Applied Water Science最新文献

The rapid progress of highly efficient thermal systems necessitates the development of novel cooling and heat transfer fluids that exceed the constraints of traditional and basic nanofluids. An investigation into the heat transfer and unsteady three-dimensional saddle-point stagnation flow of a water-based tetra-hybrid nanofluid including graphene nanoplatelets (GNP), Al₂O₃, CuO, and TiO₂ is underway in this work. The model assumes transverse magnetic field, thermal radiation, viscous dissipation, suction, Joule heating, and entropy generation in incompressible laminar flow. The governing boundary layer equations are reformulated using similarity variables and solved numerically using MATLAB’s bvp4c solver. The analysis scrutinizes the impacts of unsteadiness, radiation, magnetic field intensity, mixed convection, nanoparticle volume fraction, Brinkman and Eckert numbers, and suction on velocity, temperature, skin friction, Nusselt number, and entropy generation. According to the results, the tetra-hybrid nanofluid has better skin friction and heat transfer than the mono and hybrid nanofluids because of its higher viscosity and effective thermal conductivity. Furthermore, entropy generation escalates with more robust dissipative and radiative processes. Overall, the study finds, tetra-hybrid nanofluids show promise for high-performance thermal management and energy-efficient systems.

Traditional methods for estimating water footprints for rice production are often time-consuming and resource-intensive, highlighting the need for efficient and accurate predictive models. This study addresses this gap by evaluating the performance of seven machine learning models—Linear Regression (LR), M5P, Multi-layer Perceptron (MLP), Sequential Minimal Optimization – Support Vector Machine (SMO-SVM), Random SubSpace (RSS), Random Forest (RF), and Random Tree (RT)—in predicting the green and blue water footprints of rice in Punjab, India. Best subset regression and correlation matrix indicate that humidity, wind speed, sunshine hours, solar radiation, and total rainfall are optimal inputs for green water footprint prediction, while maximum temperature, humidity, wind speed, sunshine hours, and solar radiation are best for blue water footprint prediction. The RT model outperformed others in that, for green water footprint prediction, it achieved a correlation coefficient (CC) of 0.9991, mean absolute error (MAE) of 0.1314, root mean square error (RMSE) of 0.4553, relative absolute error (RAE) of 0.0477, and root relative squared error (RRSE) of 0.1283 during the training stage. However, during the testing stage, the RF model performed better (CC = 0.79, MAE = 154.2732, RMSE = 192.3973, RAE = 55.5602, and RRSE = 58.6433). For blue water footprint prediction, the RT model remained the best performer in both stages (training: CC = 0.9991; testing: CC = 0.9981, MAE = 0.7920, RMSE = 0.8583, RAE = 0.4440, and RRSE = 0.8290). These results suggest that machine learning can effectively support water management strategies by providing quick and reliable estimates of water footprints, which is crucial for sustainable rice production. By utilizing these models, policymakers can make informed decisions to optimize water usage and ensure sustainable agricultural practices.

