Categorization of nearshore sampling data using oil slick trajectory predictions

Marine Pollution Bulletin 150 (2020) 110577

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Categorization of nearshore sampling data using oil slick trajectory predictions Larissa Montasa, Alesia C. Fergusonb, Kristina D. Menac, Helena M. Solo-Gabrielea,

T

⁎

a

University of Miami, Coral Gables, FL, USA North Carolina A&T, Greensboro, NC, USA c UTHealth School of Public Health, Houston, TX, USA b

ARTICLE INFO

ABSTRACT

Keywords: Marine oil spills PAHs Chemical distributions Human health

Oil Spill Chemicals (OSCs) represent a risk to the environment and human health, especially in nearshore environments used for recreational purposes. Importantly, the starting point for human health risk assessment is to define the concentration of OSCs at nearshore locations. The objective of this study was to evaluate nearshore sampling data of OSC concentrations in different environmental matrices within time-space specific categories. The categories correspond to OSC concentration values for samples collected prior to nearshore oiling, post nearshore oiling and at no time impacted by oil as predicted by historic oil spill trajectories generated by an Oil Spill Trajectory Model. In general, concentration values for the post category were higher than prior which were higher than unimpacted. Results show differences in PAH concentration patterns within each matrix and for each category. Concentration frequency distributions for most chemicals in each category were log-normally distributed.

1. Introduction Oil Spill Chemicals (OSCs) are defined as the chemicals derived from spilled crude oil coupled with chemicals added during response operations to the spill, such as dispersants that are used to mitigate the impacts from the oil spill (Kujawinski et al., 2011; Paris et al., 2012; Olson et al., 2017). Following incidents in the last decade, including the 2010 Deepwater Horizon (DWH) Oil Spill (National Commission, 2011; McNutt et al., 2011; McNutt et al., 2012; Lehr et al., 2010), response agencies and researchers have collected an enormous amount of environmental samples for OSCs. Few attempts have been made to interpret this sampling data in a spatial and temporal context which addresses both, background or pre-spill concentrations and then compares them to OSC concentrations post nearshore oiling. The OSCs in crude oils include hydrocarbon compounds, non-hydrocarbon compounds, organometallic compounds and metallic compounds particularly nickel and vanadium in variable amounts (Tissot and Welte, 1984; Albers, 2003). Human health risks from an oil spill are related to the concentrations of OSCs in human exposure zones. Chemicals of concern in crude oil due to potential toxicity to humans are the volatile and semi volatile organic compounds (VOCs, SVOCs), especially the aromatic hydrocarbons, BTEX (benzene, toluene,

ethylbenzene and xylene), and the polycyclic aromatic hydrocarbons (PAHs) due to their genotoxic, mutagenic and carcinogenic properties (ATSDR, 1995; Baars, 2002; Transportation Research Board and National Research Council, 2003). Oil Spill Chemicals (OSCs) represent a risk to human health, especially in nearshore environments used for recreational purposes. At the time of DWH, publicly available estimations for nearshore impacts by oil were generated on a daily basis by the General NOAA Operational Modelling Environment (GNOME) an Oil Spill Trajectory Model (OSTM). The last decade has seen a significant increase in knowledge in the modeling of hydrodynamics, fate and transport of crude oil spilled in the marine environment (Camilli et al., 2010; Le Hénaff et al., 2012; Paris et al., 2013; Boufadel et al., 2014). These models predict surface oil slick location and mass until the oil makes landfall but do not predict the concentration of OSCs in matrices at nearshore locations. Defining the concentrations of OSCs in exposure zones is the starting point for human health risk assessment. Therefore, studies are required for estimating the concentration of individual OSCs, particularly BTEX and PAHs, that impact the nearshore environment in the days following shoreline oiling. This gap in knowledge regarding nearshore concentration values limits the scope and the refinement of risk assessment modeled from the start and until the spill has been successfully

⁎ Corresponding author: University of Miami, College of Engineering, 1251 Memorial Drive, McArthur Engineering Building Room 252, Coral Gables, FL 33146, USA. E-mail address: [email protected] (H.M. Solo-Gabriele).

https://doi.org/10.1016/j.marpolbul.2019.110577 Received 5 March 2019; Received in revised form 9 August 2019; Accepted 7 September 2019 Available online 18 December 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

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contained. This study focuses on evaluating nearshore sampling data collected by the U.S. Environmental Protection Agency (hereafter EPA) at the time of the DWH oil spill. OSC concentration measurements from samples of MC-252, the raw oil from the DWH spill were also included for the purpose of comparison. Typically, a continuous and substantial environmental sampling activity is conducted during a major oil spill such as DWH. This sampling effort is extended geographically and over a period of time. At any given day, samples collected in one location may have been collected prior to oil landfall whereas in other locations oil may have already made landfall. This sampling activity is continuous until months after the oil spill has stopped. Interpretation of the sampling results requires knowledge on the estimated oil landfall date and spatial extent with respect to the sample's collection date and specific location. One approach to gain insight into environmental sampling concentrations of OSCs collected pre and post oil landfall is a categorization process which utilizes public and timely spatial-temporal data such as the surface oil slick trajectories from GNOME an OSTM used during response operations. The objective of this study was to categorize the EPA nearshore sampling data of OSC concentrations within time-space specific categories. The time-space categories were generated by comparing the dates and locations of the sampling data with the historic daily GNOME trajectories for DWH oil slicks. The EPA environmental samples were categorized into measurements of: prior to oil spill impact, post oil spill impact, and within unimpacted areas. Differences among these categories were then evaluated statistically. Concentration frequency distributions for a subset of OSCs and concentration patterns within several matrices and for each category were compared to the MC-252 crude oil to estimate changes in the overall composition of OSCs.

