Urban Morphological Parameters Research Articles

Exploring the Impact of Urban Morphology on Building Energy Consumption and Outdoor Comfort: A Comparative Study in Hot-Humid Climates

Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology’s role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates.

A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)

Abstract. In recent years, urban canopy models (UCMs) have been used as fully coupled components of mesoscale atmospheric models as well as offline tools to estimate temperature and surface fluxes using atmospheric forcings. Examples include multi-layer urban canopy models (MLUCMs), where the vertical variability of turbulent fluxes is calculated by solving prognostic momentum and turbulent kinetic energy (TKE, k) using mixing length scale (l) and drag parameterizations. These parameterizations are based on the well-established 1.5-order k−l turbulence closure theory and are often informed by microscale fluid dynamics simulations. However, this approach can include simplifications such as assuming the same diffusion coefficient for momentum, TKE, and scalars. In addition, the dispersive stresses arising from spatially averaged flow properties have been parameterized together with the turbulent fluxes despite being controlled by different mechanisms. Both of these assumptions impact the quantification of the turbulent exchange of flow properties and subsequent air temperature predictions in urban canopies. To assess these assumptions and improve corresponding parameterization, we analyzed 49 large-eddy simulations (LES) for idealized urban arrays, encompassing variable building height distributions and a comprehensive range of urban densities (λp∈[0.0625,0.64]) seen in global cities. We find that the efficiency of turbulent transport (numerically described via diffusion coefficients) is similar for scalars and momentum but is 3.5 times higher for TKE. Additionally, parameterizing the dispersive momentum flux using the k−l closure was a source of error, while scaling with the pressure gradient and urban morphological parameters appears more appropriate. In response to these findings, we propose two changes to the previous version of MLUCM: (a) separate characterization for turbulent diffusion coefficient for momentum and TKE and (b) introduction of an explicit physics-based “mass-flux” term to represent the fraction of the dispersive momentum transport directly induced from buildings as an amendment to the existing “eddy-diffusivity” framework. The updated one-dimensional model, after being tuned for building height variability, is further compared against the original LES results and demonstrates improved performance in predicting vertical turbulent exchange in urban canopies.

Ambient temperature modelling from surface characteristics and associating urban morphology with thermal discomfort

Urban heat island assessment is of paramount importance when monitoring microclimate changes, increased heat stress, mortality and energy consumption. Simply analysing land surface temperature patterns for human comfort and health assessment is often inadequate. In this study, we attempt to resolve this inadequacy with ambient temperature modelling from multiple surface characteristics for a tropical megacity. A 336 datapoint‐based multilinear regression model was formulated to predict the maximum summer air temperature with a mean absolute error of 0.694°C. Specific locations of critical thermal environments were demarcated with Getis‐Ord statistic. Based on our results, extreme hotspots covered 10.53 per cent of the city on a daily basis at 99 per cent confidence level. The application of an outdoor thermal comfort index highlighted the existence of very strong heat stress zones over 14.67 per cent of the study area. The air temperature hotspots were verified with intra‐urban variation in carbon sequestration. This environmental parameter was used to strengthen the observed results since, carbon sequestration directly links urbanization with degradation of thermal environments. Further, five urban morphological parameters were analysed to conclude that building density and height were the most significant urban design factors leading to increased air temperature. Simultaneous local climate zone mapping depicted that heat islands were dominated by mid and high‐rise built‐up areas, of which 68.44 km2 of that area is suitable for vertical and roof gardening. Another 33.29 km of road stretch was delineated within low‐rise built‐up areas with scope for green landscaping to improve urban sustainability.

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents.

