Abstract
The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
The urbanization process is accelerating as global warming continues to intensify, resulting in increasingly high summer temperatures. As a result, the urban heat island (UHI) effect is becoming more significant (Qiu et al., 2019; Chen et al., 2016; Tang et al., 2024). In 2017, the National Climate Centre of the China Meteorological Administration announced that the 10 provincial capitals or municipalities in China with the hottest summers are Chongqing, Fuzhou, Hangzhou, Nanchang, Changsha, Wuhan, Xi'an, Nanjing, Hefei, and Nanning (Song et al., 2017), Among these, six cities are situated in the middle and lower reaches of the Yangtze River (MLYR), where the UHI effect is prominent in summer. The MLYR is characterized by a dense population and high level of urbanization. It is also situated affected by the West Pacific Subtropical High (WPSH), which results in phenomena such as air subsidence, high temperatures, low precipitation, and weak winds, or worse, extreme high temperatures and heat waves, all of which influence the UHI effect (Li et al., 2023).
Most studies have found that the WPSH has a significant impact on the weather in MLYR. The stable and strong WPSH directly triggers extreme high temperatures and heat waves in the MLYR (Li et al., 2015), and it was found that the summer extreme high temperature weather in the Jiang-Huai region of China in 2013 was associated with the combined effect of the East Asian upper level of the westerly and the WPSH (Sun 2014). In the summer of 2022, the increased intensity and westward extension of WPSH caused persistent high temperatures in the central and eastern parts of China (Ma et al., 2023), resulted in multiple extreme heat and heat waves (Li et al., 2015; Wang et al., 2018; Liu et al., 2019; Tang et al., 2023; Wang et al., 2010; Gao et al., 2018), as well as resulted droughts in the southwestern parts of China (Yang et al., 2012; Wang et al., 2015; Li et al., 2016). It is therefore evident that most of the current studies mostly analyzed the relationship between WPSH and extreme hot weather. The cumulative warming effect of multiple factors intensifies the UHI phenomenon (Gao et al., 2023). This deterioration of the living environment in densely populated urban areas leads to increased energy consumption, affects economic development, and even poses risks to human life and health (Huang et al., 2021; Nie et al., 2021). Therefore, all sectors of society should pay more attention to the UHI effect. Investigations of the temporal and spatial distributions of UHI, the factors influencing UHI in the MLYR, and the effects of WPSH on UHI are significant for local and regional climate research and have societal implications.
Some scholars have studied the UHI phenomenon under large-scale atmospheric conditions. In the large-scale climatic context, the UHI intensity is greatest under sustained high-pressure conditions and least during the passage of cold fronts (Targino et al., 2014). Meanwhile, in a study conducted in the subtropical city of Hong Kong, the highest value of UHI intensity occurs at night during the strong stable high-pressure sinks in summer (Wang et al., 2021). The lowest nighttime UHI intensity occurs during the moist conditions (Scott et al., 2018). Cities are influenced by large-scale climatic factors, leading to varying spatiotemporal characteristics in SUHI intensity (Wang & Sun, 2021; Geng et al., 2023). Additionally, the SUHI intensity is notably higher in the subtropical regions of China than in its temperature and plateau regions (Liu et al., 2022). The southern cities in the eastern monsoon region experience a significant UHI effect during autumn and winter, and the UHI intensity is higher during the night than during the day (Jia et al., 2021). Specific meteorological conditions also affect the UHI intensity. Meteorological features and weather conditions such as precipitation, wind, cloud cover, fog and air pollution are found to influence UHI effect (He 2018). In contrast to meteorological conditions such as sunny weather, strong winds, heavy precipitation, and low or moderate temperatures, low precipitation, low wind speeds, and extremely high temperatures exacerbate the intensity the UHI effect (Huang et al., 2022; Chen et al., 2023; Zheng et al., 2023). Wind direction is also a key factor influencing UHI intensity, with winds from dry environments exacerbating UHI intensity and winds from moisture-rich areas decreasing UHI intensity(Al-Obaidi et al., 2021). Sea breeze mitigates UHI intensity, and pure sea breeze perpendicular to the coastline has a greater mitigating effect on UHI intensity (Shang et al., 2024). The interaction between heat waves and UHI leads to a significant increase in nighttime UHI intensity in Athens (Founda et al., 2015). Heat waves and UHI interactions are inconsistent in tropical and temperate cities (Chew et al., 2021). Daytime heat waves, nighttime heat waves, and diurnal composite heat waves all enhance nighttime UHI intensity (Cui et al., 2023). Heat waves have been found to exacerbate UHI intensity due to meteorological factors such as low humidity, low wind speed, and high net radiation flux during heat waves (Ao et al., 2019; Ngarambe et al., 2021; Zheng et al., 2024). UHI intensity is lowest under the control of cool and dry air masses, but UHI intensity generally increases with the duration of the air mass, while the opposite occurs under warm and humid air mass conditions (Tol et al., 2023).
In the study of meteorological background and UHI, most studies have analyzed the effects of different meteorological conditions on UHI intensity, and the relationship between heat waves and UHI is more popular. Meanwhile, in the study of WPSH, most scholars studied the relationship between WPSH and heat waves. However, the current research on the relationship between WPSH and UHI is still weak. The air subsidence in the area controlled by the WPSH exacerbates the high temperatures. Thus, it is worth exploring whether the WPSH also exacerbates the UHI effect. In this study, to understand the influence of WPSH on UHI in the MLYR, the spatial and temporal distributions of UHI, the factors influencing UHI, and the influence of the WPSH on UHI were analyzed by considering two scenarios: with and without WPSH. Based on the WPSH indexes, which include an area index, intensity index, ridge line index, and western boundary index, we selected years with and without continuous WPSH in the MLYR. The UHI intensity and urban heat island proportion index (UHPI) were calculated using land surface temperature (LST) data. The factors influencing UHI under different conditions were then analyzed using the geographical detector method. Based on the results, we offer recommendations for mitigating the UHI effect and creating sustainable and resilient urban environments.
2 Materials
2.1 Study area
The MLYR is located in central-eastern China (108°21′–123°25′E; 24°38′–35°20′N; Fig. 1). Administratively, the MLYR includes Hubei, Hunan, Anhui, Jiangxi, Jiangsu, Zhejiang, and Shanghai (Zheng et al., 2014). The topography of the region is generally hilly plains with a low elevation and flat terrain, and there is little temperature difference within the region (Yang et al., 2018). The summer is often dominated by the WPSH, whereas the WPSH is less intense in other months, making the MLYR suitable as a study are for this research. By the end of 2022, the permanent population of the MLYR was 406.7 million, accounting for approximately 29% of the total population of the country. The urbanization rate of the MLYR is 67.37%. Thus, this region is densely populated, highly urbanized, and exhibits typical UHI characteristics. Therefore, studying UHI in the MLYR is of significant practical importance. The Yangtze River Delta urban agglomeration is a prominent research area for UHI studies. Research in this area primarily focuses on the long-term spatial distribution of UHI in this region and the temporal changes in UHI area and intensity (Fang et al., 2017). Previous studies have also investigated the formation mechanisms of UHI from various perspectives including urbanization (Dong et al., 2014), underlying surface properties, human activities, and vegetation cover (Chen & Ma, 2023; Du et al., 2016). The MLYR is strongly affected by UHI and is thus an appropriate area for studying UHI.
