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Fingas M.,Spill Science
39th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2016

PAHs or Polycyclic Aromatic Hydrocarbons are ubiquitous aromatic compounds also found in crude oil and are often produced as a result of combustion. Many PAHs are toxic to many species, particularly the larger PAHs. Crude oil burns and diesel fuel burns result in PAH downwind of the fire. PAH concentrations in a number of burns including test burns of crude oil and diesel fuel and at-sea burns of crude oil were examined. These included data dating 25 years ago to the present. The data were largely for the PAH concentrations in the starting oils and in the residue. In the case of some burns, the concentration of PAHs emitted as volatile compounds and adsorbed to particulate matter were available and analyzed. Crude oil burns result in polycyclic aromatic hydrocarbons (PAHs) downwind of the fire, mostly adsorbed to particulate matter, but the concentration on the particulate matter, both in the plume and the particulate precipitation at ground level, is often an order-of-magnitude less than the concentration of PAHs in the starting oil. This includes the concentration of multi-ringed (5 or 6 rings) PAHs, which are often created in other combustion processes such as lowtemperature incinerators and diesel engines. There is a slight increase in the concentration of multi-ringed PAHs in the burn residue. When considering the mass balance of the burn, however, most of the PAHs are destroyed by the fire. Destruction efficiencies are typically 99.9 % or greater. Diesel fuel contains significant levels of PAHs of smaller molecular size, the 2 to 3-ring PAHs predominating. Burning diesel results in a greater concentration of pyrogenic PAHs of larger molecular sizes. Larger PAHs are either created or concentrated by the fire. Larger PAHs, some of which are not even detectable in the Diesel fuel, are found both in the soot and in the residue; however, the concentrations of these larger PAHs are low and often just above detection limits. Overall, more PAHs are destroyed by the fires than are created. As with crude oil burns, the destruction efficiencies for diesel burns are 98% or greater, but often less than those for crude oils.


Fingas M.,Spill Science | Brown C.,Environment Canada
Marine Pollution Bulletin | Year: 2014

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 3-8. K 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. © 2014 Elsevier Ltd.


Yetilmezsoy K.,Yildiz Technical University | Fingas M.,Spill Science | Fieldhouse B.,Environment Canada
Colloids and Surfaces A: Physicochemical and Engineering Aspects | Year: 2011

Oil composition and properties including density, viscosity, asphaltene, saturate, aromatics and resin contents are responsible factors for the formation of water-in-crude-oil emulsions. These factors can be used to develop an stability index which determines states of water-in-oil emulsion in terms of either an unstable, entrained, mesostable or stable conditions. It is important to note that most of the regression models cannot capture the non-linear relationships involved in the formation of these emulsions. This study deals with the prediction of water-in-oil emulsions stability by an adaptive neuro-fuzzy inference system (ANFIS) with basic compositional factors such as density, viscosity and percentages of SARA (saturates, aromatics, resins, and asphaltenes) components.In the computational method, grid partition and subtractive clustering fuzzy inference systems were tried to generate the optimum fuzzy rule base sets. The stability estimation was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also compared to the conventional regression approach by means of various descriptive statistical indicators, such as root mean-square error (RMSE), index of agreement (IA), the factor of two (FA2), fractional variance (FV), proportion of systematic error (PSE), etc.With trying various types of fuzzy inference system (FIS) structures and several numbers of training epochs ranging from 1 to 100, the lowest root mean square error (RMSE=2.0907) and the highest determination coefficient (R2=0.967) were obtained with subtractive clustering method of a first-order Sugeno type FIS. For the optimum ANFIS structure, input variables were fuzzified with four Gaussian membership functions, and the number of training epochs was computed as 21. In the computational analysis, the predictive performance of the ANFIS model was examined for the following ranges of the clustering parameters: range of influence (ROI)=0.45-0.60, squash factor (SF)=1.20-1.35, accept ratio (AR)=0.40-0.55, and reject ratio (RR)=0.10-0.20. Results indicated that ROI, SF, AR and RR were obtained to be 0.54, 1.25, 0.50 and 0.15, respectively, for the best FIS structure.It was clearly concluded that the proposed ANFIS model demonstrated a superior predictive performance on forecasting of water-in-oil emulsions stability. Findings of this study clearly indicated that the neuro-fuzzy modeling could be successfully used for predicting the stability of a specific water-in-oil mixture to provide a good discrimination between several visual stability conditions. © 2011 Elsevier B.V.


