The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to ...be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens, which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods.
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.
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.
Oil, refined product, and pyrogenic hydrocarbons are the most frequently discovered contaminants in the environment. To effectively determine the fate of spilled oil in the environment and to ...successfully identify source(s) of spilled oil and petroleum products is, therefore, extremely important in many oil-related environmental studies and liability cases. This article briefly reviews the recent development of chemical analysis methodologies which are most frequently used in oil spill characterization and identification studies and environmental forensic investigations. The fingerprinting and data interpretation techniques discussed include oil spill identification protocol, tiered analytical approach, generic features and chemical composition of oils, effects of weathering on hydrocarbon fingerprinting, recognition of distribution patterns of petroleum hydrocarbons, oil type screening and differentiation, analysis of “source-specific marker” compounds, determination of diagnostic ratios of specific oil constituents, stable isotopic analysis, application of various statistical and numerical analysis tools, and application of other analytical techniques. The issue of how biogenic and pyrogenic hydrocarbons are distinguished from petrogenic hydrocarbons is also addressed.
•This is an oil spill remote sensing update to 2014.•Latest findings from remote sensing of the U.S. Gulf spill are included.•Includes radar processing for the first time.•Includes summary of oil ...spill thickness remote sensing.
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.
An algorithm utilizing four basic processes was described for chemical oil spill dispersion. Initial dispersion was calculated using a modified Delvigne equation adjusted to chemical dispersion, then ...the dispersion was distributed over the mixing depth, as predicted by the wave height. Then the droplets rise to the surface according to Stokes’ law. Oil on the surface, from the rising oil and that undispersed, is re-dispersed. The droplets in the water column are subject to coalescence as governed by the Smoluchowski equation. A loss is invoked to account for the production of small droplets that rise slowly and are not re-integrated with the main surface slick. The droplets become less dispersible as time proceeds because of increased viscosity through weathering, and by increased droplet size by coalescence. These droplets rise faster as time progresses because of the increased size. Closed form solutions were provided to allow practical limits of dispersibility given inputs of oil viscosity and wind speed. Discrete solutions were given to calculate the amount of oil in the water column at specified points of time. Regression equations were provided to estimate oil in the water column at a given time with the wind speed and oil viscosity. The models indicated that the most important factor related to the amount of dispersion, was the mixing depth of the sea as predicted from wind speed. The second most important factor was the viscosity of the starting oil. The algorithm predicted the maximum viscosity that would be dispersed given wind conditions. Simplified prediction equations were created using regression.
Biomarkers are one of the most important hydrocarbon groups in petroleum. Biomarkers can be detected in low quantities (ppm and sub-ppm level) in the presence of a wide variety of other types of ...petroleum hydrocarbons by the use of the gas chromatography/mass spectrometry (GC/MS). Relative to other hydrocarbon groups in oil such as alkanes and most aromatic compounds, biomarkers are more degradation-resistant in the environment. Furthermore, biomarkers formed under different geological conditions and ages may exhibit different biomarker fingerprints. Therefore, chemical analysis of biomarkers generates information of great importance to environmental forensic investigations in terms of determining the source of spilled oil, differentiating and correlating oils, and monitoring the degradation process and weathering state of oils under a wide variety of conditions. This article briefly reviews biomarker chemistry, biomarker characterization and quantification, biomarker distributions, weathering effects on biomarker composition, bicyclic biomarker sesquiterpanes and diamondoids, diagnostic ratios and cross-plots of biomarkers, unique biomarkers, application of biomarker fingerprinting techniques for spill source identification, and application of multivariate statistical analysis for biomarker fingerprinting.
Experimentation shows that oil is not strictly air boundary-layer regulated. The fact that oil evaporation is not strictly boundary-layer regulated implies that a simplistic evaporation equation ...suffices to describe the process. The following processes do not require consideration: wind velocity, turbulence level, area, thickness, and scale size. The factors important to evaporation are time and temperature.
The equation parameters found experimentally for the evaporation of oils can be related to commonly available distillation data for the oil. Specifically, it has been found that the distillation percentage at 180
°C correlates well with the equation parameters. Relationships have been developed enabling calculation of evaporation equations directly from distillation data:
(1)
percentage
evaporated
=0.165(%D)
ln(t)
where %
D is the percentage (by weight) distilled at 180
°C and
t is the time in minutes.
These equations were combined with the equations generated to account for the temperature variations:
(2)
percentage
evaporated
=0.165(%D)+0.045(T−15)
ln(t)
The results have application in oil spill prediction and modeling. The simple equations can be applied using readily available data such as sea temperature and time. Old equations required oil vapour pressure, specialized distillation data, spill area, wind speed, and mass transfer coefficients, all of which are difficult to obtain.
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