Research on the Characteristics of High-Temperature Heat Waves and Outdoor Thermal Comfort: A Typical Space in Chongqing Yuzhong District as an Example

Abstract

For the high-density urban space heat wave problem, take the core urban area of the mountainous city of Chongqing as an example, four types of typical urban functional spaces, including commercial areas, residential areas, mountain parks, and riverfront parks, were measured during a heat wave cycle, and the characteristics of high-temperature heat waves in different urban spaces were compared through the analysis of air temperature, surface temperature, relative humidity, solar thermal radiation, and other thermal environment parameters. Combined with the questionnaire research related to the heat comfort of the urban population, the physiological equivalent temperature (PET) was selected to describe the heat sensation of the human body, to summarize the elements and patterns of the influence of heat waves on heat comfort of the population in urban spaces, and to establish a prediction model of outdoor heat comfort in summer. It shows that: (1) temperatures recorded during the heat waves are influenced by urban space elements and are differentiated, with older residential areas recording the highest temperatures, followed by commercial areas, and green park areas comparing favorably with both; (2) crowd thermal comfort is correlated with the thermal environment formed by space elements, PET is significantly positively correlated with air temperature, thermal radiation and surface temperature, and significantly negatively correlated with relative humidity, air temperature and thermal radiation have more influence on thermal comfort has a greater impact, while relative humidity and surface temperature have a relatively small impact; (3) reasonable spatial form and shade planning, vegetation and water body settings, high thermal storage substrate and other design elements can alleviate high-temperature heat waves, reduce the thermal neutral temperature and improve thermal comfort. The research results provide some basis for the investigation of the formation mechanism of high-temperature heat waves in mountainous cities and the optimal design of urban spatial thermal environment.

Full text

Citation: Huang, H.; Jie, P. Research
on the Characteristics of
High-Temperature Heat Waves and
Outdoor Thermal Comfort: A Typical
Space in Chongqing Yuzhong District
as an Example. Buildings 2022,12,
625. https://doi.org/10.3390/
buildings12050625
Academic Editor: Xing Jin
Received: 24 March 2022
Accepted: 5 May 2022
Published: 9 May 2022
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buildings
Article
Research on the Characteristics of High-Temperature Heat
Waves and Outdoor Thermal Comfort: A Typical Space in
Chongqing Yuzhong District as an Example
Haijing Huang 1, 2, * and Pengyu Jie 1
1School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China;
202015131061t@cqu.edu.cn
2Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing 400030, China
*Correspondence: cqhhj@cqu.edu.cn
Abstract:
For the high-density urban space heat wave problem, take the core urban area of the moun-
tainous city of Chongqing as an example, four types of typical urban functional spaces, including
commercial areas, residential areas, mountain parks, and riverfront parks, were measured during a
heat wave cycle, and the characteristics of high-temperature heat waves in different urban spaces
were compared through the analysis of air temperature, surface temperature, relative humidity, solar
thermal radiation, and other thermal environment parameters. Combined with the questionnaire
research related to the heat comfort of the urban population, the physiological equivalent temperature
(PET) was selected to describe the heat sensation of the human body, to summarize the elements
and patterns of the influence of heat waves on heat comfort of the population in urban spaces, and
to establish a prediction model of outdoor heat comfort in summer. It shows that: (1) temperatures
recorded during the heat waves are influenced by urban space elements and are differentiated,
with older residential areas recording the highest temperatures, followed by commercial areas, and
green park areas comparing favorably with both; (2) crowd thermal comfort is correlated with the
thermal environment formed by space elements, PET is significantly positively correlated with air
temperature, thermal radiation and surface temperature, and significantly negatively correlated with
relative humidity, air temperature and thermal radiation have more influence on thermal comfort has
a greater impact, while relative humidity and surface temperature have a relatively small impact;
(3) reasonable spatial form and shade planning, vegetation and water body settings, high thermal
storage substrate and other design elements can alleviate high-temperature heat waves, reduce the
thermal neutral temperature and improve thermal comfort. The research results provide some basis
for the investigation of the formation mechanism of high-temperature heat waves in mountainous
cities and the optimal design of urban spatial thermal environment.
Keywords:
mountainous high-density cities; high-temperature heat waves; thermal environment;
thermal comfort; functional urban space
1. Introduction
The IPCC assessment reports, especially the Fifth Assessment Report (AR5), have
proven that there is no doubt about the warming of the climate system [
1
,
2
]. In the
background of the current global warming, heat waves have become an important factor
affecting the human environment in cities [
3
,
4
], and at the same time, the rapid urbanization
process has aggravated the urban heat island effect (UHI), leading to the frequent occurrence
of extreme weather phenomena. The World Meteorological Organization defines a heat
wave as “a weather process in which the maximum daily temperature is higher than
32
◦
C and lasts for more than three days” [
5
]; in a slightly different way in China, the
China Meteorological Administration defines a heat wave as “a high-temperature day
with a maximum daily temperature
≥
35
◦
C, and a high-temperature day for more than
Buildings 2022,12, 625. https://doi.org/10.3390/buildings12050625 https://www.mdpi.com/journal/buildings

Buildings 2022,12, 625 2 of 21
three consecutive days is a high-temperature heat wave” [
6
]. Some scholars analyzed the
mechanism of the UHI effect from the perspective of urban space [
7
–
11
] and found that
there is a synergistic effect between high-temperature heat waves and UHI effect [
12
], so it is
necessary to study the distribution characteristics of high-temperature heat waves and the
influencing factors of UHI from the perspective of the urban spatial thermal environment.
Urban heat waves depend not only on the characteristics of physical processes but
also on urban planning methods [
13
]. There is a strong relationship between the UHI effect
and urban configuration [
14
–
16
]. The wind and thermal environment of cities is influenced
by the land use characteristics [
17
,
18
]. At the macroscopic scale, the urban climate map
(UCmap) is now widely agreed upon and maturely applied to predict the heat island effect,
such as the climate zoning proposed by Tokyo and Stuttgart [
19
], and in terms of studying
high-temperature disaster prevention strategies from the perspective of urban planning and
architectural design, some scholars have studied the construction of disaster prevention
strategies, the optimal application of disaster prevention measures, and the prediction of
scenario simulation. The relationship between urban sprawl patterns and hazards under
scenario simulation prediction, as well as the evaluation study of existing urban high-
temperature disaster prevention systems and urban resilience, have been studied in detail
by some researchers. At the mesoscopic and microscopic scales, the interaction between
spatial and climatic factors is multi-directional and integrated, where street height to width
ratio (H/W), sky view factor (SVF), street orientation, and green cover are the main spatial
factors that are generally considered to have an impact on local heat waves. Meteorological
experimental observation methods, CFD models, and ENVI-met models are mainly used in
this dimension, e.g., Norton et al. [
20
] studied in detail the utility of different street aspect
ratios and different types of green infrastructure for mitigating extreme urban heat waves
hazards, while other scholars have studied the climate change mechanisms of urban spaces
such as neighborhoods, residential areas, rooftop gardens, public green spaces and regional
improvement strategies in urban spaces such as neighborhoods, residential areas, rooftop
gardens, and public green spaces [
21
–
25
]. In addition, vegetation and water bodies can
also ameliorate local high-temperature heat waves through transpiration, due to the release
of latent heat from vegetation cover, while increasing the energy used for its purposes.
Green areas can properly cool the surrounding areas [
26
,
27
], and transpiration from trees
decreases the temperature [
28
,
29
]. The green cover of vegetation also reduces the intensity
of direct solar irradiation and helps to convert the received solar radiation into latent heat.
Water bodies are also elements that contribute to the reduction of high urban temperatures
due to transpiration and higher specific heat capacity [
30
]. The UHI effect can also be
controlled by reducing anthropogenic heat production [31].
High-temperature heat waves are closely related to people’s outdoor thermal com-
fort perception [
32
], and human subjective thermal comfort is an important element in
high-temperature heat wave research. The effects of high-temperature heat waves are
strongest in densely populated cities [
33
], with the greatest impact and harm to the elderly,
infants, children, and people with chronic diseases [
34
]. The United Nations Environment
Programme, in its latest handbook on urban cooling published in November 2021, mentions
that the urban population exposed to high temperatures (i.e., average summer temperatures
above 35
◦
C) is expected to increase by 800% to 1.6 billion by the middle of the century [
35
].
It is particularly important to evaluate the degree of human perception of high-temperature
heat waves and improve urban populations’ thermal comfort through the optimal design
of rational urban spaces. Regarding the research on the subjective thermal perception of
the population, the research method of integrating two human thermal indicators of tem-
perature and humidity [
36
], as well as various outdoor thermal comfort evaluation models
(UTCI, PET, SET) [
37
,
38
], and the correlation effect between the thermal environment of
urban space and the thermal comfort perception of the population [
39
–
41
] have become the
focus of current research. Thermal comfort is defined as a psychological state that expresses
the human body’s satisfaction with the thermal environment [
42
]. The current objective
indexes for outdoor thermal comfort evaluation mainly contain: Physiological Equivalent

