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Quantification of atmospheric microplastic in a montane Fagus sylvatica forest in central Italy using handheld active aerosol samplers
Summary
Scientists measured tiny plastic particles floating in the air of a remote mountain forest in Italy and found them everywhere, even far from cities, with more showing up in summer than fall. This matters because it shows microplastics are traveling through the air and settling into even "untouched" natural environments, meaning we're likely breathing these particles in no matter where we live, not just in urban areas.
The atmospheric transport of microplastics plays a critical role in understanding input rates and distribution patterns across ecosystems. Forest ecosystems remain understudied with regard to microplastic contamination. Within this study we aim to evaluate microplastic concentrations in the air of a montane Fagus sylvatica forest and to identify influencing factors on sampling sites of different altitudes during the vegetation and dormant seasons, using a vertical and horizontal spatial approach. The methodology integrated simultaneous, parallel sampling with portable active aerosol samplers at multiple heights beneath the canopy. Sampling was conducted along a transect from outside towards the margins to the interior of three forests of different altitudes. Laboratory analyses featured minimal chemical application, with microplastics identified via fluorescence microscopy and µRaman spectroscopy. We observed average microplastic concentrations of 11.9 ± 7.1 MP/m³, with fragments dominating and a median size of 12 µm. Results indicated significantly higher microplastic transport into the area during summer (14.9 MP/m³) compared to autumn (8.7 MP/m³), with altitudinal differences influenced by proximity to potential sources related to human activities. Particle concentrations varied by up to 39.8 % between sampling heights, with the lowest mean abundances observed at medium height compared to under canopy and crown height. However, no significant differences were detected in the vertical or horizontal distribution. The variability among parallel samples and across sampling heights highlights the importance of this sampling approach for enhancing statistical robustness.