Applied Water Science最新文献

In the globe, there is a rise in water demand for agricultural, industrial, and domestic purposes. Single-basin solar stills (SBSS) have been a subject of research in various countries, particularly in regions with water scarcity or limited access to clean drinking water. In this work, SBSS for desalinating high-salinity water were developed, tested, and evaluated based on a developed numerical model using MATLAB R2021a program to predict the best productivity through the best selection of raw materials used to develop the SBSS. A four-inclined SBSS was fabricated and examined experimentally according to numerical model findings for best design parameters at Marsa Matrouh, 31° 21′ 10.44″N, 27°14′14.10″ E, Agricultural Station—Agricultural Research Center (ARC), Egypt. The hourly experimental results are compared with the numerical results. A good correlation between the numerical and the experimental results with variations in water, and glass temperatures of 9, and 18% respectively, and a variation in cumulative productivity by 11%. The results clearly showed that instantaneous productivity increases by decreasing water depth to 10 mm and using the SBSS unit partially insulated from the bottom of the basin. Adding insulation in front of the sides and back of tempered glass increases the shading area and decreases water temperature hence the cumulative productivity by 15%. The cumulative productivity reached 3 L for the SBSS unit partially insulated from the bottom of the basin with an area of 0.6 m² for only 12 h working system at a water depth of 10 mm.

Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting R² values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region.

The Middle East region, with its arid and semi-arid climate, is one of the regions most affected by climate change and water scarcity. To address the severe issue of water scarcity in the western region of Iraq, this study identifies optimal potential rainwater harvesting (RWH) locations. Geographic Information System (GIS) and multi-criteria decision-making (MCDM) techniques were employed to generate themed layers for RWH. The nine primary criteria considered were rainfall, elevation, slope, stream order, soil texture, land use, groundwater depth, distance from the lake, and runoff depth. A weighted overlay assessment was used to identify probable RWH locations. The analytical hierarchical process was used to weight criteria depending on the study region, hydrological and socioeconomic parameters, and literature. The consistency ratio (CR = 3.16%) was calculated to validate the optimum weights of the comparison components, from which it was found that the weights assigned to each criterion were appropriate for comparative purposes. The results indicated that the optimum location (very high suitability) for RWH is mostly in middle regions of the study area, covering 286 km² (13%), while for the other categories, high suitability is at 23% (498 km²), medium suitability at 29% (636 km²), low suitability at 21% (462 km²), and very low suitability at 14% (305 km²). Sensitivity analysis was used to identify the relative importance of the parameters and determine how each of the nine criteria influences the optimal RWH sites. These findings can assist decision makers and planners in devising strategies to mitigate the effects of climate change and increase any reclaimed area for agriculture.

This research examines the efficacy of layered double hydroxides (LDHs) in removing phosphate and nitrate from wastewater, enhanced by the memory effect and in situsynthesis techniques. LDHs were synthesized hydrothermally, initially creating carbonate-based CO₃–LDHs, which were then converted to chloride-based Cl–LDHs through anion exchange. These LDHs underwent calcination at 300 °C, 400 °C, and 500 °C to optimize their structure for enhanced adsorption capabilities. The synthesized LDHs were thoroughly characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) surface area analysis, and X-ray diffraction (XRD). Adsorption experiments in solutions with pH values between 5, 7, and 9 revealed the adsorption capacities of phosphate and nitrate on the CO₃–LDHs and Cl–LDH, respectively. The results indicated that LDHs calcined at 500 °C showed the highest adsorption performance, achieving maximum capacities of 184 mg/g for phosphate and 70.1 mg/g for nitrate. Kinetic studies confirmed that the adsorption process followed a pseudo-second-order model, demonstrating the effectiveness of the memory effect in enhancing ion exchange. The in situ synthesis of LDHs under controlled conditions significantly improved the removal rates of these anionic contaminants from wastewater, proving the potential of this method for the realistic wastewater treatment.

