Comparison of chemical sediment analyses and field oilingobservations from the Shoreline Cleanup Assessment Technique(SCAT) in heavily oiled areas of former mangrove in Bodo,eastern Niger Delta

Matthijs Bonte1, Erich R. Gundlach2, Ogonnaya Iroakasi3, Kabari Visigah3,Ferdinand Giadom4, Philip Shekwolo3, Vincent Nwabueze3, Mike Cowing5 &Nenibarini Zabbey41 Shell Global Solutions International B.V., 40 Lange Kleiweg, 2288GK Rijswijk, Netherlands2 E-Tech International Inc., Boulder, CO 80302, USA3 Shell Petroleum Development Company of Nigeria Ltd, Port Harcourt, Nigeria4 University of Port Harcourt, Port Harcourt, Nigeria5 Independent Consultant, 56 Temple Road, Epsom KT19 8HA, UK

MB, 0000-0003-3191-9290; KV, 0000-0001-8261-3924; PS, 0000-0001-6012-2884; NZ, 0000-0002-8787-7096*Correspondence: matthijs.bonte@shell.com

Abstract: Trial pitting, borehole drilling, and soil, sediment and groundwater sampling are important components of oil spillresponse and contaminated land assessment. These investigations provide detailed information on the subsurface geology andcontaminant occurrence and transport but have disadvantages including worker safety hazards, cost and time required forcompletion, and may cause cross-contamination among aquifers. An alternative to such investigations applied in oil spillresponse is the Shoreline Cleanup Assessment Technique (SCAT) approach, which relies heavily on direct visual observationsto assess the severity of oil contamination and guide cleanup efforts. Here, we compare SCAT observations of oil type, surfacecoverage and pit oiling with collected surface and subsurface sediment samples taken concurrently and analysed for a suite ofhydrocarbon constituents. Results indicate that although limited sampling and analysis is required to chemically characterizethe contamination, SCAT observations can be calibrated using limited sediment sampling and are sufficient to steer physicalcleanup methods. This is particularly evident as even closely spaced chemical samples show high variability. A coarser directvisual observation is fit-for-purpose considering the wide variability in contaminant distribution at even local levels. In thiscontribution, we discuss the limitations of the different methods.

Supplementary material: The modified SCAT data collection form, figures showing subsurface versus ground surface totalpetroleum hydrocarbons (TPH) and a variogram of TPH measured in the ground surface and subsurface samples, and datatables are available at https://doi.org/10.6084/m9.figshare.c.4534682

Thematic collection: This article is part of the Measurement and monitoring collection available at: https://www.lyellcollection.org/cc/measurement-and-monitoring

Received 28 January 2019; revised 13 May 2019; accepted 10 June 2019

The study area of Bodo Creek, located in the eastern part of theNiger Delta (Fig. 1), was exposed to two oil spillages in 2008resulting from leaks in the Trans Niger Pipeline operated by TheShell Petroleum Development Company of Nigeria Ltd (SPDC).The affected area consists of low-lying mangrove habitat, numeroustidal channels lined with very soft mud, and harder substrates alongthe upper intertidal mainland and island shorelines and alongconstructed fish ponds no longer in use. In 2015, SPDC agreed toremediate 1000 ha of oiled former mangrove areas in the BodoCreek. Phase 1 of the cleanup programme began in September 2017,lasted for about a year, and focused on reducing sediment oiling inthe top 30 cm by using surface agitation (raking) in lessercontaminated areas and deeper low-pressure, high-volume waterflushing in highly contaminated soft mud areas, most commonlyfound along intertidal channels. Phase 1 also included a mangrovereplanting pilot programme to assess seedling survival incontaminated sediments and a coring programme at 30 sites todetermine depth of oiling. Results will be provided in forthcomingpublications. A more intensive Phase 2 remediation and mangrovereplanting programme is scheduled to start in 2019. The damage to

the Bodo area is the largest documented extent of spill-relateddamage to mangroves (Duke 2016; Gundlach 2018) and the largestcleanup of oiled mangrove habitat known to the authors. Likewise,the Shoreline Cleanup Assessment Technique (SCAT) andchemical sampling programmes described herein are also thelargest ever undertaken within oiled mangrove forest habitat.

