Urban Canyon Design with Aspect Ratio and Street Tree Placement for Enhanced Thermal Comfort: A Comprehensive Thermal Comfort Assessment Accounting for Gender and Age in Seoul, Republic of Korea

Abstract

Rapid urbanization and increased human activity have negatively impacted the microclimate of cities, leading to unfavorable conditions for human thermal comfort, particularly in outdoor spaces. Thermal comfort can be improved through various means, such as adjusting the height of urban buildings, the aspect ratio of road widths, and the placement of street trees. This study employed the ENVI-met software V5.5.1 to simulate the microclimate based on aspect ratio (H/W = 1.5) and street tree spacing (6 m) similar to actual conditions with different aspect ratios (H/W = 0.5, 1.0, and 2.0) and street tree spacing (2 m) in Seoul, Republic of Korea. Thermal comfort was assessed through a comprehensive predicted mean vote (PMV) evaluation, considering the gender (male and female) and age (8, 35, and 80 years) of residents in the target area, to determine the optimal urban canyon scenario. The results of the study indicated that the height of the building and the percentage of trees had a significant impact on the temperature and PMV results. When comparing PMV results, women have higher thermal vulnerability than men, and based on age, older adults have higher thermal vulnerability. The aspect ratio of 1.5 and tree spacing of 2 m resulted in the lowest temperature of 35.91 °C at 12:00 p.m. at 0° wind direction and 36.09 °C at 90° wind direction, lower than the actual input value of 36.9 °C. The PMV values were also under the same conditions, with an average PMV by gender of 3.87 at 0° and 4.21 at 90° and an average PMV by age of 3.86 at 0° and 4.19 at 90°. This finding is significant because it can inform the development of planned cities that prioritize urban thermal comfort during summer. This can be achieved through the strategic design of urban canyons and incorporation of street trees in both new and existing cities.

1. Introduction

Urbanization is a rapidly growing phenomenon in modern society, driven by population growth and increasing migration to cities [1,2]. By the 2050s, more than two-thirds of the global population is expected to live in cities [1,3]. Urbanization causes several issues in urban environments, including the formation of urban heat islands (UHIs) [4,5,6]. It also contributes to the transformation of materials with low albedo and high heat capacity, such as concrete and asphalt, into urban environments [7]. Furthermore, the presence of impervious materials can reduce evapotranspiration, which, in turn, reduces latent heat fluxes and alters the urban thermal environment. In addition, according to the IPCC, heatwaves are expected to become more frequent and last longer [8]. Studies have demonstrated that there may be a synergistic interaction between heatwaves and UHIs, with urban temperatures increasing significantly during heatwaves compared to non-heatwave periods [9,10,11]. Therefore, the severity of heatwaves in densely populated cities must be given due consideration.

Urban morphology contributes to high temperatures in cities. Numerous studies have confirmed the significant contribution of urban morphology to surface temperature, such as urban canopy, building height and density, and vegetation [12,13,14,15]. Structures, such as skyscrapers, asphalt roads, and concrete buildings, absorb and emit heat, leading to unpleasant weather conditions in cities [16]. Densely graded asphalt and concrete have low albedo and high volumetric heat capacity, resulting in elevated surface temperatures more than 60 °C on hot summer days [17]. Temperature regulation is one of the most important functions of urban green spaces, with results showing a temperature reduction of about 1 °C [18,19]. Challenges in urban morphology raise environmental and health concerns, necessitating new approaches to urban planning and management [20,21]. Additionally, urban morphological conditions can negatively impact the health and comfort of residents [22]. The impact of urban morphology on thermal comfort has garnered increasing attention, particularly due to the phenomenon of heatwaves becoming more pronounced with urbanization [2,23]. During the summer months, the climate of major urban areas and the UHI phenomenon combine to create high levels of heat stress for residents. Exposure to heat can cause several adverse health effects, ranging from aggravation of minor pre-existing conditions to increased risk of hospitalization and death [24]. It has been estimated that approximately 50% of heat-related deaths in the West Midlands during the 2003 heatwave were due to the UHI effect [25]. A study of 85 European cities in 2015–2017 found a median 45% increase in UHI-related mortality in extreme heat [26]. An assessment of the impact of UHI on all-cause mortality in adults aged 20 years and older in 93 European cities during the summer of 2015 (June 1–August 31) found that the average urban temperature increase was 1.5 °C, attributable to about 6700 deaths. For each city, increasing tree cover to 30% would have resulted in an estimated 0.4 °C temperature decrease, potentially preventing 2644 premature deaths [27]. Groups such as the elderly and children are particularly vulnerable to high ambient temperatures [28]. Thus, urban morphology significantly contributes to the health of people living in cities.

