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A Parsimonious Parameterization of Particle Dynamics for Contaminant Mass-Balance Models of Freshwater Systems with Application to Plastic
Summary
Scientists have built a better computer model to track how plastic particles and pollutants move through rivers and lakes, accounting for how changing water flow (like after storms) sweeps them around. This matters because plastics and chemicals in our waterways can end up in drinking water and the food chain, and this improved tool—tested successfully on England's River Thames—will help researchers more accurately predict where contamination accumulates, guiding better monitoring and cleanup efforts.
Chemical contaminants and plastics are pervasive in lakes, streams, and rivers, and threaten environmental and human health. Mass-balance models, which simulate the transport and transformation of contaminants across multiple environmental compartments based on mass conservation principles, are essential for understanding chemical and plastic pollution in freshwater systems. However, many existing models neglect the spatial and temporal variability of particle transport processes driven by variable flow velocities, which is key for hydrophobic chemical contaminants and plastic. In this study, we develop a generic parameterization of particle dynamics as a function of flow velocity for contaminant mass-balance models in freshwater systems, that is parsimonious in that it does not increase data demands over current modeling approaches. We demonstrate our parameterization using the open-source Full Multi framework by replicating the classic Hjulström diagram and by validating against a shear stress baseline. Model performance evaluated by simulating suspended solid concentrations in the non-tidal River Thames from 2009 to 2017 yielded strong positive Pearson correlations ( P < 0.01) and low root-mean-square errors between simulations and observations. Extrapolating the parameterization from natural sediments to plastic particles with a broad range of sizes, densities, and weathering states allowed us to map plastic dynamics under varying flow conditions. This parsimonious parameterization represents a significant advance in process-fidelity for particle dynamics in contaminant mass-balance models, and will improve assessments of plastic and particle-associated chemical contaminants in freshwater systems.