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Modelling and Simulation of Marine Oil Snow Formation.

T R Akshaya1, Ethayaraja Mani2, K Murali3

  • 1Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, 600036, India; Institute of Organic Biogeochemistry in Geo-Systems, RWTH Aachen University, Aachen, 52056, Germany.

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Summary
This summary is machine-generated.

A new numerical model predicts marine oil snow (MOS) formation by simulating phytoplankton blooms and aggregate dynamics. This tool aids in forecasting MOS occurrence and timescales during oil spills.

Keywords:
Biological carbon pumpMarine oil snowMarine snowOil spillsPhytoplankton growthSmoluchowski coagulation kinetics

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Area of Science:

  • Oceanography
  • Environmental Science
  • Biogeochemistry

Background:

  • Marine oil snow (MOS) is formed by oil droplets aggregating with marine snow (MS).
  • MOS formation depends on phytoplankton, hydrodynamics, and environmental conditions.
  • Predicting MOS is crucial for understanding oil spill impacts.

Purpose of the Study:

  • To develop and validate a numerical model for predicting MOS formation.
  • To simulate phytoplankton bloom dynamics and aggregation processes.
  • To estimate MOS occurrence and timescales in marine environments.

Main Methods:

  • A two-module numerical model was developed: Phytoplankton Growth Module and Aggregation Module.
  • The model incorporates nutrient dynamics, environmental factors (temperature, pH, light), and hydrodynamics (mixing, shear).
  • Smoluchowski coagulation kinetics were used to simulate aggregation, with validation against experimental and satellite data.

Main Results:

  • The Phytoplankton Growth Module accurately predicted bloom occurrences.
  • The Aggregation Module effectively modeled aggregate formation influenced by turbulence and stickiness.
  • Model validation against datasets, including post-Deepwater Horizon data, showed strong agreement.

Conclusions:

  • The developed model is a robust tool for predicting MOS formation likelihood and timescales.
  • This framework is critical for assessing risks associated with marine oil spills.
  • The model's sensitivity analysis confirmed its reliability in diverse environmental scenarios.