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Article

Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics

by
Tousi Evgenia
1,2,*,
Athina Mela
2 and
Areti Tseliou
3
1
Department of Interior Architecture, University of West Attica, 12241 Athens, Greece
2
Laboratory of Urban Planning and Architectural Design, Department of Civil Engineering, School of Engineering, University of West Attica, 12241 Athens, Greece
3
School of Applied Arts and Sustainable Design, Hellenic Open University, 26335 Patras, Greece
*
Author to whom correspondence should be addressed.
Urban Sci. 2024, 8(4), 193; https://doi.org/10.3390/urbansci8040193
Submission received: 27 September 2024 / Revised: 24 October 2024 / Accepted: 26 October 2024 / Published: 31 October 2024
(This article belongs to the Special Issue Sustainable Approaches on Urban Space in Mediterranean Cities: From XS Design to XL Planning)
Browse Figures
Figure 1
<p>Methodology scheme.</p> "> Figure 2
<p>(<b>a</b>) Spatial layout of the area of study, 2D and 3D models; (<b>b</b>) 2D and 3D ENVI-met Spaces V5.6.1 model.</p> "> Figure 3
<p>ENVI-met simulation of the area of study. Potential air temperature at 10 a.m.</p> "> Figure 4
<p>ENVI-met simulation of the area of study. Potential air temperature at 3 p.m.</p> "> Figure 5
<p>ENVI-met simulation of the area of study. Potential air temperature at 8 p.m.</p> "> Figure 6
<p>ENVI-met simulation of the area of study. Mean radiant temperature at 10 a.m.</p> "> Figure 7
<p>ENVI-met simulation of the area of study, mean radiant temperature at 3 p.m.</p> "> Figure 8
<p>ENVI-met simulation of the area of study. Mean radiant temperature at 8 p.m.</p> "> Figure 9
<p>ENVI-met simulation of the area of study. PET 10 a.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 10
<p>ENVI-met simulation of the area of study. PET 10 a.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 11
<p>ENVI-met simulation of the area of study. PET 10 a.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p> "> Figure 12
<p>ENVI-met simulation of the area of study. PET 10 a.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p> "> Figure 13
<p>ENVI-met simulation of the area of study. PET 3 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 14
<p>ENVI-met simulation of the area of study. PET 3 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 15
<p>ENVI-met simulation of the area of study. PET 3 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p> "> Figure 16
<p>ENVI-met simulation of the area of study. PET 3 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p> "> Figure 17
<p>ENVI-met simulation of the area of study. PET 8 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 18
<p>ENVI-met simulation of the area of study. PET 8 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p> "> Figure 19
<p>ENVI-met simulation of the area of study. PET 8 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p> "> Figure 20
<p>ENVI-met simulation of the area of study. PET 8 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p> "> Figure 21
<p>ENVI-met simulation of the area of study. UTCI 10 a.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p> "> Figure 22
<p>ENVI-met simulation of the area of study. UTCI 3 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p> "> Figure 23
<p>ENVI-met simulation of the area of study. UTCI 8 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p> ">
Versions Notes

Abstract

:
Urban public space comfort is essential for improving quality of life, particularly as climate change affects outdoor thermal environments. This study utilizes ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors such as age and gender. The findings indicate significant disparities in thermal stress vulnerability among demographic groups. On the hottest day of July 2023, at 10 a.m., children’s PET values were approximately 2 °C higher than those of other groups. By 3 p.m., females experienced slightly higher upper-range thermal stress than males. Elderly individuals aged 80 exhibited a broad range of PET values, from 38.14 °C to 62.39 °C, with prevailing values above 56.9 °C, indicating greater vulnerability to extreme heat. Children aged 8 showed PET values ranging from 40.20 °C to 59.34 °C, with prevailing estimates between 54.2 °C and 55.7 °C. Minimum PET values for children were significantly higher than those for adults, suggesting a greater baseline level of thermal stress. Despite cooling effects in the evening, children remained exposed to more pronounced stress than elderly individuals, males, and females. The UTCI values recorded indicate a period of extreme heat stress for all demographic groups assessed. While individuals aged 35 may encounter considerable discomfort, the severity of the impact is notably more pronounced for both older adults and children. This study underscores the need for tailored management strategies and advocates for expanding ENVI-met’s capabilities to enhance urban resilience and well-being amid rising temperatures.

