The influence of urban residential block morphology on seasonal outdoor thermal comfort in severely cold regions: A case study of Harbin, China

Abstract

Outdoor thermal comfort in urban residential blocks significantly affects residents’ quality of life and health, and this effect varies seasonally. Severely cold regions experience harsh winters, and with global climate change, thermal environment problems in summer are also emerging. However, there is a lack of research on the seasonal outdoor thermal comfort in severely cold cities. This study takes 156 real residential blocks in Harbin as research cases. Through numerical simulation and statistical analysis, the influence of local climate zone (LCZ) types, block layout types, and morphological parameters on the outdoor thermal environment in winter and summer is explored. The Universal Thermal Climate Index (UTCI) is used to evaluate thermal comfort. The results show that the influence of block morphology on UTCI in winter is significantly greater than that in summer, and wind speed has a major impact on outdoor thermal comfort. Compact blocks provide better thermal comfort in both winter and summer. The seasonal thermal comfort level of blocks with hybrid layout type, which is mainly courtyard layout, is higher. This study is of great value and scientific significance for improving the urban living environment and guiding the planning and design of blocks in severely cold regions.

Graphical abstract

Keywords

Outdoor thermal comfort / Residential block / Block morphology / Local climate zone / Severely cold region

1 Introduction

With global population growth and climate change, urbanization is accelerating, and urban human residential blocks issues are becoming increasingly prominent (Manoli et al., 2019). The increasing frequency of extreme weather events has had a negative impact on the health and quality of life of urban residents (Marando et al., 2022; Salata et al., 2017). Relevant data show that in many parts of the world, the incidence and mortality rates of many diseases affected by climate have increased significantly (Alahmad et al., 2023; Thompson et al., 2022). In addition, for residents, the urban outdoor space is an important place for activities (Lai et al., 2020a). The creation of a good thermal environment can stimulate the desire to be active (Cheung and Jim, 2018), enhance the spatial experience (Li et al., 2016), and maintain public health (Rodríguez-Algeciras et al., 2021), thereby increasing the utilization rate of outdoor space. From an urban design perspective, a reasonable outdoor thermal environment design can also save energy consumption in surrounding buildings and alleviate urban heat stress, which has good economic benefits (Meng et al., 2024; Wang et al., 2019). Therefore, it is crucial to plan and design a comfortable urban outdoor thermal environment.

The most widely accepted definition of thermal comfort is a state of mind indicating satisfaction with the thermal environment (American National Standards Institute, 2013). Previous studies have shown that outdoor thermal comfort is affected by a variety of factors, including environmental parameters, population characteristics, and urban planning and architectural design measures (Abd Elraouf et al., 2022; Nikolopoulou and Steemers, 2003; Manavvi and Rajasekar, 2022). Thermal environment parameters and population characteristics vary significantly depending on the climatic context. From an urban planning perspective, a large number of studies have shown that optimization strategies for building reflective surfaces, urban underlying surfaces, and urban forms can significantly reduce outdoor temperatures and thereby alleviate urban heat stress (Sun et al., 2022; Wang et al., 2019). The larger the area of impervious urban underlying surfaces, the more heat is absorbed by the surface, which in turn affects outdoor thermal comfort (Guo et al., 2022; Lai et al., 2019; Piselli et al., 2018). The influence of urban morphology on the outdoor thermal environment is mainly achieved by changing the radiative and convective heat exchange in urban open spaces. Lai et al. (2019) found that changing the urban morphology had the greatest overall effect on thermal comfort after comparing several outdoor thermal environment improvement strategies. At the building design level, the shape of the building, the combination of building groups, and the design of the external space morphology can effectively improve the outdoor thermal environment (Banerjee et al., 2024; X. Deng et al., 2023). The above studies show that urban morphology is a key determinant in improving the urban thermal environment (Galal et al., 2020; Ibrahim et al., 2021).

1.1 Urban geometry

In recent years, a large number of scholars have studied the relationship between urban morphology and outdoor thermal comfort from the macroscale of the city, the mesoscale of the block, and the microscale of the building (Aghamolaei et al., 2023; Sun et al., 2023). At the macroscale, studies have shown that urban land use patterns and transportation networks have a significant influence on urban thermal comfort (Ali and Patnaik, 2018; Guo et al., 2022; Li and Liu, 2020; Piselli et al., 2018). Urban land use patterns with different site coverages (λ_b) have significant differences in thermal performance (Banerjee et al., 2024; Meng et al., 2024; Yin et al., 2021). Land use patterns with a high distribution of blue-green spaces can improve outdoor thermal comfort (Lai et al., 2019; Piselli et al., 2018), with the distribution of green spaces playing a decisive role (Cheung and Jim, 2018; Meili et al., 2021; Wang et al., 2019). In terms of transportation, Su et al. (2022) found that road direction is the primary factor affecting the thermal environment by analyzing radial roads centered on squares. At the mesoscale, research has mainly focused on the block, urban block, and street canyon scales (Pompei et al., 2024; Sun et al., 2023). At the block scale, research has mainly discussed the relationship between block compactness, surrounding building height, building ratio and layout morphology, and outdoor thermal comfort while also emphasizing the important influence of block context (Ignatius et al., 2015; Wai et al., 2020). At the urban block scale, studies have found that enclosure (C_p), λ_b, building average height, and floor area ratio (FAR) have a significant influence on outdoor thermal comfort. Further results show that λ_b and C_p have the greatest impact (Acero et al., 2021; X. Deng et al., 2023; Galal et al., 2020). By analyzing the thermal environment performance of typical layout types, Deng et al. (2023) showed that dispersed types of blocks provide a better thermal environment in the Nanjing area, while Apreda et al. (2020) emphasized the superior performance of enclosed types of blocks in European Mediterranean cities. At the street canyon scale, a large number of studies have focused on the height-to-width ratios, orientations, and sky view factors (SVF) of streets and have demonstrated that they are indeed key factors affecting outdoor thermal comfort (Ibrahim et al., 2021; Johansson, 2006; Krüger et al., 2011; Nasrollahi et al., 2021; Rodríguez-Algeciras et al., 2021). Further studies have shown that for the two most important influencing factors of street orientation and height-to-width ratios, changes in height-to-width ratios can also simultaneously change the impact of street orientation on pedestrian thermal comfort (Abdollahzadeh and Biloria, 2021; Ali-Toudert and Mayer, 2006; Mohite and Surawar, 2024). In addition, at the microscale of buildings, there have been relatively few studies on outdoor thermal comfort, as current research on building scale has focused mainly on indoor thermal comfort (Cao et al., 2021; Nobuo and Kakon, 2012; Xu et al., 2021). However, relevant studies have still shown that building shape, height, orientation, and spacing can affect outdoor thermal comfort (Karimi et al., 2023; Pompei et al., 2024; Sun et al., 2023).

1.2 Outdoor thermal comfort assessment

In order to obtain a more comfortable outdoor thermal environment, researchers have assessed outdoor thermal comfort from multiple aspects. Among the thermal environment parameters, air temperature (Ta), wind speed (WS), relative humidity (RH), and mean radiant temperature (MRT) are closely related to thermal comfort (Abdollahzadeh and Biloria, 2021; Dimoudi et al., 2013). At the same time, due to the influence of social background (Manavvi and Rajasekar, 2020), metabolic rate (Kumar and Sharma, 2022), and psychology (Nikolopoulou and Steemers, 2003), different groups of people tend to evaluate thermal comfort differently. The main assessment methods used in related studies include questionnaires, onsite measurements, and numerical simulations (Lai et al., 2020b; Sun et al., 2022). Questionnaire surveys collect subjective feelings about the thermal environment and other relevant information (such as gender, age, clothing, and mood during the interview) and are often accompanied by monitoring of the surrounding thermal environment (Wei et al., 2022; Xi et al., 2020; Xu et al., 2019). However, due to individual differences, psychological factors, questionnaire design, and other factors, it is difficult to form a unified standard. In addition, because the interviewee’s perception is only collected once at a specific time, it is impossible to study the dynamic effects of the thermal environment (Lai et al., 2020b; Liu et al., 2023). In the process of on-site measurement, static measurement results are more accurate, but they are less sensitive and more expensive. In contrast, mobile measurement is a more convenient and effective method, but it also has greater measurement errors (Leconte et al., 2015; Sun et al., 2022). Today, more and more studies recognize the unique advantages of simulation methods in thermal comfort research. It can link thermal comfort research at different scales and can play a role in the design and planning stages in advance (Aghamolaei et al., 2021; Li and Liu, 2020). In recent years, a large number of studies have used simulation analysis with commercial software instead of empirical models, including mainstream software such as Rayman (Taleghani et al., 2015), ANSYS Fluent (Pompei et al., 2024), ANSYS CFX (Dimoudi et al., 2014) and ENVI-met (Banerjee et al., 2024; Bedra et al., 2023; Peng and Huang, 2022). Many of these studies use a combination of methods to ensure the rigor and accuracy of their conclusions and to provide a more comprehensive evaluation of the thermal environment. A common approach is to use real meteorological data obtained from on-site measurements as input conditions or to calibrate the simulated meteorological data (Li and Liu, 2020; Liu et al., 2023).

