Abstract: As urban areas expand and water demand intensifies, the need for efficient and reliable water distribution systems becomes increasingly critical. A widely used infrastructure management approach involves partitioning water distribution networks (WDNs) into district metered areas (DMAs). However, suboptimal designs of DMA partitioning can lead to inefficiencies and increased costs. This study presents a core-periphery-informed approach for DMA design that explicitly utilises the natural division between a densely connected core and a sparsely connected periphery. Incorporating this structural framework enhances network resilience, improves water pressure stability, and optimises boundary device placement. The proposed core-periphery-informed DMA design integrates hydraulic and topological analyses to identify central and peripheral network areas, applies a community structure detection algorithm conditioned by these areas, and uses an optimisation model to determine the optimal placement of boundary devices, enhancing network resilience and reducing costs. When applied to the Modena WDN in Italy, this approach demonstrates improved pressure stability and significant cost reductions compared to traditional methods. Overall, the findings highlight the practical benefits of the core-periphery-based DMA design, offering a scalable and data-driven solution for urban water distribution systems.
Abstract: Understanding the evolution and lag effects of droughts is critical to effective drought warning and water resources management. However, due to limited hydrological data, few studies have examined hydrological droughts and their lag time from meteorological droughts at a daily scale. In this study, precipitation data were collected to calculate the standardized precipitation index (SPI), and runoff data simulated by the variable infiltration capacity (VIC) model were utilized to compute the standardized runoff index (SRI). The three-threshold run theory was used to identify drought characteristics in China. These drought characteristics were utilized to investigate spatiotemporal variations, seasonal trends, and temporal changes in areas affected by meteorological and hydrological droughts. Additionally, the interconnections and lag effects between meteorological and hydrological droughts were explored. The results indicated that (1) drought occurred during approximately 28% of the past 34 years in China; (2) drought conditions tended to worsen in autumn and weaken in winter; (3) drought-affected areas shifted from northwest to northeast and finally to southern China; and (4) the correlation between meteorological and hydrological droughts was lower in the northwest and higher in the southeast, with all correlation coefficients exceeding 0.7. The lag times between meteorological and hydrological droughts were longest (5 d) in the Yangtze River, Yellow River, and Hai River basins, and shortest (0 d) in the Tarim River Basin. This study provides a scientific basis for effective early warning of droughts.
Abstract: Addressing the growing challenge of water contamination, this study comparatively evaluated a persulfate (PDS) system activated by non-radical nitrogen-doped carbon nanotubes (N-CNTs) versus a PDS system activated by radical-based iron (Fe2+), both used for the degradation of bisphenol A (BPA). The N-CNTs/PDS system, driven by the electron transfer mechanism, achieved remarkable 90.9% BPA removal within 30 min at high BPA concentrations, significantly outperforming the Fe2+/PDS system, which attained only 38.9% removal. The N-CNTs/PDS system maintained robust degradation efficiency across a wide range of BPA concentrations and exhibited a high degree of resilience in diverse water matrices. By directly abstracting electrons from BPA molecules, the N-CNTs/PDS system effectively minimised oxidant wastage and mitigated the risk of secondary pollution, ensuring efficient utilisation of active sites on N-CNTs and sustaining a high catalytic rate. The formation of the N-CNTs-PDS* complex significantly enhanced BPA degradation and mineralisation, thereby optimising PDS consumption. These findings highlight the unparalleled advantages of the N-CNTs/PDS system in managing complex wastewater, offering a promising and innovative solution for treating complex industrial wastewater and advancing environmental remediation efforts.
Abstract: Bromate (BrO3-) is a toxic disinfection byproduct frequently formed during ozonation in water treatment processes and is classified as a potential human carcinogen. Its effective removal from drinking water is therefore a pressing concern for public health and environmental safety. This study investigated the removal of BrO3-from water using the synthesized zeolite imidazolate framework (ZIF)-67 and ZIF-67/graphene oxide (GO) nanocomposites through a comparative approach. The morphology, composition, and crystallinity of both ZIFs were characterized. The effects of four independent parameters (initial BrO3-concentration, pH, adsorbent dose, and contact time) on BrO-3 removal efficiency were examined. A strong correlation was observed between experimental and predicted values. GO enhanced BrO-3 removal not only through synergistic interactions with ZIF-67 but also by improving dispersion and providing additional functional groups that facilitate electrostatic interactions and adsorption. The Box—Behnken design was employed to evaluate both individual and interactive effects of the parameters on BrO3-removal, achieving an optimum removal efficiency of approximately 99.6% using 1.5 g/L of ZIF-67/GO at a pH value of 4 with an initial BrO3-concentration of 2 mg/L. The optimization process was further supported by desirability analysis. The BrO-3 removal mechanisms are primarily attributed to porosity, electrostatic interactions, and adsorption onto active sites. Compared to ZIF-67 alone, ZIF-67/GO demonstrated superior anion removal efficiency, highlighting its potential for water treatment applications.
