Thermal Comfort Assessment of the Beijing Historical Town Blocks: Analysis of Indices and Applications

Yang, Xin; Li, Sha; Zhang, Qi; He, Shuang

doi:https://doi.org/10.1155/2022/2381584

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AI-enabled Decision Support System: Methodologies, Applications, and Advancements 2021

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Research Article | Open Access

Volume 2022 | Article ID 2381584 | https://doi.org/10.1155/2022/2381584

Thermal Comfort Assessment of the Beijing Historical Town Blocks: Analysis of Indices and Applications

Xin Yang,¹Sha Li,²Qi Zhang,³and Shuang He⁴

Academic Editor: Rahman Ali

Received07 Dec 2021

Revised07 Mar 2022

Accepted15 Mar 2022

Published19 Apr 2022

Abstract

Thermal comfort indicates the sensation of humans towards the surrounding environment. This research work proposes a novel method for assessing thermal comfort indices of the Beijing Historical Town Blocks. Motivation. Maintaining a proper thermal comfort is essential for healthy living and promoting the use of low energy strategies; therefore, there is an intense need for research, analysis, and assessment of thermal indices for indoor environments. Objective. The research work intends to present a generalized model for outdoor thermal comfort assessment by investigating thermal comfort evaluation indices of the past ten years through the CiteSpace software and dynamic simulation system. Methodology. Based on a study of 19 streets over 6 blocks in the historical area of Beijing, validity of the four evaluation indices of thermal comfort of the street space is analyzed using real-time data monitoring, questionnaires, regression analysis, and model establishment. Statistical analysis is performed on the basis of the four commonly used thermal comfort evaluation indices along with using universal thermal climate index (UTCI) and the comfort threshold range for evaluation. Findings. Outcomes of the analysis revealed that UTCI thermal neutral value in the historic block was 20.59°C and the comfort range was 16–25°C. It was observed that UTCI indices were inversely related to the street vegetation coverage; therefore, unlike street aspect ratio and building shadow, green planting elements had a significant effect on the thermal comfort of outdoor environment. Implication. As applicability extent of the four indices is fairly broad, the proposed method is adaptable for the assessment of indoor and outdoor environments. Thermal comfort index analysis and evaluation are appropriate for advance research in microclimate environment. Application. The research work suggests improvement strategies of microclimate environment in the historical blocks and, therefore, has significant applications in addressing the challenges of outdoor thermal comfort systems.

1. Introduction

In the early 20th century, foreign scholars began to study human thermal comfort and put forward the concept of thermal comfort. With the passage of time, people have started to pay attention to the advantages and disadvantages of outdoor environment. In 1984, a study from the University of California, Berkeley, expanded the thermal comfort index to evaluate the wind speeds of the surrounding spaces of urban buildings. Shi et al. carried out a thermal comfort study of Beijing Xidan Commercial Street using numerical simulations in 2012 at the scale of urban street blocks [1]. The research team of Liu Binyi studied the thermal comfort of Shanghai urban plazas, residential areas, and other spaces in 2016 [2]. A study on the microclimate environment of public spaces in Italian cities was carried out by Finaeva in 2017 [3]. In the same year, Suminah et al. performed a field survey of the microclimates on the green spaces of surrounding apartments in Jakarta [4]. Sodoudi et al. conducted a simulation study on the microclimate thermal comfort of 25 typical green space layouts in 2018 [5]. Jason and Jones conducted a thermal comfort study on three typical cases of architectural transition spaces in Cardiff, United Kingdom, in 2019 [6]. Extensive research work has been carried out in the field of outdoor thermal comfort [7]. A large number of field studies have verified that urban spaces in different regions have local characterized thermal comfort thresholds [8]. However, as thermal comfort evaluation is based on human perception with plenty of complex influencing factors, generalization of evaluation standard appears unrealistic. This in turn affirms the complexity of thermal comfort research.

