Search Results (1,501)

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM_2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3. Full article

Biodiesel, a renewable and environmentally friendly alternative to fossil fuels, has attracted significant attention due to its potential to reduce greenhouse gas emissions. However, high production costs and complex processing remain challenges. Heterogeneous catalysts have shown promise in overcoming these barriers by offering benefits, such as easy separation, reusability, low-cost raw materials, and the ability to reduce reaction times and energy consumption. This review evaluates key classes of heterogeneous catalysts, such as metal oxides, ion exchange resins, and zeolites, and their performance in transesterification and esterification processes. It highlights the importance of catalyst preparation methods, textural properties, including surface area, pore volume, and pore size, activation techniques, and critical operational parameters, like the methanol-to-oil ratio, temperature, time, catalyst loading, and reusability. The analysis reveals that catalysts supported on high surface area materials often achieve higher biodiesel yields, while metal oxides derived from natural sources provide cost-effective and sustainable options. Challenges, such as catalyst deactivation, sensitivity to feedstock composition, and variability in performance, are discussed. Overall, the findings underscore the potential of heterogeneous catalysts to enhance biodiesel production efficiency, although further optimization and standardized evaluation protocols are necessary for their broader industrial application. Full article

Platelet-rich plasma (PRP) has become an increasingly valuable biologic approach for personalized regenerative medicine because of its potent anti-inflammatory/healing effects. It is thought to be an excellent source of growth factors that can promote tissue healing and lessen fibrosis. Although this treatment has demonstrated effectiveness in numerous disease areas, its impact on pulmonary fibrosis (PF) caused by silver nanoparticles (AgNPs) via its antiapoptotic effects remains to be explored. AgNPs were synthesized biologically by Bacillus megaterium ATCC 55000. AgNP characterization was carried out via UV–Vis spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) imaging to reveal monodispersed spheres with a mean diameter of 45.17 nm. A total of 48 male Wistar rats divided into six groups, with 8 rats per group, were used in the current study on the basis of sample size and power. The groups used were the PRP donor, control, AgNP, AgNP + PRP, AgNP + dexamethasone (Dexa) rat groups, and a recovery group. Body weights, hydroxyproline (HP) levels, and CASP3 and TWIST1 gene expression levels were assessed. H&E and Sirius Red staining were performed. Immunohistochemical studies for inducible nitric oxide synthase (iNOS) and cluster of differentiation 68 (CD68) with histomorphometry were conducted. A significant reduction in body weight (BWt) was noted in the AgNP group compared with the AgNP + PRP group (p < 0.001). HP, CASP3, and TWIST1 expression levels were significantly increased by AgNPs but decreased upon PRP (p < 0.001) treatment. Compared with those in the control group, the adverse effects of AgNPs included PF, lung alveolar collapse, thickening of the interalveolar septa, widespread lymphocytic infiltration, increased alveolar macrophage CD68 expression, and iNOS positivity in the cells lining the alveoli. This work revealed that PRP treatment markedly improved the histopathological and immunohistochemical findings observed in the AgNP group in a manner comparable to that of the Dexa. In conclusion, these results demonstrated the therapeutic potential of PRP in a PF rat model induced via AgNPs. This study revealed that PRP treatment significantly improved the histopathological and immunohistochemical alterations observed in the AgNP-induced group, with effects comparable to those of the Dexa. In conclusion, these findings highlight the therapeutic potential of PRP in a rat model of AgNP-induced PF. Full article

Predictive formulation can help reduce the number of experiments required to reach a target cosmetic product. The performance of Large Language Models from the open source Llama family is compared with that of conventional machine learning to predict the phase stability of shampoo formulations using a recently published dataset. The predictive strength is assessed for various train dataset sizes (obtained by stratified sampling of the full dataset) and for various Large Language Model sizes (3, 8, and 70B parameters). The predictive strength is found to increase on increasing the model size, and the Large-Language-Model-based approach outperforms conventional machine learning when the train dataset is small, delivering Area Under the Receiver Operating Curve above 0.7 with as few as 20 train samples. This work illustrates the potential of Large Language Models to further reduce the number of experiments required to reach a target cosmetic formulation. Full article

