Plastic pollution is globally recognised as a threat to marine ecosystems, habitats, and wildlife, and it has now reached remote locations such as the Arctic Ocean. Nevertheless, the distribution of microplastics in the Eurasian Arctic is particularly underreported. Here we present analyses of 60 subsurface pump water samples and 48 surface neuston net samples from the Eurasian Arctic with the goal to quantify and classify microplastics in relation to oceanographic conditions. In our study area, we found on average 0.004 items of microplastics per m3 in the surface samples, and 0.8 items per m3 in the subsurface samples. Microplastic characteristics differ significantly between Atlantic surface water, Polar surface water and discharge plumes of the Great Siberian Rivers, allowing identification of two sources of microplastic pollution (p < 0.05 for surface area, morphology, and polymer types). The highest weight concentration of microplastics was observed within surface waters of Atlantic origin. Siberian river discharge was identified as the second largest source. We conclude that these water masses govern the distribution of microplastics in the Eurasian Arctic. The microplastics properties (i.e. abundance, polymer type, size, weight concentrations) can be used for identification of the water masses.

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Sustainable development of the salmon farming industry requires knowledge of the biogeochemical impacts of fish farm emissions. To investigate the spatial and temporal scales of farm impacts on the water column and benthic biogeochemistry, we coupled the C-N-P-Si-O-S-Mn-Fe transformation model BROM with a 2-dimensional benthic-pelagic transport model (2DBP), considering vertical and horizontal transport in the water and upper 5 cm of sediments along a 10 km transect centered on a fish farm. The 2DBP model was forced by hydrophysical model data for the Hardangerfjord in western Norway. Model simulations showed reasonable agreement with field data from the Hardangerfjord in August 2016 (correlations between the model and observations were significant for most variables, and model biases were mostly <35%). The model predicted significant impacts on seafloor biogeochemistry up to 1 km from the fish farm (e.g., increased organic matter in sediments, oxygen depletion in bottom water and sediments, denitrification, metal and sulfur reduction), as well as detectable decreases in oxygen and increases in ammonium, phosphate and organic matter in the surface water near to the fish farm.