An Examination of the Thermal Comfort Impacts of Ficus altssima on the Climate in Lower Subtropical China during the Winter Season

Abstract

As a common practice in urban landscape design, tree planting plays an important role in improving the environment and microclimate. This study aimed to investigate the thermal comfort effects provided by trees on the surrounding environment. Using the common tree species Ficus altissima growing in lower subtropical China, the variation in temperature, humidity, and wind speed due to the tree canopy was summarized, the intensity of transpiration and cooling effects was analyzed, and the regression relationship between the indicators and thermal comfort was investigated using the physiological equivalent temperature (PET). The results revealed that various indications for thermal comfort may be described separately by one-dimensional regression equations, and three viable multiple regression equations could be created using the PET by combining physical, physiological, and microclimatic parameters.

1. Introduction

Tree cover has an important impact on reducing air temperature, increasing air humidity, and regulating wind fields [1], and plays an important function in improving the urban microclimate and enhancing residents’ quality of life. Green space infrastructure, including tree planting, can improve thermal comfort—that is, the human perception of the thermal environment—of urban outdoor spaces in hot climates; this perception is essential for understanding the relationship between green space and thermal comfort [2]. Outdoor thermal comfort can be studied and explained by different models and indicators, such as the physiological equivalent temperature (PET) [3,4], COMFA model [5], index of thermal stress (ITS) [6], universal thermal climate index (UTCI) [7], standard effective temperature (SET or SET*) [8], and predicted mean vote (PMV) [9]. The application and study of these indicators help urban planners to identify potential risk reductions associated with vegetation and to develop effective strategies to improve urban microclimates [1]. The results generated by these metrics can also be seen as an opportunity to improve outdoor thermal comfort conditions and as an important step towards sustainable urban development [10].

The number of studies that go beyond the mere air temperature lowering effect of trees by putting more emphasis on parameters that influence the heat balance of humans is continuously growing [4]. This has been accompanied by the emergence of various approaches based on the impact of trees on thermal comfort, including, first, the study of human-biometeorological parameters to assess thermal comfort in different climates and seasons [4]. These parameters are often obtained with the help of mobile or fixed meteorological stations, using calculations based on the observation of temperature, humidity, wind speed, global (solar) radiation, and atmospheric pressure. Second, questionnaire research methods have been used to study residents’ subjective perceptions of thermal comfort under specific climatic conditions [2], but the errors of such methods are relatively large, so they are often used as auxiliary indicators for analysis. Third, using remote sensing inversion and spatial information extraction methods, green space types containing trees and urban layouts at different scales are established and used to assist in analyzing the thermal comfort conditions brought about by urban greening patterns [11]. Finally, computer simulation software, such as ENVI-Met and RayMan, has been used to establish a simulation platform of trees, defining each physical parameter, physiological parameter, and meteorological background value, which can then output the thermal comfort value after platform calculation.

Presently, experimental operations on the effects of trees on thermal comfort can be divided into three categories: field measurements, numerical simulations, and their combination. Field measurements include accurate scale and scale model measurements, while numerical simulations are mainly performed by computer and wind tunnel experiments. Researchers worldwide utilize fixed weather stations and mobile measurements [2] to understand the principles and mechanisms by which the microclimate is improved by trees. Some have investigated the influence of tree height-width ratio, combination mode, and orientation [7,12], while others have analyzed the influence of different canopy and planting densities under various climatic zones [10]. Scaled outdoor experiments have been adopted in several studies [13], which have examined the effects of urban indicators on the thermal environment of a city and have reported the substantial cooling potential of tree planting on the wall and air temperature [14]. Numerical simulations of vegetation are primarily reflected by the changes in various indicators, which are used to compare the results and select the optimal scheme. Examples include the leaf area index (LAI) [15], leaf area density (LAD) [16], root area density (RAD) [17], surface vegetation cover [11,18], tree coverage ratio [18], vegetation species [12], density, and arrangement [19,20]. A hybrid approach that combines wind tunnel measurement and numerical simulations has been further proposed to examine the effect of vegetation at the pedestrian level [21].

