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A multimedia model to estimate the environmental fate of microplastic particles.
Quik, J T K; Meesters, J A J; Koelmans, A A.
Affiliation
  • Quik JTK; National Institute for Public Health and Environment (RIVM), Centre for Sustainability, Health and Environment, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands. Electronic address: joris.quik@rivm.nl.
  • Meesters JAJ; National Institute for Public Health and Environment (RIVM), Centre for Safety of Substances and Products, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands.
  • Koelmans AA; Aquatic Ecology and Water Quality Management Group, Wageningen University, P.O. Box 47, 6700 DD Wageningen, the Netherlands.
Sci Total Environ ; 882: 163437, 2023 Jul 15.
Article in En | MEDLINE | ID: mdl-37059151
ABSTRACT
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Sci Total Environ Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Sci Total Environ Year: 2023 Document type: Article
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