Oil Physical Transport and Weathering Processes: Comparison
View Latest Version
Please note this is a comparison between Version 3 by PANAGIOTA KERAMEA and Version 2 by Catherine Yang.

Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. The behavior of an oil spill in the marine environment depends on a series of physical, chemical, and biological processes that are largely determined by both the properties of leaked oil and the environmental, hydro-meteorological conditions (wave, winds, currents, solar radiation, etc.), and discharge characteristics (instantaneous/continuous, surface/deep-water). The fate and behavior of an oil spill can be influenced by the physico-chemical oil weathering processes: oil spreading, evaporation, emulsification, dissolution, photo-oxidation, biodegradation, and sedimentation, and the physical transport processes, like transport and turbulent mixing, dispersion, and resurfacing.

  • oil spill modeling
  • oil weathering processes
  • biodegradation

1. Oil Weathering Processes

1.1. Spreading

Spreading refers to the creation of a thin film, expanding over the sea surface, as soon as oil is being released [1][2]. Spreading algorithms in oil spill models provide an estimate of the spill thickness or surface area, used for modeling of many transport and fate processes such as evaporation, dispersion, and emulsification. Spreading rate and oil spill thickness depend on the sea surface temperature, oil viscosity, and density [3]. The most widely-used spreading algorithms have been developed by Fay [4][5] and Hoult [6]. The theory of gravitational spreading against viscous resistance is also followed in the Mackay’s fate algorithms [7][8][9], modified versions of which are widely used in operational oil spill models (e.g., MEDSLIK, MEDSLIK-II). Advanced oil spreading algorithms consider processes such as wind shear stresses [10], turbulent mixing and wave breaking [11], and shear spreading. These processes may result in the break-up of the slick into patches and the dispersion and partial resurfacing of oil droplets [12], as well as into natural entrainment [13]. Some recent modifications and improvements in spill spreading estimation appear in the literature (e.g., [10][12][13]). The studies of Korinenko and Malinovsky [14] have shown that at different wind speeds the slicks have an elliptical shape and are oriented in the direction of air flow and that strong winds lead to an increase in the speed of spreading the slick along the main axis. Geng et al. [11] studied the effect of waves on the movement of oil droplets, illustrating that small eddy diffusivities decreasing rapidly with depth result in large horizontal spreading and vice versa. The work of [11] suggests that two-dimensional transport models could be overestimating the spreading of oil. The association of spreading with dispersion seemingly better illustrates the recognized physics of the dispersion process, once the initial gravity-viscosity spreading is accomplished [15]. Generally, spreading is a process with specific model limitations, as it depends on oil characteristics and ocean state, and existing algorithms only partially approximate the actual surface area of real spills. As oil is weathered, it is unevenly distributed into streamers and patches due to wave action and Langmuir circulation [10]. A rigorous solution to the problem requires sea state and oil data that might not be available in the initial stage of an operational spill response. Simecek-Beatty and Lehr [1] used Langmuir circulation models to approximate the merging of oil streaks and modify existing oil spreading parametrizations by estimating a spreading surface area correction due to Langmuir effects. Their model has been validated with measurements from the 1990s North Sea field experiments, and as it requires limited data, it could be successfully incorporated into operational response oil spill models. Another source of uncertainty is that, for computational purposes, oil spill models divide the slick into Lagrangian elements (LEs) or particles and track their movement, which does not directly provide an oil concentration or thickness at specific locations. Each model treats this with a different approach; in ADIOS2 [10] for example, each LE, representing a changing volume of oil, constitutes the center of a Thiessen polygon with a surface area relative to the local density of LEs, allowing the estimation of a variable local thickness, based on the polygon area and oil volume. The approaches followed by Lagrangian oil spill models to compute oil surface area or thickness adds further uncertainty in the spreading estimation.

1.2. Evaporation

Evaporation takes place when the volatile elements of the oil diffuse from the oil and entrain the gaseous stage, while the heavier components of oil remain at sea [16][17]. Evaporation removes most of the volatile fractions of oil to the atmosphere within a few hours, leading to the reduction of oil toxicity in the marine environment [16][18]. However, these compounds are transferred to the atmosphere and in some cases (e.g., large spills close to densely-populated areas), the effects of evaporation might be more toxic [19]. On the contrary, the viscosity of remaining “weathered patches” increases [17], leading to severe physical and chemical effects on the marine environment [16]. The most widely-used analytical method to assess the rate of evaporation is based on the work of Stiver and Mackay [20]. They assessed oil evaporation by means of a mass-transfer coefficient, expressed as function of wind speed, oil spill coverage, oil vapor pressure, and sea surface temperature. This parameterization has been included in the ADIOS1 model [21][22], developed by the National Oceanic and Atmospheric Administration (NOAA). However, this method treats the oil as a uniform element, whose features alter when the slick weathers, a procedure that may decrease the precision in the estimation of oil evaporation rates and is only valid for hydrocarbons with approximately linear distillation curves [10]. Oil is actually a complicated mixture of a large number of different types of chemical compounds; therefore it is vital to differentiate among the various chemical groups, in order to accurately estimate evaporation. A more elaborated and accurate model is the pseudo-component evaporation model of Jones [23]. In the pseudo-component technique, petroleum is assumed to constitute of a comparatively limited number of discrete, non-interacting components (pseudo-components or PCs). Each pseudo-component is handled as a single item with relative vapor pressure and relative mole fraction and molecular weight, and the total evaporation rate of the slick is the sum of the individual rates. A modified version of this model is incorporated in ADIOS 2 [10], while similar approaches of pseudo-component evaporation models are used in other models like in OSCAR [24][25] and SIMAP [26][27]. Contrary to the widely-used Mackay well-mixed boundary layer evaporation model, Fingas [28][29][30] suggested a different approach for oil evaporation modeling. Fingas [29][30] argued that oil evaporation is limited by the oil diffusion and therefore it is not an issue of the wind action on the slick thickness, but on the contrary, oil temperature is the main factor determining the evaporation rate.

1.3. Emulsification

Emulsification is the process by which water is being mixed into the oil. This water-in-oil emulsion in the form of suspended small droplets is often referred to as “mousse” [16][18][31][32][33][34]. It occurs due to wave breaking, inducing sea surface turbulence, while oil composition, temperature, and viscosity play a significant role in the process [35][36][37][38]. As oil viscosity increases, a higher amount of oil emulsifies and this additionally disrupts the rate of evaporation. In parallel, the rate of emulsification expands with increasing wind speed and turbulence at the sea surface [17]. Emulsified droplets may remain in the water column for longer time periods (from months to years). The main effect of emulsification is that it creates an emulsion of considerably increased viscosity, compared to the oil initially spilled, resulting in serious implications for treatment methods. Another important negative effect of emulsification is that it increases the volume of the slick; this means that the cleanup costs are greatly increased. Thus, emulsification is a process with specific model limitations and a crucial role on the impact assessment and response in oil spill modeling. A simple emulsification algorithm has been developed by Mackay [7][8], modified versions of which are currently included in several oil spill models (e.g., MEDSLIK, MEDSLIK-II, SIMAP). A literature overview of emulsification algorithms is given in [39][40][41][42]. Fingas [43][44] and Fingas and Fieldhouse [45][46] introduced a stability index (SI) according to density, viscosity, and type of oil in order to classify the emulsification tendency of oil [47]. In addition, SINTEF’s (Selskapet for INdustriell og TEknisk Forskning) data-based oil weathering model (OWM) [48] can simulate emulsification quite well for certain types of hydrocarbons, for which laboratory or field data exist, by interpolating available data sets. However, a reliable emulsification forecasting algorithm based on environmental conditions and oil properties is not currently available to be incorporated into oil spill models.

