Abstract Improving the operational effectiveness of water structures requires effective hydraulic design. Significant changes in water levels, velocity distribution, heading up, and energy loss occur at the entrance zone of these structures because of the interaction between the canal inside slope and upstream wing walls geometry. However, prior research has not adequately assessed the integrated hydraulic effects of these characteristics. This study experimentally examines the interplay between wing wall type and canal inside slope on the hydraulic performance of water structures. A total of 360 laboratory experiments were conducted using four upstream wing wall configurations (box, broken, curved, and splayed) and three canal inside slopes (Z = H:V = 1:1, 3:2, and 2:1), representing common conditions in Egyptian irrigation canals. The findings show that hydraulic behavior is strongly influenced by both parameters. When compared to the box type, the splayed configuration exhibited the lowest energy losses and afflux, with reductions of up to 84.12% and 30.01%, respectively. Furthermore, steeper slopes were linked to higher afflux and energy losses, while the 1:1 canal inside slope continuously exhibited maximum hydraulic efficiency. New empirical relationships were developed to enable the prediction and optimization of irrigation structure performance. The outcomes support sustainable water management by increasing operational dependability, lowering maintenance requirements, and increasing hydraulic efficiency. For both new construction and rehabilitation projects, the study suggests giving priority to splayed wing walls in conjunction with a 1:1 canal inside slope, considering the soil conditions.

Agriculture is transitioning from traditional methods to precision-based approaches supported by digital technologies and smart systems. A key innovation in this transformation is precision agriculture where fertigation plays a crucial role by integrating water and nutrient management into a real-time and site-specific approach. Recent advances in precision fertigation such as IoT (Internet of things)-based sensors could improve water use efficiency (WUE) by 61–64.1% and increase crop yield by 12–35%. Irrigation based on time-domain reflectometery (TDR) approach could increase WUE by 40–56%, while GreenSeeker technology may enhance nutrient use efficiency (NUE) by 20–45%. Drip fertigation improved WUE by 9–70% and NUE by 16.5–29% and improvised soil enzyme activity such as catalase (2.81–93%), urease (1.84–31%) and phosphatase (5.34–61%). Despite these benefits, challenges such as high initial investment, environmental variability data management issues, cybersecurity concerns, limited technical knowledge, infrastructure gaps, interoperability problems and the lack of field-specific calibration issues persist. This review examines technological advancements, agronomic principles and field-level applications of precision fertigation, highlighting its contributions to enhance crop productivity and maintain long-term soil health. Drawing upon 266 relevant studies and innovations over last 2 decades, this review elaborates the implementation challenges, research gaps and future directions in scaling precision fertigation within diverse agro-ecological settings. Additionally, it examines the economic implications and policy frameworks necessary to facilitate widespread adoption, particularly in smallholder farming systems. Emphasis has been placed on the role of components and their integration with advance technology in optimizing fertigation scheduling and minimizing environmental footprints. Ultimately, this study underscores precision fertigation as a promising approach for resilient, sustainable and climate-smart agriculture.

This study evaluated physicochemical parameters and heavy metal contamination in the Dehloran watershed during the dry (September 2020) and wet (March 2021) seasons. Surface water samples (n = 62) were selected based on natural conditions and river accessibility, and groundwater samples (n = 10) were collected from low-lying areas. Physicochemical parameters covering organic load, ionic composition, nutrients, and salinity (e.g., BOD, COD, DO, EC, pH, TDS, major cations–anions, and turbidity) and heavy metals (As, Cd, Cr, Cu, Fe, Zn) were analyzed using standard methods and ICP‑MS. Paired t-test revealed significant (p < 0.05) seasonal contrasts: EC, TDS, Ca, Cl, SO4, Cu, Cr, and As were elevated in the dry season, whereas DO, TH, HCO3, and Mg increased during the wet season. Surface water had higher quality (IRWQISC = 36.4) than groundwater (IRWQIGC = 15.5), likely reflecting agricultural and anthropogenic impacts, with better quality observed upstream than downstream. Toxic parameter values were also slightly higher in surface water (IRWQIST = 55.3) than in groundwater (IRWQIGT = 54.3). PCA extracted four principal components in both seasons, dominated by salinity hardness and heavy metal factors. Piper plots revealed Ca–SO4 facies associated with gypsum rich Gachsaran formation and regional sulfur and bituminous springs. Wilcox classification showed over 60% of samples as highly saline, limiting irrigation suitability. Results from the heavy metal evaluation index (HEI) and contamination index (Cd) consistently indicated overall low levels of heavy metal contamination. These findings provide quantitative evidence supporting targeted mitigation and sustainable water resource management in semi-arid basins.

Abstract Water scarcity poses major constraints to sustainable rural development, particularly in arid regions. In Egypt, limited freshwater resources are increasingly prioritized for domestic use, compelling proposed large-scale land reclamation projects to rely on brackish groundwater. However, marginal water quality restricts cultivation to salt-tolerant crops, undermining the long-term profitability of ongoing agribusiness activities. This study is the first to evaluate the techno-economic viability of integrating decentralized desalination systems into the Moghra development area. A systematic hydrochemical assessment of 73 wells, using the Irrigation Water Quality Index (IWQI), classified 49 as “Severe Restriction” and 24 as “High Restriction”, confirming widespread concerns about groundwater suitability. A two-stage reverse osmosis (RO) desalination system powered by photovoltaic (PV) energy was designed to achieve a 70% recovery rate. An optimization model identified blending ratios that maximize post-treatment water quality while minimizing the desalinated water volume. Results showed substantial improvements: the average sodium adsorption ratio (SAR) decreased by 66%, and IWQI increased from 34 to 77. Consequently, 68 wells were reclassified as “Low Restriction” and 5 as “Moderate Restriction”, enabling a shift from salt-tolerant olives to higher-value crops (e.g., wheat–maize rotation). A cost–benefit analysis assessed trade-offs between desalination costs and resulting economic returns. Under the abstraction limit, the proposed RO–PV blending strategy yielded a 35% higher net present value (NPV) and a 15.7% internal rate of return (IRR), demonstrating both technical and financial viability. These findings provide actionable insights for policymakers, stakeholders, and investors to enhance water productivity and agricultural sustainability in arid regions.

Abstract Conventional wastewater flow-control strategies often fail to respond effectively to rapidly changing hydraulic conditions, resulting in increased overflow events, excessive energy consumption, and reduced operational resilience. Existing AI-based approaches primarily focus on prediction or process-level optimization, offering limited capability for autonomous, system-wide flow management. To overcome these limitations, this study introduces AquaFlowNet, a reinforcement learning–driven framework designed for real-time wastewater flow optimization across interconnected sewer networks. The proposed RL-DFC agent dynamically learns optimal valve and pump actions from environmental feedback, enabling adaptive control under uncertain inflow conditions. Simulation results demonstrate that AquaFlowNet achieves substantial improvements over existing rule-based and machine learning controllers, including a 52% reduction in total overflow volume, up to 90% reduction in overflow events, 35–40% improvement in peak-flow attenuation, and 230 kWh reduction in aeration-related energy demand. These findings indicate that AquaFlowNet can transform wastewater management from prediction-centric approaches into autonomous, resilient, and scalable network-level control, supporting the development of next-generation smart wastewater infrastructure.

Abstract 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.