The Effect of Street Orientation on the Temporal Variation in Thermal Environment Within Streets in Different Climate Zones

Abstract

Orientation is a key indicator affecting the street thermal environment, especially by modifying the radiation temperature. Comprehending the temporal variation in the thermal environment helps in adapting to heat exposure on streets with different orientations. Existing studies have revealed the impacts of street orientations on static thermal environments, namely, the thermal environment at a location at a certain time. However, the thermal environment is dynamically changing, yet the impact of the street orientation on this dynamic change has not yet been revealed, which is an important reference for citizens to choose appropriate streets and exposure times. This study takes the typical cities in China as examples. By simulation, the thermal data of each hour within the street were collected. Then, the thermal distribution map was initiated to display the temporal variation in the thermal environment in various oriented streets. Finally, for each oriented street, the regulatory capabilities, as well as the impacts on “hot” perception, were analyzed. Specifically, the maximum regulatory capabilities of the street orientation on PETs were about 3 °C (Harbin), 5 °C (Xi’an), 11 °C (Changsha), 10 °C (Guangzhou), 4 °C (Kunming), 4 °C (Xining), and 6 °C (Urumqi). Furthermore, taking 39 °C as the marker of “hot” PET perception, the regulatory capabilities of the street orientation on the period of “hot” perception were approximately 1 h (Harbin), 2.5 h (Xi’an), 2.5 h (Changsha), 1.5 h (Guangzhou), 5 h (Kunming), 1 h (Xining), and 5 h (Urumqi).

1. Introduction

Urbanization has led to massive heat accumulation in urban canyons, especially in the context of climate change [1,2,3]. Severe heat within urban canyons brings trouble to thermal comfort [4,5] and human health [6]. Many strategies have been used to mitigate the heat within the urban canopy [7,8,9,10,11], where urban geometry has been considered as an effective measure [12,13], especially the orientation of street [14,15], which determines the access of solar radiation to the valley and significantly changes the wind [16,17]. A great deal of research projects have explored the impacts of street orientations on static thermal environments, namely, the thermal environment in a certain place [18,19,20]. However, the thermal environment within a street is dynamically changing, yet research has not revealed the temporal evolutionary characteristics of the thermal environments of different oriented streets. Revealing the impact of street orientations on the thermal environment from the perspective of temporal evolution is crucial for citizens to avoid heat exposure [21].

Additionally, the temporal evolutionary characteristics of thermal environments in streets also depend on the climate zone [22,23]. For example, a study conducted in tropical hot-humid climate zones claimed that streets with the orientation of NE–SW had the worst thermal comfort, compared with other orientations [24]. Another study performed in UAE, a hot and dry climate country, found that streets with the orientation of S–N had the best thermal comfort [25]. Therefore, defining the impact of street orientation on the temporal variation in thermal environments should account for climate zones. China spans across seven building climate zones, with significant differences in meteorological conditions in each climate zone [26,27], which makes the issue more complicated in China. To answer the question as it pertains to China, the following works have been performed. First, the seven representative cities from the seven building climate zones are selected, considering the urban scale and location, where the typical streets in the urban heat island center are identified, which are used as the typical models in this study. Second, the Envi-met model is validated by a field experiment before it is used to simulate the thermal indicator of different oriented streets. Third, with the visual map, the temporal variation in PET corresponding to the six street orientations are illustrated in the interval of one hour, which intuitively illustrates the dynamic change in the thermal environments of different oriented streets. Finally, the general impacts, including the regulatory ability and the unacceptable duration of heat perception, of street orientations are analyzed [28]. Revealing the dynamic evolution laws, rather than the static laws of the thermal environment in different oriented streets, will provide an accurate decision-making basis for citizens’ time–space behavior [29], which proposes measures to mitigate heat exposure based on street orientations, from the perspective of citizen behavior.

2. Materials and Methods

To clarify how street orientations affect the temporal variation in thermal environments within streets of different climate zones, the four following works have been performed.

2.1. Representative Cities and Representative Streets

2.1.1. Representative Cities of 7 Building Climate Zones

China has seven building climate zones, the meteorological conditions of which vary greatly [30]. The climate conditions of the seven building climate zones are shown in

Table 1. Seven building climate zones in China.

