Applied Water Science最新文献

This study investigates the impact of transitioning from flood to drip irrigation on sugarcane cultivation in Upper Egypt. It evaluates how planting methods—cane stalks and plantlets—affect sugar quality under both systems. A selected set of crop samples was analyzed for sucrose content in the official laboratory of the Ministry of Irrigation. The results offer insights into the economic benefits of drip irrigation, highlighting its role in enhancing sugar quality and returns. Drip irrigation, particularly with plantlets, achieves a maximum sucrose content of 14.3%, a 21.2% improvement over the 11.8% under flood irrigation. This sugar quality enhancement is accompanied by a substantial yield increase: up to 9,895.6 kg/acre with drip irrigation vs. 5,351.9 kg/acre with flood irrigation—an 84.9% increase. Drip systems also show higher water-use efficiency, generating 1.45 kg of sugar per cubic meter of water, compared to 0.46 kg/m³ for flood. Application efficiencies range from 85% to 90% for drip, versus 45%–50% for flood. The study highlights the potential of drip irrigation in arid regions like Upper Egypt, where water scarcity is a major concern. Integrating modern irrigation with local conditions enhances both production and sustainability. These findings emphasize the dual benefits of higher yield and water savings, maximizing returns and reinforcing agricultural resilience under climate and water stress.

Ensuring universal access to clean water continues to be a pressing challenge worldwide. In this research, carbon black and zinc oxide nanoparticles were chemically activated using a mixture of sulfuric acid and potassium permanganate (KMnO₄) to enhance their surface reactivity. Following this treatment, a sol–gel synthesis approach was applied to produce hybrid nanostructures composed of zinc oxide (ZnO), SiO₂, and carbon black. These hybrids were then surface silanized using two different silane agents (i.e., vinyltriethoxysilane (VTES) and bis[3-(triethoxysilyl)propyl]tetrasulfide (TESPT)) at a concentration of 5%. A suite of advanced techniques was employed to characterize the materials, including XPS, FTIR, TGA, BET surface area measurements, and FE-SEM. The silanization process markedly improved the hydrophobic nature of the hybrids, with water contact angle (WCA) measurements rising from 21° to as high as 150°. Incorporating the functionalized hybrids led to a substantial decrease in the optical band gap to 2.8 eV, enhancing their photocatalytic efficiency. Mechanical testing revealed a significant boost in the reinforcement index for composites containing modified particles. Additionally, surface wettability assessments showed an increase in WCA from 91° to 151°, alongside a sharp drop in oil contact angle (OCA) from 52° to 12°, following silane treatment. These findings highlight the strong potential of silane-functionalized ZnO/SiO₂/carbon black hybrids in developing multifunctional rubber nanocomposites for simultaneous oil–water separation and photocatalytic pollutant remediation.

Preventing the degradation of soil and water resources, mitigating their associated economic and social impacts, and promoting agricultural sustainability through optimal cropping patterns have become major priorities for agricultural policymakers. This study aims to reduce water resource consumption, optimize the use of chemical fertilizers and pesticides, and enhance income and employment opportunities within the agricultural sector. For this purpose, 350 questionnaires were completed through random classification sampling in 2018–2019 in Mahidasht and Ravansar-Sanjabi in Kermanshah province, Iran. Subsequently, water-saving and sustainable cropping patterns were developed using fuzzy fractional goal programming, incorporating technical constraints related to production and livestock feed requirements. The results revealed that the physical productivity of water resources, chemical fertilizers, pesticides, and the farming employment to water resources ratio increased by about 21.4%, 3.3%, 2.5%, and 25%, respectively. The stabilization of net income, 3% improvement in farming employment, 3.2% and 2.5% reduction in chemical fertilizers and pesticides, and 30 million m³ equivalent to a 17.6% reduction in water resources consumption were other characteristics of the proposed cropping pattern. Therefore, according to the general and regional goals of policymakers, the implementation of the suggested cropping pattern should be a priority that requires the development of legal requirements, incentive, and punitive policies, and the use of agricultural promoters.

