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Article

Cooling Performance of Green Walls Under Diverse Conditions in the Urban Zone of Lower Silesia

by
Grzegorz Pęczkowski
*,
Rafał Wójcik
and
Wojciech Orzepowski
Department of Environmental Protection and Management, Wroclaw University of Environmental and Life Sciences, Pl. Grunwaldzki 24, 50-363 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(1), 269; https://doi.org/10.3390/su18010269
Submission received: 14 November 2025 / Revised: 18 December 2025 / Accepted: 24 December 2025 / Published: 26 December 2025
(This article belongs to the Special Issue Green Infrastructure Systems in the Context of Urban Resilience)
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Versions Notes

Abstract

Green facades, commonly referred to as vertical plant systems, offer sustainable solutions. They improve the energy efficiency of buildings, reduce energy consumption, and positively impact the microclimate both at the microscale and at the urban level. Their ability to regulate temperature and improve thermal comfort, including mitigating the heat island effect, makes them a valuable element of sustainable architectural design. They also contribute to reduced energy consumption, reduced noise, mitigation of air pollution, and aesthetic and wind protection. The main goal of the study was to analyse the cooling effectiveness of green walls in a transitional temperate climate zone. The study was conducted on two experimental models located on the campus of the Wrocław University of Environmental and Life Sciences and at the Research and Educational Station in a suburban area. Both locations had different characteristics: the former contained urban development, while the latter contained open and sparsely developed areas. On warm and sunny days, the cooling effects of the systems were observed independently for both locations and their exposures. For data acquisition at a distance of 5 cm from the plants, a higher data concentration and a lower variability in the mean temperature drop were observed. In the same group, on sunny days, the cooling effect averaged 4–7 °C and depended on the location. On cloudy days, the mean maximum cooling in this group did not exceed 4 °C.

1. Introduction

Environmental awareness and sustainable development in relation to energy security, including reduced energy consumption, increasingly require the implementation of innovative solutions in urban areas. These will primarily be systems based on natural architecture, particularly green facades and green roofs. In the near future, they will undoubtedly become an integral element of most architectural solutions and are currently standard within global development strategies. Society faces the priority of achieving carbon neutrality, reducing the consumption of natural resources, and minimising the carbon footprint. Green facades mitigate climate change in cities and modify their ecosystems [1]. These systems mitigate the effect of urban heat island, reduce noise, and contribute to increased biodiversity [2,3].
It should be noted that their impact can vary significantly. It will depend largely on technical solutions, vegetation cover, and location.
In this experiment, the following research question was posed:
How does the aspect of a building’s location and façade orientation affect cooling efficiency?
The aim of this work is to analyse the cooling efficiency in a transition zone of temperate climate. The evaluation was conducted in two locations: the first with dense urban development and the second with open development.
The work is structured as follows: the methodology section describes experimental models of green walls and the characteristics of the facility locations. This section also discusses laboratory tests and experimental equipment. The next section presents the impact of climate on the thermal insulation of the systems, considering two weather scenarios.
The thermal properties were assessed, including maximum and average temperatures in the plant environment. The next section discusses the results and provides a discussion. The final section presents a summary and conclusions from the research.

