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A multimedia model to estimate the environmental fate of microplastic particles
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Researchers developed SimpleBox4Plastic (SB4P), the first mathematical model capable of predicting how nano- and microplastics move through and accumulate across air, water, sediment, and soil simultaneously, accounting for processes like particle clumping, fragmentation, and attachment to natural materials. While concentration predictions carry uncertainty, the model provides a practical screening tool for assessing environmental exposure to microplastics where direct measurements are unavailable.
Nano- and microplastic (NMP) is a diverse and challenging contaminant and data on NMP concentrations are therefore not fully available for all environmental compartments. For environmental assessments of NMP, screening-level multimedia models can fill this gap, but such models are not available. Here, we present SimpleBox4Plastic (SB4P) as the first multimedia 'unit world' model capable of addressing the full NMP continuum, explore its validity, and evaluate it based on a case study for microbeads and by comparisons with (limited) concentration data. SB4P links NMP transport and concentrations in and across air, surface water, sediment, and soil, taking into account processes such as attachment, aggregation, and fragmentation, by solving mass balance equations using matrix algebra. These link all concentrations and processes known to be relevant for NMP using first-order rate constants, which are obtained from the literature. The SB4P model, as applied to microbeads, provided mass or number concentrations of NMP as the total of 'free' particles, heteroaggregates with natural colloids, and larger natural particles in each compartment at steady state. Processes most relevant in explaining observed Predicted Exposure Concentrations (PECs) were determined using rank correlation analysis. Although the predicted PECs remained uncertain due to the propagating uncertainty, inferences regarding these processes and relative distribution across compartments can be considered robust.
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