1. Introduction

Comfort in urban public spaces is a key indicator of the overall quality of life in cities and serves as a critical factor in assessing the effectiveness and appeal of public spaces [1,2]. High levels of comfort directly reflect the success of urban design, influencing how people engage with and experience their environment. Consequently, it plays a dominant role in the evaluation of public space quality, shaping perceptions of urban livability and community well-being [3]. Urban well-being and health are closely linked to outdoor activity, which provides significant social and economic benefits to cities [4,5]. To promote outdoor activities, public spaces should be attractively designed, with thermal comfort being a key factor in ensuring their usability [6]. The degree of comfort that people feel in public areas impacts their usage patterns and their perception of the quality of urban spaces [7,8]. Concerning climate change, a variety of biometeorology studies have explored how urban climates vary spatially, with a primary focus on thermal comfort. Recent years have seen a marked surge in interest within this research domain, as reflected by a substantial increase in related scholarly publications [9]. Thermal comfort is typically assessed through thermal indices, though not all of the 165 proposed indices are suitable for outdoor environments, as some overlook essential factors like solar radiation and wind speed [6]. Early studies in this field delve into the intense heatwave that affected Athens in July 1987, examined from the perspective of human biometeorology [10]. Moreover, the Physiologically Equivalent Temperature indicator has been studied [11].
Therefore, in addition to environmental factors, human-related aspects such as activity levels and clothing insulation—which influence resistance to heat exchange—also contribute to thermal comfort. Together, these six factors are known as the “six basic parameters” that shape an individual’s thermal environment [12,13]. Thermal sensation, or how a person feels in a particular thermal setting, is largely psychological and cannot be directly measured through physical or physiological terms alone [14]. A commonly used method for evaluating thermal environments is the application of thermal indices, although the term “thermal index” is rarely explicitly defined in the academic literature [6,15,16,17,18]. The heat index and analogous metrics have been criticized for their restricted generalizability. These indices often depend on “ideal” conditions and overlook contextual variables, as well as disregarding the psychological and behavioral factors that influence individual comfort. Thermal stress, on the other hand, quantifies the effect of the six basic parameters on the human body by assessing the thermal strain experienced [9]. This process is also influenced by physiological factors such as gender, age, and acclimatization. Studies show, however, that age especially plays a significant role in thermal comfort variations [19,20].
Studies centered on age differences substantiate the notion that older adults are notably more vulnerable to heat-related stress [21]. This is largely due to age-related declines in thermoregulatory capabilities, such as reduced sweat response and compromised vasodilation [22,23]. The research further highlights that older individuals correctly perceive their heightened risk of heat but often face barriers to adaptive action [24]. Children, contrastingly, showed no marked differences in subjective thermal perception compared to adults, even though their physiological susceptibility to extreme temperatures is acknowledged [25]. Overall, the data conforms to the consensus that age is a critical factor in determining thermal comfort and physiological resilience, especially under heatwave conditions [19].
The studies focusing on gender differences in thermal sensations and comfort generally found minimal significant differences between men and women. The variations observed in some contexts were attributed more to physiological responses rather than a distinct difference in perceived comfort [26]. Another important finding was that while older women showed slightly higher vulnerability to heat stress, direct thermal perception differences were not substantiated extensively [27]. Overall, the research challenges traditional assumptions about gender-based thermal comfort, indicating that perceived comfort is more closely aligned with individual and circumstantial factors than gender [22].
Outdoor thermal environments, in comparison to controlled indoor settings, show greater variability in environmental conditions [28]. Dynamic and non-uniform microclimatic conditions in public spaces can lead to an imbalance in the heat exchange between the human body and its environment [9,29]. The most common urban activities in outdoor spaces—such as standing and walking—are important considerations when assessing outdoor thermal comfort [30]. Additionally, clothing behavior varies with the seasons, depending on the climate zone of each city. Notably, even in hot climates, individuals maintain minimum clothing insulation of 0.2 clo (with 1.0 clo representing a short-sleeve shirt and shorts, according to de Freitas, 1987). Another important concept in evaluating outdoor thermal comfort is the “thermal history” of an individual [31]. For example, whether someone has recently exited an air-conditioned building significantly influences their thermal experience. However, gathering such information is difficult, and the computational simulations required for such analysis are often resource-intensive [6].
Among the tools used to assess thermal stress, the PET (Physiologically Equivalent Temperature) and UTCI (Universal Thermal Climate Index) are widely recognized [32,33]. Both measure the body’s strain responses to calculate the equivalent thermal effect in both reference and actual environments and are acknowledged as reliable indices for evaluating thermal comfort and discomfort in outdoor settings. For analyzing data related to thermal comfort in various spatial configurations, ENVI-met is an exceptionally valuable tool. It provides detailed insights into how different environmental factors influence comfort levels, making it highly effective for evaluating urban design and optimizing spaces for better microclimate performance [34,35,36]. A substantial body of the literature employs ENVI-met to explore the effects of urban greenery on microclimatic conditions during Mediterranean heatwaves. Studies like those by Magliocco and Perini and Salvalai et al. utilize ENVI-met simulations to demonstrate how different configurations and densities of urban vegetation can reduce air temperatures and alleviate the urban heat island effect. These studies provide foundational insights into how the strategic use of greenery, such as green roofs and facades, can effectively cool urban environments during heatwaves, revealing significant temperature reductions and improvements in thermal comfort indices like the Universal Thermal Climate Index (UTCI) [37,38].
Furthermore, studies by Tousi et al. and Tseliou et al. delve into comparative analyses of varying greening scenarios, investigating how changes in vegetation type and albedo affect microclimatic conditions. These simulations show that increasing tree cover or integrating green facades can significantly lower air temperatures and enhance pedestrian thermal comfort [36,39].
The current body of literature reveals a notable gap in studies that specifically utilize ENVI-met to analyze how different demographic factors, such as gender and age, influence human heat emission through simulation, as opposed to on-site empirical measurements. While ENVI-met is widely recognized as a powerful 3D microclimate simulation tool, it is more commonly applied to assess urban environmental conditions. Researchers frequently use it to explore the impact of urban design elements, such as the presence of vegetation, the configuration of buildings, and the materials used in urban settings, on the microclimate. These studies often focus on factors like heat islands, thermal comfort, and energy consumption in urban spaces, but the potential of ENVI-met to simulate human heat dissipation concerning demographic variations remains underexplored.
A study on the thermal comfort of the elderly in Sao Paulo uses ENVI-met but lacks a comprehensive approach covering a variety of demographic factors [40]. It is one of the few that begins to explore demographic differences. This indicates that while ENVI-met is widely used for evaluating thermal comfort improvements through urban interventions, the existing literature does not sufficiently address how different demographic factors such as age and gender affect thermal comfort perceptions. This remains an underexplored area warranting further research, especially in regions beyond the Mediterranean Sea.
This lack of focused research underscores an opportunity for further investigation, particularly in urban health studies where understanding how various groups experience and cope with heat could improve city planning, thermal comfort strategies, and public health interventions. Furthermore, advancing the application of ENVI-met in this direction could help bridge the knowledge gap in how age and gender-specific physiological differences manifest in microclimate interactions. This would be particularly useful in the context of rising temperatures and the increasing focus on urban resilience in response to climate change. From this point of view, this study enhances the state of the art by utilizing ENVI-met to evaluate thermal comfort with a specific focus on demographic factors, including age and gender. This approach addresses a critical gap in the existing research regarding the influence of demographic variations on heat emission in urban microclimates.

2. Materials and Methods

The research is structured into three distinct phases, as outlined in Figure 1. The first phase involves pre-fieldwork analysis, which includes a comprehensive literature review with a particular emphasis on thermal stress analysis during heatwaves in the Mediterranean region. This review focuses specifically on studies examining gender and age differences in thermal comfort during such extreme weather events. The first phase of this study culminates in the formulation of four key research questions centered on Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) values [41,42]. PET serves as a vital index for evaluating thermal comfort in outdoor environments, quantifying the impact of air temperature, humidity, wind speed, and solar radiation on human perception. It is particularly valuable in urban climate research, allowing for comparisons of thermal comfort across various demographic groups and conditions [43]. UTCI is a comprehensive index that incorporates multiple meteorological factors, reflecting physiological responses to thermal environments and facilitating the assessment of outdoor thermal stress in diverse populations and climates. PET is calculated using heat exchange models that simulate the thermal environment experienced by an individual, providing a direct measurement of comfort in degrees Celsius. UTCI, however, is derived from a more complex model that includes a broader range of physiological responses based on a reference individual, enabling a more extensive evaluation of both comfort and physiological stress in varying thermal conditions. It is important to note that PET and UTCI are the two most commonly used thermal indices among the over 160 outdoor thermal indices currently available [44].
  • RQ1: Are there any notable differences in PET and UTCI values between 35-year-old adult males and females during Mediterranean heatwaves in ENVI-met simulations?
  • RQ2: Are there any significant differences in PET and UTCI values during Mediterranean heatwaves between 35-year-old adult males and females and 80-year-old elderly individuals in ENVI-met simulations?
  • RQ3: Are there any significant differences in PET and UTCI values during Mediterranean heatwaves between 35-year-old adult males and females and 8-year-old children in ENVI-met simulations?
  • RQ4: Based on ENVI-met simulations, which demographic group—35-year-old males, 35-year-old females, 80-year-old elderly individuals, or 8-year-old children—is the most vulnerable to heat stress during Mediterranean heatwaves?
Stage 2 places a strong emphasis on data collection through fieldwork. Specifically, the authors have gathered key information pertinent to the analysis of urban geometry and the characteristics of both the built and natural environments within the study area. This includes data on flora species, construction materials, and the volume and height of buildings, culminating in the development of 2D and 3D models of the selected city blocks. Stage 3 focuses on post-fieldwork analysis, during which ENVI-met simulations were conducted. ENVI-met is an advanced 3D microclimate simulation software specifically designed to model and analyze the thermal and environmental conditions of urban environments. Utilizing sophisticated algorithms, the software simulates the interactions among various environmental factors, including air temperature, humidity, wind patterns, and solar radiation [45], with a resolution that may vary from 0.5 m to 10 m. By providing high-resolution spatial and temporal data, ENVI-met enables researchers and urban planners to evaluate the effects of different design scenarios and urban configurations on microclimatic conditions. Its applications are varied, ranging from assessing thermal comfort in public spaces to analyzing the influence of vegetation and urban morphology on heat distribution [46]. Consequently, ENVI-met serves as a crucial tool for enhancing urban resilience and guiding sustainable urban development strategies in the face of climate change. The authors selected the hottest day in July 2023, specifically the 23 July, to examine thermal stress variations across different demographic groups. On that day, the maximum air temperature was recorded at 41.7 °C at 3 p.m., while the minimum air temperature was documented as 30.9 °C at 6 a.m. Relative humidity reached a maximum of 54% at 6 a.m. and dropped to a minimum of 23% at 3 p.m. The average wind speed was measured at 4.6 km/h (1.27 m/s), with a wind direction of 180°. The above-mentioned microclimatic conditions of the examined data were used as input data of ENVI-met software v5.5.1 for the initialization of the microclimatic simulations. The total simulation time spanned 48 h. Mean radiant temperature and potential air temperature were evaluated for three specific times of the day: 10 a.m., 3 p.m., and 8 p.m. In ENVI-met simulations, mean radiant temperature (MRT) represents the combined effect of all surrounding surface temperatures and radiation (both direct solar and reflected). It quantifies the heat exchange between an individual and the environment, offering a key measure of thermal exposure and human comfort in outdoor spaces. Additionally, the ENVI-met Bio-met tool was employed to assess heat stress levels among various demographic groups, including 35-year-old males (summer clothing, 0.5 clo, preferred speed of 1.34 m/s), 35-year-old females (same settings), an 80-year-old male (0.5 clo, preferred speed of 0.9 m/s), and an 8-year-old male child (summer clothing, 0.5 clo, preferred speed of 1.10 m/s). It is important to note that these demographic settings are based on Bio-met’s default parameters. The authors evaluated PET (Physiologically Equivalent Temperature) and UTCI (Universal Thermal Climate Index) values for the three selected times of the day to address the four research questions outlined earlier. For the analysis of the PET and UTCI results, the Mediterranean scale was employed, as seen in similar studies in the Mediterranean region (Table 1) [47].
The spatial analysis of the study area, encompassing three city blocks, is illustrated in Figure 2a. The total area is estimated to be 181.91 square meters long, with a total width of 82.73 square meters. The model dimensions in ENVI-met are as follows: x-Grids = 182, y-Grids = 83, and z-Grids = 30. The grid shell sizes are set to dx = 1 m, dy = 1 m, and dz = 1 m (Figure 2b). The model is rotated at an angle of −22 degrees from the grid’s north (Figure 2b). The longitude and latitude of the area are approximately 23.67° and 37.98°, respectively. The name of the location is Nikaia-Agios Ioannis Rentis and the reference time zone is Eastern European Standard Time. Soil profiles set for the nesting grids include [0200ST] Asphalt Road (Soil A) and [0200PP] Pavement Concrete, used and dirty. The area of study, located in the Greater Athens Region, is a post-refugee urban settlement designed according to the principles of the Hippodamian grid during the interwar period [48]. Characterized by high building and population densities, it is home to vulnerable households [36]. The layout includes a communal open space in the interior of each block, with a mixture of typical blocks of flats and refugee housing organized along the perimeter [49]. The area of study presents significant research interest due to its distinctive character and the heterogeneity of its urban morphology, which can be attributed to the successive periods of construction in the region [50]. As depicted in Figure 2, building heights vary from 3 to 15 m.

