# Microclimatic analysis of the impact of irrigated vegetation in an experimental street canyon

Martina Garcia de Cezar, Severine Tomas, Bruno Cheviron, François Liron, Laurent Aprin

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Martina Garcia de Cezar, Severine Tomas, Bruno Cheviron, François Liron, Laurent Aprin. Microclimatic analysis of the impact of irrigated vegetation in an experimental street canyon. Current Research in Environmental Sustainability, 2025, 10, pp.100318. 10.1016/j.crsust.2025.100318. hal-05370331

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# Microclimatic analysis of the impact of irrigated vegetation in an experimental street canyon

Martina Garcia de Cezar<sup>a,b,*</sup>, Severine Tomas<sup>c</sup>, Bruno Chevron<sup>c</sup>, François Liron<sup>c</sup>, Laurent Aprin<sup>b</sup>

## ARTICLE INFO

Keywords:

Irrigation

Urban vegetation

Canyon street

Thermal comfort index

Microclimate

## ABSTRACT

Understanding microclimatic processes at the street-canyon scale is essential for advancing physical knowledge of urban heat and supporting effective adaptation strategies. This research investigates the influence of irrigated planters, containing climbing and shrub plants, on the microclimate of an experimental street canyon in Montpellier, France, monitored with a dense sensor network. The analysis focused on two heatwave days in August 2023, representative of the Mediterranean climate. Results show that under low wind speeds (<1 m s⁻¹), air temperature variability was mainly controlled by the distribution and intensity of solar radiation. Vegetation had a buffer and retardation effect on the warming of the nearby air regions, with mean air temperature difference between the vegetated and non-vegetated reference zones ranging from -0.17 to 0.44 °C. Comparisons between plant types indicated slightly higher air temperatures (-0.15 to 0.62 °C) and relative humidity increases (1.73 to 1.99 %) near climbing plants compared to shrubs. Irrigation produced no detectable short-term effect on air temperature, while only marginally increasing relative humidity. Thermal comfort assessment using the Universal Thermal Climate Index (UTCI) confirmed that vegetation and irrigation altered microclimatic dynamics but did not substantially reduce heat stress levels for pedestrians. Instead, radiative effects of vegetation and the morphology of the canyon—through shading and surface temperature reduction—were the dominant drivers of thermal comfort modulation in the canyon.

## 1. Introduction

Research interest in the quality of the urban microclimate is increasing rapidly due to growing urbanisation and demands for better urban environments and the revitalisation of cities, which is recognised as essential for sustainable urban development (Li et al., 2022a; Monteiro et al., 2024). The field of urban climatology was historically grounded in the study of the urban heat island (UHI), characterised by the difference in temperature between densely urbanised areas and their surroundings, especially at night (Rahman et al., 2022). While the UHI remains a central reference for understanding large-scale urban thermal patterns, contemporary research increasingly complements this perspective with microclimatic analyses at the pedestrian scale, including the assessment of thermal comfort indices. These dynamics are critical, as heat exposure strongly shapes health risks in cities (Wang et al., 2023; Yin et al., 2023). Among the various strategies to mitigate

urban overheating, vegetation is one of the most widely used and recognised for its effectiveness (Balany et al., 2020).

Despite this recognition, the role of specific types of vegetation under controlled irrigation regimes remains underexplored, particularly in street canyon contexts. The present study contributes to this field by examining the microclimatic dynamics of a street canyon, focusing on vegetation under different irrigation regimes. Specifically, it investigates two types of urban greenery — planters with climbing plants and shrubs — selected for their increasing application in Mediterranean cities such as Montpellier, France, due to their ease of implementation, high ornamental value, contrasting geometrical structures, and resilience under deficit irrigation conditions (Costello and Jones, 2014). These characteristics make them relevant for both practical implementation and scientific investigation. In addition, the study evaluates the short-term effects of irrigation on thermal comfort indices near vegetation, a factor recognised as influential but still insufficiently

<footnote><sup>a</sup> AgroParisTech, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote><sup>b</sup> GARANCE, IMT Mines Ales, Ales, France</footnote>

<footnote><sup>c</sup> INRAE, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote>* Corresponding author at: INRAE UMR G-EAU, 361 rue Jean-François Breton, 34090 Montpellier, France.</footnote>

<footnote>E-mail address: martina.garcia-de-cezar@inrae.fr (M. Garcia de Cezar).</footnote>
documented (Cheung et al., 2022b; Garcia de Cezar et al., 2025b; Mballo et al., 2021).

These local-scale interventions must be understood within the wider spectrum of adaptation strategies for outdoor thermal environments, which can be classified into three categories: environmental modifications (e.g., vegetation, shading, cooling materials), behavioural adjustments (such as clothing, activity patterns, and sun exposure), and psycho-social adaptations (including public engagement, climate awareness, and forecasting) (Shooshtarian et al., 2018). These approaches are consistent with guidelines for optimising street-canyon climates, which highlight the role of street geometry, orientation, and greenery patterns in reducing solar exposure, as well as the benefits of tree crown cover and irregular street layouts for improving shading and ventilation (Antoszewski et al., 2020; Elzeni et al., 2022). However, many of these recommendations are difficult to implement in consolidated urban areas and require context-specific evaluation (Antoszewski et al., 2020). Practical barriers—including feasibility, cost, limited effectiveness, and knowledge transfer—further complicate the transition from knowledge to action (Li and Liu, 2020; Shooshtarian et al., 2018).

The development of accurate numerical models remains essential to support reliable analysis and decision-making for the implementation of urban greenery. In this context, experimental approaches play a crucial role by providing detailed insights into complex environments such as urban canyons, as well as datasets for model validation. Several types of in-situ experiments have been conducted, depending on their objectives. Some studies rely on fixed measurements of air temperature and relative humidity at multiple neighbourhood points during short heatwave periods, typically with hourly acquisition intervals (Shi and Zhang, 2022; Zaki et al., 2020). Others employ permanent weather stations installed at approximately 2.5 m height to monitor air temperature, solar radiation, and wind speed across various urban morphologies over several months, with data collected at 1- or 5-min intervals (Jin et al., 2018; Yu et al., 2020). Mobile measurement campaigns represent another approach, deploying portable stations at pedestrian height (1.5 m) to record air temperature, radiation, humidity, wind and mean radiant temperature at high temporal resolution (e.g. 5 s). These stations are sequentially positioned at different locations, typically operating for 10 min per site with short travel and setup times (He et al., 2020). In some cases, physical measurements have been complemented with surveys of passers-by to capture subjective perceptions of thermal comfort, enabling comparison with objective climatic data (Huang et al., 2020; Ma et al., 2021; Xi et al., 2020). While these approaches provide valuable insights into the effects of materials, shading and vegetation, they often lack the spatiotemporal resolution necessary to fully characterise microclimate dynamics within a given street and to isolate the role of specific vegetation configurations.

