1 - Beijing–Tianjin–Hebei Urban Agglomeration

Introduction

With rapid urbanization, ecological and environmental problems are escalating, leading to increased demand for built-up and residential land, causing urban expansion into other land types. By 2030, urban areas are projected to double compared to 2000, with much of this expansion concentrated in Asia. An estimated 68% of the global population will reside in urban areas by 2050, mainly in Asia and Africa, posing sustainable development challenges, especially in developing countries.

Cities consume a large amount of energy (78%) and produce a significant amount of greenhouse gas emissions (over 60%), despite occupying less than 2% of the Earth’s surface. This exacerbates global warming and extreme high-temperature events. Urban expansion is influenced by various factors like physical, socioeconomic, neighborhood characteristics, land-use policies, and urban planning, which vary by location and development stage.

Increased impervious surfaces, reduced albedo, and enhanced heat conduction in urban areas lead to higher heat storage during the day, causing the Urban Heat Island (UHI) effect, which worsens air pollution and impacts human health. Cities contribute significantly to climate change and are also affected by it. The 2030 Agenda for Sustainable Development's SDG 11 aims to create green public spaces and improve urban planning for sustainable, safe, and resilient cities.

Research focuses on the impact of urban expansion on surface temperature, with scholars exploring the causes, influencing factors, and evaluation methods of the UHI effect. Studies use remote sensing to link land cover changes due to urban expansion with rising surface temperatures and heat island intensity, indicating a relationship between urban land demand and UHI. Research also examines the effect of land type transformation on Land Surface Temperature (LST) and the consistency between urban centroid movement and Surface Urban Heat Island (SUHI).

Furthermore, researchers have studied the impact of specific urban land use on UHI and the effect of land cover types on the atmospheric heat island effect. Numerical simulations have been used to study and predict the relationship between urban expansion and the UHI effect in different seasons, developing high spatial resolution spatiotemporal heat island models for thermal exposure assessment. Urban expansion is identified as the main driver of farmland and forest shrinkage, with land surface changes significantly altering the urban thermal environment and the UHI effect.

Previous studies have examined the influence of urban expansion modes on UHI, exploring the relationship between UHI effects and urban scales, areas, expansion modes, and forms. Results indicate that urban densification within limits can control UHI expansion, scope, and intensity. Research in Europe suggests urban scale has the most significant impact on the heat island effect, followed by compactness and urban extension.

The shielding effect of high-rise buildings and urban continuity can reduce UHI intensity. Urbanization's time evolution affects temperature trends on multiple scales. While the global trend of urbanization affecting temperature change is consistent, variations in the UHI effect caused by urban expansion differ across countries and regions because the density of urban built environments and activities does not increase infinitely.

In some cases, temperature remains unchanged in existing urban areas while increasing only in newly urbanized areas. During urban expansion in the eastern United States, newly expanded urban areas experience a greater annual average temperature rise than central areas, with central areas showing smaller temperature changes (0.3 to 2 ^{\circ}C). Similarly, the UHI effect results in higher temperature rises in newly urbanized areas compared to existing areas.

UHI mitigation measures are derived from the impact of landscape patterns, land cover, and utilization. Natural landscapes like forests, water bodies, and wetlands play a crucial role in alleviating high temperatures, while urban landscapes like built-up and poor land increase temperatures in the thermal environment. The impact of land cover on different urban thermal environments varies, necessitating consideration of local conditions and land cover allocation to alleviate the urban heat island effect.

The natural life cycle of green plants can increase the variability of seasonal UHI, requiring further study on its mitigation effects. The effect of urban expansion on UHI in Chinese cities is widely recognized, but current research focuses on individual large cities, with urban agglomeration studies mainly conducting macroscopic analyses and comparisons of spatiotemporal changes. The Beijing–Tianjin–Hebei (BTH) urban agglomeration exhibits significant development differences, requiring detailed analysis to reflect the environmental status of cities at different stages and to propose appropriate urban planning suggestions.

This study aims to estimate and simulate the UHI effect of the BTH region with urban expansion using land-use data and LST products with 1 km spatial resolution from 2000, 2005, 2010, and 2015. Built-up land is divided into central, expanding, unchanged rural residential, and new rural residential areas to compare temperature characteristics. The Surface Urban Heat Island (SUHI) effect is calculated based on rural comparison values to analyze spatiotemporal variation of UHI intensity. Four typical cities are selected to analyze temperature difference changes from the city center to the suburbs. Based on the results and research, adaptation strategies of city planning are proposed for central areas, expanding areas, and suburbs.

