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DWH oil spill response sampling stations of R/V Brooks McCall and Ocean Veritas (JAG, 2010, Lee & Ryan, 2010, OSAT, 2010).
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A variety of field sampling programs collected particle size information throughout the water column around the site of the Deepwater Horizon (DWH) oil spill. The particle measurements include: (1) ROV video and camera analyses of suspended particles in the water column and sessile droplets in contact with a marked grid on M/V Jack Fitz 2 cruise; (...
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Chennai, a coastal city in India with a population of over 7 million people, was impacted by a major oil spill on January 28th 2017. The spill occurred when two cargo ships collided about two miles away from the Chennai shoreline. The accident released about 75 metric tons of heavy fuel oil into the Bay of Bengal. This case study provides field obs...
Citations
... Over these long time scales, oil can be transported over hundreds of kilometers (North et al., 2015;French-McCay et al., 2019), making an accurate assessment of its fate very difficult. Some estimates have suggested that up to 70% of the spilled oil from the Deepwater Horizon (DWH) disaster may have formed a deep-sea plume (Passow and Overton, 2021;Ryerson et al., 2012) of oil, which contained droplets ranging from ≤1 μm to hundreds of μm in radius (Li et al., 2015). ...
Bacteria biodegradation of immiscible oil requires cell-droplet encounters, surface attachment, and hydrocarbon metabolism. Chemical dispersants are applied to oil spills to reduce the mean dispersed droplet size, thereby increasing the available surface area for attachment, in attempts to facilitate bacterial biodegradation. However, their effectiveness remains contentious as studies have shown that dispersants can inhibit, enhance, or have no effect on biodegradation. Therefore, questions remain on whether dispersants affect surface attachment or cell viability. Here, using microfluidics and time-lapse microscopy, we directly observe the attachment and growth of the marine bacterium, Alcanivorax borkumensis, on stationary crude oil droplets (5 μm < R < 150 μm) in the presence of Corexit 9500. We show that the average colonization time, or the time comprised of encounters, attachment, and growth, is dependent on droplet size and primarily driven by diffusive encounters. Our results suggest that dispersants do not inhibit or enhance these biophysical processes.
... M o , proportional to d D 3 , is the initial metabolizable oil mass expressed in units of equivalent bacteria (Supplementary Table 1). The average microbial degradation time for deep-water droplet sizes (10 µm to 1 mm [39][40][41] ) is governed by a tradeoff between two processes: encounters and consumption (Fig. 2). The total biodegradation time for an (1) www.nature.com/scientificreports/ ...
Immiscible hydrocarbons occur in the ocean water column as droplets of varying diameters. Although microbial oil degradation is a central process in the remediation of hydrocarbon pollution in marine environments, the relationship between droplet size distribution and oil degradation rates by bacteria remains unclear, with a conflicting history of laboratory studies. Despite this knowledge gap, the use of chemical dispersants in oil spill response and mitigation is based on the rationale that increasing the surface-area-to-volume ratio of droplets will enhance net bacterial biodegradation rates. We demonstrate that this intuitive argument does not apply to most natural marine environments, where the abundance of oil droplets is much lower than in laboratory experiments and droplet-bacteria encounters are the limiting factor. We present a mechanistic encounter-consumption model to predict the characteristic time for oil degradation by marine bacteria as a function of the initial oil concentration, the distribution of droplet sizes, and the initial abundance of oil-degrading bacteria. We find that the tradeoff between the encounter time and the consumption time leads to an optimal droplet size larger than the average size generated by the application of dispersants. Reducing droplet size below this optimum can increase the persistence of oil droplets in the environment from weeks to years. The new perspective granted by this biophysical model of biodegradation that explicitly accounts for oil–microbe encounters changes our understanding of biodegradation particularly in the deep ocean, where droplets are often small and oil concentrations low, and explains degradation rate discrepancies between laboratory and field studies.
... In addition to the sensors mentioned above, cameras are also used to detect the microdroplets and determine their sizes in the intrusion layer. For example, a digital holographic camera (Holocam), video camera or a laser light scattering instrument (e.g., Sequoia's Laser In Situ Scattering and Transmissometer [LISST] instrument) [59] were used during the DWH spill. Other systems, such as Silhouette cameras (SilCam) [60,61] and high-speed camera systems, could also be applied to detect oil droplets [62] in the future. ...
... In order to locate the submerged oil in the intrusion layer and support emergency response, samples were collected to reveal the distributions of submerged oil. The sampling process during the DWH spill was extensive [1,10,30,59,[63][64][65][66][67]. Three groups, including the U.S. Coast Guard oil response team (USCG) [26,55], scientific group [13], and damage assessment group [68,69], monitored the submerged oil by cruise-based sampling and the submarine devices (e.g., AUVs and ROVs). ...
