Advances in Meteorology
Volume 2020 (2020), Article ID 8843676, 19 pages
https://doi.org/10.1155/2020/8843676

Research Article

Field Assessment of Neighboring Building and Tree Shading Effects on the 3D Radiant Environment and Human Thermal Comfort in Summer within Urban Settlements in Northeast China

Jing Du,1,2 Lin Liu,³ Xin Chen,1,2 and Jing Liu1,2

¹School of Architecture, Harbin Institute of Technology, Harbin 150000, China
²Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150000, China
³School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China

Correspondence should be addressed to Lin Liu; liulinhit@163.com and Jing Liu; liujinghit0@163.com

Received 21 May 2020; Revised 31 July 2020; Accepted 31 August 2020; Published 23 September 2020

Academic Editor: Alastair Williams

Copyright © 2020 Jing Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Shading is one of the most effective strategies to mitigate urban local-scale heat stress during summer. Therefore, this study investigates the effects of shading caused by buildings and trees via exhaustive field measurement research on urban outdoor 3D radiant environment and human thermal comfort. We analyzed the characteristics of micrometeorology and human thermal comfort at shaded areas, and compared the difference between building and tree shading effects as well as that between shaded and sunlit sites. The results demonstrate that mean radiant temperature Tmrt (mean reduction values of 28.1°C for tree shading and 28.8°C for building shading) decreased considerably more than air temperature Ta (mean reduction values of 1.9°C for tree shading and 1.2°C for building shading) owing to shading; furthermore, the reduction effect of shading on UTCI synthesized the variation in the above two parameters. Within the shaded areas, short-wave radiant components (mean standardized values of 0.104 for tree shading and 0.087 for building shading) decreased considerably more than long-wave radiant components (mean standardized values of 0.848 for tree shading and 0.851 for building shading) owing to shading; the proportion of long-wave radiant flux densities absorbed by the reference standing person was high, leading to a relatively high long-wave mean radiant temperature, and R² between long-wave mean radiant temperature and air temperature exceeded 0.8. Moreover, the directional sky view factor (SVF) was utilized in this study, and it showed significant positive correlation with short-wave radiant flux densities, but no statistically evident correlation with long-wave radiant flux densities. Meanwhile, Tmrt was most relevant with SVFS⟶ with R² of 0.9756. Furthermore, UTCI rose two categories at the sunlit areas compared with that at the shaded areas. In contrast, Ta and Tmrt played the first positive role in UTCI at shaded and sunlit areas, respectively.

1. Introduction

Urbanization is accelerating worldwide and is affecting urban living and transforming human society in various manners [1]. Among them, increasing urban severe heat in summer is a problem for many countries, which has strongly aggravated human thermal stress within urban spaces [2]. The outdoor thermal environment is significantly affected by the design of the built and vegetation environment [3], and it affects the thermal comfort experienced by people; furthermore, people’s perception for the thermal environment as a result influences their usage of outdoor spaces [4, 5]. As outdoor leisure activities can improve people’s health and vitality [6, 7], thermally comfortable and environmentally healthy outdoor spaces in densely urban areas are of significance for citizens.

For the reasons outlined above, urban planning requires quantified information to improve outdoor space utilization for a better thermal environment and thermal comfort. Several studies indicated that shading can considerably contribute to a higher quality urban living in summer, particularly on a small scale [8, 9]. So far, related studies have been conducted in a few countries and regions with various climates, such as Hong Kong, China [10–12]; Guangzhou, China [13]; Tempe, USA [14]; Freiburg, Germany [15]; Russi, Italy [16]; and Malaysia [17]. Local shading to block the solar radiation within urban areas can be accomplished by (1) optimized design of buildings, streets, and open spaces [18–20], (2) natural shading, such as trees [12, 13, 21–24], or (3) man-made devices and structures, such as awnings or sunshades and tunnels [14, 25]. Some studies have analyzed the shading effects of these methods on the outdoor local thermal environment. For instance, height-to-width ratio (H/W) is often used as an index to assess the shading level of simple building objects or street canyon geometry [20, 26, 27]. As for the tree shading method, researchers have applied leaf area index (LAI) to describe the occlusion of tree canopies to solar radiation [28]. Sky view factor is another important factor to weigh the shading degree of complex urban structures [11, 29, 30]. Shading is utilized to obstruct the incident solar radiation directly into urban spaces and further influences the surface temperature of the surrounding surfaces such as building exterior surface, underlying surface, and tree surface [11, 23, 28, 31]. Additionally, near-surface air temperature and flow are affected because of convective heat transfer [15]. As a result, the radiant environment, air temperature, and human-biometeorology vary with shading circumstances, with outdoor local-scale energy reduction and with space cooling [28]. Studies have demonstrated that, under normal circumstances, a higher H/W, lower SVF, or higher LAI can create a more obstructed shaded area [11] and thus decrease the heat stress. Urban outdoor shading can also effectively reduce the severe heat that people receive, thus considerably influencing people’s thermal sensation and comfort. To evaluate people’s perception of the surrounding thermal environment based on the energy balance between human body and outdoor environment, several thermophysiological indices are employed, such as physiological equivalent temperature (PET) [32], outdoor standard effective temperature (OUT_SET) [33], and Universal Thermal Climate Index (UTCI) [34]. According to a study by Lee et al. [15], building and tree shade decreases PET by 13.1°C and 15.7°C, respectively, compared with the sunlit areas nearby. In summary, shading can be an effective method to ameliorate outdoor heat stress and improve thermal comfort during summer.

There are a few limitations in the existing studies: (1) they focused on the shading effects on traditional outdoor micrometeorological parameters such as air temperature and seldom concentrated on the urban outdoor radiant environment; (2) few have analyzed the shading effects considering radiant flux densities in detail, for example, directional radiant components; and (3) most existing studies have focused on a specific shading structure in a particular region and season. As a result, the comparisons between the effects of diverse shading forms, for example, shading by neighboring buildings and trees, on the radiant environment and human comfort were generally neglected. The above subjects have not been widely investigated.

In this study, exhaustive measurements and an investigation that targeted different practical effects of building and tree shading forms on the outdoor thermal and 3D radiant environment and human thermal comfort during summer were conducted in a severely cold region. The research involved the subdivision and proportion of the composition of radiant flux densities that related to short- and long-wave radiation; the reduction influence of the two shading methods on detailed directional short- and long-wave radiant flux densities from 3D environment and thus the roles of short- and long-wave radiant flux densities in Tmrt; the radiant environment features and different wavelength radiant flux densities functions at the shaded areas; and finally, the different shading effects on human thermal comfort. The research also used novel applications of the long-wave mean radiant temperature (LTmrt) and directional SVF [11] to emphasize the importance of long-wave radiant flux densities under shaded conditions and 3D directional radiant characteristics, respectively. Moreover, the research subject of this study is in a severely cold region. Unlike studies that were conducted in warm or hot areas, the local-scale ambient temperature in severely cold regions is not as high as in other climates, but the heat stress is still high during summer because of the high radiant flux densities outdoors. This indicates that the outdoor radiant environment may affect human thermal comfort differently in severely cold regions than in other regions. Therefore, research subjects with the regional features of this study provide a necessary supplement to expand the research locations and results for related fields of study.

The main objectives of this study are as follows: (1) to explore the shading effects on micrometeorological parameters, particularly on the detailed directional 3D radiant components, and on human thermal comfort; (2) to investigate the radiant characteristics under shaded conditions; (3) to propose and apply directional SVF and evaluate its relationship with 3D radiant flux densities; and (4) to probe human thermal comfort differences and the impact of micrometeorological parameters on thermal comfort under shaded and sunlit conditions. This study offers a practical research method and quantified data targeting summer conditions for developing general and specific strategies of rational urban planning.