Materials and Methods

Research Area

The Beijing–Tianjin–Hebei (BTH) urban agglomeration, located in eastern China, features diverse landforms, including grasslands and forests in the north and northwest, plains in the southeast for farmland, and a coastal east. As a major Chinese urban agglomeration, it houses 110 million people, contributing 8.5% to the national GDP. It includes Beijing and Tianjin municipalities, and 11 Hebei Province cities, covering 21.6 × 10^5 hectares, or 2.25% of China’s area. The cities are divided into northern, central, and southern parts for SUHI calculation. Research on urban expansion and the UHI effect in the region aims to promote sustainable development and climate change mitigation measures to enhance urban adaptability.

Land-Use Data

Land-use data were sourced from raster data with a 1000 m spatial resolution for the years 2000, 2005, 2010, and 2015, provided by the Resource and Environment Data Cloud (RESDC) platform from the Chinese Academy of Science (CAS) (http://www.resdc.cn/). According to the classification system of RESDC, land-use types are classified into seven primary categories: Farmland, forest, grassland, water, built-up land, unused land, and ocean; with 25 secondary classifications (Supplementary Table S1). This study focuses on the analysis of farmland, forest, grassland, water, built-up land, and unused land, excluding the ocean due to its small proportion in the study area. The urban zoning map was obtained from the website of the National Geomatics Center of China (http://ngcc.sbsm.gov.cn/ngcc/).

Remote Sensing Data of Land Surface Temperature

Temperature data for the BTH region were obtained from MOD11A2 version 6, provided by NASA (https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD11A2--6). NASA provides both daily and 8-day per-pixel land temperature and emissivity products, with a 1 km spatial resolution. The 8-day composite period aligns with the exact ground track repeat period of the Terra and Aqua platforms. The product uses atmospheric column water vapor and lower boundary air surface temperature to achieve high-quality retrievals. The emissivity is estimated based on land cover types (MCDLC1KM) using a split-window algorithm, and MCDLC1KM data are produced every three years. The product provides data from February 18, 2000, to the present, and the 8-day average temperature of MOD11A2 corresponds to the resolution of the land-use data for the BTH region. The daytime range of the 8-day product was selected for the years 2000, 2005, 2010, and 2015, and the annual average temperature was calculated for each year. The data are converted from Kelvin to Celsius (^{\circ}C).

Surface Urban Heat Island Effect (SUHI)

The SUHI reflects the intensity of the heat island effect in different areas. Due to the lack of a unified method for determining rural comparison values, existing studies use the temperature of rural residential or farmland areas as the rural comparison value. This research divides the BTH region into northern (Beijing, Zhangjiakou, Chengde, Qinhuangdao), central (Tianjin, Baoding, Langfang, Tangshan, Cangzhou), and southern (Shijiazhuang, Hengshui, Xingtai, Handan) parts and calculates the rural comparison value of each one using the following equation:

Tr = \frac{1}{n} \sum{m=1}^{n} T_m

where Tr is the mean surface temperature of rural residential pixels, Tm is the surface temperature of rural residential pixel m, and n is the total number of rural residential pixels. Table 1 shows the number of rural residential pixels of the northern, central, and southern parts in 2000, 2005, 2010, and 2015.

The SUHI of each pixel was calculated using the following equation:

SUHIi = Ti - T_r

where SUHIi is SUHI of each pixel i, Ti is the surface temperature of each pixel i, and T_r is the mean surface temperature of rural residential pixels. Based on Equation (2), the temperature difference of each part varies from -15.4 to 8.9 ^{\circ}C, with most below 5.5 ^{\circ}C. SUHI in this study was divided into six grades (Table 2).

In addition to the study of the entire BTH region, it is also necessary to discuss the spatiotemporal changes of the UHI effect at a city level. To analyze the variation of temperature difference with the change of distance from the city center (Section 3.3), we determine the radius of each circle from the city center to suburbs in selected cities. The mean temperature difference within a certain radius was calculated using the following equation:

Ct = \frac{1}{n} \sum{i=1}^{n} (TCi - Tr)

where Ct is the mean temperature difference within a radius, TCi is the surface temperature of pixel i within the radius, T_r is the rural comparison value of the northern, central or southern part that the radius belongs to, and n is the total number of pixels i within the radius.

Results

Expansion and Average Temperature of Built-up Land

From 2000 to 2015, built-up land increased from 3491 to 5003 km^2. The area of built-up land that is transferred from other land-use types was 2877 km^2, most of which was from farmland. The area of farmland, forest, grassland, water, and unused land transferred from other land types was 446 km^2, 118 km^2, 35 km^2, 273 km^2, and 22 km^2, respectively (Supplementary Figure S2). The expanded parts of built-up land distributed outward around the city center and transferred areas of other land-use types were scattered and discontinuous in the whole region (Supplementary Figure S3).

