Optimal design of experiments to improve the characterisation of atrazine degradation pathways in soil

Contamination of soils with pesticides and their metabolites is a global environmental threat. Deciphering the complex process chains involved in pesticide degradation is a prerequisite for finding effective solution strategies. This study applies prospective optimal design (OD) of experiments to identify laboratory sampling strategies that allow model-based discrimination of atrazine (AT) degradation pathways. We simulated virtual AT degradation experiments with a first-order model that reflects a simple reaction chain of complete AT degradation. We added a set of Monod-based model variants that consider more complex AT degradation pathways. Then, we applied an extended constraint-based parameter search algorithm that produces Monte-Carlo ensembles of realistic model outputs, in line with published experimental data. Differences between-model ensembles were quantified with Bayesian model analysis using an energy distance metric. AT degradation pathways following first-order reaction chains could be clearly distinguished from those predicted with Monod-based models. As expected, including measurements of specific bacterial guilds improved model discrimination further. However, experimental designs considering measurements of AT metabolites were most informative, highlighting that environmental fate studies should prioritise measuring metabolites for elucidating active AT degradation pathways in soils. Our results suggest that applying model-based prospective OD will maximise knowledge gains on soil systems from laboratory and field experiments. Highlights Bayesian model analysis can help to distinguish the active degradation pathway of pesticides. Information on degradation metabolites is crucial to understand pesticide fate. Measurements of specific guilds improve the distinction of active pesticide pathways. Prospective optimal design maximizes information gain in soil sciences.

Automated summary

This study applies prospective optimal design of experiments to identify laboratory sampling strategies that allow model-based discrimination of pesticide degradation pathways. The results highlight the importance of measuring pesticide metabolites for understanding pesticide fate in the environment. The study emphasizes the use of model-based prospective optimal design to maximize knowledge gains on soil systems from laboratory and field experiments.