Decoding the seasonal variations in the synergistic effects of multidimensional urban morphology on carbon emissions and air temperature

Impact of urban morphology on land surface temperature: A case study of the central Tokyo, Japan

Integrating Urban Morphology and Sustainability: Developing a Spatially Hierarchical Assessment Framework

Abstract. Currently, the diurnal asymmetric and nonlinear mechanisms by which urban morphology modulates the canopy urban heat island (CUHI) during heat wave (HW) periods remain underexplored. This study aims to fill this gap by focusing on the area within the Fifth Ring Road of Beijing, integrating three complementary methods: XGBoost (to identify key morphological drivers), ENVI-met (to reveal nonlinear regulatory processes), and wind environment analysis (to supplement dynamic modulation). The results show that: (1) HW periods significantly enhance CUHI intensity (CUHII) compared to non-heat wave (NHW) periods, with a 91.3 % increase in daytime and 52.7 % at night; (2) XGBoost identifies building coverage ratio (BCR) as the core daytime driver of CUHII, while sky view factor (SVF) dominates at night, and both 2D and 3D morphological indicators exert stronger effects during HW periods; (3) ENVI-met simulations reveal nonlinear mechanisms of building height/SVF: daytime thermal environments are co-driven by short-wave radiation shading and ventilation resistance (as SVF decreases), while nighttime environments are dominated by long-wave radiation accumulation by buildings; (4) Wind environment analysis further shows diurnal differences in wind's role: nighttime ventilation corridors mitigate CUHII by 33.91 %–42.09 %, while daytime prevailing winds may exacerbate downstream CUHII via thermal advection. These findings clarify the diurnal asymmetric mechanisms of CUHI and provide scientific support for urban morphological optimization under extreme heat.

Abstract Deep learning (DL) methods are an emerging tool that can be well-suited to uncover the complexity of urban vitality at a greater scale and depth. However, the use of DL in the field, even beyond vitality, remains underexplored, with no established state of the art to inform further research. This paper assesses DL’s potential as a research tool, critically examining its current status, benefits, and challenges in urban vitality studies. Through a systematic review, we analysed 208 publications that examine qualities under the dimensions of the built environment and urban activity, tying big data resources and DL methods to the study of urban vitality. A thematic analysis was conducted, identifying research contributions, big data types, resources, and DL architectures. Our findings indicate that DL shows significant promise for urban vitality research due to its scalability, multimodal capability, and fine-grained analysis, particularly of visual data. While it is well-established in analysing the built environment, its applications in human activity and spatio-temporal dynamics are only emerging. Multi-source data use is growing, enhancing robustness, but challenges such as geographic bias and high data requirements persist. Notably, over half of studies originate from China, limiting the generalizability of findings to contexts with different data availability, urban morphologies, and socio-cultural dynamics. Nonetheless, this synthesis serves as a critical assessment of DL’s possible future role in urban vitality studies.

Rapid urbanization in China has exacerbated the dual challenges of urban heat islands (UHIs) and air pollution, threatening urban sustainability. We conducted a national-scale analysis of the spatiotemporal dynamics and synergy between the surface UHI intensity, distinguished as daytime (DUHI) and nighttime (NUHI), and major air pollutants (PM2.5, PM10, NO2) in 370 Chinese cities (2000–2019). Using multi-source remote sensing, ground-based monitoring, and urban data, we applied coupling coordination and correlation analyses to quantify these interactions. Key findings reveal distinct patterns: (1) The annual mean land surface temperature (LST) rose, with the nighttime LST (NLST) increasing faster than the daytime LST (DLST). Conversely, the UHI intensity showed an overall decline, with the DUHI decreasing more than the NUHI. (2) Air pollutants displayed strong seasonality; while PM10 concentrations decreased slightly over the long term, NO2 levels rose significantly. (3) Monthly, pollutants correlated negatively with LST (R2 > 0.92 for PM2.5), suppressing the DUHI but intensifying the NUHI. Long-term, the correlation trend revealed a strengthening synergy, particularly between particulate matter and NUHI (trend R2 = 0.50). (4) Spatially, over 90% of cities exhibited high UHI–particle coordination. Key associated factors include anthropogenic activities, urban morphology, and natural mitigation factors. We conclude that disrupting the heat–pollution synergy requires integrated strategies, namely reducing emissions at the source, optimizing the urban form, and enhancing ecological regulation. This is essential for advancing low-carbon, climate-resilient urban development.

