Water science and engineering最新文献

Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.

This study examined the application of co-benefit-type wastewater treatment technology in the fish-processing industry. Given that there was a dearth of information on fish-processing industrial wastewater in Indonesia, site surveys were conducted. For the entire fish-processing industry throughout the country, the dissemination rate of wastewater treatment facilities was less than 50%. Using a co-benefit approach, a real-scale swim-bed technology (SBT) and a system combining an anaerobic baffled reactor (ABR) with SBT (ABR–SBT) were installed in a fishmeal processing factory in Bali, Indonesia, and the wastewater system process performance was evaluated. In a business-as-usual scenario, the estimated chemical oxygen demand load and greenhouse gas (GHG) emissions from wastewater from the Indonesian fish-processing industry were 33 000 tons per year and 220 000 tons of equivalent CO₂ per year, respectively. On the other hand, the GHG emissions in the co-benefit scenarios of the SBT system and ABR–SBT system were 98 149 and 26 720 tons per year, respectively. Therefore, introducing co-benefit-type wastewater treatment to Indonesia’s fish-processing industry would significantly reduce pollution loads and GHG emissions.

Froude similitude and friction similitude are the two crucial similarity conditions that are often used in physical-scale modeling of rivers. However, models often deviate from Froude similitude when dealing with real-world situations. This study developed several fixed-bed river models with various curvatures to determine the effect of Froude similitude deviation on curved channel modeling. Models were constructed according to the characteristics of the Middle Yangtze River. Differences in longitudinal slope, transverse slope, and main stream line location were measured by varying Froude similitude deviation. The deviations of longitudinal slope and velocity were negligible because friction similitude was accounted for. The transverse slope varied significantly with the Froude similitude deviation, and the main stream line varied with the curvature and Froude similitude deviation. Formulae were derived to estimate the slope deviation. These analyses helped to clarify the feasibility of the method of Froude similitude deviation for curved channels.

The structural behavior of the Xiaowan ultrahigh arch dam is primarily influenced by external loads and time-varying characteristics of dam concrete and foundation rock mass during long-term operation. According to overload testing with a geological model and the measured time series of installed perpendicular lines, the space and time evolution characteristics of the arch dam structure were analyzed, and its mechanical performance was evaluated. Subsequently, the deformation centroid of the deflective curve was suggested to indicate the magnitude and unique distribution rules for a typical dam section using the measured deformation values at multi-monitoring points. The ellipse equations of the critical ellipsoid for the centroid were derived from the historical measured time series. Hydrostatic and seasonal components were extracted from the measured deformation values with a traditional statistical model, and residuals were adopted as a grey component. A time-varying grey model was developed to accurately predict the evolution of the deformation behavior of the ultrahigh arch dam during future operation. In the developed model, constant coefficients were modified so as to be time-dependent functions, and the prediction accuracy was significantly improved through introduction of a forgetting factor. Finally, the critical threshold was estimated, and predicted ellipsoids were derived for the Xiaowan arch dam. The findings of this study can provide technical support for safety evaluation of the actual operation of ultrahigh arch dams and help to provide early warning of abnormal changes.

Nutrient release from sediment is considered a significant source for overlying water. Given that nutrient release mechanisms in sediment are complex and difficult to simulate, traditional approaches commonly use assigned parameter values to simulate these processes. In this study, a nitrogen flux model was developed and coupled with the water quality model of an urban lake. After parameter sensitivity analyses and model calibration and validation, this model was used to simulate nitrogen exchange at the sediment–water interface in eight scenarios. The results showed that sediment acted as a buffer in the sediment–water system. It could store or release nitrogen at any time, regulate the distribution of nitrogen between sediment and the water column, and provide algae with nitrogen. The most effective way to reduce nitrogen levels in urban lakes within a short time is to reduce external nitrogen loadings. However, sediment release might continue to contribute to the water column until a new balance is achieved. Therefore, effective measures for reducing sediment nitrogen should be developed as supplementary measures. Furthermore, model parameter sensitivity should be individually examined for different research subjects.

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.

