Urban Climate最新文献

{"title":"Understanding the drivers and variations of the land surface temperature across different local climate zones in China's major urban agglomerations","authors":"Xinyue Gao,&nbsp;Hangnan Yu","doi":"10.1016/j.uclim.2026.103130","DOIUrl":"10.1016/j.uclim.2026.103130","url":null,"abstract":"<div><div>Rapid urbanization and rising land surface temperatures (LST) intensify heat exposure risks and challenge sustainable development in China's urban agglomerations.</div><div>Although previous studies emphasize the dominant role of urban morphology in shaping LST, at the urban agglomeration scale, topographic and socioeconomic gradients are often strongly coupled with LCZ spatial patterns, potentially masking localized thermal mechanisms and inflating the apparent explanatory power of background variables. This study integrates the Local Climate Zone (LCZ) framework with the Optimal Parameter-based Geographical Detector (OPGD) method to investigate how natural, morphological, and socio-economic factors jointly influence LST across three major Chinese urban agglomerations: the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), the Beijing–Tianjin–Hebei region (BTH), and the Yangtze River Delta (YRD). Results reveal that thermal patterns are strongly context-dependent: in the GBA, landscape fragmentation and blue–green surface ratio interact with built-form indicators to regulate heat; in the BTH, topographic constraints amplify interactions between urban form and human activity; and in the YRD, socioeconomic intensity combines with terrain to shape thermal hotspots. Natural background factors dominate in ecological LCZs, whereas socioeconomic influences are more pronounced in high-density built-up and waterfront areas. These findings demonstrate that LCZ-related thermal effects at the urban agglomeration scale are systematically background-modulated rather than purely morphology-driven, underscoring the importance of contextual interpretation in large-scale urban climate analysis.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103130"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893627","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":"Neighborhood-level cumulative heat as a factor in estimating heat-related mortality disparity: An assessment of cardiovascular mortality in Hong Kong","authors":"Huagui Guo,&nbsp;Minghuan Zhang,&nbsp;Alan Lai,&nbsp;Yuan Shi,&nbsp;Hung Chak Ho","doi":"10.1016/j.uclim.2026.103139","DOIUrl":"10.1016/j.uclim.2026.103139","url":null,"abstract":"<div><div>Although previous studies and government protocols have highlighted the important role of cumulative heat in risk mapping, most research has not 1) examined the combined effects of neighborhood-level cumulative heat and community vulnerability on mortality, or 2) assessed mortality disparities during heat extremes based on significant risk factors identified by constructed models. This study first estimated the co-effects of daytime and nighttime cumulative heat at the neighborhood level and community vulnerability on heat-related cardiovascular deaths in Hong Kong. Our results indicated that longer nighttime cumulative heat at the neighborhood level was associated with higher cardiovascular mortality on days with heat extremes. Higher percentages of less educated individuals and unmarried people across TPUs were also associated with cardiovascular mortality on days with heat extremes; however, the effects of these community vulnerability factors were slightly lower than the effects on days without extreme heat. These results indicated that nighttime cumulative heat was an important factor contributing to added effects on days with heat extremes, while community vulnerability factors were more related to the baseline mortality pattern. Hotspots and cold spots of cardiovascular mortality were also estimated using the Getis-Ord Gi index and compared with significant risk factors to represent spatial disparities during heat extremes. The comparison between hotspots and cold spots indicated a higher risk in high-density urban areas with longer nighttime cumulative heat, greater socioeconomic deprivation, and lower levels of vegetation cover during heat extremes. These results identified areas influenced by both baseline and added effects, requiring emergency management and community health planning to reduce the adverse impacts of future extreme heat events.