Search Results (716)

Urban green spaces play a vital role in climate change mitigation through carbon sequestration and storage. However, accurately quantifying their carbon sink capability remains challenging due to complex vertical structures and spatial heterogeneity. This study proposes a comprehensive inventory framework integrating multi-source LiDAR (UAV and Backpack) with a phenology-based complementary strategy to quantify carbon dynamics across three nested scales: green space types, plant communities, and species. Two key indicators—Carbon Sequestration Efficiency (CSE) and Carbon Density (CD)—were used to evaluate both the dynamic and static aspects of carbon sink function. The results reveal a clear asynchrony between CSE and CD across scales. No single plant type performed best in both dimensions, indicating a trade-off between growth efficiency and biomass accumulation. Hierarchical clustering identified distinct plant groups with divergent carbon sink strategies, supporting nuanced vegetation selection. The dual-indicator and dual-platform approach proposed in this study advances our existing understanding of the carbon sequestration capacity of urban green spaces and provides a robust methodological foundation for data-driven low-carbon urban ecological planning. Full article

The increasing generation of biomass-derived waste has accelerated the development of sustainable strategies for its valorization into functional materials. Activated carbon (AC), due to its high surface area, tunable porosity, and chemical versatility, has emerged as a key product for applications in adsorption, catalysis, energy storage, and biosensing, among others. Recent studies have highlighted the importance of symmetry and asymmetry in determining the structural and functional performance of AC. Symmetric architectures, typically generated via templating methods, yield ordered pore networks, whereas asymmetric structures, commonly produced through direct chemical activation or heteroatom doping, exhibit hierarchical porosity and heterogeneous surface functionalities. This work critically examines the fundamentals of symmetry and asymmetry in AC materials, as well as their influence on design and use. It discusses synthesis strategies, characterization techniques, and recent approaches that enable the rational engineering of carbon structures. Application-specific case studies are presented, along with current challenges related to feedstock variability, scalability, and regulatory integration. By highlighting the interplay between structural order and functional diversity, this work provides a conceptual framework for guiding future research in the development on symmetrical and asymmetrical carbonaceous materials for sustainable waste valorization. Full article

We examined the positions of policymakers and businesses on the forest-based bioeconomy (FBE) in Slovenia, focusing on the importance of forest ecosystem services within the FBE. We also explored how businesses perceive their market potential and the role of payments for ecosystem services (PES) schemes in strengthening the FBE. We conducted interviews with 35 policymakers from the fields of forestry, the wood industry, the environment, and tourism, as well as with 24 business representatives from primary wood production, the wood industry, and forest tourism. Respondents identified fragmented land ownership (mean score on a 1–5 scale = 4.19), the lack of a strategic framework (4.12), and inefficient use of woody biomass (4.08) as key challenges to implementing the FBE in Slovenia. They highlighted knowledge transfer (4.54), investment support (4.47), and raising environmental awareness (4.44) as the main forms of state support for the FBE, while unfamiliarity with PES appears to contribute to its neglect. No significant sectoral differences were observed among policymakers regarding PES involvement; however, they viewed their role mainly in the design phase of PES and least in the phases of coordination and establishment. Greater interest in participating in PES was expressed by forest tourism businesses, despite perceiving lower market potential than those in primary wood production and the wood industry. The evident heterogeneity of stakeholder positions on the FBE calls for strong coordination and a transparent policy process involving all stakeholder coalitions to establish a coherent national strategy for the FBE. The results highlighted policymakers’ limited governance capacity and reluctance to fully implement PES as a potential solution for strengthening the FBE. The differing motivations of businesses regarding PES underscore the need for a nuanced, sector-specific approach to foster broader engagement. Full article

