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Quarterly Journal of Engineering Geology

and Hydrogeology

Comparison of Chemical Sediment Analyses and Field Oiling

Observations from the Shoreline Cleanup Assessment

Technique (SCAT) in Heavily Oiled Areas of Former

Mangrove in Bodo, eastern Niger Delta

Matthijs Bonte, Erich Gundlach, Ogonnaya Iroakasi, Kabari Visigah, Ferdinand

Giadom, Philip Shekwolo, Vincent Nwabueze, Mike Cowing & Nenibarini

Zabbey

DOI: https://doi.org/10.1144/qjegh2019-018

Received 28 January 2019

Revised 13 May 2019

Accepted 10 June 2019

© 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

Supplementary material at https://doi.org/10.6084/m9.figshare.c.4534682

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Comparison of Chemical Sediment Analyses and Field Oiling Observations from the Shoreline

Cleanup Assessment Technique (SCAT) in Heavily Oiled Areas of Former Mangrove in Bodo,

eastern Niger Delta

Running Header: SCAT versus Sediment Sampling and Chemical Analyses

Matthijs Bonte1, Erich Gundlach2, Ogonnaya Iroakasi3, Kabari Visigah3, Ferdinand Giadom4, Philip

Shekwolo3, Vincent Nwabueze3, Mike Cowing5, Nenibarini Zabbey4

1 Shell Global Solutions International B.V. – 40 Lange Kleiweg, 2288GK Rijswijk, the Netherlands 2 E-Tech International Inc. – 161 Horsfall Street, Boulder, Colorado 80302 USA 3 Shell Petroleum Development Company of Nigeria Ltd., Port Harcourt, Nigeria 4 University of Port Harcourt, Nigeria 5 Independent Consultant - 56 Temple Road, Epsom, Surrey, KT19 8HA, UK.

*Correspondence: [email protected]

Abstract

Trial pitting, borehole drilling, and soil, sediment and groundwater sampling are important

components of oil spill response and contaminated land assessment. These investigations provide

detailed information of the subsurface geology and contaminant occurrence and transport but have

disadvantages including worker safety hazards, cost and time required for completion, and may

cause cross-contamination among aquifers. An alternative to such investigations applied in oil spill

response is the Shoreline Cleanup Assessment Technique (SCAT) approach, which relies heavily on

direct visual observations to assess the severity of oil contamination and guide cleanup efforts.

Here, we compare SCAT observations of oil type, surface coverage and pit oiling to collected surface

and subsurface sediment samples taken concurrently and analysed for a suite of hydrocarbon

constituents. Results indicate that while limited sampling and analysis is required to chemically

characterize the contamination, SCAT observations can be calibrated using limited sediment

sampling and is sufficient to steer physical cleanup methods. This is particularly evident as even

closely spaced chemical samples show high variability. A coarser direct visual observation is fit-for-

purpose considering the wide variability in contaminant distribution at even local levels. In this

contribution, we discuss the limitations of the different methodologies.

Key Works: oil spills, spill assessment, Niger delta, SCAT, mangrove

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mailto:[email protected]


The study area of Bodo Creek, located in the eastern part of the Niger Delta (Fig. 1), was exposed to

two oil spillages in 2008 resulting from leaks in the Trans Niger Pipeline operated by The Shell

Petroleum Development Company of Nigeria Ltd (SPDC). The affected area consists of low-lying

mangrove habitat, numerous tidal channels lined with very soft mud, and harder substrates along

the upper intertidal mainland and island shorelines and along constructed fish ponds no longer in

use. In 2015, SPDC agreed to remediate 1,000 ha of oiled former mangrove areas in the Bodo Creek.

Phase 1 of the cleanup program began in September 2017, lasted for about a year, and focused on

reducing sediment oiling in the top 30 cm by using surface agitation (raking) in lesser contaminated

areas and deeper low-pressure, high-volume water flushing in highly contaminated soft mud areas,

most commonly found along intertidal channels. Phase 1 also included a mangrove replanting pilot

program to assess seedling survival in contaminated sediments and a coring program at 30 sites to

determine depth of oiling. Results will be provided in forthcoming publications. A more intensive

Phase 2 remediation and mangrove replanting program is scheduled to start in 2019. The damage to

the Bodo area is the largest documented extent of spill-related damage to mangroves (Duke 2016;

Gundlach 2018) and the largest cleanup of oiled mangrove habitat known to the authors. Likewise,

the SCAT and chemical sampling programs described herein are also the largest ever undertaken

within oiled mangrove forest habitat.

