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Article

Investigating the Effect of Blue–Green Infrastructure on Thermal Condition—Case Study: Elazığ, Turkey

by
Sevgi Yilmaz
1,
Yaşar Menteş
1,2 and
Elmira Jamei
3,4,*
1
Department of Landscape Architecture, Faculty of Architecture and Design, Atatürk University, 25240 Erzurum, Turkey
2
Ministry of Agriculture and Forestry, Elazığ Provincial Directorate of Agriculture and Forestry, 23119 Elazığ, Turkey
3
College of Sport, Health and Engineering (CoSHE), Victoria University, Melbourne, VIC 3011, Australia
4
Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia
*
Author to whom correspondence should be addressed.
Land 2025, 14(4), 891; https://doi.org/10.3390/land14040891
Submission received: 30 March 2025 / Revised: 7 April 2025 / Accepted: 14 April 2025 / Published: 17 April 2025
(This article belongs to the Special Issue Urban Ecosystem Services: 6th Edition)
Browse Figures
Versions Notes

Abstract

:
This study examines the thermal impacts of green and blue infrastructure in Hilalkent Neighborhood, Elazığ City, in Turkey, using ENVI-met 5.6.1 software. Six design scenarios were proposed and their impact on air temperature, relative humidity, mean radiant temperature (Tmrt), physiological equivalent temperature (PET), and wind speed during August and January was analyzed. The simulation results were verified via field measurements using the Lutron AM-4247SD Weather Forecast Station at a height of 2.0 m above the ground. Data were collected in August 2023 and January 2024. The findings of this study indicate that existing vegetation in the study area provides a cooling effect of 0.8 °C during August. The addition of 10% grass coverage further reduced air temperature by 0.3 °C, while a 20% increase in tree density led to a 0.6 °C temperature reduction. The inclusion of a 10% water surface resulted in a 0.4 °C decrease in air temperature, and the implementation of extensive roof gardens contributed to an additional 0.2 °C reduction during the August period. The combined implementation of blue–green infrastructure in the study area achieved a total cooling effect of 1.5 °C during August. During January, the proposed scenarios led to a reduction in average temperatures by 0.1 °C to 0.4 °C compared to the base scenario, which may not be favorable for thermal comfort in colder conditions. Relative humidity values decreased during the August and Tmrt values were directly proportional to air temperature changes in both August and January. The results of this study provide valuable insights for urban planners and policymakers, demonstrating the effectiveness of blue–green infrastructure in mitigating the urban heat island (UHI) effect. These findings highlight the importance of integrating climate-responsive design strategies into urban planning to enhance thermal comfort and environmental sustainability in cities.

1. Introduction

The rapid growth of urban populations has placed significant pressure on natural resources, leading to their depletion and degradation [1,2]. The ongoing trend of population growth and urbanization has profound environmental consequences, particularly affecting atmospheric conditions [3]. As cities expand, rapid urbanization has driven substantial changes in the physical environment and the ecosystems that support life [4].
According to the Intergovernmental Panel on Climate Change [5], global temperatures have already risen by 1.0 °C over the past century, with projections indicating that temperatures could stabilize at 1.5 °C between 2030 and 2052, provided that mitigation efforts are effectively implemented. However, urban expansion, particularly unplanned urban construction, has led to an increase in impervious surfaces, disrupting the natural balance between built and green environments. This disruption has exacerbated environmental degradation, contributing to unhealthy urban conditions [6].
Addressing these challenges requires an integrated approach to urban planning that prioritizes climate-sensitive design strategies. Sustainable urban development, incorporating green infrastructure and nature-based solutions, is essential to mitigating the adverse effects of urbanization and improving environmental resilience.
One of the most prominent local climate phenomena associated with urbanization is the urban heat island (UHI) effect, characterized by elevated temperatures in urban areas compared to surrounding rural environments [7]. The UHI effect occurs as densely built urban centers retain heat more efficiently than less developed areas due to factors such as increased impervious surfaces, reduced vegetation, and anthropogenic heat emissions [8,9,10]. Understanding the causes of UHIs is crucial for developing effective mitigation strategies and promoting urban sustainability.
The UHI phenomenon is a complex interplay between urban morphology, surface materials, and atmospheric conditions, all of which influence local weather patterns [11,12,13,14]. Its implications extend beyond temperature increases, affecting energy demand, thermal comfort, public health, and air pollution levels. In tropical climates, where high temperatures and humidity levels exacerbate urban warming, the effects of UHI are particularly severe [9,15]. In a study on urban areas in Central Europe, all simulation results indicate significant warming across the model domain, ranging between 0.8 and 1.1 °C. Regional climate models predict an increase in annual precipitation between 2% and 9% (with an average of 3% for Germany), with higher values occurring in winter and autumn. While the urban heat island (UHI) effect is most pronounced in summer, it is also observable in winter, further intensifying urban energy demands [16].
In countries with hot and humid climates, urban areas experience extreme temperature increases, making UHI mitigation a priority. Numerous studies have explored strategies to reduce UHI intensity in these regions [17]. The primary goal of urban heat reduction technologies is to minimize heat retention in cities by employing cooling strategies, such as increased vegetation, reflective surfaces, and improved urban design practices [18,19,20,21].
Numerous studies have been conducted to assess the impact of urban warming on city dwellers, quantify this effect, and identify planning and design strategies to mitigate the urban heat island (UHI) phenomenon. In recent years, various recommendations and mitigation strategies have been proposed and implemented in cities worldwide. These UHI mitigation strategies can be broadly categorized into three main approaches: (1) reducing the UHI effect through vegetation, (2) using cool surface materials, and (3) utilizing water surfaces.
In light of recent natural disasters in Turkey (6 February 2023), urban renewal and transformation projects must integrate UHI mitigation strategies. Proper landscape design, site-appropriate plant species selection, and best-practice urban planning approaches can improve outdoor thermal comfort and reduce heat stress in cities [22,23].
To effectively direct UHI effects, planning decisions should align with local environmental conditions to prevent further ecological degradation. Future microscale urban analyses will be essential to refining mitigation strategies and improving thermal comfort in different urban microclimates. Simulations of climate-responsive urban designs, particularly for summer and winter months, will enable city planners to anticipate potential environmental challenges [24].
This study aims to examine the cooling effects of blue–green infrastructure in Hilalkent Neighborhood, a developing urban area in Elazığ, Turkey. Using ENVI-met microclimate software, this research simulates a base scenario and additional blue–green infrastructure scenarios, including water ponds (blue infrastructure) and grass, plants, trees, and extensive roofs (green infrastructure). The goal is to identify the most effective microclimate strategies to mitigate the UHI effect in the study area. The findings will be shared with local authorities to guide future urban planning decisions and improve outdoor thermal comfort in urban environments.