Map of the research area and its location within China
2.2 Spatiotemporal databases
Most previous studies on the UHI phenomenon have used meteorological data to investigate the distribution of UHI and the influencing factors (Peng et al., 2013; Venter et al., 2021). However, the limited quantity and scattered distribution of meteorological limit large-scale studies of UHI. Satellite remote sensing data such as MODIS and Landsat data offer advantages such as comprehensiveness, continuity, and low cost, making them a focal point in recent studies on UHI. MODIS remote sensing data have a high spatial resolution and have been collected continuously for nearly two decades; furthermore, the collecting satellite transits over given areas at the same time each day. These qualities make it suitable for studying the distribution and trends of UHI over large areas (Kim et al., 2021; Deilami et al., 2018). In this study, we acquired urban surface temperature data from MODIS and calculated the surface urban heat island(SUHI) intensity in the MLYR. We downloaded and preprocessed remote sensing data for use in this study (Yin et al., 2023). The high-precision remote sensing data were resampled to 1000 m. The LST data were derived from the MODIS/Aqua satellite. The satellite passes over at 13:30 (noon) and 1:30 (night), allowing for the accurate retrieval of LST data during both day and night. The selected LST data were synthesized over an 8-day period, and the data with the highest root-mean-square value over the 8-day period were selected to effectively calculate the SUHI intensity. The LST data in units of Kelvin (K) were converted into degrees Celsius (°C), and the monthly average LST for day and night in July was obtained. Table 1 displays relevant data. The high-precision remote sensing data were resampled to 1000 m.
3 Methods
3.1 Data preprocessing
3.1.1 Standard deviation of the WPSH indexes
The MLYR is significantly affected by the WPSH in the summer. Thus, we selected July as the research month and chose years with varying spatial characteristics and intensities of the WPSH. The standard deviations of the WPSH indexes were calculated to determine the years with and without WPSH. The difference level was defined as the difference between the value of the index and the average value of the index value over the 30-year period from 1993 to 2022 (Zhao et al., 2012). The WPSH can be classified based on the standard deviation and difference levels of the WPSH indexes and examining the spatial distribution and intensity pattern of the WPSH. The standard deviations δ of the WPSH indexes in July were calculated for the 30-year period from 1993 to 2022 (Table 2), and normal values were considered those within the range of 0.5 × δ \(.\)
3.1.2 Extraction of rural background temperature
The calculation of UHI intensity requires determining the rural background temperature. In this case, adjustments were made to the original conditions based on the characteristics of the MLYR and considering the convenience, accessibility, and applicability of the selected regional indicators. The urban–rural dichotomy (Liu et al., 2017) was used to establish a unified rural background in the MLYR for calculating the SUHI intensity of each city. To minimize the error in the UHI effect caused by elevation, the urban districts were chosen as a mask, the average elevation of the urban district was calculated, and pixels with an elevation difference within ± 50 m were extracted. Regarding land cover type, farmland was selected as the land cover type due to its ability to reflect the rural background temperature. Nighttime lighting was used as a proxy for the intensity of human activities in this study. To exclude the portion of farmland affected by urbanization, we chose a pixel with a night light intensity index of 15. Finally, we used the normalized difference vegetation index (NDVI) to exclude most towns with low vegetation cover and human activities around cities. The NDVI was also used to pinpoint areas devoid of water bodies but with high vegetation cover. In the MLYR, the NDVI values of farmland areas are higher than those of urban areas but lower than those of forested areas. Therefore, pixels with an annual NDVI of 0.4–0.6 were selected. By using the above four indicators to extract the rural background, we identified the rural regions in the MLYR.
3.2 UHI indexes calculation
3.2.1 Estimation of SUHI intensity
The SUHI arises from the difference in surface temperature between urban and rural areas. The mean LST of rural pixels was used to quantify the UHI intensity, which was then classified according to the classification of SUHI (Ye et al., 2011). The SUHI intensity of pixel i (SUHIi) was calculated as
where LSTi is the temperature of pixel i (°C), n is the effective number of pixels in the background of the rural area, and LSTr is the temperature in the rural background temperature (°C). The SUHIi values were divided into seven levels: strongest cold island (< − 5.0 °C); strong cold island (− 5.0 °C to − 3.0 °C); weak cold island (− 3.0 °C to − 1.0 °C); no heat island (− 1.0 °C to 1.0 °C); weak heat island (1.0 °C to 3.0 °C); strong heat island (3.0 °C to 5.0 °C); and strongest heat island (> 5.0 °C).
3.2.2 Estimation of UHPI
UHPI is the ratio of the area with existing UHI effects to the total area of the region; UHI intensity values are assigned to pixels in a weighted manner and then used to calculate the index value. UHPI indicates the level of development of heat islands (Xu et al., 2003) within regional urban spatial units. The scale and intensity of UHI effects were compared across various spatial units and time periods in different regions. UHPI was calculated as
where m represents the level of the UHI effect (classified into seven classes, from coldest to hottest); n indicates the SUHI classes at which the urban temperature exceeds the rural temperature (i.e., classes of 5–7); j denotes the UHI effect level of these classes; \({g}_{j}\) is the weighted UHI effect level of the j-th grade; and \({a}_{j}\) is the percentage of the area occupied by SUHI level j. UHPI ranges from 0 to 1, which a greater UHPI corresponding to greater SUHI intensity and a larger proportion of the region affected.
3.3 Geographical detector model
The geographical detector method can be used to explore the spatial heterogeneity and influencing factors of geographical phenomena (Wang et al., 2017). In this paper, we used the geographical detector method to explore the factors influencing SUHI. The degree to which a factor influences the SUHI intensity (q) was calculated as
where x is the influencing factor, Y is the UHI intensity, n is the sample size of the study area, m is the number of influencing factor x classifications, nx,i represents the sample size of influencing factor i, and \({\delta }_{{T}_{x, i}}^{2}\) is the variance of influencing factor i. The values of q range from 0 to 1, with a higher q value indicating greater explanatory power on the SUHI intensity.
