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Urban heat resilience at the time of global warming: evaluating the impact of the urban parks on outdoor thermal comfort

Abstract

Background

In densely populated urban centers, increased air temperature due to urban heat island (UHI) effect can undermine the thermal comfort and health of citizens. Research has shown that large urban parks can mitigate the effect of UHIs and improve thermal comfort, especially in the warmer months of the year when temperature changes are more noticeable. This study investigated the cooling effect intensity (CEI) of the Retiro Park in the center of Madrid at three different distances from its southern edge and the impact of this cooling effect on thermal comfort from physiological and psychological perspectives. This investigation was performed by measuring microclimate data and conducting a survey simultaneously during the summer days.

Results

The results showed that the CEI of the park varies with distance from its edge. Because of this effect, air temperature within the 130 m and 280 m distance of the park was, respectively, 1.6 °C and 0.9 °C lower than the temperature at the 520 m distance (the nearest heat island). After examining the effect of the park in terms of physiological equivalent temperature (PET), it was found that the PET at the 130 m and 280 m distance of the park was 9.3% and 5.4% less than the PET in the heat island domain. More than 81% of the respondents (in all three areas) had a mental image of the park as the place where they would experience the highest level of outdoor thermal comfort, and this rate was higher in the areas closer to the park. The analysis of citizens’ responses about perceived thermal comfort (PTC) showed that citizens in areas with higher CEI had perceived a higher degree of thermal comfort from the psychological perspective.

Conclusion

This study demonstrates the significant role of large urban parks located in the core of the populated cities in providing thermal comfort for citizens from both physiological and psychological perspectives. Additionally, the results of this study demonstrated that among the environmental (natural and artificial) factors around the park (topography, urban structure, etc.), the aspect ratio has the greatest impact on thermal comfort.

Background

Climate change has had visible effects on many human communities all over the world, but these effects are more deeply felt in large, densely populated cities [19, 35]. Research has shown that urban areas are typically warmer than their rural counterparts; a difference that can be attributed to the phenomenon known as urban heat island effect (UHI) [67, 83, 90]. This effect is strong enough to cause major physical and mental health problems for citizens [16, 52, 55, 85]. Urban green spaces not only beautify the urban landscape but also moderate urban climate by increasing humidity and lowering air temperature [18, 97]. Over the years, the value of parks and green spaces for cities and their benefits for the health and welfare of citizens has been discussed and proven in many studies [15, 34, 79, 86]. In general, green spaces are an essential part of the urban landscape, as they are crucial for the stability and sustainability of cities [7, 9,10,11, 14, 76]. Hence, designing and building green spaces should be a key part of any urban plan that aims to create sustainable cities with healthy communities [19, 51, 75, 78]. Urban green spaces are known to have a cooling effect on air temperature [24, 25]. Typically, this effect can be felt not only in the park itself but also within a radius around the park, leading to improved thermal comfort in the neighborhood [31, 92, 95].

Thermal comfort has received much attention because of its great impact on the quality of life of citizens [21, 50]. According to CIBSE, human thermal comfort refers to the temperature conditions that satisfy at least 80% of individuals [22]. Human thermal comfort depends on several factors including climate, air temperature, humidity, sunlight, and air movements [74]. Ultimately, people’s perception of environmental conditions also depends on non-climatic factors such as clothing type, coping ability, age, gender, physical appearance, subcutaneous fat, fitness, diet, and skin color [23, 87].

Serious research on the subject of thermal comfort began in 1956 with the study of conditions of human thermal comfort in indoor spaces and later expanded to outdoors [68]. What makes the assessment of outdoor thermal comfort different from the analysis of indoor thermal comfort, besides the scale of work, is the massive difference between the thermal needs of different individuals, which vary with the region and study conditions [54, 64]. Following the manifestation of the consequences of climate change, the subject of thermal comfort in outdoors has attracted the attention of many researchers, especially those working in the fields of climate, urban development, and environmental research [21, 37, 50]. Over the years, researchers have introduced various indexes for measuring outdoor thermal comfort, which include Predicted Mean Vote (PMV) [26], Standard Effective Temperature (SET*) [29], which was later developed into OUT-SET* [70], Man-Environment heat Exchange (MENEX) [17], Physiological Equivalent Temperature (PET) [33], Comfort Formula (COMFA) [39], and Universal Thermal Climate Index (UTCI) [38] among others.

To analyze thermal comfort in urban environments, the measure of analysis should take into account the local climatic conditions and the four main climatic factors that affect human thermal comfort, i.e. air temperature, humidity, wind speed, and solar radiation [49, 61, 77]. Among the existing thermal comfort indexes, the PET, which has been derived from the human energy balance, not only meets the aforementioned requirement but also takes the features of human physiology into account [48, 57, 58]. Highly suitable for urban and environmental studies, PET is and is one of the four most widely used indexes in outdoor thermal comfort research (PET, PMV, UTCI, SET*). PET has also been used in a wider range of climatic regions than other indexes [71].

ASHRAE defines thermal comfort not solely as an environmental and physiological phenomenon but also as a condition of mind that expresses satisfaction with the thermal environment [6, 12]. In this definition, the term “condition of mind” refers to the fusion of mental and physiological conditions in the concept of thermal comfort [5]. Numerous studies have shown that different people have vastly different perceptions of thermal comfort and temperature preferences and also differ in their behavioral and physiological coping and their psychological habits or their expectations from the climate [45, 53, 65, 73]. These differences make it difficult to study people’s perceptions of the thermal comfort of their environment [62]. However, using methods like surveying to ask people how they feel about temperature and thermal conditions can provide a broad picture of people’s perceived thermal comfort and satisfaction with the thermal conditions of their environment [30, 42].

According to studies conducted on the subject of thermal comfort in open spaces, another factor that can significantly influence the results of such studies is people’s cognition and mental images of their environment [44, 63]. Since people’s mental conditions play a key role in their cognition of the environment [56], these conditions can massive impacts on their thermal perception of the environment and consequently their comfort conditions [3, 46, 80]. Indeed, the response of a human to an external stimulus depends on the “information” that the individual has in and about that particular situation [60]. People’s cognition and perception of their environment can be studied by the use of environmental psychology methods and instruments. One of the most common of these instruments is cognitive maps [32, 41, 89]. These maps can represent people’s knowledge of places and their importance in people’s minds, and can, therefore, be used in outdoor comfort studies to gain an insight into how important people believe a place is in terms of contribution to thermal comfort [7, 9,10,11]. In other words, these maps can help us examine the citizens’ perception of places based on their role in creating thermal comfort [43].