Varying conclusions have been reached regarding the effects of trees on microclimates from various perspectives, either through measurements or simulations. Different tree types impact radiation flux in the environment due to their variable sizes and canopy characteristics. Different temperature gradients form in and around vegetation, and their performance varies during the day and at night. Rahman et al. compared Tilia cordata Mill. and Robinia pseudoacacia L. [22] and found that tree species with a higher LAI indicated better below-canopy surface cooling. Shahidan and Jones examined forest canopy attributes under tropical climate conditions and determined that a denser foliage cover and branching habit resulted in the trees providing a more significant thermal radiation filter [23]. Massetti et al. investigated the effects of a deciduous tree species (Tilia × europaea L.) on surface temperature over various ground materials and on human thermal comfort, with an emphasis on tree shade variations due to leaf fall [6]. They found that the said species had demonstrated a two-fold benefit in terms of conditions. Sabrin et al. assessed the cooling benefits of street trees in Philadelphia, which has a humid subtropical climate, primarily considering morphological elements. They evaluated summertime thermal comfort by examining various human-biometeorological indicators such as the mean radiant temperature (MRT) and physiological equivalent temperature (PET), utilizing the RayMan model [24]. Feng et al. found that mango tree transpiration consumed 40% (sunny) to 60% (cloudy) of a canopy’s total daily energy and continued to play a role on cloudy days, with the vegetation absorbing the heat of the surrounding air [25]. Liu et al. used ENVI-MET simulations on four different types of trees (Michelia alba, Mangifera indica, Ficus microcarpa, and Bauhinia blakeana) and discovered that the model data were more stable than the measured data [26]. Leaf surface temperature, steam flux, air temperature, and humidity were lower than the observed values.

Through a review of the literature, we found that most models and indicators only consider the meteorological parameters of the environment and the heat balance of the human body in the calculation, which makes it difficult to link to the detailed properties of the objects, such as specific plant species, health conditions, branching forms, or physiological levels. Meanwhile, most of the current Chinese studies have focused on the quantitative effects of certain design elements on microclimate factors, but their effects and mechanisms are not deep enough. There are not enough single and multinomial regression equations to depict the specific associations between variables. To further clarify the effects of trees on climate and human comfort, we assessed the regulation and effects of mature Ficus altissima trees on thermal comfort in winter climates. Combining human-biometeorological parameters with computational models, we dissected the regression relationships between the thermal comfort indicator PET and various factors. Specifically, we set the targets of this study as follows:

(1)

Exploring the specific role and performance of F. altissima in various microclimatic indicators;

(2)

Revealing the distribution pattern of thermal comfort in the shade of F. altissima trees;

(3)

Comparing transpiration in different orientations of the tree canopy and differences in thermal comfort response;

(4)

Investigating the effects of various indicators on the PET and explaining the changes in the PET in combination with physical, physiological, and climatic parameters.

Compared to urban tree studies in other climatic zones, scholars tend to focus more on the response of and changes in plant ecology in hot summer climates of tropical and subtropical cities, but this study complements the information and changes on the physiological and physical properties of subtropical urban trees in the direction of adaptation to environmental microclimates and shows that cold season conditions should not be ignored. Especially for residents of tropical or subtropical cities, winter is more comfortable and suitable for people to stay than summer, while spring and autumn seasons are short, so the outdoor thermal environment in winter should be the focus of research. Therefore, it is of considerable importance and urgency to study the climatic response of major urban greening tree species under winter conditions. F. altissima was chosen as the subject of this study because it is often used as an urban greening species in lower subtropical China, and it is also widely distributed elsewhere in the world. However, it has been poorly studied in terms of climate response, thus constituting a gap in this field. This study creatively integrates factors from various directions to explain PET, establishing a link between physical, physiological, and meteorological factors, changing the traditional single-factor model with higher explanatory accuracy. It also coordinates knowledge of ecology, urban planning, design, landscape design, botany, and other multidisciplinary disciplines, so it may serve as a guide and reference for the development of interdisciplinary disciplines.

2. Material and Methods

2.1. Study Objects

This study focused on the performance of trees in regulating microclimates. We selected Ficus trees, which thrive on the open grassland of the campus of Guangdong Ocean University, as the primary research object. Being evergreen, F. altissima is a typical urban green tree species in lower subtropical China. With its thick leathery leaves, developed root system, broad crown, large tree size, and capacity for isolated planting, it provides a visual impact even in isolation and has a good shading effect. To study its microclimate effect, the canopy distribution, branching, leaf orientation, transpiration, shading effect, temperature, and humidity regulation of this species were evaluated.

2.2. Study Area and Time

Guangdong Ocean University (110°18′32″ E, 21°9′11″ N) in Zhanjiang (a city in China) was selected as the study site. The site has a typical lower subtropical monsoon climate, with its most comfortable period being from November to March. The research period was January, which typically had a mix of cloudy and sunny days, and the average daily temperature was 14–20 °C. The field measurements were carried out for six days, as shown in

Table 1. Experimental investigation schedule.