Scaled outdoor experiments have been proposed as an alternative, offering greater control over vegetation, irrigation and other canyon parameters while preserving realistic meteorological forcing. The main challenge of this method lies in maintaining representativeness with real-world conditions. Previous studies have applied this approach to evaluate, for example, the impact of tree planting across different canyon geometries on pedestrian thermal and visual comfort (Chen et al., 2021a, 2021b), the cooling influence of water bodies (Syafii, 2021), the role of vegetation cover and climatic context (Kotopouleas et al., 2021), the effects of well-irrigated trees (Mballo et al., 2021), and the performance of green walls and roofs (Djedjig, 2015). However, very few studies have investigated the spatiotemporal variation of evapotranspiration from shrubs and climbing plants, or its implications for thermal comfort at the street scale. The only studies approaching this issue assessed either the influence of green façades on surface and air temperatures as well as mean radiant temperature at multiple monitored points (Tan et al., 2014), or the impact of irrigation regimes on thermal comfort in private backyards with grass (Cheung et al., 2022a). To date, no study has explicitly addressed the combined effects of irrigation and

vegetation water balance on thermal comfort within an urban canyon context.

Addressing this gap, the present study examines how variations in vegetation evapotranspiration, regulated through irrigation, affect thermal comfort indices at the street scale. More specifically, it aims to (i) identify the key drivers of thermal comfort during periods of urban overheating, (ii) assess the potential of planters with shrubs and climbing plants to mitigate urban thermal stress, and (iii) evaluate implications for the management of urban water resources, which are typically scarce in the hottest regions of the world.

## 2. Methodology

### 2.1. Scaled outdoor experiment

At the INRAE UMR G-Eau Montpellier campus (PRESTI, 2025), France, which has a Mediterranean climate, an outdoor experiment was set up to simulate a street with planters containing climbing (species *Lonicera Japonica*) and shrub (species *Abelia GrandiFlora*) plants under different irrigation regimes during the summer months of 2023.

To overcome the common limitations of reduced-scale experiments, particularly the difficulty of maintaining coherence with real-scale surface-atmosphere fluxes, the study employed a full-scale pilot. This pilot reproduces a representative small open street of 5 m wide and 12 m long, delimitated by concrete walls of 2.4 m high and 0.6 m wide. Sensors were strategically installed to capture daily variations in key variables (e.g. thermal comfort indices and evapotranspiration) and to evaluate their interaction with plant irrigation regimes and meteorological conditions. Fig. 1 provides an overview of the experimental device, including the spatial arrangement of the air sensors and the distribution of shade across the canyon at two different times of day (12 a.m. and 6 p.m.).

Table 1 summarises the main characteristics of the sensors installed in the air and in the planter soil. Further details—such as sensor calibration procedures, precise sensor locations, irrigation strategies, and complementary information ensuring experimental reproducibility, as well as the complete dataset from one year of monitoring—are provided in the data paper (Garcia de Cezar et al., 2025a). The methodological framework developed to progressively optimise urban irrigation practices, together with the quantification of evapotranspiration from the substrate-plant-atmosphere system in the planters, is described in detail in Garcia de Cezar et al. (2024).

### 2.2. Conceptual frame

The experimental installation measures the variables continuously to assess the impact of variations in plant irrigation on thermal comfort indices in the study area, specifically the Universal Thermal Climate Index (UTCI, Bröde et al., 2012). As described by Bröde et al. (2012), the UTCI is designed to evaluate outdoor thermal conditions of human biometeorology, providing a one-dimensional value that reflects the human physiological response to “multidimensionally-defined” thermal conditions. This index incorporates a human energy balance model based on a clothing model, a thermo-physiological model, and meteorological input data, including solar radiation (Rn), air temperature (Ta), mean radiant temperature (Tmrt), relative humidity (RH), and wind speed (Va). These variables are processed through a 6th order polynomial regression to yield an equivalent temperature, which correlates with an individual's stress level. The UTCI is a dynamic index characterised by an activity level corresponding to walking at 4 km h⁻¹ (metabolic heat production of 135 W m⁻²). It relies on a clothing model that calculates the level of insulation as a function of the Ta, based on observations representative of the clothing conditions of a person living in Europe and/or North America (Havenith et al., 2012). The limit conditions for the UTCI calculation are Ta between -50 °C and + 50 °C, Tmrt up to 30 °C below Ta and 70 °C above Ta, Va at 10 m between 0.5
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Illustration of experimental full-scale pilot developed by INRAE (UMR G-Eau Montpellier, France). Mapping of the “air sensors” used in the experiment, on the left of the figure, with the legend indicating the type of sensors. Shade variation in the experiment, on the right of the figure, on 21st August 2023 at 12 a.m. and 6 p.m. (simulations conducted with the Sketchup software).</figcaption></figure>

**Table 1**

List of sensors with their specifications. The sensors positioned in the air are identified with an acquisition time step of 30 s and the sensors positioned into the soil with an acquisition time of 10 min. ¹ Accuracy for estimating mean radiant temperature from the grey globe (Thorsson et al., 2007).

<table><thead><tr><th>Variable</th><th>Sensor name</th><th>Range of values</th><th>Accuracy</th><th>Number of sensors</th></tr></thead><tbody><tr><td>Air temperature</td><td>Thermocouples type T</td><td>0 to 350 °C</td><td>±0,5 °C or 4 %</td><td>48</td></tr><tr><td>Air temperature</td><td>Hygrovue5</td><td>+20 °C to +60 °C</td><td>±0,3 °C</td><td>9</td></tr><tr><td>Air temperature</td><td>Hygrovue10</td><td>+20 °C to +60 °C</td><td>±0,1 °C</td><td>6</td></tr><tr><td rowspan="2">Relative humidity</td><td rowspan="2">Hygrovue5</td><td>25 °C and rang of 0–80 % HR</td><td>±1,8 %</td><td rowspan="2">9</td></tr><tr><td>25 °C and range of 80–100 % HR</td><td>±3 %</td></tr><tr><td rowspan="2">Relative humidity</td><td rowspan="2">Hygrovue10</td><td>25 °C and rang of 0–80 % HR</td><td>±1,5 %</td><td rowspan="2">6</td></tr><tr><td>25 °C and range of 80–100 % HR</td><td>±2 %</td></tr><tr><td>Solar radiation</td><td>Pyranometer SKS 1110</td><td>0 to 5000 W m<sup>-2</sup></td><td>typ. &lt;3 %, 5 % max</td><td>5</td></tr><tr><td>Wind speed</td><td>Wind monitor 05103 young</td><td>0 to 100 m s<sup>-1</sup></td><td>±0,3 m s<sup>-1</sup> or 1 % reading</td><td>1</td></tr><tr><td>Wind direction</td><td>Wind monitor 05103 young</td><td>0 to 360° mechanical</td><td>±3°</td><td>1</td></tr><tr><td rowspan="2">Globe temperature</td><td rowspan="2">Grey Globe</td><td>355° electrical</td><td rowspan="2">±3.5 °C<sup>1</sup></td><td rowspan="2">15</td></tr><tr><td>-50 to +250 and rang of Wind speed 0.1–4.0 m s<sup>-1</sup></td></tr><tr><td>Water content</td><td>CS650</td><td>0 to 1 m<sup>3</sup> m<sup>-3</sup></td><td>>0,05 %</td><td>24</td></tr><tr><td>Soil temperature</td><td>Thermocouples type T</td><td>0 to 350 °C</td><td>±0,5 °C or 4 %</td><td>30</td></tr></tbody></table>

m s⁻¹ and 17 m s⁻¹ and RH below or equal to 100 %.

