Varying Relative Degradation Rates of Oil in Different Forms and

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Varying relative degradation rates of oil in different forms and environments revealed by ramped pyrolysis Matthew A Pendergraft, and Brad Erik Rosenheim Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es501354c • Publication Date (Web): 08 Aug 2014 Downloaded from http://pubs.acs.org on August 12, 2014

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Environmental Science & Technology

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Varying relative degradation rates of oil in different forms and

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environments revealed by ramped pyrolysis

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Matthew A Pendergraft†

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and

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Brad E Rosenheim†‡*

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† Department of Earth and Environmental Sciences, Tulane University, New Orleans, Louisiana, 70118

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‡ Presently at College of Marine Science, University of South Florida, St. Petersburg, Florida, 33701

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* corresponding author

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Abstract

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Degradation of oil contamination yields stabilized products by removing and

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transforming reactive and volatile compounds. In contaminated coastal environments,

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the processes of degradation are influenced by shoreline energy, which increases the

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surface area of the oil as well as exchange between oil, water, microbes, oxygen, and

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nutrients. Here, a ramped pyrolysis carbon isotope technique is employed to investigate

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thermochemical and isotopic changes in organic material from coastal environments

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contaminated with oil from the 2010 BP Deepwater Horizon oil spill. Oiled beach

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sediment, tar ball, and marsh samples were collected from a barrier island and a brackish

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marsh in southeast Louisiana over a period of 881 days. Stable carbon (13C) and

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radiocarbon (14C) isotopic data demonstrate a predominance of oil-derived carbon in the

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organic material. Ramped pyrolysis profiles indicate that the organic material was

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transformed into more stable forms. Our data indicate relative rates of stabilization in the

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following order, from fastest to slowest: high energy beach sediments > low energy beach

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sediments > marsh > tar balls. Oil was transformed most rapidly where shoreline energy

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and the rates of oil dispersion and exchange with water, microbes, oxygen, and nutrients

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were greatest.

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1. Introduction When oil contaminates an environment, multiple processes physically disperse the

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oil and chemically transform it in a process referred to as weathering. The processes that

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chemically change the oil, as well as their effects, can be collectively referred to as oil

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degradation, and include evaporation, biodegradation, dissolution/water washing,

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photooxidation/photodegradation, emulsification, and adsorption/sedimentation.1-12

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These degradative processes remove and/or convert reactive and volatile compounds and

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leave behind stabilized residues that can persist in the environment.4,5,11-13

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Oil degradation rates are determined by various factors,4,5,9,14,15 with shoreline

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energy being one of the most important for coastal oil spills.1,9 Microbes can consume

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upwards of 50% of spilled oil4 and biodegradation rates are largely influenced by

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coastline energy.1,5,9 Greater shoreline energy (wind, waves, and tides) increases

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biodegradation rates by accelerating oil dispersion, which increases oil surface area

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available to microbes16-18 and the supply of oxygen and nutrients (N and P), via

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water,necessary for microbes to consume oil.19-21 High shoreline energy has been shown

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to correspond to high rates of weathering,1,9 and longer oil persistence has been observed

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on protected coastlines.22 Perhaps the longest-studied oil spill, the 1969 Florida spill in

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West Falmouth, Massachusetts, resulted in oil persisting in a protected salt marsh with

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low shoreline energy for three decades.23-25 White et al.25 concluded that physical

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processes ultimately decide the fate of residual oil components at West Falmouth.

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The large extent of the 2010 BP Deepwater Horizon oil spill (DwH) provides an opportunity to study degradation and transformation of oil deposited synchronously in



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different coastal environments. DwH is the largest accidental oil spill to date, having

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released roughly ten times the volume of oil as the 1989 Exxon Valdez oil spill in the

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Gulf of Alaska.26 Approximately 1,600 km of Gulf of Mexico shoreline and 75 linear km

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of Louisiana coastal marsh received moderate to heavy oiling from DwH.27,28 Coastal

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Louisiana is comprised of multiple environments including barrier islands directly

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exposed to the energy of the Gulf of Mexico and semi-protected marshes that receive a

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diminished, yet substantial, portion of that energy. The wetlands of this region constitute

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about 37% of the coastal wetlands of the 48 conterminous United States,29,30 support

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roughly 30% of the total United States fishing industry, and protect a network of

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infrastructure responsible for an equal proportion of the country’s oil and gas supply.30

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At the same time, the Mississippi River Delta suffers from significant subsidence-driven

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land loss estimated at 4877 km2 during 1932 to 2010,29 and expected to reach 10,000+

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km2 by the year 2100.31 Oil contamination can negatively impact marshes30,32 and

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accelerated marsh shoreline erosion due to DwH has been documented.33 Assessment of

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the degradation of oil in this ecosystem through time is important to discern where and

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how oil degradation products may contribute to further stress on the coastal wetlands.