Before the two primary spills of 2008, the number of reported spills in the area was

relatively uncommon (17 between 1986 and 2008), of which 10 were from illegal activities

(Gundlach, 2018). Illegal activities causing spillage include pipeline tapping, connection from the tap

to a vessel by (leaking) hoses, transport of stolen oil by a variety of open-hulled large (e.g. >30 m)

and small wooden vessels, and shore side refining. After 2010, the number of spills caused by illegal

actions increased dramatically (over 30 in 2010-2011). These illegal activities and associated

spillages have continued into 2018, even as cleanup and the SCAT/chemistry programs were

ongoing. Mangrove plant recovery since the primary loss in 2008 is very limited and the need for

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planting is evident. An aerial view of part of a central portion of the study area in 2015 and 2018 is

shown in Fig. 2.

The methodology to assess the distribution of oil spills and preferred methods of cleanup has

evolved over several decades: from purely scientific investigations of surface and subsurface spill

extent (e.g. Gundlach et al. 1978; Gundlach & Hayes 1978) to using SCAT. SCAT involves

collaborative field teams that include the responsible party, regulator, landholder, and

representative from the local community and non-governmental organizations. Participants all view

the site at the same time and make a consensual recommendation regarding the appropriate

response action (Owens & Sergy 2003; Santner et al. 2011). SCAT was also called upon by the

cleanup managers who are represented in the Bodo Mediation Initiative (BMI) and SPDC to monitor

and confirm when the spill-response contractor successfully completes the Phase 1 cleanup. The

BMI was established following a mediation process in 2013 led by the Dutch Embassy in Nigeria

between Bodo community and SPDC (Zabbey and Arimoro, 2017; Sam & Zabbey, 2018)

SCAT is in essence a relatively quick, straight-forward, robust and participatory approach which

contrasts with traditional land-based site assessments which rely heavily on complex investigations

and soil, sediment and groundwater sampling and analyses. Although drilling boreholes, sediment

sampling and laboratory analyses provide detailed information of the subsurface geology and

contaminant occurrence and transport, they have disadvantages including worker safety hazards,

cost, time required for completion, and may cause cross-contamination in case of deep boreholes

which connect different aquifers and are improperly sealed or drilled (Bonte et al. 2015).

Although SCAT clearly has advantages to the traditional drilling, sampling and laboratory analysis

approach, a comparison between SCAT data and chemical sample analyses has to our knowledge not

been reported in literature to assess the accuracy of the direct field observations collected during

SCAT. To bridge this gap, this work compares results of SCAT field observations to those of an

extensive chemical sampling program undertaken concurrently. Note that this report does not

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assess the tolerance of mangroves to crude oil or TPH levels, nor does it determine crude oil derived

contaminants in fish or other aquatic species. Both topics are subject of on-going investigations.

METHODS

SCAT Surveys

SCAT is a systematic method for surveying an affected shoreline following an oil spill. SCAT

methodology was developed during the response to the 1989 Exxon Valdez oil spill and has since

been applied and further refined (e.g. NOAA, 2013). It is a viable and practical technique that

maximizes the recovery of oiled habitats and resources, while minimizing the risk of further

ecological deterioration from cleanup efforts.

SCAT field procedures in Bodo initially utilized standard forms (e.g. NOAA 2013; IMO-

REMPEC 2009) but these were found inadequate to accurately describe the contaminated mud /

mangrove root environments of the area impacted by the spills. Principal among the difficulties in

using these forms was the time needed to capture the information when dealing with security

issues, boat transport, the diurnal 2+ m tide that floods the area, very soft muds making walking to

each site difficult, and the inability to determine specific horizons of oiling when oil enters a pit. The

modified SCAT data collection form is included in the supplementary material. Specific to this paper,

we use the visual estimation of percent oil on the surface and percent subsurface oil as observed on

the surface of water in a pit dug to 25 to 30 cm and after waiting approximately five minutes for the

incoming water and oil quantity to stabilize. Both surface and subsurface oil estimations were

averaged based on at least three SCAT members giving their estimation. Oil type and extent of

coverage (%) on the surface and subsurface area were categorized as sheen (silver or rainbow),

brown or black oil, or tar/asphalt. Layers of brown oil (likely with some emulsification) and black oil

(no emulsification) are thicker than sheen (Bonn Agreement 2017) and would be expected to show

higher concentrations measured by chemistry. Fig 3 illustrates the surface and subsurface oil

observations for two SCAT sites.

