Impact of ENVI-met-Based Road Greening Design on Thermal Comfort and PM2.5...

[Full title: Impact of ENVI-met-Based Road Greening Design on Thermal Comfort and PM2.5 Concentration in Hot–Humid Areas]

Citation: Du, M.; Zhao, Y.; Yang, J.; Wang, W.; Luo, X.; Zhong, Z.; Huang, B. Impact of ENVI-met-Based Road Greening Design on Thermal Comfort and PM 2.5 Concentration in Hot–Humid Areas Sustainability 2024 , 16 , 8475. https://doi.org/10.3390/ su 16198475 Academic Editor: Steve Kardinal Jusuf Received: 12 August 2024 Revised: 20 September 2024 Accepted: 27 September 2024 Published: 29 September 2024 Copyright: © 2024 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) sustainability Article Impact of ENVI-met-Based Road Greening Design on Thermal Comfort and PM 2.5 Concentration in Hot–Humid Areas Meng Du, Yang Zhao *, Jiahao Yang, Wanying Wang, Xinyi Luo, Ziyu Zhong and Bixue Huang School of Architecture and Urban Planning, Guangzhou University, 230 Guangzhou Higher Education Mega Center West Outer Ring Road, Panyu District, Guangzhou 510006, China * Correspondence: zhaoyang@gzhu.edu.cn Abstract: Road greening markedly impacts road thermal comfort and air quality. However, previous studies have primarily focused on thermal comfort or PM 2.5 individually, with relatively few addressing both aspects comprehensively, particularly in humid regions. This study combined field measurements and simulations. It employed physiological equivalent temperature (PET) and quantified the horizontal distribution of particulate matter 2.5 (PM 2.5 ). The research examines the effects of planting spacing, tree species, and tree–shrub combinations on pedestrian walkways in humid climates during both summer and winter. Using measured tree data and road PM 2.5 , a plant model was established and pollution emission parameters were set to validate the effectiveness of the ENVI-met through fitting simulations under various scenarios. The results indicated that (1) plant spacing for trees influenced both the road thermal environment and PM 2.5 levels. Smaller spacing improved thermal conditions but increased PM 2.5 . (2) trees with large canopies and high leaf area indices (LAIs) notably enhanced thermal comfort, while those with smaller canopies and dense understories facilitated PM 2.5 dispersion. The 3 m spacing resulted in a maximum absolute PM 2.5 concentration difference (C) of 5.05 µ g/m 3 in summer and a maximum mean absolute PM 2.5 concentration difference (M) in the downwind region of 2.13 µ g/m 3 in winter. (3) Combining trees with shrubs moderately improved pedestrian thermal comfort. However, taller shrubs elevated PM 2.5 concentrations on walkways; heights ranging from 1.5 m to 2 m in summer showed higher C values of 5.38 µ g/m 3 and 5.37 µ g/m 3 . This study provides references and new perspectives for the optimization of roadway greening design in humid areas in China Keywords: outdoor thermal comfort; road greening; sidewalk trees; ENVI-met; PM 2.5 1. Introduction With the acceleration of urbanization, urban environments continuously deteriorate, exacerbating the urban heat island effect and air pollution [ 1 , 2 ], which threaten urban residents’ physical and mental health [ 3 , 4 ]. Particulate matter 2.5 (PM 2.5 ), a significant contributor to air pollution [ 5 ], primarily originates from vehicle exhaust emissions on urban roads [ 6 ] and can cause respiratory and cardiovascular diseases [ 7 ]. Vegetation is crucial in mitigating the urban heat island effect and enhancing thermal comfort [ 8 ]. Optimizing urban green spaces is an effective way of alleviating the heat island effect and improving thermal comfort. In addition, trees can remove atmospheric pollutants by adsorbing particulate matter through their rough-textured leaves [ 9 ]. However, trees can also affect airflow and pollutant dispersion [ 10 , 11 ]. Therefore, an inappropriate road greening design may adversely impact air quality. When designing road greening, comprehensive consideration of the multifaceted effects on thermal comfort and air quality is necessary Increasing tree density can provide more shading and enhance the shading effect [ 12 ], thereby creating a more comfortable walking environment and increasing pedestrian walking frequency [ 13 ]. As tree spacing decreases, mean radiant temperature (T mrt ) decreases Sustainability 2024 , 16 , 8475. https://doi.org/10.3390/su 16198475 https://www.mdpi.com/journal/sustainability

Sustainability 2024 , 16 , 8475 2 of 30 exponentially, improving thermal comfort [ 14 ]. However, a higher tree density results in poor ventilation, limiting the pollutants’ dispersion [ 15 ]. PM 2.5 concentration increases with vegetation density, especially at low wind speeds and when the wind direction is perpendicular to the road [ 16 ]. Additionally, different tree species with various physical characteristics affect thermal comfort and PM 2.5 concentrations differently. Trees with larger canopies are more effective at reducing temperatures and improving thermal comfort [ 17 ]. However, they can impede air circulation, leading to the accumulation of PM 2.5 in street canyons [ 18 ]. Trees enhance thermal comfort more effectively than shrubs and grasses, with trees improving physiological equivalent temperature (PET) by approximately 2.4 folds that of ground cover plants and 1.5 folds that of shrubs [ 19 ]. However, shrubs increase PET and decrease outdoor thermal comfort at pedestrian height [ 20 ]. Regarding PM 2.5 , shrubs are more conducive to pollutant dispersion in street canyons and are the most effective at removing PM 2.5 at breathing height [ 21 ]. Moreover, combining trees with hedges was found to be more effective in reducing road particulate matter [ 22 ]. Therefore, in road greening design, planting methods are crucial and require consideration of tree spacing, tree species, and combinations of trees and shrubs The ability of trees to improve the outdoor thermal environment is region-specific [ 23 , 24 ] and heavily influenced by local climatic conditions [ 23 ]. Guangzhou has a distinct hot and humid climate with rapid urbanization and high population density [ 25 ], leading to increasingly prominent air quality issues [ 26 ]. Urban residents are vulnerable to extreme heat and PM 2.5 pollution. Current research mostly focuses on either thermal comfort or PM 2.5 , with relatively few studies examining the combined impact of both Using four roads in Guangzhou University Town, we aimed to explore the effects of road greening on outdoor thermal comfort and PM 2.5 under typical summer and winter climatic conditions in China’s hot and humid regions. Finding a roadway greening design strategy that balances thermal comfort and PM 2.5 concentration through a comprehensive analysis of planting spacing, tree species, and tree–irrigation combinations. Our findings provide scientific and reasonable recommendations for road greening design in hot and humid areas of China for improved urban environment and pedestrian experience 2. Materials and Methods 2.1. Climate Conditions and Study Areas Guangzhou (23 ◦ 08 ′ N, 113 ◦ 19 ′ E) is a typical city in the hot and humid regions of China. It experiences hot and humid summers and warm winters, with an annual average temperature of 22.2 ◦ C and humidity of 77.5% in 2023 [ 27 ]. The hottest period is from June to September, with average temperatures ranging from 28.2 to 29.2 ◦ C. January is the coldest month, with an average temperature of 14.3 ◦ C [ 28 ]. In summer, Guangzhou is influenced by subtropical high and low pressures in the South China Sea, resulting in prevailing southeasterly winds. In winter, the city experiences prevailing north winds owing to cold high-pressure systems, with higher average wind speeds in winter and lower speeds in summer (Data obtained on 18 September 2024, from https://www.weather-atlas. com/zh/china/guangzhou-climate ). Guangzhou University Town is located on Xiaoguwei Island in the Panyu District, Guangzhou, Guangdong Province. The roads were categorized into four types based on their structure and greenbelt configurations [ 29 ]: one roadbed and two belts (R 12), two roadways and three belts (R 23), three roadways and four belts (R 34), and four roadways and five belts (R 45), as shown in Figure 1 . There were no industrial pollution sources within or near the university town, and vehicular emissions were the primary source of PM 2.5 .