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103139"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932992","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 evaluation in the upcoming generation of CMIP6-Driven EURO-CORDEX regional climate simulations","authors":"Javier Diez-Sierra,&nbsp;Natalia Zazulie,&nbsp;Gaby S. Langendijk,&nbsp;Yaiza Quintana,&nbsp;Marianna Adinolfi,&nbsp;May Bohmann,&nbsp;Lars Buntemeyer,&nbsp;Erasmo Buonomo,&nbsp;Steven Caluwaerts,&nbsp;Angelo Campanale,&nbsp;Rita M. Cardoso,&nbsp;David Carvalho,&nbsp;Ana Casanueva,&nbsp;Matthias Demuzere,&nbsp;Theodore Endreny,&nbsp;Jesús Fernández,&nbsp;Nicolas Ghilain,&nbsp;Klaus Goergen,&nbsp;Tomas Halenka,&nbsp;Peter Hoffmann,&nbsp;Fuxing Wang","doi":"10.1016/j.uclim.2026.103103","DOIUrl":"10.1016/j.uclim.2026.103103","url":null,"abstract":"<div><div>The latest EURO-CORDEX simulations, downscaling CMIP6 GCMs, provide an improved representation of urban areas compared to the previous CORDEX-CMIP5 generation, partly motivated by the increasing demand for urban-scale climate information for adaptation and planning, as reflected in the IPCC Special Report on Climate Change and Cities.</div><div>In this study, we evaluate the ERA5-driven EURO-CORDEX ensemble (CMIP6 generation) for its ability to represent urban areas and simulate urban climate across nearly 100 European cities. We further assess the sensitivity of urban–rural contrasts to geographical location and urban parameterization schemes. Results show that the urban heat island effect is captured in most cities, with a more realistic representation in models that include urban canopy schemes. The urban dry island effect and wind speed attenuation are also identified, although they exhibit greater inter-model variability than temperature.</div><div>Models identify spatial patterns across the analyzed cities, including geographical climatic gradients, the urban cool island effect in arid regions, and coastal influences. Comparison with observations highlights challenges in evaluating RCMs, underscoring the need for dense and representative observational networks or alternative approaches.</div><div>This work provides the first systematic evaluation of urban climate representation in EURO-CORDEX, identifies suitable sub-ensembles, and informs future model development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103103"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884550","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":"The impacts of agricultural and urban emissions on health and on the PM2.5 composition in the most polluted Mexican city","authors":"Angélica Neria-Hernández,&nbsp;Araceli Amaya-Chávez,&nbsp;Fernando Millán-Vázquez,&nbsp;Jesús Figueroa-Lara,&nbsp;Víctor Torres-Meza,&nbsp;Rodolfo Sosa-Echeverría,&nbsp;Ana Alarcón-Jiménez,&nbsp;Violeta Mugica-Álvarez","doi":"10.1016/j.uclim.2026.103137","DOIUrl":"10.1016/j.uclim.2026.103137","url":null,"abstract":"<div><div>This study presents an integrated assessment of fine particulate matter (PM_2.5) in the Toluca Valley Metropolitan Area (TVMA), the most polluted urban region in Mexico. A year-long monitoring campaign at two representative sites combined detailed chemical characterization, carbonaceous tracers, receptor modeling, trajectory analysis, and machine learning to identify emission sources, investigate the processes controlling PM_2.5 variability, and assess their implications for human health and air quality management. Chemical characterization reconstructed 98% of the PM_2.5 mass, dominated by OC, SO₄²⁻, NH₄⁺, EC, NO₃⁻, Cl⁻, and K⁺, shifting from an alkaline potential at the urban site to an acidic balance at the periurban site. Carbonaceous tracers, together with Positive Matrix Factorization (PMF) suggested diesel exhaust, soil resuspension, biomass burning, and secondary aerosol formation as the dominant sources. HYSPLIT backward trajectories and an exploratory Random Forest analysis indicated that pollutant accumulation is driven by regional air-mass recirculation and nonlinear meteorological controls. The annual mean PM_2.5 concentration exceeded the updated WHO guideline by a factor of eight, and health risk assessment identified Pb, Ni, Cr, Mn, and Cd as the principal contributors to non-carcinogenic risk (HI &gt; 1), particularly during the cold-dry season, highlighting the importance of reducing the overall particulate burden. By integrating complementary analytical approaches, this research demonstrates that PM_2.5 pollution in the TVMA results from the interaction of local emissions, regional transport, and meteorological processes, providing a robust basis for developing multi-sector emission-control strategies and improving air quality management in urbanizing metropolitan regions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103137"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148995572","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":"Diurnal impacts of anthropogenic heat on street canyon thermal environments