The Impact of Green Walls

In the literature, green walls are defined as vertical vegetation systems offering sustainable solutions that improve the energy efficiency of buildings. Reducing energy consumption and positively impacting the microclimate at both the building and city levels is particularly important [4,5]. Their ability to regulate temperature, improve thermal comfort, and mitigate environmental impacts and the heat island effect make them a valuable element of sustainable architectural design. In densely populated areas, this can be particularly important in limiting and mitigating human impact on the environment [6,7,8,9,10,11]. They influence variables such as air temperature, humidity, and mean radiant temperature [12,13,14]. They also influence heat and mass exchange between the external environment, façades, and building surfaces, thus shaping the microclimate of the building [15,16]. The effectiveness of passive cooling increases with the level of solar radiation [17,18,19,20]. They generate economic stimulation, increase property values, and create new jobs in the horticulture and maintenance sectors. They can positively impact the value of properties where they are designed [21,22,23,24].
The properties and functionality of these systems depend on many factors. This is mainly due to the influence of atmospheric conditions, i.e., the climate at the regional level. Next, there are location conditions, including orientation and shading [4,25,26]. Therefore, climatic exposure will indicate factors such as wind speed, radiation, temperature, and relative humidity of the air. Consequently, it will have a direct impact on the performance of these systems [27]. The influence of many factors on the performance of green systems can pose difficulties and problems during analysis. A crucial aspect may be the appropriate selection of assumptions for future projects, primarily in terms of experimental measurement methodology. Therefore, characterisation of the microclimate should include multivariate analysis [28].
Considering Koppen-Geiger climate groups, the performance of green walls varies [29]. In the moderate climate group (C), a temperature improvement of up to 8 °C was observed in the built-up area [30]. Regarding the continental group (D), Varvara et al. demonstrated a reduction of 4.5 °C in Neubrandenburg, Germany. In the same group in Moscow, this reduction was only a maximum of 2.6 °C [31]. The highest temperature decrease for vertical green structures was observed in Hong Kong in the climate group (A), up to 8.4 °C [32]. In summary, it should be noted that a clearly visible climate mitigation is observed in warmer and drier climates. The influence of location, orientation, and compact development, including shading, is also notable. For example, Susca et al. [33] showed that the difference in temperature between the least and most overgrown areas in New York City was 2.0 °C. The influence of canyon width on the temperature decrease was also confirmed. Tall buildings, complex architecture, and narrow streets restrict wind circulation [34].
If the majority of the urban facades are green, the temperature reduction can reach 1 °C, which contributes to improved environmental comfort [35].
A model experiment that takes location and orientation into account provides information on the system performance. Several factors play a key role, in particular the urban canyon effect, radiation mitigation, and wind protection.

2. Materials and Methods

2.1. Design and Location

To evaluate the performance of the model green wall systems, a site was selected for future locations. The selection was primarily dictated by existing buildings and their orientation on the campus of the Wrocław University of Environmental and Life Sciences. The buildings significantly influenced the choice of orientation (east–west). The model in the second location was designed as a twin of the first. The same model is identical in terms of orientation, structure, panels, and plant species.
The experiment was carried out at two sites during the summer period from June to October 2023. The first site was located on the campus of the University of Environmental and Life Sciences in the centre of Wrocław, at latitude 51°10′ and longitude 17°3′. The second site was located at the Research and Educational Station of the University of Environmental and Life Sciences in the Swojec district, at 51°11′ and 17°14′, respectively. Both sites are in southern Poland, in the Lower Silesian Voivodeship, at an altitude of 117 metres above sea level. The entire area falls within the temperate transitional climate zone. The average annual air temperature for the 1991–2020 multi-year period is 9.6 °C, with a summer temperature of 15.8 °C. The warmest month is July with a temperature of 19.8 °C and the coldest is January (0.0 °C). The total annual precipitation for the 1991–2000 multi-year period is 576 mm, including 376 mm in the summer half-year (May–October). February is the month with the lowest precipitation (28 mm), and July with the highest precipitation (89 mm) [36]. Experimental models were used for the study, constructed as cuboidal wood-composite structures measuring 1.3 m × 1.3 m and 2.1 m high. The model solution was designed with three layered wood-based panels. To protect against weather conditions, the façade was covered with PVC panels. In the interior, 15 cm of extruded polystyrene (EPS) insulation was designed. The same EPS material was used for roof insulation. The roof cover was secured with standard bituminous material. The technical parameters and physical properties of the materials used are summarised in Table 1. The models in both locations were aligned in the same east–west direction. This model should be considered as a retention solution equipped with plant panels. The panels were designed and constructed from aluminium elements measuring 40 × 40 × 20 cm. The panel structure was coated in white. The panels were filled with a substrate and finally planted. The substrate was placed in prepared geotextile bags made of agrotextile with a base weight of 50 g m−2. To ensure proper growth conditions for the plants, a specially prepared substrate was used—a soil substrate. The substrate consists of garden soil, peat with a pH of 6.5, fine sand, and expanded clay aggregate in the garden—fine (4–6 mm) and coarse (10–16 mm). The substrate was prepared in the ratio above: 50:15:10:25. The basic retention properties and the soil water characteristic curve (SWCC) were determined using the HYPROP 2 measurement system [37]. The standard evaporation method is used for this measurement. Complete measurements were performed according to the procedure described by the manufacturer. Based on the measured data obtained, the characteristic values were determined, including the full saturation Qs = 0.858, the state at pF 2.0 and pF 3.0 of 0.55 and 0.39, respectively, and the readily available water at 16% by volume.
The vegetation panels were equipped with automatic controlled drip irrigation systems using pressure-compensated emitters with a capacity of 2 litres per minute. Irrigation was initiated three and four times per day, depending on weather conditions. Each panel was irrigated by two emitters with the same capacity as above. The volume of the panel with plants and substrate was 0.032 m3, and each emitter delivered two 33.3 cm3 of water per minute. The duration of each of the three irrigation sessions was two minutes. The final and fourth irrigation session was administered depending on the weather conditions and lasted 1.5 min. The plantings used were geranium and geranium macrorrhizum. In this case, geranium is a versatile plant, undemanding in terms of location, suitable for both sunny and semi-shady locations. It is also a plant of the Polish climate zone 5, resistant to temperatures below 20 °C.
Next, there are Heuchera alumroot varieties, including “Melting Fire” (Heuchera americana), “Palace Purple” (Heucher micrantha), “Coral Forest” (Heuchera sanguinea), and Sedum spectabile. In the case of Heuchera and Sedum, these are fully winter-hardy varieties, suitable for climate zone 4. In addition, there are grasses: Carex flacca and Carex Montana varieties, winter-hardy perennials, suitable for climate zones 5 and 6 (up to −20 °C). Each panel was planted with 9 plants of selected species, Figure 1.
For both locations, the Leaf Area Index (LAI) was measured using an AccuPar/LAI LP-80 Meter Group GmbH, München Germany (formerly Decagon Devices Inc.) device. This allowed for a general assessment of the condition and development of the plant. The measured values for the UPWr Campus, regardless of the eastern and western exposure, were slightly higher compared to the Swojec site. Similarly, in both cases, the thickness of the vegetation section did not differ significantly (Table 2).

