The FTIR imaging data were automatically processed (24). Briefly, each spectrum in the measurement file was analyzed via two library searches to confirm polymer identity using an adaptable database design (25). The library can be downloaded (https://link.springer.com/article/10.1007/s00216-018-1156-x). Each pixel identified was stored with its position, analysis quality, and, finally, assigned polymer type into a file, which was subject to image analysis based on Python 3.4 scripts and SimpleITK functions (24). This approach enabled the identification, quantification, and size determination of all polymer particles while excluding human bias (24). MP particles were assigned to size classes to reduce the complexity of the size distribution and for comparison with previous studies.

All statistical comparisons were made based on nonparametric statistics (Mann-Whitney U test, Minitab 18, Statistica 13). We tested for Spearman’s rank correlations between wind speed on the sampling day and MP quantity, as well as correlations between MP and fiber concentrations (Minitab 18; P > 0.05). The polymer composition of samples from Europe and the Arctic was compared by multivariate analyses (PERMANOVA, PRIMER-e version 6.1.16 with PERMANOVA 1.0.6) based on Bray-Curtis similarities of fourth-root transformed data of polymer types (46). The SIMPER routine of PRIMER-e was used to assess what polymer type contributed to the dissimilarity.

The annual MP and fiber fallout was calculated for the areas sampled using mean annual snowfall values for Fram Strait [200 kg m−2 (47)], Svalbard [450 kg m−2 (48)], and Davos [500 kg m−2 (49)] as tentative estimates for MP and fiber deposition rates via snow. Bremen and Heligoland were not included because snow fall in these regions is ephemeral. The density of melted snow samples was assumed as water density (1 kg liter−1).

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