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Marine Pollution Bulletin

journal homepage: www.elsevier.com/locate/marpolbul

A standardised approach to the environmental risk assessment of potentiallypolluting wrecksFreya Goodsira,⁎, Jemma A. Lonsdalea, Peter J. Mitchella, Roxana Suehringb, Adrian Farcasa,Paul Whomersleya, Jan L. Branta, Charlotte Clarkea, Mark F. Kirbya, Matthew Skelhornc,Polly G. Hillca Cefas, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UKbDepartment of Environmental Science and Analytical Chemistry (ACES), Stockholm University, SE-106 91 Stockholm, Swedenc Salvage and Marine Operations, Ash 2b #3212, MOD Abbey Wood, Bristol, BS34 8JH, UK

A R T I C L E I N F O

Keywords:Potentially polluting wrecksOilRisk assessment

A B S T R A C T

The potential risk to the marine environment of oil release from potentially polluting wrecks (PPW) is in-creasingly being acknowledged, and in some instances remediation actions have been required. However, wherea PPW has been identified, there remains a great deal of uncertainty around the environmental risk it may pose.Estimating the likelihood of a wreck to release oil and the threat to marine receptors remains a challenge. Inaddition, removing oil from wrecks is not always cost effective, so a proactive approach is recommended toidentify PPW that pose the greatest risk to sensitive marine ecosystems and local economies and communities.This paper presents a desk-based assessment approach which addresses PPW, and the risk they pose to en-vironmental and socio-economic marine receptors, using modelled scenarios and a framework and scoringsystem. This approach can be used to inform proactive management options for PPW and can be appliedworldwide.

1. Introduction

Oil can cause significant damage to the environment (Kingston,2002; Rogowska and Namieśnik, 2010) due to its toxic and persistentnature (Burns et al., 1994; Kingston, 2002; Peterson et al., 2003;Hailong and Boufadel, 2010). There are several sources from which oilcan enter the marine environment, including the natural slow release ofoil from the sea floor (seeps), oil and gas industry activities, shippingincidents and terrestrial based activities (i.e. run off and discharges).

There is an increasing concern over the pollution risk posed bypotentially polluting wrecks (PPW) (Michel et al., 2005; Faksness et al.,2015), as it is estimated that between 2.5 and 20.4 million metrictonnes (Michel et al., 2005) of oil may remain as bunker and/or cargooil within these wrecks. High profile incidents of oil release fromwrecks such as the S.S. Jacob Luckenbach (California, USA) and HMSRoyal Oak (Orkney, UK) have raised awareness of the potential of PPWas an oil polluter to the marine environment (Alcardo et al., 2007).With approximately 8500 identified wrecks worldwide, there isgrowing concern that these represent a significant source and risk ofpollution and thus pose a considerable level of threat to the marine

environment (Michel et al., 2005; Etkin et al., 2009).World War II wrecks have been submerged for over 70 years, so

pose increased potential of releasing oil due to the increased likelihoodof structural failure from corrosion of metal, compared to youngerwrecks. It is not possible to mitigate risk via intervention (e.g. removalof the oil) for all known wrecks, therefore there is a need to understandthe risk each of them pose. The first step is to understand the amountand composition of oil remaining on board and the likelihood of oilrelease to occur, to help prioritise efforts.

The oil types present in wrecks vary from heavy, persistent oils (e.g.bunker oils) to lighter, non-persistent oils (e.g. diesels or petrol). Heavyoils persist in the environment as they do not degrade readily (Law andMoffett, 2011), whereas light oils tend to degrade more quickly as theyare more bioavailable due to the small size of the compounds (ENSR,2006). Furthermore, there are a number of physical, biological andchemical factors which contribute to the weathering of oil (e.g. eva-poration, dispersion, sedimentation, microbial degradation) (Wang andFingas, 1995; Fingas, 1999; National Research Council, 2002; Plataet al., 2008), which can considerably alter the composition of oil in themarine environment (Liu et al., 2012). Oils contained in shipwrecks

https://doi.org/10.1016/j.marpolbul.2019.03.038Received 9 October 2018; Received in revised form 13 March 2019; Accepted 16 March 2019

⁎ Corresponding author at: Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, NR33 0HT, UK.E-mail address: freya.goodsir@cefas.co.uk (F. Goodsir).

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(either for operational purposes and/or as cargo) have the potential tocause widespread negative effects to marine ecosystems (Peterson et al.,2003; Michel et al., 2013) and socio-economics activities (Surís-Regueiro et al., 2007; Smith et al., 2011) and can persist in marinesediments (Laane et al., 2013; Sühring et al., 2016).

The physical properties of oil are critical for understanding itsmovement through the water column and dispersion over time, but theexact composition is often unknown for historical wrecks. Therefore, itcan be necessary to make an estimation based on what is known aboutprevalent oils from the time and location. The type and scale of oil leakscan vary, occurring as sporadic events or continuous releases of dif-fering magnitudes (Etkin et al., 2009). As a result, predicting the po-tential quantity and rate of release of oil can be difficult. What can becertain is that, as the condition of a wreck deteriorates over time thelikelihood of remaining oil being released will increase (Monfils, 2005).

Historical records and survey data (i.e. acoustic and underwaterimagery) can provide information about a wreck's condition and in-ternal integrity, and therefore the likelihood of it releasing oil. In manyinstances there is limited information relating to the condition and oilleak history of wrecks, so proxy data or expert judgement is used to fillthe gaps, reducing confidence in the assessment.

Some national authorities have investigated the potential pollutionrisks posed by wrecks and have already started addressing and mana-ging PPW within their waters (NOAA, 2009, 2013; Bergstrøm, 2014).Depending on the perceived level of risk, mitigation measures can rangefrom doing nothing, initiating monitoring programmes, to removingremaining oil. Removing oil from wrecks can be extremely costly ran-ging from a few million to tens of millions of US dollars (Etkin et al.,2009), therefore, a proactive approach is recommended to identifywrecks that pose the greatest risk to local marine ecosystems, econo-mies and communities. This requires a robust and standardised risk

assessment approach incorporating the likelihood of an oil release andthe severity of the potential impacts. Such an approach enables directcomparison of PPW and the prioritisation of appropriate managementand funding.

Risk assessment methodologies which identify and evaluate the riskfrom wrecks are well documented (see for example Michel et al., 2005;Alcardo et al., 2007; ABP Marine Environmental Research Ltd., 2007;Etkin et al., 2009; NOAA, 2013; Landquist et al., 2013, 2016; Ventikoset al., 2016). However, existing assessments lack an evaluation ofquantitative risk to local receptors under multiple oil spill scenarios.This can lead to an under- or overestimation of threats to the marineenvironment and leads to incorrect prioritisation of PPW. A reliablemeans of prioritising PPW is essential to a management programmeseeking to reduce environmental risk to as low as reasonably possible(ALARP) whilst delivering value for money.

There is little standardisation in wreck risk assessment methodolo-gies at either national or international levels. For example, a standar-dised approach is useful to national authorities which have PPW dis-tributed across the globe as a means of prioritising PPW for remediationwork. Furthermore, a standardised approach could support aims set outwithin the Nairobi Convention 2007 such as determining the hazardposed by wrecks, the risk of a potential oil release into the environment,and the legal obligation for removal of PPW from the marine environ-ment.

