Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multipolarized C-band backscatter response of oil in newly formed sea ice (NI). Artificial sea ice was grown in two cylindrical tubs at the Sea-ice Environmental Research Facility, University of Manitoba. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oilcontaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the copolarized backscatter is increased by 13-dB local maximum, while the cross-polarized backscatter is decreased by 9-dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multipolarization radar satellites.

Regional climate change scenarios predict increased temperature and precipitation in the northern Baltic Sea, leading to a greater runoff of fresh water and terrestrial dissolved organic carbon (DOC) within the second part of the 21st century. As a result, the current north to south gradient in temperature and salinity is likely to be shifted further toward the south. To examine if such climate change effects would cause alterations in the environmental fate of organic pollutants, spatial variations of DOC quality and sorption behavior toward organic contaminants were examined using multiple analytical methods. The results showed declining contents of aromatic functional groups in DOC along a north to south gradient. Similarly, the sorption of a diverse set of organic contaminants to DOC also showed spatial differences. The sorption behavior of these contaminants was modeled using poly parameter linear energy relationships. The resulting molecular descriptors indicated clear differences in the sorption properties of DOC sampled in northern and southern parts of the Baltic Sea, which imply that more organic contaminants are sorbed to DOC in the northern part. The extent of this sorption process determines whether individual contaminants will partition to biota via direct uptake or through sorption to DOC, which serves as food source for bacteria-based food-webs.

Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1415-x) contains supplementary material, which is available to authorized users.

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