A Geochemical and Hydrological Assesment of Oil and Related Compounds from the 
2010 Deepwater Horizon Oil Spil in Gulf Coastal Saltmarshes 
 
by 
 
Kirsten Nicole Guera 
 
 
 
 
A thesis submited to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Master of Science in Geolgy 
 
Auburn, Alabama 
December 14, 2013 
 
 
 
 
Approved by 
 
Ming-Kuo Le, Chair, Profesor of Geology and Geography 
Jim Saunders, Professor of Geology and Geography 
Charles Savrda, Profesor of Geology and Geography 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 ii
 
Abstract 
 
In April of 2010, the Macondo-1 wellhead ruptured in the Gulf of Mexico, releasing an 
estimated 4.9 milion barrels of crude oil. Ten saltmarsh sites along the northern Gulf coast were 
investigated to determine the fate, transport, and transformation of oil and metal contaminants 
associated with the spil. Sites ranged from heavily contaminated (Bay Jimy North, Bay Jimy 
South, Bayou Dulac, and Batiste), moderately contaminated (Walker Island, Point Aux Chenes 
Bay, and Rigolets), and pristine (Weeks Bay, Longs Bayou, and Bayou Heron). 
Weathered oil samples at heavily-contaminated sites were fingerprinted for two targeted 
biomarkers with special mas-to-charge ratios (m/z). Fragmentograms of m/z 217 and 218 reveal 
a strong correlation between oiled sediments and original MC-252 crude oil. Tarballs colected 
from the Alabama coast in September 2012, 28 months after the spil also were fingerprinted to 
the 2010 Depwater Horizon spil by fragmentograms of m/z 191, 217, and 218. Isolated plumes 
of MC-252 spiled oil thus stil exist on the Gulf of Mexico seaflor, and it is probable that these oil 
plumes wil continue to wash into coastal regions over time with the aid of storm surges or 
hurricane activity. Fragmentograms of the tarbals and weathered oil samples both indicate the 
enrichment of heavier hydrocarbon compounds due to preferential weathering and 
biodegradation of lighter compounds. 
MC-252 crude oil was identified in Gulf coastal sediments at depths down to 15 cm. 
Saltwater-freshwater dynamics may have played a role in this intrusion of crude oil. Physical tank 
experiments and numerical modeling both support the viability of two proposed intrusion 
mechanics: intrusion with a saltwater wedge and downward intrusion with onlapping seawater 
that infiltrates the sediment surface.  
Folowing the spil in 2010, BP and NOA aplied 2.1 milion galons of COREXIT 950A 
to disperse oil slicks throughout the water column. Gas and liquid chromatography-mas 
spectrometric (GC-MS and LC-MS) analyses of COREXIT 950A revealed several compounds: 
 
 iii 
light hydrocarbons, dioctyl sodium sulfosucinate (DOS), palmitic acid, and a sulfonic 
acid base to a series of polyethylene glycols (PEGs). Among these, DOCC wil serve as the most 
effective conservative biomarker to trace the occurrence and geochemical evolution of the 
dispersant in natural environments. 
Inductively coupled plasma-mas spectrometry (ICP-MS) reveals generaly low 
concentrations (a few to hundreds of pb) of various trace metals in pore waters, despite high 
levels of those trace metals in sediments. Elevated total sulfur concentrations at contaminated 
sites and at shalower depths sugest a large influx of sulfur along with crude oil. Low dissolved 
iron and very high levels of sulfide indicate that bacterial sulfate reduction has fixed most reduced 
Fe through the formation of iron sulfide solids, such as pyrite. High levels of arsenic and 
manganese in both sediments and pore water at contaminated sites indicate the influx of these 
metals with crude oil but sugest the possible sequestration of arsenic into iron sulfide solids. 
A series of geochemical models ilustrate the probable evolution of oil-inundated 
saltmarshes. An activity model indicates that pH and H
2
S conditions at oiled saltmarshes ocur 
within the stability field of pyrite under reducing conditions (pE = -3). In a system affected by 
crude oil influx, pyrite is shown to precipitate and hematite to dissolve as conditions transition 
from oxidizing to sulfate reducing (increasingly negative Eh). Sequestration of metals into pyrite 
and adsorption to hydrous ferric oxides (HFOs) in coastal marsh sediments may temporarily 
mitigate groundwater contamination concerns, but geochemical models show that a rise in pH 
from 7 to 9 would result in the desorption of over 50 !g/kg arsenic into pore waters. 
Furthermore, competing ions disolved in seawater may replace arsenic on HFO sorption sites, 
prompting additional increases of arsenic in solution. 
 
 
 
 
 
 
 
 
 
 iv 
Table of Contents 
 
 
Abstract ............................................................................................................................................ ii 
List of Tables ................................................................................................................................... vi 
List of Figures ................................................................................................................................. vii 
Introduction   ................................................................................................................................... 1 
Background   ................................................................................................................................... 5 
 Geologic Seting   ............................................................................................................... 5 
 Crude Oil   .......................................................................................................................... 6 
 Dispersants   .................................................................................................................... 11 
 Metals   ............................................................................................................................. 13 
 Iron- and Sulfate-Reducing Bacteria   .............................................................................. 16 
Methodolgy   ............................................................................................................................... 19 
Results & Discusion   .................................................................................................................. 26 
 Hydrocarbon Fingerprinting   ............................................................................................ 26 
 Fingerprinting of Oiled Sediments and Plants  ................................................... 26 
 Fingerprinting of a Tarbal  ................................................................................. 36 
 Dispersant Fingerprinting   ............................................................................................... 44 
 Trace Metals .................................................................................................................... 48 
 Geochemical Modeling   ................................................................................................... 67 
 Activity Model  .................................................................................................... 67 
 Mineralogical Reactions Resulting from Inflow of Organic Matter  ..................... 69 
 Saltwater Intrusion and Desorption of Arsenic ? pH Efects  ............................. 71 
 Saltwater Intrusion and Desorption of Arsenic ? Effects of Ionic Competition  .. 73 
 Hydrodynamics of Saltwater and Oil Intrusion   ............................................................... 78 
 Aquifer Tank Experiments  ................................................................................. 78
 
 v
 
 Permeameter Studies  ....................................................................................... 86 
 Numerical Modeling  .......................................................................................... 87 
Conclusions  ................................................................................................................................. 93 
References  .................................................................................................................................. 96 
Appendices   ............................................................................................................................... 100 
 Appendix 1: Microscale Solvent Extraction, derived from EPA Method 3570   .............. 100 
 Appendix 2: Surface Water pH and H2S Data   ............................................................. 101 
 Appendix 3: Basin2 Input Files  ..................................................................................... 102 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 vi 
List of Tables 
 
 
Table 1 Freshwater chemistry used in geochemical reaction path models .................................. 24 
Table 2 Seawater chemistry used in geochemical reaction path models ..................................... 24 
Table 3 Ratios of selected biomarkers from GC-MS tarbal analysis ............................................ 40 
Table 4 Alkylated PAHs present in tarbal ..................................................................................... 43 
Table 5 ICP-MS readings of sulfur and iron in pore waters .......................................................... 52 
Table 6 ICP-MS readings of arsenic and manganese in pore waters ........................................... 53 
Table 7 ICP-MS readings of coper and chromium in pore waters .............................................. 54 
Table 8 ICP-MS readings of cobalt and zinc in pore waters ......................................................... 55 
Table 9 Results and calculations of constant-head permeameter experiments ........................... 86 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 vii 
List of Figures 
 
 
Figure 1. Map of Gulf coast wetland sampling sites ...................................................................... 4 
Figure 2. Ful-scan GC-MS chromatograph of original BP crude .................................................... 9 
Figure 3. Ful-scan GC-MS chromatograph of weathered oil from BJS .......................................... 9 
Figure 4. GC-MS SIM fragmentogram of BP crude targeting m/z 191 ......................................... 10 
Figure 5. GC-MS SIM fragmentogram of BJS weathered oil targeting m/z 191 ........................... 10 
Figure 6. Application of dispersants via seaplane and wellhead .................................................. 11 
Figure 7. Laser ablation ICP-MS of pyrite from Weks Bay site ................................................... 18 
Figure 8. Setup of laboratory-scale tank model ............................................................................ 20 
Figure 9. Setup of constant-head permeameter for hydraulic conductivity tests .......................... 21 
Figure 10. Framework structure of 2D numerical tank model ....................................................... 22 
Figure 1. GC-MS-SIM of weathered oils on plants from BJN and BJS, m/z 217 ........................ 29 
Figure 12. GC-MS-SIM of weathered oils on sediments from BJN and BJS, m/z 217 ................. 30 
Figure 13. GC-MS-SIM of weathered oils in deper sediments from BJN and BJS, m/z 217. ..... 31 
Figure 14. GC-MS-SIM of weathered oils on plants from BJN and BJS, m/z 218 ........................ 33 
Figure 15. GC-MS-SIM of weathered oils on sediments from BJN and BJS, m/z 218 ................. 34 
Figure 16. GC-MS-SIM of weathered oils in deper sediments from BJN and BJS, m/z 218 ...... 35 
Figure 17. Ful-scan GC-MS of tarbal .......................................................................................... 38 
Figure 18. GC-MS-SIM of tarbal targeting m/z 191 ...................................................................... 39 
Figure 19. GC-MS-SIM of tarbal targeting m/z 217 ...................................................................... 41 
Figure 20. GC-MS-SIM of tarbal targeting m/z 218 ..................................................................... 42 
Figure 21. Ful-scan GC-MS chromatograph of dispersant COREXIT 950A .............................. 47 
Figure 2. LC-MS chromatograph of dispersant COREXIT 950A .............................................. 47 
Figure 23. Box and whisker plots of sulfur, iron, and arsenic in pore waters ................................ 56 
Figure 24. Box and whisker plots of manganese, coper, and chromium in pore waters ............. 57 
 
 vii 
Figure 25. Box and whisker plots of cobalt and zinc in pore waters ............................................. 58 
Figure 26. Depth plots of sulfur concentrations in pore waters and sediment .............................. 59 
Figure 27. Depth plots of iron concentrations in pore waters and sediment ................................. 60 
Figure 28. Depth plots of arsenic concentrations in pore waters and sediment ........................... 61 
Figure 29. Depth plots of manganese concentrations in pore waters and sediment .................... 62 
Figure 30. Depth plots of coper concentrations in pore waters and sediment ............................ 63 
Figure 31. Depth plots of chromium concentrations in pore waters .............................................. 64 
Figure 32. Depth plots of cobalt concentrations in pore waters and sediment ............................. 65 
Figure 3. Depth plots of zinc concentrations in pore waters and sediment ................................. 66 
Figure 34. Stability fields of Fe
2+
 with varied pH and H
2
S ............................................................. 68 
Figure 35. Geochemical reaction path of minerals in response to decreasing Eh ........................ 70 
Figure 36. Concentrations of arsenic in fluid in response to changes in pH ................................. 72 
Figure 37. Speciation of arsenic in fluid in response to changes in pH ........................................ 73 
Figure 38. Geochemical reaction of fluids in response to seawater titration  ................................ 75 
Figure 39. Geochemical reaction of sorbate in response to seawater titration ............................. 76 
Figure 40. Concentrations of arsenic in solution in response to seawater titration ....................... 77 
Figure 41. Initial and final stages of a saltwater wedge tank model .............................................. 81 
Figure 42. Ghyben-Herzberg ilustration ....................................................................................... 82 
Figure 43. Depth profile of DOC concentrations .......................................................................... 83 
Figure 4. Introduction of surficial saltwater source to tank model ............................................... 83 
Figure 45. Movement of surficial saltwater source in tank model ................................................. 84 
Figure 46. Downward and leftward movement of surficial saltwater in tank model ...................... 84 
Figure 47. Interactions of surficial saltwater source with intruding wedge in tank model .............. 85 
Figure 48. Interpretive schematic of interactions betwen saltwater intrusion sources ................ 85 
Figure 49. Regresion trend of K values from constant-head permeameter experiments ............ 87 
Figure 50. Illustration of freshwater head variance on saltwater wedge intrusion ........................ 88 
Figure 51. Transient numerical model at earliest stages, 20 years ............................................ 90 
Figure 52. Transient numerical model after developing a saltwater wedge, 160 years .............. 90 
 
 ix 
Figure 53. Transient numerical model at introduction of surficial saltwater source, 260 years ... 91 
Figure 54. Transient numerical model at spreading of surficial saltwater source, 260 years ..... 91 
Figure 5. Transient numerical model saltwater interactions, 300 years .................................... 92 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 1 
INTRODUCTION 
Petroleum hydrocarbons rank as the most ubiquitous marine contaminant in the 
world, entering the environment through natural seepages, pollution from ships and ports, 
pipeline failures, and most notably, deep-water driling acidents (Capelo et al., 207). The 
explosion and subsea rupture of BP?s Macondo-1 prospect well on April 20, 2010 resulted in the 
release of aproximately 4.9 milion barrels of crude oil over the course of 84 days (Crone & 
Tolstoy, 2010). Numerous other spils preceded the Depwater Horizon spil, but none have 
matched its extent or scale. The amount of crude oil spiled from the Macondo-1 well exceeds the 
next-largest spill, the 1989 Exon-Valdez spil, by more than 3.5 times (Maki, 191; Atlas & 
Hazen, 201). Due to our limited knowledge of the fate and transformation of oil in marine and 
coastal environments, a spil of this magnitude requires further investigation into the extent of 
environmental effects and improved remediation practices. 
The masive amounts of oil released by Macondo-1 into the Gulf met several fates. 
The imediate response of BP to the crisis was the application of dispersant solutions that 
served to spread the oil throughout the water column rather than forming a surficial slick 
(Kujawinski et al., 201). Stil, a slick formed that extended for an estimated 68,00 square miles 
across the Gulf of Mexico (Amos, 2010). Furthermore, vast quantities of crude oil made their way 
to the northern Gulf coast of the United States. 
The introduction of oil into coastal ecosystems brought on many imediate, 
damaging effects that have been well documented. Some wildlife populations experienced drastic 
decreases. Many food sources were threatened through the bioaccumulation of spil-related 
toxins such as mercury and arsenic (Delaune et al., 204; Kongchum et al., 206). Although great 
emphasis has been placed on the remediation of these immediate concerns, the detrimental 
effects of sustained contamination should not be overlooked. Iverson et al. (2010) concluded that, 
in some bird species, cumulative mortality counts from prolonged exposure to oil contaminants 
 
 2 
likely exceed counts of acute mortality. The culprit behind the continued inimical effects on wildlife 
in afected areas may be the persistence of heavier oil fragments on coastal sediments. The 
diminished presence of a once-expansive oil slick and decreased sightings of oil on the land 
surface sugest near eradication from affected areas. However, distributions of total organic 
carbon with depth (Nater et al., 2012) clearly indicate that oil contamination penetrates beyond 
the surface into deper sediments. The high organic content at oiled saltmarsh sites (Keevan, 
2012; Natter, 2012) has been shown to increase the sorption of heavier crude oil compounds 
onto sediments (Burnol & Charlet, 2010). It is also suspected that dynamics of saltwater intrusion 
may further exacerbate the oil?s penetration into deeper sediments. 
Oil contamination in wetlands also has a secondary efect. Wetlands are ecologicaly 
important to wildlife and human populations. Often, wetland sediments serve as sinks for trace 
metals that are transported and deposited by neighboring river systems, reducing the likelihood 
for those metals to enter the water column and become bioacumulated through the food chain 
(Esaid et al., 201; Ribeiro et al., 201). Trace metal concentrations are impacted naturaly on a 
smal scale by microbes within the wetlands. However, an oil spil provides an overabundance of 
carbon fod suply to the microbes, thereby enhancing microbial activities. Two types of 
microbes in particular, sulfate-reducing bacteria (SRB) and iron-reducing bacteria (FeRB), have 
been studied in relation to previous oil contamination events (Kleikemper et al., 2002; Lee & 
Saunders, 203) and the Depwater Horizon oil spil (Keevan, 2012; Nater, 2012; Valentine et 
al., 2010; Edwards et al., 2011). Toxic metals, such as arsenic and mercury, may desorb from 
sediment surfaces by bacterial iron reduction and become mobilized in pore water. In contrast, 
activity of sulfate reducers also may promote the co-precipitation of metals into newly formed 
biogenic minerals, removing them from pore water (Farquhar et al., 2002). Furthermore, co-
precipitation may help to mitigate the high concentrations of trace metals commonly associated 
with crude oil. 
This study aims to ases the geochemical evolution of selected Gulf coastal 
wetlands that experienced diferent degres of oil contamination (Figure 1). At heavily oiled sites, 
the biotransformation and persistence of heavier crude oil compounds was asesed using 
 
 3 
specific petroleum biomarkers (i.e. terpane, hopane, and steranes). These biomarkers show 
strong resistance to environmental alteration as compared to most organic compounds, thus 
providing a reliable way to correlate the spiled oils with their initial source. The chemistry of 
dispersants also was explored through a combination of liquid and gas chromatography-mas 
spectrometry (LC-MS and GC-MS) in an efort to determine the most efective method for 
assessing their occurrence and potential evolution in coastal marsh sediments. Additionally, the 
concentrations of trace metals in sediments and extracted pore waters were measured to ases 
the mobility and cycling of metals at heavily oiled sites. Finaly, this study considers potential 
hydrological and geochemical methods for improved remediation. A physical tank model and 
coresponding numerical models were created to investigate the hydrodynamics of interactions 
between oil, saltwater, and fresh water. Geochemical modeling also was conducted to investigate 
the biogeochemical changes of sediment and pore waters in response to the inflow of seawater 
and spiled oil. The geochemical models simulate (1) the effects of saltwater intrusion on metals 
desorption from iron oxides, and (2) the effects of oil-induced, bacterial sulfate-reduction on 
reactions and mobility of metals. A clear understanding of these interactions reveals the potential 
transformation of oils and metals in saltmarsh environments. The data provide critical information 
to implement long-term remediation and restoration strategies for Gulf coastal marshes. 
 
