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Article

The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia)

by
Snežana Kecman
1,*,
Nadežda Stojanović
1,
Milena Vukmirović
1,
Nevena Vasiljević
1,
Ivana Bjedov
1 and
Dragana Vujović
2
1
Faculty of Forestry, University of Belgrade, 11000 Belgrade, Serbia
2
Faculty of Physics, University of Belgrade, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Forests 2025, 16(2), 321; https://doi.org/10.3390/f16020321
Submission received: 30 December 2024 / Revised: 30 January 2025 / Accepted: 4 February 2025 / Published: 12 February 2025
(This article belongs to the Section Urban Forestry)
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Abstract

:
Small green spaces are the most common type of greenery in cities, but very little is known about their impact on thermal comfort. It has been established that larger green spaces (large city parks, urban forests, etc.) have a significant effect on the formation of thermal comfort in cities. Conversely, it has been shown that this effect is highly variable in smaller green spaces (particularly those <3 ha). This study investigated the impact of smaller green spaces (<3 ha) of various categories (parks, squares, and street tree lines) on the thermal comfort of urban open spaces. In total, 18 green spaces in Belgrade were selected, where specialised meteorological measurements were conducted during summer and winter, and the PET index and UTCI were calculated using the RayMan Pro (Version 3.1 Beta) software. Research has shown that green spaces ranging from 0.9 to 3 ha have an average difference of 4.04 °C in the PET index and 3.27 °C in the UTCI. For areas between 0.3 and 0.9 ha, the differences are 2.32 °C for PET and 2.05 °C for UTCI, while for spaces <0.3 ha, the differences are 2.19 °C for PET and 2.12 °C for UTCI. In all cases, the values of the PET index and UTCI were higher in green spaces compared to areas without greenery, with differences ranging from 2.19 to 4.04 °C for PET and 2.05–3.27 °C for UTCI. It was determined that green spaces <3 ha increased the PET index by an average of 2.75 °C and the UTCI by 2.41 °C. The results of this study showed that despite their size, small green areas can significantly improve thermal comfort. This study highlights the importance of these green spaces and provides a basis for the planning of new or renovated existing urban green spaces to mitigate the effects of climate change in cities.

1. Introduction

Climate conditions in cities are receiving increasing attention worldwide. Public, scientific, and expert discussions on climate change and its potential impact on the lives of urban residents have heightened awareness regarding ecological opportunities for the adaptation and mitigation of this omnipresent and global phenomenon. Climate scenarios indicate that Central Europe will experience significantly more intense heat waves, higher air temperatures, and more frequent and longer-lasting extreme heat events in the future. These climate change consequences have a considerable impact on public health in urban environments [1,2]. The most vulnerable groups include young children, the elderly, and individuals with cardiovascular disease. However, these consequences also affect the productivity, concentration, and sleep of other citizens [3]. Owing to the uneven distribution of green spaces, not all communities and city residents can access these areas equally. Ecological equality refers to equal access for residents to green spaces, whereas ecological inequality is evident between wealthy and poor communities, where wealthier communities benefit more from accessible green areas. According to the concept of environmental justice, all people have the right to equal access to services (e.g., green spaces) and protection from specific environmental issues, such as pollution and climate change [4]. Urban and spatial planning authorities are increasingly recognising these factors and are focusing on incorporating microclimatic conditions into urban planning and management processes, particularly in urban open spaces [5].
Randrup and Jansson [6] defined urban open spaces as unbuilt areas within a populated settlement, comprising a combination of “green” (vegetation, greenery), “blue” (water-dominated), “brown” (derelict), and/or “gray” (hard-surface) elements. Urban green spaces are defined as outdoor areas with a significant amount of greenery [7].
The intensity of urban open space usage by city residents is fundamentally influenced by the thermal experience of individuals directly exposed to existing microclimatic conditions [8]. In cities where large populations coexist, thermal comfort is an important factor for enhancing the quality of daily life. A study conducted in five European countries (the United Kingdom, Italy, Germany, Switzerland, and Greece) at 14 different locations confirmed the connection between microclimatic and comfort conditions. The results of microclimatic and human monitoring (nearly 10,000 interviews) show that air temperature and solar radiation are important factors influencing comfort, although a single parameter is not sufficient to assess thermal comfort. Wind speed and relative humidity are influenced by air temperature; at high air temperatures, wind is desirable, whereas at low air temperatures, it contributes to discomfort. The same concept applies to humidity; high humidity and high temperatures are uncomfortable for users. Another important factor is psychological adaptation, which refers to the ability to make choices to avoid discomfort (e.g., selecting a seating area to reduce discomfort) when visiting outdoor spaces. The design of urban open spaces can allow for their use even under harsh microclimatic conditions (hot or cold) by balancing exposure to and protection from various weather elements [9].
The assessment of thermal comfort today is based on thermal comfort indices. There are more than one hundred thermal comfort indices that evaluate the impact of heat and cold on the human body under various environmental conditions [10,11,12]. The four most commonly used indices are Physiological Equivalent Temperature (PET), Universal Thermal Climate Index (UTCI), Predicted Mean Vote (PMV), and Standard Effective Temperature (SET) [13,14,15]. The advantage of PET and UTCI lies in their expression in degrees Celsius (°C), making them more understandable for those not familiar with biometeorological terminology [16] and also suitable for comparison and analysis [17].
Furthermore, the range of studies on the PET index spans the global, local, and micro scales. Its widespread use is largely owing to its ability to calculate it using the RayMan model, which requires standard meteorological data and is simple to apply [13,18,19]. In addition to its application in indoor environments, the PET index is also widely used in outdoor spaces. In Germany, guidelines for urban and regional planners are recommended as indices for predicting changes in urban and regional climates [20].
The UTCI is suitable for assessing meteorological, ecological, and bioclimatic conditions, and it is claimed that this index can describe thermal comfort across all seasons, climatic conditions, and on various temporal and spatial scales. Additionally, it can be used in both outdoor and indoor environments as well as in cold or warm climates [21]. According to Blazejczyk et al. [11], the UTCI represents different climates, weather, and locations very well, unlike other indices that adequately express bioclimatic conditions only in specific situations. The UTCI even reflects small differences in the intensity of micrometeorological stimuli, which allows it to represent the temporal variability of thermal conditions better than other indices. The analyses conducted indicate the universality of the UTCI and its ability to represent bioclimatic conditions for humans across a wide range of climatic conditions. Lukić et al. [15] noted that UTCI is recommended by the World Meteorological Organization (WMO).
The UTCI, which is similar to the PET index, can be calculated using the RayMan model. The input parameters for both indices were measured at a height of 1.1 m (wind speed, in the case of the UTCI, was automatically adjusted by the model). The main difference is that PET always uses standard clothing (clo = 0.9), which may limit its applicability, whereas the UTCI adjusts clothing based on specific outdoor conditions. Additionally, UTCI is highly sensitive to wind speed [13,19,22], which can also be considered a limitation. Nevertheless, both the PET index and UTCI are widely used in outdoor environments, effectively represent different climates and urban microclimates, and are suitable for assessing the thermal comfort in all seasons. This makes them particularly relevant for this study and more broadly, for research on urban thermal environments.
A considerable number of studies on the impact of urban green spaces on the provision of thermal comfort in urban open spaces [23,24,25] are often associated with larger green spaces because their large amount of greenery significantly influences the provision of thermal comfort. A study conducted in Beijing, China, revealed that a green space of 102 ha reduced PET by an average of 2 °C and a maximum of 15.6 °C during the summer. Similarly, research on a large green area of 125 ha in Madrid, Spain, showed that the PET at a distance of 150 m (PET 29.3 °C) from the park was lower by an average of 2 °C and 2.3 °C compared to distances of 380 m (PET 31.3 °C) and 650 m (PET 31.6 °C). This demonstrates the significant effect of green spaces on the urban environment [26]. However, in cities (particularly in dense city centres), there is a notable presence of smaller green spaces (<3 ha) that can also have a significant impact, but this effect has not been sufficiently researched [27]. Sun et al. [28] investigated the impact of four small green spaces (0.1 ha, 0.3 ha, 0.6 ha, and 1 ha) on thermal comfort in a densely populated urban area in Tokyo, Japan. This study is one of the few in the field of thermal comfort. Their findings revealed a significant difference in PET between areas inside and outside the green spaces, with the greatest difference observed at noon. However, there is a lack of research in this area. Moreover, Armson et al. [29] pointed out the challenges in assessing the impact of green spaces on thermal comfort, as no green space or cities are identical to one another, nor are the areas within a single city, aside from the size of the green space. This indicates the difficulty in establishing standardised criteria for comparison in such studies. The same authors suggested that this limitation can be overcome by comparing the air temperature of green spaces with the air temperatures in the surrounding streets. This approach was applied in this study, but with a focus on thermal comfort. The impact of green spaces on thermal comfort in urban open spaces is reflected in the difference in PET and UTCI values between the green space (in the shade) and the immediate vicinity outside the green space, that is, areas without greenery (in the sun).
As smaller green spaces are the most common type of greenery in urban areas, it is important to establish guidelines and standards for sustainable planning, design, and development. This study examines the impact of smaller (<3 ha) green spaces of various types (parks, squares, and street tree lines) on the provision of thermal comfort in urban open spaces within the city of Belgrade.
The initial hypothesis was that even smaller green spaces can have a significant impact on thermal comfort in urban open spaces. To assess the influence of smaller green spaces (<3 ha) on thermal comfort, specialised meteorological measurements were designed and conducted. Thermal comfort indices were calculated based on these measurements, followed by the analysis, systematisation, and statistical processing of the data collected.
The results of this research provide a framework for establishing standards for the planning of new or renewed existing urban green spaces, with the aim of mitigating climate change in cities.