Sustainability 2024 , 16 , 8475 3 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 3 of 33 Figure 1. Study area and road types 2.2. Field Measurements and Model Validation This study focused on four di ff erent types of roads in Guangzhou University. First, tra ffi c fl ow data were collected using high-de fi nition cameras to capture tra ffi c volumes and corresponding vehicle types on the four roads. Second, on-site measurements were conducted from 8:00 to 17:00 on 20,21,24 and 25 September 2023, and 21, 22, 24, and 25 January 2024. The greenbelt planting con fi gurations along the four roads can be categorized into tree–shrub combinations, tree-only planting, and areas without trees or shrubs. Accordingly, three measurement points were established along each route, located in the tree–shrub combination area, the tree-only area, and the area without trees or shrubs, respectively. The measurement height for all points was set at 1.5 m, as shown in Figure 2. Thermal environment parameters and PM 2.5 concentrations were collected using a thermal comfort meter and an all-in-one gas detector. PM 2.5 data were collected hourly from each measurement point, with monitoring lasting > 5 min [30]. The hourly background atmospheric data for the same day were obtained from the Guangzhou Municipal Environmental Protection Bureau. The measurement instruments and their parameters are listed in Table 1. Background meteorological data during the experimental period, including air temperature (T a ), relative humidity (RH), and wind speed (Ws), were collected using a thermal comfort meter set up in an open area 100 m away. Additionally, road width, green belt width, greening con fi guration, and planting spacing in the study area were measured. Figure 1. Study area and road types 2.2. Field Measurements and Model Validation This study focused on four different types of roads in Guangzhou University. First, traffic flow data were collected using high-definition cameras to capture traffic volumes and corresponding vehicle types on the four roads. Second, on-site measurements were conducted from 8:00 to 17:00 on 20,21,24 and 25 September 2023, and 21, 22, 24, and 25 January 2024. The greenbelt planting configurations along the four roads can be categorized into tree–shrub combinations, tree-only planting, and areas without trees or shrubs. Accordingly, three measurement points were established along each route, located in the tree–shrub combination area, the tree-only area, and the area without trees or shrubs, respectively. The measurement height for all points was set at 1.5 m, as shown in Figure 2 . Thermal environment parameters and PM 2.5 concentrations were collected using a thermal comfort meter and an all-in-one gas detector. PM 2.5 data were collected hourly from each measurement point, with monitoring lasting > 5 min [ 30 ]. The hourly background atmospheric data for the same day were obtained from the Guangzhou Municipal Environmental Protection Bureau. The measurement instruments and their parameters are listed in Table 1 . Background meteorological data during the experimental period, including air temperature (T a ), relative humidity (RH), and wind speed (Ws), were collected using a thermal comfort meter set up in an open area 100 m away. Additionally, road width, green belt width, greening configuration, and planting spacing in the study area were measured.

Sustainability 2024 , 16 , 8475 4 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 4 of 33 Figure 2. Measurement points and road environments Table 1. Information on experimental equipment. Equipment Manufacturer Country of Origin Model Parameter Measuring Range Accuracy Sampling Rate Thermal comfort instrument Beijing Tianjian Huayi Science and Technology Development Co., Ltd. China SSDZY-1 Ta (°C) − 20.0–80.0 °C ±0.3 °C 1 min RH (%) 0.01–99.9% RH ±2% 1 min GlobeTemperature (°C) − 20.0–80.0 °C ±0.3 °C 1 min Ws (m/s) 0.05–5.00 m/s 5% ± 0.05 m/s 1 min Figure 2. Measurement points and road environments Table 1. Information on experimental equipment Equipment Manufacturer Country of Origin Model Parameter Measuring Range Accuracy Sampling Rate Thermal comfort instrument Beijing Tianjian Huayi Science and Technology Development Co., Ltd. (Beijing, China) China SSDZY-1 Ta ( ◦ C) − 20.0–80.0 ◦ C ± 0.3 ◦ C 1 min RH (%) 0.01–99.9% RH ± 2% 1 min GlobeTemperature ( ◦ C) − 20.0–80.0 ◦ C ± 0.3 ◦ C 1 min Ws (m/s) 0.05–5.00 m/s 5% ± 0.05 m/s 1 min All-in-one gas detector Shenzhen Keruino Electronics Technology Co., Ltd. (Shenzhen, China) China GT-1000-B 3 PM 2.5 ( µ g/m 3 ) 0–9999 µ g/m 3 ± 3% µ g/m 3 10 s

Sustainability 2024 , 16 , 8475 5 of 30 The reliability of the ENVI-met model was validated by comparing the temperature, humidity, and PM 2.5 concentration values obtained from on-site measurements with the model’s output values. The accuracy of the model was evaluated using the correlation coefficient (R 2 ), root mean square error (RMSE), and mean absolute error (MAE). The calculation formulas (1) are as follows: RMSE = s ∑ n i = 1 ( X obs , i − X model , i ) 2 n (1) MAE = ∑ n i = 1 X obs , i − X model , i n (2) where X obs represents the observed values, X model represents the values simulated by the software, and n represents the number of data points 2.3. Modeling and Parameter Setting Based on the satellite images and on-site measurement data of the case area, physical three-dimensional models of each road were constructed using the ENVI-met space module. Each road was set to a length of 300 m, with a grid resolution of 3 m × 3 m × 3 m. Threedimensional vegetation models were established according to the actual conditions of the model. The building surface materials were set to concrete, and the road materials were set according to the actual conditions (Table A 1 ). Additionally, the model used the measured wind direction and speed. The pollutant source settings in the ENVI-met were configured using the hourly traffic volume for each road. The hourly pollution source emission rate was estimated using equation (3) [ 31 ]: Q = C · E (3) where Q ( µ g/m 3 ) represents the pollutant emission rate; C (veh/h) denotes the hourly traffic flow, which were 390 veh/h, 243 veh/h, 220 veh/h, and 207 veh/h for the R 12, R 23, R 34, and R 45 roads, respectively; and E stands for the PM 2.5 emission factor, mg/(km · veh). The average emission factor for Guangzhou is 57.8 mg/(km · veh) [ 32 ]. Based on Formula (4), the average emission rates per hour are 3.32 µ g/m 3 , 3.53 µ g/m 3 , 3.90 µ g/m 3 , and 6.30 µ g/m 3 for the R 12, R 23, R 34, and R 45 roads, respectively. Pollutants are emitted across the entire width of the road at a height of 0.3 m (the height of vehicle exhaust pipes) Because of the effective simulation capability of ENVI-met for PM 2.5 concentrations over a short period of time [ 33 ], simulations were conducted in three stages (8:00–10:00, 11:00–14:00, and 15:00–17:00) after preheating, totaling 8 measurement days 2.4. Case Studies 2.4.1. Establishment of Arbor Database To accurately predict the impact of roadside greening on outdoor thermal comfort and PM 2.5 concentration in humid subtropical regions, this study selected nine common tree species in Guangzhou: Michelia alba (Ma), Ficus altissima (Fa) , Bauhinia blakeana (Bb) , Mangifera indica (Mi), Alstonia scholaris (As), Chukrasia tabularis (Ct), Dracontomelon duperreanum (Dd) , Ficus concinna (Fc), Cinnamomum camphora (Cc). The ENVI-met Albero model contains nine parameters: leaf area density, tree height, under-canopy height, leaf reflectance, crown width, root area density, root depth, root morphology, and root width. Previous studies have indicated that area density, width, root depth, and morphology have insignificant effects on the simulation of outdoor thermal environments [ 34 ]; thus, default values were used in model construction. Tree height, under-canopy height, and crown width were measured using a rangefinder, while leaf shortwave reflectance was measured using a spectrophotometer (Lambda 950). The leaf area index (LAI) was determined using

