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• This study examines the impacts of climate-resilient city (CRC) on aged people. • Five health impact mechanisms for CRC are identified. • CRC reduces health gaps across different demographic groups. • Aged people face psychological trauma risks from climate-related disasters. • Vulnerable groups in extreme weather regions benefit most from CRC policies. This study examines the health impacts of Climate Resilient City (CRC) policies using a difference-in-differences methodology. Our findings demonstrate that CRC policies significantly improve public health, particularly benefiting vulnerable populations and residents in regions with extreme temperatures. Mechanism analysis reveals that these policies enhance urban climate resilience through improved water management, air pollution reduction, energy conservation, and strengthened social capital. Moreover, our results show that CRC policies help reduce health disparities linked to differences in medical resources and climate conditions. This study provides crucial insights for policymakers in designing effective climate and public health strategies, emphasizing the importance of climate-resilient urban development. [ABSTRACT FROM AUTHOR]

• Utilized XGBoost-BA for accurate spatio-temporal modeling of asthma-prone areas. • SHAP analysis revealed seasonal impacts of climate and air pollution on asthma. • Rainfall in spring/autumn and temperature in summer/winter were key factors. • High accuracy achieved in predicting asthma-prone areas across all seasons. Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM 2.5) in spring, carbon monoxide (CO) in summer, ozone (O 3) in autumn, and PM 10 in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization. [ABSTRACT FROM AUTHOR]

• Daytime LST was the highest in the summer and nighttime in the monsoon. • Densely built-up LCZs experienced higher diurnal LST than open ones. • Height of built-up features had higher impact on night-time heat than density. • Some rural areas were as hot as urban during the day but not at night. • Heat vulnerable populations inhabited LCZs prone to overheating. Rising temperatures and rapid urbanization globally reinforce the need to understand urban climates. We investigated the influence of land cover and local climate zones (LCZs) on diurnal land surface temperature (LST) in various seasons in greater Delhi region, India, and their implications on socio-economic factors. Day LST was the highest in the summer and night LST in the monsoon, which also had the lowest diurnal differences in LST. Higher height and density of built-up features contributed to greater heat at night. During the day, open built-up and vegetated areas experienced relatively less heat than their compact equivalents. The lowest diurnal difference was in medium height compact urban zones and tall vegetation. Social inequity in access to urban cooling was indicated by large low-income and heat-vulnerable populations inhabiting the hottest LCZs. This research highlighted that even in semi-arid and subtropical climates, spatial planning policy should consider both the seasonality and diurnal differences in temperature as much as appropriate morphologies for design of thermally comfortable and climate resilient urban spaces. These policies should address the evidenced social inequities in heat exposure to reduce the adverse health impacts on vulnerable groups and therefore contribute to wider societal and economic benefits of healthier populations. [ABSTRACT FROM AUTHOR]

• Introduced WebMRT, an online T mrt prediction tool using tree-based ensemble models. • Trained on state-of-the-art measurements collected using Marty in Southwest US. • LightGBM achieved R2=0.92, RMSE=3.43, MAPE=5.33, and MBE = 0.20. • Streamlined process from input to T mrt prediction for wider stakeholder engagement. • Designed with simplicity to enhance data-driven urban climate governance. Mean Radiant Temperature (T mrt) is the most critical atmospheric variable influencing outdoor human thermal exposure and comfort in hot, dry environments. However, accurately quantifying T mrt requires time-consuming field measurements with expensive equipment or complex, resource-intensive computations. We introduce WebMRT, an online tool to predict T mrt using a data-driven approach. It features an intuitive interface using air temperature, shading status, and built environment features as predictors of T mrt for a user-selected summer day, time, and location. Utilizing a tree-based ensemble model, WebMRT is trained on state-of-the-art human-biometeorological data collected by MaRTy using LightGBM after evaluating its performance against several candidate machine learning regressors. Feature engineering was applied to the day and time input, and two additional temporal features were derived: 'Solar Altitude' and 'Minutes-from-Sunrise'. These inputs are integrated into the user interface, emphasizing simplicity and easy access for users at the frontend. After training the regressor on MaRTy datasets and employing k-fold cross-validation with ten folds, the model demonstrated strong predictive power (R2=0.92) with acceptable error (RMSE=3.43, MAPE=5.33) and bias (MBE=0.20). WebMRT also features optional fisheye photo uploads, processed using transfer learning techniques for image segmentation, further enhancing the tool's predictive accuracy, user experience, and applications towards climate action decision-making processes. [ABSTRACT FROM AUTHOR]

• Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. • Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. • Polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. • RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs. Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort. [ABSTRACT FROM AUTHOR]

• A multi-scale modeling framework is developed for urban meteorology simulations. • Conduct a quantitative assessment of the energetic impact of non-uniform surfaces. • Rivers or shaded areas mitigate heat, contrasting with the densely built-up areas. • Energy near rivers converts to ground and latent heat, thus reducing sensible heat. • Energy in built-up areas mainly converts to sensible heat, especially at nighttime. Sophisticated urban climate systems, influenced by the interaction of mesoscale circulation and microscale forcing, cannot be adequately described using solely mesoscale or microscale models. Moreover, high-quality urban morphological data are indispensable for quantifying the complex and highly fragmented wind/thermal environments with heterogeneous characteristics. This study develops an innovative multi-scale modeling framework combining the coupled mesoscale and microscale numerical models with unmanned aerial vehicle photography to accurately capture the microclimate dynamics of highly fragmented real urban climates under mesoscale weather conditions. Validated by measured data, this framework is adopted to simulate the dynamic meteorology during the heatwave in the Xiongan New Area in Hebei Province, China. Results reveal that the high-density built-up area(HDA) exhibits spatial patterns of higher temperatures, poorer ventilation, more arid, and more severe heat stress than near-river and low-density areas. Moreover, HDA tends to convert more radiation energy into ground heat storage(Q S) and sensible heat flux(Q H) during daytime, and Q S is subsequently released into Q H at night, thus deteriorating the nighttime thermal environment. Whereas, the river converts more radiation energy into Q S , with a portion transformed into latent heat flux, which benefits summer cooling. This innovative framework provides promising tools for sustainable urban climate design in realistic cities. [ABSTRACT FROM AUTHOR]

As urban populations grow and cities expand, the challenge of managing urban heat and its environmental impacts becomes increasingly critical. Traditional methods for analyzing urban temperature dynamics often fall short in precisely capturing the complexity of urban landscapes. This paper introduces the 3D Landscape Clustering (3LC) framework, a new tool designed to analyze urban temperature dynamics by factoring in landscape variables. It clusters landscapes into homogeneous groups using high-resolution 3D land cover maps. The 3LC adopts a clustering mechanism to enhance flexibility and objectivity in landscape categorization, thereby enhancing the depth and accuracy of urban climate studies and moving beyond traditional classification frameworks such as the Local Climate Zone (LCZ). Case studies demonstrate its capability to provide detailed insights into the relationships between urban landscape features and temperature variations. Additionally, the paper details how the framework can excel in multi-city analyses and outlines advanced analytical techniques. Promising research opportunities and limitations are identified. This research reshapes our approach to landscape categorization, advancing our understanding of the interactions between landscape and climate dynamics, and contributing to more sustainable, climate-resilient cities. • Clustering with detailed 3D land cover maps enhanced flexibility and objectivity. • Precisely identifies diverse landscape categories with distinct temperature profiles. • Supports quantitative-semantic querying, diverse sampling, and multi-scale analyses. • Multi-city case studies proved the framework's transferability and versatility. • Identified opportunities and challenges, advancing urban climate resilience. [ABSTRACT FROM AUTHOR]