Buildings 2022,12, 625 3 of 21
Temperature (PET), Outdoor Standard Effective Temperature (OUT_SET), Predicted Mean
Voting index (PMV), and Universal Thermal Climate Index (UTCI) [
43
–
46
]. Bedford first
proposed that thermal comfort and thermal sensation are the same and classified them into
five states subjectively [
47
], then ASHRAE began to use a 7-level thermal sensory evaluation
scale, according to different research objectives and subject sensitivity. Researchers have
different choices for the scale, the more major evaluation scales are: thermal comfort voting
(TCV), thermal sensory voting (TSV), and thermal preference voting (TPV) [
48
–
50
]. Since
2000, more and more outdoor studies have begun to focus on the actual thermal response of
the human body in specific behaviors or environments [
51
], in which objective metrics are
calibrated against subjective data from populations in different regions. Since 2010, most of
the thermal comfort studies have fitted crowd voting with thermal environment parameters
to derive different thermoneutral temperatures, reflecting the influence of climate regional
differences on thermal comfort [
52
–
54
], while some researchers have also fitted simulations
of crowd voting with thermal comfort indicators to derive prediction equations for their
studies [
55
–
57
], which use the thermal comfort parameters of PET, indicating that it has
been widely used for outdoor thermal environment evaluation, and this indicator was also
used in this study.
The studies mentioned above have provided a more comprehensive urban heat
wave research system and orientation. From the perspective of research methods and
objects, major cities and urban agglomerations in China, such as Beijing, Shanghai, and the
Guangdong-Hong Kong-Macao Greater Bay Area, have started to study the heat island
effect and high-temperature heat hazards in the urban context from a multidisciplinary
fusion perspective in the past five years in both temporal and spatial dimensions [
58
–
60
].
International research on urban heat waves is more advanced and intensive, such as con-
structing a comprehensive vulnerability evaluation framework and index system under
the influence of multiple factors including the natural environment, urban environment,
socio-economy, and public resources, which ultimately achieve the purpose of spatial
identification of vulnerable populations [
61
,
62
]. Mountain cities have outstanding summer
heat wave problems due to the compound influence of multiple factors that include their
special topography and highly dense urban form, which need to be addressed urgently.
However, domestic research on high-temperature heat hazards in mountainous cities, such
as Chongqing in China, is relatively scarce compared to that in other countries, with a
single research method. The latest remote sensing satellite data for the regional study of
heat island effect in Chongqing were retrieved in 2017 [
63
], while only in 2021, scholars
selected natural factors such as the degree of surface relief (RDLS) based on multi-source
data from the main urban area of Chongqing and used the spatial analysis method of GIS.
Spatial variability of habitat suitability in mountainous areas was quantitatively studied
from the perspective of ventilation [
64
]. However, the study of high-temperature heat
waves in functional spaces at the scale of 10m–1km is still insufficient, and there is a lack
of comparative exploration between different functional spaces. It is necessary to select
thermal environment parameters and spatial morphological elements from the local scale
to study the coupling relationship between them. Motivated by the above-mentioned
knowledge gaps, this paper aims to study the characteristics of high-temperature heat
waves and human thermal comfort at the functional space level in Chongqing, a mountain
city, in order to provide a theoretical basis and a methodological approach for improv-
ing the thermal comfort of people in mountain cities and better coping with the risk of
high-temperature heat waves. The objectives of this study are (1) to describe the thermal
environment parameters in four functional areas during a heat wave cycle to analyze local
heat wave characteristics and differences; (2) to qualitatively analyze the mitigation effects
of different spatial forms on heat waves; (3) to analyze the outdoor population thermal
comfort characteristics in Chongqing, a mountainous city, during a heat wave and the
relationships influenced by different functional spaces; (4) to establish an outdoor thermal
comfort prediction model based on the research data.

Buildings 2022,12, 625 4 of 21
2. Materials and Methods
2.1. Methodological Framework
This study concerns the main urban area of Chongqing, a mountainous high-density
city, as the research object. We have collected actual measurement data during the high-
temperature heat waves, analyzed the thermal environment from the urban spatial per-
spective, combined the PET human comfort evaluation index, explored the correlation
between PET and thermal environment elements at each measurement point, verified the
unity of human subjective thermal perception and objective model analysis, calculated the
outdoor thermal comfort in summer through a regression fitting range, established the
outdoor thermal comfort evaluation model during a summer heat wave in high-density
urban centers, investigated the effect of thermal parameters on human thermal comfort,
and have drawn final research conclusions. The methodological framework of this study is
shown in Figure 1.
On‐sitemeasurement Questionnaire
Subjective
factors
ObjectiveFactors
August
04‐06,2020
Threekindsoffourtypes
offunctionalzoning
 Air
temperature
 Surface
temperature
 Thermal
radiation
 Relative
humidity
 Windspeed
 BusinessDistrict
 Residence
 MountainPark
 RiversidePark
Conclusions
Spacefactor
SVF
H/W
Roadalignment
HighDifference
Vegetationcover
Outdooractivity
crowd
Basicinformation+
thermalsensory
information
TSV\TCV
PET
Thermoneutraltemperature
Summeroutdoorthermalcomfortevaluation
model
watercolumn
Correlation
coefficient
 OREGONSCIENTIFICWMR300
 TestoMeteorologicalinstruments
 LaserRangefinder
 Canon80Dfisheyelens
 Ta
 Ts
 RH
 TR
LinearfitR²
Five‐pointscale
Seven‐pointscale
Fittinganalysis
CorrelationandDifference Spaceoptimization
Significantlycorrelated
0.01（2‐tailed）
Significantlycorrelated
0.05（2‐tailed）
Figure 1. The methodological framework of the study.
2.2. On-Site Measurement
Chongqing is a subtropical monsoon climate zone, with an average annual temperature
of 17.6
◦
C, extremely high temperature of 42.2
◦
C, extremely low temperature of
−
1.8
◦
C,
average annual relative humidity of 70–80%, average annual wind speed of 1.12 M/S,
average high temperature of over 33.0
◦
C in July-August in summer, resulting in hot and
humid weather. For 19 days in 2020, the annual temperature exceeded 35
◦
C, with high
temperatures and heat waves in the city. The issue is very serious. The measurement area is
the Yuzhong District, which is the core of the main city of Chongqing. The Yuzhong District
is surrounded by water to the east, south, and north, connected to the land in the west and
is a narrow peninsula running east-west. It reaches 394 m above sea level at Goose Ridge,
the maximum point in the district, and 167 m above sea level at Shazuijiao, where the two
rivers converge at Chaotianmen, which is the lowest point, with a relative height difference
of 227 m. The extremes of climate and special topography make the Yuzhong District a
typical mountainous urban area affected by high temperatures and heat waves.

Buildings 2022,12, 625 5 of 21
Considering different types of urban functional spaces to determine the measurement
points, the twelve experimental measurement points cover four types of urban spaces in
three categories: commercial area (Jiefangbei), residential area (Dajing Lane), mountainous
park area (People’s Park), and waterfront park area (Riverside Park), the vegetation in
the commercial area is sporadically distributed, the vegetation in the residential area is
unevenly distributed among the measurement points, and the vegetation in the moun-
tain park area and the waterfront park area are more dense and uniform, as shown in
Table 1, Figures 2and 3for each measurement point. According to the definition of the
high-temperature heat waves by the China Meteorological Administration, the actual mea-
surement of thermal environment data was carried out in a typical high-temperature heat
wave cycle in Chongqing (2020.8.4–8.6, with the maximum temperature of 37
◦
C, 38
◦
C
and 38
◦
C for three days), and the experimental period was 9:00–20:00. Due to the large
number of measurement points and the limited number of instruments, the Dajing Lane
measurement point used the American OREGON SCIENTIFIC WMR300 professional me-
teorological system and thermal radiometer, and the rest of the measurement points used
Testo series measuring instruments, in order to ensure that all measurement points were
measured simultaneously. Through thermal environment data collection and analysis, the
impact of high-temperature heat waves on different urban functional spaces was studied.
1
Figure 2. Distribution of measurement points.
Table 1. Basic information of each measurement area.
1Road Alignment
(Long Direction)
Height to Width
Ratio/(H/D)
High Difference/m
(Measurement Points) Sky View Factor (0–1)
Business District
(Jiefangbei)
B1, B2, B3, B4
135◦3.49 1.6 0.566
Residence (Dajing
Lane)
R1, R2, R3, R4
160◦0.45 64.7 0.301
Mountain Park
(People’s Park)
P1, P2, P3
45◦0.50 40.8 0.381
Riverside Park
(Riverside Park)
P4
15◦1.65 0.0 0.258
1The E-W strike is 0◦strike.