This paper presents a standardised environmental desk based as-sessment (E-DBA) of risk posed by PPW in a multi-staged approach(Fig. 1). The E-DBA is the first of a multi-stage methodology for overallrisk assessment which builds on the strengths of previous risk assess-ments. Stage two incorporates on-site wreck integrity validation alongwith environmental measurements and sampling, and stage three in-volves intervention/remediation, should it be necessary. The

Fig. 1. Flow diagram illustrating the multi-staged methodology for overall risk assessment, and the stepped process for carrying out the environmental desk basedassessment (E-DBA).

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information collected during the stage one E-DBA and stage two on-sitewreck integrity and environmental surveys will support the decision-making process relating to stage three, remediation requirements, andassociated options at each wreck site.

The first stage of the approach reported here uses predeterminedmodelled scenarios, which can be applied to any wreck. Firstly, a long-term chronic release of oil from a wreck is modelled; and secondly, twoshort-term acute releases of oil from a wreck. Similar to previous as-sessments (NOAA, 2013), the modelled scenarios enable an estimate ofsea surface, water column, sediment and shoreline contamination froman oil release and inform an assessment of the potential impacts toecological and socio-economic marine receptors. Here the standardisedwreck assessment process is described. The approach has been devel-oped to ensure that the relative environmental risks of individualwrecks can be compared as an aid to environmental management, de-cision making and prioritisation for intervention/remediation purposes.

2. Risk assessment approach

The E-DBA for PPW builds on previous risk assessment approaches(i.e. NOAA, 2013; ABP Marine Environmental Research Ltd., 2007) andassesses the potential risk to marine receptors under the application ofnumerical models of both chronic and acute oil release scenarios. Theapproach is formed as a multi-staged approach (Fig. 1). Firstly, thelikelihood that a wreck will release oil, based on existing historicalinformation, surveys and dive reports. Secondly, the likelihood of ex-posure of ecological and socio-economic receptors to released oil usingspatially resolved modelled outputs of spill trajectory and fate. Lastly,the potential for oil exposure to harm marine receptors. The purpose ofthis scoring system is to allow comparison and ranking between dif-ferent wrecks around the world. A confidence assessment is also appliedat two stages of the assessment, the first is applied to the likelihood ofrelease and then separately to the severity of risk to marine receptors.

2.1. Likelihood of oil release

The likelihood that a wreck will release oil depends on severalfactors, of which eight are considered in this E-DBA (Tables 1 and 2).The volume and type of oil are assessed in the modelling scenarios inthe next assessment stage, and so are excluded from this stage to avoidthe same risk factor being assessed twice. Historical wreck informationis used to determine the likelihood of the release of fuel oil and oilstored as cargo. Each criterion is given a score of either low (1),medium (2), or high (3); as per Table 2. Weightings (1–3) are applied tocriteria according to their perceived influence on the likelihood of an oilrelease (Table 2).

Overall oil release likelihood scores are generated by multiplyingeach risk assessment criterion's score by its weighting and summing theresulting values. The minimum attainable oil release likelihood score is16 and the maximum score is 48. A confidence assessment is applied toindicate the suitability and accuracy of available information andhighlight where additional survey or investigative works could improvethe confidence in assessments. Confidence criteria are scored for eachcriterion as either low (1), medium (2), or high (3); as per Table 3. Theminimum obtainable confidence score is 8 which is attributed to thenumber of risk assessment criteria in Tables 2 and 3, and the maximumobtainable score is 24. The confidence score categories are as follows:low confidence (8–12) medium confidence, (13–19), high confidence(20–24).

2.2. Likelihood of exposure of marine receptors to oil

Three oil release scenarios are used to predict likelihood of en-vironmental impacts: one chronic and two acute. While it is recognisedthat there are other possible release scenarios, the scenarios here arechosen to demonstrate the range of possible outcomes through the riskassessment process.

Table 1Defined risk assessment criteria for the assessment of likelihood for wrecks to release oil (adapted from NOAA, 2013; ABP Marine Environmental Research Ltd.,2007).

Vessel depth Vessel depth influences the physical conditions that a wreck encounters. Wrecks in < 30 m of water potentially pose a higher riskbecause they are more likely to be affected by waves and storm events. Wrecks at shallower depths are therefore expected to breakapart more rapidly, increasing the chance of an oil release. Wrecks in > 100 m are less influenced by hydrodynamics.Where vessel depth information is not available, water depth is taken from admiralty charts of the last know location of a wrecksto give a best estimate. Where location is unknown, literature or historical records can be used to provide an approximatelocation.

History of leaks Historical incident, dive or observation reports may contain information on whether there have been reported leaks since thevessel sunk or whether oil from an unknown source has been observed near a wreck. This information can tell us something aboutthe structure of the vessel and whether tanks may have been ruptured in the incident, however the amount of oil leaking from thevessel will remain unknown.

Integrity of wrecks If a wreck is broken into several pieces, tanks may have been ruptured and pipes and vessels may have been broken contributingto the release of oil. However, in some cases wrecks that have been broken into several pieces still run the risk of containing someoil which may be released. Where applicable, and evidence is available, post-sinking damage i.e. from salvage or natural eventssuch as storms, will be considered.

Age of vessel at time of sinking Older vessels pose an increased potential of releasing oil due to the increased likelihood of structural failure from corrosion ofmetal, compared to younger wrecks. This has been kept separate from the ‘length of time the vessel has been submerged’ as thesetwo criteria can affect a wreck's integrity differently as vessels are likely to accumulate wear and tear during their working life,such as rust or changes to the structural integrity due to modifications. This criterion also functions as a proxy for condition ofvessel prior to sinking.

Length of time the vessel has beensubmerged

Over long periods of submergence, wreck integrity will deteriorate as it is exposed to environmental factors that may acceleratecorrosion and collapse. Therefore, the longer a wreck has been submerged, the more likely it is to structurally fail leading to arelease event.

Method of storage The method of storage reflects how securely the oil is stored and how resistant the vessel may be to release the oil. Bunker tanksare more resistant to weathering than drums stored on deck, which are more exposed to external elements and therefore have ahigher potential to break free and rupture, though these contain smaller volumes of oil.

Type of incident The type of incident relates to the severity of structural damage that occurred at the time of sinking. This can provide an idea ofthe internal damage sustained, indicating whether oil is likely to have been released at the time of sinking. More severe types ofincidents have been classified as low, as a significant portion of any oils on-board are likely to have been released at the time ofsinking.

Stability of seabed The stability of the seabed is important for assessing whether conditions are likely to change around to a wreck, potentiallycausing wreck movement and accelerating breakup. Areas of stable seabed are less likely to cause breakup of a wreck than areaswith a high degree of movement and instability.

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The chronic release scenario is a continuous release of 50 kg oil perday and is fixed between wrecks for direct comparison. This value isreached using previous information for the sunken warship HMS RoyalOak, for which the oil release was considered “noticeable” when therate of oil release exceeded 100 kg per day (Michel et al., 2005). HMSRoyal Oakwas chosen as a representative reference because this methodwas initially developed for the assessment of nearshore World War I andII wrecks assumed to contain large amounts of oil. If information isavailable regarding the expected release rate, the release rate in thechronic scenario should be adjusted accordingly, however, such data isunlikely to be available.