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Here we employ ramped pyrolysis (RP) carbon isotope analysis34 to oil-impacted

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organic material from a barrier island and a brackish marsh in order to test the hypothesis

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that oil will be removed from coastal systems of Louisiana before post-depositional oil

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transformation occurs. The RP isotope technique analyzes all acid insoluble organic

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material (OM) in a sample and produces both a pyrolysis profile related to

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thermochemical stability and an isotopic spectrum (14C and 13C) resulting from the

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different thermochemical stabilities of admixed OC.34-38 4 ACS Paragon Plus Environment

The RP isotope technique has

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been previously shown to be effective at detecting and quantifying oil contamination in

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sedimentary organic material (SOM)39 as it employs radiocarbon analyses, a sensitive

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tracer of oil-derived organic carbon (OC). Effectively, oil and residual compounds from

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its degradation lie on the completely radiocarbon-depleted endmember of the radiocarbon

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scale (∆14C = -1000‰) and recent sediment and soil OC lies on the other end (near or

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slightly above 0‰), with analytical precision on radiocarbon measurements generally

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around 5-10‰. The use of 14C for oil spill studies 25,40-45 is not as prevalent as the use of

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13 46-56

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measurements compared to stable isotope analyses. Ultimately, this approach applied to

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these environments can provide a basis for ongoing remediation efforts and efforts to

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assess the toxicity of oil contamination and its degradation derivatives.

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, mainly because of the relatively high cost and the novelty of these

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2. Methods

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2.1 Sample locations

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Grand Isle and Bay Jimmy in southeast Louisiana were sampled over the course

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of 881 days following the Macondo well blow-out that initiated DwH (Figure 1). Grand

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Isle is a subtropical barrier island at the mouth of Barataria Bay with a gently sloping

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sandy beach affected by moderate wave energy which increases episodically during

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tropical cyclones (summer) and cold fronts (winter). Sediments and tar balls at two

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locations at Grand Isle were repeatedly sampled: a relatively high energy site exposed to

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direct wave energy from the Gulf of Mexico and a relatively low energy site located on

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the lagoonal side of Grand Isle, protected from direct wave action from the Gulf of 5 ACS Paragon Plus Environment


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Mexico. The third sampling location was at Bay Jimmy, which lies within the brackish

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marsh region of northeastern Barataria Bay.57 Although an erosive marsh with ~10’s of

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km of southerly fetch, the Bay Jimmy site is considered a lower energy environment

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compared to the two barrier island sites, which have even greater southerly fetch. During

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the course of our sampling, we observed more geomorphic change at the Grand Isle sites

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than at the Bay Jimmy site.

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2.2 Sample collection and treatment

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Sediment and tar balls were collected from Grand Isle and oiled marsh plants and

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sediment were collected from Bay Jimmy. At Grand Isle, sediment was initially sampled

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from the beach surface then, after geomorphic changes mixed the oil deposits into the

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beach sediments, by digging trenches (~1 m depth) near the shoreline and sampling dark

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subsurface layers and oily sheen with a petrochemical odor at the trench bottoms (see

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abstract figure for an annotated photograph of a representative trench). Tar balls on the

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beach between the two fixed sites and an oil/tar coating on a rocky groin near the higher

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energy site were also collected from Grand Isle. We use the general term of “tar balls”

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for a non-uniform set of samples with varying proportions of oil/tar and sediments.

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Marsh samples from Bay Jimmy include clusters of oil and vegetation and the top (0-1

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cm) of a 50 cm surface core collected with a hand auger.

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Most samples were collected in glassware capped with aluminum foil (both

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precombusted at 525°C for ≥ 2 h) and a plastic lid, and stored at -4°C under nitrogen gas.

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Some samples from Bay Jimmy were collected in sealed plastic bags

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(Whirlpack®/Ziploc®) when precombusted glassware was exhausted, but the samples


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were transferred to pre-combusted glass once in the laboratory (~3 h after sampling). The

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crude oil, provided by BP (reference material ID: SOB-20100617-032; source sample ID:

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ENT-052210-OL-041/043), was sampled directly from the Macondo wellhead and stored

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at -4°C.

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Samples from Grand Isle were ground with a mortar and pestle, acidified with

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10% hydrochloric acid (HCl) to remove inorganic carbon, then repeatedly rinsed with

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deionized water, centrifuged, decanted until a pH of ~6 was reached, and then dried at

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60°C for 24 h or until completely dry. Acidification of samples was necessary to analyze

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the organic material without inclusion of thermal decomposition of carbonate-carbon. It

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has been demonstrated that rinsing of sediment and soil containing organic material after

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acid treatment can mobilize some of the organic matter58, however the resulting

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ambiguity of pyrolyzing samples containing carbonate minerals was considered a higher

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cost to our experimental approach. Thus, samples from Bay Jimmy were also acid

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treated after inspection for and removal of shell fragments, to ensure that all samples

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were treated equally for this experiment.

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2.2 Elemental and stable isotope analyses Samples were measured for organic carbon content (%OC) and bulk stable carbon

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isotopic composition (δ13C), then subjected to ramped pyrolysis carbon isotope analysis.

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Organic carbon content and stable carbon signatures were measured on 127 beach

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sediment, tar ball, and marsh samples (Figure S1, Supplemental Information) at the

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Stable Isotope Laboratory at Tulane University using an Elementar vario MICRO cube 7 ACS Paragon Plus Environment


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elemental analyzer interfaced to an Isoprime dual inlet isotope ratio mass spectrometer

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(EA-IRMS) continuously monitoring isotope ratios of CO2 peaks in the carrier gas of He.

