Urban Climate最新文献

{"title":"Urban cooling and CO2 reduction potentials of mass deployment of heat pump water heaters in Tokyo","authors":"Kazuki Yamaguchi ,&nbsp;Yuya Takane ,&nbsp;Tomohiko Ihara","doi":"10.1016/j.uclim.2025.102374","DOIUrl":"10.1016/j.uclim.2025.102374","url":null,"abstract":"<div><div>Although countermeasures against nocturnal urban heat islands that cause health hazards are required, few measures with significant cooling potential at night are known. An air-source heat-pump water heater (HPWH) absorbs heat from the atmosphere to produce hot water and simultaneously emits a cold exhaust. The mass deployment of HPWHs is expected to contribute considerably to the improvement of urban thermal environments and mitigation of CO₂ emissions. To assess these contributions, we conducted a case study in Tokyo using an urban climate and energy model. The average peak value of summer nighttime temperature drop from HPWH reached 0.31 °C at the urban scale and exceeded 1 °C in residential areas with high hot water demand. This substantial temperature impact is attributed to stable atmospheric conditions at night. Here, urban vegetation was shown to reinforce this cooling effect by further stabilizing the nighttime atmosphere. The use of the HPWH resulted in a significant direct CO₂ reduction of 41–47 % from water-heating origin, and only a marginal indirect CO₂ increase from the space-heating origin. Given that the HPWH efficiency improves under high temperatures, the benefits of urban cooling and CO₂ reduction can be optimized by seasonally switching operation times.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102374"},"PeriodicalIF":6.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644095","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":"Effective street tree and grass designs to cool European neighbourhoods","authors":"Yehan Wu ,&nbsp;Bardia Mashhoodi ,&nbsp;Agnès Patuano","doi":"10.1016/j.uclim.2025.102376","DOIUrl":"10.1016/j.uclim.2025.102376","url":null,"abstract":"<div><div>Street trees and grass are important elements for cooling cities, yet where and how to distribute them at the neighbourhood scale is still unclear. This study aims to identify effective street tree and grass design scenarios to maximise cooling in European neighbourhoods with temperate climate. 32 design scenarios were developed by combining urban design parameters of vegetation type, coverage, and spatial distribution in four neighbourhood typologies. The microclimate effects of these scenarios were then simulated using the ENVI-met model. To evaluate their cooling effects, three indices were applied: PET reduction, percentage change in thermal sensation class, and cooling efficiency. Results reveal that even fully covering streets with grass has a marginal thermal impact in reducing the mean Physiological Equivalent Temperature (PET) by up to 1.1 °C across the neighbourhood, while street trees lower PET by up to 8.7 °C. Neighbourhoods with wide radial streets have higher initial PET values and benefit more from green interventions. Strategically placing two rows of large trees on main streets is more effective for cooling than a single row on both main and secondary streets on the high-radiation side. Neighbourhood-specific practical recommendations for strategically implementing street trees and grass are provided to improve urban cooling.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102376"},"PeriodicalIF":6.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644093","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":"Numerical investigations on urban roadside vegetation for efficient mitigation of airborne ultra-fine particles pollution: Model development, validation and implementation","authors":"Siyi Bao ,&nbsp;Zhuangbo Feng ,&nbsp;Shi-Jie Cao","doi":"10.1016/j.uclim.2025.102377","DOIUrl":"10.1016/j.uclim.2025.102377","url":null,"abstract":"<div><div>Urban traffic-released ultra-fine particles (UFPs) significantly threats human health due to their distinctive composition/size and high toxicity. The roadside vegetation has potential to effectively purify UFPs. Optimal urban vegetation design relies on reliable prediction of UFPs motion and purification. The existing models mainly focused on vegetation removal of PM_2.5/PM₁₀, while less attention was paid to UFPs. Therefore, the current study proposed a turbulence-induced particle deposition model to predict the removal of size-dependent UFPs by roadside vegetation. This model simultaneously incorporated air turbulence characteristics, particle size, leaf area density (LAD), and surface roughness into UFPs removal simulation. Experimental particle size-dependent data from literature was utilized for model validation, and relative errors were below 7 %. Then this validated numerical model was utilized to optimize urban roadside vegetation design. Compared with the scene without roadside vegetation, utilization of “trees+shrubs” can decrease 57 %–95 % of UFPs concentration (1–100 nm) at sidewalks area on the downstream side of urban road, while large UFPs (&gt; 50 nm) locally accumulated on the upstream side. The UFPs concentrations in the whole target zone decreased by 11 %–78 % due to vegetation purification. The newly proposed numerical model can be used for sustainable design of roadside vegetation to effectively mitigate UFPs pollution.