Sustainability 2024 , 16 , 8475 6 of 30 TOP-1300, while the leaf area density (LAD) values at different heights within the tree canopy were calculated using the following formula (4) [ 35 ]: LAD = x Z 0 L m h − Z m h − Z n exp n h − Z m h − Z dZ (4) where 0 ≤ Z ≤ Z m with n = 6 with Z m ≤ Z ≤ h with n = 0.5 The tree model and parameters are illustrated in Figure 3 . Sustainability 2024 , 16 , x FOR PEER REVIEW 6 of 33 Previous studies have indicated that area density, width, root depth, and morphology have insigni fi cant e ff ects on the simulation of outdoor thermal environments [34]; thus, default values were used in model construction. Tree height, under-canopy height, and crown width were measured using a range fi nder, while leaf shortwave re fl ectance was measured using a spectrophotometer (Lambda 950). The leaf area index (LAI) was determined using TOP-1300, while the leaf area density (LAD) values at di ff erent heights within the tree canopy were calculated using the following formula (4) [35]: {?}{?}{?} = {?} ℎ − {?} ℎ − {?} {?}{?}{?} {?} ℎ − {?} ℎ − {?} {?}{?} (4) where 0 ≤ {?} ≤ {?} with {?} = 6 with {?} ≤ {?} ≤ ℎ with {?} = 0.5 The tree model and parameters are illustrated in Figure 3. Figure 3. Joe model and parameters 2.4.2. Case Studies As shown in Figure 4, the study was divided into three phases: determining the optimal planting spacing, selecting the best tree species, and combining trees with shrubs. First, 3 m, 6 m, and 9 m planting spacing were arranged as As on the four roads. The reference group consisted of roads with no lane trees. The models used were R 12-As-3, R 12-As-6, R 12-As- 9, R 23-As-3, R 23-As-6, R 23-As-9, R 34-As-3, R 34-As-6, R 34-As-9, R 45- As-3, R 45-As-6, and R 45-As-9. Second, nine types of lane tree species commonly found in Guangzhou were arranged on the green belts of the four roads. The reference group consisted of a road model without trees. Third, after comparing the tree species, two were selected and combined with or without shrubs of di ff erent heights: 1 m, 1.5 m, and 2 m. The combinations were labeled as Ma-1.5, Ma-2, As-0, As-1, As-1.5, and As-2. The T a , T mrt , Ws, and PM 2.5 concentrations of the pedestrian space of each model were determined. The BIO-met process was used to calculate the PET value for each scenario. The parameters were set to 35 years old, 75 kg weight, and 1.75 m height, and clothing insulation values in summer and winter were 0.5 Clo and 0.9 Clo, respectively. The metabolic rate was 164.7 W. The results were taken as a means of follow-up analysis. Figure 3. Joe model and parameters 2.4.2. Case Studies As shown in Figure 4 , the study was divided into three phases: determining the optimal planting spacing, selecting the best tree species, and combining trees with shrubs. First, 3 m, 6 m, and 9 m planting spacing were arranged as As on the four roads. The reference group consisted of roads with no lane trees. The models used were R 12-As-3, R 12-As-6, R 12-As- 9, R 23-As-3, R 23-As-6, R 23-As-9, R 34-As-3, R 34-As-6, R 34-As-9, R 45- As-3, R 45-As-6, and R 45-As-9. Second, nine types of lane tree species commonly found in Guangzhou were arranged on the green belts of the four roads. The reference group consisted of a road model without trees. Third, after comparing the tree species, two were selected and combined with or without shrubs of different heights: 1 m, 1.5 m, and 2 m The combinations were labeled as Ma-1.5, Ma-2, As-0, As-1, As-1.5, and As-2 The T a , T mrt , Ws, and PM 2.5 concentrations of the pedestrian space of each model were determined. The BIO-met process was used to calculate the PET value for each scenario. The parameters were set to 35 years old, 75 kg weight, and 1.75 m height, and clothing insulation values in summer and winter were 0.5 Clo and 0.9 Clo, respectively The metabolic rate was 164.7 W. The results were taken as a means of follow-up analysis.

Sustainability 2024 , 16 , 8475 7 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 7 of 33 Figure 4. ENVI-met model and the three stages of simulation 2.5. Thermal Comfort Evaluation Index and Quantitative Analysis of PM 2.5 2.5.1. PET Common thermal indices include the Standard E ff ective Temperature (SET*), Wet Bulb Globe Temperature (WBGT), Predicted Mean Vote (PMV) index, and Physiological Equivalent Temperature (PET) [36]. PET, derived from the human energy balance equation, considers environmental factors such as T a , RH, Ws, and T mrt , as well as personal factors such as clothing and metabolic rate, to comprehensively assess human thermal comfort [37]. The residents of hot and humid regions exhibit higher adaptability and tolerance to such environments [38]. Therefore, this study used PET to assess the thermal comfort in Guangzhou, as shown in Table 2 The ranges for PET assessment in Guangzhou were obtained from [39]. Table 2. The ranges for PET assessment in Guangzhou PET Value Thermal Sensation Grade of Physiological Stress - Very cold Extreme cold stress - Cold Strong cold stress Below 11.3 °C Cool Moderate cold stress 11.3–19.2 °C Slightly cool Slight cold stress 19.2–24.6 °C Comfortable No thermal stress 24.6–29.1 °C Slightly warm Slight heat stress 29.1–36.3 °C Warm Moderate heat stress 36.3–53.6 °C Hot Strong heat stress Above 53.6 °C Very hot Extreme heat stress 2.5.2. Quanti fi cation of PM 2.5 Distribution When people walk in pedestrian areas, particularly downwind zones, they are more likely to be exposed to roadway pollution [40]. Therefore, the distribution of PM 2.5 at the heights of people in the downwind areas of roads should be considered. By comparing the control and reference groups in downwind zones, the absolute PM 2.5 concentration Figure 4. ENVI-met model and the three stages of simulation 2.5. Thermal Comfort Evaluation Index and Quantitative Analysis of PM 2.5 2.5.1. PET Common thermal indices include the Standard Effective Temperature (SET*), Wet Bulb Globe Temperature (WBGT), Predicted Mean Vote (PMV) index, and Physiological Equivalent Temperature (PET) [ 36 ]. PET, derived from the human energy balance equation, considers environmental factors such as T a , RH, Ws, and T mrt , as well as personal factors such as clothing and metabolic rate, to comprehensively assess human thermal comfort [ 37 ]. The residents of hot and humid regions exhibit higher adaptability and tolerance to such environments [ 38 ]. Therefore, this study used PET to assess the thermal comfort in Guangzhou, as shown in Table 2 . The ranges for PET assessment in Guangzhou were obtained from [ 39 ]. Table 2. The ranges for PET assessment in Guangzhou PET Value Thermal Sensation Grade of Physiological Stress - Very cold Extreme cold stress - Cold Strong cold stress Below 11.3 ◦ C Cool Moderate cold stress 11.3–19.2 ◦ C Slightly cool Slight cold stress 19.2–24.6 ◦ C Comfortable No thermal stress 24.6–29.1 ◦ C Slightly warm Slight heat stress 29.1–36.3 ◦ C Warm Moderate heat stress 36.3–53.6 ◦ C Hot Strong heat stress Above 53.6 ◦ C Very hot Extreme heat stress 2.5.2. Quantification of PM 2.5 Distribution When people walk in pedestrian areas, particularly downwind zones, they are more likely to be exposed to roadway pollution [ 40 ]. Therefore, the distribution of PM 2.5 at the heights of people in the downwind areas of roads should be considered. By comparing the control and reference groups in downwind zones, the absolute PM 2.5 concentration differences for each model and the mean absolute PM 2.5 concentration difference in downwind areas were calculated using Formulas (5) and (6) as follows: C = a xy − b xy (5)

