Rapid Identification of Deepwater Horizon Oil Residues Using X-ray

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Rapid Identification of Deepwater Horizon Oil Residues using X-Ray Fluorescence Anna P. M. Michel, Alexandra Morrison, Charles Marx, and Helen K. White Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.8b00589 • Publication Date (Web): 26 Nov 2018 Downloaded from http://pubs.acs.org on December 4, 2018

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Environmental Science & Technology Letters

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Rapid Identification of Deepwater Horizon Oil

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Residues using X-Ray Fluorescence

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Anna P.M. Michel†*, Alexandra E. Morrison†, Charles T. Marx‡, and Helen K. White‡

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† Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic

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Institution, 266 Woods Hole Rd, Woods Hole, MA, 02543, USA.

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‡ Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford PA, 19041,

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USA

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* E-mail: [email protected]

ABSTRACT

Oil residues are found on Gulf of Mexico (GoM) beaches due to the Deepwater Horizon

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(DWH) incident.

Yet, natural seepage and other anthropogenic inputs also contribute oil

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residues to these beaches. Therefore, in order to identify the sources of oil residues found on

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beaches, especially after a spill, it is critical to have techniques that can be used in the field, can

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provide rapid source identification, and can be used easily by response workers. Here we present

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the utility of using handheld x-ray fluorescence (XRF) for rapid source identification of oil

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residues. By coupling XRF data with a machine-learning model, DWH samples could be

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distinguished from non-DWH samples with 95% accuracy. This approach allows us to analyze

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bulk samples without sample preparation, paving the way for utilizing XRF in the field for

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immediate oil residue source identification.

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INTRODUCTION

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The presence of oil residues in the marine environment is of both environmental and public

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concern.1,2 Local communities, especially those with fishing-based or recreation-based

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economies, can experience significant financial losses as a result of oil contamination on public

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beaches, particularly in the aftermath of an oil spill.3 Oil residues in the marine environment,

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however, can originate from a variety of different sources, including anthropogenic inputs such

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as spills, as well as natural oil seeps.4 Therefore, determining not only if persistent oil is present,

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but also the source of the oil is fundamental to understanding oil residue presence on beaches.

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Mixed inputs of oil from natural and anthropogenic sources are well known, particularly in the

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Gulf of Mexico (GoM) following the Deepwater Horizon (DWH) incident5,6, an area which has

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natural oil seeps7,8 and small inputs of unknown oil likely from anthropogenic sources.9 It is also

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important to monitor these oil residues over time as they contain, among other chemicals,

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persistent polycyclic aromatic hydrocarbons (PAHs), which can be toxic if exposed to humans

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and ecosystems.10 Monitoring the persistence of a specific source of oil in the marine

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environment in the field can be challenging, although is a tractable problem once samples are

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collected and analyzed in the lab.

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To determine the source of oil to the marine environment, the chemical composition of oil,

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specifically the characterization of oil-derived hydrocarbon compounds known as biomarkers,

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are typically measured.11,12 Well-defined methods of oil and biomarker analysis for source

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determination occur in specialized laboratories employing one-dimensional gas chromatography

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(GC) coupled to flame ionization detection (FID) or mass spectrometry (MS) as well as

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comprehensive two-dimensional gas chromatography (GC×GC) coupled to either FID or a time-

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of-flight MS (TOF-MS).13 Bulk oil analysis methods such as Fourier transform infrared (FT-IR)

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spectroscopy and thin layer chromatography with flame ionization detection (TLC-FID) are also

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employed to more generally characterize oil.5,9,14 The aforementioned methods are robust in their

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applications, but are both time and labor intensive, use harmful solvents, are destructive to the

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samples analyzed, require the use of a specialized laboratory, and a highly trained researcher. In

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contrast to lab-based approaches, field protocols based on physical and visual characteristics of

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oil residues have been developed. While useful, these protocols rely on human interpretation and

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are not definitive in terms of distinguishing different sources of oil.15 In this study, we sought to

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develop a rapid and portable approach that could be employed in the field to determine the

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source of oil in different samples. A hand-held X-ray fluorescence (XRF) spectrometer was used

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to analyze samples for their elemental content.16 A key benefit of this method is that it does not

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require time consuming sample preparation steps, thus making it possible to analyze a large

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number of samples in a relatively short period of time. The method itself only takes minutes to

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complete from start to finish, where other analyzation techniques can take hours. A second

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benefit is that this process is nondestructive, which allows for the preservation of samples.17

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The XRF analytical approach detailed here focuses on the analysis of elemental

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concentrations present in oil as a way to distinguish different sources of oil. Specific metals,

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primarily nickel and vanadium, have previously been used to characterize oils from different

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sources.18–20 This approach is successful in part because nickel and vanadium are two of the most

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abundant elements in oil. However, a variety of other metals are also consistently found in oil

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samples,19 but their utility with respect to determining oil source has not been extensively

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explored. Typically, a broad range of metals beyond nickel and vanadium are examined with the

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intent of understanding the toxicity of oil as well as how the concentrations of these elements

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may be altered as oil is weathered in the environment.21,22 In studies related to the DWH incident,

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for example, chromium, aluminum, iron, magnesium, nickel, lead, thallium, vanadium, cobalt,

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zinc, copper, and mercury were all examined21,23. Other studies have also measured the

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abundance of calcium, titanium, and strontium in crude oils.24,25

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For development of this XRF analytical approach, samples that had been collected from GoM

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beaches in Florida, Alabama, Mississippi, and Louisiana between 2012 and 2017 were utilized.

