Urban Thermal Environment Research Articles

Method of calculating land surface temperatures based on the low-altitude UAV thermal infrared remote sensing data and the near-ground meteorological data

The continuous urbanization has greatly changed the underlying surfaces, which leads to great impacts on the urban thermal environment. The land surface temperatures (LSTs) have gained considerable attention as a typical indicator for studying the urban thermal environment. Accounting for the deficiency of existing calculation methods in calculation principle of LSTs from the low-altitude thermal infrared remote (TIR) sensing data, this study proposes a method to retrieve the LSTs at a block scale with high temporal and spatial resolution for the effective evaluation of micro urban thermal environment. Thus, the thermal infrared radiation transmission equation is rewritten according to the actual thermal infrared transmission process. The final calculation form of LSTs is given, in which the multi-source data is employed. Ultimately, to verify the accuracy of this method in calculating the LSTs, an application case is designed. The results indicate that the temperature differences between the calculated LSTs and the measured LSTs ranges from -1.73 K to 1.45 K, and the absolute accuracy of the method is 0.09 K based on the correctly obtained multi-source data. This method is available for the quantitative analysis of the urban thermal environment and urban heat island effect at a block scale.

Changing coordination between urban area with high temperature and multiple landscapes in Wuhan City, China

Changes in urban landscapes are a direct inducement for changes in the urban thermal environment. Previous studies have focused on a single landscape in the urban thermal environment, but there is a lack of the research on the coupling effects of multiple landscapes. In this study, partial correlation analysis and spatial coupling model were used to explore the changing coordination between urban areas with high temperature and multiple landscapes (gray, green, and blue landscapes) in Wuhan City and their effects on land surface temperature (LST). The results showed that 6.22%, 0.52% and 25.91% of the blocks at the sub-district level behaved the dominant strong coupling relationship between the change trends of blue, green, as well as gray landscapes and urban thermal environment, respectively. For gray-green dominant strong coupling area, the change from blue landscape in 1988 to gray landscape in 2018 resulted in an average LST increase of 4.07 °C, which was 0.1 °C less than that in a single gray strong coupling area. It can be concluded that compared with single endothermic or exothermic landscape dominant strong coupling area, the multiple landscapes combining dominant strong coupling area will experience distinctly enhanced or weakened LST response to the same landscape change, respectively.

The influence of the landscape pattern on the urban land surface temperature varies with the ratio of land components: Insights from 2D/3D building/vegetation metrics

Recently, the impact of multidimensional building/vegetation landscape patterns on urban land surface temperature (LST) has received extensive attention. However, investigations on how two-dimensional (2D)/three-dimensional (3D) building and vegetation landscape patterns affect the urban thermal environment under different cover types are still limited. Thus, we use the boosted regression tree model to explore the separate and combined relative contributions and marginal effects of 2D/3D building and vegetation landscape patterns on the LST and to examine the impacts of differences in building and vegetation coverage. The results indicate that the mean architecture height, building coverage ratio, high building ratio, vegetation coverage ratio, and architecture height standard deviation are the main metrics affecting the LST, with relative contributions of 24.8%, 14.9%, 14.7%, 8.2%, and 7.6%, respectively. The building landscape roughness and fragmentation have the greatest influence on the LST, with contributions of 1.2 °C and 0.8 °C, respectively. Furthermore, changes in the building and vegetation coverage can significantly affect the LST. When building coverage dominates, building-based metrics largely affect the LST. When vegetation coverage dominates, the impact of vegetation landscape diversity, roughness, and fragmentation on the LST gradually increases. This study provides insights for improving urban thermal environment.

