Literature DB >> 31789156

The treatment of biodegradation in models of sub-surface oil spills: A review and sensitivity study.

Scott A Socolofsky1, Jonas Gros2, Elizabeth North3, Michel C Boufadel4, Thomas F Parkerton5, E Eric Adams6.   

Abstract

Biodegradation is important for the fate of oil spilled in marine environments, yet parameterization of biodegradation varies across oil spill models, which usually apply constant first-order decay rates to multiple pseudo-components describing an oil. To understand the influence of model parameterization on the fate of subsurface oil droplets, we reviewed existing algorithms and rates and conducted a model sensitivity study. Droplets were simulated from a blowout at 2000 m depth and were either treated with sub-surface dispersant injection (2% dispersant to oil ratio) or untreated. The most important factor affecting oil fate was the size of the droplets, with biodegradation contributing substantially to the fate of droplets ≤0.5 mm. Oil types, which were similar, had limited influence on simulated oil fate. Model results suggest that knowledge of droplet sizes and improved estimation of pseudo-component biodegradation rates and lag times would enhance prediction of the fate and transport of subsurface oil.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  Biodegradation; Blowout; Droplet size; Modeling; Oil; Sub-surface dispersant injection

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Year:  2019        PMID: 31789156     DOI: 10.1016/j.marpolbul.2019.04.018

Source DB:  PubMed          Journal:  Mar Pollut Bull        ISSN: 0025-326X            Impact factor:   5.553


  2 in total

1.  A tradeoff between physical encounters and consumption determines an optimal droplet size for microbial degradation of dispersed oil.

Authors:  Vicente I Fernandez; Roman Stocker; Gabriel Juarez
Journal:  Sci Rep       Date:  2022-03-18       Impact factor: 4.996

2.  Predicting Primary Biodegradation of Petroleum Hydrocarbons in Aquatic Systems: Integrating System and Molecular Structure Parameters using a Novel Machine-Learning Framework.

Authors:  Craig Warren Davis; Louise Camenzuli; Aaron D Redman
Journal:  Environ Toxicol Chem       Date:  2022-04-29       Impact factor: 4.218

  2 in total

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