ABSTRACT

The goals of subsea dispersant injection (SSDI) into a deep water oil and gas blowout are to increase effectiveness of dispersant treatment over that achievable at the water surface; decrease the volume of oil that surfaces; reduce human and wildlife exposure to volatile organic compounds (VOCs); disperse the oil over a large water volume at depth; enhance biodegradation; and reduce surface, nearshore and shoreline exposure to floating and surface-water entrained/dissolved oil. Potential tradeoffs include increased water column and benthic resource exposures to oil at depth. In order to better understand the implications of SSDI use, we modeled a hypothetical blowout in the northern Gulf of Mexico to predict oil fate and compare the environmental exposure for no intervention to various response options (i.e., mechanical recovery, in-situ burning (ISB), surface dispersant application, and SSDI). Probabilistic modeling was used to evaluate the influence of variable metocean conditions (i.e., wind, currents, temperature). The results showed that even with a substantial capacity of equipment applied, mechanical and ISB removed only a small fraction of the oil that would otherwise be floating or evaporate. Compared to cases without use of SSDI, SSDI reduced the size of oil droplets by an order of magnitude, substantially decreased the amount of oil on the water surface and on the shoreline, increased dissolution and degradation rates of hydrocarbons at depth, increased weathering rate of rising oil such that floating oil contained much lower content of soluble and semi-soluble hydrocarbons, decreased surface water concentrations of dissolved hydrocarbons, and decreased VOC emissions to the atmosphere and, therefore, reduced human and wildlife exposures to VOCs. The tradeoff was that with SSDI there was greater exposure to hydrocarbons in deep water. However, densities of biota are much lower in deep water than near the water surface, where sensitive early life history stages of fish and invertebrates are most abundant. This approach provides decision makers with quantitative environmental exposures with which they may evaluate risk tradeoffs regarding appropriate response strategies for mitigating impacts from oil and gas released during a deep water blowout.

INTRODUCTION

The goal of this study was to use oil spill modeling to evaluate combinations of response options and identify that combination that would minimize overall the environmental, socioeconomic and human health and safety impacts of a deep water blowout in the northern Gulf of Mexico. A Comparative Risk Assessment (CRA) approach was used to evaluate the implications various response alternatives, i.e., SSDI in addition to mechanical recovery, in-situ burning (ISB), and surface dispersant application. The objective was to provide decision makers with science-based and transparent information to enable technically-sound choices regarding appropriate strategies for mitigating impacts from oil and gas released during a deep water blowout. The modeling-based analysis quantitatively evaluated each of the considered response strategies to facilitate a comparison to select the most effective set of strategies to minimize long-term impacts. As such, this study provides quantitative information that may be used in the context of a Spill Impact Mitigation Assessment (SIMA). SIMA represents the merger of prior decision-support and comparative assessment tools (e.g., net environmental benefit analysis, NEBA, and consensus ecological risk assessment, CERA) by focusing on the trade-offs among different oil spill response (OSR) approaches aimed at reducing short-term and long-term consequences in the environment, in terms of the ecological, socio-economic and human health. This project included a novel approach to quantify the magnitude of exposure and potential recovery of valued ecosystem components (VECs) in the Gulf of Mexico. In this paper, however, we will only provide results of the modeling portion of the work. A future publication will describe the VEC exposure/recovery quantification and use of the results in a CRA utilizing a SIMA approach.

Given this context, an engagement process was established with stakeholder representatives, decision makers and others (Walker, 2017). At the onset of the project, we formed a Technical Advisory Committee (TAC) comprised of oil spill technical specialists and managers (e.g., on oil spill response, oil spill modeling, environmental effects, wildlife, socio-economic resources) that included representatives from federal and state government agencies responsible for providing input to spill response decision-making, representatives from federal and state natural resource trustees, and subject-matter experts (SMEs) from academia and industry. The role of the TAC was to (1) review the approach and assumptions for the modeling and CRA analyses and (2) to assist in making project-related decisions, recognizing potential uncertainties. As the modeling work ensued, presentations were made to the TAC to review inputs, methodologies and findings in order to improve the effort. After completion of the modeling and comparative risk analyses, a workshop was held with the TAC and other invited participants and SMEs to review the approach and findings of the modeling and the overall CRA.

