Applied Water Science最新文献

This study investigates the water hammer phenomenon using ANSYS Fluent, comparing numerical results with analytical models (Quasi, Zielke, and Brunone) and experimental data from a laboratory setup. The standard k-ε turbulence model was employed to simulate turbulent flow conditions. The findings reveal that the ANSYS-based water hammer simulation shows excellent agreement with both the experimental data and Brunone’s analytical model. Pressure variations were analyzed at two critical locations: the pipe midpoint and the valve vicinity, across different flow velocities. For instance, at a flow velocity of 0.1 m/s, the numerical model predicted a pressure head of 47.263 m near the valve, closely matching the experimental value (46.403 m) and outperforming the Zielke (45.307 m), Brunone (45.526 m), and Quasi (45.521 m) models. These results highlight the high accuracy and reliability of the numerical simulation in capturing water hammer dynamics, suggesting its effectiveness for practical hydraulic system analysis.

Iran-Afghanistan Helmand River Basin (HRB) is a significant transboundary water resource challenge. The article critically explores Afghan allegations of reduction in HRB water resources and investigates how remote sensing and reanalysis datasets can act as practical neutral conflict resolution tools. Trend analysis from 2000 to 2020 indicates a slight annual decrease in reference evapotranspiration (ET_o), while precipitation, snow cover (SC), and snow water equivalent (SWE) remained relatively stable. In contrast, agricultural area nearly doubled from 111,000 to 216,000 hectares, while agricultural water consumption increased from 1.5 to 3.2 BCM. These findings point to human activities in upstream areas as the primary source of reduced downstream water availability. The study highlights the crucial role played by remote sensing and reanalysis datasets in data-poor areas and suggests the establishment of a bilateral technical cooperation committee. The use of complementary data sources can enhance informed decision-making and lessen conflict. By facilitating collaborative transboundary water management, this Research makes a directly contributes the Sustainable Development Goal (SDG) 6.5.2, the equitable and sustainable use of water.

The present study focuses on assessment of drought risk affecting human activities in the command area of a Himalayan River Project, namely, the Gandak River Project in northern India using extensive geospatial techniques and climatic data-based drought indices. A 100-year dataset for precipitation and temperature was analyzed to characterize meteorological droughts. Major patterns in sub-regions that were once referred to as drought-prone zones were studied with the help of a pattern recognition method. Droughts were mostly caused by low rainfall, which, in turn, led to a decrease in agricultural output. This study also highlights the importance of evaluating drought risk through the use of geospatial tools to establish a meaningful connection with metrological drought events. The Normalized Difference Vegetative Index (NDVI), a vegetation index used in drought assessment studies, was based on NDVI values derived from satellite images over time. Furthermore, Rain-based Drought Indices Tool (RDIT)-based indices were analyzed using IMD method, SPI method, and Drought Indices method using Meteorological Drought Monitor (MDM) software. An attempt has been made to identify and extract drought risk areas encountering agricultural and meteorological droughts using the NDVI obtained from the LANDSAT images and rainfall data. The NDVI images were used to examine large-scale drought patterns, and their climatic impact on vegetation. NDVI values reflected the different geographical conditions quite well. The NDVI and rainfall values were found to be highly correlated. It is concluded that temporal variations of NDVI are convincingly associated with precipitation in the study area.

Cefixime (CFX), an antibiotic from the cephalosporin category, is widely prescribed for treating bacterial infections in humans and animals. Antibiotics and their metabolites in the environment cause various side effects, including acute and chronic toxicity, microbial resistance, and antibiotic resistance genes. Therefore, effective removal of these pollutants from aquatic environments is essential. This study removed the CFX antibiotic from the aqueous medium using hybrid process of electrocoagulation and adsorption. The characterization of the chitosan-bismuth oxy-iodide (CS@BiOI) biocomposite was performed using FESEM, XRD, and BET analysis. BET-specific surface area, Average pore diameter, and total pore volume of CS@BiOI biocomposite were 354.1 m² g⁻¹, 8.3 nm, and 0.092 cm⁻³ g⁻¹, respectively. Under the optimal conditions of initial CFX concentration 25.0 mg L⁻¹, pH 8.0, current density 10.0 mA cm⁻², reaction time 11.6 min, and 0.5 g L⁻¹ CS@BiOI biocomposite as the adsorbent, the maximum removal efficiency, EEC, and MEC were found to be 99.8%, 0.342 kWh m⁻³, and 0.038 g L⁻¹, respectively. According to the results obtained, the combination of coagulation, flocculation, precipitation, adsorption, and flotation processes can effectively increase the removal efficiency and can be used as a suitable method with high efficiency and relatively low cost for treating contaminated water containing antibiotics.

This study evaluates the performance of a novel aerobic postanoxic baffled reactor designed for nitrification and denitrification of domestic wastewater without an external carbon source. Instead, the raw wastewater is divided between the aerobic zone at the beginning of the plug flow and the postanoxic zone. The reactor was installed at a wastewater treatment plant to feed on real domestic wastewater from the primary settling tank. The performance of the reactor was evaluated under varying flow distribution ratios, ammonia loadings, and with the inclusion of biological media to assess treatment efficiency in subsequent compartments of the baffled reactor. The key parameters, such as COD, TN, NH₄⁺, and NO₃⁻, were monitored along the reactor. Results revealed that dividing the flow and adding biological media significantly improved NH₄⁺ and NO₃⁻ removal, indicating enhanced nitrification and denitrification. The reactor maintained its performance at different influent ammonia concentrations reaching 400 mg/L when an 80% aerobic to 20% anoxic flow distribution was employed. High-throughput sequencing identified dominant bacterial communities contributing to nitrogen transformations. These findings provide critical insights into optimizing reactor configurations for sustainable wastewater treatment.