Meanwhile, more than 60 thermal comfort indices have been proposed to evaluate thermal comfort levels better. Some of these are based on direct measurements of thermal environment variables, such as wet bulb globe temperature (WBGT), while others are rational indices calculated based on heat balance equations, including the predicted mean vote (PMV), standard effective temperature (SET), new standard effective temperature (SET*), physiological equivalent temperature (PET), MRT, and universal thermal climate index (UTCI) (Sun et al., 2022; Wang, 2021). Among them, SET∗ is mainly applicable to hot regions. In contrast, UTCI, PET, SET*, and PMV are more versatile and can be adapted to most regions (Ibrahim et al., 2021; Nie et al., 2022). PMV is mainly used to evaluate the thermal comfort of indoor environments, and when evaluating outdoor thermal comfort, it overestimates uncomfortable outdoor conditions (Ali-Toudert and Mayer, 2006; Villadiego and Velay-Dabat, 2014; Yin et al., 2021). It is also a dimensionless index that is difficult to directly compare with other outdoor thermal comfort indices (Nie et al., 2022; Wang, 2021). PET, as a commonly used index for preliminary outdoor thermal comfort research, is widely used in meteorological forecasting and urban planning and design (Abdallah and Mahmoud, 2022; Sun et al., 2022). However, the PET model does not consider the moisture content of human skin and lacks consideration of human heat dissipation (Cheng et al., 2012). In addition, several studies have shown that UTCI is more suitable than PET and PMV for different seasons in all climatic regions (Blazejczyk et al., 2012; Lai et al., 2014; Wang, 2021). Therefore, UTCI has also become the most commonly used indicator for evaluating the outdoor thermal environment in recent years (Yang et al., 2023).

1.3 Climatic conditions

At present, most of the world’s research on the urban thermal environment focuses on hot or humid areas (Abd Elraouf et al., 2022; Ali-Toudert and Mayer, 2006; Galal et al., 2020), and a considerable part of it is carried out in subtropical regions (Peng and Huang, 2022; Wang et al., 2019). However, there are relatively few studies in severely cold regions where outdoor thermal comfort is a serious problem, and the utilization rate of outdoor space is low (Sun et al., 2022; Villadiego and Velay-Dabat, 2014). However, with the changing global climate, research on outdoor thermal comfort in severely cold regions has also increased significantly in recent years (Nie et al., 2022; Yilmaz et al., 2021; Yin et al., 2021). In addition, with the popularization of the local climate zone (LCZ) system proposed by Stewart and Oke (2012), a large number of studies have now applied this new local climate classification method, aiming to provide a more systematic and universal design strategy for the outdoor thermal environment (Leconte et al., 2015). Previous studies have shown that since the thermal environment parameters depend heavily on seasonal changes in local weather and their distribution is affected by urban morphology, the thermal environment parameters and urban morphology parameters that are the focus of research in regions with different climatic conditions also show significant differences (Johansson, 2006; Sun et al., 2022; Villadiego and Velay-Dabat, 2014). In terms of outdoor thermal environment parameters, while most studies focus on Ta, WS, and MRT, humid regions pay more attention to the influence of humidity than dry regions (Achour-Younsi and Kharrat, 2016; Ali-Toudert and Mayer, 2006; Peng and Huang, 2022). For example, in hot Egypt, Abd et al. (2022), who selected a hot and humid climate as the research area, discussed the relationship between RH and outdoor thermal environment, while Galal et al. (2020), who focused on a hot and dry climate area, did not. In terms of urban morphology parameters, in addition to the generally concerned street orientation and street height-to-width ratios, compared to cold regions, hot regions will pay more attention to the influence of SVF on outdoor thermal comfort (Johansson, 2006; Nasrollahi et al., 2021). At the same time, due to the regional characteristics of the climate, hot regions mainly conduct research on a single season in the summer (Abdollahzadeh and Biloria, 2021; Boukhelkhal and Bourbia, 2016; Galal et al., 2020; Nasrollahi et al., 2021), while most of the research in cold regions is conducted in winter (Jin et al., 2020; Shui et al., 2016).

Studies have shown that the outdoor thermal environment in cold regions is more comfortable during the transition season, while the thermal comfort is poor in winter (Liu et al., 2019). However, as the global warming trend intensifies, high temperatures have frequently occurred in cold regions in recent years, and the summer heat risk issue has become a problem that cannot be ignored. As the core place of people’s daily lives, the thermal environment level of residential blocks directly affects the quality of life and physical and mental health of residents (Abdel-Ghany et al., 2013; Lai et al., 2014). Although previous studies have shown that urban morphology has an important influence on the thermal environment of blocks, current research results on cold regions are still significantly insufficient (South China University of Technology, 2013).

On the one hand, existing research has mostly focused on the outdoor thermal environment in hot or temperate regions (Abdollahzadeh and Biloria, 2021; Galal et al., 2020). Even in cold regions, most studies focus on the characteristics of cold winter climates, with insufficient response to the increasingly frequent high temperatures in summer, and a lack of comprehensive consideration of the climate characteristics of both winter and summer (Jin et al., 2020). On the other hand, existing studies have mostly used control variable methods based on ideal simplified models to simulate the influence of morphological parameters on the outdoor thermal environment (Abdollahzadeh and Biloria, 2021; Ali-Toudert and Mayer, 2006). Although the differences and trends in the research results are quite significant, the lack of consideration of actual planning and design requirements in the research conditions and the difficulty in reflecting the actual thermal environment response under the coupling of multiple parameters in the complex spatial morphology of built blocks have made it difficult to effectively apply the research results in urban construction projects.

This paper aims to study the influence of urban residential block morphology on outdoor thermal comfort in severely cold regions in winter and summer. Harbin, China, is used as an example. The outdoor thermal environment of 156 built residential blocks is simulated, and their thermal comfort is assessed. The relationship between LCZ type, layout type, and outdoor thermal comfort is quantitatively analyzed, as well as the influence of block morphology parameters on outdoor thermal comfort. This paper’s innovation lies in its research, based on morphological data from a large sample of real residential blocks in severely cold regions. The study clarifies the sensitivity of block morphology to the outdoor thermal environment in winter and summer, reveals seasonal differences in its mechanisms, and proposes a block morphology design strategy based on comprehensive optimization of multi-season thermal comfort. The results provide a theoretical foundation and scientific basis for thermal environment design in urban residential blocks in severely cold regions, and promote the shift toward climate-responsive and seasonally adaptive urban morphology design.

2 Methodology

The workflow of this study is divided into four steps, as shown in Fig. 1. First, 156 typical residential blocks in Harbin were selected as the research sample, and their local climate zone (LCZ) and layout types were classified, and their morphological parameters were calculated. Second, the outdoor thermal environment of the research sample was simulated using ENVI-met software, and the outdoor thermal comfort index was calculated using Rayman software based on the simulation results. Third, the relationship between LCZ types, layout types, morphological parameters, and outdoor thermal comfort was statistically analyzed. Finally, the research results were discussed, and suggestions and conclusions for the block design were proposed.

2.1 Research site

Harbin (45°41'N 126°37'E), known as the “Ice City” in China, is the capital of Heilongjiang Province and a megacity with the largest land area and the third largest registered population among Chinese provincial capitals. It has been approved by the State Council as an important central city in Northeast China (Yin et al., 2021). Harbin has a city area of 10,198 km² and administers 9 urban districts, including 6 main urban areas, with a population density of 577.05 people/km². Harbin is classified as a severely cold region in Zone A of the Code for thermal design of civil building (GB 50176—2016) (China Academy of Building Research, 2016) and has a mid-latitude continental monsoon climate. Harbin has the typical climate characteristics of a severely cold region with four distinct seasons. Winters are cold and dry, with a heating period lasting up to 180 days. Summers are warm, lasting up to 3 months. The transition seasons are short, and there is a large annual temperature difference (China Academy of Building Research, 1994). The average Ta in the hottest month of July and the coldest month of January are 23.8 °C and —17.5 °C, respectively. Harbin’s annual RH varies between 45% and 78%, and the average monthly WS fluctuates between 2.4 m/s and 3.7 m/s, with a prevailing S-SSW-SW wind direction. Precipitation is concentrated from June to August, and snowfall is concentrated from November to January (Kubota and Kono, 2021).

