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John F Saraceno

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This dataset was generated during the precision testing of three water-quality sondes before picking one to use for field deployment of high frequency ground-water quality monitoring. A laboratory experiment was conducted for the three sondes to simultaneously measure at hourly intervals with a setup of standard solution circulating past the sondes to simulate field conditions. The electrical conductivity experiment lasted 33 hours, the pH experiment lasted 13 hours, and the dissolved oxygen experiment failed (no data).
Categories: Data; Tags: geoscientificInformation, water
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These data sets are the electrical conductivity, pH, and dissolved oxygen time-series data generated from the short-term precision experiment and the long-term field precision analysis to characterize three commonly used water-quality sondes for the Guidelines and Standard Procedures for High-Frequency Groundwater-Quality Monitoring Station Techniques and Methods Report.
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The dataset presents 5- or 15-minute data for turbidity and Fluorescent Dissolved Organic Matter (FDOM) measured by in-stream sensors at the USGS gage Río Icacos near Naguabo (50075000), Luquillo Experimental Forest, Puerto Rico. The measurement period was 10 March, 2017 to 1 March, 2019. The USGS sensors were co-located with sensors for nitrate and specific conductance operated by University of New Hampshire (UNH), and these datasets are all used in the accompanying publication. A 4-month gap in turbidity in 2018 was mostly filled by UNH data, using a relation between USGS turbidity and co-located UNH turbidity when both sensors were operating. The main metric used in the journal paper was the ratio of coefficient...
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The authors examined the long-term precision of SC, pH, and DO sensors by analyzing the groundwater-quality time-series data generated at a deep municipal supply well (USGS station 364200119420001). The data set presented is the data collected by the RBRmaestro sonde the during the time period from February 2016 to June 2016 and September 2017 to October 2017.
Categories: Data; Tags: water
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