• Seasonal cooling was greatest in summer for all green-blue-infrastructure (GBI). • The cooling benefit in different seasons is influenced by the width and area of the GBI. • Cooling of adjacent buffer areas is greatest from blue spaces like rivers and lakes. • The buffer zones around all GBI show graduated cooling benefits. • The cooling benefits of agricultural areas varied with proximity to urban areas. Green and blue spaces are vital for mitigating urban heat island impacts but are poorly studied in arid regions. In this study, we quantify monthly and seasonal cooling for five contrasting types of green and blue infrastructure (GBI): rivers, lakes, "captured" agricultural areas, urban parks, and golf courses in the Cairo and Giza provinces of Egypt. Using Landsat-8 images of Land Surface Temperature (LST) we assessed change in LST along bisecting transects and in circle plots for three replicates of each GBI type, in each of four seasons. Cooling was greatest in summer for all GBI types. Cooling differentials of LST were greater for water bodies than for green spaces. Ordered by increasing cooling potential (May LST cooling) they were: Agricultural areas (3.3 °C), Golf courses (4.3 °C), Parks (4.4 °C), Lakes (8.2 °C) and Rivers (12.2 °C). The cooling effects extending into adjacent buffer areas were greatest for blue spaces like rivers and lakes. This paper provides the first data for cooling by less-studied GBI types in arid regions, such as golf courses and urban agriculture. It provides information to support city planners to embrace green and blue spaces within metropolitan areas and to protect them from urban sprawl. [Display omitted] [ABSTRACT FROM AUTHOR]

• This study uses machine learning to simulate underground climate change. • It examines ground temperatures and strains in a city district over time. • Results show that large-scale predictions are feasible with limited data. • This approach is up to 200 times more efficient than existing ones. Subsurface heat islands induce an underground climate change in urban areas, which can threaten public comfort and health, subsurface ecosystems, transportation infrastructure, and civil infrastructure. Meanwhile subsurface heat islands harbor a marked energy recovery potential. Despite increasing investigations, the understanding of subsurface heat islands remains limited and suffers from the lack of expedient and accurate simulation approaches. Here we explore the use of machine learning to accurately and expediently simulate subsurface heat islands in terms of ground temperature and deformation anomalies. Using the Chicago Loop district as a case study, we identify a series of physical features to establish a relationship between central drivers and effects of subsurface heat islands. We incorporate these features into a random forest model to simulate underground climate change with variable training datasets. The results indicate that ground temperature and deformation anomalies across an entire city district can be predicted based on data extracted solely from a handful of buildings. The proposed approach achieves comparable accuracy to current simulation methods but boasts a calculation speed that can be over a hundred times faster, promising to advance fundamental science while effectively informing engineering and decision-making in the mitigation of underground climate change. [ABSTRACT FROM AUTHOR]

• PV-GR annually cuts about 5.131×106 t CO 2 -eq. • PV-GR's power generation can meet about 22.13 % of Xiamen's annual electricity demand. • The ecological balance of PV-GR in Xiamen equals 3.872×105 Gha. • Deploying PV-GR in Xiamen can mitigate climate change. • PV-GR's carbon reduction far exceeds its emissions. The Photovoltaic-Green Roof (PV-GR) system, which integrates rooftop photovoltaics and green roofing, has significant potential for sustainable urban development and climate change mitigation. However, the specific effects of PV-GR are not yet clear. This paper employs methodologies including Geographic Information Systems (GIS), Denitrification-Decomposition(DNDC) Model, and solar simulation. Combined with ecological balance calculations, these methods assess PV-GR's carbon reduction benefits and its potential to mitigate climate change. Using Xiamen City as a case study, research shows that Xiamen has about 54 km² of rooftops suitable for PV-GR. Annually, PV-GR can produce about 5.931×103 tons of biomass and generate 7,427 GWh of electricity, meeting about 22.13 % of Xiamen's annual electricity demand. The annual carbon reduction from Xiamen's PV-GR is estimated at about 5.131×106 t CO 2 -eq , offsetting around 29.28 % of the city's annual carbon emissions. Over a 30-year lifecycle, PV-GR's carbon emissions and reduction benefits amount to 2.274×107 t CO 2 -eq and 1.539×108 t CO 2 -eq , respectively. The ecological footprint of deploying PV-GR in Xiamen is 6.709×104 Gha, while the biocapacity reaches 4.542×105 Gha. The global ecological balance stands at 3.872×105 Gha, suggesting that PV-GR can significantly contribute to mitigating climate change. [ABSTRACT FROM AUTHOR]

• Explored MAUP's impact on LST and its influencing factors. • Human factors showed higher LST scale sensitivity than natural factors. • Zoning and scale effects significantly altered LST factor explanatory powers. • LST factor interactions were more stable and explanatory than single-factor effects. Exploring the modifiable areal unit problem (MAUP) in land surface temperature (LST) and its influencing factors is crucial for understanding LST variation patterns and quantifying the factors' impact. Neglecting the MAUP could lead to an incomplete understanding of LST changes and their driving mechanisms. This study used optimal parameters-based geographical detector and gradient boosting regressor models to investigate the MAUP in LST and its influencing factors. The analysis covered 87 cities across seven climate zones in China, examining MAUP in 12 spatial scales to discern the scale and zoning effects on LST and its influencing factors. The research findings were as follows: (1) The sensitivity of LST influencing factors to spatial scales exhibited both spatial and temporal heterogeneity. Significant differences in the q-values of LST influencing factors were observed across various climate zones and periods (daytime and nighttime), with human factors, particularly those related to residents' work, buildings, life, and rest, showing higher spatial scale dependency than natural factors. (2) Zoning effects significantly impacted the q-values of LST influencing factors and were closely linked to the discretization methods and quantities used, which could alter the trends of these q-values. (3) Across the 12 spatial scales, more than 67.34 % of LST influencing factor interaction types were classified as bi-variable enhancement types. The q-values for LST influencing factor interactions were higher and more stable than those of single factors. LST influencing factor interactions in transitional climate zones exhibited high sensitivity to spatial scales. This research enhances our understanding of LST variations, providing valuable insights for urban climate adaptability planning and the development of climate-resilient cities. [ABSTRACT FROM AUTHOR]

• A heat-related health risk assessment framework for old neighborhoods was developed. • This framework for evaluation at neighborhood level was validated feasible. • This framework proves especially beneficial for quantifying heat-related health risk. • This framework can deliver targeted information for decision-making processes. Assessing the heat-related health risks is crucial for promoting the sustainable development of cities, particularly in the face of extreme climates and urban human settlement governance. Heat health risk assessment serves as a foundational element within the risk governance framework, serving to mitigate heat-related morbidity and mortality rates. The revitalization of old neighborhoods, especially those situated in city centers with deficient facilities, emerges as a critical imperative within this context. This study chose Changsha, a city in Central China that is severely affected by high temperatures and has numerous old neighborhoods, as a case study. We developed a heat risk framework that incorporates city level risk, exposure, vulnerability, and the adaptation of old neighborhoods. Validated through data accessed from medical facilities visit, the framework effectively reflects the distribution of heat risks. By combining qualitative and quantitative methods, we investigated the correlation between heat health risk levels and the behaviors of residents in old neighborhoods. The results indicate that urban planners should prioritize comprehensive renovations in medium-risk neighborhoods, resident behavior management in low-risk neighborhoods, and the indoor thermal environment in high-risk neighborhoods. This framework plays an important role in the assessment of future spatial risk for old neighborhoods renewal. [ABSTRACT FROM AUTHOR]