1.4. Dissolution

Oil contains very small amounts of soluble compounds (<1 mg/L), which may dissolve in water, but is still considered an important process, since the lower molecular weight aromatic hydrocarbons (monoaromatic and polynuclear aromatic hydrocarbons, MAHs and PAHs), which are both highly volatile and soluble, are the most toxic elements of oil to aquatic organisms. Therefore, dissolution plays a significant role on environmental impact assessment and on response support models. Oil can dissolve in the water column from the surface slick or from dispersed oil droplets [49][50]. Dissolution and evaporation are two competitive processes [51], although evaporation exhibits faster rates and affects larger parts of the spill. Hydrocarbon components of lower molecular weight are highly soluble in seawater and relatively more volatile. Examples include the light hydrocarbons of benzene and toluene, which can dissolve within a few hours [18][49][50]. Generally, dissolution is significant when evaporation is low [17], therefore dissolution is substantial for subsurface oil spills and dispersed oil droplets. This is due to the lack of atmospheric exposure and the higher available oil surface area per unit of volume [47]. The algorithm developed by Mackay [52] is usually applied in oil spill modeling for estimating dissolution from the surface slick. This treats dissolution as a mass flux connected to oil solubility and temperature. Considering dispersed oil, dissolution is usually handled as a mass flux across the surface area of a droplet [52]. In SIMAP [26][27], the pseudo-component approach is followed for modeling oil weathering processes, including evaporation, therefore the dissolution and the toxic effects of lower molecular weight induced by the aromatic compounds to ecosystems can be more accurately estimated.

1.5. Photo-Oxidation

Photo-oxidation occurs when oil under the influence of the sunlight generates polar, water soluble, oxygenated compounds [35]. The process depends on the type of oil [53] and on the thickness of the oil slick [30]. Thick slicks may partially oxidize, generating tar balls, which are accumulated in bottom sediments or leach off the coast long after a leak. Generally photo-oxidation has long been considered a very slow process, with thin oil films dissolving, even in bright sunlight, at rates lower than 0.1% per day [53]. Thus, photo-oxidation is considered unimportant over the first few days of a spill but becomes visible after a week or more [16][52][54]. Therefore, photo-oxidation is not contained in modern oil spill response models. However, studies following the Deepwater Horizon oil spill have indicated that under conditions of high UV light exposure, photo-oxidation can be fast, affecting a considerable fraction of the spilled oil, and can contribute to emulsification [54][55]. Moreover, photo-oxidation alters the physicochemical characteristics of the oil and its related elements, with the oxygenated parts being more polar, expanding the dispersibility and dissolution and ultimately changing the toxicity biodegradability of the oil [56][57]. Currently, the existing numerical models do not consider photo-oxidation, since limited knowledge exists on this process, parametric expressions are lacking, and the importance and rates of the process have not yet been fully studied. Kolpack [58] developed a formulation for the rate of photo-oxidation in terms of the extrapolation of laboratory works to open ocean slicks, although this concept is yet not validated [35].

1.6. Biodegradation

Biodegradation of oil by native microorganisms is one of the most significant natural processes that can attenuate the environmental effects of marine oil spills in the long term. A number of in-depth reviews on this process are cited in the literature [59][60][61][62][63]. The biodegradation rate of oil depends on the type of petroleum hydrocarbons, temperature, species of micro-organisms, and the availability of oxygen and nutrients [59][63][64]. Furthermore, the rate of oil biodegradation [65][66] increases with the available water–oil interface, which for dispersed oil droplets increases as the size of droplets decreases. The application of chemical dispersants to enhance biodegradation by increasing the interfacial region available for biological activity [53] is still debated, as studies during and after the Deepwater Horizon oil spill showed both enhancement and inhibition of biodegradation [67][68]. As knowledge is still limited on how dispersants affect microbial species and their ability to biodegrade oil [68], as well as on the effects of different dispersant-to-oil ratios and the chemistry of oil-dispersant mixtures [67], further in-depth studies are needed to evaluate the application of chemical dispersants as a response option.
Biodegradation was generally considered a long-term oil weathering process, having a significant impact only after the first seven days of a spill and with time scales reaching up to several months; however, studies following the Deepwater Horizon oil spill have shown that under specific conditions it can become a significant process at shorter time scales, within the first week of an oil spill [55]. Therefore, until recently, biodegradation was typically not included in operational oil spill models, and when it was considered, it was treated as a first-order decay process, depending only on oil composition (e.g., SIMAP [26][27]). Although several studies have been conducted measuring biodegradation rates and modeling the kinetics of dissolved oil and dispersed oil droplets, under different environmental conditions (e.g., [69][70][71][72][73][74]), these have not yet been integrated into oil spill models. Of particular importance is the recent work of Brakstad et al. [75][76][77] on oil droplet biodegradation, taking into account the droplet size distribution. This research has been incorporated into NOAA’s GNOME oil spill model, by including a new biodegradation algorithm, which is dependent on the surface area of droplets [66]. Work performed by Kapellos [78] and Kapellos et al. [79] on the effect of biofilm formation, including the development of a shrinking core model, has not yet found its way into operational oil spill models. A comparison of different modeling approaches for oil droplets biodegradation following a deep sea blowout is documented in [65], employing the TAMOC model [80], while the importance of initial oil droplet size distribution and biodegradation for the subsurface transport of oil spills is highlighted in the work of North et al. [81] using LTRANS. Generally, there is a need for a more realistic description of biodegradation kinetics in oil spill models, including oil composition, dispersed oil droplets-water interface, but also other important parameters that may limit biodegradation rate such as microbial population, biofilm formation, and availability of dissolved oxygen and nutrients, to enable a more accurate prediction and evaluation of possible bioremediation scenarios and risk assessment in the mid- and long-term. Such an attempt was recently carried out by modifying the MEDSLIK-II [82][83] model, adding modules describing biodegradation of oil dispersed or dissolved in the water column and improving existing oil transport and weathering subroutines. In this modified version of MEDSLIK-II, the pseudo-component approach has been adopted for simulating weathering processes [84][85]. Biodegradation of petroleum is modelled via Monod kinetics. The kinetics of oil droplets size reduction due to the microbe-mediated degradation at the water-oil particle interface are described by the shrinking core model (SCM) [69][86].

1.7. Sedimentation

Sedimentation of oil droplets occurs as a result of three processes: increased density of the entrained oil and surface slicks due to weathering processes; incorporation of fecal pellets by means of zooplankton or benthic organisms’ ingestion; and oil adherence or flocculation and agglomeration with suspended particulate matter (SPM) aggregates (OSA) [17][30][35][53][87][88][89][90]. For this reason, offshore OSA is a vital process to limit the transport of oil to nearshore benthic areas [90]. Generally, sedimentation of oil causes several impacts on the marine environment and for this reason it is a fundamental process for biological impact analysis and response oil spill modeling. On the other hand, oil sediments in OSA processes may or may not fall to the bottom. Recent works indicated that interactions among oil and sediments are critical in the dispersion and degradation of oil spills [91]. In regions with high SPM concentrations, increased dispersion and removal of oil is accounted for due to ingestion and adhesion [17][24]. Several parameters (e.g., temperature, salinity, wave energy, and physio-chemical oil properties) may control the OSA formation [92][93]. Moreover, the properties and characteristics of sediments constitute a significant role in OSA formation [91][93]. Due to knowledge gaps in properly expressing the detailed dynamics of sedimentation in a quantitative parameterization scheme, data are limited [49][57]. Khelifa et al. [94][95][96] introduced a Monte Carlo scheme, in terms of collision concept between particles of oil droplets and SPM to simulate the formulation of oil-mineral aggregates [15]. Recently, a conceptual development of oil–particle coagulation capability was developed by Zhao et al. [97][98]. Furthermore, the new term MOSSFA (marine oil snow sedimentation and flocculent accumulation) was identified in 2013 [99], after the Deepwater Horizon (DwH) accident, in order to assess the procedures affecting the formation and fate of oil-associated marine snow [100][101][102]. A well-defined schematic diagram of the process of MOSSFA into the water column and its driving parameters is presented in Quigg et al. [102]. Finally, the MOSSFA process has not been incorporated into any existing operational oil spill model.