METHODS

Oil Spill Models: OILMAPDeep and SIMAP

Oil spill trajectory and fate modeling was performed using two models, OILMAPDeep and and SIMAP. OILMAPDeep evaluates the nearfield dynamics of a blowout plume, and the droplet sizes produced as a result of the turbulent energy involved and application of dispersants (Spaulding et al., 2000; Crowley et al., 2014; Spaulding et al., 2015; Li et al. 2016). This determines the initial conditions for the SIMAP model, which calculates transport and fate of the oil after release from the near-field buoyant plume.

OILMAPDeep includes two subordinate models, the plume model and a droplet size model. The plume model predicts the plume evolution through the water column and the droplet size model predicts the distribution of droplet sizes in response to the release turbulence (based on oil and gas flow rates, volumetric corrections for pressure at the release depth, and aperture size at the release point) and oil properties (density, viscosity, interfacial tension). Subsurface dispersant application is implemented in the droplet model through incorporation of a reduced interfacial tension (IFT) associated with the dispersant treatment as a function of the dispersant to oil ratio (DOR) of the treated fraction of the release (Spaulding et al., 2015; Li et al., 2016). Reduction of IFT results in smaller oil droplets released into the water column. The mass and droplet size distribution output by OILMAPDeep is used as input to SIMAP, which then simulates the rise rate of the oil droplets (a function of droplet size and weathering state via its effect on oil density), dissolution (which is faster for smaller droplets), dilution and degradation (which is faster for releases with smaller droplet sizes because of increased dissolution and therefore bioavailability for biodegradation).

SIMAP (French McCay, 2003, 2004; French McCay et al., 2015, 2016) quantifies oil trajectory, concentrations of oil hydrocarbon components (as part of droplets and dissolved) in the water column, areas swept by floating oil of varying mass concentrations and thicknesses, shorelines oiled to varying degrees, and amount of oil settling to sediments. Processes simulated by SIMAP include spreading, evaporation of volatiles from surface oil, transport on the surface and in the water column, randomized dispersion from small-scale motions (mixing), emulsification, entrainment of oil as droplets into the water (natural and facilitated by dispersant application), dissolution of soluble and semi-soluble hydrocarbons (S/SS-HCs), volatilization of dissolved hydrocarbons from the surface water, adherence of oil droplets to suspended particulate matter (SPM), adsorption of semi-soluble aromatics to SPM, sedimentation, stranding on shorelines, and degradation (biodegradation and photo-oxidation). The model tracks soluble and semi-soluble components of the oil (i.e., monoaromatics (MAHs, such as benzene, toluene, ethylbenzene and xylene, BTEX), polynuclear aromatic hydrocarbons (PAHs), and soluble alkanes; i.e., S/SS-HCs), as well as insoluble volatile aliphatic hydrocarbons, separately from high-molecular weight non-volatile and insoluble components of the oil. Whole oil (containing non-volatiles and volatiles not yet volatilized or dissolved from the oil) is simulated as floating slicks, emulsions and/or tarballs or as dispersed oil droplets of varying diameter (some of which may resurface). Sublots of the spilled oil are represented by Lagrangian elements (“spillets”), each characterized by mass of hydrocarbon components and water, location, thickness, diameter, density, and viscosity. A separate set of Lagrangian elements is used to track movements of the dissolved hydrocarbons.

Mechanical recovery and ISB are simulated using polygons defining time and spatial windows where oil is removed at specified rates (i.e., oil volume per unit time). Constraints due to weather (e.g., wind speed, wave height) and weathering state (i.e., viscosity) of the oil are defined in the model inputs. The model removes oil when weather conditions allow to the extent that floating oil within an appropriate viscosity range is available at the time(s) and in the location(s) specified.

