Development of a two-dimensional horizontal plastic dispersal model, model validation and uncertainty assessment. PLUXIN Deliverables D3.1, D3.2, D3.3, D3.4 & D3.5
Toorman, E.; Achutha Shettigar, N. (2024). Development of a two-dimensional horizontal plastic dispersal model, model validation and uncertainty assessment. PLUXIN Deliverables D3.1, D3.2, D3.3, D3.4 & D3.5. KU Leuven: Leuven. 66 pp. https://dx.doi.org/10.48470/75
The presence of microplastics (MP) in open environments has drawn the attention of decision makers and the scientific community to understand the potential risks. The risk assessment of MP in the aquatic environment needs the understanding of large-scale dispersal of MP. While field data continues to be gathered, its extent remains insufficient at both temporal and spatial scales. To expand the knowledge of MP dispersal, modelling tools are essential. In this study, the numerical modelling tool openTELEMAC is used to implement the Population Balance Equation (PBE) method applied to the size distribution of the MP coupled to a (horizontally) two-dimensional (2D) depth-averaged hydrodynamic model. With the help of PBE, the dynamic evolution of the particle size distribution is tracked. The transport of MP in the aquatic environment is influenced by the presence of sediments whose mass or volume fraction outnumber the MP by several orders of magnitude. Therefore, the sediment transport model is coupled to the hydrodynamic model, to capture the influences of the sediment classes mud and sand on the transport behaviour of the MP. In addition, the TOMAWAC wave model is coupled as well for a better representation of coastal dynamics.
The calibrated model is used for understanding the MP flux at different cross-sections of the model domain. The flux values of number or mass concentrations of MP differ at different cross-sections due to the tidal asymmetry in the area. The estuary shows a particle retaining characteristics and the intertidal areas show large deposition zones of MP. However these outcomes are highly subjected to the initial condition values of the MP assumed in the study. Moreover, other sources of MP during the simulation are not considered.The outcomes of the model are subjected to uncertainties arising as a result of model parameters whose values are not certain. To estimate the overall uncertainty in the model outcome, a few important parameters of the model are selected for the uncertainty assessment study. The overall parameter uncertainties in the model outcomes are quantified and discussed.
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