2. Physical Transport Processes

2.1. Dispersion

Dispersion occurs when the waves or other turbulent events break over the oil slick surface and generate droplets of several sizes into the water column [30][103][104][105][106]. The large droplets resurface to their primary region while the smaller spread and diffuse into the water column [103]. The rate of natural dispersion is influenced by environmental frameworks (i.e., the sea state), but also by oil properties and spill characteristics (oil-film thickness, density, viscosity, oil/water surface tension), developing rapidly with low-viscosity oils in the presence of breaking waves [15][107].
Mackay [52] and Mackay et al. [7] developed an early model of wave entrainment, based on the fraction of the sea surface subjected to dispersion and the fraction of the entrained oil containing fairly small droplets to be constantly dispersed in the water column. Such parameterization considers both oil properties and oil film thickness. The fractional area of the surface slick dispersed at each time step depends on the sea state and is parameterized proportionally to the square of the wind speed. The formulation of Mackay et al. [7] has proven to succeed only at moderate wind speeds [49][108]. Delvigne and Sweeney [103] and Delvigne [109] later developed empirical formulations based on the experimental investigation of natural dispersion due to breaking waves. These commonly used models are empirical relations of the entrainment rate as a function of the dissipation of wave energy per unit area, the fractional area of the sea surface enclosed through breaking waves, and the volume of oil entrained per unit of water volume. The formulations of Mackay et al. [7] and Delvigne and Sweeney [7][103], as well as their modifications, are widely used in operational oil spill models, like ADIOS [10][21], SIMAP [26][110], OSCAR [111][112][113][114], OILMAP [115], MEDSLIK [116][117][118][119], and MEDSLIK-II [82][83]. In OpenOil [105], the method of Li et al. [119120] is followed, which is a modification of the formulation of Delvigne and Sweeney [103], parameterizing the entrainment rate via the dimensionless Weber (We) and Ohnesorge (Oh) numbers. More recent parameterizations of entrainment from wave breaking incorporated the effect of viscosity, density, and oil–water interfacial tension [120][121][122][123]. It should also be noted that the fractional area of the sea surface encased by breaking waves, used to describe the sea state, has specific model limitations and is subject to large uncertainty [10]. It is regularly parameterized via the wind speed (e.g., in [123124], subsequently used in [103]). Currently, there are numerous formulations for the wave-breaking fraction (e.g., [123][124][125][126]). However, vast uncertainty still exists in the linkage between the wind speed and wave breaking areal fraction [105]. The algorithms that are currently employed in oil spill models for natural dispersion, do not handle the knowledge gap of the process well, by assuming wave-averaged Eulerian velocities or mean dissipation rates. Future models should include the wave spectrum and white capping to improve the dispersion parameterizations and droplet formation. This is expected to improve the estimation of dissolution and biodegradation in the water column, as these weathering processes are influenced by oil droplets formation. Such an approach could also improve surface processes such as evaporation and distinguish oil partitioned between evaporation and dispersion.

2.2. Resurfacing of Submerged Oil

Resurfacing of the entrained oil droplets has as a result the movement of oil between the sea surface and the water column. The submerged oil droplets increase by virtue of their buoyancy, which is forced by means of their droplet size and the density difference among oil and water. It quickly proceeds for larger oil droplets, although the small-scale droplets remain in the subsurface for an extended period of time and can only resurface when wave turbulence decreases [10][47][126127]. Oil droplets resurfacing has been modeled by Tkalich and Chan [104] and used in OpenOil [105]. The final vertical velocity relies on the Reynolds number of the flow over the droplet, according to Stokes’ law, for low Reynolds numbers, and an experimental definition for the larger Reynolds numbers. Entrainment of surface oil and the associated droplet size spectra for the submerged oil, naturally affect the estimation of the subsequent oil resurfacing [105][106][127128]. Droplet size distributions of dispersed oil may be declared either as a number size distribution or as a volume size distribution [126127]. Although [103] noticed a power-law number size distribution, current experimental research indicates that the droplet size distribution is better expressed via a lognormal distribution [120121][126127] or as two regimes with various exponents of power-law [126127][128129]. Identified droplet size distributions depend on oil type, sea state, oil weathered state, oil-water interfacial tension, and initial oil slick thickness [120121][126127]. The diameter of oil droplets, used via the droplet size distribution in oil spill models, directly affects oil droplets resurfacing through the calculation of the advective flux due to buoyancy [105].

2.3. Turbulent Mixing

Turbulent mixing moves oil and mixes it into the water column. While buoyancy moves oil droplets in one direction, turbulent mixing transfers oil particles upwards and downwards. It affects mainly smaller oil droplets, diminishing their opportunity to resurface [47][100][104][126127]. This process has a significant role in the vertical interchange among the surface oil spill and the vertical layers of the water column [105]. The main source of uncertainty in oil spill simulations arises from uncertainties in the forcing of models, i.e., ocean circulation, wave and atmospheric coupled models, therefore reliable forecasts are essential for accurately determining the advective transport [129][130][131]. The volume of turbulent mixing is widely represented via an eddy diffusivity coefficient. The eddy diffusivity can be provided by ocean circulation models [131132] or be approximated by the wind speed (e.g., [132133]). Using eddy diffusivities provided by ocean circulation models, the exerted wind forcing is considered, as well as the advection and inertia of turbulence and buoyancy and inhibition through seawater stratification. When turbulent mixing levels are properly increased, the oil particles are maintained in the water column [120121]. Novel ocean models with real-time data present details about the vertical currents, stratification, and turbulent mixing, providing more sensible particle transport representations [12][105][133134]. Vertical mixing algorithms and parameterizations are provided by Galt and Overstreet [13], Röhrs et al. [105], and Nordam et al. [134135]. In Lagrangian particle tracking oil spill models (e.g., MEDSLIK, MEDSLIK-II, OpenOil), the turbulent flux can be expressed via a random walk process, according to Visser [135136]. From this perspective, a random vertical displacement is estimated for each particle (e.g., [105]).

2.4. Transport

Horizontal and vertical transport of oil spilled at sea are separate processes, which are vital for the circulation of oil spills in the sea water [136137]. Horizontal transport includes spreading and advection while vertical transport involves vertical dispersion and wave entrainment, turbulent mixing, and resurfacing. Horizontal transport mainly depends on advection due to ocean currents, waves, and winds, while vertical transport has a crucial aspect, affecting the horizontal transport of oil slicks and generating a mixing layer at the top of the water column via breaking waves [105]. Wind resistance is generally considered to affect only the surface slick, while ocean currents and the wave-induced Stokes drift range according to depth and are therefore also important for the movement of discrete oil parcels in the subsurface [108][137138]. Therefore, in order to simulate the transport and fate of oil spill, a well-described expression of surface slicks is demanded, together with the vertical distribution of submerged oil [105]. The common modeling technique, employed by nearly all oil spill models, to account for the effect of wind on the oil slick floating on the sea surface is to use a “wind factor” approach, i.e., the effect of wind will move oil at a certain fraction of the wind speed and at a certain angle to the wind direction [138139]. However, there is considerable dispute as to what are the most effective options for the values of the drift factor and angle in combination with the sufficient vertical resolution of ocean forecasting models to resolve the vertical structure of the current flow, so that the motion of the surface layer is computed accurately. A comprehensive review on this is given in [139140], while examples of the sensitivity of current depth in oil spill modeling are provided by [83]. Improvements in parameterization of wind drag have been introduced by [140141]. In addition, in the majority of Lagrangian oil spill models (e.g., MEDSLIK, MEDSLIK-II, OpenOil) oil particles/parcels are assigned an advective displacement according to currents, wind and Stokes drift, and a diffusive displacement given by a random walk model.

