On fitting the k-C* first order model to batch loaded sub-surface treatment wetlands

Area of Science:

• Environmental Engineering
• Biogeochemistry
• Wetland Science

Background:

• The k-C* model is a first-order kinetic model used to analyze time-series data from batch-loaded model wetlands.
• Understanding factors influencing the model's parameters (k and C*) is crucial for accurate wetland performance assessment.

Purpose of the Study:

• To evaluate the influence of plant species and temperature on the k-C* model parameters using time-series Chemical Oxygen Demand (COD) data.
• To compare the effectiveness of the Levenberg-Marquardt method versus nonlinear mixed effect regression for calibrating the k-C* model.

Main Methods:

• Time-series COD data from four plant species (sedge, bulrush, cattail) and unplanted controls in model wetlands were analyzed.
• Temperature was varied cyclically (24°C to 4°C to 24°C) over a year.
• The Arrhenius relationship was used to estimate temperature effects on model coefficients (k and C*).

Main Results:

• Model coefficients varied significantly based on plant species.
• The rate constant (k) consistently decreased as temperature increased.
• Variation in residual concentration (C*) due to temperature and species was greater than variation in k.

Conclusions:

• Plant species identity and temperature are critical factors influencing wetland performance as described by the k-C* model.
• While k decreases with temperature, C* exhibits greater variability, indicating k alone is insufficient to predict wetland efficiency.
• Statistical calibration methods impact coefficient estimates, emphasizing the need for standardized reporting.