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Seasonal and Diurnal Characteristics and Drivers of Urban Heat Island Based on Optimal Parameters-Based Geo-Detector Model in Xinjiang, China

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In the context of sustainable urban development, elucidating urban heat island (UHI) dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations and potential driving factors, cities can implement effective strategies to reduce their impacts on the environment and public health. However, the driving factors of a UHI in arid regions remain unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were 1.37 ± 0.86 °C, with the SUHII being most pronounced in summer (2.44 °C), followed by winter (2.15 °C), spring (0.47 °C), and autumn (0.40 °C). Moreover, the annual mean SUHII was stronger at nighttime (1.90 °C) compared to during the daytime (0.84 °C), with variations observed across seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime (3.91 °C) compared to nighttime (0.39 °C), with daytime and nighttime SUHIIs decreasing from summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban form, and socio-economic factors (GDP and population density) emerged as key drivers, with the GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects, measures like urban environment enhancement by improving surface conditions, blue–green space development, landscape optimization, and economic strategy adjustments are recommended.
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Citation: Chen, H.; Mamitimin, Y.;
Abulizi, A.; Huang, M.; Tao, T.; Ma, Y.
Seasonal and Diurnal Characteristics
and Drivers of Urban Heat Island
Based on Optimal Parameters-Based
Geo-Detector Model in Xinjiang,
China. Atmosphere 2024,15, 1377.
https://doi.org/10.3390/
atmos15111377
Academic Editors: Tiziana Susca,
Fabio Zanghirella and
Ferdinando Salata
Received: 28 August 2024
Revised: 29 October 2024
Accepted: 13 November 2024
Published: 15 November 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Seasonal and Diurnal Characteristics and Drivers of Urban Heat
Island Based on Optimal Parameters-Based Geo-Detector Model
in Xinjiang, China
Han Chen, Yusuyunjiang Mamitimin *, Abudukeyimu Abulizi, Meiling Huang, Tongtong Tao and Yunfei Ma
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China;
107552201118@stu.xju.edu.cn (H.C.); keyimabliz@xju.edu.cn (A.A.); 107552201123@stu.xju.edu.cn (M.H.);
107552203705@stu.xju.edu.cn (T.T.); grmayunfei@163.com (Y.M.)
*Correspondence: y.mamitimin@xju.edu.cn or y_mamitmin@163.com; Tel.: +86-15276862217
Abstract: In the context of sustainable urban development, elucidating urban heat island (UHI)
dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations
and potential driving factors, cities can implement effective strategies to reduce their impacts on
the environment and public health. However, the driving factors of a UHI in arid regions remain
unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the
potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based
Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal
that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were
1.37
±
0.86
C, with the SUHII being most pronounced in summer (2.44
C), followed by winter
(2.15
C), spring (0.47
C), and autumn (0.40
C). Moreover, the annual mean SUHII was stronger
at nighttime (1.90
C) compared to during the daytime (0.84
C), with variations observed across
seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime
(3.91 C)
compared to nighttime (0.39
C), with daytime and nighttime SUHIIs decreasing from
summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban
form, and socio-economic factors (GDP and population density) emerged as key drivers, with the
GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects,
measures like urban environment enhancement by improving surface conditions, blue–green space
development, landscape optimization, and economic strategy adjustments are recommended.
Keywords: arid zones; urban heat island; seasonal and diurnal characteristics; driving factors;
optimal parameters-based geographic detector
1. Introduction
The expansion of urban scale and rapid changes in urban form are key features of
contemporary urban development. These transformations alter the physical properties of
the original subsurface, impacting heat exchange between the surface and atmosphere, and
consequently modifying the local thermal environment [
1
]. An urban heat island (UHI)
refers to a significant temperature increase in urban areas compared to the surrounding
countryside and natural surroundings [
2
]. This phenomenon primarily stems from land
use changes due to urbanization, anthropogenic heat sources like buildings and roads,
and a lack of vegetation cover. A UHI has significant implications for the climate, energy
consumption, ecology, and human health, leading to increased energy usage, worsened
air quality, and higher urban temperatures [
3
,
4
]. Therefore, it is crucial to gain a deeper
understanding of the evolving characteristics of the UHI and its driving factors to support
initiatives aimed at reducing environmental pollution, as well as improving urban planning,
management, and overall human well-being.
Atmosphere 2024,15, 1377. https://doi.org/10.3390/atmos15111377 https://www.mdpi.com/journal/atmosphere
Atmosphere 2024,15, 1377 2 of 22
UHIs can be classified into atmospheric urban heat islands (AUHIs) and surface
urban heat islands (SUHIs) based on the data sources used in heat island studies [
5
].
An AUHI relies on atmospheric temperature data from fixed or locally mobile weather
stations, resulting in discrete point or linear data with limitations in scale conversion
accuracy and analyses of large cities [
6
,
7
]. In contrast, an SUHI utilizes remote sensing data,
enabling continuous monitoring over large areas and significantly enhancing the study
of urban thermal environments [
8
]. With advancements in airborne and satellite remote
sensing technology, researchers now have access to a wide range of remote sensing data
sources with improved spatial and temporal resolution, making them essential tools for
investigating urban thermal environments [
9
]. The primary data sources for urban surface
thermal environment research include NOAA/AVHRR, ASTER, Landsat TM/ETM+, and
MODIS [
10
,
11
]. Among them, MODIS stands out for its high temporal resolution, allowing
it to accurately capture the temporal differences of UHIs [
12
]. Therefore, MODIS has been
widely used in SUHI studies [
13
15
]. Li et al. have leveraged the MOD11A2 product
to examine the seasonal and diurnal variations in the SUHII in Chinese prefecture-level
cities [
16
]. Hamdi et al. have analyzed the seasonal and diurnal fluctuations in the UHII
within the El-Mansoura conurbation, Egypt, using MODIS data [
17
]. Khan and Shahid
have evaluated the UHII in five major cities in Pakistan utilizing MODIS data on daily
surface temperatures [
18
]. Si et al. have explored the diurnal cycle, monthly variations, and
interannual trends of SUHIIs in 34 major urban agglomerations in China by employing
MOD11A1 data from 2003 to 2019 [19].
The strength of the SUHI is commonly assessed through the surface urban heat island
intensity (SUHII) [
20
]. Research on the spatial and temporal variations in the SUHII has
a well-established background, with numerous studies highlighting significant diurnal,
seasonal, and spatial changes. At the spatial level, the UHI varies based on the climatic
conditions of different regions [
21
]. On a temporal scale, previous research has highlighted
the significant seasonal and diurnal variations in the UHI. Research conducted on over
9500 cities worldwide has shown that during summer, SUHII is typically stronger than
in winter, with daytime effects being more pronounced than nighttime effects, displaying
specific spatial distribution characteristics [
22
]. In China, it has been observed that the
SUHII is generally more pronounced at nighttime than during the daytime. Further exam-
ination of the SUHI in Beijing identified a stronger effect during nighttime compared to
daytime across various local climate zones [
23
]. Additionally, Cao et al. have concluded
that the annual average nighttime SUHII in China is notably stronger than during the
daytime [
24
]. Seasonally, the nighttime SUHII is most intense in winter; however, vari-
ations exist in both diurnal and spatial aspects of this effect, depending on the research
methodology
employed [25,26].
For instance, research on the diurnal and seasonal SUHIIs
in the Zhengzhou metropolitan area have demonstrated that the daytime SUHII peaks in
summer, while the nighttime intensity is strongest in winter [
27
]. In contrast, a prior study
by Asfa Siddiqui on the UHI in Lucknow, India, reported that both daytime and nighttime
SUHIIs were most pronounced in summer [
28
]. Xi et al. did not specifically address diurnal
SUHII variations, but they indicated that the average UHII in Hefei is strongest in summer
and weakest in winter [29].