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Values of %OC were used to calculate sample size and expected yields for RP analysis in

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order to evaluate for complete carbon recovery. Stable carbon isotopic signatures are

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expressed in δ notation and ‰ units, relative to the Pee Dee Belemnite (PDB) standard.59

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2.3 Ramped pyrolysis 14C analysis

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Sixty of the 127 samples were screened for reaction profiles using RP. Based on

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this screening, 15 representative samples were re-analyzed by RP with CO2 collection

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and isotopic analysis of the CO2. The same RP procedure was used as in previous

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studies;34-39 this procedure consists of a smooth temperature ramp of 5°C min-1, 0% O2 in

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the reaction chamber, and ~8% O2 in the combustion chamber. More details of the

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analysis can be found in Rosenheim et al.34 Individual pyrolysis peaks, representing

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thermochemically distinct components, were cryogenically collected at inflection points

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in continuous aliquots.

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Radiocarbon and stable carbon isotope analyses of the ramped pyrolysis CO2

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aliquots were measured at the National Ocean Sciences Accelerator Mass Spectrometer

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facility (NOSAMS) at Woods Hole Oceanographic Institution. Radiocarbon data,

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reported in ∆ notation and per mil (‰) units, were calculated using: 1 ∆ ≅ = − 1 × 1000

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where is the fraction of the 14C/12C ratio of the measured sample compared to that of a

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“modern” standard (1950 wood).60 We assume ∆14C to be equivalent to δ14C because


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less than three years passed between sample collection and measurement. 60 Ramped

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pyrolysis AMS radiocarbon data are corrected for an analytical blank (9.3 ± 9.3 µg

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modern blank and 0.5 ± 0.5 µg 14C-free blank for an entire run; these masses are divided

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by the number of isotopic measurements made during each run) determined by repeated

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measurements on isotope standards. Nine RP aliquots were depleted enough in 14C that

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they resulted in less 14C than our determination of blank contamination in the method.

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These values are treated as they were in Pendergraft et al.39; we use a maximum ∆14C

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limit equivalent to 2σ uncertainty of the blank to report fraction modern values and to

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calculate ∆14C values.

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3. Results Depleted radiocarbon values (-891 ±232‰; Figure 2 and Table S2) dominate the

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aliquots of CO2 collected during RP, with the exception of the marsh surface sample

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collected at 678 d, which had no visible oil. Stable carbon isotopic signatures (-27.5 ±

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2.2‰; Figure 2 and Table S2) are also generally depleted relative to Gulf of Mexico

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marine SOC (‐21.4±1.9‰)38. We calculate a geometric mean isotopic value (equivalent

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to a bulk isotopic value) using:

2 = ; ℎ = 1

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where δgm represents the geometric mean isotopic value, represents the fraction, in

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each aliquot, of total CO2 generated from the sample, and represents the associated 9 ACS Paragon Plus Environment


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isotopic ratios. ∆14Cgm (-877(±268)‰, range: -996 to 0‰) and δ13Cgm (-27.6(±2.3)‰,

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range: -28.8 to -20.5‰) characterize each whole sample subjected to RP (Table S2).

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Assuming sample composition to be a binary mixture of oil and background organic

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carbon (OCb), a Bayesian multi-source isotope mixing model was employed to the

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radiocarbon data to estimate component fractions.61 The mixing model has the same

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conventional formula as Equation 3, but δgm is replaced by δ (the measured isotopic

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composition from a sample comprised of a mixture of endmembers). The model is able

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to incorporate known variability in endmembers and analytical error associated with the

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isotopic measurements. We use the most negative, blank-corrected ∆14C value measured

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for oil using RP AMS and the associated analytical uncertainty (-998.2±1.8‰) for the oil

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endmember, and the mean ∆14C value and associated analytical uncertainty (86.9±4.0‰)

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from RP AMS analysis of two unoiled marsh samples collected pre-DwH for the OCb

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endmember. Calculated fraction oil (foil) values from radiocarbon data and the mixing

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model quantify oil in the CO2 aliquots (Table S2) and show high fractions of OC

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comprised of oil (0.899(±0.216)). Applying to the mixing model geometric mean

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isotopic data calculated using Equation 2 yields geometric mean fraction oil (foil-gm)

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values that quantify oil for entire beach sediment, tar ball, and marsh samples

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(0.88(±0.22)) analyzed by RP AMS. The evolution of CO2 over the pyrolysis ramp estimates decomposition reaction

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!"#$"$%&

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rates

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temperature is linear with time.34,36 RP data of CO2 concentration versus temperature are

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normalized to allow for comparison between samples with different amounts of carbon.

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As a result, the areas under reaction profiles are equal and can be interpreted considering

$

' for the mixture of compounds comprising the OC because


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the percent of total OC that pyrolyzes within continuous temperature ranges. Pyrolysis at

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higher temperatures results from greater thermochemical stability. Here, we consider

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temperatures of < 300°C, 300-500°C, and > 500°C as low, mid, and high, respectively.

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Time series of RP data present varying thermochemical trends amongst the

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different sample types and environments (Figure 3). At 88 d, Grand Isle sediment from

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both high and low energy sites experience a majority of pyrolysis (63% and 68% of total

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pyrolysis, respectively) at low temperatures (< 300°C), but at 678 d these samples

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pyrolyze almost entirely (82 and 89%, respectively) at mid to high temperatures (>

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300°C; Figure 3 A and B). RP data for marsh samples from Bay Jimmy (Figure 3C) are

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characterized by significant (50%) low-temperature pyrolysis persisting through 535 d

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and minimal (16%) low-temperature pyrolysis at 694 d. Tar balls from Grand Isle

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(Figure 3D) continue to yield a majority of pyrolysis (59%) below 300°C through 881 d,

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with the exception of a tar ball from 678 d that presents an anomalous reaction profile.