Fingas M.,Spill Science | Fieldhouse B.,Environment Canada
Marine Pollution Bulletin | Year: 2012

Water-in-oil mixtures such as emulsions, often form and complicate oil spill countermeasures. The formation of water-in-oil mixtures was studied using more than 300 crude oils and petroleum products. Water-in-oil types were characterized by resolution of water at 1 and 7. days, and some after 1. year. Rheology measurements were carried out at the same intervals. The objective of this laboratory study was to characterize the formed water-in-oil products and relate these properties to starting oil properties.Analysis of the starting oil properties of these water-in-oil types shows that the existence of each type relates to the starting oil viscosity and its asphaltene and resin contents. This confirms that water-in-oil emulsification is a result of physical stabilization by oil viscosity and chemical stabilization by asphaltenes and resins. This stabilization is illustrated using simple graphical techniques.Four water-in-oil types exist: stable, unstable, meso-stable and entrained. Each of these has distinct physical properties. © 2011 Elsevier Ltd.


Fingas M.,Spill Science
Proceedings of the 34th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2011

This paper is a review of oil spill tracking buoys and devices from 1970 until the present. Since the early 1970's devices for tracking oil spills have been made and tested. Very early in the oil spill technology, cardboard coupons, plywood pieces, etc., were tried. Later some devices were developed and commercialized. Some buoys were equipped with radio transmitters and later satellite transmitters. A literature review shows that over 30 devices were proposed and about 20 tested to a certain degree. Testing programs on many oil spill tracking devices were carried out. Testing using actual slicks was carried out in the 1970's and 1980's. These tests showed that two of the slick tracking devices followed oil very well. Some buoys tracked the oil slick only with large deviation and many were not found to be useful for tracking oil. Some devices did not even follow the direction of the test slicks. Later testing showed that some surrogates such as cedar oil with cedar wood chips were useful and thus testing could be carried out without actual oil. The test results are summarized and results given. Devices are listed along with the deviation from the true slicks as found in the various tests.


Fingas M.,Spill Science
Proceedings of the 38th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2015

Depending on starting oil properties, four types of water-in-oil types are created: mesostable and stable emulsions, entrained water-in-oil and unstable or 'those-that-do-not-form-any' types. Each type has unique properties and unique behaviour. Previous efforts have focussed on using a wide variety of data including physical properties and SARA data with which to predict the type of water-in-oil formed. Often SARA data are not available for oils and at most, density and viscosity are available. This paper focuses on using only density and viscosity to estimate water-in-oil type. This type of simplification is possible because certain types of water in oil emulsions have unique density/viscosity relationships. First, entrained water types have a density of 9.6 to 1.0 g/mL and a viscosity of 2300 to 200,000 mPa.s. Those oils that do not form any type of water-in-oil type are of two kinds, low viscosity oils and high viscosity oils. These types can then be screened out of the decision tree. The low viscosity oils have a viscosity < 50 mPa.s. The high viscosity oils have a viscosity greater than 200,000 mPa.s. This screening process then leaves the meso-stable and stable emulsions. The starting density and viscosity of these oils is very similar and water-in-oil types are difficult to predict. Stable and meso-stable have been separated by using the fact that the stable emulsion types have a greater deviation when predicting viscosity using density. The error rate is low for the entrained and 'do not form' types; however is high for meso-stable and stable types. Examples are given of the screening process by which one can assign water-in-oil types to starting oils.