Dates	Weather	Measurement Position	Measurement Items	Data Validity	Data Completeness	Whether to Use
6 January 2021	Cloudy, light rain	Measuring points 1–7	Microclimate data	×	√	×
7 January 2021	Medium rain	Measuring points 1–7	Physiological data	×	×	×
10 January 2021	Cloudy	Measuring points 1–7	Physical and physiological data	√	√	√
12 January 2021	Sunny	Measuring points 1–7	Physical and physiological data	√	√	√
13 January 2021	Sunny	Measuring points 1–7	Microclimate data	√	√	√
14 January 2021	Sunny	Measuring points 3, 4	Microclimate data and physiological data	√	√	√

. Due to weather effects and instrument limitations, valid data were only available for four days, on January 10 (cloudy), 12 (sunny), 13 (sunny), and 14 (sunny). The physical and physiological data of the plants were measured on the 10th and 12th, and the microclimate data were measured on the following two days.

2.3. Measurement Items

The experiment used small samples as subjects; small sample measurements have been applied to a number of studies in microclimate programs rich in relevance. For example, Takács et al. collected meteorological data on five trees in a city and constructed integrated human-biometeorological indices [27]. Kántor et al. used two human-biometeorological stations for the empirical measurements of a great Sophora japonica, which provided evidence of the beneficial small-scale human-biometeorological effects of a large shade tree during the daytime in summer [4]. A deciduous tree (Tilia × europaea L.) in Cascine Park was measured empirically by Massetti et al., and they combined the index of thermal stress (ITS) responses with the changes in comfort at different locations under the tree canopy [6]. Liu et al. analyzed the effect of trees on human energy fluxes in a humid subtropical climate region by obtaining meteorological data from four trees [28].

In this study, seven sites were set up and divided into two groups (Figure 1). Group A consisted of two shade sites under two F. altissima and one comparison site (exposure position), and those in group B consisted of three shade sites under three F. altissima and one comparison site (exposure position). The F. altissima in group A were tall and luxuriant, while those in group B were less so, especially tree No. 4, which had the sparsest foliage. The distance between the planting points of these 5 trees was more than 45 m. There was no overlapping of branches and leaves between adjacent trees and no obvious shading between tree crowns. With the help of three major types of experimental instruments (

Table 2. Field measurement instrument statistics.

Type	Instrument Name, Model, Origin		Accuracy	Measurement Range	Characteristic Description
Physical parameters		Laser rangefinder, Vertex Laser Geo, Sweden	±0.1 m	46 cm–700 m	High precision, fast response, measuring tree height, under branch height and crown width
		Tree girth ruler, C227, China	±0.3 mm	3 m (total length), 94 cm (diameter range)	Simple operation, used to measure 1.3 m high diameter at breast height
Physiological parameters		Hemi digital plant canopy analysis system, Hemi View, UK	/	/	Fast calculation of sky geometry, leaf area index, etc., based on light and shadow components
		1/10,000 balance scales, FA324C, China	±0.1 mg	0–320 g	Highly accurate weighing to help calculate transpiration rates
Microclimatic parameter		Urban multi-factor climate data acquisition instrument, HQZDZ-7, China	±0.1 °C (Ta) ±2% (RH) ±0.3 °C (Ts) ±0.1 m/s (V) ±2 w/m2 (Sr)	−40–70 °C (Ta) 0–100% (RH) −200–260 °C (Ts) 0–45 m/s (V) 300–3000 nm (Sr)	Integrating air temperature, relative humidity, soil temperature, wind speed, and solar radiation, it can automatically record data
		Globe thermometer, HQWBGT, China	±0.5 °C	0–120 °C	Acquisition of surface radiation and air temperature
		Mechanical ventilation dry and wet table, HQDHM2, China	±0.2 °C	−26–51 °C	Simple structure, measuring air temperature and humidity
		Infrared high-precision thermometer, HQSI-111, China	±0.2 °C	−10–65 °C	Portable, suitable for temperature measurement on various non-transparent surfaces

), we measured and obtained physical, physiological, and meteorological data of arborvitae. These are discussed in the following three paragraphs in relation to the instruments.

Tree features such as tree height, green coverage, shape, and permeability of the crown can influence the thermal environment [10]. Thus, we assessed several physical parameters relevant to trees, including diameter at breast height (DBH), crown width (CW), tree height (TH), and under branch height (UBH), which could help reflect the three dimensions of the trees and analyze their impact on the environment and human comfort. In this study, the tree measurements were primarily obtained using a laser rangefinder and a tree girth ruler. Since F. altissima has well-developed roots attached to its trunk, estimations and different methods were used to determine the pure DBH value at 1.3 m above the ground. The mean DBH of the sampled trees was 41–68 cm, and the UBH (>2 m) met the space use requirements of the active population. The CW was 9.9–16.1 m, and the TH was 7.64–13.65 m. Additionally, to assess the influence of tree shade on the underlying surface temperature, the bare land diameter (BLD) was also measured, which had a range of 77–225 cm.

