1. Introduction
Urbanization, modifying the natural environment with an artificial environment, has several impacts on urban environment and climate, which are global concerns [
1,
2,
3]. The heat emission of human production activities is increasing rapidly, making the city a huge heating element and exacerbating the temperature difference between the city and its surrounding suburbs, which is an important factor causing the increase of the urban heat island effect [
4,
5,
6]. Anthropogenic heat is an additional source of energy in urban areas compared to the other surfaces of the earth and results mainly from human metabolism and the consumption of energy by human activity, such as heating of residential areas, commercial centers, industry, and the discharge from vehicles [
7,
8,
9,
10]. Urban anthropogenic heat is an accompanying product of urbanization, changing the energy flow of the urban ecosystem and affecting the urban ecological processes such as the urban regional climate and atmospheric environment [
11]. Therefore, quantification of anthropogenic heat is of great significance to mitigation and control of the impact of urbanization on the environment and human society.
With the growing concern over anthropogenic heat flux (AHF), many researchers have conducted quantitative studies in this field. The methods of estimating anthropogenic heat can be grouped into three major categories: the energy-consumption inventory method, building energy modeling method, and surface energy balance method [
12]. The energy-consumption inventory, based on the energy consumption data of industries, buildings, and transportation and administrative socioeconomic data, converts these different types of energy consumption into anthropogenic heat emissions through the energy conversion experience coefficients [
13,
14]. The modeling method collects detailed energy-consumption data of a study aera through building energy modeling and geographic information modeling, to estimate anthropogenic heat flux [
15,
16]. The surface energy balance method, based on remote sensing data and meteorological data, uses empirical formulas to obtain the contribution of net surface radiation, soil heat flux, latent heat flux, and sensible heat flux to the surface energy [
17,
18]. According to the first law of thermodynamics, it uses the remainder method to estimate anthropogenic heat flux. Compared with the first two methods, the data of remote sensing and meteorological for the surface energy balance method are easy to obtain. The data have the characteristics of high spatial resolution and wide coverage and can be used to determine instantaneous anthropogenic heat flux to study the characteristics of temporal and spatial changes.
The method is based on the surface energy balance and has been applied by previous studies using remote images for research on urban anthropogenic heat. For example, Kato and Yamaguchi used ASTER and ETM+ data to conduct an anthropogenic thermal analysis in Nagoya, Japan, and found that the spatial distribution and seasonal variation trend of anthropogenic heat are consistent with actual energy consumption, which is high in developed areas and low in rural areas, and factories have high annual anthropogenic thermal energy [
19]. Zhuo et al. used ASTER data to study the anthropogenic heat and energy consumption of residential and commercial buildings in Indianapolis in the summer, and the results showed that the spatial pattern and scale of anthropogenic heat and building energy consumption are consistent [
20]. Wong et al. used HJ-1B data to analyze Hong Kong’s anthropogenic heat based on the surface energy balance method, and the results showed that anthropogenic heat flux is related to building density and building height, and the winter correlation coefficients are 0.94 and 0.62, respectively [
21]. Hu et al. used ASTER imagery and meteorological observation data to analyze Beijing’s anthropogenic heat and found that anthropogenic heat is positively correlated with the surface temperature and has a greater impact in the summer [
22].
The previous researches mainly focus on quantitatively estimating the intensity of anthropogenic heat based on different scales, analyzing spatiotemporal characteristics, influencing factors, and the impact on the environment. Research on a global scale has shown that the northern mid-latitudes contribute most to the global AHF, and the distribution of anthropogenic heat has the characteristics of regional concentration, mainly concentrated in the areas with high-density population and industries [
23,
24]. In addition, some studies mapping detailed anthropogenic heat using countries and megacities as research areas [
25,
26,
27] found that the range of anthropogenic heat in China’s large cities is between 60 to 190 W/m
2 [
28] and that the east has significantly more anthropogenic heat than the west [
29]. Factors such as the density and height of buildings, functional urban zones, and impervious surface coverage can affect the intensity of anthropogenic heat [
20,
30]. There are a lot of studies showing that the change of anthropogenic heat not only causes a variation in urban temperature but also affects the air quality of the city, affecting human health and social and economic development [
31,
32,
33,
34]. However, there is a lack of research involving analysis of the temporal and spatial characteristics of anthropogenic heat. The spatial pattern and intensity changes of anthropogenic are the result of multiple factors such as human activities, underlying changes, and economic development. Therefore, exploring the temporal and spatial characteristics of anthropogenic heat can elucidate the impact of human activities and the urban thermal environment.
