Search Results (7,026)

Compound heat and drought events (CHDEs) increasingly threaten crop production, yet conventional assessments rarely account for phenological changes in crop heat sensitivity and water demand. Here, we developed a phenology-aware daily framework for summer maize in the Huang–Huai–Hai (HHH) Plain, China, integrating stage-specific heat thresholds with a crop-coefficient-adjusted standardized precipitation evapotranspiration index (SPEI_K_C) and a fixed cultivation distribution. Using daily meteorological observations from 1980 to 2020, CHDEs were characterized across the sowing-to-jointing, jointing-to-tasseling, and tasseling-to-maturity stages. Across the growing season, CHDE frequency and mean duration increased significantly, whereas mean intensity declined. Stage-specific responses differed markedly: frequency increased across all stages, while the tasseling-to-maturity stage showed the fastest increase in frequency and a significant lengthening of duration. Potential exposure also became progressively concentrated over crop development and was highest during tasseling-to-maturity in major maize-producing areas. By incorporating phenological variation into both heat and drought characterization, this framework resolves within-season differences in compound stress that are obscured by uniform-threshold approaches and provides a crop-relevant basis for stage-targeted monitoring and adaptation. Full article

Accurate retrospective, spatially explicit land-use reconstruction is essential for Land Use, Land-Use Change and Forestry (LULUCF) greenhouse gas accounting. However, the suitability of historical geospatial databases as information sources for such reconstruction has rarely been evaluated systematically. This study proposes an objective framework for assessing their correspondence with land-use observations from the Lithuanian National Forest Inventory (NFI). The analysis was based on a reference set of 16,351 systematically distributed NFI sample points and 19 database-year datasets covering the period 1990–2022. Original database classes were harmonised with the national hierarchical LULUCF classification, and correspondence was evaluated using overall accuracy, Cramér’s V and Normalized Mutual Information (NMI), complemented by category-specific representation, precision, recall and F1 score. Correspondence varied substantially among databases according to thematic scope, spatial completeness, mapping characteristics and land-use category. Among the multi-category databases, the Georeferenced Base Cadastre (GRPK) showed the strongest overall correspondence with the NFI reference data, whereas the CORINE Land Cover series provided the longest consistent multi-temporal record extending back to 1990. Forest land and settlements, as well as particularly water-related wetland classes, were represented comparatively reliably, while grassland remained the most difficult major land-use category to identify consistently. Temporal analysis showed that database performance also varied between database versions, while boundary sensitivity analysis demonstrated that observations near mapped polygon boundaries contributed to disagreement without changing the relative advantage of GRPK over CORINE. The results demonstrate that the evaluated historical databases provide substantial and complementary information for spatially explicit LULUCF land-use reconstruction and that the proposed framework provides a transparent basis for identifying and selecting suitable information sources according to land-use category and historical period. Full article

Extreme precipitation events have grown more common as a result of global climate change, and conventional static hazard assessments find it difficult to account for the dynamic progression of flood disasters. This study considers extreme precipitation factors for different return times and creates different extreme precipitation scenarios based on multiyear historical precipitation data and actual storm events. The study proposes a method for the dynamic assessment of regional flood hazard that takes extreme rainfall scenarios into account by simulating the dynamic flood inundation processes under each scenario using the Accumulated Runoff and Flood Estimation Model (AccRo v.1.0), iterative flow accumulation, and hydrological calculations. A dynamic assessment and zoning of flood hazards was carried out in Laiyuan County, Hebei Province. The results reveal that high-hazard zones coincide with the distribution of historically badly damaged townships, concentrated in the river valley plains along the Juma River. The results show that spatial patterns are simultaneously influenced by precipitation, terrain, and the river network. In the temporal dimension, under Scenario 3, the superimposition of the 50-year return period daily maximum rainfall at the 12th hour increased the high-hazard area by approximately 110% compared with that at the 11th hour. In addition, the non-uniform multi-peak rainfall pattern in Scenario 4 represented the rise, peak, and recession stages of the flood process. A combined assessment of water depth and flow velocity can effectively distinguish between two disaster-causing modes—deep water with low flow velocity and shallow water with high flow velocity—thereby addressing the underestimation of hazard in transition zones associated with the use of water depth as a single indicator. Full article