References

  1. Simecek-Beatty, D.; Lehr, W.J. Extended oil spill spreading with Langmuir circulation. Mar. Pollut. Bull. 2017, 122, 226–235.
  2. Gług, M.; Wąs, J. Modeling of oil spill spreading disasters using combination of Langrangian discrete particle algorithm with Cellular Automata approach. Ocean Eng. 2018, 156, 396–405.
  3. Buist, I.; Twardus, E. In-situ burning of uncontained oil slicks. In Proceedings of the 7th Arctic and Marine Oil Spill Program (AMOP) Technical Seminar, Edmonton, AB, Canada, 12–14 June 1984; Environment Canada: Ottawa, ON, Canada, 1984; pp. 127–154.
  4. Fay, J. The Spread of Oil Slicks on a Calm Sea. In Oil on the Sea; Hoult, D.P., Ed.; Springer: Boston, MA, USA, 1969; pp. 53–63.
  5. Fay, J.A. Physical processes in the spread of oil on a water surface. In Proceedings of the International Oil Spill Conference, Washington, DC, USA, 15–17 June 1971; pp. 463–467.
  6. Hoult, D.P. Oil spreading on the sea. Annu. Rev. Fluid Mech. 1972, 4, 341–368.
  7. Mackay, D.; Buist, I.A.; Mascarenhas, R.; Paterson, S. Oil Spill Processes and Models: Environment Canada Manuscript Report No 8; Environment Canada: Ottawa, ON, Canada, 1980.
  8. Mackay, D.; Paterson, S.; Trudel, K. A Mathematical Model of Oil Spill Behaviour. Report to Research and Development Division, Environment Emergency Branch, Environmental Impact Control Directorate; Environment Canada: Ottawa, ON, Canada, 1980.
  9. Mackay, D.; Shiu, W.Y.; Hossain, K.; Stiver, W.; McCurdy, D. Development and Calibration of an Oil Spill Behavior Model; Toronto University (ONTARIO) Department of Chemical Engineering and Applied Chemistry: Toronto, ON, Canada, 1982.
  10. Lehr, W.; Jones, R.; Evans, M.; Simecek-Beatty, D.; Overstreet, R. Revisions of the ADIOS oil spill model. Environ. Model. Softw. 2002, 17, 189–197.
  11. Geng, X.; Boufadel, M.C.; Ozgokmen, T.; King, T.; Lee, K.; Lu, Y.; Zhao, L. Oil droplets transport due to irregular waves: Development of large-scale spreading coefficients. Mar. Pollut. Bull. 2016, 104, 279–289.
  12. Elliott, A.J. Shear diffusion and the spread of oil in the surface layers of the North Sea. Dtsch. Hydrogr. Z. 1986, 39, 113–137.
  13. Galt, J.; Overstreet, R. Development of Spreading Algorithms for the ROC; Genwest Systems Inc.: Edmonds, WA, USA, 2009; p. 68.
  14. Korinenko, A.E.; Malinovsky, V.V. Field study of film spreading on a sea surface. Oceanologia 2014, 56, 461–475.
  15. Reed, M.; Johansen, Ø.; Brandvik, P.J.; Daling, P.; Lewis, A.; Fiocco, R.; Mackay, D.; Prentki, R. Oil spill modeling towards the close of the 20th century: Overview of the state of the art. Spill Sci. Technol. Bull. 1999, 5, 3–16.
  16. Zafirakou, A. Oil Spill Dispersion Forecasting Models. In Monitoring of Marine Pollution; IntechOpen: London, UK, 2018.
  17. Horn, M. Trajectory and Fate Modelling in Support of the ExxonMobil Eastern Newfoundland Offshore Exploration Drilling Project; RPS: South Kingstown, RI, USA, 2018.
  18. Mishra, A.K.; Kumar, G.S. Weathering of oil spill: Modeling and analysis. Aquat. Procedia 2015, 4, 435–442.
  19. Ocean Studies Board; National Academies of Sciences, Engineering, and Medicine. The Use of Dispersants in Marine Oil Spill Response; National Academies Press: Washington, DC, USA, 2020.
  20. Stiver, W.; Mackay, D. Evaporation rate of spills of hydrocarbons and petroleum mixtures. Environ. Sci. Technol. 1984, 18, 834–840.
  21. Lehr, W.J.; Overstreet, R.; Jones, R.; Watabayashi, G. ADIOS-Automated Data Inquiry for Oil Spills; Environment Canada: Ottawa, ON, Canada, 1992.
  22. Overstreet, R.; Lewandowski, A.; Lehr, W.; Jones, R.; Simecek-Beatty, D.; Calhoun, D. Sensitivity analysis in oil spill models: Case study using ADIOS. In Proceedings of the International Oil Spill Conference, Long Beach, CA, USA, 27 February–2 March 1995; pp. 898–900.
  23. Jones, R. A simplified pseudo-component oil evaporation model. In Proceedings of the Arctic and Marine Oilspill Program Technical Seminar, Vancouver, BC, Canada, 11–13 June 1997; pp. 43–62.
  24. Payne, J.; Kirstein, B.; McNabb, G., Jr.; Lambach, J.; Redding, R.; Jordan, R.; Hom, W.; De Oliveira, C.; Smith, G.; Baxter, D. Multivariate analysis of petroleum weathering in the marine environment–sub Arctic. Environ. Assess. Alsk. Cont. Shelf Final Rep. Princ. Investig. 1984, 21, 423–434.
  25. Reed, M.; Daling, P.S.; Brakstad, O.G.; Singsaas, I.; Faksness, L.-G.; Hetland, B.; Ekrol, N. OSCAR2000: A multi-component 3-dimensional oil spill contingency and response model. In Proceedings of the Arctic and Marine Oilspill Program Technical Seminar, Vancouver, BC, Canada, 14–16 June 2000; pp. 663–680.
  26. French-McCay, D. Development and application of damage assessment modeling: Example assessment for the North Cape oil spill. Mar. Pollut. Bull. 2003, 47, 341–359.
  27. French-McCay, D.; Rowe, J.J. Evaluation of bird impacts in historical oil spill cases using the SIMAP oil spill model. In Proceedings of the Arctic and Marine Oilspill Program Technical Seminar, Edmonton, AB, Canada, 8–10 June 2004; pp. 421–452.
  28. Fingas, M.F. Modeling evaporation using models that are not boundary-layer regulated. J. Hazard. Mater. 2004, 107, 27–36.
  29. Fingas, M.F. Studies on the evaporation regulation mechanisms of crude oil and petroleum products. Adv. Chem. Eng. Sci. 2012, 2, 246–256.
  30. Fingas, M.F. Oil and petroleum evaporation. In Handbook of Oil Spill Science Technology; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015; Volume 2, pp. 205–223.
  31. Fingas, M.; Fieldhouse, B.; Mullin, J. Studies of water-in-oil emulsions and techniques to measure emulsion treating agents. In Proceedings of the Arctic and Marine Oilspill Program Technical Seminar, Vancouver, BC, Canada, 8–10 June 1994; p. 213.
  32. Fingas, M. Water-in-oil emulsion formation: A review of physics and mathematical modelling. Spill Sci. Technol. Bull. 1995, 2, 55–59.
  33. Fingas, M.; Fieldhouse, B.; Lane, J.; Mullin, J. Studies of water-in-oil emulsions: Long-term stability, oil properties, and emulsions formed at sea. In Proceedings of the Arctic and Marine Oilspill Program Technical Seminar, Vancouver, BC, Canada, 14–16 June 2000; pp. 145–160.
  34. Payne, J.R. Petroleum Spills in the Marine Environment: The Chemistry and Formation of Water-in-Oil Emulsions and Tar Balls; CRC Press: Boca Raton, FL, USA, 2018.