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4. Discussion

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4.1 A dominant oil signature in ∆14C and δ13C data through 881 days

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Radiocarbon and stable carbon isotopic data for CO2 aliquots produced during

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RP demonstrate a dominance of oil-derived carbon in the sediments and soils for >678 d

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after the Macondo well explosion (Figure 2, Table S1). A large portion (88%) of CO2

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aliquots were measured with radiocarbon values below -800‰ (minimum of -998‰) and

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correspondingly high calculated fraction oil values (maximum of 0.998; Table S2). For

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the tar balls, a maximum ∆14C value of -977‰, corresponding to a minimum foil value of 11 ACS Paragon Plus Environment


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0.966 (Table S2), confirms compositions dominated by oil-derived C for 881 d, including

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the sample with an anomalous reaction profile (Figure 3D).Stable carbon isotopic data

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also support the prominence of oil in the OC of most CO2 aliquots. We use δ13C to

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indicate oil in pyrolysates but not for quantification purposes because δ13C is not as

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sensitive a tracer of oil as is ∆14C and because ∆14C inputs can be constrained by a binary

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mixing model. The expected sources of carbon at the study sites and their associated

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δ13C values are: oil (-27.3‰), marine organic material (-20‰), and brackish marsh

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organic material (-16.9 to -12.5‰).39,42,57,59,62 Stable carbon isotopic data for the samples

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analyzed by RP indicate OC dominated by petrogenic carbon in all the samples, yielding

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a mean δ13Cgm value (27.6 ± 2.3)‰) comparable to that of the Macondo oil (27.3‰). RP

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CO2 aliquot δ13C data below the lowest expected endmember indicate that stable carbon

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isotopes are not uniform in the different compounds of the oil mixture and they are

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fractionated chemically and/or isotopically during RP, as previously observed.35,38,39

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Inclusion of C3 terrigenous carbon (δ13C = -34 to -23‰)63 is also possible and could

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account for δ13C values below that of oil. In the case of fractionation, this would not

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affect radiocarbon data as they are corrected for stable isotope values. No matter the

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reason, our results indicate that the process responsible for transformation of oil in these

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environments has not fractionated the oil-derived carbon isotopically.

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4.2 Evidence for chemical transformation of oil and derivatives Considering that isotopic data demonstrate the OC is dominated by oil for nearly all samples (Figure 2, Table S2), the observed changes in thermochemical stability 12 ACS Paragon Plus Environment

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(Figure 3) can be interpreted as evidence of oil stabilization through degradation.

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Therefore, we reject the null hypothesis that oil was removed from these depositional

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settings before undergoing transformation. The pyrolysis profiles is most apparent in the

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disappearance of low temperature pyrolysis over time, shifting the decomposition of the

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oil and derivatives to higher overall temperatures. This indicates more thermochemical

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stability in the OC through time.

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groups of compounds occurs fastest in higher energy beach sediments. Ramped pyrolysis

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data for sediments from the Grand Isle high and low energy sites (Figure 3 A and B,

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respectively) display similar decreases in low temperature pyrolysis and increases in high

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temperature pyrolysis. Isotopic data for each reaction profile shows oil to be the major

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source of OC at all temperatures for both sites (Figures 4 and S3-S5). Evidence of faster

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oil degradation at the high energy site than at the low energy site exists at 337 d, with

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relatively more low temperature pyrolysis (42% of total pyrolysis) persisting at the low

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energy site than at the high energy site (15%; Figure 3 A and B). Additionally, a lower

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∆14Cgm value (by 103‰) and a correspondingly higher foil-gm value (by 0.095) for the low

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energy site at 337 d (Table S2) indicate a higher proportion of oil remaining there than at

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the high energy site due to a slower rate of oil degradation and/or dispersal. Similar RP

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profiles at 678 d (Figure 3 A and B) indicate the oil at both sites had reached similar

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states of degradation by that point.

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In this study, oil transformation to more stable

Marsh RP data (Figures 3C and 4) indicate slower oil transformation than in

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beach sediment. More low temperature pyrolysis in the marsh at 535 d (50%) than in the

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low energy beach sediments at 337 d (42%) indicates greater preservation of less stable

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oil moieties in the marsh . A second peak shifted to higher temperatures between 337 d 13 ACS Paragon Plus Environment


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and 535 d (Figure 3C) is likely due to a relative increase in OCb (Figure 4 and Table S2).

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Pyrolysis at 694 d occurs fully in the temperature range of the control sample (Figures 3C

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and 4) and isotopic data confirm the lack of oil visible that day at Bay Jimmy (Figure 4

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and Table S2). This sample indicates the importance of using both the reaction shape and

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the isotopic composition to discern transformation versus complete degradation or

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erosion of the oil contamination.