A comprehensive study of how urban morphological parameters impact the solar potential, energy consumption and daylight autonomy in canyons and buildings

Over the past two decades, global greenhouse gas emissions have surged, leading to rising sea levels and climate change. Solar energy has emerged as an excellent solution to this problem. This study aims to assess daylight availability and energy consumption in urban canyons and blocks in a semi-arid climate while exploring the correlation between the heterogeneity of urban morphological parameters and key research variables. The study employed field measurements and simulations using Ladybug Tools and UMI. Data were collected from urban canyons and blocks in three areas, amounting to 1,366,982 m2. The findings indicate that areas with abundant greenery and H/W ratios exceeding 1.8 experience the most significant shading effects in urban canyons. Selecting suitable trees and placing them in strategic locations can enhance energy efficiency. Furthermore, broad urban canyons (H/W < 0.5) receive four times more solar radiation than narrow urban canyons (H/W > 1.8) with high tree density, peaking at 240 kWh/m2 during summer. In urban blocks, rooftops have the highest solar potential, with imbalances between building heights and interbuilding shading as factors that reduce solar capabilities. Tree coverage significantly affects the solar potential of facades, with some urban blocks receiving less than 5,000 kWh/m2 of solar radiation on the southern facades each month. Finally, this study examined the impact of heterogeneity within urban morphological parameters on the principal research variables. The results indicate that the heterogeneity observed in urban morphological parameters affects daylight availability and energy consumption within urban blocks.

The Concurrent Effect of Building Height Diversity and Cool Pavement Materials on Air Temperature Near the Surface of an Urban Facade: A Case Study of Shahriar Street In Esfahan, Iran

The rise in urban temperatures is a significant threat to the urban heat island effect, increasing building energy demand. This increase is worrisome because the supply of renewable energy is a big challenge. The approach to improving the urban microclimate offers a promising solution. This research investigates the concurrent effect of urban morphological parameters and physical characteristics of urban surfaces, such as cool materials, on the urban microclimate near the building's facades in an urban street. This evaluation was conducted by ENVI-met(v4). The results show that the concurrent effects of increasing building height diversity and by using cool pavement materials are more helpful in reducing the average air temperature of an urban street. Because the amount of shade and wind speed increased as building height variety increased, the absorption of solar radiation decreased as pavement material albedo increased. As a result, these two parameters reduced air temperature by 0.8 °C. Also in the combined scenario of increasing building height diversity and by using cool pavement materials, the air temperature near the building's facades was reduced by 1°C on the first and second floors and by approximately 0.5°C on the upper floors.

A new method for detecting urban morphology effects on urban-scale air temperature and building energy consumption under mesoscale meteorological conditions

Impact of urban morphology on urban-scale building energy consumption under mesoscale climate has not been elucidated. A new model was developed to examine the impact using the mesoscale Weather Research and Forecasting (WRF) model. For the first time, local climate zone and building categories (LCZBCs) are integrated into WRF, handling landuse/landcover and building configuration. The urban morphology parameters are clustered into three groups: urban structure, vegetation fraction, and impervious surface thermal properties. Our results show that urban structure induces a higher urban heat island (UHI) intensity at nighttime than does in daytime. The difference in urban-rural building/street structure is the key factor in compact and open high-rise areas, especially commercial-dominant ones, where the UHI intensity and AC load are increased by 40.33% and 34.52%, respectively. Besides, greenery is most effective to mitigate UHI problems, lessening AC demand in middle-rise and low-rise areas. Its benefit is most prominent for residential-dominant areas that could reduce the UHI intensity and AC load as much as 106.26% and 53.13%, respectively. In contrast to rural pavement, urban impervious surfaces induce negligible increases in temperature (4.75%) and AC load (2.33%). The outcome provides insights into the dominant parameters in LCZBCs and the strategy for urban (re)development.

