Nutrient Analysis in Arctic Waters Using a Portable Sensing Platform

• Published in: Frontiers in Sensors
• Main Authors: McCaul, Margaret, Magni, Paolo, Jordan, Sean F, McNamara, Eoghan, Satta, Andrea, Diamond, Dermot, Ribotti, Alberto
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2021
• Subjects: General Medicine; Arctic; Teisten; Kongsfjord*; Kongsfjorden
• Online Access: https://www.openaccessrepository.it/record/167308; https://doi.org/10.3389/fsens.2021.711592

A portable sensing platform for the detection of nutrients (PO43−, NO2−, NO3−) in natural waters has been realized through the use of rapid prototyping techniques, colorimetric chemistries, electronics, and LED-based optical detection. The sensing platform is modular in design incorporating interchangeable optical detection units, with a component cost per unit of ca. €300, and small form factor (20 cm × 6 cm x 3.5 cm). Laboratory testing and validation of the platform was performed prior to deployment at the CNR Dirigibile Italia Arctic Research Station, Ny-Aselund (79°N, 12°E). Results obtained showed excellent linear response, with a limit of detection of 0.05 μM (NO2−, NO3−), and 0.03 μM (PO43−). On the June 22, 2016 a field campaign took place within Kongsfjorden, Ny-Aselund (78.5–79°N, 11.6–12.6°E), during which 55 water samples were acquired using 10 L Niskin bottles on board the MS Teisten research vessel. 23 hydrological casts were also performed using a Seabird 19plus V2 SeaCAT Profiler CTD probe with turbidity and dissolved oxygen sensors. Water samples were subsequently analyzed for PO43−, NO2−, NO3− at the CNR Dirigibile Italia Arctic Research Station Laboratory using the adaptive sensing platform. Nutrient concentrations were compared to hydrological data to assess the processes that influence the nutrient concentrations within the Fjord. This research highlights the potential use of the adaptive sensing platform in remote locations as a stand-alone platform and/or for the validation of deployable environmental sensor networks.