A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks.

Baiqian Shi,Miao Wang,Anna Lintern,Peter M Bach,Stephen Catsamas,Dusan Jovanovic,David T Mccarthy,Ana Deletic,Peter Kolotelo

doi:10.3390/s21093056

Abstract

High-resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low-cost sensor reliably measures the rate-of-change of water level without any re-calibration by comparing with industry-standard instruments such as HACH and HORIBA’s probes. To overcome the observed drift of level sensors, we developed an automated re-calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher-resolution sensing feasible from the budget control considerations, since the regular sensor re-calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re-calibration every two weeks is required to limit the sensor’s inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real-time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high-density monitoring that cannot be achieved before with high-end loggers and sensors.

Highlights

Many researchers have emphasised that higher spatial and temporal resolution data should be collected in future via a smart water system to better understand network dynamics, to improve stormwater modelling [4,5], and to even support the existing stormwater infrastructure management in overcoming the challenges imposed by climate change and rapid urbanisation [6]
Conventional detection methods, including visual inspection, dye testing, and CCTV cameras are not necessarily efficient and effective enough to locate and eliminate most point discharge sources in an urban catchment [9,10]. This is because these measures usually require substantial on-site work and human and financial resources to conduct, thereby making high spatial and temporal resolution monitoring in near-real-time cumbersome and costly
High linear correlations were observed between the true water depths and those recorded by the six different low-cost sensors (Figure 4a–f; R2 > 0.98), but the y-intercepts of the linear correlations were off the origin (−45 to 10 mm)

Summary

Introduction

Conventional detection methods, including visual inspection, dye testing, and CCTV (closed-circuit television) cameras are not necessarily efficient and effective enough to locate and eliminate most point discharge sources in an urban catchment [9,10]. This is because these measures usually require substantial on-site work and human and financial resources to conduct (i.e., manually searching a vast urban catchment or conducting camera real-time data check), thereby making high spatial and temporal resolution monitoring in near-real-time cumbersome and costly. For illicit discharge detection, there is a need to monitor both water level and other quality parameters in real-time distributed network to eliminate the illegal behaviours from happening

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Conclusion