Fig. 2 provides a generic way of studying the propagation of irrigation effects on the microclimate characteristic of the substrate-plant-

atmosphere systems studied. Irrigation strategies and control (1) generate variations in soil water status, denoted by the experimental variables theta (soil water content) and psi (soil matric potential), which

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Illustration of the effects generated by varying irrigation on the Universal Comfort Climate Index (UTCI). Elements in light orange are linked to the potential evapotranspiration of the system (Eto), elements in blue are those altered by varying irrigation, elements within the square with dashed lines are the meteorological variables involved in calculating the UTCI. Numbers indicate 1- irrigation control, 2- microclimate variables linked to the occurrence of 3- actual evapotranspiration (AET), which has an impact on the 4- thermal comfort index. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
together with the type of plant and climatic characteristics (2) dictate evapotranspiration (3) in the study area. The intensity of evapotranspiration close to the plant generates a variation in three out of the four meteorological variables (RH, Ta, Tmrt) used to calculate UTCI (4), thus inducing a specific variation in this index. This study aims to assess to what extent irrigation control is capable of favourably affecting UTCI values, in the available experimental setting and potentially in any other setting involving irrigation, based on the same proposed methodology.

The cause-effect analysis this article aims to perform is complex to achieve because the experiment takes place outdoors. Natural variations in the climatic conditions are combined with intended modifications in irrigation strategies. Thus, seeking associated perturbations (the δ terms in Fig. 2) in the variables of interest is challenging. Even more generally, it is difficult to determine whether changes in the key control variables of UTCI (known to be Tmrt and Va) were induced by the experimental setup or brought in by natural fluctuations in the local microclimatic conditions (Provençal et al., 2016).

The present data analysis strategy developed divides sensor distribution into specific zones and utilises the mean of measurements within each zone, assuming homogeneity in those zones and facilitating result interpretation. Additionally, this approach reduces measurement uncertainty: the uncertainty of the mean tends to be lower than that of individual sensors. This reduction is based on the standard error of the mean, which is approximately the individual sensor uncertainty divided by the square root of the number of sensors in each zone (Farrance and Frenkel, 2012). The data analysis followed these steps:

(i) Selection of two heatwave days with the most comparable climatic conditions. The most intense heatwaves in the Mediterranean region of Montpellier are characterised by temperatures of up to 35 °C during the day and 22 °C at night for at least three consecutive days (montpellier.fr). This step aimed to verify the consistency of trends observed across both days. The selection process followed a prioritised sequence of climatic parameters, ensuring the closest values in the following order: Rn, Va, Ta, and RH.

(ii) Wind conditions were analysed for the two selected days and their influence on the spatial variation in temperature was checked using the thermocouple sensors, considering the average temperature on the west side (zone 01) and east side (zone 02), as shown in Fig. 3. The difference in air temperature on the two sides of the pilot was analysed for different wind intensities and times of the day, sorted in the following categories: Night Late (0 am – 6 am), Morning Early (6 am – 11 am), Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), Afternoon Late (5 pm – 8 pm), Night Early (7 pm – 12 pm). The temperature values of each zone refer to the average of the values of the sensors per zone, with an effect on the overall accuracy: for thermocouples with an

accuracy of ±0.5 °C, the average of 12 sensors yields an accuracy of about ±0.15 °C.

(iii) The impact of vegetation in the experimental street was assessed by establishing two reference zones without plants (Z05 and Z06) and two reference zones with plants (Z03 and Z04), using thermocouple sensors, as shown in Fig. 4(a). The analysis involved comparing the air temperature differences (taking the mean of all the sensors within each zone) in average, maximum, and minimum values between the different zones with and without vegetation. For thermocouples with an accuracy of ±0.5 °C, the average of 4 sensors gives an accuracy of about ±0.25 °C. To evaluate the impact of each plant type and the dynamics in the area closest to the vegetation, sensor lines equipped with thermohygrometers, identified in Fig. 4(b), (c), and (d), were used. The analysis also concerned the comparison of the differences in averages, minimum, and maximum values of air temperature and relative humidity in each zone. The accuracy of the analyses carried out for the average air temperature with thermohygrometers is at least ±0.20 °C, ±0.13 °C and ± 0.17 °C for the zones in Fig. 4(b), (c), and (d), respectively. The average accuracy of the relative humidity analyses carried out with thermo-hygrometers is at least ±1.27 %, ±0.80 % and ± 1.00 % for the zones in Fig. 4(b), (c), and (d), respectively.

(iv) Given the high number of variables studied to assess the dynamics in Fig. 2, a Pearson correlation matrix was used to evaluate their relationships. Additionally, the maximum, mean, and minimum differences across these variables in the analysed zones were calculated to quantify the range of variation at each time of day. Reference zones Z12 for climbing plants and Z16 for shrub zones (Fig. 4(d)) were selected taking the mean of all the sensors within each zone to analyse the correlation in the variations in air temperature, relative humidity, grey globe temperature, mean radiant temperature, and UTCI. Furthermore, differences in soil water content and soil temperature between the climbing plant and shrub sides were examined to assess the correlation between the microclimatic dynamics near each vegetation type and the effects of evapotranspiration.

## 3. Analysis of microclimate data in an experimental urban street canyon

### 3.1. *Selection of two overheating days*

The summer months of 2023 in Montpellier refer to the period between 21st June and 23rd September. The results for this period of solar radiation variation in the experimental device, rainfall intensity, wind speed, air temperature and relative humidity variations are shown in Fig. 5. These variables are the criteria used to select the most suitable two overheating days for more detailed investigation.

The combined analysis of multiple climatic variables lead to the identification of two overheating days with highly comparable characteristics. As shown in Fig. 5, several heatwave events occurred during the campaign, with the most intense episode—both in terms of temperature magnitude and duration—taking place between 19th and 24th August. Across these six days, solar radiation, wind speed, and relative humidity exhibited only minor fluctuations, no precipitation was recorded, maximum air temperatures consistently exceeded 35 °C, and daily averages were close to 30 °C. Supplementary material provides a detailed description of the climatic conditions for these six days, as well as the quantitative comparison of differences between consecutive days, confirming that 20th and 21st of August were the most comparable overheating days.