4 Results and analysis
4.1 Selection the years with or without WPSH control
The WPSH indexes represent the area, strength, and positions of the WPSH in the north, south, east, and west (Liu et al., 2012). As shown in Table 3, the WPSH in July 2018 was within the normal range, and the WPSH’s ridge was located at 33.3°N and 121.1°E. The west extension ridge point index was in the East in 2015 and 2021. The main body of the WPSH was located over the Pacific Ocean and was distant from the mainland of China. Therefore, July 2015, 2018 and 2021 were selected as the month when the WPSH did not affect the MLYR. In July 2022, the WPSH ridge line was located at 25.5°N and 113.1°E, and the WPSH exhibited a high intensity, large control area, and westward position. The WPSH influenced southeast China and the majority of the Yangtze River basin, including the MLYR, in July 2017, 2020 and 2022. Therefore, July 2017, 2020 and 2022 were selected as the month when the MLYR was affected by the WPSH.
4.2 Temporal and spatial distributions of UHI
4.2.1 The daytime UHI
As shown in Fig. 2, during the daytime in July 2015, large area of strongest heat island appeared in Anhui Province in the north and Shanghai City in the east of the study area. In July 2018, the UHI effect was significant during the daytime in the MLYR. The intense UHI effect was primarily concentrated in the northeast of the MLYR, with a pronounced presence in the northwestern cities of Anhui province and the urban agglomeration of the Yangtze River Delta. In July 2021, strong heat island appeared in bands in Shanghai City and urban centers in the central and southern parts of Hunan Province. It can be seen that the spatial distribution characteristics of strong and strongest heat island in the years without WPSH control are mainly: Large areas of strong heat island appeared in economically developed provincial capitals and municipalities directly under the Central Government. High-density urban buildings, dense population, and activities such as production in provincial capitals and downtown areas can significantly exacerbate the UHI effect. Xiangyang City in Hubei Province, Fuyang City in Anhui Province, Bozhou City, Huaibei City, and Suzhou City had large areas of strongest heat island. These cities are low lying, flat, and densely populated, resulting in high surface temperatures. Several cold islands with strong cooling effects were found in the western regions of the MLYR due to the high altitudes, low temperatures, and abundant forest vegetation. Some urban cold island appeared along the southeast coast due to partial moderation by the ocean, which helps to alleviate high temperatures.
Map of daytime UHI intensity in the MLYR
In July 2017, large area of the strongest heat island was observed in Shanghai City City, while strong heat island also appeared in large bands in cities in central Zhejiang Province and in multiple points in Hunan Province. In July 2020, large area of the strong heat island was observed in Shanghai City, cities in northern and southern Hunan Province, cities in northern Jiangsu Province and cities in northern Zhejiang Province. Throughout 2022, the UHI effect was widely distributed during daytime, with a high concentration observed in the capital cities of all provinces. Strong heat island effects were widely distributed in many cities in Hunan and Jiangxi provinces. A significant UHI effect was present in a band spanning Nanchang City in the north of Jiangxi Province; Quzhou City, Jinhua City, and Shaoxing City in the middle of Zhejiang Province; and across a vast area in the urban agglomeration of the Yangtze River Delta, including Shanghai City, Jiaxing City, Suzhou City, and Changzhou City. In contrast, under the control of the WPSH, the daytime UHI exhibited high overall intensity and a wide distribution. The UHI intensity was more widely spread over provincial capitals along with economically developed and densely populated cities. Overall, the distribution of strongest and strong heat island during the daytime were more extensive in the years with WPSH control compared to the years without WPSH control.
In the area distribution of various UHI intensities during the daytime (Table 4). The strongest cold island had the largest distribution area of 6.64 million km2 in 2018, and the smallest distribution area of 4.17 million km2 in 2022. The strong cold island had the largest distribution area 14.80 million km2 in 2021, and the smallest distribution area was in 2020 with 10.98 million km2. The weak cold island had the largest distribution area in 2021 with 27.26 million km2, and the smallest value was in 2020 with 21.43 million km2. It can be seen that the maximum value of the area of the cold island mainly occurred in the years without WPSH, and the minimum value was in the years with WPSH. The areas of the cold island in the years without WPSH were larger than that in the years with WPSH. The maximum value of the area of the distribution of the weak heat island was 15.56 million km2 in 2020, and the minimum value was 9.52 million km2 in 2021. The maximum value of the distribution area of strong heat island was 5.69 million km2 in 2020 and the minimum value was 3.93 million km2 in 2021. The maximum value of the distribution area of the strongest heat island was 4.42 million km2 in 2015 and the minimum value was 2.20 million km2 in 2018. It can be seen that the maximum value of the area of various types of heat island mainly occurred in the years WPSH, the minimum value occurred in the years without WPSH. Overall, the areas of heat island in the years with WPSH control were generally larger than that in the years without WPSH control, while the areas of cold island were generally smaller than that in the years without WPSH control.
4.2.2 The nighttime UHI
The UHI intensity in the MLYR at night was weak. There was no UHI effect in most areas at night in the years with WPSH, and urban cold island were distributed around the edge of the MLYR. Weak heat island was concentrated in and around the low-altitude and densely populated provincial capitals in the MLYR, while stronger heat island appeared in the centers of provincial capitals (Fig. 3). The nighttime UHI effect was more extensively spread in the years with WPSH than the years without WPSH, with significant areas of weak heat island in numerous cities in central Hunan and Hubei provinces, the urban cluster in northern Jiangxi Province, and the urban cluster in the Yangtze River Delta. The nighttime UHI effect was more pronounced in economically developed cities with dense populations, such as Shanghai City and provincial capitals. The results indicate that WPSH exacerbates the UHI effect in densely populated urban areas, posing a risk to urban safety and human settlements.
Map of nighttime UHI intensity in the MLYR
The area of the nighttime UHI distribution (Table 5), the strongest cold island had the largest distribution area of 3.82 million km2 in 2021, and the smallest distribution area of 2.74 million km2 in 2015. The strong cold island had the largest distribution area 8.78 million km2 in 2020, and the smallest distribution area was in 2022 with 5.17 million km2. The weak cold island had the largest distribution area in 2020 with 19.43 million km2, and the smallest value was in 2015 with 14.58 million km2. It can be seen that the maximum value of the area of the stronger cold island mainly occurred in the years with WPSH control, and the minimum value was in the years without WPSH control. The maximum value of the area of the distribution of the weak heat island was 26.57 million km2 in 2017, and the minimum value was 16.87 million km2 in 2021. The maximum value of the distribution area of strong heat island was 4.52 million km2 in 2017 and the minimum value was 1.66 million km2 in 2021. The distribution area of the strongest heat island was minimal and close to none. It can be seen that the maximum value of the areas of the various types of heat island mainly occurred in the years with WPSH control, and the minimum value were in the years without WPSH control. Overall, the weak heat island distribution was the widest at night, the strongest heat island distribution was smallest, and the heat island distribution was larger in the years with WPSH than the years without WPSH.
