Urban Climate最新文献

{"title":"Spatial dimensions of climate vulnerability: Ecological and economic intersections across urban and peripheral districts","authors":"Shihui Miao,&nbsp;Simson Reyes","doi":"10.1016/j.uclim.2026.103062","DOIUrl":"10.1016/j.uclim.2026.103062","url":null,"abstract":"<div><div>Biophysical conditions alone do not determine how cities are affected by climate change. Long-lasting spatial inequalities in ecological resources and economic resources have a significant impact on how cities are affected. This research presents a spatially explicit framework for evaluating structural vulnerability across administrative districts in South Korea by simultaneously analyzing thermal stress, deficiencies in vegetative infrastructure, and material limitations. Principal component analysis is used to generate composite indicators from 2022 satellite data and administrative records. Then we use unsupervised clustering to separate the data into distinct, consistent categories of vulnerability. The study identifies four types of vulnerability based on different combinations of environmental stress and socioeconomic capacity. It also shows significant differences between dense urban areas and more rural or peripheral parts. The typology serves as an analytical tool for classifying places at risk from multiple climate stressors and for supporting place-sensitive adaptation planning. In a broader sense, this study adds to the body of investigation on urban climate risk and longitudinal disparity by turning multidimensional vulnerability into a framework grounded in real-world data and usable in practice.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103062"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urbanization amplifies compound day-night heatwaves in the Yangtze River Delta urban agglomeration: Spatiotemporal characteristics and atmospheric drivers","authors":"Shiyao Li,&nbsp;Shifang Yu,&nbsp;Rui Yao,&nbsp;Peng Sun,&nbsp;Yi Liu","doi":"10.1016/j.uclim.2026.103065","DOIUrl":"10.1016/j.uclim.2026.103065","url":null,"abstract":"<div><div>Heatwaves are extreme climatic events that severely affect human health and ecosystems. The Yangtze River Delta urban agglomeration (YRDUR), a global hotspot of frequent extreme heatwaves, faces increasing risk of concurrent daytime and nighttime heatwaves due to continuous urban expansion. This study identified daytime heatwaves (DHWs), nighttime heatwaves (NHWs), and compound day-night heatwaves (CHWs), systematically characterized their spatiotemporal evolution, and examined the effects of urbanization and atmospheric circulation on the three heatwave types. The results indicate that: (1) heatwave types in the YRDUR are shifting from independent DHWs and NHWs toward CHWs, with CHWs showing the most significant intensification. The characteristics of DHWs show more pronounced decreasing trends in rural areas, with an overall weakening greater than that observed at urban stations. NHWs show limited overall urban-rural differences, but their intensity is higher in urban areas than in rural areas. (2) The enhancement effect of urbanization is most stable for compound heatwave characteristics, especially in areas with high urban development levels, while nighttime heatwave intensity has increased significantly after 2010. (3) All three heatwave types show consistent positive geopotential height anomalies (200–850 hPa) and significant mid-upper tropospheric warm anomalies. DHWs are more susceptible to dynamic disturbances, NHWs are more closely associated with mid-level thermal anomalies and stable descending motion, whereas CHWs show the strongest continuity in atmospheric vertical structure and the greatest consistency in large-scale circulation, reflecting a more stable whole-column warming background. Therefore, urbanization and anomalous atmospheric forcing jointly amplify severe heatwave risk in the YRDUR.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103065"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Downscaling urban land surface temperature using AlphaEarth satellite GeoAI embeddings: A cross-city evaluation in the US","authors":"Bijoy Mitra,&nbsp;Guiming Zhang","doi":"10.1016/j.uclim.2026.103058","DOIUrl":"10.1016/j.uclim.2026.103058","url":null,"abstract":"<div><div>Fine-scale land surface temperature (LST) data are critical for urban heat management, yet open-source LST datasets have long been limited to 30–500 m resolution. Recently, Google released its AlphaEarth Foundations (AEF), a 64-dimensional GeoAI embedding dataset covering the entire globe at a spatial resolution of 10 m. This study introduces a novel application and comprehensive evaluation of the AEF embedding dataset for downscaling 30-m Landsat LST to 10-m resolution in urban environments using deep learning. As case studies, LST downscaling was conducted across 26 US cities using an artificial neural network that takes the embeddings as input to predict LST values and is trained with spatially stratified samples. We achieved a mean root-mean-squared error (RMSE) of 1.57 °C and a mean R² of 0.733 on the test set. Spatial cross-validation reveals minimal deviations (standard deviation of RMSE ∼0.0012 on the log-transformed scale) across the folds. The prediction errors were not spatially clustered and had no significant hotspots for any of the cities. Further, SHAP analysis demonstrated that AEF layers 31, 18, and 1 had the highest feature importance for predicting LST across the cities. Finally, the model trained for each city was applied to predict LST in other cities to assess the model's cross-city geographic transferability. Model transferability succeeds across places with similar forest structure or aridity but is hindered by extreme temperatures and the distinct characteristics of tropical ecosystems. The downscaled high-resolution LST product can be used to monitor urban thermal stress and inform neighborhood-level climate adaptation planning.