Evaluation of Water Quality Data Collected using a Novel Autonomous Surface Vessel

Water quality monitoring is becoming increasingly important as human populations grow, industrial and agricultural activities expand, and climate change threatens to cause major alterations to the hydrologic cycle. With advent of new technology, autonomous surface vessels (ASVs) are able to provide data with high spatial and temporal resolution, which is critical for water quality monitoring and management. A suite of sensors has been integrated in a novel solar-powered ASV that can trave1 $\sim$ 9.26 kmph or can be stationed at a location collecting continuous data. The overarching objective of this paper is to present the efficacy of the ASV in collecting accurate water quality data by comparing these data with data from another set of independent sensors as well as laboratory analysis of water samples. In-situ water quality data from selected sites together with ASV data were collected from four study areas in Mississippi, USA. Salinity, temperature, pH, and dissolved oxygen (DO) measured by the ASV were compared with the measurements by a profiling sensor suite and ASV measured chlorophyll a, phycocyanin, colored dissolved organic matter (CDOM), turbidity, and partial pressure of carbon dioxide (pCO
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) were compared with the measurements from laboratory analysis of water samples collected from the same location and approximately at the same time as the ASV measurements. The comparisons produced correlation coefficients of 0.999, 0.985, 0.974, 0.755, 0.701, 0.633, 0.755, 0.839, and 0.999 for salinity, temperature, pH, DO, chlorophyll a, phycocyanin, CDOM, turbidity, and pCO
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, respectively and the root-mean-square deviations for salinity, temperature, pH, DO, chlorophyll a, phycocyanin, and pCO
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were 1.29 PSU, 0. $671{}^{\circ}\text{C}, 0.56,3.1\text{S}\text{m}\text{g}/\text{L}, 1.12\mu \text{g}/\text{L}$, 0.694 $\mu \text{g}/\text{L}$, and 18.8 $\mu \text{a}\text{t}\text{m}$, respectively. This ASV, along with its sensor suite, should be valuable for water quality modeling and management due to its potential to provide large amounts of accurate data.