Afforestation can contribute to climate-change mitigation when tree establishment is matched to ecological context and sustained by long-term management, but its net climatic effect is not uniformly cooling. Existing syntheses often emphasize carbon sequestration while treating biophysical, hydrological, disturbance, and socioeconomic evidence separately. Here, we provide a structured integrative review. We distinguish afforestation from reforestation, natural regeneration, forest restoration, and improved management. We explicitly assess evidence from global modeling, remote sensing, meta-analyses, long-term observations, and regional case studies. Global forests cover about 4.14 billion ha in 2025, while annual net forest loss remained about 4.12 million ha yr⁻¹ during 2015–2025. Global forests were a sink of about 3.5 ± 0.4 Pg C yr⁻¹ in the 2010s, but this existing-forest sink should not be interpreted as an afforestation-specific removal rate. Humid tropical and subtropical regions generally have the greatest potential for net climatic cooling. In contrast, afforestation at snow-covered high latitudes may cause substantial albedo-driven warming, while water-limited regions require careful species selection and conservative planting densities. Soil carbon gains are most consistent on former croplands and other low-carbon degraded lands, but responses on carbon-rich grasslands are highly variable. Long-term benefits further depend on disturbance resilience, permanence, land competition, financing, and credible monitoring. Additionally, we identify five priorities for the future: climate-smart adaptive silviculture, digital forestry with field-calibrated uncertainty, permanence and disturbance-risk accounting, sustainable forest bioeconomy, and integrated international governance and finance. Full article

Leaf water content (LWC) is an important indicator of plant water status, and its rapid estimation is essential for water diagnosis and cultivation management in fig production. Although hyperspectral sensing provides an effective means of estimating LWC, spectral redundancy and noise may hinder the extraction of water-sensitive information. Wavelet analysis can extract localized spectral information; however, single-scale wavelet features may not simultaneously preserve fine spectral details and suppress noise, and thus cannot fully characterize the complementary LWC-related responses across different scales. This study therefore developed multiscale double wavelet indices (MSDWIs) and multiscale triple wavelet indices (MSTWIs) to improve the hyperspectral estimation of fig LWC. Savitzky–Golay (SG) filtering and multiplicative scatter correction (MSC) were compared, and random forest (RF) and support vector regression (SVR) were used to evaluate the estimation performance of traditional vegetation indices (VIs), MSDWIs, MSTWIs, and their fused feature sets. The results showed that multiscale wavelet indices generally achieved higher estimation accuracy than traditional VIs, while multi-feature fusion further improved model performance. The SVR model based on the SG-preprocessed VIs+MSDWI+MSTWI feature set achieved the best validation performance (R² = 0.760, RMSE = 0.0232, and MAE = 0.0152). SHAP analysis of the optimal RF and SVR models showed that MSTWI was the dominant feature category, accounting for 57.8% and 57.0% of the total SHAP importance, respectively. These findings demonstrate that integrating multiscale wavelet indices with traditional VIs can enhance the representation of LWC-related spectral information and provide an effective approach for estimating fig LWC. Full article

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima Prefecture, Japan, to identify 1000 cells within decontaminated zones where the then immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective. Full article

Water erosion is a core issue that threatens the ecological integrity of the highland ecosystems in the Andes Mountains and the agricultural sustainability of the Ramis River basin. This study uses the Revised Universal Soil Loss Equation (RUSLE), which integrates five factors, rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P), to estimate the spatial distribution of potential water erosion rates in this basin. The results show that the very low and low erosion classes together cover 73.21% of the basin, while the high, very high, and extreme erosion classes account for 17.29% of the total area. Among these, the extreme erosion class, with an annual erosion volume exceeding 250 tons per hectare, covers 8.07% of the basin, equivalent to 1190.13 square kilometers. This extreme erosion is concentrated in steep headwater areas and five sub-basins including Cuenca Grande. Comparative model verification shows that the Ordinary Least Squares (OLS) model only identifies a positive correlation between slope gradient and potential soil loss, with an extremely low explanatory power (R² = 0.045). Its residuals exhibit significant spatial autocorrelation (Moran’s I = 0.204, p < 0.001). In contrast, the Geographically Weighted Regression (GWR) model greatly improves the model fit (R² = 0.359, RMSE = 148.288) and eliminates the spatial autocorrelation of residuals, proving that the slope-erosion relationship has spatial non-stationarity. Sensitivity analysis shows that the C factor has the highest sensitivity (0.980), followed by the LS factor (0.626). Based on these findings, this study proposes that cover and management measures such as vegetation restoration should be prioritized in high-risk headwater sub-basins. It should be noted that the values estimated in this study are potential soil loss amounts, rather than actually measured erosion values. Full article

