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Microplastic Contamination in High-Altitude Soils of Sagarmatha National Park: A Spatial Assessment with Deep Learning-Supported Detection
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Even the remote, pristine soils of Mount Everest's national park are contaminated with microplastics, with the worst pollution found near tourist hubs like Lukla and Namche rather than at higher elevations. This shows that human activity and tourism—not just distance from civilization—drive plastic pollution, a reminder that these tiny particles (which can enter our food, water, and even our bodies) are showing up in places we'd least expect, even iconic natural areas we consider "untouched."
Microplastic contamination is an emerging global concern, but its occurrence in high-altitude protected areas has been understudied. This study systematically assessed microplastic abundance, morphology, and spatial distribution in Sagarmatha National Park (SNP), Nepal, a UNESCO World Heritage Site. Soil samples were collected from 25 sites across four land-use types, including settlement, farmland, forest, and floodplain, at two depths along the Lukla–Phortse trekking corridor during the pre-monsoon season of 2023. Samples were pretreated by density separation and Fenton’s reagent digestion, and microplastics were then detected using a YOLOv11n-seg instance segmentation model. A subset of extracted particles was chemically confirmed by optical photothermal infrared (O-PTIR) spectroscopy. Microplastics were present in all samples, with concentrations ranging from 80 to 960 particles·kg−1. Fragments were the dominant morphological type, accounting for 65.7% of all particles, followed by fibers and films. Negative binomial regression revealed significant effects of land use and soil depth and their interaction on microplastic abundance. Settlement soils showed the highest concentrations with significant surface enrichment, while farmland soils showed no significant depth effect, consistent with human-dominated plastic sources and tillage-driven redistribution. Elevation was not a significant predictor of contamination. Hotspot analysis identified statistically significant clustering around Lukla and Namche, the two primary tourism hubs. The baseline established in this study provides a foundation for long-term monitoring and targeted waste management in SNP and offers insights to other protected high-altitude environments. Meanwhile, the application of deep learning and O-PTIR to balance the counting efficiency and accuracy is a novel approach that could be adopted by other researchers.
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