Building Density Research Articles

The potential of alternative water sources for potable water savings in Ukraine's residential areas

Universal access to safe drinking water is not only a fundamental need but also a basic human right. Water reuse and rainwater harvesting systems provide essential technical alternatives for water supply management. There are several alternative water sources available for reuse after some needed treatment: rainwater, stormwater, greywater, drainage water etc. The integration of on-site non-potable water reuse enhances the environmental goals of a green building by lowering the consumption of potable water and decreasing the volume of storm-water and wastewater that must be sent to the City's Wastewater Treatment Plants for treatment. The purpose of the article is to analyze two approaches for partially replacing tap water (rainwater harvesting and treated greywater reuse), to compare the constructive characteristics of these systems and calculate annual volume the potential water savings in Ukraine's residential areas using each option. Both methods offer sustainable solutions to alleviate water scarcity but differ significantly in construction, application, and operational mechanisms. For a comparative analysis of the two indicated alternative sources, are present the constructive features of water supply in the context of rainwater collection and use, and greywater. The volume of greywater generated in low-rise residential areas and rainwater that can be harvested from roofs was counted using Ukranian normative documents. The result shows at a building density of 0.4, the greywater volume is 1.45 times greater than that of rainwater; at a density of 0.54, it is 1.49 times greater under Ukraine's maximum annual precipitation of 750 mm. With comprehensive water recycling systems, it is possible to reduce freshwater demand by approximately 30% through greywater reuse and by around 10% with rainwater harvesting. These savings offer both economic and environmental benefits, making water recycling especially advantageous for water-scarce regions seeking to mitigate the impacts of climate change.

Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people’s well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Baidu Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of green coverage rate has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) The performance of the GWRF model in predicting GVI is excellent and far exceeds that of comparison models.; (4) Whether it is the green coverage rate, urban built environment or socioeconomic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect. With the help of these non-linear influences and explicit threshold effects, quantitative analyses of greening are provided, which can help to assist urban planners in making more scientific and rational decisions when allocating greening resources.

Characteristics of spatial and temporal distribution of carbon emissions and influencing factors in Hefei City based on nighttime lighting data

Urban areas are significant contributors to global carbon emissions. Investigating the spatiotemporal distribution characteristics of urban carbon emissions and their influencing factors is essential for formulating effective carbon reduction policies and enhancing urban environmental quality. This study utilized nighttime light data to analyse the spatiotemporal distribution of carbon emissions in Hefei City. We employed a Random Forest regression model to explore the impact of urban spatial morphology on carbon emissions. The findings indicated that carbon emissions in Hefei have shown a consistent annual increase without a clear spatial pattern. Regression analysis revealed a nonlinear positive correlation between carbon emissions and factors such as floor area ratio, building density, architectural volume and staggered degree. Conversely, the body mass index and enclosure factor demonstrated a nonlinear negative correlation with carbon emissions. Amongst them, the body mass index was shown to have the highest impact weight on urban carbon emission, which was 0.195, followed by architectural volume and floor area ratio, which were 0.155 and 0.145, respectively. This research highlights the complex influence of urban spatial morphology on carbon emissions and provides valuable insights for the development of urban carbon reduction strategies.

Integrated impacts of surrounding building density and gap permeability on pollutant dispersion in four-way street networks.

Gaps between buildings facing the streets can effectively accelerate the natural removal of pollutants in street canyons by enhancing ventilation and diffusion processes. This removal process is closely related to gap permeability (Pg, ratio of gap width to street length) and building density (λn) surrounding the streets. However, the integrated effects of these two parameters on pollutant dispersion remain unclear, because of the limited computational resources and the difficulty of traditional modeling methods to discrete the numerous buildings. Numerical simulations coupled with the porous media approach are performed for this purpose. Results indicate that for isolated streets surrounded by empty land (λn = 0), the high-speed airflow through the gaps causes significant flushing effects on pollutants in the street, and thus larger Pg induces better street air quality than the case without gaps (Pg = 0). Conversely, when the streets are surrounded by buildings (λn ≠ 0), the air quality is good for both smaller Pg = 0 and larger Pg = 52 %, and the worst case occurs in Pg = 24 %. Moreover, because of the blocking effect of surrounding buildings, the percentage of pollutant removal by the gaps decreases with increasing λn, which can reach up to 50 % for λn = 0, decreasing to 30 % for medium density (λn = 0.22). Notably, for high density (λn = 0.44), only when Pg > 52 % the pollutant concentration can be reduced compared to Pg = 0. The present findings can serve as principles for determining appropriate building gap layouts at different building densities, thereby improving the urban air quality more effectively.