We also measured the physiological parameters of the plants as well as the sky geometry, LAI, and transpiration rate per unit leaf area of the trees. These were measured using the Hemi digital plant canopy analysis system and 1/10,000 balance scales. The study of sky geometry and the LAI helps to analyze the characteristics of the plant canopy cover and to perform some simple calculations. The LAI of trees 1–5 was within 1.520–1.989; different degrees of the LAI indicated significant differences in the crown leaf densities of the five Ficus trees. The sky visible factor (SVF) was inversely related to the LAI, with a measurement range of 0.781–0.863. LAIDev was used to interpret the density of leaf distribution within the canopy, which could be used to evaluate tree growth. Groundcover (GC) measured the canopy coverage to the ground, which was complementary to SVF. The combined value of the two factors should be equal to 1; together, they describe the shading degree of the canopy to the ground. The transpiration rate reflects the absorption and release of the canopy to the environmental microclimate. For trees 3 and 4, we also monitored leaf transpiration in 4 directions at 20 min intervals.

Measuring microclimatic parameters is an essential step of the experiment, this experiment used fixed weather stations with the same instruments at each point. Air temperature (Ta), relative humidity (Rh), soil temperature (Ts), wind speed (V), and global radiation were readily acquired by an urban multi-factor climate data acquisition instrument. Globe temperature, dry bulb temperature, wet bulb temperature, and blade surface temperature were recorded using a globe thermometer, a mechanical ventilation dry and wet table, and an infrared high-precision thermometer. All instruments were installed at measuring points at 1.5–2 m above the ground. Regarding the planting mode and distance of the trees, measuring points 1 and 2 in group A and points 3, 4, and 5 in group B were arranged under the shade of five F. altissima trees. Measuring points 6 and 7 were used as comparison points for the two groups. The shaded points reflect the particular microclimate space formed under the tree canopy, while the comparison points represent the background conditions for both groups.

2.4. Calculation Method

The MRT is a significant influencing element for the PET, which considers the impact of the surface temperature of nearby objects on human thermal comfort and heat dissipation [3]. Tmrt (°C) was used to represent the temperature of the MRT, while the globe temperature (Tg, in °C), air temperature (Ta, in °C), wind speed (V, in m/s), globe emissivity (ε, 0.95 in this paper), and globe diameter (D, 0.15 m in this paper) needed to be considered. The formula for the MRT [29] is as follows:

$$T_{mrt}={\left[\begin{array}{c}{\left(T_g+273\right)}^4+\frac{1.1\times {10}^8\times V^{0.6}}{\epsilon \times D^{0.4}}\left(T_g-T_a\right)\end{array}\right]}^{0.25}-273$$

(1)

Transpiration rate E_G [g/(m²·h)] is related to the transpiration rate per unit leaf area [E_L, in g/(m²·h)] and the LAI [Equation (2)] [30]. According to Equation (3) [31], the latent heat of evaporation Q [cal/(m²·h)] per unit projected area caused by tree transpiration can be computed from E_G and the heat of vaporization λ. The air temperature decreased by the canopy of F. altissima can be determined according to Equation (4), where ρ represents the air density (kg/m³), c is the specific heat capacity [J/(kg·°C)], and v represents the 1000-m³ air column [32].

E_G = E_L × LAI

(2)

Q = E_G × λ

(3)

There are two ways to calculate λ in Equation (3): λ = 2498.8 – 2.33T (λ, in kJ/kg; T, in °C) [33], or λ = 597 – 5/9T (λ, in cal/g; T, in °C) [34].

$$\Delta T=\frac{Q}{\rho \times c\times \nu }$$

(4)

In this study, the PET was selected as an objective evaluation index of the impact of F. altissima on outdoor thermal comfort.

Table 3. Ranges of the thermal indices of PET for different grades of thermal perception by human beings and their physiological stress.