Regarding irrigation, the planters were equipped with drip systems, each with a single emitter per plant delivering a flow rate of 1.9 L h⁻¹,

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Illustration of zones defined for data analysis concerning the impact of wind speed and direction on air temperature, thermocouples on the west side (Z01) and east side (Z02) of the experimental urban canyon.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Illustration of zones defined for data analysis concerning the impact of plant implementation and the impact of each type of plant in the experimental urban canyon. (a) Impact of plant implementation with Z05 and Z06 as reference zones without plants and Z03 and Z04 as reference zones with plants. (b) Impact of each plant type with Z07 as reference climbing plant and Z08 as reference shrubs. (c) Analysis of the dynamics in the area closest to the plants with Z09 west line, Z10 centre line and Z11 east line. (d) Impact of each plant type and dynamics in the area closest to the plants with horizontal lines references Z12 closest to the climbing plants, Z13 second closest to the climbing plants, Z14 centre line, Z15 second closest to shrubs and Z16 closest to the shrubs.</figcaption></figure>

operated in 30-min doses according to the criteria defined for the experimental campaign. On 20th August, plants were subjected to deficit irrigation conditions, whereas on 21st August they were maintained under well-watered conditions. The latter regime applied the maximum irrigation frequency possible without drainage losses from the planters. This irrigation strategy is described in detail by Garcia de Cezar et al. (2024), while the specific schedules and irrigation frequencies for each plant type and day are reported in Table 2.

## 3.2. Impact of wind speed and direction

The distributions of wind speed and direction for 20th and 21st August 2023 are presented in Fig. 6. The results reveal a similarity in wind speed distribution between the two days: wind speed is below 1 m s⁻¹ approximately 80 % of the time, for the 30-s time steps analyses. This is considered a low wind speed by the literature (Oh and Kato, 2018; Xu et al., 2021). Indeed, field observations indicate that speeds around 1 m s⁻¹ are almost imperceptible to the human body and only slightly influence the orientation of the anemometer.

With respect to wind direction, there is a clear predominance of East-West wind channelling for approximately 90 % of the time in the 30-s time step dataset. However, a slight divergence in the predominant direction is observed between the two days. On 20th August, the predominant wind direction was from west to east 51 % of the time, while on 21st August, the direction was from east to west 49 % of the time.

Wind speed and direction are factors that modify air temperature distribution by driving convective processes and facilitating the exchange of air masses between the street canyon and its surroundings (Oh and Kato, 2018; Xu et al., 2021). However, the position of the sun impacts the position of the drop shadows thus the pattern of direct and diffuse radiation, which also have a concurring impact on temperature variations and patterns (Sanusi et al., 2016). In accordance, Fig. 7 shows a sequence of photos that illustrates the migration of shadows between different hours in the afternoon of 2nd of August 2023. To analyse the combined influence of wind speed and solar radiation, wind speed and air temperature variations are plotted over an entire day, divided into time slots of a few hours to highlight different temporal patterns (Fig. 8). Given that wind speeds were predominantly low on the selected days (20th and 21st August 2023), the air temperature differences observed between Zone 01 (Z01) to the west and Zone 02 (Z02) to the east of the

experimental setup are primarily driven by variations in incident solar radiation, modulated by the sun's position throughout the day.

For both days, from 6 am (Morning Early) to 5 pm (Afternoon Early), the east side (Z02) remained warmer for a longer period than the west side (Z01), with an average temperature difference between the sides ranging from 0.3 °C to 0.5 °C, assuming an accuracy of ±0.15 °C. The period classified as Afternoon Late, from 5 pm to 8 pm, represents the time of day when the solar radiation distribution across the experimental setup becomes more uniform, gradually shifting towards the west (Fig. 7). As a result, the data showed a shift in the temperature difference between the two zones, with an increased number of higher temperatures measured on the west side and the average difference between the sides dropping to 0 °C for both days. In the Night Early period, the west side was recorded as warmer for most of the time on both days, consistent with the stronger solar incidence on the west side later in the day, hence with the hypothesis of heat storage and delayed release as radiating heat. Furthermore, this difference on 21st August was more constant (91 %) and intense (0.2 °C), probably due to the fact that the wind speed remained in the 0–0.5 m s⁻¹ range for longer (94 %), which limited aerodynamic mixing.

Finally, during the Night Late, two different behaviours were observed between 20th and 21st August. The Night Late period of 20th August followed a cooler day, with an average temperature of 26.3 °C on 19th August, compared to 29.0 °C and 29.1 °C on 20th and 21st August, respectively. As a result, similar temperatures were recorded on both sides, with an average difference of 0 °C. In contrast, the Night Late of 21st August followed a warmer day with intense solar radiation, which likely led to greater heat accumulation on the west side, followed by a progressive heat release.

In summary, the east-facing side was warmer during the day (from Morning Early to Afternoon Early), while the west-facing side dominated in the Afternoon Late and Night Early due to heat storage and delayed release. The contrasting behaviours observed on 20th and 21st August Night Late emphasise the influence of antecedent thermal conditions and limited wind-driven mixing on nocturnal warming.

## 3.3. Impact of vegetation type and irrigation

### 3.3.1. On temperature

The impact of vegetation was estimated by analysing the variations
<figure><img src="image_6.png" /><figcaption>Fig. 5. Daily rainfall and solar radiation data from five sensors distributed across the pilot area, the WS_Pyrano sensor is located at the meteorological station in the centre of the pilot site, while Pyrano_1 to Pyrano_4 form a north-south line, with approximately 1-m spacing between each sensor, starting 1 m from the northern wall (a). Weather data collected at the centre of the pilot, showing wind speed together with the daily maximum, minimum and average of air temperature (b), then daily rainfall, daily maximum and daily minimum of air relative humidity (c). Daily-averaged data from 21st July to 23rd September 2023.</figcaption></figure>

<table><caption>**Table 2**

Irrigation scheduling applied to climbing and shrubs planters on the two selected overheating days.</caption><thead><tr><th>Plant type</th><th>20th August 2023</th><th>21st August 2023</th></tr></thead><tbody><tr><td>Climbing plants</td><td>2 irrigations (8 am and 6 pm)</td><td>3 irrigations (8 am, 12 am, 6 pm)</td></tr><tr><td>Shrubs</td><td>1 irrigation (8 am)</td><td>3 irrigations (8 am, 12 am, 6 pm)</td></tr></tbody></table>

in air temperature in reference zones without vegetation on the west and east sides of the pilot (Z05 and Z06 respectively) and in zones influenced by plants, to the west (Z03) and to the east (Z04) of the canyon (Fig. 9). Vegetated zones remained cooler for most of the analysed period on

both days, except during the Afternoon Early period (2 pm – 5 pm), when they exhibited higher temperatures. This coincided with the peak solar radiation intensity, which reached nearly 890 W m⁻² at 2 pm in the centre of the canyon. To assess the relationship between solar radiation intensity and temperature differences between zones, during periods of high solar radiation, a Pearson correlation (R) analysis was conducted (Eq. (1) (Freeman et al., 2007)).

$$R = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}} \quad (1)$$

R quantifies the strength and direction of a linear relationship between two variables, x and y, as defined in Eq. (1). Its values range from
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Graphs showing the distribution of wind speed and direction on August 20th (orange bars) and 21st (blue bars), 2023, based on temporal resolution of 30 s. Wind speed is categorized into different intervals. Wind direction is represented as percentages for the four cardinal points: west (W), south (S), east (E), and north (N). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Sequence of photos of the experimental device showing the variation in shade at different times on 2nd August 2023.</figcaption></figure>

−1 to 1: *R* > 0 indicates a positive correlation (both variables increase together), *R* < 0 denotes a negative correlation (one variable increases while the other decreases), and *R* = 0 suggests no linear relationship. The Pearson correlation between the mean temperature difference across different zones and solar radiation are presented in Table 3.