2.2. Acquisition of Measurement Data

The experiment utilised a data acquisition system (Figure 2) consisting of an IoT subsystem designed around ESP8266 modules and a set of sensors to monitor environmental factors. The modules’ functionality enabled time correction using a connexion to the pool.ntp.org server and an NTP client. A detailed description of the data acquisition system in the following sections relates to the data used in the analyses. These included the drip irrigation control system, the data acquisition subsystem using an SQL database, and the application layer enabling real-time data review. Observations also included measurements of atmospheric air temperature, relative humidity, and wind speed. Solar radiation was measured on both horizontal and vertical surfaces. Temperature and humidity were measured using Humicap HMP155 sensors from Vaisala (Vantaa, Finland). Sensors were typically installed in a radiation shield. Wind speed measurements were taken using a Windcap WMT700 sensor from Vaisala. At the Swojec location, solar radiation was measured horizontally and vertically using a Kipp Zonen CMP11 pyranometer (Kipp & Zonen, Delft, The Netherlands) and on the UPWr SR20—D2 campus, a Hukseflux pyranometer (Hukseflux, Delft, The Netherlands). The former was measured in the 285–2800 nm range, and the latter in the 285–3000 nm range. Meteorological data were measured at a 1 min interval and recorded using a Vaisala QML201C (Vaisala, Vantaa, Finland).
For the purposes of this study, temperature data at characteristic points in the green systems were obtained on the basis of measurements using profile probes (a proprietary solution). The probes consisted of Maxim Integrated 18B20 sensors. According to the datasheet, measurements with the ds18b20 sensor can be made with an accuracy of +/− 0.3 °C. Therefore, the intended use of these sensors required measuring the accuracy and taking into account the calibration error. For control purposes, a linear correction method was used between the read value and the reference value. The temperature value was corrected for two points: 0 and 100 °C for each specimen. The programme code was designed to incorporate a constant offset value obtained from the calibration of each. The measurement concept is based on a 1-Wire serial bus. The integrated probe contained six temperature sensors connected to the ESP8266 Wi-Fi transmission modules. A 1 min interval was programmed for each module. This functionality allows connexion to a database located on a remote SQL server. The controller software was designed to provide connexions to an online web service, allowing real-time access to the database. Air temperature measurements in the immediate vicinity of the green model were taken right next to the plant panel, as well as at distances of 5 and 10 cm from the plants, and in the plant substrate and the air gap behind the panel in front of the model wall. These measurements were taken on the eastern and western façades, at the centre points of the plant panels.
The design of the rectangular models presented in this study does not simulate the thermal conditions within the structure. Moreover, it does not ensure the functional usability of the technical solutions. In the remainder of this paper, the geometric characteristics and thermophysical properties of the structural elements, such as conductivity and thermal capacity, will be omitted.

