Frontal Area Index Research Articles

Exploring the influence of block environmental characteristics on land surface temperature and its spatial heterogeneity for a high-density city

A fundamental understanding of the spatial change trends and driving mechanisms of land surface temperature (LST) under urbanization is a prerequisite for the development of effective strategies to mitigate the urban heat island effect. In this study, the built-up blocks of Shenzhen, a high-density city in China, were selected as the unit of analysis. Multi-source datasets were utilized to calculate a total of 44 environmental characteristic indicators, covering four categories. In order to comprehensively analyze the influence of each environmental feature indicator on LST and spatial heterogeneity, MLR, XGBoost and MGWR models were constructed. Furthermore, the nonlinear relationship between the variables was investigated using the SHAP method. The results demonstrated that the predictive efficacy of the MGWR and XGBoost models was markedly superior to that of the MLR model. The percentage cover of forest, the average elevation, NDVI, the frontal area index and the standard deviation of building height were identified as the primary determinants of the LST. These factors account for more than 52% to the explanation of the LST distribution. The effects of the majority of landscape pattern, building form and street view indicators on LST exhibited spatial heterogeneity. Furthermore, the indicators also showed nonlinear patterns and threshold effects on LST. The findings offer valuable insights for enhancing the urban thermal environment, particularly in high-density urban areas.

Indoor PM2.5 and building energy consumption in nine typical residential neighborhoods in Nanjing under different ventilation strategies caused by traffic-related PM2.5 dispersion

Traffic-related PM2.5 seriously endangers human health and affects the energy consumption of building ventilation systems. This study investigates the impact of different ventilation strategies caused by traffic-related PM2.5 dispersion on indoor PM2.5 concentration and building energy consumption of nine typical residential neighborhoods in Nanjing using EnergyPlus software. Results of a third-floor apartment in a six-story slab-type parallel layout residential neighborhood (6F-Sp) show that strategy 1 (window opening, OW) and strategy 7 (natural and mechanical ventilation, NV&MV-TC) can maintain indoor PM2.5 concentration below 25 μg/m3. The NV&MV-TC strategy is chosen to simulate indoor PM2.5 concentrations and cooling/heating energy consumption in nine neighborhoods. The building cooling energy consumption range is 21.2–41.5 kW•h/m2, while the heating energy consumption is 14.9–74.7 kW•h/m2. In the cooling season, the hybrid ventilation strategy shows the lowest cooling energy consumption, followed by window closure with operating indoor air purifiers. Apartments employing window closure with mechanical ventilation have the highest cooling energy consumption. In the heating season, the most energy-efficient strategy is window closure with operating indoor air purifiers, followed by the hybrid ventilation strategy. The indoor PM2.5 concentration negatively correlates with the frontal area index (λf). This study provides valuable information for residential ventilation strategies and guides residents' behaviors in cooling/heating seasons.

Automated mapping process of frontal area and thermal potential indexes: GIS algorithm development and implementation

The impacts of urban areas on microclimate conditions are well-known and highlight the importance of climatological guided urban planning. The growth and evolution of Geographic Information Systems (GIS) have set a backdrop for implementing strategies to apply urban climatological knowledge in planning daily practice. This research brings algorithms to automate the Frontal Area Index (FAI) and Thermal Potential Index (TPI) mapping process. This type of algorithms type facilitates the extraction of detailed spatial information for the decision-making process, making it highly relevant for urban planning and management. Their calculation method is implemented as ESRI © ArcGIS Pro embedded Python Stand Alone Script Tools, using the Python ArcPy library to access the ArcGIS geoprocessing functions. The algorithms are described in detail, allowing their implementation in other GIS platforms. The output maps allow the urban thermal conditions and morphology assessment. The findings from this research may substantially contribute both to the advance in the urban climatology scientific field and to guide urban planners' and managers' practical decision making.

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.