4.3 Analysis of UHPI
UHPI was calculated to determine the area proportions of different levels of SUHI intensity (Table 6). The SUHI intensity was quantitatively compared across different times and locations. Daytime UHPI values were generally smaller than at night. In terms of year comparison, the daytime UHPI values were greater than 0.3 in three years, including two years with WPSH control. The mean daytime UHPI values of the years with WPSH control were slightly higher than the mean values of the years without WPSH control, which showed that the UHI effect in the study area was more intense in the years with WPSH control than the years without WPSH control.
The maximum value of the nighttime UHPI value was 0.455 in 2017, and the UHPI values greater than 0.3 had three years, such as 2017, 2015 and 2022, of which there were two years with WPSH control. Meanwhile the mean UHPI value at night in the years with WPSH control was higher than the mean value in the years without WPSH control. It can be seen that the years with WPSH control had a more significant effect on the exacerbation of UHI intensity at night.
Overall, the daytime and nighttime UHPI values of the years with WPSH control were generally larger than those of the years without WPSH control, and the WPSH had a significant exacerbating effect on the intensity of the UHI. In both the presence and absence of the WPSH, UHPI were higher during the night than the day. The difference in UHPI value between day and night were larger in the years with WPSH than the years without WPSH. The presence of the high-pressure system of the WPSH reduced the disparity in UHI intensity between day and night, resulting in continuous heating of the city throughout the day. These results indicate that WPSH exacerbated the intensity of UHI, and the effect was more pronounced at night.
4.4 Factors influencing the UHI effect
The geographical detector method was employed to analyze the factors influencing the UHI intensity during the day and night. We identified four categories of influencing factors: the air quality factors were ozone (O3), aerosol optical depth (AOD), and air quality index (AQI); the anthropogenic factors were nighttime light (NTL) and population density (POP); the surface characterization factors were elevation (DEM) and NDVI; and the meteorological factors were water vapor content (VAP) and distance from the WPSH center (WPSH).
4.4.1 Analysis of the role size of factors influencing UHI intensity
The factors affecting the daytime UHI intensity in 2015 and 2018 were mainly anthropological factors such as POP and NTL (Table 7). The VAP had the largest q value, the POP and the O3 had higher q values in 2021. Overall, the main factors were anthropogenic factors that affecting the daytime UHI intensity in the years without WPSH. The factors affecting the daytime UHI intensity were mainly POP, while the air quality factors such as AQI and O3, and the WPSH also had the larger q value in 2017. The q values of the anthropogenic factors such as POP and NTL, and the air quality factors such as AQI and O3, which q value were larger in 2020. The mainly factors affecting the daytime UHI intensity in 2022 were POP, NTL, AQI, and WPSH. Overall, the main factors affecting daytime UHI intensity in the years with WPSH control were anthropogenic factors, air quality factors and the factor of WPSH.
The main factors affecting the daytime UHI intensity were the anthropogenic factors. The primary factor influencing UHI was O3, because O3 absorbs ultraviolet short-wave radiation, warming the atmosphere. Air quality had a strong influence on the intensity of the UHI during the daytime, with O3 and AQI playing significant roles, and the q values of these two types factors were larger in the years with WPSH control, which may be due to the fact that WPSH control of the study area was accompanied by a persistent low-wind and low-rainfall weather, which made it difficult for air pollutants to diffuse, and further exacerbated the intensity of the UHI. Moreover, under the control of the stable WPSH, the MLYR had less cloudy, and the ozone in the troposphere absorbs ultraviolet rays more easily, which increased the surface temperature and exacerbates the UHI to a certain extent. The intense human activities in densely populated urban areas are also important sources of heat emission that aggravate the UHI effect. The increasing of the UHI intensity cannot be separated from the anthropogenic heat generated by human production, living and transport activities. The higher the population density of a city, the higher the degree of urbanization development, the UHI was further aggravated, in addition, the increase in population density further accelerated the large amount of anthropogenic heat emission, so social activities were important factors affecting the UHI. Taken together, the process of mitigating UHI intensity should focus on emissions of anthropogenic heat from industrial activities, transport and so on in years without WPSH control. Anthropogenic heat emitted by human activities and air quality factors needed to be focused on in years with WPSH control.
DEM played an important role in the factors affecting the nighttime UHI (Table 8). DEM had the largest q value, POP and NTL had high q values in 2015. AOD had large q value, while the anthropogenic factors also had high q values in 2018. The primary factor influencing UHI in 2021 was AOD, and larger q values for the air quality factors of O3 and AQI. Overall, the factors affecting the nighttime UHI intensity in the years without WPSH control were mainly AOD, POPand NTL. In 2017, anthropogenic factors such as POP and NTL and air quality factors such as AOD had high q values, and the q value of WPSH was also high. In 2020, O3 and AQI had large q values. In 2022, POP and AOD had large q values, and the WPSH had large q value. Overall, the main factors affecting the nighttime UHI intensity in the years with WPSH control were the POP, AOD and WPSH. The distance from the center of the WPSH was an important factor affecting UHI intensity. A smaller distance from the center of the WPSH corresponded to a greater WPSH intensity and the strong UHI effect.
Most of the studies showed that AOD affects the nighttime UHI intensity to a large extent. AOD had different effects during the day and night (Cao et al., 2016). Aerosols reflected and absorbed solar radiation during the day, which could lower near-ground temperatures. However, this cooling effect of aerosols was not significant at high temperatures. At night, aerosols played a role in heat preservation. Generally, aerosols increased the intensity of UHI at night; thus, AOD was primarily an influencing factor of UHI during the night (Deng et al., 2021; Han et al., 2022; Liang et al., 2021). The results suggest that WPSH had an important influence on UHI intensity during both day and night. A smaller distance to the center of WPSH corresponded to a greater WPSH intensity and a more significant UHI effect. So, it was important to focus on the AOD to further mitigate the nighttime UHI intensity. In the years without WPSH control, more attention should be paid to the anthropogenic factors, and in the years with WPSH control, factors such as population density factor and WPSH should also be paid attention to.
In summary, the UHI intensity was higher under the influence of WPSH than when the WPSH was absent. The main factors influencing UHI intensity were anthropogenic heat emissions from human activities, the distance from the center of WPSH, and aerosols. During the years without WPSH, O3 and other air pollutants had significant effects on UHI intensity, and population density also had an important effect. The important influencing factors during daytime were the population density and night light. During the day, industrial and transportation activities along with anthropogenic heat emissions are extensive in densely populated areas (Dewan et al., 2021; Lu et al., 2014; Wu et al., 2023; Zhou et al., 2014). Therefore, the effects of human activity on daytime UHI intensity are significant. At night, the primary influencing factor was aerosols, which have a thermal insulation effect on the ground and exacerbate the intensity of UHI at night to some extent.
