Anticipating the impact of pitfalls in kinetic biodegradation parameter estimation from substrate depletion curves of organic pollutants

Accurate and reliable estimation of kinetic parameters of pollutant biodegradation processes is essential for environmental and health risk assessment. Common biodegradation models proposed in the literature, such as the nonlinear Monod equation and its simplified versions (e.g. Michaelis-Menten-like and first-order equations), are problematic in terms of accuracy of kinetic parameters due to the parameter correlation. However, a comparison between these models in terms of accuracy and reliability, related to data imprecision, has not been performed in the literature. This task is necessary, mainly because the model selection cannot be straightforward, as shown in this work. To facilitate the comparison, novel statistics summarising the accuracy and reliability of estimations are introduced. The main objective is to establish relationships between these statistics (trough new diagnostic indicators) to limit the probability of pitfalls or to avoid the negative impact of an improper model selection. Such anticipation is an imperative need in the biodegradation modelling framework and, to the best of our knowledge, it has never been performed. In order to account for accuracy, simulated data in realistic conditions are used to highlight the magnitude of pitfalls related to the model selection for estimation of the main kinetic parameters (Kₛ, μₘ and/or Vₘ). Four scenarios related to model selection are compared for the first time and, diagnostic indicators able to anticipate relevant aspects related to accuracy and reliability are introduced. Moreover, first evidences of the impact of measurement errors in other intrinsic Monod parameters (the initial biomass concentration and the microbial yield coefficient, Y), as well as the impact of the simultaneous μₘ, Kₛ and Y estimation, on the accuracy and reliability of the estimations are reported. Despite the pitfalls shown, specific applicability of even unreliable models is highlighted, and suggestions for environmental and health risk modellers are provided accordingly.