Applied Water Science最新文献

Contaminated drinking water poses a significant threat to public health, particularly in urban areas where industrial and environmental pollutants may affect water quality. However, there is a lack of comprehensive studies that evaluate the specific health risks associated with harmful metal contaminants in drinking water. This study seeks to address this gap by assessing water quality and metal contamination using pollution indices and human health risk assessments. The findings will help to identify potential health risks for urban residents and guide the development of targeted interventions and improved water management strategies. The groundwater samples were collected from five different zones in Kasur rural area. A total of 25 samples were collected by random sampling from hand pumps during 4 months (March–June, 2021) for determining various physiochemical attributes (pH, electric conductivity, turbidity, total hardness, chloride, and phosphate) and potentially toxic elements (arsenic, cadmium, and lead) using standard protocols. Results revealed that almost all the physicochemical attributes were close to the World Health Organization (WHO) guidelines. The water quality assessment revealed that pH levels ranged from 7.4 to 9.0, electrical conductivity (EC) between 150 µS/cm and 800 µS/cm, and average turbidity of 12 ± 3.29 NTU, total hardness varied from 200 to 1000 mg/L. Chloride and phosphate concentrations averaged 304 ± 1.28 mg/L and 4.51 ± 1.99 mg/L, respectively. Cadmium levels ranged from 0.15 to 0.53 mg/L, while lead and arsenic concentrations reached up to 7.47 mg/L, exceeding the WHO guidelines. Heavy metal pollution index (HPI) values of all sites were less than critical value of 100. However, by considering the HPI classes, all the locations had high HPI (> 30) class indicating critically polluted water with heavy metals. Through exposure to drinking water, heavy metals had a significant impact on non-carcinogenic risk (HI > 1), according to the hazard index values determined by the human health risk analysis for children, infants, and adults. As compared with metals carcinogenic risk values, lead posed high risks to adults than children and infants as mean CR values for adults, children, and infants were 1.48E + 00, 1.40E + 00, and 7.60E-01, respectively. It is suggested that for drinking water supplies, there is need of installation of treatment plants in the industrial areas to minimize the risk of metal contamination and health issues.

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.

Floods, which cause loss of life and property and destruction of the environment, have devastating effects on socio-economic welfare. Slit-check dams are essential structures for managing the transport of silt and woody debris, especially in events of significant floods. The current study presents the hydraulic characteristics of slit-check dams with different geometries for experimental and numerical tests. First, the Butterfly model was produced with a 3D printer and examined experimentally. Then, the Butterfly model was validated extensively using OpenFOAM (v7) software for the numerical analysis. Finally, the other models were examined numerically using the k-ε turbulence model. The changes in water surface profile, velocity profiles, energy dissipation rates, and streamlines were comprehensively examined and discussed. The results showed that slit-check dams caused hydraulic jumps and dissipated flow energy. The Arced and Rectangular models, in particular, demonstrated a significant performance for energy dissipation, which is essential for flood management. Water surface profiles are directly affected by discharge. Moreover, the cross-sectional length of the model in question significantly affects the water surface profile. Accordingly, an increase was observed in the velocity profiles along the slit-check dam. While the maximum velocity for all unit discharge was observed in the V-shaped model, the minimum velocities were observed for the Arced and Rectangular models. Thus, the energy absorption performance of Arced and Rectangular models is higher.

Outstanding photocatalytic performance can be achieved by designing and building heterojunction photocatalysts with a suitable interfacial contact and staggered energy band structure. A simple two-step technique was used to manufacture hybrid inorganic/organic nanocomposites made of copper manganese oxide (CuMn2O4) and g-C3N4. Multiple techniques were employed to characterize the hybridized CuMn2O4/g-C3N4 heterostructure. CuMn2O4/g-C3N4 (0.2:1) efficiently destroyed 91% of erythrosine (10 ppm) below visible lamp in 90 min, being better than the performance of both CuMn2O4 and g-C3N4 and has superior stability. The primary reactive species involved in the photocatalytic breakdown of erythrosine over the nanocomposite were photogenerated superoxide ion radicals. The research results led to the proposal of a photocatalytic mechanism via the nanocomposite for the degradation of erythrosine. Based on the experimental data, a unique S-scheme model was presented to illuminate the charge transport mechanism. This work offers a straightforward method for creating innovative step-scheme photocatalysts for environmental and associated applications. This study revealed that the combination of CuMn2O4 and g-C3N4 as composites shows great potential for efficient photocatalytic dye degradation applications.

