Search Results (2,114)

Mining activity in Peru generates environmental liabilities with the potential to release toxic metals into the environment. This study conducted a comprehensive physical, chemical, mineralogical, and toxicological characterization of ten active and inactive tailings samples from the Arequipa region in southern Peru. Particle size distribution analysis, inductively coupled plasma atomic emission spectroscopy (ICP-AES), scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and the Toxicity Characteristic Leaching Procedure (TCLP) followed by ICP-MS were employed. The results revealed variable particle size distributions, with the sample of Secocha exhibiting the finest granulometry. Chemically, 8 out of 10 samples exhibited concentrations of at least two metals surpassing the Peruvian Environmental Quality Standards (EQS) for soils with values reaching >6000 mg/kg of arsenic (Paraiso), 193.1 mg/kg of mercury (Mollehuaca), and 2309 mg/kg of zinc (Paraiso). Mineralogical analysis revealed the presence of sulfides such as arsenopyrite, cinnabar, galena, and sphalerite, along with uraninite in the Otapara sample. In the TCLP tests, 5 out of 10 samples released at least two metals exceeding the environmental standards on water quality, with concentrations up to 0.401 mg/L for mercury (Paraiso), 0.590 mg/L for lead (Paraiso), and 9.286 mg/L for zinc (Kiowa Cobre). These results demonstrate elevated levels of Potentially Toxic Elements (PTEs) in both solid and dissolved states, reflecting a critical geochemical risk in the evaluated areas. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

With the growing severity of environmental pollution, people are paying increasing attention to their health. However, naturally occurring wood with health benefits and applications in human healthcare is still scarce. Natural wood exhibits a limited negative oxygen ion release capacity, and this release has a short duration, failing to meet practical application requirements. This study innovatively developed a humidity-responsive, healthy wood material with a high negative oxygen ion release capacity based on fast-growing poplar. Through vacuum cyclic impregnation technology, hexagonal stone powder was infused into the pores of poplar wood, endowing it with the ability to continuously release negative oxygen ions. The healthy wood demonstrated a static average negative oxygen ion release rate of 537 ions/cm³ (peaking at 617 ions/cm³) and a dynamic average release rate of 3,170 ions/cm³ (peaking at 10,590 ions/cm³). The results showed that the particle size of hexagonal stone powder in suspension was influenced by the dispersants and dispersion processes. The composite dispersion process demonstrated optimal performance when using 0.5 wt% silane coupling agent γ-(methacryloxy)propyltrimethoxysilane (KH570), achieving the smallest particle size of 8.93 μm. The healthy wood demonstrated excellent impregnation performance, with a weight gain exceeding 14.61% and a liquid absorption rate surpassing 165.18%. The optimal impregnation cycle for vacuum circulation technology was determined to be six cycles, regardless of the type of dispersant. Compared with poplar wood, the hygroscopic swelling rate of healthy wood was lower, especially in PEG-treated samples, where the tangential, radial, longitudinal, and volumetric swelling rates decreased by 70.93%, 71.67%, 69.41%, and 71.35%, respectively. Combining hexagonal stone powder with fast-growing poplar wood can effectively enhance the release of negative oxygen ions. The static average release of negative oxygen ions from healthy wood is 1.44 times that of untreated hexagonal stone powder, and the dynamic release reaches 2 to 3 times the concentration of negative oxygen ions specified by national fresh air standards. The water-responsive mechanism revealed that negative oxygen ion release surged when ambient humidity exceeded 70%. This work proposes a sustainable and effective method to prepare healthy wood with permanent negative oxygen ion release capability. It demonstrates great potential for improving indoor air quality and enhancing human health. Full article

The quality of water in households can be affected by plumbing design and materials, water usage patterns, and source water quality characteristics. These factors influence stagnation duration, disinfection residuals, metal release, and microbial activity. In particular, stagnation can degrade water quality and increase lead release from lead service lines. This study employs numerical modeling to assess how combined corrosion control and flushing strategies affect lead levels in household taps with lead service lines under reduced water use. To estimate potential health risks, the U.S. EPA model is used to predict the percentage of children likely to exceed safe blood lead levels. Lead exceedances are assessed based on various regulatory requirements. Results show that exceedances at the kitchen tap range from 3 to 74% of usage time for the 5 µg/L standard, and from 0 to 49% for the 10 µg/L threshold, across different scenarios. Implementing corrosion control treatment in combination with periodic flushing proves effective in lowering lead levels under the studied low-consumption scenarios. Under these conditions, the combined strategy limits lead exceedances above 5 µg/L to only 3% of usage time, with none above 10 µg/L. This demonstrates its value as a practical short-term strategy for households awaiting full pipe replacement. Targeted flushing before peak water use reduces the median time that water remains stagnant in household pipes from 8 to 3 h at the kitchen tap under low-demand conditions. Finally, the risk model indicates that the combined approach can reduce the predicted percentage of children with blood lead levels exceeding 5 μg/dL from 61 to 6% under low water demand. Full article