2. Methodology

2.1. Case Study Area and Field Measurement Scheme

2.1.1. Study Area

We took Harbin (45.75°N, 126.77°E) as the research location, which is a central city in northeast China [35]. Summertime here is ephemeral, from early July to late August; however, the solar radiation is relatively high, and air temperature may exceed 30°C [36]. In this study, shaded areas obstructed by buildings (SHA_Buil) and trees (SHA_Tree) and a sunlit background meteorological station (SUN_0), in a university campus in the city center, were selected. Figure 1 shows the location of Harbin and the distribution of each research site in the university campus. In detail, SHA_Buil was occluded by 45 m height nine-story (on the east, south, and west side of the research spot) and 20 m height four-story (on the north side of the research spot) buildings, with light gray elastic coating exterior surfaces. SHA_Tree was under tall elm trees with a mean height of 10 m. Underlying surface material under both shaded sites was seepage brick, with light gray and red colored at SHA_Buil and light red colored at SHA_Tree. In addition, the playground where SUN_0 was on was covered by imitation turf and plastic track. SHA_Buil and SHA_Tree were selected strictly according to the height and degree of the enclosure that blocked most of the direct solar radiation from all directions. Quantitatively, the sky view factor with a lens facing upward was closed to or less than 0.3, and the lateral sky view factors were close to or less than 0.15. Moreover, they also met the requirements for denizens’ outdoor activities. SUN_0 was on a playground that was sufficiently broad to represent an open space that received high radiation.

Figure 1: Location of Harbin and the distribution of the research sites in the university campus.

Measurements at all the research sites were conducted from 7:30 to 18:00 within a week, from July 28 to August 3 in 2018, simultaneously. The meteorological conditions of short-term sample days should meet the characteristics of typical seasonal weather conditions [14, 27, 37, 38]. For this study, the days for measurement were selected under clear-sky conditions with relatively intense solar radiation in July and August, which are the hottest months of the year. Moreover, according to the official historical summer weather database over the years for Harbin (refer to the data for July and August from 2002 to 2018), the daytime ranges of air temperature and global radiant flux densities were 19.0–36.0°C and 14.0–1031.0 W/m², respectively [39]. In this study, during the same period with the official weather data, the ranges of the measured air temperature and global radiant flux densities were 26.7–35.5°C and 20–1025 W/m², respectively. This indicates that the meteorological conditions during the sample measurement period in this study were representative of the norm.

2.1.2. Field Measurement Scheme

At SHA_Buil, SHA_Tree, and SUN_0, air temperature (Ta), relative humidity (RH), wind speed (), and the directional 3D short- and long-wave radiant flux densities reaching the reference standing person (Ki and Li, respectively) were recorded every 1 min at 1.1 m above ground level using three sets of tailor-made human-biometeorological measurement units. Among these, 3D Ki and Li were from the vertical (downward: ↓ and upward: ↑) and horizontal directions (easterly: E⟶, westerly: W⟶, southerly: S⟶, and northerly: N⟶). Surface temperature Ts was recorded every 1 min using button-type temperature recorders that were fixed to the measurement positions using industrial adhesive. In detail, the specific locations of the Ts recorders were as follows: five were used at SHA_Buil, among which one was pasted on the ground under the measurement unit, and four were fixed to the four surrounding building walls at 1.1 m above ground level right facing the measurement unit; another five recorders were used at SHA_Tree, and one was also pasted on the ground under the measurement unit; the other four were fixed to the four tree trunks facing the measurement unit with a distance of 1.1 m to the ground; for SUN_0, the surrounding environment was much more open, so only one recorder was fixed to the ground under the measurement unit. Generally, 4410 data points were obtained for each parameter measured above. Moreover, directional sky view factor SVFi values were obtained through fisheye photos taken adjacent to each radiometer and calculated by RayMan software [40, 41]. In detail, SVF was subdivided into five directions to represent the urban structures: lens facing upward for SVF↓ and facing lateral directions for SVFE⟶, SVFW⟶, SVFS⟶, and SVFN⟶. Table 1 lists the main technical parameters of the sensors used in this study.

Table 1: Main technical parameters of the instruments.

Location
Parameter
Instrument
Quantity/site
Range
Accuracy
Resolution

SHA_Buil/SHA_Tree/SUN_0
Ta
HoBo U23-002
1
−40∼70°C
±0.21°C
0.02°C

RH
0∼100%
±2.5%
0.03%

WFWZY-1
1
0.05∼30 m/s
±0.05 m/s
0.01 m/s

Ki
TBQ-2
6
0∼2000 W/m²
<5%
1 W/m²

Li
TBL-1
6
0∼2000 W/m²
±2%
1 W/m²

Ts
DS1922L
5 at SHA_Buil and SHA_Tree and 1 at SUN_0
−40∼85°C
±0.5°C
0.5°C

All the instruments complied with the ISO 7726 standard [42].

Because RH depends on Ta to a certain degree, it makes more sense under a human-biometeorological perspective to use the water vapor pressure () [30]. Therefore, in the following content, we converted RH to by multiplying RH and saturated water vapor pressure corresponding to various Ta. To describe and compare the data under different shaded or sunlit conditions, the seven-day mean data and standard deviation (SD) were used as statistical indices to display the overall distribution, centralization tendency, and dispersion degree of the local-scale micrometeorological variables. Figure 2 shows the mean and SD distributions of Ta, , global radiant flux densities (equal to K↓ in this study), and from all the research sites during the measurement period. The figure illustrates that there was a relatively large difference in micrometeorological conditions among the shaded and sunlit areas, as well as between different shading forms. The values and discrete degrees of the four types of micrometeorological parameters at the shaded sites were significantly lower than those at the sunlit background meteorological station, particularly for , which indicated that both building and tree shading can effectively reduce outdoor radiation.

(a)

(b)

Figure 2: Statistical distribution of micrometeorological parameters at the research sites: (a) Ta and ; (b) and .

Figure 3 shows the overall distribution, centralization tendency, and dispersion degree of the surface temperature Ts of the exterior walls, tree trunks, and underlying grounds. As can be seen, Ts at the shaded areas was evidently lower than that at the sunlit area. Mean ground surface temperature at SHA_Buil and SHA_Tree was 10.9°C and 11.4°C lower than that at SUN_0, respectively. Comparing the two shaded areas, Ts of surfaces facing the same direction at near SHA_Buil was higher than that at near SHA_Tree, with 1.1°C mean facade temperature difference and 0.5°C ground surface temperature difference; furthermore, it fluctuated more strongly at SHA_Buil than at SHA_Tree comparatively.

Figure 3: Surface temperature of the solid surfaces at different positions around the research sites.

2.2. Mean Radiant Temperature

According to the radiation budget between the radiant environment and the reference standing person, the “six-directional method” was utilized to calculate Tmrt in this study [43], as shown in the following equation:

where Kabs and Labs are the total of short- and long-wave radiant flux densities absorbed by the standing human-biometeorological reference person [44], respectively (both in W/m²). Parameter αl is the long-wave absorption coefficient, σ is the Stefan–Boltzmann constant (5.67∗10⁻⁸ W/(m²·K⁴)), and the unit of Tmrt is °C.