3. Results

3.1. Potential Air Temperature in the Area of Study at 10 a.m., 3 p.m., and 8 p.m.

As shown in Figure 3, the potential air temperature in the study area at 10 a.m. ranged from 28.9 °C to 40.5 °C. The highest temperatures were observed on the eastern sides of the blocks, while cooler areas were identified on the western sides and in the center of each block. However, hot air tends to flow through the alleyways, with wider alleyways leading to larger hot zones in the middle of the blocks.
At 3 p.m., air temperatures in the study area ranged from 36.87 °C to 48.89 °C (Figure 4). Hotter zones were concentrated on the western sides of each block, with the highest temperature recorded in the center of the middle block (Figure 4). The large building volumes create cooler sheltered areas in the center of the first block; however, air temperatures in these areas remain elevated, around 42 °C (Figure 4).
At 8 p.m., air temperatures ranged from 34.23 °C to 39.60 °C. Hot linear zones have been observed along roads and alleyways, while cooler islets are found in the center of each block (Figure 5). The size of these cooler areas varies depending on the shadows cast by nearby buildings and the presence of trees, influenced by factors such as foliage shape, tree type, and height.

3.2. Mean Radiant Temperature in the Area of Study at 10 a.m., 3 p.m., and 8 p.m.

At 10 a.m., the mean radiant temperature in the study area ranged from 25.19 °C to 83.26 °C, with the coolest areas located in shaded zones near buildings on the northwestern side of each block. Central areas of the blocks exhibited lower mean radiant temperatures where tree cover is dense, while areas without trees experienced higher mean radiant temperatures (Figure 6). Tree cover significantly reduces mean radiant temperature by offering protection from direct sunlight.
At 3 p.m., the mean radiant temperature in the study area ranged from 32.42 °C to 87.71 °C (Figure 7). Cooler areas were located along the eastern sides of each block and in the central regions where dense tree cover is present.
At 8 p.m., the study area exhibited a projected decline in mean radiant temperature, ranging from 31.09 °C to 42.46 °C. Cooler zones were predominantly observed along the perimeters of buildings along the outer side of each block, while higher temperatures were concentrated between structures, suggesting the retention of heat in the central areas of the blocks and alleyways, as depicted in Figure 8.

3.3. PET Analysis of Different Demographics

3.3.1. PET Analysis at 10 a.m.

As shown in Figure 9, the PET values at 10 a.m. on the hottest day of the heatwave ranged from 30.54 °C (Slight heat stress) to 60.69 °C (Extreme heat stress) for 35-year-old males, with the prevailing PET estimations throughout the study area ranging from 51.0 °C to 55.1 °C. Comparable values, ranging from 30.51 °C (Slight heat stress) to 61.26 °C (Extreme heat stress), were observed for females of the same age, as illustrated in Figure 10. In the case of females, however, the prevailing PET values throughout the examined area were slightly higher than that of males, varying from 55.4 °C to 57.5 °C.
On the hottest day of the heatwave, an 80-year-old male wearing summer clothing (0.50 clo) and walking at a preferred speed of 0.90 m/s experiences PET values ranging from 30.85 °C (Slight heat stress) to 61.71 °C (Extreme heat stress) at 10 a.m (Figure 11), with the prevailing PET estimations to range between 53.8 °C and 57.9 °C (extreme heat stress). In contrast, an 8-year-old male child, also in summer clothing (0.50 clo) but with a preferred walking speed of 1.10 m/s, faces PET values ranging from 32.98 °C (moderate heat stress) to 58.63 °C (extreme heat stress) with the prevailing PET estimations ranging between 53.8 °C and 57.9 °C at the same time (Figure 12). When comparing the four demographic groups on the hottest day of July 2023 at 10 a.m., children exhibit a minimum PET value approximately 2 °C higher than the other three groups. However, the same demographic also experiences a maximum PET value that is about 2.6 °C lower than the other groups. The prevailing PET estimations throughout the examined area, however, indicate that both children and the elderly population may experience similar extreme heat stress conditions. This contrast highlights both elevated baseline thermal stress and a more moderate peak PET for children. It is important to note that, although there are no striking differences among males and females of the same age, the prevailing PET values throughout the study area are, approximately, 2 °C higher than those of males. The PET analysis across the four selected demographic groups during morning hours (10 a.m.) reveals only minor variations in values, with the spatial distribution of heat exhibiting similar patterns across all examined categories. This suggests consistent thermal stress distribution regardless of demographic differences. Cooler enclaves of moderate heat stress (less than 35 °C) are found along buildings on the western side of the blocks and under dense tree-covered areas of the central open space. In all other areas of the block, the PET values indicate a range from strong heat stress to extreme heat stress for all demographic groups, according to the Mediterranean scale for PET.