Representative Cities	Building Climate Zones	Key Indicators
Harbin	Severe cold zone (I)	January mean Ta ≤ −10 °C, July mean Ta ≥ 25 °C July mean RH ≥ 50%
Urumqi	Severe cold zone (II)	−20 °C ≤ January mean Ta ≤ −5 °C July mean Ta ≥ 18 °C, July mean RH ≤ 50%
Xi’an	Cold zone (I)	−10 °C ≤ January mean Ta ≤ 0 °C 18 °C ≤ July mean Ta ≤ 28 °C
Xining	Cold zone (II)	−22 °C ≤ January mean Ta ≤ 0 °C July mean Ta ≤ 18 °C
Changsha	Hot summer–cold winter zone	0 °C ≤ January mean Ta ≤ 10 °C 25 °C ≤ July mean Ta ≤ 30 °C
Guangzhou	Hot summer–warm winter zone	January mean Ta ≥ 10 °C 25 °C ≤ July mean Ta ≤ 29 °C
Kunming	Mild zone	0 °C ≤ January mean Ta ≤ 13 °C 18 °C ≤ July mean Ta ≤ 25 °C

. Considering factors of city size and city level, Harbin (45.63° N, 127.97° E), Xi’an (34.26° N, 108.93° E), Changsha (28.15° N, 113.23° E), Guangzhou (23.27° N, 113.51° E), Kunming (25.29° N, 102.82° E), Xining (36.82° N, 101.44° E), and Urumqi (43.42° N, 87.32° E) are selected as the representative cities. The seven cities are all provincial capitals with serious heat loads in summer, shown in Figure 1.

2.1.2. Representative Streets in the Seven Cities

By visiting the geospatial data cloud platform (https://www.gscloud.cn/), this research obtains satellite remote sensing data accessed on 12 July 2023. Then, the Environment for Visualizing Images (ENVI 5.6) is used to calculate the land surface temperature [31], which is used to identify the street within the urban heat island. The ENVI equations for calculating the land surface temperature are shown below [32].

$${\mathrm{L}}_{\mathrm{s}\mathrm{e}\mathrm{n}}=\mathrm{B}\left({\mathrm{T}}_{\mathrm{s}\mathrm{e}\mathrm{n}}\right)=\left(\mathsf{\epsilon }\mathrm{B}\left({\mathrm{T}}_{\mathrm{s}}\right)+\left(1-\mathsf{\epsilon }\right){\mathrm{L}}_{\mathrm{d}}\right)\mathsf{\tau }+{\mathrm{L}}_{\mathrm{u}}$$

(1)

$${\mathrm{T}}_{\mathrm{s}}={\displaystyle \frac{{\mathrm{K}}_2}{\ln \left(\left(\frac{{\mathrm{K}}_1}{\mathrm{B}\left({\mathrm{T}}_{\mathrm{s}}\right)}\right)+1\right)}}$$

(2)

In Equations (1) and (2), L_sen is the radiance measured by the sensor (W·m⁻²·sr⁻¹·μm⁻¹), B is Planck’s law, T_sen is the radiance brightness measured by the sensor (K), ε is the land surface emissivity, τ is the atmospheric transmissivity, T_s is the land surface temperature (K), L_d is the downwelling atmospheric radiance, and L_u is the upwelling atmospheric radiance (W·m⁻²·sr⁻¹·μm⁻¹), K₁ and K₂ are the radiation constants.

After identifying the urban heat island, this research selects the serious hot spots of each city, the examples of which are shown in the first column of Figure 2. Correspondingly, the satellite imagery, as well as the spatial texture of each hot spot, are shown in the second and third columns of Figure 2. In the sub-title, the urban heat island intensity (UHII) is employed to describe the intensity of the urban heat island (UHI) of the spot in the first column. For example, a UHII of 4.90 °C means the biggest difference within the circled area is 4.9 °C.

2.1.3. Abstract Models for Representative Streets in the Seven Cities

From the examples of UHI areas, this research abstracts the pattern of street forms according to the classification of the architectural group layout [33], where buildings include a court layout (Harbin), terrace layout (Xi’an, Changsha), pavilion layout (Guangzhou), and terrace–court layout (Kunming, Xining, Urumqi). The abstract models for the streets in the seven cities are shown in Figure 3, where the size information is marked. In Figure 3, the area highlighted in red in each model is the calculated area, where the mean thermal indicator within the red mark will be calculated to indicate the effects of street orientation.

2.2. Meteorological Parameters in Representative Cities

The air temperature (Ta), wind speed (Va), and relative humidity (RH) are the three key meteorological parameters, which are collected from the China meteorological administration. As July is the hottest month in China, the average meteorological data in the interval of one hour in July for each city are analyzed, for example, the average temperature at 8:00 a.m. from July 1 to 31 is calculated as the representative temperature at 8:00 a.m.; the results are shown in Figure 4, Figure 5, Figure 6 and Figure 7.

