Effect of propanil, linuron, and dicamba on the degradation kinetics of 2,4‐dichlorophenoxyacetic acid by Burkholderia sp. A study by differential analysis of 2,4‐dichlorophenoxyacetic acid degradation data

Abstract

The successive application of distinct pesticides, or mixtures of them, is a frequent practice that could adversely affect the microbial species inhabiting soil and aquatic ecosystems. The ability of soil or aquatic microbiota to degrade a pesticide could be affected by the presence of another. If the degradation rate of the first compound is inhibited, its dissipation half‐life in the environment could be hazardously enlarged. Few studies have been made to quantify the impact on the biodegradation rate of pesticides in soils or water by the presence of other pesticides. In this work, a method for assessing the effect of a pesticide on the biodegradation rate of another, measuring its effect on the biodegradation kinetics of a single bacterial strain is presented. The mathematical analysis is a powerful tool to study the stoichiometry and kinetics of microbial processes, which was used to evaluate independently, in detail, the effect of three pesticides (propanil, linuron, and dicamba) on the biodegradation kinetics of 2,4‐dichlorophenoxyacetic acid by a strain of Burkholderia sp. It was evidenced that linuron and dicamba caused a decay of more than 40% in the top instantaneous degradation rate of 2,4‐dichlorophenoxyacetic acid, while propanil showed a minimal effect.

Variable‐volume accelerated system to evaluate the carbaryl biodegradation dynamics of a microbial community by differential analysis

“…Cell growth and carbaryl concentrations were periodically analyzed. Using Mathematica 13.2.1 (Wolfram Research, Inc., Champaign, IL, USA), the experimental data obtained in the last hours of the batch culture, when the carbaryl substrate was limiting, were fitted to logistic models, 11,12 describing x [ t ] (Eqn (14)) and s [ t ] (Eqn (15)): …”

“…By their analytical or numerical differentiation, x 0 [t] and s 0 [t] can be estimated. The variation in the physiological parameters ⊘[t] and q s [t] of the microbial community can be calculated from Eqns (11) and ( 12):…”

“…Mathematical differentiation enables estimation of the transient changes of microbial functional parameters, such as the instantaneous values of transformation efficiencies or the volumetric or specific reaction rates in a bioreactor. 11,12 Although single microbial cells within populations vary significantly in characteristics, especially in response to changes in substrate availability generating microbial heterogeneity, 13 in bioprocesses, microbial populations and microbial communities are usually described with averaged parameters. The continuous steady-state culture of MCs presents another difficulty because they involve myriads of microbial species.…”

“…Nonlinear fitting of experimental data to mathematical models is convenient, followed by their analytical or numerical differentiation. Mathematical differentiation enables estimation of the transient changes of microbial functional parameters, such as the instantaneous values of transformation efficiencies or the volumetric or specific reaction rates in a bioreactor 11,12 …”