Search Results (1,331)

Torrefaction, a mild thermochemical pretreatment process, is widely acknowledged as an effective strategy for enhancing the energy potential of lignocellulosic biomass. This review systematically evaluates the technological, environmental, and economic dimensions of lignocellulosic biomass torrefaction with the objective of clarifying its critical role in sustainable energy production and circular economy frameworks. Drawing from recent literature, the review covers process fundamentals, feedstock characteristics and operational parameters—typically 200–300 °C, heating rates below 50 °C per minute, ~1 h residence time, and oxygen-deficient conditions. The impacts of torrefaction on fuel properties, such as increased energy density, improved grindability and pelletability, enhanced storage stability, and reduced microbial degradation are critically assessed along with its contribution to waste valorization and renewable energy conversion. Particular emphasis is placed on the application of torrefied biomass (biochar) in sustainable agriculture, where it can enhance nutrient retention, improve soil quality and promote long-term carbon sequestration. This review identifies an unresolved research gap in aligning large-scale techno-economic feasibility with environmental impacts, specifically concerning the high process energy requirements, emission mitigation and regulatory integration. Process optimization, reactor design and supportive policy frameworks are identified as key strategies that could significantly improve the economic viability and sustainability outcomes. Overall, torrefaction demonstrates substantial potential as a scalable pathway for converting waste agricultural and forest residues into carbon-neutral biofuels. By effectively linking biomass waste valorization with renewable energy production and sustainable agricultural practices, this review offers a practical route to reducing environmental impacts while supporting the broader objectives of the global circular economy. Full article

Urbanization is a dominant force reshaping human settlements, driving socio-economic development while also causing significant environmental challenges. With over 56% of the world’s population now residing in urban areas—a figure expected to rise to two-thirds by 2050—land use changes are accelerating rapidly. The conversion of natural landscapes into impervious surfaces such as concrete and asphalt intensifies the Urban Heat Island (UHI) effect, raises urban temperatures, and strains local ecosystems. This study investigates land use and landscape changes in Ceredigion County, UK, utilizing remote sensing and GIS techniques to analyze urbanization impacts over two decades (2003–2023). Results indicate significant urban expansion of approximately 122 km², predominantly at the expense of agricultural and forested areas, leading to vegetation loss and changes in water availability. County-wide mean land surface temperature (LST) increased from 21.4 °C in 2003 to 23.65 °C in 2023, with urban areas recording higher values around 27.1 °C, reflecting a strong UHI effect. Spectral indices (NDVI, NDWI, NDBI, and NDBaI) reveal that urban sprawl adversely affects vegetation health, water resources, and land surfaces. The Urban Thermal Field Variance Index (UTFVI) further highlights areas experiencing thermal discomfort. Additionally, machine learning models, including Linear Regression and Random Forest, were employed to forecast future LST trends, projecting urban LST values to potentially reach approximately 27.4 °C by 2030. These findings underscore the urgent need for sustainable urban planning, reforestation, and climate adaptation strategies to mitigate the environmental impacts of rapid urban growth and ensure the resilience of both human and ecological systems. Full article

(1) Objective: Volatile organic compounds (VOCs) monitoring in industrial parks is crucial for environmental regulation and public health protection. However, current techniques face challenges related to cost and real-time performance. This study aims to develop a dynamic calibration framework for accurate real-time conversion of VOCs volume fractions (nmol mol⁻¹) to mass concentrations (μg m⁻³) in industrial environments, addressing the limitations of conventional monitoring methods such as high costs and delayed response times. (2) Methods: By innovatively integrating photoionization detector (PID) with machine learning, we developed a robust conversion model utilizing PID signals, meteorological data, and a random forest’s (RF) algorithm. The system’s performance was rigorously evaluated against standard gas chromatography-flame ionization detectors (GC-FID) measurements. (3) Results: The proposed framework demonstrated superior performance, achieving a coefficient of determination (R²) of 0.81, root mean squared error (RMSE) of 48.23 μg m⁻³, symmetric mean absolute percentage error (SMAPE) of 62.47%, and a normalized RMSE (RMSE_norm) of 2.07%, outperforming conventional methods. This framework not only achieved minute-level response times but also reduced costs to just 10% of those associated with GC-FID methods. Additionally, the model exhibited strong cross-site robustness with R² values ranging from 0.68 to 0.69, although its accuracy was somewhat reduced for high-concentration samples (>1500 μg m⁻³), where the mean absolute percentage error (MAPE) was 17.8%. The inclusion of SMAPE and RMSE_norm provides a more nuanced understanding of the model’s performance, particularly in the context of skewed or heteroscedastic data distributions, thereby offering a more comprehensive assessment of the framework’s effectiveness. (4) Conclusions: The framework’s innovative combination of PID’s real-time capability and RF’s nonlinear modeling achieves accurate mass concentration conversion (R² = 0.81) while maintaining a 95% faster response and 90% cost reduction compared to GC-FID systems. Compared with traditional single-coefficient PID calibration, this framework significantly improves accuracy and adaptability under dynamic industrial conditions. Future work will apply transfer learning to improve high-concentration detection for pollution tracing and environmental governance in industrial parks. Full article