The impacts of urban canyons morphology on daylight availability and energy consumption of buildings in a hot-summer Mediterranean climate

Proper sunlight access has a positive impact on the physical and mental health of citizens, as well as on reducing energy consumption. This capacity may be limited by inappropriate urban morphology, and urban canyons and buildings are deprived of proper daylight. The purpose of this research is to study the current solar potential on the surface of the canyons and buildings and the effect of urban morphology parameters on the amount of each of the main variables of the research in Ilam, Iran. First, solar radiation, daylight illuminance, and air temperature were measured at different points of the studied canyons for three days. After that, the studied area was modeled with Rhino-Grasshopper software, and simulation was done using Ladybug Tools and UMI plugin. The results showed that the amount of daylight available in most canyons is low. Less than 30 % of the canyon’s surfaces benefited from 6 h of direct sunlight during the measurement. The simulation results for the 62 studied buildings showed that the highest amount of daylight is related to buildings with a height of more than 10 m. 56 % of the buildings had an annual solar radiation of less than 500 mw/m2. The roofs of the buildings received the most radiation, accounting for 72 % of the total radiation. By examining the parameters of urban morphology, it was found that the most influential parameter on the amount of solar radiation was the floor area, and the height of the buildings had the most influence on energy consumption.

Comparing multiple machine learning models to investigate the relationship between urban morphology and PM2.5 based on mobile monitoring

The development of compact cities has deteriorated the air quality and threatened public health. To improve air quality, it is necessary to systematically analyze the complex relationship between urban morphology and air pollution to propose spatial optimization strategies. This study selected Nanshan District in Shenzhen, China as a case study, and used the street-level exposure to PM2.5 obtained from mobile monitoring as the dependent variable. Nine urban morphology parameters extracted from 3D building dataset and street view images, as well as other environmental parameters, were used as independent variables. A multiple linear regression model and six machine learning models were constructed for investigating the optimal one. For the study area, the results indicated that the RF and the XGBoost were the best-fit models with the R2 of 0.95 and 0.96 respectively. The frontal area index at 20 m height, the standard deviation of volume obstruction ratio, the building coverage ratio at 20 m height and the standard deviation of building height are the key urban form factors influencing PM2.5. In urban renewal and design practices, it is recommended to maintain the frontal area index within the range of 0.10–0.35, the standard deviation of volume obstruction ratio within the range of 4 × 10−5–8 × 10−5, and the standard deviation of building height below 48 m or above 64 m within the 1000 m buffer zone. These findings provide a generalizable analytical framework for understanding PM2.5 dispersion, and the differentiated applicability of models and parameters is recommended to be considered for different cities.

Study on the Correlation Analysis between Urban Morphological Factors and Microclimate Based on Empirical Methods on a University Campus in a Hot-Summer–Cold-Winter Region

With the continuous progress of urbanization, contemporary scholars are increasingly focusing their attention on the study of urban microclimates. This research aims to investigate the relationship between urban morphology and microclimates in regions characterized by hot summers and cold winters. Environmental meteorological data, including temperature, solar radiation, and wind speed during the spring and summer seasons, were collected through on-site monitoring in a specific area of Shanghai. Various methods, such as on-site surveys and drone photography, were employed to obtain and analyze a range of urban morphological parameters, including floor area ratio (FAR) and greenery ratio (GnPR). The sky view factor (SVF) was determined using a fisheye lens technique. Subsequently, the collected datasets were analyzed to assess the varying impact of distinct urban morphological factors on microclimates. The findings indicate a significant correlation between different building morphology factors and temperature and solar radiation during spring, while their associations with wind speed become more prominent in summer. Notably, greenery and pavement demonstrate stronger correlations with temperature and radiation in summer, aligning with the seasonal growth patterns of plants. These research findings provide both a theoretical foundation and practical guidelines for the urban design of environmental microclimates.