2. Literature Review

Climate change has become a critical global challenge in the 21st century, significantly impacting both natural and built environments. Urban settlements, which occupy only 2–3% of the Earth’s surface, are among the primary contributors to climate-related issues due to their high energy consumption, altered land use, and increased greenhouse gas emissions [25].
One of the most prominent local climate phenomena associated with urbanization is the urban heat island (UHI) effect, characterized by elevated temperatures in urban areas compared to surrounding rural environments [7]. In the following section, some of the key strategies that have been used to minimize the UHI have been presented.

2.1. Vegeatation

Vegetation is one of the most effective elements for mitigating urban heat. Urban green spaces provide multiple benefits, including aesthetic, hydrological, acoustic, and ecological improvements. Studies have consistently demonstrated that planting vegetation is among the most efficient strategies for cooling urban environments [3,7,26,27,28]. A scenario analysis conducted in a park in São Paulo, Brazil, using ENVI-met software during the hot humid summer of 2006 showed that vegetated areas were up to 2.0 °C cooler than non-vegetated spaces [29]. Similarly, research in the Philippine Island of Cebu indicated that increasing green space and tree cover could reduce air temperatures by an average of 0.2 °C [30]. Different urban land uses also influence surface temperatures, with green areas exhibiting a clear cooling effect [31]. Moreover, vegetation not only lowers air temperatures but also shields impervious surfaces from direct solar radiation, further contributing to thermal comfort [32].

2.2. Cool Materials

Another key strategy in reducing UHI intensity is the use of cool surface materials for roofs, pavements, building facades, and vertical surfaces. Traditional urban surfaces often have low solar reflectance and high thermal absorption, which contribute to excessive heat retention [33,34,35]. A study conducted in Melbourne, Australia, using ENVI-met software during the hot summer days of 2020, found that green roofs reduced air temperatures at roof level by 1.5 °C [36]. Similar studies have explored vertical gardens [37,38] and high-albedo outdoor cooling materials [39,40], both of which contribute to reducing urban heat.
Green roof applications have also demonstrated significant cooling effects. Reference [41] concluded that intensive green roofs can minimize UHI impacts, while [42] used ENVI-met to measure temperature reductions on green roofs at the University of Bologna in Italy. Their results indicated that green roofs decreased daytime temperatures by 0.5 °C and nighttime temperatures by 3.0 °C. Meanwhile, a scenario integrating low-reflective surfaces, trees, and water bodies reduced air temperatures by up to 4.3 °C [43].

2.3. Water

The role of water bodies in urban climate regulation is well documented. Water, similar to vegetation and cool materials, helps mitigate UHI effects [44]. Research highlights that water features are among the most effective urban cooling strategies during summer [45,46]. For example, a study in the Dora district of Beirut, Lebanon in July 2019 found that incorporating water features led to a temperature reduction of up to 5.0 °C [47].
To evaluate the impact of these mitigation strategies, tools have been introduced. Tools such as ENVI-met that enable researchers to model and analyze the effectiveness of different mitigation strategies.
This study aims to examine the cooling effects of blue–green infrastructure in Hilalkent Neighborhood, a developing urban area in Elazığ, Turkey, using ENVI-met microclimate software. This study also aims to identify the most effective microclimate strategies to mitigate the UHI effect in the study area.

3. Materials and Methods

3.1. Study Area

This Elazığ province is situated between 38°30′ and 40°0′21″ eastern longitudes and 38°0′17″ and 39°0′11″ northern latitudes, with an average elevation of 1078 m above sea level. The province spans an area of 9153 km2. According to the Köppen climate classification, Elazığ experiences a “BSk” semi-arid steppe climate (cold) and a “Csa” Mediterranean climate, characterized by cold snowy winters and hot, dry summers [48]. This study was conducted in the Hilalkent Neighborhood, located in the western part of Elazığ city, within the Eastern Anatolia region (Figure 1).