2.2 Sample selection and morphological analysis of residential blocks

2.2.1 Sample selection of residential blocks

This study selected a sample of 156 typical residential blocks in Harbin as case studies. The specific distribution is shown in Fig. 2(a). In order to make the study representative and reasonable, the selection of the sample followed the following principles: First, the year of completion of the residential blocks should be within the past 20 years, and the location should be within the boundaries of the six main urban areas of Harbin. Second, the sample of residential blocks must meet the relevant requirements of the Code for the Compilation of Regulatory Detailed Planning in Heilongjiang Province (GB23/T744-2004) (Harbin Urban Planning and Design Institute, 2004) and Harbin City Building Floor Area Ratio and Related Content Management Regulations (Standing Committee of Harbin Municipal People’s Congress, 2004). For low-rise residential projects, the site coverage (λ_b) in new urban areas should not exceed 30%, and the floor area ratio (FAR) should not exceed 0.7; in the old urban area, the λ_b should not exceed 35%, and the FAR should not exceed 0.8. For multi-story residential projects, the λ_b in both the old and new urban areas does not exceed 35%, and the FAR does not exceed 2.0. For high-rise residential projects, the FAR in the new urban area does not exceed 4.5, the FAR in the old urban area does not exceed 4.2, and the λ_b in both does not exceed 25%.

On this basis, and taking into account the diversity of the sample sites in terms of size and spatial morphology, as well as the wide range of locations, a total of 156 residential blocks in the six main urban areas of Harbin were selected as the research sample. In addition, the spatial morphology information of the research sample was studied and investigated using drawings, satellite images, field surveys, etc. The residential sites studied primarily ranged in area from 2.0 to 32.0 ha, with only 0.6% exceeding 32.0 ha (see Fig. 2(b)). Fig. 2(c) shows a statistical breakdown of the site shapes of the studied blocks, with rectangular, polygonal, trapezoidal, L-shaped, and triangular shapes accounting for 37.8%, 35.9%, 10.3%, 12.8%, and 3.2%, respectively. Further statistical analysis of the length, width, and aspect ratio of rectangular sites is shown in Fig. 2(d). Site length and width primarily ranged from 201 to 300 m, while the aspect ratio for rectangular areas primarily ranged from 1.01 to 1.50. Fig. 2(e) shows that the average number of floors for buildings primarily ranged from 4 to 33, with nearly 50.0% of the cases having an average number of floors between 6 and 9. For a detailed model of the case studies, see Appendix A.

2.2.2 Layout types and morphological parameters

The layout types used in this paper are mainly row type, courtyard type, point type, and hybrid type, which are also the most commonly used classification methods in related research (Abd Elraouf et al., 2022; China Urban Planning and Design Institute, 2018; Dwiputra, 2021; Taleghani et al., 2015; Zhang et al., 2022). Among them, the row type layout is conducive to lighting and ventilation and is the most commonly used. The courtyard type layout forms a relatively closed inner courtyard space, which is suitable for cold and windy areas, but some house orientations are poor. The point type refers to the layout of low-rise free-standing types, multi-story point types, and high-rise tower types that form relatively independent groups. Natural ventilation and lighting are improved by the staggered arrangement of buildings. The hybrid type is a combination or deformed combination of the three building layouts: row type, courtyard type, and point type. It can flexibly combine the layout of buildings with the conditions of the site. This study divides the layout types of 156 residential blocks into six types: row type, courtyard type, and hybrid type of row and point (hybrid type [R + P]), hybrid type of row and courtyard (hybrid type [R + C]), hybrid type of courtyard and point (hybrid type [C + P]), and hybrid type of row, courtyard and point (hybrid type [R + C + P]).

In this study, based on the literature review, we quantitatively describe block morphology in three dimensions: overall, horizontal, and vertical, and select morphological parameters that have been widely used in the relationship between urban morphology and outdoor thermal comfort (Liu, 2021). In terms of overall morphology, the floor area ratio (FAR) is selected as an indicator, which comprehensively reflects the degree of site development and land use. Horizontal control factors include site coverage (λ_b), site enclosure (C_p), and block orientation (D_bg). λ_b indicates the degree of building coverage on the site only in the horizontal direction; C_p is an important indicator of the degree of openness of space and can visually reflect the degree to which the building boundary encloses the internal space of the entire block; D_bg affects the internal building layout and D_bg of the site have an important influence on thermal comfort. Vertical control factors include building average height (H_avg) and building height standard deviations (H_std). The H_avg is the weighted average height of the building base area, which reflects the vertical differences between blocks with different base areas. The H_std further objectively reflects the differences in building height within the site. In addition, according to previous research on the outdoor wind environment in blocks, the influence of building orientation on WS is caused by the relative angle between the building orientation and the wind direction. Therefore, this study introduces the wind projecting angle (θ) index (Jin et al., 2017). The definitions of the morphological parameters are shown in Table 1.

2.2.3 Type classification based on the LCZ system

The LCZ system was first proposed by Stewart and Oke (2012) in 2012 as a classification framework for urban climate research. This system combines qualitative and quantitative factors to determine the type of LCZ to which a parcel belongs. The LCZ system has been widely used in the thermal environment research of many cities at home and abroad, and its effectiveness has been initially demonstrated (Lau et al., 2019). However, due to the diversity of urban morphologies and the highly urbanized characteristics of Chinese cities, the initial setting based on European cities is not entirely applicable to Chinese cities (Zhou et al., 2022). Therefore, this study combined the Standard for Urban Residential Area Planning and Design (GB 50180—2018) (China Urban Planning and Design Institute, 2018) and the actual development situation in Harbin to revise the LCZ classification method proposed by Stewart and Oke (2012). The original LCZ classification and the revised LCZ classification method are detailed in Table 2.

This study subdivides LCZ and introduces new parameter ranges. The new parameter ranges for site coverage and building average height thresholds differ significantly from the LCZ classification proposed by Stewart and Oke, but are more suitable for the actual situation of urban residential area planning and design in China. LCZ 1 and LCZ 4 represent compact high-rise buildings and open high-rise buildings, respectively; LCZ 2 and LCZ 5 represent compact middle-high rise buildings and open middle-high rise buildings, respectively; and LCZ 3 and LCZ 6 represent compact multi-story and open multi-story buildings, respectively. For details of the revised LCZ classification method, see Table 2. In addition, considering the high urbanization rate and urban development intensity in Harbin, low-rise buildings were not studied in this research.

2.3 Outdoor thermal comfort index

This study uses the UTCI to comprehensively evaluate the outdoor thermal environment. UTCI is a new comprehensive index established under the auspices of the International Society of Biometeorology (ISB) that integrates multiple disciplines and is based on Fiala’s multi-node model (Matzarakis et al., 2010). It includes a clothing thermal resistance calculation model and records the types of clothing combinations worn by the average city dweller in different real-world environments. The UTCI is defined as the Ta of a reference environment in which the dynamic physiological thermal response of the human body is equivalent to that in the actual environment under the following reference conditions: MRT equals Ta, RH is 50 % (with water vapor pressure not exceeding 20 hPa), WS at 10 m above ground is 0.5 m/s, and the metabolic rate of the human body is 135 W/m². The biggest difference between it and PET, SET∗, and PMV is that it is based on a non-steady-state model that takes into account the adaptability of the human body and can assess the thermal comfort level in different seasons in all climate zones (Jendritzky et al., 2001).

This study calculated the UTCI using RayMan software, a widely used thermal comfort index calculation software in outdoor thermal comfort research both domestically and internationally. RayMan requires input of thermal environment parameters, including Ta, WS, RH, and MRT (Daneshvar et al., 2013; Matzarakis et al., 2007). Therefore, based on the outdoor thermal environment simulation results for each residential case study, this study inputs these four thermal environment parameters at each receiving point into RayMan to calculate the UTCI index value. Finally, the average value of UTCI at each receiving point was used to measure the outdoor thermal comfort level of each study case.

2.4 Simulation of the urban outdoor thermal environment

2.4.1 Selection of a typical meteorological day

Different indicators can be used to measure the cold and heat of a region. From the perspective of human subjective feelings, the climatic conditions of the coldest and hottest months of the year can directly reflect the local cold and heat (China Academy of Building Research, 1994). In order to improve the feasibility of the simulation and make the results more representative, typical meteorological days that can represent the general climatic characteristics of January (the coldest month) and July (the hottest month) were selected as the typical calculation days for studying the outdoor thermal environment and residential thermal environment of blocks in severely cold regions in winter and summer.

