Event Title

Development of a Water Quality Mobile Monitoring Platform and Techniques for Managing Resulting Data

Location

USU Eccles Conference Center

Event Website

http://water.usu.edu

Start Date

4-5-2016 4:39 PM

End Date

4-5-2016 4:42 PM

Description

Water quality monitoring efforts typically involve the measurement of variables at fixed locations. With in situ sensors, high temporal resolution can be achieved, but spatial resolution is not captured. A mobile monitoring platform has the potential to observe fine scale spatial variability of water quality in streams or lakes to identify spatial hot spots and constituent sources, and to inform fixed monitoring efforts. In order to capture spatial variability, a mobile monitoring platform consisting of a small, tethered boat was created and outfitted with in situ water quality sensors (YSI EXO Sonde, FTS Turbidity Sensor, Turner Designs fluorometer). An on-board global positioning system (GPS) tracks the route of the boat and reports to a datalogger so that the data are temporally and spatially referenced. The platform can be guided, floated, or pulled down a stream to capture a longitudinal gradient or on a lake to capture spatial variability of surface water quality. The platform was tested along a stretch of a canal system in an urban area of Logan City and at First Dam, a reservoir on the Logan River at the mouth of Logan Canyon. Challenges encountered included maintaining control of the boat, portaging around bridges, and preventing interference of ambient conditions by operators (e.g., mobilization of sediment). Visualizations of the data show promise in revealing spatial patterns in water quality. Complex data such as these require tools for loading, storing, editing, and visualization. Data from sensors on the mobile monitoring platform are initially recorded on a datalogger and then transferred to a relational database where they can be accessed, queried, and edited. Recently developed tools were tested for loading time series data into a relational database including an Excel template and a desktop executable program. Furthermore, raw sensor data contain anomalies that need to be reviewed and edited. We also tested software for performing quality control edits on sensor data.

Comments

A poster by Phil Suiter, who is with Utah State University, UWRL, Civil and Environmental Engineering

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Apr 5th, 4:39 PM Apr 5th, 4:42 PM

Development of a Water Quality Mobile Monitoring Platform and Techniques for Managing Resulting Data

USU Eccles Conference Center

Water quality monitoring efforts typically involve the measurement of variables at fixed locations. With in situ sensors, high temporal resolution can be achieved, but spatial resolution is not captured. A mobile monitoring platform has the potential to observe fine scale spatial variability of water quality in streams or lakes to identify spatial hot spots and constituent sources, and to inform fixed monitoring efforts. In order to capture spatial variability, a mobile monitoring platform consisting of a small, tethered boat was created and outfitted with in situ water quality sensors (YSI EXO Sonde, FTS Turbidity Sensor, Turner Designs fluorometer). An on-board global positioning system (GPS) tracks the route of the boat and reports to a datalogger so that the data are temporally and spatially referenced. The platform can be guided, floated, or pulled down a stream to capture a longitudinal gradient or on a lake to capture spatial variability of surface water quality. The platform was tested along a stretch of a canal system in an urban area of Logan City and at First Dam, a reservoir on the Logan River at the mouth of Logan Canyon. Challenges encountered included maintaining control of the boat, portaging around bridges, and preventing interference of ambient conditions by operators (e.g., mobilization of sediment). Visualizations of the data show promise in revealing spatial patterns in water quality. Complex data such as these require tools for loading, storing, editing, and visualization. Data from sensors on the mobile monitoring platform are initially recorded on a datalogger and then transferred to a relational database where they can be accessed, queried, and edited. Recently developed tools were tested for loading time series data into a relational database including an Excel template and a desktop executable program. Furthermore, raw sensor data contain anomalies that need to be reviewed and edited. We also tested software for performing quality control edits on sensor data.

https://digitalcommons.usu.edu/runoff/2016/2016Posters/4