Three-dimensional City Modeling for Microclimate Simulation of Urban Areas

Abstract Increasing urbanization and building density can trigger an increase in environmental temperature, thereby influencing urban climate change. Urban management that responds to climate change is urgently needed, requiring a smarter urban design approach than before. As geospatial science and technology develop, conventional two-dimensional methods will transition to more complex three-dimensional spatial modeling. This approach has great potential in supporting climate change-responsive decision making in urban environments. Three-dimensional city modeling at the LOD 2.0 level was used for the microclimate simulation. The research was conducted in the Central Business District (CBD) of Mayjend Sungkono, Surabaya, which has special urban characteristics related to the urban climate, such as variations in the use of building functions, such as residential, village, commercial, apartment, or hotel, as well as unique types of roofs in the area. The parameters used for the simulation included air temperature (°C), relative humidity (%), wind speed (m/s), and wind direction. Three-dimensional city modeling was performed using a semi-automatic method based on LiDAR data and aerial photos. The simulation was carried out by integrating three-dimensional city modeling with the visualization of microweather parameters in Envi-Met, along with pattern indicators from building facades. In addition, a vegetation scenario was created to reduce the temperature. The second factor is a comparison between the existing conditions and the scenarios that have been created.

Centrifugal model test of deep covered karst collapse induced by static overloading of dense building groups

Centrifugal model test of deep covered karst collapse induced by static overloading of dense building groups

Air PM10,2.5 Removal by Urban Green Space Under Urban Realistic Stressors

Urbanization has significantly altered the ecological resources, functions, and services, thereby imposing specific constraints on particulate matter (PM) mitigation through green spaces. To investigate the effect of green spaces on mitigating PM10,2.5 under multiple urban stressors, this study employed combined remote sensing imagery and small-scale quantitative measurements to identify the PM within green space and street tree, and their PM differences with the square underlying surface according to a continuous scale of 60~3000 m. The results indicated that urban stressors significantly influenced air PM10 and PM2.5 mitigation, with stressors LST (land surface temperature) and RD (traffic road density) as key stressors on air PM10, while LST, ISA (impervious surface area), BH (building height), NDVI (normalized difference vegetation index), GA (green space area), and WA (water body area) were key stressors on air PM2.5. Furthermore, stressors exhibited a significant scale effect on air PM10,2.5 mitigation; for air PM2.5, stressors ISA, RD, BH and BD (building density) had a notable impact on air PM2.5 mitigation at 1500~3000 m scales, while NDVI, GA, and WA showed a significant impact at 450~600 m. For air PM10, stressors ISA, BH, NDVI, and GA revealed a continuous scale effect, with the key scales occurring at 450 m and 3000 m. In summary, urbanization stressors can combine to affect air PM10 and PM2.5 mitigation by green spaces, especially at different spatial scales, to provide practical guidance for urban planning.

Identifying localized heat zones in urban heat islands from a hill and saddle perspective

Urban heat islands are highly heterogeneous with localized heat zones (LHZs) forming hill-like patterns representing temperature peaks; however, the generation and segmentation of these LHZs remain unclear. This study focused on Jinan, Shandong Province, China, and used a watershed algorithm to segment the urban heat island into LHZs, with inner parts and segmentation lines considered as “hills” and “saddles,” respectively. The influences of urban parameters on the “hill” temperature and “hill–saddle” temperature differences were elucidated. Results showed that (1) temperatures in the LHZ hills were notably higher than those in LHZ saddles; (2) population density (PD) had the highest ability to influence the temperatures in the LHZ hills, exerting a warming effect of ∼0.7°C for PD > 10,000 persons/km2; and (3) urban parameters could drive temperature differences in two main ways. PD and building height followed monotonous upward paths, promoting the hill parameters and making them significantly higher than the saddle parameters, thereby increasing the temperature differences, whereas NDVI and building density followed “V” shaped paths, which could be expanded by unilaterally increasing the hill and saddle parameters, thereby increasing the temperature differences.