This study focuses on the synthesis and characterization of the molecularly imprinted polymer (PPy-MIP) to remove 17β-Estradiol (E2) from aqueous solutions. The MIP was synthesized using a non-covalent procedure, incorporating the target compound, E2. To synthesis PPy-MIP, a mixture of 300 μl pyrrole and 50 ml distilled water was stirred for 30 min. After adding 3 g ferric chloride as an oxidant, the solution was mixed for 2 h and stored for 48–72 h. MIP capability is compared with a non-molecularly imprinted polymer (NIP) as a control. Various factors such as pH, contact time, dosage, temperature, and concentration were investigated to optimize the performance of the PPy-MIP. The structure of the MIP was confirmed using field emission scanning electron microscopy (FESEM), infrared spectrophotometric spectrum (FTIR), and X-ray diffraction (XRD). The efficiency of the PPy-MIP in removing E2 was obtained 99.97% at optimum condition; while, the NIP achieved a removal efficiency of 69.9%. Adsorption data were fitted with Langmuir isotherms (R² 0.98) and pseudo-second-order kinetics (R² 0.99). The selectivity of the PPy-MIP toward similar compounds such as progesterone and cholesterol was also examined. To understand the adsorption process, thermodynamics, kinetics, and isotherm studies were performed. The MIP showed good reproducibility with only a slight decrease in removal efficiency after multiple absorption and reabsorption cycles. The adsorption of E2 by the MIP followed Langmuir adsorption isotherm and second-order adsorption kinetics. MIP was utilized to pre-concentrate and separate E2 in real samples (urine, blood, hospital wastewater, tap water). This method shows promise for efficient and selective removal of E2 from aqueous solutions.

The escalating depletion and irrational exploitation of global groundwater resources have led to severe ecological and environmental repercussions and exacerbated water scarcity. Therefore, effective, sustainable management remains urgent to ensure the security and balance of water resources. This study utilized an integrated approach that combines Geographic information systems (GIS), remote sensing, and the fuzzy analytic hierarchy process to assess the suitability of artificial recharge in the Mi River watershed, creating 14 thematic layers. FAHP is a crucial tool for assigning relative weights to these layers, enabling a comprehensive assessment of the suitability of artificial recharge. The study area was categorized into five suitability classes with notable seasonal variations. During the wet season, the areas were rated as follows: 5.80%, very good; 35.24%, good; 41.96%, moderate; 16.11%, poor; 0.89%, very poor. These percentages during the dry season changed to 11.02% (very good), 39.80% (good), 34.39% (moderate), 10.39% (poor), and 4.39% (very poor). The central basin regions were deemed less suitable for artificial recharge. The model's accuracy was validated by analyzing receiver operating characteristic curves derived from a dataset of 29 wells. This study provides a scientific foundation for sustainable groundwater management within the Mi River watershed and substantiates the effectiveness of GIS and FAHP in evaluating artificial recharge potential. Future research should improve data accuracy to increase model precision and extend its applicability to various geographical and environmental settings.

This paper reports the preparation of cobalt silicate (Co₂SiO₄, CSO) first by a cost-effective and simple sonochemical route, followed by the fabrication of Co₂SiO₄/g-C₃N₄ (CSO/CN) nanocomposites with different mass ratios by ultrasonic-assisted co-precipitation. We have investigated the photocatalytic performance of Co₂SiO₄, g-C₃N₄, and different Co₂SiO₄/g-C₃N₄ nanocomposites for the degradation of eriochrome black T (EB). This initial instance of CSO integrated with CN demonstrates a superior function in photocatalysis. The outcomes indicated that multiple parameters affected effectiveness, including the amount of CSO, catalyst, and EB. As a result, CSO/CN with a weight ratio of 0.1:1 is the most efficient, which means that 0.07 g of CSO/CN (0.1:1) is capable of degrading 90.0% of 10 ppm EB. Photodegradation reactions were demonstrated by the scavenger tests to be largely influenced by superoxide radicals. The kinetic investigation showed that a bigger rate constant (k = 0.0166 min^‒1) leads to higher efficiency (90.0%). The combination of CSO and CN as composites shows great potential for efficient photocatalytic dye degradation applications. Advanced materials for environmental treatment procedures may be developed as a result of further research and development in this field.