Given the role of urban green spaces in mitigating urban heat and creating thermal comfort in built-up spaces, they have a fundamental impact on the quality of life of citizens and sustainable development of urban environments. Previous research on the role of the central park in Mediterranean climates and the city of Madrid has conducted two studies regarding the cooling effect of parks on thermal comfort [7,8,9,10,11]. In one study, the northern part of the park with structured urban configuration was examined in the morning [7, 9,10,11], while in the other study, the western part of the park, located in the historical area (organic urban configuration) was examined at noon [8]. These investigations do not discuss the role of elements that can affect the intensity and distance of the cooling effect and consequently the thermal comfort. Therefore, the present study, in the continuation of two other studies, not only examines the area located on the southern side of the park, but also discuss the role of effective environmental (natural and artificial) factors (such as wind pressure, topography, urban texture, and ratio) to cover to the gap in previous studies. In this study, considering the influential factors outside of the park, we examined the cooling effect of a large central park at different distances and the consequent impact on thermal comfort from physiological and psychological perspectives.

Methods

The measuring sites

This study was performed in Madrid, Spain (40°25′08″N; 3°41′31″W), which according to Köppen–Geiger classification, has a “Hot-summer Mediterranean” climate [47]. The case chosen for examining the effect of large urban green spaces on thermal comfort was the Retiro Park, which, with an area of about 125 ha, is one of the largest parks in the center of Madrid. The area of interest was in the southern side of this park, where according to the Madrid’s UHI maps, there is a heat island at the 520 m distance from the edge of the park [66].

According to the UHI of Madrid [66], there is a heat island near the Granada-Narciso Serra district located 520 m away from the southern edge of the park (marked red in Fig. 1) [72]. For a more precise examination of the cooling effect of the park, two areas at the intersection of Gutenberg-Valderribas-Fuenterrabía and Torrejón-Agustín Querol, which are located in the orange and yellow zones at the 280 m and 130 m distance from the park, were also included in the study. All selected intersections are physically and structurally identical. For easier reference, hereafter, the Torrejón-Agustín Querol intersection (150 m) is called intersection A, the Gutenberg-Valderribas-Fuenterrabía intersection (280 m) as called intersection B, and the Granada-Narciso Serra intersection (520 m) is called intersection C.

Fig. 1
figure 1

Madrid’s UHI map (July 26, 2015) and the investigated area at the southern side of the Retiro Park (green zone) [66]. Area A is located in the yellow zone, area B in the orange zone, and area C in the red zone near one of the Madrid’s UHIs

The southern side of the Retiro Park has a regular and grid-like texture mostly consisting of seven-story buildings. The façade of most buildings in this area is made of red brick. The roads of the area are asphalt and the sidewalks are mostly made of bright colored mosaic tiles (Fig. 2).

Fig. 2
figure 2

Street views of the three investigated intersections (ac), and their Sky View Factor values (calculated by RayMan 1.2)

The data collected in this study were of two types: (I) microclimate data including air temperature, humidity, and wind speed, which were measured inside the Retiro Park and at all three intersections on 6 days starting from the summer of 2018: June 22, July 10 and 24, August 10 and 24, and September 10. (II) Survey data, which were collected by a questionnaire measuring citizens’ perceived thermal comfort and also cognitive maps whereby citizens were asked to specify the places where they feel the most thermal comfort.

All data (both microclimatic and survey) were collected on clear sunny days. At each intersection, data collection was performed over a 10 min period, during which one group collected temperature data and the other group conducted the survey. Since the peak temperature inside the park in the summer of 2017 had been observed, on average, between 13:28 and 15:08 CEST [1], all stages of data collection were scheduled to start after 13:30.

Microclimate measurements

Air temperature (Ta) and relative humidity (RH) were measured by a mobile microclimate station (HOBO MX2301A Temperature/RH Data Logger, produced by Onset Computer Corporation Co., MA, USA) with an accuracy of ± 2.5% for RH and ± 0.2 °C for Ta. Wind speed (WS) was measured by a Proster digital anemometer model MS6252a. All measurement devices were installed 1.5 m above the ground. A fisheye lens (Sigma 8 mm circular) was used to assess the sky view and take fisheye photographs. Ta and RH data were automatically logged at 1-min intervals and averaged over every 10 min. Wind speed data were also recorded manually at 1-min intervals and averaged over every 10 min. It should be mentioned that the wind direction differed in measuring days (22 Jun, N; 10 Jul, NNE; 24 Jul, SW; 10 Aug, NNE; 24 Aug, N; 10 Sep, N), due to this issue assessing the role of wind direction was inconclusive. Climatic data including Ta and RH were also collected from the AEMET station (Agencia Estatal de Meteorología) located inside the Retiro Park [1].

Survey

Throughout the study, researchers surveyed a total of 133 individuals (Nnode A = 46, Nnode B = 43, Nnode C = 44), who were randomly chosen from among people either living or working in the area. Surveys were conducted during the same 10 min in which the microclimate measurements were being made (on average, seven people per intersection per day). Respondents were from both genders and different age groups (excluding children) and had different activity levels (sitting, standing, and walking). The citizens’ perceived thermal comfort was measured by a researcher-made questionnaire consisting of two sections: questions and cognitive maps.

The cognitive map section of the questionnaire was a map of the area between the southern edge of the Retiro Park and the heat islands to its south, on which citizens were asked to mark the areas where they normally feel thermal comfort. To avoid bias, this map was drawn so that the park would make up only a small portion of the map. The obtained cognitive maps were analyzed with the software AMMA [9]. In this analysis, the maps on which the park was marked were awarded a score of 100 and the maps on which there was no mention of the park were given a score of 0 (Table 1).

Table 1 The proportional percentages questionnaire data in the three investigated intersection (Nnode A = 46, Nnode B = 43, Nnode C = 44) on all the measured days

The questions section of the questionnaire itself consisted of two parts. The first part consisted of the questions enquiring directly about the perceived thermal comfort of the respondent. These questions were designed based on the five-item Likert scale with scores ranging from 1 (very low) to 5 (very high) (Table 1). The questions included in this section were: (1) How thermally comfortable do you feel (neither too hot nor too cold)? (2) How hot do you feel? (3) How much do you feel the cooling effect of the Retiro Park? (4) How heat-tolerant are you?

The responses to this part of the questionnaire were analyzed in SPSS using statistical methods such as One-Way Analysis of Variance (ANOVA) to compare the PET values of the three examined nodes on the south side of the Retiro Park.

The second part of this section was dedicated to demographic and personal information, including age, gender, type of clothing, and activities. Given the importance of information collected in this part of the questionnaire for the calculation of PET [59], for each respondent, the type of clothing was quantified based on the Clo index and the type of activity was quantified based on relevant standards [81] (Table 1).

