Applied Water Science最新文献

The water-stressed Barind Tract (BT) in northwest Bangladesh faces critical challenges in meeting irrigation and domestic water demands, with groundwater tables declining at rates of 0.09–0.39 m per year due to over-extraction and limited natural recharge through thick clay aquitards (21–39 m). This study evaluates the potential for managed aquifer recharge (MAR) potential across three districts of BT (Godagari, Niamatpur and Mohanpur) using integrated horizontal flow treatment units. We analyzed 376.68 km of re-excavated Kharies (natural canals) and 257.01 acres of Beels (water reservoirs), incorporating seventeen years (2005–2021) of groundwater monitoring data and nineteen years of rainfall analysis (2002–2020). Field-verified recharge rates of 29 L/min for Kharies and 162.7 L/min for Beels were used to calculate potential MAR installations using the rational formula. Our analysis identified 3,315 feasible MAR installation sites (3,251 in Kharies: Godagari-966, Niamatpur-2276, Mohanpur-9; and 64 in Beels: Godagari-4, Niamatpur-14, Mohanpur-46), capable of recharging 27.10 million m³ annually from 50.61 million m³ of surface runoff, achieving 53.5% utilization efficiency. Groundwater monitoring showed declines of 6.6 m in Godagari, 5.24 m in Niamatpur, and 1.6 m in Mohanpur over the study period. Annual rainfall averaged 1231 mm, 1414 mm, and 1132 mm respectively, with 90% occurring during May–October monsoon periods. Performance monitoring of existing MAR installations demonstrated successful water quality maintenance within national drinking water standards (ECR 2023), with parameters including calcium (59.03–63.87 mg/L), iron (0.58–0.86 mg/L), and pH (7.26–7.29) remaining within acceptable limits while achieving groundwater level improvements up to 2.01 m. The integrated treatment units overcame natural recharge limitations of 2–3 mm/day through thick clay aquitards, achieving 10,000-fold improvement in infiltration rates. This assessment provides a scalable and replicable methodology for sustainable groundwater management strategies in water-stressed regions globally, with phased implementation of identified MAR installations offering a pathway to reverse groundwater depletion trends.

The current work aimed to develop a sustainable and eco-friendly method for treating leather industry wastewater using Azadirachta indica (neem) leaf extract and copper nanoparticles (Cu NPs) synthesized through a green synthesis approach from the same plant source. Three treatment phases were designed: (i) characterizing untreated (pre-treated) wastewater, (ii) treating with A. indica leaf extract at a 10:200 mL ratio, and (iii) treating with Cu NPs at 0.5 g/L in addition to A. indica extract. Key water quality parameters such as pH, chemical oxygen demand (COD), biological oxygen demand (BOD), total dissolved solids (TDS), total suspended solids (TSS), and chromium (Cr) concentration were analyzed for each case and compared to the Pakistan Environmental Quality Standards (PEQS). The results revealed considerable increases in water quality following treatment. Treatment with A. indica extract alone resulted in significant COD, BOD, TDS, TSS, and pH reductions, whereas the addition of Cu NPs increased removal efficiency even more. COD dropped from 510 to 40 mg L⁻¹, BOD from 145 to 116 mg L⁻¹, TDS from 3829 to 499 mg L⁻¹, and pH fell from 9.0 to 6.5, bringing the treated water closer to regulatory norms. Furthermore, the maximum Cr removal was 98%, equivalent to an adsorption capacity of 49 mg/g at an equilibrium pH of 5, a contact period of 2 h, and a Cu NPs dose of 0.5 g L⁻¹. To better understand the adsorption behavior and process, equilibrium data were fitted to several isotherm models, with the Langmuir isotherm model providing the best fit, implying monolayer chemisorption as the dominant mechanism for Cr removal. This green treatment strategy, which combines plant-based bioactive chemicals with metal nanoparticles, provides a cost-effective and environmentally friendly option for reducing pollution from the leather industry. The study proposes implementing this technology on an industrial scale for optimal wastewater management and environmental preservation.