A comprehensive review of the relevant literature on the factors influencing the UHI re-
veals that it is shaped by a combination of natural factors like the geographic location and cli-
mate, as well as socio-economic factors such as urbanization and
industrialization [3033].
Previous research indicates that natural factors like the geographic location, regional cli-
matic background, elevation, topography, and vegetation cover significantly impact the
UHI. Among these, vegetation cover is considered to have a particularly strong
impact [34].
Human-modified urban thermal environments are primarily influenced by surface cover
factors such as land use/land cover and urban landscape patterns. In addition, impervious
surfaces, green space areas, the urban spatial form, and density all affect the distribution
of the UHI [
35
,
36
]. Moreover, the city size also plays a crucial role in the UHI [
37
,
38
],
with suggestions that the city size and urban form may be key in its formation and
Atmosphere 2024,15, 1377 3 of 22
progression [39,40].
The characteristics of urban scale, such as buildings, roads, and popu-
lation density, can exacerbate the UHI [
41
,
42
]. Urban form, referring to the spatial arrange-
ment of buildings, roads, and green spaces within a city, can have varying effects on the heat
island effect based on different thermal characteristics of different morphologies [
43
]. For
instance, Yin et al. conducted a spatial regression analysis in Wuhan, demonstrating that
optimizing the urban form can significantly impact the management of the UHI [
44
]. In re-
cent years, a number of studies have demonstrated that socio-economic factors significantly
influence the SUHII. Wu et al. examined the drivers of the SUHII in Guangdong Province
and found that socio-economic indicators, such as population and gross domestic product,
are key determinants of the nighttime SUHII [
45
]. Similarly, Sidiqui et al. investigated the
relationships among SUHII spatial variations, land use patterns, and socio-economic factors
in the city of Greater Geelong, Australia, revealing that socio-economic and demographic
factors play crucial roles in the generation of the SUHII [46].
Nowadays, researchers employ various methods and models to investigate the rela-
tionship between the SUHII and its driving factors. Common approaches include Pearson’s
correlation analysis, Ordinary Least Squares (OLS), Geographical Weighted Regression
(GWR) model, gray correlation analysis, and geo-detector analysis [
44
,
47
50
]. The geo-
detector model, introduced by Wang and Xu [
51
], has been widely utilized to study SUHI
driving factors. For instance, Hu et al. utilized the geo-detector model to assess the
explanatory power of impact factors on the urban thermal environment in Tianjin [
52
].
Similarly, Zhao et al. identified key impact factors of the urban thermal environment in
Zhengzhou City using the geo-detector model, highlighting factors such as the Normalized
Difference Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), and
anthropogenic factors [
53
]. In addition, Xiang et al. examined the dominant factors of the
seasonal SUHII in the urban agglomeration of the middle reaches of the Yangtze River
through a combination of Spearman’s correlation analysis and geo-detector methods [
54
].
Their analysis revealed differences in dominant factors between daytime and nighttime.
Globally, semi-arid, arid, and hyper-arid areas encompass approximately 41 percent
of the world’s land area. Despite the harsh conditions prevalent in these environments,
they support over two billion people, accounting for about one-third of the global popu-
lation. This underscores their significance for human survival and habitation. Over the
past 40 years,
these regions have witnessed rapid population growth and urbanization,
resulting in significant transformations of urban landscapes and the emergence of the UHI.
And research has indicated that arid and semi-arid areas exhibit a greater UHII compared to
humid and semi-humid regions [
21
]. While previous research has delved into the seasonal
diurnal variations and driving factors of the UHI, there remains a gap in understanding
the seasonal and diurnal characteristics and drivers of this phenomenon in the arid and
semi-arid regions of northwest China, particularly in Xinjiang. As a crucial economic hub
and frontier region in the urbanization process of Central Asia and western China, Xinjiang
is grappling with significant UHI impacts and resulting ecological challenges due to its
temperate continental arid climate and delicate ecological surroundings. Thus, this study
aims to (1) analyze the seasonal and diurnal variation patterns of the SUHII across
22 cities
in Xinjiang, a region characterized by arid and semi-arid climates in northwest China;
(2) to explore
the relationship between the urban size and SUHII; (3) to evaluate the im-
pacts of natural factors, urban form, and socio-economic circumstances on the SUHII using
OPGD analysis. The study also proposes strategies and recommendations to mitigate the
UHI in Xinjiang. Furthermore, the findings of this research can offer insights and lessons for
understanding the UHI in other arid zone cities across the global urbanization landscape.
2. Materials and Methods
2.1. Study Area
Xinjiang is situated in northwestern China and spans from 73
25
E to 97
30
E and
34
30
N to 49
00
N, covering 1,664,900 km
2
, which is one-sixth of China’s total land
area [
55
]. It is the largest provincial-level administrative region in China [
56
,
57
]. The
Atmosphere 2024,15, 1377 4 of 22
topography of Xinjiang includes mountain ranges, basins, deserts, and grasslands, which
significantly impact the region’s climate and environment [
58
]. Xinjiang exhibits various
climate types, predominantly arid and semi-arid, such as temperate continental arid and
alpine climates [
59
]. Xinjiang boasts abundant light and heat resources, with a total solar
radiation ranging from 5440 to 6280 MJ/m
2
and an average annual sunshine hours of
2500–3400h, making it one of the regions in China with the highest sunshine hours [
60
].
The region maintains an average temperature of about 6–10
C over many years, but
experiences significant seasonal temperature variations. Summers are characterized by
high temperatures exceeding 30
C on average, while winters are extremely cold, with
temperatures dropping below
10
C. Xinjiang experiences limited annual precipitation,
averaging between 100 and 200 mm, leading to widespread drought conditions across most
of the region [
61
]. For this study, 22 cities in Xinjiang with urban built-up areas greater than
10 km2were selected, as depicted in Figure 1.
Atmosphere2024,15,xFORPEERREVIEW4of23
2.MaterialsandMethods
2.1.StudyArea
XinjiangissituatedinnorthwesternChinaandspansfrom73°25′Eto97°30′Eand
34°30′Nto49°00′N,covering1,664,900km2,whichisone-sixthofChinastotallandarea
[55].Itisthelargestprovincial-leveladministrativeregioninChina[56,57].Thetopogra-
phyofXinjiangincludesmountainranges,basins,deserts,andgrasslands,whichsigni-
cantlyimpacttheregion’sclimateandenvironment[58].Xinjiangexhibitsvariousclimate
types,predominantlyaridandsemi-arid,suchastemperatecontinentalaridandalpine
climates[59].Xinjiangboastsabundantlightandheatresources,withatotalsolarradia-
tionrangingfrom5440to6280MJ/m2andanaverageannualsunshinehoursof2500
3400h,makingitoneoftheregionsinChinawiththehighestsunshinehours[60].The
regionmaintainsanaveragetemperatureofabout6–10 °Covermanyyears,butexperi-
encessignicantseasonaltemperaturevariations.Summersarecharacterizedbyhigh
temperaturesexceeding30°Conaverage,whilewintersareextremelycold,withtemper-
aturesdroppingbelow−10°C.Xinjiangexperienceslimitedannualprecipitation,averag-
ingbetween100and200mm,leadingtowidespreaddroughtconditionsacrossmostof
theregion[61].Forthisstudy,22citiesinXinjiangwithurbanbuilt-upareasgreaterthan
10km2wereselected,asdepictedinFigure1.
Figure1.TopographicmapofXinjiangandthelocationsof22majorcities.
2.2.DataSourcesandPreprocessing
TheprimarydatautilizedinthisstudyincludetheMODISlandsurfacetemperature
(LST)dataset;landuse/landcover(LULC)data;naturalgeographicfeaturedata,includ-
ingprecipitation,topography,andNDVI;aswellassocio-economicdatasuchasthepop-
ulationdensity,GDP,andtheurbangreenspacerate(Table1).LSTdatawereutilizedto
quantifythetemperatureinthestudyarea,enablingthecalculationoftheSUHII.TheLST
datasetfordaytimeandnighimeon15January,15April,15July,and15October2020
Figure 1. Topographic map of Xinjiang and the locations of 22 major cities.
