A modified probabilistic oil spill model and its application to the Dalian New Port accident

被引:14
|
作者
Guo, Weijun [1 ,2 ]
Wu, Guoxiang [3 ]
Jiang, Meirong [4 ]
Xu, Tiaojian [2 ]
Yang, Zhiwen [5 ]
Xie, Mingxiao [5 ]
Chen, Xiaobo [6 ]
机构
[1] Dalian Maritime Univ, Coll Environm Sci & Engn, Linghai Rd 1, Dalian 116026, Peoples R China
[2] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116023, Peoples R China
[3] Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
[4] Offshore Oil Engn Co Ltd, Tianjin 300451, Peoples R China
[5] MOT, Tianjin Res Inst Water Transport Engn, Key Lab Engn Sediment, Tianjin 300456, Peoples R China
[6] Dongbei Univ Finance & Econ, Sch Investment & Construct Management, Dalian 116025, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
North Yellow Sea; Dalian New Port accident; Oil spill; Probabilistic model; Preconditioning factors; TRAJECTORIES; UNCERTAINTY; POLLUTION; IMPACTS; AREAS; RISK;
D O I
10.1016/j.oceaneng.2016.05.054
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
We employ a partial-condition designated probabilistic oil spill model based on a Lagrangian particle technique to simulate the Dalian New Port oil spill accident. By correlating the model with aerial images and field observations of oil slicks on the sea surface, the model is applicable for evaluating the probability spatial scattering of the area polluted by the spilled oil. Evidence shows that multiple simulations under specified conditions fit the real distribution and behavior of oil slicks more accurately than those from fully random initial conditions. The findings suggest that the effects of seasonal variations in wind and current dynamics within tidal cycles on spill distribution should be taken into account for estimating the impact of potential oil spills in the North Yellow Sea. A statistical model that considers condition selection could enhance the reliability of risk estimation and enable the application of appropriate recovery operations in contaminated seas. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:291 / 300
页数:10
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