Search Results (128)

Advance prediction and management of ground subsidence are crucial, as its occurrence can lead to human casualties and property damage, particularly in densely populated metropolitan areas. This study developed artificial intelligence (AI)-based models to predict the daily occurrence of urban ground subsidence using meteorological factors. Focusing on selected areas within South Korea, a daily time-series dataset spanning 2010–2015, the primary analysis period selected for record consistency, was constructed using daily precipitation, temperature, and ground subsidence occurrence records. The predictive performance of seasonality-based baselines, conventional machine-learning models (random forest, extreme gradient boosting (XGBoost)) and deep-learning models (long short-term memory (LSTM), LSTM-Transformer (LT)) was evaluated under a strictly chronological, leakage-free protocol with multi-seed repetition and bootstrap confidence intervals. Antecedent meteorological conditions provided predictive skill significantly beyond seasonal climatology; notably, this skill was captured most effectively by a logistic regression on a compact summary of the preceding day’s conditions (macro F1 = 0.608, ROC-AUC = 0.655), which the deep sequence models matched but did not exceed. Analyses of input sequence length showed that short windows outperformed longer ones, and temperature variables emerged as the dominant predictors, indicating that recent antecedent conditions—rather than extended meteorological sequences or model complexity—carry most of the predictive information. This study confirms the feasibility of meteorologically informed daily screening of ground subsidence risk at a prototype level. These findings are expected to facilitate the development of a more robust ground subsidence prediction system through future integration with station-level meteorological inputs and data on subsurface infrastructure and geological conditions. Full article

Peri-urban belts are where most metropolitan residents actually meet farmland, and where urban food policy and agroecological transition physically overlap. They are also where wild food plants (WFPs) grow: on verges, ditch bank, and field margins, the interstitial biodiversity that complements cultivated crops. We ask whether the knowledge that makes those plants usable survives peri-urbanisation, and whether it can serve as a proxy for the resilience of the surrounding agroecosystem. Two Northern Italian landscapes with comparable perennial monoculture but opposite positions on the urban–rural gradient were compared through a diachronic design: the peri-urban lowland of the Trevigiano (Veneto), inside the dispersed-city fabric of the Venice–Padua–Treviso region and under rapid Prosecco expansion, and the Langhe (Piedmont), a UNESCO-listed hill district of low settlement density whose agriculture is nonetheless fully industrialised around wine grapes, hazelnuts, and tourism. Interviews with 74 informants were conducted through complementary designs: a cohort-structured approach in the Trevigiano and actor-diversified and diachronic interviews in the Langhe, recorded 13 folk food taxa in the Trevigiano against 43 in the Langhe (Jaccard 0.19). The peri-urban repertoire is thin and mostly inert: recognition survives while practice does not, with gaps of up to 86 percentage points between the two, and only Humulus lupulus, anchored in ritual, dialect, and a commercial product, is transmitted intact. The non-peri-urban repertoire is broader and still mobile, relocated by younger foragers onto unmanaged refugia and partly professionalised. The proxy, however, has a precondition. WFP knowledge indexes a healthy agroecosystem only where the land is clean enough to gather from, and the Trevigiano is not: informants avoid treated margins. The one EU instrument that would have banned pesticides in public green areas was withdrawn in 2024. We conclude that peri-urban planning should treat edges, unmanaged interstitial land, and treatment transparency as food-system infrastructure, and propose paired habitat and knowledge indicators for peri-urban agroecological assessment. Full article

The co-evolution of urban–rural spatial transformation and ecosystem services represents a key scientific issue for high-quality basin development and metropolitan ecological governance. As a core growth pole in the lower Yellow River Basin, the Jinan Metropolitan Area (JMA) faces the overlapping pressures of rapid urbanization and ecological constraints, making it an appropriate case for exploring the interactions between the urban–rural gradient and ecosystem service value (ESV). Based on multisource spatiotemporal datasets for 2000–2024, this study deploys a modified equivalent-factor method, a multidimensional urban–rural gradient model, and a geographic detector to investigate ESV dynamics, the evolution of the urban–rural gradient, and their coupling relationship at a 1 km × 1 km grid scale. The results show that: (1) Total ESV in the JMA increased slightly from 2000 to 2024, with pronounced spatial heterogeneity. High-ESV areas were distributed in the southern mountainous region and along the Yellow River, whereas low-ESV areas were mainly distributed across the northern plains and urban built-up areas. Forestland and water bodies provided the primary foundation for regional ESV stability. (2) Inner-suburban areas expanded rapidly and became the dominant urban–rural transition zone, while rural areas continued to contract. Urban areas exhibited both polarization and sprawl along transportation corridors. (3) The coupling coordination between ESV and the urban–rural gradient exhibited a “high-periphery, low-middle” spatial pattern and declined slightly over time. Land-use change, population density and normalized difference vegetation index (NDVI) serve as core driving factors, with nonlinear threshold effects; pairwise factor interactions exert stronger explanatory power than individual factors. This study advances the analytical framework for examining ESV in basin-metropolitan areas and provides a reference for integrated urban–rural development and ecological protection in similar regions. Full article

