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Process-based quantification of accumulation and interaction-driven synergistic risks of microplastics and heavy metals in freshwater sediments

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This study examined microplastics and heavy metals in freshwater sediment samples using field data and computer modeling. Clay-rich sediment held more microplastics, while heavy metals built up regardless of grain size. The model found heavy metals caused most of the combined risk, with microplastics adding to it by helping metals build up and move.

Study Type Environmental

Abstract Microplastics’ ability to absorb and release metals is well known, but quantitative estimates of the strength of these interactions remain scarce. Most existing studies rely on qualitative assessments or simplistic linear models, which fall short of quantifying the strength of interactions, thereby limiting in-depth understanding of cumulative risk. This study investigates the environmental processes governing the accumulation and interaction of microplastics (MPs) and heavy metals (HMs) in freshwater sediments and quantifies their resulting synergistic risks. Using extensive seasonal field data and machine-learning–assisted modelling, the study moves beyond single-contaminant assessments to integrate accumulation mechanisms, contaminant interactions and consequence-based risk quantification within a unified framework. Bayesian regression revealed contrasting controls on accumulation, with clay-rich sediments strongly promoting MP retention, whereas HMs exhibited comparatively weak grain-size dependence. Additionally, Random Forest analysis identified the polymer- and metal-specific interaction strengths, and the role of MPs as vectors that enhance metal accumulation and mobility. A novel consequence-based synergistic risk model showed that HMs dominate the combined risk (~ 93%), while MPs contribute indirectly by amplifying metal-related risks through interaction processes. The key innovation of this work is the scalable framework that quantitatively integrates accumulation processes, contaminant interactions and consequence modelling into a unified risk assessment approach. The resulting framework advances the understanding of multi-contaminant dynamics in sedimentary environments and provides more robust predictive support for environmental management in aquatic systems.

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