  35. Spaulding, M.L. A state-of-the-art review of oil spill trajectory and fate modeling. Oil Chem. Pollut. 1988, 4, 39–55.
  36. Daling, P.S.; Moldestad, M.Ø.; Johansen, Ø.; Lewis, A.; Rødal, J. Norwegian testing of emulsion properties at sea—The importance of oil type and release conditions. Spill Sci. Technol. Bull. 2003, 8, 123–136.
  37. Ashrafizadeh, S.; Motaee, E.; Hoshyargar, V. Emulsification of heavy crude oil in water by natural surfactants. J. Pet. Sci. Eng. 2012, 86, 137–143.
  38. Komaiko, J.; Sastrosubroto, A.; McClements, D.J. Formation of oil-in-water emulsions from natural emulsifiers using spontaneous emulsification: Sunflower phospholipids. J. Agric. Food Chem. 2015, 63, 10078–10088.
  39. MacKay, D.; Zagorski, W. Studies of Water-in-Oil Emulsions; Environment Canada: Ottawa, ON, Canada, 1982.
  40. Council, N.R. Oil in the Sea, Inputs, Fates, and Effects; The National Academies Press: Washington, DC, USA, 1985.
  41. Fingas, M. The evaporation of oil spills: Prediction of equations using distillation data. Spill Sci. Technol. Bull. 1996, 3, 191–192.
  42. Fingas, M.F. The evaporation of oil spills: Development and implementation of new prediction methodology. In Proceedings of the International Oil Spill Conference, Calgary, AB, Canada, 2–4 June 1999; pp. 281–287.
  43. Fingas, M.F. Oil Spill Science and Technology: Prevention, 1st ed.; Gulf Professional Publishing: Amsterdam, The Netherlands, 2011; p. 1192.
  44. Fingas, M. Models for water-in-oil emulsion formation. In Oil Spill Science and Technology; Elsevier: Amsterdam, The Netherlands, 2011; pp. 243–273.
  45. Fingas, M.; Fieldhouse, B. Water-in-oil emulsions: Formation and prediction. In Handbook of Oil Spill Science Technology; John Wiley & Sons: Hoboken, NJ, USA, 2014; p. 225.
  46. Fingas, M.; Fieldhouse, B. Studies on crude oil and petroleum product emulsions: Water resolution and rheology. Colloids Surf. A Physicochem. Eng. Asp. 2009, 333, 67–81.
  47. Spaulding, M.L. State of the art review and future directions in oil spill modeling. Mar. Pollut. Bull. 2017, 115, 7–19.
  48. Daling, P.S.; StrØm, T. Weathering of oils at sea: Model/field data comparisons. Spill Sci. Technol. Bull. 1999, 5, 63–74.
  49. ASCE. State-of-the-art review of modeling transport and fate of oil spills. J. Hydraul. Eng. 1996, 122, 594–609.
  50. ITOPF. Fate of Marine Oil Spills. Available online: https://www.itopf.org/knowledge-resources/documents-guides/fate-of-oil-spills/ (accessed on 17 April 2014).
  51. Abianeh, O.S.; Chen, C. Modelling of evaporation and dissolution of multicomponent oil droplet in shallow water. Adv. Comput. Methods Exp. Heat Transf. 2012, 12, 231.
  52. Mackay, D. Mathematical Model of the Behavior of Oil Spills on Water with Natural and Chemical Dispersion; Environment Canada: Ottawa, ON, Canada, 1977.
  53. ITOPF. Oil Tanker Spill Statistics 2011; The International Tanker Owners Pollution Federation Limited: London, UK, 2011.
  54. Ward, C.P.; Overton, E.B. How the 2010 Deepwater Horizon spill reshaped our understanding of crude oil photochemical weathering at sea: A past, present, and future perspective. Environ. Sci. Process. Impacts 2020, 22, 1125–1138.
  55. Ward, C.P.; Sharpless, C.M.; Valentine, D.L.; French-McCay, D.P.; Aeppli, C.; White, H.K.; Rodgers, R.P.; Gosselin, K.M.; Nelson, R.K.; Reddy, C.M. Partial photochemical oxidation was a dominant fate of Deepwater Horizon surface oil. Environ. Sci. Technol. 2018, 52, 1797–1805.
  56. Wang, Z.; Fingas, M.F. Development of oil hydrocarbon fingerprinting and identification techniques. Mar. Pollut. Bull. 2003, 47, 423–452.
  57. Shankar, R.; Shim, W.J.; An, J.G.; Yim, U.H. A practical review on photooxidation of crude oil: Laboratory lamp setup and factors affecting it. Water Res. 2015, 68, 304–315.
  58. Kolpack, R.; Plutchak, N.; Stearns, R. Fate of Oil in Water Environment±Phase II, a Dynamic Model of the Mass Balance for Released Oil; University of Southern California: Washington, DC, USA, 1977.
  59. Das, N.; Chandran, P. Microbial degradation of petroleum hydrocarbon contaminants: An overview. Biotechnol. Res. Int. 2011, 2011, 941810.
  60. McGenity, T.J.; Folwell, B.D.; McKew, B.A.; Sanni, G.O. Marine crude-oil biodegradation: A central role for interspecies interactions. Aquat. Biosyst. 2012, 8, 10.
  61. Xue, J.; Yu, Y.; Bai, Y.; Wang, L.; Wu, Y. Marine oil-degrading microorganisms and biodegradation process of petroleum hydrocarbon in marine environments: A review. Curr. Microbiol. 2015, 71, 220–228.
  62. Li, Z.; McCay, D.F. Review of Hydrocarbon Biodegradation Rates for Use in Modeling Oil Fate in Seawater; RPS ASA Group: Wakefield, RI, USA, 2016.
  63. Ławniczak, Ł.; Woźniak-Karczewska, M.; Loibner, A.P.; Heipieper, H.J.; Chrzanowski, Ł. Microbial degradation of hydrocarbons—Basic principles for bioremediation: A review. Molecules 2020, 25, 856.
  64. Kostka, J.E.; Joye, S.B.; Overholt, W.; Bubenheim, P.; Hackbusch, S.; Larter, S.R.; Liese, A.; Lincoln, S.A.; Marietou, A.; Müller, R. Biodegradation of petroleum hydrocarbons in the deep sea. In Deep Oil Spills; Springer: Cham, Switzerland, 2020; pp. 107–124.
  65. Socolofsky, S.A.; Gros, J.; North, E.; Boufadel, M.C.; Parkerton, T.F.; Adams, E.E. The treatment of biodegradation in models of sub-surface oil spills: A review and sensitivity study. Mar. Pollut. Bull. 2019, 143, 204–219.
  66. Thrift-Viveros, D.L.; Jones, R.; Boufadel, M. Development of a new oil biodegradation algorithm for NOAA’s oil spill modeling suite (GNOME/ADIOS). In Proceedings of the 38th AMOP Technical Seminar, Vancouver, BC, Canada, 2–4 June 2015; Environment Canada: Ottawa, ON, Canada, 2015; pp. 143–152.
  67. Kleindienst, S.; Paul, J.H.; Joye, S.B. Using dispersants after oil spills: Impacts on the composition and activity of microbial communities. Nat. Rev. Microbiol. 2015, 13, 388–396.
  68. Rahsepar, S.; Smit, M.P.J.; Murk, A.J.; Rijnaarts, H.H.M.; Langenhoff, A.A.M. Chemical dispersants: Oil biodegradation friend or foe? Mar. Pollut. Bull. 2016, 108, 113–119.
  69. Vilcáez, J.; Li, L.; Hubbard, S.S. A new model for the biodegradation kinetics of oil droplets: Application to the Deepwater Horizon oil spill in the Gulf of Mexico. Geochem. Trans. 2013, 14, 4.