separation of PAHs into light (up to four benzene rings) versus heavy (more than four benzene rings). Although several studies conducted after the oil spill identified oxidative by-products in environmental samples, including oxidized forms of PAHs (Aeppli et al., 2012; Black et al., 2016), none were measured within the EPA environmental dataset. In addition to parameters of concern and for the purpose of estimating changes in the overall composition of OSCs this study evaluated other matching parameters including saturated hydrocarbons (SHC), bulk parameters such as Gasoline Range Organics (GRO), Diesel Range Organics (DRO), Oil Range organics (ORO) and Oil and Grease Hexane Extractable Material (HEM) and two metals: nickel (Ni) and vanadium (V). The EPA environmental data also measured chemicals associated the primary oil dispersant applied in mass during DWH, known by the trade name Corexit® 9500, followed by Corexit® 9527. These dispersants consist of different chemicals (NALCO Environmental Solutions, EC9500, 2016a and NALCO Environmental Solutions 9527, 2016b). More details about the chemicals coincident in the raw oil and environmental samples and the chemicals found in dispersants including alternate names are publicly available online through the GRIIDC database established for this study (https://doi.org/10.7266/ n7-be3h-vd24). 2.3. U.S. EPA sampling strategy The EPA processed of 1,881 samples for the four matrices that were the focus of this study: weathered oil, tar, sediments and water (Table 1). Due to the limited number of waste samples collected, results from this matrix were not included in the current study. The vast majority of the measurements fell below detection limits (> 85% of the measurements). In some cases, the detection limits were very high, and in other cases, the concentrations of the ambient samples were very low resulting in additional non-detects. Fig. 1 provides an example of the intensity of the sample collection effort and the relative number of samples that were detected (top panel) versus non-detected (bottom panel) for benzo[a]pyrene. The high detection limit relative to those detected is particularly noticeable for the inset shown for Mobile Bay, AL. In some locations it is difficult to determine if a non-detect was due to a lack of contamination or if contamination was present but not measured due to the high detection limit. This is important to take into consideration in terms of understanding the concentration ranges for all of the OSCs of concern in the sampling locations and the connected decision making processes regarding human and environmental health. Although all samples were analyzed according to the EPA approved analytical methods (Table 4.1 in U.S. EPA, 2010a, 2010b), due to the large number of non-detects, the statistics presented in the following

2. Methods 2.1. Raw oil and environmental sampling Data containing MC-252 oil physical and chemical parameters were downloaded from the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), MC-252 Oil Characterization data file (doi:https://doi.org/10.7266/N7DN43GQ). During the response to the spill a sample of the MC-252 crude oil was collected from an intervention vessel on June 19, 2010. This oil sample was collected at ambient temperature and pressure, and was considered dead with respect to the reservoir oil because it was no longer entrained with reservoir gases. This oil is defined as “raw oil” (hereafter raw oil) and is considered an unweathered oil (BP Gulf Science Data, 2013). However, it is likely that its chemical composition varied to that of the confined reservoir oil due to changes during transport through the water column. The environmental sampling data were downloaded from the EPA's Deepwater Horizon oil spill response website from (https://archive.epa. gov/emergency/bpspill/web/html/download.html). The data are also available for download through the dataset established for the current study within the Gulf of Mexico's Research Initiative's (GRIIDC) database (https://doi.org/10.7266/n7-be3h-vd24).

Table 1 Analyte Counts by Matrix and Detections for U.S. EPA Sample Collection Program. Each analyte corresponds to a specific chemical – sample combination. The samples are collected in many different locations but some sampling locations are repeated resulting in some cases with multiple samples collected from the same location but at different times.

2.2. Basis of comparison for environmental measurements

Analytes

At the time of the oil spill, the EPA collected environmental samples in nearshore (1.6 to 5 km from shoreline) and in shoreline areas (up to 1.6 km from shoreline) in Louisiana, Mississippi, Alabama and Florida from April 28th - 8 days after the initial explosion, until September 29th 2010–74 days after the well was capped (U.S. EPA, 2010a, 2010b). Using the raw oil as a reference, the matching chemical parameters measured in the EPA environmental data were identified. The parameters of concern measured in the raw oil and environmental samples included BTEX and 43 parent and alkylated homologue series PAHs. For the purpose of comparison between categories, this study included the

Weathered oil Tar Sediments Water Waste Subtotal Subtotal minus waste a

2

Total

Detected

Not detected

6,366 328 14,433 20,991 172 42,290 42,118

1,282 (20%)a 59 (18%) 1,993 (14%) 2,178 (10%) 42 (24%) 5,554 5,512 (13%)

5,084 (80%) 269 (82%) 12,440 (86%) 18,813 (90%) 130 (76%) 36,736 36,606 (87%)

Percentage with respect to total analytes for each matrix.

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Unimpacte

Fig. 1. Locations for the U.S. EPA sample collection program showing detection limits for benzo[a]pyrene in sediments. Sites with detects are shown in black. Sites with results below detection limit are shown in orange with the relative size of the symbol representing the value of the detection limit. Background image from ArcMap 10.5 (ESRI).

sections focused on measurements that were detected and were at or above the limit of quantification.

used the GNOME model's output (Zelenke et al., 2012) to produce daily oil spill trajectory maps, starting on April 24 and ending August 23, 2010 (Environmental Response Management Application, 2014). Effective August 2010 aerial surveys and satellite analyses eventually showed no recoverable oil in the spill area (NOAA, 2010). Ocean currents for GNOME trajectory forecasts were obtained from several models including those developed by NOAA, University of South Florida, Texas General Land Office-Texas A&M University and the U.S.

2.4. GNOME trajectories Spatial data layers with information for the DWH oil spill nearshore surface oil forecast were downloaded from NOAA's Environmental Response Management Application. During the DWH oil spill, NOAA 3

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GNOME Trajectory May 03, 2010

Deepwater Horizon Wellhead Forecast Heavy Forecast Medium Forecast Light Forecast Uncertainty

GNOME Trajectory June 22, 2010

Fig. 2. Two maps highlighting the Mississippi river delta and the GNOME trajectories for May 2nd and June 22nd, 2010. Background image from ArcMap 10.5 (ESRI).