Results

As mentioned, the data of this study were collected on 6 days, with approximately 14-day intervals, starting from the summer of 2018. During the data collection days, the min, mid, and max temperatures inside the park, which were measured by the AEMET station [1], were 32.4 °C, 26.1 °C, and 19.7 °C on average (Table 2). As expected (from Madrid's 2015 UHI), the selected intersections had different temperatures depending on their distances from the park (Table 3). The Torrejón-Agustín Querol intersection, which was closest to the park (130 m), had the closest average temperature (33.7 °C) to the average temperature inside the park (32.4 °C). In comparison, the Gutenberg-Valderribas-Fuenterrab intersection at a distance of 280 m from the park and the Granada-Narciso Serra intersection at a distance of 520 m from the park had higher average temperatures (34.4 °C and 35.3 °C) (Fig. 3).

Table 2 The individual and average values for air temperature (Ta) relative humidity (RH) and wind velocity (W) in the Retiro Park on all the measurement days
Table 3 Intersection mean values for air temperature (Ta), relative humidity (RH), mean radiant temperature (Tmrt), wind speed (WS), PET and PTC on the all measurement days (13:30–15:20 CEST)
Fig. 3
figure 3

The diagram illustrates the temperature range of the three investigated intersections A, B & C on the southern side of the Retiro Park, and the temperature range inside of the park. Gray rectangles: temperature range at three nodes (bottom side: Ta Node A, top side: Ta Node C). Linear range: the temperature range of the park, bottom line: Ta Min of the park, top line: Ta Max of the park

Considering these temperature differences, PET and PTC were also predicted to be different. After determining the PET of each respondent (based on gender, age, clothing type, and weather conditions at the time of the survey) with the software RayMan 1.2 [28], the average PET at distances of 130, 280, and 520 m from the park was calculated to 38.4 °C, 40.0 °C, and 42.3 °C, respectively. This suggests that thanks to the cooling effect of the park, citizens at the 130 m and 280 m distance from the park have, respectively, 3.9 °C PET and 2.3 °C PET less than those at the 520 m distance from the park (closer to the heat island) (Table 3).

The investigation of psychologically perceived thermal comfort, which was done with the help of questions and cognitive maps, showed that the cooling effect of the park has had a positive impact on the minds of citizens, as people living or working closer to the park were feeling more thermally comfortable than others who were farther away from the park and closer to one of the heat islands. After processing the respondent’s answers to direct questions about their perception of thermal comfort (at the moment of the survey), which were scored from 1 to 5 (very low = 1, low = 2, medium = 3, high = 4, very high = 5), the results showed that the mean score decreased from 3.16 for intersection A, which was 130 m away from the park, to 3.07 for intersection B, which was 280 m away from the park, and finally to the sub-average score of 2.88 for intersection C, which was 580 m away from the park (Table 3).

The analysis of the obtained cognitive maps with the AMMA software showed that out of 133 respondents, 108 people or about 81% marked the park. This means that the majority of citizens know Retiro Park as the place where they would feel the highest degree of thermal comfort. As expected, it was found that the people at the Andrés Torrejón-Agustín Querol intersection had the highest rate of reference to the park (91.3%). In comparison, 81.4% of people at the Gutenberg-Valderribas-Fuenterrabía intersection and 70.5% of people at the Granada-Narciso Serra intersection mentioned the park. The last figure is still quite high considering the relatively long distance of the Granada-Narciso Serra intersection from the park. The results of the AMMA analysis of the obtained maps were also plotted in the form of color spectra showing the places most frequently marked by citizens. These plots also show that on every data collection day, the Retiro Park was more frequently mentioned than any other public space in the study area (Fig. 4).

Fig. 4
figure 4

Cognitive map analyses using the Aram Mental Map Analyzer (AMMA) in the southern side of the Retiro Park during 6 summer days of 2018

Since the two main variables of this study were PET and PTC, statistical tests were used to find out whether there is any significant difference between the three nodes in terms of these indexes.

PET differences between the three nodes

One-Way Analysis of Variance (ANOVA) was used to compare the PET values of the three investigated area on the southern side of the Retiro. After this analysis, in cases where there were significant differences between means (P <  .05), Tukey’s honest significance test, which is a Post Hoc test, can be used to find the source of significant difference between response levels [13] (Table 4).

Table 4 ANOVA PET analyses in the three investigated selected points

Since the results of ANOVA showed a significant difference between the average PETs of nodes A, B, and C (P-value < .05), Tukey’s test was used to determine which of the response levels had a significant difference (Tables 5 and 6).

Table 5 Multiple Comparisons Tukey PET analyses in the three investigated intersections
Table 6 Tukey PET analyses in the three investigated intersections (A, B and C)

The above results can be interpreted as the presence of a significant difference between the average PETs of all three points, i.e. A, B and C, located respectively at the 130, 280 and 520 m distance from the park. Based on these statistical results, it can be confidently claimed that the cooling effect of the Retiro Park reduces PET and increases thermal comfort in nearby areas.

PTC differences between the three nodes

As mentioned earlier, the data collected by the questions included in the questionnaire were used to determine PTC. As with PET, ANOVA Analysis was used to compare PTC in the three nodes in the southern neighborhoods of the Retiro Park (Table 7).

Table 7 ANOVA PTC analyses in the three investigated selected points

Since the results of ANOVA showed a significant difference between PTC in different nodes (P-value <  .05), again, Tukey’s test was used to identify the source difference between the response levels. The results of this test are provided in the tables below (Table 8 and 9).

Table 8 Multiple Comparisons Tukey PTC analyses in the three investigated intersections
Table 9 Tukey PTC analyses in the three investigated intersections (A, B and C)

The above results suggest that there is a significant difference between average thermal comfort in the Andrés Torrejón-Agustín Querol intersection (intersection A), which is 130 m away from the park and the Granada-Narciso Serra intersection (intersection C), which is farther away from the park and closer to the heat island. This test proves that the cooling effect of the park is well perceived by the people living or working close to the park, causing them to have a significantly different level of PTC than those living or working further away from the park. However, the effective factors which have a significant role in the intensity and distance of park cooling effect and consequently its thermal comfort should be discussed.

Discussion

Research has shown that because of factors such as low albedo [91], extensive shading [82], and moisture generation and evaporation from leaf surfaces [88], the space within a park often has a lower ambient air temperature than the surrounding environments [69, 93]. The results of the present study showed that during the data collection days (June 22, July 10 and 24, August 10 and 24, and September 10), the average maximum temperature of the Retiro Park at noon hours (13:40–14:40) was 32.4 °C, but at the same time, the average air temperature at the distance of 520 m from the park was about 35.3 °C. Studies carried out in various parts of the world have shown that large urban parks (> 10 ha) can reduce the air temperature by an average of 1–2 °C within a radius of 350 m [7]. In the present study, it was found that the cooling effect of the Retiro Park on the neighborhoods to its south has led to a 0.9 °C reduction in air temperature within the 280 m distance and a 1.6 °C reduction in air temperature within the 130 m distance of this park (compared to air temperature in the nearest heat island), which is in areas closer to the park. At a distance of 130 m, it was 1.6. Indeed, the findings of this study confirm that the Retiro Park, like other large parks, generates a cooling effect that can reduce the temperature of its surrounding area. However, the focus of this study was the impact of this cooling effect on the thermal comfort of citizens from both physiological and psychological perspectives; a matter that was explored with the help of well-established tests and standards.

