Sustainable Cities and Society 74 (2021) 103174

Available online 14 July 20212210-6707/© 2021 Elsevier Ltd. All rights reserved.

Mapping local climate zones and their associated heat risk issues in Beijing: Based on open data

Yi Zhou a,b, Guoliang Zhang a,b, Li Jiang a,b, Xin Chen a,b, Tianqi Xie a,b, Yukai Wei a,b, Lin Xu c, Zhihua Pan c, Pingli An a,b, Fei Lun a,b,*

a College of Land Science and Technology, China Agricultural University, Beijing 100193, China b Key Laboratory of Land Quality, Ministry of Land and Resources, Beijing 100193, China c College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China

A R T I C L E I N F O

Keywords: Local climate zone Heat risks Open data Urban morphology Beijing

A B S T R A C T

The “Urban Heat Island” (UHI) effects in cities pose great threats to heat health risks for urban dwellers, but they present great differences inside cities. Local climate zones (LCZs), with distinct surface morphologies and physical structures, can be used to better explore differences of UHI effects inside cities and can also solve their different heat risks there. However, there still existed some gaps to map LCZs and their associated heat risks in cities, due to lack of detailed urban building data. Thus, we focused on mapping LCZs and their associated heat risks in Beijing, based on open data of four urban morphology parameters, including sky view factors, permeable surface fraction, building surface fraction and building height. Seven types of land cover LCZs and eight types of artificial built LCZs were divided. In spite of only 11% of total area, artificial built types of LCZ played vital roles in local economic development. Different types of LCZ were highly related with their urban functions, and most of artificial built LCZs were located in the Capital Functional Core Area and the Urban Functional Development Area. Open low-rise and open midrise were two important artificial built types of LCZ in Beijing, due to their high value of traditional and cultural buildings. Beijing faced serious heat risks in summer, especially for artificial built type LCZs. About 6% of total area and 58% of all residents suffered heat risks in Beijing, and most of them were located in artificial built type of LCZs. Lightweight low-rise suffered the most serious heat risks, and more than 90% of its area and residents were under heat risks. Considering climate change, about 4.0~7.4 million more people would suffer heat risks in future, and the LCZ of compact high-rise was highly sensitive to future climate change in Beijing. Urban morphology, economic function, population density and heat risks existed great differences among various LCZs in Beijing, and thus different strategies should be applied to mitigate heat risks in different LCZs for future city planning.

1. Introduction

Thanks to its better conditions, more than half global people are living in cities and it is expected to continue to be 68% by 2050 (UN, 2018). Global urban area has been expanding since 1940s and it would increase to 3.6 million km2 by 2100 (Gao & O’Neill, 2020). Urbanization and intensive human activities have significantly modified urban ther-mal conditions in cities, with reduction in latent heat flux and rise in sensible heat flux there (Grimm et al., 2008; Hart Sailor 2009; Yang et al., 2019; Mrowczynska, Skiba, Bazan-Krzywoszanska & Sztubecka,

2020). Therefore, there exist significant temperature differences be-tween urban areas and their surrounding rural areas, which has been termed as the “Urban Heat Island” (UHI) effect (Pena Acosta, Vahdati-khaki, Santos, Hammad & Doree, 2021). The UHI effect could bring in serious issues, like urban air pollution (Ulpiani, 2021), labor produc-tivity decreasing (Matsumoto, 2019), more energy consumption for cooling (Aboelata, 2020), more potentials of crime and violence (Burke, Hsiang & Miguel, 2015). More importantly, the UHI effect could greatly increase thermal stress and lead to great heat risks there (Amani-Beni, Zhang, Xie & Xu, 2018; Macintyre, Heaviside, Cai & Phalkey, 2021;

* Corresponding author. E-mail addresses: B20193030288@cau.edu.cn (Y. Zhou), glzhang@cau.edu.cn (G. Zhang), chenxin1992@cau.edu.cn (X. Chen), SY20193030370@cau.edu.cn

(T. Xie), s20193030512@cau.edu.cn (Y. Wei), linxu5048@cau.edu.cn (L. Xu), panzhihua@cau.edu.cn (Z. Pan), anpl@cau.edu.cn (P. An), lunfei@cau.edu.cn (F. Lun).