The “worst-case” acute release scenario is based on the total esti-mated volume of oil being released within a 24-h period. The volume ofoil remaining on board is estimated based on best available evidencefrom historical reports, information such as how much oil the vesselwas carrying at the time of incident, and loss of oil and internal damagesustained during the incident.

It is unlikely that all the oil would be released at once, even fol-lowing an extreme failure of wreck integrity. This is particularly thecase for large vessels where the oil is stored in multiple tanks to preventsuch an event. Therefore, a “most probable” acute scenario simulates asingle tank failure within a 24-h period. Tank volumes are determinedfrom ships plans found in historical reports. Where limited or no in-formation is available for scenario development, proxy information isused from similar ships, ideally sister ships built by the same company.If no proxy information is available, 10% of total oil volume is used.

The properties of oil, such as composition, density and viscosity, arecritical for predicting movement through the water column and dis-persion over time. The exact composition and physical-chemical prop-erties of oil from historical wrecks is often unknown and can vary de-pending on when and where they were refuelled. Oil can also varybetween tanks on the same wreck due to different fuels being loaded(ship vs aviation fuel) and inconsistent rates of biodegradation betweentanks. Pre-WWII Admiralty specifications of oil are very vague re-garding the type and origin of oils, and could include residuals oils,shale distillates and various types of crude oils, but in general specifyviscosities at low temperature (32 °F or 0 °C) and require that the fuelsare suitable to use at low ambient temperatures (Brown, 2003). Forscenario development, and where oil type wasn't specified in archivedreports, oil types are chosen from the literature and/or the OSCAR Oildatabase (SINTEF, 2013) based on prevalent oils used on vessels at thetime of sinking, the location of loading fuel/cargo and the propulsiontype of the vessel.

For the presented work, the chronic scenario is modelled using theDose-related Risk and Effects Assessment Model (DREAM) and theacute scenarios are modelled using the Oil Spill Contingency andResponse (OSCAR) component of the Marine Environmental ModellingWorkbench (MEMW; SINTEF). The proposed E-DBA is a modular as-sessment method. Therefore, other environmental fate and transportmodels are able to predict long-term low volumes as well as short-termhigh volume sub-sea releases and distribution of oil can be used. Thescenarios explore sea surface, water column, sediment and shorelineimpacts. Table 4 provides model parameters for the different scenariotypes, including oil release description, model domain and grid re-solution based on the SS Baku Standard. the model domain should ac-commodate the transport of oil particles throughout the simulation,while the grid resolution (the size of grid cells, which determines thenumber of cells within the model domain) should ensure a good balancebetween the spatial resolution of the model outputs and computationalcost. Fig. 2 presents the oil deposition in sediments (kg/m2) from thethree scenarios using information based on the SS Baku Standard.

2.2.1. Chronic modelling scenarioDREAM is a chemical dispersion model that uses 2D surface wind

and 3D current data to model how a discharge or spill disperses in thewater column (SINTEF, 2013). DREAM simulates how the componentsTa

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of a discharge behave based on physicochemical properties, biode-gradability, and hydrodynamic properties of the release. With theseparameters, DREAM calculates the Predicted Environmental Con-centration (PEC) of a discharge as concentrations of contaminant“particles” in a 3D grid around the wreck. The addition of appropriatePredicted No-Effect Concentration (PNEC) values for the discharge al-lows DREAM to establish PEC/PNEC ratios throughout a modelled vo-lume and calculates the risk to sensitive species. In the presented study,DREAM was used to establish the predicted environmental risk ofchronic oil release from PPW. However, other models could be applied(or adapted) to predict the PEC/PNEC ratio. To generate a PEC/PNEC,local predicted oil concentrations are divided by the PNEC that is basedon available ecotoxicological literature data.

In DREAM, a PEC/PNEC value of > 1 represents concentrations atwhich 5% or more of the most sensitive species are likely to be affected.The species may or may not be present in the vicinity of the release site,and are not specifically identified, but rather they represent thosespecies for which the PEC is equal to or exceeds their PNEC. The modeluses acute toxicity data for marine algae, crustaceans and fish to cal-culate the PNEC of the investigated components. In most cases, thereare few data relating to chronic toxicity or sub-lethal effects such ascarcinogenic effects and endocrine disruption, and so these are not fullyconsidered by the model.

If a wreck is within a seasonally or partially stratified water column,the chronic model is run under both winter and summer conditions.Where seasonal variation in model results is observed, risk assessmentis based on the worst case of the two model outputs. If no seasonalvariation in model results is observed, or a wreck is within a perma-nently mixed water column, only one set of model outputs is reported.

The PEC/PNEC approach is an internationally accepted standard forenvironmental risk assessment and routinely applied in both theEuropean regulation for the Registration, Evaluation, Authorisation andRestriction of Chemicals (REACH) and Stockholm Convention assess-ments. The DREAM model is routinely used in the risk assessment forproduced water and oil releases from offshore oil and gas production inaddition to accidental spill modelling (SINTEF, 2013).

These initial chronic risk predictions are used in the first assessmentof PPW that are intended to inform decisions on the prioritisation offurther, on-site assessment and survey work. Therefore, the scenariosare based on general, easily available ecotoxicological data. The pre-dictions can be further refined by conducting ecotoxicological experi-ments using species typically living in wreck habitats.

2.2.2. Acute modelling scenariosOSCAR consists of a trajectory module for transport processes (ad-

vection and dispersion) coupled to an oil weathering module for the

fate processes (evaporation, dissolution, emulsification, stranding,biodegradation, and interaction with the sediments) (Spaulding, 2017).The horizontal transport of oil in the marine environment is driven bywind, currents and, to a lesser extent, waves (Guo and Wang, 2009).Thus, it is dependent on the meteorological and hydrodynamic condi-tions occurring from the initial moment of acute oil release until thefinal fate of the oil has been determined (Spaulding, 1988, 2017; Reedet al., 1999). The fate of the released oil is typically determined overdays (e.g. light oils or when the environmental conditions favour rapidonshore stranding) or weeks (e.g. heavy oils release, far from shore)(Guo and Wang, 2009; Spaulding, 1988, 2017; Reed et al., 1999).

Single model outputs can show a great variability, which is due notonly to the variability of the initial and driving environmental condi-tions, but also due to the uncertainty in parameter values used to set upthe model (parametric uncertainty), as well as uncertainty related tothe design of the equations used to model the physical processes and theway they are solved computationally (structural uncertainty).Structural uncertainty can be addressed through a multi-model ap-proach (e.g. using different models for the fate and trajectory of spilledoil), while the parameter uncertainty can be explored by varying theparameter values. Arguably, one of the most uncertain and importantparameters is the volume of released oil, and although here two pos-sible scenarios (the most likely and the worst case volumes) are de-scribed, these scenarios can still inform our estimation of risk to marinereceptors (which can broadly be categorised as low/medium/high),especially if the two scenarios result in similar patterns of oil exposureprobabilities, only with different magnitudes. However, the mainvariability of the model outputs arises from the variability of thedriving environmental conditions, which is more thoroughly exploredby the running of multiple trajectories.

