Search Results (856)

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO₂ NWs-Fe₂O₃), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO₂ NWs-Fe₂O₃ has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m²·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO₂ NWs-Fe₂O₃-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO₂ NWs-Fe₂O₃ membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article

A comparative study was conducted to investigate membrane fouling control and treatment performance using natural surface water as the feed source. The evaluated processes included: (1) direct filtration–tubular ceramic membrane (DF-TCM, control); (2) coagulation–tubular ceramic membrane (C-TCM); and (3) coagulation–tubular ceramic membrane with concentrate recycling (C-TCM-CR). Experimental results demonstrated that under constant flux operation at 75 L/(m²·h) for 8 h, the C-TCM-CR process reduced the transmembrane pressure (TMP) increase by 83% and 35% compared to DF-TCM and C-TCM, respectively. Floc size distribution analysis and cake layer characterization revealed that the C-TCM-CR process enhanced coagulation efficiency and formed high-porosity cake layers on membrane surfaces, thereby mitigating fouling development. Notably, the coagulation-assisted processes demonstrated improved organic matter removal, with 13%, 10%, and 10% enhancement in COD_Mn, UV₂₅₄, and medium molecular weight organics (2000–10,000 Da) removal compared to DF-TCM, along with a moderate enhancement in fluorescent substances removal efficiency. All three processes achieved over 99% turbidity removal efficiency, as the ceramic membranes demonstrate excellent filtration performance. Full article

In this paper, MXene nanosheets were used as nano additives for the preparation of MXene-modified polyvinyl chloride (PVC) mixed max membranes (MMMs) for the rejection of lead (Pb²⁺) ions from wastewater. MXene nanosheets were introduced into the PVC matrix to enhance membrane performance, hydrophilicity, contact angle, porosity, and resistance to fouling. Modeling and optimization techniques were used to examine the effects of important operational and fabrication parameters, such as pH, contaminant concentration, nanoadditive (MXene) content, and operating pressure. Predictive models were developed using experimental data to assess the membranes’ performance in terms of flux and Pb²⁺ rejection. The ideal circumstances that struck a balance between long-term operating stability and high removal efficiency were found through multi-variable optimization. The optimized conditions for the best rejection of Pb²⁺ ions and the most stable permeability over time among the membranes that were manufactured were the initial metal ions concentration (2 mg/L), pH (7.89), pressure (2.99 bar), and MXene mass (0.3 g). The possibility of combining MXene nanoparticles with methodical optimization techniques to create efficient membranes for the removal of heavy metals in wastewater treatment applications is highlighted by this work. Full article

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

The escalating heat flux densities in high-performance electronics necessitate superior thermal management. This study enhanced pool-boiling heat transfer, a method offering high heat removal capacity, by leveraging Binder Jetting 3D Printing (BJ3DP) to create complex porous copper structures without the need for chemical treatments. This approach enables a reliable utilization of phenomena like capillarity for improved performance. Three types of porous copper structures, namely Large Lattice, Small Lattice, and Staggered, were fabricated on pure copper substrates and tested via pool boiling of de-ionized and de-gassed water at atmospheric pressure. Compared to a plain polished copper surface, which exhibited a critical heat flux (CHF) of 782 kW/m² at a wall superheat of 18 K, the 3D-printed porous copper surfaces showed significantly improved heat transfer performance. The Staggered surface achieved a conventional CHF of 2342.4 kW/m² (a 199.7% enhancement) at a wall superheat of 24.6 K. Notably, the Large Lattice and Small Lattice structures demonstrated exceptionally stable boiling without reaching the typical catastrophic CHF within the experimental parameters. These geometries continued to increase in heat flux, reaching maximums of 2397.7 kW/m² (206.8% higher at a wall superheat of 55.6 K) and 2577.2 kW/m² (229.7% higher at a wall superheat of 39.5 K), respectively. Subsequently, a gradual decline in heat flux was observed with an increasing wall superheat, demonstrating an outstanding resistance to the boiling crisis. These improvements are attributed to the formation of distinct vapor–liquid pathways within the porous structures, which promotes the efficient rewetting of the heated surface through capillary action. This mechanism supports a highly efficient, self-sustaining boiling configuration, emphasizing the superior rewetting and vapor management capabilities of these 3D-printed porous structures, which extend the boundaries of sustained high heat flux performance. The porous surfaces also demonstrated a higher heat transfer coefficient (HTC), particularly at lower heat fluxes (≤750 kW/m²). High-speed digital camera visualization provided further insight into the boiling phenomenon. Overall, the findings demonstrate that these BJ3DP structured surfaces produce optimized vapor–liquid pathways and capillary-enhanced rewetting, offering significantly superior heat transfer performance compared to smooth surfaces and highlighting their potential for advanced thermal management. Full article