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A combination of surface and subsurface observations was observed to steer the cleanup

activities where surface observations determined whether raking to remove tar, dead stumps and

garbage was needed, and subsurface pit observations determined the need for sediment flushing.

Where the percentage cover of black or brown oil in the pit was 35% or less, the clean-up

Phase 1 treatment was confirmed as completed. No treatment was performed in areas within ~3 m

of living mangrove as the presence of living mangroves indicates that the residual weathered oil in

the soil is not restricting mangrove growth.

To undertake cleanup and SCAT field monitoring of the Bodo Creek oil spill site, the

delineated 1,000 ha was subdivided into 200 x 200 m (4 ha) grids. Because of channels and presence

of living mangrove, grid size varied from a full 4 ha to much smaller sizes (e.g. 10’s of m2). Channels

and live mangrove areas are not included in the 1,000 ha to be treated. Two cleanup contractors

were selected for the Phase 1 cleanup encompassing 535 grids. SCAT was applied first to advise on

where and how to clean each grid and then to monitor and verify when Phase 1 cleanup was

adequately performed. As cleanup progressed, SCAT categorized the grid and 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% or greater) but not yet

started; ‘During Treatment’ where treatment started but pit oiling was still 35% or greater, indicating

that additional work was required; and ‘Post-Treatment’ where pit oiling was reduced to 35% or less

and therefore Phase 1 was completed. Sites were not repeated; e.g. sites of ‘Pre-Treatment’ in a

grid were not the same as ‘Post Treatment’. For this reason, this paper reviews overall trends and is

not site specific.

An overview of the work grids and former mangrove area (in red) is shown in Fig. 4. The

same figure also shows location of the Fig. 2 photograph as well as the sites of the two 2008 spills

discussed previously.

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SCAT surveys were undertaken in August 2015 (35 sites) and from 18 September 2017 to 30

August 2018 (911 sites). The chemical sampling team participated with SCAT during the 2015

surveys and from September to December 2017. SCAT teams included representatives from federal

government (DPR, NAPIMS and NOSDRA), state government (RSMENV), SPDC-ORP, BMI, the Bodo

community, non-governmental organisations (NACGOND) and the two cleanup contractors.

Participating personnel and organizations with acronyms are provided in the acknowledgements.

Chemical Sampling

Previous chemical sampling in the area is limited (Linden & Palsson 2013; Little et al. 2018; UNEP

2011). For this study, sediment sampling for chemical analyses was carried out before, during and

after the first phase of cleanup in conjunction with the above described SCAT site investigations.

Sampling sites were selected to ensure a wide coverage of habitats, locations, and oiling conditions.

Fig. 5 provides an overview of the location of sites reviewed in this paper.

At each chemical sample site, separate composites of five grab samples were taken from the

surface (0-5 cm depth) and subsurface (15-25 cm depth). One of the five pits was used for SCAT’s

visual surface and subsurface observations. The two selected sampling depths are based on the

observations from 2015 surveys, which indicated that the most heavily oiled sediments are located

in the top 30 cm and that the associated cleanup methodology should predominantly target this

depth of oiling.

Fig. 6 illustrates field-sampling activities. A clean stainless-steel spoon was used to take

sediment from each of five surface and subsurface locations spaced equally within ~5 m around a

center point. The sub-samples from surface and subsurface were placed into separate and clean

stainless steel bowls. After thorough mixing, a composite sample was packaged and shipped with

accompanying Chain of Custody documentation for laboratory chemical analysis by methods

detailed in the next sections. Pits were backfilled following SCAT data recording and site

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photography. In total, we utilize data from 317 SCAT sites that have observations of surface and

subsurface oiling and results from chemical analysis.