The consistency of outcomes from multiple simulations determinesour estimates of probability. While a high probability (e.g. of impact ofa certain receptor) indicates an agreement between a large fraction ofthe model runs, this in not necessarily a proof of prediction robustnesson its own, unless it could be argued that the underlying driving pro-cesses for this particular outcome are well known and well representedin the model. In this case, it is often the prevailing winds and currentsthat are broadly responsible for the high probability predictions ofimpact, and arguably these are reasonably represented by the drivingdataset and implemented in the model.

While detailed data (e.g. hydrographic, tidal currents), which oftenis not available, would be necessary for accurate predictions at smallscale or in complex topographic coastal areas, it should be also notedthat many of the receptors span large areas, often tens of kilometresacross, which together with the broad categorisation of the risk to thesereceptors, allows for a meaningful estimation of the risk.

Table 3Confidence scores for the likelihood of oil release criteria based on underlying data are applied to each risk assessment criterion, assessed as 1 (low) to 3 (high).

Confidence Score Definition

High 1 The data used are timely, the best available, robust and the outputs are well supported by evidence. Majority of experts agree.Medium 2 The data are limited and/or proxy information. There is a majority agreement between experts; however, evidence is inconsistent and there are differing

views between experts.Low 3 The data are limited and not well supported by evidence. Experts do not agree.

Table 4Basic model set-up parameters used for the three oil spill modelling scenarios based on the SS Baku Standard.

Parameter Chronic Most probable acute Worst-case acute

Release amount 50 kg/day 848 t 5057 tOil type Texas crudeOil release position 56°48.5′ N, 002°12.9′ EOil release height 2 m above seabedModel domain 70 km × 70 km × 100 m 500 km × 500 km × 100 m 500 km × 500 km × 100 mGrid resolution 150 m × 150 m × 10 m 1 km × 1 km × 10 m 1 km × 1 km x 10 m

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The variability of the environmental conditions (both short-termand seasonal), determines the predicted behaviour and fate of the oil(Fig. 3a). This variability is included by running multiple (200) tra-jectories beginning at different times over a one-year period for eachscenario, namely a stochastic simulation. The trajectories are combinedto produce a statistical output. The results of individual trajectories mayvary widely in the direction and shape of the spread of oil depending onthe release time. The results of any single trajectory may thus not re-present the worst case or be representative of the actual risk posed tothe marine receptors. Therefore, all results presented are based on thestatistical analysis of the 200 model trajectories and do not depict theextent of oil contamination from a single trajectory (Fig. 3b).

Thresholds are applied to exclude model output data that are notdeemed environmentally harmful. Each stochastic simulation scenariois set up to include a threshold value at which an impact could occur atthe sea surface, in the water column and on the shoreline (Table 5).Therefore, the post simulation statistical analysis calculates the prob-ability of oil exceeding the minimum threshold in each model grid cellat some point during the simulation period. The threshold exceedanceduration cannot be specified as a model parameter and thus can be asshort as a single time-step (e.g. 20 min) or as long as the full simulationduration (e.g. 100 days). The actual threshold exceedance time at agiven location will vary between each individual model trajectory. Thesensitivity to oil varies dependant on the receptors assessed, for ex-ample surface oil sublethal effects on marine mammals, birds and seaturtles could occur at around 1.0 g/m2, whereas commercial and re-creational fishing could be prohibited at 0.01 g/m2 (French McCay,2016). Standard thresholds were applied (Table 5) for a rapid risk as-sessment to identify priority to wrecks for further investigation.

For each of the three environmental compartments (shoreline, seasurface and water column), the probability of contamination is calcu-lated by mapping out the percentage of model trajectories where agiven cell is hit by the contaminant in exceedance of the specifiedthreshold (Fig. 3b). For computational reasons, stochastic simulationsdo not include the sedimentation of oil as this is reflected as oil thatexits the model domain. It is therefore necessary to compute sedimentcontamination probabilities using manually set up trajectories ratherthan the OSCAR stochastic simulation tool.

It is important to note that the model outputs predict the extent ofan oil release incident with no remediation actions undertaken. It ispossible that salvage efforts to remove the oil before a potential releaseinto the environment could significantly reduce the extent and magni-tude of environmental impacts.

2.3. Severity of risk to marine receptors

Determining the level of risk an oil spill poses to marine receptorsrequires interpretation of the probability and magnitude of oil ex-posure, whilst considering the sensitivity of each receptor. Marine re-ceptors are described in this paper as ecological (marine and coastaldesignated areas, marine mammals, reptiles, seabirds, fish, and pro-tected benthic species); or socio-economic (shipping, tourism, fishingand infrastructure). Thresholds have been defined for ecological andsocio-economic receptors as the minimum level of oil contamination atwhich an impact might occur at the sea surface, water column, shore-line and in the sediments, for both chronic and acute modelling sce-narios (Tables 6, 7, 8 and 9). It must be noted that national prioritieschange considerably by region, for example mangroves, seagrass beds

Fig. 2. Modelled oil release scenarios: a) chronic 50 kg/day, b) most probable acute release of 843 t and c) worst cast acute release of 5057 t based on the wreck SSBaku Standard.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

295

and coral reefs are commonly of high priority for protection in tropicalregions, whereas the protection of marine mammals and seabirds maybe prioritised in more northern latitudes. Therefore, it is possible thatecological and socioeconomic receptors and their associated thresholdscan be refined dependant on the local priorities as required.

Risk to each marine receptor is evaluated and characterised as ei-ther low (1), medium (2) or high (3). Model outputs from the threescenarios are mapped along with marine receptor spatial distributiondata to assess the potential risk.

Fig. 4 illustrates mapped outputs showing environmental and socio-economic risk associated with an acute oil release from the SS BakuStandard, a wreck off the east coast of Scotland. Risk is expressed asprobability of contamination, based on the proportion of trajectoriesreaching a receptor during the modelled acute oil spill simulations. Thisbasic method cannot give an exact probability of contamination for a

receptor that spans more than one model grid cell, it can only give aminimum probability, but further analysis of the trajectory outputs inconjunction with receptor shapefiles can provide these figures.

Where a risk is identified for a marine receptor, expert judgementalong with additional sources (e.g. tourist board information, citizenscience) is used to verify risk. For example, under an acute scenario alarge area of the sediment is shown to be at high risk of oil con-tamination. In the absence of spatial information on benthic features,e.g. cold coral reefs, it is difficult to determine whether the feature ispresent and therefore overlaps with the area at risk. However, recordsof coral reefs have been reported in or close to the area at high risk.Therefore, a high risk score is given based on the large spatial extent athigh risk of oil contamination under an acute scenario, suggesting thatthis feature is likely to be exposed to high concentrations of oil.

A confidence assessment is also applied to evaluate the level of

Fig. 3. a): Single model outputs: single model trajectories of oil released in different months, variations in slick spreading is primarily due to different windconditions. 3b) Stochastic model outputs: a probability of 50% translates to 100 of the 200 individual trajectories resulting in contamination in a location for at leastthe minimum time step during each 30-day simulation. Values shown are the maxima across the 200 trajectories. The extent of any single model trajectory is notdepicted in this figure.