PET (°C)	Thermal Perception	Grade of Physiological Stress
<4	Very cold	Extreme cold stress
4–8	Cold	Strong cold stress
8–13	Cool	Moderate cold stress
13–18	Slightly cool	Slight cold stress
18–23	Comfortable	No thermal stress
23–29	Slightly warm	Slight heat stress
29–35	Warm	Moderate heat stress
35–41	Hot	Strong heat stress
>41	Very hot	Extreme heat stress

was extracted from research defining detailed evaluation intervals [35]. The PET calculated the measured microclimate factors by substituting them into RayMan 3.1 software (Research center human biometeorology, Deutscher Wetterdienst). Meanwhile, the human body indicators were set as male, 175 cm in height, 20 years old, 65 kg, 1.0 thermal resistance of winter clothing, and 80 W/m² metabolic rate of activity.

3. Results

3.1. Microclimate Indicators Analysis

Under natural conditions, the internal control mechanisms in plants are influenced by external climatic factors such as air humidity and temperature [36]. The changes in air temperature and humidity were plotted according to the performance of groups A and B (Figure 2), which was used to explain the dynamic distribution of trees in terms of temperature and humidity. It can be seen from Figure 2a that the temperature difference between group A and the comparison points fluctuated from 0 to 1.5 °C, and the temperature at each shaded point (measuring points 1 and 2) was on average 1 °C and 0.5 °C lower than the comparison point, respectively. The temperature difference between group B and the comparison points fluctuated from −1.2 to 1.4 °C, and the temperature at each shaded point was not always lower than the comparison point (Figure 2b). This indicates that in winter, the effect of the canopy on the ambient temperature may include both cooling and insulation. In general, it seems that the temperatures at all measurement sites gradually increased over time and reached a peak around 12:00 noon, after which the temperatures basically remained stable. The temperature performance of group B was slightly higher than that of group A, at 10% higher than the average, which was partly due to the disturbance of the surrounding environment and partly due to the lower average LAI of group B. The fluctuation in the performance of measuring point 4 with a lower LAI value was more obvious than the other points, which may be caused by the fact that the vegetation canopy showed a certain azimuthal angle to the light, and the sunlight through the sparse canopy was more likely to trigger the hot spot effect [37].

From 8:00 a.m. onwards, the air relative humidity of all measuring points gradually decreased to produce a sharp decline at noon, reaching a lower value at 12:00 a.m. The fluctuation in relative humidity in the morning was significantly greater than that in the afternoon, which is likely due to the uneven closure of stomata (especially at midday) [38]. The relative humidity at the shaded points in group A (mean 35.0%) was higher than that at the comparison points (mean 31.2%), and the relative humidity at the shaded points in group B (mean 31.2%) and the comparison points (mean 31.1%) was basically the same. The mean drop in relative humidity in both groups of shaded points was relatively similar (43% and 42.7%, respectively), but the rate of humidity drop in group A (Figure 2c) was slower than that in group B (Figure 2d). By 12:00, the average drop in group A was 38.4%, and the average drop in group B was 39.1%. The difference between the shaded points and the comparison points was found to be higher in group A than in group B, at 1.4% and 0.4%, respectively. Combining the field measurements in Section 2.3 with the above illustrates that densely growing trees are more likely to keep the air relative humidity in the shade constant, thus avoiding premature environmental influences that can lead to a rapid drop in relative humidity.

In tropical climates, changing wind conditions and shade will modify the microclimate and improve human thermal comfort [39]. Since the wind fluctuates greatly and does not have a temporal pattern, it cannot be clearly displayed by a time-line diagram, and so we integrated the wind speed (V) and wind direction (Wd) of all measuring points in groups A (Figure 3a) and B (Figure 3b) in scatter plots. In Figure 3, the colored foreground indicates each shaded point, the gray background is the comparison points, and the superposition of the two can better visualize the difference between them. From the horizontal axis, the wind speed distribution of each shaded point exhibited a large difference. The smallest dispersion standard deviation (0.42) was found at measuring point 5, indicating that the wind field at this location performed most consistently. On the contrary, the dispersion standard deviation of measuring point 1 was the largest (0.73), indicating that the wind field fluctuated the most at this location. Combined with the physical and physiological parameters of the trees, the pattern of solitary trees in wind speed blocking was not obvious enough, and the blocking effect was weak. From the vertical axis, the southeast (90–180°) wind direction was dominant at all measurement sites, which was mainly influenced by the prevailing summer winds. Compared with the comparison sites, the wind directions at the observation sites were more even, i.e., the overall trend produced a decrease in the frequency of the prevailing wind direction, and the other wind directions were strengthened.

