Search Results (122)

Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels. Full article

Traffic flow prediction is important for traffic management and safety control in mountainous areas. In these environments, traffic flow is affected by complex terrain, changing weather, and mixed vehicle types, so the resulting time series often show strong fluctuation and poor stability. Many existing prediction models were developed for urban roads or flat highways, and their performance is therefore limited in mountainous scenarios. To address this problem, this paper proposes a hybrid model called DK-VCA Net. The model combines adaptive signal decomposition with a terrain-aware deep learning structure to separate useful traffic variation from complex noise. It also integrates traffic flow, speed, slope, and weather information to better describe mountain traffic conditions. The proposed method is evaluated using real traffic data collected at 5 min intervals from detection stations on the Guibi Expressway in Guizhou Province, China, during September 2020. Experimental results show that DK-VCA Net achieves better prediction accuracy than several representative baseline models, including 1D-CNN, LSTM, Transformer, STWave, and Mamba. Across the 15 min, 30 min, and 60 min forecasting tasks, the proposed model reduces the average RMSE by 14.8% compared with the conventional 1D-CNN model and by 8.9% compared with the baseline Transformer model. The ablation study further proves the effectiveness of the decomposition strategy, terrain-related features, and the attention mechanism. The results show that the proposed method is effective for traffic flow prediction in the studied mountainous highway scenario. Full article

New energy vehicle promotion (NEVP) is of great significance for the green and low carbon development of urban transportation. Based on the panel data of new energy vehicle sales, carbon emissions, and air quality in Chengdu, China, from 2014 to 2024, this paper employs multiple linear regression, distributed lag and multiple mediation pathway models to empirically examine the environmental benefits of NEVP. A heterogeneity analysis is also conducted by integrating the distribution of charging stations across urban circles. The results show that: (1) In the multiple mediation pathway model, the total effect of NEVP includes direct effect and indirect effect. Based on the total effect, the total carbon emission from the effect of NEVP is reduced by about 3.95% of the total carbon emissions, and 40% of carbon emission within the transportation sector in Chengdu. NEVP in Chengdu has a significant direct emission reduction effect, accounting for about 39.80% of the total effect, with the annual average carbon emissions being reduced by about 432,800 tons, accounting for about 1.57% of the total carbon emissions in Chengdu. In terms of indirect effects, NEVP significantly reduces carbon emissions through three pathways: industrial structure upgrading (1.02%), green consumption transformation (1.12%), and technological innovation (0.25%). However, the benefits of NEVP on improving urban air quality are limited. (2) The lag effect analysis shows that the environmental benefits of NEVP exhibit distinct characteristics of time lag and long-term persistence. (3) The environmental benefits show significant sub-circle heterogeneity. As carbon emissions decrease, the air quality of the central urban zone (the first circle) and the suburbs (the second circle) improves significantly, while the impact on the outer suburbs (the third circle) is not significant. There is an imbalance in the layout of charging piles in Chengdu. This research offers empirical evidence and policy insights for the green and low carbon development of urban transportation. Full article

The Chengdu–Chongqing Twin-City Economic Circle (CC-TCEC), located in the Sichuan Basin, frequently experiences persistent winter PM_2.5 pollution due to basin-constrained ventilation and strong meteorology–emission coupling. Using daily PM_2.5 observations from 113 monitoring stations with a strict two-year training and one-year testing split, we develop hybrid spatiotemporal forecasting models that couple a graph neural network (GCN/GAT) for inter-station spatial dependence learning with a temporal backbone (LSTM/Transformer) for evolving concentration dynamics. We adopt a rolling one-day-ahead forecasting scheme using a 7-day look-back window. Across 12-month, 6-month, and 3-month evaluation windows, the meteorology-augmented Multi-GAT-Transformer shows a slight but consistent advantage over the other tested variants, suggesting potential benefits of attention-based spatial weighting and long-range temporal self-attention under nonstationary basin pollution regimes. Spatiotemporal mappings derived from the best-performing configuration suggest that elevated winter PM_2.5 is mainly associated with low-lying areas such as the Chengdu Plain, industry clusters, and dense urban cores, with peaks that also coincide with the New Year and the pre-Lunar New Year period, suggesting a possible contribution from elevated traffic and production activity. These impacts are amplified by winter stagnation (low winds, high humidity, limited precipitation). From a policy perspective, the results support sustainability-oriented winter haze management by enabling early episode warning and hotspot prioritization. Full article