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

The depletion of water resources caused by climate change and human activities is a pressing global issue. Lake Ereen is one of the ten natural landmarks of the Gobi-Altai of western Mongolia is included in the list of “important areas for birds” recognized by the international organization Birdlife. However, the construction of the Taishir Hydroelectric Power Station, aimed at supplying electricity to the western provinces of Mongolia, had a detrimental effect on the flow of the Zavkhan River, resulting in a drying-up and pollution of Lake Ereen, which relies on the river as its water source. This study assesses the pollution levels in Ereen Lake and determines the feasibility of its rehabilitation by redirecting the flow of the Zavkhan River. Field studies included the analysis of water quality, sediment contamination, and the composition of flora. The results show that the concentrations of ammonium, chlorine, fluorine, and sulfate in the lake water exceed the permissible levels set by the Mongolian standard. Analyses of elements from sediments revealed elevated levels of arsenic, chromium, and copper, exceeding international sediment quality guidelines and posing risks to biological organisms. Furthermore, several species of diatoms indicative of polluted water were discovered. Lake Ereen is currently in a eutrophic state and, based on a water quality index (WQI) of 49.4, also in a “polluted” state. Mass balance calculations and box model analysis determined the period of pollutant replacement for two restoration options: drying-up and complete removal of contaminated sediments and plants vs. dilution-flushing without direct interventions in the lake. We recommend the latter being the most efficient, eco-friendly, and cost-effective approach to rehabilitate Lake Ereen. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Heavy metal pollution from human activities is an increasing environmental concern. This study investigates the concentrations of Cu, Pb, Zn, Cd, Hg, and As in the coastal seawater offshore of Beihai, Guangxi, in April 2021, and explores their relationships with dissolved inorganic nitrogen, phosphate, and salinity. Our results reveal higher heavy metal concentrations in the northern nearshore waters and lower levels in southern offshore areas, with surface waters generally exhibiting greater enrichment than bottom waters. Surface concentrations show a decreasing trend from the northeast to the southwest, likely influenced by prevailing northeast monsoon winds. While bottom water concentrations decline from the northwest to the southeast, which indicates the influence of riverine runoff, particularly from the Qinzhou Bay estuary. Heavy metal levels in southern Beihai waters are comparable to those in the Beibu Gulf, except for Hg and Zn, which are significantly higher in the water of the Beibu Gulf. Notably, heavy metal concentrations in both Beihai and Beibu Gulf remain considerably lower than those observed in the coastal waters of Guangdong. Overall, Beihai’s coastal seawater meets China’s Class I quality standards. Nonetheless, continued monitoring is essential, especially of the potential ecological impacts of Hg and Zn on marine life. Full article

Background/Objectives: Since breast cancer (BC) survival rates have increased to 91% at 5 years and 80% at 15 years postdiagnosis, there is a growing awareness of the importance of addressing the long-term well-being of patients. Consequently, integrative oncology, which combines standard therapies with complementary approaches (nutrition, mind–body practices, and lifestyle modifications), has emerged as a patient-centred model aimed at improving symptom management, treatment adherence, and overall quality of life (QoL). This study aims to demonstrate how integrative therapies can benefit body composition, phase angle, and fluid and electrolyte balance through bioelectrical impedance analysis (BIA). Methods: This study considers a patient who underwent BC surgery and was enrolled in the AMICO clinic for anamnesis, as well as their oncological pathology data, assessment of QoL, and BIA. The breast surgeon specialising in integrative oncology therapies prescribed the patient curcumin and polydatin, moderate physical activity, a balanced diet, and Qigong sessions. The patient underwent monitoring through haematochemical analysis, BIA, and a QoL questionnaire, with follow-up every four months. Results: Between 4 and 12 months, fat mass (FM) and body mass index (BMI) markedly decreased, whereas fat-free mass (FFM), total body water (TBW), and skeletal muscle mass (SMM) increased progressively. Moreover, the improvements in the Na/K ratio and phase angle (PhA) suggest a shift toward better electrolyte and fluid balance and enhanced cellular integrity and membrane function. Equally outstanding were her psychological benefits in terms of mood, sleep, anxiety, and melancholy. Conclusions: Patient progress in body composition, metabolic function, pain management, and psychological status measured during the 12-month follow-up demonstrates the potential benefits of an integrative approach to supportive cancer care. Full article

