Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring

Prediction of the marine spreading of low sulfur fuel oil using the long short-term memory model trained with three-phase numerical simulations

In this study, we focus on the development and validation of a deep learning (long short-term memory, LSTM)-based algorithm to predict the accidental spreading of LSFO (low sulfur fuel oil) on the water surface. The data for the training was obtained by numerical simulations of artificial geometries with different configurations of islands and shorelines and wind speeds (2.0-8.0 m/s). For simulating the spread of oils in O(102) km scales, the volume of fluid and discrete phase models were adopted, and the kinematic variables of particle location, particle velocity, and water velocity were collected as input features for LSTM model. The predicted spreading pattern of LSFO matched well with the simulation (less than 10 % in terms of the mean absolute error for the untrained data). Finally, we applied the model to the Wakashio LSFO spill accident, considering actual geometry and weather information, which confirmed the practical feasibility of the present model.

Vulnerability of biological resources to potential oil spills in the Lower Amazon River, Amapá, Brazil

Ships that transport oil or derivatives on the Lower Amazon River waterway are at a considerably high risk of suffering spills, with severe environmental and socioeconomic consequences. The present study is aimed at modeling and simulating the oil dispersion and magnitude of these accidents in terms of the vulnerability of biological resources, considering two oil types most often transported by medium-sized tankers in the region (S500 and S10). The study method was as follows: (a) secondary data were collected from local species, and the coastal sensitivity index (CSI) was calculated, obtained from Brazil's Letters of Environmental Sensitivity to Oil Spill (Cartas de Sensibilidade Ambiental ao Derramamento de Óleo (SAO)); (b) ship traffic information was obtained from Brazil's Statistical Yearbook of Waterway (Anuário Estatístico Aquaviário (ANTAQ)); (c) modeling and numerical simulation of oil spills in water were performed, in order to investigate dispersion scenarios (SisBaHia); (d) three numerical scenarios of oil plume dispersion (in May and November) were integrated to assess species vulnerability in three zones of environmental interest (I, II, and III). Some species identified in zone II were considered to be the most vulnerable (fish, plankton, aquatic mammals, amphibians, aquatic invertebrates, trees, and plants), with the mammal Sotalia fluviatilis being at risk of extinction (Gervais & Deville, 1853). The simulated scenarios showed that contingency plans should have a minimum response time of 3 h and a maximum response time of 72 h to prevent the oil plumes from dispersing as far as 170 km longitudinally, depending on the zone, season, and tidal phase. Thus, a total of 62 sites of biological resources were identified in the literature recorded from 2016. Considering them, 324 species of flora and fauna were recorded, distributed in the following seven groups: (i) 49 tree and plant species, (ii) 37 amphibian species, (iii) 2 aquatic invertebrate species, (iv) 23 invertebrate species, (v) 1 aquatic mammal species, (vi) 95 fish species, and (vii) 117 planktonic species. A failure to respond to these accidents would impact immense intact aquatic areas and ecosystems, with unpredictable consequences for local biodiversity conservation.

Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model. In the classification, K-Nearest Neighbor (KNN) obtained the best approximations in oil detection using Blue (0.453-0.520 μm), NIR (0.790-0.891 μm), SWIR1 (1.557-1.717 μm), and SWIR2 (1.960-2.162 μm) bands for 2010 spill images. In the concentration model, the mean absolute error (MAE) was 1.41 and 3.34, for training and validation data. When testing the concentration-estimation model in images where oil was detected, the concentration-estimation obtained was between 40 and 60 %. This demonstrates the potential use of ML techniques and spectral response data to detect and estimate the concentration of oil-spills.

