Adaptive measurements of urban runoff quality
Adaptive measurements of urban runoff quality
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DOI：
10.1002/2015wr018013
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发表时间：
2016-11-01
影响因子：
5.4
通讯作者：
Kerkez, Branko
中科院分区：
文献类型：
--
作者：
Wong, Brandon P.;Kerkez, Branko
介绍了一种自适应测量径流水质动态的方法，特别侧重于表征城市污染图的时间和幅度。而不是依赖于一个静态的时间表或流量加权采样，这可能会错过重要的水质动态，如果参数化不充分，新的互联网使能的传感器节点被用来自主适应他们的测量频率，实时天气预报和水文条件。这种动态的方法有可能显着提高使用有限的实验资源，如自动抓斗采样器，这将继续提供一个强大的替代采样水质动态时，在现场传感器不可用。传统的流量加权或时间加权的采样方案，依赖于预设的阈值相比，该方法的一个主要好处是能够动态地适应功能的基础水文信号。以28 km 2的城市小流域为研究对象，分析了小流域内总悬浮物（TSS）和总磷的浓度特征。水质样本是根据底层水文和实时天气预报的特征自动触发的。研究流域并没有表现出强大的第一次冲洗和事件内浓度的变化是由流动加速，其中最大的负荷TSS和总磷对应的风暴过程线的最陡峭的上升肢体。所提出的方法的可扩展性进行了讨论的背景下，更大的传感器网络部署，以及潜在的改善城市水质控制。
An approach to adaptively measure runoff water quality dynamics is introduced, focusing specifically on characterizing the timing and magnitude of urban pollutographs. Rather than relying on a static schedule or flow-weighted sampling, which can miss important water quality dynamics if parameterized inadequately, novel Internet-enabled sensor nodes are used to autonomously adapt their measurement frequency to real-time weather forecasts and hydrologic conditions. This dynamic approach has the potential to significantly improve the use of constrained experimental resources, such as automated grab samplers, which continue to provide a strong alternative to sampling water quality dynamics when in situ sensors are not available. Compared to conventional flow-weighted or time-weighted sampling schemes, which rely on preset thresholds, a major benefit of the approach is the ability to dynamically adapt to features of an underlying hydrologic signal. A 28 km(2) urban watershed was studied to characterize concentrations of total suspended solids (TSS) and total phosphorus. Water quality samples were autonomously triggered in response to features in the underlying hydrograph and real-time weather forecasts. The study watershed did not exhibit a strong first flush and intraevent concentration variability was driven by flow acceleration, wherein the largest loadings of TSS and total phosphorus corresponded with the steepest rising limbs of the storm hydrograph. The scalability of the proposed method is discussed in the context of larger sensor network deployments, as well the potential to improving control of urban water quality.