Thermal comfort in the built environment is defined as “a psychological state that expresses satisfaction with the thermal environment through subjective evaluation” [29]. Environmental variables, such as air temperature, average radiant temperature, specific humidity, and wind speed, significantly affect heat balance and determine outdoor thermal comfort [30]. One method for assessing thermal comfort is physiological equivalent temperature (PET). Based on the Munich energy balance model for individuals, PET is defined as the air temperature at which the thermal balance of the human body is balanced in a typical indoor environment (no wind or solar radiation), with the same core and skin temperatures as in the complex outdoor conditions being evaluated [31]. The UTCI-Fiala mathematical model of human thermoregulation, known as the universal thermal climate index (UTCI), has undergone extensive validation testing, adaptation, and expansion, including the incorporation of an adaptive clothing model, which enables the prediction of human temperature and regulatory responses to several common outdoor climate conditions [32]. To assess thermal comfort, various indicators and indices are used, including the predicted mean vote (PMV), which quantitatively measures thermal discomfort in urban environments [33]. PMV aids in determining the necessary breaks and protective measures for working in both indoor and outdoor environments. Measuring PMV in urban environments is crucial for understanding its impact on residents’ comfort and health. PMV is standardized by ISO 7730, thereby ensuring the reliability of its application in a variety of environments [34]. PMV evaluates thermal comfort by comprehensively considering human physiological and psychological responses, thereby enabling a human-centered approach to comfort assessment. In contrast to other metrics, PMV can be evaluated using straightforward data.

Urban canyons are a distinct urban topography characterized by canyon-like terrain surrounded by skyscrapers. The aspect ratio of a city, which is the ratio of building height to width, is an important factor in determining its urban form. Changes in aspect ratio can significantly affect the thermal characteristics within a city. Various studies have explored how an increase in tall buildings affects thermal comfort in cities [35,36]. Higher aspect ratios reinforce the UHI effect, increasing temperatures inside cities during summer. This phenomenon can negatively impact residents’ comfort and quality of life. The placement of urban vegetation is a crucial factor that significantly affects thermal comfort in urban environments. Vegetation provides shade, thereby mitigating urban heat stress. Recent research has investigated the impact of street trees’ type, density, and placement on urban environments to develop guidelines for creating comfortable and healthy urban environments [37,38,39]. In general, planting trees effectively reduces UHIs, but this process can also increase the urban moisture island, which can exacerbate heat stress due to high humidity [40]. Therefore, an optimized tree planting strategy is necessary to avoid this potential issue [40]. At the pedestrian level, the presence of vegetation improved thermal comfort by approximately two points of PMV during summer in Lecce in Italy and Lathi in Finland [41]. Tree cover is more efficient at reducing UTCI less than 3 °C during the day, with higher percentages of vegetation providing more cooling at night [42]. A previous study revealed that the interactions between surfaces, vegetation, and atmosphere in urban environments are complex and can produce distinct patterns of varying flow and temperature fields [43]. The placement and density of street trees directly affect thermal comfort.

