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

Area of Science:

• Environmental Science
• Aquatic Ecology
• Ecotoxicology

Background:

• Copper sulfate pentahydrate is a long-standing algicide for nuisance phytoplankton and cyanobacteria.
• Current copper sulfate dosing lacks scientific rigor, leading to suboptimal application and potential non-target effects.

Purpose of the Study:

• To develop a predictive multiple linear regression (MLR) model for optimal copper sulfate dosing.
• To minimize non-target ecological impacts by refining algicidal application methods.

Main Methods:

• Laboratory bioassays correlated water quality parameters (pH, hardness, alkalinity, DOC) with copper toxicity to algae.
• A predictive MLR model was built using key predictors: dissolved organic carbon (DOC) and pH.
• Field validation was conducted in an aquaculture pond over 28 days.

Main Results:

• Dissolved organic carbon (DOC) and pH were significant predictors of copper toxicity (R² = 0.813).
• The MLR-derived dose used 60% less copper than standard methods.
• Equivalent harmful algal control (95% reduction) was achieved with the MLR dose, alongside reduced impact on beneficial plankton.

Conclusions:

• Multiple linear regression (MLR) provides a scientifically sound basis for optimizing copper sulfate algicide application.
• This refined dosing strategy leads to more ecologically responsible management of harmful algal blooms.
• The MLR model facilitates reduced copper usage while maintaining efficacy.