Influential factors on park cooling effect

Other studies in the northern [7, 9,10,11] and western [8] sides of Retiro Park in Madrid have shown that the park cooling effect can reduce the air temperature during the summer days at a distance further from the park (Fig. 6). That is, in the northern area, the cooling effect of the park up to a distance of 665 m can reduce the air temperature compared to the area located in UHI between 0.6 and 1.3 °C [7, 9,10,11]. This effect is also able to reduce the air temperature in the western part of the park to the range of located at 855 m (adjacent to UHI) between 2.4 and 2.8 °C [8]. Owing to these changes, it is questionable that what role environmental factors play in the distance and intensity of the cooling effect of the park in its surroundings. The environmental natural and artificial factors around the park (wind, topography, urban configuration, aspect ratio, etc.) have a significant role in park cooling effect [94, 96]. Because the prevailing wind direction was shifty during data collection, it was not considered in this study, but other important environmental factors such as urban structure and topography were discussed.

Urban configuration

The northern area (Salamanca) and the southern area (Pacífico) of Retiro Park both have a structured urban configuration. The Cortes neighborhood is located on the western side of the park situated in the old part of Madrid and has an organic structure (Fig. 5). Based on the previous research about the role of urban texture in reducing air temperatures [27, 84, 98] and the results of this study, organic urban structures due to its structural features, can improve airflow, thereby reducing air temperatures. Accordingly, it can be said that the cooling effect of the park is paramount in the western area than in other areas (855 m).

Fig. 5
figure 5

Urban configuration of the northern area, western area, and southern area of Retiro Park

Topography

Furthermore, the topographic map of Madrid showed that the two northern and western areas of Retiro Park are at a higher altitude than the southern area. This difference in height in Pacífico area (southern area) varies between 40 and 60 m compared to the western area and 55 to 70 m compared to the northern area (Fig. 6; right map). By comparing the topographic map with UHI map, the impact of height on the intensity and the distance of the park cooling effect was illustrated (Fig. 6). Being in a lower elevation area according to studies on the effect of topography on urban heat [2, 40] can make an urban area warmer. Thus, it can be said that the southern area of the park is warmer than the northern and western areas due to the difference in height (average 35 m), as a result, the distance of the cooling effect in the urban fabric is less that than the other two areas.

Fig. 6
figure 6

The topographic map (right) and UHI map of Madrid [66] (left)

Aspect ratio

However, the most important finding of this study is thermal comfort. On the northern side of Retiro Park, the PET amount of in the area where there is a park cooling effect is on average 2 °C to 2.3 °C less than the area located in UHI [7, 9,10,11]. On the western side, the rate of PET reduction where the park cooling effect exists is 3.9 °C. According to the results of this study, the cooling effect in the south of Retiro Park has resulted in PET with on average between 2.3 and 3.9 °C less than the area located on UHI. Since this difference in the southern part of the park is akin to other areas, it illustrates that the topographic factor and urban structure cannot be considered as effective variables on thermal comfort. As the southern area, in spite of the differences in height and the urban structures, in terms of perceived thermal comfort, has similar behaviour to other areas. The data collected in all three ranges were performed at the points where the aspect ratio (H/W) was most similar to each other [7,8,9,10,11]. As the buildings of all three areas had the same height system (buildings of 5–6 floors), and the dimensions of the streets in the places where the data were collected (especially the northern and southern areas) were very similar, these similarities are also reflected in the PET difference. Aspect ratio (H/W), which is an important factor in the intensity of sunlight and shading, is among influential elements on perceived thermal comfort [4, 36]. Since the nature of such studies is to ask people and receive information from them, the respondents were overshadowed at all stages of data collection. Accordingly, the effect of aspect ratio (H/W) on the thermal comfort of residents has played a more noticeable role than other factors.

The elements inside a park can also play a significant role in the CEI of that park [82]. However, considering the limitations of this study and since its main goal was to prove the cooling effect of the park on the thermal comfort of citizens from physiological and psychological perspectives, we did not investigate the impact of internal components of the park. In previous studies, the effect of the quality of vegetation, water features, and other elements within the park on thermal comfort inside the park has been explored [91], but future studies need to accurately examine the effect of these factors on thermal comfort outside the park so that the results can be used to improve the design of urban parks for stronger cooling effects and increased thermal comfort generation in the surrounding urban landscape.

These days in the field of urban planning the issue of sustainable development and the 2030 Agenda for Sustainable Development has been of paramount importance. The results of this study indicated that the development and expansion of urban green spaces can be effective in achieving the Sustainable Development Goals (SDGs). According to this study, among 17 SDGs goals, the third goal (good health and well-being), the eleventh goal (sustainable cities and communities), and the thirteenth goal (climate action) are achievable. Given the potential of urban green space cooling effects in providing thermal comfort and the importance of sustainable development, appropriate programs to develop and improve the quality and quantity of green spaces are required.

Conclusion

In the present study, it was found that the cooling effect of the Retiro Park on the neighborhoods to its south varies with the distance from the park and diminishes at the distance of 520 m, which falls in the domain of a heat island. The cooling effect intensity (CEI) of the park is higher in the areas closer to the park, causing a 1.6 °C reduction in air temperature within the 130 m distance and a 0.9 °C reduction in air temperature within the 280 m distance of the park compared to Granada-Narciso Serra intersection (520 m away from the park).

The results showed that any increase or decrease in the CEI of the Retiro Park will change the level of thermal comfort of citizens who live or work near the park from both physiological and psychological perspectives. The amount of PET within the 130 m distance of the park was 4% less than the PET at the 280 m distance and 9.3% less than the PET at the 520 m distance, which means citizens within this distance of the park enjoy higher physiological thermal comfort. Also, the PET at the 280 m distance from the park was 5.4% less than the PET at the 520 m distance.

The analysis of cognitive maps obtained from citizens to study the impact of the park’s cooling effect on their perception of thermal comfort showed that more than 81% of respondents had a mental image of the park as the place that would provide them with the highest degree of thermal comfort. This rate was above 81% and 91% in the two districts that were nearest to the park and did not fall below 70.5% in the district that was farther from the park. The total score of the questionnaire, which was used as a direct measure of PTC, showed that citizens in the area with the highest CEI had the highest perception of thermal comfort from the psychological perspective and citizens in the area with the lowest CEI had the lowest PTC.

