Correlation Research between Blue-green Algae and Water Quality Indicators Using Unmanned Surface Vehicle

The harmful algal blooms in fresh waters have led to severe environmental problems such as mass mortalities of wild and cultured fish and shellfish, and human illnesses, which hamper the sustainability of fisheries and aquaculture. Blue-green algae (BGA) are commonly the dominant species in harmful algal blooms. Thus, studying the correlation between BGA and water quality indicators can contribute to establishing data-driven models when predicting the outbreaks of BGA. Previous studies typically used data from fixed-point sampling for correlation analysis. For specific waters, fixed-point sampling has the defects of small coverage, low sampling frequency, and poor flexibility, which is one of the reasons affecting the reliability of the analysis results. This paper uses an unmanned surface vehicle (USV) for water quality data collection. Spearman's correlation coefficient and statistical methods are used to conduct correlation analysis between BGA biomass (measured by phycocyanin) and water quality indicators. The results show a significant positive correlation between BGA biomass and chlorophyll-a, pH, water temperature, and dissolved oxygen. The results are consistent with most correlation studies and demonstrate the feasibility of using the massive sampling data collected by unmanned surface vehicle to analyze the correlation between BGA and water quality indicators.