Considering the impact of thermal comfort on users’ attendance in open areas and parks, there is a rise in the studies related to the outdoor thermal comfort. Though various studies have been conducted in thermal comfort assessment, most of the research studies focus on residential areas, urban squares, urban parks, and other fields [9]. Until now, only a few research works have targeted historical blocks. Historical blocks, being a special type of urban space, suffer from many problems, such as lack of public space, insufficient vegetation coverage, uncomfortable environment, and so on. Xiong and Yan measured microclimate in Gaochun old street in Jiangnan area [10]. They intensely analyzed the correlation between thermal comfort and microclimate factors [10]. Rosso and other foreign scholars discussed the effectiveness of building innovative materials in the thermal comfort regulation of historical blocks [11].

Since the Beijing City Master Plan 2016–2035 (BCMP) came into effect, Beijing has been under improved historical and cultural protection measures. The protected Historical Town Blocks (HTBs) have been increased to a 26% proportion. According to the representatives of the historical growth of Beijing—the capital city of China, HTBs are rich in local cultural features. However, environmental issues have become prominent, such as inadequate public spaces, insufficient communal landscape, and scattered green areas. These environmental problems diminish the local residential quality while hindering the healthy growth of the HTBs. Although limited thermal comfort studies have been conducted on HTBs, thermal comfort is one of the major environmental factors that directly affects neighborhood vigor. Establishing an appropriate research system to monitor and analyze the HTB street thermal comfort is significant for promoting overall livability.

This paper combines the real-time microclimate measurement with software simulation. The study combines the objective mechanism index evaluation of thermal comfort with the subjective perception evaluation systematically. The approach exploits simulation by the microclimate software to evaluate the current status of thermal comfort in Historical Town Blocks. The research provides a refined evaluation method for the existing urban environmental evaluation in the inventory era of Beijing. Initially, data are extracted from the China National Knowledge Infrastructure (CNKI) database and are used to analyze the knowledge map of thermal comfort indices through CiteSpace software.

The proposed method intends to summarize the commonly used thermal comfort indices with their application ranges. Following on, the method of questionnaire survey for the selection of regional thermal comfort evaluation indices is used for the tested town blocks. Regression analysis is applied to the subjective evaluation results to find out the most relevant objective evaluation index and comfort threshold range. The indices thus obtained are used as guiding measurements for the Beijing HTBs. Simultaneously, the microclimate values are measured and the RayMan software is used to derive four evaluation indices of thermal comforts. Finally, the microclimate environment of the selected sample streets in Dashilar is simulated to evaluate the current status of thermal comfort through ENVI-met, a 3D dynamic simulation software program. The research study proposes a new way of evaluating urban microclimate using the example of the environmental comfort quality of Beijing HTBs. The method of thermal comfort index proposed in this paper can be used to evaluate microclimate environment at the scale of urban block. In addition, the application and result analysis of environmental simulation based on thermal comfort index have guiding significance for the environmental improvement of historical blocks.

2. Application Analyses of Indices

Indices are considered essential for critical analysis. Key indices are used to consider different perspectives, features, and factors during evaluation. Details about the indices in thermal comfort are presented in the following sections.

2.1. Literature Review of Outdoor Thermal Comfort Indices

With global influences, CNKI database links Springer, Wiley, and Elsevier. The database is extended to academics to achieve technological achievements and to support varied formats such as journals, conferences, and patents. CiteSpace is a software program widely used for data mining. In scientific atlas analysis [12], a total of 2170 documents are used and the retrieval time span of this study is ten years (2010–2020). With data analysis, taking into consideration the traditional literature study, physiological equivalent temperature (PET), universal thermal climate index (UTCI), predicted mean vote (PMV), and outdoor standard effective temperature (SET) have been detected to be the most commonly used indices for outdoor thermal comfort research works (as shown in Figure 1).

Under the consensus of complex changes in the microclimate environment, the selection of appropriate evaluation indices is crucial. The commonly used indices PET, UTCI, PMV, and SET all are important microclimate elements that are to be taken into account to assess thermal resistance of clothing and human body’s metabolism Zhuang et al. [13]. Index selection is important for assessing varied climate situations to form an effective research foundation. Ruiz and Correa [14] compared the four thermal comfort indicators to the actual thermal sensations of subjects. The study pointed out that the predictive abilities of these indicators are insufficient. It is also declared in the research that it is necessary to establish a thermal adaptation model based on the perception of local people for specific research locations [14]. In a recent study by Saud et al. [15], a simulation software program was used to predict and assess seven outdoor thermal comfort indices. In a recent paper [16], a state-of-the-art comparison was performed among different types of spaces—indoor, transitional, and outdoor. Similarly, systemic planning for village and urban spaces was proposed by Fan et al. [17] based on thermal comfort assessment.

