Urban Climate最新文献

{"title":"Identifying priority areas for urban heat stress management using high-density sensor networks based on apparent temperature for enhancing urban environments","authors":"HaeKyung Park ,&nbsp;Yunjoo Yoo ,&nbsp;Seung Beom Seo","doi":"10.1016/j.uclim.2026.102965","DOIUrl":"10.1016/j.uclim.2026.102965","url":null,"abstract":"<div><div>In this study, a heat stress analysis and vulnerability assessment were conducted in the Seoul Metropolitan Area using high-resolution urban sensor data from the Smart Seoul Data of Things (S·DoT), a high-density urban sensor network characterized by high spatial and temporal resolution. The analysis focused on summer 2021 and used apparent temperature (AT) — a more human-centric metric incorporating humidity — to define heat stress occurrence. Unlike conventional studies that use air temperature (Ta) to define heatwaves, this study uses the term ‘heat stress’ to refer to days when AT exceeds 33 °C. The heat stress severity measured by frequency, intensity, and duration was compared across 25 residential areas to minimize variations caused by land use differences. Space-time pattern mining was then used to cluster relatively high- and low-temperature zones in Seoul based on AT. These findings were integrated with residential and pedestrian heat-stress vulnerability factors to comprehensively assess heat stress vulnerability in the city. The results revealed significant disparities in heat stress severity among Seoul's residential areas. Additionally, the space-time pattern mining identified areas with relatively higher temperatures that coincided with heat-stress vulnerability factors related to population density. The outcomes of this study can inform urban planning and policy decisions by facilitating efficient resource and financial allocation, addressing health inequalities, and the prioritization of vulnerable populations. The significance of this research lies in its pioneering approach to analyzing heat stress based on AT using S·DoT data.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102965"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167889","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":"Comparative evaluation of LULC classification methods and urban expansion simulation using CA-Markov in Hyderabad, Sindh (1990–2050)","authors":"Hifsa Rajput ,&nbsp;Jihui Yuan","doi":"10.1016/j.uclim.2026.102962","DOIUrl":"10.1016/j.uclim.2026.102962","url":null,"abstract":"<div><div>Urban expansion poses significant challenges in developing countries, affecting land-use patterns, environmental sustainability, and urban management. Hyderabad, the second-largest city in Sindh, has experienced considerable urban growth during the past three decades, highlighting the need for a quantitative assessment of its spatial dynamics. This study analyzed built-up area changes in Hyderabad from 1990 to 2022 using Landsat imagery (TM, ETM+, OLI and TIRS) and explored potential urban growth patterns up to 2050 using Cellular Automata–Markov modeling.</div><div>Six classification methods—Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), Minimum Distance (MD), Mahalanobis Distance (MHD), and Spectral Angle Mapper (SAM)—were applied to map built-up areas, and classification performance was evaluated using overall accuracy and the Kappa coefficient. The results show variations among classification outputs, reflecting differences in algorithm design and sensitivity to spectral characteristics. Several classifiers, including MLC, ANN, and SVM, achieved relatively high classification accuracy in mapping urban areas.</div><div>Directional analysis indicates that urban expansion has predominantly occurred toward the north, north-east, and south-east of the city. Shannon entropy analysis was used to examine the spatial dispersion of built-up areas, indicating a gradual shift from relatively concentrated growth patterns to more dispersed urban expansion over time.</div><div>CA–Markov simulations were used to explore potential future urban expansion scenarios for Hyderabad up to 2050 based on historical land-use transitions. The projections suggest continued outward expansion of built-up areas into surrounding peri-urban regions. Overall, the study highlights how differences in classification approaches may influence the representation of urban growth patterns and provides insights that may support sustainable urban planning and land-management strategies in Hyderabad.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102962"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168002","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":"Local mitigation, regional leakage? Climate effects of sustainable development policy in China’s resource-based cities","authors":"Liyun Wang ,&nbsp;Hongyu Cai ,&nbsp;Xu Fu ,&nbsp;Guoying Deng","doi":"10.1016/j.uclim.2026.102949","DOIUrl":"10.1016/j.uclim.2026.102949","url":null,"abstract":"<div><div>Efforts to reduce greenhouse gas emissions in resource-dependent cities are central to urban climate mitigation. This study investigates whether China’s Sustainable Development Plan for Resource-Based Cities (SDR) effectively lowers urban Global Warming Potential (GWP)—a composite measure of CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>, and N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O emissions. Using panel data for 277 cities from 2009–2021 and a quasi-experimental identification strategy complemented by spatial econometric analysis, we find that the SDR significantly