Understanding how microplastic particles move and accumulate within estuarine and coastal waters requires consideration of primary inputs (e.g., raw materials from industrial zones) as well as secondary inputs resulting from fluvial processes (i.e., materials carried into coastal waters by rivers and streams). This study presents a novel approach to achieve this aim, by comparing the individual and combined ability of Particle Tracking Models (PTMs) and seasonal rainfall data, to explain observed inputs of microplastic pellets to the ocean beaches of Santos City (south-eastern Brazil). A Lagrangian PTM based on high-resolution hydrodynamic models was used to simulate seasonal patterns of pellet dispersal from five release points within the Santos Estuarine System (SES) and nearshore waters which are known contributors to the regions microplastic debris problem. Model outputs suggested that the debris field is likely to be small within the estuary (ranging from 3.6 to 8.1 km2), intermediate at the river mouth (mean 34 km2) and greatest for near- and offshore sites (ranging from 34 to 40 km2). The spatial footprints were strongly modulated by season (and rainfall), with simulations alone unable to reconcile daily inputs of pellets observed on the beaches of Santos Bay (ranging from 2 to 51 particles m2 ⋅ d–1). Given this discrepancy, a Generalized Additive Modeling approach was employed to integrate the PTM outputs with rainfall data to improve predictions of beached particles. Results confirmed that considering fluvial processes, could significantly improve the ability to predict rates of pellet accumulation (raising the explained deviance in observed inputs from 41 to 93%). Thus, the study highlights the potential to couple widely used dispersion models with metrics that describe fluvial forcing (rainfall and estuarine flushing) in order to better understand the spatio-temporal dynamics of microplastic debris transport and accumulation within dynamic coastal environments.

中文翻译：

---

使用综合降雨数据和拉格朗日粒子跟踪模型预测巴西东南部河口和沿海水域中微塑料颗粒的扩散和积累

了解微塑料颗粒如何在河口和沿海水域移动和积累需要考虑初级输入（例如来自工业区的原材料）以及河流过程产生的次级输入（即通过河流和溪流进入沿海水域的材料）。本研究通过比较粒子跟踪模型 (PTM) 和季节性降雨数据的单个和组合能力，提出了一种实现这一目标的新方法，以解释观察到的微塑料颗粒对桑托斯市（巴西东南部）海滩的输入. 基于高分辨率流体动力学模型的拉格朗日 PTM 用于模拟来自桑托斯河口系统 (SES) 和近岸水域五个释放点的颗粒扩散的季节性模式，这些区域是该地区微塑料碎片问题的已知贡献者。模型输出表明，河口内的碎片场可能很小（范围从 3.6 到 8.1 平方公里），在河口中间（平均 34 平方公里），在近海地点最大（范围从 34 到 40 平方公里） . 空间足迹受到季节（和降雨量）的强烈调节，仅凭模拟无法调和在桑托斯湾海滩上观察到的颗粒的日常输入（范围从 2 到 51 颗粒 m2 ⋅ d-1）。鉴于这种差异，采用广义加性建模方法将 PTM 输出与降雨数据相结合，以改进对搁浅粒子的预测。结果证实，考虑河流过程，可以显着提高预测颗粒堆积率的能力（将观察到的输入的解释偏差从 41% 提高到 93%）。因此，该研究强调了将广泛使用的扩散模型与描述河流强迫（降雨和河口冲刷）的指标结合起来的潜力，以便更好地了解动态沿海环境中微塑料碎片运输和积累的时空动态。可以显着提高预测颗粒积累率的能力（将观察到的输入的解释偏差从 41% 提高到 93%）。因此，该研究强调了将广泛使用的扩散模型与描述河流强迫（降雨和河口冲刷）的指标结合起来的潜力，以便更好地了解动态沿海环境中微塑料碎片运输和积累的时空动态。可以显着提高预测颗粒积累率的能力（将观察到的输入的解释偏差从 41% 提高到 93%）。因此，该研究强调了将广泛使用的扩散模型与描述河流强迫（降雨和河口冲刷）的指标结合起来的潜力，以便更好地了解动态沿海环境中微塑料碎片运输和积累的时空动态。