Electrospun nanofiber membranes based on cellulose acetate (CA) have gained increasing attention for wastewater treatment due to their high surface area, tuneable structure, and ease of functionalization. In this study, the performance of CA membranes was enhanced by incorporating aluminum oxide (Al₂O₃) nanoparticles (NPs) at varying concentrations (0–2 wt.%). The structural, morphological, and thermal properties of the resulting CA/Al₂O₃ nanocomposite membranes were investigated through FTIR, XRD, SEM, water contact angle (WCA), pore size measurements, and DSC analyses. FTIR and XRD confirmed strong interactions and the uniform dispersion of the Al₂O₃ NPs within the CA matrix. The incorporation of Al₂O₃ improved membrane hydrophilicity, reducing the WCA from 107° to 35°, and increased the average pore size from 0.62 µm to 0.86 µm. These modifications led to enhanced filtration performance, with the membrane containing 2 wt.% Al₂O₃ achieving a 99% removal efficiency for Indigo Carmine (IC) dye, a maximum adsorption capacity of 45.59 mg/g, and a high permeate flux of 175.47 L·m⁻² h⁻¹ bar⁻¹. Additionally, phytotoxicity tests using Lactuca sativa seeds showed a significant increase in germination index from 20% (untreated) to 88% (treated), confirming the safety of the permeate for potential reuse in agricultural irrigation. These results highlight the effectiveness of Al₂O₃-modified CA electrospun membranes for sustainable wastewater treatment and water reuse. Full article

Aquaculture expansion to meet global protein demand has intensified concerns over nutrient pollution and greenhouse gas (GHG) emissions. While floating treatment wetlands (FTWs) are proven for water quality improvement, their potential to mitigate GHG emissions in marine aquaculture remains poorly understood. This study quantitatively evaluated the dual capacity of Sesuvium portulacastrum FTWs to (a) regulate dissolved inorganic nitrogen (DIN) and (b) reduce CO₂/N₂O emissions in grouper aquaculture systems. DIN speciation (NH₄⁺, NO₂⁻, NO₃⁻) and CO₂/N₂O fluxes of six controlled ponds (three FTW and three control) were monitored for 44 days. DIN in the FTW group was approximately 90 μmol/L lower than that in the control group, and the water in the plant group was more “oxidative” than that in the control group. The former groups were dominated by NO₃⁻, with lower dissolved inorganic carbon (DIC) and N₂O concentrations, whereas the latter were dominated by NH₄⁺ during the first 20 days of the experiment and by NO₂⁻ at the end of the experiment, with higher DIC and N₂O concentrations on average. Higher primary production may be the reason that the DIC concentration was lower in the plant group than in the control group, whereas efficient nitrification and uptake by plants reduced the availability of NH₄⁺ in the plant group, thereby reducing the production of N₂O. A comparison of the CO₂ and N₂O flux potentials in the plant group and control group revealed that, in the presence of FTWs, the CO₂ and N₂O emissions decreased by 14% and 36%, respectively. This showed that S. portulacastrum FTWs effectively couple DIN removal with GHG mitigation, offering a nature-based solution for sustainable aquaculture. Their low biomass requirement enhances practical scalability. Full article