  70. Yassine, M.H.; Wu, S.; Suidan, M.T.; Venosa, A.D. Aerobic biodegradation kinetics and mineralization of six petrodiesel/soybean-biodiesel blends. Environ. Sci. Technol. 2013, 47, 4619–4627.
  71. Olson, G.M.; Gao, H.; Meyer, B.M.; Miles, M.S.; Overton, E.B. Effect of Corexit 9500A on Mississippi Canyon crude oil weathering patterns using artificial and natural seawater. Heliyon 2017, 3, e00269.
  72. McFarlin, K.M.; Prince, R.C.; Perkins, R.; Leigh, M.B. Biodegradation of Dispersed Oil in Arctic Seawater at −1 °C. PLoS ONE 2014, 9, e84297.
  73. Denis, B.; Pérez, O.A.; Lizardi-Jiménez, M.A.; Dutta, A. Numerical evaluation of direct interfacial uptake by a microbial consortium in an airlift bioreactor. Int. Biodeterior. Biodegrad. 2017, 119, 542–551.
  74. García-Cruz, N.U.; Valdivia-Rivera, S.; Narciso-Ortiz, L.; García-Maldonado, J.Q.; Uribe-Flores, M.M.; Aguirre-Macedo, M.L.; Lizardi-Jiménez, M.A. Diesel uptake by an indigenous microbial consortium isolated from sediments of the Southern Gulf of Mexico: Emulsion characterisation. Environ. Pollut. 2019, 250, 849–855.
  75. Brakstad, O.G.; Nordtug, T.; Throne-Holst, M. Biodegradation of dispersed Macondo oil in seawater at low temperature and different oil droplet sizes. Mar. Pollut. Bull. 2015, 93, 144–152.
  76. Brakstad, O.G.; Almås, I.K.; Krause, D.F. Biotransformation of natural gas and oil compounds associated with marine oil discharges. Chemosphere 2017, 182, 555–558.
  77. Brakstad, O.G.; Daling, P.S.; Faksness, L.G.; Almås, I.K.; Vang, S.H.; Syslak, L.; Leirvik, F. Depletion and biodegradation of hydrocarbons in dispersions and emulsions of the Macondo 252 oil generated in an oil-on-seawater mesocosm flume basin. Mar. Pollut. Bull. 2014, 84, 125–134.
  78. Kapellos, G.E. Chapter 2—Microbial Strategies for Oil Biodegradation. In Modeling of Microscale Transport in Biological Processes; Becker, S.M., Ed.; Academic Press: Cambridge, MA, USA, 2017; pp. 19–39.
  79. Kapellos, G.E.; Paraskeva, C.A.; Kalogerakis, N.; Doyle, P.S. Theoretical Insight into the Biodegradation of Solitary Oil Microdroplets Moving through a Water Column. Bioengineering 2018, 5, 15.
  80. TAMOC—Texas A&M Oilspill Calculator. Available online: http://github.com/socolofs/tamoc (accessed on 10 October 2020).
  81. North, E.W.; Adams, E.E.; Thessen, A.E.; Schlag, Z.; He, R.; Socolofsky, S.A.; Masutani, S.M.; Peckham, S.D. The influence of droplet size and biodegradation on the transport of subsurface oil droplets during the Deepwater Horizon spill: A model sensitivity study. Environ. Res. Lett. 2015, 10, 024016.
  82. De Dominicis, M.; Pinardi, N.; Zodiatis, G.; Lardner, R. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting—Part 1: Theory. Geosci. Model Dev. 2013, 6, 1851–1869.
  83. De Dominicis, M.; Pinardi, N.; Zodiatis, G.; Archetti, R. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting-Part 2: Numerical simulations and validations. Geosci. Model Dev. 2013, 6, 1871–1888.
  84. Spanoudaki, K.; Kampanis, N.; Kalogerakis, N.; Zodiatis, G.; Kozyrakis, G. Modelling of oil spills from deep sea releases. EGUGA 2018, 20, 16401.
  85. Zodiatis, G.; Lardner, R.; Spanoudaki, K.; Sofianos, S.; Radhakrishnan, H.; Coppini, G.; Liubartseva, S.; Kampanis, N.; Krokos, G.; Hoteit, I.; et al. Oil Spill Modelling Processes; Makarynskyy, O., Ed.; Elsevier: Amsterdam, The Netherlands, 2021.
  86. Levenspiel, O. Chemical reaction engineering. Ind. Eng. Chem. Res. 1999, 38, 4140–4143.
  87. Li, H.; Bao, M.; Li, Y.; Zhao, L.; King, T.; Xie, Y. Effects of suspended particulate matter, surface oil layer thickness and surfactants on the formation and transport of oil-sediment aggregates (OSA). Int. Biodeterior. Biodegrad. 2020, 149, 104925.
  88. Sun, J.; Zheng, X. A review of oil-suspended particulate matter aggregation—a natural process of cleansing spilled oil in the aquatic environment. J. Environ. Monit. 2009, 11, 1801–1809.
  89. Gustitus, S.A.; Clement, T.P. Formation, fate, and impacts of microscopic and macroscopic oil-sediment residues in nearshore marine environments: A critical review. Rev. Geophys. 2017, 55, 1130–1157.
  90. Payne, J.R.; Clayton, J.R.; Kirstein, B.E. Oil/Suspended Particulate Material Interactions and Sedimentation. Spill Sci. Technol. Bull. 2003, 8, 201–221.
  91. Gong, Y.; Zhao, X.; Cai, Z.; O’Reilly, S.E.; Hao, X.; Zhao, D. A review of oil, dispersed oil and sediment interactions in the aquatic environment: Influence on the fate, transport and remediation of oil spills. Mar. Pollut. Bull. 2014, 79, 16–33.
  92. Loh, A.; Shim, W.J.; Ha, S.Y.; Yim, U.H. Oil-suspended particulate matter aggregates: Formation mechanism and fate in the marine environment. Ocean Sci. J. 2014, 49, 329–341.
  93. Gao, Y.; Zhao, X.; Ju, Z.; Yu, Y.; Qi, Z.; Xiong, D. Effects of the suspended sediment concentration and oil type on the formation of sunken and suspended oils in the Bohai Sea. Environ. Sci. Process. Impacts 2018, 20, 1404–1413.
  94. Khelifa, A.; Ajijolaiya, L.; MacPherson, P.; Lee, K.; Hill, P.; Gharbi, S.; Blouin, M. Validation of OMA Formation in Cold Brackish and Sea Waters; Environment Canada: Ottawa, ON, Canada, 2005; Volume 1, pp. 527–538.
  95. Khelifa, A.; Stoffyn-Egli, P.; Hill, P.; Lee, K. Effects of salinity and clay type on oil-mineral aggregation. Mar. Environ. Res. 2005, 59, 235–254.
  96. Khelifa, A.; Hill, P.S.; Lee, K. A comprehensive numerical approach to predict oil-mineral aggregate (oma) formation following oil spills in aquatic environments. Int. Oil Spill Conf. Proc. 2005, 2005, 873–877.
  97. Zhao, L.; Boufadel, M.C.; Lee, K.; King, T.; Loney, N.; Geng, X. Evolution of bubble size distribution from gas blowout in shallow water. J. Geophys. Res. Ocean. 2016, 121, 1573–1599.
  98. Zhao, L.; Boufadel, M.C.; Geng, X.; Lee, K.; King, T.; Robinson, B.H.; Fitzpatrick, F.A. A-DROP: A predictive model for the formation of oil particle aggregates (OPAs). Mar. Pollut. Bull. 2016, 106, 245–259.
  99. Daly, K.L.; Passow, U.; Chanton, J.; Hollander, D. Assessing the impacts of oil-associated marine snow formation and sedimentation during and after the Deepwater Horizon oil spill. Anthropocene 2016, 13, 18–33.