2.2. Data Sources and Preprocessing
The primary data utilized in this study include the MODIS land surface temperature
(LST) dataset; land use/land cover (LULC) data; natural geographic feature data, including
precipitation, topography, and NDVI; as well as socio-economic data such as the population
density, GDP, and the urban green space rate (Table 1). LST data were utilized to quantify
the temperature in the study area, enabling the calculation of the SUHII. The LST dataset
for daytime and nighttime on 15 January, 15 April, 15 July, and 15 October 2020 was chosen
to represent the four seasons. In this study, the MODIS product converts land surface
temperature units from thermodynamic Kelvin (K) to Celsius (
C) using a field calculator
in ArcGIS. The LULC data for the study area in Xinjiang in 2020 were obtained by cropping
vector maps of city boundaries using the Extract-by-Mask tool in ArcGIS. The urban and
rural areas were then extracted based on the land use coverage classification system of the
Institute of Geographic Sciences and Resources of the Chinese Academy of Sciences (IGSR).
Atmosphere 2024,15, 1377 5 of 22
Urban built-up areas were defined as continuous impervious surfaces greater than 10 km
2
.
Landscape pattern indices were calculated to describe urban form. Natural geographic
data provided environmental context, while socio-economic data reflected urbanization
levels and human activities.
Table 1. Datasets used in this study.
Data Type Factors
Year
Spatial Resolution Source
Land surface temperature (LST)
2020
1 km National Tibetan Plateau/Third Pole Environment Data Center
(https://data.tpdc.ac.cn/ (accessed on 24 January 2023))
Land use/land cover (LULC)
2020
30 m Resources and Environmental Science Data Center
(https://www.resdc.cn/ (accessed on 24 January 2023))
Natural geographic
data
Precipitation
2020
1 km National Tibetan Plateau/Third Pole Environment Data Center
(https://data.tpdc.ac.cn/ (accessed on 16 May 2023))
DEM
2020
30 m
NASA (https://search.earthdata.nasa.gov/ (accessed on 16 May 2023))
NDVI
2020
1 km
Socio-economic data
Population density
2020
- China Urban Construction Statistical Yearbook 2020
GDP
2020
- China City Statistical Yearbook 2021
Urban green space
rate
2020
- China Urban Construction Statistical Yearbook 2020
2.3. Methods
2.3.1. Surface Urban Heat Island Intensity
The surface urban heat island intensity (SUHII) is a measure of the UHI within a
particular area or city [
20
]. It is commonly assessed by comparing the temperature in the
urban area to that in the surrounding rural or natural areas [
62
,
63
]. Greater temperature
variances signify a stronger UHI. Its formula is as follows:
SUHI I =LSTULSTR(1)
where LST
U
is the LST of core urban areas, while LST
R
is the LST of surrounding rural or
natural areas.
2.3.2. Urban Form
In order to characterize the spatial pattern of urban form, the study selected the
urban landscape pattern index and urban fractal dimension. The landscape pattern index,
originating from landscape ecology, is a suitable method for quantifying the spatial pattern
characteristics of urban form [
64
]. It effectively characterizes the spatial composition and
structural features around elements and has been widely used in quantitative research on
urban form and land use [
65
]. In some studies, researchers have integrated the landscape
pattern index with a thermal environment analysis [66]. Seven types of landscape pattern
indices were chosen based on previous studies to quantify the spatial pattern of urban
form in this research, and the calculations were performed using Fragstats 4.2 software.
The urban fractal dimension was calculated using the box counting method [67], a simple
technique for estimating fractal dimension, implemented through Fractalyse 3.0 software.
The specific urban form factors and their descriptions are detailed in Table 2.
Table 2. Detailed description of urban form factors.
Category Variable Description Equation
Urban forms
Largest Patch Index (LPI) Percent area of the largest patch of urban land in
the total landscape area. LPI =[max(a1,a2,...,aj)]
A(2)
Landscape Shape Index (LSI)
LSI equals 1 when the landscape has the most
regular shape; the value increases with the
complexity of the landscape shape.
LSI =0.25E
A(3)
Mean perimeter–area ratio
(PARA_MN)
PARA_MN indicates the average perimeter-to-area
ratio of urban patches, representing the regularity
of urban patch shapes in the landscape.
PARA_M N =P
A(4)
Atmosphere 2024,15, 1377 6 of 22
Table 2. Cont.
Category Variable Description Equation
Urban forms
Percentage of Like Adjacencies
(PLADJ)
PLADJ indicates the percentage of cell adjacencies
involving urban patches that are similar. PL AD J =NSimlar
NTotal (5)
Patch Cohesion Index
(COHESION)
COHESION measures the physical connectedness
of urban land; it increases as the urban patch
becomes more clumped or aggregated in
its distribution. COH ESION ="1
m
j=1pij
m
j=1pij aij #
h11
Ai(6)
Aggregation Index (AI)
AI is used to determine the degree of compactness
of the landscape. AI =haii
maxaii i(7)
Urban fractal Dimension (DI)
DI is considered as a measure of compact-ness, i.e.,
compact cities have usually large values of DI. D=log N
logr (8)
2.3.3. Pearson’s Correlation Analysis
The Pearson correlation coefficient is frequently utilized to depict the linear correlation
between variables xand y[
68
]. The Pearson correlation coefficient quantifies the strength
of the correlation relationship, and its formula is as follows:
r=n
i=1(xix)(yiy)
qn
i=1(xix)2qn
i=1(yiy)2(9)
where ris the Pearson correlation coefficient, which is calculated by determining the
covariance and standard deviation of two variables. The value of rfalls within the range of
[
1, 1]. A correlation of 0 indicates no linear relationship between xand y. The closer the r
is to 1 or
1, the stronger the correlation. Conversely, the closer the ris to 0, the weaker
the correlation.
2.3.4. Optimal Parameters-Based Geographic Detector (OPGD)
Geo-detector models can quantitatively measure the significance of independent vari-
ables in relation to dependent variables based on the theory of spatial
differentiation [51].
Traditional studies of drivers of surface temperature and thermal environment differentiation
typically rely on a correlation analysis using SPSS software (https://www.ibm.com/spss
accessed on 24 January 2023) to test significance. However, this method may not be convenient
for exploring the strength of factors’ influences. The introduction of the OPGD offers a new
statistical measurement model that overcomes limitations of traditional geo-detectors, such as
the lack of collinearity and no variable assumptions. OPGD also addresses issues with data
discretization in traditional geo-detector models, eliminating the need for manual adjustments
and reducing the impacts of human subjective factors [
69
]. OPGD has found widespread
application in natural science, social science, and environmental pollution studies [
70
]. The
factor detector is utilized to analyze driving factors of spatial divergence in the UHI in this
study. The qvalue of each driver, calculated with different classification methods and numbers,
represents the explanatory power of the driver on the SUHII. Its formula is as follows:
q=1L
g=1Ngσ2
g
Nσ2(10)
where Lrepresents the stratification of the dependent or independent variable; N
g
and
σg2
denote the number of samples and the variance of stratification group g;Nand
σ2
refer to
the number of samples and the variance of the entire model, respectively.
2.4. Study Steps
In this study, we first calculated the SUHII for 22 cities in Xinjiang using MODIS LST
data (ArcGIS 10.8). Next, we calculated and organized 14 natural factors, urban size and
form factors, as well as socio-economic factors that may potentially influence the SUHII.
We then employed Pearson’s correlation analysis to assess the relationship between the
Atmosphere 2024,15, 1377 7 of 22
SUHII and its influencing factors across different seasons and during both daytime and
nighttime. Finally, we utilized the OPGD model to investigate the primary factors affecting
the SUHII in Xinjiang across various seasons and times of day.
