Urban Climate最新文献

{"title":"Coastal and anthropogenic heat impacts on PBL processes during extreme summer thunderstorm precipitation in New York City","authors":"Madhusmita Swain ,&nbsp;Jean Carlos Peña ,&nbsp;Robert D. Bornstein ,&nbsp;Jorge E. González-Cruz","doi":"10.1016/j.uclim.2025.102534","DOIUrl":"10.1016/j.uclim.2025.102534","url":null,"abstract":"<div><div>Urban extreme precipitation is a crucial study topic due to its growing societal impact and limited understanding of its processes. This study examines the extreme summer precipitation event that occurred in coastal New York City (NYC) on July 17, 2018, using observations and simulations. Severe flooding was caused throughout the City by the thunderstorm event, which had a maximum rainfall of 44 mm and an Urban Heat Island (UHI) intensity of 8 °C. The study employs two high-resolution weather model simulations: one uses the Building Effect Parameterization (BEP), while the other incorporates with the BEP, Building Energy Model (BEM), an anthropogenic heat source, and a variable building drag coefficient.</div><div>Forensics of the event show that increasing temperatures, and a strong UHI ahead of an approaching storm in the northwest initiated an urban storm cell near Bronx borough. The northwest-moving thunderstorm merged with the active urban cell and intensified the precipitation. Surface data indicates classic UHI signatures, such as pre-storm City-warming and post-storm cooling. The BEP + BEM model performed better than the BEP-only model by better simulating anthropogenic heat flux and projecting a greater UHI effect (3.8 vs. 2 °C). This improved simulation of anthropogenic heat produced high surface temperatures that strengthened low-level moisture convergence with the City, enhancing precipitation. The results highlight the key role played by anthropogenic heat in altering mesoscale atmospheric phenomena, strengthening storm intensity in coastal-urban environments. The findings of urban and anthropogenic processes on precipitation are transferable for improved urban-weather forecast and for planning resiliency of cities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102534"},"PeriodicalIF":6.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604337","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":"Using Gradient Boosting for gap-filling to analyze temperature and humidity patterns in an urban weather station network in Freiburg, Germany","authors":"Marvin Plein ,&nbsp;Gregor Feigel ,&nbsp;Matthias Zeeman ,&nbsp;Carsten F. Dormann ,&nbsp;Andreas Christen","doi":"10.1016/j.uclim.2025.102496","DOIUrl":"10.1016/j.uclim.2025.102496","url":null,"abstract":"<div><div>Weather station networks (WSNs) are essential to investigate urban–rural and intra-urban variability of urban weather and climates. However, missing data in WSN time series lead to biases in the analysis of long-term WSN climatic statistics. Here, we use Extreme Gradient Boosting to impute gaps in observational air temperature and humidity time series of a WSN in an orographically complex, mid-sized European city (Freiburg, Germany). Imputation accuracy is evaluated across meteorological variables, stations, and artificially created gaps of 1–28 days. The gap-filling procedure shows good imputation accuracy with mean RMSEs of 0.46 K for air temperature and 2.51% for relative humidity. Model performance is insensitive to gap lengths but varies between stations, with larger errors in remote and non-built-up locations. Moreover, we use the gap-filled data to investigate urban–rural and intra-urban air temperature and humidity variability from September 2022 to August 2023. During the study period, the city center was 1.1 K warmer than representative rural areas, and vapor pressure was 7% lower. The annual number of tropical nights among stations in built-up locations varied between 0 and 29. Station clustering yields groups that are not only explainable by land cover and geographic proximity but also by orographic settings.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102496"},"PeriodicalIF":6.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604341","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":"Remote sensing assessment of urban vegetation's dust retention for mitigating atmospheric particulate pollution","authors":"Wei Yang ,&nbsp;Yan Yu ,&nbsp;Wenpeng Lin ,&nbsp;Jia Song ,&nbsp;Yue Sun ,&nbsp;Yuxun Zhang ,&nbsp;Wei Zhang ,&nbsp;Yue Li","doi":"10.1016/j.uclim.2025.102535","DOIUrl":"10.1016/j.uclim.2025.102535","url":null,"abstract":"<div><div>Urbanization has significantly increased particulate emissions, while vegetation plays a key role in capturing and retaining atmospheric particles, thereby improving air quality. However, the large-scale impact of urban vegetation on dust retention remains underexplored. This study integrates Sentinel-2 data with vegetation spectral data to extend the analysis from individual points to larger urban areas. Five urban vegetation species from Xuhui and Minhang districts in Shanghai were selected for dust retention and canopy reflectance data collection. Ground-based spectral measurements were converted into Sentinel-2 spectra using a spectral response function. Ten vegetation indices (VIs), including SR, NDVI, SIPI, and ARVI, were evaluated for their correlation with dust retention, and four machine learning algorithms were compared. The optimal algorithm was selected for modeling the spatial distribution of vegetation dust retention. The results indicated that: (1) NDVI, ARVI, SIPI, and SR were sensitive to dust retention, with correlation coefficients of −0.78, −0.78, −0.77, and − 0.73, respectively; (2) Random Forest outperformed the other algorithms in estimating regional dust retention, with an <em>R</em>^<em>2</em> of 0.65, surpassing polynomial regression, stochastic gradient descent, and support vector machines. High dust retention areas were associated with continuous vegetation cover and urban greening. These findings provide valuable insights for urban green space planning and offer a scalable method for regional dust retention estimation.