• Trees' cooling impact varies with placement near buildings. • Small trees can cool as effectively as large ones if correctly placed. • Distant trees lower mean radiant temperature by over 20 °C in high-rise complexes around noon. • The cooling effect changes with tree distance from shaded walls. • Tree-cooling effectiveness decreases by 71 % to 91 % based on location. Street trees are vital in mitigating urban heat islands, with their cooling effect significantly influenced by the urban layout. Past studies explored how urban canyon characteristics—aspect ratio, building coverage—affect tree cooling, yet seldom analyzed the impact of distance between trees and buildings. Addressing this, our study evaluates tree cooling effects concerning their proximity to shaded and sunlit walls. The findings highlight that cooling effectiveness varies with the ratio of distance from the shaded wall to building height (D s h a :H), peaking within a ratio range of 0.55 to 0.7. Below a ratio of 0.3, effectiveness decreases to 9–29 %, emphasizing the importance of strategic planting distances. The result shows that when planted at an optimal distance from buildings, small trees can produce similar radiation mitigation effects to those of larger trees. This discovery advocates for thoughtful tree placement in high-density areas, optimally leveraging their shade and evapotranspiration benefits. The study provides actionable insights for urban planners and landscape architects, suggesting that careful consideration of tree placement relative to building shadows can significantly improve urban climates, offering a strategic approach to deploying green infrastructure in high-rise complexes for enhanced climate resilience. [ABSTRACT FROM AUTHOR]

High-resolution microclimate data is essential for capturing spatio-temporal heterogeneity of urban climate and heat health management. However, previous studies have relied on dense measurements that require significant costs for equipment, or on physical simulations demanding intensive computational loads. As a potential alternative to these methods, we propose a multimodal deep learning model to predict microclimate at a high spatial and temporal resolution based on street-level and satellite imagery. This model consists of LSTM and ResNet-18 architectures, and predicts air temperature (T a i r), relative humidity (R H), wind speed (ν), and global horizontal irradiance (G H I). For our study area situated at a university campus in Singapore, we collected microclimate data, street-level and satellite imagery. We conducted extensive experiments with our collected dataset to showcase our model's predictive capabilities and its practical use in generating high-resolution microclimate maps. Our model reported RMSE at 0.95 °C for T a i r , 2.57% for R H , 0.31 m/s for ν , and 225 W/m 2 for G H I. Furthermore, we observed a contribution of imagery inputs to higher accuracy by comparing models with and without such inputs. We identified hot spots at a high spatio-temporal resolution, indicating its application for issuing real-time heat alerts. Our models are released openly at the microclimate-vision GitHub repository (https://github.com/kunifujiwara/microclimate-vision). [Display omitted] • Prediction of microclimate parameters using street-level and satellite imagery. • Multimodal prediction model combining LSTM and ResNet-18 architectures. • Collecting and using microclimate data, street-level imagery, and satellite imagery. • High accuracy: RMSE at 0.95 °C (T a i r), 2.57% (R H), 0.31 m/s (ν), 225 W/m 2 (G H I). • Street-level and satellite imagery contribute to improving accuracy. [ABSTRACT FROM AUTHOR]

• Identified 95 shrinking cities in China from 2000 to 2010. • Assessed temperature at 2419 stations from 1961 to 2014. • Revealed cooling effects in shrinking cities. • This cooling exhibits significant seasonality. • Urban shrinkage contributes to mitigating regional warming. In recent years, urban areas are increasingly experiencing intense warming. Although urbanization is an important driver of warming in urban environments, it remains unclear whether depopulation trends in shrinking cities mitigate this warming effect. Here, we explored the relationship between shrinking cities and observed warming in China. Of the 356 Chinese cities, 95 were identified as shrinking between 2000 and 2010. We categorized 2419 observation stations into three groups—rural, shrinking, and non-shrinking—and generated a surface air temperature (SAT) anomaly series for each group from 1961 to 2014. Temperature differences between the shrinking and non-shrinking urban stations were investigated. Using segmented generalized least squares regression, the spatiotemporal temperature patterns across the mean (T mean), maximum (T max), and minimum temperatures (T min), diurnal temperature range (DTR) indicators, and across seasons—spring, summer, autumn, and winter—were explored. Results revealed a cooling effect in shrinking cities, with decadal decreases of 0.042 °C (–0.078 to –0.005 °C), 0.083 °C (–0.126 to –0.039 °C), and 0.029 °C (–0.062 to –0.005 °C) in regional T mean , T max , and T min anomalies, respectively. Moreover, pronounced seasonality was identified in this phenomenon—the cooling effect was most notable for T mean in spring and T max in autumn, less significant in summer, and negligible in winter. These results suggested that the population decline in shrinking cities could alleviate regional warming, having implications that could influence urban planning and climate mitigation policies. [ABSTRACT FROM AUTHOR]

• Integration of mesoscale climate simulation and urban microclimate model is presente. • City-wide UTCI mapping is achieved at a 10 m spatial resolution and hourly intervals. • Impact of urban climate drivers on outdoor thermal comfort is assessed and quantified. The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures. [ABSTRACT FROM AUTHOR]

• Vegetation changes modulate temperature dynamics across 304 Chinese cities. • Vegetation impacts on urban-rural temperature gradients, 2000–2020, assessed. • 96 % of urban areas cool as vegetation increases, effect fades toward rural zones. • Urban afforestation rose by 4.35 %, cooling 67 % of cities by 0.58 °C from 2000 to 2020. • Vegetation alters urban cooling and rural warming, reducing UHI by 0.56 ± 0.35 °C. City afforestation serves as a robust strategy for thermal mitigation. Employing multisource satellite data, we explored the impact of vegetation cover on temperature dynamics across urban and rural sectors in Chinese cities. Our analysis reveals that a 1 % increase in vegetation coverage corresponds to a reduction in surface temperature by 0.099 °C to 0.102 °C in urban environments. However, the cooling effect diminishes from urban to rural areas, where increased vegetation occasionally results in warming. Further, the temperature reduction tends to stabilize at higher vegetation levels, as demonstrated in grid-based evaluations. Significantly, variations in urban population density substantially modulate these cooling effects. Over the period 2000–2020, afforestation increased vegetation coverage by 4.35 % in urban areas, which led to cooling in 67.43 % of the analyzed cities, with an average surface temperature decline of 0.58 °C. Additionally, our findings document a notable attenuation of the urban heat island (UHI) effect over time, with an average decrease of 0.56 ± 0.35 °C. This reduction primarily results from the thermoregulatory effects of changes in vegetation coverage, which cool urban areas and may warm rural ones. Our study underscores the critical role of afforestation in urban climate management and its efficacy in attenuating urban heat waves and UHI effects. [ABSTRACT FROM AUTHOR]

• Diurnal and seasonal urban changes of thermal environment were explored. • LST showed an increasing trend during daytime and nighttime. • Diurnal temperature range (DTR) decreased significantly. • Urban greenspace impact on LST was not always a cooling effect. • Results could help promote healthy and sustainable development of city. Rapid urban expansion has significantly altered the urban surface energy balance, consequently impacting the urban thermal environment. Urban greenspace (UG) plays a crucial role in regulating urban climate. This study utilizes a seamless land surface temperature (LST) dataset to elucidate the spatio-temporal patterns of LST and diurnal temperature range (DTR) in Shenzhen, a rapidly urbanizing city in China, and the influence of UG on urban thermal environment. The UG showed a notable decrease of 16.3 % from 2000 to 2020. Over the past 21 years, both daytime LST (LSTD) and nighttime LST (LSTN) exhibited an upward trend, while DTR showed a significant downward trend, especially in autumn. Significant spatial changes in LSTD, LSTN, and DTR were observed in winter across the entire city. Moreover, our results emphasize that the impact of UG on LSTD displays non-uniform cooling and varies significantly in terms of season, greenspace quality, and geographical location. Our assessment of changes in the urban thermal environment in Shenzhen and the role of UG on LST can offer theoretical support for policymakers in developing effective urban cooling measures and healthy urban development. [ABSTRACT FROM AUTHOR]

• Urbanization effects are expressed as the urban climate and land cover change. • The land cover change effect is recognized as the major effect. • Land cover change effect is stable in time series. • Urban climate effect has experienced an increasing trend for the past two decades. Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective. [ABSTRACT FROM AUTHOR]