4.4.2 Interaction analysis of factors affecting UHI intensity
Among the years without WPSH control, the factor interactions affecting daytime UHI intensity (Fig. 4) in 2015 were in descending order as follows: VAP ∩ WPSH (0.748) > POP ∩ VAP (0.725) > DEM ∩ VAP (0.659) > POP ∩ NDVI (0.637) > NDVI ∩ WPSH (0.632). In 2018, the factor interactions were ordered as VAP ∩ WPSH (0.69) > O3 ∩ POP (0.675) > AQI ∩ POP (0.653) > POP ∩ WPSH (0.632) > AOD ∩ DEM (0.614). In 2021, the factor interactions were ordered as NTL ∩ NDVI (0.644) > O3 ∩ POP (0.627) > POP ∩ NDVI (0.606) > POP ∩ WPSH (0.604) > AOD ∩ WPSH (0.589).
Heat map of the interaction of factors affecting the daytime UHI intensity
Among the years with WPSH control, the factor interactions in 2017 were ranked as AQI ∩ POP (0.79) > O3 ∩ POP(0.772) > POP ∩ NDVI(0.75) > AQI ∩ NTL(0.726) > NTL ∩ NDVI(0.724). In 2020, the factor interactions were ranked as AOD ∩ WPSH (0.601) > POP ∩ DEM(0.571) > DEM ∩ VAP(0.571) > AQI ∩ POP(0.56) > POP ∩ VAP(0.559). In 2022, the factor interactions were ranked as POP ∩ DEM (0.707) > NTL ∩ DEM( 0.703) > O3 ∩ DEM(0.684) > AQI ∩ POP(0.683) > DEM ∩ VAP(0.673).
Overall, the interactions between POP and multiple factors such as air quality factors and surface characteristics were significant in the years without WPSH, and the two types of anthropogenic factors, such as POP and NTL, interacted significantly with WPSH control and air quality factors in the years with WPSH control.
Among the years without WPSH control, the factor interactions affecting nighttime UHI intensity (Fig. 5) in 2015 as follows: NTL ∩ DEM(0.84) > DEM ∩ VAP(0.828) > POP ∩ DEM(0.797) > DEM ∩ NDVI(0.795) > DEM ∩ WPSH(0.782). In 2018, the factor interactions were ordered as DEM ∩ NDVI(0.871) > DEM ∩ WPSH(0.867) > DEM ∩ VAP(0.845) > AOD ∩ DEM(0.839) > O3 ∩ DEM(0.838). In 2021, the factor interactions were ordered as DEM ∩ VAP(0.787) > DEM ∩ WPSH(0.786) > AOD ∩ DEM (0.764) > DEM ∩ NDVI (0.724) > AOD ∩ WPSH (0.689). Among the years with WPSH control, the order of factor interactions in 2017 were: AQI ∩ DEM(0.861) > NTL ∩ DEM(0.857) > DEM ∩ VAP(0.856) > O3 ∩ DEM(0.845) > AOD ∩ DEM(0.841).The order of factor interactions in 2020 were: AOD ∩ DEM(0.73) > DEM ∩ NDVI(0.727) > AOD ∩ AQI(0.611) > AOD ∩ WPSH(0.602) > AQI ∩ NDVI(0.589).In 2022, the order of factor interactions were: DEM ∩ VAP(0.873) > O3 ∩ DEM(0.839) > DEM ∩ NDVI(0.826) > DEM ∩ WPSH( 0.825) > POP ∩ DEM (0.823).
Heat map of the interaction of factors affecting the nighttime UHI intensity
Overall, the interactions between DEM and multiple factors were significant in the nighttime UHI. The interactions between DEM and anthropogenic factors, such as NTL and POP, were larger in the years without WPSH control. The interactions between air quality factors, such as AQI and AOD, and DEM were more significant in the years WPSH control, while there were also some interactions with anthropogenic factors.
5 Discussion
When studying UHI, scholars have typically examined the spatial and temporal distributions of UHI in major cities and the influencing factors. However, few studies have considered the diverse meteorological backgrounds of cities as contributing factors. The WPSH exacerbates airflow subsidence, resulting in weak winds and low rainfall. This creates a favorable meteorological environment for the UHI effect and intensifies the UHI phenomenon in urban areas. In this study, we demonstrated that the UHI intensity is higher during both day and night under the influence of the WPSH, with a stronger effect observed at night. It was found that the UHI intensity was enhanced under the control of the WPSH, When the WPSH was active, the distance from the center of the WPSH was found to strongly influence the UHI effect. Therefore, more attention should be paid to the UHI effect under the influence of WPSH. The factors influencing UHI also differed in the presence and absence of the WPSH. Thus, the results provide guidance for mitigating the UHI effect under different meteorological conditions. For the healthy and sustainable development of cities and the mitigation of UHI intensity in cities, the following aspects needed to be addressed. First, limiting anthropogenic heat emissions such as traffic and air-conditioning systems in the years without WPSH control, and strengthening the management of air pollutants in years with WPSH control to reduce the levels of pollutants such as ozone and aerosols in the air. Increasing vegetation cover in central urban areas with economically developed population concentrations and high building densities. Paying attention to high intensity UHI mitigation during the day, managing air pollutants such as ozone, and limiting anthropogenic heat emissions from industrial production, transport and air systems, among others. At the same time, paying attention to the distribution of large area UHI at night, and carrying out timely aerosol management to mitigate UHI at night. Secondly, increasing a certain amount of vegetation cover in the internal planning of the city; increasing the use of green roofs in the construction of buildings; and in daily management, timely using of irrigation facilities to increase the water vapor content of the soil and the air, and ultimately lower the air temperature to mitigate the intensity of the UHI.
This study has some limitations. First, although the urban and rural dichotomy was used for reference, and some adjustments were made, there are many regional differences. The UHI intensity estimated based on the urban and rural dichotomy may contain errors (Meng et al., 2018; Chakraborty et al., 2019). The academic community has not yet established a unified standard to differentiate urban and rural areas. Therefore, a more reasonable criterion for identifying rural background will be investigated in the future (Wang, 2022). Second, we compared the UHI effect in the presence and absence of the WPSH, and considered the distance from the center of the WPSH as an influencing factor. In the future, we will study the spatiotemporal distribution and evolution of UHI intensity for different locations and WPSH intensities. This analysis will help us better understand the influencing factors and develop strategies to mitigate the UHI effect.