2.3. Data Analysis and Processing

Statistical analyses aimed at identifying significant differences between temperatures at specific points and assessing the impact of solar radiation, wind speed, and air humidity on cooling efficiency were conducted using Statistica v.13, Tibco software Inc. license Poland. Identification was performed using the nonparametric Kruskal–Wallis test, which is an equivalent of the classic Anova test. The use of this nonparametric test was necessary due to the characteristics of the data distribution.
The Shapiro–Wilk test at a level of p < 0.05 was used to reject the hypothesis of normality for the study population. Post hoc tests were used for confirmed cases of significant differences between groups. Dun’s test, including Bonferroni correction, was used to definitively identify groups with differences.

3. Results

Thermal Efficiency
The impact of meteorological and climate conditions on the thermal performance of vertical green structures was analysed for two weather scenarios: sunny and cloudy days. The summer of 2023 was characterised by air temperatures in the Wroclaw metropolitan area ranging from 12.1 to 35.5 °C, with an average of 21.4 °C, and in the Swojec district from 3.7 to 33.1 °C, with an average of 19.6 °C. Considering only sunny days, the average energy reaching the surface, depending on the location, was 0.7667 MW·m2·h−1 for the UPWr Campus.
In the case of the Swojec location, it was 1.485 MW·m2·h−1. The average wind speed in the same period was 0.74 and 1.97 m2·s−1, respectively. The following section presents the temperature distribution in the immediate vicinity of the plants, at a distance of 5 cm, and in the air gap behind the panel in front of the wall, against the background of solar radiation and air temperature. The graphs show a representative sunny and cloudy day, while the second is cloudy. In both cases, the data presented includes two facades: east and west (Figure 3 and Figure 4).
A warm sunny day (5 July) and a warm cloudy day (13 July) were selected for detailed analysis. For the first case (Figure 3), the maximum radiation for both locations varied; for the UPWR campus, it was 961.9 W·m−2, and in the Swojec district it was 1219.8 W·m−2. The maximum temperatures on this day were 30.2 and 30.3 °C, respectively. The time shift in the temperatures relative to the maximum radiation was 12 and 22 min. For green facades, the measured temperatures in the foliage (gr1), regardless of the facade, were significantly lower than the air temperature, at 19.6 °C. At an average distance of 5 cm from the plants (gr2), the temperature was observed to be approximately 5 °C higher. For both cases, the time shift to reach the maximum temperature in the relation—eastern elevation and western elevation—was nearly 60 min for the UPWr campus and 120 min for the Swojec district.
On a cloudy day on 13 July, the maximum radiation intensity for the UPWr campus was 828 W·m−2 and for the Swojec district, 884 W·m−2, with maximum air temperatures of 27.7 and 26.4 °C, respectively. On this day, despite the temperature reductions around the green façade (gr1 and gr2), it can be concluded that these properties were not controlled by radiation to the same extent as on the first date, regardless of the eastern and western exposures.
On both dates, on sunny and cloudy days in the late afternoon and at night, for two locations, the temperatures in gr1 and gr2 were higher than the temperature on the control wall and the air temperature.
To assess the impact of vertical greening on structures and the cooling effect on air temperature, data groups from the entire measurement campaign were analysed, taking into account the maximum cooling value. The maximum daily averages for the entire experiment for the east and west facades for two locations are presented in box plots (Figure 5 for sunny days, Figure 6 for cloudy days). The analysis included selected groups, including the cooling effect directly in the foliage (gr1), at a distance of 5 cm from the plants (gr2), and in the air gap behind the plant panels. In each of the cases considered, the characteristics indicate a significant cooling effect for all groups. For green surfaces in gr2 (5 cm from the plants), regardless of exposure and model location, a higher data concentration and lower variability can be observed. In the same group, on sunny days, the cooling effect, depending on the location, was 4–7 °C. On cloudy days, the maximum cooling in group gr2 (5 cm from the plants) did not exceed 4 °C (third quartile Q3) and did not exceed 8 °C for non-outlier observations. It is worth noting that the cooling efficiency for the model at the UPWr Campus was higher than for the model at Swojec. In the former case, it was 7.0 °C (maximum 12.0 °C) on the eastern façade, and in the latter, it was 4.2 °C (maximum 7.1 °C). Slightly smaller differences were observed on the western façade. Similarly, for the UPWr campus, it was 6.0 °C (maximum 10.1 °C) and for Swojec, 6.1 °C (maximum 8.2 °C).
Regardless of the chosen date for the cooling effect analysis and the location of the model, the maximum effect resulting from the difference between ambient air temperature and the façade temperature occurred in the air gap behind the panels.
Statistical tests allowed for a more detailed study of the thermal behaviour of green facades. For this purpose, the relationship between outdoor temperature and solar radiation intensity was assessed. The nonparametric Kruskal–Wallis test and the median test were used. The possibility of using Anova tests for normal distributions of the variables studied was previously excluded. Analysis with the Shapiro–Wilk test at the level of p < 0.05 for each group allowed for rejection of the hypothesis of normal distribution. Comparison of the median temperature reduction as a function of solar radiation intensity partially confirmed the relationship between groups a and c (up to 400 and 600–800 W·m−2) for both locations in the Swojec district and additionally in group d (>800 W·m−2). On cloudy days, this relationship could only be observed in the western exhibition area on the UPWr campus in groups a and c (up to 400 and 600–800 W·m−2), Table 3.
Relative air humidity (RH) affected cooling efficiency in a decreasing manner with increasing relative humidity. The maximum effect was observed for values between 40 and 60%. However, for both locations, a reduction in efficiency was observed at relative humidity <40% and >60%, Table 4.
The wind had an increasing effect on the cooling efficiency of vertical facades, reaching its maximum effect at v > 3 m·s−1. Comparison of median values between groups revealed no statistically significant differences at the level of p < 0.05, as confirmed by the Kruskal–Wallis test, Table 5.

