Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections

Abstract

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors-the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T-were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0-5.0 m range, with a standard deviation of 0.44-1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22-0.23 m within the 2.0-5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

Keywords: Geographic information system; Internet of things; Sensors; Sewer chamber; Sewer monitoring.

Fig. 1

RTSMS system architecture.

Fig. 2

Circuit diagram.

Fig. 3

Circuit diagram.

Fig. 4

WSD installment schema.

Fig. 5

Testing of sensors at distances from 0.2 to 1.0 m.

Fig. 6

Testing of sensors at distances from 1.0 to 5.0 m.

Fig. 7

Deviation analysis (a) maximum fluctuation for TF-Luna Benewake (b) average deviation for TF-Luna Benewake (c) maximum fluctuation for sharp GP2Y0A41SK0F & JSN-SR04T (d) average deviation sharp GP2Y0A41SK0F & JSN-SR04T (e) maximum fluctuation for TOF400 VL53L1X (f) average deviation for TOF400 VL53L1X.

Fig. 8

Sewage monitoring algorithms integrated to GIS (a) sewer chamber section (b)wastewater overflow algorithm (c) manhole cover presence algorithm (d) pipe blocakage algorithm.

Fig. 9

Interface of RTSMS GIS.