3. Results
3.1. Seasonal and Diurnal Characteristics of the SUHI
Figure 2illustrates the seasonal and diurnal SUHII distributions of 22 cities in Xinjiang
in 2020, highlighting the high spatial heterogeneity of the SUHII in the region. The mean
SUHII of these cities in 2020 was 1.37
C (Table 3), ranging from
3.95
C (spring daytime
in Wujiaqu) to 10.90
C (winter daytime in Yining). Spatially, the annual mean SUHII distri-
bution pattern in Xinjiang cities follows northern Xinjiang (1.59
C) > southern Xinjiang
(1.36
C) > eastern Xinjiang (
0.10
C). Specifically, Urumqi had the highest annual average
SUHII value (2.86
C), while Turpan had the lowest (
0.78
C). Turpan and Wujiaqu were
the only cities with annual average SUHII values below 0
C, indicating the absence of a
heat island throughout the year. Yining had the highest annual average daytime SUHII
value (3.29
C), while Wujiaqu had the lowest (
2.11
C). Seven cities, including Altay,
Hami, Kashgar, Kuitun, Tumushuke, Turpan, and Wujiaqu, had annual average daytime
SUHII values below 0
C, suggesting no daytime heat island throughout the year. Urumqi
had the highest annual average nighttime SUHII value (3.89
C), while Turpan had the
lowest (0.30
C). All 22 cities in Xinjiang experienced a nighttime heat island throughout
the year, with annual average nighttime SUHII values above 0 C.
Figure 3illustrates the seasonal and diurnal variations in the SUHII in Xinjiang. In
2020, the SUHII values for spring, summer, autumn, and winter were 0.47
C, 2.44
C,
0.40 C,
and 2.15
C, respectively, indicating a seasonal trend of summer > winter > spring
> autumn. Analyzing the diurnal patterns, the annual average SUHIIs were higher at
nighttime (1.90
C) compared to during the daytime (0.84
C) across the year. When
looking at individual seasons, daytime SUHIIs were higher than nighttime SUHIIs in
summer (2.81
C, 2.08
C) and winter (2.61
C, 1.69
C), while the reverse was observed
in spring (0.97 C; 1.91 C) and autumn (1.10 C; 1.91 C). Notably, during spring and
autumn daytime, some cities in Xinjiang experienced SUHIIs below 0
C, suggesting the
lack of a heat island in these periods. Overall, the SUHII in Xinjiang follows a pattern
of summer daytime > winter daytime > summer nighttime > spring nighttime > autumn
nighttime > winter nighttime > spring daytime > autumn daytime, with the heat island
being more pronounced in summer than in winter, regardless of daytime or nighttime.
Atmosphere 2024,15, 1377 8 of 22
Atmosphere2024,15,xFORPEERREVIEW8of23
Figure2.SpatialdistributionsoftheSUHIIinXinjiang’s22majorcities,including(a)thespring
daytimeSUHII;(b)springnighimeSUHII;(c)summerdaytimeSUHII;(d)summernighimeSU-
HII;(e)autumndaytimeSUHII;(f)autumnnighimeSUHII;(g)winterdaytimeSUHII;and(h)
winternighimeSUHII.
Figure 2. Spatial distributions of the SUHII in Xinjiang’s 22 major cities, including (a) the spring
daytime SUHII; (b) spring nighttime SUHII; (c) summer daytime SUHII; (d) summer nighttime SUHII;
(e) autumn daytime SUHII; (f) autumn nighttime SUHII; (g) winter daytime SUHII; and (h) winter
nighttime SUHII.
Atmosphere 2024,15, 1377 9 of 22
Table 3. Descriptive statistics of the SUHII.
Annual Average
SUHII (C)
Annual Daytime Average
SUHII (C)
Annual Nighttime Average
SUHII (C)
mean 1.37 0.84 1.90
maximum 2.86 3.30 3.90
minimum 0.78 2.11 0.30
Atmosphere2024,15,xFORPEERREVIEW9of23
Figure3illustratestheseasonalanddiurnalvariationsintheSUHIIinXinjiang.In
2020,theSUHIIvaluesforspring,summer,autumn,andwinterwere0.47°C,2.44°C,0.40
°C,and2.15°C,respectively,indicatingaseasonaltrendofsummer>winter>spring>
autumn.Analyzingthediurnalpaerns,theannualaverageSUHIIswerehigherat
nighime(1.90°C)comparedtoduringthedaytime(0.84°C)acrosstheyear.Whenlook-
ingatindividualseasons,daytimeSUHIIswerehigherthannighimeSUHIIsinsummer
(2.81°C,2.08°C)andwinter(2.61°C,1.69°C),whilethereversewasobservedinspring
(0.97°C;1.91°C)andautumn(1.10°C;1.91°C).Notably,duringspringandautumn
daytime,somecitiesinXinjiangexperiencedSUHIIsbelow0°C,suggestingthelackofa
heatislandintheseperiods.Overall,theSUHIIinXinjiangfollowsapaernofsummer
daytime>winterdaytime>summernighime>springnighime>autumnnighime>
winternighime>springdaytime>autumndaytime,withtheheatislandbeingmore
pronouncedinsummerthaninwinter,regardlessofdaytimeornighime.
Figure3.Theseasonalanddiurnalvariat ionsintheSUHIIinXinjiang.
3.2.RelationshipBetweenUrbanSizeandtheSUHII
Figures4and5illustratetherelationshipbetweenurbansizeandtheSUHII.Urban
sizereferstothesizeoftheurbanbuilt-uparea,typicallymeasuredinkm2.Inthestudy
area,theurbanbuilt-upareaofthe22citiesrangesfrom13.71km2(Aral)to557.09km2
(Urumqi),withanaverageof76.59km2.Thecitiesarecategorizedintofourgroupsbased
onsize:0–25km2,25–50km2,50–75km2,andover75km2.AnalyzingtheSUHIIsofthese
citiesrevealsthatlargercitiesexhibitastrongerSUHIIduringthedaytime,whileat
nighime,allcitiesexceptthoseinthe50–75km2categoryshowasimilartrend.Thean-
nualmeanSUHIIsduringthedaytimeforcitiesof0–25km2,25–50km2,50–75km2,and
over75km2are−0.03°C,1.17°C,1.27°C,and1.29°C,respectively.Duringthenighime,
theannualmeanSUHIIsforthesecategoriesare1.62°C,1.91°C,1.38°C,and2.56°C.This
indicatesthepresenceofaUHIduringthedaytimeincitieslargerthan25km2andduring
thenighimeincitiesofallsizes.
Figure 3. The seasonal and diurnal variations in the SUHII in Xinjiang.
3.2. Relationship Between Urban Size and the SUHII
Figures 4and 5illustrate the relationship between urban size and the SUHII. Urban
size refers to the size of the urban built-up area, typically measured in km
2
. In the study
area, the urban built-up area of the 22 cities ranges from 13.71 km
2
(Aral) to 557.09 km
2
(Urumqi), with an average of 76.59 km
2
. The cities are categorized into four groups based
on size: 0–25 km
2
, 25–50 km
2
, 50–75 km
2
, and over 75 km
2
. Analyzing the SUHIIs of
these cities reveals that larger cities exhibit a stronger SUHII during the daytime, while
at nighttime, all cities except those in the 50–75 km
2
category show a similar trend. The
annual mean SUHIIs during the daytime for cities of 0–25 km
2
, 25–50 km
2
, 50–75 km
2
, and
over 75 km
2
are
0.03
C, 1.17
C, 1.27
C, and 1.29
C, respectively. During the nighttime,
the annual mean SUHIIs for these categories are 1.62
C, 1.91
C, 1.38
C, and 2.56
C. This
indicates the presence of a UHI during the daytime in cities larger than 25 km
2
and during
the nighttime in cities of all sizes.