Buildings 2022,12, 625 6 of 21
Buildings 2022, 12, x FOR PEER REVIEW 6 of 21
Table 1. This is a table. Tables should be placed in the main text near the first time they are cited.
1
Road Alignment
(Long Direction)
Height to
Width Ra-
tio/(H/D)
High Differ-
ence/m
(Measurement
Points)
Sky View Factor (0–1)
Business District (Jiefangbei)
B1, B2, B3, B4 135° 3.49 1.6 0.566
Residence (Dajing Lane)
R1, R2, R3, R4 160° 0.45 64.7 0.301
Mountain Park (People’s Park) P1,
P2, P3 45° 0.50 40.8 0.381
Riverside Park (Riverside Park) P4 15° 1.65 0.0 0.258
1
The E-W strike is 0° strike.
(a) (b) (c) (d)
Figure 3. Representative sky fisheye photos of the four measurement areas: (a) is the Jiefangbei
measurement point; (b) is the Dajing Lane measurement point; (c) is the People’s Park measurement
point; (d) is the Riverside Park measurement point.
2.3. Evaluation of Outdoor Thermal Comfort in Summer
The PET is the air temperature at which the heat income and expenditure of the hu-
man body in a typical indoor environment (without solar radiation and wind) is in equi-
librium with the same core and skin temperatures to be evaluated in complex outdoor
conditions, being an effective means of evaluating human thermal comfort sensations in
complex urban spatial environments [65–67]. In this study, questionnaire research was
used to obtain subjective human sensory data, and the PET value was calculated based on
the thermal environment data using the Rayman model so that the degree of influence of
high-temperature heat waves on human thermal comfort could be objectively reflected
through thermal comfort, thermal sensory voting and PET fitting analysis.
Questionnaires were conducted at the same time as the field measurements, where
the respondents were randomly selected from the measurement site activity population,
containing basic information (age, gender, height, weight, clothing, activity status, etc.).
Thermal comfort voting (TCV), thermal sensory voting (TSV), etc., where the thermal
comfort voting used a seven-point scale and the thermal sensory voting used a five-point
scale (the evaluation criteria of each scale is shown in Figure 4) were used to substitute
into the thermal environment data calculated using the Rayman model, as shown in Table
2.
Figure 3.
Representative sky fisheye photos of the four measurement areas: (
a
) is the Jiefangbei
measurement point; (
b
) is the Dajing Lane measurement point; (
c
) is the People’s Park measurement
point; (d) is the Riverside Park measurement point.
2.3. Evaluation of Outdoor Thermal Comfort in Summer
The PET is the air temperature at which the heat income and expenditure of the
human body in a typical indoor environment (without solar radiation and wind) is in
equilibrium with the same core and skin temperatures to be evaluated in complex outdoor
conditions, being an effective means of evaluating human thermal comfort sensations in
complex urban spatial environments [
65
–
67
]. In this study, questionnaire research was
used to obtain subjective human sensory data, and the PET value was calculated based
on the thermal environment data using the Rayman model so that the degree of influence
of high-temperature heat waves on human thermal comfort could be objectively reflected
through thermal comfort, thermal sensory voting and PET fitting analysis.
Questionnaires were conducted at the same time as the field measurements, where
the respondents were randomly selected from the measurement site activity population,
containing basic information (age, gender, height, weight, clothing, activity status, etc.).
Thermal comfort voting (TCV), thermal sensory voting (TSV), etc., where the thermal
comfort voting used a seven-point scale and the thermal sensory voting used a five-point
scale (the evaluation criteria of each scale is shown in Figure 4) were used to substitute into
the thermal environment data calculated using the Rayman model, as shown in Table 2.
Buildings 2022, 12, x FOR PEER REVIEW 7 of 21
(a) (b)
Figure 4. Subjective heat sensation voting scale; (a) is the Thermal comfort voting scale; (b) is the
Thermal sensory voting scale.
Table 2. Summary of the data used to calculate PET values.
Dajing Lane Jiefangbei Riverside Park People’s Park
Air Temperature/ Max 44.8(36.7) 40.3(35.6) 37.4(34.5) 38.2(36.0)
°C Min 31.2(33.2) 31.5(32.9) 26.6(29.1) 32.1(33.2)
AVG 35.7 35.8 34.8 35.1
Relative Humidity/ Max 68.8(54.3) 60.0(47.2) 70.0(53.4) 62.0(48.6)
% Min 42.8(45.6) 37.7(40.1) 43.0(47.5) 40.1(42.6)
AVG 52.3 47.6 53.4 48.4
Wind Speed/(m\S) Max 0.4(0.1) 3.4(1.7) 3.1(1.5) 1.5(0.3)
Min 0.0(0.0) 0.2(0.4) 0.0(0.6) 0.0(0.1)
AVG 0.3 0.9 1.5 0.4
Thermal
radiation/W/m2 Max 850.0(487.3) 581.0(329.2) 0.0(0.0) 690.0(440.0)
Min 0.0(0.0) 0.0(0.0) 0.0(0.0) 0.0(0.0)
AVG 180.9 111.1 0.0 98.2
PET/ Max 37.5(30.8) 48.6(36.5) 36.1(31.5) 49.7(37.6)
°C Min 26.7(28.2) 28.2(30.1) 22.6(26.5) 26.6(29.9)
AVG 34.2 36.4 32.1 37.6
2.4. Thermal Comfort Data Statistics and Analysis
In this study, nonlinear regression analysis in SPSS software was used to express the
relationship between voting results and PET changes in a regression equation, using PET
as the independent variable and TSV and TCV at each measurement point as the depend-
ent variables, in order to clearly demonstrate the relationship between thermal voting and
thermal comfort, and to facilitate the derivation of thermoneutral temperature. The corre-
lation between PET and thermal environmental parameters was analyzed using the sta-
tistical method of Pearson bivariate correlation coefficient in SPSS. Meanwhile, a multiple
logistic regression model in SPSS was used to integrate the effects of each thermal param-
eter to build an outdoor thermal comfort prediction model.
3. Results
The World Meteorological Organization points out that the human body’s perception
of hot and cold is not only dependent on air temperature, but also humidity, wind speed,
and solar thermal radiation. Statistical raw measurement data show that wind speed
changes irregularly, so this paper mainly analyzes the four thermal environment param-
eters of thermal radiation, air temperature, surface temperature, and relative humidity,
Figure 4.
Subjective heat sensation voting scale; (
a
) is the Thermal comfort voting scale; (
b
) is the
Thermal sensory voting scale.