Since the China Standard Weather Data (CSWD) has a long observation period and its data source is up to date, it can represent the typical meteorological conditions of each month in the region. Therefore, this paper selects a typical meteorological day based on the hourly meteorological parameter data of a typical meteorological year in Harbin City from the CSWD and uses the mean absolute percentage error (MAPE) and consistency index (CI) to measure the error between the meteorological parameter values at each hour of the day and the average value of the meteorological parameters at each hour of the month. These two indicators fully consider the relative magnitude of the average error and the sensitivity of the error value. The calculation formulas are as follows.

\begin{array}{}\text{(1)}& MAPE=\frac{1}{n}{\displaystyle \sum _{i=1}^n}\frac{\left|X_{\text{month}\mathrm{,}i}-X_{day\mathrm{,}i}\right|}{X_{\text{month}\mathrm{,}i}}\times 100\%\mathrm{.}\end{array}

Among them, MAPE is the mean percentage error, X_{day, i} is the hourly value of the meteorological parameter at the time i of the day, X_{month, i} is the mean value of the meteorological parameter at the time i of the month, and n is the number of hours in a day.

\begin{array}{}\text{(2)}& CI=1-\frac{{\displaystyle \sum _{i=1}^n}{\left({\text{X}}_{\text{day}\mathrm{,}i}-{\text{X}}_{\text{month}\mathrm{,}i}\right)}^2}{{\displaystyle \sum _{i=1}^n}{\left(\left|{\text{X}}_{\text{day}\mathrm{,}i}-{\overline{\text{X}}}_{\text{month}}\right|+\left|{\text{X}}_{\text{month}\mathrm{,}i}-{\overline{\text{X}}}_{\text{month}}\right|\right)}^2}\mathrm{.}\end{array}

Among them, CI is the consistency index, X_{day, i} is the hourly value of the meteorological parameter at time i each day, X_{month, i} is the average value of the meteorological parameter at time i in the month, month is the average of the average values of the meteorological parameters at each time in the month, and n is the number of hours in a day.

When selecting a typical meteorological day, the key meteorological elements should be analyzed, and their weights should be taken into account. On this basis, the MAPE and CI of each meteorological element are calculated for each day, and the weighted sum is calculated according to the weight coefficient so as to rank and compare the values of the two indicators, respectively. The day with the smallest MAPE and the largest CI value is selected as the typical meteorological day. Combining previous research results (Hall et al., 1978; Jiang, 2010; Pissimanis et al., 1988; Wong and Ngan, 1993; Yang et al., 2007), this paper selects three meteorological elements: Ta, WS, and total horizontal solar radiation, and sets their weighting factors to 1/6, 1/6, and 3/6, respectively. In summary, the weighted MAPE and CI of the daily meteorological data for the coldest month, January, and the hottest month, July, are calculated. The comparative ranking shows that the results of the two indicators are consistent, and January 25 and July 20 are finally selected as typical winter and summer weather days, respectively.

2.4.2 ENVl-met simulation

This study used the ENVI-met software to simulate the outdoor thermal environment of samples of residential blocks on typical summer and winter days. ENVI-met is a holistic three-dimensional microclimate CFD model developed by Bruse and Fleer (1998) in 1998. Based on the principles of fluid mechanics, thermodynamics, and atmospheric physics laws, ENVI-met can simulate complex surface-plant-air interactions in an urban environment(Huttner, 2012). Specifically, it is a grid-based model with fine resolution (0.5—10 m) and uses the standard κ-ε turbulence model and Reynolds Averaged Navier-Stokes (RANS) equations (Elraouf et al., 2022). Among all of the CFD-based models, ENVI-met is one of the most widely used tools in multiple climate backgrounds and for different urban morphologies, and its accuracy and effectiveness have been fully verified (Deng and Wong, 2020; Liu, 2021).

The ENVI-met simulation parameter settings mainly include two aspects: physical model parameters and background meteorological parameters (See Table 3). The background meteorological parameters mainly include Ta and RH at a height of 2 m, WS and direction at a height of 10 m, solar radiation intensity, and soil temperature and moisture at different depths. Based on the typical meteorological data of Harbin in a typical year in the CSWD, the initial parameters for simulating the outdoor thermal environment of residential blocks on typical meteorological days in winter and summer (January 25 and July 20) are set, as shown in Table 3. In addition, in order to avoid the influence of initial conditions, the start time of the simulation is set to 4:00 on the day before the typical calculation day, the total simulation time is set to 44 h, and the calculation results for the last 24 h are used.

The physical model parameters were simulated in Harbin, China (45°41'N, 126°37'E). The 156 research objects were modeled in ENVI-met according to the actual layout of the blocks. Among them, the Design code for residential buildings (GB50096-2011) (General Administration of Quality Supervision, 2011) stipulates that the height of residential floors should be 2.8 m. However, considering structural factors such as parapets, pitched roofs, and the height difference between indoors and outdoors, and for the sake of calculation convenience, the floor height is set to 3 m, and the buildings are set to have flat roofs. Considering the simulation calculation time and accuracy, the grid resolution of the main model area is set uniformly, the horizontal grid resolution is dx = 3 m, dy = 3 m, and the vertical direction adopts the bottom layer subdivided into 5 layers of equidistant grids, with the grid resolution of dz = 6 m, and the height of the bottom layer of grids that can be read simulation results in order of 0.1·dz, 0.1·dz + 0.2·dz, 0.1·dz + 0.4·dz, 0.1·dz + 0.6·dz, 0.1·dz + 0.8·dz, 0.1·dz + 0.8·dz, 0.1·dz +1·dz·dz, 0.1·dz + 0.6·dz, 0.1·dz + 0.8·dz, 0.1·dz + 1·dz, and the number of grids is set based on the actual spatial dimensions of different blocks (China Academy of Building Research, 2016). The number of grids is set based on the actual spatial dimensions of the different residential blocks (Jin et al., 2017). Five nesting grids arranged at intervals of soil and asphalt are set around the main model area to weaken the impact of external conditions on the simulation results of the main model area (Conry et al., 2015). Since this paper only considers the impact of the block building layout on the outdoor thermal environment, in order to avoid the impact of the underlying surface materials and vegetation on the outdoor thermal environment, this paper uniformly sets the model underlying surface to be a concrete floor commonly used in residential blocks in severely cold regions, with a reflectivity of 0.2 and a thermal conductivity of 1.51 W/(m·K) (China Academy of Building Research, 2016). The thermal performance of the building envelope refers to relevant energy-saving standards. The reflectivity of the external walls and roofs is 0.3, and the heat transfer coefficients are 0.4 W/(m²·K) and 0.25 W/(m²·K), respectively.

2.4.3 Validation study

In order to verify the accuracy and precision of the micro-climate simulation model, field measurements of the outdoor thermal environment were carried out in residential blocks with a high degree of typicality and representativeness in Harbin’s spatial morphology―Hesong Residential Block (Phase I), Hesong Residential Block (Phase II), Heyuan Residential Block and Guanjiang Shoufu―from 8:00 to 18:00 on July 18, 2016 (summer) and January 11, 2017 (winter). The residential area under test is located in the city center, with the building complex oriented 10° southeast. The exterior facades of the buildings within the block are all decorated with light-colored paint, and the underlying materials are primarily concrete and cement bricks.

Nine measurement points were set up for the on-site measurement. All were located on cement brick paving, as far away as possible from greenery and large areas of trees and shrubs to minimize their impact on the thermal environment test results. The measurement point locations and the surrounding environment are shown in Fig. 3. Measurement points C1—C3 were set in the center of the enclosed space, with C1 and C2 located in the fully enclosed space and C3 in the semi-enclosed space. Measurement points L1‒L3 were set in the center of the row-and-row space, and measurement points S1—S3 were set in the center of the square. The test instruments included a temperature and humidity recorder, a black globe temperature recorder, and a small weather station. The specific models, performance, and configuration of the test instruments are shown in Table 4. During the test, to prevent solar radiation from affecting the test results, the temperature and humidity recorder was placed in a homemade aluminum foil sleeve with open ends for good ventilation. The handheld anemometer, black globe temperature recorder, and temperature and humidity recorder were fixed together with a bracket at a height of 1.5 m above the ground.

The simulation results of ENVI-met were compared with the measurement results. As the most widely used statistical data, the root mean square error (RMSE) and the consistency index d were used to evaluate the accuracy of ENVI-met. As can be seen from Table 5, the RMSE values of Ta, RH, WS, and MRT are all within an acceptable range. At the same time, the consistency index d values of each parameter are relatively high. This shows that the simulated values are in good agreement with the measured values and that ENVI-met can reasonably simulate the outdoor thermal environment conditions in summer and winter for this experiment.

3 Result

3.1 Relationship between LCZ types of residential blocks and outdoor thermal comfort in winter and summer

Figure 4 shows the average Ta, average MRT, average wind speed ratio (Ri), and average UTCI corresponding to each LCZ type in winter and summer. LCZ 4, LCZ 5, and LCZ 6 have higher summer Ta and MRT values than LCZ 1, LCZ 2, and LCZ 3, respectively, but lower winter Ta and higher winter MRT values. This indicates that open blocks have higher MRT than compact blocks. LCZ 2 has the lowest Ta and MRT in summer, while these values are higher in winter. In contrast, LCZ 6 has higher Ta and MRT in summer but lower values in winter. In the summer, the overall distribution range and interquartile range of Ta and MRT in LCZ 3 and LCZ 6 of multi-story blocks are small, indicating that the different morphologies of various residential blocks in LCZ 3 and LCZ 6 have a small impact on temperature. In addition, in summer, MRT increases in the low-density blocks (LCZ 1, LCZ 2, LCZ 3) and in the high-density blocks (LCZ 4, LCZ 5, LCZ 6) in that order, respectively, while the opposite is true in winter. This indicates that in summer, the block MRT increases as building height decreases.