Road surface temperature evaluated with streetview-derived parameters in a hot and humid megacity

This study explores the potential of a novel method that uses streetview-derived parameters to explain road surface temperature (RST) variations. The relationships between RST and streetview-derived parameters, including view factors (VFs), sunlit/shaded status, and sunlit hours, are investigated in an urban central district in a hot and humid climate. Thermal mapping of RST was conducted in 14 traverses at different times on a hot summer day and on both sides of the street canyon. The performances of streetview-derived parameters were also compared with commonly used urban morphological parameters. Results show that the correlations between RST and streetview-derived sky view factor (SVF) and green view factor (GVF) can reach 0.92 and − 0.93, respectively, at around 11:25. Building view factor (BVF) shows a weak correlation with RST and exhibits different directions of influence at different times of the day and in different types of routes. Correlations between streetview-derived VFs and RST are scale-dependent, varying by time of day and showing different patterns on the two sides of one canyon. The correlations between RST and traditional morphological parameters (BCR, FAR and meanH) are weaker than streetview-derived ones (sunlit hours, screen status, SVF and GVF). Multiple linear regressions show that streetview-derived view factors can explain 59% ∼ 82% of the spatial variation in daily maximum RST. The study highlights the potential of using streetview-derived parameters to predict RST.

The impact of urban morphology on the building energy consumption and solar energy generation potential of university dormitory blocks

Urban morphology is a major factor affecting building energy consumption and solar potential in the urban block. The aim of this research was to evaluate the impact of urban morphology on both building energy consumption and solar energy generation potential for university dormitory blocks in Wuhan. This paper proposed a classification method for dormitory blocks, calculated the building energy consumption and solar energy generation potential of 55 blocks, and analyzed the correlation between urban morphology and three energy performance indicators: Energy Use Intensity (EUI), Solar Energy Generation Intensity (SEGI) and Net Energy Use Intensity (NEUI). Multiple regression models were used to predict energy performance by urban morphological parameters. The results indicate that different block types could lead to up to 12.25% difference in EUI, and 35.85% significant difference in building NEUI with the deployment of photovoltaic (PV) panels. The EUI is mainly affected by three morphological parameters, which are Average length of block, Shape factor and Building density; while the SEGI and NEUI are mainly affected by Average height of block, Shape factor and Sky view factor. This study could serve as guidelines for planners and policymakers in campus planning and architectural design to improve building energy conservation at block-scale.

The Influence of Refined Urban Morphological Parameters on Dynamical and Thermal Fields in a Single-Layer Urban Canopy Model

In this study, localised and non-uniform urban morphology (UM) and urban fraction (UF) parameters are implemented in a single-layer urban canopy scheme in the Weather Research and Forecasting (WRF) mesoscale meteorological model. The purpose of this research is to evaluate the effect of the refined parameterisation scheme on the simulation of dynamic and thermal fields in the urban canopy of the Guangzhou metropolitan area. The results showed that, compared with the default urban canopy parameters of the WRF model, using the localised UM parameters resulted in the most significant improvement in the 10 m wind speed simulation. In urban districts, the mean bias between the observed and simulated 10 m wind speed was reduced significantly by 59% from 2.63 m/s to 1.09 m/s during the daytime. For the thermal environment simulation during the daytime, higher UF and UM values resulted in lower surface albedos and generated narrower street canyons compared with the default modelling setting, which caused more heat to be trapped in the urban canopy and ultimately led to an increase in the surface skin temperature (TSK) and a largely increased ground heat flux (GRD). As a result, at night, more heat was transferred from the ground to the surface, producing a higher TSK. The effect of the localised UF on the sensible heat flux (HFX) was closely related to the near-surface temperature gradient. The UM caused the HFX to increase during the daytime, which was related to the near-surface heat exchange coefficient in the lower model layers. As the high-resolution UM significantly altered the urban geometry, the dynamic environment simulation resulted in a large increase in friction velocity and a decrease in wind speed.

