Urban Climate最新文献

{"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}

{"title":"Adaptability of social media data integrated machine learning algorithms in urban flood susceptibility mapping using the SHAP framework","authors":"Ronghu Miao,&nbsp;Guoru Huang,&nbsp;Jiaxuan Zheng","doi":"10.1016/j.uclim.2026.103070","DOIUrl":"10.1016/j.uclim.2026.103070","url":null,"abstract":"<div><div>Urban flooding is emerging as an increasingly severe global challenge due to climate change and urbanization. Although machine learning (ML) offers numerous solutions for urban flood susceptibility mapping, its application remains constrained. Existing research is constrained by the scarcity of traditional hydrological monitoring data, and the absence of systematic comparisons across multiple models creates uncertainty in algorithm and feature selection, leaving the related decision-making mechanisms unclear. To address these challenges, social media data was adopted as the sole basis in this study to evaluate and compare the performance of seven typical ML algorithms in urban flood susceptibility mapping. The Shapley Additive exPlanations (SHAP) framework was established to investigate the adaptability of the selected algorithms based on a multidimensional feature system while elucidating the decision-making mechanisms for selecting the most suitable algorithms and features. The results suggest that: (1) Social media data can serve as the sole source for precise urban flood identification, overcoming the real-time and spatial coverage limitations of traditional methods. (2) Different ML models show significant performance heterogeneity; reliance on a single model risks systematic bias, whereas ensemble tree models demonstrate superior predictive performance. (3) Feature importance is highly model-dependent, exhibiting contextual sensitivity and interactive influence mechanisms. Therefore, feature engineering should be based on multi-model consensus, prioritizing features with significant differences such as natural characteristics and risk exposure.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103070"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635867","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":"Multi-criteria optimization of closed-loop hydronic pavement systems in urban heat island mitigation: Balancing performance and cost","authors":"Sundeep Inti,&nbsp;Srinivasan Arumugam,&nbsp;Sumeet J. Paramaj","doi":"10.1016/j.uclim.2026.103083","DOIUrl":"10.1016/j.uclim.2026.103083","url":null,"abstract":"<div><div>Large parking lots intensify urban heat island (UHI) effect by storing solar radiation and increasing pavement temperatures and pedestrian heat exposure. Hydronic pavement systems mitigate this heating by circulating fluid through embedded pipes to extract surface heat. However, most prior studies have examined open-loop systems, single design parameters, or winter applications, with limited attention to cooling-focused optimization or thermal-comfort outcomes in closed-loop systems. This study evaluates closed-loop hydronic parking lots using a Taguchi DOE framework to identify performance-driving factors and cost-effective designs. Three variables—pipe material, initial circulating-fluid temperature, and flow rate—were tested under simulated irradiance levels of 200, 600, and 1000 W/m², representing overcast to peak-summer solar conditions. Thermal performance was quantified using peak surface-temperature reduction and the area under the temperature–time curve (AUC), which was integrated with system cost in a multi-objective optimization. Closed-loop systems reduced surface temperatures by 4.4–36.5 °C and cumulative thermal loading by 12–37%. Initial fluid temperature was the dominant performance factor, while pipe material primarily influenced cost. Surface cooling lowered mean radiant temperature by up to 2.55 °C and improved the Universal Thermal Climate Index (UTCI) by up to 0.76 °C, demonstrating measurable improvements in pedestrian thermal comfort. Overall, closed-loop hydronic pavements effectively reduce pavement heating and pedestrian exposure, and the proposed framework provides a practical, cost-informed basis for hydronic-system design and evaluation in UHI mitigation.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103083"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148692901","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":"Effect of street trees on turbulent exchange processes and flow circulation in urban street canyon: Application to air pollutant removal","authors":"Francesco Barbano,&nbsp;Erika Brattich,&nbsp;Silvana Di Sabatino","doi":"10.1016/j.uclim.2026.103049","DOIUrl":"10.1016/j.uclim.2026.103049","url":null,"abstract":"<div><div>This paper investigates the role of trees in modulating the pollutant removal from a real urban street canyon. An integrated methodology is designed to incorporate the aerodynamic effect of trees in modulating the inertial and thermal forcing that drives the in-canyon circulation. Contextually, the efficacy of exchange processes in mixing momentum and heat is evaluated at different levels within and above the street canyon using time scales and their ratios. By linking the exchange process with the relative circulation, the