6 Conclusion
We investigated the effect of WPSH on the UHI intensity in the MLYR. We analyzed the spatiotemporal distribution and influencing factors of UHI intensity by considering two scenarios (with the WPSH and without the WPSH). The characteristics of the WPSH in the MLYR were analyzed based on the standard deviation of the WPSH indexes. The UHI intensity and UHPI in the MLYR were calculated using the rural background temperature extracted using the elevation, land cover, nighttime light, and NDVI. The factors influencing UHI were analyzed using the geographic detector method, leading to the following conclusions.
Daytime UHI intensity is higher and more dispersed, while nighttime UHI intensity is weaker but more extensive. The UHI intensity was high in capital cities, municipalities, and some economically developed cities in the MLYR. The area distribution of UHI were wider in the years WPSH than in years without WPSH during day and night. The distribution area of high-intensity UHI was larger under WPSH control than in its absence. The WPSH exacerbated the UHI intensity in the MLYR, leading to high urban temperatures.
Comparative analyses of UHPI values showed that nighttime UHPI values were higher than daytime UHPI values, while the difference in nighttime UHPI values with and without the WPSH was larger than that during the daytime. Thus, the daytime UHPI values were higher under the influence of WPSH. The WPSH enhanced the UHI effect during both day and night, and the WPSH has a more significant role in exacerbating the UHI at night.
In the analysis of factors affecting UHI, the factors affecting daytime UHI intensity are mainly anthropogenic factors such as POP and NTL, anthropogenic sources of heat emissions such as industrial activities and traffic, O3 also plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and the interactions are mainly DEM and multi-factors during the nighttime.
Availability of data and materials
There are no data made available from this work for public access.
References
Al-Obaidi, I., Rayburg, S., Polrolniczak, M., & Neave, M. (2021). Assessing the Impact of Wind Conditions on Urban Heat Islands in Large Australian Cities. Journal of Ecological Engineering, 22(11), 1–15. https://doi.org/10.12911/22998993/142967
Ao, X. Y., Tan, J. G., Zhi, X., Guo, J. B., Lu, Y. W., & Liu, D. J. (2019). Synergistic Interaction Between Urban Heat Island and Heat Waves and Its Impact Factors in Shanghai. Acta Geographica Sinica, 74(09), 1789–1802.
Cao, C., Lee, X. H., Liu, S. D., Schultz, N., Xiao, W., Zhang, M., & Zhao, L. (2016). Urban heat islands in China enhanced by haze pollution. Nature Communications, 7. https://doi.org/10.1038/ncomms12509
Chakraborty, T., & Lee, X. (2019). A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation, 74, 269–280. https://doi.org/10.1016/j.jag.2018.09.015
Chen, C. H., & Ma, M. M. (2023). Characteristics and Mechanism of Urban Heat Island Based on Multi-source Remote Sensing Data. Geospatial Information, 21(10), 119–122+139.
Chen, G., Chen, Y. Q., Tan, X., Zhao, L. H., Cai, Y. N., & Li, L. (2023). Assessing the urban heat island effect of different local climate zones in Guangzhou, China. Building and Environment, 244. https://doi.org/10.1016/j.buildenv.2023.110770
Chew, L. W., Liu, X., Li, X. X., & Norford, L. K. (2021). Interaction between heat wave and urban heat island: A case study in a tropical coastal city, Singapore. Atmospheric Research, 247. https://doi.org/10.1016/j.atmosres.2020.105134
Chen, M. X., Liu, W. D., & Lu, D. D. (2016). Challenges and the way forward in China's new-type urbanization. Land Use Policy, 55, 334–339. https://doi.org/10.1016/j.landusepol.2015.07.025
Cui, F. Q., Hamdi, R., Kuang, W. H., Yang, T., He, H. L., Termonia, P., & De, M. P. (2023). Interactions between the summer urban heat islands and heat waves in Beijing during 2000–2018. Atmospheric Research, 291, 106813. https://doi.org/10.1016/j.atmosres.2023.106813
Deilami, K., Kamruzzaman, M., & Liu, Y. (2018). Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. International Journal of Applied Earth Observation and Geoinformation, 67, 30–42. https://doi.org/10.1016/j.jag.2017.12.009
Deng, Y. J., Wang, J. C., Hong, Y. Y., Tan, H. B., Chen, J. Y., & Xiang, K. L. (2021). The Impact of Aerosols on Daytime Urban Heat Island Intensity in Guangdong-Hong Kong-Macao Greater Bay Area. Scientia Geographica Sinica, 41(12), 2213–2221. https://doi.org/10.13249/j.cnki.sgs.2021.12.015
Dewan, A., Kiselev, G., Botje, D., Mahmud, G. I., Bhuian, M. H., & Hassan, Q. K. (2021). Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society, 71. https://doi.org/10.1016/j.scs.2021.102926
Dong, L. P., Jiang, Z. H., & Shen, S. H. (2014). Urban Heat Island Change and Its Relationship with Urbanization of Urban Agglomerations in Yangtze River Delta in Past Decade. Transactions of Atmospheric Sciences, 37(02), 146–154. https://doi.org/10.13878/j.cnki.dqkxxb.2014.02.002
Du H. Y., Wang D. D., Wang Y. Y., Zhao X. L., Qin F., Jiang H., & Cai Y. L. (2016). Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Science of The Total Environment, 571, 461–470. https://doi.org/10.1016/j.scitotenv.2016.07.012
Fang, Y. B., Zhan, W. F., Huang, F., Gao, L., Quan, J. L., & Zou, Z. X. (2017). Hourly Variation of Surface Urban Heat Island over the Yangtze River Delta Urban Agglomeration. Advances in Earth Science, 32(02), 187–198.
Founda, D., Pierros, F., Petrakis, M., & Zerefos, C. (2015). Interdecadal variations and trends of the Urban Heat Island in Athens (Greece) and its response to heat waves. Atmospheric Research, 161-162, 1–13. https://doi.org/10.1016/j.atmosres.2015.03.016
Gao, H. Y., Shen, X. Y., Dong, W., Zhao, L., Luo, Y. L., & Wang, Y. Q. (2023). The Synergy of Urbanization and Western Pacific Subtropical High Intensification on Compound Heat Waves in China. Transactions of Atmospheric Sciences, 46(01), 119–131. https://doi.org/10.13878/j.cnki.dqkxxb.20210311001
Gao, M., & Zheng, H. Z. (2018). Nonstationary extreme value analysis of temperature extremes in China. Stochastic Environmental Research and Risk Assessment, 32(5), 1299–1315. https://doi.org/10.1007/s00477-017-1482-0
Geng, X. L., Zhang, D., Li, C. W., Yuan, Y., Yu, Z. W., & Wang, X. R. (2023). Impacts of climatic zones on urban heat island: Spatiotemporal variations, trends, and drivers in China from 2001–2020. Sustainable Cities and Society, 89, 104303. https://doi.org/10.1016/j.scs.2022.104303
Han, M. Y., Feng, X. G., Li, F. X., Zhou, Z. H., & Li, M. (2022). Research Progress on Interaction Between Urban Heat Island and Aerosol. Remote Sensing Information, 37(04), 128–134.
