Fine sediment production in catchments and transport through rivers to floodplains and coastal areas is extremely important for riverine, coastal and marine ecosystems, nutrient transport, global biogeochemical cycles, water quality and pollution. Due to the high cost of suspended sediment monitoring technology, it is extremely difficult to obtain a complete understanding of the physical connections between climate, hydrology, fluvial processes, and sediment fluxes, which requires measurements at many locations. For this reason, we have built an open-source turbidity sensor that brings accessibility to global river research. Compared to commercial turbidity sensors ( 6000€), our low-cost version ( 200€) allows for multiple deployment and therefore a high spatial coverage of sediment fluxes. It is an optical scatter sensor with an 850 nm LED and two IR detectors, and features a temperature and pressure sensor. Our sensor is 3D-printed on a hobby printer and is programmed with Arduino IDE, making it accessible to those without high-tech workshop access and limited programming skills. It features a printed circuit board that stacks on top of an ultra low-power Arduino MKR WAN 1310, for durability and easy assembly. The sensor was tested during a flood in September 2022 on the Ötztal Ache in Tirol, Austria.
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http://dx.doi.org/10.1016/j.ohx.2023.e00395 | DOI Listing |
Sci Data
January 2025
IFREMER Délégation Océan Indien (DOI), Le Port, 97420, La Réunion, Rue Jean Bertho, France.
Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
December 2024
Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity.
View Article and Find Full Text PDFHardwareX
December 2024
University of Lyon, CNRS UMR 5600 Environnement Ville et Société, Lyon, France.
The use of low-cost sensors, with open-source code, facilitates greater spatial resolution and flexibility of environmental monitoring, thus generating more information and overcoming limitations of traditional commercial sensors. Measurement of water turbidity using submerged sensors can be problematic in that rapid biofouling requires frequent site visits to remove, clean, calibrate and replace the sensor. We therefore designed an automated system using low-cost commercially-available sensors that pumps water from the stream, samples it for turbidity and purges remaining water, leaving the turbidity sensor dry between measurements, thus greatly reducing the biofouling problem and minimizing operation costs.
View Article and Find Full Text PDFFront Microbiol
October 2024
Produce Safety and Microbiology Research Unit, Western Regional Research Center, Agricultural Research Service, United States Department of Agriculture, Albany, CA, United States.
Agricultural water is commonly treated with chlorine-based disinfectants, which are impacted by water quality. Understanding how water quality influences disinfectants such as chlorine dioxide (ClO) against pathogenic bacteria is important for creating efficacious sanitation regimens. In this study, the minimum inhibitory concentration (MIC) of ClO needed to achieve a 3-Log reduction against Shiga toxin-producing (STEC) and was compared across agricultural water samples.
View Article and Find Full Text PDFEnviron Monit Assess
November 2024
UMR 5600 Environnement Ville Et Société, Université Jean Moulin Lyon 3, CNRS, 18 Rue Chevreul Lyon 7, Lyon, France.
This study presents the process of design and development of a low-cost turbidimeter for monitoring water quality, facilitating rigorous spatial-temporal variability analysis within large-scale hydrological systems. We propose a low-cost optical turbidimeter, modifying the existent SEN0189 turbidity sensor, Arduino boards, and additional sensors for temperature compensation. We compared a low-cost system with high-tech sensors, modifying the original low-cost SEN0189 probe for enhanced environmental performance.
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