Atmosphere2024,15,xFORPEERREVIEW10of23
Figure4.BivariatemapoftherelationshipsbetweenurbansizeandtheSUHIIinXinjiang’s22major
cities.
Figure5.Mean,maximumandminimumSUHIIvaluesfordierentcitysizes.
3.3.ImpactFactorsoftheSUHII
3.3.1.DescriptiveStatisticalAnalysisofSUHIIImpactFactors
Inthisstudy,14factors,includingtheLPI(LargestPatchIndex),LSI(Landscape
ShapeIndex),PARA_MN(meanperimeter-arearatio),PLADJ(PercentageofLikeAdja-
cencies),COHESION(PatchCohesionIndex),AI(AggregationIndex),DI(Urbanfractal
Dimension),PRE(precipitation),AL(altitude),EL(elevation),NDVI(Normalized
Figure 4. Bivariate map of the relationships between urban size and the SUHII in Xinjiang’s
22 major cities.
Atmosphere 2024,15, 1377 10 of 22
Atmosphere2024,15,xFORPEERREVIEW10of23
Figure4.BivariatemapoftherelationshipsbetweenurbansizeandtheSUHIIinXinjiang’s22major
cities.
Figure5.Mean,maximumandminimumSUHIIvaluesfordierentcitysizes.
3.3.ImpactFactorsoftheSUHII
3.3.1.DescriptiveStatisticalAnalysisofSUHIIImpactFactors
Inthisstudy,14factors,includingtheLPI(LargestPatchIndex),LSI(Landscape
ShapeIndex),PARA_MN(meanperimeter-arearatio),PLADJ(PercentageofLikeAdja-
cencies),COHESION(PatchCohesionIndex),AI(AggregationIndex),DI(Urbanfractal
Dimension),PRE(precipitation),AL(altitude),EL(elevation),NDVI(Normalized
Figure 5. Mean, maximum and minimum SUHII values for different city sizes.
3.3. Impact Factors of the SUHII
3.3.1. Descriptive Statistical Analysis of SUHII Impact Factors
In this study, 14 factors, including the LPI (Largest Patch Index), LSI (Landscape Shape
Index), PARA_MN (mean perimeter-area ratio), PLADJ (Percentage of Like Adjacencies),
COHESION (Patch Cohesion Index), AI (Aggregation Index), DI (Urban fractal Dimension),
PRE (precipitation), AL (altitude), EL (elevation), NDVI (Normalized Difference Vegeta-
tion Index), PD (population density), GDP (gross domestic product), and UGSR (urban
green space rate) were selected to examine their impacts on the SUHII in Xinjiang cities.
Table 4provides the descriptive statistics for these variables, including their minimum,
maximum, mean, and standard deviation (STD) values. The results indicate that the LPI,
PARA_MN, EL, and GDP exhibit larger STD values compared to their mean values, sug-
gesting significant variation among the 22 cities in Xinjiang. Conversely, the urban form
factors LSI, PLADJ, COHESION, AI, and DI show smaller STD values relative to the means,
suggesting less variation in urban form among the cities. Particularly, DI shows a range
between 1 and 2, with a maximum value of 1.77 and a minimum value of 1.20, suggesting
fractal self-similarity in urban form and aligning with principles of fractal geometry [
71
].
Moreover, natural and socio-economic factors such as PRE, AL, NDVI, PD, and UGSR also
exhibit STD values smaller than the means, implying relatively consistent values across the
22 cities in Xinjiang. A comparison with national averages reveals that the mean values
of PRE, NDVI, and UGSR in Xinjiang cities are lower than the national average (706 mm,
0.38, and 38.24%, respectively), indicating lower precipitation levels, vegetation cover, and
urban green space in these cities. Additionally, the average PD of the 22 cities in Xinjiang is
higher than the national average of 2778 persons per square kilometer, indicating a higher
concentration of population in urban areas. This trend may be linked to the presence of job
opportunities and improved social services like transportation, healthcare, education, and
cultural amenities in urban centers as opposed to rural villages in Xinjiang. Consequently,
there is a steady migration of non-urban residents towards the cities [72].
Table 4. Descriptive statistics of the driving factors.
Category Variable N Minimum Maximum Mean STD
Urban
Forms
LPI 22 0.03 9.49 1.38 2.25
LSI 22 2.38 8.55 4.00 1.57
PARA_MN 22 14.48 841.12 153.34 240.19
PLADJ 22 97.39 98.97 98.33 0.39
COHESION 22 98.93 99.87 99.50 0.21
Atmosphere 2024,15, 1377 11 of 22
Table 4. Cont.
Category Variable N Minimum Maximum Mean STD
Natural
Factors
AI 22 97.75 99.25 98.79 0.34
DI 22 1.20 1.77 1.55 0.13
PRE 22 25.72 301.00 144.11 79.79
AL 22 421.98 1376.21 750.13 290.45
EL 22 129.98 402.41 27.07 109.42
NDVI 22 0.07 0.29 0.20 0.06
Human
Factors
PD 22 643.00 6355.00 3796.95 1247.49
GDP 22 519,300.00 33,373,200.00 4,170,059.91 6,646,827.05
UGSR 22 30.72 44.95 36.57 3.28
3.3.2. Pearson’s Correlation Analysis of the SUHII Impact Factors
The Pearson correlation analysis was conducted to examine the relationship be-
tween the SUHII and impact factors across different seasons in 2020 within each city
of the study area. The results, depicted in Figure 6, indicate seasonal and diurnal varia-
tions in the
r value
and significance level of the Pearson correlation coefficients between
the SUHII and the impact factors in Xinjiang during 2020. The significance test at the
0.1 level
revealed specific correlations. The spring daytime SUHII was significantly and
positively correlated with
AL (r = 0.802),
while the summer daytime SUHII was nega-
tively correlated with
EL (r = 0.368).
The autumn daytime SUHII was positively corre-
lated with
AL (r = 0.558),
while the winter daytime SUHII was positively correlated with
PRE (r = 0.758)
and
COHESION
(r = 0.416), and negatively correlated with AL (r =
0.411)
and
EL (r = 0.469).
Moreover, the spring nighttime SUHII exhibited a significant positive
correlation with GDP (r = 0.368), while the summer nighttime SUHII was positively cor-
related with PLADJ (r = 0.455),
GDP (r = 0.451),
and COHESION (r = 0.376). The autumn
nighttime SUHII was positively correlated with the GDP (r = 0.411), and the winter night-
time SUHII was positively correlated with the GDP (r = 0.483), PARA_MN (r = 0.405), and
PLADJ (r = 0.382), while being negatively correlated with the UGSR (r = 0.368).
Atmosphere2024,15,xFORPEERREVIEW12of23
GDP(r=0.368),whilethesummernighimeSUHIIwaspositivelycorrelatedwithPLADJ
(r=0.455),GDP(r=0.451),andCOHESION(r=0.376).TheautumnnighimeSUHIIwas
positivelycorrelatedwiththeGDP(r=0.411),andthewinternighimeSUHIIwasposi-
tivelycorrelatedwiththeGDP(r=0.483),PAR A_M N(r=0.405),andPLADJ(r=0.382),
whilebeingnegativelycorrelatedwiththeUGSR(r=−0.368).
Overall,thedaytimeSUHIIofXinjiangcitiesexhibitedanotablecorrelationwith
topographicfactors,specicallyALandEL.ThespringandautumnSUHIIsshowedpos-
itivecorrelationswithAL,whilethesummerandwinterSUHIIsdisplayednegativecor-
relationswithbothALandEL.Furthermore,duringwinter,thedaytimeSUHIIalso
demonstratedsignicantpositivecorrelationswithPREandCOHESION.Notably,there
wasasignicantpositivecorrelationbetweenthenighimeSUHIIandGDPacrossall
citiesinXinjiang.Additionally,thesummernighimeSUHIIexhibitedsignicantpositive
correlationswithPLADJandCOHESION,whilethewinternighimeSUHIIdisplayed
positivecorrelationswithPARA_MNandPLADJ,alongwithanegativecorrelationwith
theUGSR.