Before the two primary spills of 2008, the number of reportedspills in the area was relatively small (17 between 1986 and 2008),of which 10 were from illegal activities (Gundlach 2018). Illegalactivities causing spillage include pipeline tapping, connection fromthe tap to a vessel by (leaking) hoses, transport of stolen oil by avariety of open-hulled large (e.g. >30 m) and small wooden vessels,and shore-side refining. After 2010, the number of spills caused byillegal actions increased dramatically (over 30 in 2010–2011).These illegal activities and associated spillages have continued into2018, even as cleanup and the SCAT–chemistry programmes werecontinuing. Mangrove plant recovery since the primary loss in 2008is very limited and the need for planting is evident. An aerial view ofpart of a central portion of the study area in 2015 and 2018 is shownin Figure 2.

© 2019 Shell Global Solutions International BV. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License(http://creativecommons.org/licenses/by/4.0/). Published by The Geological Society of London. Publishing disclaimer: www.geolsoc.org.uk/pub_ethics

Research article Quarterly Journal of Engineering Geology and Hydrogeology

Published online July 23, 2019 https://doi.org/10.1144/qjegh2019-018 | Vol. 53 | 2020 | pp. 19–30

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Themethod to assess the distribution of oil spills and the preferredmethods of cleanup have evolved over several decades: from purelyscientific investigations of surface and subsurface spill extent (e.g.Gundlach & Hayes 1978; Gundlach et al. 1978) to using SCAT.SCAT involves collaborative field teams that include the responsibleparty, regulator, landholder and representative from the localcommunity and non-governmental organizations. Participants allview the site at the same time and make a consensual recommen-dation regarding the appropriate response action (Owens & Sergy2003; Santner et al. 2011). SCAT was also called upon by thecleanup managers who are represented in the Bodo MediationInitiative (BMI) and SPDC to monitor and confirm when the spill-response contractor successfully completes the Phase 1 cleanup. TheBMI was established following a mediation process in 2013 led bythe Dutch Embassy in Nigeria between the Bodo community andSPDC (Zabbey & Arimoro 2017; Sam & Zabbey 2018).

SCAT is in essence a relatively quick, straightforward, robust andparticipatory approach that contrasts with traditional land-based siteassessments, which rely heavily on complex investigations and soil,sediment and groundwater sampling and analyses. Althoughdrilling boreholes, sediment sampling and laboratory analysesprovide detailed information on the subsurface geology andcontaminant occurrence and transport, they have disadvantagesincluding worker safety hazards, cost and time required forcompletion, and may cause cross-contamination in the case ofdeep boreholes that connect different aquifers and are improperlysealed or drilled (Bonte et al. 2015).

Although SCAT clearly has advantages over the traditionaldrilling, sampling and laboratory analysis approach, a comparisonbetween SCAT data and chemical sample analyses has to ourknowledge not been reported in the literature to assess the accuracyof the direct field observations collected during SCAT. To bridgethis gap, this work compares results of SCAT field observationswith those of an extensive chemical sampling programme

undertaken concurrently. It should be noted that this study doesnot assess the tolerance of mangroves to crude oil or total petroleumhydrocarbons (TPH) levels, nor does it determine crude oil derivedcontaminants in fish or other aquatic species. Both topics are thesubject of continuing investigations.

Methods

SCAT surveys

SCAT is a systematic method for surveying an affected shorelinefollowing an oil spill. The SCAT approach was developed duringthe response to the 1989 Exxon Valdez oil spill and has since beenapplied and further refined (e.g. NOAA 2013). It is a viable andpractical technique that maximizes the recovery of oiled habitats andresources while minimizing the risk of further ecological deterior-ation from cleanup efforts.

SCAT field procedures in Bodo initially utilized standard forms(e.g. NOAA 2013; IMO-REMPEC 2009) but these were foundinadequate to accurately describe the contaminated mud–mangroveroot environments of the area affected by the spills. Principalamong the difficulties in using these forms was the time needed tocapture the information when dealing with security issues, boattransport, the diurnal ≥2 m tide that floods the area, very soft mudsmaking walking to each site difficult, and the inability to determinespecific horizons of oiling when oil enters a pit. Themodified SCATdata collection form is included in the supplementary material.Specific to this paper, we use the visual estimation of per cent oil onthe surface and per cent subsurface oil as observed on the surface ofwater in a pit dug to 25–30 cm and after waiting c. 5 min for theincoming water and oil quantity to stabilize. Both surface andsubsurface oil estimations were averaged based on at least threeSCAT members giving their estimation. Oil type and extent ofcoverage (%) on the surface and subsurface area were categorized as

Fig. 1. Location of the Bodo Creek study area along the eastern edge of the Niger River delta in Rivers State, Nigeria. Background satellite image iscopyright Google, image Landsat/Copernicus.

