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Limitations of a biokinetic model to predict the seasonal variations of nitrous oxide emissions from a full-scale wastewater treatment plant

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“A biokinetic model based on BioWin’s Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide(N2O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N2O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N2O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N2O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N2O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA.”

(Citation: Seshan, S., Poinapen, J., Zandvoort, M.H., et. al. – Limitations of a biokinetic model to predict the seasonal variations of nitrous oxide emissions from a full-scale wastewater treatment plant – Science of the Total Environment 917(2024)art. no. 170370 – DOI: 10.1016/j.scitotenv.2024.170370 – (Open Access))

This is an open access article distributed under the terms of the Creative Commons CC-BY license
© 2024 The Authors. Published by Elsevier B.V.

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