The correlation analysis between air temperature differences and solar radiation variation highlights the distinct thermal behaviour of vegetated and non-vegetated zones. The three periods with the highest solar intensity, along with their average radiation levels over the two evaluated days, were Morning Late (814 W m⁻²), Afternoon Early (784 W m⁻²), and Afternoon Late (328 W m⁻²).

The weak to moderate negative correlation observed between Z05-Z06 suggests that temperature variations in these non-vegetated reference zones are influenced by solar radiation but not as the dominant factor, indicating the presence of additional mechanisms regulating air

temperature. In contrast, the strong negative correlation in Z06-Z04 and Z05-Z03 during Morning Late (−0.812 and −0.637) and Afternoon Late (−0.726 and −0.725) suggests that the vegetated zones (Z04 and Z03) effectively mitigate temperature increases associated with solar radiation, reinforcing the cooling effect of vegetation.

Additionally, the weak correlation between Z06-Z04 and Z05-Z03 in Afternoon Early (0.245 and −0.083, respectively) indicates that vegetation alters the thermal response of the canyon, reducing the direct impact of radiation on air temperature. These results support the hypothesis that vegetation plays a significant role in local cooling, particularly during periods of intense solar radiation. However, further investigation into wind dynamics and evapotranspiration effects (possibly linked to irrigation) is needed to fully understand the interactions between radiation, airflow, and vegetation in shaping the microclimate within the canyon.
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Data are presented at 30-s intervals for the 20th and 21st of August. Variation in wind speed and average air temperature for reference zones in the west (Z01) and east (Z02) within the experimental setup. Different coloured sections in the graph represent the periods Night Late (0 am – 6 am), Morning Early (6 am – 11 am), Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), Afternoon Late (5 pm – 8 pm), Night Early (7 pm – 12 pm).</figcaption></figure>

Fig. 10 presents the average air temperature in the reference zones for 20th and 21th August, with an analysis accuracy of ±0.25 °C. The corresponding maximum and minimum differences between periods, calculated from the observed maximum and mean temperatures, are summarised in Table 4.

These results highlight the consistency of temperature patterns between the two days and provide an indication of the magnitude of temperature dynamics within the study area. In addition, the analysis revealed a higher inertia in the vegetated zone in terms of raising air temperature in the Morning Early period. Notably, the two vegetated zones, east and west (2.5 m apart), exhibited a significant temperature difference, suggesting an intensified cooling effect of air near the plants. A similar relationship was identified in a study that examined the impact of air temperature near a vegetated wall (Tan et al., 2014). The study found that vegetation influenced the temperature at a distance of more than 2 m from the wall, with a time lag of around 2 h in reaching the peak temperature. A maximum temperature difference of 1.5 °C was recorded between a concrete wall and a vegetated wall.

To assess the effect of plant type (climbing and shrub species) on air temperature during the hottest periods of the day (11 am to 8 pm), horizontal (East-West direction), vertical (North-South direction), and central profiles were analysed to evaluate the dynamics of the study area for the two greening strategies (Fig. 11).

Comparing temperature dynamics in the centre of the vegetated zones (Fig. 11b, zones Z07 and Z08) reveals that the side with climbing plants recorded higher maximum temperatures. The maximum temperature differences between the two zones were 0.9 °C and 1.0 °C during Morning Late, 0.8 °C and 0.7 °C during Afternoon Early, and 0.2 °C and 0.3 °C during Afternoon Late on 20th and 21st August, respectively. In terms of average temperatures, the shrub zone remained cooler for most of the time, except during Afternoon Late, when both zones exhibited similar temperatures, with a mean difference of 0 °C on both days. During other periods, the shrub zone was cooler for nearly 100 % of the time, with average temperature differences of 0.5 °C and 0.6 °C in Morning Late on 20th and 21st August, respectively, and 0.3 °C in Afternoon Early on both days. The similarity in temperature dynamics and ranges across the two days reinforces the consistency of the results. It is also important to note that the measurement precision for the analysed zones is ±0.2 °C. Additionally, the temperature differences between the two vegetated zones may be influenced by variations in solar exposure and shading on each side of the canyon, although the impact of this factor was not able to be quantified in this analysis.

Regarding the average temperature variation across the three

vertical lines from east to west (Fig. 11c, zones Z09, Z10 and Z11), the absolute variation was lower than in previous analyses, indicating less effects in this direction.

Additionally, the average temperature difference across the five horizontal lines between the two types of planters (Fig. 11d, zones Z12 to Z16), spaced 0.8 m apart, was analysed. During the hottest hours, the northernmost line near the climbing plants (Z12) remained warmer for most of the time, except during the Afternoon Late period. Conversely, the second southernmost line (Z15) had the lowest temperatures almost all the time in every period studied. The average temperature differences between the southern zones and Z12 ranged from 0 °C to 0.21 °C, 0 °C to 0.34 °C, 0.17 °C to 0.81 °C, and – 0.15 °C to 0.62 °C for zones Z13, Z14, Z15, and Z16, respectively, with a measurement accuracy for these zones of ±0.13 °C. It is worth noting that the zones after the areas closest to the planters (Z13 and Z15) showed lower temperatures than zones touching the planters (Z12 and Z16). This could be explained by the planter's aerodynamic roughness reducing wind speed in its vicinity or by the heating effect of the wood and substrate, which likely warmed the air in close proximity to the planters, or by the heating of the concrete blocks that more strongly influence the north side of the street.

### 3.3.2. On relative humidity

For the zones corresponding to the vertical lines (Fig. 4c, zones Z09, Z10, and Z11), variations in relative humidity were significantly lower than the analysis accuracy of ±0.8 %. Therefore, no further analysis is conducted for these zones.

In contrast, for the horizontal zones (Fig. 4d, zones Z12 to Z16), relative humidity dynamics near the vegetation showed that the area closest to the climbing plants recorded higher relative humidity levels compared to the area near the shrubs, as illustrated in Fig. 12. The absolute differences between the average relative humidity between the zones revealed a maximum difference of 2.3 % and 2.6 % between Zones 12 (Z12) and 16 (Z16) on 20th and 21st August, respectively, during the Morning Early period. The highest average difference between zones was found in the Afternoon Late period for both days, where the average difference was 1.7 % and 2.0 % on 20th and 21st August, respectively, again between Z12 and Z16, and with an analysis accuracy of ±1 %. It is noteworthy that the two analysed days corresponded to different levels of water stress for the plants; on 20th August, the plants were under greater water stress, while on 21st August, they were in a more comfortable condition. This may have influenced a greater difference in the impact of evapotranspiration around the plants, leading to more dynamic behaviour in this variable on 21st August.
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Air temperature variation for 21st August 2023 at 5 pm. Results (a) for the air temperature distribution of each sensor and (b) for the average temperature of each zone are presented in a figure of the pilot, where the shadow projected in the figure corresponds to the real situation for the day and time.</figcaption></figure>

**Table 3**

Pearson correlation analysis between the difference in average temperature in zones Z03, Z04, Z05 and Z06, according to the variation in solar radiation for the periods Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), Afternoon Late (5 pm – 8 pm). All correlation values are for *p*-value greater than 0.05; meaning that the correlation are statically robust.