 
 
 
 
 
 
 
 4 
 
Figure 1. Map of coastal saltmarsh sampling sites along the northern Gulf of Mexico. Red, 
yelow, and white labels indicate heavily-contaminated, moderately-contaminated, and unaffected 
sites, respectively (after Nater et al., 2012). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 5 
BACKGROUND 
 
Geologic Seting 
This study focuses on the efects of the Depwater Horizon oil spil in typical saltmarshes 
across the northern Gulf of Mexico. Al sampling sites are located within wetlands along the 
coasts of Louisiana, Misisippi, and Alabama. These wetlands and surounding coastal plains 
are covered in Holocene alluvium, deposited by rivers that feed into the Gulf of Mexico. Because 
the core samples only penetrate 30 cm into the upermost layer, the Holocene aluvium is the 
main focus in this study. The aluvium consists of fine- to medium-grained quartz sand, silt, and 
clays. Sediments in the coastal region also exhibit relatively high organic content, which is 
significant to the capacity of oil and metals sorption. 
Wetlands comonly act as the ultimate sinks for many trace metals that are transported 
and deposited by neighboring river systems (Essaid et al., 201). By sorbing to the minerals of 
wetlands, many trace metals are efectively removed from the water column, reducing the risks of 
bioaccumulation (Ribeiro et al., 2011). Many of the factors that control metals cycling are highly 
dependent on a region?s geochemical conditions. The waters of saltmarshes are typicaly anoxic 
and generally maintain a pH lower than that of seawater. Additionally, saltmarshes are rich in 
nitrogen and phosphorous, essential nutrients for effective microbial respiration (Capelo et al., 
2007).  
A thorough characterization of each sampling site is presented in the theses of Jef 
Keevan (2012) and Mike Nater (2012), including mineralogy, grain-size distribution, total organic 
carbon contents, and bulk geochemistry of sediments. 
 
 
 
 
 6 
Crude Oil 
The crude oil released by the ruptured Macondo-1 well was classified as a light, mature 
crude with an API gravity of 38.8
o
 (Rosenbauer et al., 2010). Crude oil can be an incredibly 
complex mixture, consisting of an imense variety of hydrocarbon and non-hydrocarbon 
components. The basic structure of most crude oil compounds is an isoprene (or isoterpene), 
C
5
H
8
. Multiple isoprenes are bound together to form an isoprenoid (or terpenoid), (C
5
H
8
)
 n
. An 
infinite number of isoprenoids can be created from any quantity of isoprenes and their many 
possible configurations. For example, the compound C
30
H
62
 alone can form as 4,111,846,783 
unique structures. Additionally, isoprenoids may bond with other atoms such as nitrogen, oxygen, 
and sulfur to form over 20,00 distinct compounds in various forms of C
c
H
h
N
n
O
o
S
s
 (Marshal, 
2004). Various metals are also comonly associated with crude oil, including Ni, V, Fe, Ba, Al, 
Cr, Cu, and U (Karchmer & Gun, 1952; Bal et al., 1960). 
The primary clasification of hydrocarbons is based on their fundamental structures: 
saturates are straight and branched chains; olefins are unsaturated structures with double carbon 
bonds; aromatics are conjugated rings of unsaturated bonds; polar resins have polar substituents; 
and asphaltenes are polar with very high molecular weights (Wang & Stout, 207). The scope of 
this study is limited primarily to the saturate subgroups of terpanes, hopanes, and steranes, but 
one group of aromatics also is considered. Owing to their carcinogenic nature, polycyclic aromatic 
hydrocarbons (PAHs) are an important component of crude oil. PAHs have low volatility, 
decreasing their likelihood to evaporate, and some PAHs are highly resistant to degradation 
(Arulazhagan et al., 2010). These characteristics amplify the potential of PAHs to remain traped 
within coastal sediments for extended periods. Oudot and Chailan (2010) determined that only 
82% of aromatics from the Amoco-Cadiz spil had biodegraded 23 years after the initial spil. 
The most comon cyclic isoprenoids to ocur in crude oil are limited to 5-6 carbons. By 
contrast, terpanes range from 15-30 carbons, hopanes contain 27-35, and steranes contain 
between 21-30 (Wang & Stout, 207). Therefore, hopanes, terpanes, and steranes occur in 
immensely more varieties that are each highly representative of their sources. It is this 
uniqueness in source specificity that alows for oil-to-oil corelation, or hydrocarbon fingerprinting. 
 
 7 
The practice of hydrocarbon fingerprinting was developed in the 1970?s by Chevron Corporation. 
It was developed for purposes of petroleum exploration, but its application also translates to 
environmental forensics. Fingerprinting serves to identify a likely source by characterizing the 
presence of specific biomarkers and their abundance. The chemistry of biomarkers in crude oil is 
strongly influenced by the primary controls of source-rock lithology and organic composition. 
However, their chemistry can be significantly impacted by later transformation through the 
secondary controls of in-reservoir alteration, evaporation, and biodegradation (Wang & Stout, 
2007). In instances of environmental forensics, such as the Deepwater Horizon oil spil, 
abundances of certain biomarkers also can characterize the degre of degradation experienced 
by the hydrocarbons (Rosenbauer et al., 2010; Natter et al., 2012). 
The most comon aproach to hydrocarbon fingerprinting is through gas 
chromatography-mas spectrometry (GC-MS) (Wang et al., 203). Due to the high volatility of 
many compounds in crude oil, ions wil elute through spectrometry after diferent dwel times 
based on their mass to charge (m/z) ratio. Lighter ions, those with lower m/z ratios, tend to elute 
earlier in the process than heavier compounds. Fingerprinting studies typically focus on several 
specific m/z ratios, as those ratios are known to contain especialy meaningful biomarkers. The 
most common include the hopane and terpane biomarkers of m/z ratio 191 and the steranes of 
m/z ratios 217 and 218. Analysis of a crude oil sample with GC-MS takes about 1-2 hours and 
results in chromatographs like those in Figures 2 and 3. GC-MS chromatographs depict the time 
of flight and intensities for a wide range of ion m/z ratios. For a more detailed analysis that 
focuses the entire run-time on a single m/z ratio, the oil sample is run through GC-MS selcted 
ion monitoring (GC-MS-SIM). With GC-MS-SIM, the instrument?s heightened sensitivity to a 
single m/z ratio provides a more accurate quantification of the target ion (Wang, 207). 
Environmental aplications of hydrocarbon fingerprinting begin with the characterization 
of the original source crude. In a previous study, Nater et al. (2012) analyzed via GC-MS a pure 
crude oil sample from the Macondo-1 wellhead (Figure 2). Next, Nater et al. (2012) analyzed the 
ful GC-MS chromatograph of a weathered oil sample from the heavily-contaminated surface 
sediments at the Bay Jimy South site (Figure 3). For further corelation, Natter et al. (2012) 
 
 8 
used GC-MS-SIM to specificaly analyze the 191 ratio biomarkers in a sample of Macondo-1 
crude (Figure 4) and weathered oil recovered from plants at Bay Jimmy South (Figure 5). The ful 
suite of 191 fragmentograms for al other sampling sites related to this study can be found in 
Nater et al. (2012).  The results of m/z 191 are very encouraging, so this study wil further test 
the corelation by evaluating sterane biomarkers (i.e. m/z 217 and 218) in the initial crude oil and 
weathered oils. 
Keevan (2012) and Natter (2012) drew several conclusions based on the comparisons 
between the pure crude from the Macondo-1 source and the weathered crude collected along the 
coastal wetlands. Foremost, the presence of numerous unique biomarkers in both the pure and 
weathered crude oil indicates with high certainty that the oil inundating this study?s Gulf coastlines 
is in fact sourced from the Depwater Horizon wellhead. Furthermore, the relatively low 
intensities of light hydrocarbon compounds to heavy compounds indicate incomplete degradation 
of the spiled oil in saltmarsh sediments. Heavier compounds dominate the fraction of oil 
persisting in affected sediments. 
 
 
 
 
 
 
 
 
 
 
 9 
 
Figure 2. GC-MS chromatograph of the original crude oil spiled from 
the Macondo-1 well (from Nater et al., 2012). 
 
 
 
Figure 3. GC-MS chromatograph of weathered oil collected from 
surface sediments in Bay Jimy South (from Nater et al., 2012). 
 
 
 
 10 
 
Figure 4. GC-MS-SIM fragmentogram focusing on target ion m/z 191 
of the original crude oil spiled from the Macondo-1 well (from Nater 
et al., 2012). 
 
 
 
Figure 5. GC-MS-SIM fragmentogram focusing on target ion m/z 191 
of surface sediments from Bay Jimmy South (from Nater et al., 2012). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 11 
Dispersants 
 
BP, in cordination with NOA, implemented several oil-remediation strategies folowing the 
severance of the Macondo-1 well. Due to the lack of knowledge or experience with such an 
expansive oil spil, remediation efforts were unprecedented. Public outcry for immediate action led to 
the use of potentially harmful dispersants that never before had been tested on such a large scale. 
The purpose of the aplied dispersants was to prevent acumulations of oil with higher 
molecular weight in the water column or at the water?s surface. A dispersant has two main 
components: (1) a surfactant and (2) a hydrocarbon-based solvent (Judson et al., 2010). The 
surfactant induces emulsification by coating the surface of the crude oil and lowering the tension at 
the interface betwen the oil and surounding water. Once emulsified, the oil is no longer imiscible 
with water and can move frely throughout the water column. The goal for the aplication of these 
dispersants was to hinder the formation of large, highly viscous oil slicks that could blanket coastlines 
and harm aquatic life. 
Remediation eforts included the release of 2.1 milion galons of dispersant into the Gulf of 
Mexico betwen May 15
th
 and July 12
th
 of 2010. Small aerial vessels released 1.4 milion of those 
gallons at the surface in the form of dispersants COREXIT 9500A and COREXIT 9527. The other 
0.77 milion gallons were released at the wellhead via a jet that ejected COREXIT 9500A directly into 
the stream of flowing crude oil (Kujawinski et al., 201).  
 
 
 
Figure 6. Dispersants COREXIT 950A and COREXIT 9527 as aplied by seaplanes and 
pumped directly into flow from the wellhead. 
 
 
 12 
Dispersants have ben utilized in previous oil spils but never to the extent that they were in 
the 2010 Depwater Horizon oil spil. Very litle was known about secondary efects of these 
dispersants, and even less was known about their fate and transformation at such a large scale. A 
study conducted by the EPA (2010) on smal silverside fish and mysid shrimp indicated no endocrine 
disruption; however, other studies sugest that there may be some negative environmental impacts 
associated with the dispersants COREXIT 9500A and COREXIT 9527. The component nonylphenol 
ethoxylate (NPE) of various dispersants, including COREXIT 950A, has ben reported as cytotoxic 
to mamalian endocrine cels (Judson et al., 2010). Aditionaly, dispersants may have greater 
effects on small life forms. Hamdan and Fulmer (201) report that COREXIT 950A is highly toxic to 
some microbial comunities that are directly involved in natural hydrocarbon degradation. Studies by 
BP (2010b) indicate that COREXIT 950A is the least harmful choice among dispersants. By 
contrast, Judson et al. (2010) suggests dispersants JD 2000 and SAF-RON GOLD as safer 
alternatives. 
Although the composition of COREXIT 950 is available on the USEPA website and in other 
publications (Singer et al., 196; Judson et al., 2010; Place et al., 2010), very litle is known about its 
chemical evolution in natural environments. Kujawinski et al. (201) indicated that some degradation-
resistant ingredients in dispersants (e.g., dioctyl sodium sulfosuccinate) may be used as a 
conservative biomarker to trace their transport in natural environments. Place et al. (2010) presented 
biomarker standards of COREXIT dispersants associated with m/z 81 and 241 using negative-ion 
detection mode. 
It is suspected that COREXIT dispersants may have migrated with crude oil toward the 
southeastern US coastlines, affecting several of the heavily-contaminated sites included in this 
study: Bayou Dulac, Bay Jimy North, and Bay Jimy South. This study analyzed a pure sample 
of COREXIT 9500 by LC-MS and GC-MS to characterize its chemical make-up. In a related 
study, Dr. Elizabeth Kujawinski of Woods Hole Oceanographic Institute (WHOI) is developing a 
new protocol to extract and analyze adsorbed dispersant from oiled wetland sediments.  
Colectively, these studies allow for the determination of the occurrence, fate, and transformation 
of dispersants in oiled marsh environments. 
 
 13 
Metals & Metaloids 
 
Around the world, arsenic contamination of groundwater systems represents a potential 
health hazard. The health effects of arsenic poisoning are severe but delayed by 5-20 years, and 
it is therefore difficult to determine the scale of the problem. Arsenic enters the groundwater 
naturally and through various anthropogenic practices (Smedley and Kiniburgh, 202; Saunders 
et al., 2008). Natural arsenic contamination occurs in many countries, including the United States, 
Argentina, Thailand, China, and most notably, Bangladesh. Many communities along flodplains 
experience problems with arsenic contamination as a result of reductive interactions betwen 
organic matter and minerals, which provide sorption surfaces for arsenic.  
In solution, arsenic may apear in a variety of oxidation states depending upon its origin. 
Often, As(V) ocurs as the charged anionic complex AsO
4
3-
. As(III) comonly apears as the 
charged anionic species, As(OH)
4
-
, or as the neutral species, As(OH)
3
. Due to the neutral or 
relatively low charges of arsenite complexes, As(II) tends to be more mobilized in solution. 
Concentrations of arsenic in groundwater systems are controled by two main processes: 
adsorption reactions and co-precipitation. The surfaces of minerals and amorphous precipitates 
often carry a slight charge, which encourages the adsorption of disolved ions from solution onto 
those surfaces. The net surface charge of a mineral is determined by its chemistry and other 
environmental factors, such as pH. In the case of arsenic, which typically occurs as anionic 
complexes, surfaces with a positive net charge, such as Fe- and Mn-oxides, wil yield greater 
rates of adsorption. Arsenic and other trace metals may adsorb to a variety of phases, such as 
silicates, organic mater, pyrite, or iron oxides, but the interactions betwen metals and many of 
these phases have not ben fuly characterized. The sorption reactions that have ben most 
thoroughly discussed are those of iron oxides (Dzombak & Morel, 190). The sorption reactions 
of iron oxides are especially fundamental, as iron oxides are a ubiquitous mineral coating in 
various hydrogeologic environments (Hinkle, 199). 
The interactions between arsenic and sorption surfaces are highly pH dependent 
(Saunders et al., 203). Generaly, the net surface charge of an iron oxide transitions from 
positive to negative as pH increases in the range of 6 to 9 (Dzombak & Morel, 1990). Therefore, 
 
 14 
sorption concentrations of anionic arsenic complexes on iron oxide surfaces decreases with 
increasing pH in the same range. Negatively-charged As(V) could desorb strongly when pH 
reaches the desorption front of about 8.5 (Le et al., 205). This concept is especialy important 
in the saltmarsh sediments of this study due to the potential threat of rising pH through saltwater 
intrusion (pH of 8?8.4). 
Factors other than pH also afect the fate and mobility of arsenic in geologic 
environments. Disolution of minerals that provide sorption sites decreases the available surface 
area on which ions such as arsenic may adsorb. Ions adsorbed to a mineral?s surface also may 
be replaced by more kinetically favored ions. For example, a typical saltwater contains high 
concentrations of disolved ions (e.g. silicate, carbonate, calcium, sulfate, etc.) that may compete 
with arsenic for sorption sites on hydrous oxides (Bethke, 2008; Tyrovola and Nikolaidis, 2009). 
When such a saltwater is introduced into a freshwater system, the competition between ions 
results in desorption of existing adsorbed ions from charged mineral surfaces. Similarly, when 
investigating the geochemical evolution of an area impacted by an oil spill, it is important to 
consider the efect of newly-introduced ions that comonly occur in crude oil. The influx of oil-
associated metals Ni, V, Fe, Ba, Al, Cr, Cu, and U (Karchmer & Gunn, 1952; Ball et al., 1960) 
may compete with existing sorbate for a limited number of sorption sites. 
The other primary control on disolved arsenic concentrations is mineral precipitation. 
Arsenic is a main structural component of many minerals: arsenopyrite (FeAsS), realgar (AsS), 
orpiment (As
2
S
3
), and others. In other cases, arsenic ions and their complexes adsorb to a variety 
of mineralogic and organic surfaces. However, small changes in physiochemical and redox states 
may cause the disolution of a mineral and thereby reduce the available sorbing surface area. 
The disolution of existing arsenic-bearing minerals mobilizes high concentrations of arsenic into 
solution. Conversely, the precipitation of arsenic-bearing minerals removes arsenic from solution. 
When incorporated into a mineral?s structure or bound to a mineral?s surface, arsenic is removed 
as a hazard from potential drinking water sources. Many studies have shown the strong control of 
redox conditions on the inclusion of trace metals during pyritization (Berner, 1970; Morse & 
 
 15 
Luther, 1999; Saunders et al., 208; Ye, 2010). In this study, the most important consideration is 
the effect of redox conditions induced by microbial activity following an oil spil. 
Hydrous feric oxides and iron sulfides often have strong reactions with many metals and 
metaloids. Aside from arsenic, this study also considers Cu, Pb, Co, Zn, Cr, and Mn. The 
interactions betwen pyrite and various trace metals have ben thoroughly studied. The degre 
of pyritization (DOP) is a function of acid volatile sulfides, redox conditions, and pore-water metal 
concentrations (Ye et al., 2010). Ye et al. (201) showed that the DOP in subtidal zones was 
primarily controlled by concentrations of iron and sulfur rather than other reactive trace metals. 
Stil, the DOP is strongly linked to the degre of trace metal pyritization (DTMP) in many cases. 
Both the DOP and DTMP are especialy augmented in anoxic conditions (Alvarez-Iglesias, 208; 
Ye et al., 2010), such as those comon to wetlands. The limitation of iron relative to sulfur, which 
is also a comon condition of wetlands, is another major contributor of low pyritization (Huerta-
Diaz 2010). 
 Coper (Cu) is strongly favored thermodynamicaly and kineticaly to asociate with 
pyrite, and the DTMP of Cu is especially high closer to the sediment surface (in the 6-8cm range) 
(Alvarez-Iglesias, 208) noted. Mercury (Hg) also exhibits a very high DTMP, although its exact 
association with pyrite is dificult to measure and therefore remains unclear. Based on kinetics, 
Hg likely precipitates as a sulfide solid (cinnabar, H
2
S) prior to the precipitation of pyrite (Ye et al., 
2010). However, Hg also may co-precipitate with pyrite or intermediary FeS minerals (Natter, 
2012). 
Manganese (Mn) and chromium (Cr) consistently display low DTMP. However, both are 
comonly found sorbed to the surfaces of silicates (Alvarez-Iglesias, 208). Aditionaly, the 
kinetics of Mn sugest incorporation into carbonates prior to pyritization (Otero et al., 203), and 
Cr has a very high afinity for organic mater relative to other disolved metals (Alvarez-Iglesias, 
2008). Kinetically, associations between Cr and S are highly unfavorable, and any chromium 
within pyrite is likely a product of isomorphic substitution (Otero et al., 203). 
 
