A review on the sinking mechanisms for oil and successful response technologies

Critical shear stress of sunken, No. 6 heavy fuel oil in fresh water

A series of flume- and laboratory-based experiments defined and quantified the thresholds of sunken oil transport using No.6 heavy fuel oil mixed with kaolinite clay. When the sunken oil became mobile, the current-induced bed shear stress exceeded a threshold value specific to the oil, known as critical shear stress (CSS). The oil's CSS was evaluated as a function of water velocity, water temperature, oil condition, and sediment size. Based on experimental results, the stages of oil transport were defined and empirical relationships using the oil's kinematic viscosity (vo) and sediment size were developed to predict oil CSS at each transport stage. For vo<2 × 104 cSt, multiple thresholds of movement were observed: (1) gravity dispersion, (2) rope formation, (3) ripple formation, and (4) break-apart/resuspension. When vo> 6 × 104 cSt, transport was more likely to occur as a single event with the oil remaining intact, saltating over the bed in the direction of flow.

Formation of oil-particle aggregates in the presence of marine algae

After an oil spill, the formation of oil-particle aggregates (OPAs) is associated with the interaction between dispersed oil and marine particulate matter such as phytoplankton, bacteria and mineral particles. Until recently, the combined effect of minerals and marine algae in influencing oil dispersion and OPA formation has rarely been investigated in detail. In this paper, the impacts of a species of flagellate algae Heterosigma akashiwo on oil dispersion and aggregation with montmorillonite were investigated. This study has found that oil coalescence is inhibited due to the adhesion of algal cells on the droplet surface, causing fewer large droplets to be dispersed into the water column and small OPAs to form. Due to the role of biosurfactants in the algae and the inhibition of algae on the swelling of mineral particles, both the oil dispersion efficiency and oil sinking efficiency were improved, which reached 77.6% and 23.5%, respectively at an algal cell concentration (Ca) of 1.0 × 106 cells per mL and a mineral concentration of 300 mg L-1. The volumetric mean diameter of the OPAs decreased from 38.4 μm to 31.5 μm when Ca increased from 0 to 1.0 × 106 cells per mL. At higher turbulent energy, more oil tended to form larger OPAs. The findings may add knowledge about the fate and transport of spilled oil and provide fundamental data for oil spill migration modelling.

Interaction effects of crude oil and nutrient exposure on settlement of coral reef benthos

Anthropogenic stressors increasingly cause ecosystem-level changes to sensitive marine habitats such as coral reefs. Intensification of coastal development and shipping traffic can increase nutrient and oil pollution on coral reefs, yet these two stressors have not been studied in conjunction. Here, we simulate a disturbance scenario exposing carbonate settlement tiles to nutrient and oil pollution in a full-factorial design with four treatments: control, nutrients, oil, and combination to examine community structure and net primary productivity (NPP) of pioneer communities throughout 28 weeks. Compared to the control treatment oil pollution decreased overall settlement and NPP, while nutrients increased turf algae and NPP. However, the combination of these two stressors resulted in similar community composition and NPP as the control. These results indicate that pioneer communities may experience shifts due to nutrient enrichment, and/or oil pollution. However, the timing and duration of an event will influence recovery trajectories requiring further study.

Sunken oil detection and classification using MBES backscatter data

Sunken oil incidents have occurred multiple times in the Bohai Sea over the past ten years. Currently, quick and effective sunken oil detection and classification remains a difficult problem. In this study, sonar detection experiments are conducted to obtain acoustic image samples using a multibeam echosounder (MBES) in a large seawater tank at the bottom of the area where the sunken oil is located. A series of MBES data corrections are constructed to generate backscatter strength images that can reflect the target characteristics directly. Meanwhile, eight-dimensional features are extracted, and a support vector machine (SVM) classification framework is built to classify the sunken oil and other interference targets. The results indicate that the MBES backscatter images provide an alternative approach for detecting and classifying sunken oil. The overall target classification accuracy reaches 88.5% by the SVM algorithm. Thus, this study provides a basis for further investigation of detecting sunken oil.

Integrating marine oil snow and MOSSFA into oil spill response and damage assessment

Marine snow formation and vertical transport are naturally occurring processes that carry organic matter from the surface to deeper waters, providing food and sequestering carbon. During the Deepwater Horizon well blowout, oil was incorporated with marine snow aggregates, triggering a Marine Oil Snow (MOS) Sedimentation and Flocculent Accumulation (MOSSFA) event, that transferred a significant percentage of the total released oil to the seafloor. An improved understanding of processes controlling MOS formation and MOSSFA events is necessary for evaluating their impacts on the fate of spilled oil. Numerical models and predictive tools capable of providing scientific support for oil spill planning, response, and Natural Resource Damage Assessment are being developed to provide information for weighing the ecological trade-offs of response options. Here we offer considerations for oil spill response and recovery when assessing the potential for a MOSSFA event and provide tools to enhance decision-making.

Bayesian sunken oil tracking with SOSim v2: Inference from field and bathymetric data

Sunken oil is often difficult to detect, and few oil spill models are designed to locate and track such oil. Therefore, the multi-modal Bayesian inferential sunken oil model, SOSim (Subsurface Oil Simulator), was expanded in this work for use during emergency response and damage assessment. Rather than requiring hydrodynamic data as input, SOSim v2 accepts available field concentration data, along with default or custom bathymetric data, for inference of the location and trajectory of sunken oil. Novel aspects include inference based on bathymetry and the Coriolis Effect, by constructing a prior likelihood function from sampled bathymetric data, scaled proportionally with field concentration data. SOSim v2 is demonstrated versus field data on the ITB DBL-152 oil spill in the Gulf of Mexico, with sensitivity analysis. Results suggest that the inferential approach presented can be effective for modeling relatively slow-moving pollutant masses such as sunken oil, when field concentration data are available.