A novel, cost-efficient Nanophotonic Enhanced Solar Membrane Distillation (NESMD) system, a solar-driven water desalination technology, was studied. The system features a photothermal membrane acting as a solar collector for water distillation, thus eliminating the need for an external condenser. To address the system’s vulnerability to thermal losses, a comprehensive mathematical model was developed and validated against real-world experimental data. This model represents intricately coupled heat and mass transfer within a sweeping-air NESMD system, incorporating heat loss considerations. The modeling strategy involved dividing the NESMD module into sub-cells and implementing a finite difference method for detailed analysis. This led to a series of nonlinear simultaneous equations, which were resolved via computational code using MATLAB software. The developed NESMD model exhibited commendable conformity to experimental data, exhibiting a relative percentage error of less than 10% for average permeate flux and identifying thermal losses as high as 63%. Depending on the operating conditions, heat transferred to the surroundings takes the lead among the heat loss contributors at higher feed rates (up to 25%), whereas heat conduction across the membrane dominates (up to 42%) thermal losses at low feed rates. The study established an exponential correlation between permeate production and solar energy, with a heat transfer coefficient ranging from 9.5 to 30 W m⁻² K⁻¹ and a coefficient of determination of 0.96. An integral part of this work includes calculating solar energy utilization and clarifying the system’s performance. Furthermore, this study examines the influence of diverse operational and geometric parameters, providing insights into enhancing production rates. Hence, an increase in feed layer thickness enhances freshwater production by 7%. Due to the intensification of solar irradiance, freshwater production increased ninefold, and specific energy consumption decreased by 134 kW hr m⁻³. This research underscores the potential of NESMD for sustainable desalination, providing a validated model that lays the groundwork for future advancements in membrane distillation technology.

In current study, a novel adsorbent of CuFe₂O₄/CuS magnetic nanocomposite (MNC) was constructed via a green approach for tetracycline (TC) removal. The leaf extract of the Alhagi pseudalhagi plant was employed as a green reductant agent. The features of the nanocomposite were characterized using XRD, FTIR, FESEM, TEM, BET, and VSM. Batch studies were conducted to assess the impact of parameters, including pH (3.0–9.0), adsorbent dosage (0.025–2 g/L), TC concentration (5–100 mg/L), and temperature (5–50 °C) on the TC adsorption efficiency. The antibiotic was fully removed at pH 7.0, nanocomposite dose of 1.5 g/L, time of 200 min, and TC content of 5 mg/L. Based on the thermodynamic study, the TC adsorption onto the CuFe₂O₄/CuS MNC occurred spontaneously and was primarily driven by physical interactions (physisorption). Positive values of ∆H° (enthalpy change) and ∆S° (entropy change) demonstrated that the adsorption process is naturally endothermic, and the degree of dispersion improves with rising temperature. Adsorption kinetics was well fitted by the pseudo-second-order model. The isotherm studies showed that TC can be removed by the adsorbent at a maximum of 31 mg/g. Overall, CuFe₂O₄/CuS MNC exhibited notable efficacy and cost-effectiveness (reusability: 5 times) for the TC adsorption from water.

Wastewater discharge is important in numerous areas of industries and in governance of the environmental sectors. Controlling and monitoring water pollution are essential for protecting the availability of water and upholding standards of sustainability. Thus, in the current study, the effects of pollutant discharge concentration (PDC) are considered while analyzing the flow of non-Newtonian nanofluids (NNNF) through the permeable Riga surface subject to heat radiation. Walter’s B fluid (WBF) and second-grade fluids (SGFs), two distinct types of NNNF, have been investigated. The fluid flow is expressed as a system of PDEs, which are simplified into lower order by employing similarity approach. These equations (ODEs) are solved using the Levenberg Marquardt back-propagation optimization algorithm (LMBOA) of the artificial neural network (ANN). The Matlab package “bvp4c” is used for generating the dataset in order to validate the results of the ANN-LMBOA. The dataset was developed for various flow scenarios, as well as ANN evaluation and validation. The accuracy of the ANN-LMBOA model is estimated though numerous statistical tools, i.e., histogram, regression measures, curve fitting, performance plots, and validation tables. The numerical outcomes of bvp4c package are also compared to the published literature. Which show best accuracy and resemblance with each other for the limiting case. The targeted date absolute error is accomplished within the range of 10^–4-10^–5 which confirms the outstanding accuracy of ANN-LMBOA. It is concluded form error histograms (EHs) that the EHs values for case 1–4 is lie about (3 cdot 6 times 10^{{ - 7}}), (7 cdot 83 times 10^{{ - 9}}), (- 4.7 times 10^{{ - 8}}) and (- 2 cdot 9 times 10^{{ - 6}}) respectively.