Kabs and Labs are calculated using the following equations, respectively:

where αk is the absorption coefficient of human body for short-wave radiant flux densities Ki (W/m²) and αl is the absorption coefficient of human body for long-wave radiant flux densities Li (W/m²). The standard value of the two physical quantities above is as follows: αk = 0.70 and αl = 0.97. are the angular factors of the reference standing person for Ki and Li and are set to 0.06 for the two vertical directions as well as 0.22 for the four horizontal directions [43].

The total short- or long-wave radiant flux densities absorbed by the reference standing person can be classified into the vertical and horizontal ones (, Khor,abs, , and Lhor,abs) by adding the corresponding components.

The main purposes of using Tmrt in this study are summarized as follows: (1) to comprehensively quantify the short- and long-wave radiation from the 3D environment and to evaluate the specific shading effects on the radiant environment; (2) to determine the thermal comfort index UTCI and to assess its role in human thermal comfort under different shaded and sunlit conditions.

In this study, long-wave mean radiant temperature (LTmrt) was inducted to represent the impact of long-wave radiant flux densities at the shaded areas by substituting Labs for Kabs + Labs, as shown in the following equation [11]:

2.3. Standardization Processing

In this study, the data were presented by means of difference related to the micrometeorological parameters and thermal comfort index such as Ta, Tmrt, and UTCI, and of ratio for the radiant flux densities (short- and long-wave radiant flux densities), instead of absolute values. The impact of different shading forms on the micrometeorological parameters, thermal comfort index, and radiant flux densities was then compared.

Difference of Ta, Tmrt, or UTCI was calculated as follows:

where ΔT represents the Ta, Tmrt, or UTCI difference between the building or tree shading and the sunlit background areas (ΔTa, ΔTmrt, or ΔUTCI); TSHA_Buil and TSHA_Tree are Ta, Tmrt, or UTCI at the building and tree shading areas, respectively; and TSUN_0 is Ta, Tmrt, or UTCI at the sunlit background meteorological station.

With regard to the radiant flux densities, ratios were utilized, as shown below:

where rK and rL are the standardized short- and long-wave radiant ratios of the shaded to the sunlit background areas, respectively; KSHA_Buil and KSHA_Tree are the short-wave radiant flux densities at the building and tree shading areas, respectively; LSHA_Buil and LSHA_Tree are the long-wave radiant flux densities at the building and tree shading areas, respectively; and K↓SUN_0 and L↑SUN_0 are the reference short- and long-wave radiant flux densities at the sunlit background meteorological station, respectively (downward ↓ short-wave radiant flux densities and upward ↑ long-wave radiant flux densities).

3. Results and Discussion

3.1. Overall Shading Effects by Neighboring Buildings and Trees

Based on the abovementioned calculation processing, the shading effects of neighboring buildings and trees on the micrometeorological parameters including Ta, the short- and long-wave radiant components, and thus Tmrt, and the human thermal comfort evaluation index UTCI are analyzed in this section.

3.1.1. Shading Effects on Ta

29 July 2018 was selected as the typical weather day. Figure 4(a) shows the temporal variation in Ta at the shaded and sunlit sites. Different Ta patterns and magnitudes appeared among the three areas that peak Ta was observed at noon at SUN_0 and SHA_Buil, but Ta at SHA_Tree peaked in the morning, then fluctuated at noon and early afternoon, and dropped in the late afternoon and dusk. By applying equation (5), the effect of shading by neighboring buildings and trees on Ta can be analyzed, as presented in Figure 4(b). Tree shading brought about a stronger decrease in Ta especially in the afternoon, with a mean reduction value of 1.9°C and a peak value of −2.8°C at about 12:10; while building shading caused Ta to decrease relatively smoothly with a mean reduction value of 1.2°C and a peak value of −2.0°C at 12:10. The main reason of shading’s decreasing effect on Ta is that shading can occlude a considerable part of incident solar radiation. This weakened the radiant exchange and decreased the surface temperature of the surrounding solid surfaces (see Figure 3), thus lowering the convective heat transfer between the exterior surfaces and local air. Moreover, the evapotranspiration cooling effect of the vegetation [21–23, 45], as well as the more enclosed environment (involving more occlusion to incident solar radiation), around SHA_Tree reduced Ta more than SHA_Buil.

(a)

(b)

Figure 4: Diurnal patterns of Ta and ΔTa on July 29, 2018: (a) temporal variation in Ta at the shaded and sunlit areas; (b) ΔTa between the two shaded and the sunlit background meteorological areas.

3.1.2. Shading Effects on 3D Radiant Environment

The diurnal temporal variations of short- and long-wave radiant components reaching the reference standing person are plotted in Figures 5(a) and 5(b). It can be seen that the short-wave radiant component’s variation ranges remained steady at a lower level between 27 and 107 W/m² at SHA_Buil regardless of the solar position. In contrast, at SHA_Tree, short-wave radiation fluctuated during the pre- and postperiods of high solar altitude angles from the incident directions (↓, S, and E in the morning and W in the afternoon), respectively, because of solar radiation transmission through the leaves. The highest values occurred at SUN_0, especially for K↓ and KS⟶ with the peak value reaching 1025 W/m² when the solar altitude angle was the highest during the day. Besides, KE⟶ and were symmetrically reversed at approximately the highest solar altitude angle, and they reached their peak values in the morning and afternoon, respectively. Figure 5(b) presents the changing profiles of the long-wave radiant components. It can be seen that the patterns of the long-wave radiant components and the relative magnitudes among the research sites were similar to those of the local-scale Ta, indicating that the temporal variation in Ta could affect the long-wave radiant components. There might exist a certain correlation between long-wave radiation and Ta (it will be further analyzed in Section 3.2.2). In addition, directional differences among the components at the two shaded areas were smaller than those at SUN_0. Comparing the two shaded areas, the long-wave radiant flux densities were lower at SHA_Tree than at SHA_Buil. As mentioned in Section 3.1.1, Ta could be lowered near greenery. Therefore, it is well-founded that the long-wave radiation could also be lower, as Ta was, because of the effect of the evapotranspiration of the trees around SHA_Tree.

(a)

(b)

(c)

(d)

Figure 5: Diurnal patterns of Ki, Li, rKi, and rLi on July 29, 2018: (a) temporal variation in short-wave radiant flux densities reaching the reference standing person at the measurement sites; (b) temporal variation in long-wave radiant flux densities reaching the reference standing person at the measurement sites; (c) standardized short-wave radiant components from six directions; (d) standardized long-wave radiant components from six directions.

We also used equations (6) and (7) to compare the different summer shading effects on outdoor 3D short- and long-wave radiant flux densities reaching the reference standing person on July 29, as shown in Figures 5(c) and 5(d). For rKi in Figure 5(c), before 17:00, the reduction effect of building shading on short-wave radiation had much weaker fluctuations than that of tree shading. In particular, rK at SHA_Buil remained below 0.15 until late afternoon; tree shading led to a higher standardized short-wave radiation in the short-wave incident directions from ↓, S, and E in the morning and from W in the early afternoon, which was mainly caused by the transmission of solar radiation under the tree shading condition. After 17:00, shading effects on reducing the short-wave radiation began to decrease at both shaded areas.

As delivered in Figure 5(d), the directional standardized long-wave radiation under both shaded conditions presented different patterns compared with standardized short-wave radiation that they reduced from the beginning of the measurement, reached their lowest values close to 0.75 at noon, and increased till the measurement ended. By contrast, the standardized long-wave radiation variations from different directions were larger at SHA_Buil than at SHA_Tree. A special circumstance appeared from 9:30 to 10:50 when the downward standardized long-wave radiation had a mutation, which corresponded to the time at which rK showed an abrupt change at SHA_Tree because of the radiant transmission effects.