under solar heating: Modulation by wind and buoyancy interactions","authors":"Weichen Zhang,&nbsp;Jian Hang,&nbsp;Shuojun Mei,&nbsp;Hua Yuan,&nbsp;Hanying Dong,&nbsp;Liyue Zeng","doi":"10.1016/j.uclim.2026.103102","DOIUrl":"10.1016/j.uclim.2026.103102","url":null,"abstract":"<div><div>The thermal environment of urban street canyons is controlled by a combination of canyon geometry, surface thermal properties, ambient wind, and anthropogenic heat (AH) emissions. Among these factors, the diurnal interaction between solar radiation and anthropogenic heat can substantially modify local microclimates, yet its influence on the balance between mechanical forcing and thermal buoyancy remains insufficiently understood. This study performed unsteady CFD simulations based on the Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach, incorporating time-varying solar radiation and diurnal AH emissions (8–22 W/m²) to examine how wind-buoyancy interaction regulates the thermal environment in two-dimensional street canyons under six reference wind speeds (<em>U</em>_<em>ref</em> = 1-9 m/s; Richardson number (<em>Ri</em>) from 0.006 to 14.297) and three aspect ratios (<em>AR</em> = 0.5,1,1.5). Results show that at low wind speeds (1 m/s), buoyancy-driven updrafts can effectively remove heat and limit AH-induced warming. At moderate winds (3–5 m/s), the opposition between mechanical forcing and thermal buoyancy suppresses vertical heat release and leads to the strongest heat accumulation, with a maximum pedestrian-level air temperature increase of 4.3 K. At high winds (&gt;7 m/s), wind-driven ventilation dominates, reducing warming. Critical interactions occur near <em>Ri</em>≈1, where mechanical forcing and thermal buoyancy are of comparable importance, rather than under the weakest wind conditions. AH effects diminish beyond critical wind thresholds (5 m/s for <em>AR</em> = 0.5, 7 m/s for <em>AR</em> = 1, &gt;9 m/s for <em>AR</em> = 1.5), indicating transitions to wind-dominated regimes. These findings provide insight into how ambient wind conditions modulate the combined effects of AH, solar heating, and canyon geometry on urban thermal environments, with implications for heat mitigation in dense urban canyons.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103102"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893618","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 heat island inequalities and social vulnerability in Dutch cities","authors":"Famke de Roos,&nbsp;Marco Helbich","doi":"10.1016/j.uclim.2026.103123","DOIUrl":"10.1016/j.uclim.2026.103123","url":null,"abstract":"<div><h3>Background</h3><div>Climate change is increasing urban heat exposure, while urban heat islands (UHIs) amplify these risks unevenly within cities, raising concerns for public health and climate justice. Evidence on urban heat inequalities in European cities remains limited, particularly when based on high-resolution air-temperature data.</div></div><div><h3>Aim</h3><div>To assess whether sociodemographic and socioeconomic groups are disproportionately exposed to UHI intensity and identify neighborhoods where high heat exposure coincides with social and health-related vulnerability.</div></div><div><h3>Methods</h3><div>We analyzed 592 neighborhoods across Amsterdam, Rotterdam, and The Hague. High-resolution estimates of maximum UHI intensity were combined with socioeconomic, demographic, and health indicators. City-specific associations were assessed using spatial autoregressive models, and a composite vulnerability index was mapped alongside UHI intensity to identify high-risk neighborhoods.</div></div><div><h3>Results</h3><div>Associations between UHI intensity and population characteristics varied markedly across cities. In Rotterdam, neighborhoods with higher proportions of non-European residents experienced greater UHI intensity. In Amsterdam and The Hague, neighborhoods with higher proportions of older residents experienced lower UHI intensity. In The Hague, higher proportions of women and lower socioeconomic status were associated with greater UHI intensity. High-risk neighborhoods were concentrated in dense inner-city areas, where elevated heat exposure coincided with greater social and health-related vulnerability.</div></div><div><h3>Conclusions</h3><div>Urban heat inequalities were context-specific, challenging assumptions of uniform patterns across cities. Spatially targeted, equity-oriented climate adaptation strategies are needed to reduce heat-related health risks.