Towards better exploiting object-based image analysis paradigm for local climate zones mapping

Local Climate Zones (LCZs) have demonstrated significant promise in urban climate research. Although an LCZ consists of diverse geographic objects, most object-based LCZ mapping research encounters difficulty and seems to have no advantage due to the huge intra-class differences of objects within an LCZ. To address this problem, we propose a new object-based LCZ mapping framework based on multi-level features, which consist of information from pixel and region, bridging adjacent objects through region-level features while preserving original image features. As only using remote sensing imagery makes it hard to fully depict urban form, we modified urban morphological parameters (UMPs) to help with mapping, including sky view factor (SVF), building surface fraction (BSF), and permeable surface fraction (PSF). In addition, a refined sampling strategy is proposed for object-based LCZ mapping to avoid unrepresentative objects. Experiments were conducted in three cities (Hong Kong, Nanjing, and Nanchang), and the results showed that the accuracy of built LCZ types (OAbu) with modified UMPs and region-level features performed best, since they performed about 20% better than those without UMPs and region-level features. Furthermore, compared with patch-based methods, it is demonstrated that the object-based strategy can more appropriately depict the boundary of an LCZ. With further analysis of results at different scales and with features extracted from different region sizes, we found that built types benefit more than natural types at fine scales, and the region size of 17 × 17 is satisfied. In fact, we expect this study would directly reverse the unfavorable situation of object-based image analysis (OBIA) paradigm in LCZ mapping.

Analysis and Optimization of Thermal Environment in Old Urban Areas from the Perspective of “Function–Form” Differentiation

High-density urban areas have spatial characteristics, such as complex functions, population gathering, and complex forms, that lead to more severe urban heat island effects. Systematically evaluating the thermal environmental benefits of urban spatial forms to optimize the urban physical environment is important. In this study, Tianjin’s central urban area, which is a typical representative of high-density urban areas, was selected to invert the multi-period land surface temperature by relying on the existing two- and three-dimensional morphological data set of communities. The multi-scale geographically weighted regression model was used to fit the regression relationship between the urban land surface temperature and spatial morphological parameters. From this, the influencing factors of different types of existing community spaces and their spatial stabilities were explored. The results show the following: (1) The summer surface temperature varies greatly in the central urban area, and the high-temperature areas are mainly distributed in the industrial, residential, and commercial districts. (2) The MGWR model has the better model-fitting ability. The positive influence coefficients of temperature include ISP and BD, while the negative influence coefficients are BSD, BH, NDVI, and SVF. (3) There is significant spatial heterogeneity in the impact coefficients among the blocks that can be targeted to mitigate the heat island effect. This study provides ideas for optimizing the spatial morphological parameters of surface temperature in urban centers. Future challenges include increasing the spatial morphological parameter selection range, dissecting the interactive relationships between spatial morphological parameters and their effectiveness on the surface temperature, and refining the study’s spatial and temporal granularity.

Assessing the impact of urban morphological parameters on land surface temperature in the heat aggregation areas with spatial heterogeneity: A case study of Nanjing

In recent years, many studies on urban morphology have investigated the urban heat island. However, most of the studies have focused on the whole city rather than on heat aggregation areas. Also, their assessments rarely consider local variations of LST impacting factors, and their results lack planning applicability. In this study, the main urban area of Nanjing was used as a case study. Using open data, heat aggregation areas were identified and the impact of urban morphological parameters on LST in daytime was assessed. Based on the urban morphological parameters and their responses to LST, some morphological optimization strategies for heat aggregation areas with spatial heterogeneity were proposed. The results show that forest index (FI) (91.71%), water index (WI) (90.23%) and floor area ratio (FAR) (91.94%) suggest a negative effect on LST, while impervious surface index (ISI) (79.31%) and building density (BD) (95.55%) seem to have a positive effect on LST. And the correlation with the indicators in three dimensions and LST is unclear. Spatial heterogeneity within heat aggregation areas also exists. Based on this, this study suggests targeted local strategies for LST mitigation based on the overall optimization strategy. This study contributes to sustainable urban planning by providing useful insights into the influence of urban morphological parameters on LST.