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Transformation does not seem to occur within tar ball microenvironments, where

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only a crust of oil interacts with the surroundings and most oil mass is untouched (Figure

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3D). Tar balls yielding a majority of pyrolysis (59%) at low temperatures through 881 d

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indicate preservation of the oil comprising the tar balls. A tar ball at 678 d (foil-gm =

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0.998) that resembled asphalt or black rubber upon collection presented a unique RP

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profile (Figure 3D) that we interpret as oil that has undergone different degradative

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pathway than the other tar balls. Aeppli et al.11 determined that samples with a similar

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appearance collected at Grand Isle and the Chandeleur Islands, LA did not fit the two

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dimensional gas chromatography flame ionization detection (GCxGC-FID) fingerprint of

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Macondo oil.

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The relative oil transformation rates observed in our sample sets are in agreement

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with degradation rates in previous studies. Oil degradation was fastest at the high energy

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beach site because mechanical energy was greatest there and resulted in fastest dispersion

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and surface area maximization of oil in the water and sediments and greatest exchange

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between oil, pore- and overlying waters, microbes, oxygen, and nutrients.1,9,64,65 Oil

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degradation was slower in the marsh than in the beach sediments due to less mechanical

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energy at the marsh. In addition, marshes contain high amounts of other organic material 14 ACS Paragon Plus Environment

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that can be preferentially consumed by microbial communities, thus limiting nutrients

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necessary for microbial consumption of oil17,18. Anoxia in marsh sediments can also slow

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oil degradation but is not considered to be a significant factor in this study because the

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marsh samples were collected from the surface. Tar balls degraded the slowest because

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they protect the majority of their mass from exchange with water, oxygen, and nutrients

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by a small surface area-to-mass ratio.17,66 Our findings are in agreement with other

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studies that have documented the persistence of tar balls67,68. Despite their heavily

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weathered appearance, tar balls shelter significant (~30%) concentrations of saturates,

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compounds that formed a relatively large portion (62-74%) of the Macondo crude oil.69

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Whereas Elango et al.68 presents slow degradation of oil components in tar balls, our RP

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data present an even more extreme scenario: minimal degradation of labile oil

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compounds between 46 d and 881 d after initiation of DwH. This evidence of such slow

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oil degradation in tar balls helps explain their persistence for years to decades in coastal

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environments after an oil spill.67

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4.3 Insight into oil transformation

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The stabilization of the oil-derived compounds observed in RP data likely result from

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both the loss of labile compounds and the conversion of labile compounds into more

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stable compounds (Figure 3, except 3D). Oil degradation has largely been considered in

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terms of the loss of certain compounds and has been evaluated through the use of ratios

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of GC-amenable compounds. The degradation product is more stable than the original oil

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because the more volatile and reactive compounds are preferentially lost and/or

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converted, leaving behind recalcitrant compounds (generally in the form of a growing



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unresolved complex mixture (UCM) in GC terminology). In the RP data from beach and

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marsh OC, we observe this in the loss of low temperature pyrolysis over time and the

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persistence of pyrolysis at mid and high temperatures (Figure 3). However, a question to

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be asked is whether the remaining compounds were present in the original oil or if some

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of them were generated during degradation. Recent efforts on DwH oil have provided

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new insight into the transformation of oil compounds. Two-dimensional gas

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chromatography (GCxGC) has helped show that degraded oil can be largely comprised of

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saturated hydrocarbons, present in the crude oil, as well as oxygenated hydrocarbons,

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such as carboxylic acids, that appear to be the result of biodegradation (carboxylic acids

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have been identified in biodegraded source oils for decades,70-73 yet they had been largely

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ignored in oil contamination until recently11,69,74). The identification of stable compounds

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formed from crude oil components is significant because it demonstrates that degradation

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doesn’t only imply the loss of compounds through evaporation or mineralization into

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CO2. In light of this information, we propose that the decrease in prominence of low

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temperature pyrolysis and increase in prominence of high temperature pyrolysis

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presented here (Figure 3) is likely the result of both the loss of less thermochemically

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stable compounds and their conversion into more thermochemically stable compounds.

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Compounds that pyrolyze at mid and high temperatures are likely a combination of

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saturated hydrocarbons, oxygenated hydrocarbon residues, and other compounds yet to

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be identified in degraded oil.11,69,74,75

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4.4 Constraint of specific degradation processes using ramped pyrolysis data


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Oil evaporated in the laboratory and tar from a rocky groin provide insight into

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the RP signatures of evaporation and biodegradation, the degradation processes that most

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affect oil in the environment.1,3,4,7,9,10 The residue that remained after evaporating

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Macondo crude oil at 60°C for 310 hs shows a similar RP profile as the crude oil, but

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shifted to only slightly higher temperatures (~75°C, Figure 5A). Thus, evaporation can

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account for the slight shift to higher temperatures of the prominent, low temperature peak

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in the reaction profiles of the 88 d beach sediments (Figure 3 A and B). However, the

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evaporation of crude oil in the laboratory did not result in the nearly complete loss of

365

low-temperature pyrolysis that occurred in beach sediment and marsh samples after 88 d.

366

The RP profiles of two tar deposits collected from a rocky groin at Grand Isle (Figure 4B)

367

exhibit a loss of low temperature pyrolysis similar to that observed in the sedimentary OC

368

from Grand Isle and Bay Jimmy. One sample was taken from the top of the rocky groin

369

where it was exposed to direct solar radiation and remained dry most of the time. These

370

are conditions we deem less hospitable to microbes. The other sample was collected

371

from within the rocky groin where it was out of direct sunlight and often wet from wave

372

and tidal action; conditions more hospitable to microbes. The sample from the more

373

hospitable environment displays a loss of low temperature pyrolysis relative to the

374

sample from the inhospitable environment (Figure 5B). Isotopic data for both samples

375

confirm their compositions to be largely oil (foil-gm = 0.986 and 0.910). We interpret these

376

two pairs of samples as evidence that the disappearance of low temperature pyrolysis

377

observed in sedimentary beach and marsh OC over the course of 600+ d is largely due to

378

biodegradation.