Affected by coupled environmental factors including temperature and water activity, food storage is restricted by fungal contamination, quality degradation, and energy limits. Conventional microbial growth prediction models typically rely on given initial states and environmental parameters, making it difficult to account for the effects of prior-parameter errors and thereby limiting the accurate quantification of fungal growth risk and the real-time regulation of storage environments. This paper develops a fungal growth risk prediction and optimal regulation method for food storage based on the forward reachable set (FRS). The method combines a fungal growth kinetic model for Aspergillus flavus with FRS theory to calculate the reachable domains of colony radius and cell states within a finite time horizon, adopts a risk margin to describe the maximum colony expansion relative to deterministic growth trajectories, and constructs a multi-objective index covering energy cost, fungal growth risk, quality loss, and control switching cost to select the optimal environmental control scheme. Numerical simulation results show that the risk margin reflects the expansion of fungal growth risk caused by the propagation and accumulation over time of prior-parameter errors, while the selected regulation strategy exhibits stronger conservatism. Full article

Increasing frequency and intensity of heat events pose significant challenges for the production and early establishment of urban trees. This study evaluated whether horticultural interventions could mitigate heat stress and improve growth of young red maple (Acer rubrum ‘FranksRed’) under full-sun conditions representative of urban planting environments. Six treatments (control, canopy misting, paclobutrazol, propiconazole, kaolin clay, and potassium phosphite) were evaluated over two growing seasons in the Willamette Valley, Oregon, which were characterized by hot, dry summers and episodic heat waves. Canopy temperature, soil volumetric water content, and growth were monitored using high-resolution sensor networks and analyzed using mixed-effects modeling to account for repeated measures and environmental covariates. Across both years, mean canopy temperature largely tracked ambient conditions, and treatment effects on absolute temperature were modest. However, canopy misting reduced daily canopy temperature amplitude (ΔT) and maintained the highest soil volumetric water content, while both misting and kaolin consistently reduced exposure to the highest canopy temperature thresholds. Although these reductions in cumulative thermal exposure were not statistically significant, they coincided with improved tree growth. The chemical treatments produced smaller, context-dependent effects. Despite modest temperature differences, stem caliper increased by 10–20% under misting relative to the control (p < 0.05). Growth responses indicate that small changes in canopy thermal exposure and soil water availability can translate into meaningful differences in early tree performance. These results demonstrate that the absence of strong treatment effects on mean canopy temperature does not preclude biologically relevant outcomes. Management strategies that modify canopy thermal dynamics or plant water relations may improve growth and establishment potential of young trees under increasingly extreme thermal conditions, even when ambient heat loads cannot be fully mitigated. Full article

Background/Objectives: The healthcare sector accounts for an estimated 1–5% of the global environmental footprint, and hospitals concentrate a large share of that impact. As the largest professional group, nurses are pivotal to the transition toward green and regenerative hospitals. This review aimed to map the sustainable nursing practices implemented in hospitals internationally and to characterise the contribution of nursing to reducing the environmental footprint of care. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). PubMed, Scopus, ScienceDirect, SpringerLink, SciELO and Dialnet were searched for studies published between 2021 and 2026, complemented by grey and institutional literature, citation searching and hand-searching. Records were screened in duplicate against predefined eligibility criteria, and the evidence was charted into six thematic categories aligned with the review objectives. Results: Twenty-five sources were included—11 reviews and 14 primary studies, most of them descriptive. Waste management emerged as the domain most sensitive to nursing action, whereas energy and water use were only minimally nurse-sensitive. Education, leadership and nurse engagement were the main enablers; insufficient training, resistance to change and limited investment were the recurrent barriers. Only one study explicitly addressed the regenerative transition, from a governance perspective. Conclusions: Embedding sustainable practices in nursing can reduce the environmental impact of hospitals while preserving quality of care. Nurse-sensitive environmental indicators and the integration of sustainability competencies into curricula and hospital governance are key levers for this transition, whose regenerative dimension remains incipient and requires primary, comparative research. Full article

