Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

Area of Science:

• Environmental Engineering
• Water Quality Modeling
• Wastewater Treatment

Background:

• Nitrogen removal in waste stabilization ponds (WSPs) is crucial for environmental protection.
• Accurate parameterization and uncertainty analysis are essential for effective WSP modeling.
• Dynamic simulation models require robust methods for parameter estimation and sensitivity analysis.

Purpose of the Study:

• To develop and apply an approach for parameter realization sets in pilot-scale WSP nitrogen removal.
• To perform optimal parameterization, local sensitivity, and global uncertainty analysis of a WSP dynamic simulation model.
• To identify key parameters influencing nitrogen simulation outputs in WSPs.

Main Methods:

• Utilized the R software package Flexible Modeling Environment (R-FME).
• Employed the Markov chain Monte Carlo (MCMC) method for parameter estimation.
• Integrated Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis and parameter sensitivity evaluation.
• Simulated and assessed nine parameters and concentrations of organic nitrogen (ON-N), ammonia nitrogen (NH₃-N), and nitrate nitrogen (NO₃-N).

Main Results:

• The integrated FME-GLUE model achieved good performance with Nash-Sutcliffe coefficients (0.53-0.69) and correlation coefficients (0.76-0.83).
• The model successfully simulated ON-N, NH₃-N, and NO₃-N concentrations in the pilot-scale WSP.
• The Arrhenius constant was identified as the sole parameter sensitive to ON-N and NH₃-N simulations.
• Nitrosomonas growth rate, denitrification constant, and maximum growth rate at 20 °C were found to be sensitive to ON-N and NO₃-N simulations via global sensitivity analysis.

Conclusions:

• The FME-GLUE approach provides a robust framework for parameterization and uncertainty analysis in WSP modeling.
• The study successfully identified critical parameters influencing nitrogen removal processes in WSPs.
• The findings contribute to improved understanding and management of nitrogen removal in wastewater treatment systems.