In the solar radiation (Sr) data distribution (Figure 4a), measuring point 4 in the shade was closest to the comparison point, but the overall level (median 614 W/m²) was slightly higher than the other measuring points. This is due to the sparse distribution of branches and leaves at point 4, where the light hot spots can locally increase the Sr value [37]. Different degrees of Sr reduction occur under different canopies (Figure 4b), with shaded points 1 and 3—located at the bottom of the dense canopy—being the most pronounced and showing high ΔSr variation values (maximum values of 767 W/m² and 847 W/m², respectively). However, measuring point 4 showed significant negative values (e.g., median −35 W/m² and minimum −351 W/m²), indicating that Sr increased at some time under the trees, again due to the hot spot effect. The sparsely branched F. altissima in this case effectively blocked Sr by up to 75%, while the dense canopy blocked it by up to 88%. This indicates that F. altissima with large LAI values significantly and effectively reduces the Sr level under winter weather conditions.

3.2. Transpiration

As the site conditions of soil, air, water, and nutrition were the same at the planting sites, the transpiration of these two Ficus trees was obviously different due to their different growth conditions (Figure 5). The transpiration rate of the entire plant was categorized by direction: east, west, north, and south. It was found that the transpiration rate of F. altissima No. 3 with vigorous growth was highest in the south (mean 26.22 W/m²), followed by the west (23.54 W/m²), east (19.01 W/m²), and north (15.94 W/m²). For F. altissima No. 4 with sparse growth, the transpiration rate was highest in the west (23.33 W/m²), followed by the north (18.74 W/m²), south (18.32 W/m²), and the east (17.27 W/m²). Cumulative data from the four directions indicated that the transpiration of No. 3 was inferior to that of No. 4 before 10:00 a.m., while the transpiration of No. 3 was far superior to that of No. 4 from 11:00 a.m. until the evening. This indicates that a tree crown with vigorous growth exhibits a more stable transpiration performance, and such plants have greater advantages in regulating environmental humidity.

Cooling caused by leaf transpiration can be expressed as the latent heat of evaporation Q (

Table 4. Descriptive statistics of the latent heat of evaporation and transpiration cooling of Ficus altissima. Q [Cal/(m²·h)] represents the latent heat of evaporation in the four directions of east, west, north, and south, while ∆T (°C) denotes the cooling changes caused by transpiration in those directions.

Indicators	Minimum Statistic	Maximum Statistic	Mean Statistic	Mean Std. Error	Std. Deviation Statistic	Variance Statistic
QE	37.0	399.1	177.19	23.10	108.33	11,736.42
QW	56.0	506.0	227.86	29.86	140.08	19,621.94
QS	61.2	428.7	218.70	25.59	120.02	14,405.33
QN	32.1	352.4	167.98	18.74	87.88	7723.55
∆TE	0.1	1.3	0.60	0.08	0.36	0.13
∆TW	0.2	1.7	0.76	0.10	0.47	0.22
∆TS	0.2	1.4	0.73	0.09	0.40	0.16
∆TN	0.1	1.2	0.56	0.06	0.29	0.09

) to calculate the cooling of the surrounding air due to the tree canopy (refer to formulae 2–4). Our study indicated that the maximum evaporation latent heat of F. altissima was located in the west [506 Cal/(m²·h)], and the minimum value was located in the north [352.4 Cal/(m²·h)]. Transpiration brought about significant environmental cooling, especially in the west, with a range of 0.2–1.7 °C (∆T) and an average temperature drop of 0.76 °C during the day. Transpiration in the north was the weakest, with a cooling range of 0.1–1.2 °C. The variation in temperature drop data was consistent with that of the latent heat of evaporation, with the most apparent fluctuations on the west side and the most stability on the north side. To summarize, the effect of heat evaporation and cooling on the leaves of F. altissima was the most obvious on the west side, followed by the south, east, and north sides.

3.3. Physiological Equivalent Temperature

The PET was used to study the effect of F. altissima on thermal comfort, and the relationship between the PET and multiple climate factors (e.g., Sr, Tmrt, and Ta) was explored. Figure 6 indicated that the maximum PET value at shade measuring points 1–5 was lower than that at comparison points 6–7 under sunny conditions in winter (the maximum difference was 16.3). Moreover, the variation range in the PET at the shade measuring points was relatively gentle. From 8:00 to 9:00, the shade resulted in a cold and cool state. From 9:00 to 10:00, the PET was generally in a slightly cool state. From 10:00, the PET sharply increased at all measuring points, and some measuring points crossed three intervals. From 16:00, the PET of all measuring points sharply decreased, and some of the measuring points decreased by three intervals. By 18:00, all measuring points returned to a cold and cool state. The optimum range for comfort was primarily from 11:00–16:00. For group A, with a low SVF and high LAI, the PET was primarily located in a slightly cool (close to comfortable) interval; for group B, with a relatively high SVF and low LAI, the PET was primarily located in a comfortable interval. All shade measuring points were two to three intervals lower than the comparison points. The comparison points were in a slightly warm or warm range for most of the time (six to seven hours), while the shade measuring points were within or close to the comfortable range for most of the time (five hours). The results showed that the shade of F. altissima in winter can influence human comfort, allowing people to spend most of their time in a relatively comfortable thermal environment during clear daytime weather in winter.