Sugar production generates wastewater rich in dissolved solids and organic matter, and improper disposal poses severe environmental risks, exacerbates water scarcity, and creates regulatory challenges. Conventional treatment methods, such as evaporation and chemical precipitation, are energy-intensive and often ineffective at removing fine particulates and dissolved impurities. This study evaluates membrane-based separation as a sustainable alternative for water reclamation and sugar recovery from sugar industry effluents, focusing on replacing evaporation with membrane processes, ensuring high permeate quality, and mitigating membrane fouling. Cross-flow filtration experiments were conducted on a lab-scale membrane system at 70 °C to suppress microbial growth, comparing direct reverse osmosis (RO) of the raw effluent to an integrated ultrafiltration (UF)–RO process. Direct RO resulted in rapid membrane fouling. A tight UF (5 kDa) pre-treatment before RO significantly mitigated fouling and improved performance, enabling 28% water recovery and 79% sugar recovery, maintaining permeate conductivity below 0.5 mS/cm, sustaining stable flux, and reducing membrane blocking. Additionally, the UF and RO membranes were tested via SEM, EDS, and FTIR to elucidate the fouling mechanisms. Full article

The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective B-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor B-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL. Full article

The management of end-stage kidney disease (ESKD) poses a substantial clinical and economic challenge, characterized by a growing patient burden, rising healthcare costs, and persistent unmet needs to enhance survival outcomes and quality of life. Background/Objectives: Conventional high-flux hemodialysis (HD) remains the dominant form of renal replacement therapy for ESKD but is still associated with substantial morbidity and mortality. High-volume post-dilution online hemodiafiltration (HVHDF) offers a promising alternative by enhancing the convective removal of uremic toxins. Methods: We conducted a narrative review of randomized controlled trials, meta-analyses, real-world cohort studies, and registry analyses published between 2010 and 2024. Evidence was categorized into short-term, medium-term, and long-term outcomes, including hemodynamic stability, inflammation, anemia, infection risk, cardiovascular events, cognitive decline, quality of life, and survival. Results: HVHDF improves short-term outcomes by enhancing toxin clearance, stabilizing blood pressure, reducing inflammation and oxidative stress, and improving anemia management. Medium-term benefits include improved nutritional status, reduced hospitalizations related to infections, and improved neurological and immune function. Long-term data from major trials (e.g., ESHOL, CONVINCE) and large real-world studies show consistent reductions in all-cause and cardiovascular mortality, particularly with convection volumes ≥ 23 L/session. A clear dose–response relationship supports the clinical relevance of convection volume targets. HVHDF has also shown benefits in preserving cognitive function and enhancing health-related quality of life. Conclusions: Strong and converging evidence supports HVHDF as a superior dialysis modality. Given its survival benefits, better tolerance, and broader impact on patient outcomes, HVHDF should be considered the new standard of care in dialysis, especially in light of the recent regulatory approval of the machine that provides the ability to perform HDF in the United States. Full article

Polyethersulfone (PES) membranes are essential in separation processes; however, their inherent hydrophobicity can limit their effectiveness in water-intensive applications. This study aims to enhance PES membranes by modifying them with a NiFe₂O₄–nanoclay composite nanoparticle to improve both their hydrophilicity and photocatalytic potential as a photocatalytic membrane. The nanoparticles were synthesized using the sol–gel auto-combustion method and incorporated into PES membranes through mixed-matrix embedding (1 wt% and 3 wt%) and surface coating. X-ray diffraction confirmed the cubic spinel structure of the composite nanoparticles, which followed the second order kinetic reaction during the photodegradation–adsorption of crystal violet. The mixed-matrix membranes displayed a remarkable 170% increase in water flux and a 25% improvement in mechanical strength, accompanied by a slight decrease in contact angle at 1 wt% of nanoparticle loading. In contrast, the surface-coated membranes demonstrated a significant reduction in contact angle to 18°, indicating a highly hydrophilic surface and increased roughness. All membranes achieved high dye removal rates of 98–99%, but only the coated membrane system exhibited approximately 50% photocatalytic degradation, following mixed kinetics. These results highlight the critical importance of surface modification in advancing PES membranes, as it significantly reduces fouling and enhances water–material interaction qualities essential for future filtration and photocatalytic applications. Exploring hybrid strategies that combine both embedding and coating approaches may yield even greater synergies in membrane functionality. Full article

