Water Science and Technology最新文献

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.

Electroplating wastewater contains heavy metal ions and organic matter. These contaminants not only endanger the environment but also pose risks to human health. Despite the development of various treatment processes such as chemical precipitation MBR, electrocoagulation (EC) ceramic membrane (CM), coagulation ultrafiltration (UF) reverse osmosis (RO), and CM RO. These methods are only effective for low concentrations of heavy metals and struggle with high concentrations. To address the challenge of treating electroplating wastewater with high heavy metal content, this study focuses on the wastewater from Dongfang Aviation Machinery Processing Plant. It introduces an EC and integrated membrane (IM) treatment process for electroplating wastewater. The IM comprises microfiltration (MF) membrane, nanofiltration (NF) membrane, and RO membrane. Results indicated that under specific conditions, such as a pH of 8, current density of 5 A/dm², electrode plate spacing of 2 cm, 35 min of electrolysis time, and influent pH of 10 for the IM, removal rates of Zn²⁺, Cu²⁺, Ni²⁺, and TCr in the wastewater exceeded 99%. The removal rates of chemical oxygen demand (COD), suspended solids (SS), total phosphorus (TP), total nitrogen (TN), and petroleum in wastewater exceed 97%. Following a continuous cleaning process, the membrane flux can consistently recover to over 94.3%.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Sediment microbial fuel cells (SMFCs) represent a technology that can enhance sediment quality through processes such as nutrient suppression while simultaneously generating electricity from microorganisms. Despite its importance in elucidating the principles of nutrient suppression, the complex behavior of various ions within this context has been rarely explored. Herein, we applied an SMFC and systematically evaluated alterations in ion concentrations in interstitial and overlying waters. The SMFC deployment substantially decreased Na⁺ concentrations and increased Cl^- levels in the interstitial water. This intriguing phenomenon was attributed to reactions driven by the electrodes. These reactions induced remarkable shifts in pH. Consequently, this pH shift triggered the leaching of heavy metals, particularly Fe, and decreased HCO₃^- concentrations within the interstitial water, thereby inducing the migration of other ions, including Na⁺ and Cl^-, as compensation. Moreover, the PO₄^3- concentration in interstitial water showed an increasing trend upon SMFC application, which contradicts the results of several previous reports. This increase was primarily attributed to the release of PO₄^3-caused by the leaching of Fe salts, which was triggered by the pH shift. These findings provide new insights into sediment improvement research through SMFCs, enhancing our understanding of the fundamental principles and broadening the potential applications of this technology.

The Periyar River, a vital component of Kerala's ecosystem in India, serves as a lifeline supporting agriculture, hydropower generation, and ecological equilibrium. This study adopts a multifaceted approach to address critical challenges in the Periyar basin, with a primary focus on flood mitigation due to the region's susceptibility to devastating floods. Covering a length of 67.85 km, the study intricately segments the Periyar River into distinct reaches for a comprehensive steady flow analysis, considering factors such as seasonal monsoon fluctuations, diverse catchment topography, and human-induced alterations. Utilizing advanced modeling techniques, particularly HEC-RAS software, the study effectively predicts and simulates shifts in hydraulic behavior. The results, including velocity plots and cross-sectional maps, offer accurate insights into critical parameters, enabling the identification of areas with high velocity occurrence. This information proves instrumental in making informed decisions for the construction of river restoration structures, crucial for mitigating the impact of floods. The study's findings contribute valuable tools for future forecasting and sustainable management of the Periyar River, addressing the complex interplay of natural and anthropogenic factors.

This manuscript presents a novel approach for developing an environmentally friendly and effective oil-water separation membrane. Achieving a superhydrophobic (SH) coating on textile fabric (TF) involved a two-step process. Initially, the surface roughness was enhanced by applying bio-zinc oxide (ZnO) nanoparticles obtained from Thymbra spicata L. Subsequently, the roughened surface was modified with stearic acid, a material known for its low surface energy. The bio-ZnO nanoparticles exhibit a circular morphology with an average size of 21 nm. The coating demonstrated remarkable mechanical stability, maintaining SH properties even after an abrasion length of 300 mm. Chemical stability studies revealed that the prepared membrane retained SH properties within a pH range of 5-11, which ensures robust performance. Absorption capacity measurements showcased different capacities for n-hexane (Hex), corn oil (C.O), and silicone oil (S.O), with consistent performance over 10 absorption-desorption cycles. High oil-water separation efficiencies were achieved for hexane, C.O, and S.O, emphasizing the coating's versatility. Flux rate measurements demonstrated that oil passed through the membrane efficiently, with the highest flux observed for Hex. The prepared SH membrane has superior mechanical and chemical stability and high separation efficiencies, which positions it as a promising candidate for diverse industrial applications.

1,4-Dioxane concentration in most contaminated water is much less than 1 mg/L, which cannot sustain the growth of most reported 1,4-dioxane-metabolizing pure cultures. These pure cultures were isolated following enrichment of mixed cultures at high concentrations (20 to 1,000 mg/L). This study is based on a different strategy: 1,4-dioxane-metabolizing mixed cultures were enriched by periodically spiking 1,4-dioxane at low concentrations (≤1 mg/L). Five 1,4-dioxane-metabolizing pure strains LCD6B, LCD6D, WC10G, WCD6H, and WD4H were isolated and characterized. The partial 16S rRNA gene sequencing showed that the five bacterial strains were related to Dokdonella sp. (98.3%), Acinetobacter sp. (99.0%), Afipia sp. (99.2%), Nitrobacter sp. (97.9%), and Pseudonocardia sp. (99.4%), respectively. Nitrobacter sp. WCD6H is the first reported 1,4-dioxane-metabolizing bacterium in the genus of Nitrobacter. The net specific growth rates of these five cultures are consistently higher than those reported in the literature at 1,4-dioxane concentrations <0.5 mg/L. Compared to the literature, our newly discovered strains have lower half-maximum-rate concentrations (1.8 to 8.2 mg-dioxane/L), lower maximum specific 1,4-dioxane utilization rates (0.24 to 0.47 mg-dioxane/(mg-protein ⋅ d)), higher biomass yields (0.29 to 0.38 mg-protein/mg-dioxane), and lower decay coefficients (0.01 to 0.02 d^-1). These are characteristics of microorganisms living in oligotrophic environments.