Table 5Threshold values applied to the shoreline, sea surface and water column for acute release scenarios.

Shoreline 50 kg/km which corresponds to 1 g/m2 (or the equivalent of 2 tar balls of approximately 1 cm in diameter per m2), under the assumption the shore is 50 m wide.Sea surface 0.1 t/km2 or 0.1 g/m2, equivalent to a 0.1 μm thick layer, where sheens will start to be visible.Water column 50 ppb, corresponds to the PNEC (predicted no effect concentration) of the most toxic components of oil, in this study C25 is used which was the main component

by mass fraction of the fuel oils investigated. The value will vary on the type of oil, and the contribution to toxicity made by the main components of that oil.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

296

accuracy of spatial data and suitability of underpinning information foreach receptor. Confidence criteria are scored for each criterion as eitherlow (1), medium (2), or high (3); as per Table 3. For example, in theabsence of spatial data and evidence on extent of the receptor, con-fidence is scored as low, whereas if spatial data is present and recent,confidence is score high.

2.4. Overall risk score

The overall risk posed by a wreck is assessed by generating anoverall risk score. The overall risk score is calculated by multiplying thelikelihood of release score by the severity of risk to marine receptorsscore. Ecological and socio-economic risks are kept separate so as not toassume an equal weighting.

The minimum score that can be achieved for ecological receptors is96 based on a score of 1 for each of the 6 receptors (total score 6) andthe lowest likelihood of oil release score (16). The maximum score is864 based on a score of 3 for the 6 receptors (total score 18) and thehighest likelihood of release score (48). Ecological risk is defined as low(< 252), medium (253–384) or high (385–864).

The minimum score that can be achieved for socio-economic re-ceptors is 64, based on a score of 1 for each of the four receptors (totalscore 4) multiplied by the lowest likelihood of oil release score (16).The maximum score is 576 based on a score of 3 for each of the re-ceptors (total score 12) and the highest likelihood of release score (48).Socio-economic risk is defined as low (< 168), medium (169–256) orhigh (257–576).

A confidence score is given based on 1) the robustness of dataavailable and 2) the level of agreement among a range of assessors onthe risk score assigned. This is used to identify limitations, uncertaintyand gaps in the available data. Confidence scores are assigned for eachof the assessed receptors as low (1), medium, or high (3). Scores aresummed then divided by the number of assessed receptors and multi-plied by 100 to gain a percentage score. The confidence score is used todescribe the overall level of confidence as low (< 50%), medium(50–80%) or high (> 80%).

A worked example is available in the supplementary material.

3. Discussion

This standardised risk assessment approach has been developed toassess the likelihood of oil release from a PPW and the potential of thatoil to cause harm to ecological and socio-economic marine receptors.The approach uses a combination of numerical modelling, geo-spatialdata and expert scientific judgement to quantify risks and generateconfidence scores to highlight areas of uncertainty. It attempts to ad-dress some of the current shortfalls in environmental risk assessments inrelation to PPW and builds on the strengths of previous assessmentstowards a robust approach. The aim is to assist managers in identifyingthose PPW which pose the greatest risk, to highlight where additionalsurveys or investigative works could improve the confidence of theseassessments, and to facilitate the development of appropriate man-agement and remediation strategies.

The described method uses three predetermined scenarios, in-cluding both chronic and acute oil releases to demonstrate risk of ex-posure and the magnitude of risk to key marine receptors. Multiplescenarios are essential, as the volume and duration of a release is fun-damental to understanding the movement of oil and therefore the risk itmay pose (NOAA, 2013). Large volume acute releases have the poten-tial to cover a wider area, potentially effecting many receptors, whereaslong duration chronic releases tend to be more localised but still havethe potential to impact receptors such as mariculture or benthic species.By understanding the history of a wreck, such as the incident thatcaused its sinking, and current structural condition, these scenarios canbe tailored to reflect the most likely oil spill scenario. If a wreck isknown to be leaking, the modelled scenario can be changed to reflectTa

ble6

Risk

ofch

roni

coi

lrel

ease

harm

ing

ecol

ogic

alm

arin

ere

cept

ors

issc

ored

aslo

w(1

),m

ediu

m(2

)or

high

(3).

Ther

eis

deem

edto

bea

risk

whe

nth

ePr

edic

ted

Effec

tCon

cent

ratio

n(P

EC)

exce

eds

the

conc

entr

atio

nof

cont

amin

atio

nat

whi

ch‘n

oeff

ect’

isob

serv

ed(P

redi

cted

No

Effec

tCo

ncen

trat

ion

(PN

EC),

i.e.P

EC/P

NEC

>1)

.

Risk

asse

ssm

entc

rite

ria

Rele

vant

oils

pill

mod

elou

tput

Low

(sco

reas

1)M

ediu

m(s

core

as2)

Hig

h(s

core

as3)

Mar

ine

and

coas

tald

esig

nate

dar

eas

Wat

erco

lum

n,se

dim

ent,

sea

surf

ace

and

shor

elin

e

<0.

002

PEC/

PNEC

risk

ofov

erla

pw

ithan

ypr

otec

ted

area

.0.

002–

0.2

PEC/

PNEC

risk

ofov

erla

pw

ithan

ypr

otec

ted

area

.>

0.2

PEC/

PNEC

risk

ofov

erla

pw

ithan

ypr

otec

ted

area

.

Spec

ies

and

feat

ures

ofco

nser

vatio

nin

tere

stM

arin

em

amm

als

(pin

nipe

ds)

Wat

erco

lum

n,se

asu

rfac

ean

dsh

orel

ine

<0.

2PE

C/PN

ECri

skof

over

lap

with

any

pinn

iped

haul

-out

orpu

ppin

glo

catio

n.0.

2–1

PEC/

PNEC

risk

ofov

erla

pw

ithan

ypi

nnip

edha

ul-

out

orpu

ppin

glo

catio

n.>

1PE

C/PN

ECri

skof

over

lap

with

any

pinn

iped

haul

-out

orpu

ppin

glo

catio

n.M

arin

em

amm

als

(cet

acea

nsan

dsi

reni

ans)

Wat

erco

lum

nan

dse

asu

rfac

e<

0.2

PEC/

PNEC

risk

ofov

erla

pw

ithan

impo

rtan

tkn

own

ceta

cean

orsi

reni

anha

bita

t.0.

2–1

PEC/

PNEC

risk

ofov

erla

pw

ithan

impo

rtan

tkno

wn

ceta

cean

orsi

reni

anha

bita

t.>

1PE

C/PN

ECri

skof

over

lap

with

anim

port

ant

know

nce

tace

anor

sire

nian

habi

tat.

Mar

ine

rept

iles

(tur

tles)

Wat

erco

lum

n,se

asu

rfac

ean

dsh

orel

ine

<0.

2PE

C/PN

ECri

skof

over

lap

with

any

turt

lene

stin

gsi

te.

0.2–

1PE

C/PN

ECri

skof

over

lap

with

any

turt

lene

stin

gsi

te.

>1

PEC/

PNEC

risk

ofov

erla

pw

ithan

ytu

rtle

nest

ing

site

.Se

abir

dsSe

asu

rfac

ean

dsh

orel

ine

<0.