impact on pollutant removal from the canyon is evaluated. Trees are found to sustain efficient momentum exchanges under inertial forcing as the production of turbulent momentum by the crown roughness and flow channelling in the trunk region generates fast mixing within the canyon. The faster mixing at the surface than at the canyon rooftop provides the ideal conditions for efficient momentum exchange between the canyon and the atmosphere above. Conversely, trees inhibit thermal mixing through the shading effect of their crowns, leading to inefficient exchange rates under thermal forcing. The pollutant concentrations follow inertial forcing, which favours pollutant removal from the canopy, and thermal forcing, which favours stagnation, revealing the benefits and drawbacks for air quality of a common tree deployment in the urban environment.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103049"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148692936","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":"Rapid prediction of wind field in street canyons with infrastructure elements using neural operators","authors":"Guozhu Liang,&nbsp;Ruikun Ge,&nbsp;Dongjin Cui,&nbsp;Jian Hang,&nbsp;Gang Hu,&nbsp;Cheuk Ming Mak","doi":"10.1016/j.uclim.2026.103074","DOIUrl":"10.1016/j.uclim.2026.103074","url":null,"abstract":"<div><div>Rapid and accurate prediction of wind fields is critical for optimizing the wind environment in street canyons. However, most previous studies on rapid wind field prediction have focused on idealized geometric models and neglected the effect of infrastructure elements (e.g., envelope features, street furniture, and barriers), which limits their application in wind environment optimization. This study used the Fourier Neural Operator (FNO) and Deep Operator Network (DeepONet) models to rapidly predict wind fields in street canyons with different infrastructure elements. The results revealed that different element placements affect airflow patterns: the windward placement forms upper and lower recirculation zones, the leeward placement weakens the primary vortex, and the base placement disturbs the near-ground flow. Both models achieved high prediction accuracy and the relative <em>L2</em> errors of DeepONet (5.56%–6.37%) were slightly lower than those of FNO (6.08%–7.12%). They effectively captured complex flow features, including primary recirculation zones, vortex structures, and wake regions. Errors aggregated in regions with sharp velocity gradients and flow separation around elements. Both models predicted leeward cases more accurately than windward ones. Moreover, the trained models could predict wind fields for unseen cases in approximately 0.02 s while maintaining relative <em>L2</em> errors below 7.2%. The FNO model is suitable for early-stage design exploration because it only requires geometric information, whereas DeepONet is more appropriate for detailed design validation as it requires boundary velocity data. This study provides efficient methods for rapid assessment and design optimization of wind field in street canyons.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103074"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635855","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":"Nonlinear effects of urban morphology on the thermal environment in the Jinan ancient city: An explainable machine learning approach","authors":"Chang Xu,&nbsp;Wei Xue,&nbsp;Deqing Lin,&nbsp;Xiaodong Xu","doi":"10.1016/j.uclim.2026.103073","DOIUrl":"10.1016/j.uclim.2026.103073","url":null,"abstract":"<div><div>Urban warming and increasing extreme heat exposure highlight the need to understand how fine-scale urban morphology regulates land surface temperature (LST) in compact constrained urban environments. However, nonlinear and interaction effects among morphological variables remain insufficiently understood. This study investigates morphology–LST relationships in a compact constrained urban fabric using Landsat-9 imagery, Local Climate Zone (LCZ) context, six 60 m grid-level morphological indicators, and an explainable machine learning framework. CatBoost achieved the best predictive performance (test R² = 0.722, RMSE = 1.439 °C, MAE = 1.140 °C) and was interpreted using SHAP. Results show that green coverage ratio, non-building impervious surface fraction, sky view factor, and average building height contribute more strongly to LST variation than traditional density indicators. SHAP dependence analysis reveals nonlinear and value-dependent responses, indicating that morphology–LST relationships vary across intensity ranges rather than following monotonic trends. Interaction analysis further identifies coupled effects among building density, volumetric intensity, spatial openness, and greenness. These effects are summarized into three morphology–temperature profiles: surface hardening trap, density–volume coupling, and geometry-dependent thermal turning. The study provides a transferable framework for explaining and mitigating urban heat in compact constrained urban environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"68 ","pages":"Article 103073"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635931","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}