Impact of 3-D structures on directional effective emissivity in urban areas based on DART model

Thermal infrared (TIR) remote sensing is effective to monitor urban land surface temperature (LST) on a large scale. The accuracy of the inversion of LST depends strongly on the emissivity. The emissivity is complex in urban areas and is largely dependent on three-dimensional (3-D) urban structure and different building materials, also called effective emissivity. The urban canyons have homogeneous temperatures to avoid the difficulty of defining the emissivity of non-isothermal surfaces. In this paper, we use the 3-D discrete anisotropic radiative transfer (DART) model to simulate the emission and propagation of thermal radiation in urban scenes by a flux tracking method and generate remote sensing images. The impact of 3-D structures on the effective emissivity was analyzed and simple relations between effective emissivity and 3-D structure parameters were proposed. Results show that the increase of emissivity at nadir is well related to wall frontal area index (F) and the mean sky view factor of flat surfaces (i.e., roof and road, SVFweighted), whereas emissivity directionality is well related to road's sky view factor (SVFroad). Overall, emissivity directionality (reaches 0.069 if reflectance is 0.2 for the road and 0.1 for the other components) due to material mixture and nadir emissivity increment (increase by 0.0646 for material reflectance R of 0.1) caused by 3-D structures can be larger than other emissivity sources. This study helps to understand the impact of 3-D structures and different material emissivity on urban effective emissivity and offers insights for addressing urban emissivity directionality in multi-angular remote sensing missions.

The roles of surrounding 2D/3D landscapes in park cooling effect: Analysis from extreme hot and normal weather perspectives

Urban parks are regarded as a promising measure to alleviate urban heat island (UHI) effects. Park cooling effects have been widely investigated, however, the impacts of surrounding 2D/3D landscapes on it remain uncertain, especially in extreme hot weather. Therefore, taking 70 parks in Beijing as the samples, this study investigated the impacts of surrounding 2D/3D landscapes (vegetation, water, impervious surface, and buildings) on the park cooling effects in the extreme hot and normal days. First, based on buffer method, park cooling efficiency (PCE) was determined in the extreme hot and normal days. Then, both 2D (percentage of landscape (PLAND), landscape shape index (LSI), and aggregation index (AI)) and 3D (building density (BD), building height (BH), sky view factor (SVF), and frontal area index (FAI)) metrics were used to explore how surrounding 2D/3D landscapes influence PCE based on boosted regression tree (BRT) approach. The results showed that parks demonstrated the cooling islands, and the average PCE were 7.01 and 7.49 in the extreme hot and normal days, respectively. Surrounding buildings and vegetation metrics are the most important indicator in the extreme hot and normal days, respectively, which account for 30.6% and 47.1% of the contribution of PCE. The relationships between the key surrounding landscape metrics and PCE were nonlinear in the extreme hot and normal days. This study emphasized that surrounding 2D/3D landscapes can significantly affect park cooling effects, and the impacts are nonlinear. The findings are conducive to enhance the park cooling effects by optimizing the surrounding landscapes.

Mapping urban cool air connectivity in a megacity

Urban heat islands and global warming negatively impact human health, and it is important to analyze the effects of urban cool islands (UCIs). Previous studies have used land surface temperature to define the effect of UCIs, but they have difficulty representing the spread of cool air. Here, we suggest a new method for mapping the UCI effect using a landscape connectivity model. We combined the urban heat flux to simulate the cooling potential and surface roughness to represent resistance to cool air spread from UCIs across 605 km2 of Seoul, Republic of Korea. The results identify high cool air connectivity areas in the Han River, which bisects Seoul, and along ridges that provide a cooling potential of approximately 440 W/m2. Areas with low cool air connectivity have a high frontal area index, which blocks cool air flow. The mapped cool air connectivity results have a − 0.56 correlation coefficient with monitored air temperatures, which shows that our result can be used to identify the UCIs network and to optimize the best areas for new UCIs. This research method can be utilized to plan cooling strategies for cities with few air-temperature monitoring stations.