Navy's Naval Oceanographic Office, Coastal Ocean Model (NCOM). The GNOME model was initialized from overflight observations and from the previous days' satellite imagery analysis as available through NOAA and the National Environmental Satellite, Data, and Information Service (NESDIS) Office (Environmental Response Management Application, 2014. Date of access January 2018). For the purpose of this study, July 31, 2010 was utilized as the end date for surface oil trajectories given no recoverable oil was reported from overflights after that date. Starting August 2010, the NESDIS data analysis showed a few scattered anomalies far offshore and west of the Mississippi River delta such that the threat of new shoreline impacts was low (OSAT, 2011). Geospatial data for GNOME trajectories consist of a series of four

polygons, three of which define “best guess” areas of heavy, medium and light oil slicks (Fig. 2). The uncertainty boundary delineates a fourth outer polygon within which there is a chance that oil may be located anywhere inside but is most likely to be within the “best guess” polygons. The uncertainty boundary was based on the extent of the differences among the ocean current models as well as inevitable inexactness of input data regarding predicted currents, winds, and other model inputs. For the current study, the spatial extent of the GNOME trajectories was defined by the uncertainty boundary. GNOME also predicted locations where oil could potentially beach during the forecast period. The beached oil estimates on the maps are shown as discrete point locations. For the purpose of the current study, trajectory 4

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Table 2 Bulk characteristics measured in weathered oil and tar samples, means with number of samples analyzed given in parenthesis. These bulk characteristics were not measured in the sediment or water samples. Chemical group or chemical

Identifier

Raw oil (mg/kg)

Weathered oil (mg/kg)

Tar (mg/kg)

GRO (C6-C10) DRO (C10-C28) ORO (C28-35) (C28-40)e Oil and Grease, HEMb (C4-C44)e TOC

8006-61-9c DROC10C28d OROC28C35d OILGREASEHEMd TOCd

NM 384,087 NMa NM NM

0.6231 (4) 119,808 (48) 72,859 (48) 211,615 (39) 29.63% (42)

0.07 (1) 5,300 (1) 26,000 (1) NM 5.19% (1)

a b c d e

NM = Not Measured. Hexane Extractable Material. CAS Number. U.S. EPA Identifier. Exact range depends on specific laboratory protocol.

compare the value to other measurements in a different category. The difference between the single data point and its nearest neighbor was divided by the spread for the entire set. The resulting ratio was compared with rejection values critical for a particular degree of confidence (Skoog and West, 1992). The 95% degree of confidence was selected for this study. Data analysis also involved comparing the PAH concentration patterns among the different matrices and categories. This was accomplished by ranking the mean PAH concentrations within each category for each chemical from high to low for raw oil. OSC concentrations for weathered oil and sediment in post, prior and unimpacted categories were then compared to the order (or pattern) of the chemicals found in the raw oil. Additionally, histograms of the OSCs were plotted for the various matrices to evaluate the frequency distributions of the OSCs.

polygons that describe area-wide impacts were used as opposed to the discrete locations as given by the beaching algorithm. For example, the GNOME trajectory for May 3, 2010 forecasts area-wide impact to the Mississippi River delta. As the oil spill progressed, the spatial extent for the trajectories varied. The GNOME trajectory for June 22, 2010 shows an expansion of the forecast impacts beyond the Mississippi River delta to include Mobile Bay, AL and other coastal zones (Fig. 2). 2.5. GIS processing - categorization of sampling data The georeferenced EPA sampling data was imported into ArcMap 10.5 (ESRI) and a point layer was created for each analyte across all sample locations. Each record included the analyte's sample name, sample location, sample date, analyte concentration, and detection limit among other parameters. The GNOME surface oil slick trajectory polygons corresponding to the GNOME model estimates from April 24th until July 31rst were imported in ArcMap 10.5 (ESRI) to generate a polygon layer with one record corresponding to each surface oil category polygon for the total number of trajectory days. The geometric intersection for the analyte sample locations and surface oil trajectories was calculated using spatial analysis in ArcMap 10.5 and a Python script. Results were used to classify the EPA data-set, for each matrix, into three categories. The separation was based upon the date the sample was collected as compared to the date of the first intersection by any of the four trajectory polygons with the sample location. This allowed the separation of the data into categories named prior to impact, post impact, and unimpacted, respectively. The first category (prior) corresponds to data for locations with samples collected prior to the date of the first intersecting trajectory. The second category (post) corresponds to data for locations with samples collected on the date or the time period subsequent to the date of the first intersecting trajectory. A third category (unimpacted) was defined for locations not intersected by a trajectory.

3. Results and discussion 3.1. Time-space specific categories Most samples were categorized to the post category (79% and 65% for sediments and water, respectively), with fewer samples categorized in the prior category (12% and 35% for sediment and water, respectively) (Table 1). The fewest samples corresponded to the unimpacted category (9% and 1% for sediments and water, respectively). Sampling locations for prior and post span the western Louisiana shoreline to the Florida panhandle. Whereas, sampling locations for unimpacted correspond only to the eastern reaches of the Florida panhandle (Fig. 2). 3.2. Bulk oil parameters Studies have characterized the MC-252 raw oil as a light Louisiana crude oil with over 50% low molecular weight hydrocarbons (Ryerson et al., 2012), which are known to undergo rapid weathering in marine environments. Consistently, results show that lower molecular weight hydrocarbons as measured by GRO were depleted in the weathered oil and tar, which had the lowest concentrations (Table 2). The mean DRO in weathered oil and tar were at about 1/3 and 1/100 the value in raw oil, respectively. In terms of the weathered oil compared to the tar, the percent depletion for DRO and ORO are 96% and 64%, respectively. The higher depletion for DRO may have been a result of the weathering process as DRO may have degraded more easily relative to heavier molecular weight hydrocarbons found in ORO. This observation is consistent with weathering processes such as evaporation, dissolution and biodegradation which favor the loss of low molecular weight compounds. For light crude oil, such as MC-252, evaporation alone can account for up to 75% of the losses (Albers, 2003). The raw oil characterization data did not provide a reference for oil and grease-Hexane Extractable Material (HEM) so we focused on comparing the values of DRO and HEM within the weathered oil samples. Results show that mean value for HEM is approximately 100%

2.6. Statistical analysis For each OSC, the mean and standard deviation were calculated for the concentrations within prior, post and unimpacted categories. Statistical differences between prior, post and unimpacted concentrations for a given chemical and for groups of chemicals were evaluated through t-tests assuming paired two samples for means, two-tailed with alpha at 0.05. Concentration measurements from the single raw oil sample collected at the surface above the release point were not categorized. The GIS data processing correctly assigned all weathered oil and tar samples to the post category. Thus, for the three matrices of raw oil, weathered oil, and tar, statistical differences were not run between prior, post, and unimpacted. Additionally, t-tests were only possible when comparing concentration measurements with two or more data points per OSC. When a dataset within a category for an OSC consisted of only one data point, an outlier test was conducted using a Q test to 5