Spatial pattern of anthropogenic heat flux in monocentric and polycentric cities: The case of Chengdu and Chongqing

• Anthropogenic heat flux (AHF) had spatial variations under distinct urban forms. • Building and traffic contributed more AHF than industry and human metabolism. • AHF pattern was contiguous in Chengdu and clustered in Chongqing. • High AHF gathered in Chengdu's center but dispersed in Chongqing's several centers. Although anthropogenic heat emission (AHE) has been acknowledged as a significant threat to urban thermal environment, little attention has been paid to the differences in the intensity of AHE, namely anthropogenic heat flux (AHF), under distinct urban forms. Therefore, we chose one monocentric city (Chengdu) and one polycentric city (Chongqing) in China to compare the spatial pattern of AHF. AHE was calculated from four aspects of buildings, traffic, industries, and human metabolism for the whole cities and then allocated to the blocks using the inventory-based method. The results showed that building and traffic heat accounted for major AHE. In Chengdu, annual mean AHF decreased from the urban core to the suburbs, forming a circular and contiguous pattern. In Chongqing, AHF concentrated in several hotspots of the (sub)centers, resulting in a balanced and clustered pattern. AHF from buildings and human metabolism were contiguous and concentrated within city centers. In contrast, AHF from traffic and industries were scattered and dispersed in line with the road networks and industrial patterns. The results reflected that the spatial pattern of AHF varied with different urban forms, which called for planners to consider the differences and make related policies to mitigate AHF for urban sustainability.

Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China

With rapid urbanization, urban three-dimensional morphology and its ecological effects have received more attention. However, thorough investigations into the multiple scale impact of the 2D/3D architectural morphology on urban land surface temperature (LST) remain limited. Taking Beijing as a case study area, we quantified the contributions of the 2D/3D architectural morphology indicators and revealed their marginal effects on multiple scales using the boosted regression trees (BRT) method. The results showed that (1) the building coverage ratio and building height were the most significant factors influencing the LST across all spatial scales and seasons, (2) the 3D shape index, 3D fractal, and 3D adjacency were found to be influential factors, with sum contributions varying from 6.0% to 37.7%, and (3) in summer, the 3D shape index showed a stepwise negative correlation with the LST. The 3D fractal and 3D adjacency exhibited both positive and negative correlations with the LST. When the spatial scale was 240 m, the regulation amplitudes for the 3D shape index, 3D fractal, and 3D adjacency were 2.0°C, 1.0°C and 1.0°C, respectively. These findings provide quantitative insights that can be used to improve urban thermal environments and achieve sustainable urban development by adjusting architectural morphology.

도시지표특성 모수 적용을 통한 태양복사모델 개선 효과 분석

Sudden change in an urban thermal environment has an intimate effect on resident’s daily life, prompting increased interest in and demand for improvement of urban thermal environments. In order to improve the urban thermal environment, heat vulnerability information that reflects the actual physical environment is needed in a format that can be controlled as spatial information. In addition, a quantitative method is need to analyze heat stress, as experienced by urban residents. In this study, solar radiation flux simulation results for Seoul metropolitan’s main residential and commerce area are analyzed, and the accuracy of these results is enhanced by improving the urban simulation index. The SOLWEIG model is used in this study as it can simulate high-resolution solar radiation flux and mean radiant temperature using data such as the sky view factor, reflecting shapes of buildings and vegetation, shadow pattern, temperature, humidity, and solar radiation. In order to reflect domestic condition and geographical characteristics, a correlation equation of solar altitude angle and the difference in surface temperature and atmospheric temperature by land cover is derived. Based on this, a comparative analysis is performed on the simulation result using the SOLWEIG model and solar radiation observation data.

Calculation and Expression of the Urban Heat Island Indices Based on GeoSOT Grid

The urban heat island (UHI) effect accelerates the accumulation of atmospheric pollutants, which has a strong impact on the climate of cities, circulation of material, and health of citizens. Therefore, it is of great significance to conduct quantitative monitoring and accurate governance of UHI by calculating the index rapidly and expressing spatial distribution accurately. In this paper, we proposed a model that integrates UHI information with the GeoSOT (Geographic Coordinate Subdividing Grid with One-Dimension Integer Coding on 2n Tree) grid and subsequently designed the calculation method of UHI indices and expression method of UHI spatial distribution. The UHI indices were calculated on Dongcheng and Xicheng District, Beijing, in the Summer of 2014 to 2019. Experimental results showed that the proposed method has higher calculation efficiency, and achieved a more detailed description of the spatial distribution of the urban thermal environment compared with the Gaussian surface fitting method. This method can be used for large-scale and high-frequency monitoring the level of UHI and expressing complicated spatial distribution of UHI inside the city, thus supporting accurate governance of UHI.

Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China

The urban morphology has impacts on the urban thermal environment, which has drawn extensive attention, especially in metropolitan regions with intensive populations and high building densities. This study explored the relationship between the urban morphology and spatial variation of land surface temperature (LST) in Wuhan by using the local climate zone (LCZ) and seven natural and social factors. A deep learning model (light LCZ model) was used to generate LCZ map in Wuhan, and a geographic detector model was utilized to explore the driving mechanism of LST spatial differentiation. The results show that the LST difference between LCZ classes in summer is greater than that in winter, and the LST of the built-up classes (LCZ 1–10) are significantly higher than that of the vegetation classes in summer. Among the six residential building classes (i.e., LCZ 1–6), LCZ 1 is characterized by compact and high buildings and has the largest average LST. Building density and height have a warming effect, and the building density has a stronger effect than the height. Compared with other natural and social factors, LCZ has the largest explanatory power for LST spatial differentiation in the main urban area and surrounding areas of Wuhan, with explanatory power (q) values reaching 0.660 (summer) and 0.316 (winter). The types of interaction for all pairwise cases are mutual and nonlinear. The strongest interaction is MNDWI-NDBI combination (0.780) in summer and LCZ-NDBI combination (0.460) in winter.

Spatial differentiation of neighborhood climates and their response to surrounding land cover composition: A case study in Beijing, China

Heterogeneous land cover affects near-surface heat and humidity distribution in urban areas. Effective land cover arrangements can create a more sustainable local thermal environment. However, spatial differentiation in neighborhood climates and their spatial response range to the surrounding land cover composition (LCC) in high-density urban environments remains unclear. In this study, field monitoring of the air temperature (Ta) and relative humidity (Rh) was conducted in summer (August 2016) and winter (December 2016 and January 2017) in a neighborhood in Beijing, China. A multi-radius approach was developed to quantify the effective response range of Ta and Rh at unshaded measuring points to the surrounding LCC. Our results demonstrated that the: (1) spatial distribution of Ta and Rh in a typical neighborhood varies significantly in both summer and winter and is dependent on the local land cover; (2) Ta at measurement points generally increases with growing surrounding vegetation coverage and decreases with less impervious pavement and building coverage, whereas the opposite applies to Rh; (3) response of Ta and Rh to land cover composition is spatially dependent; and (4) Ta and Rh have an effective response range of up to 200-m to surrounding vegetation coverage in both seasons, whereas their response range to pavement coverage is 150- and 100-m in summer and winter, respectively. Overall, LCC within a radius of 100–150-m has a significant impact on the Ta and Rh of the measuring points in a high-density urban neighborhood. These findings elucidate the spatial response of a neighborhood climate to surrounding land cover and demonstrate that landscape infrastructure intervention is an effective means of improving urban thermal environments.

Association analysis on spatiotemporal characteristics of block-scale urban thermal environments based on a field mobile survey in Guangzhou, China

City blocks typically exhibit diverse thermal environmental characteristics due to their self-character complex spatial morphology and lateral thermal interactions from neighboring blocks. A field survey illustrating the spatiotemporal characteristics of block-scale thermal environments is urgently required to explore the inner-associated effects. A mobile survey method was conducted with two selected typical case regions during summer in Guangzhou, China. A block-based local climate zone (LCZ) mapping method was applied to the case regions, and diversified LCZ types that coexist in irregular states were obtained. Then, a multi-step data processing method integrating temporal correction and GIS-based spatial analysis was implemented to obtain the spatiotemporal distribution atlas of air temperature in LCZ blocks. Throughout this study, the air temperatures displayed time-varying characteristics and changed significantly with LCZ types. The correlation analysis showed that the sky view factor and pervious surface fraction present significant positive effects on block-scale air temperature during the daytime. Next, a spatial heterogeneity analysis conducted by fitted semivariogram curves at different times revealed the spatial variability of the urban thermal environment. Additionally, further spatial autocorrelation analysis of thermal interactions between blocks resulted in significant spatial cluster characteristics of air temperatures in the case regions.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Efficient Plant Types and Coverage Rates for Optimal Green Roof to Reduce Urban Heat Island Effect