... ROV video identified visible droplets (<1 mm) existed from 801.6-1005.8 m and 1168-1390 m depth, with the median diameter (d 50 ) of droplets ranging from~45-50 µm [59]. The R/V Brooks McCall water samples in the layer included oil droplets in the water column around the leaking wellhead between the depths of 1000 and 1400 m. ...
Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil spill. The subsurface peeling layers of submerged oil droplets were released from the well blowout plume and moved along constant density layers (also known as isopycnals) in the ocean. The submerged oil layers were a challenge to locate during the oil spill response. To better understand and find submerged oil layers, we review the mechanisms of submerged oil formation, along with detection methods and modeling techniques. The principle formation mechanisms under stratified and cross-current conditions and the concepts for determining the depths of the submerged oil layers are reviewed. Real-time in situ detection methods and various sensors were used to reveal submerged oil characteristics, e.g., colored dissolved organic matter and dissolved oxygen levels. Models are used to locate and to predict the trajectories and concentrations of submerged oil. These include deterministic models based on hydrodynamical theory, and probabilistic models exploiting statistical theory. The theoretical foundations, model inputs and the applicability of these models during the Deepwater Horizon oil spill are reviewed, including the pros and cons of these two types of models. Deterministic models provide a comprehensive prediction on the concentrations of the submerged oil and may be calibrated using the field data. Probabilistic models utilize the field observations but only provide the relative concentrations of the submerged oil and potential future locations. We find that the combination of a probabilistic integration of real-time detection with trajectory model output appears to be a promising approach to support emergency response efforts in locating and tracking submerged oil in the field.
... www.nature.com/natrevearthenviron plankton at depth were successfully imaged with this equipment a few kilometres away from the Macondo wellhead 25,31,32 , providing oil droplet size distribution data that were previously unattainable and highlighting a technology that can be used in future deep-sea oil releases 28 . However, imaging data did not capture the oil droplet size distribution at the wellhead, so subsequent modelling 33,34 and laboratory 35,36 efforts have focused on understanding the role of oil-to-gas ratio, source geometry conditions and dispersant application on release trajectories and droplet-size distributions. ...
The 2010 Deepwater Horizon disaster remains the largest single accidental release of oil and gas into the ocean. During the 87-day release, scientists used oceanographic tools to collect wellhead oil and gas samples, interrogate microbial community shifts and activities, and track the chemical composition of dissolved oil in the ocean’s interior. In the decade since the disaster, field and laboratory investigations studied the physics and chemistry of irrupted oil and gas at high pressure and low temperature, the role of chemical dispersants in oil composition and microbial hydrocarbon degradation, and the impact of combined oil, gas and dispersants on the flora and fauna of coastal and deep-sea environments. The multi-faceted, multidisciplinary scientific response to the released oil, gas and dispersants culminated in a better understanding of the environmental factors that influence the short-term and long-term fate and transport of oil in marine settings. In this Review, we summarize the unique aspects of the Deepwater Horizon release and highlight the advances in oil chemistry and microbiology that resulted from novel applications of emerging technologies. We end with an outlook on the applicability of these findings to possible oil releases in future deep-sea drilling locations and newly-opened high-latitude shipping lanes.
... Previously reported in-situ holographic camera ("holocam") observations within the deepwater intrusion confirm the presence of "oil droplets" having 30-400 µm diameter (72) during sampling campaigns conducted after the riser pipe was cut. The holocam is an underwater digital holographic camera that captures a hologram of a precisely defined volume of water; the resulting data can be processed to derive droplet concentrations and size distributions (72). Such microdroplets may have been formed through tip streaming (73,74) under the turbulent conditions caused by the jet of petroleum fluids at the wellhead in the presence of dispersant, a process not modeled in the present version of VDROP-J. ...
Significance
Environmental risks posed by deep-sea petroleum releases are difficult to predict and assess. We developed a physical model of the buoyant jet of petroleum liquid droplets and gas bubbles gushing into the deep sea, coupled with simulated liquid–gas equilibria and aqueous dissolution kinetics of petroleum compounds, for the 2010 Deepwater Horizon disaster. Simulation results are validated by comparisons with extensive observation data collected in the sea and atmosphere near the release site. Simulations predict that chemical dispersant, injected at the wellhead to mitigate environmental harm, increased the entrapment of volatile compounds in the deep sea and thereby improved air quality at the sea surface. Subsea dispersant injection thus lowered human health risks and accelerated response during the intervention.