 
 
 16 
Iron- and Sulfate-Reducing Bacteria 
 
An oil spil afects disolved metal concentrations in wetlands in two ways. First, crude oil 
comonly contains many metals, and those metals may be directly introduced into wetlands 
through the physical migration of an oil slick. Second, an oil spil enhances microbial activity, 
which indirectly afects disolved metal concentrations by altering geochemical conditions to the 
point of mineral disolution or precipitation. 
Microbial respiration folows the same basic proces, whether authigenic or 
anthropogenic; it is merely augmented by the introduction of spiled hydrocarbons. After oxidation 
of a hydrocarbon by sulfate-reducing bacteria (SRB) or iron-reducing bacteria (FeRB) (Edwards 
et al., 201), the final step of the electron transport chain is the deposition of the remaining 
electron onto a terminal electron-accepting phase (TEAP). SRB deposit electrons onto disolved 
sulfate (SO
4
2-
), which is then reduced from 6+ to 2- and becomes a sulfide species. Similarly, 
FeRB (e.g., Geobacter), deposit electrons onto the iron of ferric minerals, reducing the iron?s 
oxidation state from 3+ to 2+ (Esaid et al., 2011). The reduction of iron creates instability within a 
mineral?s structure, causing it to disolve. Saunders et al. (208) showed that, with the adition of 
an artificial source of carbon to simulate an oil spil, disolved concentrations of ferrous iron (and 
also arsenic) increased dramatically as a result of enhanced FeRB activity. 
The activity of microbes, which fed on organic carbon, is strongly influenced by masive 
releases of hydrocarbons. Folowing an oil spil, the mass influx of hydrocarbons into coastal 
marshes enhances microbial respiration to unnaturally-high levels. As a result of the Deepwater 
Horizon spil, microbial respiration in the oil slick near the welhead increased by a factor of 
approximately five (Edwards et al., 2011). However, the study led by Edwards was focused in a 
deep-water region relatively devoid of microbial nutrients (201) as compared to shalower, 
nitrogen- and phosphorous-rich Gulf setings (Capelo et al., 207) where the samples for this 
study were colected. Therefore, it is probable that microbial activity in the coastal saltmarshes of 
this study also was augmented significantly (Natter et al., 2012). 
As Saunders et al. (208) showed, the enhanced respiration of FeRB leads to high 
concentrations of disolved ferrous iron from the disolution of ferric minerals. Along with the 
 
 17 
feric minerals, other metals within the mineral?s structure or metals adsorbed to the mineral?s 
surface enter solution. However, continued microbial activity also can stimulate the reverse efect. 
Through the disolution of feric minerals and reduction of disolved sulfate, microbial respiration 
provides an ample source of reduced iron and sulfur, which allows for the precipitation of new 
minerals. The precipitation of pyrite (FeS
2
) may decrease the concentrations of disolved metals 
with high DTMP through co-precipitation or adsorption.   
Framboidal pyrite, thought to form biogenicaly or authigenically in sediments, was found 
primarily at those sites most heavily contaminated: BD, B, and BJN (Kevan, 2012; Nater, 
2012). Framboidal pyrite also was recovered from the uper sediments of a core from the Weks 
Bay (WB) site. The biomineralization of framboidal pyrite sugests that sulfate-reducing 
conditions are wel established in the salt marsh sediments (Wilkin et al., 197). Through laser 
ablation ICP-MS of individual pyrite crystals (Figure 7), Kevan (2012) and Natter (2012) showed 
that the stoichiometry of the framboidal pyrite consisted primarily of Fe and S. The samples also 
contain elevated amounts of Mn, Pb, Zn, and V (average concentrations > 1,00 mg/kg) and 
various amounts of Co, Ni, Cu, As, Se, Hg, Mo, and Cd (ranging from a few to hundreds of 
mg/kg). They conclude that sulfides produced from sulfate reduction also may react with 
disolved metals to form insoluble metal sulfides; such a reaction would decrease the 
concentrations of dissolved metals by sequestering them in the insoluble metal sulfides. 
ICP-MS studies of sediments were caried out to characterize the metal concentrations in 
saltmarshes across the Gulf coast (Kevan, 2012). The ICP-MS determinations of metal contents 
in sediments generally ocur on the scale of pm. Another apparent trend occurs with depth; 
Keevan (2012) and Nater (2012) report that concentrations of some metals (e.g. Fe, S, Pb, Co, 
As, and Hg) increase with depth in the range of 0-15cm but most metals decrease with further 
depth in the range of 15-30 cm. Higher concentrations toward the middle of their sediment cores 
may be attributed to oil intrusion processes, and the slight enrichment of metals in the upper 20 
cm of sediments is hypothesized to be a result of increased industrial activity in the last 10 years 
(Kevan, 2012). In general, the concentrations of many elements (e.g., Ni, Cu, Pb, Zn, Sr, Co, V, 
Ba, Hg, As, S, and Fe) displayed a positive corelation with the degre of oil contamination. Metal 
 
 18 
concentrations at heavily-oiled sites were notably higher than those at pristine sites. Keevan 
(2012) and Nater (2012) noted that the average grain size of wetland sediments plays a major 
role in the retention of metals; generally, finer grains more surface area for metal sorption. 
 
Figure 7. Laser ablation ICP-MS graph of a single pyrite crystal from a 
sediment core at a depth of <20 cm in Weeks Bay (from Keevan, 2012). 
 
 
 
 
 
 
 
 
 
 
Time(sec)
I
n
t
e
n
s
i
t
y
(
C
P
S
)
0 20 40 60 80
10
2
10
3
10
4
10
5
10
6
10
7
10
8
Fe
S
Zn
Pb
As
Hg
 
 19 
METHODOLOGY 
 
Sample Colection 
In October of 2010 and January of 201, Jeff Kevan and Mike Natter colected the 
saltmarsh sediment and pore-water samples analyzed in this study. The samples, collected from 
various locations around Barataria Bay, Louisiana, were heavily contaminated by the Deepwater 
Horizon oil spil. The sites of sample colection are ilustrated in Figure 1.  At each site, samples 
were colected in the form of 30 cm cores, containing bulk sediments and pore water. Water 
quality parameters (e.g. pH, DOC, H
2
S etc.) of surface water were measured at each sampling 
site via in-situ readings using a TROL 900. After colection, core samples were quickly 
transported to a nearby wet chemistry lab at the Stenis Space Center for processing. Further 
details on sample collection, including sample processing and storage procedures, can be found 
in Nater (2012) and Kevan (2012).  
In late August of 2012, Huricane Isac made contact with the US Gulf coast. Isac?s 
intensity spured the relocation of tar balls from the ocean floor onto coastal beaches. On 
September 4, 2012, tarball samples were collected from a shoreline in Gulf State Park, Alabama 
at (30.2490?, -87.6682?). The tarballs were bagged and preserved in a freezer until the time of 
analysis. 
 
Physical Tank Experiments 
Several analyses were performed on the efects of saltwater and oil intrusion using a 
hydrologic tank model. The setup of the artificial aquifer tank is shown in Figure 8. The tank was 
initially developed by Mat Obley, a physics specialist with the Alabama Science in Motion 
program, to study the efect of sea-level rise on saltwater intrusion (Le, et al., 2012). The 
artificial aquifer is 54.6 cm long (in the flow direction), 27 cm high, and 2.5 cm wide. 
 
 20 
The main aquifer chamber is packed with smal glas beads and conected to constant-
head freshwater and saltwater reservoirs by permeable screens. Overflow outlets positioned at 
varying levels can adjust water levels in both freshwater and saltwater reservoirs. The freshwater 
chamber acts as an up-gradient recharge source. Freshwater enters the sand chamber and 
migrates toward the saltwater chamber at a fixed hydraulic gradient to establish a steady-state 
flow acros the main sand chamber. Saltwater intrusion can be initialized by injecting saltwater 
into the saltwater chamber. 
Several experiments were run using the tank model. Betwen each experiment, the tank 
was cleaned and reset to the state in Figure 8. In the preliminary experiments, diferences in the 
toe of the saltwater/freshwater interface due to adjustments in saltwater head were measured 
with respect to time. Once interfaces were established, a separate source of saltwater (dyed blue) 
was introduced into the system from directly above the sand chamber. Experiments were 
designed to test the likelihood of saltwater and oil intrusion dynamics in coastal environments. 
 
 
Figure 8. Photograph and diagramatic ilustration of tank model for experimentation into the 
effects of saltwater hydrodynamics on oil intrusion. 
 
A constant-head permeameter was used to determine the hydraulic conductivity of the 
glass beads used in tank experiments. The permeameter setup is shown in Figure 9. The glas 
beads are encased in a cylindrical chamber with a diameter of 6.4 cm, height of 12.5 cm, and a 
total area of 402.1 cm
2
. Several experiments were run at various head values, each for three 
minutes. For each experiment, the amount of discharge from the permeameter was measured 
 
 21 
and recorded to the nearest mililiter. Hydraulic conductivity values were calculated acording to 
Darcy?s law: 
 
where K = hydraulic conductivity (m/s); Q = discharge (m
3
/s); L = length of chamber (m); A = 
cross-sectional area of chamber (m
2
); and "h = the diference in constant heads between input 
and output (m). 
 
 
Figure 9. Photograph of permeameter setup. Water flows from a set head through a hydraulic 
chamber and colects in a bucket. 
 
 
Hydrologic Modeling 
Numerical modeling was conducted using Basin2 (Bethke et al., 203) to mimic the 
physical tank experiments. The input files used to generate the groundwater and solute transport 
models can be found in Apendix 3. The fundamentals of the model, outlined in Figure 10, were 
 
 22 
defined to closely resemble the idealized physical properties of the tank model. The computer 
model was created with the same dimensions as the tank, and the average hydraulic conductivity 
value was set close to that of a silty sand in coastal aquifers. Nodal spacing was constructed with 
40 nodes in the flow direction (x) and 16 nodes in the direction of depth (z). 
 
 
Figure 10. Finite diference grid used in Basin2 numerical simulations, displaying nodal structure and 
wel placement. 60x34 cm. Ocean and seaflor are located on the left side. 
 
 
The Basin2 model was run to simulate the same saltwater intrusion dynamics revealed in 
the tank experiments. To model groundwater flow and solute transport, which are controled by 
difusion, dispersion, and advection, Basin2 solves the folowing partial differential equation in the 
two-dimensional form: 
 
where C is concentration (mol/cm
3
), q is specific discharge (cm/s), t is time (s), and D is the 
hydrodynamic dispersion coefficient (cm
2
/s) that acounts for both mechanical dispersion and 
molecular difusion (Peny et al., 203).  
 
 
 
 
 
 23 
Geochemical Modeling 
Geochemist?s Workbench (GWB) (Bethke, 208) was used to calculate mixing of 
freshwater and saltwater and potential geochemical evolution of saltmarsh sediments and pore 
water in areas impacted by the Depwater Horizon spill. GWB sub-program Act2 was used to plot 
the stability fields of iron under varying pH and concentrations of H
2
S. Basic parameters used in 
the models, such as pE or Eh, were estimated as closely as posible based on field 
measurements of Eh in similar reducing waters. Temperature and pressure were set at 25
o
C and 
1.013 bars, respectively. Wherever possible, real field measurements were used within the 
models, such as H
2
S concentrations and pH values. 
Geochemical reaction-path modeling can be conducted to assess the geochemical 
evolution of a solution and solid mineral phases in response to mas transfer (Bethke et al., 
2008). In this study, several reaction path models were created using the GWB sub-program 
React to illustrate the geochemical efects of introducing saltwater into a freshwater system. The 
models simulate surface complexation processes through desorption of pre-existing ions and 
their replacement by adsorption of ions newly introduced with the saltwater (Lee & Saunders, 
2003). The surface complexation model utilizes the double-layer, hydrous feric oxide model 
developed by Dzombak and Morel (1990). A chemical analysis of Amazon River water (Hem, 
1985; Bethke, 1996) was used to represent a typical freshwater chemistry (Table 1). Similarly, the 
chemistry of the saltwater reactant was defined based on a typical surface seawater (Drever, 
1988; Bethke, 1996), shown in Table 2. In some cases, the formation of particular minerals (e.g., 
hematite, goethite, and magnetite) were suppressed; the formation of such minerals are 
kineticaly favored on a long-term scale, but an amorphous hydrous feric oxide is more likely to 
form within a short time frame.  
 
 
 
 
 
 
 24 
Table 1. Basis for freshwater composition in reaction path models for this study, based on 
compositions of Amazon River water (Hem, 1985). 
 
!"#$%&'(#")
!"#
$
%&'() *) +,-.,)
/0
12
) 343*)+,-.,)
56
$2
) 3437)+,-.,)
8&
$2
) 941) +,-.,)
:,
$2
) ;4;) +,-.,)
<&
2
) ;4=) +,-.,)
>8#1
?
) ;@) +,-.,)
!#9
$?
) 1) +,-.,)
80
?
) ;4@) +,-.,)
#
$%&'(
) A4=) +,-.,)
 
 
Table 2. Basis for seawater composition in reaction path models for this study, based on analysis 
of typical seawater chemistry (Drever, 1988; Bethke, 1996). 
 
*#'&'(#")
!"#
$
%&'() 7) +,-.,)
8&
$2
) 9;;) +,-.,)
:,
$2
) ;$@3) +,-.,)
<&
2
) ;3*73) +,-.,)
B
2
) 1@@) +,-.,)
>8#1
?
) ;9$) +,-.,)
!#9
$?
) $*;3) +,-.,)
80
?
) ;@1A3) +,-.,)
 
 
 
Trace Metals Analysis 
Sediment cores from each site were sectioned into 3 cm segments. Each segment was 
centrifuged in 50 mL tubes to isolate pore water. Pore-water geochemical analyses were 
conducted using inductively coupled plasma mas spectrometry (ICP-MS) to determine the 
presence of trace metals and their concentrations. ICP-MS analysis was performed in Dr. Alison 
Keimowitz?s lab at Vasar Colege. 
 
