Search Results (1,374)

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Background: The effectiveness of prostate-specific antigen (PSA)-based early detection of prostate cancer remains controversial and implementation-dependent. Screening policy changes have substantially altered PSA testing behavior in the United States, yet longitudinal evidence from non-organized European settings is limited. We assessed 12-year changes in awareness and utilization of PSA-based early detection and identified subgroups requiring targeted counseling. Methods: Two cross-sectional survey waves were conducted in 2009 (Study Phase 1) and 2021 (Study Phase 2) among men recruited via general practitioner practices in urban and rural regions of Germany. The survey was developed and reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). Identical questionnaires were used across phases. Endpoints were awareness of PSA-based early detection and prior PSA testing. Univariable and multivariable logistic regression evaluated independent associations with sociodemographic and behavioral factors. To assess sensitivity to compositional differences between survey waves, post-stratified weighted analyses re-aligning Study Phase 2 to the Study Phase 1 distribution of age category, educational attainment, and smoking status were conducted. Results: The analytic cohort comprised 890 men (Study Phase 1, n = 755; Study Phase 2, n = 135). Compared with Study Phase 1, Study Phase 2 participants more frequently were non-smokers (63.0% vs. 48.5%, p < 0.001) and had a university degree (38.5% vs. 30.5%, p = 0.002). In primary multivariable analyses, higher educational attainment (OR 1.71, 95% CI 1.24–2.36) and paternity (OR 1.94, 95% CI 1.25–3.01) were independently associated with greater awareness, whereas increasing age (OR 1.39, 95% CI 1.29–1.50) and higher educational attainment (OR 1.63, 95% CI 1.19–2.24) were independently associated with utilization. Study phase was not independently associated with either endpoint in primary models. In post-stratified sensitivity analyses, study phase was positively associated with utilization, indicating sensitivity of temporal contrasts to population composition. Conclusions: In primary multivariable analyses, we did not detect statistically significant temporal differences in awareness or utilization of PSA-based early detection within this German non-organized setting. The emergence of a study phase effect in weighted sensitivity analyses suggests that apparent time trends may be influenced by compositional differences between survey waves. Persistent social gradients, particularly related to educational attainment, underscore the importance of targeted, evidence-based counseling in opportunistic early detection systems. Larger, prospectively designed studies are needed to distinguish true temporal change from sampling-related effects. Full article

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature (LST) from 1 March to 31 July 2025—a recent period marked by multiple heatwaves across the region. To assess the cooling performance of wetlands, data samples were collected within the buffer zones. A Light Gradient Boosting Machine was trained to characterize the relationship between cooling intensity and a set of influencing factors (e.g., distance to wetland boundary, land use/land cover, built-up density, and green space density). The model explains approximately 91% of the variation in cooling intensity around wetlands. Notably, a machine-learning-based simulation framework was proposed to attain insights into the cooling characteristics of the riparian zone. The result indicates a mean cooling effect of about 2 °C and an effective cooling distance of 210 m from the wetland boundary. Partial dependence analysis further reveals that increasing built-up density substantially weakens cooling performance and implies that, for the conditions observed in Hue City, maintaining built-up density near wetlands below roughly 45% is favorable for sustaining effective cooling of the blue space, as indicated by the model-based partial dependence analysis. Overall, the research findings provide a data-driven basis for informing urban planning and wetland management in Hue City to mitigate heat stress. Full article

Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional data and the inability to handle severe data imbalance. This study proposes a lightweight and interpretable machine learning framework for real-time waterlogging hotspot prediction, based on a multi-dimensional feature space. Specifically, we implement a Lasso-based mechanism to distill 37 multi-source variables into five core determinants. This process effectively isolates dominant environmental drivers while filtering noise. To further overcome the recall bottleneck, we propose a Synthetic Minority Over-sampling Technique based on Weighted Distance and Cleaning (SMOTE-WDC) algorithm that incorporates weighted feature distances and density-based noise cleaning. Validating the framework on datasets from Shenzhen (2023–2024), we demonstrate that the integrated Gradient Boosting Decision Tree (GBDT) model integrated with this strategy achieves optimal performance using only five features, yielding an F1-score of 0.808 and an Area Under the Precision-Recall Curve (AUC-PR) of 0.895. Notably, a Recall of 0.882 is attained, representing a 4.6% improvement over the baseline. This study contributes a cost-effective, high-sensitivity approach to disaster risk reduction, advancing predictive urban waterlogging management. Full article

Rapid urbanisation in tropical megacities intensifies urban heat islands, especially during summer. Peri-urban wetlands help combat surface thermal stress through evapotranspiration, thermal inertia, and hydrological connectivity. However, their cooling effects are often oversimplified. This study assesses the complex cooling role of peri-urban wetlands, using a geospatial framework with Landsat imagery. We analyse land surface temperature (LST) variability and cooling patterns across the East Kolkata Wetlands (EKW). Results show a sharp thermal gradient, with waterbodies as the coolest surfaces (mean 25.4 °C) and dumping grounds as intense hotspots (mean 35.75 °C). Built-up areas adjacent to water are significantly cooler than urban cores. Cooling exhibits non-linear distance-decay and directional asymmetry, extending several kilometres but attenuated by dense western urban development. Internal thermal disruptions from dumping grounds create localised heat plumes. The findings demonstrate that wetland cooling is governed by hydrological connectivity and landscape permeability. Thus, conserving waterbody networks and mitigating thermally disruptive land uses are therefore critical. This positions peri-urban wetlands as dynamic climate-regulating infrastructure, offering a nature-based solution for urban heat adaptation that aligns with the sustainable development goals (SDGs). Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

Groundwater is a critical resource in the arid Chili River sub-basin (3246 km²) in Arequipa, southern Peru, yet the aquifer systems, their recharge mechanisms, and chemical evolution remain poorly characterized. This study integrates hydrogeological mapping, major-ion hydrochemistry (31 samples from springs and wells), and stable-isotope tracing (δ¹⁸O and δ²H, 11 sources) to delineate aquifer types, groundwater flow systems, and recharge dynamics across an elevation gradient of 2000–4000 m a.s.l. Three principal aquifer groups were identified: unconsolidated porous aquifers beneath the Arequipa urban area, fracture-controlled volcanic aquifers associated with the Chachani, Misti, and Pichupichu volcanic complexes, and sedimentary fractured aquifers of the Yura Group. Piper and Stiff diagrams reveal a chemical evolution from calcium-bicarbonate waters at high elevations to sodium-chloride waters in the lowlands, while scatter-plot analysis distinguishes local, intermediate, and regional flow systems. Elevated boron concentrations linked to borate deposits on Pichupichu volcano pose a potential health risk in supply springs such as La Bedoya. Isotopic signatures confirm that wells are recharged predominantly by high-altitude rainfall (>4000 m a.s.l.), whereas springs integrate water from multiple elevations through fractured volcanic formations. These findings provide a scientific basis for recharge-zone protection, abstraction planning, and water-quality monitoring to sustain groundwater supply under increasing urbanization and climatic variability. Full article