3.2. Field Measurements

This Elazığ province is situated between 38°30′ and 40°0′21″ eastern longitudes and 38°0′17″. This study was conducted using microclimate field measurement, and numerical simulations were conducted using ENVI-met. Field measurements of air temperature (Ta = 0 °C), relative humidity (RH—%), wind speed (V—m/s), and wind direction were conducted using the Lutron AM-4247SD Weather Forecast. Summer measurements were conducted throughout August 2023, the hottest month, while winter measurements were conducted throughout January 2024, the coldest month, at a height of 2 m above the ground (Figure 2). However, since the ENVI-met software uses 24-hour microclimate data, the 24-hour data input obtained has been used in the analyses. For this purpose, the microclimate data recorded on 11 August, the hottest day of the summer, and January 8, the coldest day of the winter, were used in the analyses. The coldest and hottest days among the recorded days were selected [49,50].
The Lutron AM-4247SD anemometer humidity/temperature meter is equipped with two probes—an anemometer and a humidity/temperature probe—enabling precise measurement of temperature, relative humidity, wind speed, and wind direction. Real-time data were recorded using an SD memory card datalogger, with a sampling period adjustable from 1 s to 3600 s. Low friction ball wings provide high accuracy at high and low speeds. It can be easily used in open and closed areas. In order to achieve high accuracy results, measurements were started after the device was calibrated. Based on the data provided, the sample size is large enough to draw meaningful conclusions that are linked to the research questions. However, as always, further validation and comparison with other datasets or longer-term studies could further strengthen the findings. Given that the approach of this research aimed to evaluate the effectiveness of each mitigation scenario, the sample size was appropriate to draw meaningful conclusions.
The data collected in this research were annual-based. However, for the purpose of simulation with ENVI-met, the hottest month of summer and the coldest month of winter were considered. In fact, large-scale research is often conducted using remote sensing, satellite images, and the Weather Research and Forecasting (WRF) model. These studies typically rely on annual weather data to reach solid conclusions. However, in our applied research, we analyzed a small-scale neighborhood using the ENVI-met software. As demonstrated in the literature, the duration in ENVI-met is limited to 24–48 h, and worst-case scenarios are often considered as inputs to obtain meaningful results.
The study area, located in the Hilalkent Neighborhood, is situated 9 km from the city center and spans 1.06 hectares. The study area consists of 42.4% impervious surfaces, 29.2% green spaces, and 8.6% open/vacant areas. The study area includes three building blocks arranged within a garden-style layout. Each building has nine floors, with an approximate height of 30 m.

3.3. Climatic Features of the Study Area

According to the Elazığ General Directorate of Meteorology, August is the hottest month of the year. Therefore, the measurements in Hilalkent Neighborhood were conducted on a clear, cloudless day on 11 August 2023 and indicated that the highest air temperature is 41.2 °C and the lowest temperature is 25.3 °C, with a daily average temperature of 33.6 °C.
The highest recorded relative humidity was 35.3% and the lowest was 8.7%, with a daily average of 20.8%. Wind speeds peaked at 2.2 m/s during the day, with an average wind speed of 1.2 m/s.
Winter measurements were conducted on 8 January 2024. On this date, the highest air temperature recorded was 7.5 °C, the lowest was −1.7 °C, and the daily average temperature was 2.6 °C, with a temperature range of 9.2 °C. The highest daily relative humidity was 83.0%, the lowest was 50.6%, and the average relative humidity was 69.6%. Wind speeds reached a maximum of 2.4 m/s during the day, with an average wind speed of 1.7 m/s. Table 1 shows the measured microclimate parameters (e.g., air temperature, relative humidity, and wind speed) for the selected days in August and January.

3.4. Preparation of the Landscape Scenarios

Six different urban heat island (UHI) mitigation scenarios were developed for Hilalkent Neighborhood, and these scenarios were analyzed under 12 different conditions based on the microclimate data of 11 August and 8 January. Urban green spaces play a crucial role in reducing the thermal environment’s intensity and mitigating the UHI effect [51], and vegetation is widely recognized as a key component of sustainable urban green design [52,53,54]. Therefore, the most effective UHI mitigation strategies were developed incorporating variations in vegetation cover, surface materials, wind speed, and wind direction.
○
Scenario 1: This scenario represents the existing conditions of the study area, incorporating the current structures, vegetation, and material types present in the site.
○
Scenario 2: Scenario depicting the study area without vegetation.
○
Scenario 3: Scenario depicting a 10% grass coverage applied to a 1000 m2 land area in the northern section of the study site to consider the impact of partial vegetation coverage on thermal regulations.
○
Scenario 4: Scenario incorporating a 10% grass coverage alongside a 20% increase in tree density within the study area to assess the combined effects of grass and additional tree cover on thermal regulation.
○
Scenario 5: Scenario incorporating a 10% increase in grass coverage, a 20% increase in tree density, and the addition of 10% water bodies to evaluate the combined effects of vegetation and water on thermal regulation.
○
Scenario 6: Scenario incorporating a 10% increase in grass coverage, a 20% increase in tree density, a 10% water surface, and the addition of three roof tops to the buildings in the study area. An extensive green roof was used in this study. The buildings are 10 stories, each featuring an extensive green roof. Green roofs are situated at 30 m height from the ground. Each building has a roof area of approximately 600 m2, resulting in a total of 1.8 hectares (3 × 600 = 1800 m2) of green space. Table 2 lists the 3D view of the study area in each scenario and shows the percentage of building, pavement, vegetation, open space, and water body ratio in each scenario.

3.5. Microclimate Simulations with ENVI-met 5.6.1

ENVI-met 5.6.1 is one of the most comprehensive models used to estimate the mitigation effects of urban heat islands [55]. This software has been employed in the simulation studies conducted within the scope of this research. In recent years, ENVI-met has become the most widely used software for evaluating microclimate data [56]. As a holistic microclimate model, ENVI-met integrates various elements of urban and landscape environments, enabling interactions between atmospheric conditions, vegetation, architectural structures, and materials. Moreover, it allows for the simulation of urban climate dynamics, facilitating detailed assessments of environmental modifications.
ENVI-met is the most widely used urban microclimate simulation tool globally, capable of performing high-resolution 3D simulations within a defined spatial domain, including ceiling, floor, and height parameters [49,57,58,59].
To run a simulation, an area input file (.INX), consisting of physical characteristics of the study area such as buildings, surface materials, roads, and vegetation, and the weather file including climatic parameters such as air temperature, relative humidity, wind speed, and direction have to be obtained.
The 3D modeling of the study areas and the creation of the *.INX format input file were conducted using SketchUp 2023. To facilitate this process, the SketchUp plugin ENVI-metINX, compatible with ENVI-met V5, was downloaded. This tool, based on vector files, assists in generating the INX model.
The climatic parameters, including air temperature, relative humidity, wind speed, and wind direction, were recorded hourly using the Lutron AM-4247SD anemometer humidity/temperature meter for mobile measurements. These data were then used to generate the *.SIM file.
Following the completion of 3D modeling and the creation of the .INX and .SIM files, the collected data were simulated. ENVI-met software operates based on the principles of fluid mechanics and thermodynamics [60]. Table 3 presents the area input file and the climatic data used as inputs for the ENVI-met simulation.
Measurements from the hottest summer month and the coldest winter month were recorded at the study site for the designated period. The obtained data were averaged to create a 24-hour dataset for system input.