 
 
 25 
Hydrocarbon Fingerprinting 
Extraction of petroleum compounds from oiled sediments is based on EPA Method 3570 
(Appendix 1). Solvents were extracted into 2 mL vials. The extracted oil samples were analyzed 
using an Agilent 5975C gas chromatograph-mas spectrometer (GC-MS) at ALS Environmental. 
Peak profiles of unique biomarkers output by the GC-MS were used to verify the potential 
corelation betwen the extracted oils and oil from the original source, Macondo-1. In the case of 
samples whose contamination sources were positively identified as the Macondo-1 well, the GC-
MS results for those samples also were used to quantify the degree of contaminant degradation 
of heavy and light compounds over time. 
After broader, more generalized results were obtained through ful-scan GC-MS, a high-
sensitivity run was conducted in GC-MS-Selected Ion Monitoring (SIM) mode. GC-MS-SIM 
focuses on specific hydrocarbon compounds with unique mas-to-charge ratios, providing data 
with much greater precision. The SIM results were used to identify specific petroleum biomarkers, 
including terpanes, hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs), highly 
weather-resistant compounds of crude oil. Biomarkers with a mas-to-charge ratio of 191 were 
examined in previous studies (Natter et al., 2012), so the main target for this study includes 
terpane and sterane compounds with mas-to-charge ratios of 171 and 181. 
COREXIT 950, the dispersant applied in response to the Deepwater Horizon spil, was 
characterized using gas and liquid chromatography-mas spectrometry (GC-MS and LC-MS) 
facilities in the Department of Chemistry and Biochemistry at Auburn University. The dispersant 
sample used in in the analysis was provided by the Florida Department of Environmental 
Protection (FDEP). Due to the nonvolatile nature of most dispersants (Plante et al., 2011), it was 
unclear whether COREXIT 9500 would produce more distinguishable peak profiles through liquid 
or gaseous form. Therefore, the sample was analyzed using both GC-MS and LC-MS. The 
resulting GC-MS and LC-MS chromatograms were analyzed to determine which produced 
characteristic peak profiles that would alow for more reliable source fingerprinting. 
 
 
 
 26 
RESULTS & DISCUSSION 
 
Hydrocarbon Fingerprinting 
 
Fingerprinting of Oiled Sediments and Plants 
 
Hydrocarbon fingerprinting techniques use gas chromatography-mas spectrometry (GC-
MS) to corelate spiled oils with their initial source. Specific oil compounds with unique mas-to-
charge ratios (m/z) can be analyzed by GC-MS operated in the Single Ion Monitoring (SIM) 
mode. Nater et al. (2012) used terpanes and hopanes (m/z 191) as geochemical biomarkers to 
trace the source, weathering, and biotransformation of oils in the coastal wetlands study areas. 
Biomarkers asociated with steranes (m/z 217 and m/z 218) are targeted in this study for oil-to-oil 
corelations. Terpanes and steranes are derived from biological precursors of terpenoids and 
steroids, compounds that make great biomarkers due to their resistance to many degradation 
processes within natural environments. 
Comparisons of m/z 217 and 218 f fragmentograms of initial BP crude oil to various 
weathered oil samples ilustrate the efects of environmental weathering on the crude oil. In GC-
MS analysis, lighter compounds elute earlier at lower temperatures, whereas heavier compounds 
have longer retention times. In full-scan GC-MS chromatographs and GC-MS-SIM 
fragmentograms (Figures 11-16), the initial BP crude sample exhibits a full spectrum of lighter 
and heavier compounds. By contrast, samples of weathered oil colected from plants, surface 
sediments, and sediments at depth, show a much stronger presence of heavier hydrocarbons 
relative to lighter hydrocarbons. The overal increased presence of heavier hydrocarbon 
compounds relative to lighter compounds occurs as a result of preferential biodegradation, 
evaporation, and photo-oxidation of lighter compounds (Wang and Stout, 2007). Although heavier 
compounds are concentrated relative to lighter compounds, it is important to note that the overal 
intensities of most compounds tend to be greatly reduced in weathered oils extracted from 
sediments. For example, GC-MS-SIM m/z 217 readings of the initial BP crude (Figure 12, top) 
 
 27 
peak at an intensity of 22,871, whereas a weathered crude oil sample collected from BJN 
sediment peaks at 1,729 (Figure 12, middle). 
The m/z 217 fragmentogram of pure BP crude oil (Figure 1, top) displays a strong 
bimodal trend in the ranges of 17-19 minutes and 21-23 minutes. The intensity of these two 
modes is nearly at the same level. An aditional smaler cluster of biomarkers centers around 16 
minutes. Of al the weathered oils analyzed, oil scraped from plants at sites BJN and BJS (Figure 
11) display the strongest correlation to the initial BP crude oil based on m/z 217 biomarkers. Al 
thre modes identified in the m/z fragmentogram of BP crude oil also apear in the weathered oil 
on plants at BJN and BJS. The first two modes (16 minutes; 17-19 minutes) are created from the 
elution of relatively lighter hydrocarbons than those that comprise the largest mode (21-23 
minutes). Prefered weathering of lighter hydrocarbon compounds apears to have lowered the 
relative intensity of the first two modes compared to the enriched heavier hydrocarbons 
comprising the latest mode. 
M/z 217 fragmentograms of surface sediments (0-3 cm) at BJN and BJS (Figure 12) also 
indicate a strong presence of BP crude. The fragmentograms of surficial oil at BJS and BJN 
contain the same thre modes identified in the BP crude oil. In adition to these, oil at BJN 
displays an individual peak at 15.12 minutes and an extra mode in the range of 24-25 minutes. 
These aditional peaks indicate the mixing of an external hydrocarbon source with BP crude oil at 
BJN. 
M/z 217 fragmentograms of sediments at depths of 12-15 cm at BJS and BD (Figure 13) 
also show evidence of a separate hydrocarbon source, independent of the BP spil. Oil recovered 
from deper sediments at BJS (Figure 13, middle) strongly displays the two clusters of peaks at 
17-19 minutes and 21-23 minutes that indicate the presence of BP crude oil; the same fingerprint 
also reveals hydrocarbons in individual peaks at 15.12 minutes and in the range of 24-25 
minutes. 
Evidence of mixing BP crude with an aditional hydrocarbon source is present in surface 
sediments at BJN and deeper sediments (12-15 cm) at BJS. Mixing is also evident in the deper 
sediments of BD; however, the fingerprint at BD is dominated by a hydrocarbon source 
 
 28 
independent of the BP spill and apears to have mixed with only a small amount of BP crude oil. 
All fingerprints that display evidence of mixing contain the same hydrocarbon anomalies. The 
same peak at 15.12 minutes and cluster of peaks at 24-25 minutes appears in BJN surface 
sediment, deper BJS sediments, and deper BD sediments. The same anomalies in each 
fingerprint sugest that sites BJN, BJS, and BD have al experienced contamination by other 
hydrocarbon sources in adition to the BP crude oil. These aditional hydrocarbons may be 
derived from other natural oil seeps in the Gulf of Mexico. The National Research Council (203) 
reported that betwen 560,00 and 1,40,00 barrels of crude oil per year sep into the Gulf of 
Mexico from natural sources. The volume of oil relased from these natural seps is much les 
than that of the Depwater Horizon spil.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 29 
 
 
 
 
 
 
 
 
 
 
Figure 11. A GC-MS-SIM m/z 217 fragmentogram of pure MC-252 crude oil (top) in comparison 
to weathered oil scraped from supresed plants at sites BJN (midle) and BJS (bottom). 
 
 
 30 
 
 
 
 
 
 
 
 
 
Figure 12. A GC-MS-SIM m/z 217 fragmentogram of pure MC-252 crude oil (top) in comparison 
to weathered oil recovered from surface sediments at sites BJN (midle) and BJS (bottom). 
 
 
 
 
 
 31 
 
 
 
 
 
 
 
 
 
Figure 13. A GC-MS-SIM m/z 217 fragmentogram of pure MC-252 crude oil (top) in comparison 
to weathered oil recovered from sediments at a depth of 12-15cm at sites BJS (middle) and BD 
(botom). 
 
 
 
 
 32 
Hydrocarbon fingerprinting with GC-MS-SIM analysis of m/z 218 reveals very similar 
results to those of m/z 217. M/z 218 fragmentograms of pure BP crude oil display two larger 
modes in the ranges of 17-19 minutes and 21-23 minutes and a smaller mode centered on the 
16-minute mark. Each of these modes is also evident in m/z 218 fragmentograms of oil scraped 
from plants at BJN and BJS (Figure 14). In addition to indicators of BP crude oil contamination, 
peaks at 18.72, 19.47, and 19.53 minutes (not seen in BP crude) suggest that these weathered 
oils were mixed with an additional hydrocarbon source. 
M/z 218 fragmentograms of oil colected from surface sediments at BJN and BJS (Figure 
15) strongly indicate the presence of BP crude oil. Fragmentograms of both BJN and BJS display 
the same thre modes identified in initial BP crude oil. In adition to these, BJN contains 
compounds that elute at 18.72 minutes and in a cluster from 24-25 minutes. Although the 
presence of BP crude oil in these sediments is clear, it is also probable that some mixing has 
occurred with an additional source. M/z 218 fragmentograms of oil collected from deeper 
sediments (12-15 cm) at BJS and BD (Figure 16) provide further evidence to suggest that BP 
crude oil has infiltrated as dep as 15 cm. Oils in deper BJS sediments contain the smaler 
mode (16 minutes) and two larger modes (17-19 and 21-23 minutes) that are reminiscent of initial 
BP crude oil; additional hydrocarbon compounds (at e.g. at 18.72 and 19.322 minutes) suggest 
moderate mixing with another source independent of the BP spil. By contrast, oils in deper BD 
sediments show signs of slight mixing with BP crude but are dominated by a diferent 
hydrocarbon source. 
Overal, m/z 217 and 218 fragmentograms provide strong indications of the presence of 
BP crude oil at saltmarsh sites around the Barataria Bay. The hydrocarbon fingerprints of BP 
crude exist in deper sediments as well, indicating possible infiltration of oil into sediments as 
deep as 15 cm. The possible mechanisms of oil infiltration wil be discussed in a later chapter. 
 
 
 
 
 
 
 
 
 
 
 33 
 
 
 
 
 
 
 
 
 
 
Figure 14. A GC-MS-SIM m/z 218 fragmentogram of initial MC-252 crude oil (top) in comparison 
to weathered oil scraped from supresed plants at sites BJN (midle) and BJS (bottom). 
 
 
 
 34 
 
 
 
 
 
 
 
 
 
Figure 15. A GC-MS-SIM m/z 218 fragmentogram of initial MC-252 crude oil (top) in comparison 
to weathered oil recovered from surface sediments at sites BJN (midle) and BJS (botom). 
 
 
 35 
 
 
 
 
 
 
 
 
 
 
Figure 16. A GC-MS-SIM m/z 218 fragmentogram of initial MC-252 crude oil (top) in comparison 
to weathered oil recovered from sediments at a depth of 12-15cm at sites BJS (middle) and BD 
(botom). 
 
 
 
 36 
Fingerprinting of a Tarball 
 Tarbals were colected from the shore in Gulf State Park, Alabama in September of 
2012, shortly after they were washed ashore by Huricane Isac. Hydrocarbon fingerprinting was 
conducted on the tarbal via GC-MS and GC-MS-SIM, targeting m/z 191, 217, and 218. Gas 
chromatography of the tarball was conducted at a diferent ramp speed and final temperature 
than the chromatography conducted on BP and BJS oil samples scraped from plants; therefore, 
the retention times of BP and BJS compounds vary from those of the tarball. 
 Ful-scan chromatographs (Figure 17) highlight the apparent enrichment of heavier 
hydrocarbon compounds in weathered oil and the tarball with respect to the BP crude oil due to 
the preferential degradation of lighter compounds. Positive identification of BP crude oil within the 
tarbal is difficult in ful-scan chromatographs due to diferences in retention times. Spectral 
matching in GC-MS-SIM m/z 191 fragmentograms (Figure 18) provides some evidence for 
positive identification. Several compounds were recorded in the fingerprints of both BP crude oil 
and the tarball. The ratios of several biomarkers are summarized in Table 3. A higher similarity in 
selected biomarker ratios of BP crude oil to the tarbal represents a greater likelihod that the 
tarbal is descended from oils released from the MC-252 wellhead. For example, Ts/Tm (Ts/Tm 
=18#-22,29,30-trisnorneohopane/17#-22,29,30-trisnorhopane) ratios of the tar bal sample and 
initial BP oil ocur in a remarkably narow range (0.913-1.070). Strong corelations are also 
notable for C29/C30 norhopane-hopane (0.530-0.586), 35S/34S pentakishomohopane-
tetrakishomohopane (0.792-0.814), and C31S/C30 homohopane-hopane (0.507-0.520) 
biomarkers. Based on the similarity of these calculated ratios, it is very probable that BP crude oil 
accounts for at least some of the hydrocarbons present in this tarball. 
 Fragmentograms of m/z 217 and 218 (Figures 19 and 20) both reveal fingerprints that are 
strongly bimodal. However, the smaler mode (centered on 16 minutes) present in m/z 217 and 
218 fragmentograms of BP crude oil and weathered oil is barely detectable in the tarball. 
Nevertheles, the two, strong modes that apear in m/z 217 and 218 fragmentograms of the 
tarbal match those in the BP crude oil and weathered oil, indicating that the persistent 
hydrocarbons associated with both modes were preserved fairly well. 
 
 37 
In al chromatographs, the only distinguishable polycyclic aromatic hydrocarbon (PAH) 
present at a high intensity is C2-phenanthrenes/anthracenes (C2-P/A) (Figure 17, bottom). 
Specific biomarkers of terpanes, hopanes, and steranes thus represent a more powerful tol than 
PAHs to fingerprint the source of oils in natural environments. A complete list of al alkylated 
PAHs detected in the tarball can be found in Table 4. Several PAHs derived from crude oils are 
potent carcinogens (Daudel & Daudel, 1966), and the long-term effects of persistent PAH 
compounds in marsh ecosystems remains unclear. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 38 
 
 
 
 
 
Figure 17. A ful-scan GC-MS chromatograph of pure MC-252 crude oil (top; from Natter et al., 
2012) in comparison to weathered oil scraped from suppressed plants at BJS (middle; from 
Keevan, 2012) and a tarbal colected from the Gulf coast 2.5 years after the Depwater Horizon 
spil (botom). 
 
 39 
 
 
 
 
 
Figure 18. A GC-MS-SIM m/z 191 fragmentogram of pure MC-252 crude oil (top; from Natter et 
al., 2012) in comparison to weathered oil scraped from suppressed plants at BJS (middle; from 
Keevan, 2012) and a tarbal colected from the Gulf coast 2.5 years after the Depwater Horizon 
spil (botom). 
 
 40 
Table 3. Ratios of several biomarkers comon to m/z 191 spectra. Closer ratios represent 
increased likelihod of matching hydrocarbon sources (ratios of BP crude oil, weathered oil/BJN, 
and surface sediment/BJN are reproduced from Natter et al., 2012). 
 
Oil samples m/z 191 
 Ts/Tm
1
 24Tri/23Tri
2
 C29/C30
3
 29D/29H
4
 35S/34S
5
 C31S/C30
6
 
BP crude oil 1.070 0.914 0.586 0.564 0.814 0.507 
Weathered oil/BJN 1.057 0.912 0.531 0.557 0.809 0.487 
Surface sediment/BJN 0.965 1.001 0.592 - - - 
Tarbal 0.913 - 0.530 - 0.792 0.520     
1
Ts/Tm = 18#-22,29,30-trisnorneohopane / 17#-22,29,30-trisnorhopane 
2
24Tri/23Tri = C24 tricyclic terpane / C23 tricyclic terpane 
3
C29/C30 = 17#,21$(H)-30-norhopane / 17#,21$(H)-hopane 
4
29D/29H = 18#(H)-30-norneohopane  / 17#,21$(H)-30-norhopane 
5
35S/34S = 17#,21$(H)-20-pentakishomohopane (22S) / 17#,21$(H)-20-tetrakishomohopane 
(22S) 
6
C31S/C30 = 17#,21$(H)-20-homohopane (22S) / 17#,21$(H)-20-hopane (22S) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 41 
 
 
 
 
 
Figure 19. A GC-MS-SIM m/z 217 fragmentogram of pure MC-252 crude oil (top; from Natter et 
al., 2012) in comparison to weathered oil scraped from suppressed plants at BJS (middle; from 
Keevan, 2012) and a tarbal colected from the Gulf coast 2.5 years after the Deepwater Horizon 
spil (botom). 
 
 42 
 
 
 
 
 
Figure 20. A GC-MS-SIM m/z 218 fragmentogram of pure MC-252 crude oil (top; from Natter et 
al., 2012) in comparison to weathered oil scraped from suppressed plants at BJS (middle; from 
Keevan, 2012) and a tarbal colected from the Gulf coast 2.5 years after the Depwater Horizon 
spil (botom). 
 
 43 
Table 4. Measurements of alkylated polycyclic aromatic hydrocarbons (PAHs) from a Gulf coast 
tarbal (ND = not detected). 
 
