Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde

Tillman, Evan F.

doi:10.3133/ofr20171118

Cited by 3 publications

(2 citation statements)

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“…Water pollution, a significant global problem, requires ongoing evaluation and international efforts to effectively manage water resources, from a broader perspective to individual dataset was obtained using IoT technology, specifically the Eureka Water Probe Manta +35 sensors deployed at each station, enabling accurate real-time monitoring of river water conditions. The Eureka Manta water quality multiprobe underwent rigorous testing at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility to assess its accuracy and compliance with standards, including ISO 7027 [22] for measuring turbidity and Standard Methods 2510 B to correct the specific conductance. The results demonstrated that the Manta met the criteria outlined in the USGS National Field Manual for continuous water quality monitors, covering parameters such as dissolved oxygen and turbidity.…”

Section: Discussionmentioning

confidence: 99%

Thailand Raw Water Quality Dataset Analysis and Evaluation

Krohkaew,

Nilaphruek,

Witthayawiroj

et al. 2023

Data

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Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.

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Section: Discussionmentioning

confidence: 99%

Thailand Raw Water Quality Dataset Analysis and Evaluation

Krohkaew,

Nilaphruek,

Witthayawiroj

et al. 2023

Data

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“…In‐situ measurements of turbidity, dissolved oxygen (DO), and water temperature were conducted using the Eureka® Manta 2 water‐quality multiprobe sonde (Eureka Inc., Austin, USA). Real‐time recording was set at continuous 5‐s intervals for 5 min at +0.25, +0.5, +1, +2, +4, and +7 m above the riverbed (Khamis et al., 2015; Kröger et al., 2010; Lee et al., 2019; Tillman, 2017). Water samples were collected using an Alpha water sampler (Wildco®, Florida, USA) from the middle of the water depth.…”

Section: Methodsmentioning

confidence: 99%

Dynamics of Suspended Particulate Matter in an Impounded River Under Dry and Wet Weather Conditions

Huynh

Kim

et al. 2023

Water Resources Research

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The dynamics of suspended particulate matter (SPM) and SPM‐associated biological and physicochemical processes in a river vary under dry and wet weather conditions and hydraulic structures, which moves a free‐flowing river to a semi‐lacustrine environment. This study investigated the response of SPM dynamics and riverbed morphodynamics to climate and anthropogenic stressors based on in‐situ observations and biogeochemical analyses of river water and riverbed sediment. The biochemical analyses and flocculation tests demonstrate abundant biopolymers and high flocculation potential of the river water during the pre‐flood period with algal bloom. This might promote SPM deposition and high‐concentration sediment layer (HCSL) formation on the riverbed. The high fractions of clay minerals and organic carbon in the riverbed sediment indicate the deposition of organic‐rich biomineral flocs. Vertical turbidity profiles with “long tails” of high turbidity near the riverbed also confirmed SPM deposition and HCSL formation. However, the highly turbulent flow conditions during the post‐flood period disturbed the tails of high turbidity and homogenized the vertical profiles of turbidity, temperature, and dissolved oxygen. Additionally, terrestrial humic substances were discharged from the watershed during this period, increasing the deflocculation and stabilization of SPM but decreasing deposition on the riverbed, thereby reducing the fractions of clay minerals and organic carbon in the sediment. This study demonstrated the interaction mechanism of SPM dynamics and riverbed morphodynamics with hydrological and biogeochemical processes in an impounded river under dry and wet weather conditions. The findings provide insights into water resource management to deal with climate change and anthropogenic stressors.

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Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

Waczak,

Aker,

Wijeratne

et al. 2024

Remote Sensing

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Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition.

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Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde

Cited by 3 publications

References 1 publication

Thailand Raw Water Quality Dataset Analysis and Evaluation

Thailand Raw Water Quality Dataset Analysis and Evaluation

Dynamics of Suspended Particulate Matter in an Impounded River Under Dry and Wet Weather Conditions

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

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