Urban neighbourhood classification and multi-scale heterogeneity analysis of Greater London

We study the compositional and configurational heterogeneity of Greater London at the city- and neighbourhood-scale using Geographic Information System (GIS) data. Urban morphometric indicators are calculated including plan-area indices and fractal dimensions of land cover, frontal area index of buildings, evenness, and contagion. To distinguish between city-scale heterogeneity and neighbourhood-scale heterogeneity, the study area of 720 km2 is divided into 1 [Formula: see text] 1 km2 neighbourhoods. City-scale heterogeneity is represented by categorisation of the neighbourhoods using a k-means clustering algorithm based on the morphometric indicators. This results in six neighbourhood types ranging from “greenspace” to “central business district”. Neighbourhood-scale heterogeneity is quantified using a hierarchical multi-scale analysis for each neighbourhood type. The analysis reveals the dominant length scales for land-cover and neighbourhood types and the resolutions with the most information gain. We analyse multi-scale anisotropy and show that small-scale features are homogeneous, and that anisotropy is present at larger length scales.

Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach

Urban morphology significantly affects the urban thermal environment. Seasonal impacts of two-dimensional (2D) and three-dimensional (3D) urban morphology on land surface temperature (LST) remain uncertain, and the impacts exist scale effects. Thus, taking Beijing as the study area, boosted regression tree (BRT) model was used to investigate the seasonal contributions of urban morphology to the thermal environment. Building density (BD), building height (BH), floor area ratio (FAR), sky view factor (SVF), and frontal area index (FAI), were used to comprehensively characterize urban morphology, and 13 scales ranging from 30 m to 600 m were used to investigate scale effects. The results showed that there are obvious spatial differences in LST and urban morphology indicators in the study area. 270 m was determined as the optimal scale for modeling in the study area. BH and BD are the domain indicators, which together contribute more than 75% of the variance of LST among four seasons, while the relative influences of SVF, FAR, and FAI are relatively low. Relationships between urban morphology indicators and LST are nonlinear among four seasons. The findings provide a scientific understanding for urban planners on mitigating the UHI effects through optimizing buildings.

Outdoor ventilation evaluation and optimization based on spatial morphology analysis in Macau

Owing to Macau's high urban development intensity, outdoor ventilation is important to enhance human comfort. In this study, four morphological indices as frontal area index (FAI), site coverage (SC), building height (BH) and floor area ratio (FAR) were mapped and analyzed by establishing a refined three-dimensional urban digital model. Then indicator analysis, numerical simulation and LST retrieval were comprehensively used to assess the present outdoor ventilation conditions of Macau. The results showed that Macau's overall spatial morphology was not conducive to ventilation. In hot season, the cool sea breeze was obstructed by high-rise buildings in coastal area of the northern peninsula and difficult to infiltrate into the inner city. In cold season, the high pedestrian level wind speed of urban blocks in coastal areas of southern island was not good for wind protection. In Macau's northern peninsula, three potential wind corridors that connecting several existing open spaces are suggested. In southern island, a clustered urban development mode is proposed to create a wind corridor network. In addition, the existing heat islands in the windward side of wind corridors can be reduced by optimizing land surface. For pedestrian-level ventilation, the wind speed of traditional neighborhoods can be enhanced by reducing site coverage and introducing high-rise buildings during urban renewal. Wind protection methods should be considered in existing coastal urban areas, and low-rise high-density development mode is recommended for coastal new town planning in southern island. This research presents an opportunity to assist urban planners and researchers in developing a comprehensive understanding of Macau's ventilation condition, and provides theoretical support for planning practices for Macau and cities with comparable conditions.

Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing

AbstractAn essential task of numerical weather prediction (NWP) is accurate surface wind speed prediction. However, current NWP models contain significant systematic errors due in part to indeterminate ground forcing (GF). This study considers an optimal virtual GF (GFo) derived by training observed and simulated data sets of 10‐m wind speeds (WS10) for summer and winter. The GFo is added to an offline surface multilayer model (SMM) to revise predictions of WS10 in China by the Weather Research and Forecasting model (WRF). This revision is a data‐based optimization under physical constraints. It reduces WS10 errors and offers wide applicability. The resulting model outperforms two purely physical forecasts (the original WRF forecast and the SMM with physical GF parameterized using urban, vegetation, and subgrid topography) and two purely data‐based revisions (i.e., multilinear regression and multilayer perceptron). Compared with the original WRF forecasting, using the GFo scheme reduces the root mean square error in WS10 across China by 25% in summer and 32% in winter. The frontal area index of GFo indicates that it includes both the effects of indeterminate GF and other possible complex physical processes associated with WS10.