Alkylated PAH 
Reading 
(mg/kg) 
 
Alkylated PAH 
Reading 
(mg/kg) 
cis/trans-Decalin 0.190 
 
C1-Fluoranthenes/Pyrenes 2.99 
C1-Decalins 0.336 
 
C2-Fluoranthenes/Pyrenes 4.11 
C2-Decalins 0.443 
 
C3-Fluoranthenes/Pyrenes 4.25 
C3-Decalins 0.635 
 
C4-Fluoranthenes/Pyrenes 5.10 
C4-Decalins 0.812 
 
Naphthobenzothiophene 2.01 
Benzo(b)thiophene ND 
 
C1-Naphthobenzothiophenes 8.65 
C1-Benzothiophenes ND 
 
C2-Naphthobenzothiophenes 9.34 
C2-Benzothiophenes ND 
 
C3-Naphthobenzothiophenes 6.62 
C3-Benzothiophenes 0.0198 
 
C4-Naphthobenzothiophenes 2.95 
C4-Benzothiophenes ND 
 
Benz(a)anthracene 0.0603 
Naphthalene 0.281 
 
Chrysene 6.77 
C1-Naphthalenes 0.573 
 
C1-Chrysenes 12.0 
C2-Naphthalenes 0.592 
 
C2-Chrysenes 10.5 
C3-Naphthalenes 0.461 
 
C3-Chrysenes 4.99 
C4-Naphthalenes 1.11 
 
C4-Chrysenes 3.09 
Biphenyl 0.179 
 
Benzo(b)fluoranthene 0.726 
Dibenzofuran 0.0481 
 
Benzo(k)fluoranthene ND 
Acenaphthylene 0.0123 
 
Benzo(a)fluoranthene ND 
Acenaphthene 0.00801 
 
Benzo(e)pyrene 1.09 
Fluorene 0.116 
 
C30-Hopane 16.3 
C1-Fluorenes 0.570 
 
Benzo(a)pyrene 0.0593 
C2-Fluorenes 5.00 
 
Perylene 0.0461 
C3-Fluorenes 9.93 
 
Indeno(1,2,3-cd)pyrene ND 
Anthracene 0.120 
 
Dibenz(a,h)anthracene 0.175 
Phenanthrene 0.294 
 
Benzo(g,h,i)perylene 0.194 
C1-Phenanthrenes/Anthracenes 6.43 
 
4-Methyldibenzothiophene 0.781 
C2-Phenanthrenes/Anthracenes 26.3 
 
2-Methyldibenzothiophene 0.318 
C3-Phenanthrenes/Anthracenes 23.4 
 
1-Methyldibenzothiophene 0.312 
C4-Phenanthrenes/Anthracenes 16.1 
 
3-Methylphenanthrene 1.29 
Retene 1.71 
 
2-Methylphenanthrene 1.26 
Dibenzothiophene 0.150 
 
2-Methylanthracene ND 
C1-Dibenzothiophenes 1.67 
 
9-Methylphenanthrene 1.90 
C2-Dibenzothiophenes 9.02 
 
1-Methylphenanthrene 1.41 
C3-Dibenzothiophenes 9.84 
 
2-Methylnaphthalene 0.578 
C4-Dibenzothiophenes 9.61 
 
1-Methylnaphthalene 0.266 
Benzo(b)fluorene 0.392 
 
2,6-Dimethylnaphthalene 0.381 
Fluoranthene 0.232 
 
2,3,5-Trimethylnaphthalene 0.143 
Pyrene 0.606 
 
Carbazole ND 
 
 
 
 
 
 
 
 
 
 44 
Dispersant Fingerprinting 
 
A pure sample of COREXIT 950A, the primary dispersant used in BP?s remediation 
efforts for the Deepwater Horizon spil, was obtained for chemical analysis. Following the spil, 
dispersant was applied directly into the flowing wellhead and aerially to the surficial Gulf oil slick, 
but it is suspected that the dispersant may have migrated to coastal wetlands along with oil 
through the physical wave action of Gulf waters. This study aims to characterize the chemical 
nature of the dispersant so that future studies may determine the fate and geochemical evolution 
of COREXIT 9500A along Gulf coast saltmarshes. COREXIT 9500A can be characterized by its 
volatile compounds through gas chromatography-mas spectrometry (GC-MS) and its non-
volatile ions through liquid chromatography-mas spectrometry (LC-MS). The chromatographs 
produced through GC-MS and LC-MS colectively serve to identify the potential compounds that 
may act as tracers in natural environments. 
NALCO, the manufacturers of COREXIT, have reported some of the ingredients in 
COREXIT 950 through Material Safety Data Shets (MSDS). Hazardous substances revealed 
by the MSDS?s include organic sulfonic acid salt, propylene glycol, and hydrotreated light 
petroleum products (NALCO). Several of these published compounds and others are revealed 
through GC-MS and LC-MS analyses of a pure COREXIT sample. 
The most prominent compounds displayed in the GC-MS chromatograph (Figure 21) are 
polyethylene glycol and light hydrocarbon compounds. Polyethylene glycol is used in the 
dispersant as a surfactant. A surfactant induces emulsification by coating the surface of the crude 
oil and lowering the tension at the interface betwen the oil and surounding water. Another 
compound that registered a high intensity was a branched hydrocarbon, C
15
H
32
 
(Dodecane,2,6,10-trimethyl). The polyethylene glycol and hydrocarbon acount for the surfactant 
and hydrocarbon-based solvent components discussed by Judson et al. (2010). These dominant 
components of COREXIT 950A are light, as indicated by their quick elution (i.e. under 10 
minutes). In Figure 21, the shaded red box outlines a region where the intensity has ben scaled 
up by a factor of 36. At this level, another peak is evident; this exaggerated peak indicates the 
 
 45 
presence of a saturated 16C-hydrocarbon fatty acid chain and carboxyl group, CH
3
(CH
2
)
14
COOH, 
or palmitic acid. 
 
 
 
 
Figure 21. Chemical analysis of dispersant COREXIT 950A via gas chromatography-mas 
spectrometry (GC-MS), indicating the presence of polyethylene glycol, light hydrocarbon 
compounds, and faty acids. 
 
 
Liquid chromatography of positive ions (Figure 2) from COREXIT 950A revealed a 
single spike of high intensity and a trend of related peaks of lower intensity. The highest individual 
peak occurs at m/z 213.1471. Within the segment of 5x exaggeration, there is a clear trend of 
rising and faling m/z values that ocur at regular intervals. This trend appears similar to the 
process of wax enrichment that occurs in many spiled oils (Wang and Stout, 2007). However, a 
key feature of this trend is the consistent spacing of 4 m/z betwen each peak. A regular 
spacing of 4 is highly indicative of polyethylene glycol (PEG). PEG is composed of repeating 
(C
2
OH
4
)
 n
 units, each of which have an atomic mass unit (amu) of 44. Although it appears 
unrelated, the high intensity peak at m/z 213.1471 falls along the repeating interval of 44 m/z. The 
peak at 213.1471 is likely created by a compound of organic sulfonic acid and PEG; a compound 
of sulfonic acid (SO
3
H; 81 amu) and 3 PEG, (C
2
OH
4
)
 3
,;132 amu) amounts to a total mass of 213 
 
 46 
amu. If this compound is a sulfonic acid, it may represent the base in a series of compounds that 
regularly increase by 4 amu with the presence of additional individual PEGs. PEGs adsorb onto 
the surface of oil to promote uniform distribution and eliminate the attractive forces that form thick 
slicks. Finaly, one peak at 467.190 does not fall within the regular PEG interval. This peak likely 
represents dioctyl sodium sulfosucinate (DOS), a neutral compound with an atomic mas of 
444.56 amu (C
20
H
37
NaO
7
S). Its presence in a liquid chromatograph indicates that the molecule 
has been ionized. The detected DOS was likely ionized by an aditional sodium (23 amu), 
resulting in the recorded m/z of 467.190. 
 
 
 
Figure 2. Chemical analysis of dispersant COREXIT 950A via liquid chromatography-mas 
spectrometry (LC-MS) ilustrating a polyethylene glycol (PEG) series, sulfonic acid base, and 
dioctyl sodium sulfosuccinate (DOS). 
 
 
Not al compounds identified can be used as reliable tracers for COREXIT 950A in the 
natural environment. Some compounds identified here (i.e. organic sulfonic acid, etc.) biodegrade 
readily and therefore wil not persist long enough in the natural environment to act as a 
biomarker. PEGs are resistant to thermal degradation when protected by water (Padfield et al., 
1990), but studies have shown PEGs to be biodegradable by some bacteria (Obradors & Aguilar, 
1991; Watson & Jones, 197). 
 
 47 
Other compounds may resist biodegradation, but it is dificult to trace their origin back to 
one particular source. Palmitic acid is one of the most common fatty acids found in animals, 
plants, and microorganisms. The palmitic acid identified in COREXIT 9500A also appears in the 
pore-water samples extracted from heavily-contaminated saltmarsh sites. However, it is likely that 
the palmitic acid in contaminated porewaters was derived from a local, biological source. 
Therefore, although palmitic acid is resistant to biodegradation, it canot act as a reliable 
biomarker because its origin cannot be traced to one particular source. The same is true of the 
branched hydrocarbon revealed in GC-MS analysis. 
Overal, DOSS likely provides the most reliable dispersant tracer. Kujawinski et al. (2012) 
reported that litle to no DOS had degraded in seawater four months after the initial aplication 
of COREXIT 9500A. It is also possible that DOS may enter groundwater along coastal 
saltmarshes through anthropogenic means, as it is a comon ingredient in some pesticides and 
supositories. Therefore, a confident identification of COREXIT 950A by DOS wil require a 
strong corelation betwen DOS concentrations and the degre of oil spil contamination. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 48 
Trace Metals 
 
In previous studies (Kevan, 2012; Natter, 2012), the bulk geochemical composition of 
saltmarsh sediments was determined through inductively coupled plasma-mas spectrometry (ICP-
MS). The results for the geochemical analysis of sediments indicated high levels of metal 
contamination as a result of the influx of spiled oil from the Depwater Horizon event. High 
concentrations of metals in sediments reflect the high capacity of fine-grained, saltmarsh sediments 
to sequester various elements (e.g. Ni, Cu, Pb, Zn, Sr, Co, V, Ba, Hg, As, S, and Fe) via adsorption 
or co-precipitation. This study quantifies the concentrations of disolved sulfur and trace metals in 
pore waters at the same saltmarsh sites. Trace element analyses were conducted using the ICP 
facility at Vasar Colege.  
Figures 23?25 display the concentrations of sulfur and various trace metals in the extracted 
pore waters from each site. A complete dataset, including removed outliers, can be found in Tables 5-
8. For easy visual grouping, depth plots are color-coded by level of contamination: heavily 
contaminated sites are shown in shades of red, intermediate sites in shades of yelow, and pristine 
sites in shades of green. Box-and-whisker plots illustrate the ranges of metal concentrations and their 
corelations with degre of contamination. 
Figure 23 (top) illustrates higher concentrations of total sulfur (representing all reduced 
and oxidized forms of H
2
S, SO
4
2-
, etc.) in pore water at heavily oiled sites. Unlike most other 
metals analyzed, sulfur concentrations were recorded on the scale of pm in both sediment and 
pore water. Measurements of total sulfur concentrations have an average range of 249ppm at 
pristine sites, 261pm at moderately contaminated sites, and 806pm at heavily contaminated 
sites. Nater et al. (2012) also reported very high levels of reduced sulfur concentrations in pore 
waters (up to 80pm) in heavily contaminated sediments. The influx of spiled oil probably 
provided new sulfur and additional substrate for sulfate-reducing bacteria that convert sulfate to 
sulfide. 
Depth profiles of sulfur concentration in pore water and sediment are shown in Figure 26. 
Total sulfur concentrations in pore water are higher near the sediment surface than at depth. The 
same trend was observed in the populations of SRB estimated by Nater et al. (2012). Total sulfur 
 
 49 
concentrations near the sediment surface are higher at the heavily contaminated sites than the 
pristine sties, likely due to higher inflow of spiled oil on the top of marsh sediments. 
Iron concentrations in pore water have an average range of 250pb at pristine sites, 
245ppb at moderately contaminated sites, and 221ppb at heavily contaminated sites (Figure 23, 
midle; Figure 27). Similarities in concentration ranges indicate no significant diference between 
oiled and pristine sites. Low disolved iron and very high levels of sulfide indicate that bacterial 
sulfate reduction has fixed most reduced Fe by forming iron sulfide solids. Biogenic sulfide 
minerals such as pyrite with distinct framboidal form are found in oiled marsh sediments (Nater et 
al., 2012). Pyrite-like sulfide is the stable mineral phase formed under sulfate-reducing conditions 
enhanced by the massive inflow of oil and organic matter. The total concentrations of iron in 
sediments are slightly higher at oiled sites and Weks Bay; the enrichment of trace metals in 
Weeks Bay is directly related to riverine input derived from industrial sources (Bonzongo and 
Lyons, 2004, Le et al., 2013). Higher iron levels in sediments at contaminated sites can be 
interpreted as the result of pyrite precipitation (and thus Fe fixation) enacted by the reducing 
conditions of increased FeRB and SRB activity. 
Ranges in arsenic concentrations in pore water (Figure 23, bottom) are highest at heavily 
contaminated sites. Measurements of arsenic concentrations have an average range of 15 ppb at 
pristine sites, 16 ppb at moderately contaminated sites, and 21 ppb at heavily contaminated sites. 
Heavily-contaminated sites BJN and BJS display the widest ranges in dissolved arsenic 
concentrations in pore water. Ranges in manganese folow the same trend; the average 
concentration range is 848pb at pristine sites, 132 ppb at moderately contaminated sites, and 
3273 ppb at heavily contaminated sites. 
Arsenic concentrations in sediments (Figure 28, right) are also notably higher at oiled 
sites than those at pristine sites.  Due to chemical complexation among organic compounds and 
metals, crude oils often contain elvated levels (up to hundreds of mg/kg) of trace metals. Higher 
levels of arsenic and other metals in sediments are likely derived from spilled oil or industrial 
sources. Pyrite-like sulfides with distinct framboidal form are found in oiled sediments and Weeks 
Bay.  Laser ablation inductively coupled plasma mas spectrometer (LA-ICP-MS) analyses show 
 
 50 
that sulfide solids contain various levels of trace metals such as As, Hg, Pb, Cu, Zn, V, and Zn 
(Kevan, 2012). These sulfide solids likely serve as local sinks for trace metals under sulfate-
reducing conditions. The formation of pyrite and other sulfide solids explains the high levels (a 
few ppm) of arsenic in bulk sediments and the low concentrations of dissolved arsenic in pore-
waters (< tens of pb). 
Chromium and coper are both comon constituents of crude oil (Karchmer and Gun, 
1952; Ball et al., 1960) and therefore may be indicative of crude oil metals contamination. Ranges 
in coper (R = 0.01) folow no trend relative to the degre of contamination: the average Cu 
concentrations in pore water are 9.6 ppb at pristine sites, 10.2 ppb at moderately contaminated 
sites, and 7.7 ppb at heavily contaminated sites. However, chromium concentrations (Figure 24, 
bottom) display a more positive correlation with the degre of contamination; the average 
concentration range is 1.9 ppb at pristine sites, 2.2 ppb at moderately contaminated sites, and 4.9 
ppb at heavily contaminated sites. Concentrations of copper at all depths are generally low 
(Figure 24, midle); every measurement is well below the maximum contamination level (MCL) 
for coper, 1.3 ppm. Spikes in copper (up to 27.4 ppb) occur around 10 cm depth at sites RG and 
WB; these spikes are very similar to those observed in iron at the same sites. In the case of both 
iron and coper, it is likely that these peaks are due to pre-spil anthropogenic factors. Spikes in 
chromium concentration also apear around a depth of 10 cm at the BJN site; the spikes in 
chromium occur only at heavily contaminated sites. Fluctuations in chromium may be a result of 
sequestration into minerals or organic mater. Chromium often displays a very low DTMP, but it 
comonly associates with silicate surfaces or organic mater (Alvarez-Iglesias, 208). The 
highest concentration of chromium, 60.8 ppb, occurs at 3 cm depth at site BD (omitted from 
Figure 31 as an outlier). This especialy high peak in chromium may be related to the influx of 
chromium along with oil contamination or other industrial sources, and the high intensity relative 
to other heavily contaminated sites may be a result of its enclosed setting and lack of tidal 
flushing.  
Concentrations of Zn in pore water range from 21.0 to 472.3 ppb (Figure 25, botom). 
Cobalt concentrations (R = 0.27471) were exceptionally low (0.0 to 5.3 ppb) (Figure 25, top). 
 
 51 
Concentrations of Co in pore water at every sampling site and at nearly al depth intervals were 
measured as <1 pb. There is no notable enrichment of Co and Zn in pore water at heavily oiled 
sites with respect to those at pristine sites. 
In sumary, concentrations of trace metals disolved in pore waters are generaly 
low (a few to hundreds of pb), despite high levels of trace metals in bulk sediments (a few to 
tens of ppm). It is very likely that high organic matter content and bacterialy-mediated sulfate 
reduction promote metal retention through the formation of sulfide solids.  The bacterial sulfate 
reduction proces can diminish the levels of trace metals dissolved in pore waters of marsh 
sediments. Several metals display strong trends with the degre of oil contamination. 
Concentrations of sulfur, arsenic, manganese, and chromium in pore waters al display a positive 
trend with increased degre of oil spil contamination. In sediments, concentrations of S, Fe, As, 
Mn, Cr, Cu, Ni, and Co also increase with increased oil contamination. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 52 
Table 5. ICP-MS results for sulfur (pb) and iron (ppb) concentrations in subsurface pore waters down to 30 cm at various Gulf coastal saltmarsh 
sites. 
 