<table><thead><tr><th rowspan="2">Zones</th><th colspan="3">Correlation with solar radiation variation per period</th></tr><tr><th>Morning Late</th><th>Afternoon Early</th><th>Afternoon Late</th></tr></thead><tbody><tr><td>(Z05 – Z06)</td><td>0.238</td><td>-0.441</td><td>-0.137</td></tr><tr><td>(Z06 – Z04)</td><td>-0.812</td><td>0.245</td><td>-0.726</td></tr><tr><td>(Z05 – Z03)</td><td>-0.637</td><td>-0.083</td><td>-0.725</td></tr><tr><td>(Z04 – Z03)</td><td>0.524</td><td>0.422</td><td>0.346</td></tr></tbody></table>

**3.4. Correlation between study variables and UTCI**

For the Pearson correlation analysis between the study variables—soil water content (δθ), soil temperature (δTs), air temperature (δTa), relative humidity (δRH), grey globe temperature (δGG), mean radiant temperature (δTmrt), and UTCI (δUTCI)—two key aspects were

considered when selecting the analysis periods and time step.

As detailed in Section 2.2, one of the boundary conditions for UTCI calculation is that wind speed at 10 m must be between 0.5 m s⁻¹ and 17 m s⁻¹. Based on the logarithmic law of Bröde et al. (2012), this implies that at 1.3 m above ground, where the anemometer in this study is positioned, wind speed should range between 0.35 and 12 m s⁻¹. To ensure compliance with this criterion, the analysis was restricted to the period between 10 am and 8 pm, as wind speeds outside this range fall below 0.35 m s⁻¹. The selected analysis periods were Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), and Afternoon Late (5 pm – 8 pm).

Regarding the time step for correlation analysis, the experimental air-related data were recorded at 30-s intervals, while soil data from the planters were recorded at 10-min intervals. To maintain consistency, the correlation analysis was conducted using a 10-min time step for both air and soil data, even though a time lag likely exists between variations in soil water content and their potential effects on air humidity and UTCI through evaporation and transpiration processes.

To evaluate the representativeness of the chosen time step, Fig. 13 compare UTCI variations using both a 10-min time step (applied in the
<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Data are presented at 5-min intervals for the 20th and 21st of August. Variation in wind speed and average air temperature for reference zones without vegetation in the west (Z05) and east (Z06), as well as reference zones with vegetation in the west (Z03) and east (Z04) within the experimental setup. Different coloured sections in the graph represent the Night Late (0 am – 6 am), Morning Early (6 am – 11 am), Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), Afternoon Late (5 pm – 8 pm), Night Early (7 pm – 12 pm).</figcaption></figure>

<caption>**Table 4**</caption>

Maximum and minimum temperature differences between periods, calculated from the observed maximum and mean air temperatures in the reference zones non-vegetated and vegetated for 20th and 21st of August (accuracy ±0.25 °C).

<table><thead><tr><th rowspan="2">Day</th><th colspan="2">Maximum temperature differences</th><th colspan="2">Average temperature differences</th></tr><tr><th>Min.</th><th>Max.</th><th>Min.</th><th>Max.</th></tr></thead><tbody><tr><td>20th August 2023</td><td>0.16 °C<br>Night Early</td><td>1.04 °C<br>Morning Early</td><td>-0.15 °C<br>Afternoon Early</td><td>0.44 °C<br>Morning Early</td></tr><tr><td>21st August 2023</td><td>0.27 °C<br>Night Early</td><td>0.9 °C<br>Morning Late</td><td>-0.17 °C<br>Afternoon Early</td><td>0.39 °C<br>Morning Early</td></tr></tbody></table>

Pearson correlation matrix) and a 5-min time step for 20th and 21st August, respectively. Since UTCI represents an equivalent temperature (°C) linked to a thermal stress category perceived by an individual, the level of detail in daily temperature fluctuations between the two time steps, while present, does not significantly impact the interpretation of thermal stress variations in the study area. The differences observed between the two time steps were minimal and did not alter the overall dynamics of the calculated stress category. Thus, the 10-min time step used for the correlation analysis is considered appropriate.

The Pearson correlation matrix (Fig. 14) was applied to assess the relationships between microclimatic and vegetation variables, focusing on Z12 (northernmost, near climbing plants) and Z16 (southernmost, near shrub plants) within the canyon (Fig. 4d). The objective was to identify the strongest correlations in the study area regarding UTCI variation and to evaluate the potential impact of evapotranspiration on the microclimate near the planters. Regarding evapotranspiration, previous studies (Garcia de Cezar et al., 2024) have shown that the experimental setup leads to higher evapotranspiration rates for climbing plants compared to shrub plants. Furthermore, the plants experienced distinct hydric conditions on the two days, which may have influenced evapotranspiration dynamics and its effects on UTCI. During the Morning Late and Afternoon Early periods on 20th August, the planters were under greater water stress, whereas on 21st August, they were in a more comfortable state and were irrigated at 12 pm. During the Afternoon Late period, both planters were irrigated at 6 pm on both days.

The absolute variation in UTCI between the climbing plant zone (Z12) and the shrub zone (Z16) reached a maximum of approximately 3 °C, with an average difference of 2.1 °C, over both days. Among the microclimatic variables influencing UTCI oscillations, mean radiant temperature and grey globe temperature emerged as the most strongly correlated (positively) and thus the primary drivers of UTCI fluctuations (Fig. 14). This aligns with findings in the literature (Provençal et al., 2016), reinforcing that radiative contributions are the dominant factor influencing thermal comfort variations in the studied domain.

Regarding the potential impact of evapotranspiration on the microclimate near the planters, the dynamics of relative humidity and soil water content variation differed between the two days. On 20th August, during the Morning Late period, no correlation was observed between differences in soil water content, relative humidity, and UTCI. In contrast, on 21st August, following irrigation at 12 pm, a negative correlation emerged between soil water content variation and UTCI, suggesting that greater differences in soil moisture, indicative of higher evapotranspiration, were associated with lower UTCI values. During the Afternoon Early period on 20th August, soil water content variation also exhibited a negative correlation with UTCI, indicating that as the soil dried out, UTCI increased. On 21st August, irrigation-induced fluctuations in soil water content during the Afternoon Early period resulted in an initial increase followed by a decrease in the difference between the zones, mirroring UTCI oscillations and indicating a positive correlation between these variables. Finally, during the Afternoon Late period, irrigation was applied at 6 pm on both days, with consistently negative correlations between soil water content variation and UTCI.