Developing the Urban Blue-Green Infrastructure as a Tool for Urban Air Quality Management

Urban structure is an important factor that shapes the process of urban ventilation and pollution dispersion. With proper planning of the urban spatial layout, city breathability can be effectively regulated, contributing to urban air quality improvement. This paper investigates the development and current management of urban systems of green and open spaces in four Polish cities: Gdańsk, Warsaw, Poznań and Wrocław, with a particular focus on the planning aspects of urban ventilation and air quality management. The initial GIS-based comparison of historical plans and the current spatial layouts of the cities show that these systems, consciously shaped at the beginning of the twentieth century, remain clearly identifiable. However, in some locations, the continuance of these systems was interrupted by later investments. The next step was to develop GIS procedures to effectively map the spatial distribution of selected urban form indicators that are related to urban ventilation, especially the frontal area index. The results made it possible to determine the main features of the current ventilation systems and to identify some of the local problem areas. The last phase of the study was to conduct a local-scale analysis of these problem areas. With this study, the applicability of various analysis and simulation tools for the purpose of improving city breathability by appropriate integrated planning and design decisions was demonstrated. The presented approach, taking into account the city- and micro-scale interactions, should be used in current planning practice to preserve the historically developed ventilation systems.

Ventilation Capacities of Chinese Industrial Cities and Their Influence on the Concentration of NO2

Most cities in China, especially industrial cities, are facing severe air pollution, which affects the health of the residents and the development of cities. One of the most effective ways to alleviate air pollution is to improve the urban ventilation environment; however, few studies have focused on the relationship between them. The Frontal Area Index (FAI) can reflect the obstructive effect of buildings on wind. It is influenced by urban architectural form and is an attribute of the city itself that can be used to accurately measure the ventilation capacity or ventilation potential of the city. Here, the FAIs of 45 industrial cities of different sizes in different climatic zones in China were computed, and the relationship between the FAI and the concentration of typical pollutants, i.e., NO2, were analyzed. It was found that (1) the FAIs of most of the industrial cities in China were less than 0.45, indicating that most of the industrial cities in China have excellent and good ventilation capacities; (2) there were significant differences in the ventilation capacities of different cities, and the ventilation capacity decreased from the temperate to the tropical climate zone and increased from large to small cities; (3) there was a significant difference in the ventilation capacity in winter and summer, indicating that that with the exception of building height and building density, wind direction was also the main influencing factor of FAI; (4) the concentration of NO2 was significantly correlated with the FAI, and the relative contribution of the FAI to the NO2 concentration was stable at approximately 9% and was generally higher than other socioeconomic factors. There was a turning point in the influence of the FAI on the NO2 concentration (0.18 < FAI < 0.49), below which the FAI had a strong influence on the NO2 concentration, and above which the influence of the FAI became weaker. The results of this study can provide guidance for suppressing urban air pollution through urban planning.

Local climate effects of urban wind corridors in Beijing

Based on a summer observational experiment and the meteorological data from 2009 to 2018, this paper investigated the local climate effects of representative 1 Level One and 2 Level Two wind corridors (LOC and LTCs, respectively) in the central urban area (CUA) of Beijing by introducing the wind speed ratio (WsR) and urban heat island (UHI) intensity indices. In addition, the impacts on the ventilation of wind corridors from the 8 spatial landscape parameters were analyzed. The results indicated that LOC and LTCs have significant ventilation benefit and certain UHI mitigation benefit varying with season, day and night and weather，which were able to accelerate winds when wind speeds exceed 0.57 m/s and 0.75 m/s, respectively. The most important influencing parameters on the ventilation capability were frontal area index and roughness length. The multiyear average WsRs of the LOC and LTCs were 57% and 31% higher than that of the CUA, respectively. The UHI reduction did not occur along the whole corridor or throughout the whole day. The maximum seasonal mean UHI reduction of the LOC during daytime and nighttime were 0.89 °C and 0.75 °C, respectively. The LTCs mainly reduced UHI during daytime with the maximum reduction of 0.30 °C.

