Quantifying Impacts of Microcosm Mass Loss on Kinetic Constant Estimation

Area of Science:

• Environmental microbiology
• Biogeochemistry
• Remediation science

Background:

• Microcosm experiments are crucial for studying microbial reductive dechlorination of chlorinated aliphatic hydrocarbons.
• These experiments often suffer from significant mass loss (5-50%) due to sampling and diffusion.
• Existing kinetic models frequently overlook this experimental mass loss.

Purpose of the Study:

• To investigate the bias in kinetic parameters when experimental mass loss is not accounted for in microcosm studies.
• To develop and present a model that incorporates mass loss for improved kinetic constant estimation.

Main Methods:

• Conducted numerical experiments fitting Monod kinetic models with and without mass loss.
• Generated over 1300 synthetic datasets based on published microcosm data.
• Performed Monte Carlo simulations to quantify parameter bias under varying mass loss conditions.

Main Results:

• Models ignoring mass loss introduced significant bias in fitted kinetic parameters, ranging from 5-45% for low mass loss and 20-120% for high mass loss.
• High mass loss simulations showed best-fit parameters frequently reaching optimization bounds.
• Failure to account for mass loss can lead to inaccurate kinetic constant estimation.

Conclusions:

• Experimental mass loss in microcosms significantly biases kinetic parameter estimation.
• This bias may explain variability in literature-reported kinetic constants for microbial dechlorination.
• The presented model offers a method to improve accuracy by including mass loss effects.