In addition to the composite sampling, for every 20 samples a blind duplicate was collected

consisting of a homogenized sample divided in two sub-samples. Each duplicate portion was

assigned its own sample number to be unknown to the analytical laboratory. At four sites, the five

individual (discrete) samples used for the composite sample were analysed independently for

comparison to assess small-scale variability.

This sampling plan follows the United States Environmental Protection Agency (US EPA)

methods and the ISO 10381 standard for sediment quality sampling. Laboratory analyses were

carried out using certified methodologies (MCERTS) issued by the Environment Agency (England)

with associated laboratory QA/QC procedures. The 2015 samples were analysed by Alcontrol (UK)

while the 2017 samples were analysed by I2 (UK).

Samples were analysed for total and fractionated TPH (total petroleum hydrocarbons),

fraction banding as defined by Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG

1998-1999), for non-volatile hydrocarbons by gas chromatography - flame ionization detector (GC-

FID) and by gas chromatography – mass spectrometry (GC-MS) for volatile hydrocarbons as well as

individually reported benzene, toluene, ethylbenzene and xylene (BTEX) components. US EPA 16

(2003) priority polynuclear aromatic hydrocarbons (PAH) were analysed individually and as a sum

parameter.

The statistical significance of the difference in TPH concentrations between different

treatment classes (i.e. No Treatment, During Treatment and Post Treatment, respectively) and the

Pre-Treatment concentration was determined with the nonparametric Mann Whitney U (MWU) test.

The statistical significance of the differences in TPH concentrations for the different SCAT

observation groups (i.e. oil sheen, brown oil and black oil) was tested with a one-way ANOVA test.

Statistical calculations were performed using the Python SciPy module (Anonymous, 2018). The

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spatial data structure was investigated by constructing variograms using the Python Pykrige module

(Anonymous, 2019).

Nigerian regulations relating to oil spill cleanup and remediation are described in

Environmental Guidelines and Standards for the Petroleum Industries in Nigeria issued in 1992 and

updated in 2018 (EGASPIN 2018). EGASPIN provides a tiered approach to determine corrective

requirements based on the Standard Guide for Risk-based Corrective Action Applied at Petroleum

Sites prepared by ASTM (2015). The first tier comprises intervention of ≥ 5000 mg/kg which is

derived for a long-term residential exposure scenario. The higher tier assessments (tiers 2 and 3) are

more complex in that it considers receptors such as persons living near or using the area of

contamination. Tier 2 and 3 assessments are being carried out to derive site specific target levels,

which will provide risk-based criteria that are considerate of local circumstances and are the topic of

a subsequent publication. Some of the shorelines, particularly in areas close to Bodo, are used by

fishers on a regular basis. Most of the other damaged areas are visited relatively infrequently.

RESULTS AND DISCUSSION

Distribution of TPH Concentrations

Fig. 7 and Table 1 present results of total TPH concentrations of surface and subsurface samples as

box-whisker plots for each of the four Phase 1 treatment conditions: No Treatment, Pre-Treatment,

During Treatment and Post-Treatment. Median and mean TPH concentration before Phase 1

cleanup (Pre-Treat) for the ground (G) surface samples are approximately 35,000 and 50,000 mg/kg,

respectively, with the 25%-ile and 75%-ile at 17,700 and 68,000 mg/kg. Mean values are typically

higher than the median (50%-ile) because of ‘hotspots’, which bias the mean upward. Surface

concentrations are considerably higher than subsurface values, with the latter having median and

mean concentrations of 12,250 and 30,300 mg/kg with the 25%-ile and 75%-ile at 2,448 and 39,250

mg/kg. It is interesting to note that although subsurface sediment concentrations are consistently

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lower than surface concentrations, the correlation between surface and subsurface collected at the

same location is very poor (R2 =0.12, Fig 1 in supplementary information) highlighting the high

degree of heterogeneity. The results of vibracoring and analyses undertaken in September / October

2018 at depths of 2 to 4 m reveal that the vast majority of petroleum hydrocarbon impact is

restricted to depths of less than 50 cm (data not reported here).