379



380

381

4.5 Other processes affecting isotopic trends Isotopic data show evidence of stable isotope fractionation at the most depleted

382

∆14C values and mixing with other OC sources at less depleted ∆14C values. At 88d,

383

radiocarbon data pooled from the two barrier island sites (Figure 6A) show that oil is the

384

only carbon source (∆14C ≈ -1000‰), and there is no mixing occurring between sources

385

of carbon. The variation in δ13C is interpreted as RP causing isotopic fractionation or

386

chemical separation of oil components that are both isotopically (δ13C) and

387

thermochemically distinct. Stable carbon isotope fractionation has been observed before

388

in RP,35,38,39 however it is important to note that radiocarbon values, which are used to

389

calculate foil, are corrected for stable carbon isotope composition,34,60 thus fractionation

390

does not affect 14C quantification. As the oil mixes with OCb in the environment and

391

∆14C values become less negative (Figure 6A), a mixing relationship, with variation in

392

both δ13C and ∆14C. dominates the fractionation signal. Increasing steepness of linearly

393

regressed data in δ13C-∆14C space at different sampling dates (Figure 6A) indicates

394

admixture of OCb over time. Data from tar samples show a similar trend but at a slower

395

rate, indicating a slower rate of incorporation of OCb into tar balls (Figure S6B). It is

396

important to note that the amounts of OCb are small, but RP isotopic analysis is sensitive

397

enough to reveal the isotopic effect. A mixing line that better encompasses this system’s

398

endmembers can be generated by linearly regressing all the marsh data together (Figure

399

6B) because earlier samples are comprised almost exclusively of oil and the marsh

400

sample from 694 d is nearly devoid of oil. Plotting δ13C versus 1/foil in a Keeling-like

401

plot76 (Figure 6B), as in Pendergraft et al.,39 accurately estimates the δ13C value of

402

Macondo oil (-27.1‰ where 1/foil = 1) to within twice the measurement error (1σ = 18 ACS Paragon Plus Environment

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403

0.1‰) of the measured value (-27.3‰).39,42 Data at lower 1/foil values (higher foil) exhibit

404

fractionation in their δ13C variation, whereas variation in both 1/foil and δ13C at higher

405

1/foil values (lower foil) demonstrates admixture of OCb.

406

4.6 Significance of results

407

Our RP isotope analysis of oil-impacted beach and marsh SOM presents

408

transformation of oil by a complex set of processes analyzing all of the OM in the

409

system, including compounds not initially present in the petroleum contamination. For

410

all three locations, higher shoreline energy implies faster dispersion of oil and greater

411

exchange with water, microbes, oxygen, and nutrients.1,5,9,16-21 Higher energy

412

environments can also imply faster and more admixture with other ambient sources of

413

organic material after the oil contamination event. Overall, isotopic data reveal that oil

414

dominated the organic carbon in coastal Louisiana after the 2010 Deepwater Horizon oil

415

spill and persisted, especially in tarballs, for 678-881+ d. Ramped pyrolysis reaction

416

profiles show oil transformation in beach sediments and on the marsh surface and oil

417

preservation in tar balls. These findings are in agreement with previous studies that

418

found oil in tar balls, aggregates, and thick emulsions can persist in the environment

419

much longer than oil from the same source but in dispersed forms.17,18 Relative oil

420

degradation rates, from fastest to slowest, for the four sample types is as follows: higher

421

energy beach sediment > lower energy beach sediment > marsh surface > tar balls.

422

Transformation of the oil seems to have been microbially mediated, with mixing

423

processes also recognized in isotope time series, particularly in the marsh, an

424

environment rich in OCb. The persistence of oil in this region is important due to the

425

vulnerability of the area where a large proportion of this oil was deposited. The presence 19 ACS Paragon Plus Environment


426

of transformed oil compounds may prolong the effects of the oil deposition, especially in

427

a sensitive and important ecosystem.

428

429

Figures

Abstract Figure. Representative trench dug at Grand Isle with inlaid ramped pyrolysis trend. Sampling focused on dark sediment layers and infiltrated groundwater with oil sheen and petrochemical odor at bottom of the trench. Image: B.E. Rosenheim. (amended by M.A. Pendergraft) 430 431


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432 433

Figure 1. Sampling locations in Bay Jimmy (A.) and at Grand Isle (B.) in southeast Louisiana. Samples collected where indicating lines meet the adjacent land.



