Human urine: A novel source of phosphorus for vivianite production

Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants

Quickly grasping the time-consuming water quality indicators (WQIs) such as total nitrogen (TN) and total phosphorus (TP) of influent is an essential prerequisite for wastewater treatment plants (WWTPs) to prompt respond to sudden shock loads. Soft detection methods based on machine learning models, especially deep learning models, perform well in predicting the normal fluctuations of these time-consuming WQIs but hardly predict their sudden fluctuations mainly due to the lack of extreme fluctuation data for model training. This work employs attention mechanisms to aid deep learning models in learning patterns of anomalous water quality. The lack of interpretability has always hindered deep learning models from optimizing for different application scenarios. Therefore, the local and global sensitivity analyses are performed based on the best-performing attention-based deep learning and ordinary machine learning models, respectively, allowing for reliable feature importance quantification with a small computational burden. In the case study, three types of attention-based deep learning models were developed, including attention-based multilayer perceptron (A-MLP), Transformer composed of stacked A-MLP encoder and A-MLP decoder, and feature-temporal attention-based long short-term memory (FTA-LSTM) neural network with encoder-decoder architecture. These developed attention-based deep learning models consistently outperform the corresponding baseline models in predicting the testing set of TN, TP, and chemical oxygen demand (COD) time series and the anomalous values therein, clearly demonstrating the positive effect of the integrated attention mechanism. Among them, the prediction performance of FTA-LSTM outperforms A-MLP and Transformer (2.01-38.48 % higher R2, 0-85.14 % higher F1-score, 0-62.57 % higher F2-score). Predicting anomalous water quality using attention-based deep learning models is a novel attempt that drives the WWTPs' operation towards being safer, cleaner, and more cost-efficient.

Selective degradation of endogenous organic metabolites in acidified fresh human urine using sulphate radical-based advanced oxidation

The human urine metabolome is complex, containing a wide range of organic metabolites that affect treatment of urine collected in resource-oriented sanitation systems. In this study, an advanced oxidation process involving heat-activated peroxydisulphate was used to selectively oxidise organic metabolites in urine over urea and chloride. Initial experiments evaluated optimal conditions (peroxydisulphate dose, temperature, time, pH) for activation of peroxydisulphate in unconcentrated, non-hydrolysed synthetic urine and real urine acidified to pH 3.0. Subsequent experiments determined the fate of 268 endogenous organic metabolites (OMs) and removal of COD from unconcentrated and concentrated real urine (80-90% mass reduced by evaporation). The results revealed >90% activation of 60 mM peroxydisulphate in real unconcentrated urine heated to 90 °C for 1 h, resulting in 43% ΣOMs degradation, 22% COD removal and 56% total organic carbon removal, while >94% of total nitrogen and >97% of urea in real unconcentrated urine were recovered. The mechanism of urea degradation was identified to be chemical hydrolysis to ammonia, with the rate constant for this reaction determined to be 1.9 × 10-6 s-1 at pH 3.0 and 90 °C. Treating concentrated real urine resulted in similar removal of COD, ΣOMs degradation and total nitrogen loss as observed for unconcentrated urine, but with significantly higher chloride oxidation and chemical hydrolysis of urea. Targeted metabolomic analysis revealed that peroxydisulphate treatment degraded 157 organic metabolites in urine, of which 67 metabolites were degraded by >80%. The rate constant for the reaction of sulphate radicals with oxidisable endogenous organic metabolites in urine was estimated to exceed 108 M-1 s-1. These metabolites were preferentially oxidised over chloride and urea in acidified, non-hydrolysed urine treated with peroxydisulphate. Overall, the findings support the development of emerging urine recycling technologies, including alkaline/acid dehydration and reverse osmosis, where the presence of endogenous organic urine metabolites significantly influences treatment parameters such as energy demand and product purity.

Subtle magnesium liberation of self-fabricated functional filler actuates highly efficient phosphorus removal from source-separated urine by SBBR

Domestic wastewater source-separated treatment has attracted wide attention due to the efficiency improvement of sewage treatment systems, energy saving, resource reuse, and the construction and operation cost saving of pipeline networks. Nonetheless, the excess source-separated urine still demands further harmless treatment. Sequencing batch biofilm reactor (SBBR), a new type of composite biofilm reactor developed by filling different fillers into the sequential batch reactor (SBR) reactor, has higher pollutant removal performance and simpler operation and maintenance. However, the phosphorus removal ability of the SBBR filling with conventional fillers is still limited and needs further improvement. In this study, we developed two new fillers, the self-fabricated filler A and B (SFA/SFB), and compared their source-separated urine treatment performance. Long-term treatment experimental results demonstrated that the SBBR systems with different fillers had good removal performance on the COD and TN in the influent, and the removal rate increased with the increasing HRT. However, only the SBBR system with the SFA showed excellent PO43--P and TP removal performance, with the removal rates being 83.7 ± 11.9% and 77.3 ± 13.7% when the HRT was 1 d. Microbial community analysis results indicated that no special bacteria with strong phosphorus removal ability were present on the surface of the SFA. Adsorption experimental results suggested that the SFA had better adsorption performance for phosphorus than the SFB, but it could not always have stronger phosphorus adsorption and removal performance during long-term operation due to the adsorption saturation. Through a series of characterizations such as SEM, XRD, and BET, it was found that the SFA had a looser structure due to the use of different binder and production processes, and the magnesium in the SFA gradually released and reacted with PO43- and NH4+ in the source-separated urine to form dittmarite and struvite, thus achieving efficient phosphorus removal. This study provides a feasible manner for the efficient treatment of source-separated urine using the SBBR system with self-fabricated fillers.

Strategies for optimizing biovivianite production using dissimilatory Fe(III)-reducing bacteria

Vivianite (Fe3(PO4)2·8H2O), a sink for phosphorus, is a key mineralization product formed during the microbial reduction of phosphate-containing Fe(III) minerals in natural systems, and also in wastewater treatment where Fe(III)-minerals are used to remove phosphate. As biovivianite is a potentially useful Fe and P fertiliser, there is much interest in harnessing microbial biovivianite synthesis for circular economy applications. In this study, we investigated the factors that influence the formation of microbially-synthesized vivianite (biovivianite) under laboratory batch systems including the presence and absence of phosphate and electron shuttle, the buffer system, pH, and the type of Fe(III)-reducing bacteria (comparing Geobacter sulfurreducens and Shewanella putrefaciens). The rate of Fe(II) production, and its interactions with the residual Fe(III) and other oxyanions (e.g., phosphate and carbonate) were the main factors that controlled the rate and extent of biovivianite formation. Higher concentrations of phosphate (e.g., P/Fe = 1) in the presence of an electron shuttle, at an initial pH between 6 and 7, were needed for optimal biovivianite formation. Green rust, a key intermediate in biovivianite production, could be detected as an endpoint alongside vivianite and metavivianite (Fe2+Fe3+2(PO4)2.(OH)2.6H2O), in treatments with G. sulfurreducens and S. putrefaciens. However, XRD indicated that vivianite abundance was higher in experiments containing G. sulfurreducens, where it dominated. This study, therefore, shows that vivianite formation can be controlled to optimize yield during microbial processing of phosphate-loaded Fe(III) materials generated from water treatment processes.