High Frequency Optical Fingerprinting of Urban Storm Runoff

城市雨水径流向受纳沃茨输送复杂的污染物混合物，从而降低生态系统健康。然而，我们对污染源、途径和个别化合物（碳氢化合物、有机物、轮胎碎片等）的命运之间的联系缺乏深刻的理解。这在很大程度上是由于收集、分类和计数颗粒所需的采样和实验室工作时间密集。为了推进机械的理解，并开发现实的基于物理的模拟河流水质，需要新的方法来收集数据，特别是在城市集水区。考虑到英国合并污水溢流（CSO）排放的频率越来越高（2021年超过37万），这一需求尤其及时，这导致了围绕新的，更强大的风暴溢流计划的持续磋商。该提案朝着实现这一目标迈出了重要一步，开发和应用新技术，以实现对城市径流的连续原位表征，分离无机沉积物，有机物和人为颗粒，如微塑料和橡胶碎片。

Urban stormwater runoff delivers a complex cocktail of contaminants to receiving waters which degrades ecosystem health. However, we lack critical understanding of the links between pollution source, pathway and the fate of individual compounds (hydrocarbons, organic matter, tyre shards, etc.). This is largely due to the time-intensive sampling and laboratory work needed to collect, classify and count particles. To advance mechanistic understanding, and develop realistic physically-based simulations of riverine water quality, new approaches to data collection are required, particularly in urban catchments. This need is especially timely given the increasing frequency of combined sewage overflow (CSO) releases in the UK (>370k in 2021) that has led to the ongoing consultation around a new, more robust, Storm Overflows plan. This proposal takes an important step towards meeting this ambition by developing and applying new technology to enable continuous, in-situ characterisation of urban runoff, separating inorganic sediment, organic matter and anthropogenic particles such as microplastics and rubber fragments.