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Unraveling Microplastics in Lakes with a Mechanism-Informed, Few-Shot Data-Driven Liquid Neural Networks
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Scientists built a smarter computer model that combines real physics with limited water-testing data to more accurately predict where microplastics travel and accumulate in lakes, tested successfully on China's Poyang Lake. This matters because knowing where microplastics concentrate—especially in areas that feed drinking water supplies or fisheries—can help identify hotspots of human exposure risk, even in places where we don't have enough monitoring stations to track pollution directly.
Simulating microplastic (MP) transport in aquatic environments faces two persistent bottlenecks: error accumulation in purely physical models and the intrinsic difficulty of machine learning methods with sparse, discontinuous field observations. Here, we introduce a mechanism-informed, few-shot data-driven framework powered by Liquid time-constant networks, the first application of continuous-time neural ordinary differential equation models to pollutant transport prediction. The framework employs the MIKE21 hydrodynamic particle tracking model as a physics knowledge generator, whose high-resolution fields augment limited measurements to train a physically constrained closed-form continuous-time (CfC) neural network. With learnable time constants and nonlinear synaptic coupling, the networks assimilate sparse observations directly without interpolation, correcting spatial distortions in nearshore zones and suppressing long-term error propagation. Demonstrated on Poyang Lake using only sparse monitoring data, the model achieves R 2 values of 0.867 and 0.895 for dry and wet seasons, respectively, markedly outperforming Random Forest and other benchmarks. SHAP analysis further identifies flow velocity, water depth, and suspended sediment concentration as dominant regional drivers. This paradigm offers both a transferable solution for lacustrine MP prediction and a new avenue for environmental simulation in data-sparse contexts.
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From particle tracking modelling to species impact forecasting: a framework for microplastic exposure risk quantification in the largest freshwater lake of China
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Using a hydrodynamic model coupled with particle tracking, researchers simulated microplastic transport, distribution, and impact zones in Poyang Lake, China's largest freshwater lake. The model revealed seasonal variation in MP accumulation hotspots driven by water level fluctuations, and predicted high-risk zones for aquatic organisms.
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