To further explore the temperature characteristics of built-up land, urban land and rural residential areas in 2000 were taken as central and unchanged rural settlement areas, and the increased parts by 2015 were taken as expanded and new rural residential areas, respectively. The average annual temperature of the central areas was 22.0 ^{\circ}C, which was the highest among the four types. The average annual temperature of expanded, unchanged rural residential, and new rural residential areas was 21.4, 21.2, and 21.2 ^{\circ}C, respectively, indicating a lower average temperature in rural than urban areas, but no significant difference between unchanged and new rural residential areas. From the temporal variation, the temperature difference between central and expanded areas decreased significantly from nearly 1 ^{\circ}C in 2000 to about 0.3 ^{\circ}C in 2015, indicating a more rapid increase in temperature in expanded areas, due to urban expansion.

Spatiotemporal Changes of SUHI

With relatively small urban land area and high coverage with forest and grassland, the majority of Chengde and Zhangjiakou showed a cold or a slightly cold effect, although small areas presented an MHI effect in the south of Zhangjiakou. The most obvious heat island effect occurred in Beijing, Shijiazhuang, Xingtai, Handan, and the junction of Tianjin, Langfang, and Cangzhou. Beijing has the largest urban land area and the most obvious expansion, accompanied by a large population influx from 2000 to 2015, so the range of the heat island effect increased significantly. The grade of SUHI of central areas changed from mostly MHI to mostly HHI, and the areas of HHI also increased obviously in Beijing. The MHI and HHI areas extended outward from the city center of Beijing during 2000–2015.

The heat island effect of Langfang, Cangzhou, and their junction with Tianjin tended to weaken. The MHI and HHI area was wide and concentrated in the middle of Langfang and the northeast of Cangzhou. However, the SUHI grade gradually decreased to the MHI effect, and the range of influence of the MHI effect also narrowed in the junction of the three cities. The southern region experienced an obvious heat island effect, which was distributed in Shijiazhuang, Xingtai, and Handan in a north–south strip. In 2000, the heat island effect of the Shijiazhuang city center was strong, and then gradually weakened. From 2000 to 2015, the MHI effect in Xingtai and Handan was particularly obvious, while the range narrowed down to Handan, and that area also decreased.

Comparing the level and scope of SUHI, cities with larger and continuous urban areas showed a higher intensity of UHI effect. From the perspective of regional scale in the BTH urban agglomeration, areas with dense distribution of cities, such as the junction of Langfang, Cangzhou, and Tianjin and the southern area with Shijiazhuang, Xingtai, and Handan, also presented obvious SUHI.

Spatiotemporal Variation of Temperature Difference of Typical Cities

The radius from the city center to the edge of urban land of Beijing, Tianjin, Handan, and Chengde was nearly 20 km, 15 km, 8 km, and 5 km, respectively. Comparing the value of each circle, the average temperature difference obviously decreased with the distance from the city center of Beijing. According to the SUHI classification in Table 2, the 20 km radius from the center of Beijing is almost in the range of the MUHI effect from 2005 to 2015. From the perspective of an annual change, the results show that the temperature difference of each circle was greater in 2005 than in 2000. However, the temperature difference in 2010 showed a decreasing trend, which was lower than that in 2005, and circles exceeding 30km from the city center were lower than in 2000. The average temperature difference of each circle of Beijing in 2015 increased significantly, exceeding that in 2000, 2005, and 2010. Moreover, the inter-annual change trend was 2015 > 2005 > 2010 > 2000 within 40 km, and the variation range of temperature difference was larger than that 40 km away, which shows that the heat island effect became stronger in the area near the city center from 2000 to 2015.

The temperature differences along the circles in Tianjin show a decreasing trend with distance from the city center. The trend of temperature difference of the circle within 5 km was 2000 > 2015 > 2005 > 2010; within 5–10 km it was 2015 > 2005 > 2010 > 2000; and 10 km away it was 2015 > 2010 > 2005 > 2000. The variation of temperature difference of circles within 0–15 km of Handan fluctuated. It was higher in the 5–10 km and 10–15 km circles in 2000, 2010, and 2015 than in the circle near the city center, indicating that this area had higher temperatures and a more obvious heat island effect. The temperature difference of circles exceeding 15 km decreased, and the interannual variation trend was 2015 > 2010 > 2005 > 2000. There was no obvious heat island effect in Chengde. Except for 2000, the average temperature difference of each circle was below 0 degree in Chengde—but it was basically in line with the trend of the highest temperature in the city center and decreasing outward. The temperature difference trend compared with rural residential areas was 2015 > 2005 > 2010 > 2000, showing a colder trend.