Rainfall-induced landslides pose a significant threat to communities and infrastructure in the eThekwini Metropolitan Region, South Africa. This paper presents a geotechnical–hydrogeological property zonation and parameterisation framework developed to support future physically based slope stability modelling. Using a weighted sum analysis in a GIS environment, the landscape was subdivided into distinct property zones by integrating lithology, slope gradient, and landform, with weights derived from a fully reproducible renormalisation of a previously published regional frequency ratio (FR) susceptibility model. This procedure provided the foundation for assigning zone-specific parameters, including effective shear strength parameters (c′ and ϕ′) and saturated hydraulic conductivity (Ksat), derived from laboratory testing, borehole pump testing analysis, and empirical relationships. The approach delineated four geotechnical–hydrogeological zones. A correlation of these zones against an inventory of 819 landslides revealed that over 82% of failures have occurred within Zones 2 and 3. While the hydrogeological conditions of these two susceptible zones range from intermediate to low permeability (K_sat = 10⁻⁵ to 10⁻⁸ m/s), which promotes transient pore pressure build-up, their high failure frequency corresponds closely with shared low shear strength (c′ = 5 kPa) and comparatively low effective friction angle (ϕ′ = 27.5–30°). This identifies shear strength as an important predisposing control on instability, relative to the inherently more stable Zones 1 and 4 (c′ = 10–15 kPa, ϕ′ = 30–35°). Consequently, this zonation and parameterisation approach advances landslide hazard assessment by providing a reproducible, model-ready dataset intended for future transient rainfall-infiltration simulations (e.g., TRIGRS), laying the groundwork for physically based early-warning systems, and supporting risk-informed urban development. Full article

Background: The absolute risk and risk ratio of stroke and large vessel occlusion (LVO) across different Cincinnati Prehospital Stroke Scale (CPSS) categories and symptom patterns remain incompletely defined. This study quantified stroke and LVO risk across CPSS categories, symptom patterns, and predefined CPSS groupings. Methods: This single-center prospective observational study included consecutive adults patients assessed by basic life support (BLS) emergency medical service crews in the Milan metropolitan area between March 2023 and December 2024. Patients were eligible if they had a prehospital stroke code based on CPSS ≥ 1, symptom onset within 24 h, a pre-stroke modified Rankin Scale score ≤ 3, and transport to Grande Ospedale Metropolitano Niguarda, a comprehensive stroke center (CSC). Final diagnoses were obtained from hospital records and neuroimaging. Absolute risks and risk ratios for stroke, ischemic stroke, and LVO were calculated across predefined CPSS categories. Discrimination was assessed using receiver operating characteristic analysis. Results: Among 876 patients, 411 (47%) had ischemic stroke, 96 (11%) intracerebral hemorrhage, and 164 (19%) LVO. The observed proportion of patients with stroke increased from 41% among those with CPSS = 1 to 78% among those with CPSS = 3, while the corresponding proportion with LVO increased from 6% to 36%. Risk ratios showed a similar gradient. Among patients with CPSS = 2, differences between symptom patterns were small. For LVO, the difference in discrimination between CPSS = 3 and the predefined grouping including CPSS = 3 or CPSS = 2 with Face and Arm deficits was small. Overall, these findings were more consistent with a severity gradient than with superiority of a specific symptom pattern or predefined CPSS grouping. Discrimination across the evaluated CPSS categories and predefined CPSS groupings was limited to modest. Conclusions: In CPSS-positive stroke-code patients assessed by BLS crews and transported to a single CSC, higher CPSS scores were associated with progressively higher observed proportions of stroke and LVO. These estimates are conditional on the study cohort and require external validation. They should therefore not be used alone to define transport pathways or guide clinical management. Full article

