National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College (AMC), University of Tasmania, Launceston, Australia.
School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, Australia.
Mar Pollut Bull. 2018 Oct;135:1117-1127. doi: 10.1016/j.marpolbul.2018.08.030. Epub 2018 Aug 30.
There is significant risk associated with increased oil and gas exploration activities in the Arctic Ocean. This paper presents a probabilistic methodology for Ecological Risk Assessment (ERA) of accidental oil spills in this region. A fugacity approach is adopted to model the fate and transport of released oil, taking into account the uncertainty of input variables. This assists in predicting the 95th percentile Predicted Exposure Concentration (PEC) of pollutants in different media. The 5th percentile Predicted No Effect Concentration (PNEC) is obtained from toxicity data for 19 species. A model based on Dynamic Bayesian Network (DBN) is developed to assess the ecological risk posed to the aquatic community. The model enables accounting for the occurrence likelihood of input parameters, as well as analyzing the time-variable risk profile caused by seasonal changes. It is observed through the results that previous probabilistic methods developed for ERA can be overestimating the risk level.
在北极地区增加石油和天然气勘探活动存在重大风险。本文提出了一种用于该地区意外溢油生态风险评估（ERA）的概率方法。采用逸度方法来模拟释放的石油的归宿和传输，同时考虑输入变量的不确定性。这有助于预测不同介质中污染物的 95%预测暴露浓度（PEC）。从 19 个物种的毒性数据中获得 5%预测无影响浓度（PNEC）。基于动态贝叶斯网络（DBN）开发了一个模型来评估对水生群落造成的生态风险。该模型能够考虑输入参数的发生可能性，以及分析由季节性变化引起的时变风险概况。通过结果可以看出，先前为 ERA 开发的概率方法可能会高估风险水平。