2. Materials and Methods

2.1. Study Area

The territory of the city of Belgrade (44°49′1″ N and 20°27′44″ E) includes an area of 3224 km2, with an urban area covering 35996 ha. According to the Köppen–Geiger climate classification, Belgrade mainly has a temperate continental climate with four distinct seasons, categorised as Cfa [30]. The mean annual air temperature is 11.7 °C. The dominant wind is Košava, which can reach speeds up to 130 km/h. The topography south of the Sava and Danube Rivers is characterised by significant variability, with the average altitude of Belgrade being 132 m (the absolute altitude of the meteorological observatory). The Belgrade region has 1,684,259 inhabitants, representing 25.3% of the total population of the Republic of Serbia. According to the law on regional development, Belgrade is designated as a city region. The Belgrade region is one of five regions (Nomenclature of Statistical Territorial Units—NSTJ2) of the Republic of Serbia. Belgrade is one of the most environmentally threatened regions in the Republic of Serbia. This is attributed to high population density, extensive economic activity, and heavy traffic (of national and regional importance), which result in increased pressure on urban spaces and resources [31,32,33].

2.2. Selection of Green Spaces

Based on the recommendations of various authors [2,3,34], this study focuses on the categories of green spaces that are identified as the most frequented, specifically those open areas in the city where there is a high frequency of residents. Common examples of small green spaces in urban areas are parks, squares, and tree-lined streets. The category of green spaces serves as the foundational basis for selecting research objects, ensuring that an equal number of green spaces from each category is represented. Therefore, parks, squares, and tree-lined streets constituted the sample of green spaces used in this study. For each of these three categories, three green spaces were selected, located in two Belgrade municipalities (Stari Grad, the inner urban core, and Novi Beograd, the outer urban area). These two parts of the city were selected for this study because of their distinct characteristics, including differences in historical development, urban structure, building architecture, and ecological features. The locations of parks, squares, and street tree lines are shown in Figure 1, and their characteristics are presented in Table 1.
Further research and analysis of the results (the differences obtained in PET and UTCI indicators) are based on the classification of green areas according to their size. Based on the results of a study by Chang et al. [27], Müller et al. [35], and Sun et al. [28], the researched green spaces were classified into three categories: small green spaces (SGS) with an area smaller than 0.3 ha, medium green spaces (MGS) with an area between 0.30 and 0.90 ha, and large green spaces (LGS) with an area ranging from 0.9 to 3 ha.
Forests 16 00321 g001
Figure 1. Location of the study area (map modified from the General Regulation Plan of the Green Areas System of Belgrade [36]).
Figure 1. Location of the study area (map modified from the General Regulation Plan of the Green Areas System of Belgrade [36]).
Forests 16 00321 g001

2.3. Selection of Coordinates for Green Space

To calculate the area of green spaces and determine the geographical location of the measurement points, the following were used: GIS Belgrade Land Development Public Agency (GIS Beoland, GDi Localis Visios, Version 1.0), ArcGIS Pro 3.0.0 (2022), and Google Earth (Version 10.72.0.2). For each point, primary coordinates were initially determined using Google Earth, and then precise positions were established through the GIS Belgrade Land Development Public Agency (GIS Beoland) and the software package ArcGIS Pro 3.0.0 (2022). Subsequently, graphical and cartographic data processing were performed using Adobe Photoshop CC 2018.

