Applied Water Science最新文献

In this study, bimetallic zeolitic imidazolate frameworks with a ZIF-11 structure with different molar ratios (Zn/Co: 3, 2, 1, 0.5, 0.33) were synthesized. Then, tetracycline (TC) adsorption was examined and compared between bimetallic and monometallic samples. Techniques such as XRD, FTIR, FESEM, EDS, and BET were used to determine their characteristics. A slight shift in peak position on XRD patterns for samples with Zn/Co ratios of 1, 2, and 3 was observed, resulting from the introduction of cobalt instead of zinc. FESEM images show that the bimetallic samples, like the monometallic ZIF-11, have a characteristic dodecahedral crystal structure. According to the obtained results and especially in the comparison between single and bimetallic samples, ZIF-11 (Zn/Co:3) appears to have the greatest surface area of 580.4 m².g^− 1. Based on the findings, ZIF-11 (Zn/Co:3) is identified as the best synthetic sample, bearing a 95.1% adsorption efficiency for TC (5 ppm) in 60 min at pH 7, along with a maximum adsorption capacity of 294.1 mg/g. The adsorption efficiency with the bimetallic sample (ZIF-11 (Zn/Co:3)) was about 2.2 times that of the monometallic sample (ZIF-11), which indicated that the addition of cobalt greatly improved the adsorption capacity, probably due to the large supply of metal sites provided by cobalt. According to the pH_PZC of ZIF-11 (Zn/Co:3) equal to 7.3, the adsorption process was investigated at normal pH. The best kinetic and isotherm models for TC adsorption on ZIF-11 (Zn/Co:3) are Elovich (R² = 0.97) and Langmuir (R² = 0.99), respectively. Adsorption mechanisms of ZIF-11(Zn/Co:3) include π-π interactions and hydrogen bonding. The structural properties of the ZIF-11 bimetallic mixture have been improved compared to its monometallic type, which has subsequently been effective in its performance in the adsorption process.

The high water solubility and long-term thermal and optical stability of Reactive Blue 248 pose a challenge for its removal from industrial wastewater. The performance of BiOI as a photocatalyst under various conditions was investigated. Additionally, machine learning models were used to model and predict the efficiency of pollutant removal under different conditions. Furthermore, the stability and reusability of BiOI were evaluated for repeated cycles of dye degradation. Organic dye oxidation and identification of active radical species were evaluated through chemical oxygen demand (COD) and radical scavenging tests, respectively. The catalyst dose and contact time significantly influence dye removal efficiency, with p-values of 0.0299 and 0.0238, respectively. The optimal conditions include a pH of 6.713, a contact time of 60 min, and a catalyst dose of 0.634 g/L. The addition of AgNO₃ as an e⁻ scavenger had no effect, keeping the efficiency at 73.87%, indicating that free electrons do not play a significant role in the degradation of RB248. However, the introduction of KI, which scavenges h⁺, led to a substantial drop in efficiency to 40.27%, confirming the crucial role of h⁺ in the photocatalytic process. The second-order kinetic model provides a more accurate description of the dye removal process. The degradation process (COD Tests) was evaluated for three initial dye concentrations of 20, 30, and 50 mg/L. These results indicate that the degradation efficiency was higher at lower initial concentrations, while the removal process was slower at higher concentrations. Among the machine learning models evaluated on the test data, the ERT model outperforms others in all key performance metrics.

