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.

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

Numerical Modelling of Oil Spill Transport in Tide-Dominated Estuaries: A Case Study of Humber Estuary, UK

Oil spills in estuaries are less studied and less understood than their oceanic counterparts. To address this gap, we present a detailed analysis of estuarine oil spill transport. We develop and analyse a range of simulations for the Humber Estuary, using a coupled hydrodynamic and oil spill model. The models were driven by river discharge at the river boundaries and tidal height data at the offshore boundary. Satisfactory model performance was obtained for both model calibration and validation. Some novel findings were made: (a) there is a statistically significant (p < 0.05) difference in the influence of hydrodynamic conditions (tidal range, stage and river discharge) on oil slick transport; and (b) because of seasonal variation in river discharge, winter slicks released at high water did not exhibit any upstream displacement over repeated tidal cycles, while summer slicks travelled upstream into the estuary over repeated tidal cycles. The implications of these findings for operational oil spill response are: (i) the need to take cognisance of time of oil release within a tidal cycle; and (ii) the need to understand how the interaction of river discharge and tidal range influences oil slick dynamics, as this will aid responders in assessing the likely oil trajectories.