International studies have reported extensively on outdoor learning in bush (Australia) or forest settings (e.g., U.K. and Nordic countries). Yet, limited studies have investigated urbanized environments comprising community facilities and city settings. This study shares early childhood teachers’ exploration and engagement with outdoor community settings in Singapore. Innovative practices emerged in response to the community and context in urbanized areas. Transformation of teaching happens during the research study when teachers shift from thinking about the local environment to thinking with, contributing to creating new ways of constructing outdoor teaching and learning experiences in an urbanized landscape. This study illustrates how teachers exploring the outdoors and thinking with places can open up conversations in building lively (and deadly) connections with the world. Full article

Afforestation substantially promotes vegetation restoration and modifies soil physical, chemical, and biological properties. The integrated effects of soil properties on soil quality, expressed via a composite soil quality index (SQI), remain unclear despite variations among individual properties. Here, five vegetation restoration treatments were selected as follows: (1) barren land (BL, control), (2) disturbed short-rotation Eucalyptus plantation (REP); (3) undisturbed long-term Eucalyptus plantation (UEP); (4) mixed native-species plantation (MF); and (5) natural forest (NF) following >50 years of restoration. Soil physicochemical properties and microbial community compositions were investigated, and soil quality was evaluated by an integrated SQI. Our results showed that vegetation restoration had strong effects on soil physicochemical properties, soil quality, and microbial communities. Most of the soil physicochemical properties exhibited significant differences among treatments. Soil dissolved organic carbon, total nitrogen, and ammonium nitrogen were the three key soil quality indicators. The SQI increased significantly with vegetation recovery intensity. In both UEP and MF, it reached levels comparable to NF, and was higher in UEP than in REP, implying that short-rotation practices impede soil restoration. In addition, microbial biomass (bacteria, fungi, arbuscular mycorrhizal fungi, actinomycetes, and total microbe PLFAs) increased from BL to NF. All plantations exhibited lower microbial biomass than NF, revealing incomplete recovery and a greater sensitivity to soil physicochemical properties. Conversely, the fungi-to-bacteria biomass ratio decreased sequentially (REP > BL > UEP > MF > NF). Strong positive correlations between microbial biomass and the SQI were observed. These results collectively indicate that afforestation with mixed tree species is optimal for rapid soil restoration, and undisturbed long-term monocultures can achieve similar outcomes. These findings highlight that tree species mixtures and reducing disturbance should be taken into consideration when restoring degraded ecosystems in the tropics. Full article

Carbon sinks are of great significance for mitigating the greenhouse effect and climate change. However, only a few carbon sink measurement methods are suitable for small-scale research, such as at the city-region scale. Methods that can accurately distinguish the high–low gradients of forest carbon sinks within small-scale areas have not yet been established. To fill this gap, we used a tree allometric growth model—the i-Tree Eco model—and applied it to Tai’an, which is a National Forest City in China. By using indicator conversion methods, we innovatively combined the China Forest Resources Inventory Geographic Information Database with i-Tree Eco. The results showed that i-Tree Eco successfully estimated the carbon sinks provided by urban–rural forests (in 2019)—the total carbon storage in Tai’an forest was 5,828,165.90 t; the average carbon storage per hectare was 37.19 tC·ha⁻¹; the total carbon sequestration was 936,789.03 tC·yr⁻¹; and the annual carbon sequestration was, on average, 5.97 tC·ha⁻¹·yr⁻¹. Our method improved the spatial resolution of carbon sequestration and storage compared to the commonly used InVEST model, from about 350 m × 350 m to 195 m × 195 m. Compared to the traditional IPCC method, the i-Tree Eco model provided greater accuracy and timeliness in small-scale carbon sequestration measurements, eliminating the need to wait for the next forest inventory to be published. Our method yielded results that covered the entire city region and better reflected the spatial heterogeneity of carbon sinks. We conclude that the innovative application of the i-Tree Eco model to urban–rural-scale carbon sink measurements provides stronger technical support for urban green space planning, as well as data guidance, in relation to local carbon mitigation strategies. Full article

