spilled in the marine

environment. Common oil constituents can be classified into four general groups: (1)

individual saturated hydrocarbons (the normal alkanes and isoprenoids); (2) polycyclic

aromatic hydrocarbons (PAils) including the dominant alkylated homologues in oil; (3) sulfur

heterocyclic aromatic hydrocarbons and related alkylated homologues; and (4) oil biomarkers

(primarily the steranes and triterpanes) which are polycyclic aliphatics. Even though aromatic

hydrocarbons (groups 2, 3, and 4) represent less than 5% of the bulk composition of most oils,

they are essential for assessing biological effects, aid in determining exposure pathways, source

characterization, and for monitoring the extent of oil weathering and degradation (Sauer and

Boehm 1991).

Once oil is discharged into the marine environment, it undergoes various physical and chemical

interactions such as spreading, drifting, dispersion, evaporation, dissolution, emulsification,

photochemical degradation, and biodegradation. These effects on bulk oil composition are

collectively called weathering and result in changes in the aromatic hydrocarbon profile of an

oil. The two primary weathering processes affecting oil in the marine environment are

evaporation and biodegradation. The chemical effects of weathering can be broken down into

four simplified stages. Samples are classified as either relatively unweathered or slightly,

moderately, or heavily weathered. Relatively unweathered oils exhibit no apparent change in

the aromatic hydrocarbon profile. Loss of compounds more volatile than naphthalene may

have occurred, but any weathering is minor. Slightly weathered oils exhibit no major changes

in the relative order or abundance of aromatic homologues. The alkylated naphthalenes are the

most abundant constituents, but may be slightly reduced; and the normal alkanes are generally

still present. The total naphthalenes are significantly depleted in moderately weathered oils;

and the total alkylated dibenzothiophenes and phenanthrenes dominate the histogram plot.

Furthermore, moderately weathered oils have a highly degraded normal alkane fraction.

Dibenzothiophenes and phenanthrenes are significantly depleted from heavily weathered oil,

and dominant constituents are alkylated napthobenzothiophenes, pyrenes and chrysenes.

Tarballs are examples of moderately to heavily weathered oil. Light petroleum products and

light crude oils, such as many South Louisiana Crude production oils, spread rapidly and are

often removed from the ocean surface by dispersion during high sea state conditions. On the

2
other hand, the very heavy crude oils, refined heavy bunker oils, and other petroleum products

with high pour points are slow to spread, exposing little surface area for the natural degradation

process. Thick oils are generally only degraded at the surface interface resulting in the

encapsulation of "fresher" oil. As a result, heavier oils are the most persistent in the

environment and are often encountered as stranded tarballs along the debris line of the beach

face. The formation of "mousse", a stable water-in-oil emulsification, may enhance the

process of tarball formation and extend environmental persistence of the lighter crude oils.

Mousse formation is common and is a prime factor for the formation of tarballs from otherwise

easily dispersible light crude oils.

Factors that influence weathering include weather conditions, the environment in which the oil

was spilled, and the type of oil. Once oil is stranded on a beach or shoreline, weathering is

modified by the microenvironment in which the oil is entrapped and the rate of natural

biodegradation at any specific location is dependent on a variety of factors. The amount of oil,

mixing energy, microfauna, and the availability of oxygen are a few of the key factors of

biodegradation. The limiting factor is often the availability of oil to the microbial community.

Other physical/chemical properties that affect the fate of individual constituents in crude oil

include vapor pressure, water solubility, the organic carbon partitioning coefficient (K0) and

octanol/water partitioning coefficients (}Q). Different oils spilled under similar conditions

may undergo entirely different weathering changes. The ability to examine a sample of

residual oil (such as a tarball) and speculate on the composition of the unweathered oil from

which it was derived is valuable information. Environmental forensic investigations to

determine a potential source of an unknown or mystery spill must exploit both morphological,

as well as, chemical compositional differences.

Biomarkers

As previously stated, petroleum spilled into the marine environment undergoes varying degrees

of environmental weathering that affect its composition. As weathering proceeds, certain

groups of oil constituents are lost in a predictable sequence. The first compounds to be

depleted are the n-alkanes and isoprenoids, followed by the lighter PAHs, then the remaining

PAHs and their alkyl homologues. As a result, unique identification of sources of oil spills

may be difficult due to the loss of these oil constituents and their distribution patterns relative

to the amount of weathering.

Biomarkers, as defined for this research, are petroleum components that remain detectable and

relatively unchanged in oil residues even after natural environmental weathering processes.

They are also typically resistant to biodegradation and are therefore, usefifi as chemical

markers. Biomarker fingerprinting by (iC/MS may be necessary for environmental samples in

which identification of spilled oil is difficult due to weathering of target compounds. Two

classes of biomarkers commonly referred to in the literature are the triterpane and sterane

compounds (Figure 1). The triterpane and sterane distributions are unique for each type of oil,

and, because of their relatively refractory nature, these compounds help to identif' a particular

oil, even oils with similar geographical origins, when other target analytes are lost. Biomarkers

suffer little interference from weathering effects because of their high resistance to degradation,

which can be attributed to their high molecular weights. As an oil becomes more degraded, the

3
concentration of biomarker compounds should increase relative to the more easily degraded

constituents (Wang and Fingas 1995b). These compounds are also useful in distinguishing a

spilled oil from other oils and sources that may be present within the same sample matrix. As a

result, biomarkers provide chemical fingerprinting information about the source, degree of

weathering, characteristics, and fate of the spilled oil.

Figure 1. Structures of (a) triterpanes and

(b) steranes, both common biomarker

compounds.

While the hopanes and steranes are useful biornarker compounds, other biomarkers from one of

the four groups of oil constituents previously listed are essential due to the fact that refinery

processes remove the triterpanes and steranes. Previous research in the IES/RCAT laboratory,

as well as other studies reported in the literature, suggest that the compounds and ratios listed in

Table 1 are most promising as biomarkers. The "n" represents normal alkane (rn/c 85), and

"C" relates to the number of carbons attached to the parent molecule. For example, C-3

dibenzothiophene indicates that the unalkylated sulfur heterocyclic parent structure has three

carbons attached as either a propyl group or a combination of methyl and ethyl groups. The

structures of a few of these compounds are displayed in Figure 2.

Table 1. Target Petroleum Biomarkers.

4

IESIRCAT Biomarkers Abbreviations

nC- 17/pristine

nC-I 8/phytane

(Table I Continued)

nC- I 7/Pris

nC- 1 8/Phy
Figure 2. Other oil biomarker compounds (a) dibenzothiophene;

(b) phenanthrene; (c) chrysene; and (d) pyrene.

Analytical Chemistry and Oil Characterization

The foremost questions asked about spilled oil are its source and quantity in various

compartments of the environment, and the risk and consequences associated with various levels

of petroleum within these compartments. Spilled oil is not evenly or uniformly dispersed

throughout the environment because many chemical compounds that make up petroleum are

not water-soluble. Consequently, spilled oil is dispersed into aquatic environments in a very

heterogeneous fashion. The physical and chemical processes that constitute environmental

weathering continually alter its composition and distribution into different facets of the

environment. The heterogeneous distribution of oil, with a continually changing composition,

causes considerable uncertainty in assessing the impacts of oil spills and chronic petroleum

releases. These factors also pose a problem in the evaluation and selection of various

technologies that are utilized to mitigate the incidence.

5

IESIRCAT Biomarkers Abbreviations

C-3 Dibenzothiophene

C-3 Phenanthrene

C-lPyrene

C-i Chiysene

l7ct (II), 2fl3(H) -30 Norhopane/l7a (H), 2fl3(H) -

Hopane

Sum of C-3 Dibenzothiophene divided by the sum of

the C-3 Phenanthrenes

C-3 DBT

C-3 Phen

C-lPyr

C-1 Chr

Nor/Hop

C-3 DBT arealC-3 Phen area
Furthermore, the complex mixture and alteration of spilled oil in the environment creates a

problem for most analytical detection tecimiques. This is due to the fact that most analytical

methods provide little or no discrimination between sources and the calculated concentrations

are often accepted as real. Other situations that complicate the ability to assess trace level oil

pollution are matrix effects and multiple pollution sources. Analytical approaches to oil spill

assessment include U.S. Environmental Protection Agency (EPA) methods for volatile

aromatic compounds and polycyclic aromatic hydrocarbons (PAH5). Unfortunately, these

methods were originally developed to assess industrial and hazardous waste (Wang and Fingas

1995a), not oil pollution. When studying oil pollution in aquatic environments, these analytical

approaches lack chemical specificity for oil compounds and are unable to differentiate sources

of contamination and quantify oil residues in complex environmental samples. To further

complicate matters, many of the petroleum compounds of interest have no commercially

available standards and identification is often based on the extensive qualitative mass

spectrometer analyses during method development. Therefore, the risks and consequences

associated with various levels of petroleum in all parts of the environment are very difficult to

accurately assess and the effectiveness of mitigating technologies are not easily ascertained. In

an effort to better identify and quantify spilled oil, document its weathered residues, and assess

the physicallchemical transformations caused by weathering, the IES/RCAT laboratory in 1990

developed an advanced GC/MS methodology and self-normalizing fingerprint indexes (SF1) for

source-fingerprint analysis of moderate to heavily contaminated samples associated with oil

spills.

Analytical methods for identif'ing oil in a marine environment should accomplish the

following tasks: detect the presence of oil; provide compound specific quantification of target

petroleum compounds; and provide data applicable to source-fingerprinting. Other criteria to

consider when developing an analytical method that targets both common oil constituents and

analytes for baseline oil pollution include the ability of the method to differentiate between

petroleum and other natural and anthropogenic sources of hydrocarbon pollution (i.e. terrestrial

plant waxes and combustion by-products).

Information about the bulk physical and chemical properties of oil provides limited data to

assess damages to the environment. Standard EPA methodologies are inadequate for assessing

petroleum pollution since they lack key target compounds characteristic of oil. Therefore, a

more chemically selective approach is required to determine the concentration of constituents

in spilled oil in environmental samples. While no standardized methodology currently exists at

a national level, there is fundamental acceptance of GC/MS for the analysis of petroleum. This

is due to the fact that GCIMS provides highly selective source-fingerprinting information along

with compound specific quantitative results for target aromatic and aliphatic hydrocarbons.

The target analytes may be a single compound or isomers quantified as a group. Target

compounds must be useful for monitoring oil weathering and biodegradation; assessing

response activities, as well as fate and effects of spilled oil; and aid in the determination of

baseline oil pollution. Furthermore, legally defensible analytical procedures and data should be

incorporated into the selection of target oil constituents. Oil constituents selected for analysis

in the IESIRCAT laboratory are displayed in Table 2. The hopanes and steranes ions are

primarily used for source-fingerprinting and generally are not quantified.

6
Table 2. List of Target Oil Constituents Analyzed for by GCIMS-SIM

in the !ESIRCAT Laboratory.

Compound Quantification Ion Abbreviation

Alkanes 85

Naphthalene 128 NAPH

C-i Naphthaienes 142 C-i NAPH

C-2 Naphthalenes 156 C-2 NAPH

C-3 Naphthalenes 170 C-3 NAPH

C-4 Naphthalenes 184 C-4 NAPH

Fluorene 166 FLU

C-i Fluorenes 180 C-i FLU

C-2 Fluorenes 194 C-2 FLU

C-3 Fluorenes 208 C-3 FLU

Dibenzothiophene 184 DBT

C-i Dibenzothiophenes 198 c-I DBT

C-2 Dibenzothiophenes 212 C-2 DBT

C-3 Dibenzothiophenes 226 C-3 DBT

Phenanthrene 178 PHEN

c-i Phenanthrenes 192 c-i PHEN

C-2 Phenanthrenes 206 C-2 PHEN

C-3 Phenanthrenes 220 C-3 PHEN

C-4 Phenanthrenes 234 C-4 PHEN

Fluoranthrene 202 FLANT

Pyrene 202 PYR

C-i Pyrenes 216 c-I PYR

C-2 Pyrenes 230 C-2 PYR

C-3 Pyrenes 244 C-3 PYR

C-4 Pyrenes 258 C-4 PYR

Chrysene 228 CHRY

c-i Chrsenes 242 c-i CHRY

c-2 Chs 256 C-2 CHRY

C-3 Chsenes 270 C-3 CHRY

c-4 ch 284 C-4 CHRY

Benzo (b) Fiuoranthene 252 B(b)F

Benzo (k) Fluoranthene 252 B(k)F

Benzo (e) Pyrene 252 B(e)P

Benzo (a) Pyrene 252 B(a)P

Perylene 252 PERYL

Indeno (g,h,i) Pyrene 276 INDPYR

Dibenzo (a,h) Anthracene 278 DIBENZ

Benzo (i,2,3-cd) Perylene 276 BENZP

Hopanes (i9i family) 191

Steranes (217 family) 217

Identification, or source-fingerprinting as it is commoniy called, is based around the use of

high-resolution GC/MS anaiysis of selected components in oil and associated residues such as

those listed in Tabie 2. Source-fingerprinting is an environmentai forensics technique that

utilizes analytical chemistry to compare samples of spilled oil to a suspected source to assess if

the oil is a positive match. Therefore, source determinations, such as a damaged tanker or

7
deliberate ocean dumping, can be derived. The results of oil fingerprinting are similar to other

forensic analyses (i.e. blood typing) in that oil-fingerprinting techniques provide stronger

evidence to prove that a sample is a nonmatch to a suspect source rather than prove, without

question, a positive match. Since oil is a very complex mixture of compounds that cannot be

completely resolved by gas chromatography, a highly selective detector such as a mass

spectrometer used in conjunction with a high-resolution chemical separation system, the GC,

discrimination of specific target compounds from the bulk oil can be achieved. The target

polycyclic aromatic hydrocarbons (PARs) listed in Table 2 represent less than 5% of the bulk

oil composition by weight, and many of the target analytes are present at the low part per

million (ppm) levels in whole or bulk oil. This list of PAHs is highly useful in differentiating

crude petroleum from byproducts of fuel combustion. This is possible because incomplete

combustion of fuels produces PARs that are characterized by 3,4, and 5 ring aromatic

compounds with few substituted alkyl homologues. For example, fluoranthene, chrysene and

benzopyrenes are more common in combustion-sourced pollution than oil pollution.