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103058"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neural Network-based downscaling method for near surface temperature in urban areas","authors":"Julia García Cristóbal,&nbsp;Jean Wurtz,&nbsp;Antoine Doury,&nbsp;Blanka Balogh,&nbsp;Valéry Masson,&nbsp;Olivier Mestre","doi":"10.1016/j.uclim.2026.103076","DOIUrl":"10.1016/j.uclim.2026.103076","url":null,"abstract":"<div><div>Urban weather forecasts at hectometric resolution are essential for effective warning systems and sustainable urban planning, especially in the context of climate change and rapid urban expansion. While numerical weather prediction (NWP) systems can simulate the Urban Heat Island (UHI) effect, their computational cost limits their operational use. This study presents a convolutional neural network (CNN) based statistical downscaling (SD) method to emulate 300 m resolution 2 m air temperature over the Paris region, using coarse resolution inputs from AROME (2.5 km) and ARPEGE (10 km) operational models. This method leverages a UNet architecture, optimized with a Sobel operator penalty loss preserving fine-scale temperature gradients, and includes static and dynamic predictors. The approach is first validated in a super-resolution framework, using degraded high-resolution Meso-NH simulations (1.2 km to 9.6 km) to assess the model’s ability to recover fine-scale details. This demonstrates the potential of the methodology, achieving a Root Mean Squared Error (RMSE) of 0.37 °C when reconstructing 300 m temperature fields. The method is further evaluated using operational AROME and ARPEGE forecasts, validating against high-resolution Meso-NH simulations and in-situ observations from Météo-France’s stations. When compared to observations, the downscaled AROME SD model achieves <span><math><mrow><mo>−</mo><mn>0.33</mn><mspace></mspace><mo>°</mo><mi>C</mi></mrow></math></span> mean bias over the urban station, and the ARPEGE SD, whose inputs do not include any urban parameterization, a mean bias of <span><math><mrow><mo>−</mo><mn>0.46</mn><mspace></mspace><mo>°</mo><mi>C</mi></mrow></math></span>. This study demonstrates the efficiency of the SD in recovering hectometric-scale patterns, including UHI intensity and spatial heterogeneity, while offering a computationally efficient alternative to traditional NWP systems.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103076"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148717259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic risks of summertime extreme heat and air pollution: Evidence from a suburban area in Shanghai, China","authors":"Xiao Liu,&nbsp;Zhongxun Li,&nbsp;Bao-Jie He","doi":"10.1016/j.uclim.2026.103081","DOIUrl":"10.1016/j.uclim.2026.103081","url":null,"abstract":"<div><div>Based on data from a suburban area in Shanghai, China, this study investigated the characteristics, daily variations, and associations of ambient temperature, relative humidity, wind speed, and air pollutants and air quality index (AQI) from 2015 to 2019. Those analyses were conducted using three different extreme heat period (EHP) definitions. Three definitions characterized 28, 32, and 21 EHP days. Further analyses showed higher ambient temperature, lower relative humidity, higher pollutant concentrations, and higher AQI during EHP compared to non-EHP (NEHP). Daily variations in all variables showed significantly lower wind speeds during the EHP. Compared with the NEHP, PM_2.5 and PM₁₀ concentrations and AQI doubled, and the O₃ concentration increased by more than 70%. Ambient temperature showed strong, negative associations with relative humidity, while it was positively correlated with wind speed, SO₂, CO, and O₃ during the EHP daytime. Wind speed was negatively correlated with all pollutants during NEHP daytime and nighttime. However, wind speed was only correlated with NO₂ during EHP daytime and NO₂ and CO during EHP nighttime. The results according to different EHP definitions were consistent. However, when analyzing associations among different variables, there were some obvious differences, which might indicate diverse pollution mechanisms under different EHP levels.