In this study, Ri was used as an indicator to evaluate the outdoor wind environment. Ri is the ratio of the WS within the site to the WS from the external flow and is mainly used to evaluate the degree of WS change caused by disturbances in the surrounding environment. The Ri of each LCZ type of block was higher in winter than in summer. The overall distribution range of Ri in summer was 0.08—0.8, while that in winter was 0.16—1.02. This reflects that the LCZ type has a more significant impact on UTCI in winter. LCZ 2 and LCZ 3 had the lowest Ri in both winter and summer. In contrast, LCZ 1 also has a low Ri in winter, but its Ri in summer remains moderate. LCZ 4, LCZ 5, and LCZ 6 have similar and relatively high RIs in both winter and summer and decrease in order. The Ri in LCZ 4, LCZ 5, and LCZ 6 are similar and higher in both winter and summer, which reflects that the low-density blocks have greater WS.

The overall UTCI range in winter is wider than in summer. The overall UTCI range in summer is 23.1 °C—27.6 °C, while in winter it is —26.9 °C——15.5 °C. The difference between the UTCI of each LCZ type is smaller in summer and larger in winter. In both summer and winter, the lowdensity blocks (LCZ 1, LCZ 2, LCZ 3) have higher UTCI than the high-density blocks (LCZ 4, LCZ 5, LCZ 6) in that order, respectively, and the difference is even greater in winter. In addition, the UTCI rises in summer in the LCZ 1, LCZ 2, LCZ 3 and LCZ 4, LCZ 5, LCZ 6 sections in that order. This shows that in summer, the UTCI of the block increases as the building height decreases. In both summer and winter, LCZ 3 has the highest UTCI, LCZ 4 has the lowest UTCI, and LCZ 5 has a moderate UTCI. In contrast, LCZ 2 has the highest UTCI in winter and remains moderate in summer. In contrast, LCZ 2 has the highest UTCI in winter and remains moderate in summer, which is the best performance of the comprehensive outdoor thermal environment in winter and summer in the severely cold region.

3.2 Relationship between layout types of residential blocks and outdoor thermal comfort in winter and summer

Figure 5 shows the average Ta, average MRT, average Ri and average UTCI for each layout type in the winter and summer seasons. The results show that the overall distribution range of Ta and MRT in the summer is smaller than in the winter for each layout type. The Ta and MRT of the row type blocks are highest in the summer and lower in the winter. The Ta of the courtyard type blocks is lowest in the summer and highest in the winter. The Ta and MRT of the hybrid type blocks are at moderate levels in both the summer and winter. This shows that as the degree of enclosure increases, Ta of the block in summer gradually decreases, and Ta of the block in winter gradually increases. Among them, the hybrid type (R + C + P) has lower Ta and MRT in summer and higher in winter. The overall distribution range and the distance between the quartile interval values of Ta and MRT in winter and summer for the hybrid type (C + P) are small, indicating that the different morphologies of the various residential block types of the hybrid type (C + P) have a small impact on temperature.

Ri in summer is lower than in winter for all layout types. The maximum difference between the median Ri values for the various layout types in summer is 0.24, which is smaller than in winter (0.4). In both summer and winter, Ri is lowest in the courtyard type blocks, higher in the row type blocks, and at a moderate level in the hybrid type blocks. Among these, the hybrid type (R + P) has relatively high Ri in both winter and summer, while the hybrid type (C + P) is relatively low. This shows that as the degree of enclosure increases, the block Ri gradually decreases.

In summer, the maximum difference between the UTCI medians of the various layout types is 1.15 °C, which is significantly smaller than in winter (5.2 °C). In summer, the UTCI of the courtyard type and hybrid (C + P) blocks is slightly higher than that of the other layout types, but in winter, it is significantly higher. In both winter and summer, the UTCI for blocks of the courtyard type and the hybrid type (C + P) are the lowest, while the UTCI for the hybrid type (R + C) and the hybrid type (R + C + P) are at a moderate level. This indicates that increasing the proportion of peripheral blocks in hybrid blocks can significantly increase winter UTCI and slightly increase summer UTCI, while increasing the proportion of determinant blocks in hybrid blocks can reduce both winter and summer UTCI to a certain extent.

3.3 Relationship between morphology parameters of residential blocks and outdoor thermal comfort in winter and summer

3.3.1 Correlation analysis of urban block morphology parameters

The correlation analysis in Table 6 reveals statistically significant relationships among FAR, λ_b, C_p, H_avg, and H_std. Among them, the FAR and H_avg showed the highest positive correlation (|r| = 0.848). FAR is significantly and weakly negatively correlated with λ_b and C_p, and a low positive correlation with H_std. H_avg shows a significant medium-high negative correlation with λ_b (|r| = 0.785) and C_p (|r| = 0.568) and a significant medium positive correlation with H_std (|r| = 0.515). λ_b and C_p are highly positively correlated (|r| = 0.781). Additionally, D_bg has a significant medium-high correlation with both θ (summer) and θ (winter).

3.3.2 Analysis of the relevance of urban block morphology parameters for outdoor thermal comfort

Table 7 shows that C_p and λ_b are significantly negatively correlated with Ta in summer but significantly strongly positively correlated with Ta in winter. H_std is significantly weakly positively correlated and negatively correlated with Ta in summer and winter, respectively. All morphological parameters are significantly correlated with Ta in winter and Ta. H_avg, FAR, θ, and H_std are significantly negatively correlated with Ta in winter, and the degree of correlation decreases in turn.

The θ is significantly weakly negatively correlated with MRT in both winter and summer. λ_b and H_std are significantly lowly negatively correlated and positively correlated with MRT in winter, respectively, but have no significant relationship with summer MRT. In addition, FAR, H_avg, D_bg, and λ_b are significantly negatively correlated with MRT in summer, and the degree of correlation decreases in turn.

λ_b and C_p are significantly strongly negatively correlated with Ri in both winter and summer. The θ, H_avg, and H_std are significantly positively correlated with Ri in both winter and summer. Among them, the degree of correlation of H_std is low. In addition, FAR is only significantly positively correlated with Ri in summer.

λ_b and C_p are significantly positively correlated with both UTCI (summer) and UTCI (winter). The D_bg is significantly negatively correlated with UTCI (summer) and positively correlated with UTCI (winter), respectively. The FAR is significantly negatively correlated with UTCI (summer) and has no significant correlation with UTCI (winter). In addition, the H_avg, θ, and H_std are significantly negatively correlated with both UTCI (summer) and UTCI (winter), among which the correlation between H_std and UTCI is relatively low.

Figure 6 further analyzes the relationship between UTCI (summer) and UTCI (winter) and the residential block morphology parameters with significant correlations. The line in the box is the median; the top and bottom edges of the box indicate the 75th and 25th percentiles of all values sorted from smallest to largest, and the top and bottom t-shaped bars indicate the maximum and minimum values, respectively.

As the FAR increases, the UTCI (summer) fluctuates between 24.75 °C and 26.6 °C, but the overall trend is still downward. This trend is particularly obvious when the FAR is in the range of 1.79—3.13. This phenomenon shows that a moderate increase in the FAR can help alleviate the heat load in summer. When the FAR increases in the range of 0.89—1.78, the UTCI (winter) shows a significant upward trend. When the FAR increases within the range of 1.79—4.03, the UTCI (winter) fluctuates more significantly without a clear trend. When the FAR is within the range of 4.04—4.49, the UTCI (winter) is significantly higher. This suggests that an excessively high FAR may reduce heat loss due to air circulation, thereby improving outdoor thermal comfort in winter.

UTCI (summer) increases slightly with an increase in λ_b from 0.08 to 0.29. Then, as λ_b continues to increase, UTCI (summer) decreases slightly. This shows that the impact of density on the outdoor thermal environment varies within different threshold ranges. When λ_b increases gradually from 0.08 to 0.22, the decrease in UTCI (winter) gradually decreases. Then, as λ_b continues to increase, UTCI (winter) fluctuates between —24 °C and —23.2 °C. As the C_p gradually increased within the range of 0.42—0.77, UTCI (summer) slowly increased, and when the C_p changed in other ranges, UTCI (summer) did not show a significant trend of change. This suggests that higher enclosure increases summer heat discomfort. When the C_p was within the range of 0.35—0.49, the median value of UTCI (winter) was the lowest, at about —24.9 °C. As the C_p increases in the range of 0.50—0.70, the median UTCI (winter) gradually decreases from a peak of —21 °C to —23.65 °C and then stabilizes.

