Queen Mary University of London, School of Mathematical Sciences, Mile End Road, London, E1 4NS, UK.
Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway.
Sci Rep. 2022 Jul 19;12(1):12346. doi: 10.1038/s41598-022-16342-9.
Human activities alter river water quality and quantity, with consequences for the ecosystems of urbanised rivers. Quantifying the role of human-induced drivers in controlling spatio-temporal patterns in water quality is critical to develop successful strategies for improving the ecological health of urban rivers. Here, we analyse high-frequency electrical conductivity and temperature data collected from the River Chess in South-East England during a Citizen Science project. Utilizing machine learning, we find that boosted trees outperform GAM and accurately describe water quality dynamics with less than 1% error. SHapley Additive exPlanations reveal the importance of and the (inter)dependencies between the individual variables, such as river level and Wastewater Treatment Works (WWTW) outflow. WWTW outflows give rise to diurnal variations in electrical conductivity, which are detectable throughout the year, and to an increase in average water temperature of 1 [Formula: see text] in a 2 km reach downstream of the wastewater treatment works during low flows. Overall, we showcase how high-frequency water quality measurements initiated by a Citizen Science project, together with machine learning techniques, can help untangle key drivers of water quality dynamics in an urbanised chalk stream.
人类活动改变了河流水质和水量，对城市化河流的生态系统产生了影响。量化人为驱动因素在控制水质时空格局中的作用对于制定改善城市河流生态健康的成功策略至关重要。在这里，我们分析了在英格兰东南部的棋溪（River Chess）进行的一项公民科学项目中收集的高频电导率和温度数据。利用机器学习，我们发现增强树比 GAM 表现更好，并且可以用不到 1%的误差准确描述水质动态。Shapley Additive exPlanations 揭示了单个变量（如河流水位和污水处理厂（WWTW）出水）之间的重要性和（相互）依赖性。WWTW 出水导致电导率的昼夜变化，这种变化全年都可检测到，并且在低流量时，污水处理厂下游 2 公里的河段的平均水温升高了 1 [Formula: see text]。总的来说，我们展示了如何通过公民科学项目和机器学习技术相结合，对高频水质测量进行分析，从而帮助我们理清城市化白垩溪流水质动态的关键驱动因素。