4. Discussion

The cooling efficiency of vertical greenery systems is achieved through shading, evaporation, and additional insulation. These systems improve the energy efficiency of buildings and mitigate the effects of the urban heat island effect. However, their effectiveness varies according to climate, building conditions, design, and plant selection. In the case of the described experiment, the research area belonged to a transitional temperate climate zone. The experiment consisted of comparing the efficiency of two technically identical solutions in different locations. In the first case, this was a location typical of dense urban development, while in the second, the model was used in an open suburban area. In both cases, the characteristics tested demonstrated a significant cooling effect for both the green areas and the space behind the panel. For green areas, the cooling effect, depending on the location, ranged from 4 to 7 °C. On cloudy days, maximum cooling did not exceed 4 °C.
The differences in microclimate on the UPWr campus compared to the suburban area were mainly characterised by different temperatures and humidity. In the former case, the spatial structure of the buildings directly influenced changes in the direction and speed of the wind, and thus the scale of turbulent exchange. Sufficiently compact buildings could also indicate surface characteristics that absorb more thermal energy. Consequently, this state of affairs will result in higher temperatures.
The literature on the subject indicates that the thermal efficiency of green walls in urban street canyons is strongly dependent on their geometry. The height and orientation of the canyon may be particularly significant. The canyon’s shape will either increase or decrease the efficiency of green wall systems, primarily the cooling capacity.
Canyon geometry significantly affects efficiency; narrower and taller canyons typically increase shading and reduce solar exposure. As a result, we achieve an enhanced cooling effect. Gleichzeitig, such conditions will contribute to reduced air ventilation, potentially trapping heat and reducing thermal comfort [38,39,40,41]. Depending on the vegetation, green wall systems can create thick boundary layers. Thus, they will increase thermal resistance, restrict airflow, and reduce temperature [42].
The optimal orientation to achieve the maximum effect will vary depending on the ratio. It has been shown that a north–south orientation is most favourable when the canyon height-to-width ratio is 1. Other orientations become optimal as the ratio increases [40,43].
In our study, nonparametric Kruskal–Wallis tests and the median temperature reduction test as a function of solar radiation intensity confirmed the relationship between the groups for both locations, i.e., up to 400 and 600–800 W·m−2. In the case of the location of Swojec, this relationship was also observed in the group > 800 W·m−2. On cloudy days, this relationship was observed only at the Western Exhibition on the campus of the Wrocław University of Environmental and Life Sciences, in groups of 400 and 600–800 W·m−2. When considering other green solutions for urban areas, such as green roofs, we always achieve a heat island reduction effect through shading, water evaporation, and lower surface temperatures. Barriuso and Urbano [27] confirm the effects of reducing urban air temperature by up to 11.3 °C.
However, their results show that green roofs and walls have varying effectiveness in mitigting extreme climate events depending on the location. According to Wong et al. [44], green infrastructure has a cooling effect on the urban environment by providing shade and evaporation. Above-ground greenery reduces peak surface temperatures by 2–9 °C, while green roofs and green walls reduce surface temperatures by up to 17 °C.
The influence of wind direction is also related to the geometry of the city and the shape of the street canyon, with deeper canyons and specific wind directions that result in variable cooling effects [45,46]. East, south, and west-facing systems typically provide better cooling and energy savings than those in the north. The optimal result will always depend on the local climate and the direction of solar radiation. The location of these structures on the windward side limits air exchange to a greater extent than on the leeward side [47,48,49].