Synergising Machine Learning and Remote Sensing for Urban Heat Island Dynamics: A Comprehensive Modelling Approach

This study evaluates the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI) effects through a place-based approach. Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and climate data in Turin, Italy. A detailed analysis of the ex-industrial Teksid area revealed a significant reduction in Surface Urban Heat Island Intensity (SUHII), with decreases of −0.94 in summer and −0.54 in winter following regeneration interventions. Using 17 variables in the Random Forest model, key determinants influencing SUHII were identified, including building density, vegetation cover, and surface albedo. This study quantitatively highlights the impact of increasing green spaces and enhancing surface materials to improve solar reflectivity, with findings showing a 19.46% increase in vegetation and a 3.09% rise in albedo after mitigation efforts. Furthermore, the results demonstrate that integrating Local Climate Zones (LCZs) into urban planning, alongside interventions targeting these key variables, can further optimise UHI mitigation and assess changes. This comprehensive approach provides policymakers with a robust tool to enhance urban resilience and guide sustainable planning strategies in response to climate change.

Multi-Objective Optimization of Outdoor Thermal Comfort and Sunlight Hours in Elderly Residential Areas: A Case Study of Beijing, China

To optimize the outdoor thermal comfort and sunlight hours of elderly residential areas in cold regions of China, we collected data on streets and building forms from 121 elderly residential sites in Beijing. Utilizing parametric modeling tools to generate ideal residential models, a multi-objective optimization algorithm was applied to identify 144 Pareto solutions. The optimal solutions were analyzed using K-means clustering and Pearson correlation analysis to examine how block form affects outdoor environmental performance. The universal thermal climate index (UTCI) in summer showed significant positive correlations (r > 0.72) with the distance between buildings (DB), building density (BD), shape coefficient (SC), and coefficient of variation for building height (CVH), and significant negative correlations (r < −0.82) with average building height (AH), floor area ratio (FAR), volume area ratio (VAR), mean building area (MA), average building volume (AV), and open space ratio (OSR). Winter UTCI was significantly positively correlated with AH, FAR, VAR, MA, and AV (r > 0.83) and significantly negatively correlated with DB, porosity (PO), SC, and CVH (r < −0.88). Sunlight hours were significantly positively correlated with DB, PO, OSR, and CVH (r > 0.84) and significantly negatively correlated with AH, BD, FAR, SC, VAR, MA, and AV (r > 0.88). Courtyard and point-building configurations performed the best across all optimization objectives. (The value of r, Pearson’s correlation coefficient, ranges from −1 to +1. r = +1: Perfect positive correlation, r = −1: Perfect negative correlation, r = 0: No linear correlation)

Risk avoidance during dispersal: Temporal and behavioral shifts in selection by non-resident eastern wolves (Canis c.f. lycaon)

Dispersal is important for connectivity and persistence of small, disjunct wildlife populations. However, in human-altered landscapes dispersing animals are often more likely than residents to use anthropogenic features that increase mortality risk. As such, quantifying spatial and temporal responses of dispersing animals to anthropogenic features will improve our understanding of the potential for small, isolated populations to expand in human-altered landscapes. The distribution of eastern wolves (Canis c.f. lycaon; a threatened species in Canada) is limited to portions of eastern Canada within and adjacent to the population core in Algonquin Park. Recently, it was found that non-resident eastern wolves dispersing outside of Algonquin Park survive poorly, primarily due to high rates of human-caused mortality. We deployed Global Positioning System collars on 26 non-resident eastern wolves. We evaluated resource selection of dispersing individuals during different movement states to better understand their behavioral responses to anthropogenic landscape features during this critical, but risky period of their life history. Eastern wolves avoided areas with high building density during the day. However, during directed movements outside the winter period, eastern wolves selected areas closer to unpaved roads and areas with higher building densities at night. During winter, eastern wolves selected areas with high building densities at night while displaying tortuous movements. We demonstrate that harvested species may increase and relax avoidance of anthropogenic features at both daily and seasonal timescales. Failing to account for temporal and state-specific variation in resource selection patterns of dispersing animals may underestimate their ability to move through human-altered landscapes.