Sustainability 2024 , 16 , 8475 8 of 30 M = ∑ x = m , y = n x = 1, x = 1 C x × y (6) Herein, C ( µ g/m 3 ) represents the absolute difference in ( x , y ) concentration between the control and reference groups at grid coordinates ( x , y ); a xy denotes the PM 2.5 in the control group at grid coordinates ( x , y ) and b xy denotes the PM 2.5 concentration in the reference group at grid coordinates ( x , y ); M ( µ g/m 3 ) is the mean absolute PM 2.5 concentration difference in downwind areas, where x and y represent the number of grids, with equal grid counts per road in summer and winter: 50 grids for R 12 and 100 grids for R 23, R 34, and R 45 3. Results 3.1. Testing Results As shown in Figure 5 , during the measurement period, summer T a ranged from 28.7 ◦ C to 39.5 ◦ C, with an average of 35.3 ◦ C. RH ranged from 47.6% to 77.6%, averaging 61.7%. T a ranged from 4.4 ◦ C to 26.1 ◦ C, with an average of 12.4 ◦ C in winter. Relative humidity ranged from 32.3% to 78.0%, averaging 55.9% across the four roads, showing noticeable differences. R 45 exhibited higher T a than the other three roads (approximately 11.5 ◦ C higher on average), while R 12, R 23, and R 34 were similar. The RH was higher in R 45 and R 34 than in R 12 and R 23. Point 3 (without trees) consistently exhibited a significantly higher T a than Points 2 and 1 across all four roads in both summer and winter. The RH at Point 1 was generally higher than at Points 2 and 3, with Point 3 exhibiting a comparatively lower RH. The standard deviation, variance, and coefficient of variation are shown in Tables A 2 and A 3 . These results aligned with the typical summer and winter climatic characteristics of Guangzhou Sustainability 2024 , 16 , x FOR PEER REVIEW 8 of 33 di ff erences for each model and the mean absolute PM 2.5 concentration di ff erence in downwind areas were calculated using formulas (5) and (6) as follows: {?} = {?} − {?} (5) {?} = ∑ {?} , , {?} × {?} (6) Herein, C (µg/m 3 ) represents the absolute di ff erence in ( {?} , {?} ) concentration between the control and reference groups at grid coordinates ( {?} , {?} ); {?} denotes the PM 2.5 in the control group at grid coordinates ( {?} , {?} ) and {?} denotes the PM 2.5 concentration in the reference group at grid coordinates ( {?} , {?} ); M (µg/m 3 ) is the mean absolute PM 2.5 concentration di ff erence in downwind areas, where x and y represent the number of grids, with equal grid counts per road in summer and winter: 50 grids for R 12 and 100 grids for R 23, R 34, and R 45. 3. Results 3.1. Testing Results As shown in Figure 5, during the measurement period, summer T a ranged from 28.7 °C to 39.5 °C, with an average of 35.3 °C. RH ranged from 47.6% to 77.6%, averaging 61.7%. T a ranged from 4.4 °C to 26.1 °C, with an average of 12.4 °C in winter. Relative humidity ranged from 32.3% to 78.0%, averaging 55.9% across the four roads, showing noticeable di ff erences. R 45 exhibited higher T a than the other three roads (approximately 11.5 °C higher on average), while R 12, R 23, and R 34 were similar. The RH was higher in R 45 and R 34 than in R 12 and R 23. Point 3 (without trees) consistently exhibited a signi fi cantly higher T a than Points 2 and 1 across all four roads in both summer and winter. The RH at Point 1 was generally higher than at Points 2 and 3, with Point 3 exhibiting a comparatively lower RH. The standard deviation, variance, and coe ffi cient of variation are shown in Tables A 2 and A 3 . These results aligned with the typical summer and winter climatic characteristics of Guangzhou. Figure 5. Measured summer air temperature ( a ), winter air temperature ( b ), summer relative humidity ( c ), and winter relative humidity ( d ) As shown in Figure 6 , during the measurement period, the variation in the PM 2.5 concentration was more pronounced in summer, with each road experiencing different traffic volumes and environmental conditions, resulting in varying PM 2.5 in the range of 11.97 µ g/m 3 –44.60 µ g/m 3 , with a mean value of 25.17 µ g/m 3 . In winter, the trend in PM 2.5 concentration was more stable, with overall similar ranges of 1.30 µ g/m 3 –41.40 µ g/m 33 with a mean value of 28.95 µ g/m 3 . The PM 2.5 concentrations on road R 34 were more

