Utilising sewer network characteristics for the identification of optimised point-based monitoring systems – INCIDENT

由于新出现的污染物，如外来生物，保护地表水和地下水免受各种影响成分的潜在影响已变得越来越具有挑战性。这些物质对环境和人类生活的影响仍然无法量化，因此是当前科学研究的重点。下水道泄漏带来了极大的安全隐患。由于下水道的跨度很大，以及与之相关的各种下水道故障类型，各种污染物的时空高度可变的渗漏可能会对我们的生态系统产生长期的影响。渗漏以及由此产生的污染物在渗流和饱和区内的时空分布取决于地下属性以及下水道管网系统的属性和几何布局。由于非饱和区的水流方向一般是垂直的，我们假设来自多个小规模下水道渗漏的污水污染物羽流作为地下水污染的一维水平线源到达含水层表面。由于几个重叠的过程和具有不同空间和时间尺度的外部影响，确定或量化每一次泄漏实际上是不可行的。然而，我们认为，通过地下水监测对这种线源及其随后出现的地下水羽流进行现场尺度识别，将足以估计危险潜在区域。城市地下水资源监测系统往往受到资金限制和城市环境土地利用的限制。因此，必须对地下水观测点的数量和空间分布进行评估，才能成功地探测到下水道污染源。我们假设，在包气带等其他系统特征中，排水管网几何形状对污染源分布有根本影响。因此，通过确认已知的下水道网络属性和基本场地信息，将有可能从地下水监测中识别和定位下水道携带的污染物羽流。此外，我们将能够利用线源(这里：污水管网)的空间分布特征来建议新的或优化现有的基于点的监测网络(这里：地下水采样)。这项建议的总体目标是利用蒙特卡罗建模方法来量化给定的地下水监测系统能够将下水道管段定位为地下污染源的预测能力。这将有助于通过多目标优化的方法，以期望的精度和定义的可接受的不确定性，从给定的下水道管网布局中推导出优化的地下水监测概念。

The protection of surface and groundwater bodies against potential impacts by various influencing components has become increasingly challenging due to emerging contaminants such as xenobiotics. The effects of these substances on the environment and human life are still not quantifiable and are therefore of focus in current scientific research. A strong potential for safety hazards is provided by sewer leakage. Due to the high span width of sewer age and the associated various sewer failure types, the spatiotemporally highly variable exfiltration of contaminants of various species may provide long-term impact on our ecosystems.Sewage exfiltration and the spatiotemporal distribution of the resulting contaminants within the vadose and saturated zone depend on underground properties as well as properties and the geometrical layout of sewer network systems. Due to the generally vertical flow direction in the unsaturated zone, we hypothesise that sewage contaminant plumes from multiple, small-scale sewer leakages reach the aquifer surface as one-dimensional horizontal line sources of groundwater contamination. It is practically unfeasible to identify or quantify every single leak due to several overlapping processes and external impacts with different spatial and temporal scales. However, we suppose that the field-scale identification of such line sources and their subsequently emerging groundwater plumes via groundwater monitoring will be sufficient for estimating regions of hazardous potential.Often, the monitoring system of urban groundwater resources is restricted from financial constraints and land use of urban environments. Therefore, it must be evaluated, which amount and spatial distribution of groundwater observation points are required for successfully detecting sewer-borne contamination sources. We hypothesise that, among other system characteristics such as the vadose zone, sewer network geometry has a fundamental influence on contaminant source distribution. Therefore, it will be possible to identify and localise sewer-borne contaminant plumes from groundwater monitoring by acknowledging known sewer network properties and basic site information. Moreover, we will be able to use the characteristics of the spatial distribution of line sources (here: sewer networks) to propose new or optimise existing point-based monitoring networks (here: groundwater sampling).The overall objective of this proposal is to quantify the prediction capability with which a given groundwater monitoring system can locate sewer pipe segments as sources of sub-surface contamination utilising Monte-Carlo modelling approaches. This will avail to deduct optimised groundwater monitoring concepts from a given sewer network layout with a desired accuracy and defined acceptable uncertainty by means of multi-objective optimisation.