2.4. Data Description

For each identified green space and the surrounding non-vegetated area, data on air temperature (ta), relative humidity (RH), and wind speed (V) were collected. Using the measured values of these meteorological parameters, the PET index and UTCI were calculated using the RayMan Pro (Version 3.1 Beta) software for meteorological data processing.
The Physiological Equivalent Temperature (PET) index was introduced by Mayer and Höppe (1987). It is based on the Munich Energy Balance Model for Individuals (MEMI), which considers the functioning of the human internal system to predict actual values, thus modelling the thermal conditions of the human body in a physiologically relevant manner. This index represents the physiological equivalent temperature at any location, whether indoors or outdoors, which corresponds to the air temperature in an indoor environment [18,37].
Meteorological parameters such as air temperature (ta), vapour pressure (VP), relative humidity (RH), wind speed (V), and mean radiant temperature (Tmrt) were used to calculate the PET index. Additionally, human parameters, including activity/metabolism (80 W for walking), clothing resistance (0.9 clo for reference clothing), height (1.75 m), weight (75 kg), gender, and age (male, 35 years), are typically predetermined by standards (the MEMI model). This approach focuses on the climate conditions across various locations rather than on individual human characteristics [18,19,37].
The Universal Thermal Climate Index (UTCI) represents the air temperature of a reference environment that induces the same physiological responses in a reference person (model) as the actual environment. The calculation is based on the UTCI–Fiala model, which divides the human body into two interdependent thermoregulation systems: an active system (thermoregulatory responses, peripheral blood flow, sweating, and thermogenesis) and a passive system (the human body and heat transfer between the environment and the person) [10,38,39].
The calculation of the Universal Thermal Climate Index (UTCI) involves predefined meteorological and non-meteorological parameters: mean radiant temperature (Tmrt), which is equal to the air temperature; vapour pressure (VP), corresponding to a relative humidity of 50%, with a constant reference value of 20 hPa used for high air temperatures (>29°C); wind speed (va) at a height of 10 m above ground level set at 0.5 m/s (approximately 0.3 m/s at a height of 1.1 m); and activity equivalent to a gentle walk at 4 km/h (1.1 m/s), corresponding to a metabolic rate of 2.3 MET (135 W/m2). The UTCI includes clothing models in which clothing characteristics are adjusted according to changing climatic conditions. This index was developed to reflect the dressing habits of people in Europe based on data collected during field studies [10,11,12].

2.5. Data Collection

Meteorological measurements were conducted over the full calendar year, encompassing both winter (outside the growing season) and summer (mid-growing season). The measurements were organised into three series, with two consecutive readings taken at a height of 1.1 metres above the ground level. In parks and squares, the instruments were positioned in the central areas of these green spaces, ensuring they were located in zones with abundant greenery and away from water sources, such as ponds and irrigation systems, as well as children’s playgrounds (due to the distinct thermal properties of different materials and their varying heat conductivity). For street tree lines, which are characterised as linear structures, the instruments were positioned at the beginning, middle, and end of the tree lines on both sides of the street. The average values of the meteorological parameters collected from these six points (for each measurement in every series) were used as data for further analysis.
The instruments were placed on soil surfaces in green spaces and on asphalt surfaces in adjacent areas without greenery to eliminate the influence of different thermal properties between the materials.
We analysed the difference in the obtained values of the thermal comfort indices (PET and UTCI) between the measurement points within the green space (in the shade) and the measurement point outside the green space, in the built environment, and on asphalt (in the sun).
Meteorological measurements were carried out on sunny days (cloudless) and on days with no strong winds (e.g., the “Košava” wind) between 12:00 and 15:00 local time (UTC+1 in winter and UTC+2 in summer). This time frame was chosen based on the recommendations of Gillner et al. [40] to minimise the surface temperature variations associated with fluctuating atmospheric conditions throughout the day. Strong winds can significantly influence and modify the thermal comfort indices PET and UTCI, particularly UTCI, which is highly sensitive to the wind speed [22,41]. For this reason, the wind speed during the study did not exceed 2.3 m/s, according to the Beaufort scale [42].
A calibrated meteorological instrument, TROTEC BA30 WP, Trotec GmbH, Heinsberg, Germany, was used for meteorological measurements, which satisfied the standard criteria for this type of research. The operational range for the air temperature measurement is from −20 to 60 °C with an accuracy of 0.1 °C, relative humidity (RH) from 0 to 99.9% with an accuracy of 0.1%, and for wind speed, from 0 to 30 m/s with an accuracy of 0.01 m/s.

2.6. Data Analysis

The database was created using the Microsoft Excel 2007 spreadsheet software, and statistical analysis was performed using the IBM SPSS Statistics software, version 25. Descriptive statistics was used to describe the sample. The one-way analysis of variance (ANOVA) was used to test the established mean differences in the impact of green spaces compared with the size of the investigated green spaces. Levene’s test was used to examine the homogeneity of variances. In cases where the assumption of homogeneity was violated, robust tests of equality of means were applied, specifically the Welch test or the Brown–Forsythe test, which are resistant to violations of this assumption. Tukey’s HSD test was used to identify statistically significant differences in the thermal comfort index values. The analysis of variance (ANOVA) is an important and essential tool that helps researchers conduct studies involving more than one experimental group. ANOVA provides significant results; however, it only identifies the differences between groups, indicating whether at least one group differs from the others. It compares means across groups (to determine if they are equal) but does not provide information on which specific pairs of means are significant. The Tukey HSD test provides precise information and identifies the main differences between the groups under consideration. It is most commonly used to test the significance of three or more variables and is widely applied in various domains. The control of the Type I error rate becomes concerning when there are more than two comparisons to test. The Tukey HSD test is the most commonly used and recommended procedure for controlling the Type I error rate [43].

