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三维全球欧拉模型中碎裂微塑料的分布与迁移

Distribution and Transport of Fragmenting Microplastics in a 3D Global Eulerian Model

Zih-En Tseng, Yue Wu, Chris Ruf, Dimitris Menemenlis, Yulin Pan

arXiv 2607.29643首次发表:更新:

AI 中文总结

研究构建首个含微塑料碎裂过程的全球三维欧拉模型,揭示碎裂对微塑料分布迁移的影响,提升了表层浓度模拟与观测的空间相关性。

AI 中文摘要

碎裂是物质破碎成更小碎片的过程,是产生微塑料(MPs)的重要机制。我们提出了首个可同时解析微塑料碎裂过程与迁移过程的全球三维欧拉模型,将粒径演变建模为从较大粒径区间向较小区间的转移,由碎裂动力学框架控制。与未考虑碎裂的参考模拟相比,识别出两个显著效应:一是微塑料的表层浓度场在水平方向上更为分散;二是微塑料会下沉至500米深度,而参考模拟在该深度的浓度可忽略不计。粒径减小会导致浮力损失,这一垂直迁移现象可解释:当粒子下沉至100米深度以下后,会促进水平次混合层的输运。海洋中较大粒子的碎裂会持续产生中性浮力粒子(直径d < 1 μm),并在主要海洋环流中积累,最终这些中性浮力微塑料的浓度在环流中心达到峰值,这一行为是现有模型未捕捉到的。此外,全球积分粒径谱随时间呈现出陡峭的幂律斜率,在25年的模拟过程中持续演化。与AOMI Level-3wm观测数据集的对比显示,该模型相较于现有模型的预测能力有显著提升:纳入碎裂过程使模拟与观测表层浓度的空间相关性从45%提升至58%。

英文摘要

Fragmentation, the breakage of matter into smaller pieces, is an important mechanism responsible for generating microplastics (MPs). We present the first global three-dimensional Eulerian model that resolves fragmentation alongside MP transport. The evolution of particle size is modeled as a transfer from larger- to smaller-size bins, governed by a fragmentation kinetics framework. Relative to a reference simulation without fragmentation, two distinct effects are identified: (1) the surface concentration field of MPs becomes horizontally dispersed, and (2) MPs sink to depths of 500 m where the reference simulation shows negligible concentration. The vertical shift can be explained by the loss of buoyancy when particle size decreases, which facilitates horizontal sub-mixed layer transport once the particles sink below 100 m depth. Neutrally buoyant particles (with diameter d < 1 um) are continuously produced in the ocean by the fragmentation of larger particles and accumulate in the major oceanic gyres. Ultimately, the concentration of these neutrally buoyant MPs peaks at the gyre centers, a behavior that is not captured by prior models. Furthermore, the globally integrated size spectrum exhibits a steepening power-law slope over time that continues to evolve throughout our 25-year simulation. Comparisons with the AOMI Level-3wm observational dataset demonstrate a meaningful improvement in predictive skill relative to previous models: including fragmentation elevates the spatial correlation between modeled and observed surface concentrations from 45% to 58%.

Comments31 pages, 8 figures

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