3.6. ENVI-met Verification

Validating the results of the ENVI-met model is crucial to ensuring reliable simulation outputs [61]. The hourly air temperature and relative humidity data measured during August and January in the study area are compared with the simulated results from the ENVI-met model. The statistical parameters used to evaluate the model’s accuracy include the correlation coefficient (R2), root mean squared error (RMSE), and mean absolute error (MAE). The measured and simulated air temperature and relative humidity values for August and January, along with their correlations, are illustrated in Figure 3.
Air temperature (°C) and relative humidity (%) are critical factors in verifying the model’s performance. The R2 values between the measured and simulated air temperature and relative humidity in the study area range from 0.909 to 0.987, indicating a strong correlation. The RMSE values for air temperature and relative humidity vary between 0.104 and 2.189, falling within an acceptable range. Similarly, the MAE values range from 0.022 to 0.457, further supporting the model’s accuracy.
While minor discrepancies exist between the measured and simulated data, the obtained R2, RMSE, and MAE values confirm the reliability of the ENVI-met predictions within this study. These results demonstrate that the developed ENVI-met model is valid and can be effectively used to assess urban thermal environments.

4. Results and Discussion

4.1. Existing Scenarios

Table 4 presents the simulation results for air temperature, relative humidity, and wind speed under the current conditions during the summer period in the study area. According to Table 4, in the existing condition, the simulation results indicate that in August, the highest average air temperature during August was recorded at 39.7 °C at 13:00, while the lowest average temperature was 26.1 °C at 05:00. Table 4 also shows that the highest average relative humidity was observed at 34.4% at 06:00, whereas the lowest average relative humidity was 12.9% at 16:00.
During January, the highest recorded average air temperature was 6.9 °C at 14:00, and the lowest average temperature was 0.1 °C at 05:00. The humidity in January was higher than in August, as the relative humidity was measured at 96.1% at 05:00, and the lowest (74.2%) at 14:00.
These Figure 4 and Figure 5 show the spatial distribution of air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenarios in August and January, respectively. The impact of the proposed scenarios on thermal environment was analyzed using five key climatic parameters: air temperature, relative humidity, mean radiant temperature (Tmrt), Physiologically Equivalent Temperature (PET), and wind speed. Given that the highest daily temperature is recorded at 13:00, the simulation results for this time are analyzed.
Rayman Pro 2.1, used to determine PET values, considers various factors such as age, sex, height, weight, clothing insulation, physical activity, and posture (sitting or standing). The model assumes a 35-year-old “typical European man” with a height of 1.75 m and a weight of 75 kg. Meteorological climate data play a supportive role in assessing areas and making informed decisions about them. However, temperature data alone are insufficient to determine the perceived temperature. In outdoor environments, multiple factors influence how temperature is felt. Rayman Pro 2.1 is employed to obtain climate data and calculate PET (°C). PET ranges for different degrees of thermal perception by humans and physiological stress on humans are given in Table 5 [62].

4.2. Air Temperature

The simulation results from the base scenario (Scenario 1) indicate that the daily average air temperature during August was 33.2 °C. According to Table 6, in Scenario 2, which represents the vegetation-free condition of the study area, the daily average air temperature increased to 34.0 °C. This suggests that the existing vegetation provides a cooling effect of 0.8 °C. Previous studies have also confirmed the cooling effect of vegetation, particularly trees [63].
In Scenario 3, where a 10% grass cover (1000 m2) is introduced, the daily average air temperature in August decreased to 32.9 °C, which was 1.1 °C lower than the vegetation-free scenario and 0.3 °C lower than the current condition. Grass-covered surfaces are significantly cooler compared to exposed impervious materials [64].
In Scenario 4, the average daily air temperature in August further decreased to 32.3 °C. This represents a reduction of 0.9 °C compared to the base scenario and 0.6 °C compared to Scenario 3. A study on Haydar Aliyev Street in Erzurum, Turkey, reported an average temperature reduction of 0.2 °C when plant density was increased by 20% [65]. Moreover, previous research indicates that mesoscale modeling predicts a reduction in maximum temperatures by 1.6 °C to 2.3 °C with increased green cover, while microscale simulations show localized cooling effects ranging from 0.5 °C to 1.4 °C [66].
Similarly, this research shows a greater temperature reduction, suggesting a more significant cooling effect due to the introduction of trees. It is also predicted that the tree species used, by not allowing sunlight to pass through to the ground, will contribute to the reduction in temperature.
These grass areas exhibit a lower cooling capacity compared to trees [67]. In this study, the addition of grass cover resulted in a temperature reduction of 0.3 °C, whereas increasing tree density led to a more significant cooling effect of 0.6 °C. These findings indicate that enhancing tree cover is a more effective strategy for urban cooling than expanding grass areas.
The combination of grass and trees provides optimal heat reduction [68]. While the addition of grass alone resulted in a temperature reduction of 0.3 °C, the scenario incorporating both grass and trees achieved a more substantial cooling effect of 0.9 °C. The findings of this study align with these observations, reinforcing the effectiveness of integrating vegetation for urban temperature mitigation.
In the fifth scenario, a 10% water surface area was added to the study area in addition to the elements in the fourth scenario. The average daily air temperature during the August period was recorded as 31.9 °C, representing a 0.4 °C reduction compared to the fourth scenario.
One of the most effective strategies for mitigating urban heat during summer is the incorporation of water features. A study conducted in the Dora district of Beirut, Lebanon, demonstrated that the application of water bodies led to a maximum ambient temperature reduction of 5.0 °C [47]. While this study also confirms the cooling effect of water, the observed reduction (0.4 °C) is considerably lower than the previously reported values. Both vegetation and water surfaces contribute to mitigating urban heat effects, but their effectiveness varies [69,70]. Individually, green spaces and water bodies may have limited cooling potential; however, their combined implementation offers the most significant cooling benefits [14,71]. The literature presents varying conclusions regarding the relative cooling capacities of green areas and water bodies. Some studies suggest that water bodies exert a stronger cooling effect than vegetation [40], while others argue that water bodies do not always surpass the cooling efficiency of green spaces [70,72,73].
In the sixth scenario, extensive roof gardens were incorporated into the buildings within the study area in addition to the previous scenarios. The average daily air temperature during the August period was recorded as 31.7 °C, reflecting a further reduction of 0.2 °C compared to the previous scenario.
A study conducted in Melbourne, Australia, found that a green roof configuration reduced air temperatures at the roof level by 1.5 °C [36]. In the present study, the simulated air temperature at a height of 2 m above the ground showed only a 0.2 °C decrease. This limited cooling effect can be attributed to the placement of the extensive roof gardens, which were designed on the tenth floor of the buildings (approximately 30 m high). Consequently, the cooling impact observed at roof level does not directly translate to significant temperature reductions at the pedestrian level (2 m above ground).
The combined implementation of grass, trees, water surfaces, and extensive roof gardens in the study area resulted in a total cooling effect of 1.5 °C in the average daily air temperature during the August period. Meanwhile, the simulation results from the base scenario (first scenario) indicate that the daily average air temperature during the January period was 3.2 °C
Figure 6 shows the air temperature variation in the six proposed mitigation scenarios in August and January. According to Figure 6, the daily average air temperatures during the January period were recorded as 3.3 °C in the second scenario, 3.1 °C in the third scenario, 3.0 °C in the fourth scenario, and 2.8 °C in both the fifth and sixth scenarios. These values indicate a reduction of 0.1 °C to 0.4 °C compared to the base scenario (Table 6).
The addition of grass, trees, water surfaces, and extensive roof gardens to the study area yielded more significant temperature reductions in summer than in winter. Previous studies have similarly found that the impact of green spaces on thermal comfort is more pronounced in summer than in winter [70,74].