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103123"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893621","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":"Physics-guided modeling reveals contrasting latent heat flux responses across urban local climate zones under extreme heat","authors":"Yujia Zhang,&nbsp;Wenjun Yang,&nbsp;Yuxia Hu,&nbsp;Wenjuan Ma,&nbsp;Zhaowu Yu","doi":"10.1016/j.uclim.2026.103136","DOIUrl":"10.1016/j.uclim.2026.103136","url":null,"abstract":"<div><div>Urban latent heat flux (LE) is a key component of urban surface energy partitioning and regulates evapotranspirative cooling during extreme heat. However, how LE responses vary across urban morphological settings remains insufficiently understood. Here, a surface-energy-balance-constrained physics-guided neural network (PGNN) was developed to simulate hourly urban LE at 100 m resolution in Shanghai under normal-summer and extreme-heat conditions. The model integrated eddy-covariance observations, ERA5-Land meteorological drivers, remotely sensed vegetation indicators, and local climate zone (LCZ) data to examine LE dynamics and drivers across heterogeneous urban environments. PGNN outperformed benchmark machine-learning models, achieving an R² of 0.763 at the training site and maintaining transferability at an independent validation site (R² = 0.657). Results showed pronounced diurnal differences in LE responses under extreme heat. Compared with normal summer conditions, LE increased during midday but declined in the late afternoon, resulting in a slightly lower daily mean LE under extreme heat. Spatially, vegetated, open, and sparsely built LCZs maintained high and stable LE, whereas compact built-up LCZs showed lower baseline LE and larger afternoon reductions. Stratified SHAP analysis showed that vapor pressure deficit and air temperature contributed positively to midday LE enhancement, while vegetation- and soil-related variables showed stronger negative contribution changes, particularly in compact built-up zones. These findings suggest that extreme heat alters urban LE through interactions between atmospheric evaporative demand and surface moisture limitations, with substantial variation across urban morphology. The PGNN framework provides a physically informed approach for high-resolution urban LE simulation and supports LCZ-specific heat adaptation and green infrastructure planning.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103136"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932993","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":"Bridging data-driven models and computational fluid dynamics for enhanced urban air pollution and analysis: A review","authors":"Ahmad Daudsyah Imami,&nbsp;Mamad Tamamadin,&nbsp;Muhammad Amin,&nbsp;Jurng Jae Yee","doi":"10.1016/j.uclim.2026.103125","DOIUrl":"10.1016/j.uclim.2026.103125","url":null,"abstract":"<div><div>Air pollution remains a critical global public health concern. Applying computational fluid dynamics (CFD) to resolve airflow and pollutant dispersion requires substantial computational resources, thereby limiting scalability for city-wide or long-term simulations, particularly when reactive chemistry is included. Machine-learning (ML) models are substantially faster but often lack a physical basis. Hybrid ML–CFD methods seek to combine these strengths: ML surrogates embedded in CFD solvers can reduce runtimes from days to minutes—sometimes seconds, —while maintaining acceptable spatial fidelity. Broader adoption of deep neural network architectures (e.g., Generative Adversarial Networks (GAN), Physics Informed Neural Networks (PINNs)) may enable urban air-quality research to achieve greater physical realism. This review critically analyzes and compares validation techniques and proposes best practices through a tiered validation framework. Robust validation is increasingly feasible by pairing hybrid models with complementary monitoring approaches, including reference-grade stations, low-cost sensors, and mobile monitors; however, the limited accuracy of low-cost devices means they are best used to supplement spatial coverage rather than as standalone ground-truth references. Additionally, this review introduces the adaptive ML–CFD framework (AMCF), which links pollutant reactivity and modeling scale to the most suitable hybrid approach, from rapid street-canyon screening to city-wide chemical simulations. By integrating physical insight with data-driven methods, hybrid ML–CFD approaches can provide actionable, scalable tools for planners and policymakers addressing the complex challenge of urban air quality management.