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Sustainable Cities and Society

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https://doi.org/10.1016/j.scs.2021.103174 Received 20 September 2020; Received in revised form 9 July 2021; Accepted 10 July 2021

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Zander, Cadag, Escarcha & Garnett, 2018), especially cardiovascular and respiratory diseases (Macintyre et al., 2021). Therefore, to quantify the UHI effect and its associated issues, especially heat risks, has gained a lot attentions (Kim & Brown, 2021; Venter, Chakraborty & Lee, 2021), with observation data (Hong, Hong, Kwon & Yoon, 2019) and remote sensing data (Jimenez-Munoz et al., 2014; Yang et al., 2019).

Densely populated cities presented great differences of their UHI effects and heat risks inside their internal urban areas, due to their distinct surface morphologies and physical structures (Oke & Stewart, 2012). To better understand the UHI effects of different urban surfaces in cities, Stewart and Oke (2012) proposed the concept of “Local Climate Zone” (LCZ) in 2012, which referred to “the region of uniform surface cover, structure, material, and human activity that span hun-dreds of meters to several kilometers in the horizontal scale”. Therefore, to identify local climate zones in cities can benefit for better estimating surface thermal, urban planning and energy consumption (Alexander & Mills, 2014; Verdonck, Demuzere, Hooyberghs, Priem & Van Coillie, 2019; Yang et al., 2020b). Present studies of LCZ mapping were widely based on remote sensing data of Landsat and Sentinel-2 (La, Bagan & Yamagata, 2020; Verdonck et al., 2017), but these studies can hardly identify the LCZ classification criteria of urban inner morphology pa-rameters. According to their data structure, two types of the GIS method were also used to map LCZs, including vector-based method and raster-based method; however, these two GIS methods were highly data-intensive and required a comprehensive set of planning data (Zheng et al., 2018). Moreover, it was hard to obtain detailed urban planning data for free, and thus there was still a great challenge to accurately map local climate zones in cities with free and open data.

Beijing, one important megacity in China, has a large population of 21.53 million, and it is facing serious UHI effect there, with its increasing rate of 0.12 ◦C/year (He et al., 2020). Therefore, urban dwellers face more and more serious heat health risks in Beijing (He et al., 2020); for example, there were 1581 people died due to heat risks in the summer of 2009 (Li et al., 2012). Climate change would pose a great threat to future heat risks in Beijing, and it can lead to the increase of 3.5–10.2% for deaths due to cardiovascular diseases there (Ren, Zhou & Wang, 2021). However, Beijing has complicated buildings with high compacted and density (Cao, Luan, Liu & Wang, 2021), and their internal surface morphologies and physical structures present great differences, due to their instinct functions. To identify different LCZs in Beijing and their associated heat risks is highly significant to maintain human health for

urban dwellers in Beijing, especially considering future climate change. As the big data development, the website of Baidu (http://lbsyun.

baidu.com/) could provide some useful and free information of urban 3D morphology (like building height and building footprint), which can be used to identify detailed and different LCZs inside cities. Therefore, based on these open and free urban morphology data, this paper aimed to (1) mapping urban local climate zones in Beijing, considering its in-ternal surface morphologies and physical structures, (2) exploring summer land surface temperature and population density for different LCZs, (3) mapping the potential areas of heat risk and their heat expo-sures in Beijing, and (4) finally discussing changes of heat risks for different LCZs under different future SSP scenarios.

