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From model to nature — A review on the transferability of marine (micro-) plastic fragmentation studies
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A review of marine microplastic fragmentation studies found significant methodological inconsistencies that limit transferability of laboratory results to natural marine conditions, and proposes standardization criteria for fragmentation experiments to enable more reliable predictions of plastic degradation at sea.
Microplastic research has experienced almost exponential growth in publications and proceeded faster than ever throughout the last years. This increase comes with a downside in terms of missing standardizations and definitions especially concerning experiments. Furthermore, incomparability and lacking transferability of fragmentation studies onto the marine environment still hinder more realistic extrapolations and accurate numerical models. This review offers a first approach to tackle this problem by converting studies into comparable dimensions by rating their experimental settings and comparing them to in-situ values, thus, assisting future research with an unbiased and fast tool to assess the applicability of studies for their calculations. For this purpose, the main influencing factors for the environmental fragmentation of plastics were identified, ranked in terms of their impact, and, subsequently, applied to 49 setups of peer-reviewed studies. The average transferability into nature of the considered laboratory studies is 41%. Unconsidered implementation of proper mechanical wear by water and sediment movement into the test setups of around 80% of all reviewed studies explains this value. However, other parameters like UV radiation implementation, also pose an obstacle in about 50% of the laboratory studies. Nevertheless, even the reviewed in-situ fragmentation studies revealed problems in transferability in some of the weathering components caused by the fixation of the samples. This review indicates that the current database on plastic fragmentation is most likely not reliable enough for robust extrapolations or numerical models. A set of recommendations for test settings is proposed to improve upcoming experiments' quality and comparability.
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