2.2. Introduction to Thermal Indices

Thermal indices or simply comfort indices are the standards used for representation of numerical relationship of climatic effects. The indices play important role in the analysis of environmental aspects, comfort, and bioclimatic factors. Details of the indices—PET, UTCI, PMV, and SET—are given below.

2.2.1. Physiological Equivalent Temperature (PET)

The PET was established by Mayer and Hoppe in 1987 and has been widely adopted for evaluating the thermal comfort of outdoor environments [18], urban planning, sustainable urban development, and urban morphological research. The indices are also used for the assessment of thermal comfort distribution in urban blocks, thermal stress assessment, and meteorological forecasting. PET is the air temperature at which the core temperature and skin temperature of a person wearing 0.9 clo for internal light activities are balanced with the environment and can maintain the same thermal equilibrium state. The PET accounts for the cover parameters of individual activities, clothing fabric, and other basic information. PET reflects the significance of people in the context of outdoor thermal comfort. PET has low sensitivity to changes in outdoor humidity. In addition, the PET does not account for the self-regulation mechanism of the human body as the human body’s latent heat dissipation factors are neglected.

Based on 2010–2020 CiteSpace mapping, extensive PET-related studies were conducted particularly in the year 2014. The main research objects covered green infrastructure, residential settlements, urban streets, traffic buildings, classical gardens, urban public spaces, parks, and green spaces, as shown in Figure 2. PET has been widely applied to urban planning and design, sustainable urban development, urban morphology research, urban thermal comfort distribution, thermal comfort and thermal stress evaluation, and weather forecasting [19].

2.2.2. Universal Thermal Climate Index (UTCI)

The UTCI was established by 45 scientists from 23 countries of the International Society of Biometeorology in 2009 [20]. It is the air temperature at which the dynamic thermal response of a person walking on a horizontal surface is balanced with the actual environment. The model for the UTCI is based on a Fiala human body model [21]. It incorporates some of the most comprehensive factors and focuses on the objectivity of human caloric balances. In terms of thermal sensory evaluation, the UTCI thermal sensory zone has a large but relatively complete temperature span. Thus, the UTCI can be used to evaluate different climate regions. Unlike the PET, PMV, SET, and other stable state indices, the UTCI is a dynamic model indicator that can be applied at the seasonal and regional scales [22].

Based on 2010–2020 CiteSpace mapping, UTCI has been widely applied to residential areas, built environment, urban squares, communal green, parks, and other outdoor public spaces (as shown in Figure 3).

Some software platforms for the design simulation and analysis have adopted the UTCI computing mode. For instance, Grasshopper, Honeybee, RayMan, and ENVI-met exploit UTCI. The software has improved the index calculations and opened a new chapter in UTCI thermal comfort index visualization [23, 24].

2.2.3. Predicted Mean Vote (PMV)

The PMV is an evaluation index of the human body’s thermal response. It represents the average value of the hot and cold sensations of most people in the same environment. In 1970, Fanger proposed a thermal equilibrium equation for the state of human comfort, using predicted average voting to reflect human thermal sensations. Later, Gagge, Jendritzky, and others added refining factors to the PMV, e.g., heat dissipation, latent heat, and outdoor heat radiation [25]. Because outdoor environments are more complicated than the indoor environments in many aspects, factors such as psychological feeling, thermo-physiological conditions, and thermal balances are needed to be considered. Based on 2010–2020 CiteSpace mapping, PMV has been widely applied to interior and exterior environment. The key objects focused in such research works include residential areas, courtyards, urban blocks, and urban parks [26, 27], as shown in Figure 4.