Quantification of oil and oily residues can be obtained from several different methods. Oil and

oil residue concentrations can be directly measured as "total petroleum hydrocarbons" using

appropriate analytical and instrumental (gas chromatograph coupled with a flame ionization

detector, or GC/FID) methods. However, due to the heterogeneous distribution of oil in

aqueous systems and the non-selective properties of the detector, this method requires

extensive replication before statistically valid results can be achieved. Alternately, oil

quantities can be inferred by examining the distribution of selected hydrocarbon components

remaining in oily residues or tarballs found in the environment. This method is based on the

fact that not all components in oil are readily degraded by natural weathering processes.

Therefore, if the quantities of hydrocarbons that remain are compared to the quantity of

undegraded components, the percent of residual oil remaining after environmental weathering

can be estimated. Comparing selected hydrocarbon components to the undegraded components

is known as biomarker normalization. Biomarker normalization shows great promise as a tool

for both identifying spilled oil and quantifying environmental residues. It is not a perfect tool

and can, in contaminated systems, overestimate the actual percent degradation. One objective

of this research is to extend and further validate the use of biomarker normalization by

incorporating certain calculated fingerprint indexes that are not subject to day-to-day variances

in an analytical laboratory.

Self-Normalizing Fingerprint Indexes

Since biomarker compounds are more resistant to enviromnental weathering processes,

compared to most other oil compounds, they can be utilized as conserved reference compounds

against which the loss of less stable oil components can be quantitatively estimated by

calculating certain ratios. These ratios may be useful in differentiating unknown spill samples

from a suspected source. Furthermore, the distributions of oil biomarkers is unique for

different types and blends of petroleum products and represent an oil-specific fingerprint to

which distinct oil samples can be correlated. Match/nonmatch determinations can be achieved

qualitatively through visual comparison of ion chromatogram patterns, and quantitatively from

calculating the ratio of one biomarker to another. Ratios of certain biomarkers are referred to

as self-normalizing fingerprint indexes (SF1) throughout this research. Figures 3 - 8 display

8
the peaks chosen from the biomarker ion chromatograms for calculating the SF1. SF1 are

calculated by using the ratio of different peaks within the same isomer having similar retention

times and identical mass to charge ratios. Choosing isomer groups that have similar water

solubilites, vapor pressures, and parent masses will result in potentially useful SF1 and

contribute to the reduction of instrumental variance effects. As instrument conditions change

because of matrix effects, colunm degradation, sensitivity, or tune degradation, both integers

used to calculate the index (assuming they are similar in molecular weight, chemistry and

quantitation ion) will be affected by the same relative degree of change; therefore, the index or

ratio of the two integers, should remain constant. After a corrected base line value and peak

heights have been determined, the SF1 are calculated by dividing peak a by peak b (labeled in

each figure).

SF1 are a quantitative teclmique capable of reducing the potential for false-positive results and

is not subject to most day-to-day laboratory variances. Goals of the SF1 approach are to reduce

investigator bias through the use of improved quantitative fingerprint techniques; and allow

investigators to distinguish subtle differences in actual spill samples that can be easily missed

by standard qualitative approaches.

In 1978 Overton and colleagues verified the application of alkyl aromatic ratios in the

investigation of the fire and oil spill at the West Hackberry, Louisiana, Strategic Petroleum

Reserve Complex (Overton et al. 1981). They found that the ratio of sulfur containing alkyldibenzothiophenes to alkyl-phenanthrenes was distinctly different between the spill oil, an

Arabian light, and the South Louisiana crude oil (domestically produced and the principal

source of chronic or periodic release in the study area). MatcWnonmatch determinations were

derived primarily from qualitative (i.e. visual) comparisons of the chromatographic profiles of

specific aromatic hydrocarbons and petroleum biomarker compounds such as the steranes and

hopanes, as well as indexes derived from specific compound ratios and simple plots.

Visual comparisons of chromatographic fingerprints provide very significant information as

opposed to comparison of a few indexes. Visual comparison of a series of analyzed samples

and their corresponding ion fingerprints can easily distinguish one type of oil from another

based on the pattern or fingerprint and the ratios of the different constituents within the ion

being analyzed. Visual match/nonmatch determinations are often criticized as being highly

subjective and potentially biased by the interpreter. However, refined analytical skills,

knowledge of oil chemistry, and experience reduce bias and provide a very efficient means for

distinguishing qualitative differences between the fingerprints being compared and can provide

further direction of analysis.

Project Goals

The overall objective of this research was to develop more accurate methods to identify and

quantify spilled oil in the environment. Biomarker normalization and self-normalizing

fingerprint indexes show great promise as a tool for both identifying spilled oil and quantifying

environmental residues. Research performed by IES/RCAT will refine the use of biomarkers

commonly analyzed in our laboratory as tools for oil spill identification and assessment; verify

9
Figure 3. Extracted ion chromatogram (m/e 85) indicating

C-l7JPristane and C-l8lPhytane Sn.

Figure 4. Extracted ion chromatogram (nile 226) indicating

C-3 DBT (a/b) SF!.

10

1lo

11x75

lOtW 3j.

& (C-Ill -

a Ekfl(C11)

PI1IM)

M 27O ItO ,.fto 2550 d IilSO 1000 XIO 3OO aigo noo ,Ao ,óo

law

law

I IO

:

':

I'

oO aIO

III

aTI OZlOO

II IilIO fllo 'ZO
Figure 5. Extracted ion chromatogram (nile 220) indicating

C-3 Then (a/b) SF1.

Figure 6. Extracted ion chromatogram (nile 216) indicating

C-i Pyr (a/b) Sn.

11

1499

03000 In.

11000 1990

0000

0000

0000

'99

:

:

00 00 51550 rasot EAD

K

Th0I-4 35.00 34.00 34 3140 34S0 3000 3010 17.00 '744 3440 1IO0 0900 40-00 I0I 4100 414' 4109 4j0I

-Q

000

000 at.

100

199

00 fl109 QISJO*OflJtt D

T40Q 345I fl09 1100 09 360 Zl00 1109 hf00 300 a 000 d09 40.00 4l 410 43135
Figure 7. Extracted ion chromatogram (Se 242) indicating

C-i Chr (a/b) Sn.

Figure 8. Extracted ion chromatogram (Se 191) indicating

Norhopane (a)IHopane (b) SF1.

12

24LW

kn24LOO nujobm,ot EOC2Ø&D

1.. 4100 42.00 4120 4200 42.10 4420 44:60 4260 4l70 45.00 4&l0 47.00 47.00

1W

6000

6020

0000 Lx.

3500 Ox.

2000

lx. Ox.

b. - Ill 04054006. 451.60&fl

4400 4620 00 6020 64.00 66.00 6620 10.00 6020 64.00 ass w 75.00 7220 7400
this method as compared to the use of other conventional ratio and TPH technologies; and

apply this technique to a tarball and oil residue survey along Louisiana's coastline. IES/RCAT

will utilize its cache of several collected and preserved reference oils commonly transported in

U.S. waters including coastal Louisiana and a few internationally documented sites. The

research originally was broken down into eight different research goals, or "tasks" that were

divided into one of four categories: preliminary stages (i.e. literature search and instrument

methodology); water degradation of one oil in nine natural waters; bioaccumulation of

biomarker compounds in clams and mussels; and classification and identification of tarballs

using SF1.

Preliminary Stages

The literature search established the foundation for oil biomarkers, past and present, which can

be compared to the oil biomarkers utilized by IES/RCAT. A comprehensive literature review

of the environmental, petroleum industry, and geological journals was conducted. Information

gained from the literature survey included the identification of various biomarkers and profiles

of biomarkers previously determined in petroleum reservoirs from around the world. Key

words utilized during the literature search are given in Table 3.

Table 3. List of Keywords Utilized in the Literature Search.

Assessment Oil

Biodegradation PAils

Biomarker Persistence

Crude Oil Petroleum

Fate Pollution

Fingerprinting Salt Marsh

Fuel Oil Spill

Hydrocarbon Weathering

Ocean

Two previously compiled bib iographies provided additional references and were beneficial in

the initial literature search. The citations for these two bibliographies are:

Light, Melvin and Joseph Lanier. 1978. Biological effects of oil pollution: a

comprehensive bibliography with abstracts. US Coast Guard Research and

Development Center Report No. CG-D-75-78: 647 pp.

GESAMP (IMO, FAO, UNESCO, WMO, IAEA, UN, UNEP Joint Group of

Experts on the Scientific Aspects of Marine Pollution). 1993. Impact of oil

and related chemicals on the marine environment. Rep Stud. GESAMP (50):

180 pp.

The literature survey resulted in 180 pertinent articles out of over 300 titles acquired and

reviewed. The 180 relevant articles appear in the Appendix titled Biomarkers of Petroleum

Products in the Marine Environment: A Bibliography.

13
The instrument methodology involved the development of an improved GCIMS analytical

method that included newly identified biomarker families in addition to those compounds

currently used for oil spill response. Accomplishment of this stage involved determinations

from the literature review and data generated from all the other experiments.

Laboratory-Simulated Weathering and Degradation Study

The water degradation experiment was initially intended to assess compositional changes in six

selected oils due to weathering with respect to biomarker normalization and compositional

changes within the laboratory. In evaluating the effectiveness of this task in conjunction with

past experience with oils weathered in the environment, it was realized that the major altering

factor is not the oil compositions that can be identified, but the different types of changes due to

environmental factors. One significant factor for oil released on water is the ability of the

natural microbial communities within the water to affect the oil composition. Therefore, the

water degradation experimental design was altered to evaluate the changes in the composition

of one oil, a South Louisiana Crude, after exposure to nine natural waters collected across the

country. Water samples were collected from the coast of Alaska, Hawaii, Florida, Louisiana,

California, and from inland waters of the Mississippi River. By evaluating the degradation

changes that occur to the oil when exposed to 28-days of laboratory simulated weathering, in

addition to a simulated nutrient influx and no nutrient inputs, environmental alterations of an

oil can be projected, and ultimately affect the emphasis of this research.

In order to determine whether significantly different bacterial communities were present in the

various waters, nutrient substrate uptake was analyzed with BiologTM microplates. The 96-well

BiologTM microplate contained a variety of carbon and nitrogen sources, as well as a redox dye

that records increases in metabolic rate (Bochner 1989). After the microplate is inoculated with

the bacterial isolate it is incubated, usually for 24 hours. The patterns of positive and negative

responses are input into a computer through a microplate density reader and the database

scanned for the best statistical match of the substrate utilization profile. Although initially

utilized for identification of bacterial isolates, the BiologTM system, with some modifications to

the procedure, has been demonstrated to be a reproducible measurement of substrate utilization

by whole bacterial communities and very useful in distinguishing among different communities

(Garland and Mills, 1991; Haaek et al., 1995; Wunsche et al. 1995). The results of the

BiologTM microplates were used for characterizing the nutrient substrate utilization patterns of

the microbial communities in each of the water samples.

The final objective of the water degradation experiment was to examine the changes in SF1 for

one type of oil pre- and post- weathering. Completion of this task relied upon the SF1

generated from pre- and post-weathered Eugene Island Crude (EIC) reference oil utilized in the

28-day laboratory simulated weathering step of the water degradation experiment. Statistical

analysis was performed to determine the stability of the oil biomarkers present in the EIC after

the 28-days of weathering, along with nutrient or no nutrient additions, and exposure to natural

microorganism populations in the different waters tested.

14
Bioaccumulation

The objective of the bioaccumulation experiment was to examine biomarker up-take in bivalves

exposed to oil of known biomarker composition from samples collected recently from Prince

William Sound, Alaska and impacted by the TN Exxon Valdez. Theoretically, biomarkers are

not significantly altered by microbial degradation; hence, there is a possibility that these lipid

soluble materials will accumulate in marine organisms and could be used as an indicator of

possible exposure during oil spills. The results of this task will establish a time frame in which

biomarker compounds may be detected in bivalves since the samples were collected ten years

after the TN Exxon Valdez incident.

Classification and Identification of Tarballs

The methods of biomarker normalization developed throughout this research were used to

analyze "unknown" environmental samples collected from three areas previously sampled that

accumulated tarballs as documented in the 1993 report titled Characterization of Chronic

Sources and Impacts of Tar Along the Louisiana Coast by Henry et al. The results of the

classification and identification of the 1998 tarballs further extended the use of the SF1 as

biomarker normalization tools to identif' and estimate relative environmental oil residues. A

semi-quantitative approach for identiing different oil sources based on pattern matches of the

(iC/MS fmgerprints and calculated self-normalizing fingerprint indexes were the final results

of this task.