In the Republic of Moldova, orchard biomass represents an important resource for the production of densified solid biofuels, with peach having the highest sustainable energy potential (33.5 ± 6.54 GJ·ha⁻¹). However, the quality of solid biofuels derived from orchard biomass is often constrained by heterogeneity in moisture content, uneven particle size distribution, and inadequate drying or blending practices along the supply chain. Optimizing the solid biofuel supply chain is therefore essential to minimize feedstock variability, ensure consistent densification quality, and reduce production costs. The aim of this study was to improve the process of producing densified solid biofuels from orchard biomass. Specifically, the study investigated how raw material moisture and particle size influence briquette density and durability, and how ternary mixtures of peach biomass, wheat straw, and sunflower residues can be optimized for enhanced energy performance. All experimental determinations were performed using validated methods and calibrated equipment. The results showed that optimal performance is achieved by shredding the biomass with 4–8 mm sieves and maintaining the moisture content between 6 and 14%, resulting in briquettes with the density of 1.00–1.05 g·cm⁻³, ash content below 3–5%, and an energy yield of 18.4–19.2 MJ·kg⁻¹. Ternary diagrams confirmed the decisive role of peach lignocellulosic residues in achieving high density, low ash content, and increased energy yield, while wheat straw and sunflower residues can be used in controlled proportions to diversify resources and reduce costs. These findings provide quantitative insights into how mixture formulation and process parameters influence the briquette quality, contributing to the optimization of solid biofuel supply chains for orchard and agricultural residues. Overall, this study demonstrates that competitive solid biofuels can be produced through careful balancing of mixture composition and optimization of technological parameters, offering practical guidelines for sustainable bioenergy development in regions with abundant orchard residues. Full article

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSI_VI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSI_VI) and AGB was enhanced by 13.60% overall, with the CGSI_CIre exhibiting the highest correlation with cotton AGB (R² = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSI_VI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field. Full article

This systematic review aimed to examine recent advances (2021–2025) in the conversion of agricultural biomass into biomaterials, biofertilizers, and bioproducts. Studies were included when addressing biomass types, pretreatment methods, conversion technologies, or resulting applications. Non-agricultural biomass, non-original research, and works outside the defined timeframe were excluded. Literature was identified in Scopus and Web of Science, complemented by Espacenet, Google Scholar, and institutional databases (USDA, FAO, IRRI, ABARES, UNICA, and CONAB, among others), totaling 108 documents referenced in this work. Risk of bias was minimized through predefined eligibility criteria and full-text assessment. Results were narratively synthesized, supported by figures and tables highlighting technological trends. Studies involving a wide range of agricultural biomasses (e.g., rice straw, corn stover, wheat straw, and sugarcane bagasse) were evaluated. Main outcomes included the development of bioplastics, biofoams, composites, hydrogels, bioceramics, biochar-based fertilizers, organic acids, enzymes, and green solvents. Evidence consistently indicated that pretreatment strongly influences conversion efficiency and that enzymatic and thermochemical routes show the highest potential for integrated biorefineries. Limitations included heterogeneity in biomass composition, variability in methodological quality, and scarcity of large-scale studies. Overall, findings underscore agricultural biomass as a strategic feedstock for circular bioeconomy models, with implications for sustainable materials, renewable energy, and low-carbon agriculture. Continued innovation, supportive policies, and improved logistics are essential for scaling biomass-based technologies. Full article

Agricultural and pastoral parks in China possess abundant biomass resources, such as crop straw and livestock manure. However, insufficient distribution generation capacity and a lack of effective coordination strategies lead to low energy utilization efficiency and high carbon emissions. To address these issues, in this study, a coordinated microgrid optimization strategy is proposed based on multi-energy complementarity. A source–load multi-energy coupling model is established by analyzing the dynamic characteristics of biomass energy flow and incorporating a flexible load demand response mechanism. An optimization model aimed at minimizing operational costs is then developed to coordinate heterogeneous energy sources. Simulations under typical wind–solar–load scenarios demonstrate that the proposed strategy improves operational economy by 12.6% and reduces carbon emissions by 23.3% compared to conventional methods through optimized allocation of demand response resources. Full article

To address the challenges of difficult recovery, significant environmental hazards associated with homogeneous catalysts, and insufficient catalytic activity of heterogeneous supports in the catalytic dehydration of fructose to produce 5-hydroxymethylfurfural (5-HMF), this study employs a straightforward nitric acid modification method to prepare an acid-activated silica gel catalyst for application in this reaction system. Through systematic investigation of the influence of modification conditions on catalyst performance and economic benefits, optimal reaction conditions were determined: DMSO as the solvent, nitric acid-modified silica gel as the catalyst, a reaction temperature of 120 °C, a solid–liquid ratio of 1:30 (g∙mL⁻¹), and a fructose-to-catalyst mass ratio of 1:1. Under these conditions, the maximum 5-HMF yield reached 91.6%. Characterization via specific surface area, pore size analysis, and acid/base site characterization (NH₃-TPD) revealed that nitric acid modification preserved the silica gel’s pore structure. Through oxidative cleaning, etching to expose silanol groups, and inducing surface defects, this process significantly increased the number of acid sites on the silica gel surface, thereby enhancing catalytic activity. This study presents a low-cost, easily recoverable, and environmentally friendly heterogeneous catalytic strategy for the efficient conversion of fructose into 5-HMF. It also provides experimental guidance for the targeted functionalization of silica-based catalytic materials, holding significant implications for advancing the high-value utilization of biomass resources. Full article