Effects of crop residue on wind erosion due to dust storms in Hotan Prefecture, Xinjiang, China

In arid and semiarid areas with frequent dust storms, reasonable planting modes and crop residue measures could prevent dust storms that originate from local farmlands and address key factors for wind erosion control. The considered crop residue modes included all harvesting with no residue in a Cyperus esculentus L. (C.) monoculture setting (AH), retention of 1 ridge and harvesting of 3 ridges in a C. monoculture setting (RH), and all harvesting with retention of Setaria italica (L.) Beauv. var. germanica (Mill.) Schrad (S.) residue in an intercropping setting of 6S. ridges and 9C. ridges (ARS). The aerodynamic roughness z0 and friction velocity u* were measured with handheld anemometers, and sediment fluxes were measured by combining multichannel sand collectors under different crop residue modes. The results indicated that the total aeolian sediment flux increased with distance away from the final belt of crop residue under both the RH and ARS modes. S. residue reduced the total aeolian sediment flux by more than 50% compared to C. residue at a distance of 4.5 m away from the residue belt during the dust storm. The sand transported height under both the RH and ARS modes was lower than that under the AH mode, and the distance required for sand to be transported to the same flux behind the S. residue belt was more than that behind the C. residue belt. The frontal area index (FAI) is a parameter that can better reflect the wind erosion control ability of C. and S. residues. S. residue attained a higher wind weakening ability than C. residue according to the positive relationship between z0, u* and FAI. During the dust storm, the relationship between the total aeolian sediment flux and FAI changed from significant to insignificant as the sand transported height exceeded C. residue height. Using standing crops intercropping with C. can provide both income from C. and wind erosion control from standing crop residues. The results can provide useful guidance for local C. harvest and planting design.

Quantifying vegetation and its effect on aeolian sediment transport: A UAS investigation on longitudinal dunes

Studying the role of vegetation in regulating aeolian sediment transport is complicated by the diversity of plant geometry and spatial distribution. Using Unmanned Aerial Systems (UAS) surveys of four partially vegetated sand dunes in the Simpson Desert, this study explored statistical associations between vegetation and the location and quantity of aeolian ripples. Employing mosaic image classifications, Digital Surface Models (DSM), and Canopy Height Models (CHM), four core independent metrics were computed: The fractional cover (fc); frontal area index (λ), mean gap length (L), and shadow casting or Shadow Area Ratio (SAR). The strongest predictor of aeolian ripple abundance was the mean scaled gap length (individually scaled by the lesser of an adjacent plant’s width or height) (Lsf-) (R2 = 0.83). Lsf- (and Lh-, which only used plant height) effectively resolved the spatial and structural distribution of vegetation, which was partially governed by the composition of functional plant types. fc was also strongly associated with ripple abundance (R2 = 0.81). Ripple cover varied continuously with fc without a clear threshold for the onset of sand transport, though the curve flattened above fc ≈ 25–30%. Moderate associations were found for SAR (R2 ≤ 0.57) and λ (R2 = 0.63). Shadow lengths (in units of plant height) of 1–3 best explained the location of ripples. The efficacy of shadow casting was affected by the signal to noise ratio in the DSMs at the scale of very small plants. UAS data nevertheless displayed strong potential for advancing the study of vegetation and aeolian activity.

Variabilities of Land Surface Temperature and Frontal Area Index Based on Local Climate Zone

Urban temperature increase brings a challenge to public health, so urban warming has been widely concerned. Within the central area of Shenyang, a 3 × 3 fishnet with nine grids was built to analyze land surface temperature (LST) and frontal area index (FAI) variations based on the local climate zone (LCZ) classification and to explore relationships among them to generate implications for using wind for urban cooling. The results indicate that LST in the 4# grid (central grid) was the highest (35.11 °C) among the nine grids. In general, a higher LST occurred in the grid with a larger building area and vice versa. LCZ-G underwent the largest temperature reduction among the 17 LCZ types. LCZ-2 had the highest LST, while the LCZ-1 had the lowest LST among the three high and intensive building types, except for the LST in the 2# and 4# grids. The FAI in Shenyang decreased from the center to the surroundings. The FAI value was greatest in the 4# grid and the lowest was in the 2# and 8# grids under the prevailing wind of summer and winter. The FAI variations suggested that prevailing SW, SSW, and S winds in summer were difficult to penetrate the central urban area, and the wind cooling effect could not perform. The prevailing NEN and EEN winds in winter could enter the central urban area, enhancing the wind cooling effect, while it was a disadvantage in winter. Overall, there was a significant positive relationship among LST, FAI, and area of LCZ-building.