2. Methods

2.1. Study area

Beijing, the capital of China, is an important economic, cultural, and technology center of China. It is located in North China (116◦20′E, 39◦56′ N), and covers a total area of 16,410.54 km2. It is the temperate semi-humid continental monsoon climate, with hot and rainy summer but cold and dry winter. Forest covers about 46.5% of its total area, mainly in the mountain area; besides, the constructed area covers an area of 3517 km2 and agriculture lands amount to 2128 km2. There are 16 municipal districts in Beijing (Fig. 1), and they are divided into four functional areas as follows (Fig. 1): the Capital Functional Core Area (CFCA), the Urban Functional Development Area (UFDA), the New Urban Development Area (NUDA), and the Ecological Conservation Development Area (ECDA). There exist great differences of social- economic levels and urban morphology, like population density, GDP per capita, and urbanization (Fig. 1). However, these districts do not present significant differences for their average building height, in spite of a little higher in the CFCA.

2.2. Data and method

2.2.1. Mapping local climate zones (LCZ) In this research, all LCZs can be divided into two different categories,

namely (1) the artificial built type and (2) the land cover type. More detailed, the artificial built type is mainly or dominantly covered by constructed lands, which is paved for compact zones with low plants or

Fig. 1. Our study area and its associated social-economic levels.

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scattered trees for open zones; thus, there are 8 types of the artificial built LCZs, including compact high-rise (CH), compact midrise (CM), compact low-rise (CL), open high-rise (OH), open midrise (OM), open low-rise (OL), lightweight low-rise (LWL), and large low-rise (LL) (the detailed information and figure can be obtained in the Supporting In-formation). The land cover type includes dense tree (DS), scatter tree (ST), bush and scrub (BS), low plants (LP), bare rock or paved (BRP), bare soil or sand (BSS) and water (W). Besides, we divided LCZs based on urban morphological parameters based on open data, including building data and the Landsat 8 OLI data (https://earthdata.nasa.gov/)Fig. 2). The thresholds of urban morphological parameters for different artificial built types were determined by typical samples from Google earth, while land cover types were divided based on the land cover map from Landsat 8 OLI. Zheng et al. (2018) pointed out that the unit area of 300 × 300 m was the best spatial scale for mapping LCZs, and thus we also used 300 × 300 m in this study. More detailed information was as follows:

(1) Mapping artificial built types of LCZs

Artificial built types of LCZs can be divided based on different urban morphological parameters (Zheng et al., 2018). Stewart and Oke (2012) used seven urban morphological parameters to map LCZs, including sky view factors, permeable surface fraction, building surface fraction, building height, aspect ratio, impervious surface fraction, and terrain roughness; then they specified their standard values to identify artificial built types of LCZs. Compared with other parameters, building morphological parameters can better present urban morphology as well identify LCZs in cities (Yang et al., 2020a). Thereby, we selected four building parameters in our study, including the sky view factors (SVF), permeable surface fraction (PSF), building surface fraction (BSF), and building height (BH). More detailed, SVF and BH were estimated from the building data, while PSF was calculated from the average NDVI data from the Landsat 8 OLI in 2019 summer (June to September) (Detailed method was presented in Table 1). Then, we selected 40 typical samples for each LCZ type from Google Earth and calculated their values of these

building morphological parameters. Thus, according to their urban morphological thresholds (see Supplement Information), we mapped different artificial built types of LCZs. More detailed, we divided these LCZs into low-rise, midrise, and high-rise based on the results of BH (Fig. 4), and then distinguished the compact morphology from open morphology with the help of BSF. However, it was still hard to distin-guish the detailed LCZ types of low-rise. In order to get more detailed map, we can obtain the large low-rise by SVF>0.6 and PSF<0.2, which was the distinctive morphology of large low-rise; then, we distinguished the compact low-rise and open low-rise based on BSF, and thus we can acquire the detailed map of LCZs in the urban area (see in Fig. 4).

Fig. 2. The framework of mapping LCZs in our study area.

Fig. 3. The calculation of sky view factor.

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However, there was no building information in rural areas, but their buildings were dominated by the type of open and low-rise. Thus, we divided the rural residential area into the LCZ of open and low-rise, which was extracted from the remote sensing data.