Green roofs are implemented to reduce the urban heat island effect; however, studies are limited to comparing the reduction in urban heat island effect before and after implementation, and the focus is on the structural stability of the building rather than urban heat island reduction. In this study, using the sky view factor (SVF) in ENVI-met, a 3D microclimate modeling program, urban spaces were classified as closed, semi-open, and open areas. Meanwhile, the green roof types were subdivided according to the vegetation coverage rates, which included grass, shrubs, and trees. The vegetation ratio was evaluated using ENVI-met to determine which of the 10 scenarios was most effective for each urban space. The thermal environment was most comfortable in semi-open areas. Therefore, the green roof scenario with 70% grass and 30% trees was effective in closed areas, 50% shrubs and 50% trees were best in semi-open areas, and 70% grass with 30% trees, or 30% grass and 70% trees, was best in open areas. This study provides a basis for creating green roof guidelines aimed at improving the urban thermal environment, as well as creating other green infrastructure elements in cities.

Behavioural thermal regulation explains pedestrian path choices in hot urban environments

Due to phenomena such as urban heat islands, outdoor thermal comfort of the cities’ residents emerges as a growing concern. A major challenge for mega-cities in changing climate is the design of urban spaces that ensure and promote pedestrian thermal comfort. Understanding pedestrian behavioural adaptation to urban thermal environments is critically important to attain this goal. Current research in pedestrian behaviour lacks controlled experimentation, which limits the quantitative modelling of such complex behaviour. Combining well-controlled experiments with human participants and computational methods inspired by behavioural ecology and decision theory, we examine the effect of sun exposure on route choice in a tropical city. We find that the distance walked in the shade is discounted by a factor of 0.86 compared to the distance walked in the sun, and that shadows cast by buildings have a stronger effect than trees. The discounting effect is mathematically formalised and thus allows quantification of the behaviour that can be used in understanding pedestrian behaviour in changing urban climates. The results highlight the importance of assessment of climate through human responses to it and point the way forward to explore scenarios to mitigate pedestrian heat stress.

Environmental Effects from Pocket Park Design According to District Planning Patterns—Cases from Xi’an, China

Rapid urban expansion leads to the continuous deterioration of the urban thermal environment, which endangers the sustainable development of the city; meanwhile, countries all over the world are becoming increasingly interested in the mitigating effects of urban green spaces on heat islands. In this context, many studies show that pocket parks play an important role in optimizing the thermal environment of urban blocks, especially with the high-intensity development of urban blocks. Moreover, many cities in China have become mature, and it is difficult to open up large green areas. Therefore, to improve the urban thermal environment in summer, it is necessary to adapt to an efficient pocket-park design strategy in blocks. To consider the impact of block development intensity on the design strategy of pocket parks, three pocket parks in blocks with different floor-area ratios in the central area of Xi’an were selected as examples. This research compares the effects of tree density and green-space layout on the thermal environment in different floor-area ratio districts on a summer day using environmental simulation (ENVI-met). The results show that the design strategies of pocket parks in urban blocks with different development intensities are different. High tree density performs better from the perspective of improving the urban thermal environment. For the low and medium development intensity blocks, the effects of the open green space and closed green space are similar, while the open green space layout can significantly improve thermal comfort in high-intensity development blocks.

