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

Achieving advanced nitrogen removal from oxytetracycline wastewater by partial nitrification-endogenous denitrification: performance, metabolic pathways, microorganism community, and potential applications

Partial nitrification-endogenous denitrification (PNED) can achieve advanced nutrient removal from wastewater. Herein, an anaerobic/oxic/anoxic/oxic-sequencing batch reactor (AOAO-SBR) was used to treat real oxytetracycline (OTC) pharmaceutical wastewater via PNED. Partial nitrification with an average nitrite accumulation ratio of 91.5 % was achieved. When the influent total nitrogen was 105 ± 10 mg/L, the effluent of it was only 5.9 mg/L, and the removal efficiency was 94.8 %. In the typical cycle, multiple nitrogen removal pathways including endogenous denitrification, simultaneous nitrification-denitrification, and denitrification contributed to 68.0 %, 20.3 %, and 7.3 %. The effluent concentration of NH4+-N was 5 mg/L, and NO2--N and NO3--N were not detected. The biodegradation pathways of OTC were proposed, 47.1 % of OTC was degraded and eight possible degradation byproducts were detected with low toxicity in the extracellular and intracellular. Moreover, Extracellular polymeric substances increased from 35.3 (mg/gVSS) to 74.4 (mg/gVSS) during 120 days, which acts as a critical role in OTC degradation. High-throughput sequencing results showed that the relative abundance of ammonia-oxidizing bacteria was 2.4 %, and nitrite-oxidizing bacteria were washed out, which was conducive to partial nitrification. Candidatus_Competibacter (13.9 %) enhanced nitrogen removal by endogenous denitrification. Thauera (13.5 %), Ottowia (9.2 %), and OLB13 (1.2 %) are the main OTC-degrading bacteria. This study provides a valuable reference to treat OTC pharmaceutical wastewater effectively.

Enhanced prediction of partial nitrification-anammox process in wastewater treatment by developing an attention-based deep learning network

In the process of partial nitrification and anaerobic ammonia oxidation (anammox) for nitrogen removal, the process offers simple metabolic pathways, low operating costs, and high nitrogenous loading rates. However, since the partial nitrification-anammox (PN-anammox) process combines partial nitrification and anammox reactions within the same reactor, strict control of dissolved oxygen (DO) is essential. Additionally, assessing treatment performance through chemical measurement involves time lag, making it challenging to recover the biological process when issue arise, especially in the PN-anammox process, where strict DO control and the sensitivity of anammox bacteria to conditions and substrates demand timely intervention. Therefore, modelling for PN-anammox process is of great significance. Because of traditional modelling methods have limitations in this process, in this study, deep learning networks were applied to model and predict the process by constructing a dataset based on long-term experiments. Specifically, Long Short-Term Memory Network (LSTM) and Densely Connected Convolutional Network (DenseNet) were employed, and an enhanced Attention-based DenseNet (AttentionNet) was developed to further improve the prediction performance. These networks were utilized for long-term continuous PN-anammox reactors for nitrogen removal in low-strength wastewater. The results demonstrated that both DenseNet and the proposed AttentionNet effectively modeled the PN-anammox processes, even under conditions of unstable influent quality and relatively poor treatment performance. The mean relative error (MRE) for DenseNet was under 15%, while AttentionNet achieved an MRE of approximately 10% or less, highlighting the superior performance of the Attention layer. These findings were further validated by Bland-Altman analysis. Additionally, further analysis of AttentionNet showed that predictions for pH, nitrite nitrogen, and total nitrogen in the effluent were more accurate than those for other output parameters.

Machine learning revealing overlooked conjunction of working volume and mixing intensity in anammox optimization

Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter. By integrating the BNGR findings with computational fluid dynamics simulation, which assessed mixing properties, it became feasible to conclude that working volume and mixing intensity co-regulated flow fields in reactors and had a significant influence on anammox performance. Furthermore, this study experimentally validated how mixing intensity affected performance, and specific input power (x), a parameter that conjugates both working volume and mixing intensity, was used to establish the relationship with ammonium removal rate (NH4+-N RR, y) y = 49.90x+1.97 (R2 = 0.94). With specific input power increased from 3.4 × 10-4 to 2.6 × 10-2 kW m-3, the ammonium removal rate exhibited a rise from 1.8 to 3.2 mg L-1h-1. Following, a relationship among input power-working volume-nitrogen removal rate was also established with a view to determining the design variables for anammox reactor. Consequently, the study highlighted the necessity to consider the working volume-mixing intensity correlation when optimizing the anammox process.