Predicting the sediment inflow to dam reservoirs, followed by implementing flushing operations for dam management, is highly essential. Estimating the sediment inflow to the dam can assist in allocating water resources from the reservoir. Machine learning techniques are efficient tools for such predictions, capable of providing accurate estimates of sediment inflow to dams. This study was conducted in two steps. In the first step, the numerical simulation of flow and sediment flushing in a dam reservoir was performed using the flow-3D model. To do this, the numerical model was calibrated using the results of an actual flushing operation of the study dam. Then, the effects of variables in this study, including the water level at the start of flushing under pressure and the number of active gates during flushing, were studied. In the second step, the sediment inflow to the dam reservoir was predicted using the least squares support vector regression (LSSVR) and multivariate adaptive regression splines (MARS). In this case, the model runtime was 4 year. The Sefidroud dam, one of the most important dams in Iran, was considered as the case study. The results showed that the numerical modeling was capable of determining the initial water level in the reservoir and the appropriate discharge for the dam. The results also demonstrated that LSSVR and MARS could effectively predict sediment outflow from the dam. Moreover, a model including the sediment inflow to the reservoir 1 and 2 days earlier exerted the most suitable prediction results with RMSE, MAE, and NSE of 4.07 kg/m³, 0.189 kg/m³, and 0.88, respectively. The results of this study indicate the successful application of machine learning models in estimating sediment outflow from dams, which can be utilized for other similar areas.

This study presents a sustainable strategy for valorizing cassava-based bioethanol residues—peels, pulp, and wood ash—into activated carbon for industrial wastewater treatment. The activated carbon was produced via chemical activation with KOH, optimizing key parameters including biomass composition, impregnation ratio, and activation temperature. The optimal condition (1:4 impregnation ratio at 700 °C) yielded a porous carbon material with enhanced surface characteristics. Adsorption experiments using real bioethanol wastewater demonstrated efficient removal of chemical oxygen demand (COD) and color under optimal operational conditions (pH 5, 60 min contact time, and 80 g/L dosage), reducing COD to 46.08 mg/L and color to 198.8 ADMI—both within Thailand’s industrial discharge standards. The adsorption process followed the Freundlich isotherm model, indicating multilayer adsorption on a heterogeneous surface. In addition to pollutant removal, the spent activated carbon retained significant calorific value (1601.23 kCal/kg), enabling its reuse as a low-grade fuel in industrial boilers. This dual-function approach not only mitigates environmental impacts from agro-industrial waste but also supports circular economy principles through resource recovery and energy reuse. The proposed method offers a cost-effective and scalable alternative for wastewater treatment in the bioethanol industry and has broader implications for sustainable waste management in related sectors.

Haloculture has emerged as an innovative solution for addressing soil salinity and freshwater scarcity, playing a significant role in sustainable agriculture. By utilizing saline water and soil resources, haloculture presents novel opportunities for cultivating crops in arid and semi-arid regions. This review article aims to explore the research background, innovative technologies, and operational challenges in the field of haloculture. Various strategies such as saline water management, selection of salt-tolerant plant species, and the application of advanced technologies like nanotechnology, biotechnology, and renewable energy are discussed as effective approaches for developing this sector. The article also highlights the role of integrated agricultural systems, genetic modification of halophyte plants, and the use of biological methods to improve saline soil properties. Recent findings indicate that utilizing halophyte plants, optimizing irrigation strategies, and employing integrated systems can significantly enhance agricultural productivity in saline environments. Additionally, research has shown that incorporating renewable energy solutions in desalination processes and powering haloculture systems can reduce operational costs and improve sustainability. This article also emphasizes implementation challenges such as high costs, the need for interdisciplinary research, limited local knowledge in saline regions, and the importance of supportive policies. Finally, recommendations are provided for future research to improve the efficiency and sustainability of saline agriculture.

This study aims to identify the most effective pretreatment method for minimizing membrane fouling in the treatment of complex tannery wastewater. Three approaches were evaluated: coagulation with ferric chloride (FeCl₂), Fenton-coagulation using FeCl₃ and hydrogen peroxide, and electrocoagulation with iron electrodes. The study examines key contaminant removal metrics, including Chemical Oxygen Demand (COD), Chromium, and Total Kjeldahl Nitrogen (TKN), followed by advanced parameters such as SUVA, Zeta potential, UV₂₅₄, and Modified Fouling Index (MFI) to assess fouling potential. Results reveal that the electrocoagulation process significantly outperforms the other pretreatment methods in mitigating membrane fouling, with an MFI value, 1660 times lower than coagulation and 370 times lower than Fenton-coagulation. These findings demonstrate that the pretreatment processes improve effluent quality and significantly reduce the potential for membrane fouling. This study contributes to optimizing membrane-based treatment systems for industrial wastewater, offering scalable solutions for sustainable water reclamation in diverse applications.

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