Quantifying 3D building form effects on urban land surface temperature and modeling seasonal correlation patterns

Multiple factors regulate urban land surface temperature (LST), including land cover, climate, and urban form, among which urban form is now receiving more and more attention. Some studies have discussed the planar effects of urban form on LST, whereas less concern has been devoted to the vertical structure of urban areas, which can have a significant effect on heat redistribution. In this paper, we quantify the three-dimensional building form (3DBF) effects on LST captured by Landsat thermal sensors over four seasons by using a random forest (RF) regression method. The five 3DBF factors of building density (BD), building height (BH), sky view factor (SVF), frontal area index (FAI), and building shadow (BS) are calculated within a grid. The seasonal correlation of the 3DBF factors with LST is analyzed, and seven correlation patterns are modeled for the city of Wuhan in China. The results show that: 1) The best grid scale for the building form analysis in a city such as Wuhan is 180 m. 2) The 3DBF factors have a significant effect on urban LST over the four seasons. BD, BH and BS are the season-stable factors, among which BD has a heating effect, which reaches a maximum of 3.6 °C in spring, while BS has a cooling effect, which reaches −3.4 °C in winter. There is also a transition point for BH between heating and cooling at a height of 10 m. 3) SVF and FAI are season-varying factors, in which SVF has cooling effect, except in summer, while FAI also has a cooling effect, but not in winter. These findings will help us to understand how building form affects urban surface temperature, and will provide a reference for urban policy makers and planners in the future.

Complexity of the relationship between 2D/3D urban morphology and the land surface temperature: a multiscale perspective.

Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban morphology on UHIs from a multiscale perspective. In this study, we chose the central area of Jinan city, which is commonly known as the "furnace," as the case study area. The 2D/3D urban morphology indexes-building coverage ratio (BCR) (for assessing the 2D building density), building volume density (BVD) (for assessing the 3D building density), and frontal area index (FAI) (for assessing 3D ventilation conditions) were calculated and derived to investigate the complexity of the relationship between 2D/3D urban morphology and the land surface temperature (LST) at different scales using the maximum information coefficient (MIC) and geographically weighted regression (GWR). The results indicated that (1) these 2D/3D urban morphology indexes are essential factors that are responsible for LST variation, and BCR is the most important urban morphology index affecting LST, followed by BVD and FAI. Importantly, the relationship between the BCR, BVD, FAI, and LST was an inverse U-shaped curve. (2) The relationship between 2D/3D urban morphology and LST variation showed a significant scale effect. With increased grid size, the correlation between the BCR, BVD, and FAI and the LST strengthened, "inflection point" of inverse U-shaped curve significantly declined, and their explanation rate of the LST first increased and then decreased, with a maximum value at the 700 m scale. Additionally, the FAI exerted a stronger negative effect, while the BCR and BVD generally had stronger positive effects on the LST as the grid size increased. This study extends our scientific understanding of the complex effect of urban morphology on the LST and is of great practical significance for multiscale urban thermal environment regulation.

Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives

Urbanization can lead to changes in urban morphology that alter the urban thermal environment and energy demand. Improving urban ventilation can alleviate the urban heat island effect and reduce urban energy demand. We categorized the ventilation conditions of 31 major cities in China into four levels based on the frontal area index and presented the natural ventilation effects for cities in five different climate zones. We found that the land surface temperature varies between 0.029 and 5.357 °C in areas under the same climate background. Improving ventilation can directly or indirectly contribute to reductions in urban energy consumption. The energy demand in well-ventilated areas can be reduced by up to 6.704%. The largest reduction in urban energy demand was achieved by improving ventilation within the temperate continental climate zone.