Regionalized risk assessment of accidental oil spills using worldwide data

Burgherr, Peter; Spada, Matteo; Kalinina, A. V.; Page, Philippe

doi:10.1201/b19094-12

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…Such information are of great interest for different stakeholders and can play an essential role in different decision-making processes. Among others, risk indicators, including their uncertainties, could be used in a holistic sustainability assessment for energy systems, where they are commonly considered in the social pillar of sustainability [1,69]; as indicators for resilience assessment [70]; to assess different types of potential scenarios, i.e., expected and extreme, as input for National Risk Assessment studies [71,72]; for insurance and reinsurance industry purposes such as, for example, to better diversify the reinsurance classifications for different refineries [73]; in other industries purposes such as, for example to regionalize the accident risk for safety assessment purposes [74].…”

Section: Discussionmentioning

confidence: 99%

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Spada

Burgherr

2020

Energies

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The accident risk of severe (≥5 fatalities) accidents in fossil energy chains (Coal, Oil and Natural Gas) is analyzed. The full chain risk is assessed for Organization for Economic Co-operation and Development (OECD), 28 Member States of the European Union (EU28) and non-OECD countries. Furthermore, for Coal, Chinese data are analysed separately for three different periods, i.e., 1994–1999, 2000–2008 and 2009–2016, due to different data sources, and highly incomplete data prior to 1994. A Bayesian Model Averaging (BMA) is applied to investigate the risk and associated uncertainties of a comprehensive accident data set from the Paul Scherrer Institute’s ENergy-related Severe Accident Database (ENSAD). By means of BMA, frequency and severity distributions were established, and a final posterior distribution including model uncertainty is constructed by a weighted combination of the different models. The proposed approach, by dealing with lack of data and lack of knowledge, allows for a general reduction of the uncertainty in the calculated risk indicators, which is beneficial for informed decision-making strategies under uncertainty.

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Section: Discussionmentioning

confidence: 99%

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Spada

Burgherr

2020

Energies

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“…Centre for Chemical Process Safety (CCPS) described “catastrophic incident” as a significant incident with major consequences that fulfill “severity threshold” criteria recognized in an industry‐wide metric . However, CCPS did not explain the phrase “severity threshold,” which is lightly described in the literature . Nonetheless, there are several definitions to describe “severity threshold” like the one from Sigma Insurance Research (Sigma), The International Emergency Disaster Database (EM‐DAT), Natural Catastrophe Service (NatCat), and Worldwide Offshore Accident Databank (WOAD).…”

Section: Methodsmentioning

confidence: 99%

“…27 However, CCPS did not explain the phrase "severity threshold," which is lightly described in the literature. [28][29][30] Nonetheless, there are several definitions to describe "severity threshold" like the one from Sigma Insurance Research (Sigma), The International Emergency Disaster Database (EM-DAT), Natural Catastrophe Service (NatCat), and Worldwide Offshore Accident Databank (WOAD). However, the Energy-related Severe Accident Database (ENSAD) provided a more extensive definition.…”

Section: 119(m)(5) Is the Resolutions And Corrective Actions Havmentioning

confidence: 99%

Incident investigation work‐aid tool for process safety management compliance in process industries

Majid

Shariff

2019

Process Safety Progress

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Incident investigations conducted in industries vary based on company policies and authorities' requirements. One of the related regulations as required by authorities is based on incident investigation elements of the process safety management (PSM) standard, 29 CFR 1910.119(m). This standard outlines the requirements for identification of catastrophic incident, incident investigation team setup, report writings, follow‐up actions on the established recommendations, communication of incident investigation findings with relevant personnel, and report record keeping. Many companies implement that the incident investigations based on 29 CFR 1910.119(m) likely lead to noncompliance due to wrong interpretations of the requirements. This article presents a structured and easy technique to conduct and implement incident investigations that comply with Occupational Safety and Health Administration (OSHA) PSM 29 CFR 1910.119(m). A framework and a work‐aid tool for incident investigation were developed and validated with a previous incident investigation of a catastrophic accident as a case study. The developed work‐aid tool could benefit the process industries to track and manage incident investigation activities systematically. It also helps to prompt users on the necessary actions to close the identified gaps that caused the accident. This technique allows users to manage incident investigation better, to avoid recurrence of the same accident in future, and at the same time to reduce adverse impacts on people, environment, and assets.

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“…The expected oil spill in each area is modeled using a Lognormal (LOGNO) distribution (e.g. Burgherr et al, 2015). In this case, at a first approximation, the spill is considered independent from the area; therefore, no spatial random effects are included.…”

Section: Model Setupmentioning

confidence: 99%

“…Landuyt et al, 2015), we test a hierarchical Bayesian model not only to update an initial estimate of the probability of occurrence of tanker oil spills (e.g. Burgherr et al, 2015), using information concerning the distribution of past accidents in the geographical region under analysis, but also to generate an overall risk map.…”

Section: Introductionmentioning

confidence: 99%

Toward the integration of uncertainty and probabilities in spatial multi-criteria risk analysis

Spada¹,

Ferretti²

2018

Safety and Reliability – Safe Societies in a Changing World

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Quantitative risk assessment supports decision-making processes in an increasing variety of contexts. Within the domain of environmental decision-making, the spatial distribution of impacts, vulnerabilities and consequences associated to different risk-mitigation alternatives calls for a different framework, i.e. spatial multi-criteria risk analysis. In this paper, we propose the combined use of a hierarchical Bayesian modelling approach and Geographic Information Systems to integrate uncertainty and model probabilities into an overall risk map. The research aims at testing the operability of the integration between Bayesian modelling and spatial analysis in the context of spatial risk processes for resource allocation. To this end, we applied the model on a case study dealing with tanker oil spill risk in the Mediterranean Sea. The innovative contribution of the study stems from both the context of application point of view and the use of Bayesian modelling to calculate not only probabilities but an overall spatial risk measure.

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Regionalized risk assessment of accidental oil spills using worldwide data

Cited by 6 publications

References 10 publications

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Incident investigation work‐aid tool for process safety management compliance in process industries

Toward the integration of uncertainty and probabilities in spatial multi-criteria risk analysis

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