An initial step in the classification and identification of tarballs included the analysis of the

eight biomarker compounds chosen in fourteen unweathered reference oils (two of which were

daily reference oils). The reference oil analysis was performed prior to the analysis of the

tarballs collected in 1998. The reference oils were obtained from the IES/RCAT oil library and

were categorized into three groups: crude oils; fuel oils; and other oils (i.e. motor oil).

Detailed (iC/MS analysis was performed on these samples and data analysis included the

calculation of the SF1. The results of this task included an attempt to correlate the oils in each

of the categories, and/or support the use of the SF1 for the biomarkers chosen to aid in source

determination since the quantities of biomarkers are different for each type of oil. The SF1 data

from the reference oils was also utilized to calculate target source areas on double SF1

scatterplots containing samples collected during 1992 and 1998, and 1998 alone.

A total of 88 new tarballs were collected in 1998. All 88 of the tarballs were characterized

morphologically, and 70 tarballs were extracted and analyzed by GC/MS. After the tarballs

were analyzed, the SF1 were calculated and a comparison between the SF1 from three sites

previously sampled in 1992 to the SF1 calculated from the 1998 tarballs from the same three

sites was performed. Double SF1 scatterplots were produced for the 1992 and 1998 tarballs,

and the 1998 tarballs only. The overall objective of the classification and identification of

tarballs was to determine whether or not the SF1 and double index scatterplots used by

IES/RCAT are suitable for oil and/or source identification purposes.

15
METHODOLOGY

General Laboratory Methodology

Established good laboratory procedures were utilized throughout the course of the research.

All of the sample preparation, experimentation, extraction, and analytical analyses were

conducted at the Institute for Environmental Studies (IES) at Louisiana State University.

Samples collected in the field were brought back to the IES/RCAT laboratory to be logged in

and given an identification number. The samples were properly stored until time of preparation

and extraction.

Quality assurance and quality control was assured through a five-point calibration curve for

target analytes and internal standards that demonstrated the linear range of the analysis. After

linearity of the five-point calibration curve was established, a reference oil (North Slope Crude)

and a continuing calibration standard containing naphthalene-d8, anthracene-d10, chrysened12, and perylene-d12 was injected every day prior to any sample analysis to verif'

instrumental performance.

GC/MS Methodology

Two different Hewlett-Packard 5890 Series GCs coupled with a HP S97lSeries Mass Selective

Detector were used for all instrumental analyses. Both instruments were equipped with 30

meter (m) by 0.250 millimeter (mm) capillary columns with a DB-5 stationary phase and a 0.25

micron inner diameter. The GCs were operated in temperature program mode with an initial

colunm temperature of 55°C held for 3 minutes and then increased to 280°C at a rate of 5°C

/minute. A final temperature of 300°C was achieved at a rate of 0.5°C/mm and held for 15

minutes. The injection temperature was set to 270°C and only high-temperature, low thermal

bleed septa were used. The GCIMS interface was maintained at 280°C. Helium gas was used

as the mobile phase. The column head pressure was 13 psi and the flow rate was 1.31

milliliters (mL)/min.

The MS was operated in Selected Ion Monitoring (SIM) mode to maximize the detection of the

target crude oil compounds. The instrument was operated such that the selected ions for each

acquisition window were scanned at a rate greater that 1.5 scans/sec. At the start of an analysis

period, the MS was tuned to perfluorotributylamine (PFTBA). Nineteen groups with differing

scan start times were established and Table 4 provides detailed information for these 19 groups.

Sample introduction into the instruments for the water degradation experiment and the tarball

analysis were achieved by manual injection. Sample introduction for the bioaccumulation

study was by a HP 6890 Series Auto Injector. One microliter (1iL) of each sample extract was

injected. Both injectors were operated in splitless mode. FIP Chemstation software was used

for control, data acquisition and data analysis.

16
Calculation of SF1

All SF1 were calculated from peak height except the C-3 DBT area/C-3 Phen area SF1 that was

determined by the division of the area sums (area under each homologues series). Peak height

was chosen over an area integration method for most of the indexes since the peaks used in the

calculation axe generally not baseline resolved. All peak height determinations were calculated

manually, corrected for baseline value, and entered into a Microsoft Excel spreadsheet. Refer

to Figures 3-8 for the peaks chosen from the biomarker ion chromatograms used in the

calculation of the SF1 adapted by IESJRCAT. After a corrected baseline value and peak heights

have been determined, the SF! were calculated by dividing peak a by peak b.

Visual Matching

Comparison of extracted ion chromatographic profiles for the indexes listed in Table 1 were

conducted to determine if any of the samples appear to be related. This process compares the

relative composition and extent of weathering for each sample analyzed, providing a detailed

interpretation of the alkylated aromatic hydrocarbon series, sterane, and biomarker distribution

patterns.

The visual method of qualitatively comparing each sample to every other sample is generally

referred to as the "standard method" and is labor intensive. By this process all the GCIMS data

is printed to create a hard copy, sorted by extracted ions and stacked in a pile. Each sample in a

single ion group is compared to the other samples with the same ion to determine if each is a

match or nonmatch. Nonmatch samples form a new pile until every chromatogram has been

Table 4. SIM Ion Grouping for Target Crude Oil Compounds.

17

Group

ID

Scan Start Time

(minutes)

Ions in Group

1 04.50 82;85;128;136;142;152; 156;166;172; 180

2 19.20 85;142;152; 154;156;164;166;l70;172;180;184

3 25.00 85;165;166;170;176;178; 179;180;184; 188

4 29.00 85;165;178;18O;184;188;192;194;198;212

5 30.00 85; 178; 182; 194;208;230;500

6 32.00 85; 179; 191;! 92; 194; 1 98;208;212;226;500

7 33.00 85; 100; 192; 198;206;208;2!2; 245;260;500

8 34.00 85; 101; 1 98;202;206;208;212;226;260

9 35.00 85;202;205;206;208;212;2l6;220;226;245

10 37.00 85;205;206;216;220;226;234;244;245;500

11 39.00 85;205;216;22O;228;230;234;240;244;248

12 42.00 85;228;230;234;240;242;244;248;258;500

13 44.00 85;230;240;242;244;248;256;258;262;270;276

14 46.50 85; 191;252;256;258;262;270;276;284

15 47.00 85; 191 ;2 17;252;256;262;270;276;284

16 48.00 85; 191 ;2 17;252;256;264;270;276;284

17 49.00 85; 191 ;2 l7;252;264;270;276;284;500

18 52.00 85; 191 ;21 7;252;253;270;276;278;284

19 54.00 85; 191 ;200;2 17;276;278;284;500
compared. The result is generally dozens of small piles of the one ion being compared with

each pile signif5ñng a different source or exhibiting significantly different weathering patterns.

The piles are then given an alphanumeric identification.

Laboratory-Simulated Weathering and Degradation Study

The oil used in this experiment was Eugene Island Crude, a type of South Louisiana crude oil.

Two, one-liter water samples collected from areas outside of Louisiana included: Coronodo

Beach, California; Jax Beach, Florida; and Kilua Beach, Hawaii. Water samples collected

within Louisiana included: Holly Beach; water from Lake Charles; Mississippi river water

collected in Port Allen; Price Lake in Rockefeller Refuge; Sabine Lake; and LSU University

Lakes. Two control waters were also used. One was just deionized water and the other was a

32 part per thousand (ppT) Instant Ocean solution. Properties such as salinity, pH, and

temperature were measured in the field. The water samples were brought back to the

IES/RCAT laboratory where they were logged in, subsampled and stored at 1°C (34°F) until

the experiment began

Two, 250mL Erlenmeyer flasks were filled with lOOmL of water from each site. The flasks

were placed on a digital scale (one at a time) and the scale was then tared to zero. A disposable

9-inch soda glass pipette was used to add 0 50g of Eugene Island Crude oil to the water. The

first flask containing the water and the oil was removed and covered with sterile gauze secured

by a rubber band. After the oil was added to the water in the second flask, it was removed and

lmL of nutrient mixture was added. The nutrient mixture contained 5g of potassium phosphate

(mono), 2.5g of potassium phosphate (dibasic), 2.5g of magnesium sulfate, 5.Og of ammonium

nitrate, and 2.5g of yeast extract in 100 mL of deionized water. The second flask was also

covered with sterile gauze secured by a rubber band. These steps were repeated twice for a

total of three samples without nutrients and three samples with nutrients for most of the water

samples.

The samples were placed on orbital shakers in two different rooms with two different

temperatures. The water samples with cooler temperatures at collection were placed on the

orbital shaker in the room with an average temp of 23.6°C (74.5°F), and the waters with

warmer temperatures at collection were placed in a room with an average temperature of

27.7°C (8 1.9°F). The rotations per minute (rpm) of the two orbital shakers was set to 100 ± 10

rpm. The flasks remained on the orbital shaker for 28-days. On the 14th day of the experiment,

an additional lmL of nutrients was added to all the flasks originally containing nutrients. This

was the only time the orbital shakers were stopped during the course of the experiment.

After 28-days, all the flasks were removed from the shakers to be extracted. Four samples,

selected at random from all of the flasks, were poured into 500m1 separatory funnels. The

250mL flasks were rinsed with lOmL of dichloromethane (DCM), which was then poured into

the corresponding separatory funnel. This step was repeated two more times for a total of three

rinses and 3OmL of DCM Each separatory funnel was inverted and vented several times and

allowed to stand for 10 to 30 minutes depending on emulsification. Extent and color of the

emulsification for each sample was recorded. A glass funnel with glass wool and anhydrous

sodium sulfate (Na2SO4) was placed on top of a 4OmL VOA vial and set under each separatory

18
funnel. The Na2504 was moistened with DCM before removal of the 13CM fraction of the

extraction in the separatory funnel. DCM was used to rinse the Na2SO4 after removal of the

organic DCM fraction and to reach a final volume of 4OmL for each extract.

After the first set of four extractions was completed, all the equipment utilized was washed and

solvent rinsed prior to the next four extractions. All extracts were capped and stored at 1°C

(34°F) until GC/MS analysis.

In addition to this degradation experiment, BiologTM microplate analyses were performed to

determine whether significantly different bacterial communities were present in the various

waters. For this study, 2mL subsaniples of the water samples collected from the various sites

were frozen at 70°C until analysis. Prior to analysis, lmL aliquots of the samples were

inoculated onto plates containing BUGM agar (Biolog) supplemented with defibrinated sheeps

blood to promote abundant growth. The plates were incubated at 37°C ('-424°F) for 72 hours.

Cotton swabs were used to transfer bacteria into test tubes containing a sterile saline solution,

with salinity adjusted with NaCI to match the salinity of the samples. The saline solution was

inoculated onto the BiologTM microplates using a multipipettor. The plates were incubated for

3 days at 37°C ('-424°F), in a modification of a technique used for soil samples (Garland and

Mills 1991). The yeast extract solution was then inoculated directly onto the BiologTM plates.

Finally, pre- and post-weathered Eugene Island Crude oil samples were analyzed. The preweathered oil sample utilized was prepared by adding 0.25 grams of ETC to 10 mL of hexane,

and was manually injected everyday prior to analysis of the extracts. The post-weathered ETC

samples were obtained from the extracted waters after 28-days of weathering on the orbital

shaker. SF1 were calculated after HP Chemstation data integration. The stability of the

biomarker SF1 was determined through statistical analysis of each individual SF1.

Bioaccumulation

Because biomarkers are not significantly altered by microbial degradation, there is the

possibility that these lipid soluble materials will accumulate in marine organisms and could be

used as an indicator of possible exposure during oil spills. Selected marine bivalves recently

collected and still exposed to the TN Exxon Valdez oil were analyzed for composition, uptake

and alteration of hydrocarbon components including the biomarker compounds. Biomarkers

normally elute in the biogenic lipids fraction (Fl) during size exclusion chromatographic

fractionation. Fl fractions from 6 mussel tissues and 6 clam tissues from different collection

sites in Prince William Sound (PWS), along with a blank and rotary evaporator blank for each

tissue type, were analyzed by the GCIMS-SIM method given previously in the methodology.

An IES/RCAT scientist collected the tissue samples during the National Oceanographic and

Atmospheric Administration's (NOAA) summer monitoring cruise of PWS in 1999. The

samples arrive at the IES/RCAT laboratory frozen and in ice chests. They are logged in, given

a LSU ID#, and stored in a freezer until they are removed to be cleaned. The external shell and

tissue of all bivalves in each sample were rinsed with deionized water to remove any particles

or extraneous material. The animals were removed from their shells and placed into a

19
precleaned jar. A Teckmar TissuemizerTM was used to homogenize each sample. All

homogenized samples were stored in a freezer until extraction.

Approximately 5g of the homogenized tissue was removed from the sample and placed into a

cleaned, 5OmL beaker and spiked with surrogate standards. Between 15 to 25g of Na2SO4 was

added to the tissues depending upon the amount of water within the tissues or until a paste

consistency was obtained. The extraction solvent, DCM, was added in 35mL aliquots to the

sample. The samples were then placed in a sonicator for 15 minutes. The solvent extract was

then filtered through additional Na2SO4 and glass wool into a round bottom flask. The

extraction procedure was carried out a total of three times for each sample.