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102535"},"PeriodicalIF":6.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604338","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":"The scale-dependent effects of urban 3D morphology on urban ozone pollution across different climate zones in China","authors":"Shengyang Hong ,&nbsp;Zhibin Ren ,&nbsp;Yunxia Du ,&nbsp;Chengcong Wang ,&nbsp;Peng Zhang ,&nbsp;Yujie Guo ,&nbsp;Zijun Ma ,&nbsp;Wenhai Hong ,&nbsp;Ruoxuan Geng ,&nbsp;Xinyu Wang ,&nbsp;Fanyue Meng ,&nbsp;Baosen Huang ,&nbsp;Guodong Wang","doi":"10.1016/j.uclim.2025.102523","DOIUrl":"10.1016/j.uclim.2025.102523","url":null,"abstract":"<div><div>Urban ozone pollution (UOP) is considered one of the most challenging environmental problems. However, how urban two- and three-dimensional (2D, 3D) morphology affect UOP is not yet fully understood. Based on Boosted Regression Tree (BRT) model, we investigated the spatial patterns and explored scale-dependent effects of urban 3D morphology and UOP across different climate zones in China. Our results indicate that Chinese urban agglomerations are characterized by medium rise and high density, with an average building height of 15.69 m and a density of 0.18. The UOP is severe in Chinese urban agglomerations during summer, especially in the Beijing-Tianjin-Hebei urban agglomerations (161.51 μg/m³), where medium-rise and medium-density urban agglomerations (169.5 μg/m³) experience the most severe UOP. At localized scales (≤1 km buffer), 3D morphological parameters—specifically high building ratio (HBR) and spatial congestion degree (SCD)—jointly explain 53.14 % of UOP variability. Threshold analyses demonstrate progressive ozone concentration reduction when HBR exceeds 15 %, whereas SCD values surpassing 6 % induce pollution escalation. But with increasing distance, the dominant factors are quickly replaced by 2D building morphology such as building density (BD) and road density (RD), with a contribution rate of 74.54 %. When BD exceeds 25 % or RD surpasses 0.39 %, the ozone concentration experiences a nonlinear increase as BD and RD continue to grow. Our study aims to provide valuable references for urban planners to reduce UOP through effective urban planning.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102523"},"PeriodicalIF":6.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604340","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":"Adaptation and mitigation of outdoor heat stress and building energy consumption during a heat wave in Nicosia, Cyprus","authors":"Giandomenico Vurro ,&nbsp;Alberto Martilli ,&nbsp;Panos Hadjinicolaou ,&nbsp;Salvatore Carlucci ,&nbsp;Jacobo Gabeiras Penas ,&nbsp;Katiana Constantinidou ,&nbsp;Jos Lelieveld","doi":"10.1016/j.uclim.2025.102507","DOIUrl":"10.1016/j.uclim.2025.102507","url":null,"abstract":"<div><div>Cities in the Eastern Mediterranean and Middle East region face rising temperatures and intensifying heatwaves that are amplified by the urban heat island effect. These challenges pose significant threats to human health, agriculture, and the water–energy nexus, emphasizing the need for in-depth analysis and effective mitigation strategies at the urban scale. To address this need, we model the effects of seven interventions over 19 days, from July 23rd to August 10th, 2021, during a heatwave in Nicosia, Cyprus. We assess three key outcomes using the Weather Research and Forecasting (WRF) model coupled with the multilayer Building Energy Parameterization/Building Energy Model (BEP/BEM) scheme: 2m air temperature, outdoor heat stress, and air-conditioning energy use. Our results demonstrate that urban trees are the most effective single intervention, reducing energy consumption by approximately 46% and decreasing heat stress-degree hours by 20–25 h over the analyzed period. The combined implementation of cool roofs and urban trees proved to be the most effective overall, reducing energy consumption by over 50% and lowering 2m air temperatures by up to 1.2 °C during the day. A promising adaptive mitigation strategy emerged through the integration of photovoltaic panels and urban trees, which reduced heat stress while generating energy that significantly contributes to cooling demands. The efficacy of these interventions varied with urban geometry, with maximum benefits in areas characterized by medium building heights and densities. These findings offer guidance for developing urban climate resilience strategies in semi-arid regions, underscoring the importance of location-specific application of heat adaptation and mitigation measures.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102507"},"PeriodicalIF":6.