The Mass Ratio Hypothesis states that the growth dynamics of dominant species influence forest species diversity by regulating the niches of subordinate and transient species. However, this prediction has not yet been empirical confirmed in subtropical forests over long term. Using data from 1995 to 2017, we examined how dominant tree species regulate species evenness and richness by analyzing their height and diameter growth in three clear-cut forests (Castanopsis carlesii (Hemsl.) Hayata, Castanopsis fissa (Champ. ex Benth.) Rehder & E. H. Wilson, and Cunninghamia lanceolata (Lamb.) Hook. stands), combined with the mean value of species niche breadth (measures the diversity of resources a species utilizes) across the community, including separate analyzes for subordinate (persistently coexisting with dominants species) and transient species (temporarily occurring species). Our results showed that an increase in height and diameter growth of dominant species had a negative effect on niche breadth of subordinate species, which in turn reduced species evenness (p < 0.01) but showed no significant relationship with species richness (p ≥ 0.05). Growth dynamics of dominants had no significant influence on the niche breadth of transient species. The early-fast growing dominant C. lanceolata significantly restricted the niche breadth of subordinate species (1.16 ± 0.23), resulting in relatively low evenness (0.49 ± 0.11). Conversely, the late-fast growing dominant C. carlesii promoted niche expansion (6.62 ± 1.20), resulting in higher evenness (0.81 ± 0.02). C. fissa -dominated strands with intermediate growth increments, exhibited moderate species evenness. These findings provide long-term empirical support for the Mass Ratio Hypothesis by demonstrating that growths of dominant species modulate niche partitioning in subordinates and thereby shape species diversity in subtropical forest communities. Full article

Clarifying the trade-offs and synergies between land use and ecosystem services in major water source river basins is enhancing regional land resource distribution and safeguarding water-related ecological environments. The Danjiangkou Reservoir Basin—the water source area of the South-to-North Water Diversion Project—land use change characteristics from 2012 to 2022 were focused on in this study. Five categories of ecosystem services, represented by six land use-related indicators, were selected for analysis. The InVEST model was utilized to conduct a quantitative assessment of their spatial and temporal variations. This study investigates the spatial variations of ecosystem services, analyzes their trade-offs and synergies, and explores the impacts of land use changes on the supply and interactions of these services. The findings reveal that cultivated land was served as the dominant source of land use conversion. Specifically, the largest areas of cultivated land conversion were to forest land (240.91 km²), followed by water bodies (144.65 km²) and construction land (38.43 km²). The selected ecosystem services exhibited distinct temporal and spatial variation: water yield, total carbon storage, and habitat quality showed upward trends, whereas total nitrogen output, total phosphorus output, and soil erosion demonstrated declining trends. Overall, the synergy and trade-off relationships among the six ecosystem service indicators weakened; however, the degree of improvement in trade-offs exceeded the decline in synergies. The integration of land use change, ecosystem service functions, and trade-off/synergy relationships into a unified analytical framework facilitates a robust theoretical foundation for basin-scale ecological management. This approach offers a scientific foundation for spatial optimization, ecological redline delineation, and resource allocation within the basin. Full article

Attackers can use Ethernet or WiFi sniffers to capture smart home device traffic and identify device events based on packet length and timing characteristics, thereby inferring users’ home behaviors. To address this issue, traffic obfuscation techniques have been extensively studied, with common methods including packet padding, packet segmentation, and fake traffic injection. However, existing research predominantly utilizes non-real-time traffic to verify whether traffic obfuscation techniques can effectively reduce the recognition rate of traffic analysis attacks on smart home devices. It often overlooks the potential impact of obfuscation operations on device connectivity and functional integrity in real network environments. To address this limitation, an online experimental framework for three fundamental traffic obfuscation techniques is proposed: packet padding, packet segmentation, and fake traffic injection. Experimental results demonstrate that the proposed framework maintains the continuous connectivity and functional integrity of smart home devices with a low system overhead, achieving an average CPU usage rate of less than 0.4% and an average memory occupancy rate of less than 2%. Evaluation results based on the random forest classification method show that the device event recognition accuracy for injected fake traffic exceeds 89%. In this context, a higher recognition accuracy indicates that attackers are more effectively deceived by the injected fake traffic. Conversely, the recognition accuracy for packet padding and packet segmentation methods is nearly zero, and a lower recognition accuracy in these cases implies a more effective implementation of those obfuscation techniques. Further evaluation results based on the deep learning classification method reveal that the packet segmentation approach significantly reduces device recognition accuracy for certain devices to below 5%, while simultaneously increasing the false recognition rate for other devices to over 95%. In contrast, fake traffic injection achieves a device recognition accuracy exceeding 90%. Moreover, the obfuscation effect of the packet padding method is found to be suboptimal, a finding consistent with existing literature suggesting that no single obfuscation technique can effectively withstand all types of traffic analysis attacks. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Solar gasification is a thermochemical process that relies on concentrated solar radiation to heat steam and biomass to produce syngas. This study uses Machine Learning to model solar gasification using steam as an oxidizer, incorporating both thermodynamic simulations and predictive algorithms, developed using Python (version 3.11.13) scripting, to understand the relationship between the input and output variables. Three models—Artificial Neural Networks, Support Vector Machines, and Random Forests—were trained using datasets including biomass composition, solar irradiance (considering a solar furnace), and steam-to-biomass ratios in a downdraft or fluidized bed gasifier. Among the models, Random Forests provided the highest accuracy (average R² = 0.942, Mean Absolute Error = 0.086, and Root Mean Square Error = 0.951) and were used for feature importance analysis. Results indicate that radiative heat transfer and steam-to-biomass ratio are the parameters that result in the largest increase in the syngas heating value and decrease in the tar contents. In terms of composition, the hydrogen contents have a direct relationship with the H₂ and tar formed, while the carbon content affects the carbon conversion efficiency. This work highlights the of feature importance analysis to improve the design and operation of solar-driven gasification systems. Full article