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103125"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884548","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":"Satellite-based modeling of radiation and energy fluxes over the Osaka Metropolitan Area and adjacent forests using Himawari-8 geostationary satellite data","authors":"Masahito Ueyama,&nbsp;Misaki Ogawa,&nbsp;Kazuki Minamide,&nbsp;Yuhei Yamamoto,&nbsp;Satoru Takanashi,&nbsp;Yoshiko Kosugi,&nbsp;Kazuhito Ichii","doi":"10.1016/j.uclim.2026.103141","DOIUrl":"10.1016/j.uclim.2026.103141","url":null,"abstract":"<div><div>Radiation and surface energy exchanges are essential for understanding the urban heat island effect, with land surface temperature (LST) serving as a key indicator of surface thermal conditions. Geostationary satellites, such as Himawari-8, provide high-frequency observations that enable detailed analyses of diurnal thermal dynamics across urban and surrounding landscapes. This study developed a satellite-based framework that integrates radiation and surface energy-budget models to provide a physically constrained diagnosis of half-hourly and seasonal surface energy partitioning using Himawari-8-derived LST. Model performance was evaluated against eddy-covariance measurements at two urban and two forest sites in the Osaka Metropolitan Area and adjacent forests, Japan. Energy fluxes were calculated using bulk and single-source models. The single-source model generally improved turbulent-flux estimates, particularly at the forest sites, with root mean square errors of 36–69 W m^-2 for sensible heat flux and 27–48 W m^-2 for latent heat flux. Its improved performance was associated with reduced propagation of LST uncertainty. Regional simulations for the Osaka Metropolitan Area showed that urban areas had lower net radiation, higher ground heat flux, and lower aerodynamic conductance than forests, resulting in lower sensible heat flux. Among urban landscapes, low-rise buildings exhibited weaker turbulent coupling, whereas compact high-/mid-rise buildings partitioned more available energy into storage and consequently had smaller turbulent fluxes. These results demonstrate the utility of geostationary thermal remote sensing not only for monitoring urban energy fluxes at high temporal resolution but also for diagnosing the surface energy partitioning underlying observed thermal patterns.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103141"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932987","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":"Urban morphology and daytime surface heat production in a rapidly transforming tropical megacity: Evidence from Ho Chi Minh City, Viet Nam","authors":"Nigel K. Downes,&nbsp;Phan Kieu Diem,&nbsp;Can Trong Nguyen,&nbsp;Nguyen Kieu Diem,&nbsp;Tran Gia Hong,&nbsp;Andrea Reimuth","doi":"10.1016/j.uclim.2026.103129","DOIUrl":"10.1016/j.uclim.2026.103129","url":null,"abstract":"<div><div>Rapid urbanisation in tropical delta cities is intensifying urban heat island effects. Yet analyses often treat warming as a generic land-cover effect rather than an outcome of distinct urban development fabrics and trajectories. This paper examines how urban morphological structures shaped dry-season land surface temperature (LST) in Ho Chi Minh City (HCMC) between 2010 and 2020 using a block-based Urban Structure Type (UST) framework that captures built form, function, and green–blue spatial structure. Dry-season Landsat LST data were analysed to assess surface thermal change, typology-level contrasts, landscape–temperature relationships, and typology-specific variable importance. Results show a citywide upward shift in dry-season LST and an outward spread of higher-temperature areas from the historical core into industrial corridors and peri-urban growth zones. A persistent thermal hierarchy is evident across both years, with the highest LST observed in industrial/logistics and dense shophouse fabrics, and lower LST in less compact built-up patches and vegetation- or water-dominated surfaces. Grid-based change analysis links the expansion of industrial, commercial/special-use, and under-construction fabrics to increased LST, whereas losses of forest and cropland are associated with warming. Landscape metrics indicate that composition and dominance, especially the areal share of a typology (PLAND), relate to LST more consistently than fragmentation metrics, while Random Forest results show that variable importance is typology-specific. The UST approach makes these findings planning-relevant by identifying hotspot-producing urban fabrics and potential surface-heat lock-in, creating a practical bridge from Earth observation to typology-specific surface-heat mitigation in rapidly transforming tropical megacities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"69 ","pages":"Article 103129"},"PeriodicalIF":7.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893622","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}