When the H_avg gradually increases between 12 and 54 m, UTCI (summer) gradually decreases, while UTCI (winter) increases significantly, indicating that the impact of building height on outdoor thermal comfort shows seasonal differences. Then, as the H_avg continues to increase, UTCI (summer) remains at around 25.5 °C, indicating the impact of increasing building height on summer thermal comfort tends to stabilize. As the H_std increased within the range of 5—31 m, the median UTCI (summer) slowly decreased from a peak of 26.2 °C to a minimum of 25.4 °C, indicating that the height differences of building groups regulate summer thermal comfort. The UTCI (winter) fluctuated between —24.15 and —23.05 °C as the H_std increased within the range of 0—22 m. Subsequently, as the H_std continued to increase, the UTCI (winter) gradually decreased.

As the D_bg increases within the range of 1°—30° south by west, the median value of UTCI (summer) gradually rises to a peak of 26.6 °C, and the median value of UTCI (winter) also jumps from a minimum of —24.55 °C to a peak of —21.2 °C. Subsequently, as the angle continues to increase, both UTCI (summer) and UTCI (winter) show a significant decline. This shows that rotating the building complex toward the south-west can significantly improve thermal comfort in winter, but it will also lead to excessive heat load in summer. When the D_bg increases within the range of 1°—56° south by east, the median value of UTCI (summer) gradually decreases to a minimum of 25.1 °C, while the median value of UTCI (winter) fluctuates around —23 °C, showing no significant trend. This suggests that rotating the building cluster toward the south-east effectively reduces summer heat loads while having little impact in winter. As the θ increases within the range of 2°—56°, the UTCI (summer) gradually decreases, and the median value of UTCI (winter) significantly drops to a minimum of —24.35 °C. As the θ continues to increase, the median value of UTCI (summer) stabilizes around 26 °C, and the median value of UTCI (winter) stabilizes near —24 °C. This change reflects the important role of wind direction on thermal comfort in summer and winter, especially in winter, when a larger θ can effectively improve thermal comfort.

3.3.3 Prediction model for outdoor thermal comfort in urban blocks

Based on the results of the correlation analysis between the block morphology parameters and UTCI, the morphological parameters that have a significant impact on UTCI are selected as independent variables. UTCI (summer) and UTCI (winter) are used as dependent variables in multiple linear regression analysis to obtain the quantitative relationship between block morphology parameters and UTCI in winter and summer (See Table 8).

Since there may not be a linear correlation between the independent and dependent variables, in order to clarify the quantitative relationship between the variables, this study uses a curve fitting method to fit the selected morphological parameters to the dependent variables using various curve types, and the curve relationship between the variables is judged based on the coefficient R² and Sig. This study focuses on several common curve models, including the linear model, quadratic curve model, cubic curve model, and logarithmic curve model.

In order to fully consider the interaction between the shape parameters in the regression model and to find the optimal combination of shape parameters that can explain the variation of each dependent variable, this study comprehensively applies the curve fitting model with significant statistical significance between each shape parameter and each dependent variable as the independent variable to the regression analysis. A stepwise multiple linear regression method was used to perform multiple regression analysis on different variable combinations, and the optimal variable combination was finally selected to construct a prediction model.

The adjusted R² of the UTCI (summer) and UTCI (winter) multiple linear regression models are 0.629 and 0.684, respectively, indicating that the models can explain 62.9% and 68.4% of the phenomena, respectively, and the goodness of fit of the models is high. According to the Durbin-Watson test table, the residuals of the two regression models do not have first-order positive autocorrelation and have passed the residual independence test. The significant value (Sig.) of the regression model in the analysis of variance is less than 0.01, and the F value is greater than 2.274, indicating that the model has extremely significant statistical significance. The Sig. in the t-test is less than 0.05, indicating that the regression coefficients are significant and there is a significant correlation between the independent variable and the dependent variable. In addition, the VIF value of the independent variable is less than 10, indicating that the model is not multicollinear.

To sum up, the prediction models for UTCI (summer) and UTCI (winter) are highly reliable. λ_b, C_p, H_avg, D_bg, and θ all have a decisive influence on UTCI (summer) and UTCI (winter). According to the standardized regression coefficients, H_avg and θ have a greater influence on UTCI (summer), while λ_b has a greater influence on UTCI (winter).

4 Discussion

4.1 Influence of LCZ types on seasonal outdoor thermal comfort

This paper explores the relationship between various LCZ types and the outdoor thermal comfort of the block in winter and summer. The results show that various LCZ types have a significant impact on UTCI (winter) but a relatively small impact on UTCI (summer), which is quite different from some existing research results (Unger et al., 2018; Villadiego and Velay-Dabat, 2014). This difference may be because this study used an LCZ classification standard designed specifically for China’s high urbanization rate and the actual situation in Harbin, and the previous classification standard was adjusted. Differences in regional climate conditions and sample selection may also be the reason for the different results.

The analysis shows that in summer, open blocks have higher Ta and MRT than compact blocks, mainly because the reduced λ_b of open blocks allows outdoor spaces to receive more solar radiation. It is worth noting that in winter, the results are completely opposite, which is consistent with the results of Sun et al. (2022). This is because the solar altitude angle is smaller in winter in higher latitudes, resulting in lower and similar outdoor solar radiation received by open and compact blocks. Compact blocks have lower WS in winter, which reduces convective heat dissipation, and there is more reflection and absorption of long-wave radiation between relatively compact buildings, resulting in higher Ta and MRT. However, Jia et al. (2024) used Stewart and Qke’s original classification of LCZs to study the same area and found that open multi-story blocks had the lowest temperatures in summer, while open mid-rise blocks had the highest temperatures in winter. This is different from the results of this study because the λ_b interval threshold in the original LCZ classification is significantly higher than the threshold of the LCZ classification adjusted in this study. The λ_b thresholds for open mid-rise blocks in the original classification method and compact mid-rise blocks in the adjusted classification method of this study are similar. Therefore, in this study, compact mid-rise blocks have the highest temperatures in winter. However, in both summer and winter, open blocks usually have lower UTCI than compact blocks, and the difference is greater in winter. Lau et al. (2019) and Maharoof (2020) also supported this conclusion in their study on urban outdoor thermal environments. That is, the outdoor thermal comfort index is proportional to λ_b. The effect of λ_b on outdoor thermal comfort can be explained by the fact that when the H_avg is similar, open blocks usually have higher WS inside, which enhances human convective heat dissipation and leads to a decrease in UTCI. At the same time, this also shows that in summer, the UTCI increment brought by Ta and MRT is not as great as the impact of WS. In winter, the impact of the LCZ type on UTCI is more significant because WS plays a key role in UTCI. In winter, the temperature of open blocks is slightly lower, and high WS intensifies the feeling of cold, resulting in significantly lower UTCI. It can be concluded that outdoor thermal comfort in cold regions is more sensitive to WS, which has also been confirmed in previous studies (Abd Elraouf et al., 2022; Yin et al., 2021).

Further analysis found that the H_avg has different effects on the thermal comfort of the outdoor thermal environment of the block in different seasons. In summer, an increase in the H_avg of the block will lead to a decrease in UTCI. This is different from the results of the study by Deng et al. (2023) on the outdoor thermal environment of urban blocks in Nanjing. This is because the research object is a building complex. Although the λ_b and H_avg are high, the building spacing is large, which leads to more direct solar radiation at the pedestrian level during the day. At the same time, there are very few green spaces and vegetation on the site. These characteristics bring a large amount of heat gain. It is worth noting that in winter, middle-high rise blocks have higher UTCI than high-rise blocks and multi-story blocks. This is mainly due to the intensification of the cold sensation caused by winter WS, especially when the solar radiation effect is weak. In addition, the Venturi effect is caused by the deep urban canyon of the high-rise blocks and the relatively open space of the multi-story blocks, which both promote airflow. Lau et al. (2019) also found that mid- and high-rise blocks had higher outdoor thermal comfort indicators when evaluating the outdoor thermal comfort of different LCZ blocks in subtropical cities.

In general, there is no significant difference between open blocks and compact blocks for summer UTCI. However, for winter UTCI, compact blocks are significantly higher than open blocks. Among them, the compact middle-high rise blocks (LCZ 2) have the highest thermal comfort in winter and a moderate level of thermal comfort in summer.

4.2 Influence of urban block layout types on seasonal outdoor thermal comfort

This paper investigates the relationship between six types of urban block layouts and outdoor thermal comfort in winter and summer. The results show that the impact of each block layout on winter UTCI is significantly greater than that on summer UTCI. Sun et al. (2022) also studied the relationship between three different urban morphologies and outdoor thermal comfort in winter and summer in severely cold regions. The results showed that the variation between different layout forms was greater in summer. This difference may be because Sun et al. used PET as the thermal comfort index, which underestimates the effect of latent heat dissipation on human body heat dissipation and overestimates the effect of thermal radiation (Cheng et al., 2012). At the same time, UTCI is more sensitive to changes in WS in cold environments with WS greater than 3 m/s (Bröde et al., 2010). Liu et al. (2019) further confirmed in their study on residential spatial morphologies and outdoor thermal comfort in severely cold regions that the difference in WS between different layout forms is more significant than other climate factors.

