In this study, we demonstrate that partial least-squares regression analysis with full cross-validation of spectral reflectance data estimates the amount of polycyclic aromatic hydrocarbons in petroleum-contaminated tropical rainforest soils. We applied the approach to 137 field-moist intact soil samples collected from three oil spill sites in Ogoniland in the Niger Delta province (5.317°N, 6.467°E), Nigeria. We used sequential ultrasonic solvent extraction–gas chromatography as the reference chemical method. We took soil diffuse reflectance spectra with a mobile fibre-optic visible and near-infrared spectrophotometer (350–2500 nm). Independent validation of combined data from studied sites showed reasonable prediction precision (root-mean-square error of prediction = 1.16–1.95 mg kg−1, ratio of prediction deviation = 1.86–3.12, and validation r2 = 0.77–0.89). This suggests that the methodology may be useful for rapid assessment of the spatial variability of polycyclic aromatic hydrocarbons in petroleum-contaminated soils in the Niger Delta to inform risk assessment and remediation.

Remote-sensing for oil spills is reviewed. The use of visible techniques is ubiquitous, however it gives only the same results as visual monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Cameras are only useful to provide documentation. In daytime oil absorbs light and remits this as thermal energy at temperatures 3–8K above ambient, this is detectable by infrared (IR) cameras.Laser fluorosensors are useful instruments because of their unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. They are the only sensor that can positively discriminate oil on most backgrounds. Radar detects oil on water by the fact that oil will dampen water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing.

This paper tests the adequacy of using the lattice Boltzmann method in large-scale oil spill modelling, such as the Lebanon oil spill. Several numerical experiments were performed in order to select the most appropriate lattice and to decide between the single- and two-relaxation time models. Large-scale oil spills require simulations with short computational times. In order to speed up the computation and preserve adequate accuracy of the model, five different flux limiting interpolation techniques were compared and evaluated. The model was validated on the Lebanon oil spill with regard to the oil-slick position and concentrations in the sea, and the beaching area on the coast. Good agreement with satellite images of the slick and field data on beaching was achieved. The main advantages of the applied method are the capability of simulating very low oil concentrations and computational times that are by an order of magnitude shorter compared to similar models.

The present paper focuses on developing a numerical oil spill model that incorporates the full three-dimensional wave–current interactions for a better representation of the spilled oil transport mechanics in complicated coastal environments. The incorporation of surface wave effects is not only imposing a traditional drag coefficient formulation at the free surface, but also the 3D momentum equations are adjusted to include the impact of the vertically dependent radiation stresses on the currents. Based on the current data from SELFE and wave data from SWAN, the oil spill model utilizes oil particle method to predict the trajectory of individual droplets and the oil concentration. Compared with the observations in Dalian New Port oil spill event, the developed model taking into account wave–current coupling administers to giving better conformity than the one without. The comparisons demonstrates that 3D radiation stress impacts the spill dynamics drastically near the sea surface and along the coastline, while having less impact in deeper water.

Continuous operational monitoring by means of remote sensing contributes significantly towards less occurrence of oil spills over European waters however, operational activities show regular occurrence of accidental and deliberate oil spills over the North Sea, particularly from offshore platform installations. Since the areas covered by oil spills are usually large and scattered over the North Sea, satellite remote sensing particularly Synthetic Aperture Radar (SAR) represents an effective tool for operational oil spill detection. This paper describes the development of a semi-automated approach for oil spill detection, optimized for near real time offshore platform sourced pollution monitoring context. Eight feature parameters are extracted from each segmented dark spot. The classification algorithm is based on artificial neural network. An initial evaluation of this methodology has been carried out on 156 TerraSAR-X images. Wind and current history information also have been analyzed for particular cases in order to evaluate their influences on spill trajectory.

Arctic shipping is on the rise, leading to increased concern over the potential environmental impacts. To better understand the magnitude of influence to the Arctic environment, detailed modelling of emissions and environmental risks are essential. This paper describes a framework for environmental accounting. A cornerstone in the framework is the use of Automatic Identification System (AIS) ship tracking data from satellites. When merged with ship registers and other data sources, it enables unprecedented accuracy in modelling and geographical allocation of emissions and discharges. This paper presents results using two of the models in the framework; emissions of black carbon (BC) in the Arctic, which is of particular concern for climate change, and; bunker fuels and wet bulk carriage in the Arctic, of particular concern for oil spill to the environment. Using the framework, a detailed footprint from Arctic shipping with regards to operational emissions and potential discharges is established.

The Egadi Marine Protected Area (MPA) on the western side of the Sicily Channel (Central Mediterranean) is exposed to a high risk of oil pollution from the tanker routes connecting the eastern and western basins of the Mediterranean Sea. Areas where an oil spill would do most damage, and thus where surveillance should be concentrated, are identified in this study by Lagrangian tracers tracked backwards in time from points along the MPA perimeter using data spanning six years from 2006 to 2011. Results indicate that the areas where oil surveillance would be most beneficial are segments of the tanker routes south of Sicily (highly frequented) and north of Sicily (scarcely frequented), both extending about 150 miles from November to March and 100 miles in the other months. The third route, close to the Tunisian shore, is the most frequented by oil tankers but the threat period is limited to November and December.

The dispersion effectiveness of dispersants containing Tween 80, Span 80, and dioctyl sodium sulfosuccinate (DOSS) was characterized using a modified Swirling Flask test, and was correlated with both initial and dynamic interfacial tension produced by those dispersants at an oil–water interface. Compositional trends in effectiveness were shown to be governed by: (1) initial oil–water interfacial tension observed upon dispersant–oil–saltwater contact; (2) rate of increase (or decrease) from the initial interfacial tension as DOSS was rapidly lost to the aqueous phase; and (3) gradually slowing kinetics of dispersant adsorption to the oil–water interface as Span 80 concentration was increased, which ultimately diminished dispersion effectiveness considerably even as dynamic interfacial tension remained <10−3mN/m. It is proposed that this third phenomenon results not only from the hydrophobicity of Span 80, but also from the dependence of mixed Tween–Span–DOSS reverse micelles’ stability in crude oil on dispersant composition.

This paper reports the results of an analysis of oil spill cost data assembled from a worldwide pollution database that mainly includes data from the International Oil Pollution Compensation Fund. The purpose of the study is to analyze the conditions of marine pollution accidents and the factors that impact the costs of oil spills worldwide. The accidents are classified into categories based on their characteristics, and the cases are compared using charts to show how the costs are affected under all conditions. This study can be used as a helpful reference for developing a detailed statistical model that is capable of reliably and realistically estimating the total costs of oil spills. To illustrate the differences identified by this statistical analysis, the results are compared with the results of previous studies, and the findings are discussed.

Modeling oil biodegradation is an important step in predicting the long term fate of oil on beaches. Unfortunately, existing models do not account mechanistically for environmental factors, such as pore water nutrient concentration, affecting oil biodegradation, rather in an empirical way. We present herein a numerical model, BIOB, to simulate the biodegradation of insoluble attached hydrocarbon. The model was used to simulate an experimental oil spill on a sand beach. The biodegradation kinetic parameters were estimated by fitting the model to the experimental data of alkanes and aromatics. It was found that parameter values are comparable to their counterparts for the biodegradation of dissolved organic matter. The biodegradation of aromatics was highly affected by the decay of aromatic biomass, probably due to its low growth rate. Numerical simulations revealed that the biodegradation rate increases by 3–4 folds when the nutrient concentration is increased from 0.2 to 2.0mgN/L.