Search Results (1,952)

Urban green infrastructure is increasingly exposed to fine-scale thermal heterogeneity generated by anthropogenic point-source heat emissions, yet the leaf-level responses of adjacent vegetation to such localized stress remain poorly understood. Here, we examined whether air-conditioner (AC) exhaust, a widespread point-source heat emitter, is associated with functional trait shifts in Fraxinus chinensis street trees, and whether easily measurable leaf traits can serve as candidate indicators for ecological monitoring. Using a matched treatment–control field comparison, we compared trees located 2 m from operating AC units with unaffected controls and quantified nine leaf functional traits together with concurrent microclimate variables. AC exhaust created a distinct compound heat–drought–wind micro-environment at the 2 m patch scale, with higher air temperature (+6.3 °C), lower relative humidity (−12.3 percentage points), and higher wind speed (5.2-fold). Exposed trees showed a coordinated shift toward more resource-conservative leaf traits: leaf dry matter content (+14.8%), tissue density (+13.6%), leaf thickness (+6.3%), and stomatal density (+11.7%) increased significantly, whereas specific leaf area (−10.6%), leaf area (−12.5%), chlorophyll content index (−4.6%), and stomatal area (−10.4%) decreased significantly. The observed “small-and-numerous” stomatal configuration suggests altered stomatal regulation, although its implications for transpiration-driven cooling require direct physiological validation. Exploratory structural equation modeling suggested associations among AC-exhaust exposure, leaf economic strategy, and stomatal traits; stomatal regulation showed the highest proportion of model-explained variance (R² = 0.598), but this value should not be interpreted as direct evidence of impairment severity or restoration potential. Leaf dry matter content, specific leaf area, and stomatal density emerged as sensitive and practical candidate indicators of AC-exhaust-associated leaf functional shifts. These findings support precautionary management near AC exhaust outlets, while specific planting-distance thresholds and zoning frameworks require future validation through distance-gradient or manipulative experiments. Full article

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

Eligmodontia dunaris is a small rodent endemic to the Atacama Desert, discovered in the Los Choros dunes (La Higuera, Coquimbo Region, Chile). This study documents new records of the distribution, as well as relevant data on the natural history of this species. Records of geographic distribution were obtained in the field and from published data. The standardized number of captures was calculated by the IDR index. The ability to concentrate urine was evaluated using the RMT renal index. Reproduction and development data were obtained from pregnant females in the field and from animals that mated in the laboratory. Fourteen new locations between the type locality “Los Choros” (Coquimbo Region) and “Diego de Almagro” (Atacama Region) were recorded. In all these localities, captures of E. dunaris were low, except during periods of the flowering desert. In the laboratory, this rodent had two to three consecutive litters in a single breeding season, producing three to five pups per litter. Renal indices measured in five adult specimens (RMT = 7.9 ± 0.8) indicated that E. dunaris can concentrate urine around 4059 ± 120 mOsm/kg. These results may suggest that this species has developed physiological and ecological strategies to colonize the extreme arid environments of northern Chile, allowing it to spread across 11,267 km² in the Atacama Desert. Full article

Introduction: Long-term ecological changes in estuarine communities are primarily driven by anthropogenic and environmental pressures. While abundance-based indicators are commonly used to assess these shifts, they often mask underlying ecological aspects related to age and/or size dynamics that may not necessarily be reflected in the abundance-based approach. Objective: This work tested a size-based indicator approach to examine the long-term changes in the size structure of the Mondego estuarine fish community (Portugal), using a 22-year dataset (2003 to 2025). Methodology: To capture the whole size structure, eight size-based indicators were applied, including mean length (MeanL), length at the 10th percentile (L10), median length (MedianL), length at the 90th percentile (L90), mean length of the 90th percentile (Lmax), size spectrum, the Large Fish index, and the Shannon index of length classes, at community and species levels and subsequently considered these in relation with with local and large-scale environmental factors. Results: Linear models identified a sharp, consistent decline in the overall size of the community, significantly correlated with the North Atlantic Oscillation index (NAO) and increasing estuarine water temperatures. A dynamic factor analysis (DFA) further identified one common trend across species for all indicators, corroborating the decrease in the overall size of the community while also acknowledging contrasting responses from different species, suggesting a heterogenous response across the fish community. Conclusions: These results highlight the importance of size-based indicators when assessing long-term ecological changes in marine ecosystems, allowing us to better understand how size structures shift, their relationship with a changing environment, and the long-term ecological outcomes in terms of community stability, resilience, recruitment, and ecosystem functioning. Full article