The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious threat to food availability. Accurate and timely mapping of paddy rice is therefore crucial. This study proposes a phenology-based mapping approach using a Convolutional Neural Network-Random Forest (CNN-RF) Hybrid model with multi-temporal Sentinel-2 and Landsat-8 imagery. Image processing and analysis were conducted using the Google Earth Engine platform. Raw spectral bands and four vegetation indices—NDVI, EVI, LSWI, and RGVI—were extracted as input features for classification. The CNN-RF Hybrid classifier demonstrated strong performance, achieving an overall accuracy of 0.950 and a Cohen’s Kappa coefficient of 0.893. These results confirm the effectiveness of the proposed method for mapping paddy rice in Indramayu Regency, West Java, using medium-resolution optical remote sensing data. The integration of phenological characteristics and deep learning significantly enhances classification accuracy. This research supports efforts to monitor and preserve paddy rice cultivation areas amid increasing land use pressures, contributing to national food security and sustainable agricultural practices. Full article

The rural border areas of Serbia have been undergoing significant demographic shifts and transformations in land use. Between 2002 and 2022, these regions experienced a continuous population decline, an increase in the average age, and a growing share of single-person households. Simultaneously, there has been a reduction in agricultural land and a noticeable expansion of forested and grassland areas, particularly in hilly and mountainous terrain. This paper aims to explore the interrelationship between demographic indicators and land cover changes in these areas. Pearson’s correlation analysis was applied to data from the national population censuses and the CORINE Land Cover datasets for 1990 and 2018. The strongest positive correlation was found between the decline in the number of households and the reduction in agricultural land. Conversely, the expansion of forested areas showed a negative correlation with most demographic indicators. The findings reflect trends similar to those observed in other Eastern European countries but also reveal specific patterns of spatial marginalization unique to Serbia. In the study, the conclusion leads to the idea that depopulated border areas are in transition between past and future functions that will be influenced by their resource base. Full article

Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance sector, using a conceptual model based on well-known new information systems adoption groundworks that are implemented with a combination of machine learning techniques based on decision trees and so-called importance–performance map analysis (IPMA). The intention to interact with a chatbot is explained by performance expectancy (PE), effort expectancy (EE), social influence (SI), and trust (TR). For the analysis, three machine learning methods are applied: decision tree regression (DTR), random forest (RF), and extreme gradient boosting (XGBoost). While the architecture of DTR provides a highly visual and intuitive explanation of the intention to use chatbots, its generalization through RF and XGBoost enhances the model’s explanatory power. The application of Shapley additive explanations (SHAP) to the best-performing model, RF, reveals a hierarchy of relevance among the explanatory variables. We find that TR is the most influential variable. In contrast, PE appears to be the least relevant factor in the acceptance of chatbots. IPMA suggests that SI, TR, and EE all deserve special attention. While the prioritization of TR and EE may be justified by their higher importance, SI stands out as the variable with the lowest performance, indicating the greatest room for improvement. In contrast, PE not only requires less attention, but it may even be reasonable to reallocate efforts away from improving PE in order to enhance the performance of the more critical variables. Full article

Primary forests harbor extraordinary biodiversity, but conversion from primary forests to secondary forests often leads to biodiversity loss and diminished ecosystem functioning. While much of the existing research has focused on plants and vertebrates, soil fauna—particularly Collembola—remain underexplored in this context. To address this gap, we conducted a comprehensive assessment of the Collembola diversity and community composition in primary and secondary forests across two regions in northeastern China. Among 5587 Collembola individuals, 69 morphospecies were identified. The Collembola abundance and Shannon–Wiener index were significantly higher in primary forests, although the species richness did not differ significantly between the forest types. In contrast, the community composition differed markedly, with several taxa found exclusively in primary forests. Notably, environmental factors exerted stronger influences on Collembola communities in primary forests, suggesting that these ecosystems may be more vulnerable to climate change and external disturbances. These findings demonstrate that primary forests play a crucial role in protecting soil fauna diversity and emphasize that future conservation efforts should focus on the strict protection of primary forests. Full article