The sustainability of water resource systems in arid regions plays a pivotal role in regional ecological security and socio-economic development. A scientific elucidation of their state evolution provides a critical foundation for water resources management decision-making. To address the assessment bias inherent in traditional static fuzzy comprehensive evaluation, which arises from the difficulty of fixed weights in effectively characterizing interannual variations in indicators and the impacts of extreme climate events, this study proposes an innovative fuzzy comprehensive evaluation model based on threshold-directed dynamic reward-penalty weighting. Using the Shule River Basin in northwestern China (2005–2023) as a case study, a threshold-based indicator system comprising five subsystems and 30 indicators was established. Initial indicator weights were determined via the entropy weight method, and a dynamic reward-penalty weighting function was constructed to enable real-time weight adaptation to system states. This dynamically adjusted framework was integrated with the fuzzy comprehensive evaluation method for system scoring, followed by a comparative analysis against conventional static fuzzy comprehensive evaluation results. Key results demonstrate that: (1) The threshold-directed dynamic reward-penalty mechanism significantly enhanced weight adaptability. For instance, a sharp 71.1 percent decline in precipitation in 2020 triggered a 33.2 percent increase in the dynamic weight of this indicator compared to its entropy weight. Conversely, in the same year, the ecological-environmental water use ratio exceeding its threshold by 27 percent resulted in a 52.9 percent reduction in its dynamic weight, thereby precisely quantifying the temporal effects of drought impact and policy intervention. (2) Subsystem scores exhibited dynamic differentiation: The socioeconomic water use subsystem exhibited the highest mean score, while significant interannual fluctuations were observed in the agricultural water use and food security subsystem and the ecosystem health and sustainability subsystem, collectively revealing the stability of regional water use structure and the heightened sensitivity of ecological and agricultural systems to climate fluctuations. (3) The basin's comprehensive water resources system score evolved through three distinct phases: a slow ascent phase (2005–2008), a fluctuating rise phase (2009–2017), and a high-quality development phase (2018–2023). This trajectory confirms the presence of a compound regulatory mechanism within the Shule River Basin's water resources system, characterized by "ecological hysteresis, policy-driven interventions, and technical compensation". This study establishes a novel dynamic analytical framework for assessing arid region water resource systems, substantiated the methodological advantage of this dynamic weighting approach in the non-stationary environments typical of arid zones.

Water pollution caused by heavy metals and dyes has emerged as a pressing global issue due to their adverse effects on human health and ecosystems. In this study, magnetic zeolite nanocomposite (Fe₃O₄–NaA) was employed as an efficient magnetic adsorbent to remove cadmium (Cd), lead (Pb), malachite green (MG), and methylene blue (MB) from aqueous solutions. The Fe₃O₄–NaA adsorbent was synthesized and characterized through scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analysis. The results confirmed nanostructure, high surface area, and high adsorption capacity of this adsorbent. The point of zero charge (pH_pzc) of the Fe₃O₄–NaA was determined to be 6.2, demonstrating its versatility for various pH ranges. Process optimization was conducted using a central composite design (CCD) matrix combined with response surface methodology (RSM) to evaluate the influence of key factors, including solution pH, Fe₃O₄–NaA nanocomposite amount, ultrasonic time, and initial analyte concentration. The optimal conditions (Fe₃O₄–NaA nanocomposite amount of 0.04 g, pH of 7, initial analyte concentration of 17 mg L^-1, and ultrasound time of 16 min) resulted in removal efficiencies ranging from 91.11% to 96.09%. Reusability tests revealed that the Fe₃O₄–NaA adsorbent retained high performance over 5 adsorption/desorption cycles, with hydrochloric acid identified as the most effective eluent for regeneration. The efficacy of Fe₃O₄–NaA nanocomposite was further validated using real water samples, where it successfully removed contaminants with high efficiency. These findings highlight the potential of Fe₃O₄–NaA nanocomposites as cost-effective and environmentally-friendly adsorbents for the remediation of contaminated water.

The increasing presence of emerging contaminants in water sources, including antibiotics and pharmaceuticals, poses a significant threat to human health and the environment. Effective removal of these pollutants remains a challenge, particularly with the growing demand for high-quality water. This study explores the use of carbon nanotubes (CNTs) non-covalently functionalized with poly[ (m-phenylenevinylene)-alt- (p-phenylenevinylene)] (PmPV) to enhance the adsorption and removal of Nitroimidazole and Tetracycline antibiotics from water. Molecular dynamics and metadynamics simulations were employed to examine the interaction mechanisms, structural stability, and adsorption behavior of these hybrid systems. The results demonstrate that non-covalent functionalization significantly enhances CNT solubility and adsorption efficiency, primarily through van der Waals and electrostatic interactions. According to the computed energies, the adsorption energy of metronidazole antibiotic molecules, at -372.03 kJ/mol, and tetracycline antibiotic molecules, at -282.57 kJ/mol, are among the highest in their respective classes (Nitroimidazole and Tetracycline antibiotics). This study provides a theoretical basis for developing efficient CNT-based water treatment technologies, emphasizing the potential of PmPV-functionalized CNTs in environmental applications.