Abstract: Reservoirs play a critical role in addressing water resources challenges. However, their vertical influence on the assembly mechanisms of different microbial communities, including prokaryotes and eukaryotes, remains unclear. This study examined the vertical diversity patterns of abundant and rare subcommunities of prokaryotes and eukaryotes in an urban reservoir, using water depth as a geographical gradient and employing high-throughput sequencing. The impact of vertical environmental heterogeneity on community structure was quantified, and key drivers of these dynamics were identified. The results indicated that the urban reservoir exhibited statistically significant differences in the vertical distribution of water temperature and oxidation/reduction potential. The a-diversity of the abundant subcommunity displayed an opposing vertical pattern compared to that of the rare subcommunity, while the b-diversity for both subcommunities of prokaryotes and eu-karyotes increased with water depth. Moreover, the distinct diversity patterns of abundant and rare subcommunities were associated with environmental heterogeneity and species adaptability. Notably, the b-diversity of the rare subcommunity of eukaryotes was primarily driven by species turnover in surface water, whereas nestedness became the dominant factor in deeper water. Furthermore, eukaryotic microbes exhibited a more pronounced response to changes in water depth than prokaryotes, consistent with the importance of heterogeneous selection to the eukaryotic community. Water temperature significantly affected the community composition of all groups, highlighting its importance in shaping community dynamics. This study provides valuable insights into the vertical distribution and assembly mechanisms of microbial communities in urban reservoirs, contributing to the protection and management of aquatic ecosystems under river regulation.
Abstract: Artificial intelligence (AI) is a revolutionizing problem-solver across various domains, including scientific research. Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods. A notable application of AI is in the photo-Fenton degradation of organic compounds. Despite the high novelty and recent surge of interest in this area, a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking. This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks (ANN) in the photo-Fenton process, with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables. It examines the types and architectures of ANNs, input and output variables, and the efficiency of these networks. The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process. This review also discusses the benefits and drawbacks of using ANNs, emphasizing the need for further research to advance this promising area.
Abstract: Membrane filtration technology has been widely utilized for microalgae harvesting due to its stability and high efficiency. However, this technology faces challenges posed by membrane fouling caused by algal cells and extracellular organic matter (EOM), which are significantly influenced by membrane material and pore size. This study compared the fouling behavior of polyvinylidene fluoride (PVDF) membranes and ceramic membranes with similar pore sizes (0.20 mm and 0.16 mm, respectively) during the filtration of Microcystis aeruginosa. The ceramic membrane exhibited a lower transmembrane pressure (TMP) growth rate and reduced accumulation of surface foulants compared to the PVDF membrane, indicating its greater suitability for filtering algae-laden water. Further investigations employed membranes fabricated from aluminum oxide powders with grain sizes of 1 mm, 3 mm, 8 mm, and 10 mm, corresponding to membrane pore sizes of 0.08 mm, 0.16 mm, 0.66 mm, and 0.76 mm, respectively, to assess the impact of pore size on ceramic membrane fouling. The results revealed that increasing membrane pore size significantly lowered the TMP growth rate and reduced the irreversibility of membrane fouling. The extended Derja-guin—Landau—Verwey—Overbeek (XDLVO) analysis indicated that large pore sizes enhanced repulsion between the ceramic membrane and algal foulants, further alleviating membrane fouling. This investigation offers new insights into optimizing membrane material and pore size for efficient filtration of algae-laden water.