Sustainability 2024 , 16 , 8475 9 of 30 variable and lower in the afternoon compared to the other three roads. The difference in PM 2.5 concentrations among the three measurement points is insignificant, with Point 3 being slightly higher than Points 1 and 2. The fluctuations in the data may reflect the effect of the cold weather on the measurement day, which resulted in a significantly more significant variance and coefficient of variation in this data set. The standard deviation, variance, and coefficient of variation are shown in Tables A 2 and A 3 . Sustainability 2024 , 16 , x FOR PEER REVIEW 9 of 33 Figure 5. Measured summer air temperature ( a ), winter air temperature ( b ), summer relative humidity ( c ), and winter relative humidity ( d ). As shown in Figure 6, during the measurement period, the variation in the PM 2.5 concentration was more pronounced in summer, with each road experiencing di ff erent tra ffi c volumes and environmental conditions, resulting in varying PM 2.5 in the range of 11.97 µg/m 3 –44.60 µg/m 3 , with a mean value of 25.17 µg/m 3 . In winter, the trend in PM 2.5 concentration was more stable, with overall similar ranges of 1.30 µg/m 3 –41.40 µg/m 33 with a mean value of 28.95 µg/m 3 . The PM 2.5 concentrations on road R 34 were more variable and lower in the afternoon compared to the other three roads. The di ff erence in PM 2.5 concentrations among the three measurement points is insigni fi cant, with Point 3 being slightly higher than Points 1 and 2. The fl uctuations in the data may re fl ect the e ff ect of the cold weather on the measurement day, which resulted in a signi fi cantly more signi fi cant variance and coe ffi cient of variation in this data set. The standard deviation, variance, and coe ffi cient of variation are shown in Tables A 2 and A 3 . Figure 6. Measured summer PM 2.5 concentrations ( a ) and winter PM 2.5 concentrations ( b ). 3.2. Model Accuracy Assessment Figures 7 and 8 depict the fi tt ing of simulated and measured values of T a , RH, and PM 2.5 concentration at each measurement point. In summer, the coe ffi cients of correlation (R 2 ) for T a , RH, and PM 2.5 concentration were 0.73–0.90, 0.71–0.94, and 0.75–0.93, respectively. In winter, the R 2 values for T a , RH, and PM 2.5 concentration were 0.72–0.92, 0.7–0.93, and 0.65–0.9, respectively. These results indicate that the established ENVI-met models are reliable and suitable for simulating the distribution of thermal environment and PM 2.5 concentration in humid climates. Although a small portion of the data had large variance and coe ffi cient of variation due to weather changes, the results of the model accuracy validation based on these raw data showed that the overall prediction accuracy of the model met the requirements. Therefore, these data were still retained to ensure data integrity and reliability. Figure 6. Measured summer PM 2.5 concentrations ( a ) and winter PM 2.5 concentrations ( b ) 3.2. Model Accuracy Assessment Figures 7 and 8 depict the fitting of simulated and measured values of T a , RH, and PM 2.5 concentration at each measurement point. In summer, the coefficients of correlation (R 2 ) for T a , RH, and PM 2.5 concentration were 0.73–0.90, 0.71–0.94, and 0.75–0.93, respectively. In winter, the R 2 values for T a , RH, and PM 2.5 concentration were 0.72–0.92, 0.7–0.93, and 0.65–0.9, respectively. These results indicate that the established ENVI-met models are reliable and suitable for simulating the distribution of thermal environment and PM 2.5 concentration in humid climates. Although a small portion of the data had large variance and coefficient of variation due to weather changes, the results of the model accuracy validation based on these raw data showed that the overall prediction accuracy of the model met the requirements. Therefore, these data were still retained to ensure data integrity and reliability Sustainability 2024 , 16 , x FOR PEER REVIEW 10 of 33 Figure 7. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and the measured value during the summer Figure 8. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and measured values during summer. Figure 7. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and the measured value during the summer.

Sustainability 2024 , 16 , 8475 10 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 10 of 33 Figure 7. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and the measured value during the summer Figure 8. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and measured values during summer. Figure 8. Relationship between simulated air temperature ( a − d ), relative humidity ( e − h ), PM 2.5 concentration ( i − l ), and measured values during summer 3.3. Influence of Tree Spacing 3.3.1. Impact of Tree Spacing on the Thermal Environment Parameters In the simulated scenarios using As (9 m canopy width), the hourly outputs of T a , T mrt , and Ws were collected and compared across different planting distances. Given that 14:00 represents the peak temperature, the analysis focused on the changes in the thermal environment during this period As shown in Figures 9 and 10 , three different planting distances on the four roads consistently reduced T a (T mrt ) on pedestrian walkways compared with the control group The cooling effect was most pronounced with the 3 m planting distance, resulting in summer reductions of 0.2 ◦ C (5.4 ◦ C), 0.5 ◦ C (3.8 ◦ C), 1.0 ◦ C (21.7 ◦ C), and 0.7 ◦ C (17.8 ◦ C) across the four roads, and winter reductions of 0.4 ◦ C (1.5 ◦ C), 0.2 ◦ C (15.0 ◦ C), 0.6 ◦ C (10.9 ◦ C), and 0.7 ◦ C (13.5 ◦ C). The cooling effect diminished gradually at 6 m and 9 m planting distances. Different planting distances significantly influenced the T mrt for R 45 Notably, in winter, the 6 m and 9 m planting distances increased T mrt at 14:00 for R 12, with the highest increase observed at 6 m (1.8 ◦ C). This could be attributed to the orientation of R 12 (north–south–east–west), where winter north winds prevail, and the street trees cannot shade the 2 PM winter sun and reduce wind speeds, increasing T mrt . The 6 m spacing, on the other hand, reduces the wind speed more compared to 9 m and increases T mrt more As shown in Figure 11 , compared with the control, planting trees at various distances somewhat reduced wind speeds at pedestrian heights, with the effect decreasing in the order 3 m > 6 m > 9 m. The 3 m planting distance reduced Ws in summer by 0.02 m/s (R 12), 0.05 m/s (R 23), 0.14 m/s (R 34), and 0.12 m/s (R 45), and in winter by 0.24 m/s (R 12), 0.07 m/s (R 23), 0.14 m/s (R 34), and 0.21 m/s (R 45). Dense planting at 3 m caused the most significant decrease in wind speed. Notably, owing to the different prevailing wind directions in summer and winter, R 45 experienced the greatest reduction in Ws during summer owing to the planting distance, while R 45 and R 12 showed more significant reductions in winter.