Understanding the impact of neighborhood-level factors on stroke prevalence is crucial for addressing existing disparities. However, there is a distinct lack of ecological studies at the census tract level that investigate the social determinants of health (SDOH) influencing stroke prevalence within the U.S. Health and Human Services Region 3 (HHS Region 3: Delaware, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia). This study adopted a multivariate modeling approach to investigate the association between the 13 indicators of the Health Opportunity Index (HOI) and stroke prevalence at the census tract level in HHS Region 3 using four HOI indicator profiles and to highlight the specific SDOHs that are most associated with stroke prevalence. The four HOI indicator profiles include: (a) neighborhood and built environment profile, (b) social and community context profile, (c) resource profile, and (d) economic profile. The methodological approach was quantitative, using secondary data. The sample size was 8021 census tracts. The HOI was estimated for each census tract in the study area. Ordinary least squares regression (OLS) analysis and spatial lag model (SLM) were run to examine whether the 13 indicators of the HOI (categorized into four profiles) reliably predict stroke prevalence and to determine the most appropriate model that best identifies the strongest predictors of stroke prevalence. The results show that affordability, education, spatial segregation, and income inequality indicators were the strongest predictors of stroke prevalence in HHS Region 3. This granular research identifies the neighborhood-level SDOH most strongly linked to stroke prevalence, which can be leveraged to guide the development of targeted public health programs, quality improvement initiatives, resource allocation, and policy creation to combat stroke-related morbidity and mortality across census tracts in HHS Region 3. For example, the built environment, encompassing factors like employment access, affordable housing, and walkability, profoundly influences stroke prevalence and provides urban planners with practical insights for developing healthier, more equitable communities, such as creating neighborhood parks to encourage physical activity, a key factor in stroke prevention. This study also provides neighborhood organizations with the evidence needed to pursue grant funding and raise awareness about the socio-structural influences on stroke outcomes in their respective neighborhoods. Lastly, the insights generated from our study can facilitate collaborative decision-making processes with communities in HHS Region 3 regarding the prioritization of neighborhood-level SDOH for targeted public health interventions. This prioritization should focus on addressing predictors of stroke prevalence that are congruent with the community’s established priorities, thereby maximizing cost savings. Full article