4. Discussion

4.1. Microclimate Parameters and PET

The temperature and humidity of each measuring point under the shade of the forest were extracted at different times and compared with the PET. It can be seen from Figure 7a that air temperature and air relative humidity both exhibited a moderate correlation with the PET (R was 0.785 and −0.714, respectively), which can be expressed by the curve equation. Among them, Ta showed a positive correlation with the PET, while Rh was negatively correlated with the PET (Figure 7b), and the former had a better fit than the latter, indicating that air temperature could better explain PET variation. The effect of V and Wd on the PET was not as obvious as that of air temperature and humidity, and the wind speed and wind direction values of each group were substituted and fitted with the PET, and it was found that both were best explained by linear equations and curvilinear equations, respectively (Figure 7c,d). The R values between the variables were low for both wind speed and wind direction (0.361 and −0.432, respectively) and became lowly correlated with the PET. Exploring the effect of Sr on the PET, Figure 7e shows that they exhibited a positive correlation and could be explained by a simple quadratic equation with a high goodness-of-fit (R of 0.829). As temperature also had a significant correlation with the PET, it can therefore be explained by Tmrt (Figure 7f), from which it can be seen that the temperature field variation brought about by radiation was more fully explained by the relationship of the PET, with a higher goodness-of-fit (R of 0.946).

In summary, among the five important climate factors, solar radiation as well as air temperature had the greatest influence on comfort, with the largest coefficient of determination. Additionally, the effect of wind speed and direction alone on the PET was weak, and thus these need to be combined with other parameters to further reflect their contribution, which corroborates the conclusion reached in another paper [40].

4.2. Transpiration and PET

Due to the great differences in physical and physiological parameters for each individual F. altissima tree, the relationship between transpiration and the PET within individuals could only be studied individually, from which a common pattern of this relationship could be concluded. First, the transpiration rates and latent heat of evaporation for F. altissima Nos. 3 and 4 in all four directions were pooled, imported into SPSS, and then correlated with the PET for regression analysis. In the results section, it is noted that the most significant performance of F. altissima in terms of transpiration rate, latent heat of evapotranspiration, and transpiration cooling was on the west side, but after splitting the orientation data, it was the east side that had a more significant fit with the PET, while all other directions were not significant. Since the transpiration rate and latent heat of evaporation had different units and differed greatly from the PET, we ultimately considered the cooling on the eastern side to express this regression relationship in order to express the complete parameters in a convenient formula (Figure 7g,h). The linear fit could be expressed by a cubic function, and the obtained results had better fitting values, with R values of 0.779 and 0.318 for Nos. 3 and 4, respectively. However, because transpiration and evapotranspiration cooling are linked effects, their regression fit was the same as the previous results for the regression relationship with the PET.

4.3. Integrative Parameters and PET

First, we discussed the physical and physiological parameters together in order to obtain a more comprehensive regression equation to explain the PET performance. A total of nine commonly used indicators (i.e., DBH, UBH, CW, TH, BLD, LAI, LAIDev, SVF, and GC) were obtained in the course of the field measurements, and only four indicators, DBH, UBH, TH, and GC, were found to be independent of each other (variance inflation factor, VIF 1.088–2.224) by the SPSS multiple covariance test. The remaining five variables were co-linear with each other and were treated as excluded variables in the multiple regression. Only two indicators were significant (p-value less than 0.05) for the PET, namely, TH and GC, indicating that TH and GC can significantly affect the change in PET. The regression coefficients of both were negative, indicating that higher values of TH and GC tend to decrease the PET in general. We have already discussed that GC + SVF = 1; that is, the lower the SVF, the lower the degree of PET, and finally, the following equations are given:

PET = −1.036TH − 10.217GC + 28.642 R² = 0.314

(5)

or

PET = −1.036TH + 10.217SVF + 18.425 R² = 0.314

(6)

