Explore the vast range of titles available.

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Background Mental health disorders appear as a growing problem in urban areas. While common mental health disorders are generally linked to demographic and socioeconomic factors, little is known about the interaction with the urban environment. With growing urbanization, more and more people are exposed to environmental stressors potentially contributing to increased stress and impairing mental health. It is therefore important to identify features of the urban environment that affect the mental health of city dwellers. The aim of this study was to define associations of combined long-term exposure to air pollution, noise, surrounding green at different scales, and building morphology with several dimensions of mental health in Brussels. Methods Research focuses on the inhabitants of the Brussels Capital Region older than 15 years. The epidemiological study was carried out based on the linkage of data from the national health interview surveys (2008 and 2013) and specifically developed indicators describing each participant’s surroundings in terms of air quality, noise, surrounding green, and building morphology. These data are based on the geographical coordinates of the participant’s residence and processed using Geographical Information Systems (GIS). Mental health status was approached through several validated indicators: the Symptom Checklist-90-R subscales for depressive, anxiety and sleeping disorders and the 12-Item General Health Questionnaire for general well-being. For each mental health outcome, single and multi-exposure models were performed through multivariate logistic regressions. Results Our results suggest that traffic-related air pollution (black carbon, NO 2 , PM 10 ) exposure was positively associated with higher odds of depressive disorders. No association between green surrounding, noise, building morphology and mental health could be demonstrated. Conclusions These findings have important implications because most of the Brussel’s population resides in areas where particulate matters concentrations are above the World Health Organization guidelines. This suggests that policies aiming to reduce traffic related-air pollution could also reduce the burden of depressive disorders in Brussels.

The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments.

Urban building rooftops represent a high-potential source of solar PV power. However, prevalent estimation methods often overlook shading from surrounding structures, causing inaccuracies. This study proposes a method to estimate rooftop PV potential by integrating open-source multi-modal spatiotemporal data. By calculating solar radiation on building rooftops under both planar and 3D conditions, we derived a solar occlusion correction ratio. Using the morphological characteristics of buildings in Kunming, we established a random forest regression model to explore the impact of shading on rooftop solar radiation. After determining the correction ratio, our method achieves second-scale estimation of solar PV potential from planar conditions to those considering 3D morphological shading, reducing the global relative error from 3.66% to 0.04% (DSM-based benchmarks; 3.66% : FLAT-based; 0.04% : our ratio-based). This research includes the creation of solar radiation maps for the main areas in Kunming, analysis of PV generation potential, energy balance, and carbon emission reduction benefits, with Wuhan further used as a test city to validate the model. The findings provide valuable insights for future policy-making on PV installations, energy consumption, and grid emissions.

Building morphological indicators are widely used for microclimate regulation, but their complexity often hinders practical understanding and application. This study aims to simplify multiple morphological indicators into a smaller set of factors, and demonstrates that the simplified factors still account for microclimate effects. The study was conducted in Changsha, China. Microclimate and morphological data were collected within buffer zones ranging from 30 to 200 m in radius. Exploratory Factor Analysis (EFA) was performed on up to 12 morphological indicators, and the resulting factor scores were analyzed through regression with microclimate indicators. The results confirmed that: (1) 6 to 9 morphological indicators can be reduced to 1 to 3 factors, retaining most of the original information. The factor extraction is influenced by both the morphological indicators and the buffer radius. (2) The factors significantly impact air temperature, relative humidity, wind speed, mean radiant temperature, and the Universal Thermal Climate Index, with the extent of influence varying across microclimate indicators and buffer radii. By consolidating the shared characteristics of multiple morphological indicators, this study offers a more concise and integrated approach for describing building morphology, and its essential relationship with the microclimate.

