Applied Water Science最新文献

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.

Water, a crucial reserve, is reducing fast in both urban and rural areas due to increasing domestic and agriculture need. Groundwater and rainwater now play a significant role in hydrological planning, affecting subsurface water quality and freshwater accessibility. This study aimed to find appropriate rainwater harvesting (RWH) location in the Kohat region applying geospatial methods. Approaches used comprise Geographic Information system, remote sensing, multi influencing factors and weighted overlay analysis considering seven main factors: slope, drainage density, rainfall, geology, lineament density, land use/land cover and soil. Every factor was assigned weight and process in ArcGIS to produce an RWH optimal map, classifying zones, poor, moderate, high, and very high suitability category. Finding show that 60.48% is highly suitable for RWH, whereas 34.92% is moderately suitable, and 4.67% is poorly appropriate. The finding provides valuable insights for viable RWH site selection in Kohat and can be useful to water protection effort locally and globally.

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.

To achieve sustainable conservation and management of wetlands, this study investigates the influence of water balance dynamics in Baiyangdian (BYD) on the cyclical evolution of wetland landscapes from 1980 to 2020, as well as elucidates the driving role of key hydrological processes in wetland degradation. To accomplish this, a “process analysis–driver identification–uncertainty assessment” research framework was established. This framework facilitates a systematic investigation into how hydrological processes induce changes in landscape patterns and enables a quantitative evaluation of uncertainties associated with water balance states. This approach enhances our understanding of the mechanisms regulating water in wetlands and the factors contributing to their degradation. The results show that between 1980 and 2020, the wetland landscape underwent staged changes of contraction, recovery, decline, and stabilization, with the dominant mudflat gradually transitioning into marsh ecosystems. PLS-SEM analysis revealed that wetland landscape patterns were predominantly influenced by water balance dynamics: Recharge factors significantly promoted lake storage variation increases and wetland expansion, thereby enhancing landscape indices, while discharge factors suppressed lake storage variation, leading to wetland contraction and diminished landscape indices. Notably, Inflow emerged as the most substantial positive driver. Across distinct phases, both recharge and discharge factors exhibited marked uncertainties, with the uncertainty in lake storage variation reaching its maximum when (:Inflow) levels were elevated alongside reduced inputs from precipitation and evapotranspiration. The cyclical responses of wetland landscapes offer a foundation for elucidating uncertainties in hydrological driver interactions and establish critical linkages between water balance dynamics and wetland landscape evolution. These findings highlight the necessity of integrating basin management with multi-source hydrological replenishment strategies in water resource allocation and wetland conservation efforts, thereby ensuring the long-term sustainability and ecological integrity of wetland ecosystems.

This study evaluated heavy metal contamination in agricultural soils irrigated with untreated industrial wastewater from two major industrial zones in Lahore, Pakistan. Soil samples from cucumber (S1), wheat (S2), green chili (S3), potato (S4), ladyfinger (S5), and tomato (S6) field were collected from six industrial sites. Heavy metals including cadmium (Cd), chromium (Cr), lead (Pb), and nickel (Ni) were analyzed using atomic absorption spectroscopy (AAS). Various indices such as the contamination factor (CF), potential contamination index (PCI), geo-accumulation index (Igeo), potential ecological risk index (PERI), and human health risk assessment (HHRA) were employed to assess pollution levels and associated health risks. Results showed that most heavy metal concentrations were within permissible limits set by the European Union (EU) and World Health Organization (WHO), except at certain sites. For instance, Cd at Site 4 was 0.085 mg/kg (above EU/WHO guideline of 0.05 mg/kg), Cr exceeded the limit (0.10 mg/kg) at all sites except Site 1, and Pb levels were higher than (0.1 mg/kg) at all sites except Sites 1 and 4. Ni concentrations surpassed guidelines (0.14 mg/kg) at all sites except Sites 1 and 2. Among all elements, Cr exhibited the highest contamination factor. The PCI results also indicated that Cr and Ni posed significant contamination potential. All soil samples exhibited PERI values exceeding 600, indicating a very high ecological risk. HHRA analysis showed that children were more vulnerable than adults to all four heavy metals, as per USEPA guidelines. This study provides a comprehensive, multi-index assessment of industrial wastewater-induced soil pollution and its implications for human health. Unlike previous research that focused on individual contaminants, this work integrates ecological and human health risk assessments, offering novel insights for urban environmental protection and sustainable agricultural practices.

The simplification of algorithms for predicting natural events, particularly in the context of environmental and agricultural applications, has gained significant attention due to the need for reliable, efficient, and adaptable models. This study aims to assess the performance of various simplified algorithms for predicting daily reference evapotranspiration (ET_O) across diverse climatic conditions. A comparative analysis of multiple models, including combination-based, mass transfer-based, radiation-based, and temperature-based approaches, was conducted to evaluate their precision and adaptability in different environmental settings. Among the combination-based models, PME and ResNet₄ exhibited outstanding performance, with standardized index (SI) values consistently below 0.1 and generalized predictive index (GPI) values under 5% across all climatic conditions, making them ideal for applications in regions with diverse environmental characteristics. Other models such as LSSVR₄ and ANF-PSO₄ demonstrated moderate effectiveness in arid climates but struggled in more humid conditions, highlighting the need for further model refinement in extreme environments. The mass transfer-based models, including A-LSTM₃ and ResNet₃, showed strong performance in very dry climates, although their precision decreased in humid regions, indicating the sensitivity of these models to changes in moisture availability. Radiation-based models such as A-LSTM₂ and ResNet₂ performed well in humid and semidry conditions, while LSSVR₂ and ANF-PSO₂ were most effective in dry climates. Temperature-based models, particularly LSSVR₁ and ANF-PSO₁, demonstrated remarkable stability across all climates, with low GPI values, making them well-suited for temperature-sensitive environments. Overall, PME emerged as the most reliable model, offering consistent high performance across all climates. The findings of this study emphasize the importance of selecting and calibrating models based on climatic variability, ensuring accurate predictions for sustainable environmental management and agricultural planning.