2.2.4. Outdoor Standard Effective Temperature (SET)

The SET is the air temperature when the average skin temperature and skin moisture degree of a person wearing standard thermal resistance clothing are balanced with the environment. In 1971, Gagge and others proposed the standard effective temperature (SET) by adding skin wetness, activity level, and clothing thermal resistance to the effective temperature index (ET) for indoor thermal comfort evaluation [28]. Later, in 2000, Pickup and others introduced an outdoor average radiation model, establishing the outdoor standard effective temperature, abbreviated as OUT-SET (which is abbreviated as SET herein). Since the SET is based on a model of two-node human body temperature adjustments, the SET ignores the temperature self-adjustments of the human body in low-temperature scenarios and heavy labor conditions. This led to a decrease in accuracy [29, 30]. According to the 2010–2020 CiteSpace mapping, SET has been widely studied in urban parks, green spaces, communal spaces in residential areas, and rural buildings (see Figure 5).

From a 10-year development perspective, the thermal comfort indices of outdoor environments are based on indoor thermal comfort studies. Due to the complexity of outdoor microclimate circumstances and the variability of human thermal perception, studies to improve index precision have been continuously undertaken. Indices are established on combined foundations of models and human perception. At present, the PET, UTCI, PMV, and SET are commonly used in the domain. Through analysis and research of measured and simulated thermal comfort index, this paper can provide a way to finely deduce thermal comfort standard and provide a new idea for study of thermal comfort at the scale of urban block.

3. Measured Historical Town Blocks and Questionnaire Survey

As time goes by, preservation and reconstruction of architectural heritage becomes necessary for its cultural, economic, and social benefits. Prior to proposing thermal comfort standard, details about the measured HTBs and public knowledge about the parameters of thermal comfort are discussed in the following sections. Questionnaire surveys and measurements were used to construct a comprehensive datascape. Regression analysis was performed to obtain the thermal comfort evaluation indices of selected significant HTBs. By taking the Dashilar historical block [31] as an example, the selected four types of thermal comfort evaluation indices were applied to establish a simulation method and provide a new approach for evaluating HTB environmental quality. Geographical map of the HTBs is shown in Figure 6.

3.1. Outline of Measured HTBs

The measured HTBs include the commercial, cultural, recreational, and residential street types. In this study, 6 blocks from 19 streets (as shown in Table 1) were selected for on-site microclimate data monitoring and for the questionnaire survey.

Each measurement range was set as a length between 250 m and 500 m and width within 100 m, covering urban buildings on both sides of the streets and the associated communal spaces.

A Kestrel5500 portable weather station and a TES1333R solar radiation meter were used to record the air temperature, relative humidity, wind direction, wind speed, solar radiation, altitude, and atmospheric pressure for microclimate data analysis (as shown in Table 2).

A typical Beijing summer (mainly from June to July) was examined during the daytime from 9 : 00 to 16 : 00. The weather was clear, dry, cloudless, and windless. Data measurement on continuous basis was conducted for 7 h. The Kestrel5500 portable weather station and TES1333R solar radiation meter were placed in such a way that the interference from other heat sources like cars and air conditioners was avoided. All the measurement points were set 1.5 m above the ground. The air temperature (in °C), relative humidity (in %), wind speed (in m/s), wind direction (in °), and solar radiation (in W/m²) were recorded with a span of 5 min to generate data. The official broadcasts from the meteorological station were recorded on the measurement days to provide references for subsequent comparison. Streets in the same historical block were monitored simultaneously, with measurement points evenly set to collect microclimate data. The data records were stored, and the microclimate characteristics of the selected streets were analyzed. The research contents fittingly satisfy the following ENVI-met simulation principles [32].