This paper proposes a replicable, city-oriented workflow to support the preliminary screening of photovoltaic (PV) opportunities in Bogotá, Colombia, by integrating (i) georeferenced spatial inventories (roofs/land), (ii) solar-resource modeling based on local meteorological stations and radiation models, and (iii) an optional 3D module (LiDAR/DSM) to refine shading and orientation losses when higher-resolution data are available. Rather than claiming a complete citywide quantification from exhaustive building-level inputs, the workflow is demonstrated through two institutional case studies (public schools) selected to represent contrasting urban morphologies. The results show how the approach consistently transforms spatial constraints and solar estimates into comparable technical and economic indicators for decision-making at the site level. Finally, a practical scale-up pathway is described to extend the same logic from pilots to citywide portfolios through batch processing of urban footprints and the progressive enrichment of inputs—from 2D GIS screening to targeted 3D refinement—while preserving transparency and traceability of assumptions. For the two case study sites, the workflow yielded preliminary PV capacities of 72.6 and 95.0 kWp, with year-1 generation of 90.2 and 115.0 MWh, respectively. The IRR values achieved were between 18.9 and 19.5%, the simple payback period was approximately five years, and the LCOE was between 0.051 and 0.053 USD/kWh. It should be noted that the generation was reported as a central estimate with ±25% tolerance to reflect interannual solar resource variability. Full article

Since the 1970s, accelerated urban development in Doha has contributed to a disjointed and inefficient city structure. While the Doha Metro has begun to address spatial and mobility-related challenges, planners continue to call for a more integrated, strategic approach to ensure safe, accessible, and efficient transit connectivity. In response, the Qatar National Development Framework provides a long-term vision for sustainable urban transformation, with a central aim of embedding the Metro system within the existing urban context and aligning expansion with Transit-Oriented Development (TOD), which promotes dense, multifunctional, pedestrian-oriented neighborhoods along transit corridors. Within this context, this study investigates how TOD strategies can enhance quality of life in mixed-use environments, focusing on the area surrounding Al Mansoura metro station and the adjacent Najma and Al Mansoura districts. Using the Integrated Modification Methodology (IMM), the analysis assesses spatial structure across density, spatial diversity, and connectivity, and derives evidence-based recommendations to improve livability and support sustainable revitalization. To broaden regional applicability, the study also compares Al Mansoura with Olaya in Riyadh—two mid-to-late 20th-century, high-density mixed-use districts undergoing TOD-driven transition—highlighting how spatial form, infrastructure legacy, and urban governance shape TOD outcomes and inform adaptable TOD frameworks for Gulf cities. Full article

Urban geophysical exploration faces significant hurdles due to strong electromagnetic interference and limited operational space, which restrict the efficiency and depth of traditional Electrical Resistivity Tomography (ERT). To overcome these limitations, this paper presents a novel ERT measurement and control system based on the Frequency Division Multiplexing (FDM) principle. Unlike conventional time-domain methods, this instrument synchronously transmits three independent AC signals at distinct frequencies. The acquisition station utilizes Fast Fourier Transform (FFT) to isolate specific frequency responses, enabling the simultaneous retrieval of apparent resistivity data for three different electrode spacings from a single transmission. The system architecture integrates low-power STM32 microcontrollers with an Android-based control terminal via Bluetooth, Wi-Fi, and NB-IoT technologies. This wireless design supports real-time current monitoring and cloud-based data synchronization. Experimental results demonstrate that the FDM operating mode significantly enhances data acquisition efficiency and anti-interference capability through frequency-domain separation. Controlled indoor and preliminary field tests indicate that FDM mode substantially improves acquisition efficiency through concurrent multi-channel measurement while effectively resolving target signals from noise. This study demonstrates the system’s technical feasibility and provides a practical foundation for future geophysical detection in time-constrained urban environments. Full article

The Green Oasis (GRO) Project is a targeted urban greening intervention designed to evaluate the environmental and health impacts of compact, high-density plantings in dense built environments. Initiated in downtown Louisville, the project transformed Founders Square, a 0.64-acre sparsely planted park, into a microforest (“Trager Microforest”), a multilayered planting of 119 trees and more than 200 shrubs. The impact of this intervention is being assessed through a randomized crossover study in which participants walk in the microforest and a nearby impervious parking lot. Physiological outcomes include heart rate, heart rate variability, arterial stiffness, and stress biomarkers measured in saliva, urine, and sweat. Environmental conditions are continuously monitored by fixed and mobile weather stations, air pollution sensors, and biodiversity surveys. Baseline assessments were conducted in 2023 and 2024, with post-planting evaluations now underway (2025–). Power calculations indicate adequate sensitivity (n ≈ 40–50) to detect changes in cardiovascular stress responses in participants. Complementary ecological measurements include soil microbiome composition, greenhouse gas fluxes, and avian diversity. This study addresses critical gaps in understanding how small-scale, high-density greening interventions affect cardiovascular resilience, stress physiology, and microclimatic regulation. By integrating environmental, biological, and human health data, GRO establishes a comprehensive framework for evaluating the efficacy of urban microforests as nature-based solutions. The results are expected to inform urban planning, public health strategies, and climate adaptation policies, demonstrating how compact greening interventions can simultaneously mitigate heat, reduce pollution, enhance biodiversity, and promote human wellbeing in dense urban cores. Full article