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sheen (silver or rainbow), brown or black oil, or tar or asphalt.Layers of brown oil (probably with some emulsification) and blackoil (no emulsification) are thicker than sheen (Bonn Agreement2017) and would be expected to show higher concentrationsmeasured by chemistry. Figure 3 illustrates the surface andsubsurface oil observations for two SCAT sites.

A combination of surface and subsurface observations were madeto steer the cleanup activities, where surface observationsdetermined whether raking to remove tar, dead stumps andgarbage was needed, and subsurface pit observations determinedthe need for sediment flushing.

Where the percentage coverof blackor brownoil in the pit was 35%or less, the cleanup Phase 1 treatment was confirmed as completed.No treatmentwas performed in areas within c. 3 m of livingmangroveas the presence of living mangroves indicates that the residualweathered oil in the soil is not restricting mangrove growth.

To undertake cleanup and SCAT field monitoring of the BodoCreek oil spill site, the delineated 1000 ha was subdivided into 200m × 200 m (4 ha) grids. Because of channels and presence of livingmangrove, grid size varied from a full 4 ha to much smaller sizes(e.g. tens of m2). Channels and live mangrove areas are not includedin the 1000 ha to be treated. Two cleanup contractors were selectedfor the Phase 1 cleanup encompassing 535 grids. SCATwas appliedfirst to advise on where and how to clean each grid and then tomonitor and verify when Phase 1 cleanup was adequatelyperformed. As cleanup progressed, SCAT categorized the gridand work to be done into four categories: ‘No Treatment’, where pit

oiling was 35% or less and therefore no Phase 1 work was required;‘Pre-Treatment’, where treatment was required (pit oiling 35% orgreater) but not yet started; ‘During Treatment’, where treatmentstarted but pit oiling was still 35% or greater, indicating thatadditional work was required; ‘Post-Treatment’, where pit oilingwas reduced to 35% or less and therefore Phase 1 was completed.Sites were not repeated; for example, sites of ‘Pre-Treatment’ in agrid were not the same as ‘Post-Treatment’. For this reason, thispaper reviews overall trends and is not site specific.

An overview of the work grids and former mangrove area (in red)is shown in Figure 4. This figure also shows the location of theFigure 2 photograph as well as the sites of the two 2008 spillsdiscussed above.

SCAT surveys were undertaken in August 2015 (35 sites) and from18 September 2017 to 30 August 2018 (911 sites). The chemicalsampling team participated with SCAT during the 2015 surveys andfrom September to December 2017. SCAT teams included represen-tatives from federal government (DPR, NAPIMS and NOSDRA),state government (RSMENV), SPDC-ORP, BMI, the Bodo commu-nity, non-governmental organizations (NACGOND) and the twocleanup contractors. Participating personnel and organizations, withfull versions for acronyms, are provided in the Acknowledgements.

Chemical sampling

Previous chemical sampling in the area is limited (UNEP 2011;Linden & Palsson 2013; Little et al. 2018). For this study, sediment

Fig. 2. Overview of the central portion ofthe Bodo Creek study area showing livingand dead (former) mangrove area.(a) photograph taken on 15 September2015 (V. Imevbore photographer); (b)Google Earth image dated 2 January2018. The location of the photograph isshown in Figure 4.

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sampling for chemical analyses was carried out before, during andafter the first phase of cleanup in conjunction with the above-described SCAT site investigations. Sampling sites were selected toensure a wide coverage of habitats, locations and oiling conditions.Figure 5 provides an overview of the location of sites reviewed inthis paper.

At each chemical sample site, separate composites of five grabsamples were taken from the surface (0–5 cm depth) and subsurface(15–25 cm depth). One of the five pits was used for the SCAT visualsurface and subsurface observations. The two selected samplingdepths are based on the observations from 2015 surveys, whichindicated that the most heavily oiled sediments are located in the top30 cm and that the associated cleanup method should predomin-antly focus on this depth of oiling.