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Table 3 Mean Concentrations of raw oil, weathered oil, tar, sediments, and water for samples evaluated as part of this study. Numbers in parenthesis correspond to the number of analytes that the criteria and therefore used to compute the mean concentrations. More details about the SHCs and total PAHs detected are provided in Tables 4 and 5, respectively. Standard deviations and additional statistics for all chemicals detected in the oil are provided in the GRIIDC database referenced earlier. Chemical group or chemical

∑

SHC Total PAH∑ Light PAH Heavy PAH BTEX∑ Benzene Ethylbenzene Toluene Xylene Metals∑ Nickel Vanadium Dispersants Propylene Glycol

MC-252 raw oil (mg/kg)

31,611 12,788 12,762 26 22,530 2,255 517 6,775 11,983 1.80 1.80 ND NM

Weathered oil (mg/kg)

Tar (mg/kg)

Sediments (μg/kg)

Post

Post

Post

9,079 (27) 685.0 (229) 673.3 (216) 11.7 (13) 0.1885 (5) ND 0.0007⁎ (1) 0.0279⁎ (2) 0.1599⁎ (2) 4.551 (95) 2.660⁎ (46) 1.890⁎ (49) ND

3,225 146.2 144.9 1.370 ND ND ND ND ND 0.520 0.320 0.200 ND

(8) (22) (20) (2)

(2) (1) (1)

§

NM 960.6 (857) 680.6 (591) 279.8 (266) 3.711 (22) 1.484a (7) ND 2.227a (15) ND 39,279 (615) 14,035a (306) 25,244a (309) 0.83a (3)

Water (μg/L)

Prior

Unimpacted

Post

Prior

Unimpacted

NM 629.3 (73) 393.0 (49) 236.3 (24) 395.55 (6) 31.15a (2) 34.10(1) 273.7a,c (2) 56.60 (1) 34,973 (152) 14,265a,b (76) 20,708b,c (76) ND

NM 119.7 (60) 80.07 (34) 39.59 (26) 13.38 (16) 1.650a (6) ND 1.131c (7) 10.60 (3) 17,887 (76) 2,993c (38) 14,894c (38) 0.76a (3)

NM 8.120 (254) 7.850 (252) 0.270 (2) 6.074 (41) ND ND 1.493a (32) 4.581a (9) 21.49 (1,114) 14.08a (250) 7.415a (864) ND

NM 0.737 (117) 0.737 (117) NDⴲ 4.280 (25) NM ND 1.026a (19) 3.253a (6) 38.65 (575) 31.90a (115) 6.749a (460) 590.0 (1)

NM 1.150 (2) 1.150 (2) ND 0.620 (1) NM NM 0.620a (1) NM 31.82 (7) 26.52a (6) 20.00a (1) NM

a,b,c

Statistical differences were evaluated for sediments and water across different categories. Mean values sharing a superscript for a given chemical are not statistically different. NA = Not Applicable. ∑ Sum of the mean concentration for each individual chemical species. § NM = Not Measured. ⴲ ND = Not Detected. ⁎ Converted from mg/L to mg/kg by adjusting for an assumed oil density of 0.9137 g per mL of weathered oil (Daling et al., 2014).

greater than the value for DRO. This provides an indication for the estimated concentration of the hydrocarbons in the C4–C44 range relative to the lower molecular weight hydrocarbons measured by DRO. HEM measures nonvolatile hydrocarbons, oils, greases and related material from both petrogenic and nonpetrogenic sources (U.S. EPA, 1998).

Table 4 Mean concentrations for saturated hydrocarbons (SHC) that were found in the weathered oil and tar samples. Values in parenthesis in the weathered oil column correspond to the number of samples available. SHCs in sediment and water samples were either not measured or were below detection limits. Values for raw oil are included for comparison. The raw oil was tested for many more SHC than shown in this table.

3.3. Specific oil parameters detected in environmental samples When evaluating the specific chemical parameters detected in the EPA environmental dataset, 18 SHCs, 36 PAHs, 4 BTEX, and two metals were identified that were detected in two or more environmental media (Table 3). Raw and weathered oil shared 13 of the 18 SHCs detected in either the weathered oil or tar (Table 4). Compared to the raw oil, mean percent depletion of the SHCs was 71% for the weathered oil and 90% for the tar matrix. The raw oil was tested for 92 geochemical markers, and 118 additional hydrocarbons classified as paraffins, isoparaffins, aromatics, naphthenes and olefins (PIANO) not included within the BTEX and SHC groupings. With the exception of two chemicals none of the PIANOs nor geochemical markers found in raw oil coincide with the chemicals detected in the EPA environmental samples. The two exceptions were in the PIANO category: styrene (aromatic) with one measurement in weathered oil (0.1040 mg/kg) (n = 1) and methylcyclohexane (acyclic) which was detected in sediments prior (0.0733 mg/kg) (n = 1) and in tar (0.1200 mg/kg) (n = 1). Cumene (another PIANO) was not tested in the raw oil but was detected in sediments prior (0.1034 mg/kg) (n = 2). Details on all other chemicals (104 parameters in total) detected within the EPA environmental samples are available at (https://doi.org/10. 7266/n7-be3h-vd24).

Formula

Chemical group or chemical

MC-252 (dead) oil (mg/kg) (n = 7)

Weathered oil (mg/kg) (n = 3)

Tar (mg/kg) (n = 1)

C17H36 C18H38 C19H40 C20H42 C21H44 C22H46 C23H48 C24H50 C25H52 C26H54 C27H56 C28H58 C29H60 C30H62 C31H64 C32H66 C33H68 C34H70

Heptadecane Octadecane Nonadecane Eicosane Heneicosane Docosane Tricosane Tetracosane Pentacosane Hexacosane Heptacosane Octacosane Nonacosane Triacontane Hentriacontane Dotriacontane Tritriacontane Tetratriacontane

4,366 3,697 3,291 3023 2,504 2,253 2,015 1,879 1,677 1,379 1,072 827 800 761 677 571 436 380

390.0 969.0 1,334 1,462 1,281 1,156 622.1 565.3 515.4 407.6 1.850 374.8 1,210 NM NM NM NM NM

NMa NM NM NM NM NM NM NM NM NM 140.0 310.0 417.0 671.0 503.0 476.0 349.0 359.0

a

(1) (2) (2) (2) (2) (2) (3) (3) (3) (3) (1) (2) (1)

NM = Not Measured.