3.2. Questionnaire Design

The research work utilizes systematic questionnaires to obtain public subjective evaluations of thermal comfort. Questionnaires were issued, while data were being simultaneously collected by the Kestral5500 for consistency. The average time for completing each questionnaire was 3–5 min. When the survey work was completed, 1091 effective feedback sheets were obtained [33]. The questionnaire content was composed of two parts: one part was related to the basic parameter information of the respondent, and the second part focused on the subjective feelings of thermal comfort. Basic parameter information included the respondent’s gender, age, height, weight, clothing, ongoing activity, and location where the survey was taken. The subjective feelings of thermal comfort included the thermal sensitivity vote (TSV), thermal comfort vote (TCV), humidity sensitivity vote (HSV), wind speed sensitivity vote (WSV), and solar radiation sensitivity vote (RSV) [34]. The TSV had seven levels: hot (+3), warm (+2), less warm (+1), moderate (0), slightly cooler (−1), cooler (−2), and colder (−3). The TCV had five levels: very comfortable (+2), comfortable (+1), slightly comfortable (0), uncomfortable (−1), and very uncomfortable (−2). The HSV had five levels: very wet (+2), wet (+1), moderate (0), dry (−1), and very dry (−2). The WSV had five levels: no wind (+2), breeze (+1), slightly strong wind (0), strong wind (−1), and very strong wind (−2). The RSV had five levels: very weak (+2), slightly weak (+1), moderate (0), strong (−1), and very strong (−2) [35].

3.3. Questionnaire Feedback Analyses

It was found that 561 males, accounting for 51.42% of the total 1091 survey respondents, participated in the questionnaire survey, while 530 females participated, accounting for 48.58%. The genders were relatively balanced. The age group was mainly 30–40 years old, accounting for 30.43%, followed by 20–30 years old, accounting for 26.76%, 40–50 and 50–60 years old, accounting for 17.51% and 18.52%, respectively, and 60–70 years old, accounting for 6.78%. Of the respondents, 34.65% were engaging in light activities, 26.21% were walking, and 11.46%, 11.64%, 7.24%, and 7.15% were engaging in moderate exercise, standing and chatting, sitting and chatting, and caring for children, respectively. The respondents were wearing summer clothes, with 82.31% in short sleeves, 39.69% in shorts, and 43.72% in trousers, as shown in Figure 7.

4. Index Correction Analysis

PET, UTCI, PMV, and SET are the commonly used indicators of thermal comfort research at the international level. Due to the impacts of climate, season, and individual human factors, the index data cannot be used for assessing the thermal comfort of specific spaces. As such, the index data have to be corrected to form a thermal comfort assessment base. The procedure followed along with the correction for the indices is discussed in the following sections.

4.1. Procedure

A method by Liu et al. (Taiwan) [36], in which the average thermal sensation corresponding to the thermal comfort index per 1°C is calculated, was adopted. The method is described as follows.(1)The measured microclimate parameters were input to the software RayMan to calculate the thermal comfort value [37]. RayMan software is used for calculating radiation fluxes of human body’s short wave and long wave to quantitatively analyze the microclimate thermal environment. RayMan’s powerful ability in the field of calculating crucial indicators of radiation flux densities and thermo-physiology has been verified [38].(2)The thermal comfort values related to the questionnaire were calculated from the data at the time corresponding to when the questionnaire was completed.(3)The thermal sensation average value was calculated at each 1°C. Taking the PET index correction in Dashilar as an example, when the temperature was 36–37°C, the thermal sensation average value was 1.31. Therefore, the PET value for the average thermal sensation of 1.31 was 36.5°C. This calculation matching method was applied to the UTCI, PMV, and SET.(4)The SPSS software was used to analyze thermal comfort values and average thermal sensations to obtain regression equations. The regression equation was used to calculate thermal neutrality values and corrected thermal comfort ranges, which enabled the assessment of the thermal comfort.

The goodness of fit (R²) and thermal neutral value (MTSV) were used to test the accuracy of the regression equation: the closer the R² value to 1, the more accurate the regression equation. When the MTSV is 0, the value of the thermal comfort index is valid if it agrees with the climatic conditions.

4.2. Thermal Comfort Index Corrections

Thermal comfort refers to the individual feelings about the temperature in the surroundings. In the proposed study, correction is performed for the thermal comfort index. Details of the analysis are presented as follows.

