Sea-ice conditions during a crude oil spill govern oil behaviour and the rate of weathering processes. This study utilized data from past mesocosm experiments conducted at the Sea-ice Environmental Research Facility and compared the sea-ice temperature, salinity, and chemical composition of light crude oil under three sea-ice conditions. An oil spill under discontinuous pack ice showed a decrease in ice thickness with an increase in oil concentration, as well as the highest photooxidation of organosulfur compounds amongst all experiments. An oil spill under a continuous ice volume demonstrated a decrease of sea-ice bulk salinity with an increase in crude oil concentration. Also, oil exhibited the lowest evaporation, along with the highest dissolution and carbonylation rates. An oil spill before ice formation demonstrated the reduction of sea-ice thickness and bulk salinity in contaminated ice. The oil in this case exhibited the highest evaporation and photooxidation rates. Tested oil-in-ice scenarios produced quantitative differences in sea-ice properties and crude oil composition.

There has been increasing urgency to develop methods for detecting oil in sea ice owing to the effects of climate change in the Arctic. A multidisciplinary study of crude oil behavior in a sea ice environment was conducted at the University of Manitoba during the winter of 2016. In the experiment, medium-light crude oil was injected underneath young sea ice in a mesocosm. The physical and thermodynamic properties of the oil-infiltrated sea ice were monitored over a three-week time span, with concomitant analysis of the oil composition using analytical instrumentation. A resonant perturbation technique was used to measure the oil dielectric properties, and the contaminated sea ice dielectric properties were modeled using a mixture model approach. Results showed that the interactions between the oil and sea ice altered their physical and thermodynamic properties. These changes led to an overall decrease in sea ice dielectrics, potentially detectable by remote sensing systems.

Accidental release of petroleum in the Arctic is of growing concern owing to increases in ship traffic and possible future oil exploration. A crude oil-in-sea ice mesocosm experiment was conducted to identify oil-partitioning trends in sea ice and determine the effect of weathering on crude oil permittivity. The dissolution of the lighter fractions increased with decreasing bulk oil-concentration because of greater oil-brine interface area. Movement of the oil towards the ice surface predominated over dissolution process when oil concentrations exceeded 1 mg/mL. Evaporation decreased oil permittivity due to losses of low molecular weight alkanes and increased asphaltene-resin interactions. Photooxidation increased the permittivity of the crude oil due to the transformation of branched aromatics to esters and ketones. Overall, the weathering processes influenced crude oil permittivity by up to 15%, which may produce sufficient quantifiable differences in the measured normalized radar cross-section of the ice.

Due to the effects of heightened warming in the Arctic, there has been an urgency to develop methods for detecting oil in (or under) sea ice, owing to increasing potential for oil exploration and ship traffic in the more accessible Arctic regions. To test the potential for radar utilizing the normalized radar cross section (NRCS) of the sea ice, an oil-in-ice mesocosm experiment was performed. Throughout the experiment, corn oil was used as a surrogate for medium crude oil, to assess oil movement tendencies in sea ice, and the resultant impact on the complex permittivity through measurement and modelling techniques. We performed a modelling study to establish the effects of corn oil on the NRCS of sea ice. The oil presence in the sea ice increased the temperature and reduced the salinity of the sea ice, thereby lowering its complex permittivity and modeled NRCS when compared to control sea ice.

This paper presents a multidisciplinary research on the thermodynamic and geophysical effects of crude oil released underneath thin sea ice, and further evaluates the ability of a combined frequency- and time-domain approach toward its detection. To this end, a controlled oil release experiment in an artificially grown sea ice mesocosm was performed during the winter of 2017 at the Sea-Ice Research Environmental Facility located at the University of Manitoba. Ice cores extracted during the evolution of the sea ice prior and post oil injection allowed the investigation of the profile’s properties and the oil distribution. Furthermore, chemical composition and microstructure analysis were performed via a gas chromatography-time-of-flight mass spectrometry and X-ray, respectively. The time-series radar signature of the profile was measured utilizing ground penetration radar at 500 MHz and a C-band scatterometer. For this experiment, it was shown that the retrieval of the oil presence underneath the young sea ice layer was feasible, provided that the measured data were utilized simultaneously in a unified cost function.

An oil-in-sea ice mesocosm experiment was conducted at the University of Manitoba Sea-Ice Environmental Research Facility from January to March 2016 in which geophysical and electromagnetic parameters of the ice were measured, and general observations about the oil-contaminated ice were made. From the experimental measurements, the presence of crude oil appears to affect the temperature and bulk salinity profiles as well as the normalized radar cross section (NRCS) of the contaminated young sea ice. The measured temperature and bulk salinity profiles of the ice, as well as the crude oil distribution within the ice, were used to model the permittivity profile of the oil-contaminated ice by adapting two mixture models commonly used to describe sea ice to account for the presence of oil. Permittivity modeling results were used to simulate the NRCS of the oil-contaminated sea ice in an effort to determine the accuracy of the models. In addition, the application of X-ray microtomography in modeling the dielectric profile of oil-contaminated sea ice was examined. The sensitivity of the permittivity models for oil-contaminated sea ice to changes in temperature, frequency, and oil volume fraction was also examined.

