Data-mining analysis of in-sewer infiltration patterns: seasonal characteristics of clear water seepage into Brussels main sewers. Issue 10 (26th November 2017)

• Record Type: Journal Article
• Authors: de Ville, N.; Le, H. M.; Schmidt, L.; Verbanck, M. A.
• Is Part Of: Urban water journal. Volume 14:Issue 10(2017)
• Page Start: 1090
• Page End: 1096
• Publication Date: 2017-11-26
• Subjects: Infiltration/Inflow -- clear water seepage -- anthropogenic tracer -- water balance -- combined sewers; Municipal water supply -- Management -- Periodicals; Water-supply -- Planning -- Periodicals; 628.1
• DOI: 10.1080/1573062X.2017.1363252
• Languages: English
• ISSNs: 1573-062X

Abstract

Abstract: Parasitic clear water infiltration is known to increase the waste water volumes in most sewerage systems. Amongst others, a problem arising from that is a significant variation of waste water pollutant concentration over time, which complicates the purification process and increases its cost. Capitalizing on new extensive databases, we propose a new method for the identification of clear water infiltration rates based on data-mining and data consolidation of long time data series. Based on a straightforward anthropogenic tracer, together with a simple but rigorous water budget, the infiltrated volumes are quantified day-by-day for the entire zone treated by a major waste water treatment plant. Brussels city is used as an example of the applicability of the method over several years, demonstrating the significant seasonal changes in sewer infiltration rates in the area and the progress achieved so far by structural sewer repair.