4.3. Relative Humidity

Figure 7 presents the August and January relative humidity simulation results for the six mitigation scenarios. Relative humidity often decreases during the day and typically increases at night. As temperature declines, the air’s capacity to hold water vapor diminishes, leading to an increase in relative humidity [43].
The simulation results from the base scenario (Scenario 1) indicate that the daily average relative humidity during August was 22.7%. In Scenario 2, which depicts a vegetation-free condition, the daily average relative humidity decreased to 22.1%. In Scenarios 3, 4, 5, and 6, the daily average relative humidity values increased to 23.4%, 23.8%, 24.1%, and 24.3%, respectively.
Vegetation has a significant impact on relative humidity [75]. Areas with vegetation tend to exhibit higher relative humidity due to evaporation driven by solar radiation [32]. Expanding urban vegetation is essential for dissipating excess heat by enhancing relative humidity. The analysis results highlight the significant impact of urban intervention strategies in mitigating pedestrian heat stress, with air temperature reductions of 3–4 °C. Additionally, a correlation was identified between leaf area density and PET levels [76].
Relative humidity, which is typically high in the early morning hours, decreases significantly in the afternoon. The elevated relative humidity observed in vegetated areas during the early morning is likely a result of surface moisture transpiration. Conversely, the lower relative humidity in vegetated areas during the afternoon may be attributed to evapotranspiration processes associated with leaf moisture content [75].

4.4. Mean Radiant Temperature (Tmrt)

Mean radiant temperature (Tmrt) is one of the key governing factors impacting outdoor thermal comfort (PET) and influenced by the albedo of surfaces and shading provided by buildings and trees [43]. It is particularly a major heat source during winter and has been identified as the most influential factor affecting outdoor thermal comfort, particularly in colder seasons [59]. Figure 8 presents the Tmrt simulation results in the six proposed mitigation scenarios for both August and January.
The simulation results from the base scenario (Scenario 1), which represents the current conditions of the study area, indicate that the daily average Tmrt during summer was 37.7 °C. In Scenario 2, which depicts a vegetation-free condition, the daily average Tmrt increased to 44.1 °C. This suggests that the existing vegetation contributes to a 6.4 °C reduction in Tmrt. In Scenarios 3, 4, 5, and 6, the daily average Tmrt values were recorded as 36.8 °C, 35.8 °C, 35.6 °C, and 35.3 °C, respectively.
During winter, the simulation results from the base scenario (Scenario 1) indicate that the daily average Tmrt was 4.5 °C. In Scenario 2, which excludes vegetation, the daily average Tmrt increased to 5.8 °C, suggesting that the presence of vegetation reduced Tmrt by 1.3 °C. In Scenarios 3, 4, 5, and 6, the daily average Tmrt values were 4.3 °C, 4.1 °C, 4.0 °C, and 4.0 °C, respectively, with the lowest values observed in the fifth and sixth scenarios.
The findings of this study align with previous research indicating that Tmrt values vary based on vegetation cover. For the model to produce accurate results, microclimate data specific to the study area should be collected. Additionally, prior studies have noted that the model’s accuracy may be reduced under extreme heat and extreme cold conditions [77].

4.5. Wind Speed

Figure 9 shows the wind speed variation in the six proposed mitigation scenarios in summer and winter. According to Figure 9, the simulation results from the base scenario (Scenario 1) indicate that the daily average wind speed during summer was 0.9 m/s. In Scenario 2, representing a vegetation-free condition, the daily average wind speed slightly increased to 1.0 m/s, suggesting that existing vegetation reduced wind speed by 0.1 m/s. In the other scenarios, the daily average wind speed remained at 0.9 m/s, similar to the base scenario. Heat events frequently occur during summer, particularly under conditions of low wind and high atmospheric pressure. In such cases, the local wind field can be highly variable, especially in heterogeneous urban environments like the study area. The cooling effects of green spaces may also vary depending on the prevailing wind direction [78,79].
For the winter period, the base scenario (Scenario 1) showed a daily average wind speed of 0.9 m/s. The results for all other scenarios remained consistent, with a daily average wind speed of 0.9 m/s, indicating minimal impact of vegetation modifications on wind speed during January. Nevertheless, several studies using ENVI-met have indicated that the software does not yield accurate results in wind analysis when wind speeds are below 2.0 m/s. This is one of the limiting problems of the software [27,80,81].