002

PEC/

PNEC

risk

ofov

erla

pw

ithan

yIm

port

ant

Bird

Are

a(I

BA).

0.00

2–0.

2PE

C/PN

ECri

skof

over

lap

with

any

IBA

.>

0.2

PEC/

PNEC

risk

ofov

erla

pw

ithan

yIB

A.

Bent

hic

feat

ures

(e.g

.ree

fs)

and

spec

ies

incl

udin

gde

sign

ated

shel

lfish

grou

nds

Wat

erco

lum

nan

dse

dim

ent

Pred

icte

dto

talf

ootp

rint

ofoi

ldep

ositi

onon

sedi

men

t<

100

km2

and

<0.

002

PEC/

PNEC

risk

ofov

erla

pw

ithpr

otec

ted

bent

hic

feat

ures

and

spec

ies.

Pred

icte

dto

talf

ootp

rint

ofoi

ldep

ositi

onon

sedi

men

tbe

twee

n10

0an

d10

00km

2or

0.00

2–0.

2PE

C/PN

ECri

skof

over

lap

with

prot

ecte

dbe

nthi

cfe

atur

esan

dsp

ecie

s.

Pred

icte

dto

talf

ootp

rint

ofoi

ldep

ositi

onon

sedi

men

t>

1000

km2

or>

0.2

PEC/

PNEC

risk

ofov

erla

pw

ithpr

otec

ted

bent

hic

feat

ures

and

spec

ies.

Fish

spaw

ning

and

nurs

ery

area

sW

ater

colu

mn

and

sedi

men

tN

okn

own

spaw

ning

ornu

rser

yar

eas.

Oil

spill

inte

ract

sw

ithkn

own

disc

rete

area

sus

edfo

rsp

awni

ngan

d/or

nurs

ery

area

.O

ilsp

illin

tera

cts

with

high

inte

nsity

spaw

ning

and/

ornu

rser

yar

eas.

Fish

(sen

sitiv

eor

char

ism

atic

spec

ies)

Wat

erco

lum

nan

dse

dim

ent

No

know

nsp

ecie

s.O

ilsp

illin

tera

cts

with

know

ndi

scre

tear

eas

used

byse

nsiti

veor

char

ism

atic

spec

ies.

Oil

spill

inte

ract

sw

ithar

eaus

edby

larg

enu

mbe

rsof

sens

itive

orch

aris

mat

icsp

ecie

s.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

297

Table7

Risk

ofch

rom

icoi

lrel

ease

harm

ing

soci

o-ec

onom

icm

arin

ere

cept

orss

core

das

low

(1),

med

ium

(2)o

rhig

h(3

).Ri

skis

asse

ssed

asan

yov

erla

pbe

twee

nth

eso

cio-

econ

omic

rece

ptor

and

mod

elle

doi

lcon

cent

ratio

nsab

ove

the

defin

edth

resh

olds

whe

rean

impa

ctha

sth

epo

tent

ialt

ooc

cur

(see

Tabl

e5)

.

Risk

asse

ssm

entc

rite

ria

Rele

vant

oils

pill

mod

elou

tput

sLo

w(s

core

as1)

Med

ium

(sco

reas

2)H

igh

(sco

reas

3)

Curr

ent

and

plan

ned

infr

astr

uctu

reO

ffsho

rew

ind

farm

sSe

asu

rfac

eN

oov

erla

pw

ithan

yw

indf

arm

.Se

ason

alov

erla

pfo

r>

5%of

aw

indf

arm

leas

ear

ea.

Year

-rou

ndov

erla

pfo

r>

5%of

aw

indf

arm

leas

ear

ea.

Offs

hore

oila

ndga

sin

stal

latio

nsSe

asu

rfac

eN

oov

erla

pw

ithan

yin

stal

latio

n.Se

ason

alov

erla

pw

ithan

yin

stal

latio

n.Ye

ar-r

ound

over

lap

with

any

inst

alla

tion.

Indu

stri

alw

ater

inta

kes

Shor

elin

eN

oov

erla

pw

ithan

yin

dust

rial

wat

erin

take

.Se

ason

alov

erla

pw

ithan

yin

dust

rial

wat

erin

take

.Ye

ar-r

ound

over

lap

with

any

indu

stri

alw

ater

inta

ke.

Aqu

acul

ture

Wat

erco

lum

nan

dse

asu

rfac

eN

oov

erla

pw

ithan

yaq

uacu

lture

faci

lity.

Seas

onal

over

lap

with

any

aqua

cultu

refa

cilit

y.Ye

ar-r

ound

over

lap

with

any

aqua

cultu

refa

cilit

y.

Tour

ism

and

leis

ure

area

sTo

uris

m(C

oast

alto

wns

,bea

chfr

onts

,and

beac

hre

sort

sar

epo

pula

rho

liday

and

recr

eatio

nala

reas

supp

ortin

ga

rang

eof

busi

ness

esan

dco

mm

uniti

es)

Shor

elin

eN

oov

erla

pw

ithan

ykn

own

tour

ist

area

s.Se

ason

alov

erla

pw

ithan

ykn

own

tour

ist

area

s.Ye

ar-r

ound

over

lap

with

any

know

nto

uris

tar

eas.

Hig

hus

ear

eas

(mon

itore

dbe

ache

s,po

pula

rdi

ving

loca

tions

,rec

reat

iona

lmar

inas

and

boat

ing

area

san

dto

uris

tre

sort

s)Sh

orel

ine

No

over

lap

with

any

high

use

area

s.Se

ason

alov

erla

pw

ithan

yhi

ghus

ear

eas.

Year

-rou

ndov

erla

pw

ithan

yhi

ghus

ear

eas.

Fish

ing

grou

nds

Dem

ersa

lSe

dim

enta

ndse

asu

rfac

e<

180

days

offis

hing

effor

tim

pact

ed.

180–

365

days

offis

hing

effor

tim

pact

ed.

>36

5da

ysof

fishi

ngeff

orti

mpa

cted

.

Pela

gic

Wat

erco

lum

nan

dse

asu

rfac

e<

180

days

offis

hing

effor

tim

pact

ed.

180–

365

days

offis

hing

effor

tim

pact

ed.

>36

5da

ysof

fishi

ngeff

orti

mpa

cted

.

Crus

tace

anSe

dim

enta

ndse

asu

rfac

e<

180

days

offis

hing

effor

tim

pact

ed.

180–

365

days

offis

hing

effor

tim

pact

ed.

>36

5da

ysof

fishi

ngeff

orti

mpa

cted

.

Ship

ping

Impo

rtan

tsh

ippi

ngla

nes

Sea

surf

ace

No

over

lap

with

any

impo

rtan

tsh

ippi

ngla

nes.

Tem

pora

ryov

erla

pw

ithan

yim

port

ant

ship

ping

lane

s.Ye

ar-r

ound

over

lap

with

any

impo

rtan

tsh

ippi

ngla

nes.

Port

sSh

orel

ine

No

over

lap

with

any

port

s.Te

mpo

rary

over

lap

with

any

port

s.Ye

ar-r

ound

over

lap

with

any

port

s.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

298

the actual rate of release.Risk assessments rely on the availability of relevant and robust data.