He, B. J. (2018). Potentials of meteorological characteristics and synoptic conditions to mitigate urban heat island effects. Urban Climate, 24, 26–33. https://doi.org/10.1016/j.uclim.2018.01.004
Huang, H. C., Yang, H. L., Deng, X., Chen, T. N., & Jia, Q. (2021). Spatial Evolution of Impact of Urban Heat Island on Residents’ Health. Remote Sensing Information, 36(04), 38–46.
Huang, Q. F., & Yan, M. (2022). Hourly Variability of the Urban Heat Island Effect Under Typical Meteorological Conditions. Journal of the Meteorological Sciences, 42(03), 402–409.
Jia, W. X., Ren, G. Y., Yu, X. J., Zhang, Y. Q., & Zhang, P. F. (2021). Difference of Urban Heat Island Effect among Representative Cities of Different Climatic Zones over Eastern China Monsoon Region. Climatic and Environmental Research, 26(05), 569–582.
Kim, S. W. & Brown, R. D. (2021). Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review. Science of The Total Environment, 779. https://doi.org/10.1016/j.scitotenv.2021.146389
Li, J., Ding, T., Jia, X. L., & Zhao, X. C. (2015). Analysis on the Extreme Heat Wave over China around Yangtze River Region in the Summer of 2013 and Its Main Contributing Factors. Advances in Meteorology, 706713. https://doi.org/10.1155/2015/706713
Li, S. P., Feng, G. L., & Hou, W. (2016). Summer Drought Patterns in the Middle-Lower Reaches of the Yangtze River Basin and Their Connections with Atmospheric Circulation before and after 1980. Advances in Meteorology, 8126852. https://doi.org/10.1155/2016/8126852
Li, Y., Gao, Q. J., You, Q., & Yue, Y. B. (2023). Intraseasonal Oscillation Features of the Two Types of Persistent High Temperature Events over Jiangnan Region. Atmosphere, 14(1). https://doi.org/10.3390/atmos14010185
Liang, Z., Huang, J., Wang, Y. Y., Wei, F. L., Wu, S. Y., Jiang, H., Zhang, X. L., & Li, S. C. (2021). The mediating effect of air pollution in the impacts of urban form on nighttime urban heat island intensity. Sustainable Cities and Society, 74, 102985. https://doi.org/10.1016/j.scs.2021.102985
Liu Q., Zhou T. J., Mao H. T., & Fu C. B. (2019). Decadal variations in the relationship between the Western Pacific subtropical high and summer heat waves in East China. Journal of Climate, 32(5), 1627–1640. https://doi.org/10.1175/jcli-d-18-0093.1
Liu, Y. H., Fang, X. Y., Zhang, S., Luan, Q. Z., & Quan, W. J. (2017). Research on Quantitative Evaluations of Heat Islands for the Beijing-Tianjin-Hebei Urban Agglomeration. Acta Ecologica Sinica, 37(17), 5818–5835.
Liu, Y. Y., Li, W. J., Ai, X., & Li, Q. Q. (2012). Reconstruction and Application of the Monthly Western Pacific Subtropical High Indices. Journal of Applied Meteorological Science, 23(04), 414–423.
Liu, Y. X., Yang, Y. B., Hu, J., Meng, X. J., Kuang, K. X., Hu, X. J. D., & Bao, Y. (2022). Temporal and Spatial Characteristics of Diurnal Surface Urban Heat Island Intensity in China based on Long Time Series MODIS Data. Journal of Geo-Information Science, 24(05), 981–995.
Lu, Y., Wang, Q. N., Zhai, Y. R., Song, Y. Y., Zhang, Y. Y., & Sun, P. (2014). Anthropogenic Heat Emissions in the Yangtze River Delta Region. China Environmental Science, 34(02), 295–301.
Ma, Y. Y., Chen, Y. T., Hu, X. X., Ma, Q. R., Feng, T. C., Feng, G. L., & Ma, D. (2023). The 2022 record-breaking high temperature in China: Sub-seasonal stepwise enhanced characteristics, possible causes and its predictability. Advances in Climate Change Research, 14(5), 651–659. https://doi.org/10.1016/j.accre.2023.09.008
Meng, Q. Y., Zhang, L. L., Sun, Z. H., Meng, F., Wang, L., & Sun, Y. X. (2018). Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China. Remote Sensing of Environment, 204, 826–837. https://doi.org/10.1016/j.rse.2017.09.019
Ngarambe, J., Oh, J. W., Su, M. A., Santamouris, M., & Yun, G. Y. (2021). Influences of wind speed, sky conditions, land use and land cover characteristics on the magnitude of the urban heat island in Seoul: An exploratory analysis. Sustainable Cities and Society, 71, 102953. https://doi.org/10.1016/j.scs.2021.102953
Nie, J. D., Zhang, J. H., & Huang, B. (2021). A Review of the Human Health Consequences of Urban Heat Island Effect. Ecological Science, 40(01), 200–208. https://doi.org/10.14108/j.cnki.1008-8873.2021.01.025
Peng, B. F., Shi, Y. S., Wang, H. F., & Wang, Y. L. (2013). The Impacting Mechanism and Laws of Function of Urban Heat Islands Effect: A Case Study of Shanghai. Acta Geographica Sinica, 68(11), 1461–1471.
Qiu, G. Y., & Zhang, X. N. (2019). China’s Urbanization and Its Ecological Environment Challenges in the 21st Century. Advances in Earth Science, 34(06), 640–649.