The modification of a screen-printed graphite electrode (SPGE) with a zeolitic imidazolate framework-67/multi-walled carbon nanotubes (ZIF-67/MWCNTs) is described for the sensitive detection of 4-aminophenol (4-AP). The ZIF-67/MWCNTs nanocomposite was prepared by a facile synthesis method and was characterized by fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) analysis, field-emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectroscopy (EDS). The synergistic effects between ZIF-67 and MWCNTs resulted in significant electrocatalysis of the redox process of 4-AP on the modified SPGE. A quantitative detection of 4-AP was achieved with a linear concentration range of 0.01–675.0 µM, a limit of detection (LOD) of 0.008 ± 0.0001 µM and a high sensitivity of 0.0718 µA/µM. The ZIF-67/MWCNTs modified SPGE sensor demonstrated good stability, repeatability, and reproducibility for the detection of 4-AP. Moreover, the assessment of the interference effect of some species on the detection of the 4-AP revealed that the designed sensor possesses good selectivity towards the 4-AP. The ZIF-67/MWCNTs modified SPGE sensor was successfully used for the determination of 4-AP in water samples with a relative standard deviation (RSD) of 3.6% and recoveries between 97.1% and 104.4%.

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In this study, Fe₃O₄/kaolinite and ε-Fe₂O₃/kaolinite nanocomposites were synthesized using apricot kernel extract as a green reductant. The prepared nanocomposites were characterized and evaluated for their adsorption efficiency in removing methylene blue (MB) from aqueous solutions. The synthesis was carried out at 80 °C using iron chloride precursors (FeCl₃·4 H₂O and FeCl₂·6 H₂O) and kaolinite. The nanocomposite physicochemical characterization (X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis and differential scanning calorimetry) confirmed the incorporation of crystalline Fe₃O₄ and ε-Fe₂O₃ structures in the kaolinite matrix (particle sizes from 30 to 70 nm). Adsorption experiments showed that 4 mg of Fe₃O₄/kaolinite and ε-Fe₂O₃/kaolinite removed 90.24% and 89.15% of MB (20 mg/L) in the first cycle, 67.95% and 77.60% in the second, and 59.53% and 57.29% in the third, respectively, within 1 h at room temperature. The adsorption experiments also showed that 4 mg of Fe₃O₄/kaolinite and ε-Fe₂O₃/kaolinite removed 29.95% and 10.59% of CoCl₂·6 H₂O (20 g/L), 27.96% and 19.42% of NiCl₂·6 H₂O (60 g/L), and 10.91% and 9.21% of Cu(CH₃COO)₂·H₂O (60 g/L), respectively, within one hour at room temperature. The adsorption isotherm data were best fitted by the Temkin model, with both nanocomposites exhibiting a maximum adsorption capacity (Qₘₐₓ) of 250 mg/g. MB adsorption could be modeled using a pseudo-second order kinetic model. The high correlation coefficients (R² = 0.991 for Fe₃O₄/kaolinite and 0.985 for ε-Fe₂O₃/kaolinite) suggested that chemisorption was the predominant mechanism. The intraparticle diffusion coefficients (24.857 mg/g·min¹/² for Fe₃O₄/kaolinite and 26.299 mg/g·min¹/² for ε-Fe₂O₃/kaolinite) indicated efficient internal diffusion. These results underscore the potential of Fe₃O₄- and ε-Fe₂O₃-based kaolinite nanocomposites as effective and green adsorbents for dye removal from wastewater.

To clarify the precursor characteristics of coal seam floor water inrush under complex hydrogeological conditions and to provide a scientific basis for hazard monitoring and early warning, the damage evolution of the coal seam floor and water inrush induced by mining and collapse column were studied. The spatial and temporal characteristics of stress, seepage, fracture development, and acoustic emission (AE) responses were examined to reveal their indicative roles in water inrush initiation. A seepage—stress—damage coupling model was established by employing AE monitoring technology and RFPA2D-Flow numerical simulation. Mining-induced variations in mechanical fields, seepage fields, and AE signals were simulated to identify early-warning indicators associated with different inrush mechanisms. The results indicate that the high confining pressure of the Ordovician limestone aquifer is posed as a major threat to the stability of the coal seam floor. Distinct displacement distributions, abrupt increases in seepage, and abnormal AE activities can be taken as precursor signals of water inrush. A strong correlation is found between fracture propagation and AE energy release under pressurized conditions, and concentrated stress zones together with AE anomalies are shown to function as key warning signs. Simulations involving collapse columns further reveal a spatial correlation between peak stress and AE energy as the mining face advances. It is concluded that the combined evolution of stress concentration, fracture expansion, seepage intensification, and AE anomalies can be effectively used as early-warning indicators of floor water inrush. The findings provide critical insights for early hazard detection.

