High Frequency Optical Fingerprinting of Urban Storm Runoff

城市雨水径流将复杂的污染物混合物输送到接收水域，从而降低生态系统的健康。然而，我们对污染源、途径和单个化合物（碳氢化合物、有机物、轮胎碎片等）的命运之间的联系缺乏批判性的理解。这在很大程度上是由于收集、分类和计数粒子所需的时间密集的采样和实验室工作。为了促进对河流水质的机理理解，并开发现实的基于物理的模拟，需要新的数据收集方法，特别是在城市集水区。考虑到英国联合污水溢出（CSO）排放的频率越来越高（2021年为370k），这一需求尤其及时，这导致了围绕一个新的、更强大的风暴溢出计划的持续磋商。该提案通过开发和应用新技术实现城市径流的连续、原位特征，分离无机沉积物、有机物和人为颗粒（如微塑料和橡胶碎片），朝着实现这一目标迈出了重要的一步。

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.