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI), were analyzed across three contrasting agricultural systems: paddy, sugarcane, and rubber, revealing distinct phenological and water stress dynamics. Radar-derived structural indices captured patterns of biomass accumulation and canopy development, with VV/VH values ranging from 4.2 to 12.3 in paddy and 5.4 to 6.0 in rubber. In parallel, optical moisture indices detected crop physiological stress; for instance, NDMI dropped from 0.26 to 0.06 during drought in sugarcane. Cross-index analyses demonstrated strong complementarity; synchronized VV/VH and RDI peaks characterized paddy inundation, whereas lagged NDMI–VV/VH responses captured stress-induced defoliation in rubber trees. Temporal profiling established crop-specific diagnostic signatures, with DpRVI peaking at 0.75 in paddy, gradual RDI decline in sugarcane, and NDMI values of 0.2–0.3 in rubber. The framework provides spatially explicit, temporally continuous, and cost-effective monitoring to support irrigation, drought early warning, and agricultural planning. Multi-year validation and field-based calibration are recommended for operational implementation. Full article

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R² = 0.83) and yield (R² = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R² = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards. Full article

In this paper, an artificial neural network (ANN) model of the MLP 5-17-1 type was developed to predict the gross calorific value (HHV) of various waste types based on ultimate analysis. The dataset comprised heterogeneous samples, including biomass, municipal and industrial waste, sludges, and derived fuels, ensuring the model’s diversity and universality. The model achieved high accuracy (R² = 0.92; RMSE = 2.36; MAE = 1.68; MAPE = 10.99%), comparable to previous research results. The heterogeneity of the samples confirmed wide variations in composition and energy properties, which are crucial for developing a universal predictive model. The results confirm that ANN is a reliable tool for assessing the energy potential of waste and highlight the importance of expanding databases and optimizing parameters in future research. Full article

This study focuses on a single-step microwave-assisted carbonization and activation method for biomasses derived from peanut shells and spruce cones. Using phosphoric acid as the activating agent, this process leads to carbon materials with a micro-mesoporous structure, favoring dye adsorption. Elemental and surface analyses confirmed that the physicochemical properties of the obtained carbons are strongly dependent on the biomass’ source. The carbon materials obtained in this way, differing in porous structure and the presence of functional groups on their surfaces, were used for static adsorption of hazardous dye crystal violet from water. The adsorption behavior of both materials fits well with the Langmuir and Freundlich isotherms, indicating a combination of monolayer and heterogeneous surface adsorption, driven primarily by physical interactions. Of these two materials, carbon derived from spruce cones was characterized by better porosity, higher surface functionality, and higher adsorption capacity, demonstrating its potential as a cost-effective and sustainable material for wastewater treatment applications. Full article

Meiofaunal assemblages are crucial components of benthic ecosystems, significantly contributing to organic matter cycling and energy transfer. However, baseline quantitative data from some upwelling systems remain limited. This study characterizes the abundance, vertical distribution, and secondary production of meiofauna at a coastal upwelling station off southern Peru (14°16′ S) for July 2006 (Neutral conditions) and May 2007 (moderate La Niña, LN), using four-replicated sediment cores sectioned into 0–1, 1–2, 2–5, and 5–10 cm layers. While Nematoda (families Desmodoridae, Chromadoridae, Monhysteridae, Oxystominidae, Comesomatidae) dominated the community (>79% in all layers, both years), the total taxonomic richness did not differ substantially between study periods nor across the sediment column for 2006 or for 2007. Total density (0–10 cm) fluctuated between 3916 ± 2202 Ind 10 cm⁻² in 2006 and 4203 ± 2274 Ind 10 cm⁻² in 2007, with non-significant changes. Biomass (µgC 10 cm⁻²) in 2006 ranged from 80 ± 24 in the 5–10 cm section to 455 ± 134 in the 2–5 cm section. The uppermost 0–1 cm layer showed 238 ± 155, while the 1–2 cm section reached 302 ± 69. In 2007, biomass was consistently higher in the surface layers, with maximum values in the 1–2 cm section (500 ± 534), followed by the 0–1 cm section (376 ± 34). Hierarchical clustering produced depth-ordered groups with high within-depth similarity (>80–90%). SIMPER results identified Desmodora, Comesomatidae, and Chromadoridae among the top contributors to within-depth similarity and to the dissimilarity observed between surface and subsurface assemblages. A depth-related gradient of community composition was detected, suggesting vertical habitat heterogeneity modulated by several environmental factors; however, PERMANOVA analysis residuals (96.73%) indicate a high variation not explained by ENSO phase, sediment section, or their interaction, suggesting other unmeasured factors explaining meiofaunal community structure. Meiofauna’s production ranged from 2.836 ± 0.049 gC m⁻² y⁻¹ in 2006 to 3.106 ± 1.566 gC m⁻² y⁻¹ in 2007. These findings expand the limited knowledge on meiofaunal abundance and metabolic demands in this ocean region, fostering future efforts for comparative analyses across latitudes, depth gradients, and oceanographic regimes. Full article