This study investigates factors influencing access to advanced sanitation services and drinking water quality in healthcare facilities (HCFs) in Addis Ababa, Ethiopia. Despite the critical role of sanitation and safe water in preventing healthcare-associated infections, many facilities face significant challenges. A convergent sequential study was conducted in 382 healthcare facilities, which involved observations of sanitation facilities, interviews with facility managers, and water quality tests of 382 samples. Additionally, five water samples from reservoirs and distributors were collected, and eight key informant interviews were conducted purposively. In this investigation, only 14.5% of healthcare facilities had advanced sanitation service, while 23.2% had basic sanitation services. Over half (62.3%) of the facilities provided limited sanitation services. Additionally, 22.7% of water samples tested positive for enterococci, 26% for Escherichia coli, 35% for fecal coliforms, and 38.8% for total coliforms. All samples analyzed for fluoride, conductivity, and total dissolved solids (TDS) were below permissible limits, except for 6.6% of the samples that exceeded the pH level of 8.5. In contrast, all water samples from reservoirs and distributors were free from bacterial contaminations, and their fluoride, conductivity, and TDS levels were within the standard. Factors such as having infection prevention committee (IPC)(AOR = 2.8, 95% CI: 1.07-7.52), trained managers on sanitation safety plan (AOR = 2.96, 95% CI: 1.10-7.94), managers trained in infection prevention (AOR = 3.6, 95% CI: 1.25-10.48), having sanitation standards (AOR = 3.5, 95% CI: 1.06-11.64), availability of sufficient budget for sanitation services (AOR = 3.2, 95% CI: 1.14-9.15), having specific annual sanitation plan (AOR = 3.6, 95% CI: 1.52-8.58), using updated WASH guidelines (AOR = 3.4, 95% CI: 1.05-11.20), absence of a sanitation safety plan (AOR = 0.22, 95% CI: 0.05-0.91), lack of regular monitoring and evaluation (AOR = 0.24, 95% CI: 0.07-0.83), and managers who did not involve in leading renovation of WASH infrastructure (AOR = 0.56, 95% CI: 0.35-0.70) were significantly associated with access to advanced sanitation service level. Access to advanced and basic sanitation services is low in HCFs of Addis Ababa. Several core factors affecting access to advanced sanitation service level has been identified. Many water samples from healthcare facilities were contaminated by bacteria. Enhancing training programs for healthcare managers and securing adequate funding are critical steps toward improving sanitation and water quality.

Water security is a critical global issue, particularly in arid and semi-arid regions like China’s Loess Plateau, where water scarcity threatens sustainable development. To address this issue, this study developed an improved emergy ecological footprint model to systematically evaluate the water resource security of Zhangjiachuan Hui Autonomous County from 2014 to 2023. The model integrated emergy theory and ecological footprint analysis to quantify the water resources emergy ecological carrying capacity (WEEC), emergy ecological footprint (WEEF), and key indicators including ecological surplus/deficit (WEES/WEED), ecological tension index (WETI), and sustainable utilization index (WSUI). The results reveal a concerning transition from ecological surplus (average WEES 0.6 hm²/cap during 2014 − 2022) to deficit (WEED − 0.8 hm²/cap in 2023), with WETI exceeding the insecure threshold (1.5) and WSUI indicating severe unsustainability (0.6) in drought years. These trends are driven by two synergistic mechanisms: (a) ecological constraints, where 85% precipitation concentration (May–October) and loess hydrogeology cause 60% interannual WEEC variability (1.5 − 2.4 hm²/cap), and (b) socioeconomic pressures, with WEEF growing 7.7% annually due to increases of 178% in livestock and of 62% in orchards. Based on these findings, this study proposes an integrated management framework featuring precision agriculture, circular livestock systems, and loess-specific rainwater harvesting. This study provides a novel framework to be used as an evaluation tool and a source of policy insights for the water resource security evaluation and sustainable utilization of regional water resources.

Accurate runoff simulation and prediction are critical for effective water resource management. While traditional physical models incorporate hydrological mechanisms, they often struggle with nonlinearity and uncertainty. Conversely, pure data-driven models such as Long Short-Term Memory (LSTM) networks excel at capturing temporal patterns but lack physical constraints. This review systematically examines the emerging paradigm of coupled LSTM models, which integrate the strengths of both approaches to boost predictive accuracy and stability. This review categorizes and analyzes three dominant coupling frameworks: the integration of LSTM with physical models to leverage physical insights for improved generalization; the combination of LSTM with signal decomposition techniques to disentangle complex hydrological signals for more accurate forecasting; and the coupling of LSTM with intelligent optimization algorithms to automate feature selection and hyperparameter tuning. Analysis reveals that these coupled models significantly outperform standalone methods across various case studies. The core findings indicate that coupling enhances model accuracy, provides insights into hydrological processes, and offers solutions for data-scarce regions through physical priors. Finally, current challenges and future directions are discussed, highlighting the potential of these integrated approaches to advance hydrological sciences and support sustainable water management decisions.