The research was conducted in order to evaluate the management of irrigation in different stages of growth on the yield of the cowpea plant using the DSSAT model. The experiment was conducted in the form of a randomized complete block design with three replications in the north of Iran during 2017 and 2018. The main treatments included irrigation with the management of 40, 60, 80, 100 and 120% of the plant’s water requirements (WRs), and the secondary treatment included irrigation in the vegetative, reproductive and complete stages (vegetative and reproductive). The outcomes indicated that the maximum yield of biomass, pod and seeds when supplying 100% of the water supply in 2017 was 4588, 3222 and 1005 kg/ha, respectively, in both vegetative and reproductive stages. In the interaction of year × WR × growth stages, the maximum water use efficiency based on yield of biomass, pod and seeds in 2018 were under irrigated conditions in the vegetative stage and in dry conditions with the average of 3.57, 2.37 and 0.75 kg/m³. The average values of water productivity components based on irrigation, irrigation and rainfall, evapotranspiration, transpiration and evapotranspiration and deep percolation altogether (WPI, WPI + R, WPET, WPT and WPETQ) was obtained as 0.61, 0.41, 0.39, 0.67 and 0.32 kg per cubic meter, respectively. The outcomes of evaluating DSSAT model showed that the values of RMSE and RMSE_n in calibration conditions were, respectively, 130 kg/ha and 5.91% for biomass yield and 92 kg/ha and 12.62% for seed yield. In the validation conditions, these indicators were, respectively, 129 kg/ha and 5.29% for biomass yield and 73 kg/ha and 8.61% for seed yield. Based on the results, treating 100% of the water requirements in the vegetative and reproductive stages and with transpiration rate of 188 mm is suggested as suitable management for the studied area.

The hydrogeochemistry of groundwater from Binji community and surroundings was assessed. The main aim of the study was to characterize the groundwater in the study area through the application of indexing and statistical methods. Measured _PH revealed 12 samples to be acidic and 15 samples to be alkaline with majority of the samples from dug wells having _PH that indicates alkalinity. The concentration of analyzed chemical parameters in sampled water revealed the dominance of Ca over other metals detected in the groundwater, with the dominance given in the order Ca˃ Mg ˃ Na ˃ K. The anions were dominated by HCO₃ in the order HCO₃ ˃ Cl ˃ SO₄ ˃ CO₃. Ca ion exceeded the Nigerian standard for drinking water quality (NSDWQ) and the World Health Organization (WHO) set limits in one sample, Mg in 14 samples while K exceeded in four samples. HCO₃ exceeded both standards in 21 samples; Total dissolved solids and electrical conductivity were above the maximum permissible limits in four samples. Five different water types were identified in the area with Ca + Mg- HCO₃ dominating other types. Computed water quality index (WQI) for drinking water gave values that are within the range of 18–77.5 indicating the groundwater to be within the excellent and good classes of suitable drinking water quality. Irrigation water quality index revealed the groundwater to be suitable except for the magnesium hazard. Correlation analysis gave a moderate positive correlation that ranges from 0.632 to 0.659 between total hardness, Mg and HCO₃, and that between Mg and HCO₃ indicates the contribution of these elements to the hardness of water. Principal component analysis gave six components with a total variance of 76.19% with eigenvalues above 1 with the range of 1.52 – 4.26. Cluster analysis performed on the hydrogeochemical parameters demarcated four clusters based on the sources, hydrogeochemical environment, and reactions between chemical variables. Correlation between many chemical species revealed formation under a common hydrogeochemical environment. Gibb’s plot showed the sources of chemical constituents of groundwater to be dominated by precipitation source. The groundwater of the area is hard water that is fresh and safe for drinking and other domestic uses.

The objective of the present study is to optimise the removal of metals such as aluminium, zinc, and copper from industrial wastewater using green-synthesised nanoadsorbents. To achieve this, the Box–Behnken experimental design and response surface methodology will be employed. We used inductively coupled plasma mass spectrometry to analyse the metals present in the wastewater. A three-factor, three-stage Box–Behnken design was used to maximise the removal of these metals from aqueous solution. This involved response surface modelling and quadratic programming based on 17 different experimental data from a batch study. The study focused on three independent variables: pH, contact time, and adsorbent amount. The nanoadsorbents were prepared using a combination of Citrus X sinensis peel and Musa Cavendish peel extract, which served as the reducing agents, to produce a combined peel extract-silver nanoparticle product. Field emission scanning electron microscopy imaging and UV–visible spectroscopic analysis unequivocally demonstrated the presence of nanoparticles, with a surface plasmon resonance at 438 nm. The optimal values of the selected variables were determined by solving the quadratic regression model and analysing the contour plots of the reaction surface. At the experimental conditions of pH = 5, contact time = 92.5 min, and adsorbent dosage = 0.1 g/L, the recovery efficiency of Al, Cu, and Zn was significantly reduced. The optimised parameters were successfully applied to wastewater collected, and the degradation of detected metal ions was tested. The experiment demonstrated an effective reduction in these metals.