The Ta of the courtyard type block shows obvious seasonal differences. It is the highest in winter and the lowest in summer. Compared with the row type, the enclosure effect of the courtyard type block will reduce the internal WS, which effectively suppresses convective heat dissipation and makes it easier to keep the interior of the block warm in winter. However, due to the severe shading of the buildings, the courtyard type block receives less solar radiation, which is also confirmed by the fact that its MRT is the lowest in both winter and summer. However, the impact of this difference is weakened in winter when the solar altitude angle is low. However, Ratti et al. (2003) compared the thermal environment performance of different layout forms in hot and arid regions and found that the courtyard form is easier to store heat in summer. The difference between their results and this paper is due to the larger solar altitude angle and solar radiation intensity in hot regions in summer, which causes the shallow courtyard to receive a large amount of solar radiation and makes it difficult to dissipate heat. Further research found that the UTCI of the courtyard type blocks in winter and summer is greater than that of the row type blocks, which is similar to the results of similar studies in temperate climates such as Tianjin and the Netherlands (Taleghani et al., 2015; Zhang et al., 2017). This is because the UTCI reduction caused by reduced solar radiation is smaller than the UTCI increase caused by lower WS inside the block, both in winter and summer. In comparison, the UTCI difference in winter is significantly larger, which further shows that WS plays a more decisive role in winter. Nasrollahi et al. (2017) also pointed out in their study on outdoor thermal comfort evaluation in hot and dry climates that the most influential factors for UTCI in summer and winter are RH and WS, respectively. Combined with the results of hybrid type blocks, it can be seen that hybrid type blocks with a larger proportion of courtyard types have significantly higher UTCI in winter, while blocks with a larger proportion of row types have lower UTCI. In addition, Muhaisen (2006) found that for Rome’s temperate-hot climate, the use of courtyard forms with a larger aspect ratio is more conducive to improving outdoor thermal comfort in winter and summer. This is because, in summer, deeper courtyards receive less radiation. In winter, due to the weakening of the impact of solar radiation, the deep form ensures minimum heat loss under the condition of receiving less radiation, thereby improving the outdoor thermal comfort level.

Overall, the thermal comfort of courtyard layouts in winter is significantly superior to that of point and determinant layouts. This is fundamentally due to the fact that human thermal comfort in severely cold regions is primarily driven by wind cooling in winter. A relatively closed layout can effectively reduce WS, effectively inhibit convective heat dissipation, and significantly improve UTCI overall. In contrast, in summer, as the solar altitude angle rises and shortwave radiation intensifies, the dominant mechanism for regulating the block’s thermal environment shifts to coordinated control of ventilation and shading. While a relatively closed layout can provide some shading effects, limited ventilation may exacerbate thermal discomfort. In summary, the impact of block layout type on thermal comfort is not static but rather dynamically responds to climate change. Therefore, seasonal block layout design strategies should be adopted based on the climatic characteristics of severely cold regions to comprehensively improve outdoor thermal comfort levels in different seasons.

4.3 Influence of urban block morphology parameters on seasonal outdoor thermal comfort

There is a significant, strong positive correlation between λ_b and C_p, a strong negative correlation with Ri in both winter and summer and a strong positive correlation with UTCI in both winter and summer. This result suggests that more compact blocks help improve outdoor thermal comfort levels in winter but reduce thermal comfort in summer. Taleghani et al. (2015) also proved in their study that the more open the form, the greater the WS. However, this study and the study by Andreou (2014) on the impact of Mediterranean urban layout on the outdoor thermal environment both proved that the greater the λ_b, the better the shading effect, and thus the higher the thermal comfort of pedestrians. This differs from the summer results of this study. This is because the intensity of summer solar radiation in this study’s cold region is relatively weak and does not play a decisive role in outdoor thermal comfort. While high-density shading can provide shade, its reduced ventilation is not easily offset by the shading effect, ultimately leading to an increase in the UTCI. This excellent winter-to-summer thermal comfort and poor summer-to-summer thermal comfort reflect the seasonal shift in the mechanisms by which λ_b and C_p influence outdoor thermal comfort: WS suppression and insulation dominate in winter, while air flow and shading control dominate in summer. This results in the same parameters having opposite effects on thermal comfort in different seasons.

The H_avg has a strong negative correlation with both λ_b and C_p. An increase in building height is usually accompanied by a decrease in λ_b and C_p. The impact of this morphological combination on thermal comfort also shows significant seasonal differences. In summer, higher H_avg further promotes air flow by enhancing shading and forming a canyon effect, significantly reducing UTCI. This finding is consistent with the results of Martinelli and Matzarakis (2017) on the effect of height-to-width ratio on courtyard thermal comfort in the Mediterranean climate zone. However, it is worth noting that in winter, the H_avg is significantly positively correlated with MRT, indicating that as the building height increases, it helps to enhance the reflection and absorption of long-wave radiation between building surfaces within the block. In addition, the dispersion of building height is significantly positively correlated with the H_avg. As the height difference between buildings increases, the solar radiation incident on the block in winter will increase, but there is no significant effect in summer. This is consistent with the results of Su et al. (2023), who simulated the outdoor thermal comfort of residential areas with different morphological parameters in warm temperate regions.

The D_bg of the block and the θ determined by the prevailing wind direction both affect outdoor thermal comfort. As the θ increases, the windward area of the building decreases, which promotes heat dissipation within the block and leads to a decrease in UTCI in both winter and summer. In addition, the block D_bg mainly affects outdoor thermal comfort by determining the amount of solar radiation received within the block. Ali-Toudert and Mayer (2006) and Ma et al. (2022) pointed out in their studies of tropical and subtropical regions, respectively, that outdoor thermal comfort is improved in both winter and summer when east-west streets, that is, due to south-facing streets, turn east or west. In addition, the study by Galal et al. (2020) also pointed out that in hot and arid climates, courtyards with longer east-west directions will suffer more heat stress, thus affecting thermal comfort. Although the above studies were conducted in a different climate zone than our study, the summer results regarding street D_bg are generally consistent. However, due to the low solar altitude and low solar radiation intensity in winter in our study area, changes in street D_bg have a smaller impact on the amount of solar radiation received and, therefore, a weaker effect on outdoor thermal comfort.

4.4 Block design strategies to improve seasonal outdoor thermal comfort

The impact of urban block form on outdoor thermal comfort is highly dependent on seasonal variations in climate regimes. Given the climatic characteristics of severely cold regions, block form design should primarily focus on improving winter outdoor thermal comfort, while also addressing summer thermal issues in the context of global warming. Therefore, regulating summer outdoor thermal comfort is a secondary criterion. Furthermore, this study reveals that windbreaks and radiant heat insulation are emphasized in winter, while ventilation and sun protection are emphasized in summer.

With regard to LCZ types, compact layouts effectively reduce WS in winter and significantly improve outdoor thermal comfort compared to open blocks. LCZ 2, or compact mid-rise blocks, exhibits the best winter outdoor thermal environment due to lower WS compared to high-rise and multi-story blocks. Furthermore, compact and open blocks achieve similar levels of outdoor thermal comfort in summer.

In terms of block layout type, the outdoor thermal comfort level of the hybrid layout block is significantly higher than that of other layout types in winter, and slightly lower than that of other layout types in summer. Therefore, based on the different seasonal climate characteristics in cold regions, a seasonally adaptive block form design strategy should be adopted. It is recommended to adopt a hybrid layout type with a courtyard type layout as the main type and a small amount of point or row type layouts, which can significantly improve the outdoor thermal comfort in winter and improve the thermal comfort in summer to a certain extent. On this basis, outdoor activity venues should be reasonably configured in combination with the seasonal outdoor thermal environment performance of each layout type (Lin et al., 2010). For example, venues enclosed by peripheral clusters can be used to arrange more winter activity venues; summer activity areas are typically placed in the more open outdoor spaces formed by point-type or row-type clusters, or on ventilation axes, and the matrix layout clusters can be tilted towards the east or west direction to further improve their summer thermal comfort. In addition, ventilation corridors should be designed according to the dominant wind direction in different seasons to optimize the microclimate. The opening direction of the courtyard block should avoid the dominant wind direction in winter to form an effective cold wind barrier, and openings should be designed as much as possible on the side of the dominant wind direction in summer to enhance internal ventilation and cooling in summer (Hong and Lin, 2015; Ng, 2009). In addition, the use of strategies such as dynamic summer sunshades and winter windbreaks to adapt to different seasonal needs can improve the thermal regulation capacity of the space (Yan et al., 2025).