There is no clear spatial pattern present in the TPH concentrations (Figure 5), other than lower

concentrations present in the far south even though that area is close to the location of one of the

2008 spills (shown as a star at lower left on previous Fig. 4). High concentrations are found

throughout the remaining area, likely due to the spread of the initial spills as well as continued illegal

activities. The absence of a spatial structure in the data is confirmed by the variograms which were

constructed for TPH analyses from ground surface and subsurface samples and the exponential

variogram model that was fitted to the data (supplementary information, Figure SI-2). The fitted

variogram models plot as horizontal lines with a nugget to sill ratio of close to 1 which confirms the

absence of any spatial structure in the data (Cambardella et al. 1994).

BTEX constituents and light aliphatic constituents are absent or significantly depleted from

all samples as compared to analyses of un-weathered Bonny Light crude oil. The 16 US EPA PAH sum

concentrations of all but one sample are below the Nigerian regulatory (EGASPIN) threshold of 40

mg/kg (which is actually for a subset of 10 PAH and not all 16 PAH defined by US EPA) and are not

further discussed.

Post Treatment TPH values show a statistically significant (p<0.01) reduction for the

subsurface samples with the median concentrations decreasing from 12,250 mg/kg to 7,250 mg/kg.

However, the median concentrations for the surface samples show an increase from 35,000 to

39,500 mg/kg, which was however not statistically significant. The No Treatment sites have

statistically significant lower TPH concentrations for subsurface samples compared to the Pre-

Treatment samples while for surface samples no statistical difference is found.

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The fractionated TPH data show that relatively heavy TPH fractions (>C16) dominate both

the Pre- and Post-Treatment conditions (Fig. 8). Compared to un-weathered Bonny light crude oil,

the sediments are enriched in heavier fractions as a result of weathering (combined volatilization

and biodegradation) that preferentially removed the more volatile and biodegradable compounds

(Brown et al. 2017a). In particular, the heavier aromatic fractions have been enriched, consistent

with findings for land farming trials showing that although biodegradation decreases concentrations

of all TPH fractions, the rate of depletion is lower for heavier fractions compared to lighter fractions

(Brown et al. 2017b). The distribution of different TPH fractions is similar both between surface and

subsurface samples as well as for Pre-Treatment and Post Treatment conditions. The physical

cleanup method applied mobilizes free oil but does not preferentially remove the 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 to be 63% (Fig. 9), above generally accepted criteria ranging between 40%

and 50% (e.g. NJDEP 2014; IDEQ 2017). The likely explanation for the large RPD is that

homogenization of the composite samples was complicated by the texture of sediment consisting of

a mixture of detritus (e.g. dead mangrove roots) and clumps of clay-to-sand sized sediments.

Additionally, contamination was present in the form of isolated pockets of residual free oil droplets

scattered through the sediment, which combined with the problematic homogenization may cause

large differences between different parts in the composite sample (see photograph in Fig. 6B).

To assess small-scale variability from the same sample site, the five individual grab samples

used for the composite sample were analysed for four sites. Samples were collected within 5 m of a

center point. Results show that TPH is highly variable on a small scale (Fig. 10). The mean of the

individual discrete samples deviates considerably from that of composite sample from the same site.

This is in line with the previously discussed results of blind duplicate samples, further suggesting the

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high RPD in blind duplicate sampling was a result of incomplete homogenization. The relative

standard deviation (ratio of standard deviation and mean) ranged between 29 and 111%, and 33%

and 96% for the surface and subsurface samples, respectively. This is in the same order of

magnitude to the average RPD for the set of blind duplicate samples.

The small-scale variability in the presence of crude in sediments that is observed visually

through the SCAT process was investigating by replicate pit oiling observations in five SCAT pits

within a 6 m radius (data shown in Table SI-3). Similar to the TPH observations, these data

demonstrate a high variability in the observed oil coverage in pits with values ranging between 5%

and 95% coverage. The relative standard deviation of these observations range between 5% and

183%, with and average RSD of 62%.

Overall, the blind duplicate and discrete samplings, and replicate SCAT pit observations,

show that TPH concentrations are highly variable over short distances. The highly heterogeneous

nature in TPH concentrations is probably the result of both the continued and spatially variable

impacts to the area over the past ten years (including during and after the spills in 2008), the

dynamic tidal environment causing variable oil settlement on intertidal sediments, and nature of the

mud-dominated sediments inhibiting internal oil movement with depth. The small-scale variability

should however also be placed in the context of the overall variation in TPH concentrations, which

varies by over three orders of magnitude as discussed further below.