(1) Mapping land cover types of LCZs

The Landsat 8 OLI data on August 17, 2019, was used to divide small blocks by the method of the multi-scale segmentation, and then they were classified into different land cover types. Thus, we can obtain the image feature of different land cover samples from Google Earth, and then we used the CART classification method to divide them into Forest (dense tree, DS), Green Park (scatter tree, ST), Scrub Land (bush and scrub, BS), Cultivated Land (low plants, LP), Transportation Facility (bare rock or paved, BRP), Bare Land (bare soil or sand, BSS) and Water

(water, W).

2.2.2. Mapping land surface temperature and its associated heat risk area Land surface temperature (LST) referred to the ground temperature

and there are three main methods to retrieve LST, including the mono- window algorithm, the split-window algorithm, and the atmospheric correction method. In this study, we used the split-window algorithm to estimate the average summer LST of Beijing by the Landsat 8 OLI data of the 2019 summer (June to September). The split-window algorithm for retrieving LST required to measure land surface emissivity values. Here, the normalized difference vegetation index (NDVI) method was used to derive land surface emissivity values. To retrieve the emissivity- corrected LST values from the Landsat data, the preprocessed thermal bands produced by radiometric calibration and atmospheric correction using the ENVI software, were used as follows:

LST =TB

1 +

(

α × TBβ

)

× ln(ε)(1)

where TB= at-satellite brightness temperature in degrees Kelvin;α=wavelength of emitted radiance (α=10.8μm for Landsat-8 OLI/TIRS band 10); β = h× c

σ(1.438×10− 2m K), σ= Boltz-mann constant (1.38× 10− 23J

K ,

h= Planck’s constant (6.626× 10− 34 J s), and c= velocity of light (2.998 108 m/s); and ε is the land surface emissivity estimated using the NDVI method. The resulting LST values were later converted from de-grees Kelvin to degrees Celsius ( ◦C)

Most of present studies had discussed the relationship between air temperature and mortality, and they pointed out that the mortality rate would increase with the increase of air temperature (Gasparrini et al., 2015; Lee et al., 2019). Furthermore, other studies discussed how the LST influenced the mortality rate (Dousset et al., 2011). Estoque et al. (2020) pointed out that heat health risk was highly associated with LST, by coupling the daily LST data and mortality data in Philippine cities; more detailed, they also presented that the threshold of heat health risk was 38.3 ◦C for LST, estimated from the MODIS Data (around 10:30 am). The Landsat data, obtained around 10:50 am, was used to estimate LST in our study, and thus we assumed that it was under heat risk with

Table 1 Urban morphological parameters for different LCZs in the urban area.

Urban morphological parameters

Definition Formula

Sky View Factor SVFi is the SVF value of a point in the non-building area (1 m × 1 m) in the LCZ sample site. n is the number of SVF points (1 m × 1 m) in the non-building area. S_Sky and ΣSb is the area of the sky and the area occupied by the building at a particular point. S_sky +ΣSb is the entire hemisphere environment at a certain point. (Fig. 3)

SVFi =

S SkyS Sky +

∑Sb

SVF =∑n

i=1SVFi

n

Building Height n is the number of buildings in the LCZ sample site. BSi is the floor area of a building. RBHi is the relative height of the building, and their range were 3~1200.

BH =

∑ni=1BSi*BHi∑n

i=1BSi

Building Surface Fraction

n is the number of buildings at the LCZ sample site. S_site is the total area of the site.

BSF =

∑ni=1BSi

S site

Permeable Surface Fraction

∑S_ per is the total area of the

permeable surface (NDVI is greater than 0.2).

PSF =

∑S per

S site

Fig. 4. The processes of mapping artificial built types of LCZs in the urban area.

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its surrounding LST of more than 38.3 ◦C. Therefore, people in this area were also assumed to be under heat risks.