Landscape urbanism in the context of the historical environment

Currently, most people live in cities, which leads to an increase in building density, a reduction in public spaces, and a violation of the ecological balance. Therefore, one of the main tasks facing architects and designers is to maintain ecological balance, which, in turn, contributes to the creation of more comfortable living conditions. This article examines the need for more effective integration of landscape urbanism techniques in the context of the historical urban environment. It is also proposed to take into account the methodology of urban space design based on the study of the environment, natural characteristics and interactions between them, economic, cultural and social aspects.

Assessment of Health-Oriented Layout and Perceived Density in High-Density Public Residential Areas: A Case Study of Shenzhen

Rapid urbanization has intensified public housing development and building density, posing significant challenges to residents’ well-being and urban sustainability. With the population of the Greater Bay Area on the rise, enhancing the spatial quality of public housing is now essential. The study proposed a quantitative framework to evaluate the relationship between the residential design elements and perceived density in high-density public housing neighborhoods. It employed a virtual reality perception experiment to analyze the relationship between significant spatial indicators and perceived density by investigating 16 high-density residential layout models in 3 configurations: Tower-Enclosed, Balanced Slab-Enclosed, and Staggered Slab-Enclosed. The results indicate that: (1) greater building height intensifies perceived density, leading to sensations of overcrowding and discomfort; (2) an increased sky ratio mitigates perceived density, fostering a more open and pleasant environment; (3) recessed residential facades enhance residents’ density perception; and (4) Staggered Slab-Enclosed Layout configurations receive the most favorable evaluations regarding perceived density. The authors attempt to go beyond current regulations to propose tailored solutions for Shenzhen’s high-density context, improving spatial efficiency and residential comfort in future public housing designs. The finding provides scientific evidence to support urban planners and policymakers in developing more resilient and sustainable high-density neighborhoods.

Influence of Urban Morphologies on the Effective Mean Age of Air at Pedestrian Level and Mass Transport Within Urban Canopy Layer

This study adapted the mean age of air, a time scale widely utilized in evaluating indoor ventilation, to assess the impact of building layouts on urban ventilation capacity. To distinguish it from its applications in enclosed indoor environments, the adapted index was termed the effective mean age of air (τ¯E). Based on an experimentally validated method, computational fluid dynamic (CFD) simulations were performed for parametric studies on four generic parameters that describe urban morphologies, including building height, building density, and variations in the heights or frontal areas of adjacent buildings. At the breathing level (z = 1.7 m), the results indicated three distinct distribution patterns of insufficiently ventilated areas: within recirculation zones behind buildings, in the downstream sections of the main road, or within recirculation zones near lateral facades. The spatial heterogeneity of ventilation capacity was emphasized through the statistical distributions of τ¯E. In most cases, convective transport dominates the purging process for the whole canopy zone, while turbulent transport prevails for the pedestrian zone. Additionally, comparisons with a reference case simulating an open area highlighted the dual effects of buildings on urban ventilation, notably through the enhanced dilution promoted by the helical flows between buildings. This study also serves as a preliminary CFD practice utilizing τ¯E with the homogenous emission method, and demonstrates its capability for assessing urban ventilation potential in urban planning.

Assessment of Critical Environmental Risk Zones Around Wetlands in Colombo, Sri Lanka, Using Satellite Remote Sensing and Multi-criteria Decision Tools

Colombo, Sri Lanka’s commercial capital, is recognized as one of the first 18 global sites that were awarded the Ramsar Wetland City accreditation, and is a prime example of the intersection between urbanization and wetland conservation. Thus, this study aims to conduct an environmental risk assessment that can provide insights into the changes in biophysical properties driven by urbanization over the 2000–2020 period. The study used Geographical Information Systems and a remote sensing approach to assess critical environmental risk zones around urban freshwater lakes in the Colombo district of Sri Lanka, including Thalangama, an environmental protection area experiencing sub-urbanization. Satellite data (2000–2020) were employed, and spectral indices, including NDVI, NDBI, NDWI, NDMI, RVI and LST, were calculated. Intersecting areas with multiple risk factors were determined, and distances from these zones to lakes were calculated to generate spatially explicit risk maps using inverse distance weighting. The building density has consistently increased from 2000 to 2020 in all three sites, with ‘very high’ density areas expanding notably. NDVI reveals a gradual increase in ‘very high’ categories until 2015. Plant-water stress analysis shows significant water stress in 2015, and water body areas have decreased after 2010 in all the sites. Multi-criteria assessment identifies critical environmental risk zones around urban lakes, indicating high vulnerability to environmental damage and degradation. Overall, the study emphasizes the importance of leveraging data and risk analysis to inform decision-making processes in environmental management. By doing so, policymakers can enhance the effectiveness of conservation measures and promote the long-term sustainability of natural resources.