Given the specific conditions analysed in this study, such as the time periods and context (planters with different plant species and varying evapotranspiration potentials), we lack comparable studies to directly evaluate the consistency of our results regarding the dynamics of relative humidity and evapotranspiration with UTCI. However, a similar scenario found in the literature examines the impact of different irrigation regimes in a private backyards (Cheung et al., 2022a). In that study, correlations for the daytime period were -0.43 and 0.51 between soil moisture difference and relative humidity with the UTCI index. For the night-time period, the correlations were 0.33 and 0.51 for the same variables. In our study, where the side with climbing plants indicates higher relative humidity and a variation in water content that is also higher (but with lower values), we have a difference in RH that increases
<figure><img src="image_12.png" /><figcaption>Fig. 11. Air temperature variation for 20th August 2023 at 11 am. Results for the air temperature distribution between sensors (a) and for the average temperature of each zone, i.e. Z07 and Z08 (b), Z09 to Z11 (c) and Z12 to Z16 (d) are presented. The drop shadow corresponds to the real situation for the day and time indicated.</figcaption></figure>

and a difference in water content that also increases, which justifies the same sign between the correlations.

# 4. Discussions

## 4.1. Irrigation, shrubs and climbing plants as an cooling strategy for urban street canyon

The evaluation of the impact of irrigated shrubs and climbing plants in mitigating urban overheating at pedestrian level is a topic that has been experimentally understudied and thus requires detailed investigation (Fu et al., 2022). The data acquisition time step and spatial discretization are crucial for accurately assessing the strategy's effect on urban thermal comfort indices, as these indices reflect specific, localised pedestrian experiences at various times of the day (Li et al., 2022b). Predicting the cooling effect of irrigating various vegetation types adds further complexity, as cooling magnitude depends on factors such as plant phenology, microclimatic conditions, height, leaf area index, wind speed, and soil type (Cheung et al., 2022b).

In the present study, despite the absence of a reference point (i.e. canyon with planters without vegetation), the spatial (0.8 m grid) and temporal (30 s) resolution of the experiment enabled the identification of specific behaviours in different delineated analysis zones. The data analysis focused on identifying the difference in characteristics in the zones near the north side of the experimental street with climbing plants and the south side with planters of shrubs. The side with shrubs maintained lower temperatures during the days evaluated, with a maximum difference of 0.2 °C to 1 °C and the climbing plants recorded the highest relative humidity, with a difference between the types of plants of a maximum of 2.6 % and an average of 2 % on 21st August. These relationships between the types of plants were emphasised by comparing two days with similar climatic conditions and comparing the performance of these plants in different water states. It suggested an increase in relative humidity on the side of the climbing plants, but did not

significantly vary the difference in air temperature and mean radiant temperature. In terms of variation in the UTCI thermal comfort index, no significant variation was identified that could improve thermal comfort in the zone with more intense irrigation.

Regarding the spatial impact of each type of plant separately, no studies were found on the same species. However, Wong et al. (2010) examined the impact on air temperature at distances of 0.15, 0.30, and 0.60 m from three types of vegetated walls. In that study, no significant temperature effect was detected beyond 0.60 m from the walls and the effects of vertical greenery systems on ambient temperature are found to depend on specific vertical greenery systems. In contrast, Tan et al. (2014) found that green walls influenced the temperature at a distance of more than 2 m from the wall. Concerning shrubs, only one other experiment was identified in which they had no cooling effect on air temperature at pedestrian height (1 m) and at a distance of 1–3 m from the shrubs (Zhang et al., 2022).

In relation to the effect of irrigation on the UTCI comfort index, Cheung et al. (2022a) observed a significant impact of 0.4 °C with 4 mm of irrigation in private backyards. However, this change was insufficient to alter the thermal stress level of the area. They highlighted that a variation of at least 7 °C in UTCI would be required to shift the stress category (Bröde et al., 2012). Cheung et al. (2022a) also emphasised that changes in mean radiant temperature and incoming shortwave radiation are primary orders influencing this issue, neither of which are directly affected by irrigation.

Thus, the potential of irrigation to reduce the UTCI should be understood as an “indirect process”, operating primarily through its capacity to alleviate water stress in urban vegetation. Under water stress, plants tend to close their stomata, which suppresses evapotranspiration, elevates leaf surface temperature, and reduces their overall cooling performance. By contrast, irrigation maintains or enhances evapotranspiration, thereby regulating leaf energy fluxes (Kibler et al., 2023), lowering leaf temperature, and in some cases promoting denser foliage growth, which amplifies the cooling effect.
<figure><img src="image_13.png" /><figcaption>**Fig. 12.** Difference in relative humidity on (a) 20th and (b) 21st August 2023, showing the maximum (MAX), minimum (MIN), and mean (MEAN) differences between Z12 and Z13 (12–13), Z12 and Z14 (12–14), Z12 and Z15 (12–15), and Z12 and Z16 (12–16). The analysed periods refer to Night Late (0 am – 6 am), Morning Early (6 am – 11 am), Morning Late (11 am – 2 pm), Afternoon Early (2 pm – 5 pm), Afternoon Late (5 pm – 8 pm), Night Early (7 pm – 12 pm).</figcaption></figure>

The extent to which irrigation reinforces this effect depends on the specific characteristics of each vegetation type, particularly their resistance to water and heat stress (Santamouris et al., 2018). Most studies addressing the influence of water stress on the cooling potential of urban vegetation rely on modelling approaches, with limited validation against experimental data directly comparing irrigated and non-irrigated conditions. This lack of validation remains a notable gap in the literature. One of the few available experimental studies (Thierry, 2024), conducted on *Malus Coccinella* 'Courtarou' in pots within a reduced-scale urban canyon, demonstrated that water restriction led to a rapid decline in transpiration (–81 % on average) by the second day compared with well-irrigated trees. This reduction was associated with a decrease in leaf area (from 2.5 to 1.8 m² per tree) and LAI (from 2.4 to 1.7 m²/m²), which in turn reduced radiative interception (from 83 % to 76 %). Despite this decline, the trees retained a notable capacity to intercept radiation and to mitigate human thermal stress, even after eight weeks of water restriction and during periods of extreme heat.

In this way, irrigation not only prolongs but also amplifies the cooling effect of different vegetation types, mitigating the decline in performance that would otherwise occur under water stress. These aspects are critical for urban planning, particularly in cities facing limited water resources, where the selection of drought-tolerant species can reduce irrigation demand while maintaining vegetation cover.

## 4.2. Limitations and outlook

The analysis in this study focused on two days of extreme heat in the experimental setup, characterised by sunny conditions and low wind speeds, to isolate and highlight the spatial and temporal impacts of irrigating climbing and shrub plants in planters. While these specific conditions may not be generalisable to cloudy or less extreme weather, projections for the coming decades suggest that such overheating days will become more frequent and intense (Perkins-Kirkpatrick et al., 2024). Therefore, the findings from these two days could be indicative of typical future summer conditions, revealing the importance of adapting urban strategies for these scenarios. Nonetheless, a more comprehensive analysis is needed, considering different weather patterns, seasons, and irrigation regimes.

