Evidence map›Paper›PMID 33925612›Full record

ArticleSensors (Basel, Switzerland)2021

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

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

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Baiqian ShiBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.ORCID 0000-0002-2097-1416
Stephen CatsamasBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.ORCID 0000-0001-8967-5853
Peter KoloteloBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.
Miao WangBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.
Anna LinternBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.
Dusan JovanovicBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.
Peter M BachSwiss Federal Institute of Aquatic Science & Technology (Eawag), 8600 Dübendorf, Switzerland.ORCID 0000-0001-5799-6185
Ana DeleticSchool of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, GPO Box 2434, Brisbane 4001, Australia.
David T McCarthyBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Wellington Road, Clayton 3800, Australia.ORCID 0000-0001-8845-6501

Funding

ARC Linkage LP160100241
6 · The paper itself

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.

Indexed as

distributed sensingelectric conductivityillegal discharge detectionlow costlow powerreal-time environmental monitoringwater IoTwater level measurement

Identifiers

PMID33925612
PMCPMC8123848

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.