4.2.1. PET Index Correction

PET index correction was computed based on the data collected from the questionnaire. In the questionnaire survey, 243, 161, 209, 126, 179, and 173 respondents participated in the Dashilar area, Nanluoguxiang area, Shichahai area, Fayuansi area, Dongsi (3–8 streets) and Fuchengmennei street area, respectively. Through regression analysis, the relation between the average thermal sensation of the respondents and the PET was obtained. As a result, unary regression equations between the HTBs’ average thermal sensation and the PET were established. The PET thermal neutral values of Dashilar, Nanluoguxiang, Shichahai, Fayuansi, Dongsi 3 street, and Fuchengmennei street were then calculated to be 22.67°C, 14.52°C, 24.49°C, 20.13°C, 22.96°C, and 9.63°C, respectively. As per the outcomes, the temperature of Fuchengmennei street was significantly lower than those of the other streets. Similarly, the R² value for Fuchengmennei street was 0.753, which was the lowest among all the measures. In this case, the PET was found to be inappropriate for evaluating the outdoor thermal comfort of Fuchengmennei street. The findings are depicted in Figure 8 and demonstrated in Table 3.

4.2.2. UTCI Index Correction

The process described above was applied to determine the relationship between the average thermal sensation of the respondents in the HTBs and the UTCI. Unary regression equations between the average thermal sensation and the UTCI were obtained. The UTCI thermal neutral values of Dashilar, Nanluoguxiang, Shichahai, Fayuansi, Dongsi (3–8 streets), and Fuchengmennei were 31.10°C, 20.28°C, 23.49°C, 22.26°C, 26.17°C, and 25.79°C, respectively. The R² values and tests of human thermal perception were relatively close. As such, the UTCI index was proven to reflect the thermal comfort status of the selected six HTBs accurately. The findings are demonstrated in Table 4 and Figure 9.

4.2.3. PMV Index Correction

In the PMV index correction, the relationships between the average thermal sensation and the PMV determined value are comparatively analyzed. As a result, the unary regression equations are obtained. The PMV thermal neutral values of Dashilar, Nanluoguxiang, Shichahai, Fayuansi, Dongsi (3–8 streets), and Fuchengmennei were calculated to be 0.84, −0.13, 0.57, −1.6, 3.32, and 0.13. Among the results, the outcome for Dongsi 3 street was significantly higher than that of the others. However, the PMV was inconsistent with human body perception. Therefore, the PMV was inappropriate for evaluating the outdoor thermal comfort of Dongsi 3 street (see Table 5 and Figure 10).

4.2.4. SET Index Correction

Using the same index correction method stated above, the SET thermal neutral calculations of Dashilar, Nanluoguxiang, Shichahai, Fayuansi, Dongsi (3–8 streets), and Fuchengmennei were 17.55°C, 7.74°C, 16.71°C, 2.56°C, 15.57°C, and 11.9°C, respectively. The neutral and R² values of Nanluoguxiang, Fayuansi, and Fuchengmennei were significantly lower. Thus, the SET was inappropriate for Nanluoguxiang, Fayuansi, and Fuchengmennei (see Table 6 and Figure 11).

4.3. UTCI for HTB Evaluation

The average thermal sensation votes were summarized to form a thermal comfort correction database. Through the regression analysis, the thermal neutral values of PET, UTCI, PMV, and SET were 15.72, 20.59, 0.46, and 9.90, respectively. The R² value of the UTCI was significantly closer to 1 than the others. It was found that the PET, PMV, and SET were inappropriate for some of the HTBs (as shown in Tables 7 and 8 and Figure 12). To summarize, through regression analysis of the tested 19 street data, UTCI thermal neutral value in the historic block was 20.59°C and the comfort range (as shown in Figure 13) was 16–25°C.

5. HTB Thermal Comfort Evaluation Example: Dashilar

Thermal comfort is evaluated on different parameters covering both the environmental aspects and the people’s sensations. To assess the method with a real world scenario, an active urban location was selected for the evaluation. Details of the analysis are given as follows.

5.1. Dashilar Spatial Analyses

Dashilar is a highly active location under contemporary urban development in Beijing. As the original town block pattern has been retained, diverse architectural styles can be found between mixed-use buildings, e.g., cultural relics, educational firms, publishing houses, bookstores, commercial buildings, and industrial units. Four streets, Yangmeizhuxie Street, Qianmen Street, Dashilar Commercial Street, and Meishi Street (partial sections from Qianmen West Houheyan Street to Dashilar Commercial Street), were selected for the study (as shown in Table 9 and Figure 14).