Salt marshes are among the most important coastal wetlands and provide critical ecological services, including climate regulation, biodiversity maintenance, and blue carbon sequestration. However, most salt marshes worldwide are shrinking, owing to the effects of natural and human factors, such as climate change and artificial reclamation. Therefore, it is essential to understand the decline in the morphological processes of salt marshes, and accordingly, the likely evolution of these marshes, in order to enable measures to be taken to mitigate this decline. To this end, this study presented an extensive systematic review of the current state of morphological models and their application to salt marshes. The emergence of process-based (PB) and data-driven (DD) models has contributed to the development of morphological models. In morphodynamic simulations in PB models, multiple physical and biological factors (e.g., the hydrodynamics of water bodies, sediment erosion, sediment deposition, and vegetation type) have been considered. The systematic review revealed that PB models have been extended to a broader interdisciplinary field. Further, most DD models are based on remote sensing database for the prediction of morphological characteristics with latent uncertainty. Compared to DD models, PB models are more transparent but can be complex and require a lot of computational power. Therefore, to make up for the shortcomings of each model, future studies could couple PB with DD models that consider vegetation, microorganisms, and benthic animals together to simulate or predict the biogeomorphology of salt marsh systems. Nevertheless, this review found that there is a lack of unified metrics to evaluate model performance, so it is important to define clear objectives, use multiple metrics, compare multiple models, incorporate uncertainty, and involve experts in the field to provide guidance in the further study.

Bio-jarosite, an iron mineral synthesized biologically using bacteria, is a substitute for iron catalysts in the Fenton oxidation of organic pollutants. Iron nanocatalysts have been widely used as Fenton catalysts because they have a larger surface area than ordinary catalysts, are highly recyclable, and can be treated efficiently. This study aimed to explore the catalytic properties of bio-jarosite iron nanoparticles synthesized with green methods using two distinct plant species: Azadirachta indica and Eucalyptus gunni. The focus was on the degradation of dicamba via Fenton oxidation. The synthesized nanoparticles exhibited different particle size, shape, surface area, and chemical composition characteristics. Both particles were effective in removing dicamba, with removal efficiencies of 96.8% for A. indica bio-jarosite iron nanoparticles (ABFeNPs) and 93.0% for E. gunni bio-jarosite iron nanoparticles (EBFeNPs) within 120 min of treatment. Increasing the catalyst dosage by 0.1 g/L resulted in 7.6% and 43.0% increases in the dicamba removal efficiency for EBFeNPs and ABFeNPs with rate constants of 0.025 min⁻¹ and 0.023 min⁻¹, respectively, confirming their catalytic roles. Additionally, the high efficiency of both catalysts was demonstrated through five consecutive cycles of linear pseudo-first-order Fenton oxidation reactions.

To our knowledge, precise data concerning the pollution in terms of qualitative and quantitative fluctuations in discharge water from the laundry sector have seldom been reported. This study investigated the chemical composition of the discharge water from a laundry industry. Over 160 chemical substances and 15 standard water parameters were monitored. The results showed that the discharge water presented both inorganic and organic polycontamination with a high degree of qualitative and quantitative variability. However, of all monitored substances, only five metals (Al, Cu, Fe, Sr, and Zn), five minerals (P, Ca, K, Na, and S), and alkylphenols were systematically present and quantifiable. For a daily average water flow of 129 m³, the released metal flux was 356 g/d. Substances, such as trichloromethane, brominated diphenyl ether (BDE) 47, and fluorides, were occasionally found and quantified. Other substances, such as chlorophenols, organo-tins, and pesticides were never identified. All the samples had quantifiable levels in the chemical oxygen demand (COD), biological oxygen demand (BOD), and hydrocarbons. Only the concentrations of Zn (8.3 g/d), Cu (21.4 g/d), and BOD (57.4 g/d) were close to or above the regulatory values: 74.0 g/d for Zn, 9.0 g/d for Cu, and 57.0 kg/d for BOD. The data obtained from this study are useful to the choice of additional treatments for the reduction of pollutant fluxes.

Algal blooms, the spread of algae on the surface of water bodies, have adverse effects not only on aquatic ecosystems but also on human life. The adverse effects of harmful algal blooms (HABs) necessitate a convenient solution for detection and monitoring. Unmanned aerial vehicles (UAVs) have recently emerged as a tool for algal bloom detection, efficiently providing on-demand images at high spatiotemporal resolutions. This study developed an image processing method for algal bloom area estimation from the aerial images (obtained from the internet) captured using UAVs. As a remote sensing method of HAB detection, analysis, and monitoring, a combination of histogram and texture analyses was used to efficiently estimate the area of HABs. Statistical features like entropy (using the Kullback–Leibler method) were emphasized with the aid of a gray-level co-occurrence matrix. The results showed that the orthogonal images demonstrated fewer errors, and the morphological filter best detected algal blooms in real time, with a precision of 80%. This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.