Urban parks can act as refuges for biodiversity, but their ecological value depends on the structure and composition of the vegetation they contain. This study evaluated the relationship between vegetation attributes and bird diversity in urban parks of metropolitan Arequipa, Peru, while considering daily variation across three time periods. Vegetation and bird communities were jointly characterized in 12 urban parks using point counts, alpha-diversity metrics, mixed models, multivariate ordinations, taxonomic beta diversity, and functional diversity metrics. We recorded 21 bird species and 17,591 individuals in 291 surveys. Bird richness responded mainly to integrated gradients of park size, tree-shrub structure, and shrub-herbaceous vegetation cover, whereas Shannon diversity increased with the total number of plant families and declined toward the afternoon. Bird composition was associated with vegetation gradients, and taxonomic beta diversity was high and dominated by species turnover. Floristically dissimilar parks also supported more differentiated bird communities. Abundance-weighted functional indices responded to the shrub-herbaceous gradient. These findings indicate that vegetation structure and floristic quality are key to enhancing the ecological value of urban parks in arid cities. Full article

Within the framework of the dual carbon strategy, investigating the spatiotemporal characteristics and driving factors of carbon sequestration in metropolitan areas through land use analysis is important for mitigating climate change and promoting regional ecological protection and sustainable development. On the basis of land use time points for five phases from the Jinan metropolitan area (JMA) covering the period from 2000 to 2024, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was coupled with the extreme gradient boosting (XGBoost)–Shapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) models to explore the spatiotemporal variations in carbon storage and its driving factors. In the last 24 years, cropland has been the predominant land use category in the JMA, representing almost 62% of the overall area. Throughout the five periods, the transition from cropland to construction land predominated, resulting in an 11.78% reduction in farmland and a 49.73% expansion in construction land. Between 2000 and 2024, carbon storage in the JMA decreased overall, with a total reduction of 3.70 Tg. The occupation of farmland for construction purposes was the primary cause of the decrease in carbon storage. The spatial pattern of carbon storage was similar to that of land use in the JMA, characterized by a distribution pattern with elevated values in the southeast and reduced values in the northwest. The SHAP analysis results demonstrated that the contributions of driving factors such as elevation, vegetation coverage, human footprint, and population density were generally high, making them the main drivers affecting carbon storage, with a significantly greater contribution of natural factors than human activity factors. The MGWR model results revealed that the digital elevation model and fractional vegetation cover positively influenced carbon storage in the JMA, whereas the population density imposed a negative effect. These results could guide the judicious allocation and utilisation of resources in urban regions, the establishment of ecological conservation areas, and the advancement of regional sustainability. Full article

Public toilets are an integral part of sustainable urban sanitation infrastructure, public health protection, and inclusive public space. SDG 6.2 calls for adequate and equitable sanitation and hygiene for all, with particular attention to women, girls, and people in vulnerable situations. This study evaluates the operational status of public toilets in central Beijing in the context of the toilet revolution. A six-dimensional quantitative scoring system was developed for metropolitan public toilets based on Maslow’s hierarchy of needs theory. The average score of public toilets in the central urban area is 0.858, 0.545, 0.648, 0.443, 0.422, and 0.400 in the six dimensions of need, which is consistent with a gradient in user satisfaction across need levels. Consistency analysis indicates no significant difference (p > 0.05) in the distribution of scores for each dimension in urban areas, whereas significant differences were observed between tourist and residential areas in the dimensions of physiological excretion and convenience and comfort. The findings suggest that future upgrades should place greater emphasis on higher-order user needs, particularly dignity, equality, and user comfort. This study provides a reference for promoting the high-quality development of sanitation services. It also provides sanitation providers and policymakers with new ideas for toilet renovation, such as improving public toilet service and sanitation management through “micro-updates” and “on-site sanitation systems”. Full article

Understanding ecological resilience (ER) across urban–rural gradients is important for differentiated management in arid metropolitan areas. Using 2 km grid data for 2013, 2018, and 2023, this study delineated travel-time-based urban–rural catchments in the Urumqi Metropolitan Area. The optimal-parameter Geodetector and multiscale geographically weighted regression were used to examine global explanatory patterns and local ER–factor associations, while spatially constrained clustering identified management types. The results showed that (1) mean ER values were 0.49, 0.50, and 0.48, respectively. ER was lower in urban centers and higher in rural areas and hinterlands. Compared with 2013, ER in 2023 was higher in urban centers and peri-urban regions but lower in rural areas and hinterlands. (2) Socioeconomic factors were more prominent in urban centers and peri-urban regions, whereas rural areas exhibited temporally variable natural and socioeconomic patterns. Elevation and soil organic matter were more prominent in hinterlands, and factor interactions were predominantly characterized by bi-factor or nonlinear enhancement. (3) Local ER–factor associations varied in direction, magnitude, and spatial scale. Negative associations with nighttime light intensity, road density, and built-up land proportion were concentrated in more urbanized areas. (4) Overlaying four spatial clusters with four catchment categories produced 16 integrated management types. These units provide a spatial basis for differentiated ecological management and regional planning in the Urumqi Metropolitan Area. Full article