those detected in the EPA samples) in the raw oil (12,788 mg/kg oil) was significantly higher in comparison to the weathered oil (685.0 mg/ kg), which in turn was higher than the total PAH concentration in the tar samples (146.2 mg/kg) (Table 3). Within the weathered oil and tar a total of 34 and 20 PAH species were detected, respectively (Table 5). LPAHs were dominant in all three matrices. PAHs detected in tar samples were predominantly alkylated PAH homologous series: C1-C3

3.4. PAH concentrations 3.4.1. PAH concentrations in raw oil, weathered oil and tar The mean total PAH concentration (only for PAHs coincident with 6

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Table 5 PAHs (parent and alkylated forms) found in the environmental samples with comparisons provided against values observed in raw oil. No. Chemical group or chemical benzene rings

MC-252 (dead) oil (mg/kg)

Weathered oil (mg/kg)

Tar (mg/ kg)

Sediments (μg/kg)

Water (μg/L)

Post

Post

Post

Prior

Un-impacted Post

Prior

Un-impacted

2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4

Light PAHs∑ Acenaphthene Acenaphthylene Fluorene C1-Fluorenes C2-Fluorenes C3-Fluorenes Naphthalene C1-Naphthalenes C2-Naphthalenes C3-Naphthalenes C4-Naphthalenes Anthracene C1-Phenanthrenes/anthracenes C2-Phenanthrenes/anthracenes C3-Phenanthrenes/anthracenes C4-Phenanthrenes/anthracenes Phenanthrene Benzo[a]anthracene Benzo[k]fluoranthene Chrysene C1-Chrysenes C2-Chrysenes C3-Chrysenes C4-Chrysenes C1-Fluoranthenes/pyrenes C2-Fluoranthenes/pyrenes C3-Fluoranthenes/pyrenes Fluoranthene Pyrene

10,258 14.89 8.623 159.4 361.0 475.0 370.1 848.3 1,951 2,500 1,690 859.7 11.68 750.1 826.5 495.6 195.0 327.5 6.963 ND 54.50 119.17 139.7 103.1 68.80 91.05 147.0 165.1 4.39 17.55

673.3 0.1638⁎ (4) 0.0145 (1) 6.461 (26) 10.05 (2) 34.00 (2) 58.50 (2) 0.0336 (1) ND 347.7 (5) 2.300 (2) 6.800 (2) 10.15 (2) 64.50 (2) 102.0 (2) 76.00 (2) 57.50 (2) 36.15⁎ (47) 13.26 (6) 0.0888 (2) 26.86⁎ (47) 29.90 (2) 23.65 (2) 14.85 (2) 13.75 (2) 23.05 (2) 21.75 (2) 29.40 (2) 2.169⁎ (20) 9.590⁎ (29)

144.9 NDⴲ ND ND 2.3 (1) 6.8 (1) 15 (1) ND ND ND 0.7 (1) 1.9 (1) ND 15 (1) 27 (1) 20 (1) 13 (1) 1.75 (2) ND ND 4.8 (2) 7 (1) 4.9 (1) 4.2 (1) 3.2 (1) 5.9 (1) 5.6 (1) 5.8 (1) ND ND

675.5 16.37a (7) 8.792a (5) 34.72a (12) NM NM NM 13.67a (16) 3.267a (3) 14.24a (13) 10.94 (5) NM 14.04 a(32) NM NM NM NM 114.3a (88) 85.17a (75) 29.68a (46) 241.68a (89) NM NM NM NM NM NM NM 35.97a (102) 57.29a (98)

393.0 7.57a (1) 10.82a (2) 4.015a (2) NM NM NM ND ND 4.720a (2) NM NM 17.88a (5) NM NM NM NM 30.89b,c (5) 61.26a,c (7) 34.34a (6) 87.32a,c (4) NM NM NM NM NM NM NM 94.05a,c (6) 40.15a (9)

69.29 ND ND NM NM NM NM 7.5a (1) 8.700a (1) 17.20a (3) NM NM ND NM NM NM NM 7.111c (9) 7.366c (3) 6.4a (1) 11.175c (4) NM NM NM NM NM NM NM 7.483c (6) 7.133a (6)

7.850 ND ND 1.809a (27) NM NM NM 0.1800a (106) ND ND NM NM ND NM NM NM NM 4.001a (89) ND ND ND NM NM NM NM NM NM NM 0.1245a (17) 1.728a (13)

0.7370 NM§ NM 0.200 a (1) NM NM NM 0.088a (91) NM NM NM NM NM NM NM NM NM 0.265a (12) NM NM NM NM NM NM NM NM NM NM 0.0800b (4) 0.104a (9)

1.150 NM NM NM NM NM NM 1.100 (1) NM NM NM NM NM NM NM NM NM 0.05a (1) NM NM NM NM NM NM NM NM NM NM NM NM

5 5 5 5 5 5 6

Heavy PAHs∑ Benzo[a]pyrene Benzo[b]fluoranthene Benzo[e]pyrene Dibenz[a,h]anthracene Indeno[1,2,3-cd]pyrene Perylene Benzo[ghi]perylene

25 2.29 6.507 12.47 2.176 ND 0.1429 2.081

11.7 4.586 (3) 1.818⁎ (5) 4.285 (2) 0.1330 (1) 0.3450 (1) ND 0.521 (1)

1.37 ND 0.62 (1) 0.75 (1) ND ND ND NM

279.8 67.24a 61.09a 47.33a 23.98a 32.14a 10.39a 37.59a

236.3 57.47a,c (7) 92.80a,c (6) ND 30.25a (2) 33.04a,c (4) ND 22.76a,c (5)

39.59 5.633c (6) 7.211c (9) 3.800a (2) ND 3.800c (4) 14.35a (2) 4.800c (3)

0.2700 ND ND ND ND 0.2700 (2) ND ND

NM NM NM NM ND ND ND

NM NM NM NM NM NM NM ND

(71) (83) (12) (16) (39) (8) (37)

a,b,c

Statistical differences were evaluated for sediments and water across different categories. Mean values sharing a superscript for a given chemical are not statistically different. NA = Not Applicable. DB = Detected but below the limit of quantification. ∑ Sum of the mean concentration for each individual chemical species. § NM = Not Measured. ⴲ ND = Not Detected. ⁎ Converted from mg/L to mg/kg by adjusting for an assumed oil density of 0.9137 g per mL of weathered oil (Daling et al., 2014).