Over the past decades, Guangzhou has been experiencing rapid industrialization and urbanization. As a result, the increased intensity of human activity has stimulated a great increase of energy consumption. Therefore, a time-series analysis of the distribution and evolution and the driving force of urban anthropogenic heat is necessary to evaluate its impact on the thermal environment. In this study, taking the urban area of Guangzhou as an example, based on Landsat time-series image data and the surface energy balance equation, the urban anthropogenic heat for winter from 2004 to 2020 was estimated. The objective of this study was to analyze the spatial distribution and the characteristics of the evolution and explore the evolutionary laws and driving factors of the urban anthropogenic thermal environment.
3. Results
3.1. Temporal and Spatial Changes of AHF
The spatial distribution characteristics of AHF in the central urban area of Guangzhou were obtained and are shown in
Figure 3. It is clear from the results that the AHF distribution in the research area changed significantly from 2004 to 2020. The anthropogenic heat expanded rapidly in the space and increased rapidly in terms of value.
The statistics of anthropogenic heat in 2004, 2009, 2014, and 2020 are shown in
Table 4. The average value and the main distribution range can reflect the overall level, and the maximum value represents an area of high energy consumption. The main distribution range of the AHF value increased from 32.70 to 90.00 W/m
2 in 2004 to 52.70 to 131.60 W/m
2 in 2020. The average values of AHF are 53.91, 59.26, 62.26 and 96.28 W/m
2, respectively. The maximum AHF in 2004 and 2020 was 245.71 and 397.95 W/m
2, respectively. From the perspective of spatial distribution, it is clear that there are obvious spatial differences at different times. Among them, the areas with high AHF in 2004 were mainly distributed along the river and port areas; high AHF areas in 2009 and 2014 were more scattered than that in 2004, moving from the southwest of the central city to the north; there was a large number of AHF peak areas in 2020 in the outer areas of the city.
The intensity of anthropogenic heat in 2004 was lower than that in 2020, and the types of anthropogenic heat change are shown in
Figure 4a. From the statistics in
Table 5, it can be seen that the largest area is the AHI type, with an area of 323.44 km
2, accounting for 21.99% of the research area. Next are the AHIS and AHS types, with 231.7 and 77.54 km
2, accounting for 15.75% and 5.27%, respectively. The smallest area is the AHD type, with 4.58 km
2, accounting for 0.31%.
A comparison was made between the urban functional land and the anthropogenic heat change. The distribution map of the urban functional land in the central city of Guangzhou is shown in
Figure 4b. It can be seen that the type of AHI was mainly distributed in the southwest of the center of the study area, and the main functional land types here are residential land and commercial land. The anthropogenic heat in residential and commercial areas mainly comes from electrical energy consumption and human metabolism. There are densely populated and densely built areas in the southwest of the center of the study area, which have been the main reason for a slight increase in anthropogenic heat. The AHIS type was mainly distributed in the outskirts of the central city where the main types of functional land are industrial land and airport land. The anthropogenic heat of industrial land mainly comes from the use of electricity and coal, and frequent air transportation in the airport area causes a lot of waste heat emissions. The new Baiyun International Airport and other transportation facilities have been constantly improving, and their convenient transportation attracted a large number of industrial migration and construction to the mainland [
43]. Therefore, the release of a large amount of waste heat because of industrial production and large-scale logistics and transportation made anthropogenic heat rise sharply [
39].