In terms of morphological parameters, it is recommended that the building complex D_bg be controlled between 31° and 56° south-east, and the θ be controlled between 24° and 34°. This can significantly improve outdoor thermal comfort in winter and to a certain extent in summer. Furthermore, for high-rise blocks, it is recommended that the λ_b be kept between 0.08 and 0.11, the C_p be between 0.50 and 0.56, and the H_std be between 23 and 26 m. Furthermore, the FAR should be maximized within the range of 4.04—4.49. For mid- and high-rise blocks, it is recommended that the λ_b be kept between 0.30 and 0.33, the C_p be between 0.50 and 0.56, the H_std be between 23 and 26 m, and the FAR be maintained between 1.34 and 1.78. For multi-story blocks, it is recommended that the λ_b be kept between 0.23 and 0.26, and the C_p be minimized within 0.57—0.84. At the same time, the FAR is recommended to be increased as much as possible within the range of 0.89—1.81.

In addition, green space plays an important role in regulating urban microclimate (Erlwein et al., 2021). Among them, trees are more effective in regulating the climate than other plant elements (El-Bardisy et al., 2016; Zölch et al., 2016). Studies have shown that the ratio of evergreen trees to deciduous trees is one of the five most important factors affecting the urban environment in cold regions (Leng and Han, 2022). Mi et al. (2020) pointed out that in cold regions, it is advisable to create shaded areas in summer by planting a large number of large-crowned deciduous trees, while ensuring sufficient sunlight in the winter to stimulate residents’ desire for more outdoor activities. Japan’s climate adaptability study (Xiao and Yuizono, 2022) showed that the planting structures in an array layout mode can more effectively improve microclimate conditions in both winter and summer. This is because the use of an appropriate ratio of deciduous and evergreen trees can simultaneously meet the climate needs of summer shading and winter wind resistance.

In addition, although the urban thermal environment has received attention in recent years, and relevant guidance documents and design standards have been promulgated and implemented, providing references for urban thermal environment design, their contents still need to be improved. The Design standard for the thermal environment of urban residential areas (JGJ 286—2013) (South China University of Technology, 2013) does not yet have any evaluation design requirements for the outdoor thermal environment of residential blocks in winter, so its guiding role for the outdoor thermal environment of residential blocks in severely cold regions is limited. In the context of promoting sustainable urban development, the development of green residential blocks has become a consensus in human society. Outdoor thermal comfort is an important part of the green performance of residential blocks, but the content of the outdoor thermal environment, according to relevant standards, is seriously insufficient. The Assessment standard for green buildings (GB/T 50,378) (China Academy of Building Research, 2024) mostly controls individual buildings as the object of control and rarely involves the quality of the outdoor thermal environment. In addition, the Standard of Sustainable Residential Areas (T/CECS 377—2018) (China Academy of Building Standards Design and Research, 2019) provides a reference for green residential block planning from the aspects of livability, ecology, energy, and travel. However, this standard pays less attention to the outdoor thermal environment and lacks consideration of seasonality and the climate characteristics of severely cold regions. This study deeply analyzed the impact mechanism of residential layout type and multi-dimensional morphological parameters on seasonal outdoor thermal comfort in cold regions and put forward detailed and targeted block morphological design suggestions. The research results can provide a theoretical basis and scientific basis for the improvement of existing standards and the formulation of relevant guiding documents in the future.

4.5 Limitations and future research

The research site of this paper was selected in Harbin, so the research results are difficult to fully apply to blocks with large differences in climatic conditions. In addition, although this study uses 156 actual residential blocks as the research object to improve the authenticity and reference value of the conclusions, due to the complexity and diversity of residential space, the survey results cannot cover all block types, spatial morphological parameters, and parameter variation ranges. In addition, various components of urban block space have an impact on outdoor thermal comfort, such as buildings, greening, water bodies, and underlying surfaces, but this paper only studies the spatial morphology of blocks. The research on outdoor thermal comfort in this paper is based on typical meteorological days in January and July with extreme seasonal climates and does not cover all periods of winter, summer, and transitional seasons. In addition to affecting human comfort, the thermal environment of the block has a significant impact on building energy consumption, resident behavior, etc. The conclusions and suggestions obtained in this study are only to improve the outdoor thermal comfort of the block, rather than as an overall strategy to improve the green performance of the block. Therefore, in future research, the research block and research objects will be expanded, the information on the spatial morphology of the block will be supplemented, and the impact of more spatial components on outdoor thermal comfort will be explored to improve the influencing factors and improve the accuracy and scientificity of the research results. In addition, the duration of the research will be extended to study outdoor thermal comfort issues under different climatic conditions at different times of the year. In addition, a comprehensive study will be conducted in combination with relevant outdoor thermal environment optimization goals, such as building energy consumption and resident behavior, to obtain more comprehensive block design recommendations.

5 Conclusion

Taking Harbin, a typical city in China’s severely cold climate zone, as an example, this paper simulates the outdoor thermal environment of 156 real residential blocks and combines statistical analysis to explore the impact mechanism and seasonal differences of LCZ type, block layout type, and block morphological parameters on outdoor thermal comfort in winter and summer. This study can provide a reference for urban residential block planning in severely cold regions and provide a theoretical basis and scientific basis for building livable cities. The results show that:

(1) Different LCZ types exhibit significant differences in seasonal outdoor thermal comfort, with the differences being particularly pronounced in winter. In winter, compact blocks (LCZ1, LCZ2, and LCZ3) exhibit significantly higher thermal comfort levels compared to open blocks (LCZ4, LCZ5, and LCZ6) due to their higher λ_b, resulting in less convective heat dissipation and greater longwave radiation accumulation. Compact mid-rise blocks, with the lowest WS and highest Ta, exhibit the highest winter thermal comfort levels. In summer, despite higher WS, open blocks receive more solar radiation, resulting in outdoor thermal comfort levels similar to those of compact blocks. Furthermore, summer outdoor thermal comfort improves with increasing block height. Based on the climate characteristics of severely cold regions, and following the principle of prioritizing winter thermal comfort with a secondary focus on summer thermal comfort, compact mid-rise blocks (LCZ 2) are the most suitable LZC type.

(2) In both winter and summer, courtyard blocks have the lowest WS, while row and point blocks have the highest WS. In winter, the dominant mechanisms regulating the outdoor thermal environment in blocks are WS reduction and heat preservation. More enclosed layouts contribute to improved thermal comfort. Outdoor thermal comfort decreases in the following order: courtyard, hybrid (courtyard + point), hybrid (courtyard + row), hybrid (courtyard + point + row), hybrid (row + point), and row. In summer, outdoor thermal comfort varies little across layout types, with row and point blocks slightly higher than other blocks. The impact of block layout type on thermal comfort dynamically responds to seasonal climate changes. Therefore, considering both winter and summer outdoor thermal comfort levels, a hybrid layout type, primarily courtyard, with a limited complement of point or row layouts, is recommended. Furthermore, outdoor activity spaces should be rationally allocated based on the seasonal outdoor thermal environment characteristics of each layout type.

(3) λ_b, C_p, H_avg, and θ show strong and significant correlations with outdoor thermal comfort, and the direction of these morphological parameters’ influence on outdoor thermal comfort differs between winter and summer. λ_b and C_p are both highly negatively correlated with Ri in both winter and summer. In winter, increasing λ_b and C_p improves outdoor thermal comfort by reducing heat loss through increased spatial compactness and enclosure. However, in summer, this inhibits ventilation, leading to a decrease in thermal comfort. Furthermore, H_avg, H_std, and θ are positively correlated with Ri in both winter and summer, respectively, reducing and enhancing outdoor thermal comfort in winter and summer. Taking into account both winter and summer outdoor thermal comfort, the recommended λ_b for high-rise blocks is between 0.08 and 0.11, the C_p between 0.50 and 0.56, and the H_std between 23 and 26 m. For mid- and high-rise blocks, it is recommended that the λ_b be within 0.30—0.33, the C_p be within 0.50—0.56, and the H_std be between 23 and 26 m. For multi-story blocks, it is recommended that the λ_b be kept within 0.23—0.26, and the C_p be kept as small as possible within 0.57—0.84. In addition, it is recommended that the D_bg be controlled at 31—56° south-east, and the θ be controlled at 24—34°.

(4) The multiple linear regression model based on morphological parameters can predict the UTCI (summer) and UTCI (winter) of residential blocks well, with an explanation degree of 68.4% and 61.0%, respectively.

This paper studies the impact of urban residential morphology in severely cold regions on seasonal outdoor thermal comfort and provides specific design suggestions for residential morphology from the aspects of LCZ type, layout type, and morphological parameters. Future research will explore the impact of more block space components on outdoor thermal comfort and improve the influencing mechanism. In addition, a comprehensive study will be conducted on optimization objectives related to the outdoor thermal environment, such as outdoor thermal comfort, building energy consumption, and resident behavior, so as to provide a more comprehensive and scientific reference for urban form design.

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