Urban air quality assessment increasingly relies on spatial interpolation to complement fixed monitoring networks; however, the reliability of geostatistical methods depends strongly on temporal conditions and pollutant characteristics. Despite extensive application, limited attention has been paid to how kriging performance varies across hours of the day and months of the year, particularly when contrasting primary pollutants driven by local emissions with secondary pollutants formed through atmospheric chemistry. This study evaluates the temporal sensitivity of Ordinary Kriging (OK) for mapping urban air pollutants in the Mexico City Metropolitan Area. Using hourly observations from the official air quality monitoring network (2021), we analyze ozone (O₃), a secondary pollutant, and sulfur dioxide (SO₂), a primary pollutant, under representative diurnal and monthly scenarios. Variogram model selection and predictive performance are assessed through leave-one-out cross-validation and external hold-out validation across multiple temporal blocks and months. Results indicate that kriging performance is highly sensitive to both hour of day and month. For O₃, smoother Gaussian variogram structures perform best during peak photochemical conditions, producing coherent regional concentration fields with gradual spatial gradients. In contrast, SO₂ exhibits stronger local variability and sharper spatial gradients, favoring exponential variogram models, particularly under stable morning atmospheric conditions associated with primary emission accumulation. Sensitivity analyses further reveal that no single variogram model is universally optimal and that interpolation accuracy depends more on temporal stratification and pollutant behavior than on variogram form alone. These findings demonstrate that geostatistical interpolation is a valuable tool for urban air quality assessment only when temporal sensitivity and pollutant-specific dynamics are explicitly incorporated. The proposed framework provides practical guidance for the responsible use of interpolated air quality maps, supports sustainable urban monitoring strategies, and contributes to more reliable exposure assessment in megacities with limited sensor coverage. Full article

Illegal solid waste dumping is a key urban sustainability challenge due to increased urbanisation and human consumption, but its prevalence and impacts across a socially differentiated gradient are seldom considered. We used street and off-street road surveys to examine the extent of illegal solid waste dumping across an income gradient in two medium-sized towns of Makhanda and Knysna in South Africa. We enumerated all dumpsites encountered in low- and high-income areas, recorded their GPS coordinates, and visually estimated size and composition using a standardised typology. We encountered 215 illegal solid waste dumpsites unevenly distributed by town (155 in Makhanda and 60 in Knysna) and income status, with the majority located in low-income areas compared to high-income areas. Most illegal solid waste dumpsites in low-income areas were small and located along roadsides and vacant plots. In both towns, illegal solid waste dumpsites were dominated by household and garden waste. The findings suggest that social differentiation matters in illegal solid waste dumping and should be factored into service provision strategies for ensuring environmental justice. We recommend that (i) municipalities should consider income heterogeneity in designing effective and equitable waste management plans, (ii) the national government should consider additional human and financial support to municipalities for efficient and equitable residential waste management, (iii) waste recycling at source (within households) should be mainstreamed in waste management strategies, and (iv) cleanup campaigns should be considered as a short-term solution to manage existing illegal solid waste dumpsites. Full article

Urban environments face increasing thermal stress from climate change and the Urban Heat Island effect, with significant implications for livability, public health, and energy sustainability. Outdoor thermal comfort is defined as the state in which conditions are perceived as acceptable, depends on interactions among meteorological, morphological, physiological, and behavioral factors. This review synthesizes the application of machine learning (ML) to outdoor thermal comfort assessment into a practice-oriented taxonomy. Research spans diverse climates and urban forms, using inputs across environmental and human domains. Supervised learning dominates. Regression approaches (linear regression, support vector regression, random forest, gradient boosting) and classification algorithms (decision trees, support vector machines, K-nearest neighbors, Naïve Bayes, random forest classifiers) are widely used to predict thermal indices such as the Physiological Equivalent Temperature and Universal Thermal Climate Index, or to classify subjective responses including thermal sensation, comfort, and acceptability. Unsupervised learning (clustering, principal component analysis) supports identification of microclimatic zones and perceptual clusters, while deep learning (multilayer perceptrons, convolutional and recurrent neural networks, generative adversarial networks) achieves superior accuracy for complex, high-dimensional, and spatiotemporal data. Algorithms such as random forests, support vector machines, and gradient boosting consistently show strong performance for both indices and subjective responses when integrating multi-domain inputs. Semi-supervised and reinforcement learning remain underexplored but offer promise for leveraging large-scale sensor data and enabling adaptive, real-time comfort management. The review concludes with a roadmap emphasizing explainable artificial intelligence, scalable surrogate modeling, and integration with simulation-based optimization and parametric design tools. Full article