The presented approach has been specifically designed with theknowledge that there is often limited data to support a comprehensiverisk assessment (Whittington et al., 2017). This methodology in-corporates information about the current and historical state of awreck, environmental variables such as currents, tides and winds usedin oil release models, and marine receptor geo-spatial and sensitivitydata. The data intensive nature of this methodology can be problematic.The availability of data is highly variable between regions and dataproviders. Furthermore, where data do exist they may be outdated, orhave licence restrictions on their use. This creates difficulties whenidentifying the number and location of potentially impacted marinereceptors, which may limit the confidence of the assessment. Poor dataabout vessel condition or sensitive receptors can lead to an under- oroverestimation of threats to the marine environment, which has thepotential to undermine the quality of management decisions. However,by scoring confidence in the data, as well as risk, this approach iden-tifies which criteria are lacking information. This provides managerswith two options, either take steps to improve confidence or take aprecautionary approach based on risk and confidences scores. Con-fidence can be improved by purchasing existing sensitivity and/or sa-tellite data and accessing dive site reports. It can be improved furtherby collecting data through high resolution multibeam echosounderand/or Remotely Operated Vehicle (ROV) surveys, which indicate thestructural integrity of the wreck and hull plating damage (i.e. howmany oil tanks remain intact). These additional data can be used tomodel more probable oil release scenarios.

An environmental risk assessment such as the one proposed hereprovides a snap shot in time, it is based on the present understanding ofthe condition of the wreck and the environmental conditions at the timeof the assessment. If no monitoring or remediation is undertaken, riskassessments may need to be updated on a periodic basis to take accountof changes to wreck integrity and the surrounding environment as wellas including new information, as and when it becomes available.

Further refinement could include a risk assessment procedure forlong-term effects of an oil release, taking account of longer-term issuessuch as accumulation in sediments. Long-term oil accumulation in se-diments represents an environmental risk as a local contaminationsource for benthic organisms, as well as higher trophic levels throughbiomagnification, and can potentially impact communities near a pol-luting wreck over several decades. Environmental surveys and numer-ical modelling could predict the effects of a long-term oil release andhelp determine the level of risk posed to the local area.

An on-site environmental survey requires robust design and im-plementation to deliver the necessary information regarding site char-acterisation and a baseline assessment of contaminants present. Modeloutputs generated in the stage one E-DBA can be used to inform siteselection and sample collection, such as probable locations of oil de-position in the sediment. Verification of any previously conductedwreck integrity assessments (whether it is structurally intact) willprovide a more complete understanding of the wreck in question at thetime of survey.

National authorities are under political pressure and moral obliga-tion to avoid environmental incidents, which can be done by taking aproactive approach to PPW management (Whittington et al., 2017).Proactive PPW management will not only limit environmental damage,but also avoid the reputational damage and clean-up costs associatedwith marine oil spills. Management decisions may include monitor andevaluate, hot tap of wreck hull and removal of oil, or partial or com-plete wreck removal. The quantitative risk assessment approach pre-sented here is a management tool for prioritising PPW, ensuring thatresources are focussed on those PPW which pose the greatest risk tomarine receptors.

Table8

Risk

ofac

ute

oilr

elea

seha

rmin

gec

olog

ical

mar

ine

rece

ptor

ssc

ored

aslo

w(1

),m

ediu

m(2

)or

high

(3).

The

prob

abili

tyof

cont

amin

atio

n,ex

pres

sed

asa

perc

enta

ge,r

eflec

tsth

efr

actio

nof

the

200

traj

ecto

ries

whi

chre

sulte

din

cont

amin

atio

nab

ove

the

thre

shol

dat

the

loca

tion

ofth

ere

cept

or.T

here

isde

emed

tobe

ari

skw

hen

the

prob

abili

tyof

cont

amin

atio

nex

ceed

sth

epr

e-de

fined

thre

shol

dfo

rea

chsp

ecifi

cre

cept

or.

Risk

asse

ssm

entc

rite

ria

Rele

vant

oils

pill

mod

elou

tput

sLo

w(s

core

as1)

Med

ium

(sco

reas

2)H

igh

(sco

reas

3)

Mar

ine

and

coas

tald

esig

nate

dar

eas

Wat

erco

lum

n,se

dim

ent,

sea

surf

ace

and

shor

elin

e<

5%pr

obab

ility

ofov

erla

pw

ithan

ypr

otec

ted

area

.5–

50%

prob

abili

tyof

over

lap

with

any

prot

ecte

dar

ea.

>50

%pr

obab

ility

ofov

erla

pw

ithan

ypr

otec

ted

area

.

Spec

ies

and

feat

ures

ofco

nser

vatio

nin

tere

stM

arin

em

amm

als

(pin

nipe

ds)

Wat

erco

lum

n,se

asu

rfac

ean

dsh

orel

ine

<5%

prob

abili

tyof

over

lap

with

pinn

iped

haul

-out

orpu

ppin

glo

catio

n.5–

50%

prob

abili

tyof

over

lap

with

pinn

iped

haul

-out

orpu

ppin

glo

catio

n.>

50%

prob

abili

tyof

over

lap

with

any

pinn

iped

haul

-out

orpu

ppin

glo

catio

n.M

arin

em

amm

als

(cet

acea

nsan

dsi

reni

ans)

Wat

erco

lum

nan

dse

asu

rfac

e<

5%pr

obab

ility

ofov

erla

pw

ithan

impo

rtan

tce

tace

anor

sire

nian

habi

tat.

5–50

%pr

obab

ility

ofov

erla

pw

ithan

impo

rtan

tce

tace

anor

sire

nian

habi

tat.

>50

%pr

obab

ility

ofov

erla

pw

ithan

impo

rtan

tcet

acea

nor

sire

nian

habi

tat.

Mar

ine

rept

iles

(tur

tles)

Wat

erco

lum

n,se

asu

rfac

ean

dsh

orel

ine

<5%

prob

abili

tyof

over

lap

with

any

turt

lene

stin

gsi

te.

5–50

%pr

obab

ility

ofov

erla

ptu

rtle

nest

ing

site

.>

50%

prob

abili

tyof

over

lap

with

any

turt

lene

stin

gsi

te.

Seab

irds

Sea

surf

ace

and

shor

elin

e<

5%pr

obab

ility

ofov

erla

pw

ithan

yIB

A.

5–50

%pr

obab

ility

ofov

erla

pw

ithan

yIB

A.

>50

%pr

obab

ility

ofov

erla

pw

ithan

yIB

A.

Bent

hic

feat

ures

(e.g

.ree

fs)

and

spec

ies

athi

ghpr

obab

ility

ofov

erla

pin

clud

ing

desi

gnat

edsh

ellfi

shgr

ound

s

Wat

erco

lum

nan

dse

dim

ent

Pred

icte

dto

talf

ootp

rint

ofoi

ldep

ositi

onon

sedi

men

t<

100

km2

and

noov

erla

pw

ithpr

otec

ted

bent

hic

spec

ies.

Pred

icte

dto

talf

ootp

rint

ofoi

ldep

ositi

onon

sedi

men

tbe

twee

n10

0an

d10

00km

2an

dno

over

lap

with

prot

ecte

dbe

nthi

csp

ecie

s.