Clear Lake in Lake County, CA, USA has experienced highly toxic cyanobacterial blooms for more than a decade, with multiple cyanobacterial taxa and cyanotoxins appearing sporadically, typically throughout much of the early-spring to late-fall seasons. Recurring blooms have been attributed to high internal nutrient loads within the lake, with hydrography and hydrology playing important but still poorly documented roles in controlling the availability of growth-limiting elements to the phytoplankton community. The lake is approximately 180 km² in areal extent and composed of three somewhat disjointed lobes, or ‘Arms’. The large size of the lake presents a formidable task for synoptic lakewide surveys and for understanding the specific features that stimulate the development and magnitude of harmful blooms. We conducted a study in August of 2020 that involved the use of an autonomous underwater vehicle and deployment of a hand-held water column profiler to describe the lakewide status of various biological, chemical, and physical features. Discrete water samples were also collected from ten stations located throughout the lake to produce a near-synoptic depiction of lake status. Additionally, a mechanically driven, continuously monitoring water-column profiler was deployed at a central lake location to document short-term temporal (minutes to months) changes in water-column structure and chemistry. Wind was a dominant feature affecting the lake’s chemistry and biology during the study, resulting in massive concentrations and dramatic spatial heterogeneity of phytoplankton biomass and cyanotoxins in the eastern and southeastern Arms of the lake, and confirmed by the analysis of discrete water samples. Unique insight into the processes leading to or prolonging blooms was revealed by the water column profiler, which demonstrated rapid development (within a few hours) of suboxic conditions during periods of calm winds. We speculate that these quiescent periods are fundamental events in the lake’s ecology, resulting in episodic ‘pulses’ of nutrient release from the sediments, which can stimulate or refuel blooms of cyanobacteria in the water column. Full article

Understory vegetation plays a pivotal role in enhancing forest biodiversity, and its restoration is crucial for sustainable forest development, energy flow, and nutrient cycling. However, the dynamics of the biomass, diversity, and species composition of understory vegetation in plantations in south China, along with their key drivers, remain poorly understood. This study investigated four mature plantation types (Pinus massoniana, Pinus caribaea, Cunninghamia lanceolata, and mixed Chinese fir–broadleaf forests) in south China through plot surveys, environmental factor measurements, and structural equation modeling (SEM) to explore the diversity, biomass allocation patterns, and driving mechanisms of understory vegetation. The results demonstrated the following. (1) The introduced Caribbean pine forests exhibited higher shrub biomass than native Masson pine forests, which was driven by their high canopy openness favoring light-demanding species (e.g., Melicope pteleifolia, IV = 33.93%), but their low mingling degree limited herb diversity. (2) Masson pine forests showed superior shrub diversity due to their random spatial distribution and higher soil total potassium (TK) content. (3) Mixed Chinese fir–broadleaf forests achieved 24.50–66.06% higher herb biomass compared to coniferous monocultures, supported by high mingling degree, random spatial configuration, and phosphorus-potassium-enriched soil, with concurrently improved herb diversity. SEM revealed that stand structure (DBH, density, mingling degree) directly drove shrub diversity by regulating light availability, while herb biomass was primarily governed by soil total phosphorus (TP) and pH. Canopy-induced light suppression negatively affected herb diversity. We recommend optimizing stand density and canopy structure through thinning and pruning to enhance light heterogeneity alongside supplementing slow-release P fertilizers in P-deficient stands. This study provides theoretical support for the multi-objective management of south China plantations, emphasizing the synergistic necessity of stand structure optimization and soil amendment. Full article