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Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies
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 m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143286/ https://www.ncbi.nlm.nih.gov/pubmed/37112254 http://dx.doi.org/10.3390/s23083913 |
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author | Rocher, Javier Jimenez, Jose M. Tomas, Jesus Lloret, Jaime |
author_facet | Rocher, Javier Jimenez, Jose M. Tomas, Jesus Lloret, Jaime |
author_sort | Rocher, Javier |
collection | PubMed |
description | 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%. |
format | Online Article Text |
id | pubmed-10143286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101432862023-04-29 Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies Rocher, Javier Jimenez, Jose M. Tomas, Jesus Lloret, Jaime Sensors (Basel) Article 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%. MDPI 2023-04-12 /pmc/articles/PMC10143286/ /pubmed/37112254 http://dx.doi.org/10.3390/s23083913 Text en © 2023 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 Rocher, Javier Jimenez, Jose M. Tomas, Jesus Lloret, Jaime Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title | Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title_full | Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title_fullStr | Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title_full_unstemmed | Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title_short | Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies |
title_sort | low-cost turbidity sensor to determine eutrophication in water bodies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143286/ https://www.ncbi.nlm.nih.gov/pubmed/37112254 http://dx.doi.org/10.3390/s23083913 |
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