Numerous studies have investigated changing aspect ratios and vegetation scenarios to increase thermal comfort in urban areas [44,45,46]. However, most of these studies reflect individual scenarios, such as urban morphology or vegetation layout, and thermal comfort studies of different human conditions, such as children and the elderly, are insufficient for assessing the thermal comfort of different urban conditions and vulnerable populations. It is essential to ascertain whether the existing urban design and street tree placement are conducive to enhancing thermal comfort. This study aimed to investigate the ability of aspect ratio and vegetation placement to mitigate heat in a highly urbanized area in Republic of Korea. Furthermore, the potential for thermal mitigation was identified through the modification of existing urban design and the implementation of revised regulations. It also evaluated the overall thermal comfort of people living in the area, considering their gender and age, and determined which aspect ratio and vegetation placement scenarios have the best ability to mitigate urban thermal problems for all urban residents.

2. Methodology

2.1. Methods

The methodology for this study comprises the following steps (Figure 1): First, we selected a high-density urban center as the target area. We collected meteorological information, such as temperature and Specific humidity, on the hottest day in the last 10 years. Additionally, we obtained location and building GIS data and vegetation layout information, which were used as inputs for the ENVI-met model. To assess thermal comfort, we examined the gender and age ratio of residents in the target area as of 2023. Subsequently, different ENVI-met urban canyon scenarios were generated based on four aspect ratios of road width to building height (H/W = 0.5, 1.0, 1.5, and 2.0), three street tree spacings (none, 2 m, and 6 m), and two wind conditions (0° and 90°) with a wind speed of 1 m/s and wind direction of 0°. The software calculated the PMV by gender and age and weighted them to perform a comprehensive thermal comfort assessment. The PMV calculation for adults considered the ratio of adult males to females, while the final PMV calculation included the ratio of children, adults, and the elderly, using population data. This thorough assessment of thermal comfort was used to determine the optimal urban canyon scenario.

2.2. ENVI-Met

The model used in this study, ENVI-met, is one of the most widely employed dynamic simulation tools for microclimate analysis and was developed by Michael Bruse of Ruhr University in Bochum, Germany [47]. The first official release of the model dates back to 1998; as of 2017, it has been used for microclimate studies by more than 1900 registered users worldwide. In most existing studies, the model has been used for the investigation of current microclimatic conditions as well as for the comparative evaluation of the performance of different mitigation strategies against UHI effects. Other scientific studies have used the ENVI-met model for air quality investigations focusing on pollutant deposition and dispersion [48,49]. ENVI-met is a three-dimensional (3D) grid-based microclimate model designed to simulate complex land–plant–air interactions in urban environments. Additionally, it functions as a 1D boundary model to initialize the simulation and define the boundary model conditions for the 3D atmospheric model. The model encompasses several features, including the 3D atmospheric model that captures atmospheric temperature and humidity, wind flow, turbulence, shortwave and longwave radiative fluxes, and dispersion and deposition of pollutants, and a soil model that simulates the balanced distribution of land surface temperature and water content in natural soils. There are also plant simulations, including transpiration rates, tree leaf temperatures, and heat and vapor exchange between plants and the atmosphere [50]. ENVI-met is a software program that simulates temperature and humidity simultaneously and calculates the latent heat associated with heat and humidity by considering the heat and moisture exchange processes in the environment. Additionally, it considers microclimatic factors such as solar radiation, building geometry, and ground surface properties in order to determine temperature and humidity. The objective of this study was to assess the impact of urban geomorphological changes at the micro scale in the urban center on thermal comfort. In contrast to other available tools, it is capable of simulating thermal changes at a higher resolution. The spatial resolution of ENVI-met is typically 1 to 5 m, and the temporal resolution of the output is hourly by default [51]. Simulated air temperatures at the 1 m from ENVI-met were analyzed in the study, as it is the meteorological parameter most closely related to human heat stress [52].

2.3. Target Area

The target area is Teheran Road, located in Gangnam-gu, Seoul (Figure 2). It is one of the major commercial and economic centers with a high concentration of various commercial facilities, buildings, hotels, and cultural facilities. The urbanization index is an indicator of the degree of urbanization of a region or country, with the main criterion being the ratio of the population living in urban areas to the total urban land area. According to Statistics Korea, the urbanization index is 100%, and the urban area ratio exceeds 80%, making it a representative urban center in Republic of Korea; therefore, it was selected as the research area.