flourenes, C3-C4 naphthalenes, C1-C4 phenanthrenes/anthracenes, C1C4 chrysenes C1-and C3 fluoranthenes/pyrenes. The corresponding parent PAHs were not measured in the tar samples. Only two of the HPAHs were measured and detected in tar samples: benzo[b]fluoranthene (0.62 mg/kg) (n = 1) and benzo[e]pyrene (0.75 mg/kg) (n = 1).

undergo weathering at a slower rate than L-PAHs and the ratio of LPAHs to H-PAHs diminishes over time. With respect to individual PAHs in the sediments matrix 21 different PAHs were detected across all categories: post had the highest number of PAHs with 21 different PAH species (14 L-PAH and 7 H-PAH), followed by prior with 16 PAHs (11 LPAH and 5 H-PAH) and in unimpacted 15 PAHs were detected (9 L-PAH and 6 H-PAH) (Table 5). Of note, benzo[k] fluoranthene and indeno [1,2,3-cd]pyrene, were not detected in the raw oil as mentioned earlier, but were detected in all three categories (Table 5). This may be indicative of background PAH concentrations related to anthropogenic or other non-DWH petrogenic sources. The most common and ubiquitous sources of anthropogenic PAHs, however, are those associated with pyrogenic inputs (Boehm, 2005). Overall, results for the post category show patterns which are indicative of recent impact by oil (i.e., higher concentration measurements for L-PAHs relative to H-PAHs).

3.4.2. PAH concentrations in sediments Within the sediment matrix, results show a difference in the values for the mean total PAH concentrations in post (960.6 μg/kg), prior (629.3 μg/kg) and unimpacted (119.7 μg/kg) (Table 3). The mean concentration measurement for total L-PAHs was higher by over 100% than H-PAHs in post sediments. This difference is diminished in prior sediments where the mean concentration for L-PAHs was approximately 67% higher than that for H-PAHs. These results are consistent with recent impact to samples in the post category by a light crude oil such as the MC-252 oil which was enriched in L-PAHs; whereas, the prior category shows values which may be indicative of mixed sources. H-PAHs

3.4.3. PAH concentrations in water Fewer chemicals were measured in this matrix in comparison to the 7

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others. Within the water matrix, results show a difference in the values for the mean total PAH concentrations in post (8.120 μg/L), prior (0.737 μg/L) and unimpacted (1.150 μg/L) (Table 3). Due to the low number of analytes, almost no statistically significant differences were found between categories in water. Fluoranthene is the exception as a significant difference was found between post and prior. The most abundant PAHs across all categories in water was naphthalene (n = 198). Naphthalene has one of the highest aqueous solubilities of the PAHs (31,700 μg/L) (Mackay and Shiu, 1977). Only one heavy PAH, indeno[1,2,3-cd]pyrene, was detected in post (n = 2) and none were detected in prior or unimpacted. Interestingly, as mentioned before, indeno[1,2,3-cd]pyrene was not detected in the raw oil. A deeper analysis into PAH water concentration distributions at specific locations may provide insights into background concentrations due to prior oil spills or non-petrogenic sources of PAHs.

The second and third highest ranking alkylated series in the raw oil were C1-C3 phenanthrenes/anthracenes and C1-C3 fluorenes. The pattern found in raw oil was very similar to that observed within the weathered oil samples despite high depletion values: the C1-C3 phenanthrene/anthracene series and C2-C3 fluorenes series remained with the second and third highest ranking concentration values within the weathered oil samples. The percent depletion for phenanthrene and alkylated-phenanthrenes ranged from 71% to 91% while the percent depletion for alkylated-fluorenes ranged from 84% to 97% (Table 5). A noticeable difference in the pattern in the weathered oil was that the concentrations of benzo[a]anthracene (13.26 mg/kg) and benzo[a] pyrene (4.586 mg/kg) were elevated with respect to the raw oil (6.963 and 2.29 mg/kg) by 90% and 100% respectively (Table 5). 3.5.2. PAH concentration patterns in sediments The unimpacted PAH concentration pattern differs to all others. A striking feature of the PAH concentration pattern in post and prior sediments is that the highest ranking PAH compounds in raw oil and weathered oil moved to the bottom tier within the sediment samples. This represents an overall change in the concentration patterns for PAHs. In contrast to the raw oil, naphthalene, C1, C2 and C3 naphthalenes ranked among the lowest concentration values (13.67, 3.267, 14.24 and 10.94 μg/kg) in post sediment samples. Phenantherenes/anthracenes and fluorenes alkylated PAH homologous series were not measured in sediments. As mentioned above, these were the second and third highest ranking compounds in the raw oil. We would expect that if these compounds had been measured, that their concentration value would be in the same order of magnitude as that detected for the C1-C3 naphthalenes homologue series (Liu et al., 2012). A new pattern is observed in which the highest mean concentration measured for an individual PAH in post is the four-aromatic ring, chrysene (241.68 mg/ kg) which ranked as a minor component in the raw oil. However, chrysene is shown consistently among the higher concentrations for the other two categories: third in rank for both prior and unimpacted. This

3.5. Overall changes in PAH concentration patterns

PAH Concentraon

3.5.1. PAH concentration patterns in weathered oil When ranking the concentrations from highest to lowest in raw oil to establish the basis for comparing concentration patterns, the profile of the compounds is similar between raw and weathered oil (Fig. 3). The main exceptions were the comparatively low levels of naphthalene and naphthalene's homologous series in weathered oil in comparison to raw oil. Naphthalene's alkylated homologue series and naphthalene had the highest concentrations in the raw oil (BP Gulf Science Data, 2013) but were one of the lowest in the weathered oil. These results are consistent with results from other studies which found that naphthalene and alkylated naphthalenes were no longer dominant in weathered oil while alkylated phenanthrenes were the dominant species. For example, Liu et al., 2012 found that alkylated naphthalenes in weathered oil became minor components relative to other alkylated PAHs. Within the weathered oil the percent depletion for naphthalene and its alkylated series ranged from 82% to nearly 100%.