3.3.2. PET Analysis at 3 p.m.

The analysis of PET values during the afternoon hours, specifically at 3 p.m., reveals distinct differences across the four demographic groups. For 35-year-old males, PET values range from 37.84 °C (moderate heat stress) to 61.58 °C (extreme heat stress) (Figure 13). This range is slightly narrower compared to their female counterparts, who experience PET values between 37.80 °C (moderate heat stress) and 61.98 °C (extreme heat stress) (Figure 14). Similarly, the prevailing PET estimations throughout the study area are slightly higher in females (56.0 °C to 58.2 °C) compared to males (55.8 °C to 57.9 °C), but narrowed compared to those obtained at 10 a.m. This indicates that as the PET values increase, the heat stress variations between the two genders decrease. Even though both genders are subjected to comparable levels of thermal stress, females tend to experience marginally higher stress in the upper range. In contrast, elderly individuals aged 80 years exhibit a broader range of PET values, from 38.14 °C to 62.39 °C (Figure 15), while the prevailing PET values are above 56.9 °C. This broader range suggests that older adults encounter a wider spectrum of thermal stress, with both the prevailing and the upper limits of their PET values extending beyond those of 35-year-olds, reflecting a higher susceptibility to extreme heat. Children aged 8 years show a distinct pattern, with PET values ranging from 40.20 °C to 59.34 °C, with the prevailing PET estimations ranging between 54.2 °C and 55.7 °C (Figure 16). The minimum PET values for children are notably higher than those for adults, indicating a greater baseline of thermal stress. However, their prevailing and maximum PET values are lower than those observed in the other examined categories but remain significant, highlighting considerable heat exposure for this age group. In summary, while all demographic groups face substantial thermal stress, the extent and variability of this stress differ by age. The elderly are most vulnerable to extreme heat, as evidenced by their higher maximum PET values, while children experience a higher baseline of thermal stress compared to adults. It is important to emphasize that the spatial distribution of PET values exhibits consistent patterns across the four demographic groups (see Figure 13, Figure 14, Figure 15 and Figure 16). Specifically, cooler zones are observed along building peripheries where shadows are cast, as well as beneath dense tree canopies in the central areas of the block.

3.3.3. PET Analysis at 8 p.m.

The PET values obtained during the evening hours, as depicted in Figure 17, Figure 18, Figure 19 and Figure 20, reveal significant differences among the four demographic groups in the context of the Mediterranean PET scale. The data indicate a general reduction in PET values with the onset of evening, aligning with the expected cooling effect. For 35-year-old males and females, PET values range from 36.06 °C to 45.52 °C and from 36.01 °C to 45.45 °C, respectively. According to the Mediterranean PET scale, these values correspond to moderate to extreme heat stress. Elderly males, aged 80, present PET values from 36.35 °C to 45.79 °C. This range reflects moderate to extreme heat stress, slightly higher than that observed in 35-year-old males and females. The prevailing PET values throughout the study area are similar for males, females, and the elderly population and vary from approximately 40 °C to 42 °C. Children, aged 8 years, have PET values ranging from 38.47 °C to 54.12 °C. Their values, while indicating moderate to extreme heat stress, show a higher minimum and a quite higher maximum value compared to the elderly, whereas the prevailing PET estimations range between 42.0 and 44 °C. This suggests that children face notable thermal stress, with both their baseline and peak exposure higher than that of the elderly. In summary, the evening PET values reveal a varied experience of thermal stress across the demographic groups according to the Mediterranean PET scale. Children exhibit broader ranges of PET values, indicating higher variability in thermal stress, showing higher minimum values, and experiencing significant stress. In contrast, elderly individuals and 35-year-old males and females experience more moderate and consistent thermal stress levels. Overall, while the cooling effect of the evening reduces thermal stress for all groups, children are exposed to more pronounced stress compared to elderly individuals males and females.

3.4. UTCI Analysis of Different Demographics

3.4.1. UTCI Analysis at 10 a.m.

The Universal Thermal Climate Index (UTCI) is an essential tool for assessing thermal comfort and stress in various environmental conditions. This analysis uses the UTCI Mediterranean scale as a reference point to understand how these values affect different groups. At 10 a.m., the UTCI values for four distinct demographic groups—35-year-old males, 35-year-old females, 80-year-old males, and 8-year-old children—range between 28.94 °C (No thermal stress) and 51.36 °C (extreme heat stress) (Figure 21), whereas the prevailing UTCI estimations range between 43 °C and 47 °C (extreme heat stress). For 35-year-old males and females, the UTCI values span from 28.94 °C to 51.36 °C. This age group generally manages moderate to high heat stress better than older adults or children due to greater physiological resilience and robustness. Nonetheless, UTCI values above 34 °C indicate significant heat stress, which may necessitate adaptation measures to prevent discomfort or health issues. In contrast, for 80-year-old males, the UTCI values also range from 28.94 °C to 51.36 °C. However, older adults are more vulnerable to heat stress because of decreased physiological resilience and potential underlying health conditions. Although the range is the same as for younger groups, the impact of values exceeding 34 °C is more pronounced for older adults, making it crucial to implement more immediate and effective heat stress management strategies. Similarly, for 8-year-old children, the UTCI values fall between 28.94 °C and 51.36 °C. Children are particularly susceptible to heat stress due to their smaller body size and higher surface-area-to-volume ratio. This means that the same UTCI range poses a significant risk of thermal discomfort and stress for children, especially at the higher end of the scale. Thus, protective measures for this group should be especially stringent to ensure their safety and comfort. While the UTCI values for all four demographic groups fall within the same range, the implications of these values vary significantly. For 35-year-olds, high UTCI values may result in less severe effects compared to older adults and children. Older adults face increased risks of heat stress, while children are highly sensitive to high UTCI values. Understanding these differences is essential for developing age-appropriate strategies to manage heat stress and ensure thermal comfort across diverse demographic groups. By considering these variations alongside the UTCI Mediterranean scale, we can better address the specific needs of each group and improve overall thermal comfort and health outcomes.

3.4.2. UTCI Analysis at 3 p.m.

On 23 July 2023, at 3 p.m., the Universal Thermal Climate Index (UTCI) values for four demographic groups—35-year-old males, 35-year-old females, 80-year-old individuals, and 8-year-old children—ranged from 35.60 °C (moderate heat stress) to 57.21 °C (extreme heat stress), as shown in Figure 22. Given this, UTCI values range from moderate heat stress to extreme heat stress. For 35-year-old males and females, the UTCI values ranging from 35.60 °C to 57.21 °C suggest exposure to extreme heat stress. While this age group typically exhibits a higher level of physiological resilience compared to older adults and children, such elevated UTCI values still pose significant challenges. The extreme conditions likely result in considerable discomfort and require substantial adaptive measures to manage heat stress effectively. In contrast, the same range of UTCI values—35.60 °C to 57.21 °C—presents a more pronounced risk for 80-year-old individuals. Older adults are generally more susceptible to the adverse effects of extreme heat due to diminished physiological resilience and the potential presence of underlying health conditions. Consequently, the extreme heat stress indicated by these UTCI values could lead to severe health consequences, necessitating immediate and comprehensive interventions to mitigate risks associated with high temperatures. For 8-year-old children, the UTCI values ranging from 35.60 °C to 57.21 °C also signify exposure to extreme thermal stress. Children, due to their higher surface-area-to-volume ratio and limited physiological capacity to regulate body temperature, are particularly vulnerable to the effects of high temperatures. The extreme conditions experienced by this demographic group at the upper end of the UTCI range necessitate urgent protective measures to ensure their health and well-being. In conclusion, the UTCI values recorded on 23 July 2023, at 3 p.m., highlight a period of extreme heat stress for all evaluated demographic groups. Although 35-year-olds may experience significant discomfort, the severity of the impact is markedly greater for older adults and children. This analysis underscores the necessity for tailored heat stress management strategies that address the specific vulnerabilities of each age group, ensuring adequate protection and mitigating health risks associated with extreme heat.

3.4.3. UTCI Analysis at 8 p.m.

On 23 July 2023, the hottest day of the month, UTCI values in the study area ranged from 33.40 °C (no thermal stress) to 40.28 °C (extreme heat stress) for 35-year-old males, 35-year-old females, 80-year-old individuals, and 8-year-old children (Figure 23). For 35-year-olds, the UTCI range of 33.40 °C to 40.28 °C indicates significant thermal stress. Although this age group generally has better physiological resilience, the extreme temperatures on this day likely resulted in considerable discomfort and necessitated effective heat management strategies. For 80-year-old individuals, these UTCI values pose a critical risk due to their reduced physiological adaptability and potential health conditions. Similarly, 8-year-old children, with their less effective thermoregulation, are particularly vulnerable. The extreme heat conditions present a significant risk, necessitating stringent protective measures. In summary, the UTCI values on 23 July 2023, even during evening hours at 8 p.m., indicate a period of very high to extreme heat stress for all demographic groups.

