Search Results (1,648)

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

As urbanization intensifies, improving street thermal comfort has become a critical issue in urban renewal. While existing studies generally assume that increasing the Green View Index (GVI) linearly improves pedestrian thermal comfort, this study identifies a significant “Decoupling Effect” in high-density commercial areas through field measurements and numerical simulations of three typical street types (commercial–service, ecological–recreational, and historical–cultural) in Shenyang. Integrating DeepLab V3 semantic segmentation with ENVI-met version 5.1.1 microclimate simulation, the results demonstrate a robust monotonic negative correlation between GVI and Physiological Equivalent Temperature (PET) in ecological streets (Spearman’s ρ = −0.692, p < 0.001), confirming the consistent cooling benefit of greenery in nature-dominated environments. However, a distinct “Threshold Effect” was identified in commercial streets using Piecewise Linear Regression (PLR). A critical breakpoint was detected at GVI = 22.08%. Below this threshold, visual greenery effectively contributes to cooling (slope = −0.454); yet, once GVI exceeds 22.08%, the cooling efficacy diminishes significantly (slope = −0.109), marking the onset of a “decoupling” phase. Specifically, despite Wenhua Road achieving a GVI of ~24.5% with a complex “three-board, four-belt” structure, its PET peak reaches 46.15 °C, approximately 5.5 °C higher than ecological streets. Mechanism analysis reveals that under peak thermal stress (Traffic Heat ≈ 75 W/m²), the high-intensity anthropogenic heat and hardscape radiation exceed the evaporative cooling threshold of vegetation. This study reveals the non-linear relationship between visual greenery and the physical thermal environment, suggesting that simply pursuing visual green quantity is ineffective in commercial canyon renewal; instead, a threshold-based synergistic optimization of canopy shading and pavement thermal performance is required. These findings provide a quantitative basis for sustainable street landscape planning and urban climate adaptation strategies in high-density cities. Full article

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article

This study proposes TNAP-DDQN, a deep reinforcement learning method for urban low-altitude UAV path planning under residential noise threshold constraints. With time cost and safety risk as the optimization objectives, operational constraints such as collision risk and maximum AGL altitude are incorporated to achieve coordinated optimization of noise compliance, operational safety, and efficiency. To mitigate action space contraction and training instability induced by multiple constraints, a Noise-Degradation-Mask-based Action Bias Network (NDM-ABN) is introduced at the action selection layer. A three-tier degradation scheme prevents empty candidate sets, while bias-based decision making is applied to approximately tied actions to stabilize the policy. Moreover, multi-step prioritized experience replay (PER) improves sample efficiency and long-horizon return modeling, and potential-based reward shaping (PBRS) transforms sparse constraint signals into auxiliary rewards. Simulation results indicate that: (1) NDM-ABN is the key module for stabilizing the noise-exposure process by suppressing high-noise actions; (2) the required AGL is related to the UAV source noise level and local noise limits, implying the need for differentiated AGL altitude classes; and (3) the maximum admissible UAV source noise level increases as the threshold is relaxed. The proposed method provides quantitative guidance for noise-entry and AGL altitude regulation, while future work will incorporate additional metrics (e.g., A-weighted equivalent sound level) to better capture noise fluctuations and short-term peaks. Full article

To address the challenge that prolonged interruptions of Global Navigation Satellite System (GNSS) signals—such as those caused by urban obstructions—hinder signal re-locking and thereby reduce the number of available satellites for integrity monitoring algorithms, this study proposes an inertial navigation-assisted GNSS re-locking method based on vehicle motion information constraints. This method leverages vehicle motion constraints to confine the primary direction of Inertial Navigation System (INS) velocity errors to the vehicle’s forward direction. Upon GNSS signal recovery, frequency error compensation is employed to mitigate Doppler errors of the previously obstructed satellites. Simulation results show that this method significantly improves the re-lock capability after a long period of satellite signal interruption, increasing the number of available satellites from 7 to 10 and optimizing the satellite geometry. At a horizontal alarm threshold of 80 m, the availability of the GNSS integrity monitoring algorithm reaches 95.7%, which is 53.7 percentage points higher than the unassisted scheme. Moreover, it can achieve 100% fault detection and identification rate even with a pseudorange deviation of 82 m, significantly improving the performance of the integrity monitoring algorithm. Full article