SULFUR (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 1146956.5 1230159.1 911652.1 1076081.5 709967.3 188691.5 572916.6 
 
626024.5 
 
3-6cm 1059862.1 925973.8 670601.4 871791.0 609049.4 109944.6 473354.0 696901.5 640001.5 786562.9 
6-9cm 816196.0 774351.9 427470.2 514679.9 347053.5 34010.4 283291.7 770637.5 611678.5 739367.3 
9-12cm 675219.4 835025.4 298125.2 523254.1 308735.4 47977.3 143084.1 732816.4 620353.9 698267.1 
12-15cm 483166.5 670030.0 195340.6 439583.2 222945.3 22275.9 235381.3 712992.1 628376.9 683693.6 
15-18cm 405993.4 491512.4 127306.3 396979.4 314004.4 18502.9 426979.8 
 
695990.2 627931.4 
18-21cm 415367.8 428411.4 89617.3 333165.3 264795.0 19521.4 
  
624570.0 648433.9 
21-24cm 377503.9 523118.3 58000.7 415249.6 321780.3 48389.2 
  
563799.0 564951.5 
24-27cm 362073.5 467438.3 71464.7 518016.6 339552.1 65077.0 
  
549146.5 645091.4 
27-30cm 322506.3 591083.9 63154.8 408346.9 
 
89086.1 
  
583480.2 597392.2 
 
IRON (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 50.8 236.7 160.1 1212.4 46.1 107.9 100.2 73.0 64.5 
 
3-6cm 71.3 129.0 52.1 317.5 100.9 69.6 28.1 33.5 129.3 45.4 
6-9cm 53.7 79.6 35.7 51.5 54.9 62.3 74.9 57.1 21.1 289.1 
9-12cm 99.1 54.2 39.0 31.3 46.7 384.1 26.9 28.4 16.3 22.3 
12-15cm 301.3 42.2 55.3 32.8 425.7 170.8 63.7 102.5 15.0 209.2 
15-18cm 60.8 54.1 41.2 201.3 32.4 236.3 45.9 33.8 61.9 32.2 
18-21cm 107.2 33.4 52.1 42.3 850.2 132.2 206.3 
 
142.7 41.9 
21-24cm 107.1 58.5 44.7 15.5 37.6 29.1 206.2 
 
197.6 532.7 
24-27cm 112.8 99.8 46.3 31.0 71.6 43.3 117.0 
 
188.8 47.8 
27-30cm 45.7 45.4 39.5 25.9 
 
38.4 95.6 
 
231.4 94.8 
 
 
 
 53 
Table 6. ICP-MS results for arsenic (ppb) and manganese (ppb) concentrations in subsurface pore waters down to 30 cm at various Gulf coastal 
saltmarsh sites. 
ARSENIC (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 6.7 12.1 10.4 11.8 4.6 25.8 2.9 6.1 2.4 
 
3-6cm 6.7 18.6 9.5 27.4 5.5 9.6 3.4 4.7 3.1 12.1 
6-9cm 8.3 24.7 8.2 8.5 4.1 3.1 4.5 5.3 2.0 5.5 
9-12cm 11.2 33.2 7.4 6.0 7.2 8.5 9.9 7.1 1.5 3.5 
12-15cm 13.5 25.3 8.2 6.4 10.0 6.0 5.3 29.4 1.4 4.5 
15-18cm 15.9 40.2 8.2 9.0 6.7 8.0 13.0 6.4 1.1 2.8 
18-21cm 19.9 41.0 10.9 7.1 16.9 4.9 1.3 
 
1.9 4.7 
21-24cm 24.7 43.7 7.1 7.6 6.6 5.0 2.4 
 
3.0 4.5 
24-27cm 30.1 36.9 8.3 6.6 4.4 5.3 2.1 
 
2.9 4.2 
27-30cm 32.1 29.8 7.0 5.8 
 
6.3 1.3 
 
11.9 3.0 
 
MANGANESE (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 429.7 964.1 5308.6 7353.1 4245.4 2984.9 524.0 23.9 543.4 
 
3-6cm 1153.1 2068.5 2422.4 1459.2 4571.5 2244.2 216.9 27.0 221.1 974.6 
6-9cm 1395.3 2332.2 1915.7 921.0 3085.7 1346.7 179.5 20.0 217.9 640.6 
9-12cm 1368.8 2652.7 1989.0 896.7 3029.1 1441.0 147.7 18.1 223.7 540.0 
12-15cm 1285.1 2522.2 1998.3 838.3 1966.4 1619.5 141.6 33.0 215.7 530.1 
15-18cm 1320.0 2007.4 1769.9 774.5 2775.2 1534.3 95.9 36.5 213.8 444.7 
18-21cm 1362.7 2166.9 1979.7 807.9 2246.3 1405.6 23.3 
 
241.1 467.8 
21-24cm 1278.9 2274.2 1980.7 992.8 1844.1 1308.4 21.2 
 
312.8 482.6 
24-27cm 1574.1 2222.9 2200.3 1093.5 1723.6 1314.1 32.1 
 
303.2 463.1 
27-30cm 1717.1 2395.2 2337.0 809.2 
 
1274.8 75.3 
 
290.0 454.7 
 
 
 
 54 
Table 7. ICP-MS results for coper (pb) and chromium (ppb) concentrations in subsurface pore waters down to 30 cm at various Gulf coastal 
saltmarsh sites. 
COPER (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 3.6 3.7 1.6 3.1 2.1 4.6 5.1 4.2 3.5 
 
3-6cm 3.6 4.0 2.1 11.3 1.1 4.6 2.8 4.7 3.2 2.9 
6-9cm 6.8 3.8 2.2 4.2 1.1 5.1 4.2 3.2 2.6 2.2 
9-12cm 3.0 4.9 2.4 4.3 27.4 22.2 4.7 2.2 5.3 2.2 
12-15cm 2.3 4.8 5.1 2.5 9.2 3.6 4.1 3.2 2.3 3.0 
15-18cm 3.7 5.5 2.3 6.8 3.6 4.2 5.0 3.9 2.8 1.8 
18-21cm 15.7 5.6 6.3 3.5 15.5 8.9 7.1 
 
3.5 3.5 
21-24cm 3.8 4.7 1.9 2.9 3.4 3.9 9.4 
 
4.7 3.5 
24-27cm 2.7 6.2 3.3 1.5 3.2 4.1 6.3 
 
3.9 3.5 
27-30cm 5.3 4.9 2.6 1.2 
 
3.3 8.7 
 
5.7 3.3 
 
CHROMIUM (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 4.4 4.6 3.2 9.0 1.2 1.1 1.6 1.2 1.6 
 
3-6cm 4.4 4.4 2.9 60.8 1.2 2.1 2.7 1.4 1.6 1.2 
6-9cm 4.5 3.9 2.9 7.7 1.0 2.8 1.5 1.4 1.1 1.6 
9-12cm 11.0 7.5 2.9 3.5 4.9 126.4 1.2 1.1 1.1 1.0 
12-15cm 4.4 3.9 3.3 4.3 65.6 1.5 1.2 1.5 1.1 1.9 
15-18cm 3.9 3.8 3.0 4.2 4.0 1.8 1.1 1.3 1.4 1.0 
18-21cm 4.3 4.5 4.3 3.9 141.2 20.6 1.1 
 
1.7 1.3 
21-24cm 4.2 4.6 2.9 3.1 3.6 2.0 0.8 
 
3.0 3.3 
24-27cm 4.6 6.2 3.4 2.4 2.6 1.1 1.5 
 
1.6 1.7 
27-30cm 4.2 3.9 3.5 1.7 
 
0.8 1.4 
 
1.9 1.4 
 
 
 
 55 
Table 8. ICP-MS results for cobalt (pb) and zinc (pb) concentrations in subsurface pore waters down to 30 cm at various Gulf coastal saltmarsh 
sites. 
COBALT (pb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 0.2 0.4 0.5 3.8 0.7 0.4 0.2 0.2 0.1 
 
3-6cm 0.3 0.5 0.2 5.3 1.0 2.9 0.2 0.5 0.1 0.2 
6-9cm 0.3 0.3 0.3 1.0 0.4 0.7 0.4 0.2 0.0 0.2 
9-12cm 0.1 0.7 0.3 0.3 0.6 2.7 0.3 0.3 0.1 0.2 
12-15cm 0.7 0.4 0.3 0.4 7.8 0.4 0.2 0.3 0.0 0.2 
15-18cm 0.3 0.5 0.4 0.5 0.4 0.4 0.1 0.1 0.1 0.1 
18-21cm 0.2 0.6 0.5 0.4 15.7 1.6 0.6 
 
0.1 0.2 
21-24cm 0.4 0.5 0.7 0.3 0.4 0.4 0.9 
 
0.2 0.2 
24-27cm 0.3 0.8 0.6 0.5 0.3 0.4 0.5 
 
0.1 0.2 
27-30cm 0.4 0.4 0.6 0.3 
 
0.3 0.3 
 
0.2 0.1 
 
ZINC (ppb) 
 
BJN BJS BB BD RG WB LB WI BH PACB 
0-3cm 21.6 123.4 45.8 98.5 21.0 144.2 110.4 131.0 122.2 
 
3-6cm 42.5 77.7 52.9 100.0 31.1 105.9 112.1 98.1 87.9 90.1 
6-9cm 177.1 70.7 32.9 95.0 51.6 101.4 136.6 95.6 96.8 94.2 
9-12cm 30.6 107.7 84.3 103.0 388.7 118.9 123.0 83.9 87.2 78.9 
12-15cm 31.5 102.0 69.7 91.5 11693.5 89.2 101.4 121.2 81.8 97.7 
15-18cm 60.2 161.2 39.4 99.7 388.6 114.1 99.6 113.6 82.2 96.3 
18-21cm 47.7 1065.2 47.9 779.9 21593.8 99.3 133.8 
 
77.5 170.4 
21-24cm 48.2 111.3 50.4 51.2 472.3 116.2 186.7 
 
98.0 139.5 
24-27cm 63.7 148.5 98.6 49.9 222.0 101.4 134.4 
 
101.6 179.4 
27-30cm 177.4 166.1 101.5 97.7 
 
96.1 104.6 
 
136.3 130.1 
 
 56 
 
 
 
Figure 23. Box and whisker plots ilustrating the ranges in concentrations of sulfur, iron, and 
arsenic at all sampling sites. Plots of sulfur and arsenic both display trends of increased 
concentration ranges with an increase in the degre of oil contamination. 
[Heavily contaminated=BJN, BJS, BB, BD; moderately contaminated=RG, WI, PACB; pristine=LB, WB, BH.] 
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 57 
 
 
Figure 24. Box and whisker plots ilustrating the ranges in concentrations of manganese, copper, 
and chromium at all sampling sites. Plots of manganese and chromium both display trends of 
increased concentration ranges with an increase in the degree of oil contamination. 
[Heavily contaminated=BJN, BJS, BB, BD; moderately contaminated=RG, WI, PACB; pristine=LB, WB, BH.] 
 
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 58 
 
 
Figure 25. Box and whisker plots ilustrating the ranges in concentrations of cobalt and zinc at all 
sampling sites. Neither cobalt nor zinc displays any strong trends with the degre of oil 
contamination. 
[Heavily contaminated=BJN, BJS, BB, BD; moderately contaminated=RG, WI, PACB; pristine=LB, WB, BH.] 
 
 
 
 
 
 
 
 
 
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 59 
 
 
Figure 26. Plot of sulfur concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). In pore waters, sulfur displays a strong trend of 
decreasing concentration with increasing depth; this profile matches that of sulfate-reducing bacteria 
(SRB) with similar decreasing trends with depth. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yelow; pristine in gren.] 
 
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 60 
 
 
Figure 27. Plot of iron concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). The strongest trend ocurs in the sediment profile, 
where iron concentrations display a corelation with degree of oil contamination. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yellow; pristine in green.] 
 
 
 
 
 
 
 
 
 
 
 
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 61 
 
Figure 28. Plot of arsenic concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). In pore waters, arsenic concentrations have highest 
ranges at sites BJN and BJS; at these sites, pore-water arsenic concentrations generally increase 
with depth. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yelow; pristine in gren.] 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 62 
 
Figure 29. Plot of manganese concentrations with depth as measured in pore waters (left) and 
sediments (right; after Kevan, 2012 and Nater; 2012). Surficial concentrations at heavily-
contaminated sites B and BD are higher than at heavily-contaminated sites BJN and BJS, which 
may ilustrate the efects of tidal flushing.  
[Heavily contaminated sites colored in shades of red; moderately contaminated in yelow; pristine in gren.] 
 
 
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 63 
 
Figure 30. Plot of coper concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). Unlike most other metals, coper in pore water does not 
display any correlation with the degree of oil contamination; however, in sediments, copper 
concentrations increase with higher degrees of oil contamination. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yelow; pristine in gren.] 
 
 
 
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 64 
 
 
Figure 31. Plot of chromium concentrations with depth as measured in pore waters. Such low 
concentrations (most <5 ppb) make interpretations dificult. At site BD, unusually high concentrations 
of chromium at depths <5 cm may indicate the efects of an enclosed environment that lacks tidal 
flushing.  
[Heavily contaminated sites colored in shades of red; moderately contaminated in yellow; pristine in green.] 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 65 
 
Figure 32. Plot of cobalt concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). Cobalt concentrations in sediments vary with degree of 
contamination; however, cobalt concentrations in pore waters show no such trend. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yelow; pristine in gren.] 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 66 
 
Figure 3. Plot of zinc concentrations with depth as measured in pore waters (left) and sediments 
(right; after Kevan, 2012 and Nater; 2012). Zinc concentrations of sediments generaly increase with 
degree of oil contamination. 
[Heavily contaminated sites colored in shades of red; moderately contaminated in yellow; pristine in green.] 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 67 
Geochemical Modeling 
 
Several types of geochemical models were constructed to investigate the evolution of 
geochemical conditions in coastal saltmarshes in response to spiled oil and saltwater intrusion. 
These models include: (1) the speciation of Fe phases in the presence of S, (2) the 
transformation of Fe oxides and sulfides in response to inflow of oil and changes in redox 
conditions, and (3) the adsorption and sorption of arsenic under diferent geochemical conditions. 
Activity-activity diagrams ilustrate the minerals thermodynamically favored to precipitate due to 
the shift in geochemical conditions augmented by spiled oil. Reaction path models ilustrate the 
partition of metals among aqueous and solid phases and their sorption reactions with pyrite and 
hydrous ferric oxides due to changes in Eh, pH, and ionic-competition processes. 
 
Activity Model 
Figure 34 displays a calculated activity diagram for an Fe-S system based on varying pH 
and activity of hydrogen sulfide. The diagram outlines the stability fields for iron phases at 
varying conditions. H
2
S and pH measurements of pore waters at oiled sites (listed in Appendix 2) 
are projected as black dots within the stability field. The clustering of Gulf saltmarsh geochemical 
conditions within the pyrite field indicates the thermodynamic stability of pyrite under reducing 
conditions. The formation of pyrite and other iron sulfides is dependent upon the activity of e- 
(pE); enhanced microbial respiration spurred by an oil spil likely creates highly reducing 
conditions, so the model in Figure 34 assumes a low pE value of -3. The geochemical 
manifestation of this activity model was shown previously by Kevan (2012) and Nater (2012), 
who reported high quantities of framboidal pyrite crystals within heavily contaminated Gulf coast 
marsh sediment. 
Oiled saltmarsh pore water is characterized by relatively high pH values and very high 
H
2
S concentrations (Nater et al., 2012). High pore water pH and sulfide concentrations reflect the 
combined efects of seawater intrusion and possibly enhanced bacterial sulfate reduction induced 
by spiled oils. Pyrite wil remain stable in neutral pH or slightly alkaline conditions, but an 
increase in pH to 8.5 or more indicates the beginning of pyrite dissolution. Aditionally, more 
 
 68 
oxidized conditions (with les negative pE) may reduce the stability of pyrite. Oiled saltmarshes 
likely maintain highly reducing conditions due to high organic carbon contents and active 
microbial processes. Organic matter, especially those associated with the heavy fraction of spiled 
oils, can remain in marsh sediments for decades (Natter et al., 2012).  
 
 
 
 
 
 
Figure 34. Plot showing stability field of Fe
2+
 under varying pH and H
2
S conditions, assuming a 
pE of -3. The measured pH and concentrations of H
2
S at each site (Appendix 2) are ploted as 
black dots. The diagram was calculated using the ACT2 subprogram of Geochemist?s 
Workbench. 
 
 
 
 
 69 
Mineralogical Reactions Resulting from Inflow of Organic Matter 
The second geochemical model was constructed to trace the mineralogical reactions 
resulting from inflow of oil and organic mater. The initial geochemical condition was set by 
equilibrating seawater (Table X) with hematite (a proxy of Fe(II) oxyhydroxides) under oxidized 
conditions (Eh = +0.1 V) at 25?C. The model then simulates the biogeochemical efects of titration 
of acetate (a proxy of organic matter) into the system. In the simulation, fluid reactants containing 
500 !mol of CH
3
COO
-
 were added into the system over the course of the reaction path. To 
calculate the efect of microbial metabolism processes associated with sulfate-reducing bacteria, 
the fluid redox potential Eh decreases linearly from +0.1 V to -0.2 V over the course of the 
reaction path. The acompanying mineralogical reactions were calculated. The transformation of 
hematite is considered as follows: 
 
4 Fe
2
O
3
 (hematite) + CH
3
COO
-
 + 15 H
+
  "  8 Fe
2+
 + 2HCO
3
-
 + 8H
2
O   (1) 
 
Here, hematite (as electron acceptor) is reduced to ferrous iron, and acetate (as electron donor) 
is broken down into simple forms of HCO
3
-
 in the system.  The reduction of sulfate in seawater 
may be expresed as: 
 
SO
4
2-
 + 2 CH
3
COO
-
 + 2H
+
 " H
2
S + 2 HCO
3
-
 + 2H
2
O   (2) 
 
Hydrogen sulfide (H
2
S) produced by bacterial sulfate reduction can quickly react with dissolved 
metals (e.g., Fe
2+
) to precipitate iron sulfides such as pyrite: 
Fe
2+
 + H
2
S(aq) "FeS(solid) + 2H
+
    (3) 
 
The modeling results (Figure 35) show the efect of decreasing redox potential (Eh) and 
the inflow of organic matter on the precipitation and disolution pathways of various minerals. 
First, hematite becomes thermodynamicaly unstable as Eh drops. Sulfide produced by bacterial 
sulfate reduction then reacts with ferous iron to form pyrite at Eh <0.1 V. Previous studies 
 
 70 
(Saunders et al., 208; Le et al., 2013) have shown that Fe-sulfides formed in reducing systems 
also sequester many other disolved metals and metaloids (e.g., As, Cu, Ni, Co, Zn, U, Pb, etc.). 
Bicarbonate produced by the breakdown of organic mater (acetate) may react with Ca and Mg in 
seawater to form carbonate minerals. However, such reactions may be kineticaly prohibited in a 
short span of time. 
 