4. Discussion

This research questions focus on analyzing the differences in Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) values during Mediterranean heatwaves, utilizing ENVI-met simulations. RQ1 investigates whether there are notable differences in PET and UTCI values between 35-year-old adult males and females during these heat events. Building on this, RQ2 explores potential significant differences in PET and UTCI values between 35-year-old adult males and females and 80-year-old elderly individuals during Mediterranean heatwaves. RQ3 further examines whether significant differences exist in PET and UTCI values between 35-year-old adult males and females and 8-year-old children in the same context. Finally, RQ4 seeks to identify which demographic group—35-year-old males, 35-year-old females, 80-year-old elderly individuals, or 8-year-old children—is the most vulnerable to heat stress during Mediterranean heatwaves, based on the findings from the ENVI-met simulations.
Research findings showed that females are more vulnerable to heat compared to males. This finding is consistent with numerous studies showing that women are at a higher risk of thermal stress and heat-related mortality (RQ1). However, a smaller body of research suggests that men may be more vulnerable than women in some cases [51,52,53,54,55].
Corresponding to RQ2, the elderly population is more vulnerable to heatwaves compared to the younger population. This finding aligns with previous research, which has shown that the elderly population is especially vulnerable to high temperatures, particularly during heatwaves [52,56,57]. To address RQ3, children experience significant thermal stress, with both their baseline and peak exposure levels exceeding those of 35 males and females as well as elderly individuals. Therefore, children display a wider range of PET values, reflecting greater variability in their thermal stress levels, characterized by higher minimum values and substantial stress. In contrast, elderly individuals, as well as 35-year-old males and females, experience more moderate and stable levels of thermal stress. Overall, while the cooling effect of the evening mitigates thermal stress for all groups, children remain subject to more pronounced stress compared to elderly individuals, males, and females. To be more specific, the PET analysis across the four selected demographic groups at 10 a.m. demonstrates only slight variations in thermal values, with the spatial distribution of heat exhibiting comparable patterns across all examined categories. This suggests a uniform distribution of thermal stress during morning hours, irrespective of demographic distinctions. PET analysis at 3 p.m., however, shows that minimum PET values for children are significantly higher than those for adults, indicating a heightened baseline level of thermal stress. Their mean and maximum PET values are lower than those observed in other demographic groups, although still considerable, underscoring notable heat exposure in this age group.
In summary, while all demographic groups experience substantial thermal stress, its magnitude and variability differ according to age. The elderly are particularly vulnerable to extreme heat, as reflected by their higher maximum PET values, whereas children face a greater baseline thermal stress in comparison to adults. The evening PET values demonstrate varying levels of thermal stress across different demographic groups, as measured by the Mediterranean PET scale. Children exhibit a wider range of PET values, indicating greater variability in thermal stress, with higher minimum values and significant levels of exposure. In contrast, elderly individuals, along with 35-year-old males and females, experience more moderate and consistent thermal stress levels. Overall, although the cooling effect of the evening mitigates thermal stress for all groups, children are subject to more pronounced stress compared to elderly individuals, males, and females. From this perspective, both children and elderly individuals may be vulnerable to the adverse health effects associated with extreme temperature increases (RQ4). This finding is aligned with other relevant studies in the field [58]. It is important to note, though, that there is often a significant emphasis on elderly individuals regarding vulnerability to heat stress, largely due to physiological and health-related factors. Senior adults may have diminished thermoregulation abilities, pre-existing health conditions, and a higher likelihood of medication side effects that can impair their body’s ability to cope with extreme temperatures [59]. However, children, particularly young ones, are also highly vulnerable to heat stress for different reasons, such as their higher surface area-to-volume ratio and immature thermoregulatory systems [60]. They may not recognize the signs of heat stress or adequately respond to them, which can increase their risk during heatwaves. While the discourse around heat stress vulnerability tends to prioritize elderly populations, it is essential to acknowledge that children, especially in extreme heat conditions, also face significant risks.
It is also important to mention that, while the UTCI values for all four demographic groups fall within a similar range (morning, afternoon, and evening hours), the implications of these values vary significantly. For 35-year-olds, elevated UTCI values tend to result in less severe effects compared to older adults and children. Older adults are at greater risk of heat stress, whereas children exhibit heightened sensitivity to high UTCI values. Given this, this study, despite being aligned with other relevant studies in the field, underscores the emphasis that should be placed not only on senior adults but also on children. Recognizing these differences is critical for the development of age-specific strategies aimed at managing heat stress and ensuring thermal comfort across various demographic groups. By considering these variations alongside the UTCI Mediterranean scale, more effective measures can be implemented to address the distinct needs of each group, ultimately improving overall thermal comfort and health outcomes.

5. Conclusions

The analysis of thermal stress across diverse demographic groups reveals significant disparities in vulnerability to heat exposure. Consistent with the existing literature, elderly individuals demonstrate heightened susceptibility to heatwaves, as evidenced by their elevated maximum Physiologically Equivalent Temperature (PET) values. In contrast, children exhibit broader and more variable ranges of thermal stress, characterized by significantly higher baseline PET values than their elderly counterparts, indicating substantial and continuous exposure to heat. Although evening cooling effects can mitigate thermal stress across all groups, children remain disproportionately affected, facing pronounced stress compared to both elderly individuals and adults. Morning PET values tend to be relatively uniform; however, afternoon data indicate that children encounter higher minimum PET values, underscoring their ongoing vulnerability throughout the day. While all demographic groups experience heat stress, the impact varies by age and gender. Specifically, the elderly are at greater risk of extreme conditions, children experience elevated baseline stress, and females exhibit increased vulnerability. Despite studies focusing on the elderly due to physiological and health-related factors, it is crucial to recognize that children also face significant risks. Their heightened vulnerability during heatwaves can be attributed to factors such as a higher surface area-to-volume ratio and immature thermoregulatory systems, which hinder their ability to cope with extreme temperatures. To be more specific, on the hottest day in July 2023, children’s PET values were approximately 2 °C higher than those of other groups at 10 a.m., with prevailing estimates ranging from 54.2 °C to 55.7 °C. In comparison, elderly individuals aged 80 exhibited PET values ranging from 38.14 °C to 62.39 °C, with prevailing values exceeding 56.9 °C. The recorded Universal Thermal Climate Index (UTCI) values indicated extreme heat stress for all demographic groups assessed. While individuals aged 35 may experience considerable discomfort, older adults and children endure more severe impacts. Given these findings, this study underscores the necessity of developing targeted age- and gender-specific strategies for managing heat stress that address the unique needs of both elderly individuals and children. By employing tools such as the UTCI and PET, along with enhancing the capabilities of ENVI-met, it is possible to improve overall thermal comfort and health outcomes, particularly in light of the increasing frequency and intensity of heatwaves attributable to climate change.