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high-spatial-resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. Firstly, the observed environmental parameters, e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF), around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 ∘C. Then, the spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the decreasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high spatial and temporal resolution (30 m and 1 h) CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Spatiotemporal heterogeneity of street thermal environments and development of an optimised method to improve field measurement accuracy

Quantification of thermal environments in cities is of significance for revealing heat-related impacts and the performance of urban cooling measures, in which field measurement has been a prevailing method. However, the accuracy of field measurement is a critical issue, especially considering the spatial heterogeneity of urban spaces and its interactions with urban heat sources. Accordingly, the selection of fixed measurement points is of importance to accurately capture urban thermal environments, which has not been well considered in previous studies. Therefore, this study examines the heterogeneity of street thermal environments and explores an approach to improving field measurement accuracy. The study was empirically conducted in two typical streets in Chongqing, a hot-humid city in China. The results indicate that both microclimatic and thermal comfort indices could exhibit strong spatiotemporal heterogeneity, whilst they were measured in a same street. At a specific time, within a street, the largest air temperature (Ta), mean radiant temperature (MRT), physiological equivalent temperature (PET) and universal thermal climate index (UTCI) difference within a same street could reach 9.4 °C, 45.9 °C, 29.6 °C and 16.3 °C. Moreover, the measured Ta, MRT, PET and UTCI deviated from the average values which are used to represent street thermal environments and thermal comfort. Ta and UTCI converged towards the averaged values stronger than MRT and PET, while only 53.4% and 67.1% of the measured Ta and only 34.3% and 40.7% of measured UTCI fell into 95% accuracy zones. A framework of optimised multi-objective mobile measurement path was proposed, based on which the optimised measurement paths were obtained, and the measurement accuracy could be improved to above 98.0%. Overall, this study provides a reference for understanding spatiotemporal heterogeneity of street thermal environments and offers an approach to overcoming this issue for high accuracy.

Improved anthropogenic heat flux model for fine spatiotemporal information in Southeast China

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images.

Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.

Influences of landform and urban form factors on urban heat island: Comparative case study between Chengdu and Chongqing

Although the degraded urban thermal environment has been widely concerned, whether monocentric or polycentric urban development can alleviate urban heat island (UHI) is still debatable, particularly considering different types of cities in plain and mountainous settings. To fill the gap, this study chose the cases of Chengdu on alluvial flatlands and Chongqing on mountainous landforms in China for comparison. Adapting to natural landforms, Chengdu has a dominated monocentric urban form, whereas Chongqing has a naturally polycentric urban form. This study found that areas with high UHI intensity were located in the urban center and peripheral suburbs in Chengdu, while those were located in the peripheral industrial zones in Chongqing. The spatial error model and random forest regression results showed that natural elements such as vegetation and water had similar influences and contributions to UHI. However, distinct landforms and urban forms played critical roles in UHI differences among the two cities. Impervious surface and building density were the dominant variables to UHI of Chengdu, while sky view factor and industrial zones were the main contributors to UHI of Chongqing. The findings called for corresponding policy strategies to optimize urban form and improve the urban thermal environment.

An experimental comparison on regional thermal environment of the high-density enclosed building groups with retro-reflective and high-reflective coatings

Highly-reflective coatings (HR-coatings) are used to reduce the building heat gain and thereby, alleviate urban heat island, but the solar radiation reflected by HR-coatings will be absorbed by the surrounding building surfaces and solar heat gain will not be reduced or even increase. To overcome this inherent optical defect of HR-coatings, Retro-reflective coatings (RR-coatings) are proposed to reflect solar radiation along the incident direction with the potential to improve the urban thermal environment. Selecting the reasonable reflective coating is important to improve outdoor thermal environment, especially for high-density enclosed building groups. According to this aim, RR-coatings and HR-coatings were covered on two small-scale models of high-density enclosed building groups. The regional albedo, wall temperature and air temperature were taken as the evaluation parameters to compare the impact of RR-coatings and HR-coatings on the regional thermal environment of the high-density enclosed building groups. Experimental results showed compared with HR-coating model, RR-coating model had better cooling potential, and its peak regional albedo was increased by 5.0%. And air temperature in enclosed space of RR-coating model was 2.3℃ lower than that of HR-coating model, but they were higher than outdoor natural air temperature. Moreover, compared to HR-coatings, covering RR-coatings could reduce the peak and average wall temperature by 2.5℃∼15.2℃ & 0.3℃∼3.8℃ in the daytime and 1.3℃∼7.9℃ & 0.2℃∼4.4℃ in the nighttime, and its application in the roof and east wall had the higher efficiency.