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。这些溪流的特点往往是沉积物很细，水流在小的床型上产生压差，水流流入、流入和流出河床（潜流交换流）。生物转化和吸附过程减少TrOC浓度在孔隙水比地表水更显着。对于许多化合物，这些过程是强烈的氧化还原依赖性，即水在河床的某些氧化还原区的停留时间是决定性的TrOC衰减。以往对河流中的地球化学过程的研究几乎完全集中在固定的床形上，尽管床形迁移在具有细沉积物的河流中很常见。底形迁移影响床内的流径、通量和氧化还原带的分布。该项目旨在研究迁移床形对TrOC衰减的影响，并确定动态流态如何影响床层流动性和TrOC衰减。为了回答这些问题，实地调查将结合水槽实验和建模。实地调查是在砂床流（埃尔佩河，德国），其中接收高负荷的处理后的废水，包括高TrOC浓度。实验装置允许水流速度的变化，从而改变床形迁移速度。2D平面光极将原位可视化氧化还原分带，并通过微点采样器实现TrOC浓度的氧化还原特异性采样。水槽实验在一个独特的水槽在本-古里安大学允许系统的变化的床形celebrates，一个动态的流态，从而提供洞察控制TrOC衰减的关键过程。在将TrOC混合物添加到水槽水中之后，测量它们在地表水中减少的时间序列。沉积物中的氧化还原带是由二维平面光电探测器和小点采样确定的。为了推广结果的反应TrOC传输模型开发任意形状，瞬态床型。基于现有的水槽数据集，机器学习被用于预测河床通量分布。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Monte Carlo /机器学习研究，以根据观察到的参数预测总系统反应速率。通过提高对溪流中TrOC衰减机制的理解，拟议的研究旨在改善溪流中TrOC命运的长期预测，并推进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.