Figure 6 illustrates field-sampling activities. A clean stainless-steel spoon was used to take sediment from each of five surface andsubsurface locations spaced equally within c. 5 m around a centrepoint. The subsamples from surface and subsurfacewere placed intoseparate and clean stainless-steel bowls. After thorough mixing, acomposite sample was packaged and shipped with accompanying

Chain of Custody documentation for laboratory chemical analysisby methods detailed below. Pits were backfilled following SCATdata recording and site photography. In total, we utilize data from317 SCAT sites that have observations of surface and subsurfaceoiling and results from chemical analysis.

In addition to the composite sampling, for every 20 samples ablind duplicate was collected consisting of a homogenized sampledivided into two subsamples. Each duplicate portion was assignedits own sample number to be unknown to the analytical laboratory.At four sites, the five individual (discrete) samples used for thecomposite sample were analysed independently for comparison toassess small-scale variability.

This sampling plan follows the ISO 10381 standard for soilquality sampling (ISO 2002). Laboratory analyses were carried outusing certified methods (MCERTS) with associated laboratoryquality assurance and quality control procedures. The 2015 sampleswere analysed by Alcontrol (UK) whereas the 2017 samples wereanalysed by I2 (UK).

Samples were analysed for total and fractionated TPH, fractionbanding as defined by the Total Petroleum Hydrocarbon

Fig. 3. Overview of SCAT observations atsites L10-1 (top) and I49-1 (bottom) withpits, SCAT surface and subsurface oilingobservations, and surface and subsurfaceTPH (mg kg−1). BO + BRO indicatesblack oil + brown oil. The locations of thesites are shown in Figure 4. Photographsby E. Gundlach.

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Criteria Working Group (TPHCWG 1998–1999), by gas chroma-tography–flame ionization detector (GC-FID) for non-volatilehydrocarbons and by gas chromatography–mass spectrometry (GC-MS) for volatile hydrocarbons as well as individually reportedbenzene, toluene, ethylbenzene and xylene (BTEX) components.Sixteen USEPA (2003) priority polynuclear aromatic hydrocarbons(PAH) were analysed individually and as a sum parameter.

The statistical significance of the difference in TPH concentra-tions between different treatment classes (i.e. No Treatment, DuringTreatment and Post-Treatment, respectively) and the Pre-Treatmentconcentration was determined with the nonparametric Mann–Whitney U test. The statistical significance of the differences inTPH concentrations for the different SCAT observation groups (i.e.oil sheen, brown oil and black oil) was tested with a one-wayANOVA test. Statistical calculations were performed using thePython SciPymodule (Anonymous 2018). The spatial data structurewas investigated by constructing variograms using the PythonPykrige module (Anonymous 2019).

Nigerian regulations relating to oil spill cleanup and remediationare described in Environmental Guidelines and Standards for thePetroleum Industries in Nigeria issued in 1992 and updated in 2018

(EGASPIN 2018). EGASPIN provides a tiered approach todetermine corrective requirements based on the Standard Guidefor Risk-based Corrective Action Applied at Petroleum Sitesprepared by ASTM (2015). The first tier comprises interventionof ≥5000 mg kg−1, which is derived for a long-term residentialexposure scenario. The higher tier assessments (tiers 2 and 3) aremore complex in that they consider receptors such as persons livingnear or using the area of contamination. Tier 2 and 3 assessments arebeing carried out to derive site-specific target levels, which willprovide risk-based criteria that are considerate of local circum-stances and are the topic of a subsequent publication. Some of theshorelines, particularly in areas close to Bodo, are used by fishers ona regular basis. Most of the other damaged areas are visitedrelatively infrequently.

Results and discussion

Distribution of TPH concentrations

Figure 7 and Table 1 present results of total TPH concentrations ofsurface and subsurface samples as box–whisker plots for each of the

Fig. 4. Phase 1 work area outline inyellow with grids, former mangrove, livemangrove, water and illegal refineriesindicated. The yellow arrow indicateslocation of the photograph in Figure 2.The location of the ‘Spills 2008’ thatinitiated these cleanup and samplingactivities is also shown. The labels on thex- and y-axes of the map representgeographical coordinates (latitude andlongitude) in decimal degrees.