3.1.3. Shading Effects on Tmrt

As shown in Figure 6(a), the temporal trends of Tmrt were primarily similar to those of the short-wave radiant flux densities from the main incident directions (downward and southerly), which was caused by the significant correlation between Tmrt and K↓ and KS⟶. It has been proved by some studies [30, 35]. Moreover, when taking Ta in the foregoing content into account, Tmrt was much higher than Ta with a mean difference (Tmrt–Ta) of 13.5°C at SHA_Buil, 15.5°C at SHA_Tree, and 40.7°C at SUN_0 during the daytime. Figure 6(b) shows the shading effects by neighboring buildings and trees on Tmrt on July 29 using equation (5). As can be seen in the figure, building shade reduced Tmrt more significantly than tree shade did before 11:00; however, after this time, the two shading forms showed similar reduction effects on Tmrt. Meanwhile, shading could significantly reduce Tmrt with mean values of 28.8°C for building shading and 28.1°C for tree shading compared with the sunlit background meteorological station. According to the results discussed in Section 3.1.2, summer shading had a stronger impact on short-wave radiant components than on long-wave radiant components. Therefore, Tmrt reduction by shading was mainly caused by the decrease in short-wave radiant flux densities. Furthermore, Tmrt reduction by shading was stronger than the Ta reduction (Figure 4), revealing that shading can lead to a more significant effect on outdoor radiant environment than on air temperature.

(a)

(b)

Figure 6: Diurnal patterns of Tmrt and ΔTmrt on July 29, 2018: (a) temporal variation in Tmrt at the shaded and sunlit areas; (b) ΔTmrt between the two shaded and the sunlit background meteorological areas.

3.1.4. Shading Effects on UTCI

The Universal Thermal Climate Index (UTCI) is an important thermophysiological index to quantify outdoor heat and cold stress based on the Fiala multinode model [34]. It is appropriate for thermal assessments on any scale as well as in all climates and seasons [34, 46]. In this study, we calculated UTCI by inputting Ta, Tmrt–Ta, and at 1.1 m above ground level and at 10 m above ground level (extrapolated from 1.1 m to 10 m above ground level by equation (8) [47]) for the shaded and sunlit areas ([46], http://www.utci.org) based on the adaptive clothing model [48] and reference human activity level of walking at a speed of 4 km/h [46]. To quantify the shading impact on UTCI, the differences between the building or tree shading and the sunlit background meteorological station on July 29 were calculated using equation (5), as shown in Figure 7. Overall, tree shading brought about a stronger decrease in UTCI than building shading did and that shading decreased UTCI with mean values of 9.2°C for tree shading condition and 7.8°C for building shading condition. Moreover, both shading conditions reduced UTCI most strongly at noon. An opposite pattern appeared from 9:30 to 10:50 where ∆UTCI was higher at SHA_Tree than at SHA_Buil, the period of which was corresponding to the radiant flux densities’ sudden increase at SHA_Tree (see Section 3.1.2). The shading reduction effect on UTCI depended on its comprehensive impact on the micrometeorological parameters, especially on Ta and Tmrt. As a result, ∆UTCI integrated the characteristics of ∆Ta along with ∆Tmrt:

where is the wind speed at a height of 10 m above ground level (m/s), is the wind speed measured by the sensors at 1.1 m above ground level in this study (m/s), z is the distance from the ground (10 m in this case), α is the mean speed exponent set to 0.33 in city-center areas, and z’ is the height of the sensors installed above ground level (1.1 m in this study).

Figure 7: ΔUTCI between the two shaded and the sunlit background meteorological areas on July 29, 2018.

To sum up, both building and tree shading had considerable reduction effects on the radiant components and thus on Tmrt, which was greater than that on Ta. Their influence on UTCI consequently integrated the characteristics of ∆Ta along with ∆Tmrt. Meanwhile, the differences between the two types of shading forms were nonnegligible.

3.2. Radiant Characteristics under Shaded Conditions

As the radiant environment is an important factor in the summer shading function, detailed analyses considering directional short- and long-wave radiant components are conducted in this section. In particular, the different influences and contributions of short- and long-wave radiation are compared.

3.2.1. Definition and Application of Long-Wave Mean Radiant Temperature

Figure 8 reveals the short- and long-wave radiant components absorbed by the reference standing person under building and tree shading conditions based on the seven-day measurement data. From Figure 8(a), we can see that the variation ranges of Ki,abs under the tree canopies were higher than those under the neighboring building obstacles because of the greater short-wave radiant flux densities transmission through the leaves under the tree shading condition. However, values of Li,abs were higher at SHA_Buil than those at SHA_Tree, as presented in Figure 8(b) This was mainly because there existed massed solid surfaces at the building shading area that received more long-wave radiant flux densities. Moreover, directional differences among the two vertical directions and the four horizontal directions were distinct in both Li,abs and Ki,abs components; the average values of short- or long-wave radiant flux densities absorbed by the reference standing person from a single horizontal direction were three to four times those of a single vertical direction. This also resulted in the mean Khor,abs or Lhor,abs being six to eight times greater than that of or . These results mainly attributed to the larger emission intensity from the horizontal directions and the higher absorption for the horizontal short- or long-wave radiant flux densities by the reference standing person of 0.22 versus for the vertical ones of 0.06.

(a)

(b)

Figure 8: Short- and long-wave radiant flux densities absorbed by the reference standing person: (a) Ki,abs; (b) Li,abs.

In urban outdoor settlements, shaded conditions do not mean the complete absence of short-wave radiation during the daytime. There still exist diffuse and a small amount of direct short-wave radiation. Some studies have proved that the proportion of long-wave radiant flux densities absorbed by the human body was relatively high in the outdoor environments [14, 30, 35]. This study further calculated and compared the proportion of Kabs and Labs in Kabs + Labs under different shaded and sunlit conditions. Figure 9 presents Kabs/(Kabs + Labs) and Labs/(Kabs + Labs) at the shaded areas in this study. Kabs/(Kabs + Labs) did not exceed 10% under the shaded condition by neighboring buildings, which implied that Labs/(Kabs + Labs) reached more than 90%. The proportion range of Kabs in Kabs + Labs under the tree shading condition was larger with a peak value of 23%, indicating a minimum Labs/(Kabs + Labs) value of 77%. By comparison, Labs/(Kabs + Labs) at the sunlit area was much lower, with a minimum value even below 65% and a maximum value below 88%. Therefore, shading not only reduced the air temperature, 3D radiant flux densities, and Tmrt , but also caused a higher portion of Labs in Kabs + Labs under shaded conditions at the same time. Similar studies were carried out in summer by Middel and Krayenhoff [14] and Lee et al. [30]. The former distinguished the decomposed contributions of directional short- and long-wave radiation on Tmrt at a series of sites during one day. They concluded that there was a higher proportion of lateral and hence total long-wave radiation at shaded areas than at sunlit areas. The latter considered clear-sky summer days from 2007 to 2010 and did not differentiate between shaded and sunlit conditions but also pointed out that the proportion of Kabs did not exceed 40% with Labs proportion not falling below 60%.

Figure 9: Proportion of Kabs and Labs in Kabs + Labs at the shaded and sunlit areas.

