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Accepted Manuscript
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
To cite this article, please follow the guidance at http://www.geolsoc.org.uk/onlinefirst#cit_journal
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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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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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