Water Science and Technology最新文献

To assess the possibility of using aerobic denitrification (AD) bacteria with high NO₂^--N accumulation for nitrogen removal in wastewater treatment, conditional optimization, as well as sole and mixed nitrogen source tests involving AD bacterium, Comamonas sp. pw-6 was performed. The results showed that the optimal carbon source, pH, C/N ratio, rotational speed, and salinity for this strain were determined to be succinate, 7, 20, 160 rpm, and 0%, respectively. Further, this strain preferentially utilized NH₄⁺-N, NO₃^--N, and NO₂^--N, and when NO₃^--N was its sole nitrogen source, 92.28% of the NO₃^--N (150 mg·L^-1) was converted to NO₂^--N. However, when NH₄⁺-N and NO₃^--N constituted the mixed nitrogen source, NO₃^--N utilization by this strain was significantly lower (p < 0.05). Therefore, a strategy was proposed to combine pw-6 bacteria with traditional autotrophic nitrification to achieve the application of pw-6 bacteria in NH₄⁺-N-containing wastewater treatment. Bioaugmented application experiments showed significantly higher NH₄⁺-N removal (5.96 ± 0.94 mg·L^-1·h^-1) and lower NO₃^--N accumulation (2.52 ± 0.18 mg·L^-1·h^-1) rates (p < 0.05) than those observed for the control test. Thus, AD bacteria with high NO₂^--N accumulation can also be used for practical applications, providing a basis for expanding the selection range of AD strains for wastewater treatment.

Hydraulic oil leaks during mechanical maintenance, resulting in flushing wastewater contaminated with dispersed nano-oil droplets. In this study, 75 mg L^-1 of polysilicate aluminum ferric (PSAF) was stirred at 350 rpm and the optimal chemical oxygen demand (COD) removal was 71%. The increase of PSAF led to more hydrolysis of Fe, and 1,175 cm^-1 hydroxyl bridged with negative oil droplets. At the same molar concentration, PSAF hydrolyzes cationic metals more rapidly than polymeric aluminum chloride (PAC). PSAF forms flocs of smaller complex structures with greater bridging. The Al-O and Si-O peaks occurred at 611 and 1,138 cm^-1, indicating the formation of Si-O-Fe and Si-O-Al bonds on the flocs surface. Higher stirring speeds did not change the free energy of the flocs surface γ^Tot, mainly because the decrease in the van der Waals force (γ^LW) offset the increase of Lewis acid-base force (γ^AB). Preserving the non-polar surface, in summary, owing to its bridging abilities and affinity for non-polar surfaces, PSAF demonstrates superior efficiency over PAC in capturing and removing oil droplets.

The current study deals with a composite broad-crested weir which is specially designed with the unique idea for a 'constant discharge coefficient (C_d) of 0.6'. It is investigated experimentally and numerically. The available designs of the weir are unable to give constant C_d over a wide range of discharge as 'C_d' itself is relative to the head over the weir crest. Therefore an attempt is made to restrict C_d value to 0.6 irrespective of the variable head on the weir crest. This is achieved by adjusting the widths of the weir. With the novel objective, C_d is frozen to constant value and instead of it, 'b' is allowed to vary. The weir so designed is capable of producing constant C_d over a wide range of discharge and hence will be helpful from the viewpoint of field applications. Under existing laboratory conditions, the research reports for emissions varying from 20 to 100% of the design discharge. The numerical performance of the CBC weir through FLOW 3D is experimentally validated to examine the crest width effect and head over weir crest. In the experiments, C_d is found to vary proportionally with discharge from 0.518 to 0.648. The R-value is 0.999, with a mean error in discharge measurement being much less.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R² are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Granular activated carbon (GAC) filtration is a commonly used method for advanced wastewater treatment. Filters can be operated continuously or discontinuously, with continuous operation not requiring feed flow interruption for backwashing and circulation (B/C). This study investigated the influence of B/C on the effluent quality of continuous filters. Two continuous GAC filters were operated for 1.5 years, with analysis of dissolved substances and particulate matter in the influent and effluent. The results indicated that various B/C modes had no impact on the removal of dissolved organic carbon and organic micropollutants (OMP), achieving an OMP removal of over 70% after 5,600 treated bed volumes (m³ treated wastewater per m³ GAC). However, it was evident that continuous B/C over 2-4 h resulted in increased turbidity, total suspended solids over 30 mg/L and total phosphorus concentrations of 1.3 mg/L in the filter effluent. Additionally, the study demonstrated that longer and more intensive B/C processes resulted in GAC size degradation with AC concentrations of up to 6.9 mg/L in the filter effluent, along with a change in GAC particle size. Furthermore, the importance of pre-filtration in reducing particulate matter in the filter influent and decreasing hydraulic head loss could be demonstrated.

