Pin-pointing groundwater infiltration into urban sewers using chemical tracer in conjunction with physically based optimization model. (15th May 2020) - Zhao, Zhichao; Yin, Hailong; Xu, Zuxin; Peng, Jian; Yu, Ziwen
Abstract: Groundwater infiltration into sanitary sewers increases hydraulic loadings of sewage collection systems and threatens wastewater treatment efficiency. However, cost-effective approach to quantify this important process still needs to be improved in order to better manage this common issue. This paper presents a method for determining the origin and amount of groundwater entering the urban sewer system. On a catchment scale, by measuring and tracking a chemical tracer (i.e., artificial sweetener acesulfame) in the urban sewers, the magnitude of daily groundwater flows in each sub-catchment could be quantified based on a Monte Carlo chemical mass balance approach. For the study site, 7.9% of the sewer length contributed 58% of the total groundwater infiltration. In the identified high-risk sub-catchment, groundwater sources and their spatial-temporal flows could be further pinpointed and elucidated by physically based numerical self-optimization model using microbial genetic algorithm method, which was verified by on-site sewer flow measurements, as well as time-series tracer concentration patterns at the terminal outlet. It was found that the diurnal variations of groundwater seepage into sewer network was linked to the in-pipe water level associated with sewage pumps operation mode, demonstrating the importance of in-pipe water level regulation in controlling groundwater infiltration. Compared with traditional visual inspection or direct flow measurement methods, the proposed approach exhibits distinct advantages in determining groundwater sources and flows in large sewer systems. Graphical abstract: Image 1 Highlights: A modeling approach is designed to detect groundwater infiltration into sewer. High-risk area was identified and spatial-temporal information generated. Chemical tracers and flows are key input parameters for the model. In-pipe water level relative to ambient groundwater level drives infiltration rate.