Applied Water Science最新文献

Simulating crop water consumption has been introduced as a valuable decision tool in food security. Such a tool is typically used to support a better understanding of how to increase water-use efficiency to satisfy optimal water management and sustainability. However, climate change is one of the most important and influential factors that restrain sustainable development, agriculture, and food security. Wheat is one of the most important and strategic products in the world and Iran. Therefore, in this study, the impacts of future climate changes on winter wheat yield, water requirement (WR), evapotranspiration (ET), and water footprint (WF) were evaluated in Qazvin Plain, Iran. As such, the outputs from five general circulation models (EC-EARTH, GFDL-CM3, MPI-ESM-MR, MIROC5, and HADGEM2-ES) were fed into the LARS-WG model to get finer spatial climate data for four future periods (P1:2021–2040, P2:2041–2060, P3:2061–2080, P4:2081–2100) considering three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). Thereafter, the projected climate change data were used in the FAO AquaCrop model to simulate the variability of wheat characteristics. The results proved the superiority of LARS-WG to model the maximum and minimum temperatures and precipitation (P) of the baseline scenario (1986–2015). Moreover, results revealed that the wheat WF will decrease in future periods. The modeling results showed that the average wheat yield and biomass will increase in future periods by 7.67 and 15.98 tons/ha, respectively, as compared to the baseline. The highest increase was recorded by the HadGEM2-ES model with RCP8.5 during 2081–2100. The average WR in the baseline was 127.14 mm, which was projected to decrease in future periods. The results show that ET will potentially increase in the period 2021–2040. As a consequence, the adapted methodology produced significantly superior outcomes and can aid in decision-making for both water managers and development planners.

Temperature monitoring across cold chain practices is an integral component of fresh produce supply chains. Numerous temperature data loggers (TDLs) are available to reduce the significant amount of food loss and waste (FLW) (equivalent to around 50%) in vegetable supply chains; however, its widespread adoption remains a challenge for the actors along the chain. This study seeks to understand the adoption of TDLs within selected Australian vegetable supply chains to address the challenge of FLW. Three representative cases of vegetable supply chains were purposively selected, including growers, packers, transporters, distribution centres along with technology providers, and industry experts. Data were collected through semi-structured interviews and analysed utilising thematic analysis. The findings indicate that members of vegetable supply chains recognise temperature management as one of the key factors for preserving quality and extending shelf life of their produce; however, they are not proactively seeking to utilise TDLs in their supply chain operations. Resistance to adoption of TDLs is deeply rooted in product-based challenges such as cost and compatibility, and process-based challenges including information sharing and product mixing. Additionally, presence of an individual’s undesirable behavioural aspects such as status-quo bias and responsibility shirking as well as prevailing social norms within the industry influence the adoption of TDLs.

This study presents a two-dimensional (2D) model for simulating groundwater level variations in sloping aquifers, where rainfall is the primary recharge source. The model uses Heaviside functions to represent spatiotemporal surface recharges and is based on the 2D linearized Boussinesq equation. Analytical solutions were derived using an integral transformation method, allowing for analysis of aquifer characteristics, such as anisotropy, slope, and hydraulic conductivity. In contrast to studies that assume total rainfall becomes recharge, this model employs Horton’s infiltration equation for more accurate estimates, showing strong alignment with field data. The results highlight the significant impact of anisotropy on groundwater flow, particularly when the hydraulic conductivity ratio ({K}_{x}/{K}_{y}) exceeds 10, leading to predominantly (X)-direction flow, with the flow rate increasing by 1.3 times compared to the scenario where ({K}_{x}/{K}_{y}=1) under slope angles ({theta }_{x}={theta }_{y}=5^circ). This model also aids in predicting groundwater behavior in small watersheds without field data.

The colored effluents causing environmental pollution pose a threat to the world. This study aims to assess the effectiveness of nickel oxide/zinc oxide/kaolin nanocomposite (NiO/ZnO/Ka) in removing methylene blue (MB) from water. Furthermore, it aims to examine the impact of synergetic adsorption/photocatalytic degradation (APCD) on the MB adsorption capacity as well as the suitability of the nonlinear adsorption isotherm and kinetic modeling in analyzing the process. The composites ZnO/Ka and NiO/ZnO/Ka were synthesized by the sol–gel method and were characterized by X-ray diffraction, Fourier transform infra-red, field emission scanning electron microscopy, and Brunauer–Emmett–Teller. The impacts of various parameters, such as pH, initial concentration of MB, dose, ionic strength, and temperature, on MB removal were studied using adsorption and APCD. The results showed that ZnO/Ka had the maximum adsorption capacity of MB (39.31 mg/g) and the maximum removal (78.61%) under optimal conditions of pH 10, clay dosage of 0.1 g/25 mL, initial concentration of MB 200 mg/L, contact time of 15 min, and 298 K, while NiO/ZnO/Ka showed the maximum adsorption capacity of MB (40.88 mg/g) and maximum removal (83.74%) at pH 7. It was also noticed that Temkin and Fritz–Schlunder models are the best isotherm models, with the highest R² (1 and 0.842) for ZnO/Ka and NiO/ZnO/Ka, respectively. Moreover, the data of adsorption and photodegradation of MB onto ZnO/Ka and NiO/ZnO/Ka were revealed to follow pseudo-first-order and Avrami kinetic models with R² (0.897) for ZnO/Ka and (0.986) for NiO/ZnO/Ka. Overall, NiO/ZnO/Ka showed better removal of MB than ZnO/Ka, and the hybrid process (photodegradation process after adsorption) enhanced the overall efficiency of MB removal than adsorption alone.