This paper centers on the preparation and characterization of both a clay support and a faujasite zeolite membrane. Additionally, the study explores the development of bacterial media to assess the performance of these prepared membranes. The faujasite zeolite membrane was created using the hydrothermal method, involving the deposition of a faujasite layer to fine-tune the pore sizes of the clay support. The clay supports were crafted from clay which was sieved to particle size Φ ≤ 63 μm, and compacted with 3.0 wt.% activated carbon, then sintered at 1,000 °C. Distilled water fluxes revealed a decrease from 1,500 L m^-2 h^-1 to a minimum of 412 L m^-2 h^-1 after 180 min of filtration. Both membranes were characterized by XRF, XRD, FTIR, adsorption-desorption of nitrogen (N₂), and SEM-EDS. PCR technique was used for the identification of the isolated Arthrobacter sp., and the retention of the bacteria on the clay support and the faujasite zeolite membrane were found to be 96 and 99%, respectively. The results showed that the faujasite zeolite membrane passed the clay support due to a narrow pore size of the faujasite zeolite membrane of 2.28 nm compared to 3.55 nm for the clay supports.

Water quality predicted accuracy is beneficial to river ecological management and water pollution prevention. Owing to water quality data has the characteristics of nonlinearity and instability, it is difficult to predict the change of water quality. This paper proposes a hybrid water quality prediction model based on variational mode decomposition optimized by the sparrow search algorithm (SSA-VMD) and bidirectional gated recursive unit (BiGRU). First, the sparrow search algorithm selects fuzzy entropy (FE) as the fitness function to optimize the two parameters of VMD, which improves the adaptability of VMD. Second, SSA-VMD is used to decompose the original data into several components with different center frequencies. Finally, BiGRU is employed to predict each component separately, which significantly improves predicted accuracy. The proposed model is validated using data about dissolved oxygen (DO) and the potential of hydrogen (pH) from the Xiaojinshan Monitoring Station in Qiandao Lake, Hangzhou, China. The experimental results show that the proposed model has superior prediction accuracy and stability when compared with other models, such as EMD-based models and other CEEMDAN-based models. The prediction accuracy of DO can reach 97.8% and pH is 96.1%. Therefore, the proposed model can provide technical support for river water quality protection and pollution prevention.

17α-methyltestosterone (MT) hormone is a synthetic androgenic steroid hormone utilized to induce Nile tilapia transitioning for enhanced production yield. This study specifically focuses on the removal of MT through the utilization of photocatalytic membrane reactor (PMR), which employs an in-house polyvinylidene fluoride (PVDF) ultrafiltration membrane modified with 1% nanomaterials (either TiO₂ or α-Fe₂O₃). The molecular weight cut-off (MWCO) of the in-house membrane falls within the ultrafiltration range. Under UV95W radiation, the PMR with PVDF/TiO₂ and PVDF/α-Fe₂O₃ membranes achieved 100% MT removal at 140 and 160 min, respectively. The MT removal by the commercial NF03 membrane was only at 50%. In contrast, without light irradiation, the MT removal by all the membranes remained unchanged after 180 min, exhibiting lower performance. The incorporation of TiO₂ and α-Fe₂O₃ enhanced water flux and MT removal of the membrane. Notably, the catalytic activity was limited by the distribution and concentration of the catalyst at the membrane surface. The water contact angle did not correlate with the water flux for the composited membrane. The degradation of MT aligned well with Pseudo-first-order kinetic models. Thus, the in-house ultrafiltration PMR demonstrated superior removal efficiency and lower operational costs than the commercial nanofiltration membrane, attributable to its photocatalytic activities.

Cr(VI) and phenol commonly coexist in wastewater, posing a great threat to the environment and human health. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress. In this study, the phenol-degrading strain Bacillus cereus ZWB3 was co-cultured with the Cr(VI)-reducing strain Bacillus licheniformis MZ-1 to enhance phenol biodegradation under Cr(Ⅵ) stress. Compared with phenol-degrading strain ZWB3, which has weak tolerance to Cr(Ⅵ), and Cr(Ⅵ)-reducing strain MZ-1, which has no phenol-degrading ability, the co-culture of two strains could significantly increase the degraded rate and capacity of phenol. In addition, the co-cultured strains exhibited phenol degradation ability over a wide pH range (7-10). The reduced content of intracellular proteins and polysaccharides produced by the co-cultured strains contributed to the enhancement of phenol degradation and Cr(Ⅵ) tolerance. The determination coefficients R², RMSE, and MAPE showed that the BP-ANN model could predict the degradation of phenol under various conditions, which saved time and economic cost. The metabolic pathway of microbial degradation of phenol was deduced by metabolic analysis. This study provides a valuable strategy for wastewater treatment containing Cr(Ⅵ) and phenol.

With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.