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Updated: Feb 14, 2026

Sampling and Identification of Microplastics in Groundwater
Published on: November 7, 2025
Microplastic convergence in high-altitude lakes of the Tibetan Plateau: Mechanisms, indicators, and risk
Nian Wei1, Jungang Lu2, Yue Ma3
1East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 300 Jungong Road, Shanghai 200090, China.
Abstract:
Microplastics (MPs) have emerged as persistent pollutants in freshwater ecosystems, yet their occurrence and convergence dynamics in remote high-altitude regions remain underexplored. This study presents an integrated assessment of MP pollution in 14 freshwater sites (>4500 m elevation) across the northern Tibetan Plateau, combining field sampling (using the stainless steel bucket), polymer characterization, environmental clustering, and geospatial risk modeling. MPs were detected at all sites, with concentrations ranging from 0.01 to 0.04 items/L. Polyethylene terephthalate (PET) dominated the polymer types, with linear morphologies and transparent particles representing the most common forms. High MP concentrations occurred in nearshore zones with elevated precipitation, surface runoff, and wind retention, indicating the importance of environmental convergence mechanisms in the absence of strong local sources. Using 16 environmental variables, unsupervised K-Means clustering revealed systemic drivers such as the Wind-Hydro Dual Control and terrain-mediated hydrological traps. To quantify convergence risk, we developed a Microplastic Risk Index (MPRI) based on 13 geospatial indicators, with topographic convergence index (TCI), runoff, and wind speed emerging as dominant predictors. The resulting risk classification delineated three zones: high, moderate, and low convergence potential. These findings highlight that MP accumulation in high-altitude lakes is shaped more by environmental retention capacity than proximity to human activity. The MPRI framework offers a transferable, mechanism-informed approach for identifying MP convergence hotspots, particularly in ecologically fragile or data-scarce regions. This study lays the groundwork for predictive risk stratification and adaptive monitoring strategies under ongoing environmental change, and the derived model parameters offer a scientific basis to support evidence-based policy-making and management.
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