Drought, a recurring natural phenomenon, poses significant challenges to water resource management and agricultural sustainability worldwide. This study examines meteorological drought characteristics in Punjab province, Pakistan, during 1991–2022 using the standardized precipitation index (SPI) and reconnaissance drought index (RDI). Drought severity was classified based on SPI and RDI thresholds (mild, moderate, severe, and extreme), enabling a systematic assessment across temporal scales. Statistical measures, including correlation (R) and Root Mean Square Error (RMSE), were employed to evaluate the consistency and accuracy of the indices. Various drought periods were identified across 3, 6, 9 and 12-month timeframes, confirming the presence of both short-term and long-term drought conditions. Extreme drought years in 1997 and 2002, severe drought in 1991, 2000, 2001, and 2005, and milder drought periods in 1993, 1999, and 2004 were identified. Strong correlations (R = 0.84–1) and low RMSE (0.03–0.085) values between SPI and RDI indices indicate their effectiveness in assessing long-term drought conditions. District-level analysis highlighted regional variability, where southern and central districts—such as Bahawal Nagar, Bahawal Pur, Multan, Muzaffargarh, Rajanpur, RYK, and Vehari—are more prone to drought due to consistently high maximum temperatures, limited rainfall, and elevated evaporation rates. Historical temperature and rainfall data from NASA Power were utilized, although limitations in spatial resolution and coverage were acknowledged. The novelty of this study lies in its combined application of SPI and RDI at multiple temporal scales with district-level resolution, providing region-specific insights for drought monitoring. The findings offer practical implications for developing localized drought management strategies and support broader climate change adaptation efforts in semi-arid regions. Future research should explore integrating additional datasets or satellite imagery for enhanced analysis, as well as incorporating socio-economic factors to better capture community vulnerability and resilience.

Due to efficient charge separation and strong visible-light absorption, g-C₃N₄-based heterogeneous photocatalyts have sparked significant interest in alleviating energy and environmental crisis. The present study reports CeO₂/PbFe₁₂O₁₉ nanocomposite with photocatalytic properties that has been designed via sol–gel auto-combustion process utilizing different carbohydrate sugars as capping agent and fuel. Also, this investigation developed an ultrasonic-assisted co-precipitation route for the creation of ternary CeO₂/PbFe₁₂O₁₉/g-C₃N₄ nanocomposites with diverse contents of nano-CeO₂/PbFe₁₂O₁₉ to promote activity toward the degradation of malachite green (MG) pollutant under visible light. The results showed that 30 mg of resulting CeO₂/PbFe₁₂O₁₉ nanostructures can achieve 46.68% degradation of MG at 10 mg L⁻¹, while nanocomposites having mass ratio of 1:1 for CeO₂/PbFe₁₂O₁₉: g-C₃N₄ showed 81.50% efficiency after 120 min at similar conditions. Radical scavenging experiments confirmed that •OH and h⁺ play a dominant role in MG degradation by CeO₂/PbFe₁₂O₁₉/g-C₃N₄ nanocomposites. These results highlight the potential application of CeO₂/PbFe₁₂O₁₉/g-C₃N₄ as a promising photocatalyst for removing water-soluble organic pollutants.