3. Results

Research on the impact of the area of green spaces on the provision of thermal comfort in urban open spaces has shown that the largest average difference in PET index values between measurement points located on green spaces and those outside them was recorded at LGS (4.04 ± 2.74 °C), followed by a slightly smaller difference at MGS (2.32 ± 1.53 °C), and the smallest at SGS (2.19 ± 1.49 °C) (Table 2). The difference in PET index values at LGS ranged from 0.1 to 10.90 °C, MGS from 0 to 6.70 °C, and SGS from 0 to 5.40 °C.
The largest mean difference in UTCI values between measurement points on green spaces and those outside the green space was recorded at LGS (3.27 ± 1.97 °C), followed by a slightly smaller difference at SGS (2.12 ± 1.32 °C), and the smallest at MGS (2.05 ± 1.14 °C). The difference in UTCI values ranged from 0.1 to 7.70 °C at LGS, 0–5.20 °C at SGS, and 0–4.4 °C at MGS. Furthermore, all the studied green spaces showed a difference compared to areas without greenery, with a range of 2.19–4.04 °C for PET and 2.05–3.27 °C for UTCI, with mean values of 2.75 °C for PET and 2.41 °C for UTCI.
The statistical parameters of the differences in the thermal comfort index for the examined green spaces are listed in Table 2.
The homogeneity of variance test determined that the variance was heterogeneous for both PET (Sig. = 0.000; p < 0.01) and UTCI (Sig. = 0.000; p < 0.01). Statistically significant differences in the mean values of the thermal comfort indices for the analysed green spaces were evaluated based on the results of the test for the equality of arithmetic means (Table 3). It was confirmed that significant differences existed in the obtained values for both PET (Sig. = 0.000; p < 0.01) and UTCI (Sig. = 0.000; p < 0.01) in relation to green space areas.
The Tukey HSD test (Table 4) revealed statistically significant differences in the PET index values between the LGS and SGS (Sig. = 0.000; p < 0.01), as well as between LGS and MGS (Sig. = 0.000; p < 0.01) at a 0.01 significance level. Specifically, LGS exhibited significantly higher PET index values than SGS and MGS, whereas no statistically significant differences were found between SGS and MGS. Additionally, the Tukey HSD test identified significant differences in the UTCI values between the LGS and SGS (Sig. = 0.000; p < 0.01) and between LGS and MGS (Sig. = 0.000; p < 0.01) at a 0.01 significance level, where LGS showed significantly higher UTCI values compared to SGS and MGS. No statistically significant differences were observed between the SGS and MGS.
The comparative analysis confirmed that LGS (0.9–3 ha) had the most significant impact on modifying the differences in the PET index and UTCI, which was expected because of the considerable presence of greenery in these green spaces. In contrast, the MGS (0.30–0.90 ha) and SGS (<0.3 ha) categories were less effective in modifying the thermal comfort index differences investigated (Figure 2 and Figure 3).

4. Discussion

4.1. Discussion of Methodological Limitations

This research contributes significantly to the understanding of how smaller green spaces influence urban microclimates. Future studies could broaden the scope to encompass various categories of green spaces such as greenery in residential blocks, kindergartens, and schools. These types of green spaces are particularly important, as they serve as vital areas where local residents of all ages spend considerable time, thereby playing a crucial role in the well-being of the community.
Further studies should consider broader sets of PET and UTCI data. The current database includes measurements from 12:00 to 15:00, which corresponds to the warmest part of the day. Therefore, it is possible to extend the time range to include morning, afternoon, and evening. Additionally, this study was conducted for two seasons, summer and winter, but it could also investigate spring and autumn as transitional seasons. Furthermore, future research should focus on the impact of the biophysical structure of green spaces on thermal comfort in open urban areas.

4.2. Discussion of the Results

Sun et al. [28] investigated the impact of four small green spaces (0.1 ha, 0.3 ha, 0.6 ha, and 1 ha) on thermal comfort in a densely populated urban area in Tokyo, Japan, and obtained similar results. The differences in PET index values measured inside and outside the green spaces ranged from 4.75 to 9.99 °C. The PET results in the 1 ha park were lower by 8.21 °C, consistent with the maximum differences observed in PET values for large green spaces (up to 10.90 °C), while the results for the 0.6 ha park were lower by 9.51 °C, which is slightly higher than the values obtained for medium green spaces (up to 6.70 °C), while the results for the 0.3 ha park were lower by 4.75 °C, aligning with the values for small green spaces (up to 5.40 °C). The smallest green space (0.1 ha) in this study showed the greatest difference in PET compared to areas outside the green space (9.99 °C), suggesting that even extremely small green areas can have a significant impact on thermal comfort. This effect is attributed to both the amount and arrangement of greenery within the green space as well as the surrounding environment.
Similar results for the UTCI were obtained by Lehnert et al. [44], who investigated the impact of green spaces (squares, parks, etc.) of varying sizes (0.61–6.34 ha) on the formation of thermal comfort in open urban spaces across four cities in the Czech Republic: Brno, Olomouc, Ostrava, and Plzeň. The difference in the UTCI values measured in green spaces and open spaces ranged from 5.5 to 8.5 °C. Notably, the results obtained from the main city square in Brno (1.17 ha) showed a UTCI difference of 8.1 °C between the values recorded in the green space and those in the open space. Similarly, UTCI differences ranging from 8.4 to 10.5 °C were obtained at the pedestrian square (1.60 ha) in Ostrava. These findings are consistent with the maximum UTCI differences of up to 7.70 °C obtained on green spaces at LGS in Belgrade. Furthermore, the research conducted by Kántor et al. [1] on thermal comfort in a square (0.75 ha) in Szeged (Hungary) indicated that PET was 9.3 °C lower than the sunlit location, aligning with the maximum PET differences of up to 6.70 °C obtained at MGS in Belgrade.
The dominant category of LGS is parks, with four out of five types of green spaces belonging to this category (80%), whereas one is a tree-lined street (20%). In the case of MGS, the distribution was different: out of the seven types of green spaces, four were tree-lined streets (57.14%), two were parks (28.57%), and one was a square (14.29%). SGS shows a significant variation as well, with five out of six types classified as squares (83.33%) and one as a tree-lined street (16.67%).
These data indicate that the dominant types of green space in LGS are parks, streets in MGS, and squares in SGS. Consequently, it can be concluded that parks have the greatest impact on modifying the differences in PET and UTCI, followed by streets and squares. This observation aligns with research by Cohen et al. [34], which found that the lowest PET values were recorded in parks (26.4 °C–27.9 °C), followed by squares (37.9 °C–40.7 °C) and streets (41.6 °C–44.8 °C). The PET difference between parks and squares ranged from 11.5 °C to 12.8 °C, while the difference between parks and streets was between 15.2 °C and 16.9 °C, with the difference between squares and streets being significantly lower at 3.7 °C to 4.1 °C.
Other studies on the impact of smaller green spaces on the provision of thermal comfort in urban open spaces have reported similar results. A study by Müller et al. [35], conducted in various open spaces in Oberhausen (Germany), utilised simulations of different scenarios in a three-dimensional numerical microclimate model (ENVI-met) and showed that even smaller areas can improve thermal comfort and reduce PET, thus creating a noticeable difference compared to the surrounding environment. Research results from Oberhausen indicate that only areas of 1 ha have a significant impact on the built environment, which aligns with the results of studies conducted in Belgrade.

4.3. Implications of the Results

The results of this study on the impact of smaller green spaces on thermal comfort in urban open spaces in Belgrade align with the findings of other authors [45,46,47], who similarly emphasised that the influence of green spaces on the microclimate is dependent on the size of the green spaces, and that the impact on the microclimate increases as the size of the green space increases. This is further corroborated by the results of a study conducted in Leipzig, Germany, which suggests that while the individual impact of small green spaces on the microclimate may not be considerable, such an effect is nonetheless present, and their cumulative impact can surpass the effect of larger green spaces on the microclimate in urban landscapes [48]. Therefore, owing to the deficiency of green spaces in the city, this should be taken into account in the planning and management of urban landscapes.
An appropriate microclimatic design for urban green spaces, including those of smaller sizes (<3 ha), can help mitigate the effects of urban overheating and create an environment that is thermally comfortable for urban living. Given the increasing frequency and intensity of heat events in cities (such as rising air temperatures, an increase in the number of tropical days in many cities worldwide, and the urban heat island effect), it is evident that precise microclimatic analysis will become an essential component of strategies for planning and managing green spaces (and other urban open spaces), serving as a tool for mitigating climate change, particularly in urban environments.