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four Phase 1 treatment conditions: No Treatment, Pre-Treatment,During Treatment and Post-Treatment. Median and mean TPHconcentration before Phase 1 cleanup (Pre-Treat) for the ground (G)surface samples are c. 35 000 and 50 000 mg kg−1, respectively,with the 25%-ile and 75%-ile at 17 700 and 68 000 mg kg−1. Meanvalues are typically higher than the median (50%-ile) because of‘hotspots’, which bias the mean upward. Surface concentrations areconsiderably higher than subsurface values, with the latter havingmedian and mean concentrations of 12 250 and 30 300 mg kg−1

with the 25%-ile and 75%-ile at 2448 and 39 250 mg kg−1. It isinteresting to note that although subsurface sediment concentrationsare consistently lower than surface concentrations, the correlationbetween surface and subsurface collected at the same location isvery poor (R2 = 0.12; Fig. SI-1 in supplementary material), high-lighting the high degree of heterogeneity. The results of vibracoringand analyses undertaken in September–October 2018 at depths of2–4 m reveal that the vast majority of petroleum hydrocarbon impactis restricted to depths of <50 cm (data not reported here).

There is no clear spatial pattern present in the TPH concentrations(Fig. 5), other than lower concentrations present in the far southeven though that area is close to the location of one of the 2008 spills(shown as a star at lower left in Fig. 4). High concentrations are

found throughout the remaining area, probably owing to the spreadof the initial spills as well as continued illegal activities. Theabsence of a spatial structure in the data is confirmed by thevariograms that were constructed for TPH analyses from groundsurface and subsurface samples and the exponential variogrammodel that was fitted to the data (supplementary material, Fig. SI-2).The fitted variogram models plot as horizontal lines with a nugget-to-sill ratio of close to unity, which confirms the absence of anyspatial structure in the data (Cambardella et al. 1994).

BTEX constituents and light aliphatic constituents are absent orsignificantly depleted from all samples as compared with analysesof unweathered Bonny Light crude oil. The 16 USEPA PAH sumconcentrations of all but one sample are below the Nigerianregulatory (EGASPIN) threshold of 40 mg kg−1 (which is actuallyfor a subset of 10 PAH and not all 16 PAH defined by USEPA) andare not further discussed.

Post-Treatment TPH values show a statistically significant (P <0.01) reduction for the subsurface samples with the medianconcentrations decreasing from 12 250 to 7250 mg kg−1.However, the median concentrations for the surface samples showan increase from 35 000 to 39 500 mg kg−1, which was, however,not statistically significant. The No Treatment sites have statistically

Fig. 5. Sampling locations (n = 317) withcolours indicating TPH concentrations forsurface (downward pointing triangle) andsubsurface (upward pointing triangle)concentrations. The labels on the x- andy-axes of the map represent geographicalcoordinates (latitude and longitude) indecimal degrees.

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Fig. 6. Photographs showing sampling in grid cell P09 (4.62582N, 7.26295E) near a live mangrove. (a) Collection of field grab samples using a steelshovel; these samples are composited in a stainless-steel bowl and homogenized. (b) Close-up of the pit (c. 0.3 m across) showing droplets of oil.(c) Homogenization of grab samples. (d) Transfer of composite sample to laboratory supplied sample containers, which were subsequently transferredto ice boxes and transported to the laboratory. Photographs by M. Bonte.

Fig. 7. Box–whisker plots of TPHconcentrations (in mg kg−1; log scale) forsurface (G) and subsurface (S) samplesunder four different conditions: NoTreatment, Pre-Treatment, DuringTreatment and Post-Treatment. Thenumber above the top x-axis representsthe number of samples in each class. Thebox extends from the lower to upperquartile values of the data, with anorange line at the median (or 50%-ile).The whiskers extend from the box toshow the range of the data between the 5-and 95%-ile. The green triangle showsthe mean value. Flier points are thosepast the end of the whiskers. An asteriskabove a box plot indicates that themedian is significantly different at a 99%confidence level (P < 0.01) from the Pre-Treatment value.

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significant lower TPH concentrations for subsurface samplescompared with the Pre-Treatment samples whereas for surfacesamples no statistical difference is found.