Scott Anna, A., Waugh Darryn, W., & Zaitchik Ben, F. (2018). Reduced Urban Heat Island intensity under warmer conditions. Environmental Research Letters, 13 (6). https://doi.org/10.1088/1748-9326/aabd6c
Shang, M. J., Cao, L. J., Guo, J. P., Guo, Z. H., Liu, L., & Zhong, S. B. (2024). Influence of pure sea breeze on urban heat island in Tianjin, China: A perspective from multiple meteorological observations. Atmospheric Research, 304, 107408. https://doi.org/10.1016/j.atmosres.2024.107408
Song, L., & Fu, S. (2017). China's Summer "Stove" List Was Re-Released, and Wuhan Dropped out of the Top Three. China Media Group, (2017–07–09) ([2024–03–04]):http://news.cnr.cn/native/gd/20170709/t20170709_523840203.shtml
Sun, J. Q. (2014). Record-breaking SST over mid-North Atlantic and extreme high temperature over the Jianghuai-Jiangnan region of China in 2013. Chinese Science Bulletin, 59(27), 3465–3470. https://doi.org/10.1007/s11434-014-0425-0
Tang, S. K., Qiao, S. B., Feng, T. S., Fan, P. Y., Liu, J. Y., Zhao, J. H., & Feng, G. L. (2023). Predictability of the unprecedented 2022 late summer Yangtze River Valley and Tibetan Plateau heatwaves by the NCEP CFSv2. Atmospheric Research, 296, 107053. https://doi.org/10.1016/j.atmosres.2023.107053
Tang, X. Z., Huang, X., Tian, J. W., Jiang, Y. H., Ding, X., & Liu, W. (2024). A spatiotemporal framework for the joint risk assessments of urban flood and urban heat island. International Journal of Applied Earth Observation and Geoinformation, 127, 103686. https://doi.org/10.1016/j.jag.2024.103686
Targino, A. C., Krecl, P., & Coraiola, G. C. (2014). Effects of the large-scale atmospheric circulation on the onset and strength of urban heat islands: a case study. Theoretical and Applied Climatology, 117(1-2), 73–87. https://doi.org/10.1007/s00704-013-0989-7
Tol, Z., & Ellis, A. (2023). Analysis of urban Heat Island intensity through air mass persistence: A case study of four United States cities. Urban Climate, 47, 101345. https://doi.org/10.1016/j.uclim.2022.101345
Venter, Z. S., Chakraborty, T. & Lee, X. H. (2021). Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Science Advances, 7(22). https://doi.org/10.1126/sciadv.abb9569
Wang, J. F., & Xu, C. D. (2017). Geodetector: Principle and Prospective. Acta Geographica Sinica, 72(01), 116–134.
Wang, H. J., & He, S. P. (2015). The North China/Northeastern Asia Severe Summer Drought in 2014. Journal of Climate, 28(17), 6667–6681. https://doi.org/10.1175/jcli-d-15-0202.1
Wang P.Y., Tang J.P., Wang S.Y., Dong X.N, & Fang J. (2018). Regional heatwaves in china: a cluster analysis. Climate Dynamics, 50 (5–6):1901–1917. https://doi.org/10.1007/s00382-017-3728-4
Wang, X. Q., & Gong, Y. B. (2010). The impact of an urban dry island on the summer heat wave and sultry weather in Beijing City. Chinese Science Bulletin, 55(16), 1657–1661. https://doi.org/10.1007/s11434-010-3088-5
Wang, W. W., Chen, B. Y., Xu, Y., Zhou, W., & Wang, X. M. (2021). Urban heat islands in Hong Kong: Bonding with atmospheric stability. Atmospheric Science Letters, 22(6). https://doi.org/10.1002/asl.1032
Wang, Y., & Sun, R. H. (2021). Impacts of Regional Climate on Urban Heat Islands in China. Acta Ecologica Sinica, 41(11), 4288–4299.
Wang, Z. H. (2022). Reconceptualizing urban heat island: Beyond the urban-rural dichotomy. Sustainable Cities and Society, 77, 103581. https://doi.org/10.1016/j.scs.2021.103581
Wu, H. T., Huang, B., Zheng, Z. J., Sun, R. H., Hu, D. Y., & Zeng, Y. (2023). Urban anthropogenic heat index derived from satellite data. International Journal of Applied Earth Observation and Geoinformation, 118, 103261. https://doi.org/10.1016/j.jag.2023.103261
Xu, H. Q., & Chen, B. Q. (2003). An Image Processing Technique for the Study of Urban Heat Island Changes Using Different Seasonal Remote Sensing Data. Remote Sensing Technology and Application, (03):129–133+185.
Yang, J., Gong, D. Y., Wang, W. S., Hu, M., & Mao, R. (2012). Extreme drought event of 2009/2010 over southwestern China. Meteorology and Atmospheric Physics, 115(3-4), 173–184. https://doi.org/10.1007/s00703-011-0172-6
Yang, Z., Ding, Q. Y., Zhou, Q. Y., & Liu, H. M. (2018). Differentiation Pattern of Population Health and Its Geographical Influencing Factors in the Middle-Lower Reaches of the Yangtze River. Geography and Geo-Information Science, 34(06), 77–84+72.
Ye, C. H., Liu, Y. H., Liu, W. D., Liu, C., & Quan, W. J. (2011). Research on Urban Surface Heat Environment Monitoring Indexes and Its Application. Meteorological Science and Technology, 39(01), 95–101. https://doi.org/10.19517/j.1671-6345.2011.01.019
Yin, Y. X., Zhang, H., An, H. M., Lei, J. P., Li, M., Song, J. Y., & Han, W. H. (2023). Heat Island Characteristics and Influencing Factors of Urban Agglomerations in the Yangtze River Economic Zone Based on GEE. Chinese Journal of Ecology, 42(01), 160–169. https://doi.org/10.13292/j.1000-4890.202212.009
Zhao, J. H., Feng, G. L., Yang, J., Zhi, R., & Wang, Q. G. (2012). Analysis of the Distribution of the Large-Scale Drought/Flood of Summer in China Under Different Types of the Western Pacific Subtropical High. Acta Meteorologica Sinica, 70(05), 1021–1031.
Zheng, Y. F., Fu, Y., & Yin, J. F. (2014). Impact of Urbanization on Summer Temperature in the Middle and Lower Reaches of Yangtze River in Atmospheric Circulation Background. Journal of Tropical Meteorology, 30(02), 293–301.
Zheng, Z. F., Lin, X. L., Chen, L., Yan, C., & Sun, T. (2024). Effects of urbanization and topography on thermal comfort during a heat wave event: A case study of Fuzhou. China. Sustainable Cities and Society, 102, 105233. https://doi.org/10.1016/j.scs.2024.105233
Zheng, Y. S., Li, W. J., Fang, C., Feng, B. Y., Zhong, Q. R., & Zhang, D. X. (2023). Investigating the Impact of Weather Conditions on Urban Heat Island Development in the Subtropical City of Hong Kong. Atmosphere, 14(2), 257. https://doi.org/10.3390/atmos14020257
Zhou, D. C., Zhao, S. Q., Liu, S. G., Zhang, L. X., & Zhu, C. (2014). Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sensing of Environment, 152, 51–61. https://doi.org/10.1016/j.rse.2014.05.017
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 42161014).
Author information
Authors and Affiliations
Contributions
Geying Lai conceived and designed the study; Jie Deng performed the data analysis and took the lead in writing the manuscript; Ao Fan polished the writing and participated in the data analysis. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Deng, J., Lai, G. & Fan, A. Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China. Comput.Urban Sci. 4, 25 (2024). https://doi.org/10.1007/s43762-024-00135-z
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s43762-024-00135-z