Climate change in Iran is significant, as reduced rainfall adversely affects both biological and social systems. This study aims to long-term predict rainfall changes based on social and economic scenarios from the sixth climate change report (Hist_SSP126_SSP245_SSP585) in the Kermanshah synoptic station. Different machine learning models, have been employed to analyze data from three CMIP6 public circulation models. These models are well-established for classification and prediction tasks. The ML-based downscaling models will estimate monthly rainfall for three time periods: 2026–2050, 2051–2075, and 2076–2100. These predictions will be made under three different scenarios: SSP1, SSP2, and SSP5. Historical monthly rainfall data from a Kermanshah station (1990–2014) have been divided for model training and testing. The models were checked and adjusted using MAE, MSE, RMSE, R², and NSE to see how well they performed. Results show no significant changes in the prediction results for SVR and RF models, with the best climate models varying by region. In all scenarios, the CANESM5 model closely matches the Random Forest predictions. Projected declines in annual rainfall range from 31% to 33% across scenarios and periods, with a multi-scenario average of 32% by 2100.

Understanding the effects of climate change on watershed health is essential for effective ecosystem management. The main purpose of the current research is to evaluate the health of the Nekarood Watershed in northern Iran using a conceptual model based on reliability (R_el), resilience (R_es), and vulnerability (V_ul) under various climate change scenarios. Climate data for the periods 2021–2050 and 2051–2080 were simulated using six models under the Shared Socioeconomic Pathways (SSP) scenarios and downscaled with the LARS-WG model. The physically based Soil and Water Assessment Tool (SWAT) model was employed to predict discharge, sediment, nitrate (NO₃⁻), and phosphate (PO₄³⁻) under these scenarios. Under baseline conditions, the watershed’s R_el, R_es, and V_ul indices were 0.46, 0.53, and 0.85, respectively, while the watershed health index (WHI) was 0.59, indicating a moderate status based on high flow discharge, low flow discharge, sediment, NO₃⁻, and PO₄³⁻ at the watershed outlet. Under the SSP126 scenario, the WHI is projected to change by − 2.7% for 2021–2050 and − 6.6% for 2051–2080. For SSP245, the changes are − 6.1% and + 0.7%; for SSP370, − 5.7% and − 5.0%; and for SSP585, − 3.2% and − 18.7% for the respective future periods. This spatial modeling approach enhances decision-makers’ understanding of temporal changes in the Nekarood Watershed and supports the simulation of climate impacts on land characteristics, including pollutant loads and overall watershed health.

Managing and modeling different water resources in arid regions is the key to an accurate estimation of water uses and achieving agricultural sustainability under limited water. In this study, four Egyptian Nile Delta Governorates namely Ad Dakahliyah, Al Gharbiyah, Kafrash shaykh and Dumyat were selected as a primary wheat-producing location for modelling water footprint for green (WF_g) and blue (WF_b) colours. Seven water footprint models were established in 2006–2016 based on monthly open access results. These models were varied in volume and structure of the independent variables. Besides, these models were compared and evaluated using five machine learning algorithms, including random forest, support vector regression, Bagging, Boosting, and Matern 5/2 Gaussian process. The results of this study revealed that Model 2 utilizing the M5/2 GPR algorithm was the best prediction model for blue and green WFs in the Ad-Daqahliyah governorate. Its characteristics were R² = 0.94, RMSE = 15.53 m³/ton, and MAE = 14.49 m³/ton; and R² = 1, RMSE = 0.32 m³/ton, and MAE = 0.19 m³/ton, respectively. As well, the best predictive model for blue WF in the Dumyat was Model 2 at the boosting algorithm, which obtained R² = 0.74, RMSE = 28.56 m³/ton, and MAE = 21.06 m³/ton). In contrast to the other models, Model 1 with M5/2 GPR gave the best simulation for the estimation of green WF, with high R² = 0.96, RMSE = 2.95 m³/ton, and MAE = 2.17 m³/ton. In addition, Model 1 at M5/2 GPR was the best model for predicting blue WF at Al Gharbiyah producing a high coefficient of determination (R² = 0.88) and less error (RMSE = 24.53 m³/ton, and MAE = 16.28 m³/ton). Model 6 at the M5/2 GPR algorithm had the best performance metrics (R² = 1.00, RMSE = 0.32 m³/ton, and MAE = 0.21 m³/ton). Model 2 with M5/2 GPR produced the best results among the models for blue and green WFs in the Kafr ash-Shaikh site, with an R² of 0.97, an RMSE of 11.74 m³/ton, and an MAE of 7.87 m³/ton; R² = 1.00, RMSE = 0.34 m³/ton, and MAE = 0.23 m³/ton, respectively. These models achieved high performance and less residual errors according to statistical analysis methods. Thus, the developed models were proven to produce satisfactory results and will be a precise tool for the process of decision making for water-managers.