Buildings 2022,12, 625 7 of 21
Table 2. Summary of the data used to calculate PET values.
Dajing Lane Jiefangbei Riverside
Park
People’s
Park
Air Temperature/◦C
Max 44.8(36.7) 40.3(35.6) 37.4(34.5) 38.2(36.0)
Min 31.2(33.2) 31.5(32.9) 26.6(29.1) 32.1(33.2)
AVG 35.7 35.8 34.8 35.1
Relative Humidity/%
Max 68.8(54.3) 60.0(47.2) 70.0(53.4) 62.0(48.6)
Min 42.8(45.6) 37.7(40.1) 43.0(47.5) 40.1(42.6)
AVG 52.3 47.6 53.4 48.4
Wind Speed/(m\S)
Max 0.4(0.1) 3.4(1.7) 3.1(1.5) 1.5(0.3)
Min 0.0(0.0) 0.2(0.4) 0.0(0.6) 0.0(0.1)
AVG 0.3 0.9 1.5 0.4
Thermal radiation/W/m2
Max 850.0(487.3) 581.0(329.2) 0.0(0.0) 690.0(440.0)
Min 0.0(0.0) 0.0(0.0) 0.0(0.0) 0.0(0.0)
AVG 180.9 111.1 0.0 98.2
PET/◦C
Max 37.5(30.8) 48.6(36.5) 36.1(31.5) 49.7(37.6)
Min 26.7(28.2) 28.2(30.1) 22.6(26.5) 26.6(29.9)
AVG 34.2 36.4 32.1 37.6
2.4. Thermal Comfort Data Statistics and Analysis
In this study, nonlinear regression analysis in SPSS software was used to express
the relationship between voting results and PET changes in a regression equation, using
PET as the independent variable and TSV and TCV at each measurement point as the
dependent variables, in order to clearly demonstrate the relationship between thermal
voting and thermal comfort, and to facilitate the derivation of thermoneutral temperature.
The correlation between PET and thermal environmental parameters was analyzed using
the statistical method of Pearson bivariate correlation coefficient in SPSS. Meanwhile, a
multiple logistic regression model in SPSS was used to integrate the effects of each thermal
parameter to build an outdoor thermal comfort prediction model.
3. Results
The World Meteorological Organization points out that the human body’s perception
of hot and cold is not only dependent on air temperature, but also humidity, wind speed,
and solar thermal radiation. Statistical raw measurement data show that wind speed
changes irregularly, so this paper mainly analyzes the four thermal environment parameters
of thermal radiation, air temperature, surface temperature, and relative humidity, and
visually reflects the changes of each parameter during the high-temperature heat waves
at each measurement point by drawing a line graph (Figure 5). The calculations yielded
the mean intensity of thermal radiation in each measurement area, ranked as follows:
Dajing Lane (180.9 W/m
2
) > Jiefangbei (111.1 W/m
2
) > People’s Park (98.2 W/m
2
) >
Riverside Park (0 W/m
2
), the mean air temperature ranked as follows: Jiefangbei
(35.8 ◦C)
> Dajing Lane (35.7
◦
C) > People’s Park (35.1
◦
C) > Riverside Park (34.8
◦
C), the mean
relative humidity was ranked as follows: Riverside Park (53.4%) > Dajing Lane (52.3%) >
People’s Park (48.4%) > Jiefangbei (47.6%). The mean surface temperature was ranked as
follows: Dajing Lane (42.1
◦
C) > Jiefangbei (39.6
◦
C) > People’s Park (39.4
◦
C) > Riverside
Park (
31.5 ◦C
). Overall, the data show functional spatial differentiation, discussing the
relationship between urban spatial characteristics and changes in thermal environment
parameters for each of the four urban spaces.

Buildings 2022,12, 625 8 of 21
Buildings 2022, 12, x FOR PEER REVIEW 9 of 22
and visually reflects the changes of each parameter during the high-temperature heat
waves at each measurement point by drawing a line graph (Figure 5). The calculations
yielded the mean intensity of thermal radiation in each measurement area, ranked as fol-
lows: Dajing Lane (180.9 W/m2) > Jiefangbei (111.1 W/m2) > People’s Park (98.2 W/m2) >
Riverside Park (0 W/m2), the mean air temperature ranked as follows: Jiefangbei (35.8 °C)
> Dajing Lane (35.7 °C) > People’s Park (35.1 °C) > Riverside Park (34.8 °C), the mean rel-
ative humidity was ranked as follows: Riverside Park (53.4%) > Dajing Lane (52.3%) >
People’s Park (48.4%) > Jiefangbei (47.6%). The mean surface temperature was ranked as
follows: Dajing Lane (42.1 °C) > Jiefangbei (39.6 °C) > People’s Park (39.4 °C) > Riverside
Park (31.5 °C). Overall, the data show functional spatial differentiation, discussing the re-
lationship between urban spatial characteristics and changes in thermal environment pa-
rameters for each of the four urban spaces.
Firstly, in the crowd thermal comfort analysis, the TCV and TSV characteristics of
each measurement area were analyzed based on the questionnaire survey results. Sec-
ondly, the crowd polling and PET of different measurement points were fitted separately
to obtain the regression equation and calculate their thermoneutral temperatures. Finally,
the connection between PET changes and urban spatial elements at each measurement
point was analyzed to calculate the correlation between thermal environment factors and
PET, establishing the main urban area of Chongqing summer outdoor thermal comfort
prediction model during high-temperature heat waves in the main urban area of Chong-
qing.
(a) (b)
(c) (d)
Figure 5. Folding line diagram of thermal environment factors for each measurement point: (a) Ther-
mal Radiation; (b) Air Temperature; (c) Surface Temperature; (d) Relative Humidity.
3.1. Thermal Environment Characteristics of Different Measurement Areas
3.1.1. Thermal Environment Results of Business Areas
Jiefangbei has a larger sky openness and street height to width ratio, fewer trees, and
a larger area exposed to direct sunlight. The measured results show that the commercial
area has the highest overall air temperature, the second highest thermal radiation and
Figure 5.
Folding line diagram of thermal environment factors for each measurement point:
(a) Thermal Radiation; (b) Air Temperature; (c) Surface Temperature; (d) Relative Humidity.
Firstly, in the crowd thermal comfort analysis, the TCV and TSV characteristics of each
measurement area were analyzed based on the questionnaire survey results. Secondly, the
crowd polling and PET of different measurement points were fitted separately to obtain the
regression equation and calculate their thermoneutral temperatures. Finally, the connection
between PET changes and urban spatial elements at each measurement point was analyzed
to calculate the correlation between thermal environment factors and PET, establishing the
main urban area of Chongqing summer outdoor thermal comfort prediction model during
high-temperature heat waves in the main urban area of Chongqing.
3.1. Thermal Environment Characteristics of Different Measurement Areas
3.1.1. Thermal Environment Results of Business Areas
Jiefangbei has a larger sky openness and street height to width ratio, fewer trees, and
a larger area exposed to direct sunlight. The measured results show that the commercial
area has the highest overall air temperature, the second highest thermal radiation and
surface temperature, and the lowest relative humidity among the four measurement areas;
the air temperature and humidity change slowly, while the thermal radiation and surface
temperature fluctuate widely. It indicates that the stronger solar irradiation directly affects
the air temperature in this area, which in turn intensifies the transpiration of air–water
vapor and increases the relative humidity, while its streets are in the NW-SE direction and
the buildings on both sides have a certain blocking effect on solar radiation. The surface
temperature and thermal radiation show the variability of the measurement points, in
which the intensity of high-temperature heat waves at measurement points B1 and B2
are higher than that at measurement points B3 and B4, in particular, the average value
of surface temperature is 7.8
◦
C higher and the average value of thermal radiation is
177.4 W/m
2
larger. It is analyzed that it is formed by the absence of any shading around
measurement points B1 and B2, the high SVF (0.653 and 0.705), the high heat storage
granite sub-bedding surface with high solar radiation, reflected radiation from buildings,
and the ground. Whereas measurement points B3 and B4 are slightly less affected by the