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Although we analyzed samples in the laboratory to develop the XRF approach, since the XRF

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used is a handheld system, the same approach could be utilized directly in the field. The samples

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analyzed included oil residues that originated from the 2010 DWH incident as well as oil from

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natural seeps, other anthropogenic spills, road asphalt, and samples that visually resembled oil,

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but did not contain oil-derived hydrocarbons. Subsequent analysis of the element content data by

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machine learning approaches was then used to distinguish DWH oil residues from other sources

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of oil and non-oil samples. The machine learning approaches employed included a rule-based

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classifier, which uses IF-THEN rules for its predictions, as well as a decision tree classifier,

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which is structurally organized as a flow-chart-like structure. These two approaches were chosen

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based on their interpretability, which unlike traditional statistical approaches, produce outcomes

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in the form of element content data, that can be deployed in the field when analyzing

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environmental samples.

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EXPERIMENTAL

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Sample Collection. Between June 2012 and June 2017, 119 individual oil residue samples were

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collected from GoM beaches as previously described.9,14 This sample set includes oil patties

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originating from the DWH incident (62 samples), tar residues originating from natural seepage

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offshore (27 samples), unknown oil residues (13 samples; 6 of which also contain sand), and

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non-oil samples that have the appearance of oil, but do not contain any oil residues (17 samples).

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All samples were collected in pre-combusted glass jars (450 °C, 8 h), shipped to Haverford

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College, Haverford, PA, and kept frozen until further analysis. The classification of these

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samples as DWH oil patties, tar residues, unknown oil residues, and non-oil samples is described

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in White et al., 2016 and Morrison et al., 2018.9,14

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X-Ray Fluorescence of samples.

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Instruments X-MET 7500 x-ray fluorescence spectrometer, a portable and handheld device with

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detection limits on scales of 1 ppm.16 No extraction steps were performed on the samples prior to

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analysis. The minimum size of samples analyzed was approximately 2 cm x 3 cm x 1 cm. In

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order to consistently analyze the interior of each sample, sand patties and oil residues were sliced

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in half with a solvent rinsed razor blade prior to analysis. Samples were placed on a Mylar

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circular window film (6 µ thick, 64 mm diameter) on the XRF x-ray detection site. The XRF

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standard “soil” method was used, with a 60 second detection time.26 Samples collected are

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heterogeneous mixtures of oil, sand, and other natural organic and inorganic material.14,15 To

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capture the variation within individual samples, 5 data points were taken per sample, each the

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average of 3 measurements. Each of the 5 data points was taken sequentially from a different

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location on the inside of the sample, with approximately 2-5 millimeters distance between each

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data point. Element concentration data were therefore collected in triplicate for a total of 595

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sub-samples, representing 119 individual samples, each measured in 5 places. To evaluate the

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heterogeneity of the samples further, eight samples representing different Gulf of Mexico coastal

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beaches were solvent-extracted according to previously described methods (see White et al.,

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2016) so that the resulting oil-residue extracts and remaining unextractable materials (primarily

XRF spectroscopy was performed using an Oxford

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sand and shells) could be analyzed separately from one another.14 The extracts and unextracted

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residues were then dried overnight at room temperature prior to XRF analysis.

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Machine Learning Analysis. The 595 XRF samples from 119 unique samples were used to

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train interpretable machine learning models to infer the presence or absence of DWH oil from

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XRF data. The element concentration data has been deposited with Figshare, with the DOI

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10.6084/m9.figshare.7272644. All element concentrations were used as trainable features, with a

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binary class variable (source of oil is DWH or non-DWH). The training set used to construct the

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model was comprised of a random selection of 66% of the samples (393 sub-samples, 79 unique

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samples). The remaining 33% of the samples were held out as a test set (202 sub-samples, 39

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unique samples) so that the accuracy of the model with respect to unseen data could be

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determined. Sub-samples from the same patty were grouped together into the training set or test

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set to ensure the model did not have prior exposure to samples similar to those in the test set. The

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PART rule-based classifier27 and C4.5 decision tree classifier28,25 were used for the binary

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classification task of determining if a sample contained oil residues from the DWH oil spill.

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RESULTS AND DISCUSSION

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Elements detected by X-ray Fluorescence. A total of 25 elements were detected across the

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range of samples. In order of typical average abundance across all samples collected the

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elements are: calcium (Ca), iron (Fe), potassium (K), titanium (Ti), zirconium (Zr), strontium

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(Sr), manganese (Mn), vanadium (V), molybdenum (Mo), thorium (Th), barium (Ba), rubidium

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(Rb), nickel (Ni), cadmium (Cd), zinc (Zn), copper (Cu), lead (Pb), cobalt (Co), chromium (Cr),

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mercury (Hg), thallium (Tl), antimony (Sb), tantalum (Ta), selenium (Se), and gold (Au).

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Minimum, median, maximum, and 1st and 3rd quartile concentrations of these elements for each

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sample type are provided (Table S1; Supporting Information (SI)) along with the concentrations

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of all 25 elements for each sample category (Figure S1-S4; SI). Often, the samples were found

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mixed with sand. The most common elements detected in DWH extracted oil residues (without

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the sand matrix) compared to the most common elements in the sand matrix highlight variations

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in element concentrations within the extracted oil and sand even when separated from one

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another (Figure S5; SI).

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Element content can predict source of DWH oil. The rule-based and decision tree models

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yielded 96% and 95% classification accuracy, respectively (detailed in Figure S6; SI). The two

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models made decisions based on very similar features, primarily considering of mercury (Hg),

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copper (Cu), calcium (Ca), zinc (Zn), potassium (K), strontium (Sr), zirconium (Zr) and iron (Fe)

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(Table 1). The concentrations of these eight elements for all DWH and non-DWH samples are

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shown in Figure 1.

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Table 1. Rules and the number of each sample type classified as either DWH or non-DWH oil by each of these rules.

Rule 1 Rule 2 Rule 3

Rule a

Classification

If Hg > 7 and Cu 2869, Zn