Accurately assessing the accessibility of fire services is critical for enhancing urban safety and the resilience of the built environment. However, existing studies often lack a systematic analysis of spatiotemporal dynamics across an entire municipality. To address this gap, this study develops a citywide dynamic assessment framework for Shanghai, integrating GIS with real-time traffic data across 240 consecutive intervals to assess the service accessibility of 195 fire stations in relation to 7973 key units of fire safety. The principal findings are threefold. First, the results reveal significant urban–suburban heterogeneity in emergency response times. Notably, the proximity advantage of fire stations in central urban areas is offset by traffic congestion, and the marginal benefit of traffic speed improvement exhibits a sharp decline once the average speed exceeds a critical threshold of 13.7–21.0 km/h. Second, the accessibility ratio demonstrates a clear temporal pattern, being highest on holidays and lowest during weekday peak hours, and follows a nonlinear spatial decline from the urban centre to the periphery. This pattern is influenced more critically by the matching of supply and demand than by fire station density alone. Third, the analysis identifies dynamic vulnerability hotspots, which display a ‘bimodal (M-shaped)’ pattern on weekdays and a ‘unimodal (A-shaped)’ pattern on weekends and holidays. This spatiotemporal mismatch shows that central urban areas, despite higher station density, can suffer from both high fire risk and low accessibility, revealing structural patterns consistent with the ‘Inverse Care Law’ in emergency service provision. This study concludes that merely improving traffic conditions is insufficient; optimising the spatial matching of resources is paramount for effective urban disaster prevention. By developing a refined dynamic assessment framework, this study advances current knowledge by focusing on demand locations consistent with actual fire regulatory priorities and examining spatiotemporal patterns across both urban and suburban areas, thereby providing quantitative, evidence-based support for the strategic planning of fire stations and the enhancement of infrastructure resilience. Full article

This study analyzes heat waves (HWs), cold spells (CSs), and mean temperature trends in Türkiye’s three major metropolises (Istanbul, Ankara, and Izmir) using long-term station data. HW and CS events were defined via a percentile-based threshold approach, utilizing daily maximum (T_max) and minimum (T_min) temperature data from a total of 15 meteorological stations. Temporal trends in annual and seasonal wave frequencies, alongside mean temperature series, were evaluated using the Mann–Kendall test and Sen’s slope estimator. The findings indicate that HW frequencies have significantly increased across the majority of stations, whereas CS frequencies have decreased at most locations. It was determined that while HWs predominantly concentrate in summer and CSs in winter, heat extremes can extend into transitional seasons. Mean temperatures exhibit a statistically significant upward trend across all stations. Furthermore, HWs have become more prominent and CSs have dissipated more rapidly in urban and coastal stations. These results reveal that the risk of heat extremes is escalating while cold extreme events are weakening in Türkiye’s major cities due to warming climate conditions. Full article

Spring vegetation phenology is highly sensitive to climate change; however, climate drivers and their threshold responses at the urban scale remain insufficiently and systematically quantified. Focusing on 31 provincial capitals and municipalities in mainland China, this study integrated MODIS MCD12Q2-derived start-of-season (SOS) for spring green-up and TerraClimate climate data (2001–2023) at a 500 m grid resolution. SOS trends were characterized using the Mann–Kendall test and the Theil–Sen slope estimator. Building on these trend metrics, we developed an XGBoost–SHAP framework using the interannual rate of temperature change (tem_slope) and the interannual rate of precipitation change (pre_slope) as input features, to quantify the nonlinear contributions of climate-change rates to SOS trends and to identify key thresholds. Results indicate that the multi-year mean SOS across China’s provincial capitals and municipalities is primarily distributed between approximately DOY 74 and 138, exhibiting a clear spatial pattern of earlier green-up in the south, later green-up in the north, and delayed green-up on plateaus, with pronounced shifts in distribution centers and dispersion among climatic zones and cities. At the city level, the mean SOS trend shows an overall advancing rate of 0.81 d·year⁻¹ (i.e., the average of city-mean Sen slopes across the 31 cities). Pixel-level trend analyses show that advancing and delaying trends commonly coexist within most cities; among pixels with significant or marginally significant SOS trends identified by the Mann–Kendall test (MK p < 0.10) across all cities, advancing and delaying SOS pixels account for 75.02% and 24.98%, respectively. At the city scale, the proportions of advancing versus delaying pixels vary markedly among cities, forming directional structures characterized by advance-dominant, delay-dominant, or bidirectional coexistence patterns. SHAP dependence relationships further reveal that the effects of tem_slope and pre_slope on SOS trends are generally nonlinear and piecewise, with substantial heterogeneity across climate zones and cities. The identified tipping points and associated sensitive ranges collectively delineate spatially differentiated climate-sensitive intervals, which define the nonlinear response boundaries of spring SOS to sustained warming and precipitation changes. This study provides quantitative evidence for regional differences in urban spring phenological responses to climate change across major Chinese cities and offers a methodological reference for identifying actionable climate thresholds in urban greening design and climate-adaptive management. Full article

