Abstract
Five activated sludge models describing N2O production by ammonium oxidising bacteria (AOB) were compared to four different long-term process data sets. Each model considers one of the two known N2O production pathways by AOB, namely the AOB denitrification pathway and the hydroxylamine oxidation pathway, with specific kinetic expressions. Satisfactory calibration could be obtained in most cases, but none of the models was able to describe all the N2O data obtained in the different systems with a similar parameter set. Variability of the parameters can be related to difficulties related to undescribed local concentration heterogeneities, physiological adaptation of micro-organisms, a microbial population switch, or regulation between multiple AOB pathways. This variability could be due to a dependence of the N2O production pathways on the nitrite (or free nitrous acid—FNA) concentrations and other operational conditions in different systems. This work gives an overview of the potentialities and limits of single AOB pathway models. Indicating in which condition each single pathway model is likely to explain the experimental observations, this work will also facilitate future work on models in which the two main N2O pathways active in AOB are represented together.
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Acknowledgments
The research project and fellowship of Mathieu Pocquet was supported by the National French Research Agency (ANR). Bing-Jie Ni acknowledges the support of an Australian Research Council Discovery Project (DP130103147). The work at Université Laval benefitted from the financial support obtained through the TECC project of the Québec Ministry of Economic Development, Innovation and Exports (MDEIE) and the research project funded by the Flemish Fund for Scientific Research (FWO—G.A051.10). Peter Vanrolleghem holds the Canada Research Chair in Water Quality Modelling.
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Spérandio, M., Pocquet, M., Guo, L. et al. Evaluation of different nitrous oxide production models with four continuous long-term wastewater treatment process data series. Bioprocess Biosyst Eng 39, 493–510 (2016). https://doi.org/10.1007/s00449-015-1532-2
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DOI: https://doi.org/10.1007/s00449-015-1532-2