Buildings 2022,12, 625 9 of 21
high-temperature heat wave because of the shading effect of the tree canopy and urban
street canyons. In general, the high level of artificiality, high density of buildings and
people in the commercial land area, and the high-temperature heat waves are obvious.
3.1.2. Thermal Environment Results of Residential
Due to its 64.7 m site height difference, cramped internal streets, low sky openness,
and uneven sub-bedding surface material, the thermal environment parameters of Dajing
Lane are more complicated to change. Among the four measurement areas, the residential
thermal radiation and surface temperature are the highest, the air temperature and relative
humidity are the second highest (the difference with the highest value is small), and
the high-temperature heat wave is the most serious. This analysis is due to the early
construction of Dajing Lane, which had no underground parking garage, many green areas
were changed to hard cement pavement due to the demand for surface parking, poor heat
dissipation on the ground, uneven distribution of greenery, and the street direction of the
measurement point is close to E-W direction, which is exposed to direct sunlight for a long
time, resulting in higher thermal environment parameters. The measurement point R1 has
the highest thermal radiation (850 W/m
2
) and air temperature (44.8
◦
C), and the largest
temperature difference (12.6
◦
C), which is located at the lowest point of the settlement
(elevation 183.3 m), surrounded by very open sky (sky openness 0.821), no trees for shade,
and the lower mat surface is hard concrete pavement, which is subject to extremely strong
thermal radiation and large fluctuation of air temperature. Measurement point R2 has
the highest surface temperature (64.3
◦
C), and the highest temperature (
64.3 ◦C
), this
measurement point is located at the entrance of Dajing Lane, the highest point of the
settlement (elevation 248 m), the sky openness is medium (0.566), the height to width ratio
is large (3.49), there is no vegetation around, the lower bedding surface is made of asphalt
pavement (it is exposed to sunlight for a long time), and there is a large fluctuation of
surface temperature for three days (temperature difference 32.1
◦
C) Measurement point
R3 is located at the northern end of the settlement, the dense tree crown of the site, it
has the smallest sky openness (0.301), and the lowest values of each thermal parameter
Measurement point R4 is located in the middle of the settlement elevation difference
(elevation 221 m), and the values of each thermal parameter are slightly lower than those
of R1 and R2. In summary, Dajing Lane is an old mountain settlement, which is affected
by multiple spatial factors such as elevation, sky openness, street height and width ratio,
greenery, etc., resulting in large differences in the characteristics of high-temperature heat
waves at different measurement points.
3.1.3. Thermal Environment Results of Mountain Park
Most of the vegetation in People’s Park consists of dense and tall trees, and the
lower bedding surface is mostly mud and grass bricks, with low SVF and height to width
ratio. The overall changes of the thermal environment parameters were smooth and
showed a high degree of stability during the measurement period, showing little fluctuation.
Reasonable landscape vegetation and the consistency of the substrate in the mountain park
are important spatial factors for the formation of thermal stability. Measurement point P1 is
surrounded by dense trees and is more shaded, and its mean surface temperature (35.5
◦
C)
and mean thermal radiation (24.2 W/m
2
) are lower than the other two measurement points.
A brief maximum thermal radiation value (690 W/m
2
) was observed at measurement
point P3, which was analyzed as a result of the tree canopy not forming full coverage
at this measurement point during the tree placement, resulting in high solar radiation
and reflected radiation around it for a certain period. In general, it seems that the high
vegetation rate, suitable street height to width ratio, and low SVF together determine a less
different thermal environment at each measurement point in People’s Park, which is less
affected by high-temperature heat waves than the two types of urban functional spaces,
commercial areas, and residential areas, which are highly built up.

Buildings 2022,12, 625 10 of 21
3.1.4. Thermal Environment Results of Riverside Park
Within the riverside park, the trees are tall and dense, with a large canopy diameter
for good shading, and more mud and grass on the lower bedding surface. Measurement
point F4 is near the river, with the lowest elevation (147 m) and the smallest SVF (0.258),
which is obviously affected by the river wind. Its surface temperature, air temperature,
and heat radiation are the lowest values of all functional areas and measurement points,
but the relative humidity is the highest, although the highest value of air temperature is
greater than 35
◦
C for three consecutive days at the high-temperature heat wave effect is not
obvious. It indicates that the waterfront park effectively regulates the thermal environment
through the high heat storage of the water surface and the reflection effect of solar heat
radiation, the heat dissipation effect of river wind on the environment, and the shading
effect of tree canopy on solar radiation, etc., to play a certain mitigating effect on the urban
high-temperature heat waves.
In summary, the characteristics of high-temperature heat waves in each measurement
area vary depending on the spatial elements. Reflection of water surface and high heat
storage of water bodies affect the surrounding thermal environment. Tree shading and
the degree of street cramp affect the values of each thermal parameter by blocking solar
insolation. Hard substrates, such as cement and concrete, have a certain heating effect on
air temperature, while soft substrates such as soil and grass bricks have a better mitigation
effect on the thermal environment; the higher the elevation, the more heat radiation
the measurement point receives. The higher the elevation, the greater the amount of
thermal radiation received by the measurement point, as well as the more obvious the
high-temperature heat wave effect.
3.2. Summer Outdoor Crowd Voting Analysis
The ages of the questionnaire respondents ranged from 6 to 89 years old, with the
majority being between 36–59 years old and the proportion of men and women essentially
equal. Figure 6shows the distribution of thermal comfort votes (TCV), with 46–69% of
people voting “slightly uncomfortable” to “uncomfortable” in four measurement points
(B1–B4) in Jiefangbei and three measurement points (R1, R2, R4) in Dajing Lane;
52–80%
of
respondents voted “slightly uncomfortable” in two parks (P1–P4), 52–80% of respondents
voted “moderate” and above, and “comfortable” at P2 (3%) and P4 (4%); however, no
respondents chose “very comfortable” and “comfortable” at all points. None of the respon-
dents chose “very comfortable” and “very uncomfortable” for all points. Figure 6shows
the distribution of thermal sensory voting (TSV), with Jiefangbei R1 and R2 having the
most “slightly unacceptable” and “not at all acceptable” votes, at 45% and 49%, respectively,
and Riverside Park P4 having the most “completely acceptable” votes (38%). “Acceptable”
accounted for the most (38%). R1 and R2 measurement points of Dajing Lane had no “fully
accepted”. This shows that, like the previous thermal environment parameters, the thermal
sensation and thermal comfort of the crowd are also affected by different spatial elements.
The park provides abundant shade areas, and people feel more comfortable in shady places,
while the square in the urban commercial area and the activity site in the residential area
have more hard surfaces and less vegetation, and the external air conditioner generates a
lot of heat, so most respondents have poor thermal comfort.
Based on the PET data calculated from the thermal environment parameters, the
relationship between the mean values of thermal susceptibility voting (TSV) and thermal
comfort voting (TCV) and PET was statistically analyzed by SPSS and fitted to obtain the
regression curves for each measurement area (Figure 7). The slope of the TSV regression
curve was similar in the four measurement areas, with R
2
above 0.8, while the correlation
between TCV in Jiefangbei and Riverside Park was slightly lower than 0.8. The analysis
was performed as the excessive outdoor sunlight in the Jiefangbei measurement area
contrasts with the indoor cold air and affects people’s thermal sensation. While the comfort
sensation in the Riverside Park measurement area is more stable, the correlation between
their thermal sensation and PET is slightly weaker.

Buildings 2022,12, 625 11 of 21
Buildings 2022, 12, x FOR PEER REVIEW 12 of 22
had no “fully accepted”. This shows that, like the previous thermal environment param-
eters, the thermal sensation and thermal comfort of the crowd are also affected by differ-
ent spatial elements. The park provides abundant shade areas, and people feel more com-
fortable in shady places, while the square in the urban commercial area and the activity
site in the residential area have more hard surfaces and less vegetation, and the external
air conditioner generates a lot of heat, so most respondents have poor thermal comfort.
(a)
(b)
Figure 6. Crowd Heat Comfort Voting: (a) Thermal Sensory Voting (TSV); (b) Thermal Comfort
Voting (TCV).
Based on the PET data calculated from the thermal environment parameters, the re-
lationship between the mean values of thermal susceptibility voting (TSV) and thermal
comfort voting (TCV) and PET was statistically analyzed by SPSS and fitted to obtain the
regression curves for each measurement area (Figure 7). The slope of the TSV regression
curve was similar in the four measurement areas, with R2 above 0.8, while the correlation
between TCV in Jiefangbei and Riverside Park was slightly lower than 0.8. The analysis
was performed as the excessive outdoor sunlight in the Jiefangbei measurement area con-
trasts with the indoor cold air and affects people’s thermal sensation. While the comfort
sensation in the Riverside Park measurement area is more stable, the correlation between
their thermal sensation and PET is slightly weaker.
Figure 6.
Crowd Heat Comfort Voting: (
a
) Thermal Sensory Voting (TSV); (
b
) Thermal Comfort
Voting (TCV).
A nonlinear fit was performed for the relationship between TCV and PET at different
measurement points (Figure 8), and the regression equation was obtained as follows.
TCVRiverside Park =−0.844 + 0.362PET −0.010PET2(R2= 0.773) (1)
TCVDajing Lane = 10.085 −0.371PET + 0.001PET2(R2= 0.845) (2)
TCVJiefangbei = 18.439 −0.903PET + 0.010PET2(R2= 0.695) (3)
TCVPeople’s Park = 6.940 −0.261PET + 0.0017PET2(R2= 0.836) (4)
When TCV = 0, the corresponding PET values of each measurement point are
33.70 ◦C
for Riverside Park, 29.53
◦
C for Dajing Lane, 31.208
◦
C for Jiefangbei, and 34.22
◦
C for Peo-
ple’s Park; when TCV
≥
1, the corresponding PET thermal comfort range is
33.70 ±3.64 ◦C
,
29.53
±
3.17
◦
C, 31.20
±
3.21
◦
C, and 34.22
±
6.43
◦
C. The analysis showed that the thermal
neutral temperature at the measuring point of Dajing Lane was the lowest and its ther-
mal comfort range was the narrowest; the thermal comfort range of People’s Park was
the widest, and the thermal neutral temperature of Riverside Park was close to that of
People’s Park.