Discussion

Extending from central areas to suburbs, the mean surface temperature decreased from 21.72 ^{\circ}C to 18.5 ^{\circ}C, and there is an obvious difference between the two areas. Central areas face a greater UHI effect and the possibility of heatwaves occurring. The increased UHI effect will bring more risks to social and economic losses with high population density and dense commercial activities in central areas, so that is the key area to promote the mitigation of the effect. The UHI effect in expanding areas is expected to be lower; however, affected by population increase and urban construction, the temperature difference between expanding and central areas gradually decreases, and it is important to prevent intensification of the heat island effect in expanding areas. Suburbs usually have more vegetation coverage, which plays an important role in urban ecosystem maintenance and mitigation of the heat island effect.

Studies have shown that population and economic activity lead to anthropogenic heating, and the heat island effect is usually strongest in central urban areas. Developing adaptation strategies for UHI and climate change to improve the resilience of cities has been considered as the future direction of city planning. From the results of studies in cities listed in Table 3, the common view is that special materials used in buildings to increase albedo and adding green spaces and wetlands in cities are effective ways to reduce temperature or save energy. Combined with the results of SUHI in Section 3.2 and Beijing UHI showing a significant temperature decrease in vegetation areas, green space is an accepted mitigation strategy, but the focus differs in different areas in cities.

However, for city planning, central areas, expanding areas, and suburbs need specific and suitable mitigation measures to improve their resilience to the UHI effect. Central areas usually have dense population and buildings, and there are problems with old buildings and narrow blocks. Referring to studies on mitigating the UHI effect in London, Seoul, and Nagoya, small gardens and small wooded green spaces can reduce the temperature in the area effectively, and are appropriate even in parking lots in central areas that do not have large areas for increased green spaces. Moreover, for dense and old buildings, it is recommended to change black to white roofs to increase the albedo, or green roofs to reduce the absorbed solar energy, and then reduce indoor temperature. Simultaneously, referring to Tokyo, recycling water resources is a simple and economical way to reduce road surface temperature.

For expanding areas, although the UHI effect is weaker than in central areas, with the acceleration of urban construction, the improvement of infrastructure, the migration of population, and the settlement of enterprises, the temperature will rise with additional economic activities. Different from central areas, expanding areas should take mitigation measures against rising temperatures as an indispensable part of planning. For example, planning can restrict approval to the development of urban land, formulate environmental and energy-saving standards for buildings, and allocate green, residential, industrial, and commercial land reasonably to reduce the impact of human activities.

Suburbs are green barriers to urban areas and the main areas for eco-tourism development. Although there is no obvious UHI effect in the suburbs, measures of preventing ecological damage are necessary to maintain their important role in the mitigation of climate change.

Mitigating the UHI effect and reducing the occurrence of extreme weather, such as high temperature and heatwaves, mainly depends on controlling human activities. Promoting a reasonable urbanization mode is the main solution. Transferring from scale development to people-oriented urbanization mode, setting up different dimensions of planning from the community to the city level, and guiding the green consumption mode to reduce residential energy consumption and economic growth is the direction of future planning for improving adaptability to climate change.

Conclusion

With combined land-use and surface temperature data, we analyzed the spatiotemporal characteristics of urban land expansion and the heat island effect in the BTH urban agglomeration. There was an obvious increase in urban land around the central area during the 2000–2015 period. Compared with the built-up land, the average temperature of the central area was higher than that of expanding and rural areas, but the temperature difference between expanding and central areas obviously narrowed.

The intensity of SUHI showed significant spatial change patterns. SUHI in Beijing was the largest, and the temperature differences decreased with the extension outward from the center. The intensity of SUHI in the junction of Langfang, Cangzhou, and Tianjin showed a decreasing trend compared to 2000. Intense SUHI occurred in the southern part of the BTH region, but the areas narrowed to Handan in 2015.

The average temperature difference between the circles of Beijing, Tianjin, and Chengde decreased outward from the central area. With the expansion of urban areas, the UHI effect became more obvious in the central area. Based on the results and existing research, cool roofs, green spaces, and wetlands are recognized as some of the effective measures to reduce the temperature. However, for city planning, central areas, expanding areas, and suburbs need specific and suitable mitigation measures to improve their resilience to the UHI effect.

Comparing the existing studies, this study not only provides results of SUHI of the region, but also reflects the spatiotemporal variation characteristics of urban expansion and the UHI effect of cities at different development levels. For city planning, this study emphasizes the spatial circles of cities when analyzing the urban heat island effect through the division of central areas, expanding areas, and suburbs, which provides an important perspective for policymakers and planners to pay attention to specific planning in different spatial areas to improve the sustainability, and resilience of the whole city.