This study presented a facile and sustainable method to synthesize a novel magnetic composite of CuFe₂O₄/CuS via a simple green route. Alhagi pseudalhagi extract was effectively employed as a bio-reductant and capping agent for the composite fabrication. Then, the adsorption effectiveness of the CuFe₂O₄/CuS nanocomposite was screened for the decontamination of 20 mg L⁻¹ Reactive Red 195 (RR195) dye in aqueous environment. Batch adsorption experiments were systematically deployed to comprehend the impact of various operational parameters on the RR195 removal efficiency. The best uptake RR195 rate (95%) was maintained under the optimized operational conditions, including a pH value of 3.0, an adsorbent dosage of 1.5 g L⁻¹, an initial RR195 concentration of 20 mg L⁻¹, a contact time of 200 min, and a temperature of 20 °C. The adsorption Kinetics obeyed a pseudo-second-order kinetic model (R² = 0.999), and the attained equilibrium outcomes were best elucidated by the Langmuir isotherm model (R² = 0.965), singing a homogeneous and monolayer adsorption mechanism. Thermodynamic investigations revealed that the adsorption process is appropriate, spontaneous and endothermic, with ∆H° and ∆S° values of 60.99 kJ mol⁻¹ and 275.409 J mol K⁻¹, respectively. In vitro, the reusability analysis conformed that the as-constructed adsorbent consistently achieved a suitable stable RR195 rate with only a 28% reduction in the elimination efficiency after twelve consecutive runs. Eventually, the acquired findings manifested a robust potential of the CuFe₂O₄/CuS composite for the adsorption effectiveness, proving it a sustainable and promising for the decontamination of RR195 from water environments.

This study aims to investigate the effects of climate change on the Blue water footprint (BWFP) and Green water footprint (GWFP) of oranges in the El-Beheira and Al-Sharkia governorates in the Nile Delta using data from 2013 to 2022. Various machine learning (ML) models, including LASSO, RF, XGB, and CATBOOST, as well as hybrid models like XGB-RF, RF-LASSO-CAT-LASSO, XGB-LASSO, XGB-CAT, XGB-CAT-LASSO, XGB-RF-LASSO, and stacked ensemble models, were employed to assess the impact of climate change in Egypt and determine the most accurate model for predicting BWFP and GWFP of orange crops. The models were tested under different scenarios (Sc) involving climate parameters, crop parameters, and remote sensing indices (Normalized Difference Vegetative Index (NDVI), Normalized Difference Moisture Index (NDMI), Enhanced vegetation index (EVI), Soil-adjusted Vegetation Index (SAVI), Green Chlorophyll Index (GCI), and Land Surface Temperature (LST)). The CatBoost-RF hybrid model in Sc5 (Pe_eff, Tmax) showed the highest accuracy of 1.00 for BWFP, while the LASSO model in Sc3 had the lowest accuracy of 0.846. For GWFP, all hybrid models achieved a maximum accuracy of 1.0. The R² value for predicting BWFP was highest at 0.98 under Sc2 using RF-LASSO hybrid models and lowest at 0.63 under Sc3 with RF. As for GWFP, the highest R² value of 1.0 was obtained under Sc1 with XGB-CatBoost-LASSO, while the lowest value of 0.07 was seen under Sc3 with XGB-RF-LASSO. The lowest RMSE value of 0.15 was observed with the RF-LASSO hybrid model, while the highest value of 0.31 was seen with the LASSO model in Sc2 for BWFP. For GWFP, the minimum and maximum RMSE values were 0.01 and 0.06 under XGB-RF-LASSO and CatBoost models, respectively, in Sc4 (Pe_eff, T_max, T_min). The study suggests utilizing hybrid models, either double or triple, especially for RF-LASSO and XGB-CAT-LASSO, to effectively predict the BWFP and GWFP of oranges in arid regions.

Groundwater recharge is a key indicator for sustainable groundwater use and water resources management. Although groundwater recharge depends on climatic and land-surface conditions, previous WetSpass-M applications have often underestimated the role of vegetation because they typically assume static vegetation. To address this gap, this study quantitatively evaluated the impact of vegetation dynamics—represented by Sentinel-2–derived leaf area index (LAI)—on the water balance and groundwater recharge in the Sapghyocheon basin, South Korea. LAI showed strong spatiotemporal and seasonal variability, and its magnitude and temporal dynamics differed among vegetation types, thereby affecting individual water balance components. In particular, in paddy fields, agricultural practices (e.g., transplanting, crop growth, and harvesting) led to distinct changes in interception, with a marked decrease after harvest. During 2020–2021, the mean annual groundwater recharge ratio was approximately 22% (20–23%). Simulated annual recharge ranged from 294 to 335 mm/year across the basin, and the model reasonably reproduced observed surface runoff (R² = 0.95). When examined by land-use type, the groundwater recharge fraction in summer in coniferous forests (31%) was about twice that in fields (16%), indicating a clear difference in groundwater-recharge potential among vegetation types. These findings suggest that variations in vegetation type and phenological timing can directly and indirectly affect groundwater recharge. Therefore, explicitly accounting for vegetation dynamics can improve the detail and reliability of water balance and groundwater recharge assessments. However, because the analysis is based on a relatively short (2020–2021) study period and limited observational data, the conclusions should be viewed as an initial step rather than fully generalizable results.