• Developed a novel methodology to simulate UHI intensity. • Showed UHI intensity variability across different LCZs in seven U.S. cities. • Analyzed UHI impact on HDD and CDD in various climatic regions. • Explored UHI influence on energy use and cost in residential building prototypes. The Urban Heat Island (UHI), which causes urban areas to be warmer than rural counterparts, impacts buildings' energy demands for heating and cooling. Conventional weather data, typically gathered at non-urban sites like airports, are used to create Typical Meteorological Year (TMY) files for building energy assessments. However, this data doesn't account for urban temperature effects, impacting the accuracy of these assessments. This study proposes a novel methodology that couples Local Climate Zones (LCZs) with the Urban Weather Generator (UWG) to produce urban-specific weather data reflecting UHI effects for more accurate energy simulations. LCZs categorize urban neighborhoods into landscape types based on building heights, proximity, greenspace, etc., which regulate the magnitude of the UHI. The UWG uses LCZ parameters to estimate UHI intensity based on existing weather conditions. Together, they generate city-specific TMY files tailored to individual neighborhoods. Here, modified TMY files for seven U.S. cities located in different climates, were generated and used in residential building energy simulations. The UHI effect increases Cooling Degree Days (CDD) and decreases Heating Degree Days (HDD), but energy demand impacts vary by city and LCZ type. This methodology provides a simple means for incorporating the impact of UHI into building and urban energy simulations. [ABSTRACT FROM AUTHOR]

• Spatiotemporal UHI variations response to dynamic rural-urban transitions. • Three-stage analysis reveals long-term UHI and urbanization traits. • UHI patterns differ in two cities based on surrounding crop cycles. • The profiles in three stages show the horizontal and vertical expansion of the city. Urban heat island (UHI) not only reflects the environmental thermal comfort and energy consumption, but also affects the urban meso‑scale climate. There are many researches related with UHI mainly focusing on urban and rural area, while neglecting dynamic rural–urban transition especially in a rapid urbanization in China. Beijing and Zhengzhou are studied by using city clustering algorithm (CCA) and boundary generation algorithm (BGA) to delineate the urban, peri‑urban and rural boundaries from 2000 to 2023 within three stages. Fourier transform model was used to identify the UHI patterns. Results show: 1) Two cities have undergone obvious expansions in 20 years, with a consistent mean LST decrease from urban to peri‑urban and rural areas in three stages. 2) The distribution of UHII was more consistent in Beijing, while it varied more in Zhengzhou across seasons. 3) The UHI patterns notably differ, with Zhengzhou experiencing variable patterns and Beijing consistently showing oblate patterns. 4) The profiles of UHII and NDVI in two cities varied seasonally and reflected urban expansions in terms of longitude and latitude. Understanding the long-term changes and patterns of urban heat islands in different cities will provide information for formulating adaptive policies for urban sustainability. [Display omitted] [ABSTRACT FROM AUTHOR]

• UGS cooling effect changed with urbanization. • UGS cooling gradient, intensity, range, and distance increased with urbanization. • Cooling efficiency showed an initial ascent followed by a subsequent decline. • Dominant factors influencing UGS cooling effect shifted with urbanization. The significance of urban green space (UGS) in alleviating the urban heat island effect has attracted increasing attentions. The size, growth dynamics, and surroundings of UGS evolve with urbanization. However, the impact of these changes on the UGS cooling remains unknown, hindering the formulation of targeted greening policies at different urbanization stages. Focused on the Three Ring Road area in Changsha City, comprehensive urbanization indexes from 2000 to 2020 were computed, identifying the periods characterized by high-speed growth, transition, and steady development. By calculating the cooling indicators of UGS, employing partial correlation analysis and geographic detectors, this study clarified the determinants of the cooling effect of UGS. Results revealed that urbanization significantly impacted the cooling effect of UGS, with the cooling gradient, intensity, range, and distance exhibiting a gradual increase. Meanwhile, cooling efficiency demonstrated an initial ascent followed by a subsequent decline. Notably, the pivotal factors influencing the cooling effect of UGS shifted from external factors (surrounding construction land and ecological land) to internal factors (growth status), and eventually to the synergy of both internal and external factors. This study contributed valuable insights for delineating optimal UGS cooling strategies across diverse urbanization stages to improve urban climate adaptability. [ABSTRACT FROM AUTHOR]

• A high-resolution air temperature measurement was applied in Beijing CUHI research. • Discrepancies among LCZ properties impact the spatiotemporal pattern of CUHI. • The research reveals the effects of LCZ spatial pattern on regional air temperature. • Distinct mitigation strategies were put forward in four subareas in Beijing. Urban heat island (UHI) is a widely concerned urban climate which impacts billions of residents worldwide. Nevertheless, inadequate rigorous air temperature (T air) measurement has become an obstacle to UHI research and mitigation, especially in megacities like Beijing. In this study, we applied a 1-km hourly T air dataset interpolated from records of 521 stations in Beijing. The dataset enables us to analyze UHI patterns on a block scale and the impacts of landscapes combined with the local climate zones (LCZ) scheme. Our results indicate that: (1) T air and its temporal variation in each LCZ vary significantly, which are highly relevant to their morphology and thermal-radiative properties. (2) LCZ 1 and LCZ 2 have the highest nocturnal UHI intensities (UHII). Discrepancies among LCZs also cause various temporal variations in their UHII. (3) The spatial pattern of LCZs affects regional temperature. Area ratio and aggregation extent of LCZ 1&2 show significant positive correlations with regional temperature, while LCZ A&D&G have the opposite regularity. (4) Distinct mitigation strategies are put forward for each subarea in Beijing with various endowments of population and the LCZ structure. The study provides insight into Beijing UHI and plausible mitigation advice in megacities like Beijing. [ABSTRACT FROM AUTHOR]

• Global mean I c is characterized by evident weekly rhythms during most seasons. • Global annual mean I c is reduced by 0.10 K during weekends relative to weekdays. • Global mean I s only displays a pronounced weekly rhythm during winter daytime. • Both global I c and I s reductions during weekends tend to increase with urban ISP. Investigating the weekly rhythms of urban heat islands (UHIs) is critical for gaining deep insights into the urban climate changes caused by periodic cycles of human activities. However, the weekly rhythms of both canopy and surface UHIs (termed I c and I s) remain poorly understood at a large spatial scale. Leveraging daily screen-level air temperature (T a) and satellite land surface temperature (T s) observations (2015∼2020), we show that the weekly rhythms of I c averaged for all selected cities exhibit an evident peak-and-valley pattern across most seasons, with the annual mean I c reduction during weekends of 0.09 ± 0.01 K (mean ± S.E., p < 0.05) for daytime and 0.10 ± 0.01 K (p < 0.05) for nighttime when compared to weekdays. In contrast, the I s only displays a pronounced weekly rhythm during winter daytime, with the corresponding I s reduction during weekends of 0.07 ± 0.01 K (p < 0.05) relative to weekdays. These findings remain robust against potential observation errors in T a and T s. From an intra-city perspective, both I c and I s reductions during weekends escalate with increasing urban impervious surface percentage. This study illustrates the significance of short-term cycles of human activities in shaping global urban climates. [ABSTRACT FROM AUTHOR]