Figure6.CorrelationsbetweendrivingfactorsandtheSUHIIin2020.
3.3.3.TheResultsoftheOPGDAnalysis
TheOPGDwasutilizedtoidentifytheimpactfactorsinuencingtheSUHIIacross
dierentseasonsineachcityofXinjiangin2020.Figure7illustratesthattheselectedkey
factorshavevaryingdegreesofinuenceonSUHIIacrossdierentseasonsineachcity
withinthestudyarea.ThemaindriversofthedaytimeSUHIIinspringincludeAL,LSI,
EL,andGDP,whileEL,NDVI,LPI,GDP,PLADJ,andDIplaykeyrolesinsummer.In
autumn,signicantfactorsareEL,GDP,AL,andPRE,andduringwinter,majordrivers
arePRE,COHESION,NDVI,AL,andPLADJ.ForthespringnighimeSUHII,the
Figure 6. Correlations between driving factors and the SUHII in 2020.
Atmosphere 2024,15, 1377 12 of 22
Overall, the daytime SUHII of Xinjiang cities exhibited a notable correlation with
topographic factors, specifically AL and EL. The spring and autumn SUHIIs showed
positive correlations with AL, while the summer and winter SUHIIs displayed negative
correlations with both AL and EL. Furthermore, during winter, the daytime SUHII also
demonstrated significant positive correlations with PRE and COHESION. Notably, there
was a significant positive correlation between the nighttime SUHII and GDP across all
cities in Xinjiang. Additionally, the summer nighttime SUHII exhibited significant positive
correlations with PLADJ and COHESION, while the winter nighttime SUHII displayed
positive correlations with PARA_MN and PLADJ, along with a negative correlation with
the UGSR.
3.3.3. The Results of the OPGD Analysis
The OPGD was utilized to identify the impact factors influencing the SUHII across
different seasons in each city of Xinjiang in 2020. Figure 7illustrates that the selected key
factors have varying degrees of influence on SUHII across different seasons in each city
within the study area. The main drivers of the daytime SUHII in spring include AL, LSI,
EL, and GDP, while EL, NDVI, LPI, GDP, PLADJ, and DI play key roles in summer. In
autumn, significant factors are EL, GDP, AL, and PRE, and during winter, major drivers are
PRE, COHESION, NDVI, AL, and PLADJ. For the spring nighttime SUHII, the influential
factors are EL, PD, NDVI, GDP, PLADJ, AI, and LPI, while crucial factors for the summer
nighttime SUHII are the GDP, PLADJ, NDVI, PD, AI, and PARA_MN. Important factors
for the autumn nighttime SUHII include PD, GDP, PLADJ, COHESION, LPI, PRE, DI, and
AI, and for the winter nighttime SUHII, significant factors are PRE, GDP, PLADJ, and AI.
Notably, all these factors demonstrate an explanatory power exceeding 50%. This analysis
reveals that the SUHII in each city is impacted by a combination of natural environmental,
urban size and form, and socio-economic factors.
Among all driving factors, GDP has the greatest impact on SUHII, with an explanatory
power of more than 50% for all study periods, except winter daytime. The other drivers
exhibit significant seasonal and diurnal variations. For instance, LSI affects the SUHII only
during spring daytime, PARA_MN impacts the SUHII solely during summer nighttime,
AL influences
the SUHII exclusively during daytime, while PD and AI affect the SUHII
solely during nighttime. The study highlights the various urban form factors, natural
factors, and socio-economic factors that influence the SUHII during both daytime and
nighttime. Urban form factors such as LSI, LPI, PLADJ, DI, and COHESION play roles
in the SUHII during the day, while natural factors like AL, EL, NDVI, and PRE impact
the SUHII across different seasons. Socio-economic factors like the GDP are also found to
affect the SUHII during the day. At night, factors such as LPI, PLADJ, DI, COHESION, AI,
and PARA_MN influence the SUHII, while natural factors like EL, NDVI, and PRE play a
role in the SUHII during specific seasons. Socio-economic factors like the GDP and PD are
also identified as impacting the SUHII at night. Overall, the study highlights the seasonal
variability in the drivers of SUHII.
Atmosphere 2024,15, 1377 13 of 22
Atmosphere2024,15,xFORPEERREVIEW13of23
inuentialfactorsareEL,PD,NDVI,GDP,PLADJ,AI,andLPI,whilecrucialfactorsfor
thesummernighimeSUHIIaretheGDP,PLADJ,NDVI,PD,AI,andPARA_MN.Im-
portantfactorsfortheautumnnighimeSUHIIincludePD,GDP,PLADJ,COHESION,
LPI,PRE,DI,andAI,andforthewinternighimeSUHII,signicantfactorsarePRE,GDP,
PLADJ,andAI.Notably,allthesefactorsdemonstrateanexplanatorypowerexceeding
50%.ThisanalysisrevealsthattheSUHIIineachcityisimpactedbyacombinationof
naturalenvironmental,urbansizeandform,andsocio-economicfactors.
Figure7.DetectingtheimpactofasinglefactorontheSUHIIusingtheOPGDmodel.including(a)
thespringdaytime;(b)summerdaytime;(c)autumndaytime;(d)winterdaytime;(e)spring
nighime;(f)summernighime;(g)autumnnighime;and(h)winternighime.
Figure 7. Detecting the impact of a single factor on the SUHII using the OPGD model. including
(a) the spring daytime; (b) summer daytime; (c) autumn daytime; (d) winter daytime; (e) spring
nighttime; (f) summer nighttime; (g) autumn nighttime; and (h) winter nighttime.
Atmosphere 2024,15, 1377 14 of 22
4. Discussion
4.1. Seasonal and Diurnal Characteristics of the SUHI in an Arid Region
This study delves into the spatial distribution pattern of the SUHII in 22 cities in
Xinjiang, a region located in the arid zone of Central Asia. It focuses on exploring the
seasonal and diurnal differences in the SUHII in cities within the arid zone, aiming to
uncover the drivers behind the SUHII in these urban areas. The results showed that
the spatial distribution pattern of the annual average SUHII in Xinjiang cities follows
a trend of North Xinjiang > South Xinjiang > East Xinjiang, possibly linked to regional
economic disparities. Urumqi stands out with the highest SUHII at 2.86
C, which is
attributed to its status as the largest city in Xinjiang with substantial urban development,
industrial activity, and construction intensity compared to other cities [
73
]. Overall, cities
in Xinjiang exhibit significant heat islands, with summer and winter generally showing
stronger effects compared to spring and autumn. The SUHII is more pronounced in winter
and summer than in spring and autumn owing to seasonal climatic variations, such as
low temperatures and atmospheric stability in winter, as well as high solar radiation and
reduced adaptability of the evaporative system in summer. Additionally, anthropogenic
influences, including heat emissions from heating and cooling activities, urban subsurface
characteristics that promote high heat storage and the reduction of green spaces, and
atmospheric pollution factors, such as the greenhouse effect and atmospheric stability,
contribute to this phenomenon. Diurnal variations are more pronounced in spring and
autumn, while less evident in summer and winter. Notably, the annual average SUHII is
higher at nighttime than during the daytime, aligning with previous research indicating that
the nighttime SUHII surpasses the daytime SUHII in arid regions [
74
]. This phenomenon
may be attributed to materials like asphalt, bricks, and concrete absorbing and retaining
solar radiation during the daytime, releasing it gradually at nighttime [
75
]. On the other
hand, this difference may be attributed to the fact that the annual nighttime mean SUHII in
Xinjiang is consistently above 0
C, while the annual daytime mean SUHII is below 0
C in
seven cities. This results in a stronger annual nighttime mean SUHII than daytime SUHII
in Xinjiang. The lower daytime mean SUHII in these cities could be due to their desert
surroundings, where the built-up areas experience lower temperatures during the daytime
compared to the surrounding deserts, possibly due to the cooling effect of vegetation [
76
,
77
].