Fluorescence-estimated oil concentration (Foil) in the Deepwater Horizon subsea oil plume

Tracking the subsea oil plume during the 2010 Deepwater Horizon Oil Spill (DWH) was conducted using in situ fluorescence via vertical profilers (n = 1157) and discrete sample chemical analyses (n = 7665). During monitoring efforts, discrete samples provided a coarse picture of the oil plume footprint, but the majority of the samples were below standard analytical detection limits for petroleum hydrocarbons. In situ fluorescence data improved the spatial and temporal resolution of the subsea oil plume characterization. Here we synthesized millions of continuous fluorescence data points from hundreds of contemporaneously discrete samples collected to demonstrate how fluorescence could serve as a proxy for Benzene-Toluene-Ethylbenzene-Xylene (BTEX) concentration. Data mined from Gulf Science Data repository were well correlated, and geographically and temporally aligned to provide direct comparisons. Described here are the methods used to calibrate the fluorescence data and to spatially approximate the three-dimensional geographic extent of the oil plume.

Comparison between the Lagrangian and Eulerian Approach for Simulating Regular and Solitary Waves Propagation, Breaking and Run-Up

The present paper places emphasis on the most widely used Computational Fluid Dynamics (CFD) approaches, namely the Eulerian and Lagrangian methods each of which is characterized by specific advantages and disadvantages. In particular, a weakly compressible smoothed particle (WCSPH) model, coupled with a sub-particle scale (SPS) approach for turbulent stresses and a new depth-integrated non-hydrostatic finite element model were employed for the simulation of regular breaking waves on a plane slope and solitary waves transformation, breaking and run-up. The validation of the numerical schemes was performed through the comparison between numerical and experimental data. The aim of this study is to compare the two modeling methods with an emphasis on their performance in the simulation of hydraulic engineering problems.

Exploring the effects of microalgal biomass on the oil behavior in a sand-water system

This study focused on the impact of microalgal biomass on the oil behavior in a sand-water system. The microalgal biomass was characterized, and the interaction between microalgal biomass and oil was analyzed through Fourier transform infrared (FTIR) spectroscopy. The effects of different conditions including microalgal biomass dose, pH, temperature, and salinity on the oil behavior were investigated. A two-level factorial analysis was also used to further explore the interactions of these conditions. The microalgal biomass was found to be the most influential parameter for the residual crude oil on sand. Higher microalgal biomass dose resulted in less residual oil on sand. The remaining oil decreased with increasing solution pH from 4 to 7, and an increase of remaining oil was observed when the pH was further increased above 7. In addition, temperature and salinity could affect the removal of crude oil in the presence of microalgal biomass. Increasing the temperature could result in less residual oil on sand and there was higher oil removal at the high salinity. The effects of microalgal biomass on the oil behavior could also be impacted by environmental conditions. The results of this study indicate that the presence of algae in the oiled shoreline can be considered in the comprehensive evaluation of spill risk and prediction of oil fate.

Visual Appearance of Oil on the Sea

The visual appearance of oil spills at sea is often used as an indicator of spilled oil properties, state and slick thickness. These appearances and the oil properties that are associated with them are reviewed in this paper. The appearance of oil spills is an estimator of thickness of thin oil slicks, thinner than a rainbow sheen (<3 µm). Rainbow sheens have a strong physical explanation. Thicker oil slicks (e.g., >3 µm) are not correlated with a given oil appearance. At one time, the appearance of surface discharges from ships was thought to be correlated with discharge rate and vessel speed; however, this approach is now known to be incorrect. Oil on the sea can sometimes form water-in-oil emulsions, dependent on the properties of the oil, and these are often reddish in color. These can be detected visually, providing useful information on the state of the oil. Oil-in-water emulsions can be seen as a coffee-colored cloud below the water surface. Other information gleaned from the oil appearance includes coverage and distribution on the surface.