Further investigation into the influence of various elements in the study area, including plant roughness and potential interference from instrumentation, is crucial. Intrusive instrumentation could disrupt airflow patterns and create localised hotspots, impacting the spatial dynamics of the variables analysed (Wong et al., 2010). Previous studies have shown that high vegetation density or leaf coverage generally produces cooler environments. However, measuring this temperature reduction presents challenges: (i) the temperature decrease alone is relatively small, around 0.1 °C, and (ii) surrounding air movement due to wind diffuses the already minimal temperature difference
<figure><img src="image_14.png" /><figcaption>**Fig. 13.** Variation in equivalent UTCI temperature (°C) for (a) 20th and (b) 21st August, between 10 am and 8 pm, with 10-min (solid line) and 5-min (dashed line) time steps. The purple lines refer to the Z12 zone close to the climbing plants (to the north), while the grey lines refer to the Z16 zone close to the shrubs (to the south), as shown in Fig. 4d. The column on the right of the graph indicates the thermal stress categories associated with the different equivalent UTCI temperatures. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

(Jayalakshmy and Philip, 2010). In this study, some observed variations approached sensor resolution limits, adding complexity to the interpretation. Nonetheless, these findings provide practical insight into the extent of influence exerted by the studied elements.

Regarding the accuracy of the analysis and the sensors used, caution is required when interpreting the results due to their proximity to the sensor resolution limit. The method employed, which involved dividing the study area into zones, proved to be an effective strategy for simplifying the complex study environment. This approach mitigated challenges in interpreting an environment with multiple variables and high microclimatic variability. Zoning highlighted the most significant variables in the domain and improved analytical precision. Concerning the uncertainty of the UTCI thermal comfort index evaluated in this study, complementary analyses are underway to estimate the uncertainty of the grey globe sensor fabricated in our laboratory. However, studies indicate that even in worst-case scenarios, where measurement uncertainty leads to a variation of ±6 °C in mean radiant temperature, this would result in a variation of only ±2 °C in UTCI, which is considered acceptable (Weihs et al., 2012). This is because UTCI integrates multiple microclimatic variables (five in total), so even if the most influential variable has moderate precision, the error propagation is mitigated when combined with the other variables.

As a future perspective, this analysis will further evaluate the suitability and potential of the grey globe sensor in estimating one of the key

parameters in such studies: mean radiant temperature. Its advantages include being more cost-effective, smaller, and less intrusive in the analysed environment compared to the three-direction radiometer method, albeit with lower precision. Additionally, compared to the black globe, it reaches thermal equilibrium more quickly, has an emissivity more representative of the human body, and studies suggest it offers better precision than the black globe (Grosdemouge, 2020).

Further investigation is also needed into the performance of the radiation shelters used in this study. The naturally ventilated shelters used in the present study are more susceptible to radiative measurement errors, which may affect the spatial variation of the recorded variables (De Podesta et al., 2018; De Ridder et al., 2023). The accuracy of microclimatic measurements, particularly in complex environments, remains an active area of research. Studies indicate that both passive and actively ventilated shelters can introduce measurement errors depending on microclimatic conditions (Yang et al., 2019). Consequently, efforts are directed towards developing new types of instrumentation, as well as improving existing systems through a better understanding and modelling of error sources, in order to enhance the reliability of microclimatic analyses (Yang et al., 2021).

## 5. Conclusion

This study examined the microclimate of an experimental street
<figure><img src="image_15.png" /><figcaption>Morning late Pearson's correlation on 20th August</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Afternoon Early Pearson's correlation on 20th August</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Afternoon Late Pearson's correlation on 20th August</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Morning late Pearson's correlation on 21st August</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Afternoon Early Pearson's correlation on 21st August</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Afternoon Late Pearson's correlation on 21st August</figcaption></figure>

<caption>**Fig. 14.** Pearson correlation matrix and table containing the average, maximum and minimum between differences in soil water content (δtheta), soil temperature (δTs), air temperature (δTs), relative humidity (δRH), grey globe temperature (δGG), mean radiant temperature (δTmrt), and UTCI (δUTCI). The air-related variable differences compare Z12, representing the average of the horizontal line of sensors closest to the planters with climbing plants (north of the device), with Z16, horizontal line closest to the shrubs (south of the device). Soil-related variables reflect differences in soil water content and surface soil temperature between the easternmost planters, comparing climbing plants with shrubs. The analysed periods refer to Morning Late, Afternoon Early and Afternoon Late on 20th and 21st August 2023. Only the statistical significant (*p* < 0.05, *t*-test) correlations are shown, with the darker red cells indicating the most negative correlations and the darker blue cells the most positive correlations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</caption>
canyon influenced by planters with climbing and shrub plants under varying irrigation conditions. The results showed that, under low wind speeds (<1 m s⁻¹), air temperature variability was primarily driven by the distribution and intensity of solar radiation. Vegetation delayed the rise in air temperature, particularly during the morning, resulting in cooler temperatures near the plants throughout the day, except between 5 and 8 pm. The average air temperature variation due to vegetation ranged from -0.17 to 0.44 °C. *Maximum* differences in air temperature and relative humidity between zones with climbing and shrub plants indicated that the northern zone near climbing plants recorded higher air temperatures and relative humidity. Irrigation slightly increased relative humidity, but no effect on air temperature was detected.

Overall, the findings suggest that irrigation and plant type used in the specific context of this experiment can influence the microclimate and contribute to small reductions in air temperature, although these effects are not sufficient to substantially modify thermal comfort indices. This highlights the importance of considering irrigation as operating primarily through its capacity to alleviate water stress in urban vegetation. By maintaining or enhancing evapotranspiration, irrigation regulates leaf energy fluxes, lowers leaf temperature, and, in some cases, promotes denser foliage growth, thereby amplifying the cooling effect and contributing indirectly to reductions in incoming shortwave radiation and mean radiant temperature, which are the UTCI key drivers.

Future research should investigate the seasonal influence of irrigation on urban vegetation, as well as undertake cost-benefit analyses of such practices. From a planning perspective, particular attention should be given to cities facing limited water resources, where the selection of drought-tolerant species can minimise irrigation demand while maintaining vegetation cover. Such evidence-based strategies are essential for guiding urban design and policy in the context of climate change adaptation.

## Credit authorship contribution statement

Martina Garcia de Cezar drafted the original manuscript, performed data curation, and contributed to visualisation, methodology, conceptualisation, and formal analysis. Bruno Cheviron, Séverine Tomas, and Laurent Aprin reviewed the manuscript, contributed to visualisation, methodology, data curation, and editing, and were responsible for project administration and supervision. Martina Garcia de Cezar and François Liron oversaw the overall supervision of data acquisition.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We acknowledge the supporting of the PRESTI INRAE UMR G-Eau technical team for their support in building and maintaining the experiment and the team of developers for their support in assembling the experiment dataset.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crsust.2025.100318.

## Data availability

Data will be made available on request.

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