Applied Water Science最新文献

With its increasingly serious and continuous need, effective spatiotemporal water quality prediction has become key to effective pollution control and decision-making. Current research primarily focuses on utilizing continuous time monitoring data to predict trends in time series within specific sections. However, the lack of spatially continuous and reliable observations limits the ability to achieve full spatial coverage prediction. To address this limitation, this study proposes an integrated framework, named SELC, which utilizes the Soil and Water Assessment Tool (SWAT), Environmental Fluid Dynamics Code (EFDC), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM), to predict the continuous spatiotemporal water quality of the Xiaoqing River Basin (China) using discrete cross-section monitoring data and mechanism model simulation. The SELC model framework integration is as follows: The CNN training uses on-site monitoring data and high-resolution spatial simulations from the coupled SWAT-EFDC models. LTSM is used to generate future temporal forcing data for SELC at monitoring sections. The verification results showed that CNN successfully replicated the spatially continuous distribution of pollutants, and the prediction results were highly consistent with the trend, peak position, and minimum value EFDC simulation results. In the verification, the average coefficients of determination (R2) of the model were 0.62 (NH₃-N) and 0.65 (chemical oxygen demand, COD), confirming its reliability. This study achieved high-resolution spatiotemporal water quality prediction by using only segmented monitoring input and future scenario prediction, thus overcoming the limitation of sparse spatial data. This framework provides a practical tool for identifying high-risk pollution areas and periods and supports targeted aquatic environmental management.

Biochar is a cost-effective, porous material with a high carbon content, making it an excellent candidate for adsorption applications. However, its adsorption performance can be further enhanced by incorporating metal oxide nanoparticles. Magnesium oxide (MgO) nanoparticles possess a highly porous structure, providing numerous active sites for adsorption. When loaded into biochar, they disperse more effectively, reducing the risk of particle clumping and enhancing the overall adsorption performance. In this work, a widely distributed Mediterranean saltbush plant, Atriplex hamilus, biomass has been used for the first time to fabricate three composites; MgO@biochar-A(BCC-1), MgO@biochar-B(BCC-2) and MgO@biomass. A one-step,cost-effective pyrolysis process was adopted for the PO₄³⁻ removal from synthetic- and poultry wastewater. The as prepared composites were verified using different characterization techniques. Transmission electron microscopy(TEM) and Scanning electron microscopy(SEM) analysis revealed the formation of rod, rhomboid and spherical shapes of BCC-1, BCC-2 and MgO@biomass. X-Ray Diffraction(XRD) results confirmed the crystalline nature of MgO@biochar. Thus, emphasize that MgO-NPs were successfully loaded on biochar via surface complexation and ion exchange mechanisms. Batch adsorption experiments demonstrated maximum PO₄³⁻ uptake capacities;q_m of 129.80, 74.79, and 18.40 mg g⁻¹ for BCC-1, BCC-2, and MgO@biomass, respectively, at a dose of 0.2 g L⁻¹ and time of 60 min. Moreover, the removal efficiency of PO₄³⁻ reached a maximum of 84% using BCC-1 from real poultry wastewater. The reusability of MgO@biochar proved their effectiveness in PO₄³⁻ removal up to 4 consecutive cycles. The kinetic, isothermal models and contour plots for interactive factor effects were provided. Based on data collected, BCC-1 acquired the maximum adsorption capacity. Accordingly, this nanocomposite could be considered as a good candidate for PO₄³⁻ removal and recovery from wastewater.

This study examines the biosorption of Methylene Blue (MB), Basic Blue 41 (BB41), Reactive Red 120 (RR120), Methyl Red (MR), and Trypan Blue (TB) dyes, commonly used in the textile industry, using Juniperus drupacea cone as an biosorbent. The effects of biosorption time, initial dye concentration, temperature, pH, and particle size on dye removal efficiency were investigated. Characterization techniques such as SEM-EDX, FTIR, isotherm, kinetic, thermodynamic, and intraparticle diffusion analyses were performed. The Langmuir isotherm model indicated monolayer biosorption for MB and BB41, whereas the Freundlich isotherm suggested heterogeneous biosorption for RR120 and MR. The biosorption process followed the pseudo-second-order kinetic model, highlighting the dominance of chemical interactions. Thermodynamic analysis confirmed that MB and BB41 biosorption was spontaneous, while MB biosorption was exothermic. Intraparticle diffusion analysis suggested that biosorption was not solely controlled by intraparticle diffusion but also influenced by surface biosorption. The highest removal efficiency was recorded as 97.77% for MB under optimal conditions (pH 10, 55 °C, 75 μm biosorbent size). BB41 exhibited a maximum removal efficiency of 92.63%, with increasing biosorption at higher temperatures. The results demonstrate that Juniperus drupacea cone is an efficient and environmentally sustainable biosorbent for dye removal from wastewater. The study contributes to sustainable wastewater treatment technologies and offers a promising alternative for valorizing underutilized plant materials. These findings support the use of low-cost biosorbents in environmental applications and provide a foundation for future research on industrial-scale implementation.