2.4. Data

In this study, the weather data for 1 August 2018, which had the highest daily maximum temperature (39.6 °C) in the past 10 years, was used as input to ENVI-met. The data were generated by AWS 400 (latitude 37.5134°, longitude 127.04671° and elevation 12.66 m), a weather station in Gangnam-gu, 2.57 km from study area. As shown in Figure 3, we used hourly data for 1 August 2018.

Furthermore, to incorporate the spatial characteristics of the target area into the scenario model, we used building information provided by the National Land Information Platform at the Korea National Geospatial Information Institute under the Ministry of Land, Infrastructure and Transport. The width of the road in the target area is 50 m and the width of the sidewalk is 10 m. The average height of the buildings is about 105 m, the average length of the buildings is about 40 m and 42 m, and the distance between the buildings is 10 m. Based on this information, we examined the building height per floor to create an aspect ratio scenario. Additionally, the “Street Tree Planting Management Manual” written by the Korea Forest Service in 2022 and information on the location of street trees in Seoul provided by the Seoul Metropolitan Government were examined to build the ENVI-met model environment. Mainly, 20 m high platanus trees were placed at 6 m intervals. The trees were set to be 14 m in diameter, and 0.34 m in diameter at breast height. The soil was a loamy soil within 2 m of the tree in accordance with street tree management manual [53].

Table 1. Information on buildings in the study area.

	Length (m)
Road width	50
Sidewalk width	10
Building interval	10
Building width	40/42
Building height	105
Tree interval	6
Tree height	20
Tree crown	14
Diameter at breast height	0.34

summarizes the street width, sidewalk width, building spacing, and street tree information in the city.

2.5. Predicted Mean Vote (PMV)

The PMV is the most comprehensive and widely used thermal environment evaluation index for assessing thermal comfort and represents the expected feeling of warmth. Danish scientist P.O. Fanger extensively researched the heat exchange process between the human body and the external environment, analyzed its principles in depth, and proposed PMV, a comprehensive thermal comfort index that represents the thermal response of the human body based on the human comfort equation. Fanger developed PMV by applying the results of experiments conducted on 1300 American and European students on heat exchange and their responses to thermal environments caused by physical factors to the formula [33]. The PMV is divided into seven levels (−3, −2, −1, 0, +1, +2, +3) to categorize thermal comfort. ISO standard 7730 defines the comfort range to satisfy occupants as −0.5 to 0.5, where −3 represents a cold feeling, 0 represents a calm and pleasant state, and +3 represents a hot feeling [54].

To determine thermal comfort, Fanger examined the relationship between the average radiant temperature, air flow, humidity, and other factors, along with the metabolic rate of the human body and the thermal resistance of clothing, which mainly affect the thermal environment. In essence, PMV is an indicator of the average cold and hot sensations of most people in the same environment. It represents a formula that synthesizes various factors to assess thermal comfort while also considering the adaptability of the physiological regulation function of the human body to environmental factors.

2.6. Scenario Model

In this study, different scenarios were created to investigate the thermal comfort of different aspect ratios and vegetation placements. The aspect ratio of the target area was approximately 1.5. Furthermore, the aspect ratios were categorized as 0.5 (35 m), 1.0 (70 m), 1.5 (105 m), and 2.0 (140 m) depending on the building height while maintaining the street width. As for the vegetation, the city of Seoul placed 20 m platanus trees as street trees. The vegetation was spaced at 6 m, which is the conventional spacing. A scenario with trees spaced 2 m apart was included to assess the thermal mitigation effects of more dense tree placement. The models generated using these scenarios are shown in Figure 4. Additionally, as input data, the wind speed was fixed at 1.0 m/s and the wind direction was N–S (0°) and E–W (90°). The final synthesized ENVI-met setting is shown in Figure 5 and

Table 2. ENVI-met setting.