Because summer shading can greatly increase Labs/(Kabs + Labs), we applied LTmrt by substituting Labs for Kabs + Labs under shaded conditions (see equation (4)). LTmrt was the degenerated Tmrt considering only long-wave radiant flux densities absorbed by the reference standing person [11]. It also reacted to the mean surface temperature of surrounding objects within shaded areas where the long-wave radiant flux densities played a more essential role. Figure 10 shows the distributions of LTmrt at the two shaded research sites. As can be seen, LTmrt at SHA_Buil fluctuated more strongly (with a peak of 41.8°C and a mean value of 39.6°C) than that at SHA_Tree (with a peak of 40.4°C and a mean value of 38.8°C). This again confirmed the results shown in Figure 8(b) that the Li,abs values and variation range were higher at SHA_Buil than those at SHA_Tree.

Figure 10: Long-wave mean radiant temperature at the two shaded areas.

3.2.2. Relationship between LTmrt and Ta

The outdoor radiant environment is the decisive factor for urban micrometeorological and human-biometeorological conditions. First, as shown in the foregoing section, shading can reduce Ta slightly. As the atmosphere is almost transparent to solar radiation (short-wave), it is very weak for the atmosphere to increase air temperature by receiving solar radiation directly. In general, convective heat transfer between the local atmosphere and the solid surfaces (including underlying surfaces, neighboring building exterior walls, and vegetation surfaces) is the principal factor causing Ta to change, in which the surface temperature of the solid surfaces plays a direct role. The surface temperature varies with the incident solar radiation, which affects the radiation exchange. By taking the surface temperature at the same position (the underlying ground surface) of the three research sites for instance (Figure 3), the mean Ts at the shaded research sites was approximately 11.1°C lower than that at the sunlit sites, thus reducing Ta to a certain extent. As a result, shading not only decreased radiation intuitively but also decreased Ta indirectly.

LTmrt reflects the long-wave radiant flux densities emitted from the surrounding solid surfaces. Therefore, the surface temperature has a higher explanatory power for the outdoor long-wave radiant flux densities emissions under shaded conditions [11]. As proved in the previous paragraph, Ta was significantly influenced by the surface temperature. Thus, we analyzed the relationship between these two affected factors influenced by surface temperature of the shaded areas.

First of all, the time during the measurement was divided into four periods: morning (7:30–10:00), noon (10:00–13:00), afternoon (13:00–16:00), and dusk (16:00–18:00) according to the solar altitude angle. As shown in Figure 11, the solar altitude angle range was 6–60° during the research period. 30° and 50° were selected as the degree nodes to classify, and their nearest sharp-time points were set as the division bases. Then, the analysis of variance was conducted at SHA_Buil and SHA_Tree taking LTmrt, Ta, and time into account. Finally, the estimated marginal mean results at the two shaded areas are obtained, as shown in Figure 12.

Figure 11: Time division of the measurement period.

(a)

(b)

Figure 12: Estimated marginal mean LTmrt under temporal variation in Ta at the building and tree shaded areas: (a) SHA_Buil; (b) SHA_Tree.

It can be seen that LTmrt varied with Ta and elapsed time. On the one hand, the time segments for LTmrt and Ta to reach their highest values were different at the two shaded sites. In detail, the highest LTmrt with the corresponding Ta appeared in the afternoon and at dusk at SHA_Buil; however, LTmrt and Ta were higher at noon and in the afternoon than at other periods at SHA_Tree. It can be interpreted that, as time lapsed, the heat accumulation increased till afternoon and dusk at SHA_Buil, where there were numerous structures with strong heat storage capacity. However, because of the transmission at SHA_Tree, radiation from various directions from the external environment was allowed to enter the research space to heat solid surfaces such as trunks. As is known, radiation is strongest around midday, which directly causes LTmrt and the corresponding Ta during noon and afternoon being higher than those in the morning and at dusk.

On the other hand, from the trend in the scatter plots between the estimated marginal mean LTmrt and Ta, linear increase in the estimated marginal mean LTmrt with Ta was found at both SHA_Buil and SHA_Tree. Therefore, Table 2 summarizes their relationship that is quantified by the coefficient of determination R² (LTmrt and Ta at the two shaded areas satisfied the normal distribution), based on the data throughout the measurement period. Evidently, the correlation coefficients were intensely significant at the significance level sig = 0.000 regardless of SHA_Buil, SHA_Tree, and the total values. Therefore, LTmrt had a significant positive correlation with Ta, in which the correlation was stronger at SHA_Buil. Meanwhile, the difference in the analyses of variance and regression results between the building and tree shading was attributed to the urban microclimatic factors owing to the neighborhood: the organization, disposition, and material of the surrounding environment. To conclude, Ta can offer a basic statistical comprehension to a local-scale LTmrt at shaded areas.

Table 2: Linear fitting analysis between estimated marginal mean LTmrt and Ta at the building and tree shading areas and the total.

Research site
SHA_Buil
SHA_Tree
Total

R²
0.976
0.814
0.835

Significance
0.000
0.000
0.000

From the results in this section, we discover that summer shading also led to variations in the short- and long-wave proportions. LTmrt varied with Ta and elapsed time under different shaded conditions. Yet how the neighboring building and tree obstacles, that are ascribed to urban morphology, block radiation and quantitatively influence the radiant components should be investigated further, as presented in the following section.

3.3. Quantified Directional Urban Morphology and Radiant Environment

The shading effects on urban local-scale microclimate are mainly caused by the blocking of direct short-wave radiation. As mentioned in Section 3.1.2, shading primarily influenced short-wave radiant flux densities from the incident directions of the direct radiation to the greatest extent. Furthermore, shading of the direction-dependent short-wave radiant flux densities had consequences for other radiant flux densities. Ultimately, different shading modes mainly affect the short- and long-wave radiant flux densities in the spatial distribution and range. Their temporal variations primarily change with the solar altitude and orientation. Moreover, the urban morphology reflected by SVF of the research site plays an essential role in the microclimate. Thus, directional SVF was proposed to examine the dependence of microclimate on the urban structure in terms of different shading methods in this study [11].

Table 3 summarizes the SVFi from five directions at the research sites. From the SVF images at SHA_Tree, we can infer that it offered a higher probability of short-wave radiant flux densities entering the space from more tilt angles. To conclude, although the directional SVFi values at SHA_Tree were lower than those at SHA_Buil in the corresponding direction, the short-wave radiant flux densities reaching the tree shading site still maintained a higher level and a stronger fluctuation (see Figure 8(a)).

Table 3: Summary of SVFi from five directions.

Direction
SHA_Buil
SHA_Tree
SUN_0

Downward↓

Easterly E⟶

Westerly W⟶

Southerly S⟶

Northerly N⟶

To analyze the detailed relationship between the radiant components and SVFi, five directional short-wave (long-wave) averaged radiant data at each research site were calculated over the seven measurement days and were associated with the five corresponding directional SVFi values [11, 14]. Therefore, fifteen data points in total were linearly fitted for the directional short- or long-wave and the corresponding SVFi with the same direction, as presented in Figure 13. Evidently, directional SVFi caused the short-wave radiant flux densities to linearly increase and the long-wave radiant components to decrease. However, no clear dependence of long-wave radiant flux densities on SVFi was found because of the exceedingly low R². Therefore, SVF had a higher explanatory power for the short-wave radiant flux densities, but this was not evident for the long-wave radiant flux densities.

Figure 13: Linear fitting between seven-day mean short- or long-wave radiant flux densities and directional SVFi.

We further investigated the impact of the directional SVFi on Tmrt. The nondirectional Tmrt of the three research sites was averaged over the seven measurement days [14, 15, 20, 30, 49] and was linearly fitted with the five directional SVFi and the mean SVF, with three data points around one fitting line, as shown in Figure 14. Table 4 summarizes R² values, indicating that Tmrt was strongly correlated with the mean SVF, in which Tmrt was most relevant to SVFS⟶. Figure 14 simultaneously shows that the slope values were all positive, which once again proved that Tmrt was mainly governed by short-wave radiant flux densities, that shading in terms of a lower SVF brought out a lower Kabs + Labs even if the long-wave radiant flux densities were increasing.