The hourly Ta of the seven representative cities in July is shown in Figure 4, where a big difference in Ta between different cities can be seen. Changsha, the representative city of the hot summer–cold winter climate zone, has the highest temperature in July, while Xining, the representative city of the cold zone II, has the lowest temperature, where the Ta difference reaches 15 °C.

The maximum Va for each hour in July is analyzed, which is shown in Figure 5. Among these seven cities, Xi’an, the representative city of cold zone I, has the lowest wind speed. Changsha has a maximum wind speed during the daytime and Xining has a maximum wind speed during the night. Generally, the mean Va calculated from the maximum Va of each hour ranges from 2.0 m/s to 4.0 m/s.

The difference in relative humidity (RH) among the seven cities is more significant, shown in Figure 6. Harbin has the maximum RH, varying from 70% to 95%. Urumqi has the lowest RH, ranging from 25% to 35%. The relative humidities of the other five cities are within the range of 45% to 90%.

The solar radiation for seven cities is shown in Figure 7. Solar radiation varies significantly with the time change. The solar radiation occurs earliest in Harbin and latest in Urumqi, which may affect the temporal variation in the thermal environment significantly.

2.3. Envi-Met Modeling and Validation

Envi-met, an advanced urban climate modeling tool [34], is employed as the research tool in this study, which has been proved to be sufficient to support urban design decisions [35,36]. Before the simulation, its accuracy is evaluated by a field experiment. The experiment is located at 28.14° N, 112.99° E, where the micro-weather station is placed on a street, as shown in Figure 8. The sensors in the micro-weather station include an air temperature sensor, relative humidity sensor, wind speed sensor, and black globe temperature sensor, which are all 1.5 m above the ground. The accuracy of the sensors is shown in

Table 2. Details of these sensors used in this field experiment.

Equipment	Indicators	Measurement Range	Precision	Range of Error
JT2020-M23553 (a)	black globe temperature	−20~100 °C	0.1 °C	±0.5 °C
JT2020-M23553 (b)	wind speed	0~10 m/s	0.1 m/s	±0.5 m/s
JT2020-M23553 (c)	air temperature	−20~100 °C	0.1 °C	±0.5 °C
YGY-CJY-4 (a)	wind direction	0~360°	1°	±3°
YGY-CJY-4 (b)	wind speed	0~70 m/s	0.1 m/s	±0.5 m/s
YGY-CJY-4 (c)	Ta/RH	−40~100 °C/0~100%	0.1 °C/0.1%	±0.3 °C/±0.5%
YGY-CJY-4 (d)	horizontal surface radiation	0~2000 W/m2	1 W/m2	±5 W/m2

, where multi functional environment tester (JT2020-M23553) is produced in Beijing, China, and portable weather station (YGY-CJY-4) is produced in Wuhan, China.

The R square (R²) [35], root mean square error (RMSE) [37], and mean absolute error (MAE) [38] are used to test the accuracy, the formulas of which are shown below (Formulas (3)–(5)).

$${\mathrm{R}}^2=1-{\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{m};\mathrm{i}}-{\mathrm{X}}_{\mathrm{s};\mathrm{i}}\right)}^2}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{m};\mathrm{i}}-\overline{\mathrm{X}}\right)}^2}};\overline{\mathrm{X}}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{X}}_{\mathrm{m};\mathrm{i}}}{\mathrm{n}}}$$

(3)

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{m};\mathrm{i}}-{\mathrm{X}}_{\mathrm{s};\mathrm{i}}\right)}^2}{\mathrm{n}}}}$$

(4)

$$\mathrm{M}\mathrm{A}\mathrm{E}={\mathrm{n}}^{-1}\sum _{\mathrm{i}=1}^{\mathrm{n}}\left|{\mathrm{X}}_{\mathrm{m};\mathrm{i}}-{\mathrm{X}}_{\mathrm{s};\mathrm{i}}\right|$$

(5)

In the above equation, X_m; i represents the measured value, X_s; i represents the simulated value, and n represents the number of obtained data. The R² is 0.96 (PET); RMSE is 4.6 °C (PET); and MAE is 4.2 °C (PET). Compared with previous studies that issued the confidence interval of RMSE and MAE [39,40,41], this experiment confirms that Envi-met is a reliable tool for simulating outdoor thermal indicators.

