Skip header and navigation

3 records – page 1 of 1.

An ecological risk assessment model for Arctic oil spills from a subsea pipeline.

https://arctichealth.org/en/permalink/ahliterature295359
Source
Mar Pollut Bull. 2018 Oct; 135:1117-1127
Publication Type
Journal Article
Date
Oct-2018
Author
Ehsan Arzaghi
Rouzbeh Abbassi
Vikram Garaniya
Jonathan Binns
Faisal Khan
Author Affiliation
National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College (AMC), University of Tasmania, Launceston, Australia.
Source
Mar Pollut Bull. 2018 Oct; 135:1117-1127
Date
Oct-2018
Language
English
Publication Type
Journal Article
Abstract
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 (PEC95%) of pollutants in different media. The 5th percentile Predicted No Effect Concentration (PNEC5%) 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.
PubMed ID
30301010 View in PubMed
Less detail

An ecological risk assessment model for Arctic oil spills from a subsea pipeline.

https://arctichealth.org/en/permalink/ahliterature296515
Source
Mar Pollut Bull. 2018 Oct; 135:1117-1127
Publication Type
Journal Article
Date
Oct-2018
Author
Ehsan Arzaghi
Rouzbeh Abbassi
Vikram Garaniya
Jonathan Binns
Faisal Khan
Author Affiliation
National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College (AMC), University of Tasmania, Launceston, Australia.
Source
Mar Pollut Bull. 2018 Oct; 135:1117-1127
Date
Oct-2018
Language
English
Publication Type
Journal Article
Keywords
Arctic Regions
Bayes Theorem
Ecology - methods
Models, Theoretical
Petroleum Pollution
Risk Assessment - methods
Water Pollutants, Chemical
Abstract
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 (PEC95%) of pollutants in different media. The 5th percentile Predicted No Effect Concentration (PNEC5%) 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.
PubMed ID
30301010 View in PubMed
Less detail

Effects of cold environments on human reliability assessment in offshore oil and gas facilities.

https://arctichealth.org/en/permalink/ahliterature256434
Source
Hum Factors. 2014 Aug;56(5):825-39
Publication Type
Article
Date
Aug-2014
Author
Alireza Noroozi
Rouzbeh Abbassi
Scott MacKinnon
Faisal Khan
Nima Khakzad
Source
Hum Factors. 2014 Aug;56(5):825-39
Date
Aug-2014
Language
English
Publication Type
Article
Keywords
Cold Temperature
Decision Making
Extraction and Processing Industry
Human Engineering
Humans
Maintenance
Oil and Gas Fields
Risk assessment
Stress, Physiological
Task Performance and Analysis
Abstract
This paper proposes a new methodology that focuses on the effects of cold and harsh environments on the reliability of human performance.
As maritime operations move into Arctic and Antarctic environments, decision makers must be able to recognize how cold weather affects human performance and subsequently adjusts management and operational tools and strategies.
In the present work, a revised version of the Human Error Assessment and Reduction Technique (HEART) methodology has been developed to assess the effects of cold on the likelihood of human error in offshore oil and gas facilities. This methodology has been applied to post-maintenance tasks of offshore oil and gas facility pumps to investigate how management, operational, and equipment issues must be considered in risk analysis and prediction of human error in cold environments.
This paper provides a proof of concept indicating that the risk associated with operations in cold environments is greater than the risk associated with the same operations performed in temperate climates. It also develops guidelines regarding how this risk can be assessed. The results illustrate that in post-maintenance procedures of a pump, the risk value related to the effect of cold and harsh environments on operator cognitive performance is twice as high as the risk value when performed in normal conditions.
The present work demonstrates significant differences between human error probabilities (HEPs) and associated risks in normal conditions as opposed to cold and harsh environments. This study also highlights that the cognitive performance of the human operator is the most important factor affected by the cold and harsh conditions.
The methodology developed in this paper can be used for reevaluating the HEPs for particular scenarios that occur in harsh environments since these HEPs may not be comparable to similar scenarios in normal conditions.
PubMed ID
25141591 View in PubMed
Less detail