2.2.3. Mapping population density Point of Information (POI) represented the non-geographic mean-

ingful point on maps, like schools, residential areas, supermarkets, and so on. Thus, POI is closely related to human activities and population distribution (Wang, Fan & Wang, 2020), and we used the POI data to estimate the population distribution of Beijing in this study. At first, we collected the POI data of Beijing in 2019 from Baidu API, and then estimated the total number of POI in each grid (300 × 300) and each municipal district. The relationship between the POI number and pop-ulation in the 16 municipal districts was established by the linear regression method, and the population spatial distribution in each grid can be calculated according to this relationship (more detailed see SI).

3. Results

3.1. Mapping local climate zones in Beijing

The land cover type of LCZs accounted for 89% of the total area in Beijing (Fig. 5), dominated by dense tree (DS, 54%) and low plants (LP, 14%). More detailed, DS was mainly located in the northern and western mountain areas, which played a vital role in providing ecosystem ser-vices for Beijing; LP mainly referred to croplands, and they were located in the plain area of ECDA and NUDA in Beijing. Large areas of scatter tree (ST) and water (W) were also located in the urban area, and thus they were highly important for public rest in the urban area of Beijing. Although all artificial built types of LCZs together contributed only 11% to the total area of Beijing, they played the vital role in social activities and human daily lives. Open low-rise (OL) had the largest share (36%) of the artificial built type, which was the common LCZ in the rural resi-dential area. As a long-history city, buildings in Beijing were quite different from other modern megacities (like Hongkong). The compact density and high-rise buildings were dominant in the urban areas of Hongkong; however, building height was strictly limited to protect valuable ancient buildings in Beijing (especially in some parts of the CFCA), and thus compact high-rise (CH) and open high-rise (OH) totaled to only 11% of the artificial built LCZ in Beijing. The LCZ of open midrise (OM) was the dominant type of LCZs in urban areas, covering the area of 265 km2. “Alley” was a highly traditional and famous architecture in Beijing, and it was also a typical building type of compact density and low-rise; therefore, there were a huge area of compact low-rises (CL) in

the core area of Beijing. For functional areas, the distribution of LCZs were highly associated with their urban functions. More detailed, CFCA and UFDA (Fig. 5c) had high urbanization rate and a great number of social-economic activities, and thus their local climate zones were dominated by the artificial built type, like CM, CL, OH and OM; how-ever, NUDA and ECDA played important roles in ecological protection and thus their LCZs mainly were land cover types.

3.2. Mapping the summer lst for different LCZs

The summer LST averaged at 36.22 ◦C in Beijing (Fig. 6), and it was more than 3 ◦C higher in the artificial built type (38.37 ◦C) than the land cover type (34.85 ◦C). The LCZs of lightweight low-rise (LWL) were mainly located in city villages of Beijing, and they presented the highest summer LST of 39.90 ◦C. It was because their buildings were compact and outdated, since they were easy to absorb heat but hard to cool in summer. Dominated by “Alley”, the LCZ of compact low-rise (CL) had poor airflow and thus they also presented relatively high summer LST in Beijing. In 2016, the Beijing government planned to construct 5 venti-lation corridors to improve air circulation in the built-up area, which could benefit for cooling the summer LST in the LCZs of compact high- rise and open high-rise. The LCZs of scatter tree (mainly urban park and green space) presented the highest summer LST among different land cover types of LCZs; however, their summer LST was relatively lower than their surroundings, and thus they could present important role in alleviate the UHI effect in cities. On the contrary, with large vegetation and far away from the urban area, the LCZ of dense tree (DS) in Beijing presented the lowest summer LST of 33.29 ◦C.

3.3. Mapping population in different LCZs

About 21 million people lived in Beijing in 2019, but there existed great differences among different districts and zones. Thanks to its high society development, more than 67% of people live in the areas of CFCA and UFDA (Fig. 7), which only accounted for 8% of the total area in Beijing. The LCZ of compact high-rise (CH) was mainly dominated by intensive commercial buildings and residential buildings, and thus it presented highly densely populated there; however, only 0.65 million people lived in the LCZ of CH due to its tiny area. In contrast, about 4 million people were living in the LCZ of open high-rise (OH), in spite of its low population density. The largest population (about 6.8 million) were distributed in the LCZ of open midrise (OM), due to its large area and important living function.