Buildings 2022,12, 625 12 of 21
Buildings 2022, 12, x FOR PEER REVIEW 13 of 22
(a)
(b)
(c)
(d)
(e) (f) (g) (h)
Figure 7. Analysis of the mean thermal sensory vote (TSV) and mean thermal comfort vote (TCV)
and PET fit for each measurement area: (a) Jiefangbei (TCV); (b) Dajing Lane (TCV); (c) People’s
Park (TCV); (d) Riverside Park (TCV); (e) Jiefangbei (TSV); (f) Dajing Lane (TSV) (g) People’s Park
(TSV); (h) Riverside Park (TSV).
A nonlinear fit was performed for the relationship between TCV and PET at different
measurement points (Figure 8), and the regression equation was obtained as follows.
TCVRiverside Park = −0.844 + 0.362PET − 0.010PET2 (R2 = 0.773) (1
)
TCV
Dajing Lane
= 10.085 − 0.371PET + 0.001PET
2
(R
2
=
0.845)
(2)
TCV
Jiefangbei
= 18.439 − 0.903PET + 0.010PET
2
(R
2
=
0.695)
(3)
TCV
People’s Park
= 6.940 − 0.261PET + 0.0017PET
2
(R
2
=
0.836)
(4)
When TCV = 0, the corresponding PET values of each measurement point are 33.70
°C for Riverside Park, 29.53 °C for Dajing Lane, 31.208 °C for Jiefangbei, and 34.22 °C for
People’s Park; when TCV ≥ 1, the corresponding PET thermal comfort range is 33.70 ± 3.64
°C, 29.53 ± 3.17 °C, 31.20 ± 3.21 °C, and 34.22 ± 6.43 °C. The analysis showed that the ther-
mal neutral temperature at the measuring point of Dajing Lane was the lowest and its
thermal comfort range was the narrowest; the thermal comfort range of People’s Park was
the widest, and the thermal neutral temperature of Riverside Park was close to that of
People’s Park.
A fitting analysis of the relationship between TSV and PET (Figure 8) yielded the
following regression equation.
TSVRiverside Park = 8.831 − 0.381PET + 0.002PET2 (R2 = 0.828) (5
)
TSV
Dajing Lane
= 31.499 − 1.688PET + 0.021PET
2
(R
2
=
0.848)
(6)
TSV
Jiefangbei
= 16.552 − 0.765PET + 0.008PET
2
(R
2
=
0.812)
(7)
TSV
People’s Park
= 10.943 − 0.441PET + 0.0035PET
2
(R
2
=
0.888)
(8)
When TSV = 0, the corresponding PET values of each measurement point are 27.01
°C for Riverside Park, 29.45 °C for Dajing Lane, 33.08 °C for Jiefangbei, and 33.98 °C for
People’s Park. When TSV = −0.5~0.5, the corresponding PET thermal neutral ranges are
Figure 7.
Analysis of the mean thermal sensory vote (TSV) and mean thermal comfort vote (TCV)
and PET fit for each measurement area: (
a
) Jiefangbei (TCV); (
b
) Dajing Lane (TCV); (
c
) People’s Park
(TCV); (d) Riverside Park (TCV); (e) Jiefangbei (TSV); (f) Dajing Lane (TSV) (g) People’s Park (TSV);
(h) Riverside Park (TSV).
Buildings 2022, 12, x FOR PEER REVIEW 14 of 22
27.01 ± 1.85 °C, 29.45 ± 1.17 °C, 33.08 ± 2.30 °C, and 33.98 ± 2.57 °C. It can be seen that the
thermal neutral temperature of Riverside Park is the lowest, which indicates that the sen-
sitivity of human thermal sensation is the least, because the measurement point here is
near the riverside, with dense trees and better heat dissipation and absorption, and shad-
ing of thermal radiation so that people are less concerned about thermal sensation and
can take the lower PET as a moderate state of feeling. The People’s Park has the highest
thermal neutral temperature, which is analyzed because it is located in the city central
area, near Jiefangbei, and the elevation is higher, which is strongly influenced by solar
thermal radiation and urban thermal radiation, although the vegetation conditions are
rich, and the air temperature and thermal radiation are higher according to the previous
data, so the crowd takes the higher PET as the thermal comfort state.
In addition, the analysis of the thermally neutral temperature and threshold range of
PET at all measurement points compared with the standard PET thermal comfort range
(18 °C ≤ PET ≤ 23 °C) shows that its maximum value (36.55 °C) is higher than the standard
value by about 14 °C, indicating that the standard of outdoor thermal comfort feeling in
summer and the acceptance threshold for high temperature are higher in the main urban
area of Chongqing in high density. On the one hand, this is due to the mediation of people
who are active outdoors in summer by reducing clothing, wearing shading products, and
taking cooling measures, and on the other hand, it also indicates that people’s thermal
adaptation ability has improved in their long-term life.
(a)
(b)
Figure 8. Nonlinear fitting of crowd heat voting to PET: (a) Relationship between TCV and PET for
thermal comfort voting in each measurement area; (b) Relationship between TSV and PET for ther-
mal sensory voting in each measurement area.
3.3. Thermal Comfort Effect Analysis
Figure 9 visualizes the changes of PET at twelve measurement points in four meas-
urement areas during one day (9:00–20:00). Most of the measurement points showed a
trend of increasing and then decreasing, and a few of them showed a flat change during
the day, which is consistent with the trend of the measured data of thermal radiation and
air temperature above. Within the measurement period, the PET at each point in the typ-
ical summer space of Chongqing’s Yuzhong District during the heat wave was the lowest
at 20:00, indicating that the human thermal comfort was the best at this time. The highest
point appeared at the residential area R1 (57.8 °C), and the lowest point was P4 in River-
side Park (21.3 °C). Compared with other measurement points, the PET values at five
measurement points B1, B2, R3, P3, and P4 were lower and more stable, which is con-
sistent with the fact that these sites have good shading conditions and fast heat dissipation
Figure 8.
Nonlinear fitting of crowd heat voting to PET: (
a
) Relationship between TCV and PET for
thermal comfort voting in each measurement area; (
b
) Relationship between TSV and PET for thermal
sensory voting in each measurement area.
A fitting analysis of the relationship between TSV and PET (Figure 8) yielded the
following regression equation.
TSVRiverside Park = 8.831 −0.381PET + 0.002PET2(R2= 0.828) (5)
TSVDajing Lane = 31.499 −1.688PET + 0.021PET2(R2= 0.848) (6)
TSVJiefangbei = 16.552 −0.765PET + 0.008PET2(R2= 0.812) (7)