To concentrate the solvent extract, the sample was rotary evaporated to approximately 8mL

final volume. The concentrated extract was split into 4mL for lipid analysis and 4inL for PAH

analysis. The samples were then fractionated using alumina/silica gel solid phase extraction.

The first fraction, or the Fl fraction, is usually composed of lipid compounds in hexane. A

final Fl fraction volume of 4mL was stored in a vial until concentrated under a gentle stream of

nitrogen to a final volume of 0.lmL A lmL Hamilton Gas tight syringe was used to transfer

the 0. lmL sample into a 2mL autosampler vial containing 100 kL wide opening inserts. Ten

microliters of a 100 jiglmL internal standard were added to each of the samples. The vials were

capped with aluminum/PTFE lined crimp caps.

An HP 6890 Auto Injector was used to inject lj.tL onto the GC column for this task. After

analysis, HP Chemstation software was utilized to integrate the compounds listed in Table 2 on

page 7. After integration, the peak areas for each of the samples were transferred into a

spreadsheet where the concentrations, in parts per billion, were calculated.

Class Wcation and Iden4flcation of Tarballs

Fourteen reference oil samples were prepared by adding 0.25 grams (g) of oil into lOmL of

hexane. Oils selected from the IES/RCAT oil library included: Arabian Light Crude; Basrah

Crude; Bunker C; Diesel No. 2 Fuel Oil; Eugene Island Crude; Hondo; ff0 380; Louisiana

Blend; Mega Borg Light Crude; North Slope Crude; South Louisiana Crude; 55 Block 126

Condensate; Strategic Petroleum Reserve Crude; and Valvoline SAE 30. The North Slope

Crude oil was used to check daily instrument responses. The Eugene Island Crude oil was used

during the water degradation experiment. One microliter of the oil in hexane was manually

injected into the GC/MS. SF1 were calculated as stated previously and entered into a

spreadsheet. The SF1 for all the oils were divided into three types of oilS crude; fuel; and other.

Statistical analysis was performed to determine, if any, correlation within the three categories.

Tarballs for the 1998 classification and identification field study were collected from three sites

in the southwestern portion of the Louisiana Gulf Coast that were sampled previously in 1992.

Nine original sampling locations were designated in 1992 and are given in the report titled:

Characterization of Chronic Sources and Impacts of Tar along the Louisiana Coast by C.B.

Hemy, P.O. Roberts, and E.B. Overton (1993). Only three of the nine original station locations

were revisited in 1998 (Figure 9). The three stations revisited were: Martin's Beach; Holly

Beach; and Rutherford Beach.

20
MARTINS BEACH

RUTHERFORD BEACH OLLY BEACH

92° at

21

4

91°.. - ;C5° MILES

I

I I, 0 50 KILOMETERS

-_____ p - Søffl .t W 4??- 300

Figure 9. Sampling locations along the Louisiana coastline for stranded oil and

tar survey, 1998.

Each sampling location was subdivided during sampling into backshore and foreshore regions.

The backshore region is defined as the area behind the high tide debris line and includes the

storm berm area. The foreshore area included the high tide debris line down to the water's

edge. The differentiation between the foreshore and backshore was intended to distinguish

between recently deposited oil (i.e. from the last high tide) and oil deposited in the upper beach

from past storm activities. Tarball collection was performed by systematically walking each

sampling location, collecting all tarballs which were greater than a few millimeters in size, and

wrapping each individually in aluminum foil. All samples were stored at ambient temperature

while in the field.

For the 1998 survey, a total of 52 tar balls were collected from Martins Beach (47 from the

foreshore, one from midshore, four from backshore); 10 tarballs were collected from Holly

Beach (eight from the foreshore, two from the backshore); and 26 tarballs were collected from

Rutherford beach (25 from the foreshore, one from the midshore). The cumulative tarball total

was 88, of which 70 were sampled and analyzed. Eighteen samples were not extracted due to

the fact that they were smaller than the cutoff weight established during the morphological

characterization step of analysis; or were pieces of asphalt too hard to sample.

Once the tarballs were brought back to the lab, they were weighed, and visually and physically

characterized. Morphological characterizations included: outside/inside color, extraneous

materials, pliability, and any comments regarding the outside/inside texture. The color

categories were black, black-red, brown, grey, green, and white. Extraneous materials were

visually estimated as a percentage of organic, sand, and shell. Materials considered as organic

matter were seaweed and other plant material, and occasionally worms. Barnacles on the

outside surface were considered when determining the percent range for shell composition.

Pliability was also determined and ranked from 0 to 5 by the extent the tarball would bend

when manual pressure was applied. A value of 0 represents solid tarballs without any

pliability, while 5 indicated stranded tar that is almost fluid at ambient laboratory room

temperature. A pliability ranking of 3 is representative of tar pieces that can bend without

- 29°

89°
breaking. The pliability characteristic can be related to some degree to the residual oil's pourpoint and provide insight to the extent of weathering.

Each tarball was cut in half (after being weighed) and any additional comments were noted in

regards to the appearance of the interior portion of the sample. Common interior descriptions

were glassy, slightly glassy, shiny, slightly shiny, and dull. These descriptions were used in

conjunction with a description of the texture of the tarball interior which included hard,

relatively soft, soft, waxy, grainy/gritty, and rocky. All of these observations were qualitative

only and decided on by the researcher.

A 0.20 to 0.30g core of each tarball was sampled and placed into individual 4OmL VOA vials.

The average weight of the samples was 0.22g. Two grams of Na2SO4 were added to all the

vials. Ten milliliters of hexane were then added to each vial. The samples were sonicated in a

cold bath for 15 to 20 minutes. After sonication, the vials were refrigerated at 1°C (34°F) until

GC/MS analysis. After GC/MS analysis, the samples were integrated and the SF1 were

calculated as stated previously in the methodology and entered into a spreadsheet.

Visual classification of the tarballs was based on a source classification scheme established by

Henry et al. in the 1993 report Characterization of Chronic Sources and Impacts of Tar along

the Louisiana Coast. The tarballs for this report were classified into one of nine categories:

relatively unweathered, high aromatic; relatively unweathered, high paraffin; relatively

unweathered bimodal wax; weathered, high aromatic; weathered, high paraffin; weathered,

bimodal unresolved complex mixture (UCM); weathered, bimodal wax; weathered, bimodal

UCM, and wax (trimodal); and unclassifiable.

A slightly modified version of the same source classification was utilized to separate the

tarballs collected during the 1998 survey (i.e. high was not used as a descriptive of the type of

oil and only eight categories, instead of nine, were established for the 1998 tarballs). The

categories for the 1998 tarballs were classified into one of eight categories: relatively

unweathered, aromatic; relatively unweathered, paraffin; relatively unweathered, wax/bimodal

wax; weathered, aromatic; weathered, paraffin; weathered, wax/bimodal wax; weathered,

UCM; and unclassifiable.

SF1 from the tarballs collected in 1992 were then compared to the SF1 calculated for the tarballs

collected in 1998. Data for 27 of the 1992 tarballs was obtained from Appendix II in Henry et

al.'s 1993 report. Data for the 1998 samples totaled 69 tarballs. Double index scatterplots

were then generated with the SF1 data from both years. SF1 data from twelve of the fourteen

reference oils (excluding North Slope Crude and Eugene Island Crude) were also plotted to

determine, if possible, source of the tarballs andlor if any of the tarballs from both years

matched each other. Calculating ±20% of the x-axis SF1 and y-axis SF1 for each reference oil

established target match zones.

The 1998 tarballs were plotted alone on another set of double SF1 scatterplots with the

reference oil target match zones. The tarballs included in the target zones were visually

compared to the reference oil and/or other tarballs in the zone. Match/nomnatch determinations

22
were then made. Data evaluation also included an extensive visual matching process for the

eight biomarker fingerprints.

RESULTS AND DISCUSSION

Laboratory-Simulated Weathering and Degradation Study

The original goal of the water degradation study was to assess compositional changes in six

selected oils due to weathering with respect to biomarker nonnalization and compositional

changes within the laboratory. However, after some consideration, it was decided that studying

the ability of microbial communities to naturally degrade oils would be more prudent. The

major altering factor, in regards to oil degradation in the marine environment, is the effect

caused by the natural microbial communities within the water. This task evaluated the changes

in composition of one oil, Eugene Island Crude (EIC), after exposure to nine natural waters

collected across the country and two control waters, after 28-days of weathering.

Two analytical endpoints, percent change in total target aromatic hydrocarbons (TTAH) and

the stability of the SF1, were chosen to assess the biodegradation and biomarker persistence of

the one oil (EIC) in the nine water samples and the two control waters. The change in TTAH is

reflective of bulk oil loss; while the SF1 represent specific classes of biodegradation-resistive

hydrocarbons, or biomarkers.

Table S gives the percent reduction in total target aromatic hydrocarbons after the 28-day

weathering period for the nine water sites and Figure 10 shows the sum of TTAH in the

samples with and without nutrients analyzed after 28-days of weathering. Abiotic factors that

may affect the degradation of the ETC were controlled to some extent; the oil was the same and

temperatures remained relatively constant in both experimental locations. One important

abiotic factor not capable of being replicated in the laboratory study was the effect of tides.

Oxygen and nutrients, often the limiting factors in real world situations, were provided to the

microbial communities present in the different waters (i.e. the flasks were covered with sterile

gauze and nutrients were provided to half the flasks). Biodegradation potentials of each water

with additional nutrients were readily apparent in the percent reduction of TTAH.

Table 5. Percent of TTAII Reduction for the Nine Water Samples

After 28-Day Weathering Experiment.

23

% Reduction,

No Nutrients

% Reduction,

With Nutrients

Fresh Water Sites

Lake Charles, LA 37 87

Port Allen, LA 15 68

(Mississippi River water)

Rockefeller Refuge, LA 43 88

Sabine Lake, LA 19 49

LSU University Lakes, LA 39 93

(Table 5 Continued)
Figure 10. Average TTAH for each site and two controls.

BiologTM microplate analyses were also performed for the nine water sites in order to provide a

preliminary characterization of the microbial communities present in the waters and their

ability to utilized a variety of carbon sources. The results of the BiologTM microplate analyses

indicate that the bacteria in the different water samples had widely varying metabolic

capabilities. For purposes of comparison among samples, carbon sources were grouped

according to compound type and then the response to compounds within the class averaged.

Full responses (indicated by high color development in the well) was given a rating of 100, low

response a rating of 50, and no response a rating of 0. Compound classes included (1)

polymers, (2) carbohydrates, (3) esters, (4) carboxylic acids, (5) brominated chemicals, (6)

amides, (7) amino acids, (8) aromatic chemicals, (9) amines, (10) alcohols, and (11)

phosphorylated chemicals. The total number of sources used was also calculated. Figure 11

provides the total sources, as calculated from the BiologTM results, used by the indigenous

microorganisms from each water. The carbon source of interest for correlation to the

laboratory weathering is the aromatic chemicals. Figure 12 displays the percent aromatic

chemicals utilized by the indigenous microorganisms present in each of the water samples.

24

% Reduction,

No Nutrients

% Reduction,

With Nutrients

Salt Water Sites

Coronodo Beach, CA 17 76

buy Beach, LA 30 90

Jax Beach, FL 0 74

Kilua Beach, HI 0 35
The results of the BiologTM microplates do not correlate well with the TTAH endpoint. The

results of the 28-day weathering experiment demonstrate that LSU University Lakes water with

the additional nutrients had the highest percent change, 93%, in total target aromatic

hydrocarbons. This is interpreted as the water from University Lakes and the indigenous

microbial community present utilized the oil more efficiently than the other waters. The lakes

are subjected to chronic, urban runoff that results in a constant flux of nutrients and oxygen

demands. The results of the BiologTM microplates show the opposite of this conclusion. LSU

University Lakes had a low response in both total source utilization and aromatic chemical

utilization. This could be due to several factors. The aromatic chemicals provided as a carbon

source in the BiologTM microplates consisted of urocanic acid, inosine, uridine and thymidine.

The carbon source in the 28-day weathering experiment was a crude oil that was analyzed for

40 different petroleum hydrocarbons. The nutrient substrates also differed. BiologTM utilized

BUGM agar supplemented with defibrinated sheep's blood; the 28-day weathering study

utilized a nutrient mixture containing potassium mono-phosphate, potassium dibasic phosphate,

magnesium sulfate, ammonium nitrate and yeast extract. Larger samples of water should have

been taken for the BiologTM analyses to provide a more sufficient sample to be cultured. Only 2

mL of water from each collection site was saved for the microplate analysis.

The differences in nutrient substrate and carbon sources available in the natural environment

and in the experimental flasks could have affected the combined metabolic utilization of the

microbial community present in each of the waters tested. As a result, dominant species may

have been different for the BiologTM and the 28-day weathering experiment. Furthermore,

Smalla et al. (1998) found that the relative abundance of populations present in the inocula

changes during incubation of the BiologTM plates, resulting in a dominance of species that are

best able to compete with the substrate concentrations in the microplates and under the

conditions of incubation. Both experiments provide information on the microbial populations

as a whole and how they work together to degrade sources of nutrients provided for them.