  100. Foekema, E.M.; van Eenennaam, J.S.; Hollander, D.J.; Langenhoff, A.M.; Oldenburg, T.B.P.; Radović, J.R.; Rohal, M.; Romero, I.C.; Schwing, P.T.; Murk, A.J. Testing the Effect of MOSSFA (Marine Oil Snow Sedimentation and Flocculent Accumulation) Events in Benthic Microcosms. In Scenarios and Responses to Future Deep Oil Spills; Springer: Cham, Switzerland, 2020; pp. 288–299.
  101. Daly, K.L.; Vaz, A.C.; Paris, C.B. Physical Processes Influencing the Sedimentation and Lateral Transport of MOSSFA in the NE Gulf of Mexico. In Scenarios and Responses to Future Deep Oil Spills; Springer: Cham, Switzerland, 2020; pp. 300–314.
  102. Quigg, A.; Passow, U.; Daly, K.L.; Burd, A.; Hollander, D.J.; Schwing, P.T.; Lee, K. marine oil snow sedimentation and flocculent accumulation (MOSSFA) events: Learning from the past to predict the future. In Deep Oil Spills; Springer: Cham, Switzerland, 2020; pp. 196–220.
  103. Delvigne, G.A.L.; Sweeney, C.E. Natural dispersion of oil. Oil Chem. Pollut. 1988, 4, 281–310.
  104. Tkalich, P.; Chan, E.S. Vertical mixing of oil droplets by breaking waves. Mar. Pollut. Bull. 2002, 44, 1219–1229.
  105. Röhrs, J.; Dagestad, K.F.; Asbjørnsen, H.; Nordam, T.; Skancke, J.; Jones, C.E.; Brekke, C. The effect of vertical mixing on the horizontal drift of oil spills. Ocean Sci. 2018, 14, 1581–1601.
  106. Zeinstra-Helfrich, M.; Murk, A.J. Effects of Oil Properties and Slick Thickness on Dispersant Field Effectiveness and Oil Fate. In Deep Oil Spills; Springer: Cham, Switzerland, 2020; pp. 155–169.
  107. Buist, I.; Potter, S.; Mackay, D.; Charles, M. Laboratory studies on the behavior and cleanup of waxy crude oil spills. In Proceedings of the International Oil Spill Conference, San Antonio, TX, USA, 13–16 February 1989; pp. 105–113.
  108. Reed, M.; Turner, C.; Odulo, A. The role of wind and emulsification in modelling oil spill and surface drifter trajectories. Spill Sci. Technol. Bull. 1994, 1, 143–157.
  109. Delvigne, G.A. Natural dispersion of oil by different sources of turbulence. In Proceedings of the International Oil Spill Conference, Tampa, FL, USA, 29 March–1 April 1993; pp. 415–419.
  110. French-McCay, D.; Isaji, T. Evaluation of the consequences of chemical spills using modeling: Chemicals used in deepwater oil and gas operations. Environ. Model. Softw. 2004, 19, 629–644.
  111. Reed, M.; Rye, H. A three-dimensional oil and chemical spill model for environmental impact assessment. In Proceedings of the International Oil Spill Conference, Long Beach, CA, USA, 27 February–2 March 1995; pp. 61–66.
  112. Reed, M.; Aamo, O.M.; Daling, P.S. Quantitative analysis of alternate oil spill response strategies using OSCAR. Spill Sci. Technol. Bull. 1995, 2, 67–74.
  113. Aamo, O.; Downing, K.; Reed, M. Calibration, verification, and sensitivity analysis of the IKU oil spill contingency and response (OSCAR) model system. Report 1996, 42, 4048.
  114. Aamo, O.M.; Reed, M.; Downing, K. Oil spill contingency and response (OSCAR) model system: Sensitivity studies. In Proceedings of the International Oil Spill Conference, Fort Lauderdale, FL, USA, 7–10 April 1997; pp. 429–438.
  115. Spaulding, M.; Kolluru, V.; Anderson, E.; Howlett, E. Application of three-dimensional oil spill model (WOSM/OILMAP) to hindcast the Braer spill. Spill Sci. Technol. Bull. 1994, 1, 23–35.
  116. Lardner, R.; Zodiatis, G.; Loizides, L.; Demetropoulos, A. An operational oil spill model for the Levantine Basin (Eastern Mediterranean Sea). In Proceedings of the International Symposium on Marine Pollution, Monaco, 5–9 October 1998; IAEA: Vienna, Austria, 1999.
  117. Lardner, R.; Zodiatis, G.; Hayes, D.; Pinardi, N. Application of the MEDSLIK Oil Spill Model to the Lebanese Spill of July 2006; European Group of Experts on Satellite Monitoring of Sea Based Oil Pollution; European Communities: Brussels, Belgium, 2006; pp. 1018–5593.
  118. Zodiatis, G.; Lardner, R.; Hayes, D.; Georgiou, G.; Pinardi, N.; De Dominicis, M.; Panayidou, X. The Mediterranean oil spill and trajectory prediction model in assisting the EU response agencies. In Proceedings of the Congreso Nacional de Salvamento en la Mar, Cadiz, Spain, 2–4 October 2008; pp. 2–4.
  119. Li, C.; Miller, J.; Wang, J.; Koley, S.; Katz, J. Size distribution and dispersion of droplets generated by impingement of breaking waves on oil slicks. J. Geophys. Res. Ocean. 2017, 122, 7938–7957. Zodiatis, G., Lardner, R., Spanoudaki, K., Sofianos, S., Radhakrishnan, H., Coppini, G., Liubartseva, S., Kampanis, N., Krokos, G., Hoteit, I., Tintoré, J., Eremina, T., and Drago, A. . Oil spill modelling assessment; Oleg Makarinskyy, Eds.; Elsevier: Amsterdam, The Netherlands, 2021; pp. 145-197.
  120. Li, Z.; Spaulding, M.; McCay, D.F.; Crowley, D.; Payne, J.R. Development of a unified oil droplet size distribution model with application to surface breaking waves and subsea blowout releases considering dispersant effects. Mar. Pollut. Bull. 2017, 114, 247–257. Li, C.; Miller, J.; Wang, J.; Koley, S.; Katz, J. Size distribution and dispersion of droplets generated by impingement of breaking waves on oil slicks. J. Geophys. Res. Ocean. 2017, 122, 7938–7957.
  121. Zeinstra-Helfrich, M.; Koops, W.; Murk, A.J. How oil properties and layer thickness determine the entrainment of spilled surface oil. Mar. Pollut. Bull. 2016, 110, 184–193. Li, Z.; Spaulding, M.; McCay, D.F.; Crowley, D.; Payne, J.R. Development of a unified oil droplet size distribution model with application to surface breaking waves and subsea blowout releases considering dispersant effects. Mar. Pollut. Bull. 2017, 114, 247–257.
  122. Reed, M.; Johansen, O.; Leirvik, F.; Brørs, B. Numerical Algorithm to Compute the Effects of BreakingWaves on Surface Oil Spilled at Sea; Final Report Submitted to the Coastal Response Research Center; Report F; SINTEF Institute for Materials and Chemistry: Trondheim, Norway, 2009; p. 131. Zeinstra-Helfrich, M.; Koops, W.; Murk, A.J. How oil properties and layer thickness determine the entrainment of spilled surface oil. Mar. Pollut. Bull. 2016, 110, 184–193.
  123. Holthuijsen, L.; Herbers, T. Statistics of breaking waves observed as whitecaps in the open sea. J. Phys. Oceanogr. 1986, 16, 290–297. Reed, M.; Johansen, O.; Leirvik, F.; Brørs, B. Numerical Algorithm to Compute the Effects of BreakingWaves on Surface Oil Spilled at Sea; Final Report Submitted to the Coastal Response Research Center; Report F; SINTEF Institute for Materials and Chemistry: Trondheim, Norway, 2009; p. 131.