• Application of cool surface on street canyon reduces up to 30% sensible heat. • Combined effect of cool roof, wall and cool road can reduce canyon air temperature. • Cool Canyon provides improved thermal comfort to pedestrians in UTCI. • View factor with the cool surface plays an important role on pedestrian comfort. The Urban Heat Island (UHI) effect has been a cause of concern on human-induced impacts to urban climate. Construction materials absorb incoming solar radiation, causing additional sensible heat release to the urban environment resulting in urban air temperature changes. It has been proposed that covering the built-up surfaces such as building roofs and walls as well as road pavements, with cool surfaces which reflect most of the incoming solar radiation, could reduce heat absorption by construction materials and the subsequent heat release to urban environment. This paper, reports an experimental study of cool coating in a full-scale site setting located in Singapore during the summer months of tropical climate. The experimental site consists of four parallel industrial buildings forming two similar side-by-side street canyons. One of the canyons ('Cool Canyon') was cool coated on the roofs, walls and the road pavement whereas the other canyons remained as it was ('Conventional Canyon') as control. Air temperature, surface temperature, sensible and latent heat fluxes in the two canyons were monitored for analysis of thermal exchanges between the build-up surfaces and the canyon micro-environment. The Cool Canyon had up to 30% reduction in sensible heat release from the built-up surfaces which resulted in the air temperature in Cool Canyon being cooler than that in the Conventional Canyon by up to 2°C around 16:00 h. An analysis of pedestrian thermal comfort in the two canyons was also conducted using Universal Thermal Climate Index (UTCI). When compared to the Conventional Canyon, the Cool Canyon provides improved thermal comfort to pedestrians with up to 1.5°C reduction in UTCI. The paper also presents a critical analysis on thermal comfort due to the cool road in the middle of the street canyon. [ABSTRACT FROM AUTHOR]

• This article examines the importance of urban policy in climate change management. • It discusses the complexity of urban resilience in managing climate change. • It frames urban resilience as a policy framework for climate change in Saudi Arabia. • It provides a research agenda for urban resilience in the Global South. While climate change has traditionally driven the search for appropriate urban policy responses, a recent trend in urban studies and planning in the Global South has been to argue for resilience. Considering this call seriously, this article draws on critical literature on urban resilience and secondary data, using Saudi Arabian cities to demonstrate that urban policy-based reflections on climate change are imperative in the pursuit of urban resilience. By laying the groundwork for the conceptualization of urban resilience within and beyond urban studies, we use multiple urban cases across Saudi Arabia to establish approaches towards climate resilience, arguing that an understanding of urban resilience in urban studies and planning is critical to analyzing and combating the impacts of climate change in cities in the Global South. This leads us to argue for the need to explore urban resilience in relation to climate vulnerability, precarity, and urbanization. We conclude by advancing a research agenda on urban resilience in the Global South that draws upon the urban planning ethics of responsibility for climate change management. [ABSTRACT FROM AUTHOR]

• Chinese cities are more resilient to heavy rainfall than to extreme heat. • Nationally, areas near the Hu Line are the least resilient to heavy rainfall. • The potential long-term negative impact of extreme weathers is up to seven months. • Areas dominated by the secondary sector need to address extreme weather impacts. • Developed economies are vulnerable to climate hazards despite having high defense. Given the unprecedented climate change, extreme weathers have become more intense and frequent, causing severe socio-economic impacts. Urban resilience is vital for mitigating extreme events, but little is known about its city-level response to such shocks in China. Here, we aim to investigate the persistent effects of extreme heat and heavy rainfall on Chinese cities, ultimately revealing urban resilience. We use monthly nighttime lights from 2013–2019 as a proxy for urban functioning. Our results suggest that cities are less resilient to extreme heat than to heavy rainfall, yet the adverse effects of heavy rainfall can persist for up to seven months. Importantly, we reveal for the first time that areas near the Hu Line are vulnerable to heavy rainfall (e.g., Beijing, Tianjin and Chongqing), and cities in the Yangtze River basin are most affected by extreme heat (up to 24.8 % loss of nighttime light intensity). There is an urgent need to address severe weather impacts in regions dominated by secondary sector, while developed economies are vulnerable to climate hazards, despite having high defense. Our findings identify urban climate risk hotspots and underlying impact mechanisms, providing valuable insights into climate mitigation policies and urban development strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

• Increasing urban surface albedo improves outdoor thermal environment. • The cooling impact of high-albedo urban surfaces varies across contexts. • Increasing road albedo mitigates UHI in fringe LCZs. • Increasing wall and roof albedo is more effective in central LCZs. • Albedo-induced temperature changes show pronounced seasonal characteristics. The urban heat island (UHI) effect can exacerbate various environmental challenges related to high temperatures in urban areas. Increasing urban surface albedo is an effective strategy to mitigate UHI. However, the efficacy of high-albedo urban surfaces across varying urban contexts remains poorly understood. In this study, we leverage Urban Weather Generator to systematically simulate the effects of high-albedo roads, walls, and roofs on urban microclimate across Tianjin. Our simulation covers a typical meteorological year, representing typical weather conditions from January to December. Our results reveal that increasing road albedo is more effective in mitigating UHI in fringe areas, whereas increasing wall and roof albedo is more effective in mitigating UHI in central areas. Local climate zones with an urban aspect ratio of about 0.5 can obtain a maximum reduction of road surface temperature (-6 °C) and wall surface temperature (-3 °C). The temperature changes induced by albedo changes show evident seasonal characteristics: the road temperature decreases significantly in summer, while the wall temperature decreases significantly in spring and autumn. Our results could help guide UHI mitigation policies and urban planning in cities hoping to enhance urban surface albedo to balance urban growth and climate resilience. [ABSTRACT FROM AUTHOR]

• Open source data and a dense in-situ air temperature measurement network were used. • The ratio of biologically vital areas impacts city thermal conditions the most. • Build-up indicators with vertical features present thermal regime better than others. • The area of radius of at least 100 m is the best for evaluating thermal conditions. • Spatial development particularly influences minimum and average air temperature. In the face of climate change and the growing frequency of thermally burdensome conditions, urban morphology and city planning are becoming increasingly important to human health. Architects and policymakers use various tools, including spatial development indicators describing the characteristics of built-up areas, to mitigate urban climate. This study purposed to (1) indicate which widely used spatial development indicators most adequately explained the thermal conditions of the environment, and (2) investigate how large an area most accurately represents the thermal features recorded at a given air temperature measurement site. Selected indexes were used to characterize each of the 21 measuring sites located in Warsaw. First, five spatial development indicators were calculated for three distances from each site. Then, based on data from the air temperature monitoring network, 17 selected thermal characteristics were calculated. The results of the analyses indicated that the Ratio of Biologically Vital Area has the highest impact on the thermal regime with particularly strong influence on the lowest daily temperature values. The paper proves that the thermal conditions of a given location in the diverse space of a city are more precisely represented by a large area than by its nearest surroundings. [Display omitted] [ABSTRACT FROM AUTHOR]

• Air temperature can be predicted using features extracted from satellite imagery. • 10 % increase in building coverage may cause a rise of 0.28 °C in air temperature. • 10 % increase in vegetation coverage may cause good cooling effect within an area. • Well-trained machine learning models can help understand the general urban climate. • Well-trained neural networks can help assess and map air temperature distribution. Given the seriousness of the urban heat problem, the relationship between urbanization and air temperature has become a critical concern worldwide. In this study, common urban planning indicators, including the building coverage ratio (BCR), floor area ratio (FAR), and fractional vegetation cover (FVC), were extracted from satellite images to determine the intensity of urban development. On-site measurements and machine learning (ML) were used to observe and analyze the relationship between the intensity of urban development and air temperature. From the on-site measurement results, the air temperature in downtown Taipei decreased by an average of approximately 0.32 °C with every 10 % increase in the FVC. However, it increased by an average of approximately 0.28 °C and 0.03 °C with every 10 % increase in the BCR and FAR, respectively. The results obtained from the ML models demonstrated the same trend, with minor differences from the on-site measurement results, which were regarded as reasonable and acceptable. In this study, a more convenient method was proposed to extract urban planning indicators, describe the intensity of urban development within an area, and help estimate air temperature in areas without measuring instruments. The relationship determined herein may aid in the decision-making process of the balance of urbanization and vegetation. [ABSTRACT FROM AUTHOR]