Abstract: Maintaining low nitrate concentrations in aquaponic systems is crucial for improving water quality and maximizing the growth efficiency of fish and vegetables. Downflow hanging sponge (DHS) and upflow sludge blanket (USB) reactors have shown potential for wastewater treatment, but their use in aquaponic systems is relatively underexplored, particularly for overall performance and efficiency. In this study, a DHS reactor was coupled with a denitrifying USB reactor in an aquaponic system comprising Nile tilapia (Oreochromis niloticus) and kale (Brassica oleracea L. var. acephala DC). The USB reactor achieved a nitrate removal rate of 80.8% ± 20.5%. The specific growth rate of tilapia was 6.11% per day from day 16 to day 30. On day 45, kale growth achieved stem lengths of (4.1 ± 1.2) cm, root lengths of (12.2 ± 6.0) cm, and leaf counts of (6.3 ± 2.0) leaves per plant. Changes in the microbial communities within the reactors positively contributed to denitrification, resulting in a nitrogen utilization efficiency of 88.3%. The DHSeUSB aquaponic system effectively maintained optimal water quality and stable parameters (pH, dissolved oxygen, and temperature). It regulated ammonia levels well and achieved 80.8% ± 20.5% removal rates for nitrite and nitrate after day 10. Microbial analysis highlighted significant shifts in the microbial communities within the DHS and USB reactors, under-scoring their critical roles in nitrification and denitrification. Therefore, the DHS—USB aquatic system has the potential to improve agricultural production efficiency and promote sustainable development.
Abstract: The sedimentary bed morphology modulated by the wake flow of a wall-mounted flexible aquatic vegetation blade across various structural aspect ratios (AR = l/b, where l and b are the length and width of the blade, respectively) and incoming flow velocities was experimentally investigated in a water channel. A surface scanner was implemented to quantify bed topography, and a tomographic particle image velocimetry system was used to characterize the three-dimensional wake flows. The results showed that due to the deflection of incoming flow, the velocity magnitude increased at the lateral sides of the blade, thereby producing distinctive symmetric scour holes in these regions. The normalized morphology profiles of the sedimentary bed, which were extracted along the streamwise direction at the location of the maximum erosion depth, exhibited a self-similar pattern that closely followed a sinusoidal wave profile. The level of velocity magnitude enhancement was highly correlated to the postures of the flexible blade. At a given flow velocity, the blade with lower aspect ratios exhibited less significant deformation, causing more significant near-bed velocity enhancement in the wake deflection zone and therefore leading to higher erosion volumes. Further investigation indicated that when the blade underwent slight deformation, the larger velocity enhancement close to the bed can be attributed to more significant flow deflection effects at the lateral sides of the blade and stronger flow mixing with high momentum flows away from the bed. Supported with measurements, a basic formula was established to quantify the shear stress acting on the sedimentary bed as a function of incoming flow velocity and blade aspect ratio.
Abstract: Offshore wind power plays a crucial role in energy strategies. The results of traditional small-scale physical models may be unreliable when extrapolated to large field scales. This study addressed this limitation by conducting large-scale (1:13) experiments to investigate the scour hole pattern and equilibrium scour depth around both slender and large monopiles under irregular waves. The experiments adopted Keulegan—Carpenter number (NKC) values from 1.01 to 8.89 and diffraction parameter (D/L, where D is the diameter of the monopile, and L is the wave length) values from 0.016 to 0.056. The results showed that changes in the maximum scour location and scour hole shape around a slender monopile were associated with NKC, with differences observed between irregular and regular waves. Improving the calculation of NKC enhanced the accuracy of existing scour formulae under irregular waves. The maximum scour locations around a large monopile were consistently found on both sides, regardless of NKC and D/L, but the scour hole topography was influenced by both parameters. Notably, the scour range around a large monopile was at least as large as the monopile diameter.
Abstract: Scour around bridge pier foundations is a complex phenomenon that can threaten structural stability. Accurate prediction of scour depth around compound piers remains challenging for bridge engineers. This study investigated the effect of foundation elevation on scour around compound piers and developed reliable scour depth prediction models for economical foundation design. Experiments were conducted under clear-water conditions using two circular piers: (1) a uniform pier (with a diameter of D) and (2) a compound pier consisting of a uniform pier resting on a circular foundation (with a foundation diameter (Df) of 2D) positioned at various elevations (Z) relative to the channel bed. Results showed that foundation elevation significantly affected scour depth. Foundations at or below the bed (Z/D ≥ 0) reduced scour, while those projecting into the flow field (Z/D < 0) increased scour. The optimal foundation elevation was found to be 0.1D below the bed level, yielding a 57% reduction in scour depth compared to the uniform pier due to its shielding effect against downflow and horseshoe vortices. In addition, regression, artificial neural network (ANN), and M5 model tree models were developed using experimental data from this and previous studies. The M5 model outperformed the traditional HEC-18 equation, regression, and ANN models, with a coefficient of determination greater than 0.85. Sensitivity analysis indicated that flow depth, foundation elevation, and diameter significantly influenced scour depth prediction, whereas sediment size had a lesser impact.