  124. Callaghan, A.H.; Deane, G.B.; Stokes, M.D. Observed physical and environmental causes of scatter in whitecap coverage values in a fetch-limited coastal zone. J. Geophys. Res. Ocean. 2008, 113, C05022. Holthuijsen, L.; Herbers, T. Statistics of breaking waves observed as whitecaps in the open sea. J. Phys. Oceanogr. 1986, 16, 290–297.
  125. Zhao, D.; Toba, Y. Dependence of whitecap coverage on wind and wind-wave properties. J. Oceanogr. 2001, 57, 603–616. Callaghan, A.H.; Deane, G.B.; Stokes, M.D. Observed physical and environmental causes of scatter in whitecap coverage values in a fetch-limited coastal zone. J. Geophys. Res. Ocean. 2008, 113, C05022.
  126. Johansen, Ø.; Reed, M.; Bodsberg, N.R. Natural dispersion revisited. Mar. Pollut. Bull. 2015, 93, 20–26. Zhao, D.; Toba, Y. Dependence of whitecap coverage on wind and wind-wave properties. J. Oceanogr. 2001, 57, 603–616.
  127. Zeinstra-Helfrich, M.; Koops, W.; Murk, A.J. Predicting the consequence of natural and chemical dispersion for oil slick size over time. J. Geophys. Res. Ocean. 2017, 122, 7312–7324. Johansen, Ø.; Reed, M.; Bodsberg, N.R. Natural dispersion revisited. Mar. Pollut. Bull. 2015, 93, 20–26.
  128. Li, Z.; Spaulding, M.L.; French-McCay, D. An algorithm for modeling entrainment and naturally and chemically dispersed oil droplet size distribution under surface breaking wave conditions. Mar. Pollut. Bull. 2017, 119, 145–152. Zeinstra-Helfrich, M.; Koops, W.; Murk, A.J. Predicting the consequence of natural and chemical dispersion for oil slick size over time. J. Geophys. Res. Ocean. 2017, 122, 7312–7324.
  129. Barker, C.H.; Kourafalou, V.H.; Beegle-Krause, C.J.; Boufadel, M.; Bourassa, M.A.; Buschang, S.G.; Androulidakis, Y.; Chassignet, E.P.; Dagestad, K.-F.; Danmeier, D.G. Progress in Operational Modeling in Support of Oil Spill Response. J. Mar. Sci. Eng. 2020, 8, 668. Li, Z.; Spaulding, M.L.; French-McCay, D. An algorithm for modeling entrainment and naturally and chemically dispersed oil droplet size distribution under surface breaking wave conditions. Mar. Pollut. Bull. 2017, 119, 145–152.
  130. Hou, X.; Hodges, B.R. Hydrodynamic Uncertainty in Oil Spill Modeling; Center for Research in Water Resources, University of Texas at Austin: Austin, TX, USA, 2013. Barker, C.H.; Kourafalou, V.H.; Beegle-Krause, C.J.; Boufadel, M.; Bourassa, M.A.; Buschang, S.G.; Androulidakis, Y.; Chassignet, E.P.; Dagestad, K.-F.; Danmeier, D.G. Progress in Operational Modeling in Support of Oil Spill Response. J. Mar. Sci. Eng. 2020, 8, 668.
  131. Warner, J.C.; Sherwood, C.R.; Arango, H.G.; Signell, R.P. Performance of four turbulence closure models implemented using a generic length scale method. Ocean Model. 2005, 8, 81–113. Hou, X.; Hodges, B.R. Hydrodynamic Uncertainty in Oil Spill Modeling; Center for Research in Water Resources, University of Texas at Austin: Austin, TX, USA, 2013.
  132. Sundby, S. A one-dimensional model for the vertical distribution of pelagic fish eggs in the mixed layer. Deep Sea Res. Part A Oceanogr. Res. Pap. 1983, 30, 645–661. Warner, J.C.; Sherwood, C.R.; Arango, H.G.; Signell, R.P. Performance of four turbulence closure models implemented using a generic length scale method. Ocean Model. 2005, 8, 81–113.
  133. Sperrevik, A.K.; Röhrs, J.; Christensen, K.H. Impact of data assimilation on E ulerian versus L agrangian estimates of upper ocean transport. J. Geophys. Res. Ocean. 2017, 122, 5445–5457. Sundby, S. A one-dimensional model for the vertical distribution of pelagic fish eggs in the mixed layer. Deep Sea Res. Part A Oceanogr. Res. Pap. 1983, 30, 645–661.
  134. Nordam, T.; Kristiansen, R.; Nepstad, R.; Röhrs, J. Numerical analysis of boundary conditions in a Lagrangian particle model for vertical mixing, transport and surfacing of buoyant particles in the water column. Ocean Model. 2019, 136, 107–119. Sperrevik, A.K.; Röhrs, J.; Christensen, K.H. Impact of data assimilation on E ulerian versus L agrangian estimates of upper ocean transport. J. Geophys. Res. Ocean. 2017, 122, 5445–5457.
  135. Visser, A.W. Using random walk models to simulate the vertical distribution of particles in a turbulent water column. Mar. Ecol. Prog. Ser. 1997, 158, 275–281. Nordam, T.; Kristiansen, R.; Nepstad, R.; Röhrs, J. Numerical analysis of boundary conditions in a Lagrangian particle model for vertical mixing, transport and surfacing of buoyant particles in the water column. Ocean Model. 2019, 136, 107–119.
  136. Li, Y.; Zhu, J.; Wang, H. The impact of different vertical diffusion schemes in a three-dimensional oil spill model in the Bohai Sea. Adv. Atmos. Sci. 2013, 30, 1569–1586. Visser, A.W. Using random walk models to simulate the vertical distribution of particles in a turbulent water column. Mar. Ecol. Prog. Ser. 1997, 158, 275–281.
  137. Drivdal, M.; Broström, G.; Christensen, K. Wave-induced mixing and transport of buoyant particles: Application to the Statfjord A oil spill. Ocean Sci. 2014, 10, 977. Li, Y.; Zhu, J.; Wang, H. The impact of different vertical diffusion schemes in a three-dimensional oil spill model in the Bohai Sea. Adv. Atmos. Sci. 2013, 30, 1569–1586.
  138. Lardner, R.; Zodiatis, G. Modelling oil plumes from subsurface spills. Mar. Pollut. Bull. 2017, 124, 94–101. Drivdal, M.; Broström, G.; Christensen, K. Wave-induced mixing and transport of buoyant particles: Application to the Statfjord A oil spill. Ocean Sci. 2014, 10, 977.
  139. Zodiatis, G.; Lardner, R.; Alves, T.M.; Krestenitis, Y.; Perivoliotis, L.; Sofianos, S.; Spanoudaki, K. Oil spill forecasting (prediction). J. Mar. Res. 2017, 75, 923–953. Lardner, R.; Zodiatis, G. Modelling oil plumes from subsurface spills. Mar. Pollut. Bull. 2017, 124, 94–101.
  140. Zhu, H.; You, J.; Zhao, H. An experimental investigation of underwater spread of oil spill in a shear flow. Mar. Pollut. Bull. 2017, 116, 156–166. Zodiatis, G.; Lardner, R.; Alves, T.M.; Krestenitis, Y.; Perivoliotis, L.; Sofianos, S.; Spanoudaki, K. Oil spill forecasting (prediction). J. Mar. Res. 2017, 75, 923–953.
  141. Zhu, H.; You, J.; Zhao, H. An experimental investigation of underwater spread of oil spill in a shear flow. Mar. Pollut. Bull. 2017, 116, 156–166.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback