Sustainable Development Goal 15: Life on Land (147982)

Acacia melanoxylon R.Br. demonstrates strong biological nitrogen–fixation capacity and favourable economic returns, making it a promising candidate for the development of subtropical forestry in South Asia. It is a fast–growing leguminous tree species widely promoted for cultivation in China, and it is also one of the ideal tree species for improving soil fertility in forest lands. What are the synergistic mechanisms between A. melanoxylon-Eucalyptus stands and pure Eucalyptus spp.? Current theories regarding A. melanoxylon–Eucalyptus systems remain relatively fragmented due to the lack of effective silvicultural measures, resistance studies, and comprehensive ecological–economic benefit evaluations. The absence of an integrated analytical framework for holistic research on A. melanoxylon–Eucalyptus systems makes it difficult to summarise and comprehensively analyse their growth and development, thereby limiting the optimisation and widespread application of their models. This study employed CiteSpace bibliometric analysis and qualitative methods to explore ideal tree species combination patterns, elucidate their intrinsic eco–economic synergistic mechanisms, and reasonably reveal their collaborative potential. This study systematically reviewed silvicultural management, stress physiology, ecological security, and economic policy using the Chinese and English literature published from 2010 to 2025. The narrative synthesis results indicated that strip intercropping (7:3) is widely documented as an effective model for creating vertical niche complementarity, whereby canopy light and thermal utilisation by A. melanoxylon species improve subsoil nutrient cycling by enhancing stand structure. A conceptual full–cycle economic assessment framework was proposed to measure carbon sequestration and timber premiums. Correspondingly, this conversion of implicit ecological services into explicit market values acted as a critical tool for decision–making in assessing benefit. A three–dimensional “cultivation strategy–physiological ecology–value assessment” assessment framework was established. This framework demonstrated how to move from wanting to maximise the output of an individual component to maximising the value of the whole system. It theorised and provided guidance on resolving the complementary conflict between “ecology–economy” in the management of sustainable multifunctional plantations. Full article

This study investigates the mechanisms of topographic steering and the resultant track morphology of typhoon-like vortices over complex terrain. Leveraging a dynamic model based on potential vorticity (PV) conservation, we conducted a comprehensive sensitivity analysis over both an idealized bell-shaped mountain and the realistic topography of Taiwan. Results indicate that a triad of controls governs track evolution: vortex intensity (α), terrain geometry ($d{h}_B^{*}/{dt}^{*}$), and interaction time (impinging angle $\gamma$). To quantify predictability, we introduce the Track Divergence Percentage ($t_d$), which partitions the phase space into distinct Track Diverging (TDZ) and Converging (TCZ) Zones. The results demonstrate that vortex intensity, terrain-induced forcing, and interaction time jointly organize a regime-dependent predictability landscape, characterized by distinct zones of track divergence and convergence separated by a dynamically balanced trajectory. This framework provides a physically interpretable explanation for why small perturbations in initial conditions can lead to qualitatively different track outcomes near complex terrain. Rather than aiming at direct forecast skill improvement, this study provides a physically interpretable diagnostic framework for understanding terrain-induced track sensitivity and uncertainty, with implications for interpreting ensemble spread in forecasting systems. Full article

The photopolymer resins commonly utilized in stereolithography (SLA) additive manufacturing are non-renewable, brittle in nature and have low impact and thermal insulation properties, limiting their applications in sustainable and functional applications. To overcome these shortcomings, this paper introduces the initial research on the use of Polybutylene Adipate Terephthalate (PBAT), a biodegradable polymer, into SLA resins to create partially sustainable micro-composites with enhanced mechanical and thermal capabilities. PBAT micropowder was mixed with standard resin at 1, 5 and 10 wt% and 3D printed using SLA. To determine performance and interfacial morphology, mechanical testing (tensile and impact), thermal conductivity measurements and SEM fracture surface analysis were carried out. Introduction of PBAT significantly increased toughness, flexibility and the impact strength of the 1% PBAT composite stood at 168.63 J/m² with 68.69 J/m² of pure resin whereas the 10% PBAT sample was found to be 16% more efficient in thermal insulation. These findings indicate that partially replacing the photopolymer resin with biodegradable PBAT can enhance impact strength and thermal insulation while reducing the overall amount of petrochemical resin required. The article provides a new avenue of eco-friendly, high-performance photopolymer composites to facilitate sustainable additive manufacturing. Full article

Clay-mediated microbial degradation has been demonstrated as a low-cost, efficient, and eco-friendly strategy for remediating crude oil-contaminated soils. Despite substantial laboratory studies, field tests remain scarce. In this study, montmorillonite treatment was applied to a crude oil production well pad shut down for 753 days. Post-treatment analyses included soil physicochemical parameters (water content, redox potential, pH, elemental analysis, and total organic carbon), crude oil content/composition (gas chromatography–mass spectrometry), microbial biomass (deoxyribonucleic acid concentration), and community structure (high-throughput sequencing), with parallel comparisons to untreated control areas. Results indicated that montmorillonite enhanced the crude oil biodegradation rate (37.27% vs. control 33.00%), increased microbial biomass (83.08% vs. control 35.06%), and enriched biodiversity (7 genera vs. control 0). Specifically, it exhibited the most pronounced promotion effects on saturated hydrocarbon degradation (73.42% vs. control 60.89%) and aromatic hydrocarbon degradation (45.77% vs. control 29.60%). This study not only provides field evidence for clay-mediated microbial remediation but also lays a foundation for developing composite remediation approaches (e.g., nutrient supplements, catalysts, or functional microbial consortia) in future research and practical applications. Full article

No method to assess the risks of petroleum hydrocarbon pollutants C6–C9 in soils on construction land in China has been established. At one decommissioned petroleum refinery site in northwestern China, we performed an innovative tier 3 risk assessment method using carbon fraction proportions. Using HJ 25.3 guidelines, the risk-screening value for soil contamination of land by petroleum hydrocarbons was 192 mg kg⁻¹ for industrial land use. However, based on site-specific parameters, this value was 226 mg kg⁻¹, with a corresponding contaminated soil volume of 381,904 m³. A tier 3 risk assessment incorporating carbon fraction proportions and site-specific parameters yielded a risk control value of 2370 mg kg⁻¹ and reduced the soil volume requiring decontamination to 87,047 m³, potentially saving CNY 324 million (~USD 45.5 million as of November 2025) in remediation costs. Therefore, implementing a tier 3 risk assessment for C6–C9 pollutants can optimize remediation strategies and enhance the precision and scientific rigor of petroleum hydrocarbon-contaminated soil remediation. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

Urban areas often suffer from enduring environmental issues, including flooding, biodiversity loss, heat island effects, and air and soil pollution. The Miyawaki method of afforestation, characterized by dense planting of native species on remediated soil, has been proposed as a rapid, nature-based solution for restoring urban ecological function. This study aims to evaluate early-stage changes in soil health following Miyawaki-style microforest establishment in formerly redlined neighborhoods in Elizabeth, New Jersey. Specifically, it investigates whether this method improves soil permeability, carbon content, and microbial activity within the first three years of planting. Three microforests aged one, two, and three years were assessed using a chronosequence approach. At each site, soil samples from within the microforest and adjacent untreated urban soil (control) were compared. Analyses included physical (porosity, dry density, void ratio), chemical (total carbon), and biological (microbial respiration, biomass, metabolic rate, carbon use efficiency) assessments. Soil permeability was estimated via the Kozeny–Carman equation. Microforest soils showed significantly greater porosity (p = 0.015), higher void ratios (p = 0.009), and reduced compaction compared to controls. Soil permeability improved dramatically, with factors ranging from 5.99 to 52.27. Total carbon content increased with forest age, reaching 2.0 mg C/g in the oldest site (p < 0.001). Microbial metabolic rate rose by up to 287.5% (p = 0.009), while carbon use efficiency also improved, particularly in the older microforests. Within just one to three years, Miyawaki microforests significantly enhanced both the physical and biological properties of degraded urban soils, signaling rapid restoration of soil function and the early return of ecosystem services. Full article

This study addresses the global challenge of superficial soil contamination by heavy metals, focusing on differentiating natural geogenic sources from anthropogenic contributions in complex industrial–urban environments. We develop an integrated geostatistical and multivariate framework combining soil metal concentration analysis with AERMOD atmospheric dispersion modeling using a comparative multi-model machine learning approach (including Extreme Gradient Boosting, Random Forest, and Ridge Regression). Applied to the industrialized area of Taranto, Southern Italy, this approach incorporates spatial autocorrelation and multiple environmental predictors to identify contamination patterns and sources. The results reveal variable predictive accuracy across metals, with RF generally outperforming the other algorithms. The model achieved its highest performance for copper (R² = 0.58, RMSE = 25.82), Tin (R² = 0.53, RMSE = 5.95), and chromium, while showing instability for others. These disparities highlight the differential influence of remote sensing data on contamination mapping. The framework advances the quantitative assessment of soil pollution by linking atmospheric deposition and spatial processes with causal interpretability. Full article

Reliable estimates of regional energy consumption are essential to planning sustainable development and achieving decarbonization; however, this information is still not available for several regions worldwide. In this work, we propose a methodological framework that uses satellite-derived Nighttime Light (NTL) imagery and machine learning to predict regional electricity consumption one year ahead. The methodology follows three stages: First, a Random Forest regression model is used to identify the relationship between NTL data and regional energy consumption. Thereafter, NTL values for the year ahead are forecasted using NTL values from previous years. Lastly, the obtained result is applied to estimate regional energy consumption from predicted NTL values for the year ahead. The country of Mexico is considered a case study to apply and validate this methodology, reproducing spatial consumption patterns with high correlation to official data ($R^2>0.85)$, thus confirming the success of this proposal. The proposed methodology demonstrates how energy demand can be estimated, even in areas of scarce information, providing a transparent and replicable approach for energy monitoring in data-limited regions. Full article

Brake wear particles (BWPs) represent a major source of non-exhaust particulate matter from road traffic, contributing substantially to human exposure, particularly in urban environments. While traditionally associated with coarse and fine fractions, mounting evidence shows that brake systems emit large quantities of ultrafine particles (UFPs; <100 nm), which dominate number concentrations despite contributing little to mass. This paper synthesizes current knowledge on BWP formation mechanisms, physicochemical characteristics, environmental behavior, and toxicological effects, with a specific emphasis on UFPs. Mechanical friction and high-temperature degradation of pad and disc materials generate nanoscale primary particles that rapidly agglomerate yet retain ultrafine structural features. Reported real-world and laboratory number concentrations commonly range from 10³ to over 10⁶ particles/cm³, with diameters between 10 and 100 nm, rising sharply during intensive braking. Toxicological studies consistently demonstrate that UFP-rich and metal-laden BWPs, particularly those containing Fe, Cu, Mn, Cd, and Sb compounds, induce oxidative stress, inflammation, mitochondrial dysfunction, genotoxicity, and epithelial barrier disruption in human lung and immune cells. Ecotoxicological studies further reveal adverse impacts across aquatic organisms, plants, soil invertebrates, and mammals, with evidence of environmental persistence and food-chain transfer. Despite these findings, current regulatory frameworks address only the mass of particulate matter from brakes and omit UFP number-based limits, leaving a major gap in emission control. Full article

This study analyzes the establishment of Local Agri-Food Systems (LAFSs) in the triple-border region between the states of Minas Gerais, Rio de Janeiro, and São Paulo, by identifying and mapping potential areas of primary peasant agri-food production. An integrated analysis of data sources was treated, processed, and integrated into a common spatial support. Land use and land cover data were used from demographic and agricultural censuses, from the Rural Environmental Registry, agrarian reform settlement projects and conservation units. Our study revealed that 23.73% of the regional area has potential for peasant production, identifying four regions that stand out in terms of this potential. The area presented livestock and animal husbandry as the main agri-food chain, with potential for processing within the territory itself, in addition to extractive activities in the Atlantic Forest biome. The results indicate that there are possibilities for the establishment of LAFSs as a local development strategy associated with social inclusion and environmental responsibility, although there is a need to expand and strengthen the transportation and marketing channels for products from these short chains. The cartographies produced aim to contribute as auxiliary instruments to land use planning and management, seeking to strengthen LAFSs at different scales of governance. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

Machine learning (ML) algorithms are widely applied across various fields due to their ability to extract high-level features from large training datasets. However, their use in geochemical prospecting and mineral exploration remains limited because mineralization—a rare geological event—often results in insufficient training samples for supervised ML. Generating adequate training data is thus essential for applying supervised ML in this domain. In this study, we augmented training samples by utilizing adjacent samples centered around known mineral deposits and then employed random forest (RF) modeling to identify multivariate geochemical anomalies associated with mineralization. To evaluate the robustness of data augmentation and gain insights into the geochemical survey data, we applied interpretable ML techniques—feature importance and partial dependence plots (PDPs)—to clarify the data processing within mineral prospectivity mapping. The proposed methodology was tested in the Pangxidong Area, South China. The identified geochemical anomalies show strong spatial correlation with known mineral deposits, while feature importance rankings and PDPs validate the effectiveness of the proposed methodology. This practice enhances the applicability of supervised ML in geochemical prospecting and mineral exploration as well as the application of interpretable techniques for understanding data processing of multi-geoinformation. Full article

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

New Zealand fur seals (Arctocephalus forsteri) were severely exploited by historical hunting. However, recently assessed colonies in New Zealand are mostly thought to be growing or stable. The exceptions are three colonies (Wekakura Point, Cape Foulwind and Taumaka Island) on the West Coast of the South Island (‘WCSI’), previously documented as in decline. We used mark-recapture and morphometric data to update understandings of pup abundance and condition at these colonies. Pup abundance has continued to decline. In 2025, 186 (95% CI = 178–194) pups were estimated at Wekakura Point, 131 (95% CI = 122–140) at Cape Foulwind and 566 (95% CI = 555–577) at Taumaka Island, representing declines of 83%, 71% and 61% from the respective maxima in the 1990s. Rates of decline have slowed at Wekakura Point and Cape Foulwind since 2016 but have increased at Taumaka Island. Pup condition demonstrated substantial interannual variation. Cape Foulwind pups had the greatest average mass and body condition index score, followed by Wekakura Point and then Taumaka Island. There have been consistencies between years of particularly low pup abundance and condition across the colonies, suggesting common stressors; however, there are likely also some localised factors. Emerging diseases and marine environmental change are evaluated as potential drivers. Full article

The reinforcement of high-water-content, low-permeability soft soils presents a critical challenge in marine and coastal engineering. While electroosmotic dewatering is a promising technique, its widespread application is often hindered by issues such as high energy consumption and limited strength gain. However, the specific mechanisms by which marine-derived biopolymers modify soil properties and microstructure to enhance electroosmotic efficiency and significantly improve the post-treatment bearing capacity remain insufficiently understood. To address this gap, this study investigates the use of Sodium Alginate (SA) to enhance the electroosmotic dewatering performance of coastal soft soil. Laboratory experiments were conducted using carbon felt electrodes with varying SA mass fractions (0.0%, 0.2%, 0.5%, and 1.0%). The study integrated macroscopic monitoring with Scanning Electron Microscopy (SEM) to evaluate the electroosmotic efficiency and mechanical property evolution. The results demonstrate that the cross-linked structure of SA gel effectively bridges soil particles and fills inter-granular pores, significantly increasing the liquid limit (from 32.34% to 49.15% at 1.0% SA) and mitigating soil cracking. This microstructural alteration enhanced electrical conductivity and accelerated drainage; the average water content reduction increased from 12.78% (0.0% SA) to 20.86% (1.0% SA). Notably, the 0.5% SA treatment improved the average bearing capacity to approximately 86 kPa (about 7 times that of 0.0% SA) with only a 21% increase in the energy consumption coefficient. This study confirms that utilizing SA for electroosmotic reinforcement effectively modifies soil properties to provide a marine solution for coastal soft soil foundation treatment. Full article

Anti-Poverty Programmes (APPs) are closely linked to rural livelihoods, yet comparative evidence on how participants and non-participants differ in livelihood-capital composition and income-generation patterns remains limited in ecologically fragile pastoral regions. This study draws on a cross-sectional household survey conducted in Northern Tibet in July 2020, covering 696 households—including 225 APP participants and 471 non-participants. Using the Sustainable Livelihoods Framework and the entropy weight method, we construct multidimensional livelihood-capital indices (human, social, natural, physical, and financial capital) and compare the two groups. We further apply Ordinary Least Squares (OLS) regressions to examine factors associated with per capita net income. The results reveal substantial heterogeneity in livelihood capital and income across both groups. APP participants exhibit higher human-capital scores, largely driven by a higher share of skills training, whereas they show disadvantages in physical and financial capital relative to non-participants. Natural capital shows no statistically significant difference between the two groups under the local grassland contracting regime. Significant differences are observed and identified in certain dimensions of social capital. Regression results suggest that income is positively associated with skills training, contracted grassland endowment, and fixed assets, with skills training showing the strongest association. For participants, herd size and labour capacity are not statistically significant correlates of income; for non-participants, larger herds and greater labour capacity are associated with lower income. Taken together, the findings indicate that APP participation is associated with stronger capability-related capital (notably training) alongside persistent constraints in productive assets and financial capacity. Policy implications include improving the relevance and quality of training, strengthening cooperative governance and market linkages, and designing complementary packages that connect skills, inclusive finance, and productive asset accumulation. Given the cross-sectional design and administratively targeted certification of programme participation, the results should be interpreted as context-specific associations rather than strict causal effects. Full article

Advanced prostate cancer, particularly castration-resistant disease, remains challenging to treat due to intratumoral heterogeneity, immune exclusion, and a suppressive tumor microenvironment. Within this ecosystem, cancer-associated fibroblasts shape tumor–stroma communication, but their marked heterogeneity and plasticity complicate classification and make indiscriminate fibroblast depletion potentially ineffective or even harmful. This review summarizes recent progress in fibroblast origins, functional subtypes, and fibroblast-driven mechanisms that promote tumor progression and therapy resistance, as well as emerging therapeutic opportunities in prostate cancer. We conducted a structured literature search of PubMed, ScienceDirect, and major publisher platforms (including Nature and SpringerLink) from database inception to 15 February 2025, supplemented by targeted manual screening of reference lists. Evidence from single-cell/spatial-omics and mechanistic studies indicates that prostate tumors contain multiple fibroblast programs that occupy distinct niches yet can interconvert. Across these studies, it was found that these fibroblasts contribute to immune suppression, extracellular matrix remodeling and stromal barrier formation, angiogenesis, and metabolic support, collectively limiting drug penetration and reinforcing immune evasion; therapeutic pressure can further rewire fibroblast states and resistance-associated signaling. Overall, the literature supports a shift toward function- and subtype-directed intervention rather than “one-size-fits-all” targeting, with promising directions including precision targeting and reversible reprogramming, rational combination strategies, and localized delivery approaches that reduce stromal barriers while preserving tissue homeostasis in high-risk and treatment-refractory prostate cancer. Full article

The unique environmental settings and increasing human activity in Northeast China have intensified gully erosion, threatening food security and sustainable development. However, systematic studies of environmental thresholds driving gully erosion remain scarce. This study analyzed erosion gullies across four typical regions of Northeast China using Google Earth imagery (2011 to 2021) and field survey data (2021) to investigate the (1) conditions under which gullies most frequently form and develop and (2) conditions conducive to gully stabilization. Results showed that, in semi-humid areas, gullies mainly developed on cultivated land with a gradient of 6–15°, though catchment area thresholds varied. In contrast, in the semi-arid mountain and hilly area, developing gullies grew fastest in forested areas with low vegetation coverage. Overall, while there were differences across the four regions, gullies were most likely to form on cultivated land, while stabilized gullies were concentrated in forested areas. These findings indicate that the conversion of cultivated land to forested land slows the development of erosional gullies. In addition, rainfall promotes the formation of new gullies and inhibits the growth of eroded gullies by reducing the effective drainage area. The results provide a theoretical basis for the prevention and control of gully erosion. Full article

Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports. Full article

Background and Objectives: Hospital length of stay (LOS) after breast cancer surgery is a key indicator of postoperative recovery, healthcare quality, and hospital resource utilization. Traditional statistical approaches have identified general correlates of LOS but remain limited in predictive accuracy, particularly in heterogeneous real-world surgical populations. Machine learning (ML) models may offer improved performance by capturing nonlinear interactions among clinical, pathological, and operative factors. This study aimed to evaluate ML algorithms for LOS prediction and to identify determinants of prolonged hospitalization in a contemporary breast cancer cohort. Materials and Methods: We conducted a retrospective cross-sectional study of 198 consecutive breast cancer patients who underwent surgery between January 2022 and December 2023 at a single tertiary care center. Clinical, pathological, and surgical data were extracted from electronic medical records. Three regression models—multiple linear regression, Random Forest, and Gradient Boosting—were trained to predict continuous LOS, and three classification models were applied to prolonged LOS (≥10 days). Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and area under the curve (AUC). Feature importance was analyzed for the best-performing model. Results: The median LOS was 7 days (IQR 5–10), ranging from 1 to 26 days. Breast-conserving surgery showed the shortest LOS (median 3 days), while mastectomy with immediate reconstruction resulted in the longest stays (median 8 days). Random Forest regression achieved the lowest prediction error (MAE 2.31 days; RMSE 2.82; R² = 0.37), outperforming Gradient Boosting and substantially surpassing linear regression (MAE 8.63 days; R² = –8.17). Key predictors included age, surgical complexity, reconstruction modality, BMI, implant capacity, and tumor burden. Classification models yielded modest AUCs (0.545–0.589) with low sensitivity, indicating limited discriminative performance for dichotomized LOS outcomes. Conclusions: Machine-learning models, particularly Random Forest, substantially improve LOS prediction compared with classical regression and provide clinically meaningful insights into the drivers of hospitalization after breast cancer surgery. Continuous LOS modeling is more informative than binary thresholds. These findings support integrating ML-based tools into perioperative planning, resource allocation, and patient counseling in breast surgical care. Full article

As the climate crisis intensifies, the importance of carbon mitigation policies has become increasingly prominent. Rural regions, serving as one of China’s major carbon emission sources, are poised to become key focus regions for emission reduction. However, significant disparities in rural development levels and carbon emissions across China’s regions necessitate tailored energy sustainability and carbon mitigation strategies. Notably, advanced and emerging/developing nations exhibit substantial differences in research priorities and practical pathways, offering multifaceted insights for China’s rural carbon emission research. Adopting a hybrid bibliometric and narrative approach, the study retrieves data from the Web of Science, applies CiteSpace for bibliometric visualization, and synthesizes thematic developments in the international literature through a narrative analysis, with a discussion of the implications for China. The findings reveal distinct trajectories: over the past 25 years, advanced countries have shifted their research focus from air quality improvement to low-carbon mitigation, while emerging and developing countries have transitioned from energy demand toward air quality enhancement, with emerging momentum toward low-carbon strategies. By reviewing 95 relevant articles, this study summarizes the differences between the two in terms of their main lines of research. Building on these differences, this study proposes targeted research priorities for advanced and emerging/developing regions of China. Full article

Artificial cultivation of the endangered medicinal plant Sinopodophyllum hexandrum is a key strategy for resource protection and supply, yet cultivation can cause soil degradation and microbial disorder, while the effect of cultivation on the microbial community and its relationship with soil nutrients remains unclear. This study aimed to explore the effects of artificial cultivation on the soil–microorganism–nutrient cycling system of Sinopodophyllum hexandrum, a rare medicinal plant. We compared three groups (Native-wild, Mix-wild, Mix-cultivated) by analyzing soil physicochemical properties, microbial diversity, community structure, co-occurrence networks, and multi-nutrient cycling drivers. Geographic position drove spatial (landscape scale) heterogeneity of soil nutrients, while cultivation shaped its vertical (soil depth) counterpart. Cultivation altered the natural vertical nutrient pattern via surface fertilization, causing nutrient surface retention. Microbial communities exhibited wild-specific/cultivation-specific responses, bacteria were slightly more sensitive to cultivation effect than fungi. Cultivation altered microbial network complexity depending on the host and increased instability, with only bacterial network associations correlating with soil factors. Fungal diversity and specific taxa became core drivers of multi-nutrient cycling. This study clarifies cultivation’s regulatory mechanism on S. hexandrum’s soil–microorganism system, providing a basis for optimizing cultivation management and protecting this endangered species. Full article

As China’s industrialization progresses, the transformation of site properties across various regions has become increasingly common. Concurrently, with the relocation and market exit of some enterprises, the land occupied by the original factory sites has been developed for other uses. This study provides a comprehensive evaluation of soil and groundwater contamination levels and the associated ecological and health risks in abandoned industrial lands. The investigation focused on analyzing heavy metal and polycyclic aromatic hydrocarbon (PAH) contamination using various assessment methods, including the single-factor pollution index, Nemerow composite pollution index, and potential ecological risk index. These methods were used to assess the contamination levels of 11 heavy metals in both soil and groundwater. Additionally, health risk assessments for PAHs were conducted using the Incremental Lifetime Cancer Risk (ILCR) and Carcinogenic Risk (CR) models, considering both direct and indirect exposure pathways. The results indicated that the average concentration of each heavy metal in the soil did not exceed the screening thresholds, with all Nemerow index values falling below 1, suggesting that the site is not significantly polluted. Ecological risk assessment further revealed that most heavy metals posed minor risks, while some localized areas showed slight enrichment. Health risk assessments for PAHs indicated that, although the risks for both adults and children were within acceptable limits, the ingestion pathway for children showed a slightly higher risk compared to adults. The groundwater quality met Class IV standards, indicating no significant pollution. These findings provide data support and reference for future land-use planning, environmental management, and remediation strategies for abandoned industrial sites. Full article

Tuber crops cultivated in basalt-derived soils are influenced by naturally high geochemical backgrounds, which may elevate heavy metal(loid) levels and associated health risks. To clarify the geochemical controls governing metal accumulation, this study analyzed rock, soil, and tuber (sweet potato and yam) samples from the Qiongbei volcanic area of Hainan Island, China. Concentrations of eight heavy metal(loid)s (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) and 22 nutrient-related indicators (N, P, K, SOC, S, Se, Fe, Mn, and their available fractions) were determined. Soil contamination and potential human health risks were evaluated using the pollution index and the health risk model. The results showed that 11.1–55.6% of soil samples exceeded pollution thresholds for Cr, Cu, Ni, and Zn, reflecting typical basaltic high-background characteristics. In contrast, heavy metal(loid) concentrations in tuber crops were relatively low and jointly regulated by parent material composition and soil nutrient status. Non-carcinogenic risks (HI) were below 1, indicating acceptable exposure levels, while carcinogenic risks were mainly associated with Cd, Cr, and Pb, with total carcinogenic risk (TCR) exceeding 1 × 10⁻⁴, suggesting potential health concerns. Strong correlations between soil nutrients (N, P, K, SOC, S, Se, Mn, and Fe) and plant uptake of As, Cd, Cu, and Cr indicate that nutrient availability plays a crucial role in controlling heavy metal(loid) bioavailability. The volcanic soils exhibited a “high total content–low bioavailability” pattern. Enhancing soil Se, SOC, available N, and slowly available K (SAK) can effectively reduce Cd and other high-risk metal accumulation in tuber crops. These findings elucidate the key geochemical processes influencing heavy metal transfer in volcanic agroecosystems and provide a scientific basis for safe agricultural utilization and health risk prevention in high-background regions. Full article

Background and Objectives: Oral nicotine pouches (ONPs) are rapidly expanding nicotine products with limited evidence from the Middle East, particularly among young adults. This study assessed the awareness, perceptions, and use of ONPs among university students in Jazan, Saudi Arabia. Materials and Methods: A cross-sectional survey (November 2024–April 2025) used multistage stratified random sampling across six colleges at Jazan University. A self-administered questionnaire captured sociodemographic characteristics, tobacco-use history, ONPs awareness (aided), ever use and current use (past 30 days), and self-reported perceptions items across nine domains. Multivariable logistic regression estimated adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Results: Among 624 students (mean age = 20.9 ± 1.7 years; 50.5% female), ONPs awareness was 69.7%, ever use 11.5%, and current use 7.5%. Awareness and use were higher among males and other tobacco users (p < 0.001). In multivariable models, male sex predicted awareness, ever use, and current use; rural residence was linked to lower awareness (aOR = 0.67; 95% CI 0.45–0.98), and being a medical student was linked to lower current use (aOR = 0.08; 95% CI 0.003–0.51) Most students perceived ONPs as addictive (80%) and harmful (68%), yet accessible (61%) and attractive (55%). Conclusions: ONPs awareness and use were high, particularly among males and tobacco users. Despite recognizing potential harm, students viewed ONPs as accessible and attractive. Ongoing surveillance, education, and balanced regulation are needed to guide harm-reduction policy and prevent unintended nicotine uptake. Full article

3D concrete printing (3DCP) is an emerging intelligent construction technology that enables the direct transformation of digital models into physical components, thereby facilitating the precise fabrication of complex geometries. This study investigates the anisotropic mechanical properties and construction applicability of low-carbon 3D printed concrete for reusable formwork systems. Axial compression, flexural, and splitting tensile tests were conducted to examine mechanical anisotropy, and the effects of steel slag and iron tailings replacement levels on mechanical performance were evaluated. Carbon emission analysis was also performed. Using the coefficient-of-variation TOPSIS method, an optimal printable low-carbon mixture was identified, comprising 30% steel slag, 40% iron tailings sand, and 0.3% fibre content, balancing both mechanical performance and environmental benefits. To address the challenges associated with printing large monolithic formwork units, such as excessive weight and demoulding difficulties, three connection strategies for curved wall modular reusable formwork were designed. Finite element analyses were conducted to assess the strength and stiffness of each strategy, and an optimized connection configuration was proposed. The findings demonstrate the feasibility of accurately fabricating complex architectural components using low-carbon 3D printed concrete, providing theoretical and practical support for the industrialized production of large-scale, geometrically complex structures. Full article

Radiata pine (Pinus radiata D. Don) is New Zealand’s dominant plantation species, supporting carbon sequestration under the national Emissions Trading Scheme. However, existing stand-level carbon models cannot estimate individual tree carbon stocks which are often required for modern remote sensing-based forest inventories. This study developed comprehensive allometric equations for predicting tree-level carbon in radiata pine using an extensive dataset of 894 trees spanning ages 1–42 years across eight New Zealand locations. We fitted 12 models predicting stem wood, bark, branch, and foliage biomass from varying combinations of tree height, diameter at breast height, stand age, stand density and wood density. Models incorporating both height and diameter achieved excellent accuracy for stem wood and bark (R² > 0.99, log-transformed scale), while inclusion of age, stand density and wood density substantially improved crown component predictions (R² = 0.95 for branches and 0.93 for foliage). Biomass predictions were converted to carbon using component-specific and age-dependent carbon fractions derived from New Zealand radiata pine, avoiding biases from generic conversion factors. The resulting equations provide a tiered system accommodating different data availability levels and are directly compatible with LiDAR-derived tree attributes. These models provide a robust framework for accurate individual-tree carbon estimation, supporting both operational plantation management and robust carbon accounting across New Zealand’s radiata pine estate. Full article

In arid regions, the ecological restoration of limestone tailings requires sustainable strategies, yet the synergistic effects of substrate optimization and native plant selection remain poorly understood. In this study, we systematically evaluated substrate amendments and native species for rehabilitating limestone tailings in Northern China’s arid zone using a controlled pot experiment. An orthogonal L9(3⁴) experimental design was employed to test three factors: the soil-to-tailings ratio (1:2, 1:1, and 2:1), moisture level (30%, 45%, and 60% of field capacity), and nitrogen addition (0, 5, and 10 g N m⁻²). Five native grass species (Pennisetum centrasiaticum, Setaria viridis, Leymus chinensis, Achnatherum splendens, and Eleusine indica) were grown under these treatment conditions, and plant biomass and key soil nutrient variables were measured. Stepwise regression, structural equation modeling, and principal component analysis were applied to assess plant growth responses and soil nutrient dynamics. The results indicated that a 2:1 soil-to-tailings substrate maintained at 60% moisture content maximized biomass production across all species. Soil total potassium consistently correlated positively with biomass (Standardized β: 0.397–0.603), whereas available potassium showed a negative relationship (Standardized β: −0.825–−0.391). Nutrient dynamics ultimately governed biomass accumulation, accounting for 57.8–84.2% of the biomass variation. P. centrasiaticum ranked as the most effective species, followed by S. viridis, L. chinensis, A. splendens, and E. indica. We concluded that successful restoration under these experimental conditions hinged on key factors: using a 2:1 soil-to-tailings substrate, maintaining 60% soil moisture, and strategically combining deep-rooted P. centrasiaticum with shallow-rooted S. viridis to exploit complementary resource use. This work provides fundamental data and a conceptual framework for rehabilitating arid limestone tailings in similar ecological settings, based on controlled experimental evidence. Full article

Significant achievements have been made in the control of point source pollution. However, agricultural non-point source pollution (AGNPSP) has become a serious threat to ecological environment quality and is now the main source of pollution in the Yangtze River Basin. The topographical features of the upper Yangtze River region are primarily characterised by hilly and mountainous terrain, marked by steep slopes and pronounced undulations. This renders the land susceptible to soil erosion, thereby becoming a significant conduit for the entry of AGNPSP into water bodies. Consequently, there is an urgent need to identify critical sources, areas and periods of AGNPSP and to promote the effective prevention and control of such pollution. The present study adopted the Yongchuan District of Chongqing, a region characterised by hilly and mountainous terrain in the upper reaches of the Yangtze River, as a case study. The research, conducted from 2018 to 2021, sought to identify the “critical sources—areas—periods“ of AGNPSP. In order to surmount the challenge posed by the absence of fundamental data, the study constructed and integrated three models. The export coefficient model was used to calculate the pollution load, the pollutant load intensity model was used for spatial analysis, and the equal-scale pollution load equation was used to assess the contribution degree of different pollutants. Furthermore, the study developed a monthly pollutant flux model to accurately identify the critical pollution periods within the year. In conclusion, the research results have indicated the necessity of a governance strategy that is to be implemented with utmost priority. This strategy is to be based on the following hierarchy: critical sources, areas, and periods. The results of the study indicate the following: (1) The pollutants that exhibit the greatest contribution in Yongchuan District are total nitrogen (TN)and chemical oxygen demand (COD), accounting for 34% and 33%, respectively. The primary source of pollution is attributed to livestock and poultry breeding, accounting for 49.7% of the total pollution load. (2) The critical area of AGNPSP in Yongchuan District is located in the south of the district and primarily comprises Zhutuo Town, Hegeng Town and Xianlong Town. Among the critical areas identified, livestock and poultry farming accounts for 68% of the pollution load. (3) The monthly variation of pollutant fluxes demonstrates a single peak pattern, with the peak occurring in June. The data indicates that the flux of pollutants in June and July accounted for 37% of the total, thus identifying these months as critical periods for the management of AGNPSP in Yongchuan District. The critical source–area–period analysis indicates that the comprehensive management strategy for AGNPSP should focus on critical sources, areas and periods. Furthermore, it should adopt a prioritised, zoned and phased management approach. This approach has the potential to promote cost-effective and efficient prevention and control, thereby facilitating the achievement of sustainable agricultural development. Full article

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article

Tick-borne diseases (TBDs) pose a significant threat to livestock productivity and rural livelihoods in South Africa, particularly among resource-poor communal farmers. This narrative review synthesises findings from case studies on communal farmers’ knowledge, attitudes, and practices (KAPs) toward TBDs and their control. The analysis reveals that while many farmers can identify TBDs and their symptoms, significant gaps exist in understanding acaricide resistance and effective tick management. Socioeconomic factors, including age, gender, education, and access to veterinary services, strongly influence knowledge and practices. Indigenous ethnoveterinary practices are commonly used alongside conventional methods, although their efficacy remains understudied. The review emphasises the importance of integrated pest management, participatory approaches, and targeted awareness campaigns. A One Health framework is recommended to enhance surveillance, collaboration, and sustainable TBD control. Empowering farmers through training and inclusive communication strategies is crucial for mitigating the impacts of TBDs on communal farming systems. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

The Chang–Zhu–Tan Metropolitan Area, the first national-level metropolitan region in central China, faces a prominent conflict between urban expansion and the quality of the ecological environment (EEQ) amid rapid urbanization. Investigating the ecological evolution of this area holds both significant scientific and practical value. This study leverages the Google Earth Engine (GEE) platform and long-term Landsat remote sensing imagery to explore the spatiotemporal variations in EEQ in the Chang–Zhu–Tan Metropolitan Area from 2002 to 2022. A modified remote sensing ecological index (MRSEI) was developed by incorporating the Air Quality Difference Index (DI), and changes in EEQ were analyzed using Sen slope estimation and the Mann–Kendall test. Apart from that, using 2022 data as an example, the Optimal Parameter Geodetector (OPGD) was employed to evaluate the impacts of multifarious driving factors on EEQ. The main findings of the study are as follows: (1) In comparison with the traditional remote sensing ecological index (RSEI), MRSEI can more effectively reflect regional differences in EEQ. (2) The overall EEQ in the region is relatively good, with over 60% of the area classified as “excellent” or “good”. The spatial distribution follows a pattern of “higher at the edges, lower in the center”. (3) The EEQ trend in the study area generally suggests reinforcement, though central areas such as Kaifu District and Tianxin District exhibit varying degrees of degradation. (4) Human factors have a greater impact on EEQ than natural factors. Land Use and Land Cover Change (LUCC) is the primary driver of the spatial differentiation in the regional ecological environment, with the interaction of these factors producing synergistic effects. The results of this study strongly support the need for ecological protection and green development in the Chang–Zhu–Tan Metropolitan Area, offering valuable insights for the sustainable development of other domestic metropolitan regions. Full article

Poly(lactic acid) (PLA) has emerged as a leading bio-based polymer due to its renewability, processability, and biodegradability, yet its broader adoption remains constrained by limitations in thermal stability, mechanical performance, and end-of-life control. This review provides a comparative and application-oriented overview of recent advances in PLA from synthesis and catalyst landscapes to structure–property–biodegradation relationships and practical applications. Representative polymerization routes and catalyst systems are critically compared in terms of achievable molecular weight, stereochemical control, scalability, and sustainability. Key structure–property modification strategies—including stereocomplex formation, blending, and copolymerization—are quantitatively evaluated with respect to thermal and mechanical properties, highlighting inherent trade-offs. Importantly, environment-specific biodegradation behaviors are assessed using representative quantitative metrics under industrial composting, soil, marine, and enzymatic conditions, underscoring the strong dependence of degradation on both material design and testing environment. Finally, application-driven requirements for food packaging, fibers, and agricultural materials are discussed alongside regulatory considerations, processing constraints, and qualitative cost positioning relative to conventional polymers. By integrating recent representative studies into comparative tables and synthesis-driven discussions, this review offers design guidelines for tailoring PLA-based materials toward targeted performance and sustainable deployment. Full article

Global CO₂ concentrations are gradually increasing, and forests, as the main terrestrial carbon pool, are attracting growing attention in mitigating climate change. However, the impacts of forest types, species diversity, structural diversity, and environmental factors on the carbon sequestration mechanisms of subtropical forests remain unclear. This study established 45 forest plots (20 m × 20 m) in Lishui City, aiming to investigate the relationships between forest diversity, environmental factors, and carbon storage of subtropical forests among different forest types. Results showed that coniferous forests had the lowest species diversity (0.86), which exhibited extremely significant differences from broad-leaved forests (1.47, p < 0.01) and coniferous broad-leaved mixed forests (1.58, p < 0.01). The carbon storage of broad-leaved forests was 97.50 t·ha⁻¹, which was higher than that of coniferous broad-leaved mixed forests (77.08 t·ha⁻¹) and coniferous forests (75.57 t·ha⁻¹). The carbon storage of coniferous forests was significantly positively affected by species diversity (p < 0.05). Tree height was the most significant structural diversity factor affecting forest carbon storage (p < 0.05). The results of the structural equation model (SEM) showed that the proportion of broad-leaved trees in forests and structural diversity had a significant positive effect on carbon storage (p < 0.01). Species diversity had a non-linear relationship with carbon storage. The ecological niche complementarity effect and selection effect interacted with changes in species diversity. When the species diversity was lower than 1.12 (Shannon–Wiener index), the ecological niche complementarity effect dominated and promoted carbon sequestration; when it was above this threshold, the selection effect dominated and weakened carbon sequestration. This study recommends prioritizing the planting of broad-leaved tree species during afforestation and paying attention to the current status of forest diversity. Full article

Karst regions cover about 12% of Earth’s land surface and exhibit high uncertainty in soil organic carbon (SOC) pools due to strong spatial heterogeneity. This study quantifies the association between rock fissure network morphology and SOC pools across three karst rocky habitat types in the Maolan National Nature Reserve (Guizhou, China): Type I (predominantly sub-horizontal and weakly connected fissures), Type II (oblique and moderately connected fissures), and Type III (predominantly subvertical and highly connected fissures). Fissure network morphology was characterized using quantitative network morphology metrics, and SOC pools (content, density, and stock) were measured from field samples (with long-term sequestration estimated). Type I habitats showed the highest SOC content, density, stock, and sequestration estimates, whereas Type III habitats consistently showed the lowest values. Across habitats, SOC density and stock were negatively associated with metrics reflecting steeper fissure orientation, greater spatial heterogeneity, and higher network connectivity, while SOC content was positively associated with fissure network complexity. These findings highlight fissure network morphology as an important structural dimension for explaining SOC variability in karst rocky habitats and suggest incorporating fissure information into SOC assessment and habitat-specific soil and vegetation management in karst landscapes. Full article

Massive phytoplankton blooms threaten lake ecosystems, causing significant ecological and socio-economic damage. While remote sensing is vital for monitoring, the vertical stratification of algae influences light propagation and distorts remote sensing reflectance signals. This effect is particularly understudied in high-latitude lakes, leaving a gap in understanding phytoplankton biomass patterns. To address this, our study investigated three high-latitude water bodies: Lake Hulun, Fengman Reservoir, and Lake Khanka. We collected water samples from three depths based on total and euphotic zone depth and developed layer-specific inversion models for chlorophyll-a (Chal) and phycocyanin (PC) using a random forest algorithm. These models demonstrated strong performance and were applied to Sentinel-3 OLCI imagery from 2016–2024. Our results show that Chla generally decreases exponentially with depth, whereas PC exhibits a Gaussian-like vertical distribution with a pronounced subsurface maximum at approximately 1 m. In addition, a significant positive correlation between Chla and PC was observed in surface waters. In Lake Khanka, the northern basin exhibited a significant interannual increase in phytoplankton biomass. At 3 m, PC correlated negatively with turbidity and responded strongly to cyanobacterial blooms, while organic suspended matter correlated positively with Chla. This work establishes a robust framework for multilayer water quality monitoring in high-latitude lakes, providing critical insights for eutrophication management and cyanobacterial bloom early warning. Full article

Background: Catheter-related bloodstream infections (CRBSIs) caused by environmental organisms are uncommon, and polymicrobial cases are even rarer. Methods: We describe the first case of catheter-related bloodstream infection caused by two infrequent environmental organisms—Rhizobium radiobacter and Pseudomonas oryzihabitans—occurring as a co-infection. Results: The patient’s occupation involved frequent exposure to moist, soil-contaminated environments. Although these bacteria are often considered contaminants, they are capable of causing invasive infections such as bacteremia, which can be life-threatening. Conclusions: This case underscores the emerging pathogenic potential of R. radiobacter and P. oryzihabitans co-infection, particularly in patients with underlying malignancies or end-stage renal disease who have indwelling vascular devices, and highlights the importance of considering occupational and environmental exposures in the differential diagnosis of unusual pathogens. Full article

The digitalization of finance has accelerated the diffusion of FinTech and raised new questions about how AI, data security and blockchain shape consumer behaviour. This article examines current FinTech users, focusing on mobile banking, security perceptions, AI-enabled personalisation and trust in blockchain. A structured online survey of 191 adult users was analysed with descriptive statistics, chi-square tests and three multiple linear regression models. Results show that adoption is overwhelmingly mobile centric: 84.8% primarily use mobile banking applications, accessed almost exclusively via smartphones (96.9%). Data security is the dominant decision criterion, rated “very important” by 83.3% of respondents. While 70.1% believe AI can substantially improve the FinTech experience, trust depends on transparent explanations of how algorithms operate and on guarantees of personal data protection. Regression models indicate that usage intensity is higher among younger, higher-income users and those who perceive simplified interfaces as encouraging, whereas positive views of AI are broadly shared and not segment-specific. Trust in blockchain is linked to a pro-technology mindset rather than to socio-demographic or urban–rural differences. The findings highlight “secure convenience” and explainable AI as central conditions for sustainable FinTech engagement. Full article

This study identifies key determinants shaping countries’ Sustainable Development Goals performance and develops classification models for predicting country group membership based on the SDG Index. The research addresses the urgent need to optimize development policies amid limited resources and the approaching 2030 Agenda deadline. Using data from 154 countries (2024), the analysis reveals that key SDG determinants are fundamentally method-dependent: discriminant analysis identified Goals 10, 6, 15, and 5 as most influential for differentiating countries by SDGI level, while Random Forest identified Goals 4, 9, and 2 as the most important predictors. This divergence reflects fundamentally different analytical perspectives—linear contributions to group separation versus complex nonlinear interactions and synergies between goals—with critical policy implications for prioritization strategies. Correlation analysis demonstrates that sustainable development dynamics operate differently across development stages: high-development countries show strongest associations with technological advancement and institutional capacity, while low-development countries exhibit compensation effects where basic infrastructure provision occurs alongside lagging human capital development. The discriminant model achieved 94.08% overall accuracy with perfect classification for extreme SDGI categories, while the Random Forest model provides complementary insights into interactive pathways. The scientific contribution lies in demonstrating that perceived variable importance depends on analytical framework rather than representing objective reality, and in providing validated classification tools for rapid assessment in data-limited contexts. These findings offer actionable guidance for evidence-based resource allocation and policy prioritization in the critical final years of SDG implementation. Full article

The global literature underscores a set of human wellbeing challenges and opportunities for forestland exploitation, albeit the lack of region-specific evidence. This concerns the Congo Basin, the second-largest forest ecosystem in the world. This study uses the case of the Campo Ma’an Landscape to: (i) analyze the challenges linked to the exploitation of forestland resources, and (ii) explore forest resource exploitation opportunities in the landscape. The study employed a random sample of 200 natural resource-dependent households drawn from four study zones—Niete, Campo, Ma’an and Akom II. This was complemented by focus group discussions (n = 4), key informant (n = 6) and expert (n = 6) interviews. The descriptive and inferential analyses led to the following results: First, economic, technical, socio-cultural and institutional challenges affect the sustainable exploitation of forestland resources in the Campo Ma’an Landscape. The economic challenges of forest (B = −0.389, p = 0.01) and land resource exploitation (B = −0.423, p = 0.006) significantly affect sustainable exploitation compared to other challenges, leading to biodiversity loss and deforestation. These constitute a threat to planetary health systems. Almost all households rely on forestland resources for their livelihoods and development, with opportunities for land resource exploitation outweighing those in forest resource exploitation. Protected area management and agriculture are affected owing to competing interests among farmers, conservationists and other land users. Thus, short-term economic gains are prioritized over long-term sustainability, putting the resource landscape at risk of degradation and future uncertainties. Integrated stakeholder engagement, capacity building, and policy revision could enhance the planetary health approach by linking the social, economic and environmental dimensions of forestland resource management. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

This study examines urban–water synergy as the spatial coordination between urban expansion and water systems. Using land-use data from 2000 to 2020, the central urban areas of Jingzhou and Anqing are analyzed as representative small and medium-sized cities. Urban–water synergy is assessed across three dimensions: land-use synergy, pathway synergy, and directional synergy. These dimensions are quantified using four indicators: Urban–Water Interaction Intensity (UWII), Urban–Water Interaction Displacement (UWID), Spatial Path Alignment Distance (SPAD), and Directional Alignment Angle (DAA). The results show that Jingzhou and Anqing exhibit two distinct urban–water synergy modes: a convergent interaction mode characterized by increasing alignment in land-use interactions, spatial pathways, and directional tendencies, and a divergent synergy mode characterized by persistent separation across these dimensions. Differences between these synergy modes are associated with expansion pressure, physical template, and institutional mechanisms. Spearman rank correlation and principal component analysis suggest that institutional mechanisms constitute an independent analytical dimension and may be relevant for interpreting potential non-linear changes in urban–water interaction patterns. Based on these findings, this study discusses governance implications centered on institutional effectiveness, supported by spatial restoration and expansion regulation, for informing urban–water synergy governance in small and medium-sized cities. Full article

This article presents the results of the Innovation Support Program (ISP), designed to enhance the market readiness of 12 bio-based innovators from six European rural regions: Northern Sweden, Mazovia (Poland), Upper Austria, Pays de la Loire (France), Strumica (Macedonia), and Andalusia (Spain). Over three years, the ISP applied a modular and flexible methodology, beginning with a cross-regional needs analysis to identify knowledge gaps, followed by a call for Expressions of Interest to select promising bio-based solutions, and concluding with tailored support delivered through regional Task Forces. These provided mentoring and capacity-building activities focusing on business modeling, market analysis, and funding opportunities. The program identified market access as a major barrier to scaling up and noted that many solutions followed Social and Solidarity Economy principles, prioritizing social and environmental impact over profit. Through targeted assistance and knowledge exchange, the ISP strengthened local innovation capacity and contributed measurable progress in companies’ Technology Readiness Levels (TRLs) and Key Performance Indicators (KPIs). Positioned within the framework of the EU Bioeconomy Strategy, the ISP demonstrates how combining regional insights with a structured support framework can effectively accelerate the scaling of bio-based solutions, highlighting the need for iterative, long-term support to sustain regional bioeconomy growth. Full article

Phosphorus (P) is a key nutrient limiting crop growth and productivity, particularly in saline-alkali soils with low P availability. Arbuscular mycorrhizal fungi (AMF) have the potential to enhance P uptake in alfalfa (Medicago sativa L.); however, the synergistic effects and underlying biological mechanisms by which AMF improve P acquisition and utilization efficiency under varying P application levels remain unclear. To explore P acquisition strategies associated with AMF status, root morphology traits, rhizosphere carboxylate exudation, soil properties and microbial biomass, we conducted a pot experiment growing alfalfa in saline-alkali soil under four P application levels (0, 5, 10, and 20 mg kg⁻¹), with or without AMF inoculation. Our results showed that AMF colonization and P application synergistically increased alfalfa biomass and shoot/root P concentrations. Notably, at a low P application level of 5 mg kg⁻¹, the mycorrhizal contribution to P absorption and P-utilization efficiency reached their highest levels, while both declined under high P conditions (20 mg kg⁻¹), suggesting an interaction between P availability and AMF efficacy. Structural equation modeling (SEM) and regression analysis revealed that rhizosphere carboxylate concentrations were positively associated with P-utilization efficiency, whereas soil available P, microbial biomass P (MBP) and carbon (MBC) negatively affected it. Among these factors, AMF-induced enhancement of rhizosphere carboxylate exudation played a critical role in promoting P-utilization efficiency in alfalfa under low-P conditions. In contrast, higher P availability reduced rhizosphere carboxylate concentrations, resulting in lower P-utilization efficiency. In conclusion, the combination of AMF colonization and low P application synergistically improves P acquisition and utilization efficiency in alfalfa, providing valuable insights for sustainable nutrient management in saline-alkali soils with limited P availability. Full article

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, Stepney, Australia) sensors and satellite-derived vegetation indices (NDVI) from Sentinel-2 were collected across 25 sites in Hungary. Temporal soil moisture dynamics were encoded using a Long Short-Term Memory (LSTM) neural network, designed to capture soil-specific hydrological response behavior from time-series data. The resulting latent embeddings were subsequently used within an ordinal regression framework to predict ordered soil texture classes, explicitly enforcing physical consistency between classes. Model performance was evaluated using leave-one-soil-out cross-validation, achieving an overall classification accuracy of 0.54 and a mean absolute error (MAE) of 0.50, indicating predominantly adjacent-class errors. The proposed approach demonstrates that soil texture can be inferred from dynamic environmental responses alone, offering a transferable alternative to fraction-based regression models and supporting scalable sensor calibration and digital soil mapping in data-scarce regions. Full article