5. Conclusions

Two experimental green wall models were used to assess the impact of green walls on temperature and, consequently, system performance. The first was located at the Wrocław University of Environmental and Life Sciences campus in the centre of Wrocław, and the second was located at the Research and Educational Station in a suburban area. The two locations had different characteristics, characterised by dense urban development in the former and open urban development in the latter. A common feature was the climatic region, classified as a transition zone of temperate climate. Preliminary data from the entire experimental period, containing 2.4 million samples, and meteorological data, corresponding to nearly 500,000 samples for each station, were used in the detailed analysis.
Analysis of average temperature reductions on sunny and warm days revealed a significant cooling effect for green surfaces, regardless of the location and exposure of the model. Greater data concentration and lower variability were observed for the green gr2 surfaces. In the same group, the cooling effect on sunny days, depending on the location, was 4–7 °C. On cloudy days, maximum cooling in this group did not exceed 4 °C. It was also observed that the model located in the densely populated area of the city centre of Wroclaw had higher cooling efficiency compared to the model located in Swojec. In the former case, on the eastern facade, it was 7.0 °C with a maximum of 12.0 °C, and in the latter case it was 4.2 °C with a maximum of 7.1 °C. In the western facade, the trend was similar, but the efficiency differences between the models were smaller. Regardless of the date and location for which the cooling effect was analysed, the maximum effect resulting from the difference between atmospheric air temperature and facade temperature occurred in the air gap behind the panels. The daily temperature profile on the exterior surface of the wall behind the panels was practically flat. Consequently, this may result in greater thermal stability of the external wall. The air gap also acts as a thermal buffer and can increase the thermal stability of the wall. It was also found that the time delay of the temperature change on green surfaces on a warm, sunny day relative to maximum radiation was twice as long at the Swojec location compared to the UPWr campus (12 and 22 min). At the same time, in the case of air temperature, the time lag to reaching maximum temperature on the same surfaces relative to the eastern and western facades was almost 60 and 120 min, respectively.

Author Contributions

Conceptualization, G.P. and R.W.; methodology, G.P. and W.O.; validation, G.P.; formal analysis, G.P., R.W. and W.O.; resources, G.P. and W.O.; writing—review and editing, G.P., W.O. and R.W.; supervision, G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UPWrWroclaw University of Environmental and Life Sciences
Posterior air gapspace between the wall and the plant panel

