Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Memorial University of Newfoundland, Faculty of Engineering and Applied Science, St. John's, NL, A1B 3X5, Canada.
Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Memorial University of Newfoundland, Faculty of Engineering and Applied Science, St. John's, NL, A1B 3X5, Canada; College of Environmental Science and Engineering, Peking University, Beijing, 100871, China.
Environ Pollut. 2020 Jul;262:114294. doi: 10.1016/j.envpol.2020.114294. Epub 2020 Mar 2.
Human factors/errors (such as inappropriate actions by operators and unsafe supervision by organizations) are a primary cause of oil spill incidents. To investigate the influences of active operational failures and unsafe latent factors in offshore oil spill accidents, an integrated human factor analysis and decision support process has been developed. The system is comprised of a Human Factors Analysis and Classification System (HFACS) framework to qualitatively evaluate the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response); coupled with quantitative data analysis by Fuzzy Set Theory and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to enhance decision making during response operations. The efficiency of the integrated human factor analysis and decision support system is tested with data from a case study to generate a comprehensive priority rank, a robust sensitivity analysis, and other theoretical/practical insights. The proposed approach improves our knowledge on the significance of human factors/errors on oil spill accidents and response operations; and provides an improved support tool for decision making.
人为因素/失误（例如操作人员的不当行为和组织的不安全监督）是溢油事故的主要原因。为了调查海上溢油事故中主动作业失效和不安全潜在因素的影响，开发了一种综合人为因素分析和决策支持过程。该系统由人为因素分析和分类系统（HFACS）框架组成，用于定性评估与溢油准备和响应（例如溢油发生、溢油监测、决策/应急预案规划和溢油响应）多个作业阶段相关的各种因素和错误的影响；结合模糊集理论和逼近理想解的排序方法（Fuzzy-TOPSIS）进行定量数据分析，以增强响应作业期间的决策。通过案例研究中的数据测试了综合人为因素分析和决策支持系统的效率，以生成全面的优先级排序、强大的敏感性分析和其他理论/实际见解。所提出的方法提高了我们对人为因素/失误对溢油事故和响应作业的重要性的认识；并为决策提供了改进的支持工具。