Sustainability 2024 , 16 , 8475 11 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 11 of 33 3.3. In fl uence of Tree Spacing 3.3.1. Impact of Tree Spacing on the Thermal Environment Parameters In the simulated scenarios using As (9 m canopy width), the hourly outputs of T a , T mrt , and Ws were collected and compared across di ff erent planting distances. Given that 14:00 represents the peak temperature, the analysis focused on the changes in the thermal environment during this period. As shown in Figures 9 and 10 , three di ff erent planting distances on the four roads consistently reduced T a (T mrt ) on pedestrian walkways compared with the control group. The cooling e ff ect was most pronounced with the 3 m planting distance, resulting in summer reductions of 0.2 °C (5.4 °C), 0.5 °C (3.8 °C), 1.0 °C (21.7 °C), and 0.7 °C (17.8 °C) across the four roads, and winter reductions of 0.4 °C (1.5 °C), 0.2 °C (15.0 °C), 0.6 °C (10.9 °C), and 0.7 °C (13.5 °C). The cooling e ff ect diminished gradually at 6 m and 9 m planting distances. Di ff erent planting distances signi fi cantly in fl uenced the T mrt for R 45. Notably, in winter, the 6 m and 9 m planting distances increased T mrt at 14:00 for R 12, with the highest increase observed at 6 m (1.8 °C). This could be a tt ributed to the orientation of R 12 (north–south–east–west), where winter north winds prevail, and the street trees cannot shade the 2 PM winter sun and reduce wind speeds, increasing T mrt . The 6 m spacing, on the other hand, reduces the wind speed more compared to 9 m and increases T mrt more. Figure 9. E ff ects of di ff erent planting distances of R 12, R 23, R 34, and R 45 on air temperature (T a ) during summer ( a – d ) and winter ( e – h ). Figure 9. Effects of different planting distances of R 12, R 23, R 34, and R 45 on air temperature (T a ) during summer ( a – d ) and winter ( e – h ) Sustainability 2024 , 16 , x FOR PEER REVIEW 12 of 33 Figure 10. E ff ects of di ff erent planting distances on mean radiant temperature (T mrt ) on R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) As shown in Figure 11, compared with the control, planting trees at various distances somewhat reduced wind speeds at pedestrian heights, with the e ff ect decreasing in the order 3 m > 6 m > 9 m. The 3 m planting distance reduced Ws in summer by 0.02 m/s (R 12), 0.05 m/s (R 23), 0.14 m/s (R 34), and 0.12 m/s (R 45), and in winter by 0.24 m/s (R 12), 0.07 m/s (R 23), 0.14 m/s (R 34), and 0.21 m/s (R 45). Dense planting at 3 m caused the most signi fi cant decrease in wind speed. Notably, owing to the di ff erent prevailing wind directions in summer and winter, R 45 experienced the greatest reduction in Ws during summer owing to the planting distance, while R 45 and R 12 showed more signi fi cant reductions in winter. Figure 11. E ff ects of di ff erent spacings on Ws of R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ). Figure 10. Effects of different planting distances on mean radiant temperature (T mrt ) on R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) 3.3.2. Impact of Tree Spacing on PET Figure 12 summarizes the PET values at different planting distances from 8:00 to 17:00 Planting trees at various distances significantly reduced PET values, with the greatest reduction observed at 3 m spacing, followed by 6 m and 9 m. Compared with the control, during summer, planting As at different distances reduced PET values by up to 20.3 ◦ C (R 12-3), 20.2 ◦ C (R 12-6), 18.1 ◦ C (R 12-9), 17.2 ◦ C (R 23-3), 16.6 ◦ C (R 23-6), 14.5 ◦ C (R 23-9), 17.8 ◦ C (R 34-3), 17.0 ◦ C (R 34-6), 14.5 ◦ C (R 34-9), 17.0 ◦ C (R 45-3), 15.7 ◦ C (R 45-6), and 11.7 ◦ C

Sustainability 2024 , 16 , 8475 12 of 30 (R 45-9). The PET values for R 45 were mostly influenced by the 3 m planting distance, with a maximum difference of 5.3 ◦ C, while the least affected was R 12, with a difference of 2.1 ◦ C. This effect could be attributed to the higher number of As at 3 m, compared with R 12, providing stronger cooling effects. Therefore, the 3 m planting distance showed more significant reductions in PET values, compared with 9 m, which was particularly effective in enhancing thermal comfort during hot summers. In winter, PET values were reduced by up to 3.3 ◦ C (R 12-3), 3.2 ◦ C (R 12-6), 3.1 ◦ C (R 12-9), 12.6 ◦ C (R 23-3), 12.4 ◦ C (R 23-6), 11.6 ◦ C (R 23-9), 5.8 ◦ C (R 34-3), 5.6 ◦ C (R 34-6), 4.9 ◦ C (R 34-9), 5.9 ◦ C (R 45-3), 5.5 ◦ C (R 45-6), and 4.6 ◦ C (R 45-9) when planting at various distances. While all distances reduced PET values, R 23, R 34, and R 45 maintained PET values above 11.3 ◦ C during the daytime, while some PET values for R 12 were slightly below 11.3 ◦ C (10.5–11.3 ◦ C), potentially causing discomfort due to cold temperatures. Therefore, planting distances should be carefully considered in colder winter weather for their impact on road PET values, as overly dense tree planting can unexpectedly reduce PET values and increase cold sensation. Roads with more green areas or higher tree densities showed significant distance-related effects Sustainability 2024 , 16 , x FOR PEER REVIEW 12 of 33 Figure 10. E ff ects of di ff erent planting distances on mean radiant temperature (T mrt ) on R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) As shown in Figure 11, compared with the control, planting trees at various distances somewhat reduced wind speeds at pedestrian heights, with the e ff ect decreasing in the order 3 m > 6 m > 9 m. The 3 m planting distance reduced Ws in summer by 0.02 m/s (R 12), 0.05 m/s (R 23), 0.14 m/s (R 34), and 0.12 m/s (R 45), and in winter by 0.24 m/s (R 12), 0.07 m/s (R 23), 0.14 m/s (R 34), and 0.21 m/s (R 45). Dense planting at 3 m caused the most signi fi cant decrease in wind speed. Notably, owing to the di ff erent prevailing wind directions in summer and winter, R 45 experienced the greatest reduction in Ws during summer owing to the planting distance, while R 45 and R 12 showed more signi fi cant reductions in winter. Figure 11. E ff ects of di ff erent spacings on Ws of R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ). Figure 11. Effects of different spacings on Ws of R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) At a moderate planting distance of 6 m, Fa and Mi significantly reduced T a to 1.4 ◦ C (R 34 summer) and 1.3 ◦ C (R 23 summer); conversely, Ma showed comparatively weaker cooling effects by up to 0.4 ◦ C (R 34 summer). The other species exhibited effects between these extremes (Figure A 1 ). Regarding T mrt reduction, Fa and Cc showed the highest cooling effect, reducing T mrt by up to 26.8 ◦ C (R 34 summer) and 16.9 ◦ C (R 23 winter), whereas Ct and Ma showed the least reduction (Figure A 2 ). Fc significantly impacted wind speeds, reducing them by up to 0.4 m/s (R 12 summer), whereas Ma had the smallest impact, reducing wind speeds by up to 0.1 m/s (R 12 summer). Other species fell between these extremes in their effects on wind speed (Figure A 3 ). 3.3.3. Effects of Spacing Distance on the Absolute PM 2.5 Concentration Difference At 8:00 AM during peak traffic hours, PM 2.5 concentration changes are particularly significant. Comparing PM 2.5 variations in different scenarios at this time, as shown in Figure 13 , positive values indicate an increase in concentration, whereas negative values indicate the opposite. Regardless of whether it is winter or summer, as the planting spacing increased, PM 2.5 dispersed from the leeward side to the windward side under the influence of wind, showing a decreasing trend of PM 2.5 concentration in the order 3 m > 6 m > 9 m.