5. Conclusions

5.1. In-Depth Analysis and Interpretation of Results

Research on the impact of smaller green spaces (<3 ha) on the thermal comfort of urban open spaces in Belgrade has confirmed significant differences in the values of the thermal comfort indices PET and UTCI in relation to the area of green spaces. Based on these findings, the following conclusions were drawn:
The greatest impact on the thermal comfort of urban open spaces, in terms of the PET index, has green spaces ranging from 0.9 to 3 ha (mean difference in index values is 4.04 °C). The impact is significantly lower in areas of 0.3–0.9 ha (2.32 °C) and minimal in areas <0.3 ha (2.19 °C).
The range of thermal comfort index (PET) values for the studied green spaces of 0.9–3 ha spans from 0.1 to 10.90 °C; for areas of 0.3–0.9 ha, from 0 to 6.70 °C; and for those smaller than 0.3 ha, from 0 to 5.40 °C.
The greatest impact on the thermal comfort of urban open spaces, in terms of the UTCI, has green spaces ranging from 0.9 to 3 ha (mean difference in UTCI values is 3.27 °C), slightly smaller in areas smaller than 0.3 ha (2.12 °C), and the smallest in areas ranging from 0.3 to 0.9 ha (2.05 °C).
The differences in the PET index values for the examined areas of green spaces are statistically significant at the p < 0.01 level. A statistically significant difference in the PET index was observed between green spaces of 0.9–3 ha and those <0.3 ha, as well and green spaces between 0.9–3 ha and 0.3–0.9 ha. However, although differences were found in green spaces between <0.3 ha and 0.3–0.9 ha, they were not statistically significant.
A statistically significant difference in the UTCI was confirmed in green spaces between 0.9–3 ha and those smaller than 0.3 ha, as well as in green spaces between 0.9–3 ha and those ranging from 0.3 to 0.9 ha. However, while differences exist between green spaces <0.3 ha and those ranging from 0.3 to 0.9 ha, these are not statistically significant.

5.2. Summary of This Study’s Key Findings, Implications, and Contributions

It was established that, in all cases, the values of the PET index and UTCI were higher in green spaces than in areas without greenery. The difference ranged from 2.19 to 4.04 °C for the PET index and from 2.05 to 3.27 °C for the UTCI. In other words, the studied green spaces <3 ha increased the PET index by an average of 2.75 °C and the UTCI by an average of 2.41 °C. The hypothesis that smaller green spaces can have a significant impact on the formation of thermal comfort in open urban areas was confirmed. Green spaces of 0.9–3 ha have a more pronounced impact on the provision of thermal comfort in urban open spaces compared with areas of 0.3–0.9 ha and those <0.3 ha.
Therefore, it is necessary for cities to incorporate the consideration of existing and planning of new smaller green spaces (<3 ha) into their future development strategies and planning documents, with a focus on their spatial distribution. This will ensure a higher level of connectivity and thus improve their ability to mitigate the effects of urban heat islands, as these areas directly influence the provision of thermal comfort in urban open spaces. Furthermore, through the implementation of planning and legal frameworks, it is necessary to ensure the protection of existing green spaces (<3 ha), particularly those located in the most densely populated urban centres.

Author Contributions

Conceptualization, S.K., N.S., M.V. and N.V.; Data curation, S.K.; Investigation, S.K.; Methodology, S.K., N.S. and M.V.; Resources, S.K.; Supervision, N.S. and M.V.; Validation, D.V.; Visualisation, I.B.; Writing—original draft, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are available upon request.

