Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings

Lagrangian modelling of oil concentrations at sea: A sensitivity analysis to the grid resolution and number of Lagrangian elements

This paper presents a novel method to select the optimal combination of grid resolution and number of Lagrangian elements (LEs) required in numerical modelling of oil concentrations at sea. A sensitivity analysis in terms of grid resolution and the number of LEs, was carried out to understand the uncertainty that these user-dependent parameters introduce in the numerical results. A dataset of 211,200 simulations performed under 400 metocean patterns, 6 initial volumes, 11 grid resolutions, and different numbers of LEs (100 to 500,000), was used to analyze the sensitivity of the model along different Thresholds of Concern. Results show the importance of a correct selection of the number of LEs and the grid resolution in Lagrangian modelling of surface oil concentrations. The method proposed will allow selecting the optimal combination of these parameters to find an optimal balance between the accuracy and the computational cost of the simulation.

Reconstruction mechanism and strategy of global maritime green supply chain against the backdrop of nuclear pollution

Owing to the nuclear accident at Fukushima, everyone involved in the supply chain began to reflect on the ongoing efforts to minimize the environmental impact. Being part of the green supply chain, it is important for maritime companies to restructure their mechanism and strategy for the future. This study analyzes the framework of the reconstruction mechanism and strategy of the global maritime green supply chain against the backdrop of nuclear pollution in four dimensions, namely, ecology, management, economy, and society. On the basis of these four dimensions, 20 suitable evaluation indicators are constructed to develop an expert questionnaire. These indicators are used to identify the importance of the maritime green supply chain through the fuzzy analytic hierarchy process so that decision makers can discover the intrinsic key competencies. According to the results of the study, the indicators of critical success factors (CSFs) are as follows: 1. "Reduction of waste discharge and pollution treatment"; 2. "Construction of environmentally friendly fleets, desulfurization retrofit, and fleet replacement to comply with the International Maritime Organization's regulations and improve energy efficiency"; 3. " Regular audit, assessment, and review of compliance with environmental laws and regulations"; 4. "Priority access to ports and increased profitability by participating in the Green Ship Incentive Program"; and 5. " Dedicated management units and departments responsible for policies related to green capabilities." The results provide a reference for the decision making of maritime companies to identify the key reconstruction mechanism and CSFs in the maritime green supply chain and explore future service models as a way to build competitive advantage and ensure sustainable operations. This study integrates the ecological theory and concept of the green supply chain management. Furthermore, the results of this study provide policy implications for stakeholders, such as maritime companies and governments, in implementing and promoting green supply chain practices.

An innovative coupling technique for integrating oil spill prediction model with finite volume method-based ocean model

Marine oil spill pollution have increased recent years, threatening the safety of the marine environment. This paper proposes a coupling technique: Fast Mapping & Addressing(FMA) that integrates the oil spill model with oceanographic model. The FMA technique is based on hash function and spatial quadtree algorithm to achieve efficient addressing from an unstructured to a structured grid. The oil spill model simulates the oil spill process, while the ocean model simulates the ocean currents. The efficiency is improved about ten times compared to the interpolation algorithm. Results reveal the difference between the simulation results of the ocean model and the measured data is minimal, with an MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of about 0.06 m. Moreover, two oil spill events in China's nearshore were selected to simulate and verify the results. Indeed, our model's results agree with the observed data, demonstrating that our model can achieve a satisfied simulation of oil spill.

Oil Spill Environmental Risk Assessment and Mapping in Coastal China Using Automatic Identification System (AIS) Data

The rapid expansion in shipping traffic, oil tankers, and oil field exploration in coastal and marine areas has, inevitably, resulted in the occurrence of many oil spill accidents. Oil spill accidents, which cause serious socio-economic, health, and environmental risks in coastal and marine areas, are a global concern. An oil spill pollution risk distribution map, combining multiple spill sources, is an effective tool by which to identify high-risk areas, which may help decision-makers in adopting contingency response and integrated coastal management. However, the assessment of oil spill distribution and risk assessment has been restricted, due to their heavy dependence on laboratory experiments and model simulations lacking reliable shipping data, which often derive inaccurate mapping results. This study combines the automatic identification system (AIS) and other data to precisely quantify the spatial extent of accident risk in coastal China. Based on oil quantity, oil spill rate, and accident probability, the ship, oil storage tank, submarine pipeline, and oil platform accidents spill risk index is analyzed. Next, combined with the sensitive degree of a coastal area, considering environmental and social issues, the oil spill environmental risk index is calculated. The oil spill pollution risk level is classified into five categories based on the oil spill pollution risk index, namely the low-risk zone, relatively low-risk zone, moderate-risk zone, relatively high-risk zone, and high-risk zone. The relatively high oil spill environmental risk concentration zone is located in the Bohai Sea, inter-border area between the Yellow Sea and Bohai Sea, the Yangtze River estuary, south of the Taiwan Strait, and the Pearl River estuary. The high-risk zone in the Bohai Sea is 36,018 km2 in area, with an average risk value of 32.23, whereas the high-risk area in the Pearl River estuary is only 14,007 km2. The high-risk area proportions in Tianjin are 23.5%, while those in Fujian, Hainan, Jiangsu, and Guangxi are very low. The low-risk area proportion in Hainan Province is 62%, while the value in Tianjin is only 2.9%. This study will be helpful in assisting decision-makers in mapping the influence area of oil spills and adopting the important strategies and effective management and conservation countermeasures for ship accidents in the coastal areas of China.

Assessing the exposure risk of large pelagic fish to oil spills scenarios in the deep waters of the Gulf of Mexico

Exposure risk is assessed based on modeling suitable habitat of large pelagic fish and oil spill scenarios originating at three wells located in the western GM's deep waters. Since the fate of the oil depends on the oceanographic conditions present during the accident, as well as the magnitude and duration of the spill, which are not known a priori, the scenarios used are a statistical representation of the area in which oil spilled from the well could be found, given all possible outcomes. The ecological vulnerability assessment identified a subset of bony fish with low-medium vulnerability and elasmobranchs with medium-high vulnerability. The oiling probability and exposure risk of both bony fish and elasmobranchs hotspots vary by well analyzed. Thus, these results provide essential information for a risk management plan for the assessed species and others with economic or conservation importance distributed in the GM and worldwide.

Deep oil spill hazard assessment based on spatio-temporal met-ocean patterns

Oil spill risk assessments are important tools for the offshore oil and gas industries to minimize the consequences of deep spills. The stochastic modeling required in this kind of studies, is generally centered on surface transport and based on a Monte Carlo selection of hundreds or thousands of met-ocean scenarios from reanalysis databases, to create an ensemble of spill simulations. We propose a new integrated stochastic modeling methodology including both surface and subsurface transport, based on the specific selection of the most relevant environmental conditions through data-mining techniques. The methodology was applied to evaluate oil contamination probability as a consequence of a simulated deep release in the North Sea. Our results show the effectiveness of the proposed methodology to select representative evolutions of met-ocean conditions and to obtain pollution probabilities from an integrated subsurface and surface oil spill stochastic modeling, while assuring a manageable computational effort.

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.