... No comparisons were made to observations. Observations have recently become available for the oil droplet size distribution data in the surrounding environment of the DWH oil spill in Davis and Loomis (2014), Li et al. (2015) and Spaulding et al. (2015) with which the population-balance models (e.g., Zhao et al., 2015) could be tested. Spaulding et al.'s (2015) review and analysis of the laboratory subsurface dispersant injection studies (Belore, 2014;Brandvik et al., 2014) and the DWH oil spill field observations (Davis and Loomis, 2014) indicated that: (1) the maximum droplet size may not necessarily be dependent on the release orifice diameter, particularly when the opening is large, and (2) the breakup of oil droplets may not be solely dependent on the Weber number, but potentially other dimensionless numbers as wella point consistent with Johansen et al. (2013Johansen et al. ( , 2015. ...
An oil droplet size model was developed for a variety of turbulent conditions based on non-dimensional analysis of disruptive and restorative forces, which is applicable to oil droplet formation under both surface breaking-wave and subsurface-blowout conditions, with or without dispersant application. This new model was calibrated and successfully validated with droplet size data obtained from controlled laboratory studies of dispersant-treated and non-treated oil in subsea dispersant tank tests and field surveys, including the Deep Spill experimental release and the Deepwater Horizon blowout oil spill. This model is an advancement over prior models, as it explicitly addresses the effects of the dispersed phase viscosity, resulting from dispersant application and constrains the maximum stable droplet size based on Rayleigh-Taylor instability that is invoked for a release from a large aperture.
... Thus, the Holocam data provides evidence of small (volume mean diameter <300um) chemically dispersed oil droplets in deep and intermediate waters. Further discussions of the Holocam and other particle size data are in Li et al. (2015). ...
The droplet size distribution (DSD) formed by gas-saturated oil jets is one of the most important characteristics of the flow to understand and model the fate of uncontrolled deep-sea oil spills. The shape of the DSD, generally modeled as a theoretical lognormal, Rosin-Rammler or non-fundamental distribution function, defines the size and the mass volume range of the droplets. Yet, the fundamental DSD shape has received much less attention than the volume median size (d50) and range of the DSD during ten years of research following the Deepwater Horizon (DWH) blowout. To better understand the importance of the distribution function of the droplet size we compare the oil rising time, surface oil mass, and sedimented and beached masses for different DSDs derived from the DWH literature in idealized and applied conditions, while keeping d50 constant. We highlight substantial differences, showing that the probability distribution function of the DSD for far-field modeling is, regardless of the d50, consequential for oil spill response.
Competing time scales involved in rapid rising micro-droplets in comparison to substantially slower biodegradation processes at oil-water interfaces highlights a perplexing question: how do biotic processes occur and alter the fates of oil micro-droplets (<500 μm) in the 400 m thick Deepwater Horizon deep-sea plume? For instance, a 200 μm droplet traverses the plume in ~48 h, while known biodegradation processes require weeks to complete. Using a microfluidic platform allowing microcosm observations of a droplet passing through a bacterial suspension at ecologically relevant length and time scales, we discover that within minutes bacteria attach onto an oil droplet and extrude polymeric streamers that rapidly bundle into an elongated aggregate, drastically increasing drag that consequently slows droplet rising velocity. Results provide a key mechanism bridging competing scales and establish a potential pathway to biodegradation and sedimentations as well as substantially alter physical transport of droplets during a deep-sea oil spill with dispersant.
Advances in microfluidics technology has enabled many discoveries on microbial mechanisms and phenotypes owing to its exquisite controls over biological and chemical environments. However, emulating accurate ecologically relevant flow environments (e.g. microbes around a rising oil droplet) in microfluidics remains challenging. Here, we present a microfluidic platform, i.e. ecology-on-a-chip (eChip), that simulates environmental conditions around an oil droplet rising through ocean water as commonly occurred during a deep-sea oil spill or a natural seep, and enables detailed observations of microbe-oil interactions at scales relevant to marine ecology (i.e. spatial scales of individual bacterium in a dense suspension and temporal scales from milliseconds to weeks or months). Owing to the unique capabilities, we present unprecedented observations of polymeric microbial aggregates formed on rising oil droplets and their associated hydrodynamic impacts including flow fields and momentum budgets. Using the platform with Pseudomonas, Marinobacter, and Alcarnivorax, we have shown that polymeric aggregates formed by them present significant differences in morphology, growth rates, and hydrodynamic impacts. This platform enables us to investigate unexplored array of microbial interactions with oil drops.