Figure 14: Linear fitting between seven-day mean Tmrt and the directional SVF as well as the mean SVF.

Table 4: Summary of R² in linear fitting between seven-day mean Tmrt and the directional SVFi as well as the mean SVF.

Direction
↓
E⟶
W⟶
S⟶
N⟶
Mean SVF

R²
0.7174
0.8913
0.4540
0.9756
0.1209
0.7341

Significance
0.000
0.000
0.000
0.000
0.000
0.000

Similarly, Middel and Krayenhoff also demonstrated that the lateral short-wave radiation correlated more strongly with SVF↓ (with R² of 0.78 at noon and 0.70 in the afternoon) than the lateral long-wave radiation did (with R² of 0.02 at noon and 0.08 in the afternoon). Besides, Tmrt was found to have R² values of 0.66 at noon and 0.64 in the afternoon related to SVF↓ during the daytime [14].

In brief, it has turned out that directional SVFi values were meaningful for the study of the effects of shading on outdoor directional radiant components and the comprehensive radiant parameter Tmrt.

3.4. Human Thermal Comfort (Distributions and Influencing Micrometeorological Parameters)

The effects of shading on micrometeorological parameters such as radiant components and air temperature have been discussed in the previous sections. The outdoor shaded or sunlit conditions would affect the denizens’ thermal experience and thermal comfort. Therefore, further analyses of the impact of different shading forms on human thermal comfort will be explored in this section.

3.4.1. UTCI Distribution

Jin et al. conducted the outdoor meteorological measurements and thermal sensation and comfort questionnaire survey also in Harbin and determined the ranges of UTCI that represent people’s feeling of comfort based on the field research [50]. In this study, we utilized the UTCI values categorized in terms of thermal stress reclassified by them (Table 5). Seven-day UTCI values were separated into different thermal stress categories, as shown in Figure 15. At the shaded areas, UTCI was primarily distributed over “strong heat stress” and “moderate heat stress,” in which the proportion of “moderate heat stress” at SHA_Tree was 22% higher than that at SHA_Buil. By contrast, UTCI at the sunlit areas was raised by two categories under similar background weather conditions that the thermal stress was categorized as “extreme heat stress,” “very strong heat stress,” and “strong heat stress.” It indicates that the outdoor heat stress during summer daytime was at a high level, which also reflected the necessity of summer shading to improve human thermal comfort outdoors.

Table 5: Thermal classification of UTCI equivalent temperatures for Harbin [50].

UTCI range (°C)
Thermal stress category

>49.4
Extreme heat stress

40.9 to 49.4
Very strong heat stress

29.1 to 40.9
Strong heat stress

23.0 to 29.1
Moderate heat stress

−3.8 to 23.0
No thermal stress

−7.2 to −3.8
Slight cold stress

−18.3 to −7.2
Moderate cold stress

−25.6 to −18.3
Strong cold stress

−30.2 to −25.6
Very strong cold stress

<−30.2
Extreme cold stress

Figure 15: Histograms of seven-day UTCI distributions.

3.4.2. Impact of Micrometeorological Parameters on Human Thermal Comfort

Urban design is interested in human-biometeorological results, whose application contributes to the citizens’ thermal comfort, health, outdoor activities, work efficiency, and well-being [51]. Based on all the seven-day field measurement data at shaded and sunlit areas, the partial correlation analysis results in Table 6 demonstrated that UTCI was positively determined by Ta, Tmrt, and but negatively depended on on summer days. It can be seen that both Ta and Tmrt were highly correlated with UTCI, with the partial correlation coefficient r values exceeding 0.96. Nevertheless, subtle differences appeared when distinguishing shaded and sunlit sites, that UTCI firstly depended on Ta at the shaded areas, followed by Tmrt: but conversely, Tmrt played the most important role in UTCI at the sunlit areas where the radiant flux densities were significantly high and denizens experienced strong heat stress. The reason can be explained as follows. The radiation exchange, quantified by Tmrt, is the key meteorological variable in summer. At shaded areas in the daytime, there is usually a strong correlation between Tmrt and Ta [52]. Meanwhile, there is less incident short-wave radiation entering these enclosed spaces. This is the reason why we found that Ta was the first positive variable for the human thermophysiological perception to the outdoor environment. In other words, Ta had the role of a proxy variable for the radiation exchange under shaded condition. While at the sunlit area, Tmrt was still the dominant factor for UTCI.

Table 6: Partial correlation analysis results of UTCI depending on the microclimatic parameters at the shaded and sunlit research sites.

Research sites
Index
Ta
Tmrt

at 10 m

SHA_Buil and SHA_Tree
r
0.991
0.968
0.920
−0.919

Sig
0.000
0.000
0.000
0.000

SUN_0
r
0.990
0.996
0.807
−0.970

Sig
0.000
0.000
0.000
0.000

r: partial correlation coefficient; Sig: significance.

The above outcomes were also illustrated by Lee et al., who indicated that the correlation between human thermal comfort and Tmrt was slightly stronger than with Ta on clear-sky summer days [15, 30]. In comparison, Middel and Krayenhoff conducted day-night measurements in 22 sunlit or shaded sites on the hottest day in Tempe and found that UTCI was most sensitive to Ta, whereas it was about half as sensitive to Tmrt as Ta [14]. The differences of the results between their research and this study are mainly because (1) their study considered not only the daytime but also the evening and night after sunset when solar radiation passed off and Ta was dominant and (2) they focused on all the sunlit areas together with the shaded sites, so the relationship between UTCI and each individual micrometeorological variable under shaded and sunlit conditions was not identified.

The relatively low partial correlation coefficient values between UTCI and as well as indicated that these two microclimatic parameters at the shaded and sunlit areas played a more subsidiary part in the perception of outdoor heat by denizens, which was also proved by Middel and Krayenhoff using sensitivity analysis [14].

So far, human thermal comfort within urban settlements affected by neighboring building and tree shading has been taken into account by implementing the “human-oriented” concept in urban planning and design objectives.

4. Conclusions

This study investigates the summer shading effects caused by neighboring buildings and trees and analyzes the difference between shaded and sunlit conditions as well as that between different shading forms in a typical city in the severely cold region. Summer shading can significantly reduce outdoor heat stress and offer the diversity in thermal environment and spaces for denizens. Based on the analyses, we obtained the following conclusions:

(1)Shading led to a stronger reduction on Tmrt than on Ta, and its effect on UTCI synthesized the variation in the above two parameters that building shading decreased Tmrt, Ta, and UTCI with mean values of 28.8, 1.2, and 7.8°C and tree shading of 28.1, 1.9, and 9.2°C, respectively, compared with the background meteorological site with sunlit condition.(2)Within the shaded areas, short-wave radiant components decreased considerably more than long-wave radiant components owing to shading; the proportion of Labs in Kabs + Labs was high, and it led to a relatively high LTmrt, which had an R² with Ta exceeding 0.8, and reached their highest values in the afternoon and at dusk at SHA_Buil and at noon and in the afternoon at SHA_Tree.(3)Directional SVFi exhibited a significantly positive correlation with short-wave radiant flux densities; however, the relationship between SVFi and 3D long-wave radiant flux densities was not statistically evident. Meanwhile, Tmrt was most relevant with SVFS⟶ with a R² of 0.9756.(4)UTCI at the sunlit areas rose two categories compared with that at the shaded areas under the similar background weather conditions; Ta and Tmrt played the first positive part in UTCI under shaded and sunlit conditions, respectively.(5)Future research will aim to conduct long-term measurements considering urban morphology variations. and to obtain more novel insights.