This study examined residents’ perceptions, preferences, and experiences of urban green spaces in four regional units of the Region of Attica—West Athens, Central Athens, South Athens, and Piraeus—demonstrating how demographic diversity, urban morphology, and external stressors—such as extreme heat and the COVID-19 pandemic—shape green space use. The results show that, while green spaces are essential for health, well-being, and social cohesion, their distribution is uneven, which limits their availability (27.3%) and access (21.8%) to residents. Main concerns expressed by residents when visiting green spaces and open green spaces are poor maintenance (50.7%), lack of security (36.7%), and socially irresponsible behaviour (e.g., littering, vandalism) (32.8%). Extreme heat emerged as a major constraint on outdoor activities, particularly affecting women and the elderly. Household-associated outdoor areas (balconies, courtyards, and verandas) were highly valued (59.8%), highlighting the role of private green spaces in dense urban environments. Major metropolitan parks were the most visited and valued by residents for providing contact with nature (23.0%) and benefiting from stress relief (54.0%) while practicing their favourite activity, though their use was limited during heatwaves (30.3% of the residents do not visit). Most activities during and after the COVID-19 pandemic were reported unchanged, though reported increases in walking (34.3%) and park visits (28.3%) demonstrate the importance of green spaces in fostering urban resilience. However, the reported lack of engagement in gardening (48.0%), indoor plant care (41.2%) and bird/wildlife watching (58.3%) suggest missed opportunities for ecological and cultural enrichment. Overall, the study underscores the urgent need for integrated planning strategies to improve accessibility, maintenance, and equity in green space provision. By aligning with the sustainable development goals, the four regional units of the Region of Attica can transform its green infrastructure into an inclusive, resilient system that supports public health, social inclusion, and climate adaptation.

Rio de Janeiro’s favelas house over 20% of the city’s population in just 5% of its territory, with Rio das Pedras emerging as a critical case study: ranking as Brazil’s fifth most populous favela and its most vertically intensified. This study quantifies how uncontrolled vertical growth in informal settlements disrupts microclimate dynamics, directly impacting thermal comfort. Using high-resolution geospatial analytics, we integrated digital surface models (DSMs) derived from LiDAR and photogrammetric data (2013, 2019, and 2024) with microclimatic simulations to assess urban morphology changes and their thermal effects. A spatiotemporal cadastral analysis tracked vertical expansion (new floors) and demolition patterns, while ENVI-met simulations mapped air temperature anomalies across decadal scenarios. Results reveal two key findings: (1) rapid, unregulated construction has significantly altered local airflow and surface energy balance, exacerbating the urban heat island (UHI) effect; (2) microclimatic simulations consistently recorded elevated temperatures, with the most pronounced impacts in densely built zones. These findings underscore the need for public policies to mitigate such negative effects observed in informal settlement areas.

The subsurface of cities is warming up-undergoing an underground climate change caused by subsurface urban heat islands (SUHIs). This phenomenon poses hazards while offering opportunities for sustainable urban heating. Although the morphology of urban areas above the ground is renowned for markedly influencing surface heat islands, the impact of the underground urban morphology on SUHIs remains largely unexplored. This study aims to (i) extend the definition of quantitative variables for analysing the urban morphology from the surface to the subsurface of cities and (ii) systematically examine how the underground urban morphology affects the intensity of SUHIs. Using three-dimensional (3D), time-dependent finite element simulations, we assess the role of different underground morphological features of cities in the development and intensity of SUHIs, such as heat source dimensions and distribution, green-space density and ground properties. Results indicate that the size and density of underground heat sources primarily drive the overall intensity of SUHIs, strongly depending on the presence of groundwater flow and only secondarily on the ground thermo-physical properties and the presence of green areas at the surface, whose influence substantially vary with depth. These findings enhance the understanding of the mechanisms governing SUHIs and provide insights to mitigate them globally.This article is part of the theme issue 'Urban heat spreading above and below ground'.

Air pollution has become one of the most pressing environmental challenges affecting public health, urban ecosystems, and climate stability. Vegetation plays a crucial role in mitigating air pollution through various biophysical and biochemical processes, yet its effectiveness depends on multiple contextual factors. This literature survey aims to analyze and synthesize existing research on the role of vegetation in improving air quality, focusing on the mechanisms involved and the determinants that influence its performance across different environments. The study employs a systematic literature review approach, drawing from peer-reviewed journal articles, conference proceedings, and government reports published in the last two decades. The analysis highlights key mechanisms such as pollutant absorption through leaf surfaces, particulate matter deposition, and microclimate regulation that contribute to air purification. The findings also reveal that vegetation effectiveness is shaped by species characteristics, plant density, urban morphology, climatic conditions, and maintenance practices. The study concludes that strategic vegetation planning can significantly reduce urban air pollutants and enhance environmental health. These insights hold important implications for urban planners, environmental policymakers, and public health authorities in designing green infrastructure and sustainable urban landscapes. Further empirical research is recommended to quantify vegetation’s differential impact under varying ecological and socio-economic settings.

This study investigates how future climate change will influence urban microclimates in Athens, Greece, focusing on two representative districts classified as Local Climate Zones (LCZ2 and LCZ3). Using the ENVI-met model, microclimate simulations were conducted to assess projected air temperature variations under moderate (Representative Concentration Pathways RCP4.5) and high (RCP8.5) emission scenarios for mid- and late-century conditions. The analysis reveals a consistent warming trend across both districts, with average air temperature increases of approximately 2–3 °C by mid-century and up to 4.5 °C by the end of the century. Morphological characteristics were found to significantly affect thermal behavior: areas with wider street canyons exhibited higher temperatures due to increased solar exposure, whereas shaded inner courtyards remained relatively cooler. The study’s novelty lies in its integration of high-resolution urban microclimate modeling with climate scenario analysis for a Mediterranean metropolis, a combination seldom explored in previous research. The findings underline the importance of incorporating urban morphology into climate adaptation planning, supporting the design of low-carbon and thermally resilient urban forms in densely built environments.

