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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The growing population and increasing urbanization have led to a surge in domestic wastewater generation, posing significant challenges for effective and sustainable treatment. The present study demonstrates a novel and sustainable approach for the onsite treatment of domestic wastewater using an integrated settler-based biofilm reactor (ISBR) with efficient biogas generation. The ISBR provides an optimized environment for the growth of biofilm, facilitating the removal of organic pollutants and pathogens. Moreover, the ISBR enables the recovery of a valuable resource in the form of biogas, thus enhancing the overall utility of the treatment process. The performance of the ISBR was comprehensively evaluated at laboratory scale through treating the actual domestic wastewater generated from the hostel of Manipal University Jaipur. The ISBR system was operated under an ambient environment at a hydraulic retention time (HRT) of 24 h. The results demonstrated remarkable efficiency in terms of chemical oxygen demand (COD), total suspended solids (TSS), and coliforms removal, with average removal efficiency being more than 90%. According to the COD mass balance analysis, 48.2% of the influent COD was recovered as bioenergy. The chromatogram revealed a high percentage of methane gas in the collected biogas sample. The field emission scanning electron microscope (FESEM) analysis of the accumulated sludge in the ISBR system depicted the morphology of methanogenic bacteria. Both the experimental and theoretical results confirmed the feasibility and sustainability of the ISBR system at the onsite level.

In Egyptian desert, growing plants is difficult due to harsh climate (hot at the daytime and cold at the night), infertile soil, low average rainfall and lack of fresh water for irrigation purposes. A set of simple transparent solar stills are integrated with a new solar driven agriculture greenhouse (GH). The stills are placed at the GH roof to use the extra solar radiation (above that required for plant photosynthesis process) for water desalination. In addition to water desalination concept the solar still units even reduce the cooling load during the daytime. A net of aluminum metal coated with black colour is placed on the base of the solar still units to raise the water temperature (enhance desalination process) and provide partially shading for the GH. Using aluminum net decreases also the number of solar still units required to produce the required amount of GH fresh water leading to a significant cost reduction.The main objectives of this work are sizing of the aluminum net, spacing between solar still units to obtain the threshold of plant requirements. Also fresh water production and greenhouse climatic conditions that plant needs (temperature, relative humidity, air velocity and amount of oxygen) are simulated.Numerical simulation was carried out for the hottest day of Borg Elarab, Alexandria (Egypt). 

Petroleum oil pollution is a worldwide problem that results from the continuous exploration, production, and consumption of oil and its products. Petroleum hydrocarbons are produced as a result of natural or anthropogenic practices, and their common source is anthropogenic activities, which impose adverse effects on the ecosystem’s nonliving and living components including humans. Phytoremediation of petroleum hydrocarbon-polluted soils is an evolving, low-cost, and effective alternative technology to most traditional remediation methods. The objective of this study is to evaluate the phytoremediation potentiality of Vinca rosea for crude oil-contaminated soil by understanding its properties and involvement in the enhanced degradation of crude oil. The remediation potentiality was determined by evaluating the total petroleum hydrocarbon degradation percentage (TPH%) and changes in the molecular type composition of saturated and aromatic hydrocarbon fractions. TPH% was estimated gravimetrically, and changes in the molecular type composition of saturated and aromatic fractions were measured using gas chromatography and high-performance liquid chromatography, respectively. Sulfur concentration was measured using X-ray fluorescence. Cadmium and lead quantification was measured using Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES). The results revealed that V. rosea enhanced total petroleum hydrocarbon (TPH) degradation and altered the molecular composition of the crude oil. The saturated hydrocarbons increased and the aromatic hydrocarbons decreased. The saturated hydrocarbon fraction in the crude oil showed a wider spectrum of n-paraffin peaks than the oil extracted from unplanted and V. rosea-planted soils. Polyaromatic hydrocarbon degradation was enhanced in the presence of V. rosea, which was reflected in the increase of monoaromatic and diaromatic constituents. This was parallel to the increased sulfur levels in planted soil. The determination of sulfur and heavy metal content in plant organs indicated that V. rosea can extract and accumulate high amounts from polluted soils. The ability of V. rosea to degrade TPH and alter the composition of crude petroleum oil by decreasing the toxicity of polyaromatic hydrocarbons in soil, as well as its capability to absorb and accumulate sulfur and heavy metals, supports the use of plant species for the phytoremediation of crude oil-polluted sites.