Nomenclature

: | Global radiation, W/m²
Ki: | Short-wave radiant flux densities from different directions (i: ↓, ↑, east, west, south, or north), W/m²
Ki,abs: | Short-wave radiant flux densities from different directions absorbed by the reference standing person (i: ↓, ↑, east, west, south, or north), W/m²
K↑: | Upward short-wave radiant flux densities, W/m²
K↑abs: | Upward short-wave radiant flux densities absorbed by the reference standing person, W/m²
K↓: | Downward short-wave radiant flux densities, W/m²
K↓abs: | Downward short-wave radiant flux densities absorbed by the reference standing person, W/m²
Kabs: | Total of short-wave radiant flux densities absorbed by the reference standing person, W/m²
KE⟶: | Easterly short-wave radiant flux densities, W/m²
KE⟶,abs: | Easterly short-wave radiant flux densities absorbed by the reference standing person, W/m²
Khor,abs: | Short-wave radiant flux densities absorbed by the reference standing person from horizontal directions, W/m²
KN⟶: | Northerly short-wave radiant flux densities, W/m²
KN⟶,abs: | Northerly short-wave radiant flux densities absorbed by the reference standing person, W/m²
KS⟶: | Southerly short-wave radiant flux densities, W/m²
KS⟶,abs: | Southerly short-wave radiant flux densities absorbed by the reference standing person, W/m²
: | Short-wave radiant flux densities absorbed by the reference standing person from vertical directions, W/m²
: | Westerly short-wave radiant flux densities, W/m²
: | Westerly short-wave radiant flux densities absorbed by the reference standing person, W/m²
Li: | Long-wave radiant flux densities from different directions (i: ↓, ↑, east, west, south, or north), W/m²
Li,abs: | Long-wave radiant flux densities from different directions absorbed by the reference standing person (i: ↓, ↑, east, west, south, or north), W/m²
L↑: | Upward long-wave radiant flux densities, W/m²
L↑abs: | Upward long-wave radiant flux densities absorbed by the reference standing person, W/m²
L↓: | Downward long-wave radiant flux densities, W/m²
L↓abs: | Downward long-wave radiant flux densities absorbed by the reference standing person, W/m²
Labs: | Total of short-wave radiant flux densities absorbed by the reference standing person, W/m²
LE⟶: | Easterly long-wave radiant flux densities, W/m²
LE⟶,abs: | Easterly long-wave radiant flux densities absorbed by the reference standing person, W/m²
Lhor,abs: | Short-wave radiant flux densities absorbed by the reference standing person from horizontal directions, W/m²
LN⟶: | Northerly long-wave radiant flux densities, W/m²
LN⟶,abs: | Northerly long-wave radiant flux densities absorbed by the reference standing person, W/m²
LS⟶: | Southerly long-wave radiant flux densities, W/m²
LS⟶,abs: | Southerly long-wave radiant flux densities absorbed by the reference standing person, W/m²
LTmrt: | Long-wave mean radiant temperature, °C
: | Short-wave radiant flux densities absorbed by the reference standing person from vertical directions, W/m²
: | Westerly long-wave radiant flux densities, W/m²
: | Westerly long-wave radiant flux densities absorbed by the reference standing person, W/m²
RH: | Relative humidity, %
R²: | Coefficient of determination
r: | Partial correlation coefficient
SD: | Standard deviation
Sig: | Significance
SVFi: | Directional sky view factor (i: ↓, east, west, south, or north)
SVF↓: | Downward SVF
SVFE⟶: | Easterly SVF
SVFN⟶: | Northerly SVF
SVFS⟶: | Southerly SVF
SVFW⟶: | Westerly SVF
Ta: | Air temperature, °C
Tmrt: | Mean radiant temperature, °C
Ts: | Surface temperature, °C
UTCI: | Universal Thermal Climate Index, °C
: | Wind speed, m/s
: | Water vapor pressure, hPa.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (grant no. 51438005). The authors thank the students for helping during the measurement campaign.

References

B. Cohen, “Urbanization in developing countries: current trends, future projections, and key challenges for sustainability,” Technology in Society, vol. 28, no. 1-2, pp. 63–80, 2006.

K. W. Oleson, A. Monaghan, O. Wilhelmi et al., “Interactions between urbanization, heat stress, and climate change,” Climatic Change, vol. 129, no. 3-4, pp. 525–541, 2015.

R.-L. Hwang, T.-P. Lin, and A. Matzarakis, “Seasonal effects of urban street shading on long-term outdoor thermal comfort,” Building and Environment, vol. 46, no. 4, pp. 863–870, 2011.

M. Nikolopoulou, N. Baker, and K. Steemers, “Thermal comfort in outdoor urban spaces: understanding the human parameter,” Solar Energy, vol. 70, no. 3, pp. 227–235, 2001.

I. Eliasson, I. Knez, U. Westerberg, S. Thorsson, and F. Lindberg, “Climate and behaviour in a Nordic city,” Landscape and Urban Planning, vol. 82, no. 1-2, pp. 72–84, 2007.

H. Pitt, “What prevents people accessing urban bluespaces? A qualitative study,” Urban Forestry & Urban Greening, vol. 39, pp. 89–97, 2019.

A. A. Hakim, H. Petrovitch, C. M. Burchfiel et al., “Effects of walking on mortality among nonsmoking retired men,” New England Journal of Medicine, vol. 338, no. 2, p. 94, 1998.

T.-P. Lin, K.-T. Tsai, R.-L. Hwang, and A. Matzarakis, “Quantification of the effect of thermal indices and sky view factor on park attendance,” Landscape and Urban Planning, vol. 107, no. 2, pp. 137–146, 2012.

M. Hadianpour, M. Mahdavinejad, M. Bemanian, and F. Nasrollahi, “Seasonal differences of subjective thermal sensation and neutral temperature in an outdoor shaded space in Tehran, Iran,” Sustainable Cities and Society, vol. 39, pp. 751–764, 2018.

H. D. Cheung and T. M. Chung, “Analyzing sunlight duration and optimum shading using a sky map,” Building and Environment, vol. 42, no. 9, pp. 3138–3148, 2007.

A. Lai, M. Maing, and E. Ng, “Observational studies of mean radiant temperature across different outdoor spaces under shaded conditions in densely built environment,” Building and Environment, vol. 114, pp. 397–409, 2017.

L. Kong, K. K.-L. Lau, C. Yuan et al., “Regulation of outdoor thermal comfort by trees in Hong Kong,” Sustainable Cities and Society, vol. 31, pp. 12–25, 2017.

S. Zheng, J.-M. Guldmann, Z. Liu, and L. Zhao, “Influence of trees on the outdoor thermal environment in subtropical areas: an experimental study in Guangzhou, China,” Sustainable Cities and Society, vol. 42, pp. 482–497, 2018.

A. Middel and E. S. Krayenhoff, “Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: introducing the MaRTy observational platform,” Science of the Total Environment, vol. 687, pp. 137–151, 2019.

H. Lee, J. Holst, and H. Mayer, “Modification of human-biometeorologically significant radiant flux densities by shading as local method to mitigate heat stress in summer within urban street canyons,” Advances in Meteorology, vol. 2013, Article ID 312572, 13 pages, 2013.