Surface dispersant applications are simulated in SIMAP by specifying as model inputs the applied dispersant volume, assumed effectiveness (percent of the oil treated), DOR (i.e., volume of oil treated per volume of dispersant encountering the oil), time and spatial window(s) when applied, and constraining weather and oil weathering characteristics (e.g., viscosity limit for dispersion via dispersant application). The SIMAP model’s surface entrainment algorithm adjusts entrainment rate of a treated parcel of oil to reflect the DOR, level of turbulence (a function of wind speed), and oil characteristics (viscosity) at the time and location of dispersant application. This results in more floating oil dispersing into the water column and a change in entrained droplet size distribution to smaller droplet sizes (related to the DOR and level of turbulence), which in turn increases the dissolution rate of soluble components and thus their biodegradation rates due to this increased bioavailability.

Oil Spill Modeling Approach

A hypothetical deep water blowout in the northeastern Gulf of Mexico (in De Soto Canyon) was modeled assuming varying response strategies:

  1. No intervention (natural attenuation);

  2. Mechanical recovery;

  3. Mechanical recovery, ISB, and surface dispersant application (MBSD);

  4. Subsea dispersant injection (SSDI), in addition to MBSD.

The first step was to perform stochastic modeling and analysis to define a continuous time-series of metocean conditions that served as the base case for the second step of the modeling, which was to perform individual (deterministic) model runs evaluating oil fate and exposure with alternative response strategies. The influence of seasonal and annual variation in metocean conditions (i.e., wind, currents, salinity, temperature) on trajectory and fate of the hypothetical oil blowout was evaluated through a series of stochastic model runs to provide uncertainty bounds due to varying environmental conditions on the results for specific trajectories examined in more detail. For the stochastic modeling, runs were performed for each of 100 spill randomly-selected start dates and times during 2001 to 2010 to sample the potential time-varying sequences of metocean conditions that could occur during a release. The results provided in maps and as statistics describe the range of distances and directions oil is likely to travel, as well as the potential likelihoods and magnitudes of oil exposures to be expected.

Conditions and results were examined to determine individual runs that produced either “worst case” or median exposure to oil at the sea surface, in the water column, or on shorelines. Typically, worst-case and median exposure for the sea surface, water column, and shoreline don’t correlate to the same input / metocean conditions, e.g., worst-case shoreline exposure is likely the result of relatively consistent winds driving the oil to shore, while these conditions transfer oil off the surface more rapidly. For this study, we chose two base cases from the no-intervention stochastic runs for the second phase deterministic modeling, where alternative response options were examined. This paper focuses on the results for a base case representing the “worst case” for shoreline oiling; it being the 97th highest of the 100 runs in terms of shoreline length oiled. A second model run, that closest to median for both surface and shoreline oil exposure, was also run with the same set of response options (to be reported elsewhere).

Modeled Scenario

Table 1 lists the scenario specifications modeled. The location (Figure 1) is 120 nmiles (222 km) from the nearest shoreline (which is Apalachicola on the Florida Panhandle, to the northeast) and about 176–204 nmiles from the nearest ports where logistics would be based (i.e., 176 nmiles – Passagoula, MS; 178 nmiles – Mobile, AL; 204 nmiles – Port Fourchon, LA).

Table 1.

Spill scenario inputs for modeling (for no intervention and response altenative simulations).

Spill scenario inputs for modeling (for no intervention and response altenative simulations).
Spill scenario inputs for modeling (for no intervention and response altenative simulations).
Figure 1.

Spill site (circle with cross), maximum surface oil exposure at any time after the spill (main map), and shoreline oiling (inset map), for “worst case” run for shoreline oil with no response intervention.

Figure 1.

Spill site (circle with cross), maximum surface oil exposure at any time after the spill (main map), and shoreline oiling (inset map), for “worst case” run for shoreline oil with no response intervention.

The duration of the release assumes well shutdown via a capping stack, accomplished by 21 days after the blowout starts. The Marine Well Containment Company’s (MWCC) functional specification (accepted by BSEE) states that the capping stacks will typically be in the field and installed around day 6. However, the timing of shut-in will depend on a number of factors and criteria including the well condition, location of the well, availability of vessels, debris removal requirements, connector type, etc.