Autogenous shrinkage is a key factor contributing to early-stage cracking in high-performance concrete (HPC), which significantly affects structural durability and service life. As core components of HPC, supplementary cementitious materials (SCMs) can significantly improve concrete workability, mechanical properties, and durability, as well as reduce the risk of shrinkage cracking in HPC, by regulating hydration kinetics, pore structure, and microstructural evolution. The primary objective of this review is to elucidate the differential mechanisms by which different active pozzolanic materials regulate the autogenous shrinkage of HPC. This paper elucidates the patterns and mechanisms by which typical SCMs in HPC (such as fly ash, slag, silica fume, limestone powder, and nano-silica) affect the autogenous shrinkage of HPC. It analyzes the influence of key factors—including the type of SCMs, dosage, particle characteristics, water-to-binder (w/b) ratio, and composite blending on the autogenous shrinkage of HPC. Research indicates that highly reactive SCMs (such as silica fume and nano-silica) accelerate the self-drying process and increase autogenous shrinkage, whereas low-reactivity SCMs (such as fly ash) suppress autogenous shrinkage through dilution effects and by prolonging the hydration cycle. The combined use of multiple SCMs can achieve synergistic control of autogenous shrinkage and mechanical properties. Furthermore, this paper reviews existing autogenous shrinkage prediction models that account for the influence of SCMs and outlines future research directions. At the same time, this review identifies the limitations that currently exist in the research: there is a lack of a unified quantitative theoretical framework for the synergistic effects of multicomponent admixtures. The applicability of prediction models under multi-field coupling of temperature, humidity, and corrosive media is limited. And there is insufficient experimental data on the long-term shrinkage behavior of new low-carbon admixtures such as rice husk ash and calcined clay, which requires further dedicated research. Full article

Mountain sandy patches are typical indicators of aeolian degradation in arid and semi-arid zones; however, few studies have systematically analyzed their static spatial morphological features and statistical correlations with environmental variables. Taking the Sarbulak River Basin in the Ili River Valley of Xinjiang as the study area, this study extracts multiple morphological metrics of mountain sandy patches from high-resolution UAV orthophotos and adopts the XGBoost-SHAP framework combined with correlation analysis to quantitatively analyze patch morphological traits and their statistical links with environmental predictors. The main results are as follows: (1) Elongated geometry dominates mountain sandy patches with diverse auxiliary shapes, and the average major axis of all patches reaches 16 m. Every pair of morphological indicators shows significant positive correlations at p < 0.01 level. (2) The model’s relative predictive importance varies markedly across predictors. Wind speed ranks first with a normalized SHAP contribution of 34.7%, followed by precipitation (18.7%), NDVI (13.0%), and grazing intensity (9.0%). The four predictors jointly account for over 75% of total predictive signals and constitute a wind–water–vegetation–grazing statistical association system. All predictors show obvious nonlinear responses to mountain sandy patch occurrence with distinct statistical thresholds. (3) Strong combined statistical correlations exist between wind speed, precipitation, NDVI, temperature, elevation, and grazing intensity, and multi-variable combinations correspond to a higher probability of large-scale sandy patches. This paper summarizes key threshold intervals derived from SHAP dependence curves: patches tend to expand when wind speed ranges from 2.10 to 2.15 m/s; precipitation below 219.7 mm presents negative correlations with patch distribution; NDVI within 0.17–0.29 corresponds to positive marginal associations with sandy patch occurrence; grazing intensity exceeding 3.60 SU/ha matches frequent patch enlargement; and areas above 645.9 m elevation display higher patch prevalence. Full article