It appears as though the temperature of the two experimental locations may have had an effect

on the degradation potential of the nine water sites. The temperature of each water was

recorded at the time of collection, along with other physical characteristics such as salinity and

pH. When the waters were brought back to the laboratory, they were stored in a refrigerator

until sample preparation, at with time the samples were removed. Two different experimental

locations were established to correspond to the approximate collection temperature of the water

samples. One room had an average temperature of 23.3°C (73.9°F) and the other room had an

average temperature of 27.4°C (81.4°F). The percent reduction in TTAH for the samples in the

room with the warmer average temperature, with one exception, was almost one to three orders

of magnitude higher than the percent reduction in TTAH for the samples in the room with the

cooler temperature. The one exception was the water sample from Sabine Lake. Average

ambient temperature of the two experimental locations appears to only affect the percent TTAH

reduction for the no nutrients. Table 6 contains the physical characteristic data and the percent

reduction of TTAH for the nine water samples and is sorted based on the average temperature

of the two rooms.

25
100

90

80

I

70

60

50

40

30-

20

10

0 I, I, /j%/f,

tP 0# ... > ,

C,0

Collection Site

'u'.''

Figure 11. Total sources utilized by microorganisms from Biolog TM results.

100

90

80

70

60

50

40

30

20

10

0

--I..'..

, ,. 4 M$' \\C; ç>%6 ç 4# $ \

0 C'0 Collection Site

Figure 12. Percent aromatic chemicals utilized by microorganisms from BiologTM results.

26
Table 6. Physical Characteristic Data and % Reduction in TTAH for the Water Samples Based

on the Experimental Location Average Temperature.

*Average temperature of location 1 = 27.4°C; average temperature of location 2 = 23.3°C

SF1 were calculated for the ETC oil in the nine different waters and two control waters, with and

without nutrients, after the 28-day weathering experiment. The different collection sites were

divided into no nutrients and with nutrients, and statistical analyses were performed (averages,

standard deviations, and the percent relative standard deviation were calculated). Table 7

provides the SF1 data and statistical results for all the collection sites without nutrients after 28-

days of weathering; and Table 8 provides the SF1 data and statistical results for the collection

sites with nutrients after 28-days of weathering. Table 9 displays the daily SF1 data for the EIC

reference oil.

Table 7. SF1 Data and Statistical Results for All Collection Sites, Without Nutrients.

27

Water Sample Temp at Collection

(°C)

Experimental

Location*

%Reduction,

No Nutrients

% Reduction,

With Nutrients

Holly Beach 27.8 1 30 90

Lake Charles 30.0 1 37 87

Rockefeller Refuge 28.3 1 43 88

Sabine Lake 28.3 1 19 49

University Lakes 28.9 1 39 93

Coronodo 16.1 2 17 76

Jax Beach 23.3 2 0 74

Port Allen 25.0 2 15 68

Kilua None Given 2 0 35

Site

nC-17!

Pñs

nC-181

Phy

C-3

DBT

C-3

Phen

C-3DBT!

C-3Phen

C-I

Pyr

C-i

Chr

Nor!

Hop

Coronodo Beach, CA 2.30 1.90 1.90 4.90 1.10 0.71 2.30 0.91

Coronodo Beach, CA 2.50 2.00 2.10 5.00 1.90 0.71 2.30 0.87

Coronodo Beach, CA 2.30 2.00 2.10 4.60 1.90 0.73 2.30 0.92

DiControl 2.60 2.10 2.10 4.30 1.90 0.74 2.20 0.94

DI Control 2.70 1.80 2.00 4.70 1.90 0.75 2.20 0.93

DI Control 2.50 2.00 2.10 5.00 1.90 0.72 2.40 0.97

Holly Beach, LA 2.50 2.00 i.90 4.30 1.80 0.79 2.00 0.91

Instant Ocean 2.40 1.90 1.90 4.70 1.70 0.74 2.20 0.94

JaxBeach,FL 2.40 2.00 2.00 4.80 1.80 0.79 2.30 0.86

Kilau Beach, HI 2.50 2.10 2.00 4.60 1.70 0.80 2.20 1.00

Lake Charles, LA 2.40 2.00 2.00 4.60 2.00 0.75 2.30 0.85

PortAllen, LA 2.00 1.60 2.00 4.50 1.90 0.78 2.40 0.92

Port Allen ,LA 2.10 1.60 2.00 4.60 1.80 0.77 2.30 0.85

PortAllen,LA 2.20 1.70 2.00 4.90 1.80 0.78 2.30 0.92

Rockefeller Reffige, LA 2.60 2.10 2.00 4.90 1.90 0.76 2.30 0.86

Sabine Lake, LA 2.70 2.00 2.10 4.90 1.80 0.83 2.20 0.91

University Lakes, LA 2.40 1.80 1.90 4.50 1.80 0.74 2.30 0.82

(Table 7 Continued)
Table 8. SF1 Data and Statistical Results for All The Collection Sites, With Nutrients.

Table 9. SF1 Data and Statistical Results for Daily EIC Reference Oil.

28

nC-17/

Pris

nC-18/

Phy

C-3

DBT

C-3

Phen

C-3DBT/

C-3Phen

C.1

Pyr

C-i

Chr

Nor!

Hop

Average 2.42 1.92 2.01 4.69 1.80 0.76 2.26 0.90

Standard Deviation 0.19 0.16 0.07 0.22 0.20 0.03 0.09 0.05

Percent Relative Standard

Deviation 8.04 8.50 3.73 4.73 10.94 4.43 4.11 5.22

Site

nC-17/

Pnis

nC-18/

Phy

C-3

DBT

C-3

Phen

C.3DBT/

C-3Phen

C-i

Pyr

C-i

Chr

Non

Hop

Coronodo Beach, CA 0.00 0.00 2.20 4.80 1.70 0.69 2.20 0.94

CoronodoBeach,CA 0.00 0.00 2.10 5.10 1.80 0.73 2.20 0.92

Coronodo Reach, CA 0.00 0.00 2.20 5.70 1.80 0.00 2.20 0.90

DI Control 0.61 0.00 2.20 4.90 1.80 0.76 2.50 0.89

DI ControL 1.80 0.00 2.00 5.10 1.80 0.72 2.30 0.84

DI Control 0.71 0.00 2.20 4.90 1.90 0.74 2.40 0.88

Holly Beach, LA 0.00 0.00 2.10 7.30 2.20 0.00 4.50 0.89

Instant Ocean 0.06 0.04 2.10 5.00 1.80 0.77 2.20 0.91

Jax Beach, FL 0.00 0.00 1.50 6.40 2.00 0.00 3.20 0.90

Jax Beach, FL 0.52 0.20 2.50 4.80 1.90 0.70 2.20 0.91

Jax Beach, FL 0.00 0.00 2.00 6.10 2.00 0.85 4.30 0.92

Kilua Bch, HI 0.11 0.01 2.10 5.20 1.90 0.80 2.40 0.92

Kilua Bch, HI 0.21 0.13 2.20 5.50 1.80 0.84 2.30 1.00

Kilua Bch, HI 0.03 0.03 2.00 4.30 1.80 0.74 2.30 0.87

Lake Charles, LA 0.90 0.15 1.40 8.10 1.70 0.45 3.20 0.86

Port Allen, LA 0.00 0.00 2.00 5.30 1.60 0.65 2.20 0.92

Port Allen, LA 0.00 0.00 2.10 4.40 1.70 0.76 2.40 1.00

Pod Allen, LA 0.00 0.00 2.20 6.10 1.60 0.84 2.56 0.89

Rockefeller Refuge, LA 0.00 0.00 1.80 7.80 1.80 0.54 3.20 1.00

Sabine Lake, LA 0.61 0.17 1.90 5.30 1.90 0.73 2.40 0.90

University Lakes, LA 0.00 0.00 2.30 6.70 1.50 0.00 2.50 0.88

University Lakes, LA 0.37 0.00 2.40 6.20 1.70 0.00 4.40 0.95

University Lakes, LA 0.00 0.00 1.90 4.30 1.00 0.00 13.80 0.90

Average 0.26 0.03 2.06 5.62 1.77 0.54 3.21 0.91

Standard Deviation 0.44 0.06 0.25 1.07 0.22 0.34 2.42 0.04

Percent Relative

Standard Deviation 170.22 198.96 12.13 18.95 12.69 62.93 75.43 4.64

EIC Ref Oil

nC-17/

Pris

nC-IS!

Phy

C-3

DBT

C-3

Phen

C-3DBT/

C-3Phen

C-i

Pyr

C-i

Chr

Nor!

Hop

Rep 1 2.30 2.00 1.80 4.20 1.80 0.75 2.20 0.90

Rep 2 2.40 1.90 1.80 4.10 1.70 0.77 2.00 1.00

Rep 3 2.20 1.90 1.80 4.50 1.80 0.73 3.80 0.97

(Table 9 Continued)
The SF1 for the ETC after 28-days of weathering and no nutrients in the nine waters and two

controls were acceptable based on the percent relative standard deviation (%RSD), which

ranged from 4% to 11%. Acceptable data can be defined in many ways and is usually governed

by the criteria established in different scientific fields. For the Environmental Protection

Agency, acceptable data is anything less than or equal to 25% RSD. The SF1 for the daily ETC

analysis had similar results to the weathered EIC. Percent relative standard deviations for the

daily EIC SF1 ranged from 3% to 22%.

The nC-i7fPris and nC-18/Phy SF1 for all the waters with nutrients would not be acceptable to

use for accurate determinations of source based on their %RSD, 170% and 199% respectively.

However, they should not be entirely overlooked due to the fact that they would provide

information in regards to the extent of weathering. The C-i Pyr and C-i Chr SF1 were also

unacceptable as shown by their %RSD (63% and 75% respectively).

When each water sampling site was analyzed individually, most of the waters with the

additional nutrients had nC-17/pris and nC-i8/phy SF1 that were either not within acceptable

data limits (%RSD of 25 or less) or no peaks were present in the ion chromatogram to calculate

the SF1 (this would result in an SF1 of 0.00). There were three sites out of nine and two

controls that contained unacceptable relative standard deviations (>25% RSD) for SF1 other

than the nC-i7/Pris and nC-l8IPhy. Coronodo beach, no nutrients, had an unacceptable C-3

DBT arealC-3 Phen area SF1 (RSD=28%) and an unacceptable C- 1 Pyr SF1 for the samples

with nutrients (RSD=87%). Jax Beach had unacceptable data for C-i Pyr and C-I Chr SF1 for

samples containing nutrients (RSD=88% and 33% respectively). The third site with

unacceptable data was University Lakes with nutrients; %RSD for the C-3 DBT area/C-3 Phen

area SF1 was 26% and the C-1 Chr SF1 was 88%. All of the other individual sites, with and

without nutrients had SF1 ranging from 0 to 25% RSD. Despite some of the individual site

data, the water degradation study provides the best evidence of the stability of the biomarker

compounds analyzed (refer again to Tables 7, 8, and 9).

The SF1 that would provide the best information in regards to proving that a sample of spilled

oil is a match/nonmatch to a suspect source would be C-3 DBT; C-3 Phen; C-3 DBT arealC-3

Phen area; and Nor/Hop (refer to Figures 4, 5, and 8 respectively for fingerprint). These

compounds and their calculated SF1 will distinguish one oil type from another, even oils of

29

EIC Ref Oil

nC-17/

Pris

nC-iS!

Phy

C-3

DBT

C-3

Phen

C-3DBT!

C-3Phen

C-i

Pyr

C-I

Chr

Nor!

Hop

Rep4 2.30 1.90 1.90 6.50 1.70 0.85 2.30 0.94

RepS 2.40 2.00 1.90 4.30 1.70 0.84 2.20 0.91

Rep 6 2.40 1.80 1.90 4.30 1.70 0.78 2.30 0.94

Rep 7 2.30 1.90 2.00 7.00 1.70 0.76 2.20 0.91

RepS 2.40 1.90 2.00 4.80 1.80 0.75 2.10 0.93

Rep 9 2.50 2.00 1.90 4.10 1.70 0.79 2.30 0.97

Rep 10 2.30 1.90 2.00 4.60 1.90 0.81 2.10 0.93

Average 2.35 1.92 1.90 4.84 1.75 0.78 2.35 0.94

Standard Deviation 0.08 0.06 0.08 1.04 0.07 0.04 0.52 0.03

Percent Relative Standard

Deviation 3.62 3.29 4.30 21.43 4.04 5.07 22.09 3.36
similar geographical origin, when other target biomarkers and analytes are lost. An

investigator using these four SF1 will have the ability to distinguish subtle differences in actual

spill samples that can be easily missed by standard qualitative approaches (i.e. visual

matching). The C-3 DBT, C-3 Phen, C-3 DBT area/C-3 Phen area, and Nor/Hop SF1, when

used in conjunction with double SF1 scatterplots with a 20% target zone for the reference oil

and visual comparisons, will further assure the investigator in determining matchlnonmatch of

the unknown sample to the reference oil and other unknown samples. The remaining SF1, (nC17/Pris; nC-18/Phy; C-i Pyr; and C-i Chr) appear to be greatly affected by weathering (%RSD

were greater than 25 for the ETC after 28-days of weathering). Regardless of the fact that these

four SF1 were not within acceptable statistical ranges, visual examination of the corresponding

fingerprint will still aid the investigator in match!nonmatch determinations.

