Oil spills from global tankers: Status review and future governance

Chen, Jihong; Zhang, Weipan; Wan, Zheng; Li, Sifan; Huang, Tiancun; Fei, Yijie

doi:10.1016/j.jclepro.2019.04.020

Cited by 246 publications

(83 citation statements)

References 39 publications

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“…With an increase in international maritime traffic over time, ship accidents such as collisions, sinking, and oil leakages tend to occur more frequently [1]. These accidents occasionally spill large quantities of crude oil in coastal regions, threatening the safety of coastal areas and causing significant economic losses.…”

Section: Introductionmentioning

confidence: 99%

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Park

Foucher

et al. 2021

Remote Sensing

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With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

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Section: Introductionmentioning

confidence: 99%

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Park

Foucher

et al. 2021

Remote Sensing

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“…As the most widely used fossil energy in the world, oil is mainly transported by sea, but it is also prone to oil spill pollution (Wan and Chen, 2018; Chen et al., 2019). In contrast, liquefied natural gas (LNG) transport is more environmentally friendly.…”

Section: Introductionmentioning

confidence: 99%

“…Linying Chen et al. (2018) studied the feasibility of distributed simulation models for autonomous and coordinated multi-ship traffic simulation. Xin et al.…”

Section: Introductionmentioning

confidence: 99%

Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system

Liu

2021

J. Navigation

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Over the past few decades, the number of liquefied natural gas (LNG) ships and terminals has been increasing, playing an important role in global clean energy transportation. However, the traffic capacity of LNG shipping in port areas is limited because of its high safety requirements. In view of this problem, a novel model is proposed to study the ship traffic in a port area by combining cellular automaton (CA) and multi-agent methods. Taking the CNOOC Tianjin LNG Terminal as an example, the ship traffic in Tianjin Port is simulated. Based on the simulation results, the LNG ship traffic capacity and its impact on the general shipping traffic flow under different special traffic rules are obtained. This model can provide theoretical support for optimising the port traffic organisation for LNG ships.

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“…Which has caused severe environmental, health and social-economic phenomenon (Jiao et al, 2019). Evidence to this, is the differences in major oil spill between 1970s, 1980s and 1990s with one million, 661,000, and 702,000 tons of oil spills respectively and 2000 with 74,000 tones (Chen et al, 2019). The world at large has suffered several oil spills which includes, Canada Atlantic Empress (2,100,000-2,400,000 Barrels), Angola ABT Summer (1,800,000 -1,900,000 barrels) , France Amoco Cadis oil Tanker (1,600,000-1,700,000 Barrels), South African ship tank fire of Castillo De Bellver (1,850,000 barrels), West Indies Fergana Valley (2,000,000 plus Barrels), Mexico Ixtoc (3,300,000 Barrel), Persian Gulf Iran -Iraq War (1,900,000 Barrels), Italy, Uzbekistan and Kuwait first gulf war in 1991 (240,000,000 gallons) (Balogun et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model

Yekeen

Balogun

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This study developed a novel deep learning oil spill instance segmentation model using Mask-Region-based Convolutional Neural Network (Mask R-CNN) model which is a state-of-the-art computer vision model. A total of 2882 imageries containing oil spill, look-alike, ship, and land area after conducting different pre-processing activities were acquired. These images were subsequently sub-divided into 88% training and 12% for testing, equating to 2530 and 352 images respectively. The model training was conducted using transfer learning on a pre-trained ResNet 101 with COCO data as a backbone in combination with Feature Pyramid Network (FPN) architecture for the extraction of features at 30 epochs with 0.001 learning rate. The model’s performance was evaluated using precision, recall, and F1-measure which shows a higher performance than other existing models with value of 0.964, 0.969 and 0.968 respectively. As a specialized task, the study concluded that the developed deep learning instance segmentation model (Mask R-CNN) performs better than conventional machine learning models and semantic segmentation deep learning models in detection and segmentation of marine oil spill.

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Oil spills from global tankers: Status review and future governance

Cited by 246 publications

References 39 publications

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system

Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model

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