The research also revealed that the summer and winter SUHIIs are stronger during the
daytime, aligning with previous findings [
78
]. However, the study noted that spring and
autumn SUHIIs are stronger at nighttime, possibly influenced by the seasonal variations
in Xinjiang. The findings also suggest that the seasonal difference in the SUHII is greater
during the daytime, indicating that the SUHII is more stable at nighttime and aligning
with the findings of Wu et al. [
74
], Peng et al. [
78
] and Wang et al. [
79
]. In addition,
our study observed a decreasing trend in the SUHII from summer to winter during the
daytime, consistent with the findings of Zhou et al. [
80
]. However, unlike the study by
Zhou et al. [
80
], our research also identified a decreasing trend in the SUHII at nighttime,
possibly due to the unique diurnal temperature difference in arid regions. The decrease in
the SUHII from summer to winter was attributed to surface cover and urban activities in
summer, which led to higher temperatures compared to suburban areas. In contrast, both
the city and suburbs were covered in snow and ice during winter, resulting in minimal
temperature variations.
4.2. Driving Factors of the SUHII
4.2.1. Impacts of Natural Factors on the SUHII
The role of the natural environment as a key determinant of the UHI phenomenon
is widely acknowledged. Our research identified EL, NDVI, PRE, and AL as significant
natural factors influencing the SUHII in cities across Xinjiang. Among these factors, EL
was found to have a more pronounced effect on the SUHII compared to AL, as confirmed
by previous studies [
74
]. The OPGD analysis revealed that EL had a greater impact on
the SUHII during the daytime in the hot season and at nighttime in the cold season.
Atmosphere 2024,15, 1377 15 of 22
Pearson’s correlation analysis further demonstrated that EL exerts a significant inhibitory
effect on the SUHII during daytime in both summer and winter. This suggests that cities
situated at a lower EL exhibit a stronger SUHII compared to rural areas, consistent with
previous research [
81
]. However, in other seasons, the relationship between EL and the
SUHII was either insignificant or demonstrated a slight positive correlation, contrary to
previous findings. This discrepancy was also observed in studies conducted in Jaipur City,
India, and 201 prefecture-level cities in China. Researchers attributed this phenomenon
to the complex impact of urban expansion on factors such as solar radiation, surface
roughness, and vegetation cover, resulting in uncertainties regarding its influence on the
SUHII [
16
,
82
]. Additionally, the unique characteristics of different cities were identified
as factors contributing to this variability. The results from the OPGD indicate that the
impact of the NDVI on the SUHII varies based on daytime and nighttime, with a greater
effect observed during the daytime in summer and winter, and during nighttime in spring
and autumn. The Pearson correlation analysis revealed that the relationship between the
NDVI and SUHII in 22 cities in Xinjiang was not statistically significant. This contrasts
with previous studies that suggest a significant negative correlation between the NDVI
and SUHII, highlighting the unique climate conditions in the study area that influence this
relationship across different seasons. Previous research by Mathew, Khandelwal, and Kaul
demonstrated that the influence of the NDVI on the SUHII is more pronounced in winter
and monsoon seasons compared to summer [
82
], supporting the notion that the association
between the NDVI and SUHII is season-dependent. Additionally, Fujibe observed a lack
of correlation between the SUHII and changes in urban surface cover, which aligns with
Fumiaki Fujibe’s suggestion that urban warming may be more strongly linked to internal
changes such as increased commercial activities and building heights rather than the spatial
coverage of urban surfaces [83].
4.2.2. Impacts of Urban Size and Urban Form on the SUHII
Numerous studies have indicated that urban size and urban form are crucial factors in
the formation and development of UHIs. In relation to urban size, our analysis of 22 cities in
Xinjiang revealed a consistent trend wherein larger cities exhibited a stronger SUHII, which
aligns with previous research [
40
,
84
86
]. They also found that the UHI is more pronounced
in large cities compared to smaller ones, primarily due to the high density of buildings and
roads in urban areas. These man-made structures absorb heat rapidly and have a low heat
capacity, which results in swift urban warming. Furthermore, the dense population and
frequent human activities in large cities contribute to significant heat emissions, further
intensifying the UHI. However, a contrasting finding has been reported by Su et al., who
observed a higher SUHII in medium and large cities compared to mega-cities in Chinese
urban settings [
37
]. Su attributed this disparity to the advanced functional area planning,
well-structured blue–green spaces, and efficient traffic management prevalent in mega-
cities, which mitigate the influence of urban size on the SUHII. Consequently, medium
and large cities exhibit higher SUHII levels than mega-cities. In our investigation, as the
built-up area of all 22 Xinjiang cities is less than 600 km
2
, our results align with previous
studies focusing on small to medium-sized cities. In terms of urban form, our study
identified PLADJ, AI, LPI, COHESION, DIMENSION, LSI, and PARA_MN as significant
factors influencing the SUHII in Xinjiang cities, with PLADJ and AI emerging as the factors
with the most impact. The OPGD revealed that the influence of PLADJ on the SUHII
was more pronounced at nighttime compared to daytime across all seasons. Additionally,
Pearson’s correlation analysis showed that PLADJ positively impacted the SUHII during
daytime in summer and winter, as well as at nighttime in all seasons. Previous studies
by
Chen et al. [87]
and Chunling et al. [
88
] in Beijing and Wuhan also reported similar
findings, with correlation coefficients of 0.305 and 0.7555, respectively. The results of the AI
and PLADJ analyses showed similarities, with AI having a greater impact on the SUHII at
nighttime than during the daytime in all seasons, positively promoting the SUHII during
nighttime. This aligns with the conclusions of Wang et al. [
89
] and Shen et al. [
90
] that
Atmosphere 2024,15, 1377 16 of 22
higher AI values, indicating more agglomerated built-up areas, lead to a more severe
SUHII. The effects of LPI, COHESION, DIMENSION, LSI, and PARA_MN on the SUHII
varied seasonally, consistent with findings from Chen et al. [
87
]. It can be inferred that the
combined effects of multiple factors contribute to the understanding of the SUHII across
different seasons.
4.2.3. Impacts of Socio-Economic Factors on the SUHII
With the rapid urbanization and industrialization, the impacts of socio-economic
factors on the SUHII are increasingly significant. Our research identified the GDP and
PD as key socio-economic factors influencing the SUHII in cities in Xinjiang. Through
Pearson’s correlation analysis and OPGD results, we determined that the GDP plays a
crucial role in enhancing the SUHII across all seasons, except winter daytime, in 2020.
Specifically, the GDP shows a positive correlation with the SUHII, particularly at nighttime,
with a statistically significant relationship. This finding aligns with previous studies [
91
],
where Chen also observed a minor impact of the GDP on surface temperature during
winter daytime compared to other seasons. Other studies [
23
,
92
98
] have reported a
strong positive correlation between the GDP and SUHII (0.39
r
0.92) at various scales,
highlighting the impact of urban human activities and socio-economic development on
UHIs. Additionally, the OPGD results indicated that PD has a greater impact on the
SUHII at nighttime across all seasons, suggesting that PD primarily affects the SUHII
during nighttime. The Pearson correlation analysis revealed that PD promotes the SUHII at
nighttime in summer and suppresses it in spring and autumn. These results are consistent
with previous research [
16
], where Li found a positive correlation (r = 0.09) between PD and
the SUHII during summer nighttime in a study of 201 prefecture-level cities in east–central
China, with the opposite trend observed at nighttime during other seasons.
