From SimpleBox4nano to SimpleBox4micro: application to a case study on microbeads

, Quik Joris, Meesters Johannes, Koelmans Albert A..

Although field studies have detected microplastics, quantitative estimate of exposure concentrations to nano- and microplastics is lacking, e.g. due to fragmented field campaign's. Furthermore, the detection methods applied are generally not applicable to nanoplastics, making it a big data gap in knowledge on fate and occurrence in the environment. A commonly applied tool in assessing fate and exposure to chemicals and particles is the use of multimedia fate models. Here, we present a study on the fate of microbeads at regional scale using an extension of SimpleBox4nano ( for use with nano- and microplastics. So called Predicted Exposure Concentrations (PECs) are presented for air, soil, water and sediment compartments. The uncertainty in the PECs is due to uncertainty in the emission estimates and uncertainty and variability of input parameters describing fate of the NMPs. In the air and soil compartments the uncertainty in release rates from microbead used as abbrasives explains the most uncertainty in PECs. For the water and sediment compartment particle density, size and the fragmentation or degradatation rate constant explain most of the variation in PECs. As expected a large effect is found based on density, with low density NMPs largely remaining in the water fase in the fresh water compartment and transported to sea. PECs for sea water show a great sensitivity to the fragmentation or degradation rate, where unrealisticly high PECs are calculated when underestimating this process. This multimedia fate model for nano and microplastics provides a convenient tool for assessing exposure to different types of nano and microplastics at a screening level. Specifically, for nanoplastics this model provides an invaluable asset in future risk assessment studies as there is currently no adequate detection method. Further validation of the SimpleBox4nano model using high quality monitoring data is recommended.

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