2.4. The Thermal Perception Standard

The physiological equivalent temperature (PET), Predicted Mean Vote (PMV), Wet Bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI) are used to evaluate the thermal pressure within streets [42,43], where the PET is the most commonly used. In this study, the PET is employed to identify the time when the thermal sensation is beyond citizens’ tolerance. The PETs of different oriented streets are calculated with the Bio-met package of Envi-met [44]. Following the standards of the PET investigated in China [45], the following definitions are provided: 24–31 °C is defined as “comfortable”, 20–24 °C is defined as “slightly cold”, 31–35 °C is defined as “slightly warm”, 35–39 °C is defined as “warm”, 39–43 °C is defined as “hot”, and more than 43 °C is defined as “very hot”.

3. Results

This study builds the street models in the Envi-met platform, where each model is set in six directions. Then, the meteorological conditions are set according to the meteorological parameters presented in Section 2.2 of this paper. The simulation starts at 1:00 and runs for 32 h. This study chooses the data from 8:00 to 8:00 of the next day to reveal the effects of the temporal variation in the thermal environment within streets in different climate zones.

The average thermal indicators within the red areas of Figure 3 are calculated, including air temperature (Ta), relative humidity (RH), wind speed (Va), and mean radiant temperature (T_mrt), which are the factors of the physiological equivalent temperature (PET). Then, with the visual map, the temporal variation in the PET corresponding to the six street orientations are illustrated using the interval of one hour. Finally, the period when the PET is beyond citizens’ tolerance in each oriented street is clarified. The data analysis process is shown in Figure 9.

The impacts of street orientation on the temporal variation in the PET are visually displayed in Figure 10, where the horizontal axis indicates the time, the vertical axis indicates the street orientations, and the Z value means the PET. For example, in the sub-figure (Harbin), the PET value of the S–N direction street at 11:00 a.m. is 41 °C, which means the mean PET of the S–N-oriented streets at 11:00 was 41 °C. Totally, 144 (6 × 24) mean PETs covering 8:00 a.m. to 8:00 a.m. on the next day of the six oriented streets are displayed in the visual map, followed by the PET isotherms of the six oriented streets, which are obtained using the Kriging algorithm of SURFER. Figure 10 intuitively displays the impacts of street orientations on the temporal variation in the PET within streets in different climate zones.

Specifically, in Harbin city, for all the six oriented streets, the hottest PETs all occur around 13:00. The S–N oriented street has the hottest PET, where the highest value was 45 °C. Compared with the W–E-oriented streets, S–N-oriented streets increased the average PET by 1.2 °C. Particularly, the difference in PETs between S–N and W–E-oriented streets at 13:00 reaches 3 °C. Taking 39 °C (very hot perception of PET) as the standard, the NE-30°-oriented street has the earliest hot perception result, from 9:00 to 15:00. For other oriented streets, the “hot perception” occurs between 9:00 and 10:00, and ends at 14:30 to 16:00, among which, the “hot perception” lasts the longest in the S–N-oriented streets, ending at 16:00.

In Xi’an city, the hottest time occurs at 15:00 for all oriented streets and the “hot perception” occurs between 10:00 and 11:00 and ends at 17:20 to 18:00. NW-60°-oriented streets experience the most severe heat, with the “hot perception” covering the period of 10:00 to 17:30. The NE-30°-oriented street experiences the lowest “hot perception”, with the “hot perception” period covering 11:00 to 16:40. Furthermore, compared with the NW-60°-oriented streets, the NE-30°-oriented streets reduce the peak PET by 5 °C.

For Changsha city, W–E-oriented street seriously exacerbates the heat, and the S–N-oriented street reduces the heat most. The “hot perception” of W–E-oriented streets begins at 8:00 and ends at 18:20, while in the S–N-oriented streets, the “hot perception” begins at 9:40 and ends at 17:30. In the view of the peak PET of all the oriented streets, the peak of the W–E-oriented streets is 57 °C and the peak of S–N-oriented streets is only 46 °C, with the difference being 11 °C.

Different from Changsha city, NE-60°-oriented streets seriously accumulate heat in Guangzhou city, with the highest PET being 54 °C. Additionally, NW-30° and NW-60°-oriented streets reduce the heat powerfully, with the highest PET only being 45 °C. In Guangzhou city, the hottest PET occurs between 14:00 and 15:00 for all six oriented streets. The “hot perception” ranges from 8:30 to 17:30 for the NE-60°-oriented streets, and 9:30 to 17:00 for NW-30° and NW-60°-oriented streets.

The pattern of Kunming city is like Harbin, in terms of street orientation regulating the thermal environment. In Kunming city, NW-60°-oriented streets are hotter than others, where the “hot perception” begins at 12:00 and ends at 17:00, with the highest PET being 42 °C at 15:00. NE-30°-oriented streets experience the lowest PET, where the peak PET is only 38 °C, not reaching the bottom line of “hot perception”.