Author Contributions

Conceptualization, T.E.; methodology, T.E.; software, T.E. and A.T.; validation, A.T., T.E., and A.M.; formal analysis, T.E., A.T., and A.M.; investigation, T.E.; resources, T.E., A.M., and A.T.; data curation T.E. and A.T.; writing—original draft preparation, T.E.; writing—review and editing, A.M. and A.T.; visualization, T.E.; supervision, A.T.; project administration, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Acknowledgments

Special thanks to G.C., Department of Survey Engineering, University of West Attica for providing the ENVI-met software license to be used for the purposes of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Vukmirovic, M.; Gavrilovic, S.; Stojanovic, D. The improvement of the comfort of public spaces as a local initiative in coping with climate change. Sustainability 2019, 11, 6546. [Google Scholar] [CrossRef]
  2. Mela, A.; Vryzidis, I.; Varelidis, G.; Tsotsolas, N. Urban Space Quality Evaluation Using Multi-Criteria Decision Analysis-Based Framework. In Multicriteria Decision Aid and Resource Management: Recent Research, Methods and Applications; Springer: Cham, Switzerland, 2023; Part F1272; pp. 59–84. [Google Scholar] [CrossRef]
  3. Bao, Y.; Gao, M.; Luo, D.; Zhou, X. Urban Parks—A Catalyst for Activities! The Effect of the Perceived Characteristics of the Urban Park Environment on Children’s Physical Activity Levels. Forests 2023, 14, 423. [Google Scholar] [CrossRef]
  4. Anderson, J.; Ruggeri, K.; Steemers, K.; Huppert, F. Lively Social Space, Well-Being Activity, and Urban Design: Findings From a Low-Cost Community-Led Public Space Intervention. Environ. Behav. 2017, 49, 685–716. [Google Scholar] [CrossRef]
  5. Jackson, L.E. The relationship of urban design to human health and condition. Landsc. Urban Plan. 2003, 64, 191–200. [Google Scholar] [CrossRef]
  6. Fischereit, J.; Schlünzen, K.H. Evaluation of thermal indices for their applicability in obstacle-resolving meteorology models. Int. J. Biometeorol. 2018, 62, 1887–1900. [Google Scholar] [CrossRef]
  7. Bartesaghi Koc, C.; Osmond, P.; Peters, A. Evaluating the cooling effects of green infrastructure: A systematic review of methods, indicators and data sources. Sol. Energy 2018, 166, 486–508. [Google Scholar] [CrossRef]
  8. Mela, A.; Varelidis, G. Effects of the COVID-19 Pandemic on the Use and Attitudes Towards Urban Public Spaces. J. Sustain. Archit. Civ. Eng. 2022, 31, 85–95. [Google Scholar] [CrossRef]
  9. Peng, Y.; Feng, T.; Timmermans, H.J.P. Expanded comfort assessment in outdoor urban public spaces using Box-Cox transformation. Landsc. Urban Plan. 2019, 190, 103594. [Google Scholar] [CrossRef]
  10. Matzarakis, A.; Mayer, H. The extreme heat wave in Athens in July 1987 from the point of view of human biometeorology. Atmos. Environ. Part B Urban Atmos. 1991, 25, 203–211. [Google Scholar] [CrossRef]
  11. Matzarakis, A.; Mayer, H.; Iziomon, M.G. Applications of a universal thermal index: Physiological equivalent temperature. Int. J. Biometeorol. 1999, 43, 76–84. [Google Scholar] [CrossRef]
  12. Shooshtarian, S.; Ridley, I. The effect of individual and social environments on the users thermal perceptions of educational urban precincts. Sustain. Cities Soc. 2016, 26, 119–133. [Google Scholar] [CrossRef]
  13. Ma, X.; Tian, Y.; Du, M.; Hong, B.; Lin, B. How to design comfortable open spaces for the elderly? Implications of their thermal perceptions in an urban park. Sci. Total Environ. 2021, 768, 144985. [Google Scholar] [CrossRef] [PubMed]
  14. De Castro Fontes, M.S.G.; Aljawabra, F.; Nikolopoulou, M. Open urban spaces quality: A study in a historical square in Bath—UK. In Proceedings of the 25th Passive and Low Energy Architecture International Conference 2008, Dublin, Ireland, 22–24 October 2008. [Google Scholar]
  15. Wang, X.; Meng, Q.; Zhang, L.; Hu, D. Evaluation of urban green space in terms of thermal environmental benefits using geographical detector analysis. Int. J. Appl. Earth Obs. Geoinf. 2021, 105, 102610. [Google Scholar] [CrossRef]
  16. Ruiz, M.A.; Correa, E.N. Adaptive model for outdoor thermal comfort assessment in an Oasis city of arid climate. Build. Environ. 2015, 85, 40–51. [Google Scholar] [CrossRef]
  17. Tseliou, A.; Melas, E.; Mela, A.; Tsiros, I.; Zervas, E. The Effect of Green Roofs and Green Façades in the Pedestrian Thermal Comfort of a Mediterranean Urban Residential Area. Atmosphere 2023, 14, 1512. [Google Scholar] [CrossRef]
  18. Tousi, E.; Mela, A.; Tseliou, A.; Theofili, E.; Varelidis, G. Elements of urban design to ameliorate urban heat island. In Proceedings of the Changing Cities VI: Spatial, Design, Landscape, Heritage and Socioeconomic Dimensions, Rhodes, Greece, 24–28 June 2024; p. 5765. [Google Scholar]
  19. Stapleton, J.M.; Larose, J.; Simpson, C.; Flouris, A.D.; Sigal, R.J.; Kenny, G.P. Do older adults experience greater thermal strain during heat waves? Appl. Physiol. Nutr. Metab. 2014, 39, 292–298. [Google Scholar] [CrossRef]
  20. Bills, R. Cold comfort: Thermal sensation in people over 65 and the consequences for an ageing population. In Proceedings of the 9th International Windor Conference 2016: Making Comfort Relevant, Windsor, UK, 7–10 April 2016; pp. 156–167. [Google Scholar]
  21. Pandolf, K.B. Aging and human heat tolerance. Exp. Aging Res. 1997, 23, 69–105. [Google Scholar] [CrossRef]
  22. Atkins, W.C.; McKenna, Z.J.; Jarrard, C.P.; Foster, J.; Crandall, C.G. Thermoregulatory Responses to Very Hot and Dry Heat Exposure: Interactions Between Age and Sex. Physiology 2024, 39, 726. [Google Scholar] [CrossRef]
  23. McKenna, Z.J.; Foster, J.; Atkins, W.C.; Belval, L.N.; Watso, J.C.; Jarrard, C.P.; Orth, B.D.; Crandall, C.G. Age alters the thermoregulatory responses to extreme heat exposure with accompanying activities of daily living. J. Appl. Physiol. 2023, 135, 445–455. [Google Scholar] [CrossRef]
  24. Eady, A.; Dreyer, B.; Hey, B.; Riemer, M.; Wilson, A. Reducing the risks of extreme heat for seniors: Communicating risks and building resilience. Health Promot. Chronic Dis. Prev. Can. 2020, 40, 215–224. [Google Scholar] [CrossRef]
  25. Ravanelli, N.; Morris, N.; Morrison, S.A. 24-H Movement Behaviour, Thermal Perception, Thirst, and Heat Management Strategies of Children and Adults During Heat Alerts: A Pilot Study. Front. Physiol. 2023, 14, 1–9. [Google Scholar] [CrossRef] [PubMed]
  26. Kwak, J.; Chun, C.; Park, J.S.; Kim, S.; Seo, S. The gender and age differences in the passengers’ thermal comfort during cooling and heating conditions in vehicles. PLoS ONE 2023, 18, e294027. [Google Scholar] [CrossRef] [PubMed]
  27. Leach, O.K.; Cottle, R.M.; Fisher, K.G.; Wolf, S.T.; Kenney, W.L. Sex differences in heat stress vulnerability among middle-aged and older adults (PSU HEAT project). Am. J. Physiol. Integr. Comp. Physiol. 2024, 327, R320–R327. [Google Scholar] [CrossRef] [PubMed]
  28. Aghamolaei, R.; Azizi, M.M.; Aminzadeh, B.; O’Donnell, J. A comprehensive review of outdoor thermal comfort in urban areas: Effective parameters and approaches. Energy Environ. 2023, 34, 2204–2227. [Google Scholar] [CrossRef]
  29. Matzarakis, A.; Nastos, P. Heat Waves in Athens. In Proceedings of the 8th Hellenic Conference on Meteorology, Climatology and Atmospheric Physics, Athens, Greece, 24–26 May 2006; pp. 153–160. [Google Scholar]
  30. Kántor, N.; Lin, T.P.; Matzarakis, A. Daytime relapse of the mean radiant temperature based on the six-directional method under unobstructed solar radiation. Int. J. Biometeorol. 2014, 58, 1615–1625. [Google Scholar] [CrossRef]