The results of this study demonstrate the critical role of large urban parks in generating thermal comfort for citizens from both physiological and psychological perspective. Considering the ongoing and upcoming effects of climate change on the temperature of densely populated urban centers, future studies need to focus on finding practical solutions to strengthen the cooling effect of urban parks and expand their area of effect in line with the objectives of sustainable urban development.

Abbreviations

UHI:

Urban heat island

CEI:

Cooling effect intensity

PET:

Physiological equivalent temperature

PTC:

Perceived thermal comfort

References

  1. AEMET (2018) Agencia Estatal de Meteorología. https://www.aemet.es/es/portada

  2. Alcoforado MJ, Andrade H (2006) Nocturnal urban heat island in Lisbon (Portugal): main features and modelling attempts. Theor Appl Climatol 84(1–3):151–159. https://doi.org/10.1007/s00704-005-0152-1

    Article  Google Scholar 

  3. Aljawabra F, Nikolopoulou M (2010) Influence of hot arid climate on the use of outdoor urban spaces and thermal comfort: do cultural and social backgrounds matter? Intell Build Int 2(3):198–217. https://doi.org/10.3763/inbi.2010.0046

    Article  Google Scholar 

  4. Alobaydi D, Bakarman MA, Obeidat B (2016) The impact of urban form configuration on the urban heat island: the case study of Baghdad, Iraq. Procedia Eng 145:820–827. https://doi.org/10.1016/j.proeng.2016.04.107

    Article  Google Scholar 

  5. American Society of Heating R and A-C E (2017) 2017 ASHRAE handbook: fundamentals. In: ASHRAE handbook: fundamentals

  6. American Society of Heating Refrigerating and Air-Conditioning Engineers Inc (2015) 2015 ASHRAE handbook—heating, ventilating, and air-conditioning applications (SI Edition). American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. https://www.engineeringvillage.com/share/document.url?mid=kna_14693509155b12b472539b410178163171&database=kna

  7. Aram F, Higueras García E, Solgi E, Mansournia S (2019) Urban green space cooling effect in cities. In: Heliyon, vol 5, issue 4, Elsevier Ltd. https://doi.org/10.1016/j.heliyon.2019.e01339

  8. Aram F, Solgi E, Baghaee S, Higueras García E, Mosavi A, Band S (2020) How parks provide thermal comfort perception in the metropolitan cores; a case study in madrid mediterranean climatic zone, Preprints 2020, 2020080284. https://doi.org/10.20944/preprints202008.0284.v1

  9. Aram F, Solgi E, García EH, Mohammadzadeh SD, Mosavi A, Shamshirband S (2019) Design and validation of a computational program for analysing mental maps: aram mental map analyzer. Sustainability 11(14):3790. https://doi.org/10.3390/su11143790

    Article  Google Scholar 

  10. Aram F, Solgi E, García EH, Mosavi A, Várkonyi-Kóczy AR (2019) The cooling effect of large-scale urban parks on surrounding area thermal comfort. Energies 12(20):3904. https://doi.org/10.3390/en12203904

    Article  Google Scholar 

  11. Aram F, Solgi E, Holden G (2019) The role of green spaces in increasing social interactions in neighborhoods with periodic markets. Habitat Int 84:24–32. https://doi.org/10.1016/j.habitatint.2018.12.004

    Article  Google Scholar 

  12. ASHRAE (2010) ASHRAE Standard 55-2010. ASHRAE Inc., 2010, 42

  13. Barnett V, Neter J, Wasserman W (1975) Applied linear statistical models. J R Stat Soc Ser A Gen 138(2):258. https://doi.org/10.2307/2984653

    Article  Google Scholar 

  14. Belmeziti A, Cherqui F, Kaufmann B (2018) Improving the multi-functionality of urban green spaces: relations between components of green spaces and urban services. Sustain Cities Soc 43:1–10. https://doi.org/10.1016/J.SCS.2018.07.014

    Article  Google Scholar 

  15. Bertram C, Rehdanz K (2015) The role of urban green space for human well-being. Ecol Econ 120:139–152. https://doi.org/10.1016/J.ECOLECON.2015.10.013

    Article  Google Scholar 

  16. Błażejczyk A, Błażejczyk K, Baranowski J, Kuchcik M (2018) Heat stress mortality and desired adaptation responses of healthcare system in Poland. Int J Biometeorol 62(3):307–318. https://doi.org/10.1007/s00484-017-1423-0

    Article  Google Scholar 

  17. Blazejczyk K, Krawczyk B (1994) Bioclimatic research of the human heat balance. Institute of Geography & Spatial Organisation, Polish Academy of Science, Warsaw, p 28

    Google Scholar 

  18. Brown RD, Vanos J, Kenny N, Lenzholzer S (2015) Designing urban parks that ameliorate the effects of climate change. Landsc Urban Plann 138:118–131. https://doi.org/10.1016/j.landurbplan.2015.02.006

    Article  Google Scholar 

  19. .Bulkeley H (2013) Cities and climate change. In: Cities and climate change. Taylor and Francis. https://doi.org/10.4324/9780203077207

  20. Buyadi SNA, Mohd WMNW, Misni A (2015) Vegetation’s role on modifying microclimate of urban resident. Procedia Soc Behav Sci 202(December 2014):400–407. https://doi.org/10.1016/j.sbspro.2015.08.244

    Article  Google Scholar 

  21. Chen L, Ng E (2012) Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities 29(2):118–125. https://doi.org/10.1016/j.cities.2011.08.006

    Article  Google Scholar 

  22. CIBSE (2015) CIBSE guide a: environmental design. In: Envrionmental design, vol 19, issue 8, pp 38–46. www.cibse.org

  23. Djongyang N, Tchinda R, Njomo D (2010) Thermal comfort: a review paper. In: Renewable and sustainable energy reviews, vol 14, issue 9, Elsevier Ltd. https://doi.org/10.1016/j.rser.2010.07.040

  24. Du H, Cai W, Xu Y, Wang Z, Wang Y, Cai Y (2017) Quantifying the cool island effects of urban green spaces using remote sensing Data. Urban For Urban Greening. https://doi.org/10.1016/j.ufug.2017.06.008

    Article  Google Scholar 

  25. Fan H, Yu Z, Yang G, Liu TY, Liu TY, Hung CH, Vejre H (2019) How to cool hot-humid (Asian) cities with urban trees? An optimal landscape size perspective. Agric For Meteorol 265:338–348. https://doi.org/10.1016/j.agrformet.2018.11.027