Addressing national goals for ecological conservation in the Yellow River Basin, this study focuses on its Henan segment (HYRB). We developed a VOR-SQ assessment framework by augmenting the classic Vitality–Organization–Resilience model with ecosystem services and an enhanced ecological quality indicator. Using multi-source remote sensing and statistical data, we examine the spatiotemporal evolution of ecosystem health in the HYRB from 2000 to 2020. The XGBoost-SHAP algorithm was applied to identify nonlinear drivers and threshold effects. Key findings indicate (1) a persistent “high west, low east” health gradient with an overall declining trend; western mountains remain healthy, while eastern plains, urban, and intensive agricultural areas show degradation. (2) Natural factors—evapotranspiration (ET), elevation, NDVI, and slope—dominate health dynamics, with critical thresholds (~1153 mm, ~457 m, ~0.76, ~10.5°, respectively) beyond which their impacts shift markedly. (3) Anthropogenic factors (GDP, population/road density) contribute less globally but cause strong local negative disturbances in plains. For instance, road density > 434 km/km² or population density > 159 persons/km² reverses their effects from positive to negative. Accordingly, we propose tailored strategies: western conservation, central farmland optimization, and eastern development control. By coupling the VOR-SQ framework with XGBoost-SHAP, this study offers a robust diagnostic tool for ecosystem health and adaptive governance in fragile socio-ecological systems. Full article

Accurately estimating urban above-ground vegetation carbon density (UAGVCD) is crucial for assessing urban carbon sinks, but it is difficult due to varying spatial patterns, complex land covers, and differences caused by terrain. This study measures UAGVCD in Shenzhen, China, using an explainable remote sensing and machine-learning approach. We combined Landsat 8 spectral bands, vegetation indices, texture metrics, and terrain-based variables with 195 field measurements of carbon density to develop an Extreme Gradient Boosting (XGBoost) model. We evaluated model performance with spatial block cross-validation, using block sizes of 2 km, 5 km, and 10 km to account for spatial autocorrelation. The results show that the XGBoost model performed reliably during spatially independent validation, with the 5 km block showing the best accuracy (train R²= 0.917 ± 0.086, RMSE= 5.53 ± 3.97 Mg ha⁻¹; validation R² = 0.617 ± 0.055, RMSE = 10.25 ± 1.39 Mg ha⁻¹). Smaller blocks gave more varied results, while larger blocks led to a significant drop in accuracy (validation R² = 0.380 ± 0.297 at 10 km). Predictions showed clear differences in UAGVCD, with higher values in mountainous and green areas and lower values in highly developed regions. SHapley Additive exPlanations (SHAP) analyses suggested that both spectral and topographic factors play a significant role in UAGVCD. Additionally, the relationships between spectral data and carbon density showed strong nonlinear responses affected by terrain. These findings highlight the importance of spatially explicit validation and explainable machine learning for reliable urban vegetation carbon mapping. Full article

Addressing meso-scale sensing voids and resource misallocations, this study constructs an integrated “Performance Sensing–Bottleneck Diagnosis–Configuration Identification” framework to evaluate the spatiotemporal evolution of resilience across Xi’an’s districts (2018–2023). This research operationalizes a diagnostic-driven analytical pipeline coupling multi-source parameters with the CRITIC method to complement static stock accounting with dynamic performance sensing. This logic integrates Dagum Gini decomposition to pinpoint spatiotemporal bottlenecks and fuzzy-set QCA (fsQCA) to uncover driving pathways, utilizing an “Obstacle–Correlation” matrix to provide an objective basis for antecedent selection. The results show the following: (1) A “V-shaped” spatiotemporal trajectory and 2020 “resilience inversion” (dipping to 0.364) highlight the sensitivity of dynamic performance sensing in exposing latent vulnerabilities. (2) Persistent “center-periphery” gradients exist, with administrative siphoning driving 66.7% of inequality; diagnosis identifies distinct spatiotemporal pathologies: rigid spatial constraints in urban cores versus service imbalances in expansion zones. (3) Three equifinal pathways and an “asymmetric cancellation” effect prove that resilience hinges on configurational fit rather than linear stacking, where extreme single-dimension shortfalls neutralize collective gains. By bridging situational pathologies and governance pathways, this framework provides a robust empirical basis for the refined allocation of resources in complex environments. Full article