  31. Rupp, R.F.; Kim, J.; de Dear, R.; Ghisi, E. Associations of occupant demographics, thermal history and obesity variables with their thermal comfort in air-conditioned and mixed-mode ventilation office buildings. Build. Environ. 2018, 135, 1–9. [Google Scholar] [CrossRef]
  32. Tseliou, A.; Melas, E.; Mela, A.; Tsiros, I. Environmental Impact of Urban Design Elements in a Mediterranean City. Environ. Sci. Proc. 2023, 26, 76. [Google Scholar] [CrossRef]
  33. Koletsis, I.; Tseliou, A.; Lykoudis, S.; Tsiros, I.; Lagouvardos, K.; Psiloglou, B.; Founda, D.; Pantavou, K. Validation of ENVI-met microscale model with in-situ measurements in warm thermal conditions across Athens area. In Proceedings of the 17th International Conference on Environmental Science and Technology, Athens, Greece, 1–4 September 2021; Volume 17, pp. 1–4. [Google Scholar] [CrossRef]
  34. Mohajer, H.R.H.; Ding, L.; Santamouris, M. Developing Heat Mitigation Strategies in the Urban Environment of Sydney, Australia. Buildings 2022, 12, 903. [Google Scholar] [CrossRef]
  35. Skoufali, I.; Battisti, A. Microclimate of Urban Canopy Layer and Outdoor Thermal Comfort: A Case Study in Pavlou Mela, Thessaloniki. Urban Sci. 2019, 3, 84. [Google Scholar] [CrossRef]
  36. Tousi, E.; Tseliou, A.; Mela, A.; Sinou, M.; Kanetaki, Z. Exploring Thermal Discomfort during Mediterranean Heatwaves through Softscape and Hardscape ENVI-Met Simulation Scenarios. Sustainability 2024, 16, 6240. [Google Scholar] [CrossRef]
  37. Magliocco, A. La vegetazione in ambiente urbano: Comfort e riduzione del fenomeno isola di calore. TECHNE J. Technol. Archit. Environ. 2014, 8, 155–162. [Google Scholar]
  38. Salvalai, G.; Blanco Cadena, J.D.; Quagliarini, E. Greenery as a mitigation strategy to urban heat and air pollution: A comparative simulation-based study in a densely built environment. TEMA 2023, 9. [Google Scholar] [CrossRef]
  39. Tseliou, A.; Melas, E.; Mela, A.; Tsiros, I. Evaluating the effects of green roofs and green façade as an urban heat island adaptation strategy. In Proceedings of the ICED 2023: 24th International Conference on Engineering Design, Bordeaux, France, 24–28 July 2023; Volume 436, p. 01018. [Google Scholar] [CrossRef]
  40. Dardin, A.; Monteiro, L. Impact of extreme weather events on the thermal comfort of vulnerable populations in the city of Sao Paulo. In Proceedings of the Comfort at The Extremes 2023, Ahmadabad, India, 13–15 December 2023. [Google Scholar] [CrossRef]
  41. Höppe, P. The physiological equivalent temperature—A universal index for the biometeorological assessment of the thermal environment. Int. J. Biometeorol. 1999, 43, 71–75. [Google Scholar] [CrossRef] [PubMed]
  42. Jendritzky, G.; Havenith, G.; Weihs, P.; Batchvarova, E. Towards a Universal Thermal Climate Index UTCI for Assessing the Thermal Environment of the Human Being. Final Report COST Action 730, Freiburg, Germany. 2009. Available online: https://www.cost.eu/actions/730/ (accessed on 30 October 2023).
  43. Zhang, S.; Zhang, X.; Niu, D.; Fang, Z.; Chang, H.; Lin, Z. Physiological equivalent temperature-based and universal thermal climate index-based adaptive-rational outdoor thermal comfort models. Build. Environ. 2023, 228, 109900. [Google Scholar] [CrossRef]
  44. Kumar, P.; Sharma, A. Study on importance, procedure, and scope of outdoor thermal comfort—A review. Sustain. Cities Soc. 2020, 61, 102297. [Google Scholar] [CrossRef]
  45. Langer, I.; Sodoudi, S.; Cubasch, U. Using the ENVI-MET program to simulate the micro climate in new Town HASHTGERD. In Proceedings of the International Conference on Computing, Networking and Digital Technologies 2012, Sanad, Bahrain, 11–13 November 2012; pp. 61–64. [Google Scholar] [CrossRef]
  46. Bruse, M. Simulating microscale climate interactions in complex terrain with a high-resolution numerical model: A case study for the Sydney CBD Area (Model Description). In Proceedings of the International Conference on Urban Climatology & International Congress of Biometeorology, Sydney, Australia, 8–12 November 1999; pp. 1–6. [Google Scholar]
  47. Tseliou, A.; Tsiros, I.X. Modeling urban microclimate to ameliorate thermal sensation conditions in outdoor areas in Athens (Greece). Build. Simul. 2016, 9, 251–267. [Google Scholar] [CrossRef]
  48. Tousi, E. Changing Socio-Spatial Identities. The case of the Asia Minor Refugee Urban Settlements in the Greater Athens-Piraeus Region in Greece. J. Sustain. Archit. Civ. Eng. 2024, 35, 153–167. [Google Scholar] [CrossRef]
  49. Tousi, E. Challenges on urban socio-spatial cohesion. The case of social housing complexes in the regional administrative area of piraeus in Greece. J. Sustain. Archit. Civ. Eng. 2021, 29, 21–32. [Google Scholar] [CrossRef]
  50. Tousi, E. Initial planning and current situation of refugee housing and outdoor public spaces in the post-refugee urban settlement of Nikea in Attica. Athens Soc. Atlas 2024. [Google Scholar] [CrossRef]
  51. Wan, K.; Feng, Z.; Hajat, S.; Doherty, R.M. Temperature-related mortality and associated vulnerabilities: Evidence from Scotland using extended time-series datasets. Environ. Health A Glob. Access Sci. Source 2022, 21, 99. [Google Scholar] [CrossRef]
  52. Yatim, A.N.M.; Latif, M.T.; Sofwan, N.M.; Ahamad, F.; Khan, M.F.; Mahiyuddin, W.R.W.; Sahani, M. The association between temperature and cause-specific mortality in the Klang Valley, Malaysia. Environ. Sci. Pollut. Res. 2021, 28, 60209–60220. [Google Scholar] [CrossRef] [PubMed]
  53. Saucy, A.; Ragettli, M.S.; Vienneau, D.; de Hoogh, K.; Tangermann, L.; Schäffer, B.; Wunderli, J.-M.; Probst-Hensch, N.; Röösli, M. The role of extreme temperature in cause-specific acute cardiovascular mortality in Switzerland: A case-crossover study. Sci. Total Environ. 2021, 790, 147958. [Google Scholar] [CrossRef] [PubMed]
  54. Ingole, V.; Sheridan, S.C.; Juvekar, S.; Achebak, H.; Moraga, P. Mortality risk attributable to high and low ambient temperature in Pune city, India: A time series analysis from 2004 to 2012. Environ. Res. 2022, 204, 112304. [Google Scholar] [CrossRef] [PubMed]
  55. Alahmad, B.; Shakarchi, A.F.; Khraishah, H.; Alseaidan, M.; Gasana, J.; Al-Hemoud, A.; Koutrakis, P.; Fox, M.A. Extreme temperatures and mortality in Kuwait: Who is vulnerable? Sci. Total Environ. 2020, 732, 139289. [Google Scholar] [CrossRef] [PubMed]
  56. Wichmann, J. Heat effects of ambient apparent temperature on all-cause mortality in Cape Town, Durban and Johannesburg, South Africa: 2006–2010. Sci. Total Environ. 2017, 587–588, 266–272. [Google Scholar] [CrossRef] [PubMed]
  57. Wang, D.; Lau, K.K.L.; Ren, C.; Goggins, W.B.; Shi, Y.; Ho, H.C.; Lee, T.-C.; Lee, L.-S.; Woo, J.; Ng, E. The impact of extremely hot weather events on all-cause mortality in a highly urbanized and densely populated subtropical city: A 10-year time-series study (2006–2015). Sci. Total Environ. 2019, 690, 923–931. [Google Scholar] [CrossRef]
  58. Ebi, K.L.; Vanos, J.; Baldwin, J.W.; Bell, J.E.; Hondula, D.M.; Errett, N.A.; Hayes, K.; Reid, C.E.; Saha, S.; Spector, J.; et al. Extreme Weather and Climate Change: Population Health and Health System Implications. Annu. Rev. Public Health 2020, 42, 293–315. [Google Scholar] [CrossRef]
  59. Kovats, R.S.; Hajat, S. Heat stress and public health: A critical review. Annu. Rev. Public Health 2008, 29, 41–55. [Google Scholar] [CrossRef]
  60. Xu, Z.; Sheffield, P.E.; Su, H.; Wang, X.; Bi, Y.; Tong, S. The impact of heat waves on children’s health: A systematic review. Int. J. Biometeorol. 2014, 58, 239–247. [Google Scholar] [CrossRef]
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Figure 1. Methodology scheme.
Figure 1. Methodology scheme.
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Figure 2. (a) Spatial layout of the area of study, 2D and 3D models; (b) 2D and 3D ENVI-met Spaces V5.6.1 model.