The rapid growth of plant-based biodegradable tableware, driven by plastic restrictions, necessitates rigorous safety assessments of potential chemical contaminants like per- and polyfluoroalkyl substances (PFASs). This study comprehensively evaluated PFAS contamination risks in commercial sugarcane pulp tableware, focusing on the residues of five target PFASs (PFOA, PFOS, PFNA, PFHxA, PFPeA) and their migration behavior under simulated use and takeout conditions. An analysis of 22 samples revealed elevated levels of total fluorine (TF: 33.7–163.6 mg/kg) exceeding the EU limit (50 mg/kg) in 31% of products. While sporadic PFOA residues surpassed the EU single compound limit (0.025 mg/kg) in 9% of samples (16.1–25.5 μg/kg), the levels of extractable organic fluorine (EOF: 4.9–17.4 mg/kg) and the low EOF/TF ratio (3.19–10.4%) indicated inorganic fluorides as the primary TF source. Critically, the migration of all target PFASs into food simulants (water, 4% acetic acid, 50% ethanol, 95% ethanol) under standardized use conditions was minimal (PFOA: 0.52–0.70 μg/kg; PFPeA: 0.54–0.63 μg/kg; others < LOQ). Even under aggressive simulated takeout scenarios (50 °C oscillation for 12 h + 12 h storage at 25 °C), PFOA migration reached only 0.99 ± 0.01 μg/kg in 95% ethanol. All migrated levels were substantially (>15-fold) below typical safety thresholds (e.g., 0.01 mg/kg). These findings demonstrate that, despite concerning residue levels in some products pointing to manufacturing contamination sources, migration during typical and even extended use scenarios poses negligible immediate consumer risk. This study underscores the need for stricter quality control targeting PFOA and inorganic fluoride inputs in sugarcane pulp tableware production. Full article

The paper investigates the process of degumming substandard soybean oil using an enzyme complex of phospholipases to prepare it as a feedstock for biodiesel production. Dehumidification is an important refining step aimed at reducing the phosphorus content, which exceeds the permissible limits according to ASTM, EN, and ISO standards, by re-moving phospholipids. The enzyme complex of phospholipases includes phospholipase C, which specifically targets phosphatidylinositol, and phospholipase A2, which catalyzes the hydrolysis of phospholipids into water-soluble phosphates and lysophospholipids. This process contributes to the efficient removal of phospholipids, increased neutral oil yield, and reduced residual oil in the humic phase. The use of an enzyme complex of phospholipases provides an innovative, cost-effective, and environmentally friendly method of oil purification. The results of the study demonstrate the high efficiency of using the phospholipase enzyme complex in the processing of substandard soybean oil, which allows reducing the content of total phosphorus to 0.001% by weight, turning it into a high-quality raw material for biodiesel production. The proposed approach contributes to increasing the profitability of agricultural raw materials and the introduction of environmentally friendly technologies in the field of renewable energy. Full article

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability. Full article

Rizhao Reservoir, Shandong Province, China, as a key regional water supply hub, provides water for domestic, industrial, and agricultural uses in and around Rizhao City by intercepting runoff, which plays a central role in guaranteeing water supply security and supporting regional development. This study systematically collected 66 surface water samples to elucidate the hydrochemical characteristics within the reservoir area, identify the principal influencing factors, and clarify the sources of dissolved ions, aiming to enhance the understanding of the prevailing water quality conditions. A systematic analysis of hydrochemical facies, solute provenance, and governing processes in the study area’s surface water was conducted, employing an integrated mathematical and statistical approach, comprising Piper trilinear diagrams, correlation analysis, and ionic ratios. Meanwhile, the entropy weight-based water quality index (EWQI) and irrigation water quality evaluation methods were employed to assess the surface water quality in the study area quantitatively. Analytical results demonstrate that the surface water system within the study area is classified as freshwater with circumneutral to slightly alkaline properties, predominantly characterized by Ca-HCO₃ and Ca-Mg-SO₄-Cl hydrochemical facies. The evolution of solute composition is principally governed by rock–water interactions, whereas anthropogenic influences and cation exchange processes exert comparatively minor control. Dissolved ions mostly originate from silicate rock weathering, carbonate rock dissolution, and sulfate mineral dissolution processes. Potability assessment via the entropy-weighted water quality index (EWQI) classifies surface waters in the study area as Grade I (Excellent), indicating compliance with drinking water criteria under defined boundary conditions. Irrigation suitability analysis confirms minimal secondary soil salinization risk during controlled agricultural application, with all samples meeting standards for direct irrigation use. Full article

The aim of this study was to evaluate the influence of genotype and diet on geese from crossbreeding meat lines Tapphorn (T) and Eskildsen (E). This study was conducted on 240 crossbred geese assigned to two dietary groups: an SBM diet group fed a standard soybean-based diet and an LPS diet group fed a yellow lupin-based diet. Birds were reared under identical management conditions and slaughtered at 17 weeks of age. The following traits were recorded: meat colour (CIELab), pH₂₄, cooking loss, breast and thigh muscle texture (shear force and energy), and sensory traits. The results showed a significant effect of both genotype and diet on meat quality. The LPS diet lowered shear force and energy (by ~11%, p < 0.001), reduced cooking loss in breast muscles (by ~5%, p < 0.001), and improved the juiciness and flavour of thigh muscles. The ET genotype positively influenced the meat colour intensity (lower L*, higher a*), while the lupin-based diet improved technological parameters, especially the water-holding capacity. The results confirm that replacing soybean meal with yellow lupin protein is an effective nutritional strategy that can improve goose meat quality and sustainability without compromising the sensory quality. These outcomes support developing soy-free feeding strategies in goose production to meet consumer expectations and reduce reliance on imported feed. Full article

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article