Sustainability 2024 , 16 , 8475 13 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 14 of 33 Figure 12. E ff ect of tree spacing on PET. 3.3.3. E ff ects of Spacing Distance on the Absolute PM 2.5 Concentration Di ff erence At 8:00 AM during peak tra ffi c hours, PM 2.5 concentration changes are particularly signi fi cant. Comparing PM 2.5 variations in di ff erent scenarios at this time, as shown in Figure 13, positive values indicate an increase in concentration, whereas negative values indicate the opposite. Regardless of whether it is winter or summer, as the planting spacing increased, PM 2.5 dispersed from the leeward side to the windward side under the infl uence of wind, showing a decreasing trend of PM 2.5 concentration in the order 3 m > 6 m > 9 m. In summer, the maximum value for the absolute PM 2.5 concentration di ff erence (C) reached 5.05 µg/m 3 (R 45) when the plant spacing was 3 m. On roads such as R 23 and R 45, where there are no nearby buildings, natural winds are obstructed by trees, thereby inhibiting PM 2.5 dispersion. Therefore, smaller plant spacing resulted in higher PM 2.5 . On roads with buildings, such as R 12 and R 34, both buildings and trees hindered pollutant Figure 12. Effect of tree spacing on PET In summer, the maximum value for the absolute PM 2.5 concentration difference (C) reached 5.05 µ g/m 3 (R 45) when the plant spacing was 3 m. On roads such as R 23 and R 45, where there are no nearby buildings, natural winds are obstructed by trees, thereby inhibiting PM 2.5 dispersion. Therefore, smaller plant spacing resulted in higher PM 2.5 . On roads with buildings, such as R 12 and R 34, both buildings and trees hindered pollutant dispersion, leading to higher PM 2.5 concentrations in those areas. As a result, smaller planting spacing increases pollution in the pedestrian areas of the roadway, especially on roads with a high number of greenbelts and buildings In winter, with a plant spacing of 3 m, the PM 2.5 concentration significantly increased across the entire neighborhood, especially in areas with lower wind speeds. This effect was exacerbated by both the low wind speeds and the 3 m plant spacing, which hindered PM 2.5 dispersion. On the R 23 road, the C value reached a maximum of 2.13 µ g/m 3 Although winter typically has lower overall pollutant concentrations at pedestrian heights

Sustainability 2024 , 16 , 8475 14 of 30 owing to above-average wind speeds compared with summer, different plant spacing still significantly influenced PM 2.5 pollution from traffic emissions, with smaller spacing exacerbating PM 2.5 pollution 1 Figure 13. Effect of spacing on the absolute PM 2.5 concentration difference 3.3.4. Effect of Tree Spacing on the Mean Absolute PM 2.5 Concentration Difference in Downwind Areas As shown in Figure 14 , during summer, the mean absolute PM 2.5 concentration difference(M) for the three spacing intervals on all four roads was greater than 0, indicating an increasing trend in PM 2.5 concentration downwind. The highest and lowest M values were at 3 m and 9 m spacings, respectively. Among the four roads, R 45 exhibited the highest M values: 2.13 µ g/m 3 (3 m), 1.96 µ g/m 3 (6 m), and 1.70 µ g/m 3 (9 m). During winter on R 34, all three sets of M values were <0, indicating that tree planting benefits reduction in PM 2.5 concentration on this road, with 3 m spacing showing slightly better results than 6 m and 9 m spacings. However, the M values for the other three roads remained above 0, with significantly higher M values at 3 m spacing compared with 6 m and 9 m spacings. This

Sustainability 2024 , 16 , 8475 15 of 30 suggests that at 3 m spacing, PM 2.5 pollution is exacerbated on sidewalks, decreases slightly at 6 m spacing, and disperses more favorably at 9 m spacing. Therefore, when designing roadway landscaping, a larger planting spacing should be selected to minimize pollution due to PM 2.5 pollution concerns. And in the winter R 34 scenario, choosing smaller spacing can instead reduce PM 2.5 pollution Sustainability 2024 , 16 , x FOR PEER REVIEW 16 of 33 3.3.4. E ff ect of Tree Spacing on the Mean Absolute PM 2.5 Concentration Di ff erence in Downwind Areas As shown in Figure 14, during summer, the mean absolute PM 2.5 concentration difference(M) for the three spacing intervals on all four roads was greater than 0, indicating an increasing trend in PM 2.5 concentration downwind. The highest and lowest M values were at 3 m and 9 m spacings, respectively. Among the four roads, R 45 exhibited the highest M values: 2.13 µg/m 3 (3 m), 1.96 µg/m 3 (6 m), and 1.70 µg/m 3 (9 m). During winter on R 34, all three sets of M values were <0, indicating that tree planting bene fi ts reduction in PM 2.5 concentration on this road, with 3 m spacing showing slightly be tt er results than 6 m and 9 m spacings. However, the M values for the other three roads remained above 0, with signi fi cantly higher M values at 3 m spacing compared with 6 m and 9 m spacings. This suggests that at 3 m spacing, PM 2.5 pollution is exacerbated on sidewalks, decreases slightly at 6 m spacing, and disperses more favorably at 9 m spacing. Therefore, when designing roadway landscaping, a larger planting spacing should be selected to minimize pollution due to PM 2.5 pollution concerns. And in the winter R 34 scenario, choosing smaller spacing can instead reduce PM 2.5 pollution. Figure 14. The di ff erence in PM 2.5 concentration in the lower air area of the sideways and the average value of PM 2.5 during the summer ( a ) and winter ( b ). 3.4. Impact of Tree Species Based on the previous section, planting trees with a spacing of 6 m along roadside green belts moderately a ff ected PET and PM 2.5 . This section discusses the in fl uence of different tree species at a 6 m plant spacing on PET and PM 2.5 . 3.4.1. Impact of Tree Species on PET Figure 15 illustrates the impact of di ff erent tree species on PET values along R 12, R 23, R 34, and R 45 roads. On R 12, compared with the control group, Ma increased PET values by 0.1 °C at 2:00 PM. Other tree species generally decreased PET values, with Fa showing the most signi fi cant reduction of up to 1.4 °C. During winter, trees generally increased afternoon PET values by 0.7–1.8 °C, with Mi showing the highest increase, while only Fa decreased PET by 0.9 °C. On R 23, during summer, tree species reduced PET values by − 0.1–1 °C, with Fa achieving the greatest reduction and Ma causing a slight increase in PET. In winter, various tree species reduced PET by 2.8–6 °C, with Cc achieving the highest reduction and Ct the lowest. For R 34 in summer, PET values decreased by 0.4–6.5 °C, and in winter, the decrease ranged from 0.6 °C to 4.3 °C. Fa performed e ff ectively in summer, and Cc was more e ff ective in winter than Ct. In R 45, summer PET values decreased by 0.8–3.8 °C, and winter PET values decreased by 1.2–6.6 °C. Fa achieved the most signi fi cant reduction, while Ct showed the least reduction. In conclusion, Fa and Cc e ff ectively reduced the PET values, whereas Ma and Ct showed relatively limited e ff ects. Notably, planting trees on R 12 increased the PET values Figure 14. The difference in PM 2.5 concentration in the lower air area of the sideways and the average value of PM 2.5 during the summer ( a ) and winter ( b ) 3.4. Impact of Tree Species Based on the previous section, planting trees with a spacing of 6 m along roadside green belts moderately affected PET and PM 2.5 . This section discusses the influence of different tree species at a 6 m plant spacing on PET and PM 2.5 3.4.1. Impact of Tree Species on PET Figure 15 illustrates the impact of different tree species on PET values along R 12, R 23, R 34, and R 45 roads. On R 12, compared with the control group, Ma increased PET values by 0.1 ◦ C at 2:00 PM. Other tree species generally decreased PET values, with Fa showing the most significant reduction of up to 1.4 ◦ C. During winter, trees generally increased afternoon PET values by 0.7–1.8 ◦ C, with Mi showing the highest increase, while only Fa decreased PET by 0.9 ◦ C. On R 23, during summer, tree species reduced PET values by − 0.1–1 ◦ C, with Fa achieving the greatest reduction and Ma causing a slight increase in PET. In winter, various tree species reduced PET by 2.8–6 ◦ C, with Cc achieving the highest reduction and Ct the lowest. For R 34 in summer, PET values decreased by 0.4–6.5 ◦ C, and in winter, the decrease ranged from 0.6 ◦ C to 4.3 ◦ C. Fa performed effectively in summer, and Cc was more effective in winter than Ct. In R 45, summer PET values decreased by 0.8–3.8 ◦ C, and winter PET values decreased by 1.2–6.6 ◦ C. Fa achieved the most significant reduction, while Ct showed the least reduction In conclusion, Fa and Cc effectively reduced the PET values, whereas Ma and Ct showed relatively limited effects. Notably, planting trees on R 12 increased the PET values in the area, possibly because of increased T mrt during the afternoon, which is a primary factor influencing thermal comfort [ 13 , 41 ]. Additionally, although tree species markedly reduced PET values during winter, post-planting PET values remained above 11.3 ◦ C throughout most of the day. Therefore, tree species that enhance thermal comfort in the summer should be prioritized.

