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.

由于异生物质等新兴污染物的出现，保护地表水体和地下水体免受各种影响成分的潜在影响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。下水道泄漏存在很大的安全隐患。由于下水道年龄跨度大以及相关的各种下水道故障类型，各种物种污染物的时空高度变化的渗漏可能会对我们的生态系统产生长期影响。污水渗漏和包气内由此产生的污染物的时空分布饱和区取决于地下特性以及下水道网络系统的特性和几何布局。由于非饱和区的流向通常是垂直的，我们假设来自多个小规模下水道泄漏的污水污染物羽流到达含水层表面，作为地下水污染的一维水平线源。由于多个重叠过程以及不同时空尺度的外部影响，识别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此类线源及其随后出现的地下水羽流进行现场规模识别将足以估计潜在危险区域。城市地下水资源的监测系统通常受到财政限制和土地使用的限制的城市环境。因此，必须评估需要多少地下水观测点和空间分布才能成功检测下水道污染源。我们假设，除了包气带等其他系统特征之外，下水道网络的几何形状对污染物源分布具有根本性影响。因此，通过了解已知的下水道网络特性和基本站点信息，可以通过地下水监测来识别和定位下水道污染物羽流。此外，我们将能够利用线源（此处：下水道网络）的空间分布特征来提出新的或优化现有的基于点的监测网络（此处：地下水采样）。该提案的总体目标是量化给定地下水监测系统可以利用蒙特卡罗建模方法将下水道管段定位为地下污染源的预测能力。这将有助于从给定的下水道网络布局中通过多目标优化以所需的精度和定义的可接受的不确定性推导优化的地下水监测概念。