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

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),...

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Detalles Bibliográficos
Autores principales: Shi, Baiqian, Catsamas, Stephen, Kolotelo, Peter, Wang, Miao, Lintern, Anna, Jovanovic, Dusan, Bach, Peter M., Deletic, Ana, McCarthy, David T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123848/
https://www.ncbi.nlm.nih.gov/pubmed/33925612
http://dx.doi.org/10.3390/s21093056
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author Shi, Baiqian
Catsamas, Stephen
Kolotelo, Peter
Wang, Miao
Lintern, Anna
Jovanovic, Dusan
Bach, Peter M.
Deletic, Ana
McCarthy, David T.
author_facet Shi, Baiqian
Catsamas, Stephen
Kolotelo, Peter
Wang, Miao
Lintern, Anna
Jovanovic, Dusan
Bach, Peter M.
Deletic, Ana
McCarthy, David T.
author_sort Shi, Baiqian
collection PubMed
description 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.
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spelling pubmed-81238482021-05-16 A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks Shi, Baiqian Catsamas, Stephen Kolotelo, Peter Wang, Miao Lintern, Anna Jovanovic, Dusan Bach, Peter M. Deletic, Ana McCarthy, David T. Sensors (Basel) Article 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. MDPI 2021-04-27 /pmc/articles/PMC8123848/ /pubmed/33925612 http://dx.doi.org/10.3390/s21093056 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shi, Baiqian
Catsamas, Stephen
Kolotelo, Peter
Wang, Miao
Lintern, Anna
Jovanovic, Dusan
Bach, Peter M.
Deletic, Ana
McCarthy, David T.
A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title_full A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title_fullStr A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title_full_unstemmed A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title_short A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks
title_sort low-cost water depth and electrical conductivity sensor for detecting inputs into urban stormwater networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123848/
https://www.ncbi.nlm.nih.gov/pubmed/33925612
http://dx.doi.org/10.3390/s21093056
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