Bioaccumulation

Up-take of biomarkers and, in general PAHs, were examined in bivalves exposed to oil from

the TAT Exxon Valdez. The samples were collected in 1999 from Prince William Sound,

Alaska, 10 years after the TN Exxon Valdez spill. Since biomarkers are not usually altered by

microbial degradation, there is a possibility that these lipid soluble materials will accumulate in

marine organisms and could be used as an indicator of possible exposure during oil spills.

Since biomarkers normally elute with biogenic lipids during chromatographic fractionation (Fl

fraction), random samples of seven Fl mussel tissues and six Fi clam tissues were analyzed by

GC/MS. Figure 13 is a histogram of the mussel tissue analysis. One mussel sample (LSU ID#

N9201-22) included in the Fl fraction analysis was taken from a dock that undergoes chronic

pollution from boats. This sample had the highest concentration of both total target aromatic

hydrocarbons (TTAH; synonymous with total polycyclic aromatic hydrocarbons or PAH5) and

biomarker compounds; however, a baseline concentration of biomarker compounds has not

been established for biological samples from the beginning of the TN Exxon Valdez spill.

Figure 14 is a histogram of the clam tissue analysis. Both histograms have the TTAH and

biomarker concentrations given in parts per billion (ppb).

The Fl analysis provides important information about the uptake and utilization of petroleum

hydrocarbons by filter feeding organisms after a duration of time that would be difficult to

perform in a laboratory based experiment. Overall, the biomarker concentrations for both

tissue types were low. The PAH and biomarker concentrations in the Fl fractions from the

clam tissues were lower than the mussel. Detection limits of the GC/MS utilized for the

analysis was 0.01 ppb. The low levels of both PAHs and biomarkers can be viewed in one of

two ways: the mussel and clam tissues almost have non-detectable quantities of petroleum

hydrocarbons; or biomarker compounds are not at detectable concentrations in biological

samples 10 years after exposure to oil. The concentrations of PAHs in the tissue samples from

Prince William Sound have been relatively low the past few years.

Two factors that may explain the low levels are the metabolic capabilities of the mussels and

clams, and the age of the population sampled in 1999. Generally, biomarkers are resistant to

biodegradation, which implies bacterial degradation and not enzymatic transformation. The

life span of the mussel and clam organisms themselves is another factor. The population

30
sampled in 1999 may have been either newly established populations, therefore exposed to

lower concentrations of oil to begin with; or the population was well established and was able

to readily metabolize the petroleum compounds. Unfortunately, biomarker analysis or SF1

were not calculated for any of the samples from previous years, and as a result, a baseline

biomarker concentration was not established. In regards to the literature review concerning

biological tissue samples, the focus was on bioavailability of PAMs compounds in the marine

environment and not biotransformation.

Figure 13. Total polycyclic aromatic hydrocarbons (PAils) and biomarker

concentrations in Fl fractions from 1999 PWS mussel tissues.

2 1.8

1.6 = IA C. 3' 1.2

0.8

0.6.

o 0.4

0.2

4 #1

Clam Sample ID#

o Biomarkers

flAH

Figure 14. Total polycyclic aromatic hydrocarbons (PARs) and biomarker

concentrations in Fl fractions from 1999 PWS clam tissues.

31
Class jfication and Identification of Tarballs

Fourteen reference oils, including the two reference oils utilized throughout the research (NSC

and EIC), from the oil library at IES/RCAT were analyzed and SF1 were calculated. The oils

were categorized into three groups: crude oils; fuel oils; and other. There were a total of nine

crude; three fuel; and two other. The ratios generated from these reference oils are given in

Table 10.

Table 10. SF1 Profiles for Fourteen Reference Oils.

The SF1 profiles given in Table 10 display the varying characteristics of biomarker compounds

present in different types of oils. Even though no correlation can be made within the three

categories, the SF1 are representative of each individual oil type analyzed; therefore, supporting

the SF1 approach to source matchlnonmatch determinations. Sources of an unknown sample

can be eliminated based on the SF1 profiles given for these fourteen oils since the biomarker

distributions are relatively unique for each reference oil.

SF1 are capable of providing evidence to prove that a sample of spilled oil is a matchlnomnatch

to a suspect source. The SF1 profiles given in Table 10 provide a baseline for relatively

unweathered or recently spilled oil. As time and weathering progress, certain SF1 will remain

more constant when dealing with environmental forensic determinations, as demonstrated by

the weathering experiment. For example, the nC-l7IPris and nC-18/Phy ratios are both in the

n-alkane fraction, which is generally the first group of oil compounds to be affected by

weathering. Therefore, if the oil has been in the environment for more than a week or so,

instrumental response of these compounds may be low, which would in tum make calculating

these two SF1 difficult. On the other hand, SF1 such as the C-3 DBT, C-3 Phen, C-3 DBT

arealC-3 Phen area, and Nor/Hop had %RSD less than 11% after 28-days of weathering with

no additional nutrients. Therefore, these four SF1 would be the best option for match/nonmatch

32

C171

Pris

C181

Phy

C3

DBT

C3

Phen

C3DBT/

C3 Then

Cl

Pyr

Cl

Chr

Nor!

Hop

Crude Oils

Arabian Ligh 6.34 3.18 1.27 3.06 3.28 0.35 2.22 1.36

Basrah 1.72 2.59 1.61 2.64 1.64 0.70 2.15 0.88

Eugene Island 2.35 1.92 1.90 4.84 1.75 0.78 2.35 0.94

Hondo 1.79 0.95 1.14 1.34 1.90 0.55 1.09 0.75

LA Blend 1.75 1.85 2.79 5.88 0.83 0.93 2.28 0.67

MegaBorgLight 1.97 1.77 2.40 26.77 0.38 1.04 0.79 0.74

North Slope 2.23 2.39 2.20 1.18 0.80 0.57 2.02 0.65

So. Louisiana 1.62 2.34 2.99 4.75 0.55 0.70 2.46 0.53

SPR 2.80 2.20 1.83 1.58 1.30 0.49 1.73 0.77

Fuel Oils

Bunker C 3.35 2.64 1.67 10.44 0.84 0.98 1.71 0.92

Diesel No.2 3.03 2.72 9.30 21.38 0.46 1.39 2.66 0.00

IFO 380 2.58 2.37 1.39 7.10 0.72 0.99 1.63 0.79

Other Oils

SS Block Condensate 2.32 3.60 3.89 1.95 0.37 1.06 1.89 0.68

Valvoline 1.79 1.12 1.95 3.22 2.82 0.00 0.00 1.19
determinations for crude oils with similar physical properties. Another important factor to

consider is the extent of oil refinement. Some fuel oils, such as diesel in Table 10, are devoid

of the triterpane biomarker compounds due to the refinery process. Furthermore, certain oils

contain differing levels of sulfur would be reflected in the C-3 DBT index. Oils with higher

levels of sulfur will have lower C-3 DBT SF1; oils with lower levels of sulfur will have higher

C-3 DBT SF1. In conclusion to the reference oil analysis, just as relative abundances of

biomarker compounds in individual oils are unique, so are the SF1, making them a very

valuable tool for the characterization of oil.

Biomarkers as a tool were applied to tarball samples collected in 1992 and 1998. SF1 data from

three of nine sites surveyed in 1992 were obtained from Appendix II of Characterization of

Chronic Sources and Impacts of Tar Along the Louisiana Coast (Henry et al. 1993). SF1 were

also calculated for the 1998 tarballs. The SF1 calculated for the tarballs collected from both

tarball surveys were separated by beach and plotted on double SF1 scatterplots. The beaches

were Martin's, Rutherford, and Holly. SF1 data from 1992 included 17 tarballs collected from

Martin's beach, three tarballs from Rutherford, and seven tarballs from Holly (a total of 27).

These same three sites from 1992 were revisited in 1998. SF1 data from 1998 included 40

tarballs from Martin's beach, 21 tarballs from Rutherford, and eight from Holly (a total of 69).

Two different double SF1 plots were utilized. One plot consisted of the C3 Phen SF1 vs. the C3

DBT arealC3 Phen area. The other plot was Nor/Hop vs. C3 DBT arealC3 Phen area. Twelve

out of the fourteen reference oils (excluding NSC and EIC) were also plotted on the graphs. A

20% relative standard deviation of each SF1 utilized in the double index plots was calculated

for each reference oil and considered as that reference oil's positive match target area.

Acceptance of scientific data is usually governed by EPA standards, in which data is

considered valid when the relative standard deviation is 25% or less. The 20% variance used to

establish the reference oil target zones is based on EPA standards, but a slightly lower range so

that the chance for false positive matches is decreased.

No matches were visually confirmed between any of the 1992 and 1998 tarballs. Two tarballs

from the 1992 survey, one from Martin's Beach and the other from Rutherford Beach, when

plotted on the Nor/Hop vs. C3 DBT areaIC3 Phen area, fell into the target zone for the Bunker

C and South Louisiana Crude reference oils. Two 1998 samples (again one from Martin's and

the other from Rutherford) were also plotted on the Nor/Hop vs. C3 DBT areaJC3 Phen area,

fell into the South Louisiana Crude and SS Block Condensate target zones. The 1992 and 1998

samples within the South Louisiana Crude range did not match upon visual examination.

Mother tool for characterizing oil types based on visual scrutinization was established by

Henry et al.'s 1993 report. Tarballs collected and analyzed in 1992 for this report were

classified into one of nine categories: relatively unweathered, high aromatic; relatively

unweathered, high paraffin; relatively unweathered bimodal wax; weathered, high aromatic;

weathered, high paraffin; weathered, bimodal unresolved complex mixture (UCM); weathered,

bimodal wax; weathered, bimodal UCM, and wax (trimodal'); and unclassifiable. The most

common classification identified for the 1992 tarballs was the relatively unweathered, high

paraffin which represented 32% of the samples analyzed. A total of 26% of the samples

analyzed contained the bimodal wax component that is generally believed to be related to crude

oil tanker washing or sludge discharges resulting from cleaning waxy residues from the sides of

33
storage tanks or cargo holds (Butler et al. 1973). Cleaning bunker fuel tanks and fuel lines may

also result in a similar wax signature.

A slightly modified version of the same source classification was utilized to separate the

tarballs collected during the 1998 survey (i.e. high was not used as a descriptive of the type of

oil and only eight categories, instead of nine, were established for the 1998 tarballs). The 1998

tarballs were classified into one of eight categories: relatively unweathered, aromatic;

relatively unweathered, paraffin; relatively unweathered, wax/bimodal wax; weathered,

aromatic; weathered, paraffin; weathered, wax/bimodal wax; weathered, UCM; and

unclassifiable. The most common classification identified for the 1998 tarballs was weathered,

UCM which represented approximately 57% of the samples analyzed. These tarballs are

indicative of weathered crude oils and the normal alkane distribution suggest that the oils were

a mid to heavy range. A total of 17% of the samples analyzed were categorized into the

unclassifiable category. Samples in the unclassifiable category were oils that did not fit any of

the other classifications and generally were so heavily weathered, or not oils at all, that they

could not be classified with any confidence.

Outside of the comparison between the 1992 and 1998 samples, the data forjust the tarballs

collected in the 1998 survey was processed and interpreted at several levels. Morphological

characterizations were determined for all 88 tarballs collected. GCJMS analysis was performed

for 70 of the 88 tarballs. The data generated from the GC/MS analysis were compared based

on eight weathering stages and six extracted ion chromatograms/fingerprints (mle 85, 191, 216,

220, 226, and 242). The SF1 were calculated from the ion chromatograms and were plotted on

double SF1 scatterplots to determine if any of the samples containing oil appeared to be related,

or if any of the samples fell within the reference oil target match zones previously plotted.

Morphological Appearance

The weight distribution of the tarballs is a function of the amount of oil and the amount of

adsorbed material. As indicated by the morphological characterizations, numerous samples

contained sand and other extraneous debris. The percentage of weight contributed from debris

(not oil) could possibly account for 50% of the weight of some samples. It is practically

impossible to remove these included materials; therefore, the weight values presented are over

estimations of the true values. The total weight of the tarballs collected was 3054.15g.

Weights ranged from 0.15g to 296.01g. A cutoff weight of 0.70g was established for sampling

and analyzing due to the fact that tarballs weighing less than the cutoff were too small to

sample

A wide gradation of colors was observed and each tarball was classified as black, brown, grey,

green, and dark red. Of the 88 tarballs collected, 84 (-95%) were black, followed by grey (two

tarballs or -2%) on the outside surface as shown in Figure 15. The remaining colors were less

than 2% of the total. The majority of the inside color of the tarballs was black (82 tarballs, or

'-93%) followed by brown (2 tarballs, or -2%). The distribution of inside color for tarballs

collected in the 1998 survey is displayed in Figure 16. Two samples were too small for the

inside color to be described.

34
Color often suggests an oil type. The dark green tarballs appeared to be globs of heavy grease

or lube oil. The pitch-black tarballs appear to be sourced either from a high pour-point bunker

oils or other refined products derived from heavy petroleum residuum. Brown tarballs often

suggest oil that has been oxidized and moussed during the weathering process; often these

represent spilled crude oils.

90

85

80

75

70

65

.2 60

55

j- 50

' 45

.- 40

. 35

30

25

20

Is

10

S0

Black

I (77/

Black Red Grey Brown

Outside Color Category

Figure 15. Surface color distribution of tarballs collected in 1998 survey.

90

8580

75

70

55

50

V

Black Brown Green

H

White

Inside Color Category

Figure 16. Inside color distribution for tarballs collected in 1998 survey. Two

were too small to be characterized for inside color.

