Evaluation of low-cost sensors for real-time water quality monitoring

Xavier, Felipe; Martins, Leila Droprinchinski; Oyamada, Márcio Seiji; Spanhol, Fábio Alexandre; Coutinho, Fábio Rizental; Pfrimer, Felipe Walter Dafico; Camargo, Edson Tavares de

doi:10.5753/sbesc_estendido.2022.228152

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…RuthEllen et al, (2011) also found that water parameter testing strips were highly inaccurate while electronic meters were the most accurate but costly and require greater degree of care and maintenance. The findings of this study align with the research conducted by Xavier et al (2022), which assessed the data obtained from a turbidity sensor (DFRobot turbidity sensor SEN0189) in comparison to a reference device. The study revealed that during the initial 8-hour test period, the SEN0189 provided results closely resembling those of the reference sensor.…”

Section: Discussionsupporting

confidence: 85%

Digitalization in Small-Scale Urban Recirculation Aquaculture: Data Analytics in Sub-Saharan Africa

Aghaji,

Benjamin,

Buchenrieder

2023

Preprint

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Food and nutrition insecurity, affecting 30% of the global population in 2020, poses a significant challenge to achieving Sustainable Development Goal (SDG) 2 - Zero Hunger. Urbanization, particularly in sub-Saharan African cities e.g. Lagos, Nigeria, exacerbates these issues, impacting resources and contributing to informal settlements. There is emphasis on innovative solutions in urban farming, with Recirculating Aquaculture Systems (RAS) emerging as one of the promising approach due to its efficiency in space and capital utilization. However, maintaining optimal conditions in RAS, crucial for SDG 2 and 11, requires robust water quality monitoring. This study explores the availability of digital tools for water quality monitoring in small-scale urban RAS, evaluating handheld devices and IoT sensors' reliability through t-test statistical method. The results aim to guide practitioners in selecting effective monitoring tools, contributing valuable insights for sustainable aquaculture in urban areas, particularly in sub-Saharan Africa, where access to affordable digital solutions is pivotal for success and can attract youth to agri-food technologies.

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Section: Discussionsupporting

confidence: 85%

Digitalization in Small-Scale Urban Recirculation Aquaculture: Data Analytics in Sub-Saharan Africa

Aghaji,

Benjamin,

Buchenrieder

2023

Preprint

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“…In his tests, the author reports relative errors of 1.6%. In the work by Xavier et al [33], the LCS SEN0189 only survived immersion for the first 8 hours of a 42-hour test. However, during the 8-hour test phase, the SEN0189 delivered results that were very similar to those of the reference sensor.…”

Section: Experiments With Turbidity Sensorsmentioning

confidence: 99%

“…Without being able to calibrate the DFRobot sensor, the authors therefore decided not to test it. However, an evaluation of the sensor was carried out on [33], where the sensor was found to be fragile when left submerged for long periods and susceptible to infiltration. Section 3.4 describes the results of [33] when the results on turbidity are discussed.…”

Section: Calibration Of Turbidity Sensorsmentioning

confidence: 99%

A multi low-cost sensors analysis for remote and real-time water quality monitoring system

Slongo,

Lindino,

Martins

et al. 2024

Preprint

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Low-cost sensors integrated with the Internet of Things can enable real-time envi-ronmental monitoring networks and provide valuable water quality informationto the public. However, the accuracy and precision of the values measured bythe sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturersare tested for measuring five water quality parameters: pH, dissolved oxygen,oxidation-reduction potential, turbidity, and temperature. The low-cost sensorsare evaluated for each parameter by calculating the error and precision com-pared to a typical multiparameter probe assumed as a reference. The comparisonwas performed in a controlled environment with simultaneous measurements ofreal water samples. The relative error ranged from -0.33 to 33.77% and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative toquickly detect changes in water quality parameters. Further studies are necessaryto establish a guideline for the operation and maintenance of low-cost sensors.

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“…The data collected is diverse and can include variables such as temperature, humidity, geographic location, heart rate, images of open or closed environments, and others. In the context of water quality monitoring, the data collected includes parameters such as potential of Hydrogen, Dissolved Oxygen (DO), turbidity, Oxidation-Reduction Potential (ORP), and others [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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

Camargo

Spanhol

Slongo

et al. 2023

Sensors

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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.

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Evaluation of low-cost sensors for real-time water quality monitoring

Cited by 5 publications

References 6 publications

Digitalization in Small-Scale Urban Recirculation Aquaculture: Data Analytics in Sub-Saharan Africa

Digitalization in Small-Scale Urban Recirculation Aquaculture: Data Analytics in Sub-Saharan Africa

A multi low-cost sensors analysis for remote and real-time water quality monitoring system

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

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