From Challenges to Opportunities: Towards Future \nStrategies and a Decision Support Framework for \nOil Spill Preparedness and Response in Offshore \nNewfoundland and Labrador
Notice bibliographique
Résumé
As a major energy source worldwide, oil products are increasingly produced and consumed. Despite progress in reducing spillage through a variety of technological and regulatory measures, along with improving industry practices, oil spills continue to occur. On a daily basis, hundreds to thousands of spills are likely to occur worldwide in many different types of environments such as on land, at sea, and in inland freshwater systems. Multiple sources of spillage are involved, such as tankers, offshore platforms, drilling rigs and wells, as well as through a variety of processes of transportation, refining, storage and utilization of refined petroleum products and their by-products. Spills occur for diverse reasons including structural failures, operational errors, weather-related events, earthquakes, human negligence, and even vandalism or terrorism. The biggest contributor to oil pollution in the world’s oceans (some 45%) is operational discharges from tankers. Most oil spill occurrences (72%) are on a small scale and the overall amount of these small spills accounts for less than 1% of the total spillage. The largest spills (over 30 tonnes) rarely occur (0.1% of incidents) yet involve nearly 60% of the total amount spilled. \n \nThe project’s objective was to gain insight to current methodologies and technologies in oil spill response and countermeasures and to formulate a new strategic and decision making framework for supporting oil spill diagnosis, warning and emergency response in a cost-efficient and environmental friendly manner. The cold weather and harsh offshore conditions in Newfoundland and Labrador and their effects will be considered in the study. The main objectives of the research included: \n• Collect and analyze background information and data of historical oil spills and associated environmental, economic and societal impacts as well as relevant policies and regulations \n• Review current offshore oil spill response and countermeasure protocols and practices \n• Review the natural and social conditions, spill prevention, monitoring and analysis, assessment and modeling, and response and clean-up technologies, as well as their effectiveness and suitability in harsh environments prevailing in NL offshore areas \n• Identify knowledge gaps and technical challenges in offshore oil spill, monitoring, analysis, modeling, responses, and countermeasures particularly in harsh environmental conditions \n• Formulate a general decision making framework for integrating methods and techniques during oil spill monitoring, early warning, assessment, simulation, response and cleanup processes \n• Recommend oil spill management strategies and disclose the research and development needs particularly for NL offshore industry and regulatory authorities. \n \nBased on the comprehensive review, this study made general recommendations to help guide research and development efforts in oil spill response and countermeasures from the aspects of impact assessment, regulations and coordination, monitoring and analysis, modeling and prediction, preparedness and response, countermeasures, and decision making. Some special recommendations given to future investment and R&D efforts in order to address the identified knowledge gaps and technical challenges associated with the NL’s harsh offshore environment include: \n• Uncertainties associated with the weather and ocean conditions and the impacts on spill modeling and response decision making; \n• Integration of monitoring, assessment, simulation and optimization into offshore oil spill response decision making; \n• Capability of existing technologies in harsh environments and novel technologies customized with the specific characteristic of NL offshore oil spills; and \n• Adaptation of long-term contingency plans, management strategies, on-site response decision, and operational technologies to harsh environmental conditions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,032 | 0,023 |
| Science ouverte | 0,007 | 0,011 |
| Intégrité de la recherche | 0,008 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».