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Das T, Goerlandt F. Bayesian inference modeling to rank response technologies in arctic marine oil spills. MARINE POLLUTION BULLETIN 2022; 185:114203. [PMID: 36272316 DOI: 10.1016/j.marpolbul.2022.114203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/17/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate technologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can efficiently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.
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Affiliation(s)
- Tanmoy Das
- Department of Industrial Engineering, Dalhousie University, Halifax, Nova Scotia, Canada.
| | - Floris Goerlandt
- Department of Industrial Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
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2
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Towards Sustainable Management of Anchoring on Mediterranean Islands—Concession Support Concept. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse10010015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The focus of this paper is to define anchorage management model for concession planning purposes to provide quality support to experts in spatial planning when developing maritime spatial plans. The research aim is to develop an anchorage management model that includes decision and concession support concept. Decision support concept is defined in order to support the processes of identifying potential anchorage locations, their evaluation and comparison, and finally, the priority ranking and selection of locations for their construction. The final step is modelling the concession support concept that includes financial analysis to concession parameters definition. The problem of decision making and concession of the anchorage location selection is complex and ill-structured because of the unsystematic and ad-hoc decisions by all included stakeholders. Additionally, the involvement of several stakeholders’ groups with different preferences and background knowledge, a large amount of conflicting and seemingly incomparable information and data, and numerous conflicting goals and criteria impact final decisions. The proposed concepts overcome the above obstacles in order to enable the construction of anchorages in a way of optimal use of maritime space. The model is tested on the island of Brač, Croatia. The methods used to solve the task are SWARA (The Stepwise Weight Assessment Ratio Analysis) for defining the criteria weights and ELECTRE (Elimination and Choice Expressing Reality) for ranking anchorage locations.
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Yang Z, Chen Z, Lee K, Owens E, Boufadel MC, An C, Taylor E. Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives. MARINE POLLUTION BULLETIN 2021; 167:112313. [PMID: 33839574 DOI: 10.1016/j.marpolbul.2021.112313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
Marine oil spills pose a significant threat to ocean and coastal ecosystems. In addition to costs incurred by response activities, an economic burden could be experienced by stakeholders dependent on coastal resources. Decision support tools for oil spill response (OSR-DSTs) have been playing an important role during oil spill response operations. This paper aims to provide an insight into the status of research on OSR-DSTs and identify future directions. Specifically, a systematic review is conducted including an examination of the advantages and limitations of currently applied and emerging decision support techniques for oil spill response. In response to elevated environmental concerns for protecting the polar ecosystem, the review includes a discussion on the use of OSR-DSTs in cold regions. Based on the analysis of information acquired, recommendations for future work on the development of OSR-DSTs to support the selection and implementation of spill response options are presented.
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Affiliation(s)
- Zhaoyang Yang
- Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada
| | - Zhi Chen
- Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada.
| | - Kenneth Lee
- Ecosystem Science, Fisheries and Oceans Canada, 200 Kent Street, Ottawa, Ontario K1C 0E6, Canada
| | - Edward Owens
- Owens Coastal Consultants Ltd., Bainbridge Island, WA 98110, USA
| | - Michel C Boufadel
- Center for Natural Resources, Department of Civil and Environmental Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Chunjiang An
- Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada
| | - Elliott Taylor
- Polaris Applied Sciences, Inc., 755 Winslow Way East #302, Bainbridge Island, WA 98110, USA
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4
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van Beest FM, Nygård H, Fleming V, Carstensen J. On the uncertainty and confidence in decision support tools (DSTs) with insights from the Baltic Sea ecosystem. AMBIO 2021; 50:393-399. [PMID: 32885402 PMCID: PMC7782639 DOI: 10.1007/s13280-020-01385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 08/12/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
Ecosystems around the world are increasingly exposed to multiple, often interacting human activities, leading to pressures and possibly environmental state changes. Decision support tools (DSTs) can assist environmental managers and policy makers to evaluate the current status of ecosystems (i.e. assessment tools) and the consequences of alternative policies or management scenarios (i.e. planning tools) to make the best possible decision based on prevailing knowledge and uncertainties. However, to be confident in DST outcomes it is imperative that known sources of uncertainty such as sampling and measurement error, model structure, and parameter use are quantified, documented, and addressed throughout the DST set-up, calibration, and validation processes. Here we provide a brief overview of the main sources of uncertainty and methods currently available to quantify uncertainty in DST input and output. We then review 42 existing DSTs that were designed to manage anthropogenic pressures in the Baltic Sea to summarise how and what sources of uncertainties were addressed within planning and assessment tools. Based on our findings, we recommend future DST development to adhere to good modelling practise principles, and to better document and communicate uncertainty among stakeholders.