Urban green spaces (UGS) are critical regulators of carbon sequestration in industrial cities; however, the configuration mechanisms underlying their carbon dynamics remain insufficiently understood. This study investigates how landscape configuration influences carbon sequestration capacity in Lanzhou and Baotou using multi-temporal datasets from 2000, 2011, and 2022. Net primary productivity (NPP) derived from the CASA model was employed to represent carbon sequestration capacity. An integrated XGBoost-SHAP framework was applied to identify dominant configuration metrics, nonlinear responses, and structural thresholds. The XGBoost model showed stable predictive performance across the three periods, with test-set R² values ranging from 0.470 to 0.510 in Lanzhou and from 0.325 to 0.379 in Baotou. The results reveal systematic and persistent differences in configuration-driven controls between the two cities. In Lanzhou, aggregation-related metrics, particularly COHESION, consistently exert the strongest influence across all three periods, indicating that spatial cohesion and connectivity function as primary stabilizing mechanisms in a mountainous, valley-constrained urban system. Carbon sequestration performance increases once sufficient structural integration is achieved, with aggregation thresholds remaining relatively stable, for example AI values of approximately 0.31–0.34 across 2000–2022, reflecting the importance of maintaining ecological continuity under semi-arid climatic stress. In contrast, Baotou is more strongly regulated by fragmentation-related metrics, especially edge density (ED) and division index (DIVISION), suggesting that its relatively open terrain and industrial spatial structure render carbon sequestration more sensitive to patch separation and edge proliferation. Here, fragmentation acts as a dominant structural constraint, limiting vegetation productivity once spatial disintegration intensifies; for example, ED thresholds shifted from approximately −0.23 in 2000 to −0.56 in 2022. Landscape–carbon relationships exhibit pronounced nonlinear and threshold-dependent behavior in both cities. Rather than responding gradually to structural modification, NPP shifts across identifiable transition points that remain broadly stable over time; for instance, Lanzhou’s AI threshold remains within 0.31–0.34, whereas Baotou’s ED threshold changes from −0.23 to −0.56 across 2000–2022, indicating that these thresholds represent intrinsic structural characteristics of the respective urban ecological systems. However, the magnitude and configuration logic of these thresholds differ between Lanzhou and Baotou, confirming the existence of city-specific nonlinear regimes. These findings demonstrate that urban carbon sequestration operates through context-dependent configuration pathways shaped by terrain, climatic constraints, and long-term spatial organization. The study advances understanding of how structural heterogeneity governs carbon dynamics in arid and semi-arid industrial cities and provides a quantitative basis for configuration-sensitive land planning. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Arsenic (As) and mercury (Hg) are trace elements of major environmental and public health concern. Their relevance is due to their well-documented toxicological effects. In rapidly urbanizing port-industrial cities, soil contamination by these elements represents a critical challenge. This situation compromises sustainable urban development and environmental governance. This study had three main objectives: First, to evaluate the contamination status of As and Hg in urban soils using multiple geochemical indices; Second, to assess the potential human health risks associated with exposure in the urban environment of Talcahuano; Third, to identify the relative contributions of geogenic and anthropogenic sources based on spatial distribution patterns. A total of 420 soil samples were collected. These included 140 topsoil samples (TS; 0–10 cm), 140 subsoil samples (SS; 10–20 cm), and 140 deep-soil samples (DS; 150 cm). Arsenic concentrations were determined using hydride-generation atomic absorption spectrometry (HG-AAS). Mercury concentrations were measured by cold-vapour atomic absorption spectrometry (CV-AAS). Median As concentrations were 2.7 mg kg⁻¹ in TS, 3.1 mg kg⁻¹ in SS, and 2.5 mg kg⁻¹ in DS. The corresponding median Hg concentrations were 0.2 mg kg⁻¹ in TS and 1.4 mg kg⁻¹ in both SS and DS. Spatial distribution maps were generated through ordinary kriging interpolation. Geochemical baseline values were calculated using the median + 2 × MAD approach. The resulting baseline values were 7.8 mg kg⁻¹ for As and 3.6 mg kg⁻¹ for Hg. Contamination assessment was conducted using the geoaccumulation index (Igeo), enrichment factor (EF), and contamination factor (Cf). Results indicate that most soils are classified as uncontaminated. Enrichment levels were minimal and contamination factors were low. Nevertheless, isolated outliers were identified. These included one significantly enriched As sample and several moderately enriched or slightly contaminated Hg samples. Human health risk assessment incorporated the Hazard Index (HI) and Total Carcinogenic Risk (TCR). Results indicate that neither non-carcinogenic nor carcinogenic risks exceed acceptable thresholds at any investigated soil depth. Spatial analysis suggests that anthropogenic activities are the dominant sources of As and Hg in the study area. Traffic emissions and industrial activities appear to be the primary contributors. Full article