Buildings 2022,12, 625 13 of 21
TSVPeople’s Park = 10.943 −0.441PET + 0.0035PET2(R2= 0.888) (8)
When TSV = 0, the corresponding PET values of each measurement point are
27.01 ◦C
for Riverside Park, 29.45
◦
C for Dajing Lane, 33.08
◦
C for Jiefangbei, and 33.98
◦
C for
People’s Park. When TSV =
−
0.5~0.5, the corresponding PET thermal neutral ranges are
27.01
±
1.85
◦
C, 29.45
±
1.17
◦
C, 33.08
±
2.30
◦
C, and 33.98
±
2.57
◦
C. It can be seen that
the thermal neutral temperature of Riverside Park is the lowest, which indicates that the
sensitivity of human thermal sensation is the least, because the measurement point here
is near the riverside, with dense trees and better heat dissipation and absorption, and
shading of thermal radiation so that people are less concerned about thermal sensation and
can take the lower PET as a moderate state of feeling. The People’s Park has the highest
thermal neutral temperature, which is analyzed because it is located in the city central area,
near Jiefangbei, and the elevation is higher, which is strongly influenced by solar thermal
radiation and urban thermal radiation, although the vegetation conditions are rich, and
the air temperature and thermal radiation are higher according to the previous data, so the
crowd takes the higher PET as the thermal comfort state.
In addition, the analysis of the thermally neutral temperature and threshold range of
PET at all measurement points compared with the standard PET thermal comfort range
(
18 ◦C≤
PET
≤
23
◦
C) shows that its maximum value (36.55
◦
C) is higher than the standard
value by about 14
◦
C, indicating that the standard of outdoor thermal comfort feeling in
summer and the acceptance threshold for high temperature are higher in the main urban
area of Chongqing in high density. On the one hand, this is due to the mediation of people
who are active outdoors in summer by reducing clothing, wearing shading products, and
taking cooling measures, and on the other hand, it also indicates that people’s thermal
adaptation ability has improved in their long-term life.
3.3. Thermal Comfort Effect Analysis
Figure 9visualizes the changes of PET at twelve measurement points in four mea-
surement areas during one day (9:00–20:00). Most of the measurement points showed a
trend of increasing and then decreasing, and a few of them showed a flat change during
the day, which is consistent with the trend of the measured data of thermal radiation
and air temperature above. Within the measurement period, the PET at each point in the
typical summer space of Chongqing’s Yuzhong District during the heat wave was the
lowest at 20:00, indicating that the human thermal comfort was the best at this time. The
highest point appeared at the residential area R1 (57.8
◦
C), and the lowest point was P4 in
Riverside Park (21.3
◦
C). Compared with other measurement points, the PET values at five
measurement points B1, B2, R3, P3, and P4 were lower and more stable, which is consistent
with the fact that these sites have good shading conditions and fast heat dissipation on the
lower cushion. Site R1 (Dajing Lane residential area) maintained a high index for three
days, which is related to its highest elevation, increased direct sunlight and cramped streets.
Site P4 (Riverside Park) had a stable and comfortable PET index for three days, and its
shading conditions were the best among all sites. It can be seen that there is a corresponding
connection between PET values and urban spatial elements.
The Pearson correlations between PET values and thermal environment elements were
further calculated using SPSS software and sorted out to obtain Table 3. The correlation
between PET, relative humidity and air temperature varied among measurement points,
among which R2, P1, P2, P3 measurement points did not have a significant correlation
with relative humidity, while P1, P2, P3 measurement points did not have a significant
correlation with air temperature. Further comparing the measurement points, the corre-
lation coefficients between PET and thermal environment elements also differed slightly.
The correlation coefficients between air temperature and PET at measurement points B1,
B2, B3, B4, and R2 with better SVF were significantly larger than those between surface
temperature and PET, and the correlation coefficients between thermal radiation and PET
at points R4, P1, and P2 with higher elevation and better tree shading were significantly
larger than those three points. All the thermal environment factors at P4 showed a strong

Buildings 2022,12, 625 14 of 21
correlation with PET. It can be seen that PET is directly related to thermal environment
factors, and thermal environment factors are closely related to urban spatial elements,
so the outdoor thermal comfort of the population in summer is related to urban spatial
elements, and human comfort varies in different urban functional areas due to factors such
as SVF, elevation, street orientation, and street height to width ratio.
Buildings 2022, 12, x FOR PEER REVIEW 14 of 21
streets. Site P4 (Riverside Park) had a stable and comfortable PET index for three days,
and its shading conditions were the best among all sites. It can be seen that there is a
corresponding connection between PET values and urban spatial elements.
The Pearson correlations between PET values and thermal environment elements
were further calculated using SPSS software and sorted out to obtain Table 3. The corre-
lation between PET, relative humidity and air temperature varied among measurement
points, among which R2, P1, P2, P3 measurement points did not have a significant corre-
lation with relative humidity, while P1, P2, P3 measurement points did not have a signif-
icant correlation with air temperature. Further comparing the measurement points, the
correlation coefficients between PET and thermal environment elements also differed
slightly. The correlation coefficients between air temperature and PET at measurement
points B1, B2, B3, B4, and R2 with better SVF were significantly larger than those between
surface temperature and PET, and the correlation coefficients between thermal radiation
and PET at points R4, P1, and P2 with higher elevation and better tree shading were sig-
nificantly larger than those three points. All the thermal environment factors at P4 showed
a strong correlation with PET. It can be seen that PET is directly related to thermal envi-
ronment factors, and thermal environment factors are closely related to urban spatial ele-
ments, so the outdoor thermal comfort of the population in summer is related to urban
spatial elements, and human comfort varies in different urban functional areas due to fac-
tors such as SVF, elevation, street orientation, and street height to width ratio.
Figure 9. The change of PET at each measurement point during the actual measurement period.
Figure 9. The change of PET at each measurement point during the actual measurement period.
3.4. Summer Outdoor Thermal Comfort Evaluation Model
To further reveal the relationship between the overall comfort of the outdoor environ-
ment and thermal environment parameters, a multiple linear regression method was used
to establish an outdoor thermal comfort prediction model during the summer heat wave
in the main urban area of Chongqing, with air temperature, relative humidity, thermal
radiation and surface temperature as independent variables and overall comfort voting
value (OCV) as dependent variables, with the following relationship equation.
OCV = −0.547Ta −0.449R −0.216RH −0.146LST + 6.250 (9)
where: OCV is the overall comfort voting value, ranging from 1 to 7; Ta is air temperature
(
◦
C); R is thermal radiation (W/m
2
); RH is relative humidity; and LST is the land surface
temperature (
◦
C). From the analysis of the above equation, it can be seen that air temper-
ature (Ta), thermal radiation (R), relative humidity (RH), and land surface temperature
(LST) are all negatively correlated with thermal comfort, and from the coefficients in the
equation, the four independent variables have the following weighting on thermal comfort
in descending order: air temperature > thermal radiation > relative humidity > land surface
temperature. In the central urban area of Chongqing, air temperature and thermal radi-
ation during the high-temperature heat waves have a greater impact on human thermal
comfort. Relative humidity and surface temperature have a relatively small impact on
thermal comfort perception, with surface temperature having the smallest impact (only

Buildings 2022,12, 625 15 of 21
0.146), which has been analyzed by some scholars because impermeable surfaces in the city
retain more heat compared to the atmosphere [
68
], so changes in surface temperature have
little impact on human thermal comfort significantly. It is suggested that even in shaded
areas, excessive air temperature and thermal radiation will still make people feel hot and
uncomfortable; whereas when the temperature and thermal radiation are constant, exces-
sive relative humidity will also reduce the heat exchange between the human body and the
surrounding environment, resulting in the feeling of “hot and humid” [
69
]. In summary,
the mitigation measures for outdoor heat waves in summer should focus on reducing the
outdoor temperature and in the meantime reducing the radiation heat production.
Table 3. Correlation analysis of PET and thermal environment factors at each measurement point.
Measurement
Points PET TR Ta Rh LST
B1 PET Pearson correlation 1 0.634 * 0.972 ** −0.915 ** 0.639 *
Sig. (2-tailed) 0.027 0.000 0.000 0.025
B2 PET Pearson correlation 1 0.469 0.948 ** −0.842 ** 0.604 *
Sig. (2-tailed) 0.124 0.000 0.001 0.037
B3 PET Pearson correlation 1 0.928 ** 0.926 ** −0.717 ** 0.916 **
Sig. (2-tailed) 0.000 0.000 0.000 0.000
B4 PET Pearson correlation 1 0.963 ** 0.952 ** −0.724 ** 0.701 *
Sig. (2-tailed) 0.000 0.000 0.008 0.011
R1 PET Pearson correlation 1 0.938 ** 0.788 ** −0.689 * 0.694 *
Sig. (2-tailed) 0.000 0.002 0.013 0.012
R2 PET Pearson correlation 1 0.906 ** 0.921 ** −0.666 0.819 **
Sig. (2-tailed) 0.000 0.000 0.018 0.001
R3 PET Pearson correlation 1 0.356 0.845 ** −0.838 ** 0.874 **
Sig. (2-tailed) 0.256 0.001 0.001 0.000
R4 PET Pearson correlation 1 0.959 ** 0.779 ** −0.673 * 0.875 **
Sig. (2-tailed) 0.000 0.003 0.016 0.000
P1 PET Pearson correlation 1 0.815 ** −0.042 0.041 0.708 **
Sig. (2-tailed) 0.001 0.897 0.898 0.010
P2 PET Pearson correlation 1 0.884 ** 0.315 −0.223 0.698 *
Sig. (2-tailed) 0.000 0.319 0.487 0.012
P3 PET Pearson correlation 1 −0.089 0.467 −0.552 0.280
Sig. (2-tailed) 0.784 0.126 0.063 0.378
P4 PET Pearson correlation 1 / 0.952 ** −0.864 ** 0.910 **
Sig. (2-tailed) / 0.000 0.000 0.000
**: Significantly correlated at the 0.01 level (2-tailed); *: Significantly correlated at the 0.05 level (2-tailed).
4. Discussion
In the study, the characteristics of high-temperature heat waves and the evaluation
of outdoor crowd thermal comfort of typical urban spaces in Chongqing, a mountainous
city, were analyzed more comprehensively. It was shown that different urban spaces have
different morphological elements that determine the characteristics of being affected by
high-temperature heat waves through influencing their thermal parameter indicators. It
was found that sparse greenery, dense pedestrian flow, single street orientation, a large
number of external air conditioning units, overly open streets, and more hard pavement on
the lower mat are common problems that exacerbate high-temperature heat waves in the