The fractionated TPH data show that relatively heavy TPHfractions (>C16) dominate both the Pre- and Post-Treatmentconditions (Fig. 8). Compared with unweathered Bonny Lightcrude oil, the sediments are enriched in heavier fractions as a resultof weathering (combined volatilization and biodegradation) thatpreferentially removed the more volatile and biodegradablecompounds (Brown et al. 2017a). In particular, the heavier aromaticfractions have been enriched, consistent with findings for landfarming trials showing that although biodegradation decreasesconcentrations of all TPH fractions, the rate of depletion is lower forheavier fractions compared with lighter fractions (Brown et al.2017b). The distribution of different TPH fractions is similar bothbetween surface and subsurface samples as well as for Pre-Treatment and Post-Treatment conditions. The physical cleanupmethod applied mobilizes free oil but does not preferentially removethe lighter TPH fractions (unlike biodegradation).

Data reproducibility and variability

For TPH, an average relative percentage difference (RPD)between primary and blind duplicate samples was determined tobe 63% (Fig. 9), above generally accepted criteria rangingbetween 40 and 50% (e.g. NJDEP 2014; IDEQ 2017). The likelyexplanation for the large RPD is that homogenization of thecomposite samples was complicated by the texture of sediment

consisting of a mixture of detritus (e.g. dead mangrove roots) andclumps of clay-to-sand-sized sediments. Additionally, contam-ination was present in the form of isolated pockets of residual freeoil droplets scattered through the sediment, which combined withthe problematic homogenization may cause large differencesbetween different parts in the composite sample (see photographin Fig. 6b).

To assess small-scale variability from the same sample site, thefive individual grab samples used for the composite sample wereanalysed for four sites. Samples were collected within 5 m of acentre point. Results show that TPH is highly variable on a smallscale (Fig. 10). The mean of the individual discrete samples deviatesconsiderably from that of composite sample from the same site. Thisis in line with the previously discussed results of blind duplicatesamples, further suggesting that the high RPD in blind duplicatesampling was a result of incomplete homogenization. The relativestandard deviation (ratio of standard deviation and mean) rangedbetween 29 and 111%, and 33% and 96% for the surface andsubsurface samples, respectively. This is in the same order ofmagnitude as the average RPD for the set of blind duplicatesamples.

The small-scale variability in the presence of crude oil insediments that is observed visually through the SCAT process wasinvestigating by replicate pit oiling observations in five SCAT pitswithin a 6 m radius (data shown in Table SI-3 in supplementarymaterial). Similar to the TPH observations, these data demonstrate ahigh variability in the observed oil coverage in pits with valuesranging between 5 and 95% coverage. The relative standard

Table 1. TPH (mg kg−1) for surface and subsurface sediment samples

No Treatment Pre-Treatment During Treatment Post-Treatment

Ground surfaceMean 40 300 49 500 93 200 62 60025% percentile 38 000 17 700 37 500 13 97550% percentile (median) 38 000 35 000 55 000* 39 50075% percentile 41 500 68 000 82 750 87 750Number of samples 3 121 26 163SubsurfaceMean 600 30 300 21 700 17 40025% percentile 450 2448 1915 222350% percentile (median) 690* 12 250 6800 7250*75% percentile 810 39 250 19 800 23 700Number of samples 3 122 24 169

*The median is significantly different at a 99% confidence level (P < 0.01) from the Pre-Treatment value.

Fig. 8. TPH fraction distribution for freshBonny Light crude oil and groundsurface and subsurface sediment samples,before and after cleanup. Ali, aliphatics;Aro, aromatics. Bonny #1 shows crudeoil fractions from Brown et al (2017b).G, surface samples; S, subsurfacesamples; PreT, Pre-Treatment conditions;PostT, Post-Treatment conditions.

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deviation of these observations ranges between 5 and 183%, with anaverage RSD of 62%.

Overall, the blind duplicate and discrete samplings, and replicateSCAT pit observations, show that TPH concentrations are highlyvariable over short distances. The highly heterogeneous nature ofTPH concentrations is probably the result of both the continued andspatially variable impacts to the area over the past 10 years

(including during and after the spills in 2008), the dynamic tidalenvironment causing variable oil settlement on intertidal sediments,and the nature of the mud-dominated sediments inhibiting internaloil movement with depth. The small-scale variability should,however, also be placed in the context of the overall variation inTPH concentrations, which ranges over three orders of magnitude asdiscussed further below.

Fig. 9. Results from blind duplicatesampling and analysis (n = 28).

Fig. 10. TPH (in mg kg−1) from fivediscrete samples taken within 5 m of acentral point at each site. Grey bars showTPH, the red line shows the mean of thefive discrete samples and the green lineshows the composite sample result fromthe same site.