3.2. Analysis of the Transition Matrix
The distribution of different levels of AHF in 2004 and 2020 is shown in
Figure 5. It is clear to see that the area of medium-AHF zone expanded significantly, while the area of low-AHF zone shrunk. The area changes of different levels of AHF are shown in
Table 6. During the period 2004–2020, the zero-AHF zone and low-AHF zone accounted for the largest area, exceeding 80%. The next is the medium-AHF zone, and the smallest area is the high-AHF zone, with less than 0.3% of the total research area. From the overall dynamic process, it can be seen that the area of the medium-AHF zone and high-AHF zone increased, while the area of the zero-AHF zone and low-AHF zone decreased. During the study period, the zero-AHF zone decreased significantly, which accounted for a decrease from 63.70% to 56.69%, reduced by 103.06 km
2, and the period of the fastest decrease was during 2004 to 2009, from 63.70% to 58.86%, with the area decreased by 71.09 km
2. For the low-AHF zone, its area increased first and then decreased: during the period 2004 to 2009 and 2009 to 2014, the area of low-AHF zone was increasing steadily, with the proportion increased from 35.85% to 40.86% and the area increased by 73.79 km
2. During the period 2014 to 2020, the area of low-AHF decreased significantly, from 40.86% to 26.56%, with the area decreased by 210.41 km
2. During the period 2004 to 2020, the proportion of the medium-AHF zone increased significantly, from 0.46% to 16.48%, with the area increased by 235.65 km
2, and the fastest growth period was during 2014 to 2020, with the area increased by 213.96 km
2. The proportion of the high-AHF zone increased slightly, from 0.001% to 0.28%, with the area increased by 4.05 km
2. Therefore, during period of 2004 to 2009 and 2009 to 2014, the transfer-out of the zero-AHF zone and the transfer-in of the low-AHF zone were the main changes, while during the period of 2014 to 2020, The transfer-out of the zero-AHF zone and low-AHF zone and the transfer-in of the medium-AHF zone were the main changes. In all, the level types of AHF in the central urban area from 2004 to 2020 changed extremely significantly, and the overall situation is unbalanced.
According to the transfer matrix (
Table 7), the direction and quantity of changes among different level types of AHF could be analyzed. It can be seen that the various level types of AHF were transferred in and out during the period 2004–2020, and the transfer volume between types became more complicated.
The low-AHF zone was the largest of the transferred-out areas, with 217.06 km2, which mainly transferred to the medium-AHF zone. The next was the zero-AHF zone, with an area of 132.19 km2, which mainly converted to the low-AHF zone and medium-AHF zone, with transfer rates of 8.41% and 5.57%, respectively. The smallest transfer-out area was the high-AHF zone, with area only of 0.01 km2.
The largest of the transfer-in areas was the medium-AHF zone, with an area of 237.64 km2, and the main contribution was from the zero-AHF zone and low-AHF zone. The second was the low-AHF zone with an area of 80.43 km2, and the main contribution came from the non-built-up area. The smallest was the high-AHF zone of 4.06 km2.
The absolute number of transfer-in and transfer-out areas of the low-AHF zone was relatively large; thus, the changes in low-AHF were the most intense, while the number of transfer-in and transfer-out areas in the high-AHF zones was small, which indicated that the high-AHF zones were relatively stable.
3.3. Spatial Migration of Gravity Center
The gravity center of different levels of AHF in the central urban area of Guangzhou is shown in
Figure 6 and can be used to explore the migration trace of different levels of AHF and reveal the temporal and spatial evolution of anthropogenic heat.
This study used the center of the study area as a reference. Spatially, the gravity center of the low-AHF zone moved to the northeast during 2004–2020, but the gravity centers of the low-AHF zone in the four years were all located in the southwest near the center of the study area, which indicated that the distribution of the low-AHF zone was uniform, and the southwest is relatively denser than the northeast. The gravity center of medium-AHF zone migrated to the northwest, from the southeast of the study area, and the gravity centers of medium-AHF zone in the four years were all located in the southern part of the center of the study area. This indicated that the southern part of the medium-AHF zone was denser than the north. The gravity center of the high-AHF zone migrated to the southwest. In 2004 and 2009, the gravity centers of the high-AHF zone were located in the northeast of the study area. And then it migrated to the north, located in the southeast of the center of the study area in 2014, while it was located in the southwest of the center of the study area in 2020.