Many of the samples collected contained extraneous matter including organic debris (i.e.

seaweed), sand, and shell fragments. These observations are not unusual. The sticky nature of

oil floating on the water's surface and repeated strandings often result in the accumulation of

35
organic debris and beach substrate. The potential for extraneous material to become

incorporated in the tarball is a function of physical weathering processes as well as the

physicallchemical composition of the spilled oil. Very high pour-point oils do not easily spread

and are limited to surface encrustations only. Figures 17, 18, and 19 present the relative

contribution of organic matter, sand and shell fragments to the entire tarball population.

Organic debris from pelagic materials and beach erosion is a very ubiquitous feature in the

marine environment and was the predominant extraneous material encountered in all the 88

collected tarballs. Sand was more common than shell for most of the tarballs collected. This is

consistent with the distribution of beach substrates; sandy beaches are more numerous than

shell beaches along the northern Gulf of Mexico.

100

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

5

0

0-20% 21.40% 4l-60% 61-80% 81-l00%

Percent Organic Mailer

Figure 17. Percent organic matter distribution for tarballs collected in

1998 survey.

100.

95

90

85

80.

75

70

65

L 60 a

I- 50 C

' 40 i 35

. 30 Z2 SSa.rS/

0-20% 2140% 4h60% 6140% 81-100%

Percent Sand

Figure 18. Percent sand distribution for tarballs collected in 1998 survey.

36
35

30

25

20

15

10

Figure 19. Percent shell distribution for tarballs collected in 1998 survey.

Each tarball was also characterized for its pliability. Pliability is generally associated with the

physical/chemical composition of the spilled oil and the extent of weathering which has

occurred. Highly weathered tarballs tend to become nonpliable, often brittle or very difficult to

break. These pliability characterizations were qualitative only. Each sample was rated from 0

to 5. A rating score of 0 is nonpliable and nonbreakable using normal hand pressure. A score

of 5 represents stranded tar that was almost fluid. Figure 20 displays the pliability score for the

tarballs collected in 1998. The majority of the tarballs sampled for this research had a score of

1, which represents tarballs that are difficult to bend with normal hand pressure. Less than 7%

of the tarballs collected were nonpliable (score of 0) and none were scored as very fluid

(pliability score of 5).

I

Zero One Two Three Four Five

Pliability Score

Figure 20. Tarball pliability score for 1998 samples.

37

90

80

70

60

I.* 50

C

I- 40 4. Ez

30

20
Analytical Chemistry Results

Of the 88 tarballs collected during the 1998 survey, 70 were sampled and analyzed by GCJMS.

Eighteen samples were not extracted due to the fact that they were smaller than the cutoff

weight established during the morphological characterization step of analysis; or were pieces of

asphalt too hard to sample. The first stage of data processing for the GC/MS analyses for each

of the 70 samples was classification into one of eight classifications adapted from

Characterization of Chronic Sources and Impacts of Tar Along the Louisiana Coast (Henry et

al. 1993). Some of the earlier classification schemes were based primarily on alkanes (Boehm

et al. 1981), and more recently, polycyclic aromatic hydrocarbons (PAHs) classification have

been published (Sauer et al. 1993). Any weathering classifications are most useful when they

describe a specific oil type. The classification scheme used for this project consisted of eight

categories, which provided basic chemical information related to compositional change in the

aromatic hydrocarbon fingerprint. The eight classifications are as follows:

Relatively unweathered, aromatic. Oils classified into this category were identified

by a nC-18/phytane SF1 ratio of greater than 1 and were enriched with target

aromatic hydrocarbons. Oils in this classification are often representative of slightly

weathered, refined, blended fuel oils.

Relatively unweathered, paraffin. Oils classified into this category were

identified by a nC-18/phytane SF1 ratio of grater than 1 and were enriched

with normal paraffins between nC-iS and nC-33 often with the most

abundant normal paraffin being nC-19. Oils in this classification generally have

high pour-points and many may be representative of heavy fuel oils.

Relatively unweathered, wax/bimodal wax. These tarballs are characterized by a

nC-18/phytane SF1 ratio greater than 1 and, in some instances, a bimodal

distribution of normal alkanes with a pronounce wax component from nC-21

extending to nC-37. These oils are believed to be representative of crude oil and

heavy fuel oil tank washings or sludge discharges.

Weathered, aromatic. Oils classified into this category were identified by a nC1 8/phytane SF1 ratio of less than 1 and were enriched with target aromatic

hydrocarbons. Oils in this classification are often representative of weathered

refined fuel oils and some highly weathered crude oils.

Weathered, paraffin. Oils classified into this category were identified by a nC1 8/phytane SF1 ratio of less than 1 and were enriched with normal paraffins between

nC-15 and nC-33. Oils in this classification have high pour-points and many may

be derived from heavy, high pour-point fuel oils.

Weathered, wax/bimodal wax. These tarballs are characterized by a nC-i 8/phytane

ratio less than 1 and in some instances, a bimodal distribution of normal alkanes

with a pronounced nC-21 extending to nC-37 wax component. These oils are

38
Visual Matching

An exhaustive matrix comparison process that utilized all the chromatographic data and

provided separation of the tarballs by source was the next step of the data interpretation. This

comparison involved visual scrutinization of each ion fingerprint for all biomarker ions chosen

for analysis. After analysis, a hard copy of all the ion fingerprints for each tarball was printed

out and the biomarker ions were separated from the rest of the ions and stacked into separate

piles. Each tarball fingerprint in each pile was visually compared to each other to determine if

any matched or were nonmatches. Matched samples went into one pile and nonmatch samples

formed a new pile until every chromatogram had been compared. Each source was assigned an

alphabetical identification such as Source A and Source B. The comparisons were completed

for each tarball analyzed. Through this manual process and previous experience, certain ion

components within the samples were shown to be quite unique and descriptive.

believed to be representative of crude oil and fuel oil tank washings and sludge

discharges as noted from the unweathered bimodal wax.

Weathered, unresolved complex mixture (UCM). These tarballs are characterized

by a range of nC-18/phytane SF1 ratios and contain some normal alkanes and

aromatic hydrocarbons, but could not be distinguished as weathered aromatic oils.

All tarballs in this classification exhibited a large UCM in both the TIC and m/e 85.

Oils in this classification are oflen highly weathered crude oils.

Unclassifiable. Oils that did not fit any of the above classifications. Generally,

these oils were so heavily weathered, or not oils at all, that they could not be

classified with any confidence.

Table 11 provides a summary of the GC/MS tarball classifications for the samples analyzed in

the 1998 collection. The most common classification identified was weathered UCM, which

represented --57% of the samples analyzed. Figures 21, 22, 23, 24, 25, 26, and 27 show

chromatographic ion fingerprints of oils typically classified as one of the eight categories,

except Unclassified.

Table 11. Distribution of All Tarballs into Classifications.

Relatively Unweathered

Aromatic

Paraffin

Wax/Bimodal Wax

Relatively Weathered

Aromatic

Paraffin

Wax/Bimodal Wax

UCM

Unclassifiable

Number of

Samples*

2

9

2

3

40

12

% of

Total

--1.4

1.4

2.9

-42.9

--2.9

4.3

5 7.1

-47.1

39
(a)

(b)

Figure 21. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a relatively unweathered, aromatic oil.

40

bundance

140000-

120000-

100000-

80000 -

60000-

rime-->0LsLL44*JLø4: to.00

Ion 85.00 (61.70 to 65.70): HP8195E.D

20.00 30.0 40.00 50 00 60.00 70.00

150000

140000

130000

120000

110000

100000

90000

000

70000 0000

50000

40000

30000

::::

time--s

-

I -i4

TICt 00P295E0

J - 0 20.00 30.00 ,0. 00 50.00 60-00 '0-00
(a)

(b)

Figure 22. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a relatively unweathered, paraffinic oil.

41

Ion., Q5.00 (84.70 to 65.10): H28161C.D

120000-

100000

60000 -

60000-

40000

2000:

rime--> io.00 20.00 30.00 40.00 50 00 60.00 70.00

thundaoca

500000

410000

400000

350000

300000

250000

200000

150000

100000

50 000 ?j-->

TIC: H?eI6lC.D

10.00 20.00 30.00 40.00 SO 00 60.00 70.00
(a)

0')

Figure 23. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a relatively unweathered, bimodal wax.

42

'ibundance

90000 -

80000 -

70000:

60000 -

50000-

40000 -

30000 -

20000-

1000:

rime-->

r t4

Ion 85.00 (84.70 to 85.70): HP8169D.D

10.00 20.00 30.00 40.00 50.00 60.00 70.00

und.nc. 100000 95000

90000

65000

60000

75000

70000

65000

60000

55000 S0000 45000

40000

35000

30000

25000

20000

5000

TIC,

H

0800690.0

j

Tim.--> 10.00 10.00 3000 40.0 50.00 60.00 '0.00
(a)

(b)

Figure 24. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a weathered, aromatic oil.

43

%bund&nca

50000-

45000-

400002

35000 -

30000-

25000

20000

15000

10000

5000-

Ion

-

85.00 (94.70 to 85.70): HP8162B.D

0 .. i...,

Time--> 10.00 20.00 30. 0 40.00 50 00 60.00 70.00

,bu,dance

75000

70000

65000

60000

55000

50000

45000

40000

35000 30000

25000

20000

15000

10000

5000

0 TIne-->

TIC:

J-r

HP01620.D

10.00 20.00

p 30.00p 40.00I 50.00 I'I40.00 '0.00
(a)

(b)

Figure 25. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a weathered, paraffinic oil.

44

Zbundance

250000 -

200000-

150000 -

100000-

50000-

Ion 85.00 (84.70 to 85.7OhsHPO182D.D

time--> 10.00

0 '.,.. I 20.00 30.00 40.00 50.00 60.00 70.00

bundanc. 260000

240000

220000

200000

160000

160000

140000 20000

100000

10000

60000

40000

20000 71..-->

TIC, 10P11620.

10.00 20.00 30.00 40.00 00.00 60.00 70.00
(a)

(b)

Figure 26. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a weathered, bimodal wax.

45

bundance

300000-

250000

200000.

150000.

100000:

50000.

0

Time-->

i1L1

10.00

Ion

_i[

85.00 (84.70 to 95.70); HP8161D.D

)

20.00 30.00 40.00 50 00 60.00 70.00

bu,od.nc. 550000-

500000 450000-

400000 -

350000 - 300000. 250000-

200000

150000 -

'00000

5000: Xl>

TIC. 01P31610.D

10.00 20.00 30.00 40.00 50 0 6 .00 70.00
(a)

(b)

Figure 27. Normal alkane distribution (a) and Total Ion Chromatogram

(b) for a weathered, UCM.

46

bundance

40000

35000-

30000-

25000 -

20000 -

15000 -

100o0-

5000

0

Ion 85.00 (64.70 to 85.70): HPB1Ø9C.D

Dime--) 10.00 20.00 30.00 40.00 50.00 60.00 70.00

untoc. 40000 46000- 44 000- 42000- 40000- 30000

36000 - 34000 - 32000- 30000 - 20000 - 26000 - 24000. 22000- 20000 20000- 16000- 04000- 02000 - 10000 - 000G. 6200 - 4000- 2000LI i

TIC, 00P0109C.D

T0> 10.00 20.00 30. 0 40.0 50.00 6000 10 00
Source-fingerprinting of the 70 tarballs analyzed identified 66 different sources. Four matches

were found through visual comparison and double SF1 scatterplots. The matches included one

tarball matching Valvoline; one tarball matching ilondo Crude; one tarball matching SPR

crude; and two tarballs that matched each other but did not match one of the reference oils. An

interesting observation is the wide distribution of sources from the three sampling locations.

Apparently, the tarballs collected represent oil contamination from small petroleum and/or

bilge waste discharge.

SF1 and Scatterplots

The initial group of SF1 scatterplots included all eight of the biomarkers chosen by IES/RCAT.

As a result of the visual comparison, a selection of three of the SF1 were chosen and consisted

of two SF1 per plot (i.e. C-3 Phen vs. C-3 DBT area/C-3 Phen area; and norhopane/hopane vs.

the C-3 DBT areal C-3 Phen area). These indexes exhibited resistance to weathering during the

water degradation experiment and displayed chromatographic uniqueness. Figures 28 and 29

are two examples of the double SF1 plots.

All the samples analyzed were plotted as a scatterplot in an attempt to further confirm sources.

The reference oils prepared during the initial stages of the characterization and identification of

tarballs were also plotted and target match zone were calculated based on a 20% standard

deviation of the SF1 chosen. A total of 29 possible matches, either to one of the reference oils

or to another tarball, were determined from the two different double SF1 scatterplots. However,

only four of the matches were confirmed by visual comparison. Since the initial visual

comparison indicated an abnormal distribution of several non-related sources (66 different

sources), it was decided that principle component analysis would not provide the discrimination

necessary for statistical source-fingerprint results; therefore, scatterplots were created instead.

Henry (1995) established that the use of principle component analysis did not provide the

degree of source resolution anticipated in a tarball population containing 67 different sources in

a sample of 133. Had each source not been differentiated by visual comparison, identification

of which tarballs were related statistically could not be determined to any degree of confidence.

Factors that contributed to the loss in discrimination included the limited value range of the

index values used and the extremely high number of different sources in the study population.