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Affiliation(s)
- Floris M. van Beest
- Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
| | - Henrik Nygård
- Finnish Environment Institute SYKE, Marine Research Centre, Latokartanonkaari 11, 00790 Helsinki, Finland
| | - Vivi Fleming
- Finnish Environment Institute SYKE, Marine Research Centre, Latokartanonkaari 11, 00790 Helsinki, Finland
| | - Jacob Carstensen
- Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
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5
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Parviainen T, Goerlandt F, Helle I, Haapasaari P, Kuikka S. Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 278:111520. [PMID: 33166738 DOI: 10.1016/j.jenvman.2020.111520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 09/15/2020] [Accepted: 10/13/2020] [Indexed: 06/11/2023]
Abstract
The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.
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Affiliation(s)
- Tuuli Parviainen
- University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland.
| | - Floris Goerlandt
- Aalto University, Department of Mechanical Engineering, Marine Technology, P.O. Box 15300, FI-00076, Aalto, Finland; Dalhousie University, Department of Industrial Engineering, Halifax, Nova Scotia, B3H 4R2, Canada
| | - Inari Helle
- Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; University of Helsinki, Environmental and Ecological Statistics Group, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland.
| | - Päivi Haapasaari
- University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland
| | - Sakari Kuikka
- University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland
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6
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Hu G, Mohammadiun S, Gharahbagh AA, Li J, Hewage K, Sadiq R. Selection of oil spill response method in Arctic offshore waters: A fuzzy decision tree based framework. MARINE POLLUTION BULLETIN 2020; 161:111705. [PMID: 33022490 DOI: 10.1016/j.marpolbul.2020.111705] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/20/2020] [Accepted: 09/21/2020] [Indexed: 06/11/2023]
Abstract
A fuzzy decision tree (FDT) based framework was developed to facilitate the selection of suitable oil spill response methods in the Arctic. Hypothetical oil spill cases were developed based on six identified attributes, while the suitability of three spill response methods (mechanical containment and recovery, use of chemical dispersants, and in-situ burning) for each spill case was obtained based on expert judgments. Fuzzy sets were used to address the associated uncertainties, and FDTs were then developed through generating: i) one decision tree for all three response methods (FDT-AP1) and ii) one decision tree for each response method and the development of linear regression models at terminal nodes (FDT-LR). The FDT-LR approach exhibited higher prediction accuracy than the FDT-AP1 approach. A maximum of 100% accurate predictions could be achieved for testing cases using it. On average, 75% of suitable oil spill response methods out of 10,000 performed iterations were predicted correctly.
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Affiliation(s)
- Guangji Hu
- School of Engineering, University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
| | - Saeed Mohammadiun
- School of Engineering, University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
| | - Abdorreza Alavi Gharahbagh
- Department of Electrical and Computer Engineering, Islamic Azad University, Shahrood Branch, Shahrood 1584743311, Iran.
| | - Jianbing Li
- Environmental Engineering Program, University of Northern British Columbia, 3333 University Way, Prince George, BC V2N 4Z9, Canada.
| | - Kasun Hewage
- School of Engineering, University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
| | - Rehan Sadiq
- School of Engineering, University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
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Liu Z, Callies U. A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight. WATER RESEARCH 2020; 169:115196. [PMID: 31670089 DOI: 10.1016/j.watres.2019.115196] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/12/2019] [Accepted: 10/14/2019] [Indexed: 06/10/2023]
Abstract
Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis.