Urban form and seasonal PM2.5 dynamics: Enhancing air quality prediction using interpretable machine learning and IoT sensor data

This study investigates the critical issue of how urban form characteristics influence PM2.5 concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM2.5 concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM2.5 levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.

Land Use and Land Cover Change Analysis in Brazilian Urban centres: an approach using Cellular Automata and Neural Networks

Abstract. During the 1970s, Brazil experienced most of its population living in cities for the first time. Historically, urbanization has been intensely related to land use and cover changes, amplifying climatic and ecological stress in recently established metropolises. This study aims to analyze, through classified Remote Sensing images and the Cellular Automata and Artificial Neural Networks (CA-ANN) machine learning model, land use and land cover trends in regions that have experienced accentuated demographic growth in the decades 2000 and 2010. The methodology consists of: i) identifying areas with a high density of buildings, ii) defining the variables that drive land use change, and iii) proposing a methodology for predicting changes in the urban area. The results indicate that the urban class prediction presented high precision (≥ 0.74) and recall (≥ 0.86) indices. Forest class also presented a high precision score (≥0.72), showing an elevated prediction hit rate. Furthermore, the proposed methodology improved the results obtained in previous works for the same cities, presenting higher Kappa values in all cases.

Analysis of Building Distribution and Accessibility of Kanigoro Urban Roads

The Kanigoro Settlement Area's land is mostly utilized for residential and agricultural purposes. Modifications to the city's construction and development are necessary to turn Kanigoro into a sustainable urban region, particularly in the field of urban morphological pattern development. City or area morphology may be analyzed using three primary components: building density, land use patterns, and road network patterns. This research aims to investigate the urban morphology pattern in Kanigoro Urban Area, focusing on road accessibility and building dispersion. In order to accomplish this goal, land use and building distribution were identified using satellite image data from 2011, 2015, 2019 and 2023, along with questionnaire data on residential location selection. The findings demonstrate that elongated patterns dominate settlement patterns in Kanigoro, with group patterns and the main road network following closely behind. Kinship and historical factors also play a role in this pattern. During the study, there was a notable increase in settlement growth, particularly on main transportation routes. The population density analysis shows that there is a concentration of population in the city center and some rapidly developing residential areas as a result of this rapid settlement growth. According to the road accessibility analysis, most areas are served by arterial roads, but there are still some areas with limited accessibility, especially with regard to collector roads. Based on the research results, there is a need for sustainable integrated spatial planning, adequate infrastructure, improving the quality of the collector road network and developing integrated public transportation.

An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality

Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R2 = 0.987 and root mean square error (RMSE) = 0.15 mg/m3, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R2 values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.

Spatial suitability of urban land use models for poverty alleviation in the cities of Nigeria

Many models depicting the spatial dimensions of poverty are based on cities in developed countries, raising questions about their appropriateness for urban spaces in developing countries. This study evaluates the spatial suitability of these models in understanding poverty distribution and informing alleviation efforts related to Sustainable Development Goal (SDG). One in cities of developing nations. A grid sampling technique in ArcGIS 10.8 was used to select 1098 households across 56 wards in three mid-sized Nigerian cities. Household data were collected through structured questionnaires using GeoODK, and Maxar high-resolution imagery was used to assess urban building density. The Foster-Greer-Thorbecke (FGT) poverty indices and Pearson correlation were applied for analysis. Results showed that over 29 wards had a poverty incidence and gap index greater than 0.20, while poverty severity remained below 0.20 in most wards. High building density was more prevalent in the city’s interior. Significant correlations were found between the income poverty gap (p = 0.005, r = 0.367), poverty severity (p = 0.009, r = 0.346), and building density. The study concludes that these models are suitable for informing poverty alleviation policies in mid-sized cities of developing countries, especially Nigeria.