Fig. 5. The spatial distribution of LCZs in Beijing (a. the map of different LCZs in Beijing; b. the proportion of LCZs; c. the area of LCZs in different functional areas.) note: the study area is in the coverage of remote sensing image.

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3.4. Heat risks of different LCZs

A total area of 973.57km2 (about 6%) in Beijing was under heat risks, but more than 58% of total population (about 12.54 million) were exposed to be under heat risk in Beijing (Fig. 8). Moreover, 82% of heat risk areas were located in the artificial built type of LCZs. The LCZ of Bare Soil or Sand (BS), scattered in the urban fringe area, contributed 35% to the total heat risk area of the land cover type, due to its low vegetation. For the artificial type, the LCZ of open low-rise (OL) contributed to the largest area (254 km2) of heat risk in Beijing, but its heat population exposure (2.3 million) was a little smaller than the LCZ of open midrise (4.0 million). With compact density and outdated buildings, more than 90% of area and population in the LCZ of light- weight low-rise (LL) were under heat risks. For different functional areas, the heat risk area and its heat exposure in CFCA were less than other functional areas, but their proportion was relatively higher.

4. Discussion

4.1. Urban morphological parameters compared with other studies

Considering their inner urban morphology, LCZs can be used to present differences of UHIs inside cities (Das & Das, 2020; Lau et al., 2019), especially in megacities. Different with previous studies based on the WUDAPT method (Oliveira et al., 2020; Wang, Ren, Xu, Lau & Shi, 2018), our study mapped LCZs of Beijing by one more efficient method, based on various open data. Meanwhile, it is of high significance to identify their thresholds of urban morphology, considering their distinct built types and land covers. As an international and populated megacity, Beijing was occupied with different types of buildings and land covers, and thus its building density is more compact than that in Uppsala, Nagano and Vancouver; thus, the lowest value of SVF (0.1) in Beijing was lower than that in the other three cities of 0.2 by Stewart et al. (2012). The important type of “Alley” was common and compact buildings in Beijing, and thus the largest BSF of compact low-rise can amount to 0.8, which was much higher than results in Stewart and Oke

Fig. 6. The summer LST of LCZs in Beijing (a. the summer LST distribution in Beijing; b. the summer LST of LCZs; c. the summer LST of LCZs in different func-tional areas.).

Fig. 7. The population of LCZs in Beijing (a. the population distribution in Beijing; b. the population of LCZs; c. the population of LCZs in different functional areas.).

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(2012). The height boundary for high-rise buildings was around 25 m in our studies, similar to other cities (Wang et al., 2018; Zheng et al., 2018). Although there existed a few differences between our results and Stewart and Oke (2012), both of these results presented similar trends for different LCZs (more detailed information see in SI). For example, the upper boundary of BSF in the LCZ of compact rise was 2 times higher than that in the LCZ of open rise, illustrating that they presented similar

threshold trends for different LCZs among different cities (Stewart and Oke, 2012). Therefore, we can compare our results with other previous studies.

4.2. The urban morphology and its summer lst

Urban morphology played a vital role in mapping different LCZs, and

Fig. 8. The heat risk of Beijing (a. the heat risk area distribution in Beijing. b. the proportions of LCZs in the heat risk area. c. the proportions of LCZs in heat population exposure.).

Fig. 9. The urban morphology in heat risk area and their relationship with LST.