Fig. 3. Pattern of PAHs from top to bottom in: raw oil, weathered oil, post, prior and unimpacted categories. Full nomenclature for chemicals listed at bottom of plot is provided in Table 5. M = Not Measured, Ð = Not Detected. 8

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40

Post

35 30

Benzo(a)pyrene

25

Benzo(b)fluoranthene

20

Chrysene

15

Fluoranthene

10 5 0

Frequency

0.4

0.8

1.2

1.6

2

2.4

2.8

3.2

3.7

6

Prior 5 4 3 2 1 0 6 5

Unimpacted

4 3 2 1 0 0.4

0.8

1.2

1.6

2

2.4

2.8

3.2

3.7

Log10 Concentra!on (µg/Kg) Fig. 4. Histogram for 4 PAHs in Post, Prior and Unimpacted categories.

trend is consistent with other studies (e.g., Dickey and Huettel, 2016). Within post phenanthrene and benzo[a]anthracene follow chrysene in concentration rank. The PAH concentration pattern for the prior and unimpacted categories show similar results in terms of the raw oil's highest ranking PAH compounds moving to the bottom tier. However, results show differences in concentration patterns between each category. Flouranthene and benzo[b]fluoranthene have the first and second ranking concentration values in the prior category. As mentioned before flouranthene and benzo[b]fluoranthene were measured as minor components in the raw oil (BP Gulf Science Data, 2013). The pattern in the prior category is similar to patterns for PAHs from pyrogenic sources.

For example a study conducted by Stout et al., 2001, found that the first to third highest ranking PAHs in pyrogenic sources were flouranthene, benzo[b]fluoranthene and chrysene which coincide with the ranking for PAHs in the prior category. PAH distributions for the unimpacted category show C2-naphthalene, perylene, and chrysene as the first, second and third ranking concentration values. While C2-naphthalene ranks first similar to the raw oil, the unimpacted category shows a noticeable difference in the concentration pattern. Within the raw oil, the concentration of alkylated PAHs (C2-naphthalene, 2,500 mg/kg) were much higher than their corresponding parent PAHs (naphthalene, 848 mg/kg). Within the 9

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unimpacted category C2-naphthalene's concentration was very low (15.08 μg/kg) and in the same order of magnitude as naphthalene (7.5 μg/kg). This differentiating trend between PAH patterns in the raw oil and unimpacted category is repeated: C2-naphthalene was three orders of magnitude higher than perylene in raw oil (2,500 mg/kg and 0.14 mg/kg) but in unimpacted C2-naphtalene and perylene (17.20 μg/ kg 14.35 μg/kg) were in the same order of magnitude. Moreover, perylene was measured as a minor component in the raw oil but ranked second highest in the unimpacted category. These distributions might indicate that for the unimpacted categories the PAH source is of mixed origin. Given that raw oil is not the likely source of prior and unimpacted categories the differences in concentration patterns provide insights into background concentrations and into the combinations of PAHs which may have been found in the environment prior to DWH.

test with a 95% degree of confidence yielded a statistical basis for rejection of this data point. However, the observation of higher BTEX in locations prior to forecasted oil by GNOME trajectories may be due to non DWH sources. Results show no statistically significant difference for benzene in sediments for post to prior (p = 0.08), post to unimpacted (p = 0.62) and prior to unimpacted (p = 0.08) and as well for toluene assuming that the 542 μg/kg outlier is removed. We did not compute statistical differences for ethylbenzene and xylene due to the low n values which did not permit for statistical computations. For water, there was no significant difference in BTEX concentrations in post (6.074 μg/L) and prior (4.280 μg/L) samples. However, unimpacted water samples had a much lower concentration given that only toluene was detected (0.62 μg/L).

3.5.3. PAH concentration patterns in water Given the high lipophilicity of most OSCs, fewer chemicals were detected in water samples. The highest PAH mean concentration measured for water was for the three-aromatic ring phenanthrene (4.001 μg/L) followed by fluorene (1.809 μg/L) and pyrene (1.728 μg/ L). This pattern is consistent with the aqueous solubility for these chemicals, (1,290 μg/L, 260 μg/L and 135 μg/L respectively) (Mackay and Shiu, 1977). Although naphthalene has the highest aqueous solubility of the PAHs, it ranks fourth within the water matrix. This pattern is consistent with the concentration patterns found in the weathered oil and sediments: naphthalene was depleted in the weathered oil and ranked among the lowest concentration values in sediments.

3.8. Metal concentrations and frequency distributions For the five metals measured in the raw oil (Ba, Co, Fe, Ni, V), we chose to focus our discussion on nickel and vanadium given they were detected in all four environmental matrices and are known constituents of oil from other spills (Lewan, 1984). All five metals were detected in the weathered oil and tar samples although only two (Fe, Ni) of the five were detected in the raw oil (BP Gulf Science Data, 2013). Vanadium was below detection limits for the raw oil (detection limit 500 μg/kg), but was included in the main discussion due to its ubiquity in the environment, its presence in the environmental matrices plus the high detection limit for the raw oil measurement. The vanadium concentration range for Louisiana crude oil has been measured in other studies at up to 4,000 μg/kg (Nowell et al., 2013) and for MC-252 raw oil specifically at 200 μg/kg (Liu et al., 2012). Nickel and vanadium concentrations were elevated in weathered oil with respect to the raw oil (Table 3). However, the concentrations for these two metals decreased in the tar samples by an order of magnitude. The concentrations in sediment samples were higher than that observed in the raw and weathered oil. These results suggest the accumulation of these compounds in the environment or that a considerable background concentration existed. Unlike other OSCs, sampling activity for these two metals resulted in a high number of detects (n = 309, 76 and 38 for post, prior and unimpacted respectively for vanadium). The concentration range for vanadium within sediments and across all categories was 2,993–25,244 μg/kg. Background concentrations for nickel and vanadium in sediments vary according to location in the Gulf of Mexico, concentrations have been identified by other studies and measured as high as 32,000–42,000 μg/kg for nickel and 14,000–50,000 μg/kg for vanadium (Kennicutt, 2017). In terms of the categorization, different results were observed for nickel versus vanadium. For nickel mean concentrations in sediments were not statistically different between categories. Similarly, for vanadium they were not statistically different between prior to unimpacted (p = 0.14). However, statistical differences were observed for vanadium mean concentrations between post to prior (p = 0.02) and post to unimpacted (p < 0.01). In summary, nickel showed no statistical difference between the categories whereas vanadium did. In terms of vanadium frequency distributions, results vary between categories (Fig. 5). Concentrations in the unimpacted category were predominantly more frequent in the lower value range whereas the highest measured concentrations correspond to the post category. Concentrations in the prior category were log normally distributed with a mean in the same range as for the post category. The post category shows mixed trends. Areas included in this study contain very productive oil exploitation and transportation activities which have been known to impact specific locations. This may account for the variable concentrations. Further study is required to determine the high variability and the cause of the high vanadium concentrations in sediments and tar.