Potamogeton crispus (P. crispus), with strong nitrogen uptake capacity, plays an important ecological role during winter and early spring when most aquatic plants are inactive. Its presence can also influence microbial denitrification in sediments by regulating oxygen levels and organic carbon availability. In this study, an indoor hydroponic simulation system was used to systematically evaluate the effects of P. crispus under different nitrogen-loading conditions on nitrogen removal from water, changes in sediment carbon and nitrogen fractions, microbial community structure, and greenhouse gas fluxes. The results showed that P. crispus effectively removed TN, NH₄⁺-N, NO₃⁻-N, and NO₂⁻-N, maintaining strong denitrification capacity even under high-nitrogen loading. Under all nitrogen conditions, TN removal exceeded 80%, while NH₄⁺-N and NO₃⁻-N removal efficiencies surpassed 90%, with effective suppression of NO₂⁻-N accumulation. Rhizosphere-mediated regulation by P. crispus enhanced the transformation and stabilization of DOC and NO₃⁻-N in sediments, while also mitigating nitrogen-induced disturbances to carbon–nitrogen balance. The plant also exhibited strong CO₂ uptake capacity, low CH₄ emissions with a slight increase under higher nitrogen loading, and N₂O fluxes that were significantly affected by nitrogen levels—showing negative values under low nitrogen and sharp increases under high-nitrogen conditions. Correlation analyses indicated that CO₂ and N₂O emissions were mainly regulated by microbial taxa involved in carbon and nitrogen transformation, while CH₄ emissions were primarily driven by methanogenic archaea and showed weaker correlations with environmental factors. These findings highlight the importance of water restoration during low-temperature seasons and provide a theoretical basis for integrated wetland management strategies aimed at coordinated pollution reduction and carbon mitigation. Full article

Methane (CH₄) emissions from dairy farming represent a substantial yet under-quantified share of agricultural greenhouse gas emissions. This study provides an in-depth, satellite-based fingerprinting analysis of methane emissions from Canada’s dairy sector, using Sentinel-5P/TROPOMI data. We utilized a robust quasi-experimental design, pairing 14 dairy-intensive zones with eight non-dairy reference regions, to analyze methane emissions from 2019 to 2024. A dynamic, region-specific baseline approach was implemented to remove temporal non-stationarity and isolate dairy-specific methane signals. Dairy regions exhibited consistently higher methane concentrations than reference areas, with an average methane anomaly of 17.4 ppb. However, this concentration gap between dairy and non-dairy regions notably narrowed by 57.23% (from 24.42 ppb in 2019 to 10.44 ppb in 2024), driven primarily by accelerated methane increases in non-dairy landscapes and a pronounced one-year contraction during 2022–2023 (−39.29%). Nationally, atmospheric methane levels rose by 3.83%, revealing significant spatial heterogeneity across provinces. Notably, an inverse relationship between the initial methane concentrations in 2019 and subsequent growth rates emerged, indicating spatial convergence. The seasonal analysis uncovered consistent spring minima and fall–winter maxima across regions, reflecting the combined effects of seasonal livestock management practices, atmospheric transport dynamics, and biogeochemical processes. The diminishing dairy methane anomaly suggests complex interplay of intensifying background methane emissions from climate-driven wetland fluxes, increasing fossil fuel extraction activities, and diffuse agricultural emissions. These findings underscore the emerging challenges in attributing sector-specific methane emissions accurately from satellite observations, highlighting both the capabilities and limitations of current satellite monitoring approaches. Full article