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thus it was of high importance to discuss the relationship between urban morphology and its summer LST. However, LST can be affected by many factors (such as population, land use, human activities, and so on), which would inevitably influence the relationship between urban morphology and LST. Therefore, we divided each urban morphology parameters into 100 groups by the equal interval method, and then we estimated their average morphology parameter values and their average LSTs for each group. Besides, the least square method was used to analyze their relationship (Fig. 9). With stronger human activities and more impervious surface (larger BSF), the artificial built type of LCZs presented more heat absorption but less heat diffusion (Das & Das, 2020); thus, the summer LST increased with higher results of BSF. On the contrary, with more permeable surface and vegetation, the land cover types of LCZs (such as dense tree) could absorb ambient heat and thus cool air temperature (Wang et al., 2019); thus, the summer LST decreased with the increase of PSF. However, more complicated rela-tionship was found between summer LST and BH. The summer LST was directly affected by surface energy balances and surface net radiation (Kuang et al., 2014). High buildings could block parts of solar radiation into their surrounding surface areas, and thus their summer LSTs pre-sented a decreasing trend with the increase of building height, when BH was less than 100 m. However, the skyscrapers, with its height larger than 100 m, were mainly located in relatively open areas, and thus its surrounding area could have more solar radiation but less shadows;

therefore, their summer LSTs presented higher in the area of skyscrapers (BH>100 m) than high buildings (20 m <BH <100 m). Moreover, there were more compact buildings in low-rise or midrise (BH<20), and its summer LST presented the increasing trend with its building height. The nonlinear relationship was found between LST and SVF; more detailed, the relationship between SVF and the summer LST presented the trend of “inverted U shape”, with the increasing trend before the SVF of 0.6 but decreasing after the SVF of 0.8.

4.3. Heat risks and future climate change

Future heat risks in Beijing were discussed under different SSPs, considering urban morphology, summer LST and future climate data (More detailed information see SI). The summer LST in Beijing would average at 38.33 ◦C ~39.97 ◦C by the year of 2100 (Fig. 10), about 2~4 ◦C higher than nowadays; furthermore, the summer LST of the artificial built type would amount to 39.33~40.98 ◦C in future. Taking the 38.3 ◦C as the boundary, the total area of heat risk could amount to 1864km2

at least, about twice higher than present. By the year 2100, about 16.5~18.9 million residents in Beijing would suffer heat risks, and 88% of total population would be under heat risks under the scenario of SSP 585, particularly for the LCZ of compact high-rise. With some environment-friendly strategies, about 14% of less population would be out of heat risk in future under SSP126 than that under SSP585; thus,

Fig. 10. The heat risk map in Beijing during the period of 2081~2100.

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these results highlighted the importance of mitigation practices and adaption practices for future climate change (Ucal & Xydis, 2020). There were more energy demands and intensive buildings in CFCA, where it was hard for heat diffusion; thus, CFCA would be more sensitive to future climate change, and 2.6 million people (about 96%) in the CFCA could suffer heat risk in future. Besides, the artificial built type was also more sensitive to climate change than the land cover type; more detailed, for the artificial type, about 72% of its total area could be under heat risk in 2100, compared with 11% of the land cover type (More detailed information see SI). It is of high importance to focus on heat risks in intensive buildings and populated areas in future, considering potential risks of air pollution and health issues. Therefore, future urban planning should take detailed LCZs and heat risks into accounts, espe-cially considering future climate change.

4.4. Implications

As discussed above, lower building density and green or blue space were always conducive to mitigate heat risks in cities, which should be taken into accounts for future city planning, especially considering future climate change. Besides, urban morphology and function, popu-lation density and heat risks also existed great differences among different LCZs in Beijing. Therefore, different practices and strategies should be applied for different LCZs. High population density, wretched buildings, poor living condition and low wages commonly existed in city villages of Beijing, where were also facing serious heat risks at present and in future; therefore, it is of high importance and urgency to renewal these city villages, not only for better city scape but also for heat risk mitigation. The LCZ of open midrise presented relatively lower building density, but it had the largest population under heat risks; therefore, these areas of open midrise can increase some green spaces or blue spaces (like small lake) for mitigate heat risk in future. Although people are facing great heat risks in “alleys”, it is of high significance to protect these traditional buildings, due to their great cultural and historical values; therefore, it is feasible to improve their cooling system in these areas. The area of compact high rise and CFCA were highly sensitive to climate change, which also played important roles in economic devel-opment in Beijing. Thus, as the hot spots of future urban planning, climate change adaptation and mitigation measurements should be applied to avoid future heat risk and also to achieve future sustainable development.