Treated wastewater is increasingly utilized for irrigation and artificial groundwater recharge, particularly in dry regions such as the Middle East, where water scarcity demands sustainable resource management. However, reclaimed water may contain trace levels of pharmaceutical compounds, which can pose environmental and health concerns if not properly managed. These compounds could leach through the soil layers and contaminate groundwater, particularly when reclaimed water is used for irrigation and artificial recharge. Therefore, this study aimed to assess the potential for percolatoin through soil and leaching of eleven pharmaceutical compounds in four different soil types following the OECD 312 protocol. Solid phase and ultrasound-associated extractions were used to extract these compounds from 80 samples (16 leachate samples and 64 soil samples) which were subsequently analyzed using LC-MS/MS. The extraction results showed high recovery for most of the investigated compounds, reaching up to 109%, with an average recovery of 64%. The soil column results indicated that caffeine and cephalexin exhibited higher mobility, as they were detected in the leachates collected from the soil columns. In contrast, the other compounds showed strong sorption to soil particles, with notable accumulation in the top 5 cm of the soil profile. Caffeine and cephalexin penetrated up to 30 cm of the soil column and were detected at low concentrations (≤ 0.42% of their initial spiked concentration) in the leachate. These findings suggest that these caffeine and cephalexin may pose a potential risk of groundwater contamination due to their slight mobility in certain soil types. Overall, it can be concluded that most of the studied compounds exhibit limited leaching behavior across the four different studied soil types, indicating a negligible risk of potential pollution of groundwater from wastewater discharge or reuse.

Surface waters across Ghana are deteriorating due mainly to poor farming practices, illegal mining activities and also climate change. In the Central region this has led to a rise in the dependence on groundwater as potable water supply. However, the Central Region is known to be characterized by high unsuccessful rates of borehole drilling for groundwater, which usually results in waste of time and resources. The need to delineate groundwater potential areas in the region has long been felt. This study sought to map and delineate the groundwater potential zones of the Central Region by integrating input variables such as lineaments map deduced from magnetic survey data, digital elevation model, geology, soil type, land use/land cover, drainage density, slope and flow accumulation maps using Fuzzy Logic in a GIS software. The final groundwater potential map of the area was validated using borehole yield data and the reliability testing executed using the area under curve operation technique. Results of the study revealed that the region is characterized by very low to high groundwater potential zones. High groundwater potential zones cover the least of about 1083.7 km², representing 11.17% and moderate groundwater potential zones cover about 1978 km² constituting 20.4%. About 3461.16 km² of the region representing 35.68% and 3176.88 km² (32.74%) were found to have low and very low groundwater potential respectively. The final output revealed that the high potential areas are mainly located in the central part of the region which is mainly occupied by fractured granitoids. The low groundwater potential areas are mostly encountered in the southeastern but are found also in the northern and central part of the region and fall mainly on non-porous metavolcanics rocks. It is anticipated that the groundwater prospectivity map could be used as a valuable source for sustainable water resource management and development in the Central Region and also serve as guide for drilling campaigns in the region.

Effective water resource management requires a comprehensive understanding of hydrological processes, particularly the interactions between surface water and groundwater systems. In the Astaneh-Kouchesfahan region of northern Iran, strong hydraulic connections exist between rivers and aquifers, making it an ideal case for integrated modeling. This study proposes an innovative hybrid approach that combines numerical and soft computing techniques to simulate surface water–groundwater (SW–GW) interactions. First, the Soil and Water Assessment Tool (SWAT) model was employed to simulate surface hydrology and estimate aquifer recharge from rivers. The estimated recharge was then coupled with a groundwater model developed using MODFLOW to simulate subsurface flow and quantify river–aquifer exchanges. In the third stage, several machine learning models, including Gaussian Process Regression (GPR), Least Squares Support Vector Regression (LSSVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-PSO, and ANFIS-RSA, were applied to predict the magnitude of SW–GW interactions using input variables such as topography, groundwater level, surface recharge, river leakage, riverbed properties, and river stage. The results demonstrated that SWAT provided reliable recharge estimates for groundwater modeling, while MODFLOW revealed stronger river–aquifer exchanges in the southern part of the basin, where rivers predominantly recharge the aquifer. Conversely, in much of the northern region, the aquifer discharged to rivers. Among the machine learning models, LSSVR achieved the best performance (RMSE = 2748.6 m³/month, MAE = 1884.1 m³/month, NRMSE = 0.36, NSE = 0.87). Although the standalone ANFIS model showed limited accuracy, its performance was significantly improved when optimized with RSA and PSO algorithms. Moreover, the extent of SW–GW interactions in a given cell was strongly influenced by the interactions in adjacent cells and by surface recharge. Overall, the proposed integrated numerical–soft computing framework offers a cost-effective and accurate tool for modeling surface water–groundwater interactions and can be effectively applied to similar hydrological systems.

