Abstract—Hydropower plants represent the largest category of renewable energy sources. However, they have a common environmental issue: they reduce the amount of dissolved oxygen in the rivers where they discharge the turbine outflow. Estimating dissolved oxygen levels in a practical manner represents an ongoing challenge for energy generation companies. This study presents a comprehensive model for predicting dissolved oxygen concentration using statistical regression techniques. The model is validated using data from El Quimbo hydropower plant in Colombia to predict dissolved oxygen in the water discharged into the Magdalena River. The approach uses Ordinary Least Squares regression to calibrate the river's dynamic model of oxygen concentration.
The results show that the model explains 79.8% of the variability in dissolved oxygen concentration (R2 = 0.798). The estimation of the required oxygen injection indicates that the highest demand occurs in September, reaching 39.99 tons per month. Model performance was assessed using statistical criteria, obtaining an Akaike Information Criterion (AIC) of 873.3 and a Schwarz-Bayesian Information Criterion (BIC) of 882.9. While this model provides a valuable initial tool for optimizing oxygen injection strategies to mitigate environmental impacts and ensure adequate water quality in hydroelectric projects, the paper also discusses future work on implementing a real-time control system for water oxygenation using more sophisticated machine-learning models.
Keywords—dissolved oxygen concentration, hydropower plant, ordinary least squares, water quality, estimation of dissolved oxygen levels
Cite: Víctor Manuel Hincapié Toro, Juan Pablo Romero Sánchez, Juan Esteban De la Calle, Amalia Avendaño Sánchez, Luis Daniel Benavides Navarro, Agustín Marulanda Guerra, "Estimation of Dissolved Oxygen Concentration in El Quimbo Hydropower Plant," International Journal of Environmental Science and Development vol. 17, no. 1, pp. 1-8, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).