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103081"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148717260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How will climate change reshape urban-rural blue-green spaces? Multi-stage dynamic prediction and adaptation zoning in Wuhan","authors":"Xiaosi Zhang,&nbsp;Jizhong Shao","doi":"10.1016/j.uclim.2026.103044","DOIUrl":"10.1016/j.uclim.2026.103044","url":null,"abstract":"<div><div>Predicting climate-driven dynamics of urban-rural blue-green spaces (URBGS) is essential for proactive urban climate adaptation, requiring a modelling framework that integrates non-stationary climate characteristics, multiple climate impact pathways, and multi-stage risk representation. This study develops a Climate-driven Cellular Automata (Clim-CA) model that integrates multi-dimensional climate impact mechanisms and multi-stage conversion processes through three core components: conversion potential, conversion threshold, and conversion probability. Applied to predict changes in Wuhan's blue-green spaces by 2040 under the SSP5–8.5 scenario, the model addresses three conversion pathways: Green Space Degradation (GSD), Blue Space Shrinkage (BSS), and Blue Space Expansion (BSE). The Figure of Merit (FoM) values ranged from 0.380 to 0.450, indicating moderate performance. Results reveal that climate impacts exhibit significant non-linear thresholds (temperature: 15.75 °C; precipitation: 1579 mm) and synergistic effects. The three pathways present differentiated risk patterns: GSD farmlands are dominated by moderate degradation (60.7%), while forests show mainly mild degradation (57.5%); BSS features mild-to-moderate shrinkage, converting primarily to perennial marsh (41.2%) and intermittent marsh (52.9%); BSE shows limited, sporadic impact at localized drainage nodes and forest paludification areas. Adaptation zoning based on conversion stage spans is proposed to support differentiated resilience strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103044"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148538322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Mitigation + adaptation” climate policy synergy and urban ecological resilience: Evidence from China","authors":"Lingfu Zhang,&nbsp;Yongfang Dou","doi":"10.1016/j.uclim.2026.103046","DOIUrl":"10.1016/j.uclim.2026.103046","url":null,"abstract":"<div><div>IPCC AR6 points out that human activities have raised global temperatures by 1.1 °C. The urban heat island effect has worsened. Extreme heatwaves, heavy rainfall and coastal floods occur frequently. These disasters have hit economic and financial systems. Ecosystem vulnerability has become increasingly obvious. High quality urban development is facing prominent challenges. Climate policies have become an important means to effectively address climate risks and facilitate the high quality development of cities. This paper focuses on the coordinated governance of “mitigation + adaptation” climate policies. Taking the carbon emission trading policy pilot (mitigation climate policy) and the sponge city policy pilot (adaptation climate policy) as research carriers, it uses the DID model to analyze the impact mechanism of the dual policy pilots (DPP) on urban ecological resilience (UER). The research findings show that the DPP significantly enhance UER. After robustness tests, this impact still holds. The impact is more significant in central cities and non ecologically fragile areas. Mechanism analysis reveals that the enhancement of green innovation capabilities and the optimization and upgrading of industrial structure are the pathways through which the DPP enhance UER. The research results of this paper can provide Chinese experience and practical paradigms for the global implementation of climate actions and sustainable development goals, and the construction of a coordinated and efficient climate governance system.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103046"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148538706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moderating effect of individual characteristics and neighborhood greenness on the association between ambient temperature and habitual physical activity levels","authors":"Mandy A.G. Peggen,&nbsp;Janneke I.A. Vloet,&nbsp;Koen M. van der Sluijs,&nbsp;Milou Netten,&nbsp;Maarten Hogeweij,&nbsp;Dick H.J. Thijssen,&nbsp;Esmée A. Bakker,&nbsp;Maria T.E. Hopman,&nbsp;Thijs M.H. Eijsvogels,&nbsp;Coen C.W.G. Bongers,&nbsp;On behalf of the Nijmegen Exercise Study Collaboration","doi":"10.1016/j.uclim.2026.103056","DOIUrl":"10.1016/j.uclim.2026.103056","url":null,"abstract":"<div><h3>Background</h3><div>Ambient temperature could impact physical activity behavior, as extreme temperatures may be a barrier for physical activity. Adopting a within-subject design, we examined the association between ambient temperature and habitual physical activity levels and explored how individual characteristics and neighborhood greenness influenced this relation.</div></div><div><h3>Methods</h3><div>Physical activity data was obtained for 8 consecutive days using a thigh-worn triaxial accelerometer during three measurement periods with distinct ambient temperatures: COLD (5.9 ± 0.4 °C), MODERATE (13.9 ± 3.4 °C) and WARM (26.0 ± 0.7 °C). Physical activity parameters were compared between conditions using linear mixed model analysis, with MODERATE as the reference. The moderating effects of age, sex, cardiovascular health status, baseline physical activity levels, and neighborhood greenness on the association between ambient temperature and physical activity was subsequently explored.