5.2. Simulated Evaluation of Dashilar Thermal Comfort

Thermal comfort simulation was carried out through ENVI-met, a specialized software program for 3D urban dynamic microclimate simulation. The software has been upgraded to offer more functionality. Due to its advantages in the comprehensive simulation performance of multiple effects, the software suits well for simulation and research pursuits [39, 40]. The initial values were the same for all the streets in ENVI-met. The data collection time was 9 : 00 to 19 : 00 on July 15th, 2018, corresponding to a total of 10 hours. The simulated roughness length was the system default value of 0.01 while the simulated solar radiation factor was 1, and the simulated cloud cover was 0%. The initial air temperature was 29.5°C, the wind speed was 1.5 m/s, the wind direction was 75°, and the relative humidity was 50%. The UTCI simulation distribution maps were generated for 10 : 00 and 16 : 00. The corrected UTCI index was used to evaluate the Dashilar thermal comfort.

Through calculations, it was found that at 10 : 00, the average values of the UTCI were in the following order: Dashilar Commercial Street > Meishi Street > Yangmeizhuxie Street > Qianmen Street. At 14 : 00, the order was as follows: Dashilar Commercial Street > Yangmeizhuxie Street > Meishi Street > Qianmen Street. The thermal comfort of Qianmen Street was found to be the best, Yangmeizhuxie Street and Meishi Street were moderately comfortable, and Dashilar Commercial Street was the least comfortable. Further detailed evaluations were carried out for the four streets.

The warm, hot, and dry hot zones of Yangmeizhuxie Street were determined. At 10 : 00, the warm zone covered 41.18% of the full tested street length. The hot zone covered was 58.82%, and the dry hot zone covered was 0.0%. The warm zone was mainly in linear and patched patterns in which linear patterns were concentrated along the southeast side of the street. At 14 : 00, the warm zone covered was 0.0% of the full tested street length. The hot zone covered was 83.82%, and the dry hot zone covered was 16.18%, as shown in Figure 15.

For Qianmen Street, at 10 : 00, the warm zone covered was 76.03%, the hot zone covered was 23.97%, and the dry hot zone covered was 0.0%. The hot zone was mainly in dotted and patched patterns in the north. At 14 : 00, the warm zone covered was 4.07%, the covered hot zone was 93.99%, and the dry hot zone was 1.94%. The warm zone was in the shape of continuous spots, concentrated along the central line in the south. The dry hot zone was concentrated to the north, as shown in Figure 16.

For the internal space of Dashilar Commercial Street, at 10 : 00, the warm zone covered was 18.20%, the covered hot zone was 79.38%, and the dry hot zone was 2.42%. The warm zone was in a linear pattern, mainly on the south part of the street. The dry hot zone had a spotty pattern, mostly in the concave space in the north. At 14 : 00, the warm zone covered was 0.0%, the hot zone covered was 40.31%, and the dry hot zone covered was 59.69%, as shown in Figure 17.

For Meishi Street (partial), at 10 : 00, the covered warm zone was 23.47%, the hot zone was 76.53%, and the dry hot zone was 0.0%. The warm zone, in a patched pattern, was mainly on the east part of the street with some on the west part. At 14 : 00, the covered warm zone was 0.0%, and the hot zone was 56.41%. The dry hot zone coverage increased to 43.59%. The dry hot zone was in a continuous patched pattern, mostly near the street central line, as shown in Figure 18.

From the test data above, the simulation comparisons of the street thermal comfort conditions (as shown in Table 10) showed that the Dashilar UTCI indices were inversely related to the street vegetation coverage—the larger the vegetation areas, the better thermal comfort. The Dashilar UTCI indices had no evident correlation with the street aspect ratio or the building shadow coverage. Thus, green planting elements had a significant effect on the thermal comfort of the tested Dashilar streets. The existing Dashilar vegetation catchments were uneven. Qianmen Street, which was wider than the other streets, was under a renewal process during these tests, where new locust trees and plants were being added to improve the landscape coverage. The other narrow streets have limited space for new vegetation [41]. Vertical landscaping will be appropriate for future thermal comfort promotion in Dashilar [42].