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Figure 2. Radiocarbon (∆14C) and stable carbon (δ13C) data for the CO2 aliquots produced and collected during RP display a dominance of oil in the OC for this sample set, with the exception of the aliquots from the marsh sample from 678 d. Dashed lines are at the isotopic signatures of the Macondo oil. Symbols indicate sample type: diamond – beach SOM; triangle – marsh SOM; circle – tar ball. Colors represent sampling date and are scaled to the color bar. 434 435 436 437 438 439 440 441 442 443 444 445 446


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Figure 3. RP reaction profile trends over time for four samples types. Individual plots display evolution of CO2 over a smooth temperature ramp to 800°C, and plots are colored based on time from the well blow-out. Profiles for Macondo crude oil (black) and a control sample from unoiled marsh (white) given for reference.A. Grand Isle beach sediments from the high energy site. B. Grand Isle beach sediments from the low energy site. C. Marsh samples from Bay Jimmy within Barataria Bay. D. tar samples from grand Isle. 447 448



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Figure 4. RP isotopic data for marsh samples. A. Trend in profiles over time. Profiles for Macondo crude oil (black) and a control sample from unoiled marsh (white) given for reference. B.-D. Profiles with isotopic data for samples at 337, 535, and 694 d. Left axis in top panel is CO2 evolution. Right axis is ∆14C. Horizontal bars indicate temperature intervals over which the aliquots of CO2 were collected. Bottom panel plots δ13C (points in white) for the same intervals. Tmax is temperature at which maximum pyrolysis occurs. Geometric mean isotopic values are shown by dashed lines in green (∆14C) and blue (δ13C). Errors on the isotopic measurements are smaller than the symbols used to plot the values. 449 450 451 452 453 454 455 456 457 458


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Figure 5. A. Oil evaporated at ~60°C shows a loss of compounds below that temperature yet continues to present a dominant low temperature peak. B. Tar sampled from a rocky groin presents a loss of oil compounds that pyrolyze at low temperatures, similar to the oil in beach sediment and marsh samples. 459 460 461 462

463

464

465

466



Figure 6. A. Carbon isotopic compositions of aliquots of sediment samples produced by RP AMS. Dark blue – 88 d; light blue – 337 d; orange – 678 d. The dotted lines are at the isotopic values for Macondo crude oil. Variation in d13C with minimal variation in ∆14C is interpreted as evidence of fractionation. Slopes of linear regressions for each date are interpreted to be proportional to degree of mixing (addition of OCb to the oil). B. Plotting δ13C vs. 1/foil for all CO2 aliquots from marsh samples produces a Keeling-like plot that yields a d13C estimate for oil of -27.1‰ at 1/foil = 1. 467 468

469

Supplemental Information


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%OC (black circles, left axis) and δ13C (red diamonds, right axis) over time for 127 beach sediment (A.), tar balls (B.), and marsh samples (C.). Right axis marked at δ13C values for Macondo oil (-27.3‰, black), marine OC (-20‰, blue), and brackish marsh OC (-16‰, green). Figure S1.

