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A Probabilistic Risk Framework for Microplastics Integrating Uncertainty Across Toxicological and Environmental Variability: Development and Application to Marine and Freshwater Ecosystems
Summary
Scientists have developed a new way to estimate how risky microplastic pollution really is for wildlife in oceans and rivers, by better accounting for the fact that lab studies often use different plastic particles than what's actually found in nature. This more careful method suggests we may need to be more cautious about microplastic exposure than previous estimates indicated, though it also reveals just how much uncertainty remains in the science. While this study focuses on marine and freshwater ecosystems rather than direct human health effects, it matters because understanding environmental risks is a key step toward figuring out what microplastics might mean for people too.
Quantitative risk assessment for microplastics (MPs) is complicated by misalignments between environmentally relevant particles and those used in toxicity studies. While previous approaches addressed this using ecologically relevant metrics (ERMs) and species sensitivity distributions (SSDs), they did not account for alignment uncertainty or intra-species variability. Here, we present a novel probabilistic framework that propagates uncertainty through ERM alignments using Monte Carlo (MC) simulation, paired with a modified probabilistic SSD model. Using high-quality data from the updated Toxicity of Microplastics Explorer (ToMEx 2.0), we compared hazard thresholds derived by three approaches: traditional SSD, Monte Carlo + SSD, and Monte Carlo + probabilistic SSD. The latter produced the most health-protective thresholds, which also had the highest relative uncertainty - 5 to 140 times greater than traditional SSDs - while Monte Carlo + SSDs produced the lowest uncertainty. Food dilution ERMs and freshwater environments showed greater uncertainty than tissue translocation and marine, respectively. Sensitivity analysis identified ERM alignments as the dominant source of uncertainty, contributing up to two orders of magnitude variation. This framework highlights the importance of accounting for data harmonization uncertainty in risk assessments and identifies key research needs, including improved models for tissue translocation and more representative environmental particle characterization