This paper presents an overview of the research conducted to date at the University of Manitoba Sea-ice Environmental Research Facility pertaining to the microwave remote sensing of oil spills in the presence of sea ice. As such, a description of the facility and the design of oil-in-sea ice experiments is provided along with a summary of the primary findings of the research. Future plans to expand on work done with remote sensing of oil spills in freezing environments are also discussed.

This paper presents a multidisciplinary case study on a crude oil injection experiment in an artificially grown young sea ice environment under controlled conditions. In particular, the changes in the geophysical and electromagnetic responses of the sea ice to oil introduction are investigated for this experiment. Furthermore, we perform a preliminary study on the detection of oil spills utilizing the normalized radar cross section (NRCS) data collected by a C-band scatterometer is presented. To this end, an inversion scheme is introduced that retrieves the effective complex permittivity of the domain prior and after oil injection by comparing the simulated and calibrated measured NRCS data, while roughness parameters calculated using lidar are utilized as prior information. Once the complex permittivity values are obtained, the volume fraction of oil within the sea ice is found using a mixture formula. Based on this volume fraction, a binary detection of oil presence seems to be possible for this test case. Finally, the possible sources of error in the retrieved effective volume fraction, which is an overestimate of the actual value, are identified and discussed by macrolevel and microlevel analyses through bulk salinity measurements and X-ray imagery of the samples, as well as a brief chemical analysis.

This paper presents an experiment on remote sensing of oil infested sea ice, and the detection of this contaminant. To this end, an overview of our previously developed electromagnetic inversion algorithm is first presented. This algorithm has been able to reconstruct the complex permittivity profile of snow-covered sea ice, and also retrieve some of its thermodynamic and geophysical properties. Next, a description of our oil-in-sea ice experiment is presented in which crude oil is injected underneath an artificially-grown young sea ice as the resulting radar cross section response is temporally measured. The volume fraction of oil is then indirectly retrieved using the measured radar data via a modified inversion strategy. Although the reconstructed volume fraction is an over-estimation, it has a potential to trigger a warning system. Finally, the reasons behind this over-estimation are discussed.

Oil spills may be detected in ice-covered waters through the observed changes in its normalized radar cross section ( NRCS) data. In this paper, the NRCS sensitivity to the presence of an oil layer beneath the young sea ice is investigated for various sea ice versus oil layer thicknesses. To this end, time-series measurements on artificially-grown sea ice performed at Sea-ice Environmental Research Facility (SERF) at University of Manitoba are used to model the dielectric profile of a young sea ice. Next, three scenarios are introduced that consider the absence, or presence of the oil layer. To achieve our goal, the NRCS values associated with each case are simulated utilizing the boundary perturbation theory. Subsequently, the discrepancies between different scenarios are calculated. The simulated discrepancies indicate the possibility of a successful oil detection through inversion algorithms that utilize NRCS data.

Oil spills may be detected in ice-covered waters through the observed changes in its normalized radar cross section (NRCS) data. In this paper, the NRCS sensitivity to the presence of an oil layer beneath the young sea ice is investigated for various sea ice versus oil layer thicknesses. To this end, time-series measurements on artificially-grown sea ice performed at Sea-ice Environmental Research Facility (SERF) at University of Manitoba are used to model the dielectric profile of a young sea ice. Next, three scenarios are introduced that consider the absence, or presence of the oil layer. To achieve our goal, the NRCS values associated with each case are simulated utilizing the boundary perturbation theory. Subsequently, the discrepancies between different scenarios are calculated. The simulated discrepancies indicate the possibility of a successful oil detection through inversion algorithms that utilize NRCS data.

This paper presents a compact ac/dc electric field sensor with adjustable sensitivity and measurement range. The sensor is fabricated using an SOI micromachining process, and consists of a metallized membrane supported by micro-springs. The sensor operates by monitoring membrane displacement due to incident electric fields. Unlike field mills, this sensor does not have rotating parts, avoiding associated wear and maintenance issues. High sensitivity is achieved by using a laser position monitoring system. Modulation of the incident electric field with a bias voltage on the sensing membrane is used to give the sensor a wide measurement range, from sub-1 V/m to MV/m. Bench top experimental measurements with both dc and ac fields demonstrated a resolution of 0.1 V/m and sensitivity of 180 mV/(V/m), with the sensor operating at resonance. Measurements under 20 kV simulated power line 75 cm from the sensor demonstrated a resolution of 17 Vim and sensitivity of 360 mV/(kV/m). (C) 2016 Elsevier B.V. All rights reserved.

This paper focuses on the use of electromagnetic inverse scattering and inverse source algorithms, collectively referred to as electromagnetic inversion algorithms, for three application areas: microwave biomedical imaging, near-field antenna measurements and characterization, and sea ice remote sensing. We discuss the benefits of using the electromagnetic inversion framework, e.g., its quantitative accuracy and resolution as well as its systematic treatment of the available data, and also consider the challenges associated with the use of this framework, e.g., the use of appropriate numerical modelling and inversion algorithms.