Rapid electrification of road transport and growing shares of variable renewable generation are pushing urban low-voltage feeders toward their operating limits. Uncoordinated electric vehicle (EV) charging can create transformer overloads, voltage violations, and unfair delays, while most existing smart charging schemes either ignore distribution network constraints or treat fairness and risk in an ad hoc way. This paper proposes a city-scale hierarchical scheduling framework that coordinates EV charging and vehicle-to-grid (V2G) services under renewable variability. In the upper layer, a LinDistFlow-based optimal power flow computes feeder-constrained power envelopes and shadow prices over a rolling horizon, capturing transformer and voltage limits under photovoltaic (PV) uncertainty. In the lower layer, each station solves a queue-aware receding-horizon optimization that allocates charging/V2G set points across plugs using α-fair and lexicographic objectives, with conditional value-at-risk (CVaR) constraints on waiting times and state-of-charge (SoC) shortfalls. A digital twin of a medium-sized city with 24 stations (238 plugs) on five feeders and PV shares between 25% and 55% is used for evaluation. Compared with uncoordinated charging and myopic baselines, the proposed scheduler reduces feeder peak loading and PV curtailment while improving user experience and equity: average waits and 90% CVaR of waits are lowered, the Gini coefficient of waiting times drops (e.g., from 0.31 to 0.22), and SoC shortfalls are significantly reduced, all while respecting voltage limits. Each receding-horizon step executes in under 30 s on commodity hardware, indicating that the framework is practical for real-time deployment in city-scale smart charging platforms. Full article

Microplastic (MP) pollution poses a significant and emerging threat to global marine ecosystems; however, regional data for the Caribbean remain limited. This study presents a spatial and temporal characterization of MPs in surface and mid-waters of the Colombian Caribbean (Atlántico and Magdalena departments), which were analyzed as independent compartments due to methodological differences in sampling strategies. Sixteen sampling stations were established across two anthropogenic influence zones: Zone 1 (nearshore/bather zone) and Zone 2 (offshore). MPs were quantified and characterized according to shape, color, size, and polymer composition using attenuated total reflectance Fourier transform infrared microspectroscopy (µATR-FTIR) and multivariate techniques. MPs were detected in 100% of samples. Surface water MP abundance was higher in Magdalena (4.5 MPs m⁻³) than in Atlántico (1.7 MPs m⁻³). Mid-water MP concentrations reached maximum values during the high rainfall season in Atlántico, reflecting localized hydrological and anthropogenic influences rather than vertical gradients. Higher concentrations were generally observed in the nearshore Zone 1 compared to offshore Zone 2, although these differences were not consistently statistically significant. Fibers and fragments were the predominant shapes, and synthetic–natural polymer blends, polyethylene terephthalate (PET), polypropylene (PP), and polyacrylic acid (PAA) were the most prevalent. Generalized Additive Models (GAM) indicated that strong fluvial inputs and proximity to urban and riverine sources were factors driving MP distribution. Additionally, the detection of polymers reported in the literature as rare and high-risk, such as acrylonitrile butadiene styrene (ABS), acrylonitrile styrene acrylate (ASA), styrene–ethylene–butylene–styrene (SEBS), and polyvinyl stearate (PVS), highlights the complexity of MP sources in the region. Overall, these results provide the first spatial and temporal characterization of MPs in the surface and mid-water of the Colombian Caribbean and identify critical contamination hotspots that warrant targeted mitigation strategies. Full article

Against the backdrop of global climate change, the rising frequency and intensity of extreme weather events pose severe challenges to urban transport and commercial systems. As a core capacity for managing uncertainty and risk, urban resilience requires infrastructure to resist shocks, recover rapidly, and adaptively evolve. From a resilience perspective, this study develops a comprehensive evaluation system for spatial accessibility between subway stations and commercial complexes, operationalized by 21 indicators across five dimensions: Connectivity, Redundancy, Robustness, Dynamic adaptability, and Comfort. Spatial accessibility is simulated and measured using sDNA spatial network analysis, while an in-depth questionnaire survey collects, feeds back, and validates users’ subjective perceptions. By constructing a dual evaluation model that integrates spatial configuration and behavioral psychology, we find that the integrated development of subway stations and commercial complexes can maintain stable functional performance and sustained vitality under complex urban conditions by optimizing connectivity, enhancing redundancy, and improving adaptability. This is manifested in the expansion of residents’ pedestrian networks and the spillover of social service functions. In parallel, underground spaces can be transformed into resilient infrastructure to enhance civil air defense performance and provide diversified evacuation routes. The findings offer theoretical support and practical guidance for the construction of resilient cities. Full article