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Relation between chemistry and visual field observations

Comparisons between oil type and TPH concentrations arepresented in Figures 11 and 12. In addition, Tables SI-1 and SI-2in the supplementary material summarize the key statistics for TPHconcentrations grouped by oil type. The comparison between SCATdescribed oil type and TPH concentrations indicates a goodcorrelation for subsurface samples (Fig. 11, lower panel). Thedifferent SCAT groups have statistically different TPH concentra-tions, which is confirmed by an ANOVA test. Observations of‘silver sheen’ in the waters of a 25–30 cm test pit have a medianTPH value of slightly less than 2000 mg kg−1 and a 75%-ile valueof c. 4000 mg kg−1, whereas brown and black oil have highermedian TPH values of 8000 and 20 000 mg kg−1, respectively. Therelative standard deviation (RSD) of TPH in the classes for thesubsurface shown in Figure 11 range between 115% for black oiland 219% for silver sheen (Table SI-1 in supplementary material),which is higher than that of the small-scale sampling discussed inthe previous section (33–96%). The higher RSD of TPHconcentrations within each SCAT category can be caused by acombination of the following factors.

(1) SCAT observations were made in one pit whereas theanalysed composite sediment sample consisted of five grabsamples. For future work, it is recommended to take the averageof three SCAT pits to account for the high degree of variability.

(2) Sheening and the presence of droplets of oil in a SCAT pitcan occur over a range of TPH concentrations. This implies thatSCAT observations are not expected to provide an exact proxy forTPH analyses, but rather that a certain SCAT observation canprovide a bandwidth of TPH concentrations.

The ground surface samples do not show a correlation betweenSCAT field observations and chemical analysis results (Fig. 11,upper panel); this is confirmed by the ANOVA test, which showedno statistical difference in TPH concentrations for the differentSCAT groups. The difference between ground surface andsubsurface TPH correlations with SCAT observations is probablythe result of the black organic-rich type colour of the sediment(believed to be a result of the high organic matter content andsulphate-reducing conditions) on which it is hard to differentiateweathered oil from sediment. Field reports indicate that only whenoil was seen pooled on water or where oiling was fresh (black) couldit be clearly seen on the sediment surface, whereas the oiling of pitwaters could be discerned more easily and more consistently. Inaddition, the surface sample included sediments 0–5 cm deep,whereas the visual observation was solely on the sediment surface(0 cm deep) and therefore does not include oil incorporated into thesediment.

Pit oiling of ≤25% brown or black oil may be one criterion toprovide a visual means to confirm completion of Phase 2 treatment

Fig. 11. Box–whisker plots of TPHconcentrations (in mg kg−1; log scale) fordifferent oil types as delineated by SCATsurveys for surface and subsurfacesamples. The box extends from the lowerto upper quartile values of the data, withan orange line at the median (or 50%-ile). The whiskers extend from the box toshow the range of the data between the 5-and 95%-ile. Flier points are those pastthe end of the whiskers. The numberabove the upper x-axis indicates thenumber of samples in each group.

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(compared with the performance criterion of <35% applied in Phase1). This is believed to represent an oil level that can practicably beachieved with flushing and physical agitation. In parallel to SCATand cleanup activities, pilot replanting of mangrove seedling isbeing carried out to determine if this cleanup target is adequate. Acomparison of SCAT data and chemical data shows that the visualcriterion corresponds to a median subsurface concentration of c.4000 mg kg−1 (Fig. 12). For 75% of the sites sampled Post-Treatment (upper box limit), a TPH concentration <10 000 mg kg−1

was found. The sites that do not meet the criterion have a 50%-ileand 75%-ile concentration of 20 000 and 35 000 mg kg−1. TPHconcentrations measured in ground surface samples are muchhigher, reflecting the inability of SCAT observations to reliablydetect surface oiling as compared with chemical values taken from0–5 cm depth.