Landfill leachate circuits that operate without discharge, by recirculating aerated leachate onto the waste mass, are widespread in South-East Europe, yet their long-term behaviour is rarely documented with sample-level data. This study reports a six-year (2020–2025) seasonal monitoring campaign at a sanitary landfill in northern Serbia (alluvial aquifer of the Sava River, transboundary Danube basin) and re-examines it with a transparent multivariate protocol. Seventy-two leachate samples (collection well, aeration lagoon, sedimentation lagoon; n = 24 each, 30 parameters), 28 realised surface-water campaigns, and six years of groundwater summaries were evaluated by principal component analysis/factor analysis (PCA/FA, Varimax normalized), hierarchical cluster analysis, PERMANOVA, non-parametric paired tests and, for benchmarking, supervised machine learning. The pooled leachate model (n = 72; 21 variables; KMO = 0.700; Bartlett χ² = 956, p < 0.001) retained four factors by parallel analysis, explaining 61.6% of total variance; after rotation the factors accounted for 27.7%, 14.3%, 10.4%, and 9.3%. Factor 1 grouped organic load with particle-reactive metals (COD, BOD₅, Fe, Ni, Cr, As, Zn), Factor 2 a reduced sulfur–fluoride–BTEX signature, Factor 3 temperature-driven nitritation, and Factor 4 a nitrate–manganese redox contrast. Crucially, paired campaign-by-campaign comparison showed no removal of the dominant pollutants along the circuit. Median COD, BOD₅ and NH₄-N were not lower in the sedimentation lagoon than in the collection well, while pH rose from 8.08 to 8.75 (p < 0.001); only Cu, Pb, NO₃-N, and NO₂-N decreased significantly. The circuit therefore homogenises and concentrates dissolved load rather than removing it. Downstream surface water was significantly enriched in electrical conductivity (+110 µS/cm), total dissolved solids, NH₄-N, and NO₂-N relative to upstream (Wilcoxon, p < 0.05), and groundwater showed episodic conductivity up to 12,760 µS/cm and NH₄-N up to 102 mg/L. Cytotoxicity (MTT) confirmed biological relevance, with MRC-5 viability falling to 37% after 24 h exposure to 50 vol.% groundwater (Pw3) versus 60% in A549 cells. A random-forest classifier separated circuit units far better than PCA-based discrimination (76.4% versus 54.2% cross-validated accuracy) and distinguished the 2020–2021 pandemic period from 2022–2025 with 94.2% accuracy, a period effect also confirmed by PERMANOVA (R² = 7.2%, p < 0.001). The results indicate that closed-loop recirculation without an engineered discharge barrier transfers, rather than eliminates, contaminant load, and that after-care of such systems requires mass-balance monitoring and polishing treatment. Full article

In the last decade, significant progress has been made in understanding the crucial role of melanin in energy production in cells and tissues (which has been estimated to account for up to 90% of the total in organisms). This suggests the need to reconsider the central role of glucose and ATP, which instead are thought to play a primary role in building biomass. The mechanism by which melanin absorbs a wide range of the electromagnetic (em) spectrum (from FIR to far UV and ionizing radiation, helping organisms to cope with exposure to X- or γ rays) and dissociates water molecules (releasing oxygen, hydrogen, and free energy) has been observed in a number of experimental settings to occur in biological systems. This mechanism has also been reproduced in laboratory settings and has shown valuable applications in water revitalization and purification treatments. In terms of bioenergetics, these findings highlight the importance for mammals (including humans) of sunlight exposure (which provides the full em spectrum) while maintaining adequate hydration to maintain good homeostasis and optimal health. This process of hydrolysis and oxygen production through melanin and light has been proposed by Arturo Solìs Herrera and thus termed “human photosynthesis”. However, the proposed dissociation of water molecules (with an initial electron transition at around 7 eV and an ionization threshold of 12.62 eV) is not explainable within the still semi-classical vision of quantum mechanics (QM), especially when triggered by photons with much lower energy. In contrast, a description of water and biological matter in terms of quantum field theory (QFT) that takes into account the key role of coherence and the interplay with hydrophilic surfaces offers a coherent and physically grounded interpretive framework. In this paper, we propose a possible semi-quantitative theoretical framework for this fundamental biological process, which appears to underpin most metabolisms in heterotrophic organisms by interpreting water and living matter in terms of quantum electrodynamics (QED). This may open new perspectives in biochemistry and medicine. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article