    Article  Google Scholar 

  26. Fanger PO (1973) Assessment of man’s thermal comfort in practice. Br J Ind Med 3(4):313–324. https://doi.org/10.1136/oem.30.4.313

    Article  Google Scholar 

  27. Faroughi M, Karimimoshaver M, Aram F, Solgi E, Mosavi A, Nabipour N, Chau KW (2020) Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship. Eng Appl Comput Fluid Mech 14(1):254–270. https://doi.org/10.1080/19942060.2019.1707711

    Article  Google Scholar 

  28. Fröhlich D, Gangwisch M, Matzarakis A (2019) Effect of radiation and wind on thermal comfort in urban environments—application of the RayMan and SkyHelios model. Urban Clim 27(October 2018):1–7. https://doi.org/10.1016/j.uclim.2018.10.006

    Article  Google Scholar 

  29. Gagge AP, Fobelets AP, Berglund LG (1986) Standard predictive index of human response to the thermal environment. ASHRAE Trans 92(pt 2B):709–731

    Google Scholar 

  30. Hadavi S, Kaplan R, Hunter MCR (2018) How does perception of nearby nature affect multiple aspects of neighbourhood satisfaction and use patterns? Landsc Res 43(3):360–379. https://doi.org/10.1080/01426397.2017.1314453

    Article  Google Scholar 

  31. Hamada S, Ohta T (2010) Seasonal variations in the cooling effect of urban green areas on surrounding urban areas. Urban For Urban Greening 9(1):15–24. https://doi.org/10.1016/j.ufug.2009.10.002

    Article  Google Scholar 

  32. Heft H (2013) Environment, cognition, and culture: Reconsidering the cognitive map. J Environ Psychol 33:14–25. https://doi.org/10.1016/J.JENVP.2012.09.002

    Article  Google Scholar 

  33. Höppe P (1999) The physiological equivalent temperature—a universal index for the biometeorological assessment of the thermal environment. Int J Biometeorol 43(2):71–75. https://doi.org/10.1007/s004840050118

    Article  Google Scholar 

  34. Hunter RF, Cleland C, Cleary A, Droomers M, Wheeler BW, Sinnett D, Nieuwenhuijsen MJ, Braubach M (2019) Environmental, health, wellbeing, social and equity effects of urban green space interventions: a meta-narrative evidence synthesis. Environ Int 130:104923. https://doi.org/10.1016/J.ENVINT.2019.104923

    Article  CAS  Google Scholar 

  35. IPCC (2019) Special report on climate change and land. ipcc, August 2017, 2019. https://www.ipcc.ch/report/srccl/

  36. Jamei E, Ossen DR, Rajagopalan P (2017) Investigating the effect of urban configurations on the variation of air temperature. Int J Sustain Built Environ 6(2):389–399. https://doi.org/10.1016/j.ijsbe.2017.07.001

    Article  Google Scholar 

  37. Jamei E, Rajagopalan P, Seyedmahmoudian M, Jamei Y (2016) Review on the impact of urban geometry and pedestrian level greening on outdoor thermal comfort. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2015.10.104

    Article  Google Scholar 

  38. Jendritzky G, Havenith G, Weihs P, Batchvarova E (2009) Towards a Universal Thermal Climate Index UTCI for assessing the thermal environment of the human being. In: Final report COST Action 730. https://www.utci.org/cost/documents.php

  39. Kenny NA, Warland JS, Brown RD, Gillespie TG (2009) Part A: Assessing the performance of the comfa outdoor thermal comfort model on subjects performing physical activity. Int J Biometeorol. https://doi.org/10.1007/s00484-009-0226-3

    Article  Google Scholar 

  40. Ketterer C, Matzarakis A (2014) Human-biometeorological assessment of the urban heat island in a city with complex topography—the case of Stuttgart. Germany Urban Clim 10(P3):573–584. https://doi.org/10.1016/j.uclim.2014.01.003

    Article  Google Scholar 

  41. Kitchin RM (1994) Cognitive maps: what are they and why study them? J Environ Psychol 14(1):1–19. https://doi.org/10.1016/S0272-4944(05)80194-X

    Article  Google Scholar 

  42. Klemm W, Heusinkveld BG, Lenzholzer S, Jacobs MH, Van Hove B (2015) Psychological and physical impact of urban green spaces on outdoor thermal comfort during summertime in The Netherlands. Build Environ 83:120–128. https://doi.org/10.1016/j.buildenv.2014.05.013

    Article  Google Scholar 

  43. Klemm W, Heusinkveld BG, Lenzholzer S, van Hove B (2015) Street greenery and its physical and psychological impact on thermal comfort. Landsc Urban Plann 138:87–98. https://doi.org/10.1016/j.landurbplan.2015.02.009

    Article  Google Scholar 

  44. Knez I, Thorsson S (2006) Influences of culture and environmental attitude on thermal, emotional and perceptual evaluations of a public square. Int J Biometeorol 50(5):258–268. https://doi.org/10.1007/s00484-006-0024-0

    Article  Google Scholar 

  45. Knez I, Thorsson S (2008) Thermal, emotional and perceptual evaluations of a park: cross-cultural and environmental attitude comparisons. Build Environ 43(9):1483–1490. https://doi.org/10.1016/j.buildenv.2007.08.002

    Article  Google Scholar 

  46. Knez I, Thorsson S, Eliasson I, Lindberg F (2009) Psychological mechanisms in outdoor place and weather assessment: towards a conceptual model. Int J Biometeorol 53(1):101–111. https://doi.org/10.1007/s00484-008-0194-z

    Article  Google Scholar 

  47. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World Map of Köppen-Geiger Climate Classification—(updated with CRU TS 2.1 temperature and VASClimO v1.1 precipitation data 1951 to 2000). Meteorologische Zeitschrift, 15(3), 259–263. https://koeppen-geiger.vu-wien.ac.at/shifts.htm

  48. Krüger E, Rossi F, Drach P (2017) Calibration of the physiological equivalent temperature index for three different climatic regions. Int J Biometeorol. https://doi.org/10.1007/s00484-017-1310-8

    Article  Google Scholar 

  49. Lai D, Guo D, Hou Y, Lin C, Chen Q (2014) Studies of outdoor thermal comfort in northern China. Build Environ 77:110–118. https://doi.org/10.1016/j.buildenv.2014.03.026

    Article  Google Scholar 

  50. Lai D, Liu W, Gan T, Liu K, Chen Q (2019) A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.01.062

    Article  Google Scholar 

  51. Lee H, Mayer H, Chen L (2016) Contribution of trees and grasslands to the mitigation of human heat stress in a residential district of Freiburg, Southwest Germany. Landsc Urban Plann 148:37–50. https://doi.org/10.1016/j.landurbplan.2015.12.004

