Predicting microalgae growth and phosphorus removal in cold region waste stabilization ponds using a stochastic modelling approach

A stochastic ecological model with an integrated equilibrium temperature model was developed to predict microalgae growth and phosphorus removal in cold region waste stabilization ponds (WSPs). The model utilized a Monte Carlo simulation to account for parameter uncertainty. The equilibrium temperature model was parameterized using field data collected from two WSPs in Nunavut, Canada, from 2012 to 2014. The equilibrium temperature model provided good agreement with field data on a daily time step. The full model was run using historic (1956–2005) temperature and solar radiation data from five communities (Baker Lake, Cambridge Bay, Coral Harbour, Hall Beach, Resolute) in Nunavut, Canada. The communities represented a range of geographical locations and environmental conditions. Logistic regression on pooled model outputs showed that mean July temperature and mean treatment season temperature (June 1–September 15, ice-free period) provided the best predictors for microalgae growth. They had a predictive success rate of 93 and 88%, respectively. The modelled threshold (50% probability from the Monte Carlo simulation) for microalgae growth was 8.7 and 5.6 °C for the July temperature and mean treatment season temperature, respectively. The logistic regression was applied to each community (except Sanikiluaq) in Nunavut using historic climate data and a probability of microalgae growth was calculated. Based on the model results, soluble phosphorus concentrations consistent with secondary treatment could be achieved if WSP depth is less than 2 m. The model demonstrated a robust method to predict whether a microalgae bloom will occur under a range of model parameters.