Figure 2. (a) Spatial layout of the area of study, 2D and 3D models; (b) 2D and 3D ENVI-met Spaces V5.6.1 model.
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Figure 3. ENVI-met simulation of the area of study. Potential air temperature at 10 a.m.
Figure 3. ENVI-met simulation of the area of study. Potential air temperature at 10 a.m.
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Figure 4. ENVI-met simulation of the area of study. Potential air temperature at 3 p.m.
Figure 4. ENVI-met simulation of the area of study. Potential air temperature at 3 p.m.
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Figure 5. ENVI-met simulation of the area of study. Potential air temperature at 8 p.m.
Figure 5. ENVI-met simulation of the area of study. Potential air temperature at 8 p.m.
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Figure 6. ENVI-met simulation of the area of study. Mean radiant temperature at 10 a.m.
Figure 6. ENVI-met simulation of the area of study. Mean radiant temperature at 10 a.m.
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Figure 7. ENVI-met simulation of the area of study, mean radiant temperature at 3 p.m.
Figure 7. ENVI-met simulation of the area of study, mean radiant temperature at 3 p.m.
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Figure 8. ENVI-met simulation of the area of study. Mean radiant temperature at 8 p.m.
Figure 8. ENVI-met simulation of the area of study. Mean radiant temperature at 8 p.m.
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Urbansci 08 00193 g009
Figure 9. ENVI-met simulation of the area of study. PET 10 a.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 9. ENVI-met simulation of the area of study. PET 10 a.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g010
Figure 10. ENVI-met simulation of the area of study. PET 10 a.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 10. ENVI-met simulation of the area of study. PET 10 a.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g011
Figure 11. ENVI-met simulation of the area of study. PET 10 a.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
Figure 11. ENVI-met simulation of the area of study. PET 10 a.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
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Urbansci 08 00193 g012
Figure 12. ENVI-met simulation of the area of study. PET 10 a.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
Figure 12. ENVI-met simulation of the area of study. PET 10 a.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
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Urbansci 08 00193 g013
Figure 13. ENVI-met simulation of the area of study. PET 3 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 13. ENVI-met simulation of the area of study. PET 3 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g014
Figure 14. ENVI-met simulation of the area of study. PET 3 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 14. ENVI-met simulation of the area of study. PET 3 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g015
Figure 15. ENVI-met simulation of the area of study. PET 3 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
Figure 15. ENVI-met simulation of the area of study. PET 3 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
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Urbansci 08 00193 g016
Figure 16. ENVI-met simulation of the area of study. PET 3 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
Figure 16. ENVI-met simulation of the area of study. PET 3 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
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Urbansci 08 00193 g017
Figure 17. ENVI-met simulation of the area of study. PET 8 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 17. ENVI-met simulation of the area of study. PET 8 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g018
Figure 18. ENVI-met simulation of the area of study. PET 8 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
Figure 18. ENVI-met simulation of the area of study. PET 8 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.
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Urbansci 08 00193 g019
Figure 19. ENVI-met simulation of the area of study. PET 8 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
Figure 19. ENVI-met simulation of the area of study. PET 8 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.
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Urbansci 08 00193 g020
Figure 20. ENVI-met simulation of the area of study. PET 8 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
Figure 20. ENVI-met simulation of the area of study. PET 8 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.
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Urbansci 08 00193 g021
Figure 21. ENVI-met simulation of the area of study. UTCI 10 a.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
Figure 21. ENVI-met simulation of the area of study. UTCI 10 a.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
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Urbansci 08 00193 g022
Figure 22. ENVI-met simulation of the area of study. UTCI 3 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
Figure 22. ENVI-met simulation of the area of study. UTCI 3 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
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Urbansci 08 00193 g023
Figure 23. ENVI-met simulation of the area of study. UTCI 8 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
Figure 23. ENVI-met simulation of the area of study. UTCI 8 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.
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Table 1. PET and UTCI orginal and Mediterranean scale and thermal stress assessment.
Table 1. PET and UTCI orginal and Mediterranean scale and thermal stress assessment.
PET (°C)Mediterranean Scale Thermal Stress AssessmentUTCI (°C)Mediterranean Scale Thermal Stress Assessment
Original Scale Original ScaleWarm Period
>41.1 Extreme heat stressAbove 46Above 39.9Extreme heat stress
35.1 to 41.0Higher than 39.1Strong heat stress38 to 4638.3 to 39.9Very strong heat stress
29.1 to 35.031.2 to 39.1Moderate heat stress32 to 3836.8 to 38.3Strong heat stress
23.1 to 29.023.8 to 31.2Slight heat stress26 to 3234.0 to 36.8Moderate heat stress
18.1 to 23.014.8 to 23.8No thermal stress9 to2627.0 to 34.0No thermal stress
13.1 to 18.05.2 to 14.8Slight cold stress0 to 924.6 to 27.0Slight cold stress
8.1 to 13.00.7 to 5.2Moderate cold stress−13 to 023.0 to 24.6Moderate cold stress
4.1 to 8.0Less than 0.7Strong cold stress−27 to −1321.5 to 23.0Strong cold stress
<4.0 Extreme cold stress−40 to −2720.2 to 21.5Very strong cold stress
Below −40Below 20.2Extreme cold stress
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MDPI and ACS Style

Evgenia, T.; Mela, A.; Tseliou, A. Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics. Urban Sci. 2024, 8, 193. https://doi.org/10.3390/urbansci8040193

AMA Style

Evgenia T, Mela A, Tseliou A. Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics. Urban Science. 2024; 8(4):193. https://doi.org/10.3390/urbansci8040193

Chicago/Turabian Style

Evgenia, Tousi, Athina Mela, and Areti Tseliou. 2024. "Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics" Urban Science 8, no. 4: 193. https://doi.org/10.3390/urbansci8040193

APA Style

Evgenia, T., Mela, A., & Tseliou, A. (2024). Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics. Urban Science, 8(4), 193. https://doi.org/10.3390/urbansci8040193

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