	Size
Model dimension	125(x) × 125(y) × 150(z)
Size of grid	2 m × 2 m × 1 m
W (Total width)	70 m
Wb (Building interval)	10 m
Wr (Road width)	50 m
Ws (Sidewalk width)	10 m
LX, LY (Building width)	40 m, 42 m
H (Building height)	35 m, 70 m, 105 m, 140 m
WT (Tree interval)	2 m, 6 m
HT (Tree height)	20 m
H/W (Aspect ratio)	0.5, 1.0, 1.5, 2.0
Wind speed	1.0 m/s
Wind direction	N–S (0°), E–W (90°)

. Also, the lateral boundary conditions for the simulation used open lateral boundary conditions. It is that the values of the next grid point close to the border are copied to the border for each time step.

The collection of accurate data regarding the height and weight of a population stratified by age is inherently challenging. Consequently, analysis is predicated upon the utilization of readily available information from ISO 7730. The PMV calculation was divided by gender (35-year-old male and 35-year-old female) and age (8-year-old child, 35-year-old adult male, and 80-year-old male) to determine thermal comfort.

Table 3. Values of key variables in the PMV calculation.

	Male (35)	Female (35)	Old Male (80)	Child (8)
M (W/m2)	86.21	83.22	82.14	117.18
fcl	0.5

lists the values of the main variables used in the PMV calculation. The surface area covered by clothing (f_cl) was 0.5 clo for both men and women during summer. The metabolic rate (M) was 86.21 W/m² for adult men, 83.22 W/m² for adult women, 82.14 W/m² for the elderly, and 117.18 W/m² for children [34].

Furthermore, we multiplied the weights based on the actual ratio of gender and population to obtain a more accurate PMV summation result. After calculating the adult composite PMV, which reflects the gender ratio in

Table 4. Percentage of population by age and gender ratio.

Percentage of Population by Age			Gender Ratio
Child	Male	Old male	Male	Female
11%	75%	14%	48%	52%

, the final composite PMV was analyzed by multiplying the calculated weights to reflect the population ratio by age, and thermal comfort was evaluated.

3. Results

3.1. Results of Temperature, Humidity, and MRT Changes by Scenario

Depending on the aspect ratio and tree placement, there are 12 scenarios with four aspect ratios (0.5, 1.0, 1.5, and 2.0), an aspect ratio of 1.5 and tree intervals (2 m, 6 m), and two wind directions (0°, 90°). In the average temperature results graph (Figure 6), the line represents the average temperature, and the area around the line represents the maximum and minimum temperatures of the scenario-driven roads after simulation. The temperature results at 12:00 p.m. are summarized in

Table 5. Temperature result at 12:00 p.m. (wind direction 0°/90°).

Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
39.36 °C/37.95 °C	38.44 °C/37.07 °C	37.04 °C/36.23 °C	37.21 °C/35.99 °C	35.91 °C/36.03 °C	36.20 °C/36.18 °C

. The average temperature generally decreased as the aspect ratio increased, with the most significant improvement observed at aspect ratio 1.5, which is 1.4 °C and 0.84 °C for the 0° and 90° wind directions, respectively. At aspect ratio 2.0, the change decreased dramatically, and the average temperature at 0 °C wind direction increased. This is because the shading effect (temperature-lowering effect) caused by the increase in building height is insignificant compared to the increase in heat retention (temperature-raising effect) due to the increase in building size (temperature-lowering effect) as the building height increased from 105 m (aspect ratio 1.5) to 140 m (aspect ratio 2.0) [55,56]. Additionally, the placement of street trees demonstrated a significant temperature reduction effect, with the lowest average temperature observed at the aspect ratio of 1.5 and street tree spacing of 2 m. It is crucial to acknowledge that the temperature reduction achievable through building geometry alterations is constrained by realistic limitations. The incorporation of additional tree placement can facilitate temperature reduction, thereby enhancing human thermal comfort.