The assumed GOR (2000 scf/stb) is typical of the range of potential gas contents for reservoirs in the Gulf of Mexico (BOEM, 2013). Higher GORs are possible when first drilling into a reservoir, and lower GOR values (e.g., 500 scf/stb) are typical of producing wells.

Environmental Data

For geographical reference, a rectilinear grid was used to designate the location of the shoreline, the water depth (bathymetry), and the shore or habitat type. National Oceanic and Atmospheric Administration (NOAA) Office of Response and Restoration (OR&R) Environmental Sensitivity Index (ESI) data were used to define habitat types (http://response.restoration.noaa.gov/esi). ESI shoreline data were reclassified to a simpler habitat classification, i.e., rocky, cobble, sand, mud, wetland, and artificial (man-made) shore types. Bathymetric data were obtained from the General Bathymetric Chart of the Oceans Digital Atlas (GEBCO, 2009) one arc-minute gridded data set, which is based on quality-controlled ship depth soundings interpolated using satellite-derived gravity data as a guide.

For currents, the US Naval Research Laboratory’s HYbrid Coordinate Ocean Model (HYCOM) + NCODA Gulf of Mexico 1/25° Reanalysis product GOMu0.04/expt_50.1 was used [http://tds.hycom.org/thredds/catalog/datasets/GOMu0.04/expt_50.1/data/netcdf/catalog.html and http://hycom.org/data/gomu0pt04/expt-50pt1]. Wind data used to force the hydrodynamics, and for the oil spill modeling, were the NOAA NCEP Climate Forecast System Reanalysis (CFSR) Selected Hourly Time-Series Products, January 1979 to December 2010 (Saha et al., 2010) [ http://rda.ucar.edu/datasets/ds093.1/]. Monthly mean water temperature and salinity were from the World Ocean Atlas 2001 (WOA01, Boyer et al., 2004), compiled and maintained by the US National Oceanographic Data Center (www.nodc.noaa.gov).

After the Deepwater Horizon (DWH) spill, components of the MC252 oil were identified on the sediments in the offshore area (Valentine et al. 2014), indicating oil sedimentation. Because mineral SPM concentrations are typically very low in the offshore Gulf of Mexico (D’Sa et al. 2007, 2008; Salisbury et al. 2004), transport of oil dispersed in the water column to the seafloor likely resulted from oil droplets becoming less buoyant after weathering and biodegradation, facilitated by droplet adherence to organic matter and settling of marine snow (Passow et al. 2012; Passow 2014), as well as the unsuccessful top-kill activities where considerable oil and SPM was released from the well into the water column. Given the large volume of oil and sediments released by the failed top-kills, the density of the top-kill sediments (as opposed to near-neutral density of marine snow), and the footprint of contamination being near the DWH wellsite (Valentine et al. 2014), the top-kill material likely accounted for the majority of the oil flux to the sediments near the wellsite. In this study, it was assumed that a top-kill operation was not performed, and oil sedimentation was only mediated by baseline ambient SPM.

Based on review of baseline SPM concentrations in the Northern Gulf of Mexico, the mean SPM in offshore blue waters is 3 mg/L, while SPM concentrations in nearshore waters are ~5–10 mg/L and up to 50 mg/L near the Mississippi Delta (D’Sa et al. 2007, 2008, Salisbury et al. 2004). A synoptic map of SPM was defined for use in the oil spill model by combining results from field and modeling studies with satellite imagery depicting suspended sediment plumes (French et al., 2016; based on Salisbury, et al., 2004; D’sa, et al., 2007; Lee, et al., 2012).

The horizontal diffusion coefficient is assumed to be 50 m2/s for floating oil, 2 m2/s in surface waters (above 40m), and 0.1 m2/s in waters below 40 m. The vertical diffusion coefficient is assumed 10 cm2/s in surface and 0.1 cm2/s in deep waters. These values are based on empirical data reviewed for the deep water Gulf of Mexico (French McCay et al., 2015; based on Okubo and Ozmidov (1970), Okubo (1971), Csanady (1973), Socolofsky and Jirka (2005)).

