Reevaluating copper algaecide dosing to manage water quality: a multiple linear regression approach

Copper sulfate pentahydrate has been extensively used to control the growth of nuisance phytoplankton, including toxigenic cyanobacteria, in freshwater systems for more than 100 years. While the use of copper is well-studied, the dosing methodologies employed are less understood and lack a rigorous scientific basis. The present study aimed to develop a predictive multiple linear regression (MLR) model based on basic water quality parameters that can be used to determine an optimal algicidal dose that minimizes non-target effects on the overall aquatic ecosystem. This model was developed from a series of comprehensive controlled laboratory bioassays relating key water quality parameters, such as pH, hardness, alkalinity, and dissolved organic carbon (DOC), to algal copper toxicity. These bioassays demonstrated that DOC and pH were the most important predictors of copper toxicity to phytoplankton (r2 = 0.813, p < 0.0001). Subsequently, a rigorous field-based test of the novel MLR-derived dose was conducted using a replicated, 28-day experiment in an active aquaculture pond. The MLR-based dose, which contained 60% less copper than the standard dose, resulted in equivalent control of harmful algae (95% reduction) to the higher standard dose. Furthermore, the MLR dose caused less harm to the overall beneficial phytoplankton and zooplankton communities than the alkalinity-based dose. These results show that MLR can be used for the development of more ecologically sound methods of controlling harmful algal blooms.