Relation between chemistry and visual field observations

Comparison between oil type and TPH concentrations are presented in Figures 11 and 12. In

addition, Tables SI-1 and SI-2 in the Supplementary Information summarize the key statistics for TPH

concentrations grouped per oil type. The comparison between SCAT described oil type and TPH

concentrations indicates a good correlation for subsurface samples (Fig. 11 lower panel). The

different SCAT groups have statistically different TPH concentrations, which is confirmed by an

ANOVA test. Observations of ‘silver sheen’ in the waters of a 25-30 cm test pit have a median TPH

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value of slightly less than 2,000 mg/kg and a 75%-ile value of ~4,000 mg/kg while brown and black oil

have higher median TPH values of 8,000 to 20,000 mg/kg, respectively. The relative standard

deviation (RSD) of TPH in the classes for the subsurface shown in Figure 11, range between 115% for

black oil to 219% for silver sheen (Table SI-1), which is higher than that of the small-scale sampling

discussed in the previous section (33%-96%). The higher RSD of TPH concentrations within each

SCAT categories can be caused by a combination of the following factors: i) SCAT observations were

made in one pit while the analyzed composite sediment sample consisted of 5 grab samples. For

future work, it is recommended to take the average of 3 SCAT pits to account for the high degree of

variability. ii) Sheening and the presence of droplets of oil in a SCAT pit can occur over a range of TPH

concentrations. This implies that SCAT observations are not expected to provide an exact proxy for

TPH analyses, but rather that a certain SCAT observation can provide a bandwidth of TPH

concentrations.

The ground surface samples do not show a correlation between SCAT field observations and

chemical analyses results (Fig. 11 upper panel), which is confirmed by the ANOVA test that showed

no statistical difference in TPH concentrations for the different SCAT groups. The difference

between ground surface and subsurface TPH correlations with SCAT observations is likely the result

of the black organic rich type color of the sediment (believed to be a result of the high organic

matter content and sulfate-reducing conditions) on which it is hard to differentiate weathered oil

from sediment. Field reports indicate that only when oil was seen pooled on water or where oiling is

fresh (black) was it able to be clearly seen on the sediment surface whereas the oiling of pit waters

was easier and more consistently able to be discerned. In addition, the surface sample included

sediments 0 to 5 cm deep, while the visual observation was solely on the sediment surface (0 cm

deep) and therefore does not include oil incorporated into the sediment.

Pit oiling of ≤25% brown/black oil may be one criterion to provide a visual means to confirm

completion of Phase 2 treatment (compared to the performance criterion of <35% applied in Phase

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1). This is believed to represent an oil level which can practicably be achieved with flushing and

physical agitation. In parallel to SCAT and cleanup activities, pilot replanting of mangrove seedling is

carried out to determine if this cleanup target is adequate. A comparison of SCAT data and

chemical data shows that the visual criterion corresponds to a median subsurface concentration of

approximately 4,000 mg/kg (Fig. 12). For 75% of the sites sampled Post Treatment (upper box limit),

a TPH concentration <10,000 mg/kg was found. The sites that do not meet the criterion have a 50%-

ile and 75%-ile concentration of 20,000 mg/kg and 35,000 mg/kg. TPH concentrations measured in

ground surface samples are much higher, reflecting the inability of SCAT observations to reliably

detect surface oiling as compared to chemical values taken from 0-5 cm depth.

CONCLUSIONS

Results show that the assessed clean up area is impacted by hydrocarbons which are widely spread

and highly variable both spatially and with depth. Spatial variability is both on a broad scale over the

entire work area (several km in length) and on very small scales (within 10 m). Chemical sampling

provides important data to characterize the hydrocarbons present, demonstrating that it is primarily

composed of highly weathered crude oil, enriched in heavy hydrocarbons compared to un-

weathered Bonny Light crude oil and with low BTEX and PAH components. The high spatial

variability that is present both on small and large scales however limits the applicability to directly

steer and confirm completion of cleanup activities using a single chemical-based analytical criterion.

Given this variability, SCAT pit observations can be considered sufficiently reliable to guide

confirmation of Phase 2 cleanup in Bodo in conjunction with a risk assessment according to EGASPIN

requirements. Another alternative being reviewed is to reduce the level of contamination in the

sediments sufficient that mangroves can be re-planted and survive, aiding the further degradation of

remaining oil through phytoremediation processes. Results from seven planted sites after one 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 surface, 420-59,000 mg/kg subsurface). Continued spillages,

however, will affect and kill replanted mangroves.