This study investigates the multidimensional impacts of rooftop greening in urban public buildings through integrated energy simulations and urban spatial analysis. Focusing on a school in Szombathely, Hungary, the research employs Energy Plus to quantify energy efficiency improvements while exploring theoretical frameworks for urban-scale benefits. Three core propositions are examined: Rooftop vegetation enhances building energy performance through thermal regulation; Strategic green roof placement enables climate resilience and social comfort improvements; Systematic implementation can create interconnected green networks redefining public space functionality. Simulation results demonstrate a 22% reduction in annual cooling energy demand, with peak reduction of 23.1% in July during summer months. Heating energy savings average 6% annually, reaching 10.5% in February. Theoretical projections suggest significant improvements in pedestrian thermal comfort zones through municipal-scale implementation. The study proposes a conceptual framework for transforming isolated green roofs into continuous urban networks. Spatial analysis indicates potential dual benefits: enhanced heat island mitigation through coordinated vegetation placement and increased social interaction opportunities via strategically connected green spaces. This approach advocates redefining rooftops as multifunctional infrastructure addressing climate resilience and social needs. Methodologically, the research bridges building physics simulation with urban morphology studies, it pioneers an approach that scales localized interventions to urban social experiences. By analyzing how a single school's green roof modifies both its energy profile and surrounding community interactions, we establish a replicable model for converting individual building upgrades into city-wide social space networks. The innovation lies in correlating energy-saving metrics with spatial accessibility parameters to identify "social multiplier nodes" - public buildings whose roof greening can disproportionately enhance neighborhood connectivity. This human-centered methodology empowers planners to prioritize sites where technical improvements concurrently activate underutilized aerial territories as community assets. Importantly, the study demonstrates how standardized building retrofit data can inform place-specific social space design, challenging the conventional dichotomy between energy efficiency projects and public realm development. The outcomes recast green roofs as social infrastructure catalysts, providing policymakers with evidence that technical interventions in public architecture inherently carry untapped potential for reconfiguring urban relationship networks. This paradigm shift positions roofscapes not merely as energy-saving surfaces, but as deliberate instruments for choreographing social encounters across vertical urban dimensions.

Abstract. Urbanization’s vertical shift underscores the need for accurate building height estimation to support sustainable planning. Existing methods, limited by low-resolution data and poor generalization, cannot resolve individual buildings. We propose a training-free approach leveraging the foundation model Depth Anything V2 for relative depth estimation from high-resolution remote sensing (RS) imagery. To address GPU memory constraints, RS images are cropped into overlapping patches, and their depth predictions are unified using a height-weighted graph optimization with Levenberg–Marquardt refinement, prioritizing building-related errors. A viewpoint bias filter, modeled as terrain variation, converts relative depth to height by subtracting a morphologically derived DEM. Experiments on Google satellite imagery with 0.5 m resolution over 20 km2 in Wuhan, validated against airborne LiDAR, show an R2 of 0.73 for building heights, significantly outperforming the state-of-the-art MLSBRN whose R2 is only 0.25 and which underestimates tall buildings. Without annotation or training, our scalable method accurately estimates individual building heights, generalizes across complex urban morphologies, and provides a robust solution for 3D urban studies.

Unveiling the impact of 2D/3D urban morphology on thermal comfort across urban-rural gradients using machine learning

Modulation mechanisms of seasonal PM2.5 by 2D/3D urban morphology under background meteorological conditions: Insights from a GAM-based analysis

Urban morphology and residential building net-zero energy performance: A global meta-analysis

Urban morphology and energy self-sufficiency: A comparative study of residential blocks in eight global cities

Abstract Reaching the European Commission’s rooftop-solar target of 560 GW by 2050 demands precise building-level analysis beyond broad mapping. This study presents and validates a detailed workflow for urban photovoltaic (PV) assessment, integrating Geographic Information Systems (GIS) with PV*SOL Premium simulations, and evaluates the influence of roof types and urban morphology. Rather than relying on generalized, area-based estimates, this method utilizes detailed 3D simulations and geometric assessments of rooftops in Bologna, Italy. Roof segments with low technical performance or excessive shading are excluded, thus reducing common overestimations associated with simpler approaches. As a result, the workflow provides a clearer evaluation of both technical and economic feasibility for large-scale rooftop solar deployment. Key indicators such as energy yield, shading losses, performance ratio, return on investment, and payback period are considered. On average, this method achieved a return on investment of 6.83% and an annual CO₂ emission reduction of nearly 197,500 kilograms. Although demonstrated specifically in Bologna, this workflow can be adapted for other cities with similar urban characteristics. These findings offer practical benchmarks for more reliable urban solar planning.

Multi-objective optimization framework of tropical urban block morphology for balanced daylight illuminance, visual comfort, and energy load in central community gymnasium

Multi-objective optimization of urban block morphology for thermal comfort and air quality: An integrated CFD-machine learning framework