Fingas M.,Spill Science
Proceedings of the 38th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2015

This paper is a summary of physical parameters and the spill behaviour of diluted bitumens and other bitumen products as found in recent literature. There are three basic types of diluted bitumen based on the diluents used to lower the bitumen viscosity. The most typical type is diluted with condensates and these are called Dilbits, alternatively there are some diluted with naphtha. A newer type that is used for local transport is diluted with C4 or a butanes mixture (sometimes including C5 or a pentanes mixture), often along with condensates. Synthetic crude is sometimes used as a diluent and this type is called Synbit. Other variants on these include Railbit, similar to Dilbit but diluted half as much and Neatbit, which is not diluted at all. Each of these bitumen products has different properties initially when spilled, however with time, generally revert to the properties of the starting bitumen except for Synbit which tends to change much less. The properties of the starting bitumens vary widely, as do the diluents, resulting in variable products with variable behaviours. Included in the paper will be a summary of the properties of several bitumen products and predicted changes that these undergo with weathering.


Fingas M.,Spill Science
Proceedings of the 33rd AMOP Technical Seminar on Environmental Contamination and Response | Year: 2010

This paper summarizes the theory of oil-in-water emulsion stability resulting in oil spill dispersion re-surfacing. A model was constructed utilizing four basic processes. Initial dispersion was an input, 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. Given this coalescence, and the re-dispersion effectiveness, the dispersion in the water column decreases at an exponential rate with dispersion half-lives ranging from 120 to 250 minutes. More than 200 runs were carried out with variations of the models. The runs show that the most important factor to both the residual dispersion effectiveness and the half-life, is the mixing depth of the sea. The second most important factor is the initial dispersion effectiveness assigned and then the re-dispersion effectiveness assigned. It is noted that many dispersion destabilising processes were not included because these processes are poorly understood and/or inputs for oil spill dispersions could not be found. The model outputs illustrate the instability of oil-in-water emulsions and the various influences on the instability. The long term fate of the oil is not modeled.


Fingas M.F.,Spill Science
Proceedings of the 35th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2012

Thickness of oil spills is an important but a poorly understood topic. Examination of the limited data that exists shows that we do not understand the thickness nor profiles of oil spills. These profiles seem to vary with conditions such as age of the slick, wind, oil type and sea conditions. This paper examined several possible profiles and concluded that simple analysis did not show which, if any, were probable. Several slicks were analyzed for ratios of thin to thick portions. It was found that the old rule-of-thumb that 90% of the oil was in 10% of the area, was not correct. Analysis of a few slicks where full photographs were available, showed that the thick portion of the slick ranged from 4 % to larger than the sheen area. No simple algorithm for the thickness distribution is then available. It is thought that the slick area is a function of time since spilled, oil type, wind conditions, slick history and wave/current conditions. Means to measure slick thickness, remotely or directly, are reviewed. More than 20 concepts are present and reviewed. Many of these are judged not to be viable for a variety of scientific reasons. Only two technical means are available to remotely measure oil thickness, passive microwave radiometry and laser-acoustic measures. Neither are, at this time, highly developed. One company does make a commercial microwave radiometer, but data from these units are not readily available. Visual means to ascertain oil thickness is restricted by physics to 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 the slick, the thickness of which is unknown but may range over several orders-of-magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, however, little research effort is devoted to this task. Several viable concepts have been developed but require further work and verification. Furthermore, we currently do not have tools, not even simple rules by which to gauge thickness regimes of slicks for calibration of new instruments.


Fingas M.,Spill Science
Proceedings of the 35th AMOP Technical Seminar on Environmental Contamination and Response | Year: 2012

This paper is a review of petroleum chemistry. The traditional separation of Saturates, Aromatics, Resins and Asphaltenes (SARA) oil components is summarized. Details 011 these groupings and many compounds within the groupings and where available, the typical amounts found 111 some oils, is given. A detailed look at compounds found 111 oil and the amount of these is presented. The compounds are related to the overall composition and the SARA composition. Details of more than 500 compounds will be given 111 the paper. The composition can be related to bulk properties and bulk composition. Examples of this are given.

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