Mangrove wetlands, acting as significant traps for marine debris, have received insufficient attention in previous research. Here, we conduct the first comprehensive investigation into the magnitude, accumulation, source, and fate of marine debris across seven mangrove areas in the northern South China Sea (MNSCS) during 2019–2020. Systematic field surveys employed stratified random sampling, partitioning each site by vegetation density and tidal influence. Marine debris were collected and classified in sampling units by material (plastic, fabric, styrofoam), size (categorized into small, medium, and large), and origin (distinguishing between land-based and sea-based). Source identification and potential risk assessment were achieved through the integration of debris feature analysis. The results indicate relatively low debris levels in MNSCS mangroves, with plastics dominant. More than 70% of all debris weight with plastics (48.34%) and fabrics (14.59%) is land-based, and more than 70% comes from coastal/recreational activities. More than 90% of all debris items with plastics (52.50%) and Styrofoam (36.32%) are land-based, and more than 90% come from coastal/recreational activities. Medium/large-sized debris are trapped in mangrove wetlands under the influencing conditions of local tidal level, debris item materials, and sizes. Our study quantifies marine debris characteristics, sources, and ecological potential risks in MNSCS mangroves. From environmental, economic, and social sustainability perspectives, our findings are helpful for guiding marine debris management and mangrove conservation. By bridging research and policies, our work balances human activities with ecosystem health for long-term sustainability. Full article

Cellulose nanocrystals possess unique properties such as high surface area and excellent biocompatibility. They can disrupt strong hydrogen bonds and other intermolecular forces that hinder the solubility of certain molecules thus enhancing the solubility of poorly soluble materials. The main challenge in formulating poorly soluble drugs lies in their limited therapeutic efficacy due to inadequate solubility and bioavailability. Therefore, an innovative approach such as using cellulose nanocrystals to enhance the solubility is highly needed. The aim of this research is to study the potential of ramie (Boehmeria nivea L. Gaud) as a source of cellulose nanocrystals in the development of microspheres for the solubility enhancement of poorly soluble drugs. Nanocrystalline cellulose was isolated from the ramie (Boehmeria nivea L. Gaud) by optimizing hydrolysis conditions with varying acid concentrations and reaction times. Characterizations were performed by measuring particle size, pH, and sulfate content, followed by morphological study by SEM, functional group analysis, and thermal analysis. The use of sulfuric acid in the hydrolysis process of flax cellulose at 45 °C, as the type of acid that gives the best results, at 50% acid concentration for 60 min produces cellulose nanocrystallines with a particle size of 120 nm, sulfate concentration density of 133.09 mmol/kg, crystallinity of 96.2%, and a yield of 63.24 ± 8.72%. Furosemide was used as the poorly soluble drug model and its solubility enhancement in the form of furosemide/RNCC microspheres was evaluated through saturated solubility testing and in vitro dissolution. This study demonstrated that RNCC could improve the solubility of furosemide, which contributes to developing sustainable drug formulations and eco-friendly delivery systems for poorly soluble drugs. Full article

The complex geological environment in western Sichuan, China, leads to frequent debris flow disasters, posing significant threats to the lives and property of local residents. In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models were trained with data in Songpan County and used for debris flow susceptibility prediction in Mao County, using small watersheds as assessment units. Seventeen key feature factors based on multi-source remote sensing data encompassing topography and geomorphology, geological structures, environmental elements, and human activities were selected as input parameters after assessment with Pearson correlation analysis. Model performance was rigorously evaluated through ten-fold cross-validation, and hyperparameter optimization was employed to enhance predictive accuracy. To assess the models’ robustness, the trained models were applied to the neighboring Mao County for cross-regional validation. The results consistently indicate that elevation, seismic nucleation density, population density, and distance to roads are the primary controlling factors influencing susceptibility. Comparative analysis between the Songpan and Mao County reveals that the RF model significantly outperforms SVM and XGBoost in accuracy and robustness. Therefore, the RF model is better suited for debris flow susceptibility assessment in western Sichuan. Although the effectiveness of this model may be limited by the relatively small sample size of debris flow events in the dataset and potential variations in environmental conditions across different regions, it still holds promise for providing a scientific basis and decision-making support for disaster mitigation in comparable areas of western Sichuan. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

Optically stimulated luminescence (OSL) dating is an important method for determining the ages of late Quaternary sediments. However, partial bleaching of quartz in fluvial sediments remains a challenge, with debates on grain-size effects in different sedimentary environments. The aim of this paper is to explore the bleaching degree and its influencing factors of different grain-size quartz in fluvial sediments from the Yanchi section in the Daluze area, North China Plain. According to sedimentological methods and grain size analysis, lacustrine and fluvial layers were identified, and the ages of sediments were determined by OSL and ¹⁴C methods. The key findings are as follows: (1) Fine-grained quartz can be better bleached than coarse/medium-grained quartz for early–middle Holocene fluvial sediments. (2) The OSL method can yield reliable ages for early–middle Holocene fluvial sediments, while it overestimates these for late Holocene fluvial sediments. This probably results from variations in sediment sources and hydrodynamic conditions. (3) The dating results show that there are three fluvial activity periods in the Daluze area: 10.8~10.2 ka, 5.3~4.7 ka, and after 1 ka. This paper provides a reliable chronological framework for the evolution of regional sedimentary environments and offers references for luminescence dating of fluvial sediments in similar environments. Full article