Many previous studies on volatile organic compounds (VOCs) have focused on Photochemical Assessment Monitoring Station (PAMS) VOCs at a single site, yet there is limited understanding of the spatial heterogeneity of both PAMS VOCs and oxygenated VOCs (OVOCs) across multiple functional zones at the city scale. To better understand the characteristics, sources and the effects of VOCs on O₃, we conducted simultaneous measurements of 71 VOCs (57 PAMS VOCs and 14 OVOCs) at three urban sites (Taoyuan, TY; Jinyuan, JY; Xiaodian, XD) and one suburban site (Shanglan, SL) in Taiyuan, a heavily industrialized city in northern China, during the summertime of 2022 and 2023. Total VOCs (TVOCs) concentrations were comparable at SL (21.9 ± 7.7 ppbv) and JY (21.9 ± 8.7 ppbv), but higher than those at TY (20.3 ± 6.3 ppbv) and XD (19.5 ± 6.4 ppbv). OVOCs were the dominant component at all sites, accounting for over 60% of TVOCs, with formaldehyde as the most abundant species. Ozone formation potential (OFP) followed the order of SL (119.6 ± 47.7 ppbv) > JY (112.0 ± 58.2 ppbv) > TY (100.4 ± 34.2 ppbv) > XD (97.1 ± 34.1 ppbv), with OVOCs contributing over 75% to the total OFP. Positive matrix factorization (PMF) resolved seven sources, with secondary formation as the largest contributor at all sites (24.6–32.5% of TVOCs, 30.5–37.0% of OFP). The second-largest source of VOCs and OFP exhibited a systematic spatial gradient: biogenic sources at SL (22.0%, 28.9%), gasoline vehicle exhausts at TY (22.5%, 21.8%), coking sources at JY (23.9%, 22.8%), and combustion sources at XD (23.6%, 26.0%). The lack of OVOCs could lead to an overestimation of primary sources and an underestimation of photochemical processing in source apportionment studies. These findings demonstrate that zone-specific measures should be complemented by regional precursor reductions for effective O₃ mitigation in Taiyuan. Full article

Under the background of the “dual carbon” strategy and regional coordinated development, the synergistic evolution of low-carbon logistics, regional economy, and ecological environment in the Yellow River Basin has become a key pathway to achieving high-quality development. Taking nine provinces (autonomous regions) within the basin as the study area, this paper constructed a coupling coordination evaluation index system for the LREES (Low-carbon Logistics–Regional Economy–Ecological Environment System), and measured the comprehensive development level of each subsystem using the entropy weight method. Based on the coupling coordination degree model, the temporal evolution of the three systems from 2010 to 2024 was systematically evaluated. In addition, global and local spatial autocorrelation models were introduced to identify spatial clustering patterns, while the obstacle degree model was used to identify key constraints at both the criterion and indicator levels. The results revealed that: the overall development level of the LREES systems steadily increased, with reduced regional disparities; the coupling coordination degree showed a trend of “fluctuating rise–gradual coordination,” with the average value increasing from 0.450 to 0.623, indicating continuously enhanced synergy; spatially, a gradient pattern of “downstream > midstream > upstream” emerged, accompanied by significant positive spatial autocorrelation; resource endowment and development scale were major constraints, while construction level, operational efficiency, and governance capacity were secondary. High-frequency obstacle indicators included per capita water resources, total import and export volume, and urban sewage treatment capacity. These findings offer theoretical support and policy guidance for promoting green transformation, enhancing system synergy, and advancing coordinated regional development in the Yellow River Basin. Full article