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

利用下水道网络特征来识别优化的基于点的监测系统 â 事件

• 批准号: 402833446
• 负责人: Professor Dr. Peter Dietrich
• 金额: --
• 依托单位: Institut für Grundwasserwirtschaft
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/402833446?language=en
• 关键词: Utilising; sewer; network; characteristics; identification

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.

由于新兴的污染物（如异种生物），对各种影响成分的潜在影响的保护越来越具有挑战性。这些物质对环境和人类生活的影响仍然无法量化，因此在当前的科学研究中是重点。下水道泄漏提供了强大的安全危害潜力。由于下水道年龄的高宽度和相关的各种下水道衰竭类型，各种物种的污染物的空间高度可变的去渗透可能会对我们的生态系统产生长期的影响。划分剥离和饱和区域内质量和地下质量的铺设以及地下的质量以及地下的质量以及水分的质量均依赖于水分以及水分的空间分布。由于不饱和区域中的一般垂直流动方向，我们假设从多个小规模的下水道泄漏到含水层的污染物羽流到含水层的表面，这是地下水污染的一维水平线源。由于几个重叠的过程以及具有不同的空间和时间尺度的外部影响，识别或量化每个泄漏的情况实际上是不可行的。但是，我们假设对这种线条来源的现场尺度识别及其随后通过地下水监测的新兴地下水羽流足以估算危险潜力的区域。通常，城市地下水资源的监测系统受到城市环境的财务约束和土地利用的限制。因此，必须对其进行评估，成功地检测下水道污染源需要地下水观察点的数量和空间分布。我们假设，除其他系统特征，例如vadose区域，下水道网络几何形状对污染物源分布有根本影响。因此，通过确认已知的下水道网络属性和基本现场信息，可以从地下水监测中识别和定位下水道污染物羽毛。 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.这将有助于从给定的下水道网络布局中扣除优化的地下水监测概念，并具有所需的准确性，并通过多目标优化定义了可接受的不确定性。