Buildings 2022,12, 625 16 of 21
study area, which is consistent with the findings of previous studies [
27
,
32
,
33
]. While this
study focused on the scale of urban functional areas (10 m–1 Km), the data revealed that
urban high-temperature heat waves characteristics have functional spatial variability. The
data from this study show that old settlements have the most severe heat waves among
the four study areas, and the functional spatial variability should be considered while
developing retrofitting plans. Different from other cities, Chongqing’s unique topographic
conditions often require consideration of height differences when considering spatial
morphological factors, which directly determine the magnitude of heat radiation received
by the area and local ventilation problems. Outdoor crowd thermal comfort studies show
that people’s heat perception is closely related to the space they are in, and the correlation
between PET and crowd voting is also influenced by spatial morphological elements, such
as the outdoor sunlight and indoor air conditioning at the Jiefangbei measurement site.
The contrasting relationship between cold air affects the accuracy of PET. The calculation
of the maximum value of PET thermal neutral temperature (36.55
◦
C) as about 14
◦
C
higher than the standard value, the higher standard threshold of outdoor thermal comfort
temperature in summer in the main city of Chongqing, indicates that the thermal neutral
temperature in Chongqing, a humid and hot region, is also affected by regional differences
in climate, where the crowd appears to be adaptive to climatic conditions and the thermal
adaptation ability has improved. Examining the Pearson correlation between the values
of PET and the elements of the thermal environment reveals that there are also small
differences between the measurement points, and the correlation coefficients are larger for
the measurement points where the spatial elements have a stronger effect on the mitigation
of high-temperature heat waves. At the same time, we believe that the relationship between
vegetation characteristics and thermal environment needs to be studied separately. In this
paper, we mainly analyze the shading effect of vegetation at each measurement point, and
analyzing factors such as Leaf Area Index (LAI) and orthogonal experimental design using
ENVI-met software can provide effective data support. In summary, it can be concluded
that improving outdoor crowd thermal comfort needs to be considered in a local context,
and the degree of local high-temperature heat waves and crowd outdoor thermal comfort
in summer are ultimately related to regional spatial elements, and differentiated measures
need to be considered in design strategies and improving human feelings. This study found
that the proportion of thermal environment factors affecting human thermal comfort was
different by establishing a thermal comfort model, and the influence of surface temperature
was not imagined to be strong, which is different from the findings of previous studies,
where the influence of surface temperature was not focused on in the objective data,
mainly verified in the studies of Zhou [
53
] and Lai [
54
]. However, the areas studied in
this paper are the different urban spaces in the mountainous city of Chongqing, which
forms the characteristics and focus of our study. The results of this study have found
some very worthy points for discussion, which we will further explore on the basis of this
study subsequently.
•The results of this study may only apply to mountainous humid-heat regions similar
to Chongqing.
•
In the measured study, only the outdoor crowd thermal comfort in summer was
studied, lacking the winter measured control.
•
In the follow-up study, multiple measurements should be conducted in the selected
measurement areas to ensure the continuity and accuracy of the data, and further
simulation modeling should be used to make the study’s conclusions more accurate.
5. Conclusions
This manuscript addresses the problem of high-temperature heat waves in moun-
tainous urban spaces. Taking Chongqing, a typical mountainous city, as an example, we
analyze the characteristics of high-temperature heat waves and human thermal comfort
in three types of four urban functional spaces, namely, commercial area, residential area,
mountain park, and riverfront park, in the Yuzhong District, Chongqing, based on the

Buildings 2022,12, 625 17 of 21
measured data of the thermal environment in one high-temperature heat wave cycle. Ini-
tially, we reveal the influence of different types of urban space composition forms on high
temperature heat waves and outdoor thermal comfort, with the following conclusions.
1.
Urban high-temperature heat waves are related to the distribution of urban spatial
environmental elements, high temperature heat waves in mountainous high-density
urban spaces show obvious zoning differences and complex influencing factors. Spa-
tial environmental elements such as SVF, vegetation planning, site elevation, street
alignment, and height to width ratio have an impact on thermal parameters such
as thermal radiation, air temperature, surface temperature, and relative humidity.
A vegetation and greenery setting with a rich canopy layer, combined with low re-
flectivity underlayment material, can effectively reduce thermal radiation and lower
temperature. Suitable SVF, street height to width ratio, and orientation, combined
with water arrangement, can mitigate high-temperature heat waves by blocking solar
radiation, promoting urban ventilation and improving wet heat environment.
2.
Urban high-temperature heat wave characteristics have functional-spatial variability,
and different spaces have different mitigating capacities for it. Research shows that old
residential areas have the most severe heat waves, followed by commercial areas, and
better parks and green areas, indicating that old residential areas are more thermally
vulnerable than commercial areas and are the key targets for urban energy-saving
renovation. At the same time, the spatial elements that induce high-temperature heat
waves differ in different functional spaces, and the differences in functional spaces
should be considered when formulating mitigation strategies for high-temperature
heat waves. Commercial areas mainly consider such elements as underlayment
materials, SVF, greening arrangement, and ventilation channels, while residential
areas focus on such elements as vegetation arrangement and green space planning,
street height to width ratio, and orientation, site elevation and shading, etc. Green
space, water bodies, and landscape shading in parks are important elements.
3.
Human thermal comfort is correlated with urban spatial elements and thermal envi-
ronment. High-density commercial areas have small SVF, few green trees, and many
hard impermeable substrates, which lead to a generally poor thermal environment
effect of high temperature, high humidity, and strong thermal radiation for human
thermal sensory voting. Residential areas have a large number of vegetation of a
single type, as well as uneven distribution of greenery, plus cramped roads, and
concrete and other substrates, which exacerbate the degree of heat and humidity
affecting thermal comfort. Parks which are rich in vegetation, green water bodies,
good shading, and heat dissipation conditions create a more comfortable thermal
environment, in which waterfront parks have the best thermal environment and ther-
mal comfort performance, and the lowest thermal neutral temperature, closer to the
standard value of PET as a moderate state. It indicates that the thermal environment
composed of different urban spatial elements may be a key factor in determining the
thermal comfort of people with different outdoor activities.
4.
Modeling outdoor thermal comfort during high-temperature heat waves based on
research data, which showed that air temperature, relative humidity, thermal radiation,
and surface temperature were negatively correlated with thermal comfort, and the
proportion of the influence of each parameter was: air temperature > thermal radiation
> relative humidity > surface temperature. To cope with urban heat wave disasters
lowering air temperature, reducing solar radiation, and then lowering humidity
and surface temperature through reasonable planning and arrangement of urban
spatial form and greening elements is an important direction to improve human
thermal comfort.
Based on the findings of this research, the study of more diverse urban space types,
longer measurement years and more diverse outdoor population thermal comfort indexes
can provide a basis for the next step of exploring the mitigation mechanisms of urban heat
waves, and the optimal design of urban spaces suitable for human thermal comfort.

Buildings 2022,12, 625 18 of 21
Author Contributions:
Conceptualization, H.H. and P.J.; methodology, H.H. and P.J.; software, P.J.;
validation, P.J.; formal analysis, P.J.; investigation, H.H. and P.J.; resources, H.H.; data curation,
P.J.; writing—original draft preparation, P.J.; writing—review and editing, H.H.; visualization, P.J.;
supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was funded by the National Social Science Foundation of China: “Research
on the Prevention, Control and Management Mechanism of Heat Wave Disasters in High-density
Cities in Mountainous Areas, grant number 19BGL004; The National Natural Science Foundation of
China: A typological approach and energy-saving potential of energy-saving renovation of existing
residential buildings: A case study of Chongqing, grant number 52078071”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to privacy restrictions.
Acknowledgments:
Thanks to fellow graduate students for providing technical support for the
practical part of this experiment.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
Nomenclature
UHI Urban heat island
UTCI Universal Thermal Climate Index
PET Physiological equivalent temperature
SET Standard equivalent temperature
UCmap Urban climate map
H/W Street aspect ratio
SVF Sky view factor
CFD Computational fluid dynamics
OUT_SET* Outdoor SET
TCV Thermal comfort voting
TSV Thermal sensory voting
TPV Thermal preference voting
PMV Predicted Mean Vote
TR Thermal radiation
Ta Air temperature
Ts Surface temperature
WS Wind speed
RH Relative humidity
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