• Climate change and urban growth combined caused surface runoff increases in an urban agglomeration. • At regional and city scales, climate change was the main driver of the runoff change. • At sub-city scale, urban growth might have the greatest effect on the runoff change. • Solutions at multiple scales are needed to surface runoff in mega-cities. Climate change and urban growth have been shown to alter hydrological processes, particularly surface runoff. While there is widespread evidence of the combined effects of climate change and urban expansion on surface runoff at the watershed scale, little is known about their combined effects at different scales in large-scale urban agglomerations. Using integrated spatial models, we simulated the combined effects of climate change and urban growth on surface runoff in the Beijing-Tianjin-Hebei urban agglomeration, China, in 2030. We found that the surface runoff increased under both climate change and urban growth scenarios but the relative contributions of these two factors to surface runoff change varied from the regional to the sub-city scale. At regional and city scales, climate change was the main control on surface runoff change, while urban growth had the most effect on surface runoff change in the main urban areas of some cities, i.e., the sub-city scale. The results indicate that one single factor does not give reliable information for managing surface runoff in megacities, as the effects of other factors are ignored. These findings highlight that integrated solutions must be tailored to the appropriate spatial scale to effectively regulate surface runoff change across large-scale urban agglomerations. [ABSTRACT FROM AUTHOR]

• A 39-day microclimatic experiment was conducted in a medium South American city. • The study found that the UHI effect can significantly increase the cooling demand. • The magnitude of the UHI effect is dependent on the LCZ. • The study provides valuable insights into the UHI effect on residential buildings. • Simplified methodology based on CDD was applied to study the energy consumption. The intensification of urban growth and climate change are affecting the energy and thermal performance of buildings. In recent years, the issue of building resilience to urban heat island (UHI) conditions has become increasingly relevant. The energy performance of buildings can vary significantly in different areas of the same city, regardless of their size. The aim of this study was to evaluate the energy effects of UHI intensity differences in various local climate zones (LCZs) of Valdivia city, Chile, on a typical residential dwelling. A simplified methodology was used, based on the assessment of cooling degree days variations and heat gains variations inside the studied house through the thermal envelope. Valdivia has a homogeneous urban morphology, and three types of low-rise LCZs prevail in the city (LCZ 3, 6, and 9). The results showed that the average cooling demand for a 66 m2 residential dwelling during 39 summer days was 158 kWh for LCZ 9, 219 kWh for LCZ 6, and 289 kWh for LCZ 3, compared to the rural demand of 114 kWh. These results suggest that the energy effects of UHI can be significant, and that it is important to study the microclimatic conditions in various LCZs for a correct understanding of the UHI energy effects on different buildings. [ABSTRACT FROM AUTHOR]

Highlights • The nearly-zero energy district concept is discussed, based on a literature review. • The aggregation of buildings shows advantages by the share of costs and resources. • Urban morphology and climate should be considered in districts performance assessment. • Urban morphology and climate influence districts renewable energy systems potential. • Studies focusing on NZED are few and more research is needed to fulfil its principles. Abstract The nearly zero-energy concept aims to achieve a significant reduction of energy consumption in the buildings' sector, while promoting the renewable energy dissemination. In order to move beyond the individual building boundary and to consider the urban context influence, this article presents a critical review on the aspects of applying the nearly zero-energy principle to the intermediate urban scale known as district , from an architectural and urban planning perspective. A contextualization on the definition of district is proposed, as well as a delimitation of the various urban scales and respective levels of detail, regarding the establishment of the nearly zero-energy district concept. Key urban elements as morphology, climate and public spaces are identified in literature, namely the geometric indicators that potentially influence districts' performance. The developed methodologies for calculating districts' energy performance and the respective metrics are explored as well. At the aftermath, challenges for further research opportunities are discussed, namely the need to develop methods to evaluate the real impact of the reviewed urban elements, to appraise the interrelations between climatic and morphological indicators, and especially to accurately include them in the energy performance assessment methodologies of districts. [ABSTRACT FROM AUTHOR]

Highlights • A meta-analysis of the Envi-met evaluation results of 52 studies is performed. • The median of the reported MAE and RMSE of Tair is 1.34 °C and 1.51 °C respectively. • The cooling potential of trees and cool materials, reported in 97 studies is analyzed. • The median peak temperature drop due to urban trees is 1.0 °C. • The median peak Tair drop due to cool pavements and extra trees is 2.0 °C. Abstract Increased urban air temperatures due to urbanization affect the buildings' energy performance and the outdoor thermal comfort. Aiming on the establishment of adaption strategies, urban microclimate studies using numerical simulation tools are gaining great scientific attention. The ENVI-met model is one of the most widely employed dynamic simulation tools. Considering its increasing use, this review aims to perform a meta-analysis of the reported evaluation results, reflecting the capability of the model to accurately calculate the diurnal profile of microclimatic variables. The analysis relies on the definition of the range of the reported metrics and on the investigation of the relationship between the various indices, applied for the model evaluation. Secondly, the study assesses the existing ENVI-met simulation results, indicating the cooling potential of urban greenery and cool materials regarding air, surface and mean radiant temperature modifications. The analysis revealed that the model can be considered as a helpful tool for urban climate analysis, provided that its limitations and features are accounted for by the user during the interpretation of the simulation outcome. Concerning the performance of the examined mitigation strategies, the most popular technology is the urban greenery while the mitigation potential is increased when different strategies are combined. [ABSTRACT FROM AUTHOR]

Abstract Outdoor thermal comfort significantly influences the users' experience in urban places; affecting their extent of usage. The paper aims to identify the outdoor thermal benchmarks for the temperate oceanic (Cfb) climate zones in Australia. It examines the perception of thermal comfort in two urban places in Melbourne city. Field measurements were conducted during summer and winter seasons along with 2123 valid questionnaires and observations in both contexts. Micrometeorological parameters were measured and used to calculate the mean radiant temperature and the physiological equivalent temperature (PET). The questionnaire provided information regarding the thermal sensations and preferences of the users using ASHRAE seven-point and McIntyre scales respectively. The quantitative analysis is used to calculate the range of outdoor thermal comfort in Melbourne. This was ranged between 20 °C and 25 °C PET. Additionally, the neutral and preferred temperature were found to be 20.4 °C and 19.2 °C respectively. Variations in different benchmarks were observed with different seasons and types of urban places. The results endorse the significant impact of thermal adaptation factors on the users' comfort levels and acceptability for micrometeorological environments. The findings also identify the different thermal benchmarks that help urban designers creating comfortable outdoor places within the oceanic temperate climatic zones. [ABSTRACT FROM AUTHOR]

Highlights • Wind-tunnel (WT) tests and CFD simulations were performed on a reduced-scale urban model. • The impact of different sets of inflow conditions was quantified. • Mean wind velocity, turbulent kinetic energy and yaw and pitch angles values of WT test and CFD simulations were compared. • The agreement between WT and CFD results was quantified using four validation metrics. Abstract Wind flow modeling in urban areas is usually performed by means of Wind-Tunnel (WT) testing or Computational Fluid Dynamics (CFD) simulations. Results obtained with both techniques can be affected by the boundary conditions. This study aims at investigating how two sets of inflow conditions, termed set 1 and set 2 and calculated respectively using the equations proposed by Richards and Hoxey (1993) and Tominaga et al. (2008), may affect the accuracy of the results in terms of mean wind speed, turbulent kinetic energy, yaw and pitch angles when predicting wind flows in urban areas. 3D steady RANS simulations were performed for a selected urban area (“ Quartiere La Venezia ” in Livorno, Italy). WT tests on the same urban model were used to validate the CFD simulations. Mean wind profiles at 25 positions in the urban area were compared and the statistical performance was quantified using four metrics for both sets of inflow conditions. The results obtained using the two sets of inflow conditions showed comparable performances in terms of wind flow predictions in the urban canopy, which means that at the building scale there is no need to use more accurate conditions because they are as effective as the simpler ones. [ABSTRACT FROM AUTHOR]

