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Optimizing Autonomous Sampling for Improved Detection of Dissolved Nitrogen Inputs Sustaining Harmful Cyanobacterial Blooms in Freshwater Lakes

Authors: Ibrahim Salman, Dalton Hite, Annie Bourbonnais, Ioannis Rekleitis

Abstract: In freshwater lakes, harmful cyanobacterial blooms often thrive due to increased dissolved nitrogen inputs, primarily from nitrate. To tackle the challenge of detecting dissolved nitrogen inputs, we present a novel autonomous system specifically designed to continuously monitor nitrate influx from tributaries in lacustrine environments. We deployed an Autonomous Surface Vehicle (ASV) with a state-of-the-art Ultraviolet (UV) nitrate sensor. Further, we enhanced its mon- itoring capability with conventional water quality sensors for temperature, pH, dissolved oxygen, turbidity, and total algae. The ASV systematically navigated the intake regions, capturing a detailed spatio-temporal map of nitrate concentration as it dispersed into the lake ecosystem. Our field deployments confirm the system’s effectiveness, highlighting its potential to improve our understanding of nutrient dynamics in freshwater environments significantly.

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@inproceedings{SalmanOceans2023, author = {Ibrahim Salman and Dalton Hite and Annie Bourbonnais and Ioannis Rekleitis}, booktitle = {MTS/IEEE OCEANS - Gulf Coast}, title = {Optimizing Autonomous Sampling for Improved Detection of Dissolved Nitrogen Inputs Sustaining Harmful Cyanobacterial Blooms in Freshwater Lakes}, year = {2023}, volume = {}, number = {}, pages = {}, keywords = {}, doi = {} }