An approach for estimating microbial growth and chlorinated ethenes biotransformation in groundwater based on discrete observations

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate μeff and the real self-limiting coefficient of microbial growth keff,sl, to simplified the number of independent parameters. A parameter estimation method based on the quasi-Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E < 0.3). The monitoring duration and the sampling schedule have significant impacts on estimating the biological parameters, and large errors would be induced when the data from the periods of extremely low substrate concentration or microbial growth decline were involved in parameter estimation. The research suggested that the proposed kinetic model provided a new insight to express the limitation of microbial population growth due to the available substrates and environmental factors, and is hoping to be applied in actual CE-contaminated sites. [Display omitted] •The CE biodegradation in substrate-limited groundwater was explored.•A kinetic model was developed for simulated the sequential biodegradation of CEs.•The proposed kinetic model was validated with hypothetical and published datasets.•A discrete observation-based approach for estimating parameters was proposed.•Sampling frequency and duration notably impact parameter estimation accuracy.