4.6. Physiologically Equivalent Temperature (PET)

Physiologically Equivalent Temperature (PET) is a thermal index derived from the human energy balance [62]. The evaluation results based on scenario analyses were arranged according to the PET value ranges given in Table 5 and presented in Figure 10. The simulation results from the base scenario (Scenario 1), which represents the current conditions of the study area, indicate that the daily average PET during summer was 39.4 °C. In Scenario 2, representing a vegetation-free condition, the daily average PET increased to 40.5 °C, suggesting that the existing vegetation contributed to a 0.9 °C reduction in PET. In Scenarios 3, 4, 5, and 6, the daily average PET values were recorded as 39.3 °C, 39.0 °C, 38.7 °C, and 38.3 °C, respectively.
For the winter period, the base scenario (Scenario 1) indicated a daily average PET of 9.6 °C. In Scenario 2, which excludes vegetation, the daily average PET slightly increased to 9.7 °C, demonstrating a minimal reduction of 0.1 °C due to existing vegetation. In Scenarios 3, 4, 5, and 6, the daily average PET values were recorded as 9.5 °C, 9.3 °C, 9.2 °C, and 9.2 °C, respectively. This finding is in line with the previous studies that found mitigation strategies have greater effectiveness on thermal comfort compared to those for winter [70,82].
ENVI-met Software Limitations: When the boundaries of the study area are expanded, or when the number of plants is very dense or excessive, the simulation time increases and it often results in errors. This software operates with x, y, and z coordinates in spatial scenarios. Problems arise when these coordinates are increased. As stated in the study by [83], having only 24-hour data input in the analyses has also been highlighted as an important limitation. Another limitation is the small number of plant species available for scenario selection. Additionally, when the measured wind speed in the area is below 2 m/s, the simulations, particularly the winter versions, are considered unreliable [27,80,81]. Another drawback is that simulations for vertical water surfaces, which have become increasingly common in urban spaces, cannot be performed. It is hoped that this feature will be added in later versions.

5. Conclusions

This study, conducted in Elazığ, Turkey, examined the impact of six mitigation strategies including the use of blue–green infrastructure on microclimate conditions for both August and January. The ENVI-met model was used to evaluate the impact of the proposed scenarios on the number of climatic parameters including air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed. In the scenario without vegetation, the highest recorded temperature during the August period was 34.0 °C, which was 0.8 °C higher than the current condition. Scenario analyses indicate that the combined use of blue and green infrastructure is the most effective strategy for reducing temperatures during the summer months. In particular, the simultaneous implementation of both strategies resulted in a 1.5 °C decrease in temperature during the August, which is considered a significant gain in terms of thermal comfort. Factors such as vegetation can influence the rate of ground temperature loss during cold winter nights, thereby affecting fluctuations in air temperature. Accordingly, urban settlement areas, including roadsides, sidewalks, and open spaces, should be enriched vegetatively, taking landscape plantation characteristics into account.
While no significant variations were observed in wind speed across the scenarios, the blue–green scenario exhibited the highest relative humidity. The cooling efficiency of blue–green infrastructure during the August months was nearly 45% greater than in the current situation. However, this scenario also resulted in a 0.4 °C temperature reduction in winter, making it less advantageous during colder months. However, it should not be forgotten that there is no water on the water surfaces in winter. For this reason, it is recommended to use blue-green infrastructure in August. In particular, water surfaces should be incorporated into pedestrian walkways and urban green spaces. These water surfaces not only enhance thermal comfort but can also be designed to collect rainwater in urban areas. Therefore, it is crucial for urban planners to integrate blue–green infrastructure solutions, particularly in urban renewal projects.
For this microclimate area, it has been observed that an extensive green roof over residential structures in urban settlements reduces the ambient temperature by 0.2 °C. In this case, it would be more appropriate for local authorities to decide on the implementation of extensive green roofs after conducting a cost–benefit analysis because it cannot be said that extensive green roof applications make a significant contribution to temperature reduction. However, it should be noted that the area covered by the roof surface is limited in this analysis.
Analyses conducted for the August months have determined a temperature difference of 2.1 °C between an empty space and the blue–green infrastructure scenario. While green spaces and water bodies individually have limited cooling potential, their combined implementation provides the most significant cooling benefits. Therefore, urban planners should ensure their integrated use in designs. When used alone, grass areas provide a temperature reduction of 0.3 °C, whereas the addition of trees in the same space results in a temperature decrease of nearly 1.0 °C. Therefore, to improve thermal comfort in urban spaces, grass or ground cover plants should be incorporated beneath tree canopies. In winter, selecting ground surface materials involves a balance between natural, near-natural, and artificial options. However, their suitability is primarily limited to areas with cool summers and harsh winters.
All scenario results have shown that as vegetation increases and water is added, the relative humidity of the environment also increases. This research showed that the incorporation of blue–green infrastructure can be considered a long-term strategy for mitigating urban warming and improving thermal comfort in August, but careful considerations have to be taken into account to balance the impact in winter. This study also identified limitations in the ENVI-met model. For example, tree species available in the model were fully represent the native trees in the study area, which may impact the accuracy of the simulations. In recent years, vertical water bodies have been introduced in cities to address space constraints; however, the ENVI-met model does not support such configuration. Another limitation of the tool was related to the wind speed—when wind speeds drop below 1.0 m/s, the model produces similar results, reducing its reliability.
The findings of this study provide valuable insights that can guide urban planners in integrating climate knowledge into planning practices. By incorporating blue–green infrastructure strategies, urban planners can effectively address climate-sensitive urban design, ensuring that cities become more resilient to rising temperatures and extreme weather conditions. This research highlights the importance of evidence-based planning approaches that balance ecological, thermal, and relative humidity to increase urban livability and sustainability.
Therefore, this and similar studies should be taken into account in urban design and planning practices from local to national scales. The results of these studies should be taken into account in legal regulations to be made in urban areas.