Oil and Pseudo-Component properties

A typical and well-characterized Gulf of Mexico light crude oil is used for modeling. Detailed property data for HOOPs blend crude oil was obtained (2016 May 3) from http://www.exxonmobil.com/crudeoil/about_crudes_diana.aspx. The oil density at 16°C is 0.854 g/cm3 (API 34.2), dynamic viscosity is 8.43 cp at 20°C, and IFT is 16.6 mN/m. Based on its asphaltene (1.19%) and resin (6.79%) content and behavior of similar light crude oils (Fingas and Fieldhouse, 2012), after weathering the oil is assumed to form a mesostable water-in-oil emulsion (mousse) up to a maximum water content of 59% water.

Concentrations of volatile and soluble to semi-soluble hydrocarbons were calculated for the 19 oil pseudo-components used in the SIMAP oil fates model (French McCay et al., 2015, 2016) using fractions of the oil volatilized in boiling cut temperature ranges and measurements of hydrocarbon concentrations using Gas Chromatography / Mass Spectrometry (GC/MS), operated in the selected ion monitoring mode (SIM), performed by NewFields Environmental Forensics Practice LLC (Rockland, MA) in May 2016 (personal communication, Thomas Parkerton, ExxonMobil, May 2016). S/SS-HCs, defined using the octanol-water partition coefficient (Kow) as those with log(Kow)<6, were assigned to pseudo-components AR1 – AR9, and the remaining fractions were assigned to boiling cut-defined pseudo-components AL1 – AL8, as summarized in Table 2. Hydrocarbons within, and physical-chemical properties of each, pseudo-component are available in French McCay et al. (2015).

Table 2.

Fraction of HOOPS oil in each pseudo-component.

Fraction of HOOPS oil in each pseudo-component.
Fraction of HOOPS oil in each pseudo-component.

Primary degradation, i.e. the loss of the initial hydrocarbon compounds via biodegradation and photo-oxidation by ultraviolet light (UV), was modeled using a first-order degradation equation and pseudo-component specific rates. Biodegradation rates of hydrocarbons in seawater (Table 3) were based on reviews by French McCay et al. (2015) to develop component-specific rates for S/SS-HCs (i.e., the AR pseudo-components). The non-soluble aliphatic (AL) components’ biodegradation rates were obtained from the analyses of groups of similar molecular-weight aliphatics within separate boiling cut-based fractions by Brakstad et al. (2015) and Brakstad and Faksness (2000). The residual oil rates are applied to compounds that were not included in the AR or AL components. For floating and shoreline oil, the half-lives of AR and non-AR components are assumed 69 days and 690 days, respectively, based on reviews in French McCay (2004).

Table 3.

First-order biodegradation rates (as half-lives) for each pseudo-component.

First-order biodegradation rates (as half-lives) for each pseudo-component.
First-order biodegradation rates (as half-lives) for each pseudo-component.

Photo-oxidation rates were developed using Chen et al.’s (2001) estimates of surface photolysis rates for individual aromatics at 40° N at midday in summer, averaged for the PAH compounds falling within each of the PAH pseudo-components (AR5, AR6, AR7 and AR8). Mean midday midsummer rates were adjusted to a daily midsummer average rate by multiplying by the ratio of the average daily UV light in summer to the midday summer light intensity reported by Mills et al. (1985), and then adjusted to each season using ratios of the mean incident UV intensities by season. These water surface rates were averaged over a 20-meter deep surface layer (the estimated depth of penetration of sufficient UV light to be photoactive; Lay et al., 2015) using an exponential decay model and an extinction coefficient of 0.06/m for 380 nm calculated from radiometer data collected by French McCay et al. (2010) in the offshore Gulf of Mexico. The photolysis rates are summarized in Table 4.

Table 4.