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614243","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":"Rapid prediction of pollutant dispersion in residential blocks using generative adversarial networks","authors":"Dongjin Cui ,&nbsp;Shuaiyong Liu ,&nbsp;Xiaowen Xu ,&nbsp;Pengfei Lin ,&nbsp;Gang Hu","doi":"10.1016/j.uclim.2025.102533","DOIUrl":"10.1016/j.uclim.2025.102533","url":null,"abstract":"<div><div>The morphological layout of urban residential blocks has a significant impact on pollutant dispersion. However, the optimization of pollutant dispersion-oriented residential blocks studies used to rely on computational fluid dynamics (CFD) simulations with abstract models, which not only disconnects from the actual design situation, but also limits the efficiency of scenario iteration. Therefore, in order to realise real-time prediction of pollutant concentration fields for arbitrary layouts at the early morphological design stage of urban settlements, this study clusters 1997 residential blocks in Shenzhen, China, and constructs a CFD dataset representative of Shenzhen's residential block. Then, based on this dataset, the prediction performance of three GAN models (Pix2Pix, CycleGAN and Pix2PixHD) on pollutant dispersion under two scenarios (design optimization and real-time monitoring) is trained and compared, and the impacts of two optimization methods (training stability and training data optimization) on the performance of the models are explored. The results show that the clustered dataset can reflect the real Shenzhen residential blocks characteristics. On the test set, the MAE of Pix2PixHD reaches 0.132 and the inference time is less than 1 s. This study is the first to realise second-scale prediction of pollutant dispersion in urban residential blocks by coupling a clustered residential block dataset with generative adversarial networks, thereby establishing the methodological and data foundation for future cross-city generalization and real-time morphology optimisation.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102533"},"PeriodicalIF":6.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572483","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":"An air pollution early-warning system with the mechanism of dynamic evaluation","authors":"Ranran Li ,&nbsp;Ziyu Zhen ,&nbsp;Xiaobin Li ,&nbsp;Haimin Miao ,&nbsp;Xiaoxue Wei","doi":"10.1016/j.uclim.2025.102531","DOIUrl":"10.1016/j.uclim.2025.102531","url":null,"abstract":"<div><div>Due to the increasing energy consumption, the issue of air pollution would continue to attach attention. Although technological progress has improved the relative efficiency of energy, the task of air pollution monitoring cannot be ignored. The existing air pollution early-warning methods based on univariate time series forecasting suffer from multiple limitations. To take measures to monitor air quality status effectively, an air pollution early-warning framework is built based on statistical comprehensive evaluation and multiscale forecasting. It can settle the problems, such as accuracy improvement bottlenecks and diminishing spatial comparability. Through analyzing pollutant characteristics, the original time series is transformed into fuzzy sets, through which practical air quality levels and pollution condition ranking would be obtained. Only when the air quality classes meet the varying patterns of multivariable time series does the improvement of the forecasting accuracy make sense for air pollution dynamic monitoring. So, a multiscale forecasting method-oriented data characteristic identification is introduced to enhance the performance of the forecast engine. By recognizing the different component features, the optimal forecast allocation strategy is selected, and the data prediction errors would not increase. The results of comparative experiment indicate that the proposed model can realize dynamic evaluation of air quality and reliable air pollution early-warning performance.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102531"},"PeriodicalIF":6.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570844","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":"Spatial network characteristics and driving factors of low-carbon technology innovation efficiency in northern China","authors":"Dongri Han ,&nbsp;Yuhan Li ,&nbsp;Yingying Geng","doi":"10.1016/j.uclim.2025.102529","DOIUrl":"10.1016/j.uclim.2025.102529","url":null,"abstract":"<div><div>China's ecological civilization construction has entered a critical period with carbon reduction as the key strategic direction. Exploring the network structure of low-carbon technology innovation efficiency can offer a scientific basis and practical reference for advancing national green and low-carbon development. Taking 131 prefecture-level cities in the northern region as research objects, this paper employs the accelerated genetic algorithm-based projection pursuit model, modified gravity model and social network analysis to explore the spatial network correlation characteristics and influencing factors of low-carbon technology innovation efficiency during 2010–2023. The results show that the low-carbon technology innovation efficiency in the northern region presents a pattern of “the east region &gt; the central region &gt; the northeast region &gt; the west region”, with local dynamic characteristics and unbalanced characteristics co-existing. The overall network density of low-carbon technology innovation efficiency in the northern region presents a