K. Fabbri, G. Canuti, and A. Ugolini, “A methodology to evaluate outdoor microclimate of the archaeological site and vegetation role: a case study of the Roman Villa in Russi (Italy),” Sustainable Cities and Society, vol. 35, pp. 107–133, 2017.

N. Makaremi, E. Salleh, M. Z. Jaafar, and A. GhaffarianHoseini, “Thermal comfort conditions of shaded outdoor spaces in hot and humid climate of Malaysia,” Building and Environment, vol. 48, pp. 7–14, 2012.

F. Ali-Toudert and H. Mayer, “Numerical study on the effects of aspect ratio and orientation of an urban street canyon on outdoor thermal comfort in hot and dry climate,” Building and Environment, vol. 41, no. 2, pp. 94–108, 2006.

F. Ali-Toudert and H. Mayer, “Effects of asymmetry, galleries, overhanging façades and vegetation on thermal comfort in urban street canyons,” Solar Energy, vol. 81, no. 6, pp. 742–754, 2007.

J. Holst and H. Mayer, “Impacts of street design parameters on human-biometeorological variables,” Meteorologische Zeitschrift, vol. 20, no. 5, pp. 541–552, 2011.

L. V. de Abreu-Harbich, L. C. Labaki, and A. Matzarakis, “Effect of tree planting design and tree species on human thermal comfort in the tropics,” Landscape and Urban Planning, vol. 138, pp. 99–109, 2015.

N. J. Georgi and K. Zafiriadis, “The impact of park trees on microclimate in urban areas,” Urban Ecosystems, vol. 9, no. 3, pp. 195–209, 2006.

B.-S. Lin and Y.-J. Lin, “Cooling effect of shade trees with different characteristics in a subtropical urban park,” HortScience, vol. 45, no. 1, pp. 83–86, 2010.

R.-L. Hwang, T.-P. Lin, M.-J. Cheng, and J.-H. Lo, “Adaptive comfort model for tree-shaded outdoors in Taiwan,” Building and Environment, vol. 45, no. 8, pp. 1873–1879, 2010.

F. Gugliermetti and F. Bisegna, “Daylighting with external shading devices: design and simulation algorithms,” Building and Environment, vol. 41, no. 2, pp. 136–149, 2006.

E. Jamei, P. Rajagopalan, M. Seyedmahmoudian, and Y. Jamei, “Review on the impact of urban geometry and pedestrian level greening on outdoor thermal comfort,” Renewable and Sustainable Energy Reviews, vol. 54, pp. 1002–1017, 2016.

S. Achour-Younsi and F. Kharrat, “Outdoor thermal comfort: impact of the geometry of an urban street canyon in a mediterranean subtropical climate—case study Tunis, Tunisia,” Procedia—Social and Behavioral Sciences, vol. 216, pp. 689–700, 2016.

R. Berry, S. J. Livesley, and L. Aye, “Tree canopy shade impacts on solar irradiance received by building walls and their surface temperature,” Building and Environment, vol. 69, pp. 91–100, 2013.

J. Unger, “Connection between urban heat island and sky view factor approximated by a software tool on a 3D urban database,” International Journal of Environment and Pollution, vol. 36, no. 1–3, pp. 59–80, 2009.

H. Lee, H. Mayer, and D. Schindler, “Importance of 3-D radiant flux densities for outdoor human thermal comfort on clear-sky summer days in Freiburg, Southwest Germany,” Meteorologische Zeitschrift, vol. 23, no. 3, pp. 315–330, 2014.

S. Leuzinger, R. Vogt, and C. Körner, “Tree surface temperature in an urban environment,” Agricultural and Forest Meteorology, vol. 150, no. 1, pp. 56–62, 2010.

H. Mayer and P. Höppe, “Thermal comfort of man in different urban environments,” Theoretical and Applied Climatology, vol. 38, no. 1, pp. 43–49, 1987.

J. Pickup and R. d. Dear, “An outdoor thermal comfort index (Out_SET)–Part 1–the model and its assumptions,” in Biometeorology and Urban Climatology at the Turn of the Millennium, World Meteorological Organization, Geneva, Switzerland, 2000.

K. Błazejczyk, G. Jendritzky, P. Bröde et al., “An introduction to the Universal thermal climate index (UTCI),” Geographia Polonica, vol. 86, no. 1, pp. 5–10, 2013.

J. Du, C. Sun, Q. Xiao, X. Chen, and J. Liu, “Field assessment of winter outdoor 3-D radiant environment and its impact on thermal comfort in a severely cold region,” Science of The Total Environment, vol. 709, Article ID 136175, 2020.

MOHURD, “Code for design of civil buildings,” Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing, China, 2005, GB 50352-2005.

T. F. Zhao and K. F. Fong, “Characterization of different heat mitigation strategies in landscape to fight against heat island and improve thermal comfort in hot-humid climate (Part I): measurement and modelling,” Sustainable Cities and Society, vol. 32, pp. 523–531, 2017.

E. Johansson and R. Emmanuel, “The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka,” International Journal of Biometeorology, vol. 51, no. 2, pp. 119–133, 2006.

Climate.OneBuilding, 2020, http://climate.onebuilding.org/WMO_Region_2_Asia/CHN_China/index.html#IDHL_Heilongjiang-.

A. Matzarakis, F. Rutz, and H. Mayer, “Modelling radiation fluxes in simple and complex environments—application of the RayMan model,” International Journal of Biometeorology, vol. 51, no. 4, pp. 323–334, 2007.

A. Matzarakis, F. Rutz, and H. Mayer, “Modelling radiation fluxes in simple and complex environments: basics of the RayMan model,” International Journal of Biometeorology, vol. 54, no. 2, pp. 131–139, 2010.

ISO, International Standard 7726, Ergonomics of the Thermal Environment—Instruments for Measuring Physical Quantities, International Standard Organization, Geneva, Switzerland, 1998.

P. Höppe, “A new method to determine the mean radiant temperature outdoors,” Wetter and Leben, vol. 44, no. 1–3, pp. 147–151, 1992.

H. Mayer, “Urban bioclimatology,” Experientia, vol. 49, no. 11, pp. 957–963, 1993.

T. R. Oke, Boundary Layer Climates, Methuen, London, UK, 1987.

P. Bröde, D. Fiala, K. Błażejczyk et al., “Deriving the operational procedure for the universal thermal climate index (UTCI),” International Journal of Biometeorology, vol. 56, no. 3, pp. 481–494, 2011.

ASHRAE, ASHRAE Fundamentals Handbook, American Society of Heating, Refrigerating and Air-conditioning Engineers, Atlanta, GA, USA, SI edition, 2001.

G. Havenith, D. Fiala, K. Błazejczyk et al., “The UTCI-clothing model,” International Journal of Biometeorology, vol. 56, no. 3, pp. 461–470, 2012.

F. Lindberg, S. Thorsson, D. Rayner, and K. Lau, “The impact of urban planning strategies on heat stress in a climate-change perspective,” Sustainable Cities and Society, vol. 25, pp. 1–12, 2016.

H. Jin, S. Liu, and J. Kang, “Thermal comfort range and influence factor of urban pedestrian streets in severe cold regions,” Energy and Buildings, vol. 198, pp. 197–206, 2019.

G. Mills, “Progress toward sustainable settlements: a role for urban climatology,” Theoretical and Applied Climatology, vol. 84, no. 1–3, pp. 69–76, 2006.

H. Lee and H. Mayer, “Urban human-biometeorology supports urban planning to handle the challenge by increasing severe heat,” in Proceedings of the PLEA2013—29th Conference, Sustainable Architecture for a Renewable Future, Munich, Germany, September 2013.