Response and self-regulation of PD/A granular sludge to oxytetracycline stress

This paper investigated the operational characteristics and self-regulation mechanism of the partial denitrification/anammox (PD/A) granular system under the stress of oxytetracycline (OTC), an emerging pollutant that accumulates in municipal wastewater treatment plants through various pathways, posing significant challenges for its future promotion in engineering applications. The results indicated that OTC concentrations below 100 mg/L intensified its short-term inhibition on the PD/A granular sludge system, decreasing functional bacterial activity, while between 150 and 300 mg/L, PD's NO3--N to NO2--N conversion ability diminished, and Anammox activity was significantly suppressed. Under long-term high OTC stress (20-30 mg/L), nitrogen removal suffered, and batch tests revealed significant inhibition of PD's NO3--N to NO2--N conversion, dropping from 73.77 % to 50.17 %. Anammox bacteria activity sharply declined from 1.81 to 0.39 mg N/gVSS/h under OTC stress. Extracellular polymeric substances (EPS) content rose from 185.39 to 210.86 mg/gVSS, indicating PD/A sludge's self-protection mechanism. However, EPS content fell due to cell lysis at high OTC (30 mg/L). The decreasing relative abundance of Candidatus_Brocadia (2.32 % to 0.93 %) and Thaure (12.63 % to 7.82 %) was a key factor in the gradual deterioration of denitrification performance. This study was expected to provide guidance for the PD/A process to cope with the interference of antibiotics and other emerging pollutants (short-term shock and long-term stress).

Insights into the response of anammox process to oxytetracycline: Impacts of static magnetic field

The long-term effects of oxytetracycline (OTC) with a high concentration on the anaerobic ammonium oxidation (Anammox) process were evaluated, and the role of static magnetic field (SMF) was further explored. The stress of OTC at 50 mg/L had little effect on the nitrogen removal of anammox process at the first 16 days. With the continuous addition of OTC and the increase of nitrogen loading, the OTC inhibited the nitrogen removal and anammox activity severely. During the 32 days of recovery period without OTC addition, the nitrogen removal was further deteriorated, indicating the inhibition of OTC on anammox activity was irreversible and persistent. The application of SMF alleviated the inhibition of OTC on anammox to some extent, and the specific anammox activity was enhanced by 47.1% compared to the system without SMF during the OTC stress stage. Antibiotic efflux was the major resistance mechanism in the anammox process, and tetA, tetG and rpsJ were the main functional antibiotic resistance genes. The addition of OTC weakened the metabolic interactions between the anammox bacteria and the symbiotic bacteria involved in the metabolism of cofactors and secondary metabolites, leading to the poor anammox activity. The adaptability of microbes to the OTC stress was improved by the application of SMF, which can enhance the metabolic pathways related to bacterial growth and resistance to environmental stress.

Elucidating nitrogen removal performance and response mechanisms of anammox under heavy metal stress using big data analysis and machine learning

In this study, machine learning algorithms and big data analysis were used to decipher the nitrogen removal rate (NRR) and response mechanisms of anammox process under heavy metal stresses. Spearman algorithm and Statistical analysis revealed that Cr⁶⁺ had the strongest inhibitory effect on NRR compared to other heavy metals. The established machine learning model (extreme gradient boost) accurately predicted NRR with an accuracy greater than 99%, and the prediction error for new data points was mostly less than 20%. Additionally, the findings of feature analysis demonstrated that Cu²⁺ and Fe³⁺ had the strongest effect on the anammox process, respectively. According to the new insights from this study, Cr⁶⁺ and Cu²⁺ should be removed preferentially in anammox processes under heavy metal stress. This study revealed the feasible application of machine learning and big data analysis for NRR prediction of anammox process under heavy metal stress.

Deciphering response effect and underlying mechanism of anammox-based nitrogen removal process under exposures to different antibiotics via big data analysis

Recently, little research has been devoted to the systematic investigation regarding the effect of different antibiotics on anammox-based system and its underlying mechanism. In this study, a critical inhibition concentration to the anammox-based system was obtained: 2, 0.5 and 5 mg/L for OTC, TC and SM, respectively. However, SPM had no significant inhibition. Furthermore, Exp model and Monod model were capable to describe the inhibition period, while Gauss model was suitable for the recovery period. A universal machine learning model could accurately predict the NRR (R² over 0.9), especially when biomass information data was introduced. As a qualitative analysis, the inhibition effect of TC and OTC was strongest. The abundance of nitrogen functional genes was negatively correlated with antibiotics, while antibiotic resistance genes showed the opposite trend. Overall, the inhibition ratios of OTC, TC, SPM and SM on anammox process were calculated to be 91%, 82%, 50% and 30%, respectively.

Reaction of the anammox granules to various antibiotics and operating the anammox coupled denitrifying reactor for oxytetracycline wastetwater treatment

The anaerobic ammonium oxidation (anammox) basedtechnology has been considered as an economic and efficient way to remove nitrogen. However, the anammox bacteria could be strongly inhibited by antibiotics. In present research, inhibiting properties of oxytetracycline, penicillin and polymyxin sulfate upon the anammox activity were investigated through batch experiment. The results implied that anammox activity was significantly inhibited by oxytetracycline and polymyxin sulfate. The non-competitive inhibiting model showed that the inhibiting constants (Ki) of oxytetracycline and polymyxin sulfate were 188.5 and 17.7 mg/L, respectively. Meanwhile, the anammox process was not suppressed while the concentration of penicillin reached 3000 mg/L. In long-run experiment, the influent oxytetracycline concentration of the anammox coupled denitrifying reactor was operated at 20 mg/L. It was observed that the anammox performance completely deteriorated, while the NO₂^--N removing efficiency reached 15.8%. The obtained findings could provide important instruction for the treatment of antibiotic contaminated wastewater.