The revitalization of vitality in historic cultural districts can enhance a city’s cultural attractiveness and promote the upgrading of the urban cultural industry and sustainable development. Revealing the threshold and synergistic effects of different districts’ scene elements on district vitality helps to identify the distribution patterns of district vitality and provides a basis for managerial decision-making. This study first uses a geographic information system (ArcGIS) to overlay Baidu heatmaps with the street-network distribution in order to depict the spatiotemporal heterogeneity of district vitality and to compute vitality values by partitions at the district scale. Subsequently, based on an explanatory framework that integrates the physical space and subjective cognition, multi-source data such as street-view panoramas and points of interest (POIs) are quantified to obtain scene-element values for each unit area. Then, the scene-element values and vitality values are integrated into a consolidated database. Additionally, the LightGBM model and the SHAP method are employed to evaluate each element’s marginal contribution and relative importance to district vitality, thereby screening out the key scene elements. Finally, by means of SHAP dependence plots and interaction-effect analysis, the threshold intervals of the key elements and their synergistic relationships are identified, revealing the nonlinear threshold effects and synergies by which scene elements influence spatial vitality. The results show that during rest days, district vitality exhibits stronger diffusion, and the synergistic effect between Leisure-Facility Attractiveness and Street-Network Accessibility is the most prominent in enhancing vitality. High Exhibition-Facility Attractiveness is difficult to sustain crowds on its own; only when Leisure-Facility Attractiveness is likewise high does its effectiveness increase significantly. When Transport Accessibility is within the 0.20–0.40 interval, the positive effect of Leisure-Facility Attractiveness is significantly amplified. An excessive Traditional–Modern Facility Mix readily leads to homogenization of districts; therefore, when introducing modern business formats, local cultural characteristics must be retained. Overall, the generation of district vitality relies more on the synergy between material factors and subjective cognition than on improvements to any single element. The findings of this study provide suggestions for the planning of scene elements and the enhancement of vitality in historic cultural districts. Full article

Viral suppression is a cornerstone of HIV management, essential for improving health outcomes and preventing transmission. However, varying definitions of suppression, ranging from ≤1000 copies/mL (controlled) to ≤200 (clinically suppressed) and ≤50 (untransmittable), complicate the assessment of progress toward global UNAIDS 95–95–95 goals. Our study evaluated progress in achieving viral suppression among people living with HIV (PLHIV) in Tajikistan between 2010 and 2024 using cross-sectional data from the Ministry of Health and Social Protection of Population registry. Viral load was measured using real-time PCR, and suppression was assessed across three thresholds (≤1000, ≤200, ≤50 copies/mL). We examined associations between viral suppression and demographic factors using Chi-square tests and logistic regression models. Across all thresholds, suppression rates remained below the UNAIDS 95-95-95 target goals. At the ≤50 copies/mL threshold, 77% of males and 83% of females achieved suppression, with males demonstrating lower odds of achieving viral suppression. Regional disparities were evident, with Khatlon and Sughd showing the lowest viral suppression rate (72.2% and 76.8%, respectively) and lower odds of achieving viral suppression compared to Dushanbe. Urban–rural differences were also observed (78.3% vs. 81.1%), though odds ratios using logistic regression models were not significant. Findings highlight persistent demographic and regional disparities, underscoring the need for targeted interventions to achieve equitable viral suppression in Tajikistan. Our findings also highlight associations and do not imply causal inference. In addition, authors acknowledge that interpretation of viral suppression outcomes is limited by the absence of data on treatment regimens, duration, adherence, CD4 counts, and behavioral factors. Full article

This study addresses a research gap in integrated environmental and spatial assessments of municipal solid waste (MSW) systems in rapidly growing secondary cities in Central Asia. Using a mixed-method approach that combines field audits, GIS-based spatial analysis, environmental monitoring, and greenhouse gas modeling, the study evaluates waste composition, infrastructure coverage, and ecological risks in Shymkent, Kazakhstan. The results reveal uneven distribution of legal waste containers, a 5–7% annual increase in illegal dumping sites, and dust (TSP) concentrations exceeding WHO thresholds near active disposal zones. Spatial hotspot mapping identifies critical pressure areas in peripheral districts, while morphological audits show a rising share of plastics and construction debris. The findings support district-specific policy interventions, infrastructure modernization, and behavior-driven recycling incentives. The proposed methodology provides a replicable framework for sustainable MSW governance in urban contexts. These results contribute to evidence-based municipal waste governance and regional sustainability planning. Full article