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Affiliation(s)
- Zengkai Liu
- College of Electromechanical Engineering, China University of Petroleum, Qingdao, 266580, China.
| | - Ulrich Callies
- Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, 21502, Germany
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8
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Cantorna D, Dafonte C, Iglesias A, Arcay B. Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105716] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Liu Z, Callies U. Implications of using chemical dispersants to combat oil spills in the German Bight - Depiction by means of a Bayesian network. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 248:609-620. [PMID: 30836242 DOI: 10.1016/j.envpol.2019.02.063] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 02/19/2019] [Accepted: 02/20/2019] [Indexed: 05/23/2023]
Abstract
Application of chemical dispersants is one option for combatting oil spills, dispersing oil into the water column and thereby reducing potential pollution to coastal areas. Efficiency of dispersant application depends on oil characteristics, sea and weather conditions. Potential environmental impacts must also be taken into account. Referring to the German Bight region (North Sea), we show how probabilistic Bayesian network (BN) technology can integrate all these aspects to support contingency planning. Expected effects of chemical dispersion on oil spill drift paths are quantified based on comprehensive numerical ensemble simulations. Ecological impacts are represented just in simplified terms focusing on nearshore seabird distributions. The intuitive and interactive BN summarizes expected benefits from chemical dispersion depending on where and under which weather conditions a hypothetical pollution occurs.
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Affiliation(s)
- Zengkai Liu
- Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, 21502, Geesthacht, Germany; College of Electromechnical Engineering, China University of Petroleum, 266580, Qingdao, China.
| | - Ulrich Callies
- Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, 21502, Geesthacht, Germany.
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10
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Amir-Heidari P, Raie M. Response planning for accidental oil spills in Persian Gulf: A decision support system (DSS) based on consequence modeling. MARINE POLLUTION BULLETIN 2019; 140:116-128. [PMID: 30803625 DOI: 10.1016/j.marpolbul.2018.12.053] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 12/27/2018] [Accepted: 12/30/2018] [Indexed: 05/12/2023]
Abstract
Different causes lead to accidental oil spills from fixed and mobile sources in the marine environment. Therefore, it is essential to have a systematic plan for mitigating oil spill consequences. In this research, a general DSS is proposed for passive and active response planning in Persian Gulf, before and after a spill. The DSS is based on NOAA's advanced oil spill model (GNOME), which is now linked with credible met-ocean datasets of CMEMS and ECMWF. The developed open-source tool converts the results of the Lagrangian oil spill model to quantitative parameters such as mean concentration and time of impact of oil. Using them, two new parameters, emergency response priority number (ERPN) and risk index (RI), are defined and used for response planning. The tool was tested in both deterministic and probabilistic modes, and found to be useful for evaluation of emergency response drills and risk-based prioritization of coastal areas.
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Affiliation(s)
- Payam Amir-Heidari
- Department of Civil Engineering, Sharif University of Technology, P.O. Box. 11365-11155, Tehran, Iran
| | - Mohammad Raie
- Department of Civil Engineering, Sharif University of Technology, P.O. Box. 11365-11155, Tehran, Iran.
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11
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An Integral Approach to Sustainable Decision-Making within Maritime Spatial Planning—A DSC for the Planning of Anchorages on the Island of Šolta, Croatia. SUSTAINABILITY 2018. [DOI: 10.3390/su11010104] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The planning of nautical tourism development and especially, the planning of its supporting infrastructure development, is important topic of the maritime spatial planning. The focus of research is the integration of multicriteria analysis and stakeholders within concept modeling that will provide support to the spatial planning specialists in the design of plans related to the development of anchorage capacities for small vessels. It examines economic, environmental, ecological, social, and civil engineering concerns related to the use of coastal water. It is a complex and ill-defined civil engineering problem because of multiple stakeholders with diverse interests, numerous conflicting goals and criteria, huge quantities of information and data, limited resources, etc. The research is concentrated on an integral approach to sustainable decision-making within maritime spatial planning by the modeling decision support concept to the processes of identification, validation, comparison, and the selection of locations for anchorage construction, based on multicriteria methods, goal analysis, and the logic of the decision support system. The concept is tested on the island of Šolta, Croatia, and has been proven as being an applicable, consistent, efficient, and effective methodology for the planning of the anchorage locations.
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12
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Ha MJ. Modeling for the allocation of oil spill recovery capacity considering environmental and economic factors. MARINE POLLUTION BULLETIN 2018; 126:184-190. [PMID: 29421086 DOI: 10.1016/j.marpolbul.2017.11.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/05/2017] [Accepted: 11/06/2017] [Indexed: 06/08/2023]
Abstract
This study presents a regional oil spill risk assessment and capacities for marine oil spill response in Korea. The risk assessment of oil spill is carried out using both causal factors and environmental/economic factors. The weight of each parameter is calculated using the Analytic Hierarchy Process (AHP). Final regional risk degrees of oil spill are estimated by combining the degree and weight of each existing parameter. From these estimated risk levels, oil recovery capacities were determined with reference to the recovery target of 7500kl specified in existing standards. The estimates were deemed feasible, and provided a more balanced distribution of resources than existing capacities set according to current standards.