Characterization of Non-Neutral Urban Canopy Wind Profile Using CFD Simulations—A Data-Driven Approach

Horizontally averaged wind profiles inside the urban canopy are used in many studies and numerical models. The existing analytical models are only applicable to a small range of aspect ratios and mostly neutral atmospheric conditions due to their underlying assumptions. In this study, a surrogate model for predicting horizontally averaged wind profiles in the street canyons of an idealized urban canopy for a wide range of urban morphologies and thermal forcing scenarios is developed with the help of machine learning techniques and computational fluid dynamics (CFD) simulation data. The influence of urban morphological parameters, atmospheric stability and wind conditions on the urban canopy wind flow is modeled using machine learning algorithms applied on CFD simulation results. The numerical model is validated using wind-tunnel data. Steady-state Reynolds averaged Navier–Stokes (RANS) simulations with a standard k-ϵ turbulence model for 252 different simulation conditions are performed on an idealized building geometry that consists of a regular array of 4 × 4 cubes. The simulation results are averaged horizontally to obtain the mean velocity and temperature profiles. Surrogate models are developed using the simulation outputs as training examples and the best model is chosen by comparing the performance of different machine learning models. The surrogate artificial neural network (ANN) model of this study outperforms the current state-of-the-art models in the prediction of horizontally averaged mean wind profile inside the urban canopy. The mean error (ME) and root-mean-square error (RMSE) of the discrete point prediction ANN model of this study are 0.016 m/s and 0.060 m/s, respectively, which is significantly lower compared to the best of the legacy models for which the errors are 0.048 m/s and 0.387 m/s, respectively.

Modeling urban canopy air temperature at city-block scale based on urban 3D morphology parameters– A study in Tianjin, North China

Urban 3D morphology significantly influences the outdoor thermal environment. Understanding the influence of urban expansion in both horizontal and vertical landscapes helps the canopy urban heat island (CUHI) effect mitigation. However, the microscale numerical CUHI models are difficult to be applied for large-area CUHI effect studies. On the other hand, the mesoscale CUHI models make a wider study area workable but lost some of the model accuracies. To perform a large-area study on the CUHI effect with relatively light computing costs and fine accuracy, this paper builds a canopy air temperature predicting model at city-block scale with urban 3D morphology parameters including building coverage ratio (BCR), grass coverage ratio (GCR) and the mean value of building height (BH) to obtain the citywide block-mean 2-m temperature (T2M). The model accuracy was validated through RMSEs and comparison with the mereological station data. The proposed model shows an RMSE of 0.286 straight °C and an R-square of 0.83. Using the validated model, Tianjin with an area of 647 km 2 was performed to investigate the effects of vertical landscape on the canopy air temperature under different scenarios between 2010 and 2016, including the changes in landcover and building heights. It finds that a 40% increase in BCR may lead to the highest canopy air temperature, and the increase of BH may lead to an increase in the canopy air temperature in low-rise and high-rise building areas, but there is an opposite trend in multi-story and mid-rise building areas.

Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns.

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.

Multicollinearity issue for the parameterization of urban ventilation potential with urban morphology

The purpose of this paper is to verify the effectiveness of urban morphological parameters as variables that can be used in the system to assess the efficiency of urban ventilation and provide insight into the direction of future index improvement. Through a verification process performed by numerical analysis using CFD, urban ventilation performance is quantitatively reproduced for various building shapes, and the corresponding results are investigated in terms of relationship with urban morphological parameters. Although the urban ventilation performance can be well evaluated by urban morphology, the multicollinearity issue was raised due to the high correlation between parameters. To confirm this issue in the actual urban condition, the present work analyzed the correlation between parameters for the entire Tokyo area in Japan. The results showed high correlations between the building plan area ratio and the building frontal area ratio. However, the correlation coefficients are different depending on the vertical scale of urban configuration. Based on the analysis results, it was confirmed that understanding the spatial characteristics of urban areas is an important aspect of improving the evaluation system. This result provides essential information for developing an assessment system for urban sustainability and environmental performance using urban morphological parameters.