Building performance simulations often rely on standardised meteorological datasets, which may not accurately reflect urban microclimates. This study highlights the complexity of temperature dynamics near buildings influenced by multiple factors. A static correction factor is unfeasible for adjusting meteorological data from suburban stations to city centres. To address this, air temperature variability in different urban locations was analysed using data from a city-centre weather station, 32 facade-mounted sensors and thermographic imaging. The selected locations for analysis are representative of approximately 25% of the housing stock in Wrocław, Poland. This was compared with standard meteorological data from Wrocław II, located at an airport. These analyses formed the basis for developing a predictive machine-learning model to account for thermal variability based on building type, location, facade orientation and facade materials. Focusing on the summer period to assess nocturnal cooling potential, the model achieved high accuracy, with R2 > 0.93 in training and 0.92 in validation. These findings underscore the need for microclimate-informed meteorological adjustments in building simulations. Practice relevance Implementing corrections to outdoor temperature in urban environments can significantly enhance the accuracy of energy modelling, particularly for natural ventilation strategies addressing urban overheating. However, due to the complexity of temperature dynamics near buildings, defining a single static correction factor for climate parameters such as temperature is unlikely to be effective. Analysis indicates that even multiple regression approaches result in correction factors that are difficult to apply in practice. Adjustments must be determined separately for facade orientation, building height, external wall structure and urban location. The proposed method enables the generation of dynamic, high-spatial-resolution temperature data, incorporating facade orientation and specific building floor heights. These refined datasets can be used to adjust meteorological station measurements, improving the accuracy of simulations. When applied to a typical meteorological year dataset, this approach may offer a way to better represent thermal variability in urban environments, supporting more context-sensitive building-energy modelling.

High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.

This study investigated the variation of outdoor air temperature in the shaded area covered by buildings in an urban university campus in Malaysia. In-situ field measurements were conducted to measure the distribution of outdoor air temperature at eight different locations for seven days. Meanwhile, the building-induced shadows were generated using the AutoCAD Revit software to investigate the air temperature change. The study used four urban morphological parameters namely building to greenery ratio, sky view factor (SVF), and height-to-street width (H/W) ratio. The relationship between building-induced shadow and outdoor air temperature (Tout) obtained from the in-situ measurement was investigated. The results showed that the building-induced shadows could lower air temperature. It can be noted that a high ratio of building to greenery resulted in a higher air temperature. In contrast, the area with a low SVF value due to the combination of prolonged shading by buildings and trees had a lower air temperature. Thus, the area with a high building ratio, low greenery ratio, higher SVF value, and low H/W ratio potentially has a higher outdoor air temperature. Conclusively, combination of building shading created by appropriate ratio of building morphology and sufficient greenery able to improve the microclimate of a campus area.

The rapid urbanization of urban areas in China has brought about great variation in the layout of cities and serious air pollution. Recently, the focus has been directed toward understanding the role of urban morphology in the generation and spread of atmospheric pollution, particularly in PM2.5 emissions. However, there have been limited investigations into the impact of three-dimensional (3D) features on changes in PM2.5 concentrations. By analyzing a wealth of data on building structures based on a mixed linear model and variance partition analysis in the Yangtze River Delta throughout 2018, this study sought to examine the associations between PM2.5 concentrations and urban building form, and further compared the contributions of two-dimensional (2D) and 3D building features. The findings revealed that both 2D and 3D building forms played an important role in PM2.5 concentrations. Notably, the greater contribution of 3D building forms on PM2.5 concentrations was observed, especially during the summer, where they accounted for 20% compared to 7% for 2D forms. In particular, the building height range emerged as a crucial local factor affecting PM2.5 concentrations, contributing up to 12%. Moreover, taller buildings with more variability in height were found to aid in the dispersion of pollution. These results underscore the substantial contribution of 3D building morphology to PM2.5 pollution, contrasting with previous studies. Furthermore, compact buildings were linked to lower pollution levels, and an urban landscape characterized by polycentric urban structures and lower fragmentation was deemed more favorable for sustainable urban development. This study is significant in investigating the contribution of 3D morphology to PM2.5 and its importance for pollution dispersion mechanisms. It suggests the adoption of a polycentric urban form with a broader range of building heights in urban planning for local governments in the Yangtze River Delta.

Due to the rapid densification of cities, improving outdoor comfort is becoming increasingly important. To address this need, the current study introduces a methodology to evaluate outdoor comfort in the proximity of typical buildings in Tallinn, Estonia. The microclimate simulation software ENVI-met was employed to investigate the outdoor comfort conditions. The research outcomes show that the building's form, height, density, and orientation change consistently the pedestrian comfort around the buildings. The findings suggest that the integrated analysis of different building morphologies, massing, orientation, and their influences on the surrounding microclimate, thermal, and wind comfort are important.