3.6. PAH frequency distributions in time-space specific categories We compared the frequency distributions for two L-PAHs (chrysene and fluoranthene) and two H-PAHs (benzo[a]pyrene and benzo[b] fluoranthene) in the post, prior and unimpacted category. These four chemicals were chosen as they were measured and detected in all three categories as well as having a number of analytes (n > 2) in the prior and unimpacted category (Fig. 4). A statistically significant difference was found between post and unimpacted. However, results for all four chemicals showed no statistically significant difference between post and prior (Table 5). Results showed a lognormal distribution for individual chemicals and for all four chemicals together. The lognormal distribution was particularly apparent for the post category presumably due to the higher n values. For benzo[b]fluoranthene in the prior category, a lognormal distribution was also observed. As expected, the highest concentrations were found in the post category consistent with recent impact by oil. For all four chemicals, chrysene had the highest measured concentration and fluoranthene was most frequent in the post category. 3.7. BTEX concentrations BTEX chemicals were among the highest concentration within the raw oil (Table 3). Among BTEX, xylene had the highest mean concentration of 11,983 mg/kg, followed by toluene, benzene, and then ethylbenzene. The concentrations decreased significantly in weathered oil with lower levels in sediment and water followed by no detects in the tar samples. BTEX species are lost rapidly after an oil release due to the high volatility and solubility of these compounds. This might explain why BTEX compounds are present in the lower concentration ranges in the weathered oil matrix; particularly for weathered oil samples included in this study since these were sampled in nearshore and shoreline locations days after the oil was released. Within the sediment matrix, mean BTEX concentration distributions were higher in samples from prior than for post category mainly due to one toluene measurement (542 μg/kg) the value for which is considerably higher than the second highest value (5.39 μg/kg) in the same category or in the data as a whole (n = 18). An outlier test using a Q 10

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affected by high background levels due to PAHs that originated from non-DWH petrogenic, natural and anthropogenic sources. In order to distinguish the sources for the PAHs in the prior category, additional analysis, including source characterization and parent-to-alkylated PAH ratios, are recommended. This applies in particular for estuarine and coastal regions with high anthropogenic sources of PAHs. Consideration should also be given to the uncertainty in the inputs for the model used to generate the oil slick trajectories such as the variability in ocean and wind currents. We also consider the variability in the environmental measures. Although all samples were analyzed according to the EPA approved analytical methods the number of different laboratories used to process the samples may have contributed to the variability. As a result, uncertainty lied in both the GNOME model inputs as well as practical limitations associated with the use of various laboratories given the need for rapid results for public purposes. Notwithstanding the limitations related to uncertainty in the GNOME model inputs and others cited above, we visualize a distinct difference between the categories, by also assessing differences in mean concentration values, the difference in frequency distributions and concentration patterns. The results reflect the potential of using timely and publicly available trajectory data for the categorization of nearshore sampling data. Ultimately, these methods may be used to interpret environmental sampling results conducted during an oil spill to guide decision-making in a well-timed manner. Typically decision-making regarding public health is effected by comparing environmental concentrations to risk screening levels (RSLs). Shortly after the spill, RSLs were developed for nearshore water exposures (U.S. EPA, 2010a, 2010b) and sediment exposures (Florida Department of Health, 2010) using the limited exposure factor data available at the time. The results of this study provide ranges of concentration for post, prior and unimpacted categories and their frequency distributions. This information can be integrated with improved exposure factor data to forecast physical health outcomes associated with oil spill chemicals along the coast. Results can be potentially used to increase the certainty of health risk assessments in nearshore zones.

80

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60 50 40 30 20 10 0

-0.01 0.2

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Log10 Concentra on (µg/Kg) Fig. 5. Histogram for vanadium in sediment Post, Prior and Unimpacted categories.

3.9. Dispersants Only four dispersant-related chemicals were tested and only one (propylene glycol) was detected and measured in the EPA dataset. It was found in both sediments and water. Concentrations were detected in post (0.8267 μg/kg) (n = 3) and unimpacted (0.7600 μg/kg) (n = 3) sediment categories and in the water in the prior category (590 μg/L (n = 1)). No statistical differences in concentration levels were detected in any of the categories, likely due to the very small number of detects. The other three dispersant-related chemicals which were tested but not detected included (a)2-butoxy ethanol, (b) bis(2-ethylhexyl) sodium sulfosuccinate and (c) 1-(2-butoxy-1-methylethoxy)-2-propanol. CAS registry numbers, formal chemical names and aliases are publicly available online through the GRIIDC database for this study (https:// doi.org/10.7266/n7-be3h-vd24). 4. Conclusions

Acknowledgements

Overall, we observed that the mean concentration values for bulk parameters, PAHs, BTEX and metals are different among each category. This difference is most prevalent in the sediments matrix for all parameters. We conclude that the mean concentration values for PAHs in the post category are indicative of concentrations following oil landfall whereas the mean concentration values for the prior category are indicative of background concentrations. In general, concentration values for the post category were higher than prior which was higher than unimpacted. This trend is more prevalent for light PAHs than for heavy PAHs consistent with the chemical composition of the light Louisiana crude raw oil. In addition, we conclude the levels of heavy PAHs are due to the sediments adsorbing the heavier compounds which persist longer. Studies have determined that their toxicity may be relevant for several years (Ho et al., 1999; Reed et al., 1999; Reddy et al., 2002). Some statistical differences were observed between the categories. Overall results show that there was a statistically significant difference between post and unimpacted categories for concentrations measured in the sediments matrix. Generally, no overall statistically significant difference was observed between post and prior. Vanadium, flouranthene and phenanthrene, were the exceptions with a statistically significant difference found between post and prior in sediments and water. Possibly the high number of non-detects played a role in the lack of statistically significant difference between the post and prior category for the majority of the chemicals. In effect because a number of analytes in the lower concentration ranges were not detected, the results for the statistical analysis could have been impacted. Specifically, the prior category may have had more samples below the detection limit thus reducing the number of data points available for statistical analysis. The statistical analysis between post and prior categories may also have been

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