470



471

Page 28 of 38

Table S2. RP AMS data. Sample DB475

DB639

day count 88

337

RP Temp. µ mol C Limit, °C

‰

362

53.32

0.0108 0.0016 -989

2

-27.9

0.991

446

22.74

0.0170 0.0029 -983

3

-27.6

0.986

714

25.09

0.0181 0.0026 -982

3

-

0.985

272

15.79

0.0486 0.0065 -952

7

500

56.54

0.2150 0.0019 -787

2

-27.2

824

48.70

0.0851 0.0022 -916

2

-28.0

21.52

0.1398 0.0065 -861

7

15.01

0.0218 0.0106 -978 11

139.26 0.0029 0.0008 -997 20.92

0.0102 0.0051 -990

1

702

41.68

0.0107 0.0025 -989

3

285

31.92

0.0133 0.0033 -987

3

499

28.92

0.0751 0.0036 -925

4

763

17.91

0.0174 0.0059 -983

6

487

17.41

0.1436 0.0081 -857

8

824

15.41

0.0407 0.0101 -960 10

347

133.00 0.0028 0.0009 -997

1

-29.3

6

-27.6

-996

1

-28.9

4

149.52 0.0018 0.0007 -998

1

626

28.59

0.0074 0.0037 -993

4

337

49.90

0.0064 0.0032 -994

3

710

28.60

0.0236 0.0055 -977

5

100.73 0.0265 0.0018 -974

2

400 676 329

49.19

0.1165 0.0031 -884

3

175.04 0.0446 0.0008 -956

1

0.998

0.977 0.989 0.991 -28.5

-990

3

-28.9

-996

1

-26.2

0.983 -27.9 -28.5

0.993 -28.4

0.991

-28.5

0.963

0.987 0.998

-26.6 -29.4

0.981 -28.6

-27.2 -944

2

-28.7

1

-28.5

0.994 0.978

-27.7

0.977

-27.3

0.961

-25.8 -869

0.998 0.987

-29.2 3

0.868 0.961

-27.6

-987

0.987 0.933

-28.4

0.0081 0.0041 -992

410

-28.8

-28.2 -905

39.48

5

0.924 0.867

-27.7

-26.5

6

881

2

0.955

-28.4

-26.8 -963

0.994

0.805

-28.8

3

0.0135 0.0054 -987

-27.9

-28.6

0.0039 0.0034 -993

0.0119 0.0059 -988

DB656

-29.3

31.17 29.55

678

1

661

18.53

DB341

-995

-29.4

700

694

-28.4

6

199

DB707

6

0.0127 0.0063 -987

678

535

-909

17.14

DB653

DB659

-29.8

401 315

337

2

5

507

DB657

-

0.997

DB646

881

-28.4

foil

-28.1

418

DB654

‰

2

-860

1

‰

3

752

-989

δ13C δ13Cgm

0.0044 0.0022 -996

336

46

1σ

0.0067 0.0026 -993

88

DB649

‰

14 ∆ Cgm

29.22

DB658

678

1σ

24.52

503

DB665

∆14C

205

678

337

1σ

162

DB643

DB670

Fm

0.893

441

50.94

0.1336 0.0021 -867

2

-27.9

681

88.72

0.3049 0.0016 -697

2

-24.5

335

39.40

0.9694 0.0027

-38

3

391

29.40

0.9760 0.0039

-31

4

-20.8

713

84.00

0.9153 0.0029

-91

3

-20.9

334

25.61

0.0125 0.0063 -987

6

722

32.06

0.0162 0.0049 -984

5

238

13.70

0.0169 0.0077 -983

8

356

26.80

0.1104 0.0037 -890

4

-27.2

0.899

676

52.60

0.1169 0.0021 -884

2

-25.8

0.895

-66

-986

2

4

-18.4

-27.7

0.879 0.723 -20.2

0.109 0.165 -26.7

-25.8 -900

2

-27.5

0.115

0.986 0.985

-26.4

0.980

all radiocarbon data corrected for RP AMS blank; error propagated for gemoetric mean (gm) error

472

all δ13Cgm 1σ between 0.0 and 0.1; f OCb = 1 - f oil


Figure S3. RP isotopic data for high energy beach sediment samples. Profiles for Macondo crude oil (black) and a control sample from unoiled marsh (white) given for reference. A. Trend in profiles over time. B.-D. Profiles with isotopic data for samples at 88, 337, and 678 d. Left axis in top panel is CO2 evolution. ∆14C data points plot on right axis. Horizontal bars indicate temperature intervals over which the aliquots of CO2 were collected. Bottom panel plots δ13C for the same intervals. Tmax is temperature at which maximum pyrolysis occurs. Geometric mean isotopic values and dashed lines in red (∆14C) and blue (δ13C). Errors on the isotopic measurements are smaller than the symbols used to plot the values. Radiocarbon values here are not blank-corrected, which would only cause minimal change, on the scale of the size of the data points.

Page 29 of 38 Environmental Science & Technology

473

474

29

ACS Paragon Plus Environment

475

Figure S4. RP isotopic data for low energy beach sediment samples. Profiles for Macondo crude oil (black) and a control sample from unoiled marsh (white) given for reference. A. Trend in profiles over time. B.-D. Profiles with isotopic data for samples at 88, 337, and 678 d. Left axis in top panel is CO2 evolution. Right axis is ∆14C. Horizontal bars indicate temperature intervals over which the aliquots of CO2 were collected. Bottom panel plots δ13C for the same intervals. Tmax is temperature at which maximum pyrolysis occurs. Calculated bulk isotopic values are shown by dashed lines in red (∆14C) and blue (δ13C). Errors on the isotopic measurements are smaller than the symbols used to plot the values. Radiocarbon values here are not blank-corrected, which would only cause minimal change, on the scale of the size of the data points.


30


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476

Figure S5. RP isotopic data for tar samples. Profiles for Macondo crude oil (black) and a control sample from unoiled marsh (white) given for reference. A. Trend in profiles over time. B.-E. Profiles with isotopic data for samples at 46, 507, 678, and 881 d. Left axis in top panel is CO2 evolution. Right axis is ∆14C. Horizontal bars indicate temperature intervals over which the aliquots of CO2 were collected. Bottom panel plots δ13C for the same intervals. Tmax is temperature at which maximum pyrolysis occurs. Calculated bulk isotopic values are shown by dashed lines in red (∆14C) and blue (δ13C). Errors on the isotopic measurements are smaller than the symbols used to plot the values. Radiocarbon values here are not blank-corrected, which would only cause minimal change, on the scale of the size of the data points.

Page 31 of 38 Environmental Science & Technology

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Figure S6. A. Carbon isotopic compositions of aliquots of sediment samples produced by RP AMS. Dark blue – 88 d; light blue – 337 d; orange – 678 d. The dotted lines are at the isotopic values for Macondo crude oil. Slopes of linear regressions for each date are interpreted to be proportional to degree of mixing (addition of OCb to the oil). At 88 d, variation in δ13C data with minimal variation in ∆14C data is interpreted as evidence of fractionation. At 337 d and 678 d, increasing variation in ∆14C with respect to variation in δ13C is interpreted as an increasing degree of incorporation of OCb. B. When treated similarly, data from tar balls produce less steep slopes, implying less incorporation of OCb and less mixing of oil into the environment when in the form of tar balls. Dark blue – 46 d; yellow – 507 d; orange – 678 d; maroon – 881 d. 477


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478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519

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Acknowledgements

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The authors thank Dr D Finklestein (Hobart and William Smith Colleges) and Dr A

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Schimmelmann (Indiana University) for their assistance in field work and experimental

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design. We also thank all who assisted with sample collection and preparation. Analyses

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were funded by NSF grants EAR-1058517 and EAR-1045845 to BER and by the

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Consortium for Advanced Research on Transport of Hydrocarbon in the Environment

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(CARTHE). MAP was partially funded by Louisiana Sea Grant (NOAA) awarded to Dr

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N Gasparini and by CARTHE. Dr C Taylor (Tulane University) is acknowledged for

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donating a portion of her Macondo oil for this study.


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Varying Relative Degradation Rates of Oil in Different Forms and

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