Identifying the coupling effect mechanisms of particulate matter (PM) in different functional areas on the atmospheric environment will help to carry out graded precision prevention and control measures against pollution within mining cities. This study monitored the pollution of three different functional areas in Wuhai, a typical mining city in Inner Mongolia. PM₁, PM_2.5, PM₁₀, and TSP were sampled and analyzed for chemical fractions both in the daytime and at night in spring, summer, autumn, and winter. The results showed that the average daily concentrations of PM were generally higher in the mining area than in the urban and sandy areas in different seasons. The results of the Kerriging analysis showed that the urban area was affected the most when specific ranges of high PM concentrations were detected in the mining area and specific ranges of low PM concentrations were detected in the sandy area. PMF results indicated that the source of pollutants in different functional areas and seasons were dust, industrial and traffic emissions, combustion, and sea salt. The contributions of dust in PM with different particle sizes in the mining and sandy areas were as high as 49–72%, while all the pollutant sources accounted for a large proportion of pollution in the urban area. In addition, dust was the largest source of pollution in summer and winter, and the contribution of combustion sources to pollution was higher in winter. Health risks associated with Cr were higher in the sandy area, and non-carcinogenic risks associated with Mn were higher in the mining area during spring and summer, while there was a greater impact on human health in the urban area during autumn and winter. The results of this study revealed the coupling effect mechanisms of different functional areas on the local atmospheric environment and contribute to the development of regional atmospheric defense and control policies. Full article

The frequency of road accidents in specific locations is determined by a number of variables, among which an important role is played not only by common determinants such as inappropriate behavior of road users, but also by external factors characterizing a given location. Taking this into account, the main objective of the study was to answer the question of which variables determine that the intensity of car accidents is higher in certain parts of the city of Poznań compared to other locations. The study was based on source data from the police Accident and Collision Records System (SEWiK). For the purposes of the analysis, two variants of the regression method were used: ordinary least squares (OLS) and geographically weighted regression (GWR). The obtained results made it possible to identify variables that increase the likelihood of a traffic accident in specific parts of the city, and the variables that proved to be statistically significant include the size of the built-up area and the number of traffic lights. The results obtained using the GWR technique indicate that the way in which the analyzed features influence road accidents can vary across the city, which may emphasize the complexity of the analyzed phenomenon. The results can be used by relevant entities (transport traffic planners and many others) to create road safety policies. Full article

Coastal cities and low-lying areas are increasingly vulnerable, and accurate data is needed to identify where interventions are most required. We compared 53 cities affected by a 1 m increase in land levels and a 2 m rise in sea levels. The geographical scope of this study covered selected coastal cities in Europe and northern Africa. Data were sourced from the European Environment Agency (EEA) in the form of prepared datasets, which were further processed for analysis. Statistical methods were applied to compare the extent of urban flooding under two sea level rise scenarios—1 m and 2 m—by calculating the percentage of affected urban areas. To assess social vulnerability, the analysis included several variables: MAPF65 (Mean Area Potentially Flooded for people aged 65 and older, indicating elderly exposure), Age (the percentage of the population aged 65+ in each city), MAPF (Mean Area Potentially Flooded, representing the average share of urban area at risk of flooding), and Unemployment Ratio (the percentage of unemployed individuals living in the areas potentially affected by sea level rise). We utilized t-tests to analyze the means of two datasets, yielding a mean difference of 2.9536. Both parametric and bootstrap confidence intervals included zero, and the p-values from the t-tests (0.289 and 0.289) indicated no statistically significant difference between the means. The Bayes factor (0.178) provided substantial evidence supporting equal means, while Cohen’s D (0.099) indicated a very small effect size. Ceuta’s flooding value (502.8) was identified as a significant outlier (p < 0.05), indicating high flood risk. A Grubbs’ test confirmed Ceuta as a significant outlier. A Wilcoxon test highlighted significant deviations between the medians, with a p << 0.001, demonstrating systematic discrepancies tied to flood frequency and sea level anomalies. These findings illuminated critical disparities in flooding trends across specific locations, offering essential insights for urban planning and mitigation strategies in cities vulnerable to rising sea levels and extreme weather patterns. Information on coastal flooding provides awareness of how rising sea levels affect at-risk areas. Examining factors such as MAPF and population data enables the detection of the most threatened zones and supports targeted action. These perceptions are essential for strengthening climate resilience, improving emergency planning, and directing resources where they are needed most. Full article