The specific humidity results for each scenario varied between the two wind conditions (Figure 7), as did the average temperature; however, the correlation and trend of the two results were generally similar. The specific humidity decreased significantly in the morning, with the lowest specific humidity between 3:00 p.m., and then increased again. The results of specific humidity at 12:00 p.m. are summarized in

Table 6. Specific humidity at 12:00 p.m. (Wind direction 0°/90°).

Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
14.83%/14.85%	14.83%/14.98%	14.97%/15.06%	14.93%/15.02%	13.81%/13.82%	13.81%/13.84%

. As with the average temperature, the specific humidity at 12:00 p.m. tended to be higher as the aspect ratio increased from 0.5 to 1.5, and the specific humidity from 1.5 was the largest for both 0° and 90° wind directions. However, at aspect ratio 2.0, the change decreased sharply, and the average humidity at 0° wind direction was lower than before, as was the average temperature. The impact of humidity on the outcomes of this study is subject to variation depending on the time of day. This is due to the fact that the application of humidity data varies at different times of day. Therefore, the results of this study are more influenced by the placement of trees and buildings than by humidity. Furthermore, despite the fluctuations in humidity levels throughout the day, the relative differences in outputs, such as positional PMV, for varying building and tree placements demonstrate a comparable pattern.

The graph (Figure 8) shows the MRT as a line, with the maximum and minimum temperatures for the post-simulation scenarios represented by the ranges around the line. Upon examining these results, it is evident that the MRT improved as the aspect ratio approached 2.0 for both wind conditions. This is in contrast with the temperature and specific humidity results, which showed reversed outcomes for a wind direction of 0° with an aspect ratio of 2.0. The results of MRT at 12:00 p.m. are summarized in

Table 7. MRT at 12:00 p.m. (wind direction 0°/90°).

Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
58.42 °C/58.43 °C	55.16 °C/54.79 °C	51.93 °C/50.45 °C	49.94 °C/49.7 °C	43.78 °C/42.87 °C	46.08 °C/44.58 °C

. The simulation results indicate that the MRT peaked at 12:00 p.m. and subsequently declined, except for the aspect ratio of 0.5, which peaked again at 4:00 p.m. The mean MRT at 12:00 p.m. decreased as the aspect ratio increased from 0.5 to 1.5. The largest change in this value occurred when going from an aspect ratio of 1.0 to 1.5, with respective changes of 3.23 °C and 4.34 °C, as in the previous two results. In addition, as with the average temperature, the temperature reduction effect of tree placement was significant. However, the magnitude of this effect was significantly greater than that of the average temperature, with a difference of 5.85 °C and 5.87 °C in each wind direction for the aspect ratio of 1.5 at 6 m tree spacing. The MRT is a measure of the temperature influenced by solar radiation. However, the presence of street trees and increased local shade significantly improve this metric. Based on the results, the aspect ratio of 1.5 and tree spacing of 2 m emerged as the optimal scenario for the 0° and 90° wind directions.

3.2. Results of PMV by Scenarios

PMV results for various scenarios involving different aspect ratios, tree spacing, and wind direction, considering age and gender, are presented in Figure 9 and

Table 8. Average PMV at 12:00 p.m. (wind direction 0°/90°).

	Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
Child	5.23/5.10	4.55/4.41	4.06/4.03	3.86/3.76	3.52/3.70	3.49/3.73
Female	5.87/5.70	4.97/4.78	4.31/4.29	4.05/3.93	3.55/3.81	3.50/3.88
Male	5.76/5.59	4.88/4.70	4.25/4.23	4.00/3.88	3.52/3.79	3.47/3.83
Old	5.92/5.75	5.01/4.82	4.34/4.31	4.07/3.95	3.56/3.85	3.51/3.90