Acknowledgments

This research was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Contracts No. 451-03-65/2024-03/200169 (5 February 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kántor, N.; Kovács, A.; Takács, Á. Small-scale human-biometeorological impacts of shading by a large tree. Open Geosci. 2016, 8, 231–245. [Google Scholar] [CrossRef]
  2. Klok, L.; Rood, N.; Kluck, J.; Kleerekoper, L. Assessment of thermally comfortable urban spaces in Amsterdam during hot summer days. Int. J. Biometeorol. 2019, 63, 129–141. [Google Scholar] [CrossRef] [PubMed]
  3. Klemm, W.; Heusinkveld, B.G.; Lenzholzer, S.; Van Hove, B. Street greenery and its physical and psychological impact on thermal comfort. Landsc. Urban Plan. 2015, 138, 87–98. [Google Scholar] [CrossRef]
  4. Chen, Y.; Yue, W.; La Rosa, D. Which communities have better accessibility to green space? An investigation into environmental inequality using big data. Landsc. Urban Plan. 2020, 204, 103919. [Google Scholar] [CrossRef]
  5. Hitchmough, J.D. Urban Landscape Management; Inkata Press: Sydney, Australia, 1994; p. 594. [Google Scholar]
  6. Randrup, T.B.; Jansson, M. Introduction: Urban open space governance and management–the long-term perspective. In Urban Open Space Governance and Management, 1st ed.; Jansson, M., Randrup, T.B., Eds.; Routledge, Taylor & Francis Group: London, UK, 2020; pp. 2–10. [Google Scholar]
  7. M’Ikiugu, M.M.; Kinoshita, I.; Tashiro, Y. Urban Green Space Analysis and Identification of its Potential Expansion Areas. Procedia-Soc. Behav. Sci. 2012, 35, 449–458. [Google Scholar] [CrossRef]
  8. 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]
  9. Nikolopolou, M.; Lykoudis, S. Thermal comfort in outdoor urban space: Analysis across different European countries. Build. Environ. 2006, 41, 1455–1470. [Google Scholar] [CrossRef]
  10. Błażejczyk, K.; Broede, P.; Fiala, D.; Havenith, G.; Holmér, I.; Jendritzky, G.; Kampmann, B.; Kunert, A. Principles of the New Universal Thermal Climate Index (UTCI) and its Application to Bioclimatic Research in European Scale. Misc. Geogr. 2010, 14, 91–102. [Google Scholar] [CrossRef]
  11. Blazejczyk, K.; Epstein, Y.; Jendritzky, G.; Staiger, H.; Tinz, B. Comparison of UTCI to selected thermal indices. Int. J. Biometeorol. 2012, 56, 515–535. [Google Scholar] [CrossRef]
  12. Błażejczyk, K.; Jendritzky, G.; Bröde, P.; Fiala, D.; Havenith, G.; Epstein, Y.; Psikuta, A.; Kampmann, B. An introduction to the Universal Thermal Climate Index (UTCI). Geogr. Pol. 2013, 86, 5–10. [Google Scholar] [CrossRef]
  13. Matzarakis, A.; Muthers, S.; Rutz, F. Application and comparison of UTCI and PET in temperate climate conditions. Finisterra 2014, 49, 21–31. [Google Scholar] [CrossRef]
  14. Lai, D.; Lian, Z.; Liu, W.; Guo, C.; Liu, W.; Liu, K.; Chen, Q. A comprehensive review of thermal comfort studies in urban open spaces. Sci. Total Environ. 2020, 742, 140092. [Google Scholar] [CrossRef]
  15. Lukić, M.; Filipović, D.; Pecelj, M.; Crnogorac, L.; Lukić, B.; Divjak, L.; Lukić, A.; Vučićević, A. Assessment of Outdoor Thermal Comfort in Serbia’s Urban Environments during Different Seasons. Atmosphere 2021, 12, 1984. [Google Scholar] [CrossRef]
  16. 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] [PubMed]
  17. Zhang, L.; Wei, D.; Hou, Y.; Du, J.; Liu, Z.; Zhang, G.; Shi, L. Outdoor Thermal Comfort of Urban Park—A Case Study. Sustainability 2020, 12, 1961. [Google Scholar] [CrossRef]
  18. Matzarakis, A.; Rutz, F.; Mayer, H. Modelling Radiation fluxes in simple and complex environments—Application of the RayMan model. Int. J. Biometeorol. 2007, 51, 323–334. [Google Scholar] [CrossRef]
  19. Fröhlich, D.; Gangwisch, M.; Matzarakis, A. Effect of radiation and wind on thermal comfort in urban environments—Application of the RayMan and SkyHelios model. Urban Clim. 2019, 27, 1–7. [Google Scholar] [CrossRef]
  20. Honjo, T. Thermal Comfort in Outdoor Environment. Glob. Environ. Res. 2009, 13, 43–47. [Google Scholar]
  21. Ghani, S.; Mahgoub, A.O.; Bakochristou, F.; ElBialy, E.A. Assessment of thermal comfort indices in an open air-conditioned stadium in hot and arid environment. J. Build. Eng. 2021, 40, 102378. [Google Scholar] [CrossRef]
  22. Fröhlich, D.; Matzarakis, A.; RayMan Pro—A Tool for Applied Climatology. Modelling of Mean Radiant Temperature and Thermal Indices, RayMan Manual Version 0.1. 29 August 2018. Available online: https://www.urbanclimate.net/rayman/RayManManual.pdf (accessed on 17 July 2022).
  23. Li, Y.; Song, Y. Optimization of Vegetation Arrangement to Improve Microclimate and Thermal Comfort in an Urban Park. Int. Rev. Spat. Plan. Sustain. Dev. 2018, 7, 18–30. [Google Scholar] [CrossRef]
  24. Aram, F.; Solgi, E.; Garcia, E.H.; Mosavi, A. Urban heat resilience at the time of global warming: Evaluating the impact of the urban parks on outdoor thermal comfort. Environ. Sci. Eur. 2020, 32, 117. [Google Scholar] [CrossRef]
  25. Wei, D.; Yang, L.; Bao, Z.; Lu, Y.; Yang, H. Variations in outdoor thermal comfort in an urban park in the hot-summer and cold-winter region of China. Sustain. Cities Soc. 2022, 77, 103535. [Google Scholar] [CrossRef]
  26. Aram, F.; Solgi, E.; Higueras García, E.; Mosavi, A.; Várkonyi-Kóczy, A.R. The cooling effect of large-scale urban parks on surrounding area thermal comfort. Energies 2019, 12, 3904. [Google Scholar] [CrossRef]
  27. Chang, C.R.; Li, M.H.; Chang, S.D. A preliminary study on the local cool-island intensity of Taipei city parks. Landsc. Urban Plan. 2007, 80, 386–395. [Google Scholar] [CrossRef]
  28. Sun, F.; Zhang, J.; Yang, R.; Liu, S.; Ma, J.; Lin, X.; Su, D.; Liu, K.; Cui, J. Study on Microclimate and Thermal Comfort in Small Urban Green Spaces in Tokyo, Japan—A Case Study of Chuo Ward. Sustainability 2023, 15, 16555. [Google Scholar] [CrossRef]
  29. Armson, D.; Stringer, P.; Ennos, A.R. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 2012, 11, 245–255. [Google Scholar] [CrossRef]