    Article  Google Scholar 

  52. Lemonsu A, Viguié V, Daniel M, Masson V (2015) Vulnerability to heat waves: impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Clim 14:586–605. https://doi.org/10.1016/j.uclim.2015.10.007

    Article  Google Scholar 

  53. Lenzholzer S, Klemm W, Vasilikou C (2018) Qualitative methods to explore thermo-spatial perception in outdoor urban spaces. Urban Clim 23:231–249. https://doi.org/10.1016/j.uclim.2016.10.003

    Article  Google Scholar 

  54. Lin TP (2009) Thermal perception, adaptation and attendance in a public square in hot and humid regions. Build Environ 44(10):2017–2026. https://doi.org/10.1016/j.buildenv.2009.02.004

    Article  Google Scholar 

  55. Luber G, McGeehin M (2008) Climate change and extreme heat events. Am J Prev Med 35(5):429–435. https://doi.org/10.1016/j.amepre.2008.08.021

    Article  Google Scholar 

  56. Mansournia S, Bahrami B, Farahani LM, Aram F (2020) Understanding children’s perceptions and activities in urban public spaces: the case study of Zrêbar Lake Waterfront in Kurdistan. Urban Stud. https://doi.org/10.1177/0042098020903008

    Article  Google Scholar 

  57. Matzarakis A, Amelung B (2008) Physiological equivalent temperature as indicator for impacts of climate change on thermal comfort of humans. Seasonal Forecasts Clim Change Hum Health. https://doi.org/10.1007/978-1-4020-6877-5_10

    Article  Google Scholar 

  58. Matzarakis A, Mayer H, Iziomon MG (1999) Applications of a universal thermal index: physiological equivalent temperature. Int J Biometeorol 43(2):76–84. https://doi.org/10.1007/s004840050119

    Article  CAS  Google Scholar 

  59. Matzarakis A, Rutz F, Mayer H (2010) Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. Int J Biometeorol 54(2):131–139. https://doi.org/10.1007/s00484-009-0261-0

    Article  Google Scholar 

  60. Moskaliuk J, Burmeister CP, Landkammer F, Renner B, Cress U (2017) Environmental effects on cognition and decision making of knowledge workers. J Environ Psychol 49:43–54. https://doi.org/10.1016/J.JENVP.2016.12.001

    Article  Google Scholar 

  61. Ng E, Cheng V (2012) Urban human thermal comfort in hot and humid Hong Kong. Energy Build 55:51–65. https://doi.org/10.1016/j.enbuild.2011.09.025

    Article  Google Scholar 

  62. Nikolopoulou M (2011) Outdoor thermal comfort. Front Biosci S3(4):1552–1568. https://doi.org/10.2741/s245

    Article  Google Scholar 

  63. Nikolopoulou M, Baker N, Steemers K (2001) Thermal comfort in outdoor urban spaces: understanding the human parameter. Sol Energy 70(3):227–235. https://doi.org/10.1016/S0038-092X(00)00093-1

    Article  Google Scholar 

  64. Nikolopoulou M, Lykoudis S (2006) Thermal comfort in outdoor urban spaces: analysis across different European countries. Build Environ 41(11):1455–1470. https://doi.org/10.1016/j.buildenv.2005.05.031

    Article  Google Scholar 

  65. Nikolopoulou M, Steemers K (2003) Thermal comfort and psychological adaptation as a guide for designing urban spaces. Energy Build 35(1):95–101. https://doi.org/10.1016/S0378-7788(02)00084-1

    Article  Google Scholar 

  66. Núñez Peiró M, Sánchez-Guevara Sánchez C, Neila González FJ (2017) Update of the urban heat Island of madrid and its influence on the building’s energy simulation. In: Mercader-Moyano P (ed) Sustainable development and renovation in architecture, urbanism and engineering. Springer International Publishing, Berlin, pp 339–350. https://doi.org/10.1007/978-3-319-51442-0_28

    Chapter  Google Scholar 

  67. Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108(455):1–24. https://doi.org/10.1002/qj.49710845502

    Article  Google Scholar 

  68. Orosa JA (2009) Research on the origins of thermal comfort. Eur J Sci Res 34(4):561–567

    Google Scholar 

  69. Park J, Kim JH, Lee DK, Park CY, Jeong SG (2017) The influence of small green space type and structure at the street level on urban heat island mitigation. Urban For Urban Greening 21:203–212. https://doi.org/10.1016/j.ufug.2016.12.005

    Article  Google Scholar 

  70. Pickup J, De Dear R (2000) An outdoor thermal comfort index (Out - Set * )—Part I—the model and its assumptions. In: Biometeorology and urban climatology at the turn of the millenium. conference ICB-ICUC vol 99, pp 279–283. https://www.researchgate.net/profile/Richard_De_Dear/publication/268983313_An_outdoor_thermal_comfort_index_OUT-SET_-_Part_I_-_The_model_and_its_assumptions/links/567a4b6308ae40c0e27e9397.pdf

  71. Potchter O, Cohen P, Lin TP, Matzarakis A (2018) Outdoor human thermal perception in various climates: a comprehensive review of approaches, methods and quantification. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2018.02.276

    Article  Google Scholar 

  72. Román E, Gómez G, de Luxán M (2017) Urban heat Island of Madrid and its influence over urban thermal comfort. In: Mercader-Moyano P (ed) Sustainable development and renovation in architecture, urbanism and engineering. Springer International Publishing, Berlin, pp 415–425. https://doi.org/10.1007/978-3-319-51442-0_34

    Chapter  Google Scholar 

  73. Ruggiero G, Rapuano M, Iachini T (2019) Perceived temperature modulates peripersonal and interpersonal spaces differently in men and women. J Environ Psychol 63:52–59. https://doi.org/10.1016/J.JENVP.2019.04.004

    Article  Google Scholar 

  74. Rupp RF, Vásquez NG, Lamberts R (2015) A review of human thermal comfort in the built environment. In: Energy and buildings, vol 105, Elsevier Ltd, pp 178–205. https://doi.org/10.1016/j.enbuild.2015.07.047

  75. Shahab S, Viallon FX (2019) A transaction-cost analysis of Swiss land improvement syndicates. Town planning review. Liverpool University Press, Liverpool. https://doi.org/10.3828/tpr.2019.34

    Book  Google Scholar 

  76. Shahab S, Viallon FX (2020) Swiss land improvement syndicates: ‘Impure’ Coasian solutions? Plann Theory. https://doi.org/10.1177/1473095220923629

    Article  Google Scholar 

  77. Sharmin T, Steemers K, Matzarakis A (2015) Analysis of microclimatic diversity and outdoor thermal comfort perceptions in the tropical megacity Dhaka Bangladesh. Build Environ. https://doi.org/10.1016/j.buildenv.2015.10.007