Land use change driven by accelerated urbanization in Mongolia has precipitated significant degradation of urban riverine ecosystems over the past two decades. This study investigates hydrological transformations in the Selbe River Catchment of Ulaanbaatar, a cold semi-arid urban system undergoing intensive densification. Using the Site-scale Urban Water Mass Balance Assessment (SUWMBA) framework, we quantified water cycle dynamics across four temporal intervals (2008, 2010, 2018, and 2023), capturing shifts in surface runoff, infiltration, and evapotranspiration associated with land use transitions. Calibration and validation employed discharge records from the Selbe-Dambadarjaa gauging station. Results show that total inflows increased from 223 to 312 mm between 2008 and 2023, driven by a more than twentyfold rise in imported water (from 1 to 22 mm). Evapotranspiration declined by roughly one-third, while infiltration displayed a threshold-type non-linear response—rising sharply between 2010 and 2018 before decreasing again in 2023 as imperviousness intensified. Model performance weakened after 2018, underscoring the limitations of conventional hydrological frameworks in rapidly urbanizing contexts. A redevelopment scenario for the Selbe Sub-Center, aligned with the Ulaanbaatar City Master Plan 2040, projected substantially reduced evapotranspiration (132 mm) and markedly increased stormwater runoff (270 mm), reflecting expanded impervious cover and diminished vegetation. Imported water and wastewater flows (each 386 mm) also increased due to full connection to centralized supply and sewerage infrastructure, indicating a shift toward engineered water pathways and reduced hydrological connectivity to the Selbe River. These findings highlight the urgency of water-sensitive urban design and provide evidence directly informing Mongolia’s 2040 Urban Master Plan and decentralization strategy. The study establishes methodological precedent for applying SUWMBA to cold, semi-arid catchments and contributes quantitative insights for integrated land–water management policies. Full article

Bangladesh generates approximately 3000 tons of plastic waste daily, and high mismanagement leads to substantial discharge into soils, rivers, and oceans. Limited research exists on plastic pollution along Cox’s Bazar in southeastern Bangladesh, with no studies spanning the entire coast; this study provides the first comprehensive assessment of the full coastline. This study investigates the abundance, types, and distribution of macro-, meso-, and microplastics in sediments from 23 stations covering Tourism, Active, and Less Active areas. Plastics were classified by size, shape, color, and polymer composition using stereomicroscopy and Fourier Transform Infrared Spectroscopy (FTIR), while spatial patterns of microplastic polymers were analyzed using Inverse Distance Weighted (IDW) interpolation. A total of 11,558 plastic particles were identified, with microplastics dominating (409.04 particles/m²), followed by mesoplastics (60.7 particles/m²) and macroplastics (32.8 particles/m²). Expanded polystyrene (EPS) and fragments were the most prevalent shapes, while transparent-white particles dominated in color. Polystyrene (PS), polypropylene (PP), and polyethylene (PE) comprised over 95% of polymers. IDW mapping highlighted Tourism, urban, and industrial zones as microplastic hotspots, with higher abundances in tourism areas. These findings provide a baseline for monitoring coastal plastic pollution and emphasize improved plastic management and recycling, contributing globally to understanding contamination in rapidly urbanizing, tourism-driven developing regions. Full article

The metro system is a key driver of urban land use development; however, its spatiotemporal impact mechanisms remain insufficiently understood. This study investigates the effects of metro development on land use types and development intensity in Wuhan, China, from 2014 to 2019, and employs a Geographically and Temporally Weighted Regression (GTWR) model to capture the spatiotemporal heterogeneity of these impacts. Results show that metro construction significantly promotes land use transformation along metro lines, especially from non-construction land to residential and commercial uses, while also increasing development intensity. GTWR analysis further reveals that metro network characteristics, station attributes, and built environment features surrounding stations strongly influence land development. These impacts exhibit pronounced spatiotemporal heterogeneity, becoming more pronounced over time as the metro network extends into suburban areas. The findings provide valuable insights for urban and transportation planners, supporting the formulation of strategies for integrated land use development and metro network expansion. Full article

Monitoring the horizontal distribution of PM_2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM_2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM_2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O₄) differential slant column densities (O₄ dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM_2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM_2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM_2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O₄ dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM_2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM_2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM_2.5 pollution transport event was captured: PM_2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM_2.5 distributions. Full article