  30. Milovanović, B.; Ducić, V.; Radovanović, M.; Milivojević, M. Climate regionalization of Serbia according to Köppen climate classification. J. Geogr. Inst. “Jovan Cvijić” SASA 2017, 67, 103–114. [Google Scholar] [CrossRef]
  31. Official Gazette of the City of Belgrade. Regional Spatial Plan of the Administrative Area of the City of Belgrade; Official Gazette of the City of Belgrade: Belgrade, Serbia, 2011; p. 38. [Google Scholar]
  32. City of Belgrade. Official Website of the City of Belgrade. Available online: https://www.beograd.rs/en/ (accessed on 3 May 2020).
  33. Republic Institute for Statistics. Regions in the Republic of Serbia 2022; Republic Institute for Statistics: Belgrade, Serbia, 2023. [Google Scholar]
  34. Cohen, P.; Potchter, O.; Matzarakis, A. Daily and seasonal climatic conditions of green urban open spaces in the Mediterranean climate and their impact on human comfort. Build. Environ. 2012, 51, 285–295. [Google Scholar] [CrossRef]
  35. Müller, N.; Kuttler, W.; Barlag, A.B. Counteracting urban climate change: Adaptation measures and their effect on thermal comfort. Theor. Appl. Climatol. 2014, 115, 243–257. [Google Scholar] [CrossRef]
  36. General Regulation Plan of the Green Areas System of Belgrade. 2019. Available online: https://www.urbel.com/planovi/1547/PGRSZP_plan_namena_LIST_03-pregledna.pdf (accessed on 11 March 2020).
  37. 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]
  38. Fiala, D.; Havenith, G.; Bröde, P.; Kampmann, B.; Jendritzky, G. UTCI-Fiala multi-node model of human heat transfer and temperature regulation. Int. J. Biometeorol. 2011, 56, 429–441. [Google Scholar] [CrossRef] [PubMed]
  39. Pecelj, M.; Matzarakis, A.; Vujadinović, M.; Radovanović, M.; Vagić, N.; Đurić, D.; Cvetković, M. Temporal Analysis of Urban-Suburban PET, mPET and UTCI Indices in Belgrade (Serbia). Atmosphere 2021, 12, 916. [Google Scholar] [CrossRef]
  40. Gillner, S.; Vogt, J.; Tharang, A.; Dettmann, S.; Roloff, A. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. Landsc. Urban Plan. 2015, 143, 33–42. [Google Scholar] [CrossRef]
  41. Chen, Y.C.; Matzarakis, A. Modification of physiologically equivalent temperature. J. Heat Isl. Inst. Int. 2014, 9–2, 26–32. [Google Scholar]
  42. Republic Hydrometeorological Institute. Guidelines for Measurement and Observations at a Standard Climatological Station; Republic Hydrometeorological Institute: Belgrade, Serbia, 2020. [Google Scholar]
  43. Nanda, A.; Mohapatra, B.B.; Mahapatra, A.P.K.; Mahapatra, A.P.K.; Mahapatra, A.P.K. Multiple comparison test by Tukey’s honestly significant difference (HSD): Do the confident level control type I error. Int. J. Stat. Appl. Math. 2021, 6, 59–65. [Google Scholar] [CrossRef]
  44. Lehnert, M.; Tokar, V.; Jurek, M.; Geletič, J. Summer thermal comfort in Czech cities: Measured effects of blue and green features in city centres. Int. J. Biometeorol. 2021, 65, 1277–1289. [Google Scholar] [CrossRef]
  45. Lin, B.S.; Lin, C.T. Preliminary study of the influence of the spatial arrangement of urban parks on local temperature reduction. Urban For. Urban Green. 2016, 20, 348–357. [Google Scholar] [CrossRef]
  46. Vaz Monteiro, M.; Doick, K.J.; Handley, P.; Peace, A. The impact of greenspace size on the extent of local nocturnal air temperature cooling in London. Urban For. Urban Green. 2016, 16, 160–169. [Google Scholar] [CrossRef]
  47. Park, J.; Kim, J.H.; Lee, D.K.; Park, C.Y.; Jeong, S.G. The influence of small green space type and structure at the street level on urban heat island mitigation. Urban For. Urban Green. 2017, 21, 203–212. [Google Scholar] [CrossRef]
  48. Jaganmohan, M.; Knapp, S.; Buchmann, C.M.; Schwarz, N. The Bigger, the Better? The Influence of Urban Green Space Design on Cooling Effects for Residential Areas. J. Environ. Qual. 2016, 45, 134–145. [Google Scholar] [CrossRef]
Forests 16 00321 g002
Figure 2. Graphical review of the differences in the PET thermal comfort index for the analysed areas of green spaces.
Figure 2. Graphical review of the differences in the PET thermal comfort index for the analysed areas of green spaces.
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Forests 16 00321 g003
Figure 3. Graphical review of the differences in the UTCI for the analysed areas of green spaces.
Figure 3. Graphical review of the differences in the UTCI for the analysed areas of green spaces.
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Table 1. Characteristics of the studied green spaces (maps modified from GIS Beoland).
Table 1. Characteristics of the studied green spaces (maps modified from GIS Beoland).
Pionirski Park (P-01)Akademski Park (P-02)Pančićev Park (P-03)Park in Blok 22 (P-04)Park of Republic of Argentine (P-05)Park in Blok 63 (P-06)
Forests 16 00321 i001
A: 44°48′42.0″ N 20°27′52.3″ E		Forests 16 00321 i002
A: 44°49′12.8″ N 20°27′29.9″ E		Forests 16 00321 i003
A: 44°49′29.7″ N 20°27′48.8″ E		Forests 16 00321 i004
A: 44°48′41.0″ N 20°25′36.6″ E		Forests 16 00321 i005
A: 44°48′09.2″ N 20°22′30.1″ E		Forests 16 00321 i006
A: 44°48′23.4″ N 20°22′52.9″ E
B: 44°48′36.0″ N 20°27′52.6″ EB: 44°49′08.5″ N 20°27′29.6″ EB: 44°49′29.9″ N 20°27′50.6″ EB: 44°48′40.1″ N 20°25′32.9″ EB: 44°48′10.2″ N 20°22′27.4″ EB: 44°48′21.4″ N 20°22′54.4″ E
Area: 2.43 ha (LGS)Area: 1.46 ha (LGS)Area: 1.03 ha (LGS)Area: 0.70 ha (MGS)Area: 0.58 ha (MGS)Area: 1.02 ha (LGS)
Square Dom Vojske (S-07)Square Kopitareva gradina (S-08)Square Gundulićev Venac (S-09)Square on Corner of St. Bulevar Mihajla Pupina and John Kennedy (S-10)Square on Corner of St. John Kennedy and Louis Adamic—South (S-11)Square on Corner of St. John Kennedy and Louis Adamic—North (S-12)
Forests 16 00321 i007Forests 16 00321 i008Forests 16 00321 i009Forests 16 00321 i010Forests 16 00321 i011Forests 16 00321 i012
A: 44°49′00.6″ N 20°27′41.1″ EA: 44°48′57.2″ N 20°28′07.7″ EA: 44°49′11.8″ N 20°28′10.5″ EA: 44°49′54.2″ N 20°24′37.1″ EA: 44°49′58.4″ N 20°24′18.8″ EA: 44°49′58.4″ N 20°24′18.8″ E
B: 44°49′00.9″ N 20°27′42.1″ EB: 44°48′57.7″ N 20°28′06.6″ EB: 44°49′11.5″ N 20°28′11.1″ EB: 44°49′56.0″ N 20°24′35.6″ EB: 44°49′57.7″ N 20°24′19.9″ EB: 44°50′01.0″ N 20°24′20.0″ E