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Affiliation(s)
- Min-Jae Ha
- Dept. of Coast Guard Studies, Chonnam National University, Yeosu 59626, Republic of Korea.
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13
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Albuquerque MTD, Gerassis S, Sierra C, Taboada J, Martín JE, Antunes IMHR, Gallego JR. Developing a new Bayesian Risk Index for risk evaluation of soil contamination. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 603-604:167-177. [PMID: 28624637 DOI: 10.1016/j.scitotenv.2017.06.068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 06/08/2017] [Accepted: 06/09/2017] [Indexed: 05/23/2023]
Abstract
Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.
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Affiliation(s)
- M T D Albuquerque
- Instituto Politécnico de Castelo Branco, 6001-909 Castelo Branco, Portugal; CERENA/FEUP Research Center, Portugal.
| | - S Gerassis
- Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain
| | - C Sierra
- Departamento de Transportes, Tecnología de Procesos y Proyectos, Universidad de Cantabria, Campus de Torrelavega, Spain
| | - J Taboada
- Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain
| | - J E Martín
- Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain
| | - I M H R Antunes
- ICT/University of Minho, Braga, Portugal; CERENA/FEUP Research Center, Portugal
| | - J R Gallego
- INDUROT and Environmental Technology, Biotechnology, and Geochemistry Group, Universidad de Oviedo, Campus de Mieres, Asturias, Spain
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14
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Wu B, Yan X, Wang Y, Zhang D, Guedes Soares C. Three-Stage Decision-Making Model under Restricted Conditions for Emergency Response to Ships Not under Control. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2017; 37:2455-2474. [PMID: 28437861 DOI: 10.1111/risa.12815] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 07/22/2016] [Accepted: 02/03/2017] [Indexed: 06/07/2023]
Abstract
A ship that is not under control (NUC) is a typical incident that poses serious problems when in confined waters close to shore. The emergency response to NUC ships is to select the best risk control options, which is a challenge in restricted conditions (e.g., time limitation, resource constraint, and information asymmetry), particularly in inland waterway transportation. To enable a quick and effective response, this article develops a three-stage decision-making framework for NUC ship handling. The core of this method is (1) to propose feasible options for each involved entity (e.g., maritime safety administration, NUC ship, and ships passing by) under resource constraint in the first stage, (2) to select the most feasible options by comparing the similarity of the new case and existing cases in the second stage, and (3) to make decisions considering the cooperation between the involved organizations by using a developed Bayesian network in the third stage. Consequently, this work provides a useful tool to achieve well-organized management of NUC ships.
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Affiliation(s)
- Bing Wu
- Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, China
- Center for Marine Technology and Ocean Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Xinping Yan
- Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, China
- National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China
| | - Yang Wang
- Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, China
- National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China
| | - Di Zhang
- Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, China
- National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China
| | - C Guedes Soares
- Center for Marine Technology and Ocean Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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15
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Li P, Cai Q, Lin W, Chen B, Zhang B. Offshore oil spill response practices and emerging challenges. MARINE POLLUTION BULLETIN 2016; 110:6-27. [PMID: 27393213 DOI: 10.1016/j.marpolbul.2016.06.020] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Revised: 06/02/2016] [Accepted: 06/03/2016] [Indexed: 06/06/2023]
Abstract
Offshore oil spills are of tremendous concern due to their potential impact on economic and ecological systems. A number of major oil spills triggered worldwide consciousness of oil spill preparedness and response. Challenges remain in diverse aspects such as oil spill monitoring, analysis, assessment, contingency planning, response, cleanup, and decision support. This article provides a comprehensive review of the current situations and impacts of offshore oil spills, as well as the policies and technologies in offshore oil spill response and countermeasures. Correspondingly, new strategies and a decision support framework are recommended for improving the capacities and effectiveness of oil spill response and countermeasures. In addition, the emerging challenges in cold and harsh environments are reviewed with recommendations due to increasing risk of oil spills in the northern regions from the expansion of the Arctic Passage.
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Affiliation(s)
- Pu Li
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X5
| | - Qinhong Cai
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X5
| | - Weiyun Lin
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X5
| | - Bing Chen
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X5.
| | - Baiyu Zhang
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X5.
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