Biogenic nanoparticles produced using plant and microbial sources have emerged as low cost and environmentally benign alternatives for wastewater treatment applications. This review examines the underlying mechanisms of plant and microbe mediated nanoparticle synthesis, highlighting how naturally occurring biomolecules act as reducing, stabilizing, and capping agents to regulate nanoparticle surface characteristics. The discussion outlines key practical advantageous, including lower energy inputs, avoidance of hazardous reducing agents, use of renewable biological resources, and the potential for in situ or decentralized production, while also noting constraints like variability in plant extracts or microbial cultures. Applications in the removal of organic dyes, heavy metals, and pharmaceuticals are discussed with emphasis on performance indicators such as adsorption capacity, degradation efficiency, selectivity, and nanoparticle recovery and reuse. Alongside future opportunities for advancing green nanotechnologies through improved standardization, process control, integration with existing treatment systems, and comprehensive lifecycle under techno-economic evaluations. A comparative assessment indicates that plant-based synthesis is typically rapid, scalable, and suitable for high throughput production due to its procedural simplicity and abundance of phytochemicals. In contrast microbial synthesis generally allows finer control over nanoparticles size, shape and crystallinity. Unlike existing reviews that largely describe individual synthesis approaches or application specific studies, this review offers a critical, integrative comparison of biogenic nanoparticle synthesis routes, highlighting key performance and practical limitations across systems. The analysis indicates that no single biogenic route is universally optimal; rather, application driven selection is required, balancing efficiency, scalability and environmental capability. These insights clarify current progress while identifying priority directions for advancing biogenic nanomaterials towards real-world wastewater treatment applications.

Climate change profoundly impacts hydropower productivity, a cornerstone of renewable energy, necessitating advanced predictive tools for sustainable water-energy management. This study presents novel machine learning (ML) frameworks to forecast climate-induced variations in hydropower output by synergistically integrating climate, hydrological, and operational data with reanalysis datasets. Distinct from existing approaches, our methodology introduces unique contributions, including synthetic climate scenario generation via Generative Adversarial Networks (GANs), neural network-driven feature ranking to prioritize key climate variables, and robust preprocessing techniques such as outlier detection, normalization, and time-series feature engineering. Using a dataset of 650 records with 11 features from a hydropower plant in the Middle East, split into 70% training, 15% validation, and 15% testing subsets, we evaluated the performance of ARIMA, GAN, Autoregressive Deep Neural Network (ARDNN), and Long Short-Term Memory (LSTM) models using RMSE and R² metrics. The LSTM model outperformed the others, achieving an RMSE of 2892.61, a MAPE of 1.3237, and an R² of 0.9985, owing to its superior ability to capture long-term temporal dependencies. These advancements surpass traditional models by offering enhanced predictive accuracy and adaptability, enabling optimized resource management and bolstering the resilience of hydropower systems against climate variability, thus contributing significantly to global sustainable energy strategies.

With its increasingly serious and continuous need, effective spatiotemporal water quality prediction has become key to effective pollution control and decision-making. Current research primarily focuses on utilizing continuous time monitoring data to predict trends in time series within specific sections. However, the lack of spatially continuous and reliable observations limits the ability to achieve full spatial coverage prediction. To address this limitation, this study proposes an integrated framework, named SELC, which utilizes the Soil and Water Assessment Tool (SWAT), Environmental Fluid Dynamics Code (EFDC), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM), to predict the continuous spatiotemporal water quality of the Xiaoqing River Basin (China) using discrete cross-section monitoring data and mechanism model simulation. The SELC model framework integration is as follows: The CNN training uses on-site monitoring data and high-resolution spatial simulations from the coupled SWAT-EFDC models. LTSM is used to generate future temporal forcing data for SELC at monitoring sections. The verification results showed that CNN successfully replicated the spatially continuous distribution of pollutants, and the prediction results were highly consistent with the trend, peak position, and minimum value EFDC simulation results. In the verification, the average coefficients of determination (R2) of the model were 0.62 (NH₃-N) and 0.65 (chemical oxygen demand, COD), confirming its reliability. This study achieved high-resolution spatiotemporal water quality prediction by using only segmented monitoring input and future scenario prediction, thus overcoming the limitation of sparse spatial data. This framework provides a practical tool for identifying high-risk pollution areas and periods and supports targeted aquatic environmental management.