. Based on the results, the decrease in thermal comfort is seen with increasing building height and vegetation placement, which generates more cooling. It is evident that the child’s PMV remained consistently lower (more comfortable) across all scenarios because all other conditions were the same, except for the metabolic rate. The child with the highest metabolic rate had the lowest average of PMV. The other results followed the same order of metabolic rate: adult males, adult females, and the elderly. This finding confirms that older people are more vulnerable to heat. Furthermore, the values varied with wind direction, with higher air temperatures observed at 0° wind than at 90° wind, resulting in higher PMV values at 0° wind than at 90° wind. However, in the tree scenario, PMV values at 0° wind were found to be less than 90°. The reason for this is the transfer of cooling from the shade between the buildings at a wind direction of 90°. At 0° wind, there is no cooling effect on the heated open street area (91 m to 160 m on the X-axis) in the scenario without trees, but with trees there is an effective cooling transfer from the trees in the open area. Depending on the wind direction, it is seen that the values at the border of the corresponding direction, where values are entered into the model, are lower or higher than the surrounding values. Especially, the scenario with trees at 0° wind has a higher maximum value than at 90° wind, but a lower average and minimum value. This is because the cells on this border are influenced only by the input values, unlike calculations with in-model interactions.

3.3. Results of Comprehensive Thermal Comfort Evaluation by Scenarios

The PMV of adult males and females was calculated by multiplying the results of weights to reflect the gender ratio of Gangnam-gu in Figure 10. From the results (

Table 9. Average comprehensive thermal comfort evaluation by gender ratio (wind direction 0°/90°).

Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
6.47/6.16	5.70/5.37	4.95/4.85	4.65/4.42	3.87/4.21	4.15/4.27

), the results of the PMV calculation for the main road area were higher than those for men, which reflects the PMV values for women. Similar to the average temperature, humidity, and MRT results, the highest PMV improvement occurred when the aspect ratio was 1.5, and street tree placement had a significant effect. The optimal thermal comfort scenario for adults of both sexes is an aspect ratio of 1.5 and tree spacing of 2 m. Finally, based on the above adult PMV results, we calculated the final total PMV by reflecting the population ratio by age to evaluate total thermal comfort.

The results of the comprehensive evaluation of PMV of adults, the elderly, and children, reflecting the age ratio in Gangnam-gu in Figure 11. It is showed that the PMV of the elderly tended to be higher than those of children and 35-year-old males (

Table 10. Average comprehensive thermal comfort evaluation age gender ratio (wind direction 0°/90°).

Aspect Ratio 0.5	Aspect Ratio 1.0	Aspect Ratio 1.5	Aspect Ratio 2.0	Aspect Ratio 1.5 + Tree Interval 2 m	Aspect Ratio 1.5 + Tree Interval 6 m
6.41/6.10	5.65/5.33	4.92/4.82	4.62/4.40	3.86/4.19	4.14/4.25

). In addition, the deviation tended to decrease, with the maximum PMV decreasing and the minimum PMV increasing. PMV improves with increasing street tree coverage and decreasing tree spacing, with street trees having a greater effect on thermal comfort.

4. Discussion

The results indicate that the urban canyon scenario with an aspect ratio of 1.5 and 2 m tree spacing had the lowest temperature, MRT, and PMV. Of course, because both PMV and MRT showed better results at an aspect ratio of 2.0, it can be assumed that the 2 m tree spacing at an aspect ratio of 2.0 would result in better thermal comfort. However, based on the magnitude of improvement, the optimal urban canyon scenario is an aspect ratio of 1.5 with 2 m tree spacing. The PMV results by gender indicate that women have a higher PMV than men, depending on their metabolic rate. Additionally, when age was considered, children with the highest metabolic rate had the lowest PMV, while the elderly with the lowest metabolic rate had the highest PMV, indicating their susceptibility to heat. The comprehensive PMV assessment for each gender tended to be higher than the PMV for men, reflecting the PMV for women. The comprehensive PMV results by age indicated lower PMV for children, although still higher than that of a 35-year-old man, reflecting the PMV of the elderly, who are more vulnerable to heat. The results by urban scenario show that both composite PMV results indicated lower PMV for higher aspect ratios and denser tree placement. It is possible that achieving a 2 m spacing in vegetation arrangements may present spatial challenges. The findings of this study indicate that increasing the number of trees by narrowing the spacing can negatively impact thermal comfort. Previous studies have demonstrated comparable outcomes as the number of trees increases [41].