reduces local GWP emissions by 3.8%. The mitigation effect operates primarily through industrial restructuring and improvements in energy efficiency. However, significant spatial spillovers emerge: while treated cities experience substantial GWP reductions, neighboring cities exhibit increases, suggesting displacement of emission-intensive activities to nearby areas. The policy’s mitigation effect is more pronounced in cities with diversified industrial structures and greater structural flexibility. These findings underscore the importance of coordinated regional climate governance, as localized policies may shift rather than eliminate emissions. Insights from China’s SDR offer broader implications for designing effective climate mitigation strategies in resource-dependent urban regions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102949"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167877","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 surface relief classes with LCZs for an improved heat island methodology: Demonstration in a small Brazilian city","authors":"Leandro de Godoi Pinton ,&nbsp;Iain Douglas Stewart ,&nbsp;Tainá Medeiros Suizu","doi":"10.1016/j.uclim.2026.102972","DOIUrl":"10.1016/j.uclim.2026.102972","url":null,"abstract":"<div><div>The Local Climate Zone (LCZ) classification has standardized the treatment of built form and land-cover controls on urban temperature patterns, yet the framework does not explicitly treat the role of surface relief. This study addresses the need to incorporate surface relief features in each LCZ to support a deeper understanding of local thermal climates in and around cities. To this end, we propose a statistical approach based on LCZ relief classes to select representative sites and evaluate spatiotemporal characteristics of urban heat island magnitudes and intra-urban temperature differences. We demonstrate the approach using nighttime air temperature data collected along mobile survey routes in a small Brazilian city with relatively simple relief. Results show that LCZ relief classes improve recognition of thermal contrasts within urban and non-urban landscapes, revealing clear clusters of air-temperature values and heat island patterns across distinct landform units. We found consistently warmer and comparable conditions among representative urban sites on the plateau, whereas built-up areas across other landform units at lower elevations (slope and step) exhibited temperature regimes influenced by localized relief. Explicit treatment of local topography through LCZ relief classes also improves site metadata documentation, thereby strengthening the physical interpretation of inter-city heat island magnitudes and inter-zone temperature contrasts at a regional scale. Further research is required in cities with more complex topographies at both meso- and local scales, allowing a pathway toward more consistent practices in the analysis, interpretation, and communication of urban temperature effects.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102972"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167887","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":"Linking urban form, solar radiation exposure, and human perception at street scale: A street-view-based analysis in a high-density subtropical city","authors":"Ningjun Chen,&nbsp;Pu Wang,&nbsp;Ning Xu","doi":"10.1016/j.uclim.2026.102985","DOIUrl":"10.1016/j.uclim.2026.102985","url":null,"abstract":"<div><div>Street-scale solar radiation affects both pedestrian thermal exposure and spatial perception; yet the mechanisms linking urban morphology, radiation exposure, and human perception remain insufficiently understood. Taking central Shenzhen as a case study, this study develops a “morphology–radiation–perception” framework by integrating fisheye projection, a solar trajectory model, semantic segmentation, and street-view perception prediction based on 28,780 Baidu Street View panoramic images collected between May and October. From a pedestrian perspective, street-level solar radiation was estimated and its perceptual effects analyzed. The results reveal significant spatial heterogeneity in solar radiation exposure across central Shenzhen, with an average radiation level of 2209.601 Wh/m² for all sampled roads. Road-type differences were substantial: expressways showed the highest average radiation exposure (2641.340 Wh/m²), while local roads had the lowest value (1910.438 Wh/m²). A significant nonlinear relationship was identified between solar radiation and overall environmental perception. Perceptual evaluation reached a relatively high level at approximately 2000 Wh/m² but declined markedly once radiation exceeded 3000 Wh/m². Mediation analysis further showed that solar radiation acts as a key mechanism linking street morphology and environmental perception. Specifically, sky openness influenced perception mainly through increased radiation exposure, whereas urban greenery both reduced radiation exposure and improved perceptual quality. This study clarifies how urban morphology affects pedestrian environmental experience via solar radiation in high-density subtropical cities and provides a scalable street-view-based approach to support climate-responsive street design and refined urban regeneration.