pattern of “the east region &gt; the central region &gt; the northeast region &gt; the west region”, with obvious node characteristics. As time passes, the overall network structure of the spatial correlation of low-carbon technology innovation efficiency in the northern region develops toward the trend of density, diversification and robustness. The bidirectional overflow plate is developing rapidly. Beijing, Tianjin, Jinan and Zhengzhou have become important central cities to radiate and drive the development of surrounding areas. The spatial convergence analysis shows that the convergence characteristics and catch-up effect of low-carbon technology innovation efficiency of cities in the northern region are significant. Environmental regulation, reserve of innovative talents, innovation environment and FDI emerge as important factors influencing the formation and development of low-carbon technology innovation efficiency network in the northern region.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102529"},"PeriodicalIF":6.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556637","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":"Urban digital twin for assessing and understanding urban Heat Island impacts","authors":"Lidia Vitanova,&nbsp;Dessislava Petrova-Antonova,&nbsp;Evgeny Shirinyan","doi":"10.1016/j.uclim.2025.102530","DOIUrl":"10.1016/j.uclim.2025.102530","url":null,"abstract":"<div><div>Urban Digital Twin (UDT) provides knowledge and technologies for data-driven decision support for a broad group of stakeholders towards developing sustainable cities and communities. However, its implementation is associated with several problems, including data integration and quality, model complexity and uncertainty, computational resources, spatial and temporal resolution, and validation. Addressing these challenges requires innovative technological solutions and development efforts in the fields of urban modelling and digital twin technologies. This study proposes an approach for performing climate simulations using the state-of-the-art Weather Research &amp; Forecasting Model (WRF) and integration of the results with very high resolution, representing the relevant features of air temperature distributions over a wide area, within an UDT. The impacts of Urban Heat Islands (UHIs) on air temperatures were investigated, analysed and visualised based on data collected from heterogeneous sources in Sofia City, Bulgaria. The results show a strong UHI effect on temperature of around 6.0 °C in the central part of the city compared to the suburbs at 2100 Local Standard Time (LST) in August 2018, due to urbanisation and energy consumption from buildings. The study indicates that areas experiencing an increase in temperature and energy consumption require high attention and the implementation of feasible measures.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102530"},"PeriodicalIF":6.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563633","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":"Health effects of summer extreme heat and humidity in urban Rio de Janeiro (Brazil) by demographic and educational level","authors":"José Antonio López-Bueno ,&nbsp;Renata Libonati ,&nbsp;Djacinto Monteiro dos Santos ,&nbsp;Miguel Ángel Navas-Martin ,&nbsp;Julio Díaz ,&nbsp;Cristina Linares ,&nbsp;Ana Russo ,&nbsp;Ricardo Trigo","doi":"10.1016/j.uclim.2025.102524","DOIUrl":"10.1016/j.uclim.2025.102524","url":null,"abstract":"<div><h3>Background</h3><div>Local studies on heat-related mortality in Brazil are limited, hindering targeted Heat-Health Action Plans (HHAPs).</div></div><div><h3>Methods</h3><div>This is a time series on the Metropolitan Region of Rio de Janeiro (MRJ) between 1997 and 2019. The epidemiological threshold (TTrh) for extreme heat events (EHEs) was identified using temperature-mortality associations, adjusting for trends, seasonality, and autoregression (Box-Jenkins). EHEs are those summer days (DJF) with maximum temperatures &gt; TTrh. Attributable mortality (AM%) was estimated by time-controlled Poisson GLM models. Dependent variable was natural-cause mortality, and independent variables were EHE intensity in °C (Theat), duration of the EHE, rank of the event (order), and their corresponding lagged variables. Subgroup analyses included age, sex, race, and education.</div></div><div><h3>Results</h3><div>The TTrh was 34 °C (73rd percentile). RH contributes to explain the underlying effects of heat on health, but it did not improve the predictive power of the model. The most vulnerable groups were the elderly, women, less educated people, and Black and Brown. The most significant EHEs are among the first of each season.</div></div><div><h3>Conclusion</h3><div>This is the first study to establish an epidemiological heat wave threshold for MRJ. HHAPs should be triggered when forecasts predict Tmax above 34 °C. A climatological approach is inadequate for early warnings. Prevention should prioritize women, lower socioeconomic groups, and the elderly. Although humidity did not improve model performance, MRJ's high humidity levels may pose risks under combined heat-humidity events, highlighting the need to consider synoptic conditions in future protocols.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"62 ","pages":"Article 102524"},"PeriodicalIF":6.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549455","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}