Conclusions

Results show that the assessed cleanup area is affected byhydrocarbons that are widely spread and highly variable bothspatially and with depth. Spatial variability is both on a broad scaleover the entire work area (several kilometres in length) and on verysmall scales (within 10 m). Chemical sampling provides importantdata to characterize the hydrocarbons present, demonstrating thatthey are primarily composed of highly weathered crude oil, enriched

in heavy hydrocarbons compared with unweathered Bonny Lightcrude oil and with low BTEX and PAH components. The highspatial variability that is present on both small and large scales,however, limits the applicability to directly steer and confirmcompletion of cleanup activities using a single chemical-basedanalytical criterion. Given this variability, SCAT pit observationscan be considered sufficiently reliable to guide confirmation ofPhase 2 cleanup in Bodo in conjunction with a risk assessmentaccording to EGASPIN requirements. Another alternative beingreviewed is to reduce the level of contamination in the sedimentssufficient that mangroves can be replanted and survive, aiding thefurther degradation of remaining oil through phytoremediationprocesses. Results from seven planted sites after 1 year show 82%survival of 346 seedlings planted and good growth (mean: +46%plant height) in spite of high TPH levels (2210–87 000 mg kg−1

surface, 420–59 000 mg kg−1 subsurface). Continued spillages,however, will affect and kill replanted mangroves.

We conclude that for the relatively large affected area under studyhere, with challenges pertaining to site access and sample shipment,visual SCAT observations can effectively be calibrated with alimited set of chemical field data. As such, the study provides anexample of the application of SCAT as a speedy and reliable modelto assess and manage cleanup endeavours in mangrove and tidal flatenvironments. The comparison between SCAT observations and thesediment chemical characterization did, however, reveal that SCAT

Fig. 12. Box–whisker plots of TPHconcentrations (in mg kg−1; log scale)of Post-Treatment samples for differenttypes of SCAT surface and subsurface oilobservations classified according to aproposed close-out criterion for Phase 2under which SS plus BO ≤25%. SS,silver sheen; BO, black oil; BRO,brown oil.

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observations for surface oiling are a poor indicator for surficialsediment TPH concentration. This contrasts with subsurface (or pit)SCAT observations, which showed a much better correlation withTPH concentrations. Calibration of SCAT observations (pureproduct or sheen type) to sediment chemical analyses is alsoprobably effective for rapid field assessment of other hydrocarbonproducts (e.g. different crude oils or gasoline spills), but thisrequires further work given that the solubility and visual appearancewill vary.

Acknowledgements We would like to thank many people whoparticipated in this programme for their persistence and good humour in spiteof very difficult circumstances. These include: from the Bodo community:P. K. Lenu, N. N. Vite and F. Zorbilade; from the Bodo Mediation Initiative(BMI) Directorate: P. Kpenu and O. Nwokoro; from the Department of PetroleumResources (DPR): B. Atiyegoba; from the National Oil Spill Detection andResponse Organisation (NOSDRA): S. Oladipo, S. Ogwuche and A. Ismail B.;from National Petroleum Investment Management Services (NAPIMS): T. Etok;from Rivers State Ministry of Environment (RSMENV): H. Chukwu, Mankie-Tanen B. and C. Atteng, from The Shell Petroleum Development Company ofNigeria Ltd (SPDC): A. Chibuzor, D. Essien, F. Igbodo, I. Olere, J. Edeh,O. Sowagbein, T. Mzaga and V. Chukwudi; from National Coalition on GasFlaring and Oil Spills in the Niger Delta (NACGOND): N. Zabbey; from cleanupcontractor Giolee-Lamor: A. McArthur, A. Dumbari, C. Alozie, S. Rickaby,L. Deeko, F. Beubizua, P. Parigas, K. Ekweozor and A. Abudun; from contractorInkas: A. Cooke, S. Johnson, F. Nwabueze, J. Shekwolo, B. Kpenu, C. Dogalah,P. M. Baabel and Z. K. Noble; and from the chemical sampling crew(GeoTerrain): K. Adesida, D. Adebayo, P. Ugbehe, J. Olanrewaju, D. Yisa,S. Ademiluyi, N. West (née Aburo), S. Babatunde, B. Ismail, E. Amadi,F. IhedIohanma, I. Emefo, K. Briggs, F. Uche (née Uruakpa) and M. Taiwo.Thanks also go to I2 laboratory and ALcontrol laboratory (UK) for undertakingthe chemical analysis. Finally, we thank J. Smith and G. Devaull of Shell GlobalSolutions for providing a thorough review of the draft paper. The views expressedare those of the authors and may not reflect the policy or position of SPDC or itsJoint Venture partners.

Funding The Shell Petroleum Development Company of Nigeria Ltd(SPDC) funded the study and cleanup activities described in this paper.

Scientific editing by Stephen Buss; Birgitta Beuthe

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