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102985"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231447","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":"Mechanisms of ozone formation in heavily polluted cities using interpretable machine learning","authors":"Bin Sun ,&nbsp;Yan Han ,&nbsp;Zhier Bao ,&nbsp;Yuyi Du ,&nbsp;Jiawei Zhou ,&nbsp;Liuyi Zhang ,&nbsp;Yang Chen","doi":"10.1016/j.uclim.2026.102954","DOIUrl":"10.1016/j.uclim.2026.102954","url":null,"abstract":"<div><div>Central China has suffered severe ozone (<em>O</em>_<em>3</em>) pollution in recent years. However, the secondary generation process of <em>O</em>_<em>3</em> influenced by meteorological conditions and precursors is complex. Thus, controlling its concentration levels and quantifying the contributions of these factors remains quite challenging. This study developed an interpretable <em>O</em>_<em>3</em> estimation and analysis framework based on a <em>LightGBM</em>-SHAP model, using hourly ambient air quality and meteorological data during 2019–2022 from 32 national monitoring stations across five cities in Henan Province, central China. The model demonstrated robust estimation performance, achieving an <em>R</em>^<em>2</em> of 0.87 and an <em>RMSE</em> of 17.15 μg/m³. Based on SHAP analysis, this study provides a quantitative evidence of the statistical associations regarding the factors influencing <em>O</em>_<em>3</em> pollution. Specifically, we quantified the seasonal variations in the influence of nine key factors, assessed their individual contributions to <em>O</em>_<em>3</em> increases (e.g., temperature contributed +23.45 μg/m³), and revealed a temporary “storage and release” mechanism of morning <em>NO</em>_<em>2</em>. Three major regimes of <em>O</em>_<em>3</em> pollution were identified using the K-means clustering algorithm: “Hot-Dry”, “Stagnant Accumulation”, and “Hot-Humid”. Each regime exhibited a distinct profile, highlighting the need for tailored, prioritized mitigation strategies. This study not only extends the application of explainable artificial intelligence methods in atmospheric science, but also provides a scientific basis for differentiated <em>O</em>_<em>3</em> pollution control, regional early warnings, and targeted mitigation strategies in heavily polluted areas.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102954"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231315","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":"A novel approach for calibrating the stress categories of thermal indices","authors":"Yasser Ibrahim ,&nbsp;Ibrahim Elwy ,&nbsp;Hany Mokhtar ,&nbsp;Ahmed A. Khamis ,&nbsp;Mahmoud Atef","doi":"10.1016/j.uclim.2026.102952","DOIUrl":"10.1016/j.uclim.2026.102952","url":null,"abstract":"<div><div>This paper presents a novel approach to calibrate the Universal Thermal Climate Index (UTCI) stress categories. Subjective thermal sensation votes (TSVs) are determined using questionnaire survey of 1030 individuals across 15 monitoring campaigns. Objective thermal stress (UTCI) is determined from on-site meteorological measurements, corresponding to the survey time, spanning various seasonal environmental conditions, in the hot-arid climate of Cairo, Egypt, and is then compared against subjective thermal sensation votes. Boundary thresholds are first derived using three common calibration techniques, namely, Linear Regression (LR), Ordinal Logistic Regression (OLR) and the Percentage Dissatisfied (PD) fitted curve, and their abilities to predict subjective votes are evaluated. Leveraging the parametric capabilities of Grasshopper for Rhino3D, a new methodological framework is then presented through multi-objective optimisation of per-class recalls. The Hypervolume Estimation (HypE) algorithm is employed, with the boundary thresholds as design parameters, along with the geometric-mean recall, minimum per-class recall and Quadratic Weighted Kappa (QWK) as objective functions. The Pareto front comprises solutions with up to 0.2 lower Mean Absolute Error (MAE) and up to 15% higher average recall, compared to LR. The final optimised scale achieves 7% higher average recall, 12% higher precision, and 0.1 higher QWK, relative to OLR, providing a balanced prediction across all stress categories. The optimised neutral range indicates higher tolerance to heat stress in hot-arid climates than that of the original no thermal stress range. The framework's generalisation to other thermal indices across different climatic regions is discussed, highlighting its applicability for broader thermal comfort assessment.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102952"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231309","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-scale concurrent modeling of air pollutants and greenhouse gases over Greater Boston. Part 2: Chemical evaluation and sensitivity simulations","authors":"Yiyue Yang ,&nbsp;Yang Zhang ,&nbsp;Ying Wang ,&nbsp;Eeshan Basu ,&nbsp;Siqi Ma ,&nbsp;Daniel Tong","doi":"10.1016/j.uclim.2026.102959","DOIUrl":"10.1016/j.uclim.2026.102959","url":null,"abstract":"<div><div>Criteria air pollutants (CAPs) and greenhouse gases (GHGs) are often co-emitted atmospheric chemicals that bear intertwined consequences on human health and the environment. This work evaluates performance of the newly developed air quality model that concurrently simulates CAPs and GHGs over triple-nested domains in Greater Boston. The model satisfactorily reproduces ozone with normalized mean bias (NMBs) within ±15% and normalized mean errors (NMEs) within 25% in most months and reduced NMBs and NMEs at finer grid resolutions. NMBs and NMEs of fine particulate matter (PM_2.5) can reach beyond benchmarks due to uncertainties in meteorology, emissions, chemical