Next, TH and GC (or SVF) were retained, climate parameters were introduced, and the indicators Sr, Ta, Rh, V, Wd, and Tmrt were jointly substituted into SPSS to investigate the relationship between them and the PET. The significance (p-value) was less than 0.05 for four indicators: Ta, V, Tmrt, and GC, and the regression coefficients of Ta and Tmrt were greater than zero, which means that the higher the air temperature and the average radiation temperature, the greater the PET value. Conversely, the regression coefficients of V and GC were less than zero, which indicates that the higher the wind speed and the higher the groundcover, the smaller the PET value will be. We tested these four independent variables and found that all had a VIF < 5, suggesting no multiple co-linearity in the independent variables. Observation of the scatter distribution of the residuals revealed that they were located near the diagonal, indicating that the residuals followed a normal distribution. The final test DW (Durbin Watson) value was 2.179, which is relatively close to 2, indicating that there was no serial correlation between the samples. Therefore, the four retained indicators could fully explain the variation in PET, and the explanation rate reached 97.9%, leading to the following equation:

PET = 0.573Ta + 0.39Tmrt − 3.098V − 1.346GC + 1.627 R² = 0.979

(7)

Finally, all the previous physical, physiological, and climatic parameters were integrated, and a total of 11 indicators of transpiration rate, latent heat of evaporation, and transpiration cooling were substituted into SPSS for regression analysis. Repeating the above tests and calculations, only the wind speed and mean radiation temperature met the regression requirements of Equation (8), indicating that the important parameters affecting comfort in the thermal environment were mainly driven and controlled by Tmrt and wind speed [8,10].

PET = 0.510Tmrt − 4.203V + 6.789 R² = 0.960

(8)

5. Conclusions

In this paper, physical, physiological, and climatic parameters were obtained for F. altissima in lower subtropical China in winter conditions. The parameters were extracted, selected, analyzed, and combined using statistical principles and methods, with emphasis on their interpretation of the thermal comfort index PET. The main conclusions were as follows:

(1)

Under winter conditions in the southern subtropics, the F. altissima canopy may both warm and cool the environment, and may both reduce solar radiation in the shade and increase local solar radiation. These manifestations were related to the corresponding LAI under physiological indicators as well as being a two-way consequence of the hot spot effect.

(2)

A dense tree canopy is more conducive to maintaining a stable temperature and humidity in the shade and also allows for a more uniform wind distribution across the canopy. Unfortunately, the attenuation of wind by single trees was very weak, and no significant wind resistance was observed in this project.

(3)

Shade can influence human comfort by keeping people in relative thermal comfort for most of the daytime sunny weather in winter. Changes in the thermal comfort index PET could be explained by microclimate variables, transpiration, and so forth. Among them, solar radiation and air temperature were the climatic factors with the highest degree of explanation for the PET. While the transpiration of the entire plant could not be used to measure comfort, the transpiration and cooling effect on the east side could reasonably explain the PET in the shade of the forest.

(4)

By combining and refining physical, physiological, and climatic indicators and using multiple regression analysis, three models were developed that could explain the development of the PET. The first one combined physical and physiological parameters and found that TH and GC could partially explain the degree of thermal comfort. The second one combined physical, physiological, and climatic parameters and found that four indicators, Ta, V, Tmrt, and GC, could highly explain the thermal comfort changes. The third one used the comprehensive index Tmrt combined with wind speed and again confirmed that they are the controlling indexes affecting the direction of comfort.

In summary, this study applied the relationship between human-biometeorological parameters and computational models to specific forms and species of trees while making an innovative exploration of the relationship between physical, physiological, and meteorological parameters and thermal comfort. There are many species in the world with characteristics similar to those of F. altissima, which are widely distributed, have well-developed root systems, and are heavily branched, making them ideal for urban landscaping. Studying the effect of F. altissima on the urban thermal environment will help to advance the depth of research on thermal comfort in relation to plant climate. This study also provides basic information to begin the improvement of plant databases, which is beneficial for urban planning scholars and ecologists to make decisions on tree species selection.

The present work has some shortcomings and limitations, such as the difficulty in establishing ideal tree samples during the experiment and the short period, which is why the measurement results could not be used to produce a systematic database. The field measurements were limited by the weather. Humidity disturbances on rainy days had an impact on the accuracy of the instrument data, and the data regularity on cloudy days was not pronounced enough, so most of the data were analyzed for sunny winter days. In addition, of the three of the major study parameters (physical, physiological, and microclimate parameters), microclimate parameters are the most well studied, while the depth of the study of physiological parameters is limited to the transpiration of plants. There is a broader space for exploration here. In the future or with the development of technology, the climatic adaptation study of tree microclimates could become more extensive, the contributions of the present study may help guide future work, giving us a more comprehensive perspective, and the interpretation of PET and the description of thermal comfort may become closer to complete.