This research revealed variations in thermal comfort across different urban scenarios, as well as age and gender conditions, highlighting the need to consider such conditions for achieving urban thermal comfort and what happens to thermal comfort when it reflects the conditions of different people. However, it has several limitations. First, the model considered only one wind speed and two wind directions, which is different from reality, where both the wind direction and wind speed are variable [52]. Additionally, since this study is limited to analysis and evaluation after modeling and simulation with ENVI-met software V5.5.1, validation of both observations and models in the study area is necessary [53]. It is necessary to validate the observed and simulated PMV results for the factors required to calculate PMV in the target area [57]. The model boundary where the values are entered may reflect higher or lower values than those in the model, which can lead to errors in the analysis process. Therefore, it is necessary to design a model that reduce the error before simulation. The next limitation is related to PMV calculation. Although PMV is widely used to predict outdoor thermal comfort, it is based on an energy balance approach that does not account for differences in thermal adaptation and preferences of occupants living in different climatic regions [54]. The comfort equation used to calculate PMV is an old empirical equation created by Europeans in 1986; thus, it may differ from the actual thermal comfort evaluation of current Koreans. The calculation of PMV is dependent upon a number of factors, including those pertaining to the human body and clothing. The provision of PMV simulation results based on changes in these factors, presented in 3D form, can assist architects, engineers, and non-experts in making informed decisions by offering a readily comprehensible format for the dissemination of information [58]. Furthermore, there may be errors due to the substitution of commonly used values, such as gender-age-specific metabolic rates and clothing area coefficients, into the equation for PMV calculation. Lastly, in setting up the modeling scenario, we chose Gangnam-gu, Teheran-ro, as the study site; however, we were unable to simulate other cities with different elevation differences and to consider other vegetation species, such as Ginkgo biloba and other vegetation arrangements, in addition to the Platanus used in the modeling.

5. Conclusions

This study focused on Teheran-ro, Gangnam-gu, Seoul, Republic of Korea, a representative urban center, using data for 1 August 2018, the highest daily maximum temperatures in Republic of Korea in the past 10 years. Employing ENVI-met software V5.5.1, we simulated the microclimate according to the aspect ratio of building height and street width and the placement of street trees. Thermal comfort was analyzed by checking the comprehensive PMV index reflecting gender and age to confirm the variations in thermal comfort by urban canyon scenario. A summary of the results of this study is as follows:

• The urban canyon scenario with an aspect ratio of 1.5 and 2 m tree spacing had the lowest temperature, MRT, and PMV.
• It can be assumed that the 2 m tree spacing at an aspect ratio of 2.0 would result in better thermal comfort, given that both PMV and MRT showed better results at an aspect ratio of 2.0.
• Considering gender and age, it has been shown that women have a higher PMV than men due to their metabolic rate, while the elderly have a lower metabolic rate and are more vulnerable to heat, and urban design should take this into account.

The significance of this research is that it can be applied to the design of new cities and the redesign of existing cities to ensure thermal comfort in cities through urban canopy, which takes into account variations in building height, denser placement than the spacing of existing street tree regulations, etc. In order to conduct micro-scale urban environment analysis, it is possible to construct a 1 m grid city model and arrange the positioning of buildings and trees in order to assess the thermal comfort experienced by actual residents. To obtain more accurate thermal comfort results, the thermal comfort of the actual occupants can be assessed through surveys. It is expected to not only improve the quality of life of citizens by increasing the thermal comfort of the city but also enhance the energy efficiency of the city by reducing electricity consumption by the city’s cooling system, which has low energy consumption efficiency.