Nanotechnology has introduced an innovative approach in which nanoscale materials are synthesized through green chemical strategies by integrating with plant biotechnology. This eco-friendly, cost-effective, and novel method enables the production of plant-mediated nanoparticles that play a crucial role in the degradation and removal of environmental pollutants. In this study, we explored the degradation of malachite green dye using a magnetically recyclable silver nanocatalyst in the presence of a reducing agent (NaBH₄). Silver nanoparticles were synthesized via a simple, robust, and low-cost biochemical reduction process using the leaf broth of Alhagi maurorum, followed by magnetization with magnetic nanoparticles. The synthesized nanocatalyst was characterized using UV-Vis and FTIR spectroscopy. The catalytic efficiency of the nanocatalyst was evaluated for the degradation of malachite green under UV-Vis light. Key parameters influencing dye removal, such as initial dye concentration, pH, catalyst dosage, and reducing agent concentration were systematically studied. Kinetic analysis at varying dye concentrations revealed that the degradation followed pseudo-first-order kinetics. The results demonstrated that optimal degradation occurred at pH 7, with 3 mM NaBH₄, 50 mg of catalyst, and 40 ppm of dye concentration. Additionally, the magnetic nature of the nanocatalyst enabled its easy recovery and reuse, making it a promising and sustainable solution for water purification and dye pollution mitigation.

This study explored the treatment of Haldia industrial belt wastewater (HIBWW), which contains high pollutant loads from chemical facilities in the Haldia region of West Bengal, India. The study uses a customized electrocoagulation cell to remove TSS and other contaminants. The electrocoagulation cell consists of iron and aluminum as the anode and cathode. Key operating parameters, including the pH, oil concentration, electrolysis time, and current density, were optimized using a central composite design within response surface methodology. Adsorption isotherm, kinetic, and thermodynamic analyses were conducted to uncover the adsorption mechanism and evaluate the efficiency of the electrocoagulation system for HIBWW. An optimal TSS removal rate of 72% was achieved with 92% desirability at pH 5, initial oil concentration of 50 mg/L, current density of 30 mA/cm², and reaction time of 35 min, higher than the value reported earlier. Additionally, the removal efficiencies for turbidity, total dissolved solids, chemical oxygen demand, and biological oxygen demand were 90%, 75%, 53%, and 63%. Based on the isotherm analysis, the Langmuir model produced the best fit (R² = 0.9986), suggesting monolayer adsorption on the floc surface. The first-order kinetic model exhibited a higher regression coefficient than the second-order model, indicating physisorption-dominated adsorption driven by charge neutralization and particle aggregation. The Gibbs free energy (∆G^o: − 24.16 kJ/mol to − 20.60 kJ/mol) also suggested that adsorption occurred through physisorption. The treatment cost for HIBWW including equipment, waste disposal, power consumption, and labor, varies from approximately ₹470/m³ to ₹479/m³. The findings of the present study offer guidelines for improving the treatment efficiency of HIBWW and support environmental sustainability in accordance with United Nations 2030 Sustainable Development Goals and the long-term sustainability roadmap for 2050.