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We conclude that for the relatively large impacted area under study here, with challenges

pertaining to site access and sample shipment, visual SCAT observations can effectively be calibrated

with a limited set of chemical field data. As such, the study provides an example of the application

of SCAT as a speedy and reliable model to assess and manage cleanup endeavors in mangrove and

tidal flat environments. The comparison between SCAT observations and the sediment chemical

characterisation did however reveal that SCAT observations for surface oiling are a poor indicator for

surficial sediment TPH concentration. This contrasts to subsurface (or pit) SCAT observations, which

showed a much better correlation with TPH concentrations. Calibration of SCAT observations (pure

product or sheen type) to sediment chemical analyses is also likely effective for rapid field

assessment of other hydrocarbon products (e.g. different crudes or gasoline spills), but this requires

further work given that the solubility and visual appearance will vary.

ACKNOWLEDGEMENTS

We would like to thank many people that participated in this program for their persistence and good

humor in spite of very difficult circumstances. These include from the Bodo Community: Peter K.

Lenu, Nicholas Nekabari Vite and Felix Zorbilade; from the Bodo Mediation Initiative (BMI)

Directorate: Pobari Kpenu and Oby Nwokoro; from the Department of Petroleum Resources (DPR):

Boma Atiyegoba; from the National Oil Spill Detection and Response Organisation (NOSDRA): Sola

Oladipo, Sunday Ogwuche and Ahmed Ismail B.; from National Petroleum Investment Management

Services (NAPIMS): Theresa Etok; from Rivers State Ministry of Environment (RSMENV): Harrison

Chukwu, Mankie-Tanen B. and Claude Atteng, from The Shell Petroleum Development Company of

Nigeria Ltd (SPDC): Akwukwaegbu Chibuzor, Dickson Essien, Franklin Igbodo, Ijasan Olere, Jahswill

Edeh, Otonyemie Sowagbein, Timothy Mzaga and Victor Chukwudi; from National Coalition on Gas

Flaring and Oil Spills in the Niger Delta (NAGOND): Neninbarini Zabbey; from cleanup contractor

Giolee-Lamor: Andrew McArthur, Ahoega Dumbari, Chidi Alozie, Simon Rickaby, Ledum Deeko, Faith

Beubizua, Peons Parigas, Kris Ekweozor and Ayoola Abudun; from contractor Inkas: Alan Cooke,

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Steven Johnson, Francis Nwabueze, Jonah Shekwolo, Barida Kpenu, Celestine Dogalah, Prince Mene

Baabel and Zabbeh Kpobari Noble; and from the chemical sampling crew (GeoTerrain): Kunle

Adesida, Deji Adebayo, Prosper Ugbehe, Joel Olanrewaju, Dimas Yisa, Seun Ademiluyi, Nicon West -

Nee Aburo, saac Babatunde, Baba Ismail, Emem Amadi, Felicitas IhedIohanma, Ifeanyi Emefo,

Kingsley Briggs, Faith Uche (nee Uruakpa) and Moses Taiwo. Thanks also goes to I2 laboratory and

ALcontrol laboratory (UK) for undertaking the chemical analysis. Finally, we thank Professor

Jonathan Smith and George Devaull of Shell Global Solutions for providing a thorough review of the

draft manuscript.

The Shell Petroleum Development Company of Nigeria Ltd (SPDC) funded the study and

cleanup activities described in this paper. The views expressed are those of the authors and may not

reflect the policy or position of SPDC or its Joint Venture partners.

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List of Figures

Fig 1. Location of the Bodo Creek study area along the eastern edge of the Niger River delta in

Rivers State, Nigeria.

Fig 2. Overview of the central portion of the Bodo Creek study area showing living and dead

(former) mangrove area. A is from 15 September 2015 (V. Imevbore photographer) and B is from

Google Earth dated 2 January 2018. Arrows indicate the same location of living mangrove on

both images. The location of the photo is shown in Fig 4

Fig 3. Overview of SCAT observations at sites L10‐1 (top) and I49‐1 (bottom) with pits, SCAT

surface and subsurface oiling observations and surface and subsurface TPH (mg/kg). BO+BRO =

black oil + brown oil. The locations of the sites are shown in Fig 4. Photos by Erich Gundlach.