The phenomenon of global warming and urban heat island has raised the attention for the outdoor thermal comfort. Vegetation in the improvement of urban micro-climate has been confirmed by a large number of scholars. The problem of the uncomfortable summer climate in China's Cold region didn't get enough research. In this paper, the summer thermal comfort of the street canyon has been studied. The main research intent is to analyze the relationship between the street tree pattern and the thermal comfort of street canyon. It’s demonstrated that, in north-south street, it is suitable for the arrangement of street tree to be at the centre on one side of the street. The higher the height of the street tree, the more comfortable thermal environment the street gets. However, when the ratio of the street tree height to the street building is greater than 10:18, the improvement effect decreases. In east-west street, planting the street tree clinging to the street buildings can provide a better thermal comfort improvement effect. The result of this study can serve as a guide to the greening design in China B-cold region and other regions with similar climatic conditions. [ABSTRACT FROM AUTHOR]

The large-scale urbanization has changed the surface characteristics of cities, seriously affected the urban heat balance, and worsened the urban thermal environment. The regulation of vegetation coverage on urban climate has received extensive attention. This study aims to assess and predict changes in the urban thermal environment of Hangzhou by monitoring the variation of the urban thermal field variance index (UTFVI) affected by fractional vegetation cover (FVC) changes. The results indicate that the area of high level FVC will likely decrease by 52.91%, while the area of lower level FVC will likely increase by 33.83% from 1990 to 2030. Furthermore, the area of the strongest level UTFVI shows a significant trend of expansion, increasing by 467.299 km2 from 1990 to 2030, while the area of other levels UTFVI shows different degrees of decrease. Specifically, the area of strong and stronger levels UTFVI decreased by 149.641 km2 and 271.464 km2, respectively. The spatial correlation between the FVC and UTFVI changes is significant and negative, with p -values less than 0.01 and negative coefficients. The absolute value of the coefficient will decrease from 0.416 to 0.246 from 1990 to 2030, and the correlation between FVC and UTFVI changes shows a decreasing trend. The linear regression analysis demonstrates a significant negative correlation between the FVC and UTFVI, with coefficients ranging from −0.470 to −0.280 and p -values less than 0.001, further indicating that the FVC has a mitigating effect on the urban thermal environment. This study provides new insights into thermal environment governance and urban sustainable development. [ABSTRACT FROM AUTHOR]

• The local cooling and warming effects of different site types were compared. • The thermal effects of the four site types can be described using the same method. • Land cover properties highly influence the local cooling and warming effects. • Water has the strongest overall thermal impact on cities. • Planning strategies were discussed based on different types and overall effects. Local temperature changes caused by urban sites strongly influence regional climate and the urban heat island effect. The social functions and natural attributes of different site types result in different local thermal impacts; however, current studies have poorly considered a uniform methodology to describe the local cooling and warming effects of these sites. Therefore, this study proposes a uniform multi-perspective methodological framework to assess the cooling/warming effects and influencing factors of different site types, considering parks, water, industrial parks, and independent factories in four northern Chinese cities. The results show that the cooling/warming effects of the four site types can be described using the same methodology. First, parks have the greatest cooling gradient and the least cooling distance, intensity and efficiency, water and independent plants show similar cooling/warming effects, and industrial parks have the least stable warming distance and gradient. Second, biophysical parameters and coverage of impervious surfaces, vegetation, and water strongly influence cooling/warming effects and have opposite effects both internal and external to the sites. Third, considering synergistic thermal effects, the quantitative accumulation of land surface temperature showed the strongest overall thermal impact from water and the unequal distribution of cooling sites in the four cities. [ABSTRACT FROM AUTHOR]

• This study presents a novel Decision Support Model to map opportunities for climate adaptation. • High resolution data are essential to map land use in both public and private space. • The Decision Support Model supports action-driven implementation of climate adaptation. • The DSM shows a relationship between urban design and different adaptation measures. • The DSM can support in evaluating adaptation efforts in different spatial contexts. [ABSTRACT FROM AUTHOR]

Nowadays, in the field of urban climate there is a scientific need to compare Land Surface Temperature (LST) with screen-level air temperature (2mTair). This is done in our study for Iași city, based on MODIS imagery and in situ observations. We assess firstly the Surface Urban Heat Island (SUHI) under clear-sky conditions, outlining that SUHI is well expressed during the year, while its geometry tends to be more compact and regular during nighttime. In summer daytime SUHI extension reaches its peak, being outlined by the 35°C isotherm, while in winter SUHI is absent during the day. LST increases clearly when imperviousness ratio (IMD) overpasses the 10% threshold, and its maximum is reached for IMD higher than 80%. The comparison with the in situ observations indicates that the LST – 2mTair differences are the highest during daytime in spring and summer (+ 5 to +7°C), while during winter no major differences are observed. For the nighttime the LST is generally 1 to 3°C lower than 2mTair. We also found that intense radiative conditions are prone to increase both the SUHI intensity, and the difference between LST and 2mTair. The results are meant to support the policies aiming to mitigate the heat island effects on human population. [ABSTRACT FROM AUTHOR]

• Mitigating urban heat island from the perspective of cold island connectivity. • A method for constructing the combined cooling pattern is proposed;. • The key cooling sources are identified based on the morphospatial pattern analysis;. • Cooling network and key nodes are identified based on circuit theory;. • Proposals for climate adaptation planning at three levels: "surface-line-point". Many studies have proposed strategies to mitigate the urban heat island effect from the perspective of landscape patches, yet few studies have mitigated the urban heat island effect from the perspective of cold island connectivity and network. This study proposes a new method (Combined cooling pattern) to connect cold islands: surface (Cooling sources) - line (Networks)- point (Cooling spaces, heating spaces). Initially, the cooling sources are identified by the morphological spatial pattern analysis method and connectivity analysis. Then, to enhance the connectivity of the cooling sources, we applied circuit theory to construct the cooling network and identify the critical nodes. Finally, three levels of cooling strategies are developed. Taking Wuhan as an example, the results show that 27 cooling sources, 58 cooling corridors, 69 cooling spaces and 26 heating spaces are identified. A regional cooling network should be constructed to mitigate the urban heat island effect, and effective cooling measures should be implemented in key node areas. The method adopted can provide new ideas for sustainable urban development and urban climate adaptation planning. [ABSTRACT FROM AUTHOR]

• The urban microclimate effects are considered in an urban scale building simulation. • Building energy model is coupled with a high-resolution urban climate model. • Average overheating risks are underestimated by 140% without considering urban microclimate. • The building thermal performance is highly affected by neighboring urban contexts. • Subdivided flats in old buildings are more susceptible to the extreme heat event. Urban microclimate conditions could be an important factor influencing building thermal performance. However, most studies on urban building energy modeling (UBEM) use the typical meteorological year (TMY), often developed from observations of exposed/ rural sites, as input weather data. This study aims to assess the impacts of urban heat on building thermal performance by coupling a high-resolution urban climate simulation with UBEM. The simulated building thermal performance was compared with and without urban microclimate considerations, using weather data from the TMY, weather observations from an urban oasis, and outputs from the urban climate model. The physical parameters of typical building archetypes used in the UBEM were calibrated against indoor-measured data during summer days. Without considering urban microclimate, there was an underestimation of up to 140% of the average overheating risks of urban buildings. Furthermore, neighboring urban contexts and building ventilation rates considerably affected the thermal performance of individual buildings within high-density urban areas. The study reveals that neglecting the influence of the urban microclimate can result in a notable error in the building thermal performance assessment even at the urban level. [ABSTRACT FROM AUTHOR]

Urban planning, building design and landscaping can all provide strategies for urban heat island mitigation, urban microclimate improvement and directly affect people’s living environment and thermal comfort. This study aims to contribute to urban design for a new residential area of 10,000 m 2 area and urban aspect ratio (UAR) of 1.8, which is planned to be built in Osaka, Japan. In order to explore an appropriate design for the planned new residential area via rational building envelope and landscaping designs to better improve the urban microclimate, the effect of urban albedo (UA) and urban green covering (UGC) on the urban microclimate of the planned new residential area in terms of affecting outdoor air temperature and mean radiant temperature was analyzed using the environmental analysis model, ENVI-met. A total of six scenarios with UA of 0.3 and 0.7, UGC of 0%, 20% and 40%, were simulated. It showed that the scenario simulated with low UA of 0.3 and UGC of 20% has the greatest potential in improving urban microclimate of the planned new residential area in Osaka among the six simulated scenarios. [ABSTRACT FROM AUTHOR]