 
 
Figure 35. Plot showing reaction paths of hematite, pyrite, and dolomite as Eh changes from 
positive (0.1 V) to negative (-0.2 V). Hematite is shown to disolve, whereas pyrite and 
dolomite are shown to precipitate. 
 
 
 
 
 
 
 71 
 
Saltwater Intrusion and Desorption of Arsenic ? pH Effects 
The third geochemical model employed the surface complexation theory of Dzombak and 
Morel (190) to simulate the desorption of arsenic species associated with oxide minerals in 
response to saltwater intrusion in coastal environments. The intrusion of seawater in shalow 
alluvial aquifers may lead to desorption of arsenic from the surface of hydrous feric oxides 
(HFOs) due to pH effects and ionic competition for HFO sorbing sites. The initial system contains 
1 kg of fresh groundwater (Table 1) equilibrated with 1 g of Fe(OH)
3
 at pH 7. The groundwater 
contains 1 !g/kg of As(II), 1 !g/kg  of As(V), and 0.05 molal of NaCl. The calculation prevents 
the redox coupling betwen As(III) and As(V). Feric hydroxides used in the simulation have high 
specific surface areas (60 m
2
/g) for sorption reactions.   The surface of HFOs is composed of 
weakly sorbing sites (density = 0.4 mol/mol mineral) and strongly sorbing sites (density = 0.01) 
(Dzombak and Morel, 190; Bethke, 208). The simulations assess desorption of various arsenic 
species in response to pH increases caused by saltwater intrusion. Seawater pH ranges from 7.5 
to 8.5. 
Modeling results (Figure 36) illustrate the changes of dissolved arsenic concentrations as 
pH increases from 7 to 9. At a starting pH of 7, 19.5 mg/kg of As are sorbed onto HFOs (19.22 
mg/kg as As(V) and 0.28 mg/kg as As(II). Therefore, at near-neutral pH conditions, arsenic 
shows a strong tendency to adsorb to the surfaces of HFOs. Iron oxyhydroxides with a net 
positive surface charge have a high capability to adsorb the negatively-charged anionic 
complexes of As(V), such as AsO
4
3-
. By contrast, an HFO?s adsorption capabilities for the non-
ionic complexes (e.g., As(OH)
3
) of As(II) are por. The adsorption and desorption tendencies of 
all complexes are affected by changes in pH. Figure 37 shows significant desorption of As(V) and 
minor desorption of As(II) as pH increase from 7 to 9. At higher pH, the surfaces of HFOs 
become negatively charged and tends to expel anions of As(V) complexes. The rise in arsenic 
concentrations as pH passes through the range of typical seawater simulates the potential 
desorption reactions induced by saltwater intrusion. At a final pH of 9, arsenic concentrations in 
sorbate decreased to 19.0 mg/kg, whereas the final arsenic concentrations in fluid increased to 
 
 72 
over 500 !g/kg (Figure 37), fifty times higher than the maximum contamination level of arsenic 
set by the EPA. 
 
 
 
Figure 36. Model results showing a rise of total arsenic concentration in solution due to 
desorption processes from HFOs as pH increases from 7 to 9, passing through the range of 
typical seawater.  
 
 73 
 
Figure 37. Plot of model results showing a rise of arsenic concentrations in solution for each 
arsenic complex of As(II) and As(V) as pH increases from 7 to 9. 
 
 
 
Saltwater Intrusion and Desorption of Arsenic ? Effects of Ionic Competition 
The fourth model was conducted to investigate arsenic desorption resulting from the 
introduction of competing ions (e.g. silicates, carbonate, calcium, sulfate, etc.) with seawater. This 
model begins with a 1 kg basis of typical freshwater (se Table 1) in equilibrium with As-sorbed 
HFOs. Figure 38 illustrates the changes in fluid chemistry as increasing amounts of seawater 
(Table 2) are titrated into the system. As they acount for a large portion of disolved ions in 
seawater, Ca
2+
 and SO
4
2- 
increase at a linear rate with the steady titration of seawater. The aded 
saltwater contains no dissolved arsenic; therefore, the only possible source of increasing arsenic 
within the fluid is the desorption of arsenic from HFOs in the initial freshwater system. Calculated 
 
 74 
concentrations of these ions in HFO sorbate (Figure 39) further suport this hypothesis. In the 
early stages of saltwater titration, the concentration of ions Ca
2+
 and SO
4
2-
 increase within the 
fraction of sorbate; in contrast, AsO
4
3- 
decreases in sorbate concentration as it is desorbed and 
replaced by Ca
2+
 and SO
4
2-
. Similar to the effects of rising pH, the fraction of sorbed arsenic 
decreases and the concentration of arsenic in solution increases to more than 25 !g/kg (Figure 
40). However, in this case, the removal of sorbed arsenic is due to the high concentrations of 
competing ions within the intruding saltwater. 
Two controling processes appear to be occurring in Figure 39. In the earliest stages of 
seawater titration, changes in Ca
2+
 and SO
4
2-
 sorbate concentrations are positive, whereas 
arsenic concentrations are decreasing. This is likely due to the competing ion effect betwen 
newly introduced ions and previously sorbed arsenic ions. As titration of seawater continues, 
sorbing surfaces of oxides are quickly occupied by various ions present in seawater. The sorbed 
concentrations of Ca
2+
 and SO
4
2-
 in sorbate peak after the addition of only about 0.1 kg of 
seawater and then level off (Figure 39). The cesation of sorption at higher salinities indicates 
that HFO surfaces poses a finite number of sorption sites, which is consistent with the 
Langmuir sorption isotherm. It is unclear why the concentrations of Ca
2+
 and SO
4
2- 
in sorbate 
slowly decline after the adsorption capabilities of HFOs peak. The adition of seawater and 
subsequent increase in ionic strength may reduce the stability of the HFOs and their overal 
adsorption capacity. 
Each of the factors discused here may impact desorption of arsenic and other metals in 
coastal wetlands. Saltwater intrusion raises the overal pH of a system, which may alter the 
overall surface charge of iron oxyhydroxides and therefore encourage the desorption of arsenic 
anionic complexes. Further desorption from HFOs may occur as a result of ionic replacement by 
the introduction of competing ions disolved in seawater. Minor desorption also may occur as a 
result of iron oxyhydroxide disolution, which decreases the availability of sorbing sites. Metals 
contamination from spiled oil threatens the coastal water resources. The sequestration of toxic 
metals into pyrite and hydrous feric oxides (HFOs) may help to mitigate groundwater 
contamination concerns. The modeling results demonstrate that the mobility and transformation 
 
 75 
of trace metals are strongly influenced by multiple factors, including Eh, pH, ionic strength, 
sorption, and mineral precipitation and disolution. 
 
 
 
 
Figure 38. Plot of model results showing the reaction path of disolved ions Na
+
, Cl
-
, AsO
4
3-
, 
and As(OH)
4
-
 through titration with saltwater. Na
+
 and Cl
- 
increase linearly during titration, 
whereas arsenic complexes increase at the rate that they are desorbed from hydrous ferric 
oxide surfaces. 
 
 
 
 
 76 
 
Figure 39. Plot of model results showing the reaction path of sorbate constituents Ca
++
, SO
4
2-
, AsO
4
3-
, and As(OH)
4
-
 through titration with seawater. Whereas Ca
++
 and SO
4
2-
 increase in 
sorbate concentration, AsO
4
3-
 rapidly decreases as a sorbate component due to replacement 
by Ca
++
 and SO
4
2-
. 
 
 
 
 
 
 
 
 
 
 
 
 77 
 
 
Figure 40. Plot of model results showing how concentrations of arsenic in solution increase 
as a result of titration with seawater. Arsenic complexes are desorbed from the surface of 
HFOs as sorbing sites are replaced by competing ions introduced by seawater. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 78 
Hydrodynamics of Saltwater and Oil Intrusion 
 
Aquifer Tank Experiments 
 
Elevated levels of organics, whose fingerprint matches that of the MC-252 wellhead, 
were found at depths down to 15 cm in Gulf wetland sediments (Natter et al., 2012). It was 
suspected that saltwater intrusion dynamics along Gulf coastlines might control the presence of 
crude oil down to such depths. To investigate the interactions of saltwater, freshwater, and oil, a 
laboratory-scale hydrogeologic tank, designed by Mat Obley (Le et al., 2012) was employed in 
several modeling experiments. 
Saltwater intrusion at depth: The first tank experiment was developed to locate the 
position of a saltwater wedge. To facilitate observations over a reasonable timeframe, the tank 
was packed with glas beads with high permeability (se Permeameter Study below). Highly 
permeable media alow the hydrologic system to quickly adjust to a steady state in response to 
changes in hydraulic head. For the experiment, freshwater was first transmited through the tank 
via the freshwater chamber at a fixed hydraulic gradient. Once saturated with freshwater, 
saltwater (salinity = 0.48 molal, dyed red) was injected into the system at a constant rate via a 
constant-head saltwater chamber. Overflow outlets controled the water levels in both the 
freshwater and saltwater chambers. The first saltwater wedge (Figure 41, top) was created with a 
saltwater head of 25.5 cm and freshwater head of 27.0 cm (relative to the botom of the tank). 
The dense saltwater rapidly intruded along the botom of the aquifer from left to right to form a 
steady saltwater wedge. Once the first wedge was stabilized, the saltwater head was raised by 
0.8 cm to 26.3 cm to simulate saltwater intrusion proceses. The rise in saltwater head prompted 
the progradation of the wedge to the right until it once again stabilized after about 3.5 minutes. 
Propagation of the saltwater front ilustrates the dynamics of density-driven saltwater 
intrusion outlined in the Ghyben-Herzberg equation (Freze and Chery, 1979). The Ghyben-
Herzberg relationship equates z to roughly 40#h
f
 (Figure 42), where z is the depth of freshwater 
below sea level and h
f
 is the height of freshwater above sea level. Acording to the Ghyben-
Herzberg equation, an increase in saltwater head by 0.8 cm, or a decrease in freshwater head by 
the same amount, would lead to a decrease of z value by 32 cm (i.e., saltwater front wil rise by  
 
 79 
 
 
 
 
 
Figure 41. Photographs of initial (top) and final (bottom) stages in the first tank experiment to 
build a steady freshwater-saltwater interface in response to a 0.8 cm increase in saltwater head. 
Total time elapsed: 3.5 minutes. 
 
 
 
 
 
 80 
 
 
Figure 42. Illustration of the Ghyben-Herzberg equation, which governs the saltwater-freshwater 
interface under hydrostatic conditions (Freze and Chery, 1979). 
 
 
32 cm). The experimental results (Figure 41), however, resulted in a lower z (<10 cm). In the 
tank setup and real aquifer situations, freshwater flow tends to push the saltwater further offshore. 
The Ghyben-Herzberg equation assumes a simple hydrostatic (i.e., no-flow conditions) balance 
between freshwater and saltwater, thus underestimating the depth to the saltwater front. A more 
accurate position of the saltwater front can be determined for any given boundary condition by 
groundwater flow and solute transport modeling (se Numerical Modeling section below). 
At heavily oiled Louisiana sites, greater pore-water levels of disolved organic carbon 
(DOC) are found at depth (Figure 43), indicating mixing of saltwater, freshwater, and oil below the 
sediment surface. DOC in near-surface sediments may be reduced by freshwater recharge from 
rainfal or shalow fresh groundwater. This tank experiment demonstrates that it is also very likely 
that seawater and perhaps oil may invade along the deeper portion of the marsh sediments. This 
result sugests that not al spiled oil rose to the surface; a fraction of oil, especialy heavier 
compounds, may have spread downward through the water column and ultimately contaminated 
underlying sediments. 
Saltwater infiltration along the surface: In the second tank experiment, a new source of 
saltwater was introduced at the sediment surface to simulate conditions in which seawater and 
h
f
z
ocean
seawater
Salty
groundwater
Fresh-
water
interface
 
 81 
spiled oil wash ashore during high tide. The experiment began with the formation of a stable 
interface between freshwater and red-dyed saltwater (h = 25.5 cm; salinity = 0.48 molal). Then, a 
separate saltwater solution of the same salinity, dyed blue, was aded to the system from the top 
surface of the glass beads (Figure 44). This experiment was designed to show the interactions of 
a deeper saltwater wedge with seawater onlapping onto the coast above; it was hypothesized 
that the onlaping seawater at the surface (dyed blue) would migrate downward through the 
freshwater aquifer due to its higher density. The infiltrating saltwater, due to mixing with 
surounding freshwater, would become diluted and less dense than the salty groundwater below 
the saltwater-freshwater interface. 
Figure 4 shows a stable red saltwater wedge and the introduction of blue saltwater 
through a funel at the tank?s surface. As more blue saltwater was aded to the system, it 
became clear that the blue saltwater?s primary direction of movement was toward the left and 
bottom of the tank. The blue saltwater infiltrated down from the surface, mixed with surrounding 
freshwater, and then flowed left under the hydraulic gradients of the freshwater regime. 
Originaly, the blue saltwater did not reach the deep saltwater wedge interface as it discharged 
into the left saltwater chamber. To increase interaction betwen the blue and red saltwater, the 
input location of the blue saltwater was shifted further to the right (Figure 45). Figure 46 shows 
the first stage of interaction betwen blue and red saltwater after continued input of blue 
saltwater. As the blue plume reached the saltwater wedge, the saltwater-freshwater interface was 
pushed downward and formed local depresions, but the toe of the wedge remained in place. 
Finaly, Figure 47 shows the continued interactions of red and blue saltwater after the input of 
blue saltwater ceases. 
At the onset of the experiment, the interface betwen freshwater and red saltwater was 
linear; however; the introduction of blue saltwater disrupted the linear interface. At the end of the 
experiment (Figure 47), the interface betwen red and blue saltwater apears scaloped. 
Additionaly, some smal red saltwater fingers can be seen extending upward several centimeters 
into the descending blue plume. 
 
 82 
The interactions of the two saltwater sources observed in the tank experiments reveal 
possible oil intrusion mechanisms along the Gulf coast. If the blue saltwater near the surface also 
contained a crude oil mixture, the experiments ilustrate one mechanism by which the spiled oil 
potentially infiltrated into coastal sediments as dep as 12-15 cm. Along the interface of red and 
blue saltwater (Figure 47), blue saltwater is being driven left by freshwater flow and upward as it 
reaches the barrier of denser red saltwater. The efect of these two directional forces creates 
scalops in the uper boundary of the red saltwater wedge. The mixing and swirling at the 
saltwater wedge front also creates small upwellings of red saltwater. A schematic of this 
interpretation is shown in Figure 48.  
The presence of MC-252 crude oil and high pore water DOC contents at depths > 15 cm 
may be the result of either of the two intrusion processes illustrated in the tank experiments. It is 
possible that heavier fractions of crude oil penetrated into deeper sediments through either the 
movement of a saltwater wedge at depth or through the infiltration of onlaping seawater. The 
initial saltwater wedge occurs as a result of diferences in density and is controlled by the 
diferences in saltwater and freshwater heads. The infiltration of surficial saltwater is controlled by 
both density-driven and hydraulic gradients of freshwater (i.e., topographic-driven) flow. 
 
 
 
 
 83 
 
Figure 43. Plot showing the depth profile of disolved organic carbon (DOC) in sediments at 
various saltmarsh sites (Nater et al., 2012). Contaminated sites show much higher DOC levels 
compared to those at non-contaminated sites. 
 
 
Figure 44. Photograph showing the introduction of a second saltwater source (dyed blue) near 
the tank?s surface.  
 
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 84 
 
Figure 45. Photograph showing the transition of the blue saltwater sources further toward the 
center of the tank alows blue saltwater to reach the red saltwater wedge at depth.  
 
 
 
 
Figure 46. Photograph showing how blue saltwater follows a downward and leftward movement 
toward the original red saltwater-freshwater interface. 
 
 
 85 
 
Figure 47. Photograph of blue saltwater reaching the red saltwater wedge.  Red saltwater fingers 
can be seen growing upward through blue saltwater. 
 
