Identifying the Correct Biotransformation Model from Polychlorinated Biphenyl and Dioxin Dechlorination Batch Studies

Area of Science:

• Environmental Science
• Biotechnology
• Chemical Engineering

Background:

• Biotransformation of hydrophobic compounds like PCBs presents parameterization challenges due to low concentrations and high variability.
• Accurate modeling is crucial for understanding and predicting the environmental fate of persistent organic pollutants.

Purpose of the Study:

• To identify the most appropriate biotransformation model for batch data of hydrophobic compounds under various conditions.
• To compare the performance of zero-order, first-order, Monod, and Best models using Monte Carlo simulations.

Main Methods:

• Monte Carlo simulations were performed with varying initial substrate concentration (S0), half saturation concentration (KS), maximum substrate utilization rate (qmax), and measurement error.
• Four biotransformation models (zero-order, first-order, Monod, Best) were fitted to simulated data.
• Akaike's information criterion corrected for small sample size (AICc) was used to select the best model.

Main Results:

• The first-order model outperformed others with 10% measurement error.
• With 1% measurement error and 10 data points, the Monod model was preferred when S0 > KS and mass transfer was not limiting; otherwise, the first-order model was indicated.
• Neither the Best nor zero-order models consistently performed best.

Conclusions:

• For highly hydrophobic compounds (e.g., PCBs, dioxins) with limited aqueous solubility, a first-order model often provides a fit as good as or better than more complex models.
• Model selection depends on measurement error, data points, and substrate concentration relative to KS.
• The first-order model is a robust choice for batch biotransformation data of hydrophobic contaminants.