4.5. Novelty and limitations

Detailed building parameters are important to identify and map LCZs inside cities, but these data can be hardly to obtain for free. With the big data development, some websites (like the Baidu map in our study) can provide free, open and useful building data, and thus we used them to identify LCZs in Beijing with the novel data system. Besides, we targeted to explore differences of heat risks for different LCZs, which was highly important to achieve human health for urban dwellers, especially considering future climate change. Then, we also aimed to explore urban morphology parameter thresholds for mapping LCZs in Beijing, with complicated, high compacted and density buildings there. Although our study can provide some useful information and suggestions for future urban planning in Beijing, there were still some limitations here. With the same level of heat stress, different people could present totally different heat risks and healthy issues, due to their distinct age, health condition, occupation and incomes. Therefore, we will try to identify different boundaries of heat risk for different people in future studies. Besides, present free and open data cannot illustrate future building parameters, and thus we did not take future urban surface morphologies into account, when estimating future heat risks in Beijing.

Conclusions

In spite of some limitations, we used free and open data of four urban morphology parameters to identify local climate zones (LCZ) and its associated heat risks in Beijing, and then we aimed to provides some useful information for future urban planning. The land cover type of LCZS dominated in Beijing (89%), including dense tree, scatter tree, bush and scrub, low plants, bare rock or paved, bare soil or sand and water. Although they together accounted for only 11% of the total area in Beijing, 8 types of artificial built LCZs played the vital role in eco-nomic development and social activities. Different from other mega-cities, the LCZs of open low-rise and open midrise were important artificial built types of LCZ in Beijing, due to their high value of tradi-tional and cultural buildings. Due to their economic roles in Beijing, the function area of CFCA and UFDA were mainly occupied by artificial built types of LCZ. The summer LST was 3.5 ◦C higher in the artificial built type than that in the land cover types. About 6% of total area and 58% of residents were under heat risks in Beijing, and most of them were located in artificial built types of LCZs. For different LCZs, the type of open low-rise had the largest heat risk area (254km2), while the type of open midrise had the largest population with heat risk (4 million). The LCZ of lightweight low-rise suffered the most serious heat risks, and more than 90% of its area and residents were under heat risks. Considering climate change, more urban areas and more residents could be under heat risks in future, with 88% of population facing heat risks under SSP585. The area of compact high rise and CFCA were highly sensitive to climate change, which should gain more attentions in future. Besides, in spite of some differences, the thresholds of urban morphology for different LCZs presented the similar trend in this study with previous studies. Urban morphology and function, population density and heat risks also existed great differences among different LCZs in Beijing, and thus different practices and strategies should be applied to mitigate heat risks in different LCZs for future city planning, especially for the LCZ of compact high rise and the area of CFCA. Be-sides, other measurements should also be taken to mitigate heat risks in future, such as urban renewal of city villages, increasing blue and green spaces in the LCZ of open midrise, improving the cooling system in al-leys, and so on.

Supporting information

Fig. SI-1. The range of different urban morphological parameters based on typical LCZ samples

Fig. SI-2. The classification confusion matrix of LCZs Fig. SI-3. The summer LST validation Fig. SI-4. The scatter map between POI and population Fig. SI-5. The location of meteorological stations and their urban

morphology Fig. SI-6. The future LST of LCZs under different SSPs (note: unit is

◦C; SSP1 represents SSP126, SSP2 represents SSP245, SSP3 represents SSP370, SSP4 represents SSP585)

Fig. SI- 7. The future heat risks of LCZs and functional areas under different SSPs

Declaration of Competing Interest

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.

The authors declare the following financial interests/personal re-lationships which may be considered as potential competing interests:

Acknowledgments

This work was supported by the National Key Research and Devel-opment Plan of China [No. 2018YFA0606300].

Y. Zhou et al.

Sustainable Cities and Society 74 (2021) 103174

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Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.scs.2021.103174.

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