boundary conditions, and physical schemes for urban processes. Uncertainties in carbon dioxide simulations are mainly due to lack of reliable emission inventory and observations. To identify major sources of uncertainty, sensitivity simulations are carried out to investigate the impacts of planetary boundary layer (PBL) schemes, primary particle emission, urban canopy models (UCMs) and chemical boundary conditions. Compared to Yonsei University (YSU) PBL scheme used in the baseline, using Mellor, Yamada, Nakanishi and Niino Level 3 (MYNN3) reduces biases and errors for most meteorological and chemical parameters in January 2023, but not necessarily in July 2023. Using multilayer UCMs yields better prediction of PM_2.5 diurnal variations at urban sites but worse results at non-urban sites. Adjusting primary particle emissions and chemical boundary conditions improves model performance in January and July 2023. This work demonstrates that addressing uncertainties of UCM-PBL coupling and input data quality would benefit future model simulations at fine scales in urban areas.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102959"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231314","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":"Assessing the impact of urban climate anomalies and exogenous drivers on human development","authors":"Ying Li ,&nbsp;Ting Yu ,&nbsp;Yung-ho Chiu","doi":"10.1016/j.uclim.2026.102984","DOIUrl":"10.1016/j.uclim.2026.102984","url":null,"abstract":"<div><div>Global climate change exerts profound impacts on health, the economy, society, and ecosystems, posing severe challenges to human development. Existing research on human development efficiency assessment primarily focuses on dimensions such as education, income, and health, but commonly ignores climate risk factors. This study addresses this research gap by constructing a DDF-BOD model that incorporates climate risk as an exogenous variable, and applies it to assess 31 provincial-level administrative regions in China from 2016 to 2022. The results reveal that when climate risk is considered, the average human development efficiency score increases from 0.824 to 0.895. This suggests that traditional assessment methods, by neglecting this external pressure, may systematically underestimate the true efficiency level. Under this improved assessment framework, nine provinces achieved optimal efficiency. However, two provinces still scored below 0.7, indicating significant room for improvement. Furthermore, this study explores the factors contributing to lower efficiency and proposes targeted policy recommendations for provinces at different efficiency level, toward enhanced climate resilience and promote sustainable human development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102984"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231441","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":"Integrating multi-dimensional urban green space configuration in land surface temperature modelling: A seasonal and methodological evaluation for southern Delhi","authors":"Dyvavani Krishna Kapuganti ,&nbsp;Kailash C. Tiwari ,&nbsp;Gopala Areendran","doi":"10.1016/j.uclim.2026.102969","DOIUrl":"10.1016/j.uclim.2026.102969","url":null,"abstract":"<div><div>Increasing urban growth and global warming necessitate effective temperature mitigation strategies to support sustainable development goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action), especially in developing megacities such as Delhi. This study (i) investigates seasonal relationship between vertical (3D) and horizontal (2D) spatial configuration of urban green spaces (UGS) and land surface temperature (LST) and (ii) evaluates the effectiveness of non-spatial, spatial and non-parametric regression methods in explaining LST variability using GEDI LiDAR, Sentinel 2 A and Landsat 8 data for New Delhi and South Delhi (NSD). GEDI-derived vertical metrics reliably captured the 3D structure of UGS when validated against laser rangefinder measurements (avg. seasonal R² = 0.76), with vertical height (Vert_HT), canopy complexity (fhd), and fractional gap (pgap_th) emerging as strong predictors. Among 2D metrics—interspersion-juxtaposition (IJI) exhibited strong negative associations with LST during summer, indicating enhanced cooling over interspersed landscape in summertime. In contrast, shape-complexity metrics such as perimeter-area fractal dimension (PAFRAC) and edge density showed a seasonal cooling in post-monsoon conditions, while proportional adjacency (PLADJ) demonstrated cooling across transitional season. Integrating 3D metrics with 2D improved LST prediction, reducing RMSE by ∼33% (from 0.6 °C to 0.4 °C) during post-monsoon season. Comparative modelling revealed superior performance of spatial regression models, with geographically weighted regression emerging as the most robust approach in the NSD region (R² ∼ 0.73 avg. across UGS classes and seasons), indicating strong spatial dependency in landscape-LST relationships. Although non-parametric machine learning models demonstrated moderate LST explanatory power, their performance varied significantly across UGS classes and seasons, influenced by vegetation density and activity. The findings provide actionable insights for climate-resilient landscape planning and management in rapidly urbanising environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102969"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167785","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}