This study projects future water demand scenarios in the Upper Indus Basin, focusing on reference, high population growth, increased irrigation, and lower population growth scenarios. The baseline scenario indicates a significant rise in water demand from 35.74 billion cubic meters (BCMs) in 2020 to 60.28 BCM by 2035, driven by population growth and increased domestic water consumption. High population growth exacerbates this demand, reaching 62.96 BCM by 2035. This research aims to address domestic water needs under various growth scenarios, considering factors such as population growth rate and per capita consumption. The study employs integrated hydrological modeling to simulate water demand under different socioeconomic conditions. Key methods include analyzing baseline water demand, projecting future scenarios, and evaluating the impact of increased irrigation and population growth on water resources. Results reveal that without intervention, stagnant water supply management will lead to severe water shortages. Increased irrigation, influenced by a 3% growth in irrigated land, pushes agricultural water demand to 56.37 BCM by 2035. Mitigation efforts, such as a 15% reduction in domestic water consumption, could decrease overall demand to 51.23 BCM by 2035. Further reductions are explored through a 50% cut in agricultural water consumption, involving efficient irrigation techniques. The study highlights the critical role of technology and farmer awareness in achieving these reductions, despite current irrigation scheme losses of 20%. A lower population growth scenario shows a contrasting trend, with water demand decreasing to 49.11 BCM by 2035, attributed to a 1.8% population growth rate and decreased per capita consumption to 82 m³ per day. These findings underscore the importance of proactive water management strategies, technological advancements, and demographic considerations in addressing future water demand challenges in the Upper Indus Basin. This research provides proper insight into the impact of varied socioeconomic scenarios on water resources and the necessity for strategic interventions.

Understanding household water dynamics is crucial for achieving SDG 6 targets. This study explores the impact of 16 socio-demographic variables on household water demand in a tropical Nigerian community from February 2023 to January 2024, surveying eighty diverse households monthly. Descriptive and inferential statistics were applied to the survey data. Females constituted 85.8%, with 98.0% aged at least 18 and 73.6% having secondary education. Factorability of the dataset was confirmed (KMO = 63.4, p < 0.005). Analysis identified seven key variables, explaining 72.03% of observed variance: household size, water source reliability, time cost of obtaining water, water storage strategy, consumptive water use, monthly income, and water source management type. Further scrutiny revealed two variable groups, contributing 42.3% (VAR 1 and VAR 2) and 51.4% (VAR 3, 4, 5, and 6) of total absolute variance, respectively. This analysis is vital for effective household water planning and management, especially in resource-limited regions. Extracted variables warrant attention from industry stakeholders, with subsequent investigations revealing robust relationships (55.5–99.1%) among variables. This understanding is pivotal for institutionalizing policies and strategic decision-making in household water supply planning and management. It offers comprehensive insights for aligning practices with SDG 6 goals, ensuring sustainable and equitable access to water resources.

Remediation of water pollution or removal of pollutant molecules by efficient substrates with long life is very important and challenging. Techniques based on adsorption and extensive use of two-dimensional (2D) transition metal carbides (MXenes) with the presence of terminal functional groups have provided a high potential in the separation of organic aromatic pollutants. In this work, a 2D substrate of the MXenes family named V₂CT_x is designed to investigate the adsorption behavior of several types of dye organic pollutants using the molecular dynamics simulation technique based on Newton’s laws in the aqueous phase. Several simulation boxes are designed, which are placed in two groups, discrete simulation boxes and co-loading (Mxn-Mix) boxes. Several analyses, including root-mean-square deviation, interaction energies, radial distribution function, mean square displacement, hydrogen bond (HB) number, and the number of contacts, have been used to analyze the results. The simulation results and interaction energy show that all the dye analytes used can interact with MXene (Mxn), which indicates that MXene can be an effective adsorbent to remove pollutant molecules. Our results confirm that the adsorption process of analytes by V₂CT_x substrate is selective. The analysis of adsorption behavior shows that the loading process is spontaneous in all systems, and the values of the interaction energy for the most stable complexes are −149.95 and −104.85 kJ/mol corresponding to crystal violet and brilliants blue analytes, respectively, in both groups of discrete and Mxn-Mix simulation boxes. The strong adsorption can be attributed to the cationic nature of analytes and their nucleophilic aromatic parts, which caused strong coul interactions for the adsorption of these molecules. The HB and π–π interactions are also responsible for the adsorption of dye molecules here. The obtained results also indicate that in addition to the cationic nature, other factors, such as the linearity of the molecular structures, the charge of the dye molecules, and the molecular mass of the tested pollutants, are effective in the adsorption process. Current studies show that the Mxn nanostructure is an excellent substrate of adsorbent material that has high efficiency for the separation of organic dyes in aqueous media. It is hoped that this research can be a very good class for other target pollutants in the future.