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Figure 1. Experimental models on the campus of the Wroclaw University of Environmental and Life Sciences (a) and at the Research and Educational Station in the Wroclaw-Swojec district (b,c), cross-section of the panel with plants and a multi-probe (d).
Figure 1. Experimental models on the campus of the Wroclaw University of Environmental and Life Sciences (a) and at the Research and Educational Station in the Wroclaw-Swojec district (b,c), cross-section of the panel with plants and a multi-probe (d).
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Figure 2. Overview of system and data collection.
Figure 2. Overview of system and data collection.
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Figure 3. Air temperature and solar radiation as well as the temperature in the immediate vicinity of plants gr1 (gr1 foliage), at a distance of 5 cm from plants gr2 and in the air gap behind the panels on the eastern and western facades on a warm, sunny day on 5 July 2023.
Figure 3. Air temperature and solar radiation as well as the temperature in the immediate vicinity of plants gr1 (gr1 foliage), at a distance of 5 cm from plants gr2 and in the air gap behind the panels on the eastern and western facades on a warm, sunny day on 5 July 2023.
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Figure 4. Air temperature and solar radiation, as well as temperature in the immediate vicinity of plants gr1 (gr1 foliage), at a distance of 5 cm from plants gr2 and in the air gap behind the panels on the eastern and western facades on a cloudy day on 13 July 2023.
Figure 4. Air temperature and solar radiation, as well as temperature in the immediate vicinity of plants gr1 (gr1 foliage), at a distance of 5 cm from plants gr2 and in the air gap behind the panels on the eastern and western facades on a cloudy day on 13 July 2023.
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Figure 5. Maximum average cooling value for the entire test period t [°C] of the green surfaces gr1 (in plants), gr2 (5 cm from the plants) and in the air gap behind the panel in relation to air temperature on dry sunny days on the eastern and western facades for the location of the UPWr campus and the Swojec district.
Figure 5. Maximum average cooling value for the entire test period t [°C] of the green surfaces gr1 (in plants), gr2 (5 cm from the plants) and in the air gap behind the panel in relation to air temperature on dry sunny days on the eastern and western facades for the location of the UPWr campus and the Swojec district.
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Figure 6. Maximum average cooling value for the entire test period t [°C] of the green surfaces gr1 (in plants), gr2 (5 cm from the plants) and in the air gap behind the panel in relation to air temperature on cloudy days in the eastern and western facades for the location of the UPWr campus and the Swojec district.
Figure 6. Maximum average cooling value for the entire test period t [°C] of the green surfaces gr1 (in plants), gr2 (5 cm from the plants) and in the air gap behind the panel in relation to air temperature on cloudy days in the eastern and western facades for the location of the UPWr campus and the Swojec district.
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Table 1. Geometrical characteristics and physical properties of experimental models.
Table 1. Geometrical characteristics and physical properties of experimental models.
LocationStructure Dimensions [m]Layer Material (in Order from the Outside In)Layer Thickness
[m]		Total Thickness
[m]		Thermal Conductivity
λ
[W·m−1·K−1]		Density
[kg m−1]		Specific Heat Cp [J kg−1 K−1]
Campus Center, Wrocław University of Environmental and Life Sciences
Swojec District		1.3 × 1.3
×2.05		PVC panel
 
