Deciphering and predicting anammox-based nitrogen removal process under oxytetracycline stress via kinetic modeling and machine learning based on big data analysis

Abstract Anaerobic ammonium oxidation (anammox) is an advanced nitrogen removal process that is widely used in the nitrogen removal of various antibiotic containing wastewaters due to its high efficiency and energy saving characteristics. However, as a widely used antibiotic, the inhibitory effect of oxytetracycline (OTC) on anammox is unclear. In this study, the effect of OTC on the anammox-based nitrogen removal process was revealed by kinetic model and machine learning models. Statistical analysis showed that anammox started to be inhibited when the OTC concentration reached 2 mg/L. The inhibition and recovery periods were simulated under OTC stress. During the inhibition period, the R2 fitted by Exp model was higher, and the simulated maximum nitrogen removal rate (NRR) was between 0.47 and 17.05 kg/(m3·d). During the recovery period, both Boltzmann and Gauss models fit well. In addition, the machine learning model of the artificial neural network predicted the NRR more accurately, indicating that the importance of environmental factors was lower than the effluent parameters. Spearman correlation analysis showed that the NRR was negatively correlated with OTC under both short-term and long-term OTC stress. Furthermore, the hydraulic retention time and water quality parameters played an important role in the short-term and long-term experiment, respectively. Finally, redundancy analysis demonstrated that the abundance of nitrogen functional genes, such as hydrazine dehydrogenase, nitrite/nitric oxide oxidoreductase and hydrazine synthase, was negatively correlated with the amount of OTC, while antibiotic resistance genes showed the opposite trend.