In this study, class A pan coefficient (K_Pan) values were simulated via five machine learning models, namely the ANN, the AAN-REPTree, the ANN-SMO SVM, the ANN-Linear regression, and the ANN-Bagging model, by using daily meteorological data of the meteorological station of Ouled Ben Abdelkader region, which has semi-arid microclimate in the northwest region of Algeria. To determine the optimal combination of inputs, a variety of input-target pairs were tested by a variety of machine learning models, resulting in seven possible input scenarios: T_mean, RH_min, RH_max and Wind Speed were found to be the best input combinations. The results of models were analyzed (i.e., correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE)) to find their accuracy. As the best optimal condition, four input variables were introduced for the models ANN, ANN-REPTree, ANN-SMO SVM, ANN-Linear regression, and ANN-Bagging, with R = 0.9941 and 0.984, MAE = 0.0018 and 0.0037, RMSE = 0.0068 and 0.0016, RAE = 3.8863 and 7.6858, and RRSE = 10.9301 and 18.0686 in the training and testing phases, respectively. This hybrid model (ANN-Bagging) has demonstrated its utility in a scenario where there is a strong connection among the variables which includes KPan, and its feasibility to display the model in a feasible state.

For a source of water supply, it is crucial to understand the hydrological system and identify the anthropogenic stress factors for sustainable use of water resources. This study employs hydrochemical and isotopic data to evaluate the groundwater flow system of the Gwanin Water Intake Plant (GWIP), where Quaternary basaltic rocks have erupted over granite bedrock. Groundwater geochemistry is distinctly categorized by chemical weathering of the bedrocks based on Ca + Mg vs. HCO₃ + 2SO₄, which is supported by a hierarchical cluster analysis of measured parameters. The correlation between Cl and SO₄ showed that groundwater dominated by basalt weathering was more susceptible to contamination. Meanwhile, the correlation between NO₃ and Cl suggests differences in the relative contributions of various contamination sources depending on aquifer lithology. Evaporation signatures in in δ¹⁸O and δ²H indicate that local recharge from impounded water in paddy fields is the primary driver of these differences. ⁸⁷Sr/⁸⁶Sr ratios in groundwater indicate that the differentiation in chemical weathering is due to the distinct aquifers associated with different bedrock types. The clear difference in δ¹³C-DIC between surface water and groundwater suggests that their interaction is largely restricted. Based on the ⁸⁷Sr/⁸⁶Sr results, an end-member mixing analysis using SiO₂ and δ¹⁸O reveals that the contributions of impounded water from paddy fields and the Naengjeong Reservoir on GWIP range from 19% to 26%. These results underscore the need for managing contamination sources originating from the reservoir and paddy fields to ensure the sustainable use of GWIP.

Urban vegetation and water resources are the most vital components for maintaining ecological balance and ensuring environmental resilience in rapidly growing cities. This study focuses on Rajshahi, the greenest city in Bangladesh, with three primary objectives: (i) to analyze land cover and surface water dynamics, (ii) to assess spatiotemporal groundwater depletion, and (iii) to examine groundwater quality trends in relation to declining water tables. Multi-temporal Landsat imagery and long-term hydrological records (1990–2024), were used to evaluate urban landscape transitions and spatial groundwater levels status. Multidisciplinary approaches including image preprocessing, image classification, change detection, water indices calculation, spatial interpolation, and accuracy assessment were performed for sensible results. In addition, groundwater quality was assessed through in-situ measurements, major ion analysis via titration, and trace metal detection using UV spectrophotometry and atomic absorption spectrophotometry (AAS). Findings revealed a 543.56% increase in built-up areas, while vegetation and surface water bodies declined by 78.29% and 79.34%, respectively, over 34 years. During this time interval groundwater levels dropped by 6.38 m in pre-monsoon and 4.25 m in post-monsoon periods. Hydro-chemical analyses from 2010, 2017, and 2024 showed increasing concentrations of dissolved ions in groundwater, with very strong positive correlations (r ≈ 1) between water table decline and rising electrical conductivity (EC), total hardness (TH), and total dissolved solids (TDS) indicating worsening groundwater quality due to prolonged mineral interaction. However, this study provides critical evidence to support integrated urban planning and water resource policies for sustaining ecological integrity and managing future urban growth.

This study presents a comprehensive investigation into recent advancements in pyramid solar stills (PSS), focusing on how internal and external modifications have enhanced both performance and sustainability. The research critically examines the limitations of conventional solar stills in providing clean water and proposes innovative solutions to improve their productivity. Internal improvements like the integration of phase change materials (PCMs), Nanoparticles (e.g., TiO₂ and CNT-water Nanofluids), and energy storage materials (e.g., paraffin wax and quartz rock), meaningfully improve desalination output. PCM integration alone enhances water productivity by 35 to 101.5%, while Nanoparticle application assures an efficiency gains ranging between 6.1 to 54.4%. External modifications such as the integration of solar collectors, reflectors, and forced condensation systems, has increased water productivity. Statistically, the with water yield increases to 194% with a thermal efficiency up to 62.4%. Hybrid systems, that integrate multiple modifications, establish the greatest performance enhancements, delivering up to a 166% productivity growth when PCMs and reflectors are utilised in tandem. The results highlight that optimised PSS, developed through multidisciplinary approaches, offer a potential, sustainable, and cost-effective solution for freshwater production. However, a number of barriers linked to component integration and large-scale applications remain. More importantly, the associated findings of this review have stated a foundational framework to advance the design and operation of solar desalination technologies.