Herein, a novel method is presented for enhancing the thermal desalination process of saline water and seawater using atmospheric pressure plasma (APP). The effect of APP treatment combined with thermal heating (APP-TH) on the energy consumption, conductivity, and pH of seawater and saline water is investigated. Utilizing scanning electron microscopy and X-ray diffractometry, the evolution of the morphology, structure, and chemical composition of precipitated crystals is characterized. The APP-TH method reduces the energy consumption for desalination by 40.5% for saline water and by 52.82% for seawater when compared to the TH-only method. The pH value remains approximately unchanged, decreasing slightly for the saline water from 7.1 for untreated saline water to 7.05 after APP-TH treatment. However, after APP-TH treatment, the pH value of the seawater increased slightly, from 7 to 7.8. The total dissolved salts decreased after APP-TH treatment, lowering the conductivity of the saline water from 65,000 µS/cm to 160 µS/cm and the conductivity of the seawater from 58,200 µS/cm to 243 µS/cm. Moreover, the size of precipitated crystals from saline water is 31.47 nm after APP-TH treatment, compared to 55.59 nm after TH-only treatment. They also dropped from 41 nm to 39.5 nm for seawater. Compared with traditional approaches, this research proposes an optimistic solution to address global potable water scarcity issues.

Various critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ET_o). In this context, our study aimed to enhance the accuracy of ET_o prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)–artificial neural network (ANN) (codename: ABC–ANN). To this end, historical (1979–2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ET_o prediction. Our results showed that the highest ET_o prediction accuracy was obtained with ABC-ANN (Train R²: 0.990 and Test R²: 0.989), (Train R²: 0.986 and Test R²: 0.986), (Train R²: 0.991 and Test R²: 0.989) and (Train R²: 0.988 and Test R²: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ET_o prediction.

This work aimed to investigate the kinetic energy budget and moisture transport of a case of cyclogenesis that causes intense rains over north and middle parts of Saudi Arabia on November 23–25, 2022. The study of kinetic energy (KE) and its budget concludes that the majority of the KE was concentrated at 400 hPa and above, coinciding with the powerful activity of the subtropical jet stream during the period of cyclogenesis. The KE generation through cross-contour flow serves as a major energy source. During the cyclogenesis process, KE dissipation from grid to subgrid scales is a major energy sink, while the horizontal flux divergence of KE acts as a source of KE. The study of moisture transport through the attributes of moisture-flux components and the dispersion of perceptible water during the cyclogenesis reveals that within the lower tropospheric layer, the rotating component of moisture flux brings moisture from two primary regions: One zone spans the Arabian Sea and includes the south Red Sea, north of Ethiopia, and central Sudan; the other region covers the Mediterranean Sea and the North Atlantic. The primary moisture source in the middle layer is located over central Africa, with origins traced back to the Atlantic Ocean, Arabian Sea, and Indian Ocean.

Groundwater levels vary from region to another and sometimes in different zones in the same country due to different boundary conditions and extraction rates. Therefore, understanding intricate aquifer systems and predicting how they will react to hydrological changes require the use of groundwater models. In Egypt, the groundwater levels in the Nile Delta aquifer decrease causing problems to the delta ecosystem while it is rising in Aswan area due to the presence of Nasser Lake causing several damages to the city’s buildings and infrastructures. In order to maximize its benefits and lessen the harm brought on by inadequate groundwater management in the city of Aswan, the height of the groundwater level in that city was examined, appraised, and groundwater management scenarios were established in this study. To achieve the objectives of the study, a simulation of Aswan aquifer’s groundwater system is built based on a quasi-three-dimensional transient groundwater flow model using MODFLOW. The model was calibrated and verified. Four management scenarios are tested. The fifth scenario, in this scenario, the four scenarios combined together at the same time and with the same conditions and ratios were proposed to be implemented. The results of the proposal to implement the four scenarios together showed that the rates of decline in groundwater levels in the last stage will be 12.44%. The study results reveal that a better understanding of the simulated long-term average spatial distribution of water balance components is useful for managing and planning the available water resources in the Aswan aquifer.

Detecting and quantifying pharmaceutical compounds in various environmental matrices is complex and challenging. This difficulty stems from the trace levels at which these compounds are found and the lack of analytical methods that are rapid, cost-effective, and portable. To address these challenges, this study aimed to develop microfluidic paper-based analytical devices (μ-PADs) using beeswax screen printing for fabrication. Key parameters, including reaction time, concentration, reagent volume, and channel length, were optimized using response surface methodology. Under optimal conditions of 5 ppm sample concentration, 10 μL reagent volume, 10 min reaction time, and 2 cm channel length, the analytical performance of the μPAD was evaluated and compared with the standard UV–Vis spectrophotometry method. The microfluidic analytical device demonstrated detection limits at 0.03 μg/ml, compared to 0.01 μg/ml for the UV–Vis spectrophotometer. Although the sensitivity of µ-PADs in this study (0.03 μg/ml) is lower than that of UV–Vis (0.01 μg/ml), it represents an improvement over the previous µ-PAD report (1 μg/ml) on the same analytes. Both methods exhibited commendable precision, with a relative standard deviation below 2%. Additionally, recovery rates were acceptable and comparable, ranging from 86.8 to 99.6% for µ-PADs and 96.5–99% for UV–Vis. The analytical performance evaluation suggests that µPADs provide excellent sensitivity, precision, and accuracy for trace-level paracetamol analysis. A paired t-test further confirmed no statistically significant difference between the two methods, underscoring the promising potential of µ-PADs for trace-level paracetamol quantification in water samples without conventional analytical instruments.