Evidence map›Paper›PMID 37177633›Full record

ReviewSensors (Basel, Switzerland)2023

Low-Cost Water Quality Sensors for IoT: A Systematic Review.

Edson Tavares de Camargo, Fabio Alexandre Spanhol, Juliano Scholz Slongo, Marcos Vinicius Rocha da Silva, Jaqueline Pazinato, Adriana Vechai de Lima Lobo, Fábio Rizental Coutinho, Felipe Walter Dafico Pfrimer, Cleber Antonio Lindino, Marcio Seiji Oyamada and 1 more

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
11.8field-weighted citation impact, top 1% of its field
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

17 citing papers in PubMed, 103 citations in OpenAlex.

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  12. Feature-Based Normality Models for Anomaly Detection.Sensors (Basel, Switzerland) · 2025
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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

11 authors at 3 institutions in 1 country.

Edson Tavares de CamargoFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0002-6520-9142
Fabio Alexandre SpanholFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0002-9603-8067
Juliano Scholz SlongoFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0001-5188-9696
Marcos Vinicius Rocha da SilvaFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0002-4417-4358
Jaqueline PazinatoFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.
Adriana Vechai de Lima LoboSanitation Company of Paraná (SANEPAR), Curitiba 80215-900, Brazil.
Fábio Rizental CoutinhoFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0002-3016-989X
Felipe Walter Dafico PfrimerFederal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil.ORCID 0000-0002-0586-1585
Cleber Antonio LindinoWestern Paraná State University (UNIOESTE), Toledo 85903-000, Brazil.ORCID 0000-0003-2465-0764
Marcio Seiji OyamadaGraduate Program in Computer Science, Western Paraná State University (UNIOESTE), Cascavel 85819-110, Brazil.ORCID 0000-0002-6354-8917
Leila Droprinchinski MartinsFederal University of Technology-Parana (UTFPR), Londrina 86036-370, Brazil.ORCID 0000-0003-2176-6534
Universidade Tecnológica Federal do Paraná · BRUniversidade Estadual do Oeste do Paraná · BRUniversidade Federal do Paraná · BR

Funding

Fundação Araucária Public Call 17/2021 - Paraná Research Program in Environmental Sanitation - PPPS.São Paulo Research Foundation (FAPESP) 2020/05135-5
6 · The paper itself

Abstract

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.

Indexed as

environmental measurementsenvironmental monitoringInternet of Thingslow-cost sensorremote sensorremote water quality monitoringwater quality

Identifiers

PMID37177633
PMCPMC10181703
OpenAlexW4367599007

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.