Post-fire rehabilitation structures are widely used in Mediterranean burned landscapes to reduce runoff and sediment transfer, yet their ecological associations with early vegetation recovery remain insufficiently documented. This observational study assessed vascular plant composition, species richness, vegetation cover, plant density, aboveground biomass, and soil properties across log barriers, wattles, and log dams in the burned landscape of Ancient Olympia, western Greece. The study area belongs to the humid climatic class of the United Nations Environment Programme (UNEP) aridity framework based on the Thornthwaite aridity index, providing a comparatively wetter Mediterranean post-fire context. Paired depositional and eroded microsites in operationally restored post-fire areas were monitored in 2022 and 2023. The sampling design comprised nine plots and 18 microsites (n = 9 plots, 18 microsites). Generalized estimating equations (GEE), change-score models, principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine associations of monitoring year, microsite condition and rehabilitation structure type with soil and vegetation patterns. A total of 27 vascular plant species belonging to 16 families were recorded. The average vegetation cover increased from 39.17 ± 21.44% in 2022 to 75.11 ± 12.90% in 2023. Model-based marginal estimates with 95% confidence intervals indicated a large positive increase in vegetation cover over this period. Further, rapid early recovery was indicated by large increases in species richness, plant density and biomass. Depositional microsites were associated with stronger recovery signals than eroded ones, characterized by a larger increase in vegetation cover, density, biomass and species richness. Among rehabilitation structures, log dams showed the highest cumulative floristic richness and a broader observed floristic spectrum, although the species-level contingency analysis provided only marginal evidence for structure-associated differences in floristic composition. Changes in selected soil properties including total nitrogen (total N), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), pH, electrical conductivity (EC), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K), were detected between 2022 and 2023; the multivariate soil pattern was driven primarily by mineral nitrogen, pH, and EC. These findings suggest that, under operational post-fire restoration conditions, rehabilitation structures are associated not only with erosion-control functions but also with microsite differentiation that may shape early plant establishment and biodiversity recovery in Mediterranean burned landscapes. Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Urban Park vegetation plays a crucial role in mitigating air pollution by serving as a natural sink for gaseous and particulate pollutants, thereby enhancing the ecological sustainability of cities. Identifying tree species with high tolerance to air pollution is therefore essential for effective urban park planning and management in highly polluted urban environments. This study evaluated the air pollution tolerance of selected tree species commonly found in urban parks of Kandy City, Sri Lanka, using the Air Pollution Tolerance Index (APTI). Five tree species—Terminalia catappa (Indian almond), Cassia fistula (golden shower tree), Pongamia pinnata (Indian beech), Madhuca longifolia (butter tree), and Tabebuia rosea (pink poui)—were assessed at two urban park locations representing contrasting pollution levels, identified based on ambient SO₂, NO₂, and PM_2.5 concentrations. APTI was calculated using four leaf biochemical parameters: pH, ascorbic acid content, relative water content, and total chlorophyll content. Leaf samples were collected from ten replicates of each species at both sites. Madhuca longifolia exhibited the highest APTI values (17.06 at the HP site and 25.17 at the LP site), followed by Cassia fistula, Terminalia catappa, Tabebuia rosea, and Pongamia pinnata. These findings suggest that the identified species, particularly Madhuca longifolia and Cassia fistula, are well-suited for urban greening and can contribute to mitigating air pollution impacts. However, these findings are constrained by a single cross-sectional sampling term, limited species screening, sequential data collection variances, and fixed mathematical equations. Consequently, future research should implement continuous multi-station monitoring arrays, expand species diversity, establish localized biochemical weightings, and initiate long-term multi-seasonal tracking to resolve temporal dynamics in tropical urban ecosystems. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Understory vegetation communities in chestnut (Castanea sativa L.) forests feature unique biodiversity patterns and high conservation value, yet the complex drivers of these communities remain poorly quantified. This study investigates the combined effects of structural, microclimatic, and topographic parameters on understory biodiversity in the mountainous region of Chalkidiki, Northern Greece. Using a nested plot design (n = 30), we integrated analytical in situ microclimatic monitoring with hemispherical photography (HemiView canopy image analysis system) to accurately quantify canopy architecture (canopy cover and solar radiation parameters), while a detailed vegetation inventory of vascular plants was performed to determine plant community structure and composition. Generalized Additive Models (GAMs) were employed to model Shannon Diversity (H’) and a weighted rarity index (RSR) representing complementary aspects of understory biodiversity. Our results reveal that the tree slenderness of the dominant stand serves as a robust proxy for stand competition and compactness. Lower slenderness values, reflecting reduced overstory competition, were significantly associated with enhanced light availability and potentially with microclimatic stability, which in turn supported higher levels of species diversity and rarity. Distinct ecological trends were observed between diversity and rarity. Shannon diversity was highest in closed forest environments characterized by lower temperatures, low stand slenderness values, southern aspects, and lower elevations, with the final model explaining 66.1% of the variance (n = 27). In contrast, species rarity was primarily driven by stand slenderness and low disturbance levels (explaining 54.6% of the variance), with the majority of rare species occurring in undisturbed stands (n = 30). These findings suggest that targeted, low-intensity management for competition promotes structurally stable stands and microclimatic buffering, facilitating the preservation of understory biodiversity. Full article

Understanding how ecological quality and human activity co-evolve in densely populated watersheds is essential for sustainable land management, yet spatially explicit long-term evidence remains limited. This study investigated the spatiotemporal dynamics and coupling coordination between ecological quality and multi-dimensional human activity intensity in the Huai River Basin (approximately 269,000 km²) from 2012 to 2024. An Improved Remote Sensing Ecological Index (IRSEI) was constructed by integrating EVI, wetness, dryness, land surface temperature, and a salinity index through annual principal component analysis. A composite Human Activity Intensity (HAI) index combining nighttime light, built-up intensity, and population density was derived with objectively determined weights. The coupling coordination degree (CCD) model and a pixel-level four-quadrant classification were then applied to characterize the human–environment interaction. Results showed that the basin-wide mean IRSEI declined from 0.564 in 2012 to 0.516 in 2020, before recovering to 0.566 in 2024, while HAI increased moderately by 16.9%. CCD improved slightly from 0.451 to 0.480, indicating limited but positive coordination gains. Four-quadrant transitions revealed that high-ecology, low-activity areas expanded, low-ecology, low-activity areas contracted, whereas low-ecology, high-activity zones persisted as stable pressure cores. These findings demonstrate that ecological recovery and human activity intensification can coexist spatially, but persistent high-pressure areas require targeted management interventions. Full article