Urban development is characterized by drastic land use and land cover changes, rooted in urban population growth, with concomitant anthropogenic stressors contributing to global climate changes. While there is extensive literature on predicting future urban climate using biogeophysical models, the direct link between emergent meteorological patterns and the population dynamics remains obscure. In this study, we propose an operative model for predicting regional urban warming using a population growth model based on different scaling laws. We choose the annual average air temperature as the climatic indicator and relate the historical temperature data to demographic data for 10 megapolitan areas in the United States. Linear regression analysis shows that the coefficients of determination range from 0.164 to 0.563 for the study areas, indicating a significant correlation between urban population and climate dynamics. Specifically, the Phoenix Metropolitan Area is selected as a testbed where the regression is applied for correlating the population growth and trend of urban warming. Based on the predicted population dynamics, the annual average temperature in Phoenix is projected to increase by 0.65–3.40 °C in the next 35 years. [ABSTRACT FROM AUTHOR]

Hanoi City, Vietnam, presented the Hanoi Master Plan 2030 in 2011 to cope with rapid population growth and urbanisation. The main objective of this two-part study was to investigate the impacts of land use changes from the master plan on urban climate in Hanoi by 2030. This paper focuses on the assessment of the proposed green strategies in the master plan. Firstly, numerical simulations using the Weather Research and Forecasting were conducted for the present land use condition as well as the condition proposed by the master plan under the same extremely hot summer conditions of June 2010. Secondly, the results of the master plan condition were further compared with other scenarios comprising different green strategies for assessing their cooling effects. Based on the results, the daytime peak air temperature in the master plan condition was projected to maintain almost the same level as the current condition. However, the high temperature areas with temperature of 40–41 °C, would expand widely over the planned built-up areas. Further results showed that the green strategies were not necessarily effective at cooling all of the built-up areas, though these strategies largely reduced the air temperature within the green spaces, particularly at night. [ABSTRACT FROM AUTHOR]

• Non-stationarity detection and stage division are used to understand climate change. • Performance assessment under various Shared Socio-economic Pathways scenarios. • An antecedent dry day has a significant impact on urban hydrology. • Fully centralized or decentralized layouts are vulnerable to long-term climate change. • Decentralization to an appropriate degree can enhance the coupled system. Integrated grey-green infrastructure (IGGI) is considered as a promising facility for mitigating urban flooding during extreme rainfall events. However, most IGGI strategies rely heavily on historical precipitation data, while ignoring the long-term effects of dynamic and non-stationary climate change. This study explored a novel approach to assess the hydrological performance under Shared Socio-economic Pathways (SSPs) based on long-term rainfall time series, facilitating the exploration of the optimal solution to urban runoff alleviation in response to climate change. A case study of Guangzhou, China, was applied for model validation. The results indicated that through non-stationarity analysis and stage division, the future time series of the three SSPs were identified with an abrupt point and divided into two stages. By assessing the hydrological performance of the optimal solutions at various historical and future stages, both fully centralized IGGI and decentralized IGGI are found vulnerable to long-term climate change. Based on a certain extent of trade-off between centralization and decentralization, an appropriate degree of decentralization was observed to significantly enhance the hydrological performance of the whole IGGI system, particularly for reducing peak flow following extreme rainfall events. The authors believe that the findings could provide a novel perspective on hydrological performance assessment for IGGI in response to non-stationary and multi-scenario climate change in urban catchments with high built-up density. [ABSTRACT FROM AUTHOR]

• Arid and tropical cities showed high sensible heat of 90.33 W m−2 and 108.48 W m−2. • Temperate climate showed increasing annual trends for both UHI and ΔHI. • The later period (2001–2014) demonstrated greater UHI and ΔHI trends. • The UHI was more intense compared to ΔHI. • UHI was positively correlated with temperatures in warm and hot cities while ΔHI correlated with precipitation in cool cities. Rising temperatures that occur in cities resulted in the urban heat island (UHI) which could also amplify the thermal heat stress, leading to climate-related human mortality. Our study investigated the UHI effect and thermal heat stress of heat index (HI) in the U.S. cities of arid, continental, temperate, and tropical climate zones. A long-term simulation of the UHI and HI was conducted using Community Land Model version 5.0 from 1990 to 2014. The relationship between UHI and HI with climate and energy flux variables are analyzed with divided study periods (1990–2000 and 2001–2014) for the urban impact assessment. Higher sensible heat was reported, particularly in the arid and tropical cities. Further, temperate climate showed increasing annual trends for UHI and ΔHI (urban-rural difference of HIs). The later period (2001–2014) demonstrated greater UHI and ΔHI trends, implying a notable change in climate and urban expansion. The UHI, ranging from 0.73 °C to 2.07 °C, was more intense compared with ΔHI, at 0.14 °C to 1.23 °C. The UHI was positively correlated with temperatures in hot cities, while precipitation is the main driver in the continental city. For ΔHI, the relationship with temperatures were dominant in all climate zones. [ABSTRACT FROM AUTHOR]

• A comprehensive review on urban heat island is discussed. • The impact of several variables on urban heat island is analyzed. • A significant research gap exists in connectivity infrastructures. • The significance of different assessment tools are critically analyzed. • This review provides the future research scope of urban heat island. The global boom in urbanization and infrastructure, especially in developing countries, has resulted in the urban heat island (UHI). This recent review analyzes the current development of UHI to understand the impacts of various environmental measures on UHI. Further, this study identifies how UHI research is conducted based on multiple parameters and scenarios. The study grouped the variables under eight different measuring criteria and the most used variables from the literature. Several objectives are identified, and an analysis of the tools used to measure the relevant UHI variables is also performed. The major findings from the earlier studies are critically examined. A significant research gap exists in connectivity infrastructure, which has not been included in most UHI studies, including above-ground metro lines and highway infrastructure. The present review helps analyze the impact of environmental metrics on the UHI phenomenon in various ways to meet sustainable development goal-11 effectively. Thus, the presented recent review on the impact analysis of different environmental measures on UHI is beneficial to the scientific and architectural community and the betterment of society. [ABSTRACT FROM AUTHOR]

• Urban green spaces' CE tended to be larger in drier cities. • AT dominated CE in most cities while WS dominated CE in cities with larger WS. • CE responded oppositely to meteorological variables before/after Fc turning point. Increasing land surface temperature (LST) is one of the concerns associated with urbanization. Urban green spaces are gaining attention given its well-documented heat mitigation effects. However, the cooling efficiency (CE) of green spaces, which can be quantified as the negative ratio of LST changes to per unit change of vegetation cover fraction (Fc), has received less attention. CE can be influenced by climate background, but the impacts remain unclear. Here we selected LST and meteorological data across 7 days of heatwaves in six major African cities and analyzed the CE and its response to meteorological variables. The mean CE generally ordered by arid (Cairo, 7.51 and Kano, 6.81), temperate (Cape Town, 9.21, Durban, 5.95 and Nairobi, 5.88), and tropical (Accra, 3.26) cities. CE showed a positive response to temperature dew point difference and wind speed. Air temperature dominated the CE in most cities, but in cities with higher wind speeds, wind speed exerted greater influences. CE in all cities experienced significant changes when the Fc reached a specific value (Fc turning point) and CE's response to meteorological variables also reversed following the threshold. The clarification of climatic impacts and Fc turning points provides climate-tailored recommendations for urban green space planning. [ABSTRACT FROM AUTHOR]