RESUMO
The development of numerical models for investigating the risks and impact caused by human activities to the marine environment is important. Herein, the recently developed ChemicalDrift Lagrangian dispersion model was coupled to a toxicokinetic model and applied to investigate emissions of polycyclic aromatic hydrocarbons (PAHs) discharged from oil and gas production facilities as produced water. The performance of the model was evaluated with available data from a monitoring survey conducted at two oil fields. The survey provided exposure concentrations by means of passive samplers and bioaccumulation data in caged mussels; multiple depths and locations were assessed. The study included 26 PAHs and alkylated derivatives, showing good agreement between the model and the survey measurements. The compounds dominating the scenario were naphthalenes and phenanthrenes. Model provided contamination gradients were in agreement with the survey results, with levels decreasing with distance away from the main sources and with higher concentrations at 20â¯m depth. ChemicalDrift and the toxicokinetic model provided detailed time series, showing peaks of C1-naphthalene bioaccumulation significantly higher than values accumulated at the end of the monitored period. The utilised model was able to separate the relative contributions of multiple platforms and to identify the major contamination sources, providing a valuable and versatile tool for assessing the impact of discharges at sea.
RESUMO
Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations.
RESUMO
This paper presents a new dynamic environmental risk model, with intended use within a new, dynamical approach for risk based ship traffic prioritisation. The philosophy behind this newly developed approach is that shipping risk can be reduced by directing efforts towards ships and areas that have been identified as high priority (high risk), prior to a potential accident. The risk model proposed in this paper separates itself from previous models by drawing on available information on dynamic factors and by focusing on the ship's surroundings. The model estimates the environmental risk of drift grounding accidents for oil tankers in real time and in forecast mode, combining the probability of grounding with oil spill impact on the coastline. Results show that the inherent dynamic risk introduced by an oil tanker sailing along the North Norwegian coast depends, not surprisingly, significantly upon wind and ocean currents, as well as tug position and cargo oil type. Results of this study indicate that the risk model is well suited for real time risk assessment, and effectively separates low risk and high risk situations. The model is well suited as a tool to prioritise oil tankers and coastal segments. This enables dynamic risk based positioning of tugs, using both real-time and projected risk, for effective support in case of a drifting ship situation.