In Xining city, NE-60°-oriented streets are the hottest, followed by the W–E-oriented streets. The “hot perception” of NE-60°-oriented streets lasts about 1 h at 15:00. Additionally, the hottest PET of W–E-oriented streets only reaches 38 °C and lasts only several minutes. However, for the NW-30° and NW-60°-oriented streets, the hottest PETs are only 34 °C.

The pattern of Urumqi city is similar to that of Kunming, in terms of street orientation regulating the thermal environment. NE-60°-oriented streets are the hottest, followed by the W–E-oriented streets. For the NE-60°-oriented streets, the “hot perception” begins at 13:00 and ends at 19:00, with the highest PET being 45 °C at 16:00. For the NW-30° and NW-60°-oriented streets, the peak PETs are 39 °C and last only several minutes.

To quantify the general impacts of street orientation, this study statistically analyzes the general data of PETs in all six oriented streets, as shown in Figure 11.

Generally, for Harbin city, compared with the W–E-oriented streets, the S–N-oriented streets increased the mean PET by 1.2 °C. Xi’an and Kunming show the same pattern, where NE-30°-oriented streets decrease the PET by 3.38 °C and 3.48 °C, compared with that of NW-60°-oriented streets, respectively. For Changsha city, S–N-oriented streets have the lowest PET, while the lowest PET of Guangzhou city occurs in NW-30°-oriented streets. Xining and Urumqi city show the similar pattern, where NW-60°-oriented streets, respectively, decrease the PETs by 4.82 °C and 3.27 °C, compared with the mean PET of NE-60°-oriented streets.

4. Conclusions

This research clarifies the impacts of street orientation on the temporal variation in thermal environments within streets. Detail analyses are conducted from the view of peak values of PETs, as well as the period of “hot” perception, caused by various oriented streets. Generally, the maximum regulatory capabilities of street orientation on PETs are about 3 °C (Harbin), 5 °C (Xi’an), 11 °C (Changsha), 10 °C (Guangzhou), 4 °C (Kunming), 4 °C (Xining), and 6 °C (Urumqi). Furthermore, taking 39 °C as the “hot” PET perception, the regulatory capabilities of street orientation on the period of “hot” perception are approximately 1 h (Harbin), 2.5 h (Xi’an), 2.5 h (Changsha), 1.5 h (Guangzhou), 5 h (Kunming), 1 h (Xining), and 5 h (Urumqi). Specifically, in Harbin, NE-30°-oriented streets extend the “hot” perception by beginning 1 h earlier, compared to S–N-oriented streets. In Xi’an, NW-60°-oriented streets prolong the “hot” perception by starting 1.5 h earlier and ending hours later, compared with NE-30°-oriented streets. In Changsha, compared with the S–N-oriented streets, the W–E-oriented streets extend the “hot” perception by starting 1.5 h earlier and ending hours later. For Guangzhou, compared with the S–N-oriented streets, the NE-60°-oriented streets prolong the “hot” perception by starting 1 h earlier and ending a half hour later. In Kunming, compared with the NE-30°-oriented streets, the NW-60°-oriented streets extend the “hot” perception by starting 3 h earlier and ending 2 h later. In Xining, the NE-60°-oriented streets prolong the “hot” perception by starting half an hour early and ending half an hour late, compared with the NW-30°-oriented streets. For Urumqi, compared with the NW-60°-oriented streets, the NE-60°-oriented streets extend the “hot” perception by starting five quarters of an hour early and ending an hour and a half late.

Existing studies have proved that different climate zones have exclusive street orientations that benefit their thermal comfort [46,47,48] from a static perspective, and the conclusions are based on cross-sectional results at a certain point in time. However, the impacts of street orientation on thermal environments are dynamic. This research reveals the impact of street orientations from a dynamic view, clarifying impacts both on peak value and the appearance of “hot” perception. Some of the findings differ to those of existing research, especially the impact intensity of street orientations on thermal perception [49,50]. This is attributed to previous studies preferring to compare the thermal index at 12 o’clock, while this study found that not all climate zones experience the maximum difference at 12 o’clock. This study presents the impact of street orientations on the evolution of thermal environments from a temporal perspective. This study begins to reveal the impact of street orientations from a dynamic view. Further studies are needed to explore the relevant issues, including what is the performance of street orientations on dynamic thermal environments in other cities in the seven climate zones? What are the applicable boundaries of the conclusions drawn from seven typical cities?