Lignocellulosic biomass is an exciting renewable resource for producing sustainable biofuels, thanks to its abundance and low environmental impact. However, its intricate structure makes it tough for enzymes to break it down effectively. Only efficient pretreatment methods can solve these problems. Among these, mechanical pretreatment methods are particularly good for industry because they are easy to use, do not require chemicals, and make it easier to achieve biomass. This systematic review adhered to the PRISMA protocols and used text analysis with VOSviewer to examine 33 academic articles published between 2005 and 2025. It highlighted two main types of mechanical pretreatment: size reduction (which includes grinding, crushing, and shredding) and densification (like pelletizing and briquetting). The results show that mechanical pretreatment can significantly boost biofuel yields by increasing surface area, lowering crystallinity, and allowing better enzyme penetration. Energy consumption remains a major hurdle for the overall sustainability of biomass conversion processes. This research provides a comprehensive review of current mechanical techniques, detailing their operational settings and performance metrics while also offering suggestions for optimizing biomass conversion processes. By promoting the use of mechanical pretreatment in biofuel production systems, the findings align with the principles of a circular economy and contribute to the development of greener energy sources. Full article

Groundwater is a crucial source of water supply and an important ecological element globally. Research on the dynamic characteristics of groundwater and their causative mechanisms is fundamental to objectively evaluating groundwater resources and their sustainable utilization. Based on the large amount of hydrogeological data collected and analyzed in typical hilly plain areas, a multi-factor weighted comprehensive evaluation system (MFWCES) based on GIS was used to evaluate the response of groundwater dynamics to combined stress elements in Tangshan City. The study area is located in the plains and hilly regions of Tangshan City. The evaluation system was based on seven influencing factors, including hydraulic conductivity, soil media, aquifer thickness, depth of groundwater, land use type, extraction intensity of groundwater, and groundwater evaporation. The results of groundwater dynamics in the study area were obtained by weighted comprehensive evaluation, with their score size ranging from 2.4 to 12.7. The spatial distribution of groundwater dynamics was classified into four categories: rapid response (10.3–12.7), dual response to precipitation and anthropogenic extraction (9.6–10.3), delayed response (7.6–9.6), and strong superimposed response to human activities (2.4–7.6). The related conclusions will provide key references for regional water resource planning, ecological protection, and the development of differentiated groundwater management strategies under compound stress. Full article

A quantitative literature review of restoration techniques and supporting management strategies used throughout the Caribbean and Western Atlantic from 1998 through 2024 was compiled using references from the Web of Science to highlight those with potential for reef replenishment. From 93 sources listed, 74 publications were relevant and categorized into subtopics based on the most prevalent restoration techniques. Roughly half the studies focused on three general topics: the benefits of restoring Acropora species, studies utilizing micro-fragmentation and fragment nurseries, and outplanting techniques. Other subtopics, each with at least three references, included optimizing substrates and artificial reefs, enhancing larval recruitment, emphasizing the role of herbivory, improving management practices, and addressing the impacts of tourism and community engagement. The information from the references was compiled to determine the overlap among categories and the ways in which techniques and management strategies might be applied simultaneously to enhance restoration outcomes. Additionally, sources were analyzed according to time and location of publication to better visualize the emergence of this area of research and restoration efforts. An increase in publications was observed from 2014 to 2024, associated with the rise in major events impacting coral reefs. The major locations for published research were the Florida reef tract and Puerto Rico, though restoration studies were also reported from the Bahamas and sites around the Caribbean. Criteria to assess the success of techniques included coral survival, recruitment, coral coverage, habitat structure and complexity, and biomass of marine life, including fish and invertebrates that inhabited a restored reef. Most restoration efforts utilized either fragmentation or assisted sexual breeding, followed by cultivation in nurseries or labs. Outplanting success depended on fragment size, attachment style, and site selection, with less-intrusive techniques and intermediate planting densities promoting survival. Tools like GAO maps can guide site selection based on herbivore presence and algal coverage. Monitoring is critical to ensuring coral survival, especially after the first year of outplanting, while community involvement can foster public engagement in reef conservation. Full article