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102377"},"PeriodicalIF":6.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642525","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 climate risk assessment under climate and land use changes impact: A multi-dimensional approach","authors":"Hao Wu ,&nbsp;Yifeng Qin ,&nbsp;Dobri Dunchev ,&nbsp;Shengquan Che ,&nbsp;Boryana Ivanova","doi":"10.1016/j.uclim.2025.102379","DOIUrl":"10.1016/j.uclim.2025.102379","url":null,"abstract":"<div><div>This study presents a multi-dimensional approach to assess urban climate risks under the dual pressures of climate change and land-use transformations, with a focus on the city of Shanghai. By integrating climate, land-use, and socio-economic factors, our approach provides a comprehensive framework to evaluate the potential impacts of future climate and land-use scenarios on urban environments. Utilizing the patch-generating simulation (PLUS) model and downscaled Climate Model Intercomparison Project Phase 6 (CMIP6) data, this research projected land-use patterns and climate indicators for 2030 under various Shared Socioeconomic Pathways (SSPs). The results reveals a complex interplay between climatic hazards, urban development, and socio-economic dynamics, which highlights the pattern of higher extreme climate risks in the northwest and lower in the southeast of Shanghai. By 2030, while land transformation is projected to decrease, the increase in impervious surfaces is expected to persist. The Climate Risk Index (CR) for Shanghai, under scenarios SSP126, SSP245, and SSP585, is primarily influenced by climatic factors, with extreme precipitation and heatwaves being significant. The findings underscore the necessity of a holistic approach to urban climate risk assessment, emphasizing the primary influence of climatic factors, followed by land-use changes, with socio-economic factors playing a less pronounced role. This study enhances the understanding of urban climate risk within the context of global environmental changes and provides a replicable methodology for other urban centers confronting similar challenges.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102379"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642526","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":"Climate change effects on the spatial and temporal distribution of extreme precipitation in the Mid-Atlantic region","authors":"Majid Mirzaei ,&nbsp;Adel Shirmohammadi ,&nbsp;Alfredo Ruiz-Barradas ,&nbsp;Lars J. Olson ,&nbsp;Masoud Negahban-Azar","doi":"10.1016/j.uclim.2025.102382","DOIUrl":"10.1016/j.uclim.2025.102382","url":null,"abstract":"<div><div>Global environmental stability is significantly impacted by climate change, with an increasing frequency and intensity of extreme weather events posing substantial risks to infrastructure, ecosystems, and communities. In this study, a comprehensive analysis of extreme precipitation events in Maryland is provided, utilizing the latest CMIP6 models to assess historical (1951–2022) trends and project future scenarios from 2022 to 2100 under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585). Extreme precipitation events were analyzed by fitting a Generalized Logistic (GLO) distribution to daily observational precipitation data, with events above the 95th percentile identified as extreme. The frequency and magnitude of these events were determined by counting annual occurrences, calculating mean magnitudes, and assessing their probability across different climate scenarios. Additionally, Depth-Duration-Frequency (DDF) curves were developed to estimate expected precipitation amounts for various durations and return periods, with a focus on the associated uncertainties. The findings indicate that the probability of extreme precipitation events has slightly increased over the past seven decades, with notable variability between years, while the magnitude of these events has remained relatively stable. Future projections suggest a considerable rise in both the frequency and magnitude of extreme events, particularly under the SSP585 scenario, with the most severe impacts expected in certain regions of Maryland (Anne Arundel County and Dorchester County) and on the global level. This highlights the varying degrees of vulnerability across the state and underscores the need for targeted adaptation strategies. These results emphasize the critical importance of emissions mitigation to limit the severity of future extreme weather events and suggest that robust, region-specific adaptation measures will be essential in managing the increasing risks associated with climate change.