Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

Legal Framework for Marine Oil Pollution from Ships in Thailand

Marine oil pollution from ships has been a constant threat to Thai waters. This article examines the domestication of the provisions of relevant international conventions to which Thailand is a party regarding the prevention, preparedness, response and compensation of claimants of marine oil pollution damage. The current Thai legal framework does not provide for adequate protection of the marine environment. Thailand has not yet become a party to many relevant international conventions and does not provide adequate compensation to victims of marine oil pollution incidents. This article provides recommendations for the improvement of the legal framework of Thailand regarding these issues.

Risk Reduction of Marine Oil Spill using Clusters of Fruit Peel Pellets

Globalization has led to massive rise in cross border trade. Increase in E-commerce business has contributed to this too. As a result, a continuous and complex logistics network of supply chains operate across the globe round the clock. Transportation of goods is the most essential activity of this network. Statistics reveals that approximately 90 percent of world’s goods are transported via sea route. This intense network of shipment poses a huge threat to marine ecosystem in numerous ways. One of the most dangerous events for marine ecosystem is oil spill. Oil spill from vessels and pipelines are a major reason behind mortality of marine wildlife. The present work focuses on developing a cost-effective sustainable solution for mitigation of marine oil spill. It was found from existing literature that peels of some fruits have substantial oil absorbing capacity. This fact was experimentally validated and a reduced scale prototype was designed aiming for the task of deployment in case of oil spill in marine waters. The experimental results show that the proposed model floats on water after absorbing oil up to its saturation value. It was also found that through appropriate mechanical setup, absorbed oil can be extracted for reuse or processing.