Land use and climate change always induce significant changes in various parameters of the hydrologic cycle (e.g., surface runoff, infiltration, evapotranspiration). The Wadi El-Assiuti downstream area in the Eastern Desert of Egypt is one of the most promising areas for development that is suffering from insufficient water availability and inadequate water quality for different purposes. The main goal of this research is to evaluate the changes in groundwater quality, land use, and climate in association with geology and flooding during three periods within the years 1997–2019 in the downstream portion of Wadi El-Assiuti in the Eastern Desert of Egypt, using spatiotemporal variation associated with groundwater hydrochemical analysis and GIS techniques. About 133 groundwater samples were collected to examine groundwater quality changes over time. Different groundwater quality indices were calculated, and the results show that TDS levels of groundwater in the study area ranged between 1080–2780 mg/L, 672–4564 mg/L, and 811–6084 mg/L, while SAR levels varied within 6.15–15.34, 1.83–28.87, and 1.43–30.57 for the years 1997, 2007, and 2019, respectively. Both RSBC and SSP values exhibited significantly increasing trends over time. KR values were within 1.36–4.06 in 1997, 0.58–14.09 in 2007, and 0.35–14.92 in 2019; MAR values were within 6.9–45.2 in 1997, 20.79–71.5 in 2007, and 17.71–75.81 in 2019; and PI values were within 60.16–83 in 1997, 45.56–101.03 in 2007, and 42.51–148.88 in 2019. Across the entire study area, ongoing land use changes increased from 1.1% in 1997 to 4.1% in 2019. Findings pointed to the significant contribution of the deep Nubian Sandstone Aquifer to the groundwater aquifer at Wadi El-Assiuti through fractures and deep faults. Given the climatic conditions from 1997–2019, these changes may have affected water quality in shallow aquifers, especially with increasing evaporation. Realizing the spatiotemporal variation of the aquifer recharge system, land use development, and climate change clearly would help in water resource management. This study revealed that flooding events, deep-seated geologic structures, and land use development associated with human activities have the highest impact on groundwater quality.

The food and agricultural industries have numerous practical advantages to be gained from the use of cold plasma technology. This paper attempts to showcase the possible uses of cold plasma in the food sector, while also highlighting the most recent developments and market trends. The efficiency of cold plasma in enhancing food products’ quality and shelf life has been demonstrated in several investigations. This review has concentrated on current research into how this technology affects various food chain production stages. Cold plasma has become a cutting-edge non-thermal technique that can be used to ensure food safety. The precise mechanism underlying the effectiveness of cold plasma is still unclear. Understanding these mechanisms and potential elements that can restrict or increase their effectiveness and results is crucial to further enhancing and implementing cold plasma treatment in food processing. The main objective of this review is to investigate the use of plasma, its exceptional characteristics, and its advantages in safe, sustainable food production. In particular, this review summarizes recent studies on the use of cold plasma for microorganisms and pesticides treatment, compiling them and discussing their content. As reported in the literature, a critical point has also been reviewed about some diverse plasma configurations. A comparative study of the efficacy of cold plasma in environmental applications (microorganisms/pesticides) has also been reviewed from the literature.

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

With the rising population, environmental pollution, and social development, potable water is reducing and being contaminated day by day continually. Thus, several researchers have focused their studies on seas and oceans in order to get potable fresh water by desalination of their saltwater. Solar still of basin type is one of the available technologies to purify water because of free solar energy. The computational fluid dynamic CFD model of the solar still can significantly improve means for optimization of the solar still structure because it reduces the need for conducting large amount of experiments. Therefore, the main purpose of this study is presenting a multi-phase, three-dimensional CFD model, which predicts the performance of the solar still without using any experimental measurements, depending on the CFD solar radiation model. Simulated results are compared with experimental values of water and glass cover temperatures and yield of fresh water in climate conditions of Sheben El-Kom, Egypt (latitude 30.5° N and longitude 31.01° E). The simulation results were found to be in acceptable agreement with the experimental measured data. The results indicated that the daily simulated and experimental accumulated productivities of the single-slope solar still were found to be 1.982 and 1.785 L/m2 at a water depth of 2 cm. In addition, the simulated and experimental daily efficiency were around 16.79% and 15.5%, respectively, for the tested water depth.