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102382"},"PeriodicalIF":6.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628184","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":"Significant contribution of urban morphological diversity to urban surface thermal heterogeneity","authors":"Jiyao Zhao ,&nbsp;Le Yu ,&nbsp;Lei Zhao ,&nbsp;Haohuan Fu ,&nbsp;Peng Gong","doi":"10.1016/j.uclim.2025.102383","DOIUrl":"10.1016/j.uclim.2025.102383","url":null,"abstract":"<div><div>Urban heat island (UHI) effect, exacerbated by global warming, poses significant public health threats and socioeconomic damages. Previous UHI studies have largely overlooked thermal heterogeneity within urban areas, as existing research on urban morphology and intra-city thermal variability is mostly limited to the city scale due to the difficulty of acquiring detailed urban morphology data on a larger scale, which result in a lack of comprehensive global understanding of the relationship between urban morphological diversity and thermal heterogeneity. To address this gap, our study utilized satellite-based land surface temperature (LST) observations and the global local climate zone (LCZ) dataset to quantify the urban morphological diversity and urban surface thermal heterogeneity, and further investigate the relationship between them across 1024 cities worldwide. Our results demonstrate a robust correlation between urban morphological diversity and urban thermal heterogeneity, with urban morphological diversity accounting for a median of 15.40 % and 20.57 % of daytime and nighttime variability respectively. This relationship is further influenced by the regional background climate, shaping the intracity thermal variabilities. Across different climate regimes, an increase in urban morphological diversity is consistently associated with elevated urban thermal heterogeneity in each region of the world. These findings highlight the need for policymakers to address intracity thermal heterogeneity and emphasize the importance of tailored urban heat mitigation strategies that account for urban morphological diversity and local climate conditions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"61 ","pages":"Article 102383"},"PeriodicalIF":6.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620196","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 urban green space morphology and vegetation composition on seasonal land surface temperature: a case study of Beijing's urban core","authors":"Xiaoting Sun ,&nbsp;Panfei Fang ,&nbsp;Shaodong Huang ,&nbsp;Yuying Liang ,&nbsp;Jia Zhang ,&nbsp;Jia Wang","doi":"10.1016/j.uclim.2025.102367","DOIUrl":"10.1016/j.uclim.2025.102367","url":null,"abstract":"<div><div>Urban green spaces play a crucial role in mitigating the Urban Heat Island (UHI) effect by cooling surface temperatures. This study used Landsat 8 data and urban green space surveys, applying Radiative Transfer Equation (RTE) and Boosted Regression Tree (BRT) models to analyze how green space morphology and vegetation composition influence land surface temperature (LST) across seasons. Results show that the percentage of green space area (PLAND) has the greatest impact on LST year-round. In summer and winter, additional morphological factors, such as Largest Patch Index (LPI), Mean Patch Size (MPS), and Edge Density (ED), also play important roles. A cooling effect is observed when PLAND exceeds thresholds of 34.5 % in spring, 44.5 % in summer, 39.6 % in autumn, and 37.4 % in winter, though the effect diminishes with further increases. Trees provide the highest contribution to LST reduction in all seasons, with particularly strong effects in summer (66.8 %) and autumn (65.7 %). Optimizing urban green space design through strategic planning and a balance of vegetation types can significantly enhance temperature regulation, reduce UHI effects, and improve urban ecological quality.