ArticleSensors (Basel, Switzerland)2023
Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies.
Article 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 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Multi-Wavelength Calibration of a Low-Cost High-Range Turbidimeter: Analysis of the Dispersion Regime.ACS omega · 2025Article
- Embedded IoT Design for Bioreactor Sensor Integration.Sensors (Basel, Switzerland) · 2024Article
- Automatic Suppression Method for Water Surface Glints Using a Division of Focal Plane Visible Polarimeter.Sensors (Basel, Switzerland) · 2023Article
- Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Eutrophication is the excessive growth of algae in water bodies that causes biodiversity loss, reducing water quality and attractiveness to people. This is an important problem in water bodies. In this paper, we propose a low-cost sensor to monitor eutrophication in concentrations between 0 to 200 mg/L and in different mixtures of sediment and algae (0, 20, 40, 60, 80, and 100% algae, the rest are sediment). We use two light sources (infrared and RGB LED) and two photoreceptors at 90° and 180° of the light sources. The system has a microcontroller (M5stacks) that powers the light sources and obtains the signal received by the photoreceptors. In addition, the microcontroller is responsible for sending information and generating alerts. Our results show that the use of infrared light at 90° can determine the turbidity with an error of 7.45% in NTU readings higher than 2.73 NTUs, and the use of infrared light at 180° can measure the solid concentration with an error of 11.40%. According to the determination of the % of algae, the use of a neural network has a precision of 89.3% in the classification, and the determination of the mg/L of algae in water has an error of 17.95%.
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Registered trials
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