Area: 0.10 ha (SGS)Area: 0.05 ha (SGS)Area: 0.05 ha (SGS)Area: 0.26 ha (SGS)Area: 0.34 ha (MGS)Area: 0.20 ha (SGS)
Street Tree Lines on Cara Dušana (STL-13)Street Tree Lines on George Washington (STL-14)Street Tree Lines on Vanizelos St. (STL-15)Street Tree Lines on St. Antifascist Struggle (STL-16)Street Tree Lines on Gandhi St. (STL-17)Street Tree Lines on Nehru St. (STL-18)
Forests 16 00321 i013Forests 16 00321 i014Forests 16 00321 i015Forests 16 00321 i016Forests 16 00321 i017Forests 16 00321 i018
A: 44°49′25.0″ N 20°27′36.3″ EA: 44°48′59.5″ N 20°28′07.0″ EA: 44°49′12.7″ N 20°28′12.9″ EA: 44°48′48.2″ N 20°25′26.7″ EA: 44°48′28.1″ N 20°23′06.3″ EA: 44°48′18.7″ N 20°22′41.3″ E
B1: 44°49′26.3″ N 20°27′31.4″ EB1: 44°48′56.4″ N 20°28′14.1″ EB1: 44°49′11.6″ N 20°28′16.3″ EB1: 44°48′44.2″ N 20°25′24.8″ EB1: 44°48′25.4″ N 20°23′06.5″ EB1: 44°48′27.4″ N 20°22′36.2″ E
B2: 44°49′25.6″ N 20°27′33.0″ EB2: 44°48′58.0″ N 20°28′12.0″ EB2: 44°49′11.8″ N 20°28′14.6″ EB2: 44°48′45.1″ N 20°25′25.6″ EB2: 44°48′25.8″ N 20°23′06.1″ EB2: 44°48′23.4″ N 20°22′39.3″ E
B3: 44°49′24.6″ N 20°27′34.8″ EB3: 44°49′00.2″ N 20°28′09.2″ EB3: 44°49′12.0″ N 20°28′13.1″ EB3: 44°48′46.5″ N 20°25′26.9″ EB3: 44°48′26.6″ N 20°23′05.5″ EB3: 44°48′20.0″ N 20°22′42.1″ E
B4: 44°49′25.0″ N 20°27′35.6″ EB4: 44°48′59.8″ N 20°28′08.3″ EB4: 44°49′11.6″ N 20°28′13.2″ EB4: 44°48′47.4″ N 20°25′26.2″ EB4: 44°48′27.3″ N 20°23′06.3″ EB4: 44°48′20.7″ N 20°22′40.4″ E
B5: 44°49′26.1″ N 20°27′33.6″ EB5: 44°48′57.5″ N 20°28′11.4″ EB5: 44°49′11.5″ N 20°28′14.6″ EB5: 44°48′45.3″ N 20°25′24.3″ EB5: 44°48′26.5″ N 20°23′07.0″ EB5: 44°48′23.3″ N 20°22′38.3″ E
B6: 44°49′27.0″ N 20°27′31.9″ EB6: 44°48′56.0″ N 20°28′13.3″ EB6: 44°49′11.3″ N 20°28′15.9″ EB6: 44°48′43.8″ N 20°25′23.0″ EB6: 44°48′25.9″ N 20°23′07.5″ EB6: 44°48′27.1″ N 20°22′35.2″ E
Area: 0.33 ha (MGS)Area: 0.51 ha (MGS)Area: 0.15 ha (SGS)Area: 0.65 ha (MGS)Area: 0.35 ha (MGS)Area: 0.99 ha (LGS)
Table 2. Descriptive statistics of the differences in mean values of calculated thermal comfort indices for the analysed area of green spaces.
Table 2. Descriptive statistics of the differences in mean values of calculated thermal comfort indices for the analysed area of green spaces.
The Difference in the Thermal Comfort Index *Area of Green SpacesNMeanStd. DeviationStd. Error95% Confidence Interval for Mean
Lower Bound Upper Bound		Min.Max.
PET (°C)SGS722.18751.486740.175211.83812.53690.005.40
MGS842.31551.533130.167281.98282.64820.006.70
LGS604.04002.743760.354223.33124.74880.1010.90
Σ2162.75192.085030.141872.47223.03150.0010.90
UTCI (°C)SGS722.12081.323350.155961.80992.43180.005.20
MGS842.04881.137270.124091.80202.29560.004.40
LGS603.26501.973800.254822.75513.77490.107.70
Σ2162.41061.558750.106062.20162.61970.007.70
* The difference in the obtained values of the thermal comfort index (PET and UTCI) between the values obtained on the green spaces (in the shade) and the one outside it, i.e., in the built-up area (in the sun).
Table 3. Robust tests of equality of means (Welch and Brown–Forsythe) for the mean values of the investigated thermal comfort index differences for the analysed area of green spaces.
Table 3. Robust tests of equality of means (Welch and Brown–Forsythe) for the mean values of the investigated thermal comfort index differences for the analysed area of green spaces.
The Difference in the Thermal Comfort Index *Statistic adf1df2Sig.
PET (°C)Welch11.4552123.3430.000
Brown-Forsythe16.5912125.2030.000
UTCI (°C)Welch9.5082122.9160.000
Brown-Forsythe12.7512141.4970.000
a Asymptotically F distributed. * The difference in the obtained values of the thermal comfort index (PET and UTCI) between the values obtained on the green spaces (in the shade) and the one outside it, i.e., in the built-up area (in the sun).
Table 4. Statistical parameters of the Tukey test for the impact of green space areas on the investigated differences in thermal comfort index values.
Table 4. Statistical parameters of the Tukey test for the impact of green space areas on the investigated differences in thermal comfort index values.
The Difference in the Thermal COMFORT Index *(I)
Area of Green Spaces		(J)
Area of Green Spaces		Mean
Difference
(I-J)		Std. ErrorSig.95% Confidence Interval
Lower Bound Upper Bound
PET (°C)SGSMGS−0.127980.310510.911−0.86080.6049
LGS−1.85250 *0.337960.000−2.6502−1.0548
MGSSGS0.127980.310510.911−0.60490.8608
LGS−1.72452 *0.326800.000−2.4958−0.9532
LGSSGS1.85250 *0.337960.0001.05482.6502
MGS1.72452 *0.326800.0000.95322.4958
UTCI (°C)SGSMGS0.072020.236410.950−0.48600.6300
LGS−1.14417 *0.257310.000−1.7515−0.5369
MGSSGS−0.072020.236410.950−0.63000.4860
LGS−1.21619 *0.248820.000−1.8035−0.6289
LGSSGS1.14417 *0.257310.0000.53691.7515
MGS1.21619 *0.248820.0000.62891.8035
* The difference in the obtained values of the thermal comfort index (PET and UTCI) between the values obtained on the green spaces (in the shade) and the one outside it, i.e., in the built-up area (in the sun).
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Kecman, S.; Stojanović, N.; Vukmirović, M.; Vasiljević, N.; Bjedov, I.; Vujović, D. The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia). Forests 2025, 16, 321. https://doi.org/10.3390/f16020321

AMA Style

Kecman S, Stojanović N, Vukmirović M, Vasiljević N, Bjedov I, Vujović D. The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia). Forests. 2025; 16(2):321. https://doi.org/10.3390/f16020321

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Kecman, Snežana, Nadežda Stojanović, Milena Vukmirović, Nevena Vasiljević, Ivana Bjedov, and Dragana Vujović. 2025. "The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia)" Forests 16, no. 2: 321. https://doi.org/10.3390/f16020321

APA Style

Kecman, S., Stojanović, N., Vukmirović, M., Vasiljević, N., Bjedov, I., & Vujović, D. (2025). The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia). Forests, 16(2), 321. https://doi.org/10.3390/f16020321

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