Composite oriented strand board (OSB)
EPS
Composite (OSB)		0.01
 
0.025
 
 
0.1
0.025		0.160.17
 
0.14
 
 
0.23		1.35
 
600
 
 
13.5		900
 
1700
 
 
1800
Table 2. Vertical greenery systems of the campus of the Wroclaw University of Environmental and Life Sciences and the Swojec district.
Table 2. Vertical greenery systems of the campus of the Wroclaw University of Environmental and Life Sciences and the Swojec district.
Experimental WallLeaf Area Index (LAI)Medium Thickness [m]Total [m]
WallGapPlants
Campus Center, Swojec District:
Geranium makrorrhizum, Heuchera alumroot,
Heuchera americana,
Heuchera Palace Purple, Heuchera sanguinea,
Carex flacca,
Carex Montana		2.5–4.0
2.5–3.5		0.160.040.25
0.20		0.45
0.40
Table 3. Averages of the median temperature reduction period in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of solar radiation intensity. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Table 3. Averages of the median temperature reduction period in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of solar radiation intensity. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Median Temperature Reduction Sunny Day Foliage 5 cmSolar Radiation Intensity on a Horizontal Surface (W·m−2)
0–400400–600600–800>800
Campus UPWr
Eastern exposure3.916.467.66 a7.91
Western exposure3.385.426.94 a7.22
Swojec district
Eastern exposure2.543.964.654.44
Western exposure0.97 c,d4.114.38 a4.91 a
Median Temperature Reduction Cloudy Day Foliage 5 cmSolar Radiation Intensity on a Horizontal Surface (W·m−2)
0–400400–600600–800>800
Campus UPWr
Eastern exposure3.405.075.965.57
Western exposure2.83 c4.145.56 a5.26
Swojec district
Eastern exposure2.924.184.425.26
Western exposure2.103.453.725.05
a,c,d median temperature values marked with a different letter differ statistically at the p < 0.05 level, Kruskal–Wallis test, a, c, d—groups, solar radiation intensity.
Table 4. Averages of the median period of temperature reduction in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of relative humidity. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Table 4. Averages of the median period of temperature reduction in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of relative humidity. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Median Temperature Reduction Sunny Day Foliage 5 cmRelative Air Humidity RH (%)
<4040–5051–6061–7071–80>80
Campus UPWr
Eastern exposure6.9 e7.81 e,f5.353.882.24 a,b0.38 b
Western exposure6.36.9 e5.05.042.17 b0.02
Swojec district
Eastern exposure4.25 f4.38 f3.973.371.64−1.5 a,b
Western exposure3.643.934.363.311.78−2.0
Median Temperature Reduction Cloudy Day Foliage 5 cmRelative Air Humidity RH (%)
<4040–5051–6061–7071–80>80
Campus UPWr
Eastern exposure-6.33 f4.914.373.561.45 a
Western exposure-5.834.034.243.391.19
Swojec district
Eastern exposure6.04.583.902.513.521.60
Western exposure4.161.942.423.023.921.57
a,b,e,f median temperature value marked with a different letter differ statistically at the p < 0.05 level, Kruskal–Wallis test, a, b, e, f—groups, relative air humidity.
Table 5. Period-averaged median temperature reductions in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of wind speed. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Table 5. Period-averaged median temperature reductions in the plant environment in the experimental models of the UPWr campus and the Swojec district during the day on sunny and cloudy days as a function of wind speed. Analysis using the nonparametric Kruskal–Wallis test, post hoc comparisons for mean ranks with Bonferroni correction.
Median Temperature Reduction Sunny Day Foliage 5 cmWind Speed v (m·s−1)
<0.50.5–1.01.1–2.02.1–3.0>3
Campus UPWr
Eastern exposure4.64.725.466.216.71
Western exposure4.144.144.785.625.76
Swojec district
Eastern exposure2.663.633.793.464.35
Western exposure4.603.193.462.783.82
Median Temperature Reduction Cloudy Day Foliage 5 cmWind Speed v (m·s−1)
<0.50.5–1.01.1–2.02.1–3.0>3
Campus UPWr
Eastern exposure3.333.994.424.585.11
Western exposure3.984.363.934.04.77
Swojec district
Eastern exposure0.592.792.423.014.02
Western exposure2.222.671.591.971.41
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Pęczkowski, G.; Wójcik, R.; Orzepowski, W. Cooling Performance of Green Walls Under Diverse Conditions in the Urban Zone of Lower Silesia. Sustainability 2026, 18, 269. https://doi.org/10.3390/su18010269

AMA Style

Pęczkowski G, Wójcik R, Orzepowski W. Cooling Performance of Green Walls Under Diverse Conditions in the Urban Zone of Lower Silesia. Sustainability. 2026; 18(1):269. https://doi.org/10.3390/su18010269

Chicago/Turabian Style

Pęczkowski, Grzegorz, Rafał Wójcik, and Wojciech Orzepowski. 2026. "Cooling Performance of Green Walls Under Diverse Conditions in the Urban Zone of Lower Silesia" Sustainability 18, no. 1: 269. https://doi.org/10.3390/su18010269

APA Style

Pęczkowski, G., Wójcik, R., & Orzepowski, W. (2026). Cooling Performance of Green Walls Under Diverse Conditions in the Urban Zone of Lower Silesia. Sustainability, 18(1), 269. https://doi.org/10.3390/su18010269

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