The migration distances of different levels of AHF are shown in
Table 8, which were significantly different. The shortest migration distance was the low-AHF zone with 1.31 km during 2004 to 2020, and its migration distances were 0.32 km in 2004–2009, 0.59 in 2009–2014, 0.55 km in 2014 to 2020, respectively. The next was the medium-AHF zone with 13.19 km, and its migration distances were 3.26 km, 5.3 km, and 5.7 km, respectively. The longest migration distance was the high-AHF zone with 19.06 km, and its migration distances were 2.31 km, 12.2 km, and 16.24 km, respectively. Based on
Table 6 and
Figure 5, the area and distribution of low-AHF played the main control role in the study area during the period 2004–2020. Therefore, the gravity center of migration distance of the low-AHF zone was relatively short. The medium-AHF was mainly distributed in the southeast of study area in 2004, while the medium-AHF gradually became the main type in 2020, concentrated in the southwest and northwest, and scattered in the north. Therefore, the gravity center of medium-AHF had a large shift. The high-AHF zone had a small change in area but a large change in space. The high-AHF zone was mainly located in the east in 2004. However, it expanded to the north and southeast in 2020. To sum up, the low-AHF zone had relatively stable spatial changes during the period from 2004 to 2020, while the medium-AHF zone and high-AHF zone changed more drastically.
5. Conclusions
In this study, the central urban area of Guangzhou City was taken as an example to estimate the temporal and spatial variation of anthropogenic heat in different urbanization periods. The energy balance model was used based on remote sensing images. The transfer matrix and the migration of the gravity center was used to analyze the change characteristics of the anthropogenic heat and the drivers of anthropogenic heat change from population, GDP, and industry aspects were also explored. The main conclusions are as follows:
The overall change trend of anthropogenic heat in the central urban area of Guangzhou was enhanced during the period from 2004 to 2020, and the degree of its enhancement was related to the types of urban functional land. The central area of Guangzhou is in a stage of rapid urbanization, which has stimulated a substantial increase in energy demand and supply, and the intensity of anthropogenic heat has also increased. The increase in the intensity of anthropogenic heat in industrial areas and airport areas is greater than that of commercial and residential areas.
There are obvious differences both in area and space for different types of anthropogenic heat with urban development. In terms of area expansion, the areas of the zero-AHF zone, low-AHF zone, and medium-AHF zone changed drastically, with the zero-AHF zone and low-AHF zone transferring out and medium-AHF zone transferring in as the main form, while the area change of the high-AHF zone was relatively stable. The urbanization of Guangzhou’s central urban area has led to an increase in the demand for urban energy consumption, which is also the main reason for the unbalanced state of the single conversion of anthropogenic heat from low-AHF areas to high-AHF areas. Spatially, the distance change of the low-AHF zone is relatively stable; the migration distance is 1.31 km2. The distance changes of the medium-AHF zone and the high-AHF zone are more intense, with migration distances of 13.19 and 19.06 km, respectively. Some large-scale industrial areas were identified in the study area in 2020 with the improvement of transportation equipment, while industrial areas were mainly distributed in port areas in 2004. This is the main reason for the large changes of space in the medium- and high-AHF areas.
The expansion and enhancement of the influence of human activities, such as the increase in urban population, rapid economic development, and increased industrial production activities, have stimulated the emission of anthropogenic heat, which contributes to the positive impact on the intensity of anthropogenic heat and the occurrence of significant urban anthropogenic heat change. Analysis of the characteristics of temporal and spatial changes in anthropogenic heat and studies on the laws and influencing factors of anthropogenic heat changes will provide references for urban planning and construction in mitigating urban heat islands.