Metropolitan fringe areas are transitional spaces where urban expansion, industrial relocation, residential spillover, transport infrastructure, and ecological recreation jointly reshape land-use functions. However, urban vitality research has mainly focused on central urban areas, leaving limited evidence on how activity intensity is organized in metropolitan fringe spaces. Taking the Chengdu Metropolitan Area, China, as a case, this study delineates metropolitan fringe areas using POI kernel density analysis and density–distance relationships, calibrated with impervious-surface and remote-sensing evidence, measures village-level relative activity intensity using Baidu heatmap data, and examines nonlinear associations between built-environment variables and urban vitality using LightGBM and SHAP. The results show that vitality across the delineated fringe does not form a uniform core–periphery gradient. Instead, high-vitality units are clustered around industrial parks, residential spillover zones, transport corridors, public-service nodes, and ecological recreation spaces. Population density, nighttime light, gross domestic product, building density, and commercial POI density are the main predictors in both weekday and weekend models. Several variables show nonlinear and saturation-like associations rather than simple linear effects. These findings suggest that higher predicted fringe vitality is more likely to occur where population concentration, development intensity, service provision, transport linkage, and socioeconomic activity coexist, rather than where densification occurs alone. The study extends urban vitality research to metropolitan fringe areas and provides evidence for differentiated land-use planning in urban-rural transition zones. Full article

In the context of urban stock renewal and metropolitanization, radial rail transit corridors in megacities need to transition from singular transportation functions to composite development carriers. This study proposes a hybrid multi-criteria decision-making (MCDM) model combining the Interval-Valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) with the entropy weight method, featuring an adaptive weighting mechanism that balances subjective expert judgment and objective data patterns. Novel corridor-scale indicators (i.e., hub functional matching, jobs–housing proximity, and gradient stability) are introduced, alongside a three-stage renewal pathway identification method. A case study of five Shanghai corridors (Lines 5, 9, 11, 16, and 17) reveals that jobs–housing spatial proximity is the primary vitality dimension (weight 0.244), while functional mix exhibits a pattern that identifies it as a common shortcoming across all corridors. The five corridors are classified into three vitality tiers, with five typical syndromes diagnosed. A sensitivity analysis confirms the robustness of corridor rankings to weighting and normalization choices, but reveals their sensitivity to the jobs–housing proximity threshold, which validates the 10 km standard. The proposed model offers quantitative diagnostics and differentiated renewal strategies, providing planning references for corridor renewal. Full article

The groundwater resources are increasingly stressed in the Islamabad–Rawalpindi metropolitan area of Pakistan due to unplanned urbanization, growth of industries, and inadequate waste management. In this study, the aquifer vulnerability was evaluated in the productive alluvial zone of Islamabad Watershed using a Geographic Information System (GIS)-based DRASTIC model and critically comparing it with independent measured contamination of groundwater, which is a common weakness in many machine-learning-based DRASTIC studies considering the vulnerability index as the model input. The data from 21 boreholes supplied by the Capital Development Authority (CDA) were used to map seven hydrogeological parameters in ArcGIS Pro at a 30 m resolution. The DRASTIC Index values ranged from 69 to 188, with 12.9% of the mapped watershed (209.3 km²) being rated as Very High vulnerability, mainly in the shallow western urban alluvium where water tables are below 5 m. Single-parameter sensitivity analysis showed that the most influential factors of the index were impact of the vadose zone (S_i = 1.14) and depth to water table (S_i = 1.09). A Random Forest model was trained on independently measured nitrate instead of the DRASTIC Index, but had a poor predictive skill (cross-validated R² = 0.08), and the SHapley Additive exPlanations (SHAP) analysis suggested that increased vulnerability (shallow water table and high recharge) was correlated with lower nitrate concentrations. The inverse relationship between groundwater intrinsic vulnerability and measured nitrate was statistically significant when compared to 233 groundwater samples collected at the same locations during two different campaigns (2018 and 2024) (pooled Pearson r = −0.27, p < 0.001; Spearman ρ = −0.19, p = 0.007; Kruskal–Wallis H = 14.10, p = 0.003). Levels of nitrate in both Low and Moderate vulnerability zones (6.0 and 7.7 mg/L, respectively) were higher than in Very High zones (3.4 mg/L). The inverse direction was consistent across both campaigns and robustly significant in the 2024 dataset (ρ = −0.33, p < 0.001), which covered a wider contamination gradient; in the 2018 dataset, only the parametric test was significant. Nitrate showed no significant difference between land-use classes (H = 7.23, p = 0.065) and was found as a few individual high concentrations, suggesting that these were not diffuse loading issues or intrinsic susceptibility, but were likely influenced by point sources. These results show that intrinsic DRASTIC vulnerability is useful to identify areas vulnerable to potential future contamination, but does not explain the current distribution of contamination in this aquifer, which is influenced by point-source loading and residence-time effects. To provide effective groundwater protection, intrinsic vulnerability assessment must be complemented with specific monitoring of point sources. Full article

Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon emission and sequestration accounting by land-use type; (ii) intra-provincial spatial-interaction analysis operationalized through two complementary tools—the Ecological Support Coefficient (ESC) and Economic Contribution Coefficient (ECC), which characterize the local economy–ecology relationship within each city, and a gravity-based emission-linkage model that uses GDP, population, emissions, and inter-city distance to characterize the network structure of inter-city emission attraction; and (iii) the intPLUS model, which combines random-forest-derived transition probabilities with patch-generation rules to simulate multi-scenario land-use trajectories. The framework is applied to Jiangsu Province, China, across 13 prefecture-level cities, using 1995–2020 historical data and three 2030 scenarios. Model performance is validated against observed 2020 land use, with an overall Kappa coefficient of 0.82. The results reveal a stable “high-south–low-north” gradient in emissions, a contrasting “high-ECC/low-ESC” versus “low-ECC/high-ESC” combining pattern across southern and northern Jiangsu, and a hierarchical core–periphery emission-linkage network anchored on the southern metropolitan cluster. Scenario projections for 2030 show clear divergence in provincial carbon budgets, with emissions of $\mathrm{12,326.59}\times {10}^4$ t, $\mathrm{11,745.26}\times {10}^4$ t, and $\mathrm{13,243.42}\times {10}^4$ t under natural development, ecological protection, and economic development, respectively, and corresponding sequestration of $-87.10\times {10}^4$ t, $-100.29\times {10}^4$ t, and $-85.61\times {10}^4$ t. Rather than treating carbon accounting and land-use simulation in isolation, this workflow bridges the analytical gap between physical land-use transitions and socioeconomic spatial emission spillovers, translating structural interactions into actionable spatial planning strategies. Relying on widely available data, the framework demonstrates strong methodological transferability for comparable subnational systems, provided that local parameters and sink coefficients are properly recalibrated. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Predicted cardiovascular age (heart age) translates the risk-factor profile into an equivalent age, which may facilitate interpretation of estimated cardiovascular risk. Excess cardiovascular age describes, in years, the integrated burden of modifiable risk factors and its distribution in the population. This study aimed to quantify socioeconomic and geographic inequalities in predicted excess cardiovascular age among Peruvian adults using standardized inequality measures, and to describe its temporal variation from 2014 to 2024. We analyzed ENDES Peru 2014–2024 data for adults aged 30–74 years. Cardiovascular age was estimated using the body mass index (BMI)–based non-laboratory Framingham equation, and excess was defined as the difference between cardiovascular age and chronological age. Weighted means and 95% confidence intervals were estimated accounting for the complex survey design. Socioeconomic inequalities were assessed using absolute and relative gaps between extreme wealth quintiles (Q5–Q1), the Slope Index of Inequality (SII), the Relative Index of Inequality (RII), and the concentration index/curve. Among 163,889 participants, mean excess cardiovascular age was 9.64 years (95% CI: 9.48–9.80), with similar estimates in women (9.73; 95% CI: 9.52–9.94) and men (9.54; 95% CI: 9.33–9.75). Temporal variation was observed, peaking in 2021 (10.91; 95% CI: 10.57–11.25). Excess increased with wealth (Q1: 7.14 vs. Q5: 11.25 years), with an SII of 5.04 years (95% CI: 4.71–5.37) and a concentration index of 0.087. The gradient was steeper in men (SII 6.14) than in women (SII 3.90). Geographically, Metropolitan Lima had higher excess than the Highlands (11.17 vs. 7.45 years), and urban areas exceeded rural areas (10.28 vs. 7.25 years). In Peru, adults aged 30–74 years had a mean predicted excess cardiovascular age of about 10 years, with a consistent pro-rich and urban/coastal concentration pattern, more pronounced among men. Because this metric is derived from a risk prediction equation, these findings should be interpreted as surveillance-oriented evidence of inequalities in estimated risk-factor burden, not as evidence of observed cardiovascular disease, subclinical cardiovascular damage, causal mechanisms, or tested intervention effects. Full article