    Article  Google Scholar 

  78. Shashua-Bar L, Pearlmutter D, Erell E (2009) The cooling efficiency of urban landscape strategies in a hot dry climate. Landsc Urban Plann 92(3–4):179–186. https://doi.org/10.1016/j.landurbplan.2009.04.005

    Article  Google Scholar 

  79. Shen Y, Sun F, Che Y (2017) Public green spaces and human wellbeing: mapping the spatial inequity and mismatching status of public green space in the Central City of Shanghai. Urban For Urban Greening 27(August 2016):59–68. https://doi.org/10.1016/j.ufug.2017.06.018

    Article  Google Scholar 

  80. Shin J (2016) Toward a theory of environmental satisfaction and human comfort: a process-oriented and contextually sensitive theoretical framework. J Environ Psychol 45:11–21. https://doi.org/10.1016/J.JENVP.2015.11.004

    Article  Google Scholar 

  81. Streinu-Cercel A, Costoiu S, Mârza M (2008) Models for the indices of thermal comfort. J Med Life, 1(2):148–156. https://www.scopus.com/inward/record.url?eid=2-s2.0–77953083744&partnerID=40&md5=8298d1538cdd80241154863d771d7ab6

  82. Sun S, Xu X, Lao Z, Liu W, Li Z, Higueras García E, He L, Zhu J (2017) Evaluating the impact of urban green space and landscape design parameters on thermal comfort in hot summer by numerical simulation. Build Environ 123:277–288. https://doi.org/10.1016/j.buildenv.2017.07.010

    Article  CAS  Google Scholar 

  83. Taha H (2017) Characterization of urban heat and exacerbation: development of a heat island index for California. Climate 5(3):59. https://doi.org/10.3390/cli5030059

    Article  Google Scholar 

  84. Taleb D, Abu-Hijleh B (2013) Urban heat islands: potential effect of organic and structured urban configurations on temperature variations in Dubai, UAE. Renew Energy 50:747–762. https://doi.org/10.1016/j.renene.2012.07.030

    Article  Google Scholar 

  85. Tan J, Zheng Y, Tang X, Guo C, Li L, Song G, Zhen X, Yuan D, Kalkstein AJ, Li F, Chen H (2010) The urban heat island and its impact on heat waves and human health in Shanghai. Int J Biometeorol 54(1):75–84. https://doi.org/10.1007/s00484-009-0256-x

    Article  Google Scholar 

  86. Twohig-Bennett C, Jones A (2018) The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res 166:628–637. https://doi.org/10.1016/J.ENVRES.2018.06.030

    Article  CAS  Google Scholar 

  87. van Hoof J, Hensen JLM (2007) Quantifying the relevance of adaptive thermal comfort models in moderate thermal climate zones. Build Environ 42(1):156–170. https://doi.org/10.1016/j.buildenv.2005.08.023

    Article  Google Scholar 

  88. Vidrih B, Medved S (2013) Multiparametric model of urban park cooling island. Urban For Urban Greening 12(2):220–229. https://doi.org/10.1016/j.ufug.2013.01.002

    Article  Google Scholar 

  89. Wang J, Schwering A (2015) Invariant spatial information in sketch maps—a study of survey sketch maps of urban areas. J Spat Inf Sci 11(11):31–52. https://doi.org/10.5311/JOSIS.2015.11.225

    Article  Google Scholar 

  90. Ward K, Lauf S, Kleinschmit B, Endlicher W (2016) Heat waves and urban heat islands in europe: a review of relevant drivers. Sci Total Environ 569–570:527–539. https://doi.org/10.1016/j.scitotenv.2016.06.119

    Article  CAS  Google Scholar 

  91. Xu X, Sun S, Liu W, García EH, He L, Cai Q, Xu S, Wang J, Zhu J (2017) The cooling and energy saving effect of landscape design parameters of urban park in summer: a case of Beijing, China. Energy Build 149:91–100. https://doi.org/10.1016/j.enbuild.2017.05.052

    Article  Google Scholar 

  92. Yan H, Wu F, Dong L (2018) Influence of a large urban park on the local urban thermal environment. Sci Total Environ 622–623:882–891. https://doi.org/10.1016/j.scitotenv.2017.11.327

    Article  CAS  Google Scholar 

  93. Yu Z, Chen T, Yang G, Sun R, Xie W, Vejre H (2020) Quantifying seasonal and diurnal contributions of urban landscapes to heat energy dynamics. Appl Energy. https://doi.org/10.1016/j.apenergy.2020.114724

    Article  Google Scholar 

  94. Yu Z, Guo X, Jørgensen G, Vejre H (2017) How can urban green spaces be planned for climate adaptation in subtropical cities? Ecol Ind 82:152–162. https://doi.org/10.1016/j.ecolind.2017.07.002

    Article  Google Scholar 

  95. Yu Z, Xu S, Zhang Y, Jørgensen G, Vejre H (2018) Strong contributions of local background climate to the cooling effect of urban green vegetation. Sci Rep 8(1):1–9. https://doi.org/10.1038/s41598-018-25296-w

    Article  CAS  Google Scholar 

  96. Yu Z, Yang G, Zuo S, Jørgensen G, Koga M, Vejre H (2020) Critical review on the cooling effect of urban blue-green space: a threshold-size perspective. Urban For Urban Greening. https://doi.org/10.1016/j.ufug.2020.126630

    Article  Google Scholar 

  97. Zhang Y, Murray AT, Turner BL (2017) Optimizing green space locations to reduce daytime and nighttime urban heat island effects in Phoenix, Arizona. Landsc Urban Plann. https://doi.org/10.1016/j.landurbplan.2017.04.009

    Article  Google Scholar 

  98. Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plann 102(1):54–63. https://doi.org/10.1016/j.landurbplan.2011.03.009

    Article  Google Scholar 

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Acknowledgements

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Funding

This work is supported by the Hungarian State and the European Union under the EFOP-3.6.116-2016-00010 project and the 2017-1.3.1-VKE-2017-00025 project. Support of Alexander von Humboldt is aknowledged.

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Conceptualization, FA and EHG; methodology, FA and ES; software, FA and AM; validation, FA, ES and EHG; formal analysis, FA and AM; investigation, FA; data curation, AM and ES; writing—original draft preparation, FA and ES; writing—review and editing, EHG and AM; visualization, FA and AM; supervision, EHG, AM, and ES. All authors read and approved the final manuscript.

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Correspondence to Amir Mosavi.

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Aram, F., Solgi, E., Garcia, E.H. et al. Urban heat resilience at the time of global warming: evaluating the impact of the urban parks on outdoor thermal comfort. Environ Sci Eur 32, 117 (2020). https://doi.org/10.1186/s12302-020-00393-8

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