 
 
Figure 48. Schematic diagram showing the interpreted interactions of red saltwater (denser) and 
dark blue saltwater (lighter) along the face of the red saltwater wedge. Scalloping and small 
upwellings are created along the interface due to competing directional flows of freshwater and a 
component of upward density-driven flow. 
 
 
 86 
Permeameter Studies 
In a separate setup, a constant-head permeameter (Figure 9) was used to determine the 
hydraulic conductivity of the beads used in the idealized physical tank model. The experiments 
measure the flow rates Q through a sand layer under a controled constant head gradient (!h/!l). 
The head gradient can be controled by adjusting water levels in two constant-head reservoirs. 
The hydraulic conductivity, K, of tested sample was calculated using Darcy?s equation: Q = -
KA!h/!l.  The results of the permeameter experiments are shown in Figure 49 and Table 9. 
Average hydraulic conductivity was calculated as 0.075 ?0.0115 cm/s. This value falls within the 
10
-3
 ? 10
-1
 cm/s range of a typical unconsolidated sand (Feter, 200). 
 
Table 9. Head and volumetric data used to calculate discharge and hydraulic conductivity of 
glass beads. Constants: chamber height (!l) = 0.125m; chamber area (A) = 0.03217m
2
; time = 
180 s. 
 
Head (!h,m) Volume (m
3
) Q (m
3
/s) K (m/s) K (cm/s) 
0.61 0.002695 0.0000150 0.000954 0.095 
0.71 0.002880 0.0000160 0.000876 0.088 
0.81 0.003060 0.0000170 0.000815 0.082 
0.91 0.003200 0.0000178 0.000759 0.076 
1.01 0.003440 0.0000191 0.000735 0.074 
1.11 0.003575 0.0000199 0.000695 0.070 
1.21 0.003710 0.0000206 0.000662 0.066 
1.31 0.003835 0.0000213 0.000632 0.063 
1.41 0.003980 0.0000221 0.000609 0.061 
 
 
 
 
 87 
 
 
Figure 49. Regresion trend of hydraulic conductivity (K) values derived from constant-head 
permeameter experiments. 
 
 
 
 
 
 
 
Numerical Modeling 
 
Numerical models were constructed using the program Basin2 to simulate saltwater 
intrusion at the same scales of tank experiments.  The physical and numerical models both 
represent a simplified version of a real world coastal seting. 
Steady-State model: The first model (Apendix 3) simulates steady-state hydrologic 
conditions and accounts for salt transport by advection. Groundwater salinity at the land surface 
is set to zero and groundwater at the seafloor is set to 0.48 molal, the same as in the tank model. 
The model domain (Figure 10), 60 cm long, is composed of a sandy layer with permeability of 
0.01 darcy (10
-7
 m/s). The permeability of 0.01 darcy is similar to that of fine-grained, silty sand, 
but lower than that of glass beads used in the tank experiments.  Figure 50 illustrates that 
freshwater table relief (0.4 to 0.2 cm over 60 cm) acros the land surface drives the flow of 
freshwater from right to left. The steady-state model ilustrates a saltwater-freshwater interface at 
equilibrium and the invasion of saltwater along the bottom of the aquifer, as seen in the physical 
tank experiments.  The modeling results also demonstrate how a rise of sea level by 0.2 cm 
causes the saltwater front to advance upward and landward. 
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 88 
 
 
 
Figure 50. Model results showing predicted groundwater flow and saltwater intrusion under 
conditions of maximum freshwater table of 0.4 cm (top) and 0.2 cm (botom) above sea level. 
Sea-level rise of 0.2 c causes salty groundwater to move upwards and landwards. Colors map 
groundwater salinity from 0 (blue, corresponding to freshwater) to 0.48 molal (red, corresponding 
to seawater).  Arows show predicted flow vectors. Small tick marks denote sea level. A dark 
contour line tracks the position of salinity level 0.3 molal. 
 
 
 
 89 
Transient model: The second model (Apendix 3) investigates the interactions over time 
of two saltwater sources, one from the seafloor and one from the surface. First, the model is 
given time to develop a stable saltwater-freshwater wedge interface similar to that produced in 
the steady-state model. Due to limitations of the Basin2 software, time restrictions alow only for a 
smaler wedge to form over the alotted time. In the simulations, the saltwater front is tracked by a 
contour of 0.3 molal. Figure 51 shows the early development of a wedge at 20 model years, and 
Figure 52 shows the growth and propagation of the wedge at the end of 1600 model years. 
Betwen 150 and 300 model years, an aditional source of saltwater of the same salinity was 
introduced at the surface at a location 40 cm from the left boundary. During this time, the 
transient model ilustrates the movement and interactions of the initial saltwater wedge and a 
second surficial saltwater source. Figure 53 shows the first introduction of the second, surficial 
saltwater source. By 280 years, the surficial saltwater input forms a plume that migrates both 
downward and to the left, similar to that observed in the tank experiments. The plume?s direction 
of movement is controlled primarily by freshwater table slope and density. Finally, at 3000 model 
years (Figure 5), the surficial saltwater migrates downward, mixes with freshwater through 
dispersion, and then flows laterally toward the ocean under the freshwater gradient. 
Numerical modeling reveals some insight into posible intrusion dynamics that may 
explain the presence of crude oil identified in sediments as deep as 15 cm. Similar to the tank 
models, the numerical models reveal a stable saltwater wedge that intrudes along the deper 
aquifer from the left. By contrast, the surficial plume of saltwater infiltrates downward by gravity 
and laterally by hydraulic gradient, gradually approaching the deep wedge interface. Both tank 
and numerical models indicate that the MC-252 crude oil could have reached deeper sediments 
either as a result of infiltration of a surface plume or the intrusion of a deep saltwater wedge from 
below. 
 
 
 90 
 
Figure 51. Transient model at 20 model years, the begining stages of saltwater wedge 
development. 
 
 
Figure 52. Transient model at 160 model years, ilustrating the extent of a saltwater wedge 
before a secondary source of surficial saltwater is added to the system (at 150 model years).  
 
 91 
 
Figure 53. Transient model at 260 model years, showing the first sign of saltwater infiltration 
from a secondary surficial point.  
 
 
Figure 54. Transient model at 280 model years as the surficial source of saltwater begins to 
form a slight plume that primarily migrates downward and toward the saltwater wedge at depth.  
 
 
 92 
 
Figure 55. Transient model at 300 model years, ilustrating the impact of the surficial saltwater 
source on the original saltwater wedge interface.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 93 
CONCLUSIONS 
 
This study investigated the fate, transport, and transformation of oil and metal 
contaminants at ten selected northern Gulf saltmarsh sites (i.e., Bay Jimy North, Bay Jimy 
South, Bayou Dulac, Bay Batiste, Rigolets, Point Aux Chenes Bay, Walker Island, Weks Bay, 
Longs Bayou, and Bayou Heron) that were impacted to varying degres by the 2010 Depwater 
Horizon oil spil. 
Oil was scraped from plants and extracted from surface sediments and deper sediments 
(down to 15 cm) at heavily contaminated saltmarshes. Hydrocarbon fingerprinting via GC-MS and 
GC-MS-SIM confirms that the source of these oils is the Macondo-1 well of the Deepwater 
Horizon spil. Weathered oil on plants and in surface sediments is almost entirely Macondo-1 
spiled crude; oil recovered from deeper sediments (12-15 cm) is partially MC-252 crude oil but 
also displays evidence of mixing, possibly with oil from a separate, natural oil seep. 
Chromatographs also show that weathered MC-252 oils remaining in saltmarshes have ben 
enriched in heavier hydrocarbons by the preferential degradation of lighter hydrocarbons. 
Furthermore, tarbals brought to the Alabama Gulf shore by Huricane Isac, 28 months after the 
spil, were also positively tied to oil spiled from Macondo-1 by remarkably narrow ranges in 
selective biomarker ratios: 0.913-1.070 for Ts/Tm (Ts/Tm =18$-22,29,30-trisnorneohopane/17$-
22,29,30-trisnorhopane), 0.530-0.586 for C29/C30 norhopane-hopane, 0.792-0.814 for 35S/34S 
pentakishomohopane-tetrakishomohopane, and 0.507-0.520 for C31S/C30 homohopane-hopane.  
GC-MS and LC-MS analyses of dispersant COREXIT 950A revealed several 
compounds: light hydrocarbons, dioctyl sodium sulfosuccinate (DOS), palmitic acid, and a 
sulfonic acid base to a series of polyethylene glycols (PEGs). Of the compounds identified, DOS 
likely acts as the most reliable tracer of COREXIT 950A in natural environments. DOS is highly 
resistant to biodegradation (Kujawinski et al., 2012) and its limited anthropogenic uses wil likely 
allow for a strong correlation of environmental samples to a source. DOS was identified through 
 
 94 
LC-MS at m/z 467.190. In future studies, it may be determined whether the degradation-
resistant COREXIT 950A dispersant has migrated from the gulf oil slicks to certain coastal 
saltmarsh environments. If DOS can be identified at m/z 467.190 in a sample, it may be used 
as a conservative biomarker to trace the occurrence and geochemical evolution of dispersant in 
natural environments.  
Physical and numerical models reveal possible intrusion mechanisms responsible for the 
occurrence of MC-252 crude oil in coastal saltmarsh sediments at depths down to 15 cm. 
Physical modeling in a laboratory-scale tank reveals dynamic interactions betwen a dep 
saltwater wedge and a surficial, onlapping saltwater source. The position of the saltwater wedge 
is controlled by density and differences in freshwater and saltwater head, whereas the infiltration 
of surficial saltwater source is controlled by density and the hydraulic gradient of freshwater flow. 
High activity at the scaloped interface between these two mixing saltwater sources may be 
enough to entrain heavier compounds of crude oil deeper in the subsurface. Numerical models 
reveal similar intrusion dynamics; a stable saltwater wedge intrudes along the deeper aquifer 
from the ocean, whereas the surficial plume of saltwater infiltrates downward by gravity and 
migrates lateraly by hydraulic gradient. Both tank and numerical models indicate that the MC-252 
crude oil could have reached deper sediments either as a result of infiltration of a surface plume 
or the intrusion of a deep saltwater wedge from below. 
The concentrations of various trace metals in pore waters are generaly low (a few to 
hundreds of ppb), despite high levels of those trace metals (on the level of pm) in sediments. 
Concentrations of sulfur, arsenic, manganese, and chromium in pore water al display strong 
corelations with the degre of oil contamination. Sulfur ranges from 249 pm at pristine sites to 
806 ppm at contaminated sites, arsenic from 15 pb to 21 ppb, manganese from 848 ppb to 3273 
ppb, and chromium from 1.9 ppb to 4.9 ppb. Sulfur and iron concentrations in sediments also 
display a positive correlation with the degree of contamination, as do several trace metals 
(arsenic, coper, nickel, and cobalt) with moderate to high degres of trace metal pyritization 
(DTMP). Manganese and chromium, which typicaly exhibit a low DTMP, also display a positive 
corelation with the degre of contamination. It is very likely that the abundance of spiled 
 
 95 
hydrocarbons, together with bacterialy-mediated iron and sulfur reduction, promotes metal 
sequestration through precipitation of sulfide solids. The formation of sulfide solids may be 
responsible for the elevated concentrations of metals in sediments relative to their dissolved 
concentrations in pore water. 
Results of geochemical modeling further suport the hypothesis that the enhanced 
respiration of iron- and sulfate-reducing bacteria in coastal saltmarshes results in the 
sequestration of dissolved trace metals into sulfide solids, such as pyrite. An activity model 
indicates that pH and H
2
S conditions at oiled saltmarshes are clustered within the stability field of 
pyrite under sulfate-reducing conditions (pE = -3). Enhanced microbial respiration spurred by the 
oil spil likely created such a reducing environment in which pyrite is thermodynamicaly stable. 
Reaction-path modeling of mineral transformations in a ferrous-sulfate system shows the 
precipitation of pyrite as redox potential (Eh) becomes increasingly negative past an Eh of 
approximately -0.8 V. Seawater titration models illustrate the impacts of rising pH and the 
introduction of competing ions. At near-neutral pH, arsenic shows a strong tendency to adsorb to 
hydrous ferric oxides (HFOs), but when pH rises to 9, arsenic in solution increases by over 500 
ug/kg. Furthermore, additional calcium, sulfate, and bicarbonate ions introduced with seawater 
intrusion encourage desorption of arsenic from HFOs. These competitive ions replace arsenic in 
the sorbing sites of HFOs, resulting in tens of ug/kg increase of arsenic in solution. By contrast, 
the sequestration of toxic metals and metalloids such as arsenic into pyrite and HFOs may 
reduce groundwater contamination. The geochemical modeling results and field data suggest that 
the mobility and transformation of metals are strongly influenced by Eh, pH, the availability of 
competing ions, and mineral dissolution/precipitation. Future geochemical fluctuations in these 
tidaly-influenced coastal marshes may prompt desorption of toxic metals such as arsenic from 
pyrite and HFOs. 
 
 
 
 
 
 
 96 
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 100 
APPENDICES 
 
 
APPENDIX 1 ? Microscale Solvent Extraction, derived from EPA Method 3570 
 
Materials: 
 
1. Anhydrous sodium sulfate 
2. Sediments 
3. PTFE glas extraction tubes 
4. 25 mL glass syringe and 25mm syringe filters 
5. Dichloromethane (DCM) 
6. Rotator 
7. Centrifuge 
8. Glas pasteur pipetes 
 
Method: 
 
1. Add aproximately 5 grams of anhydrous sodium sulfate to a pre-cleaned PTFE 
extraction tube with a PTFE screw cap. 
2. Weigh 3 grams of sediments into the tared extraction tube. 
3. Add 10mL of DCM to the extraction tube and cap tightly. 
4. Shake the tube vigorously until slury is fre-flowing. 
5. Add more sodium sulfate and manualy mix as necessary to produce a fre-flowing, finely 
divided slurry. 
6. Extract the samples by rotating end over end for at least 24 hours (at 70%). 
7. Centrifuge at 300rpm for 10 minutes. 
8. Transfer liquid phase (extract) with pasteur pipete to 16x10m glas extraction vial. 
9. Extract the soil once more by ading aproximately 5mL of DCM to the sample, caping 
the extraction tube tightly, and shaking vigorously by hand for 2 minutes. More sodium 
sulfate can be aded at this point as necessary to dry the extract and break up any 
clumps that may have formed. 
10. Repeat steps 7 to 8. 
11. Filter liquid phase using glas syringe and syringe filter to another 16x10m glas vial. 
12. Dry organics in a vent hod with nitrogen gas for 30 minutes. 
13. Add 1.5mL of hexane chromatography grade. 
14. Transfer the extract to a 2mL auto sampler vial. Add 15ul of internal standard. Cap the 
vial and store in the frezer until analysis. 
 
 
 
 
 
 
 
 101 
APPENDIX 2 ? Pore Water pH and H2S (ppb) Data (from Natter et al., 2012) 
 
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 102 
APPENDIX 3 ? Basin2 Input Files 
 
Basin2 Input File: Steady-State Model 
 
run = steady; start = 0 yrs 
nx = 40;  delta_z = 2 cm 
temperature = vertical; heat_flow = 1 HFU 
salinity = full; salt_flux = 0 
left = closed, right = closed 
 
 
rock ss 
    B_perm = -1.12; A_perm = 0 
    alpha_L = 0; alpha_t = 0 
end_rock 
 
 
x_well(cm)  0  20  21  60   
 
 
strat 'Upper beds' 
    t_dep = 0 yrs 
    X(ss) = 100% 
    column    water_depth(cm)  thickness(cm)  surface_conc(molal) 
    w(1)            5             27                0.48 
    w(2)            0             32                0.48 
    w(3)            0             32                0.0 
    w(4)           -0.2           32.2              0.0 
 
 
 
 
Basin2 Input File: Transient Model 
 
 
run = transient; start = 0 yrs; end = 3000 yrs 
nx = 40;  delta_z = 2 cm 
temperature = vertical; heat_flow = 1 HFU 
salinity = full; salt_flux = 0 
left = closed; right = closed 
print_step = 200 yrs 
 
 
rock ss 
    B_perm = -1.12; A_perm = 0 
    alpha_L = 0; alpha_t = 0 
end_rock 
 
 
 
 103 
x_well(m)  0   0.2  0.21  0.4  0.6   
 
 
strat 'Upper beds' 
    t_dep = 0 yrs 
    X(ss) = 100% 
    column    water_depth(cm)  thickness(cm)  surface_conc(molal) 
 
    w(1)            5             27                0.48 
    w(2)            0             32                0.48 
    w(3)            0             32                0.0 
    w(4)            &              &                0.0 
    w(5)           -1.0           33                0.0 
 
 
strat 'Upper beds' 
    t_dep = 1500 yrs 
    X(ss) = 100% 
    column    water_depth(cm)  thickness(cm)  surface_conc(molal) 
 
    w(1)            5             0                0.48 
    w(2)            0             0                0.48 
    w(3)            0             0                0.0 
    w(4)            &             &                0.0 
    w(5)           -0.2           0                0.0    
 
     
strat 'Upper beds' 
    t_dep = 3000 yrs 
    X(ss) = 100% 
    column    water_depth(cm)  thickness(cm)  surface_conc(molal) 
 
    w(1)            5             0                0.48 
    w(2)            0             0                0.48 
    w(3)            0             0                0.0 
    w(4)            &             &               0.48 
    w(5)           -0.2           0                0.0