On October 6th and 8th, undergraduate students from University of South Florida (USF) St. Petersburg environmental science lab made a field trip to the Clam Bayou water quality and meteorological station. The Clam Bayou station is operated through a partnership with YSI Xylem and the USF Coastal Ocean Monitoring Program.
During the tour, the students learned about the YSI Xylem EXO2 Multi-Parameter Water Quality Monitoring sonde from YSI Xylem representatives Brock Houston and Scott Wolf. The sonde measures salinity, dissolved oxygen, pH, water temperature, turbidity, chlorophyll, blue green algae, and fluorescent dissolved organic matter and depth. This water quality data augments the meteorological measurements from the same location which include air temperature, wind speed and gust, relative humidity, precipitation, and barometric pressure. Clam Bayou serves as a demonstration site highlighting the value of coastal environment data.
Students compared different methods for measuring water quality. The sonde provided an example of in situ autonomous water quality monitoring. The students then learned more traditional field sampling methods where they collected water and took the samples back to the lab for testing. Once testing is complete, they will compare their results to the sonde data.
Webinar: Developing low-cost and open-source technologies for smart coastal communities
Join us Thursday, February 22nd at 12 PM ET for SECOORA's Coastal Observing in Your Community Webinar Series! Our speaker this month is Dr. Phil Bresnahan from the University of North Carolina Wilmington.
SECOORA Request for Quotes: Operate Nationwide Coastal Web Camera Network
SECOORA is soliciting proposals focused on installing, maintaining, and operating web cameras to scale from a regional to a national network of coastal web cameras (WebCOOS).
Meet the Winner of the 2023 SECOORA Data Challenge
Kaylee Mooney, a graduate student from Florida Gulf Coast University, is the winner of the SECOORA Data Challenge for her proposal Implementing Vulnerability into Historic Hurricane Normalizations.