6. Discussion

It is scientifically proved that alterations in microclimate influence the quality of surrounding environment. This in turn affects—more precisely, negatively affects—the health and comfort of local dwellers. Therefore, the use of bioclimatic principles is always preferred in urban planning and landscape architecture. Akin to the standards of indoor, enhancement of outdoor thermal comfort by remodeling the physical attributes is becoming the core research area.

In this research study, subjective findings and computed measurements are considered in the evaluation method. Unlike other contemporary research works, the proposed hybrid approach takes into account the micrometeorological measurements and participants’ responses obtained through the questionnaire surveys during the field campaigns. Statistical analysis of the thermal comfort of 19 street spaces in 6 blocks in Beijing HTBs was conducted using the UTCI for simulated evaluations. To summarize findings of the evaluation, UTCI is a suitable index for the comprehensive evaluation of the thermal comfort in HTBs. However, as discussed in the literature, for outdoor thermal comfort, scholars mostly select PET or SET index for evaluation. For instance, Xiong and other scholars opted PET index for evaluation thermal comfort of Jiangnan historical blocks [10]. Similarly, Xue et al. used SET index to evaluate outdoor thermal comfort in south area of China [43]. The reliability is worth further verification to select different thermal comfort indices to evaluate different areas. Unlike other indices, the obtained R² value for UTCI was significantly closer to 1; therefore, UTCI was utilized for the evaluation. Results of the regression analysis revealed that UTCI thermal neutral temperature in historic blocks is 20.59°C and the comfort range is 16–25°C. Other focusing findings of the study are the spatial thermal comfort distribution map of Dashilar and the simulation data. Thermal comfort zones were divided into warm, hot, and dry hot zones. The street thermal comfort was correlated with the vegetation coverage and the aspect ratio.

For simulation, the 3D visualization software—ENVI-met—was used. Through the simulated evaluation analyses, thermal comfort data were visually compared and the indices were examined, including thermal stress grading map. Results of the simulation comparison showed that the Dashilar UTCI indices were inversely related to the street vegetation coverage and street aspect ratio. This indicates that a healthier thermal comfort mainly depends on a larger vegetation area.

As per the outcomes of this research, vertical landscaping is proposed for future thermal comfort promotion in Dashilar. Lastly, findings of the study form a scientific framework for comprehensive HTB thermal comfort evaluation.

7. Conclusion and Future Work

Thermal comfort of a locality has significant impact on the lifestyle of the dwellers. This research work examines the determinant factors of thermal comfort. In a systematic way, thermal comfort research was conducted on Beijing HTBs in summer season. Regression analyses were performed using the SPSS software over the responses of participants collected via the specifically designed questionnaires. The commonly used thermal comfort evaluation indices were used in the analysis. The UTCI was chosen to be a suitable evaluation index for thermal comfort studies of the HTBs in Beijing. A hybrid approach combining micrometeorological measurements, subjective findings, and simulation was followed in the evaluation. To determine the thermal comfort evaluation index and threshold range, mutual verification was performed to compare the subjective perception and objective index-based measurement. This research method is reproducible and can be used as a reference for the study of thermal comfort environment at block scale. Moreover, the conclusion has a guiding significance for refined transformation of historical blocks, a special type of urban space.

Though the study successfully discovers significant findings in the realm of thermal comfort evaluations, the research is not devoid of limitations. As the environmental interference factors affect accurate measurement of data, inputs to the statistical analysis were not absolutely precise. Due to the limited public space, high building density, low vegetation coverage, and sultry summer, HTB microclimate data were of poor quality. The research work also lacks the use of numerical simulation to minimize the influence of interference factors. Besides, physical characteristic of vegetation, radiation, and personal thermal comfort were not considered in the study. Since the four objective mechanism indices were selected in the evaluation, our future work is to apply the other indices as well. It is also one of our future strategies to carry out further experimental work in more diversified locations. This will not only support the current findings but also open new research dimensions in the outdoor thermal comfort assessment.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors thank Xinsheng Lu and Tong Yao for a lot of first-hand experimental operation work. This research was funded by the Beijing Natural Science Foundation (no. 8202017) and China University of Mining and Technology Talent Foundation (no. 102519082).

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