Author Contributions

Conceptualization, S.Y., Y.M. and E.J.; methodology, S.Y., Y.M. and E.J.; software, Y.M. and S.Y.; formal analysis, S.Y. and Y.M.; investigation, S.Y., Y.M. and E.J.; resources, S.Y. and Y.M.; data curation S.Y. and Y.M.; writing-original draft preparation, S.Y., Y.M. and E.J.; writing-review and re-editing, S.Y., Y.M. and E.J.; supervision, S.Y., Y.M. and E.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Administration System of Scientific Research Project (BAP), Ataturk University of TURKIYE (Grant No: FDK-2022-11528), (ADEP-YOK—Grant No: FBA-2024-13536—Grant No: FBA-2024-14152). The Scientific and Technological Research Council of Türkiye (TUBITAK 1001 Grant No-119O479) and the Turkish State Meteorological Service (MGM) for sharing their data free of charge.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Location of the study area, measurement sites (indicated by three green buildings) (top), and 3D view of the study area (bottom).
Figure 1. Location of the study area, measurement sites (indicated by three green buildings) (top), and 3D view of the study area (bottom).
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Figure 2. Devices used to conduct the field measurements, including a Lutron AM-4247SD anemometer humidity/temperature meter.
Figure 2. Devices used to conduct the field measurements, including a Lutron AM-4247SD anemometer humidity/temperature meter.
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Figure 3. Correlation between measured and simulated air temperature and relative humidity during August (top) and January (bottom).
Figure 3. Correlation between measured and simulated air temperature and relative humidity during August (top) and January (bottom).
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Figure 4. Spatial distribution of air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in August.
Figure 4. Spatial distribution of air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in August.
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Figure 5. Spatial distribution of air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in January.
Figure 5. Spatial distribution of air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in January.
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Figure 6. Air temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom).
Figure 6. Air temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom).
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Land 14 00891 g007
Figure 7. Relative humidity variation in the six proposed mitigation scenarios in August (top) and January (bottom).
Figure 7. Relative humidity variation in the six proposed mitigation scenarios in August (top) and January (bottom).
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Land 14 00891 g008
Figure 8. Mean radiant temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom).
Figure 8. Mean radiant temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom).
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Land 14 00891 g009
Figure 9. Wind speed variation in the six proposed mitigation scenarios in August (top) and January (bottom).
Figure 9. Wind speed variation in the six proposed mitigation scenarios in August (top) and January (bottom).
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Land 14 00891 g010
Figure 10. Physiological equivalent temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom). Land 14 00891 i013: 29.1–41.0 °C < warm–very hot; Land 14 00891 i014:13.0–29.0 °C slightly cool–slightly warm; Land 14 00891 i015: 8.1–4.0 °C ≤ cool–very cold.
Figure 10. Physiological equivalent temperature variation in the six proposed mitigation scenarios in August (top) and January (bottom). Land 14 00891 i013: 29.1–41.0 °C < warm–very hot; Land 14 00891 i014:13.0–29.0 °C slightly cool–slightly warm; Land 14 00891 i015: 8.1–4.0 °C ≤ cool–very cold.
Land 14 00891 g010
Table 1. Measurement values for the summer and winter periods.
Table 1. Measurement values for the summer and winter periods.
11 August (Summer)8 January (Winter)
TimeAir-T (°C)Relative-H(%)Wind-S
(m/s)		Air-T
(°C)		Relative-H(%)Wind-S
(m/s)
00:00 30.122.41.00.177.91.5
01:00 29.523.40.8−0.278.21.7
02:00 29.023.91.1−0.578.51.6
03:00 28.125.61.2−1.080.31.8
04:00 26.328.21.4−1.281.72.1
05:00 25.331.71.8−1.783.02.4
06:00 26.935.31.30.776.52.0
07:00 28.632.81.01.677.61.8
08:00 32.727.50.81.977.91.7
09:00 34.124.11.23.173.31.9
10:00 35.922.50.84.368.71.6
11:00 38.518.71.45.861.21.5
12:00 40.115.62.16.355.31.5
13:00 41.210.52.27.251.91.4
14:00 40.59.51.57.550.61.3
15:00 39.812.31.26.953.31.6
16:00 39.18.70.95.356.81.7
17:00 38.314.31.34.059.51.8
18:00 37.516.61.23.564.71.5
19:00 36.117.21.32.868.21.4
20:00 34.217.91.02.270.11.9
21:00 33.218.51.21.673.52.0
22:00 31.720.81.31.175.42.1
23:00 30.821.30.80.576.72.0
Table 2. Characteristics and 3D views of six proposed mitigation scenarios (red dot: measurement location; gray color: building roof; orange color: building façade; green color: vegetation).
Table 2. Characteristics and 3D views of six proposed mitigation scenarios (red dot: measurement location; gray color: building roof; orange color: building façade; green color: vegetation).