Summary of daily averaged, depth integrated photolysis half-lives (days) applied to the PAH pseudo-components in the upper 20 m.

Summary of daily averaged, depth integrated photolysis half-lives (days) applied to the PAH pseudo-components in the upper 20 m.
Summary of daily averaged, depth integrated photolysis half-lives (days) applied to the PAH pseudo-components in the upper 20 m.

Response Alternatives, Assumptions and Inputs

Surface Application of Dispersants: In model runs including surface application of dispersants, it was assumed that sufficient dispersant supplies were available to treat all actionable floating oil and that application was not restricted. The geographic area where surface dispersant use was assumed to occur was in pre-approval areas in Area Contingency Plans: not within a 5 nmile radius exclusion zone of the release site, in less than 10 m water depth, or within 3 nmiles of a shoreline. Aerial dispersant application was assumed to occur beginning on day 2 of the event at an effective application rate of 1 part dispersant to 20 parts oil (DOR = 1:20) during 12 hours (daylight) each day. The dispersant application was assumed effective when oil thickness exceeded 8 μm (0.0003 in, NOAA, 2010) and on weathered and emulsified oil up to a viscosity of 20,000 cp.

Subsea Application of Dispersants: The DOR for oil treated by SSDI was assumed to be 1:100, which appears to be the most efficient effective ratio based on testing to date (e.g., Brandvick et al., 2013). The SSDI application was assumed to treat 100% of the released oil, start on day 6, and be continuous, 24 hours per day and 7 days a week.

Mechanical removal and ISB: During the DWH oil spill response, once fully mobilized, essentially all available equipment for mechanical removal and ISB was applied. Thus, the achieved removal rates reflect performance and operational capacity. The modeled capacity was assumed to be the maximum monthly average volume removal rate per day over the period of the DWH response, which, based on Lehr et al. (2010), was 10,829 bbl/day of oily water in June 2010, equivalent to 2,166 bbl/day of oil removed. In June 2010, ISB removed an average of 5,372 bbl of oil per day (Lehr et al., 2010). In the model, operations were assumed to start on day 2 and to occur 12 hours a day, except for a 5-nmile exclusion zone around the release site, when environmental conditions were suitable (Etkin et al., 2006) on oil > 8 μm thick (NOAA, 2010).

RESULTS

Based on the OILMAPDeep model analysis, the buoyant plume for this scenario was trapped in an intrusion ~385m above the release point, i.e., at 1015 m below the water surface, with a radius of 77 m. The SIMAP model runs were therefore initialized with releases of free oil droplets at 1015 m within a radius of 77 m. Based on the OILMAPDeep droplet size model (Li et al., 2016), the volume median diameter (VMD) of untreated oil was 3118 μm, and that of the dispersant-treated oil was 267 μm. The droplet size distribution was treated as lognormal, where the VMD is the mean and log(sigma) = 0.5 (Spaulding et al., 2015; Li et al. 2016).

Figure 2 shows the percentage of oil in different phases and environmental compartments over time for the four alternative response options modeled using the worst-case shoreline run as base case. The model results show that removal by mechanical means and ISB amounted to a small fraction of the oil that would otherwise be floating or evaporate. The addition of surface dispersant application to these removal operations only slightly changed the mass balance. Removal and surface dispersant application were primarily limited by the amount of actionable floating oil and daylight. SSDI reduced droplet sizes so that the oil rose more slowly, dispersed and traveled further from the release point, and that which surfaced did so later and as sheens. Compared to the No-Intervention and MBSD cases, SSDI substantially reduced the amount of thick (actionable) oil and mousse on the surface and the shoreline (Figure 3), increased the dissolution rate of S/SS-HCs (BTEX, PAHs, soluble alkanes) and so their degradation rates, increased the weathering rate of rising oil such that floating oil contained much less S/SS-HCs, and decreased VOC emissions to the atmosphere (Figure 2).

Figure 2.

Mass balance over time for the worst-case shoreline model run with alternative response options.

Figure 2.

Mass balance over time for the worst-case shoreline model run with alternative response options.