Principle component analysis may have been useful if the study population demonstrated a

more normal distribution.

The double SF1 scatter plots that were generated did not provide a high degree of identification,

but were considered a useful screening tool. The resulting scatterplots were often more

confusing than enlightening, yielding "inconclusive" results for all double SF1 plots attempted.

The major factor influencing the interpretation of the scatterplots was the various stages of

weathering and sources for the tarballs sampled. Therefore, it has been concluded that double

SF1 scatterplots are more efficient and defensible when one source oil and a few unknown

samples have been collected for analysis.

47
7

6

I

L

55

C3 Phenatithrene sIb

10 xRefl0 .Martins Beach 1998

IS

Figure 28. C3 Phenanthrene versus C3 Dibenzothiophene/C3 Phenanthrene area double

SF1 scatterplot for tarballs from Martin's beach.

4

.

05

00 0' 02 03 04 0.5 06 01 08 09 10 II 12 3 14 IS 16 17 II 19 20

Norhopane/Hopane

xReference Oils Martins Beach 1998

Figure 29. Norhopanelllopane versus C3 Dibenzothiophene/C3 Phenanthrene area double

SF1 scatterplot for tarbails from Martin's beach.

The SF1 values were validated for instrumental variability from the percent relative standard

deviation calculated from the daily North Slope Crude (NSC) reference oil. The %RSD for the

daily NSC ranged between 3.63 and 9.17%. Individual %RSD were as follows: nC17/pristane, 3.63%; nC-18/phytane, 9.17%; C-3 DBT a/b, 6.08%; C-3 Phen a/b, 4.21%; c-i

Pyr a/b, 7.59%; C-I Chr a/b, 5.13%; Nor/Hop, 5.33%; and C-3 DBT area/C-3 Phen area,

3.83%. One goal of this research was to choose SF1 that were unaffected by changes in

48

S

0
instrumental operation. Each index, except for the C-3 DBT arealC-3 Phen area, was hand

calculated on the basis of peak height rather than area to reduce integration error due to peaks

not being baseline resolved. The C-3 DBT arealC-3 Phen area SF1 is based on total area

represented by each isomer group.

Of the 70 tarballs analyzed by detailed GC/MS, 57% had fingerprints indicative of weathered

crude oils. The normal alkane distribution observed from the GC/MS analysis indicate that the

majority of the weathered crude oils were a mid to heavy range. Light oils spilled in the marine

environment spread very thinly on the water's surface and disperse by natural processes, such

as storms, and usually do not form tarballs. However, some of the tarballs were most likely

formed due to the formation of stable water-in-oil emulsifications, or mousses. Mousse

formation is common for many crude oils and is a prime factor for the formation of tarballs

from otherwise easily dispersible light crude oils. Very heavy crude oils are prone to form

tarballs due to the fact that they are slow to spread and expose little surface area for natural

degradation processes.

CONCLUSIONS AND RECOMMENDATIONS

Biomarker compounds, by definition, are universal in crude oils and petroleum products.

These compounds are generally more resistant to environmental weathering than most of the

other oil constituents. The distribution of biomarker compounds is unique for each oil and

different sources of petroleum exhibit different oil fingerprints. SF1 calculated from these

fingerprints provide a stable and useful tool for determining a match or nonmatch for different

oil residues present in some environmental samples. The presence and detection of biomarker

compounds is an indicator of some sort of oil exposure or contamination. Analytical chemistry,

biomarker normalization determined by SF1, and visual comparisons of oil biomarker

fingerprints, provide environmental scientists with valuable information regarding the

identification of spilled oil and quantification of oil residues.

As far as general methodology is concerned, CC/MS operated in selected ion monitoring mode

provides detailed compositional and comprehensive information on saturated and aromatic

hydrocarbons, without requiring involved sample clean-up procedures prior to sample

extraction and analysis. High-resolution GC/MS analysis and source-fingerprinting techniques

were employed throughout all experimental stages to validate and extend the use of biomarkers

as tools for oil spill identification and assessment. Eight (8) petroleum biomarkers were chosen

for detection and analysis through a literature search and from previous research. Selfnormalizing fingerprint indexes, or SF1, calculated from the GC/MS analysis of a water

degradation experiment; a bioaccumulation study; and the classification and identification of

tarballs assisted in the validation of the eight biomarkers chosen by LSU IES/RCAT.

Visual comparison played an important role in distinguishing gross, and in some cases subtle,

differences in the biomarker fingerprints. This method is often the first step in GCIMS data

interpretation of spilled oil. Visual comparison of a small number of samples is very efficient

for matchlnonmatch determinations. A suggested method (Figure 30) for data interpretations

for match/nonmatch determinations is as follows: print out hard copies of biomarker ions and

49
the TIC; separate each copy into separate biomarker ions; compare other copies of each

biomarker ion to one another to determine a visual match/nonmatch; calculate the SF1 and

compare each value with the others in the group; plot the reference oil with the 20% target

match zone on one of the double index scatterplots from above and then plot the samples; and

finally, reconfirm matches with a visual comparison.

SF1 reflect the relative rates at which selected oil biomarkers are dissipated during the

combined physical, chemical, and biological processes that constitute oil weathering. The

effects of weathering on the value of biomarker indexes are generally greatest in the period

inmediately following spillage or leakage. For SF1 to be valid throughout the weathering

processes, they must consist of components that are truly conserved during oil weathering and

not display significantly different physical properties (Whittaker and Pollard 1997). The SF1

that meet this criterion and provide the best tool for oil spill identification, both visually and

statistically, are C-3 DBT, C-3 Phen, C-3 DBT area/C-3 Phen area, and Norhopane/Hopane.

These compounds, as demonstrated by the water degradation experiment, remain stable even

after weathering and will distinguish one oil source from another, even oils of similar

geographical origin, when other biomarkers are lost (i.e. nC-17/Pris and nC-l8fPhy).

Generally, the simpler the chemical structure, the easier it is to degrade, which is the case for

nC- 1 7/Pristane and nC- 1 8/Phytane. The latter two SF1 are straight-chain hydrocarbons that are

metabolized by microorganisms through successive oxidation and cleavage of terminal methyl

groups of a hydrocarbon molecule. The C-3 DBT, C-3 Phen, C-3 DBT arealC-3 Phen area, and

Norhopane/Hopane are branched or cyclic compounds that experience slower

biotransformation rates due to the presence of secondary, tertiary, and quaternary carbon atoms

within the basic hydrocarbon structure, rendering them more difficult to metabolize (Whittaker

and Pollard 1997).

Even though four of the eight SF1 are statistically valid, it is recommended that GC/MS

analysis and visual examination include all eight because they provide very valuable

information about the extent of weathering and biodegradation from their respective

fingerprints. Visual examinations of chromatographic fingerprints provide very significant

qualitative information, as opposed to the comparison of a few quantitative indexes, and can

easily distinguish one type of oil or weathering stage from another based on the pattern of the

fingerprint alone.

One significant aspect of oil released on water is the ability of natural microbial communities

within the water to affect the oil composition; or in other words, their biodegradation potential.

The key elements of biodegradation include the amount of oil, the mixing energy, the types of

microfauna, and the availability of oxygen. The water degradation experiment assessed the

effects of microorganism populations present in different water sources in two ways: BiologTM

microplate analyses and percent reduction of total target aromatic hydrocarbons (TTAH). The

organisms in the different water samples had widely varying metabolic capabilities.

Unfortunately, the results from the BiologTM analyses and the %TTAH calculated from the

GC/MS data did not correlate. This is due to several factors: differing carbon sources;

differing nutrient substrates; and differing dominate microbial communities. However, the

experiment provided the best data in regards to the stability of the SF1 chosen by IES/RCAT.

50
Form one pile and designate as group "A'.

Print out hard copies of data.

Separate ions 85, 191, 216, 220,226, and 242 into six piles.

Calculate SF1 based on Figures 3-8.

Vt

Compare each sample in each ion pile to one another.

N

Label this sample with the same group designation (i.e. if' it matches A,

lab& sample as A and pu in group A pile.

/

Take next sample and compare to previous samples.

V

Does it match any of the other samples?

Plot SF1 and reference oil values on scatterplots.

V

Reconfirm matches by visual comparison.

Form two piles and designate as groups "A" and "B".

V

Label this sample as "C".

Figure 30. Suggested method for match/nonmatch determinations of unknown environmental oil

samples to a reference oil based on visual comparison and SF1 data.

51

Yes. No

N

Continue on in the same manner with the rest of samples until finished.

V

Calculate standard deviation and 200/ x -axis, y-axis values for reference oil.
The uptake of biomarkers and, in general PAHs, was examined in bivalves exposed to oil from

the TN Exxon Valdez. Theoretically, biomarkers are not significantly altered by microbial

degradation, and since biomarkers are lipid soluble materials, they will accumulate in marine

organisms and could be used as an indicator of possible exposure during oil spills. Selected

marine bivalves were collected during the 1999 NOAA Shoreline Monitoring Program of

Prince William Sound, Alaska and analyzed for PAH content and biomarker concentrations.

The analysis provided important information about the uptake and utilization of petroleum

hydrocarbons by filter feeding organisms after a duration of time (10 years) that would be

difficult to perform in a laboratory based experiment. Overall, the biomarker and PAH

concentrations for both mussel and clam tissues were low, and have been relatively low the past

few years. Unfortunately, a baseline concentration of biomarker compounds has not been

established for biological samples from the beginning of the TN Exxon Valdez incident.

When spilled oil is weathered to the state of a tarball, the oil is generally considered less

acutely toxic than the fresh crude oil or the refined petroleum product from which it was

derived. This is due to the loss of the more water soluble mono- and di-aromatic hydrocarbons,

such as the benzenes and naphthalenes, by evaporation and dissolution processes. However,

tarballs are important environmental samples to analyze due to the fact that oil remaining in the

tarball is enriched with the possible carcinogenic and chronically toxic 3,4, and 5 ring aromatic

hydrocarbons such as chiysene and benzo(a)pyrene.

The classification and identification of tarball samples collected in 1998 suggests that the

majority of the tarballs collected were from mid- to heavy-range crude oil sources. Of the 70

tarballs extracted and analyzed, four matches were found through visual comparison and

double SF1 scatterplots. The double SF1 scatterplots were beneficial as a preliminary

identification step of an unknown sample to a source oil. However, the various stages of

weathering and abnormal distribution of several non-related sources limited the scatterplots

ability to discriminate, within defensible limits, a match or nonmatch to a source. Since the

distribution of sources was extensive, it is believed that the tarballs collected represent oil

contamination from small petroleum andlor bilge waste discharge.

In conclusion, the analysis of biomarker compounds can be further advanced through the use of

tandem mass spectrometry. Triple-stage quadrupole (TSQ) mass spectrometers are currently

available and several researchers have been developing new GC-MS/MS techniques for the

analysis of complex mixtures, such as oil and biomarker compounds above the nC-40 region

(Philip and Oung 1988).

The water degradation experiment could be further advanced with a longer-term oil weathering

study. The duration of the study would be anywhere from one to two months. Several oils

could be tested in several types of natural waters and extracted and sub-sampled for microbial

analysis at select intervals throughout the experiment (i.e. days 0, 7, 14, 28, etc). Instrumental

analysis would be by GCIMS or GC-MS7MS and data analysis would include calculation of

SF1. This would provide fingerprints of the various stages of weathering for each of the oils

tested, which in turn, would be a valuable tool for oil spill responders. A more detailed study

of the microbial affects on oil could coincide with the longer-term weathering study and

characterize the fluctuation of dominant species in the microbial population throughout the

52
study. This would include BiologTM characterization of the microbial communities present in

the waters and their ability to utilize a variety of carbon sources. The dominant communities

identified from the BiologTM analyses, using its carbon and nutrient sources, could then be

compared to the dominant species identified from the sub-samples taken throughout the water

degradation study. This analysis would clarify the differing results between the carbon source

utilization from the BiologTM analyses and the GC/MS % reduction of petroleum hydrocarbons.

The important bioaccumulation factors that need further exploring are how bivalves metabolize

petroleum hydrocarbons throughout their life cycle. Generally, biomarker compounds are

resistant to biodegradation, which implies bacterial degradation and not biological

transformation by the indigenous enzymes in the marine bivalves. A laboratory- simulated oil

spill that would expose populations of mussels and clams to an oil would help establish

concentrations of the biomarker compounds throughout one to several life cycles of the

animals. Both Fl, biogenic fractions, and F2, PAH fractions, of the extracted tissues would be

analyzed by GC/MS or GC-MS/MS. Results from an experiment such as this would aid

environmental scientists and oil spill responders in determining recent and conceivably

previous oil exposure to biological specimens.

Developing more quantitative schemes for classifying and identifying tarballs would be

difficult due to the varying stages of weathering encountered throughout sampled populations.

Double SF1 scatterplots and principle component analysis is one way of obtaining quantitative

schemes; however, they are most effective when there is only one source in question with

several unknown samples. Further experimentation, such as the recommended water

degradation study, could enhance the ability of scientists to develop more quantitative schemes

for classifying and identifying tarballs. At this point, classification of tarballs into different oil

compositions based on qualitative methods (i.e. morphological descriptions) and visual

scrutinization of biomarker fingerprints remain the best tools for oil spill responders and

environmental scientists.

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The Department of the Interior Mission

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