Dynamic hyporheic zone: Impact of non-steady stream flow and moving streambeds on the fate of trace organic contaminants

微量有机化合物（troc）是一组不同的化学品，如药品，农药和个人护理产品。高TrOC负荷发生在接收处理过的废水的低地溪流中。这些溪流的特征通常是细小的沉积物，其中水流过小河床产生压力差，水流进、进、出河床（浅流交换流）。生物转化和吸附过程比地表水更显著地降低孔隙水中的TrOC浓度。对于许多化合物来说，这些过程是强烈依赖于氧化还原的，也就是说，在河床的某些氧化还原区，水的停留时间对TrOC的衰减是决定性的。先前对河流生物地球化学过程的研究几乎完全集中在固定的河床上，尽管河床迁移在细沉积物的河流中很常见。床型迁移影响床内流动路径、通量和氧化还原带的分布。该项目旨在研究迁移河床对TrOC衰减的影响，并确定动态流动模式如何影响河床迁移和TrOC衰减。为了回答这些问题，实地调查将与水槽实验和建模相结合。实地调查是在一条沙质溪流（德国的Erpe河）中进行的，该河流接收高负荷的处理废水，包括高TrOC浓度。实验装置允许改变水流速度，从而改变河床迁移速度。二维平面光电器件将在原位可视化氧化还原带，并通过微点采样器实现氧化还原特异性采样TrOC浓度。在本-古里安大学一个独特的水槽中进行水槽实验，允许系统地改变河床速度，动态流动状态，从而深入了解控制TrOC衰减的关键过程。在水槽水中加入TrOC混合物后，测量了其在地表水中减少的时间序列。沉积物中的氧化还原带通过二维平面光电二极管识别，并通过微点采样。为了推广结果，针对任意形状的瞬态床型，建立了反应性TrOC输运模型。机器学习用于基于现有水槽数据集预测河床通量分布。使用水槽和River Erpe数据集校准流量和反应参数，然后进行大规模蒙特卡罗/机器学习研究，以根据观察到的参数预测总系统反应速率。通过提高对河流中TrOC衰减机制的理解，本研究旨在改善河流中TrOC命运的长期预测，并提出改善淡水系统水质的修复策略。

Trace organic compounds (TrOCs) are a diverse group of chemicals such as pharmaceuticals, pesticides and personal care products. High TrOC loads occur in lowland streams that receive treated wastewater. These streams are often characterized by fine sediments in which water flow over small bedforms creates pressure differences and water flows into, within and out of the streambed (hyporheic exchange flow). Biotransformation and sorption processes reduce TrOC concentrations in pore water more significantly than in surface water. For many compounds, these processes are strongly redox-dependent, i.e. the water residence time in certain redox zones of the streambed is decisive for TrOC attenuation. Previous research on biogeochemical processes in streams has focused almost exclusively on stationary bedforms, although bedform migration is common in streams with fine sediments. Bedform migration affects the distribution of flow paths, fluxes, and redox zones within the bed. The project aims to investigate the effects of migrating bedforms on TrOC attenuation and to determine how dynamic flow regimes affect bed mobility and TrOC attenuation. To answer these questions, field investigations will be combined with flume experiments and modelling. The field investigations are carried out in a sandbed stream (River Erpe, Germany), which receives high loads of treated wastewater including high TrOC concentrations. The experimental setup allows variation of the stream water velocity and thus of the bedform migration celerity. 2D-planar optodes will visualize redox zonation in-situ and enable redox-specific sampling of TrOC concentrations by minipoint samplers. Flume experiments in a unique flume at Ben-Gurion University allow systematic variation of bedform celerity, a dynamic flow regime and thus provide insight into the key processes controlling TrOC attenuation. After the addition of TrOC mixtures to the flume water, time series of their decrease in surface water are measured. The redox zones in the sediment are identified by 2D-planar optodes and sampled by minipoints. In order to generalize results a reactive TrOC transport model is developed for arbitrarily shaped, transient bedforms. Machine learning is used to predict streambed flux distributions based on existing flume datasets. Flow and reaction parameters are calibrated using flume and River Erpe datasets and then a large-scale Monte Carlo / machine learning study is performed to predict total-system reaction rate from observed parameters. By improving the mechanistic understanding of TrOC attenuation in streams, the proposed research aims to improve long-term predictions of TrOC fate in streams and advance remediation strategies to improve water quality in freshwater systems.