Management of Dredging Activities in a Highly Vulnerable Site: Simulation Modelling and Monitoring Activity

Unfortunately, more and more contaminants, such as heavy metals and other organic micro-pollutants, degrade the good ecological status of marine systems. The removal of contaminated sediments from harbours through dredging activities may cause harmful changes in the environment. This present work shows how monitoring the activity and validated numerical models can be of great help to dredging activities that can cause environmental impacts due to the increase of the suspended solid concentration (SSC) and their dispersion and deposition far from the dredging point. This study is applied to a hypothetical dredging project in a very vulnerable coastal site in Southern Italy, the Mar Piccolo Basin. A statistical analysis of the simulated parameter SSC was carried out to numerically estimate its spatial (vertical and horizontal) variability, thereby allowing an evaluation of the potential environmental effects on the coastal area.

The Influence of Oil leaking rate and Ocean Current Velocity on the Migration and Diffusion of Underwater Oil Spill

Severe environmental pollution and huge economic losses would be caused by submarine oil spill with the increasing development of petroleum energy in sea. In order to predict the law of migration of oil spill from submarine pipelines accurately, the volume of fluid (VOF) model and realizable k-ε turbulence model were employed to establish numerical simulation of submarine oil spill, and the experiments were used to verify the feasibility of the numerical models. Different oil leaking rate and ocean velocity were simulated in the study. The simulation results indicated that comparing with oil leaking rate (set vertical migration velocity, U_o), current velocity (set horizontal migration velocity, U_w) has a greater influence on the migration of the oil spilling; the actual vertical migration velocity (U_o1), actual horizontal migration velocity (U_w1) and R₁ (the ratio of U_o1 and U_w1) are positively correlated with R (the ratio of U_o and U_w), and they both fluctuate within a small range no matter how large R is; when 20 ≤ R ≤ 150, R₁ fits a linear fit curve with curve as R₁ = 0.66932 + 0.00215 R, which can provide a theoretical reference to the recovery system of underwater pipeline oil spilling emergency.

A mesophotic black coral forest in the Adriatic Sea

A forest of the black coral Antipathella subpinnata was found from 52 to 80 m depth in three different sites at Tremiti Islands Marine Protected Area (MPA; Mediterranean Sea), with two of them hosting a monospecific forest on horizontal and vertical substrates. Colonies of A. subpinnata showed a mean density between 0.22 ± 0.03 and 2.40 ± 0.26 colonies m⁻² (maximum local values of 2.4–7.2 colonies m⁻²). The link between the local distribution of A. subpinnata and the main oceanographic features confirmed the fundamental role of the currents in shaping the distribution of the species in presence of hard substrata. This black coral forest represents the only one known thus far in the Adriatic Sea, but it could be linked with other unseen forests all over the Mediterranean Sea. The associated megafauna highlights the importance of these forests as habitat for species of both conservation and commercial importance but, at the same time, makes such habitat a target for fishing practices, as many lost fishing gears were found within the coral forest. The enlargement of the MPA borders and the enforcement of controls in the area of the A. subpinnata forest is urgently needed for the proper conservation of this protected species.

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.

Monitoring Systems and Numerical Models to Study Coastal Sites

The present work aims at illustrating how the joint use of monitoring data and numerical models can be beneficial in understanding coastal processes. In the first part, we show and discuss an annual dataset provided by a monitoring system installed in a vulnerable coastal basin located in Southern Italy, subjected to human and industrial pressures. The collected data have been processed and analysed to detect the temporal evolution of the most representative parameters of the inspected site and have been compared with recordings from previous years to investigate recursive trends. In the second part, to demonstrate to what extent such type of monitoring actions is necessary and useful, the same data have been used to calibrate and run a 3D hydrodynamic model. After this, a reliable circulation pattern in the basin has been reproduced. Successively, an oil pollution transport model has been added to the hydrodynamic model, with the aim to present the response of the basin to some hypothetical cases of oil spills, caused by a ship failure. It is evident that the profitable prediction of the hydrodynamic processes and the transport and dispersion of contaminants strictly depends on the quality and reliability of the input data as well as on the calibration made.

A Review of Oil Spill Remote Sensing

The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day–night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.