Fig 4. Phase 1 work area outline in yellow with grids, former mangrove, live mangrove, water

and illegal refineries indicated. The yellow arrow indicates location of the photograph in Figure

2. The location of the ‘Spills 2008’ that initiated these cleanup and sampling activities is also

shown. The labels on the X- and Y- axis of the map represent geographic coordinates (latitude

and longitude) in decimal degrees.

Fig 5. Sampling locations (n=317) with colours indicating TPH concentrations for surface (triangle

point down) and subsurface (triangle pointing up) concentrations. The labels on the X- and Y- axis

of the map represent geographic coordinates (latitude and longitude) in decimal degrees.

Fig 6. Photographs showing sampling in grid cell P09 (4.62582N, 7.26295E) near a live mangrove.

Photo A shows collection of field grab samples using a steel shovel which are composited in a

stainless-steel bowl and homogenized. Photo B shows a close-up of the pit (~0.3m across)

showing droplets of oil. Photo C shows homogenization of grab samples. Photo D shows transfer

of composite sample to laboratory supplied sample containers which were subsequently

transferred to ice boxes and transported to the laboratory. Photos by M. Bonte

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Fig 7. Box-whisker plots of TPH concentrations in mg/kg (log scale) for surface (G) and

subsurface (S) samples under four different conditions: No Treatment, Pre-Treatment, During

Treatment and Post Treatment. The number above the top x-axis represents the number of

samples in each class. The box extends from the lower to upper quartile values of the data, with

an orange line at the median (or 50%-ile). The whiskers extend from the box to show the range

of the data between the 5- and 95%-ile. The green triangle shows the mean value. Flier points

are those past the end of the whiskers. A * symbol next to a box plot indicates the median is

significantly different at a 99% confidence level (p<0.01) from the Pre-Treatment value.

Fig 8. TPH fraction distribution for fresh Bonny Light crude oil and ground surface and subsurface

sediment samples, before and after cleanup. Ali and Aro represent aliphatics and aromatics,

respectively. Bonny #1 shows crude oil fractions from Brown et al (2017b). G shows surface

samples. S shows subsurface samples. Pre-T represents Pre-Treatment conditions. PostT

represents Post Treatment conditions.

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

Fig 10. TPH in mg/kg from five discrete samples taken within 5 m of a central point at each site.

Grey bars show TPH, the red line shows the mean of the five discrete samples and the green line

shows the composite sample result from the same site.

Fig 11. Box-whisker plots of TPH concentrations in mg/kg (log scale) for different oil types as

delineated by SCAT surveys for surface and subsurface samples. The box extends from the lower

to upper quartile values of the data, with an orange line at the median (or 50%-ile). The whiskers

extend from the box to show the range of the data between the 5- and 95%-ile. Flier points are

those past the end of the whiskers. The number above the upper x-axis indicates the number of

samples in each group.

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Fig 12. Box-whisker plots of TPH concentrations in mg/kg (log scale) of Post Treatment samples

in mg/kg (log scale) for different types of SCAT surface and subsurface oil observations classified

according to a proposed close-out criterion for Phase 2 under which SS plus BO ≤25%. SS

indicates silver sheen, BO indicates black oil and BRO indicates brown oil.

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Tables

Table 1. TPH (mg/kg) for surface and subsurface sediment samples. Bold numbers with *

indicate the median is significantly different at a 99% confidence level (p<0.01) from the Pre-

Treatment value.

No Treatment Pre-Treatment During Treatment Post Treatment

GROUND SURFACE

Mean 40,300 49,500 93,200 62,600

25% Percentile 38,000 17,700 37,500 13,975

50% Percentile (median) 38,000 35,000 55,000* 39,500

75% Percentile 41,500 68,000 82,750 87,750

Number Samples 3 121 26 163

SUBSURFACE

Mean 600 30,300 21,700 17,400

25% Percentile 450 2,448 1,915 2,223

50% Percentile (median) 690* 12,250 6,800 7,250*

75% Percentile 810 39,250 19,800 23,700

Number Samples 3 122 24 169

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