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

This study investigates the spatiotemporal evolution of ecological environment quality and its driving mechanisms in the Sichuan section of the Yellow River Basin using Landsat imagery from 2000 to 2023. The Remote Sensing Ecological Index (RSEI) was constructed on the Google Earth Engine platform, and a comprehensive evaluation model was developed using principal component analysis. Sen’s slope, the Mann–Kendall test, and the Hurst exponent were applied to assess temporal trends and future persistence, while the optimal parameter-based Geodetector model was used to identify the driving factors of spatial differentiation. Results show that: (1) ecological environment quality exhibits a fluctuating but overall increasing trend, with a multi-year mean RSEI of 0.58, indicating a transition from “moderate” to “good–excellent” conditions; (2) spatially, ecological quality demonstrates significant heterogeneity and clear altitudinal gradients, with better conditions in the northwest than in the southeast, where low- and mid-altitude areas show higher ecological quality and stronger improvement, whereas high-altitude areas remain relatively poor due to strong natural constraints; (3) the spatial differentiation is jointly driven by multiple factors, among which precipitation and temperature are dominant, elevation exerts a fundamental constraint, and human activity plays a relatively minor role, while the interaction between climate and topographic factors shows the strongest explanatory power. These findings provide insights into the evolution and drivers of ecological environment quality in high-altitude regions and support ecological protection and regional management in the upper Yellow River Basin. Full article

Accurate bathymetric information for inland lakes is essential for water resource management, ecological monitoring, and environmental research. However, the accuracy and robustness of remote sensing-based bathymetric retrieval are often constrained by the complex optical properties of inland waters and the limited representation of conventional inversion features. To address these challenges, this study systematically compared the performance of a multiband logarithmic ratio model and three machine learning models, including Random Forest (RF), XGBoost, and AdaBoost, for inland lake bathymetric retrieval. Furthermore, a synergistic retrieval framework integrating chlorophyll-a concentration (Chla) and a Water Optical Index (WOI) was proposed. The results show that: (1) The overall accuracy of the Random Forest, XGBoost, and AdaBoost models constructed with the integration of chlorophyll-a concentration and WOI ($R^2=0.93$, $0.93$, and $0.91$; MAE $=0.06$ m, $0.07$ m, and $0.12$ m; RMSE $=0.14$ m, $0.14$ m, and $0.16$ m) outperforms that of models using only multispectral band information ($R^2=0.93$, $0.91$, and $0.82$; MAE $=0.06$ m, $0.07$ m, and $0.14$ m; RMSE $=0.14$ m, $0.16$ m, and $0.22$ m). Moreover, all these machine learning models significantly outperform the traditional numerical model ($R^2=0.27$; MAE $=0.29$ m; RMSE $=0.45$ m), with the Random Forest model achieving the best overall performance. This indicates that the proposed method offers higher applicability and retrieval accuracy in complex inland lake environments. (2) The optimal Random Forest model integrating chlorophyll-a concentration and WOI achieved high-precision bathymetric inversion for inland lakes ($R^2=0.93$, MAE $=0.06$ m, RMSE $=0.14$ m). Based on the three-dimensional bathymetry derived from this model, the estimated lake storage capacity was $1072.11\times {10}^4$ m³, compared with a measured volume of $1094.27\times {10}^4$ m³, yielding a relative error of $-2.03\%$. This result provides reliable and highly accurate data to support water resource management. Full article

The ecological consequences of hydraulic engineering on riverine environments have intensified the need for scientifically grounded ecological flow regimes. To ensure habitat suitability during critical fish spawning periods, this study developed habitat preference curves by correlating physiological parameters with key hydro-environmental drivers. A habitat suitability index (HSI) model was established using fuzzy logic, integrated with a genetic algorithm (GA) to simultaneously optimize fuzzy membership functions and inference rules. This model was applied to simulate the relationship between the weighted usable area (WUA) and discharge for various fish egg types in the reach downstream of the Fengman Dam, ultimately facilitating the determination of an optimized ecological pulse flow hydrograph. The results reveal distinct hydro-environmental preference variations among species. Specifically, drifting eggs require specific hatching cycles supported by higher flow magnitudes and velocities. Conversely, adhesive eggs experience a significant reduction in suitable habitat area under high-flow and high-velocity conditions. These findings suggest that reservoir water resource allocation must be tailored to the life-history requirements of target species to maximize spawning success. This study provides a robust scientific framework for eco-friendly reservoir scheduling and the conservation of regulated river ecosystems. Full article