Sheffield Water Centre, Department of Civil and Structural Engineering, University of Sheffield, Sheffield S1 3JD, UK.
Severn Trent Centre, 2 St Johns Street, Coventry CV1 2LZ, UK.
Water Res. 2024 Jan 1;248:120838. doi: 10.1016/j.watres.2023.120838. Epub 2023 Nov 4.
Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events.
地表水质经常受到雨水等急性污染源的影响，如污水溢出。由于河流系统缺乏高分辨率数据，因此很难了解短期粪便污染暴露的风险。本文提出了一种实用的建模方法，用于预测基于集水区源负荷的水文路径的大肠杆菌水平升高的到达时间和持续时间，该方法的特点是采用分布式和遥感技术（包括污水溢出监测）。该模型使用具有漫滩径流和暴雨溢出影响的英国流域的新的高分辨率大肠杆菌数据集进行了校准和验证。在不同的季节性条件下，对河流进行了不同的降雨事件后的大肠杆菌进行了每小时/两小时的采样。该模型很好地估计了降雨事件后大肠杆菌升高的到达时间和持续时间。模拟结果表明，流域内的主要污染源是特定事件的，在短时间、高强度降雨事件后，污水溢出更为严重。