The study aimed to assess the suitability of Lelliso River water for drinking and irrigation. The Weighted Arithmetic Water Quality Index (WQI) and Water Pollution Index (WPI) were applied to evaluate its quality. Irrigation suitability was further examined using united state salinity laboratory (USSL) and Wilcox diagrams. Pollution sources were identified through Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), while health risks were assessed using the Hazard Index (HI). According to the Weighted Arithmetic Water Quality Index (WAWQI), Lelliso River water was unsuitable for drinking (223.8-424.4 in rainy season; 79.6–197 in dry season). For irrigation, quality ranged from good to unsuitable (50.8-157.7 rainy; 51.6-138.9 dry). The Water Pollution Index (WPI) for drinking was 0.69–0.92 (rainy) and 0.74–1.05 (dry), while for irrigation it was 0.38–0.67 (rainy) and 0.49–0.81 (dry). Overall, the river was unsuitable for drinking but safe for irrigation. The USSL diagram confirmed suitability for irrigation, whereas the Wilcox diagram placed all samples in the S3 category, indicating unsuitability. With the highest hazard index (HI = 1.165) at S1 during the rainy season surpassing the acceptable limit, suggesting possible non-carcinogenic risk, health risk assessment revealed ingestion as the predominant exposure channel, especially among children’s. Soil erosion, rock weathering, solid waste disposal, and urban and agricultural runoff were found to be the primary sources of pollution by PCA and HCA. Overall, the river is unfit for drinking, and presents health risks to children due to nitrate.

Predicting water usage is a crucial aspect of organization and management of water resources. Developing a highly precise model for forecasting water consumption is significant in enhancing regional water resource management and supporting sustainable growth in the socioeconomic sector. This study investigates the primary elements that influence water consumption. This study examines the water consumption prediction model to enhance the efficiency of selecting its parameters and offers insights for analyzing regional water usage as well as for planning and managing water resources. Utilizing data on water consumption and its influencing factors from 2003 to 2023, it aims to forecast future water usage. This research employs the developed Inception-V4 (IV4) network by Enhanced Single Candidate Optimization (ESCO), for analyzing regional water consumption. The extracted effective factors have been utilized as input samples for the developed model to forecast water consumption over the next 15 years. The findings indicate that the RMSE and the MAE for modeling that have been made using the developed IV4 are 0.0519 and 0.0407 respectively. This result shows superior performance compared to other models in terms of prediction error and trend accuracy.

Climate change affecting arid and semi-arid regions increases the periodicity and intensity of droughts resulting in a need for the development of effective groundwater exploration techniques. Here, the availability of near-surface groundwater in the Main Karoo Basin (MKB) is evaluated using multivariate machine learning models. These models integrate 21 conditioning factors ranging from spectral indices, topographical features, geological formations, and hydrological parameters. Among the five machine learning (ML) models tested, the Fast Tree Decision Learning models achieved the highest classification accuracy (81.4%) and a robust Receiver Operating Characteristic (ROC) area curve of 0.87. The resultant near-surface groundwater prospectivity model showed a statistically significant (p < 0.00001) alignment with the spatial locations of high-yielding boreholes, springs, and groundwater-dependent vegetation. Areas with a high potential for near-surface groundwater were identified along the Drakensberg Escarpment, the Cape Fold Belt, and along the eastern MKB adjacent to the Indian Ocean. In the arid western MKB, localized zones identified to be highly prospective for near-surface groundwater coincide with the intersections of drainage networks and major geological structures. Geo-hydrologically, these areas are characterized by borehole yields exceeding 9 L/s. This study illustrates the effectiveness the ML models that harness regional datasets in characterizing prospective areas for near-surface groundwater in data-scarce, arid environments.

Spur dikes are flow regulators used in a wide range of streams and open channels. Recognizing the site-specific nature of their design, this study experimentally and numerically investigated the impact of spur dike height, length, and spacing on upstream water levels within a sediment-free open channel. Reynolds-averaged Navier-Stokes (RANS) equations and the renormalization group (RNG) k-ε turbulence model are used for the numerical analysis. Contrary to classical one-sided spur settlements existing in the literature where height primarily causes localized effects with minimal upstream impact, the configurations examined in this study led to a significant increase in upstream water levels, especially as the spur length-to-channel width ratio (l/B) increased. The findings showed that the length of the spurs also causes significant changes in the upstream water level for large discharges. The analysis indicated that spur length, alongside height, can elevate the upstream water levels by up to 350% at low Froude numbers, depending on its placement within the channel. Notably, the spacing between spurs exhibited the least influence on upstream water levels; increasing this distance resulted in a reduction of the upstream water level and induced secondary flow formation in the inter-spur regions. These results highlight the importance of considering spur length when designing these flow regulators, especially in scenarios where upstream water level management is the primary objective. The findings offer valuable guidance to engineers on how to optimize the geometric configuration of grouped spur dikes to effectively regulate flow under different hydraulic conditions. The results also provide practical insights for developing design strategies that balance water level control, flow stability, and channel conveyance efficiency in river engineering applications.