Estimation of cyanobacteria in the Iowa River using long-term data from the Des Moines and Raccoon Rivers to inform drinking water management

Harmful algal blooms, which are high concentrations of cyanobacteria, can impact water quality and drinking water sources. Negative health impacts can arise from drinking contaminated water in the liver, kidneys, and gastrointestinal system. The state of Iowa does not require regular monitoring of cyanobacteria concentrations at drinking water plants. Monitoring has been conducted for the Des Moines River and Raccoon River, but not the Iowa River. Many factors influence the growth of cyanobacteria, including temperature, pH, flow, nutrient levels, turbidity, precipitation, and wind speeds. Using a model to estimate cyanobacterial burden is intended to help inform future testing practices and mitigation strategies. Using a model to give guidance to water treatment plants on when harmful algal blooms may be at risk can promote efficient and effective testing.

This study models the growth of cyanobacteria based on these environmental factors. Temperature, pH, flow, and nitrate levels were found to be key drivers when individually modeled. High temperatures, low flows, low nitrates, and pH levels closer to 9 when modeled estimated higher cyanobacterial cell counts. Temperature and pH were had the strongest influence on the model, followed by flow. Nitrates held minimal strength on the model. These parameters were combined into one model alongside the months to account for seasonal variations to create a final model used to predict the Raccoon River and Iowa River. The model was compared to cyanobacteria cell concentrations in the Raccoon River to determine the applicability to other rivers in the state of Iowa. The results were significantly correlated, but indicated other river qualities may be useful for modeling.

The model was then used to predict the Iowa River cyanobacteria cell concentrations based on monthly profiles of the Iowa River’s temperature, pH, flow, and nitrate data. The months June-October demonstrated higher risk, while December-February demonstrated low risk. The other months showed higher risks of elevated cyanobacterial cell counts when temperature and pH were high, and flows were low.

Next steps may include regular testing for cyanobacteria in the Iowa River, determining the effectiveness of current drinking water treatment practices in use, and the development of communication strategies for times when concentrations exceed acceptable limits. This study attempts to estimate cyanobacterial risk for mitigation and planning purposes for water treatment plants that utilize rivers in the state of Iowa as their water source.