Figure 3.

Volume of surface floating and shoreline oil over time for the no-intervention and SSDI plus mechanical, in situ burning and surface dispersant response cases.

Figure 3.

Volume of surface floating and shoreline oil over time for the no-intervention and SSDI plus mechanical, in situ burning and surface dispersant response cases.

Areas exposed to floating oil were calculated by summing over all model time steps the area swept by Lagrangian Elements with an oil thickness exceeding various thresholds (as g/m2, where 1 g of non-emulsified oil/m2 is about 1 μm thick). This calculation included areas that may have been exposed multiple times. The results (Figure 4) show that SSDI, as compared to No-Intervention and MBSD, substantially reduced the cumulative footprint of floating oil. Figure 4 also shows the summed exposure of (stationary) biota in the water column to total PAH (AR5 + AR6 + AR7 + AR8) concentrations > 10 μg/L, calculated by summing the volume exceeding the threshold times duration exposed. The results show that water column exposure in the upper 20 m decreased with MBSD, and more so with SSDI (because less oil surfaced), but that exposure in deeper water increased with SSDI.

Figure 4.

Exposure to surface floating oil (km2-days) above indicated thresholds (left and center panel) and water exposure (m3-days) above 10 μg/L total PAH concentration.

Figure 4.

Exposure to surface floating oil (km2-days) above indicated thresholds (left and center panel) and water exposure (m3-days) above 10 μg/L total PAH concentration.

Variations in mass balance and exposure metrics due to environmental conditions were quantified by the coefficient of variation (COV, i.e., standard deviation/mean) of results of 100 stochastic runs for each of 3 response alternatives (Table 5). The results show similar maximum amounts of oil on the surface, in the water column, degraded, and (especially) evaporated among the 100 runs for a single response alternative, i.e., the date and time and environmental conditions do not greatly influence these results. In contrast, shoreline oiling varies considerably, depending mainly on the prevailing wind direction and the differences among the response alternatives are not significant. For this spill site far from land, most runs resulted in little shoreline oiled (and a skewed distribution, such that the median was 0 km oiled).

Table 5.

Exposure metrics (mean, coefficient of variation due to environmental conditions.)

Exposure metrics (mean, coefficient of variation due to environmental conditions.)
Exposure metrics (mean, coefficient of variation due to environmental conditions.)

CONCLUSIONS

The oil spill modeling analysis presented herein showed that even with a substantial capacity of equipment applied, mechanical and ISB removed only a small fraction of the oil that would otherwise be floating or evaporate. Compared to the No-Intervention and MBSD cases, SSDI reduced the size of oil droplets by an order of magnitude, substantially decreased the amount of oil on the water surface and on the shoreline, increased dissolution and degradation rates of hydrocarbons at depth, increased weathering rate of rising oil such that floating oil contained much less S/SS-HCs, and decreased VOC emissions to the atmosphere and therefore reduced human and wildlife exposures to VOCs. The cumulative footprint of floating oil, the amount of actionable thick oil, and the amount of oil coming ashore were substantially reduced by SSDI, as compared to the other response alternatives. Water concentrations of PAHs were reduced in surface waters by SSDI dispersing more oil at depth. The tradeoff was that with SSDI there was greater exposure to PAHs and other S/SS-HCs in deep water. However, densities of biota are much lower in deep water than near the surface, where sensitive early life history stages of fish and invertebrates are most abundant.

The scenario examined was for a single, deep water location in the northeastern Gulf of Mexico, far offshore. Results could vary at other locations with different release conditions. More shoreline exposure would be expected from releases closer to shore. However, the scenario in this study is expected to be representative of the general fate behavior of oil and gas blowouts of similar release rate and conditions in much of the deep water areas of the Gulf of Mexico.

ACKNOWLEDGEMENTS

This work was supported by the American Petroleum Institute (API), Subsea Dispersant Injection Program. The authors appreciate the insightful and constructive comments made by the TAC and other technical reviewers, which greatly contributed to the reliability, applicability, and relevance of the modeling results.

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