Modeling Escherichia coli removal in constructed wetlands under pulse loading

Area of Science:

• Environmental Science
• Water Quality Management
• Wastewater Treatment

Background:

• Manure-borne pathogens pose significant risks to global water quality and public health.
• Pathogen transport from agricultural lands to water bodies via surface runoff and tile drains is a major concern.
• Effective management of pathogen removal in aquatic systems is crucial for preventing disease outbreaks.

Purpose of the Study:

• To develop and validate a robust model for estimating pathogen removal in surface flow wetlands under pulse loading conditions.
• To compare the performance of a novel adaptive neuro-fuzzy inference system (ANFIS) model against a mechanistic model for Escherichia coli removal.
• To identify key factors influencing pathogen removal dynamics in constructed wetlands.

Main Methods:

• Development and validation of adaptive neuro-fuzzy inference systems (ANFIS) models using experimental data from pulse-loaded constructed wetlands over winter and summer seasons.
• Application of a mechanistic fecal coliform removal model using the same experimental datasets for comparative analysis.
• Utilizing tracer study data, specifically E. coli concentrations at inflection points, to enhance model predictability.

Main Results:

• The ANFIS model demonstrated a significantly improved ability to describe the dynamics of E. coli removal under pulse loading conditions.
• The mechanistic fecal coliform removal model exhibited poor performance, characterized by a lower coefficient of determination and higher root mean squared error compared to ANFIS models.
• E. coli concentrations at tracer study inflection points were identified as critical parameters for enhancing model prediction accuracy.

Conclusions:

• Adaptive neuro-fuzzy inference systems (ANFIS) offer a superior approach for modeling pathogen removal in constructed wetlands compared to traditional mechanistic models, especially under dynamic loading conditions.
• The developed ANFIS model provides a more accurate and reliable tool for assessing and managing pathogen risks in surface flow wetlands.
• Understanding E. coli dynamics, particularly at specific points identified through tracer studies, is essential for improving the predictive power of water quality models.