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102367"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580193","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 landscape characteristics impact land surface temperature in the context of urban spatial heterogeneity: A case study from Beijing, China","authors":"Wei Chen ,&nbsp;Jianjun Zhang ,&nbsp;Chenyan Huang ,&nbsp;Shu Fu","doi":"10.1016/j.uclim.2025.102366","DOIUrl":"10.1016/j.uclim.2025.102366","url":null,"abstract":"<div><div>Research on the influence of landscape characteristics on land surface temperature (LST) at the block scale remained relatively limited, particularly in contexts where urban structures exhibited spatial heterogeneity. This study categorized urban spaces of Beijing, China, by using the Self-Organizing Feature Map (SOFM) network model and analyzed the impact of landscape characteristics on LST. Results showed a maximum mean LST difference of 4.23 °C between urban spaces. Patch density (PD), aggregation index (AI), and urban porosity (Por) had a significant impact on LST in any urban space, whereas the influences of other landscape pattern indices were comparatively weaker. The influence of landscape pattern indices on LST was constrained by urban spaces, which indicating an overall trend of “urban bare spaces” &gt; “urban park spaces” &gt; “urban building spaces”. The sensitivity of LST to landscape pattern index was higher in residential building and administrative office building space. A notable positive spatial autocorrelation of LST, with the urban space of commercial buildings exhibiting a stronger prevalence of high-high (H<img>H) clustering characteristics. These results can help urban planner to be utilized to reduce urban heat by adjusting the distribution of urban spaces and the landscape characteristics.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102366"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549263","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":"Exploring the advantages of artificial neural networks in predicting children's thermal perception and their potential application","authors":"Xiaoyun He ,&nbsp;Kerry A. Nice ,&nbsp;Yuexing Tang ,&nbsp;Long Shao","doi":"10.1016/j.uclim.2025.102378","DOIUrl":"10.1016/j.uclim.2025.102378","url":null,"abstract":"<div><div>While numerous thermal comfort models have been developed to predict human thermal comfort levels in outdoor areas under varying weather conditions, these indexes are generally designed for adults. To assess the suitability of thermal comfort models, the Universal Thermal Climate Index and a multiple linear regression (MLR) model based on Predicted Mean Vote factors, to predict children's outdoor thermal sensation votes (TSV), field investigations were conducted in a Harbin park across multiple seasons. In addition, two new artificial neural network (ANN) models, with single and double hidden layers, were developed and validated to address a wider range of input parameters than the traditional models, clothing levels and metabolic rates, as well as accounting for a wider range of ages, body weights and heights. The results demonstrated that: 1) the ANN models outperformed the traditional models; 2) The two-hidden-layer ANN model slightly outperformed the one-hidden-layer model; 3) sensitivity analysis identified the top four parameters influencing the prediction of children's TSV in Harbin as mean radiant temperature (0.259), air temperature (0.200), globe temperature (0.161), and children's metabolic rate (0.110). These findings will offer valuable insights for optimizing thermal environments in urban parks, reducing children's thermal stress, and advancing intelligent park services.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102378"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636334","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 jobs-housing balance on neighborhood traffic and air quality","authors":"Xiaoke Sun ,&nbsp;Runyuan Zou ,&nbsp;Junshi Xu","doi":"10.1016/j.uclim.2025.102361","DOIUrl":"10.1016/j.uclim.2025.102361","url":null,"abstract":"<div><div>This paper presents an analysis of the employment and residential functions of Xi'an City People's Stadium and the factors influencing ambient fine particle (PM_2.5) concentrations. The spatial distribution of employment and home locations in Xi'an extends to the surrounding in the form of “employment-mixed-residential-mixed”. Based on this result, the PM_2.5 concentration data, environmental factors, traffic flow data, and the jobs-housing imbalance factor are selected to fit the PM_2.5 concentration of the City People's Stadium, with an adjusted R² of 0.97. The dissipative structure theory is used to analyze the PM_2.5 at the City People's Stadium, identifying the factors leading to a threshold exceedance for PM_2.5. This study tests a 20 % reduction in traffic flow in Xi'an to verify the model's effectiveness at detecting threshold exceedance.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102361"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561862","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}