4.3. Policy Suggestions
The SUHII in Xinjiang exhibits clear spatial variations, as well as distinct seasonal
and diurnal characteristics. When developing policies to address the SUHII, it is crucial to
prioritize the identification of regions and seasons with pronounced SUHIIs, strategically
harnessing positive factors while minimizing the impacts of negative factors and devising
tailored strategies for different regions [
99
]. Additionally, the complexity of reducing the
SUHII necessitates adaptable policies for individual cities, with a comprehensive approach
that considers the entire urban system rather than solely focusing on the UHI. Based on our
findings, we propose the following insights for mitigating the SUHII in Xinjiang cities. First
of all, it is crucial to manage the expansion of impervious surfaces and artificial buildings,
adjust the urban subsurface structure, and enhance the development of urban blue and
green spaces [
100
102
]. Utilizing environmentally friendly building materials like fiber-
reinforced plastic and bamboo–glass fiber composites instead of traditional materials such
as steel, glass, and concrete, along with implementing green roofs and reflective surfaces,
can effectively control the SUHII [
103
,
104
]. Green roofs help lower surface temperatures,
while reflective surfaces increase surface albedo, resulting in a more efficient cooling
effect [
105
,
106
]. Secondly, it is essential to manage urban size and optimize the spatial
layout of the city’s landscape. In order to combat the SUHII while also controlling the
expansion of large built-up areas, a balance must be struck between SUHII mitigation and
economic growth through the development of satellite cities and new towns. Finally, it is
advisable to diversify surface cover types and decrease the proximity of similar surfaces by
incorporating parks, urban ventilation corridors, and other measures to break the spatial
continuity of the city [
107
109
]. Additionally, adjustments to the industrial structure and
optimization of population distribution are essential. Large industrial enterprises can
boost GDP growth by refining their industrial layout, enhancing production efficiency,
and minimizing the environmental impacts of industrial activities in urban areas. Long-
term strategies to combat the SUHII include reducing anthropogenic heat emissions by
promoting energy conservation, enhancing energy efficiency, and encouraging the use
Atmosphere 2024,15, 1377 17 of 22
of public transportation. It is noteworthy that Xinjiang serves as a significant renewable
energy source for power generation. With its abundant wind and solar energy resources,
the gradual replacement of fossil fuels with renewable energy sources may represent an
effective approach for the future [104,110].
4.4. Limitations and Prospects
This study still has some limitations and uncertainties. In this study, we analyzed
the seasonal and diurnal variations in the SUHII in Xinjiang, utilizing MODIS data as the
primary source. MODIS data offer significant advantages for studying the SUHII due to
their extensive global coverage and high temporal resolution, which facilitate continuous
and dynamic monitoring of surface temperatures. This capability provides essential in-
sights into the spatial and temporal distribution of the SUHII and its trends. However,
MODIS data also have limitations; specifically, their spatial resolution is inadequate for
analyzing fine urban structures, which may result in an inaccurate representation of small
temperature differences within the city. Additionally, precipitation data for natural factors
is only available on a monthly basis, making it impossible to analyze the impact factors of
daytime and nighttime separately. Similarly, GDP and PD data for socio-economic factors
are annual, preventing the assessment of seasonal variations in the impact strength, and
the physical mechanism by which socio-economic factors affect the surface heat island is
unclear. Secondly, the selection of socio-economic factors is incomplete, omitting trans-
portation, pollution, and infrastructure, which limits a comprehensive understanding of
human factors’ impacts on regional heat islands. Thirdly, the study only considers the two-
dimensional urban form, neglecting the complexity of three-dimensional urban landscapes
and their impacts on the SUHII. Finally, Pearson’s correlation analysis is a fundamental yet
effective method in the study of the UHI. However, it is important to note that Pearson’s
correlation analysis can only describe the strength and direction of the relationship between
two variables; it does not provide insights into causality and is limited in its ability to
quantify spatial heterogeneity. In future UHI studies, geographically weighted regression
models can be utilized, as they can offer better explanations of causality and effectively
quantify spatial heterogeneity. Additionally, the study focuses on the city as a whole using
average data with lower spatial resolution, lacking detailed research at the grid scale and
within the city. Further research should conduct a more detailed analysis of regional heat
islands by examining the SUHII at the grid scale in arid zones, considering impact factors
at seasonal and diurnal scales, accounting for the impacts of three-dimensional landscapes,
and utilizing detailed results to mitigate the SUHII.
5. Conclusions
This study quantifies the seasonal and diurnal variations in the SUHII in Xinjiang,
while investigating how natural factors, urban size and form, and socio-economic factors
contribute to the regional heat island. The research utilizes land use data to calculate
relative surface temperatures and characterize the SUHII. The study further computes
the urban scale pattern index based on the urban built-up area range, and examines the
seasonal differences in SUHII through Pearson’s correlation analysis and OPGD. Key
findings include the following: (1) the SUHII in Xinjiang is typically more pronounced
in summer and winter compared to spring and autumn. Moreover, the annual average
SUHII is higher at nighttime than during the daytime, stronger during the daytime than
at nighttime in summer and winter, and more pronounced at nighttime than during the
daytime in spring and autumn. (2) The seasonal disparity of the SUHII is more significant
during the daytime than at nighttime, with a decreasing trend observed from summer to
winter for both daytime and nighttime SUHII. (3) Our study indicates that the drivers of
the SUHII are intricate and seasonal rather than fixed. Natural factors like EL and NDVI,
urban size and form factors like PLADJ and AI, and socio-economic factors like the GDP
and PD play significant roles in influencing the SUHII in Xinjiang. Among these factors,
socio-economic factors, especially the GDP, have the greatest impact on the SUHII across
Atmosphere 2024,15, 1377 18 of 22
all seasons, except daytime in winter. Therefore, there is an urgent need to address the
increasing SUHII and its negative effects on both the population and the environment.
To combat this issue, it is crucial to enhance the urban environment, manage urban size,
optimize urban landscapes, and reorganize economic activities and population distribution.
Furthermore, promoting the adoption of new technologies and increasing public awareness
about environmental and self-protection measures can also mitigate the impacts of the
UHI. The methodology employed in this study can be replicated to examine the UHI and
urban comfort in other arid zone cities, and future research could focus on analyzing the
correlations between UHI changes and urban industries, transportation, and air pollution.
The main contribution of this study is its comprehensive analysis of seasonal and
diurnal variations in the SUHI and its potential drivers across 22 cities in Xinjiang, China.
This research addresses the existing gap in understanding the factors influencing SUHI
dynamics in arid regions. Furthermore, the study provides a scientific foundation for
comprehending the SUHI phenomenon in these areas and significantly contributes to the
development of effective urban environmental improvement strategies. These strategies in-
clude optimizing surface conditions, enhancing blue–green spaces, and adjusting economic
policies to mitigate the SUHI while promoting sustainable urban development.
Author Contributions: Conceptualization, H.C. and Y.M. (Yusuyunjiang Mamitimin); data curation,
H.C.; investigation, H.C., T.T. and Y.M. (Yunfei Ma); formal analysis, H.C.; software, H.C.; vali-
dation, H.C. and T.T.; writing—original draft, H.C.; supervision, Y.M. (Yusuyunjiang Mamitimin);
writing—review and editing, Y.M. (Yusuyunjiang Mamitimin), M.H. and A.A.; methodology, Y.M.
(Yusuyunjiang Mamitimin); resources, M.H.; visualization, M.H.; project administration, Y.M. (Yunfei
Ma). All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Third Xinjiang Scientific Expedition and Research Program
of the Ministry of Science & Technology of People’s Republic of China (grant number 2022xjkk1100).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in the study are included in the
article; further inquiries can be directed to the corresponding author.
Acknowledgments: We would like to acknowledge the funding from the Third Xinjiang Scientific
Expedition and Research Program of the Ministry of Science & Technology of People’s Republic of
China. We also would like to thank the anonymous reviewers for their constructive comments that
improved the quality of this manuscript.
Conflicts of Interest: The authors declare no conflicts of interest.
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