</div></div><div><h3>Results</h3><div>Participants (<em>n</em> = 151, 62 ± 11 years) had lower daily step counts in WARM (10,576 ± 3395 steps/day, <em>p</em> = 0.003) and COLD (10,641 ± 3483 steps/day, <em>p</em> &lt; 0.001) compared to MODERATE (11,602 ± 3636 steps/day). LIPA and MVPA were lower in COLD (244 ± 62 min/day, <em>p</em> = 0.004, and 91 ± 30 min/day, <em>p</em> &lt; 0.001, respectively) compared to MODERATE (255 ± 66 min/day and 104 ± 36 min/day, respectively). Sitting time (545 ± 89 min/day) did not differ across conditions (<em>p</em> = 0.68). Baseline physical activity was an effect modifier for step count, sitting time, and MVPA, whereas neighborhood greenness was an effect modifier for sitting time.</div></div><div><h3>Conclusion</h3><div>Colder ambient temperatures were associated with reduced physical activity levels (<em>i.e.</em>, lower step count, LIPA, and MVPA), while warmer ambient conditions were only associated with reduced step count. Effects were more prominent in the most active <em>versus</em> least active individuals, with little impact of neighborhood greenness.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103056"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seven-year satellite assessment of NO2 hotspot persistence and relative exposure inequality in Bursa, Türkiye","authors":"Burak Caliskan","doi":"10.1016/j.uclim.2026.103080","DOIUrl":"10.1016/j.uclim.2026.103080","url":null,"abstract":"<div><div>Nitrogen dioxide (NO₂) remains a major air-quality concern in urban-industrial regions, yet long-term hotspot persistence, population co-location, and industrial-zone associations are rarely evaluated together. This study analyzed Sentinel-5P/TROPOMI tropospheric NO₂ columns over Bursa, Türkiye, for 2019–2025 using QA-filtered observations and a fixed 500 m reference height to express the column field as a surface-equivalent relative NO₂ indicator. The transformed product was compared with ground-based measurements at three monitoring stations and showed significant temporal agreement, with daily Pearson correlations of 0.482–0.730 and monthly correlations of 0.587–0.860. However, systematic negative bias confirmed that the product should be interpreted as a relative indicator rather than an absolute surface concentration. Long-term trends were evaluated using the Mann-Kendall test and Theil-Sen estimator, the COVID-19 effect using the Wilcoxon signed-rank test, and spatial clustering using annual Getis-Ord Gi* statistics. The NO₂ indicator declined significantly in 2020, but the provincial long-term trend was not statistically significant, and the main hotspot structure remained stable. Persistent hotspots, defined as pixels classified as hotspots in at least five of seven years, were concentrated in the northern urban-industrial corridor. These areas covered only 17.36% of the study area but contained 83.55% of the provincial population, while approximately 74% of pixels within organized industrial zone buffers were persistent hotspots. Because absolute guideline compliance could not be reliably assessed, the study characterizes relative spatial exposure inequality. The findings identify priority corridors for denser monitoring and spatially targeted air-quality management.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103080"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148692931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Kuala Lumpur as a tropical city on cloud formation: Numerical experiments using the WRF-UCM model","authors":"Sharifah Faridah Syed Mahbar,&nbsp;Nobuyasu Suzuki,&nbsp;Hiroyuki Kusaka","doi":"10.1016/j.uclim.2026.103050","DOIUrl":"10.1016/j.uclim.2026.103050","url":null,"abstract":"<div><div>Previous satellite-based studies have shown that Greater Kuala Lumpur (GKL) consistently exhibits a higher cloud frequency than surrounding rural areas. To investigate the underlying mechanisms, numerical experiments were conducted using the Weather Research and Forecasting (WRF) model for 227 hot days without daytime precipitation during the wet season (DJF) and 141 hot days without daytime precipitation during the dry season (JJA). The results show that urbanised areas within GKL exhibit higher sensible heat fluxes and deeper boundary layers than surrounding rural regions. Despite lower latent heat fluxes and reduced near-surface relative humidity, which would normally suppress cloud formation by raising the lifting condensation level (LCL), cloud frequency is enhanced over GKL. This enhancement can be explained by two mechanisms. First, the deeper urban boundary layer largely offsets the negative effect of an elevated LCL. Second, urban heat island and modified sea-breeze circulations enhance moisture advection from surrounding rural areas and adjacent oceans, increasing moisture within the urban boundary layer. The spatial distribution of urban-induced cloud frequency exhibits a clear seasonal dependence. During DJF, the enhancement extends across the GKL region, whereas during JJA it is mainly confined to inland urban areas. Areas of enhanced horizontal moisture flux convergence closely correspond to increased cloud frequency. These seasonal differences suggest that urbanisation modifies interactions among the background monsoonal flow, sea-breeze circulation, and urban heat island circulation, thereby altering the location and intensity of moisture convergence and the associated cloud response. These findings provide new insights into urban-induced cloud formation in tropical cities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103050"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}