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Over the latest decades, oil marine pollution has posed a vital threat for global ocean health, since spillages of any scale are related to environmental, social and financial impacts. The worldwide increase in oil and gas demand, and the parallel rise in oil and gas production, exploiting particularly coastal and offshore marine deposits, have significantly increased the risk of accidental oil release to the sea. In the present study, an operational oil spill model was applied to test the oil dispersive properties and to reveal the relative magnitude of weathering processes, after an accidental oil spill release along the main tanker transportation route in the North Aegean Sea. Numerical simulations were implemented using the OpenOil transport and fate numerical model, a subclass of the OpenDrift open-source trajectory framework. This model integrates algorithms with several physical processes, such as oil entrainment, vertical mixing, oil resurfacing and oil emulsification. The oil dispersion model was coupled to real-time met-ocean forecasts received from NOAA-GFS and CMEMS. Present simulation results have focused on the impact of turbulent kinetic energy, induced by the background flow field, on the horizontal spreading of particles, as well as on the evolution of oil mass balance and oil mass properties.

We introduce a model for the dynamics of oil in suspension, appropriate for shallow waters, including the nearshore environment. This model is capable of oil mass conservation and does so by evolving the oil on the sea surface as well as the oil in the subsurface. The shallower portion of the continental shelf poses compounding unique modeling challenges. Many of these relate to the complex nature of advection and dispersion of oil in an environment in which wind, waves, as well as currents all play a role, as does the complex bathymetry and the nearshore geography. In this study we present an overview of the model as well as derive the most fundamental of processes, namely, the shallow water advectiion and dispersion processes. With regard to this basic transport, we superate several fundamental challenges associated with creating a transport model for oil and other buoyant pollutants, capable of capturing the dynamics at the large spatio-temporal scales demanded by environmental and hazard mitigation studies. Some of the strategies are related to dimension reduction and upscaling, and leave discussion of these to companion papers. Here we focus on wave-filtering, ensemble and depth-averaging. Integral to the model is the proposal of an ocean dynamics model that is consistent with the transport. This ocean dynamics model is detailed here. The ocean/oil transport model is applied to a couple of physically-inspired oil-spill problems in demonstrate its specialized capabilities.

Knowledge of the droplet size distribution (DSD) of spilled oil is essential for the accurate prediction of oil transport, dissolution, and biodegradation. Breaking waves play important roles in oil droplet formation in oceanic environments. To understand the effects of breaking waves on oil DSD, oil spill experiments were designed and performed in a large-scale wave tank. A plunging breaker with a height of about 0.4 m was produced using the dispersive focusing method within the tank. Oil placed within the breaker resulted in a DSD that was measured using a shadowgraph camera and found to fit a Gaussian distribution (µ = 1.2 mm, σ2 = 0.29 mm2). For droplets smaller than 1500 µm, the number-based DSD matched the DS1988 correlation [1], which gives N(d) ~ d-2.3, but this was N(d) ~ d-9.7 for droplets larger than 1500 µm. An order of magnitude investigation revealed that a Gaussian volume-based DSD results in a number-based DSD that may be approximated by d-b (with b ≈ 2) for small diameters (relative to the mean), which explains the occurrence of the DS1988 correlation. With the measured wave hydrodynamics, the VDROP model was adopted to simulate the DSD, which closely matched the observed DSD. The present results reduce the empiricism of the DS1988 correlation.