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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.3913
Main Authors: Rocher, Javier, Jimenez, Jose M, Tomas, Jesus, Lloret, Jaime
Format: Article
Language:English
Subjects:
Algae
Aquaculture
arduino
Biodiversity
Biological diversity conservation
Calibration
Design
Digital signal processors
Economic aspects
Environmental Monitoring
environmental pollution
Errors
Eutrophication
Humans
infrared LED
Infrared radiation
Infrared Rays
Laboratories
Light
Light sources
Light-emitting diodes
Low cost
Methods
Photoreceptors
Quality standards
RGB LED
Rivers
Sensors
solids
Turbidity
Water Quality
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Summary: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%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23083913