Institute of Environmental Science and Research (ESR), Christchurch, New Zealand.
Institute of Environmental Science and Research (ESR), Christchurch, New Zealand.
Water Res. 2022 Jun 30;218:118485. doi: 10.1016/j.watres.2022.118485. Epub 2022 Apr 21.
A groundwater monitoring network surrounding a pumping well (such as a public water supply) allows for early contaminant detection and mitigation where possible contaminant source locations are often unknown. This numerical study investigates how the contaminant detection probability of a hypothetical sentinel-well monitoring network consisting of one to four monitoring wells is affected by aquifer spatial heterogeneity and dispersion characteristics, where the contaminant source location is randomized. This is achieved through a stochastic framework using a Monte Carlo approach. A single production well is considered that results in converging non-uniform flow close to the well. Optimal network arrangements are obtained by maximizing a weighted risk function that considers true and false positive detection rates, sampling frequency, early detection, and contaminant travel time uncertainty. Aquifer dispersivity is found to be the dominant parameter for the quantification of network performance. For the range of parameters considered, a single monitoring well screening the full aquifer thickness is expected to correctly and timely identify at least 12% of all incidents resulting in contaminants reaching the production well. This proportion increases to a global maximum of 96% for a network consisting of four wells and very dispersive transport conditions. Irrespective of network size and sampling frequency, more dispersive transport conditions result in higher detection rates. Increasing aquifer heterogeneity and decreasing aquifer spatial continuity also lead to higher detection rates, though these effects are diminished for networks of 3 or more wells. Statistical anisotropy has no effect on the network performance. Earlier detection, which is critical for remedial action and supply safety, comes with a significant cost in terms of detection rate, and should be carefully considered when a monitoring network is being designed.
一个围绕着抽水井（如公共供水）的地下水监测网络，可以在可能的污染源位置通常未知的情况下，实现早期污染物检测和缓解。本数值研究调查了由一个到四个监测井组成的假想监测井监测网络的污染物检测概率如何受到含水层空间异质性和弥散特征的影响，其中污染源位置是随机的。这是通过使用蒙特卡罗方法的随机框架来实现的。考虑了一个单一的生产井，该井导致靠近井的汇聚非均匀流。通过最大化考虑真实和假阳性检测率、采样频率、早期检测和污染物迁移时间不确定性的加权风险函数来获得最佳网络布置。发现含水层弥散度是量化网络性能的主要参数。在所考虑的参数范围内，一个单独的监测井筛选整个含水层厚度，预计可以正确和及时地识别至少 12%导致污染物到达生产井的所有事件。对于由四个井和非常弥散的输运条件组成的网络，这一比例增加到全球最大值 96%。无论网络规模和采样频率如何，弥散性更强的输运条件会导致更高的检测率。增加含水层非均质性和减少含水层空间连续性也会导致更高的检测率，尽管对于 3 个或更多井的网络，这些影响会减弱。统计各向异性对网络性能没有影响。对于补救行动和供应安全至关重要的早期检测，在检测率方面需要付出巨大的代价，因此在设计监测网络时应仔细考虑。
