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Spatial distribution of microplastic contamination on alpine glaciers
Summary
Researchers examined microplastic contamination in supraglacial debris from 13 glaciers across the Alps in Italy, France, Switzerland, and Austria, collecting 70 surface samples between 2020 and 2022. They found putative microplastics in every sample with a mean of 14 items per sample, with one glacier showing significantly higher anthropogenic particle concentrations than others, consistent with atmospheric transport as the primary input mechanism.
Microplastics (MPs) are found in environments ranging from deep sea to high altitudes. The primary mechanism of contamination in high altitude environments is thought to be atmospheric transport. In this study, we examined MP contamination in supraglacial debris of 13 glaciers in the Alps, covering Italy, France, Switzerland, and Austria. The study sites were: Clapier, Glacier Blanc, Mont Miné, Prè de Bar, Mandrone, Lupo, Preda Rossa, Platigliole, Sforzellina, Fellaria, Fellaria Ovest, Dosdè, Pasterze and the Calderone Glacier (Apennines). We collected 70 samples of supraglacial debris from the glaciers' surface between 2020 to 2022. The presence of putative MPs was confirmed in every sample, with a mean of 14 ± 6.39 SE items/sample. Due to time constraints, we characterized the polymeric composition of MPs in only 25 samples out of 70, and first results are focused on anthropogenic particles (APs). The Lupo Glacier showed a significantly higher concentration of APs than the other glaciers: 1,434.78 ± 471.60 SE APs/kg. We tested population density in a 100 km buffer, altitude, and surface of the glaciers as predictors for APs concentration in our sites. Only a marginally significant correlation between population density and particles concentration was found. No significant results were obtained repeating the analyses on identified MPs only, and the polymeric composition of identified MPs did not differ among glaciers. However, more time is needed to complete MPs identification. Our study highlights the difficulties in identifying the predictors of MPs contamination in remote areas and the need to develop complex models to understand contamination patterns. Also see: https://micro2024.sciencesconf.org/559498/document