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Numerical Simulation of Transportation of Marine Microplastics: A Review
Original title: Numerical Simulation of Transportation of Marine Microplastics: A Review
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This Chinese-language review synthesizes numerical simulation studies of microplastic transport in marine environments, examining how currents, wind, waves, topography, and extreme sea conditions affect microplastic distribution. The authors recommend future modeling efforts focus on spatio-temporal distribution using combined sampling and simulation data, with improved empirical parameters for processes such as biofouling and resuspension.
The temporal and spatial discontinuity of microplastic sampling data restricts the investigation on their source,sink,transport pathway and trend. Numerical simulation combined with sampling investigation can comprehensively study the effects of microplastic characteristics,meteorology and hydrodynamics on the distribution and transportation of microplastics. In this paper,the studies of microplastic numerical simulation were reviewed from the aspects of numerical simulating research and their applications in microplastic tranportation,and the results were summarized as follows:The construction of the main driving force(current); the influence of environmental factors,such as wind,waves,topography and extreme sea conditions on the properties of microplastics with different characteristics(particle size,density,shape)and their tranportation; the application of numerical simulation in the study of microplastic removal. Based on progress on the study of numerical simulation of marine microplastics,the future directions were pointed out that the further simulating studies should focuson the spatio-temporal distribution and evolvement of microplastics by combining sampling data and numerical model, the simulating research on the relationship between microplastic parameters (roughness,wind drag coefficient,settling rate,resuspension rate and biofouling rate)and(meteorological and ocean)dynamic condition. Moreover,the results of simulating sensitivity experiments should be compared with sampling and laboratory testing data to improve the empirical parameters and formulas of numerical model.
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