ENVI-met ViewsSketchUp Views
Scenario 1 Land 14 00891 i001existing conditions of the study areaLand 14 00891 i002
Scenario 2 Land 14 00891 i003the study area without vegetation Land 14 00891 i004
Scenario 3 Land 14 00891 i005+10% grass coverageLand 14 00891 i006
Scenario 4 Land 14 00891 i007+10% grass coverage
+20% increase in tree density		Land 14 00891 i008
Scenario 5 Land 14 00891 i009+10% increase in grass coverage
+20% increase in tree density
+10% water bodies		Land 14 00891 i010
Scenario 6 Land 14 00891 i011+10% increase in grass coverage
+20% increase in tree density
+10% water bodies
+extensive green rooftops		Land 14 00891 i012
Table 3. ENVI-met input data.
Table 3. ENVI-met input data.
LocationHilalkent Neighborhood
Climate typeUrban ecosystem
Simulation timeAugust and January
Total simulation time24 h for one alternative
Field size (x, y, z)41 m × 39 m × 15 m
Grid size (m) (x, y, z)5 × 4 × 3
Rotation (0° 360°) [0.0 N]0
Measurement time11.08.202308.01.2024
Basic meteorological inputUnshadedUnshaded
Average wind speed (m/s)1.21.7
Wind direction
(0:N.90:E.180:S.270:W)		90 °C90 °C
24 h average air temperature33.62.6
24 h average relative humidity20.869.6
Minimum air temperature (°C)/h25.3 °C/05:00−1.7 °C/05:00
Maximum air temperature (°C)/h41.2 °C/13:007.5 °C/14:00
Minimum humidity (%)/h8.7%/16:0050.6%/14:00
Maximum humidity (%)/h35.3%/06:0083.0%/05:00
Sky visibility ratioOpenOpen
Table 4. ENVI-met hourly simulation results.
Table 4. ENVI-met hourly simulation results.
SummerWinter
TimeAir Temperature (°C)Relative Humidity (%)Wind Speed (m/s)Air Temperature (°C)Relative Humidity (%)Wind Speed (m/s)
MinMaxAverageMinMaxAverageMinMaxAverageMinMaxAverage
01:0027.030.129.023.329.326.11.00.34.02.383.094.788.70.8
02:0026.629.628.623.829.926.61.00.03.21.684.496.590.60.8
03:0026.228.928.025.330.927.81.0−0.52.61.086.498.792.90.8
04:0025.528.026.927.232.229.61.0−0.72.20.687.8100.494.50.8
05:0024.927.426.129.334.032.01.0−1.21.70.189.4102.296.10.8
06:0025.227.726.831.036.134.41.00.72.01.082.4100.293.30.8
07:0025.929.028.031.136.333.91.01.42.41.782.899.793.40.8
08:0027.532.330.827.434.731.01.01.62.51.983.099.993.70.9
09:0029.833.832.824.430.827.70.92.43.72.879.497.191.30.9
10:0031.535.734.527.728.424.90.93.34.63.976.396.590.40.9
11:0033.738.136.819.225.321.10.94.25.95.169.792.585.80.9
12:0035.440.038.716.122.217.90.94.86.85.964.488.380.80.9
13:0036.541.339.711.518.914.40.95.47.66.660.584.376.70.9
14:0035.041.239.710.417.513.00.95.87.86.958.881.974.20.9
15:0034.540.939.312.418.614.00.95.77.56.660.982.274.60.9
16:0033.640.338.610.117.812.90.95.16.45.664.384.577.10.9
17:0032.639.337.713.820.315.20.94.05.14.366.787.580.00.9
18:0031.737.936.616.022.917.40.93.34.43.671.491.284.00.9
19:0030.136.135.017.325.018.00.92.83.93.174.994.187.10.9
20:0028.734.533.718.126.519.80.92.33.42.677.096.389.20.9
21:0027.733.632.718.727.820.50.91.83.02.280.498.791.90.9
22:0026.732.531.720.729.722.10.91.32.61.882.4100.793.80.9
23:0025.931.730.921.330.922.90.90.82.21.384.1102.495.50.9
Table 5. The thermal stress categories of the PET index [62].
Table 5. The thermal stress categories of the PET index [62].
PET [°C]Grade of Physiological StressThermal Sensitivity
≤4 Extreme cold stressVery cold
4.1–8 Strong cold stressCold
8.1–13 Moderate cold stressCool
13.1–18 Slight cold stressSlightly cool
18.1–23 No thermal stressComfortable
23.1–29 Slight heat stressSlightly warm
29.1–35 Moderate heat stressWarm
35.1–41 Strong heat stressHot
41< Extreme heat stressVery hot
Table 6. Simulation results on air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in August and January.
Table 6. Simulation results on air temperature, relative humidity, mean radiant temperature, physiological equivalent temperature, and wind speed for the six proposed mitigation scenario at 1:00 pm in August and January.
Period1. Scenario2. Scenario3. Scenario4. Scenario5. Scenario6. Scenario
Air temperature (°C)August33.234.032.932.331.931.7
January3.23.33.13.02.82.8
Relative humidity (%)August22.722.123.423.824.124.3
January87.687.387.987.388.088.1
Mean radiant temperature(°C)August37.744.136.835.835.635.3
January4.55.84.34.14.04.0
PET (°C)August39.440.539.339.038.738.3
January9.69.79.59.39.29.2
Wind speed (m/s)August0.91.00.90.90.90.9
January0.90.90.90.90.90.9
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Yilmaz, S.; Menteş, Y.; Jamei, E. Investigating the Effect of Blue–Green Infrastructure on Thermal Condition—Case Study: Elazığ, Turkey. Land 2025, 14, 891. https://doi.org/10.3390/land14040891

AMA Style

Yilmaz S, Menteş Y, Jamei E. Investigating the Effect of Blue–Green Infrastructure on Thermal Condition—Case Study: Elazığ, Turkey. Land. 2025; 14(4):891. https://doi.org/10.3390/land14040891

Chicago/Turabian Style

Yilmaz, Sevgi, Yaşar Menteş, and Elmira Jamei. 2025. "Investigating the Effect of Blue–Green Infrastructure on Thermal Condition—Case Study: Elazığ, Turkey" Land 14, no. 4: 891. https://doi.org/10.3390/land14040891

APA Style

Yilmaz, S., Menteş, Y., & Jamei, E. (2025). Investigating the Effect of Blue–Green Infrastructure on Thermal Condition—Case Study: Elazığ, Turkey. Land, 14(4), 891. https://doi.org/10.3390/land14040891

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