Pred

icte

dto

talf

ootp

rint

ofoi

lde

posi

tion

onse

dim

ent

>10

00km

2

Fish

spaw

ning

and

nurs

ery

area

sW

ater

colu

mn

and

sedi

men

tN

oov

erla

pw

ithan

ysp

awni

ngor

nurs

ery

area

s.O

verl

apw

ithdi

scre

tear

eas

used

for

spaw

ning

and/

ornu

rser

yar

ea.

Ove

rlap

with

high

inte

nsity

spaw

ning

and/

ornu

rser

yar

eas.

Fish

(sen

sitiv

eor

char

ism

atic

spec

ies)

Wat

erco

lum

nan

dse

dim

ent

No

over

lap

with

any

spec

ies.

Ove

rlap

with

disc

rete

area

sus

edby

sens

itive

orch

aris

mat

icsp

ecie

s.O

verl

apw

ithar

eaus

edby

larg

enu

mbe

rsof

sens

itive

orch

aris

mat

icsp

ecie

s.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

299

Table9

Risk

ofac

ute

oilr

elea

seha

rmin

gso

cio-

econ

omic

mar

ine

rece

ptor

ssco

red

aslo

w(1

),m

ediu

m(2

)orh

igh

(3).

The

prob

abili

tyof

cont

amin

atio

n,ex

pres

sed

asa

perc

enta

ge,r

eflec

tsth

efr

actio

nof

the

200

traj

ecto

ries

whi

chre

sulte

din

cont

amin

atio

nab

ove

the

thre

shol

dat

the

loca

tion

ofth

ere

cept

or.T

here

isde

emed

tobe

ari

skw

hen

the

prob

abili

tyof

cont

amin

atio

nex

ceed

sth

epr

e-de

fined

thre

shol

dfo

rea

chsp

ecifi

cre

cept

or.

Risk

asse

ssm

entc

rite

ria

Rele

vant

oils

pill

mod

elou

tput

sLo

w(s

core

as1)

Med

ium

(sco

reas

2)H

igh

(sco

reas

3)

Curr

ent

and

plan

ned

infr

astr

uctu

reO

ffsho

rew

ind

farm

sSe

asu

rfac

e<

5%pr

obab

ility

ofov

erla

pof

oilw

ithan

yw

indf

arm

.5–

50%

prob

abili

tyof

over

lap

ofoi

labo

ve5%

ofa

win

dfar

mle

ase

area

.>

50%

prob

abili

tyof

over

lap

ofab

ove

5%of

aw

indf

arm

leas

ear

ea.

Offs

hore

oila

ndga

sin

stal

latio

nsSe

asu

rfac

e>

5%pr

obab

ility

ofov

erla

pof

anin

stal

latio

n.5–

50%

prob

abili

tyof

over

lap

ofan

inst

alla

tion.

>50

%pr

obab

ility

ofan

yov

erla

pof

anin

stal

latio

n.

Indu

stri

alw

ater

inta

kes

Shor

elin

e>

5%pr

obab

ility

ofov

erla

pw

ithan

ypo

wer

stat

ion.

5–50

%pr

obab

ility

ofov

erla

pw

ithan

ypo

wer

stat

ion.

5–50

%pr

obab

ility

ofan

yov

erla

pw

ithan

ypo

wer

stat

ion.

Aqu

acul

ture

Wat

erco

lum

nan

dse

asu

rfac

e<

5%pr

obab

ility

ofov

erla

pw

ithan

yaq

uacu

lture

faci

lity.

5–50

%pr

obab

ility

ofov

erla

pw

ithan

yaq

uacu

lture

faci

lity.

>50

%pr

obab

ility

ofov

erla

pw

ithan

yaq

uacu

lture

faci

lity.

Tour

ism

and

leis

ure

area

sTo

uris

mSh

orel

ine

<5%

prob

abili

tyof

any

shor

elin

eim

pact

.5–

50%

prob

abili

tyof

any

shor

elin

eim

pact

.>

50%

prob

abili

tyof

any

shor

elin

eim

pact

.H

igh

use

area

sSh

orel

ine

<5%

prob

abili

tyof

over

lap

with

any

high

use

area

.5–

50%

prob

abili

tyof

over

lap

with

any

high

use

area

.>

50%

prob

abili

tyof

over

lap

with

any

high

use

area

.

Fish

ing

grou

nds

Dem

ersa

lSe

dim

enta

ndse

asu

rfac

e<

180

days

offis

hing

effor

tim

pact

edin

area

of50

–100

%pr

obab

ility

ofoi

lcon

tam

inat

ion.

180–

365

days

offis

hing

effor

tim

pact

edin

area

of50

–100

%pr

obab

ility

ofoi

lcon

tam

inat

ion.

>36

5da

ysof

fishi

ngeff

orti

mpa

cted

inar

eaof

50–1

00%

prob

abili

tyof

oilc

onta

min

atio

n.Pe

lagi

cW

ater

colu

mn

and

sea

surf

ace

<18

0da

ysof

fishi

ngeff

orti

mpa

cted

inar

eaof

50–1

00%

prob

abili

tyof

oilc

onta

min

atio

n.18

0–36

5da

ysof

fishi

ngeff

orti

mpa

cted

inar

eaof

50–1

00%

prob

abili

tyof

oilc

onta

min

atio

n.>

365

days

offis

hing

effor

tim

pact

edin

area

of50

–100

%pr

obab

ility

ofoi

lcon

tam

inat

ion.

Crus

tace

anSe

dim

enta

ndse

asu

rfac

e<

180

days

offis

hing

effor

tim

pact

edin

area

of50

–100

%pr

obab

ility

ofoi

lcon

tam

inat

ion.

180–

365

days

offis

hing

effor

tim

pact

edin

area

of50

–100

%pr

obab

ility

ofoi

lcon

tam

inat

ion.

>36

5da

ysof

fishi

ngeff

orti

mpa

cted

inar

eaof

50–1

00%

prob

abili

tyof

oilc

onta

min

atio

n.

Ship

ping

Impo

rtan

tsh

ippi

ngla

nes

Sea

surf

ace

<5%

prob

abili

tyof

over

lap

with

impo

rtan

tshi

ppin

gla

nes.

5–50

%pr

obab

ility

ofov

erla

pw

ithim

port

ant

ship

ping

lane

s.>

50%

prob

abili

tyof

over

lap

with

impo

rtan

tsh

ippi

ngla

nes.

Port

sSh

orel

ine

<5%

prob

abili

tyof

over

lap

with

apo

rt.

5–50

%pr

obab

ility

ofov

erla

pw

itha

port

.>

50%

prob

abili

tyof

over

lap

with

apo

rt.

F. Goodsir, et al. Marine Pollution Bulletin 142 (2019) 290–302

300

Acknowledgments

This work is funded by and is part of the ongoing research projectC6107 Environmental Desk Based Assessments for the Ministry ofDefence (MoD). The MoD has defined a programme of work to under-take standardised E-DBAs taking account of oil pollution from wrecksfor which they are responsible.

Appendix A. Worked wreck example

Supplementary data to this article can be found online at https://doi.org/10.1016/j.marpolbul.2019.03.038.

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