Sustainability 2024 , 16 , 8475 16 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 17 of 33 in the area, possibly because of increased T mrt during the afternoon, which is a primary factor in fl uencing thermal comfort [13,41]. Additionally, although tree species markedly reduced PET values during winter, post-planting PET values remained above 11.3 °C throughout most of the day. Therefore, tree species that enhance thermal comfort in the summer should be prioritized. Figure 15. Impact of tree species on the PET value of pedestrian space. 3.4.2. E ff ects of Tree Species on PM 2.5 Absolute Concentration Di ff erence When the planting spacing was 6 m, Figure 16 illustrates the distribution of C values between the di ff erent tree species and the control group. Dispersed PM 2.5 can accumulate in speci fi c areas owing to the obstructive e ff ects of trees and buildings, leading to increased concentrations. In summer, the maximum C values for PM 2.5 on the four roads were 0.47 µg/m 3 (Fc), 0.60 µg/m 3 (Fa), 1.54 µg/m 3 (Fa), and 5.99 µg/m 3 (Fa) for R 12, R 23, R 34, and R 45 roads, respectively. R 12 and R 23 roads showed a relatively limited increase in PM 2.5 concentration, whereas R 34 and R 45 roads exhibited a signi fi cant increase. In winter, the maximum C values for PM 2.5 on the four roads were 0.31 µg/m 3 (Fc), 3.17 µg/m 3 (Fa), 1.10 µg/m 3 (Fa), and 0.99 µg/m 3 (Fc) for R 12, R 23, R 34, and R 45 roads, respectively. The increase in PM 2.5 concentration was signi fi cant on the R 23 road and least signi fi cant on the R 12 road. Across the four roads, PM 2.5 concentrations notably increased with Fa and Fc, whereas Ma and Ct showed a less signi fi cant increase. In summer, the maximum C values were 3.39 µg/m 3 (Ma) and 3.47 µg/m 3 (Ct) on R 45, and in winter, they were 1.22 µg/m 3 (Ma) and 1.29 µg/m 3 (Ct) on R 23. Bb, Cc, Dd, and As had PM 2.5 that fell between these two categories of trees. Figure 15. Impact of tree species on the PET value of pedestrian space 3.4.2. Effects of Tree Species on PM 2.5 Absolute Concentration Difference When the planting spacing was 6 m, Figure 16 illustrates the distribution of C values between the different tree species and the control group. Dispersed PM 2.5 can accumulate in specific areas owing to the obstructive effects of trees and buildings, leading to increased concentrations In summer, the maximum C values for PM 2.5 on the four roads were 0.47 µ g/m 3 (Fc), 0.60 µ g/m 3 (Fa), 1.54 µ g/m 3 (Fa), and 5.99 µ g/m 3 (Fa) for R 12, R 23, R 34, and R 45 roads, respectively. R 12 and R 23 roads showed a relatively limited increase in PM 2.5 concentration, whereas R 34 and R 45 roads exhibited a significant increase. In winter, the maximum C values for PM 2.5 on the four roads were 0.31 µ g/m 3 (Fc), 3.17 µ g/m 3 (Fa), 1.10 µ g/m 3 (Fa), and 0.99 µ g/m 3 (Fc) for R 12, R 23, R 34, and R 45 roads, respectively. The increase in PM 2.5 concentration was significant on the R 23 road and least significant on the R 12 road Across the four roads, PM 2.5 concentrations notably increased with Fa and Fc, whereas Ma and Ct showed a less significant increase. In summer, the maximum C values were 3.39 µ g/m 3 (Ma) and 3.47 µ g/m 3 (Ct) on R 45, and in winter, they were 1.22 µ g/m 3 (Ma) and 1.29 µ g/m 3 (Ct) on R 23. Bb, Cc, Dd, and As had PM 2.5 that fell between these two categories of trees In addition, to explore the effects of tree morphological indicators on PM 2.5 concentrations more deeply, the correlations of tree height, crown spread, height under a branch, and LAI with PM 2.5 concentrations were further analyzed, as shown in Table 3 . Calculation of the correlation between morphological indicators of trees and PM 2.5 concentrations showed that crown width had the greatest effect on PM 2.5 concentration, especially in summer, with correlation coefficients of 0.817 ( p < 0.01) and 0.696 ( p < 0.05) in the R 12 and R 34 paths, respectively, showing strong positive correlations. Tree height was also positively correlated with PM 2.5 concentration in summer, but its effect was slightly weaker than crown height. In winter, the correlation coefficient of tree height in R 23 roads reached 0.900 ( p < 0.01), showing a significant positive correlation. These suggest that increasing tree height and crown spread may increase PM 2.5 concentrations. Under-branch height had a smaller effect on PM 2.5 concentrations, while LAI showed a significant negative correlation in winter (R 34, − 0.800, p ≤ 0.01), suggesting that larger LAI may contribute to lower PM 2.5 concentrations. These results suggest substantial differences in the effects of tree morphometric indicators on PM 2.5 concentrations across seasons and sites, with tree

Sustainability 2024 , 16 , 8475 17 of 30 morphometric indicators having a more significant impact in the summer and a weaker effect in the winter, similar to HE et al. [ 31 ]. Sustainability 2024 , 16 , x FOR PEER REVIEW 18 of 33 Figure 16. E ff ects of tree species on PM 2.5 absolute concentration di ff erences. Figure 16. Effects of tree species on PM 2.5 absolute concentration differences.

Sustainability 2024 , 16 , 8475 18 of 30 Table 3. Calculation of the correlation between morphological indicators of trees and PM 2.5 concentration. In this table, the symbol ‘*’ indicates p < 0.05, and ‘**’ indicates p < 0.01 Summer Winter Tree Morphological Indicators Scene Pearson Correlation Coefficient p -Value Tree Morphological Indicators Scene Pearson Correlation Coefficient p -Value Tree Height R 12 0.703 * 0.035 Tree Height R 12 − 0.45 0.224 R 23 0.306 0.424 R 23 0.900 ** 0.001 R 34 0.721 * 0.028 R 34 0.767 * 0.016 R 45 0.772 * 0.015 R 45 0.117 0.765 Crown Width R 12 0.817 ** 0.007 Crown Width R 12 0.407 0.277 R 23 0.777 * 0.014 R 23 0.661 0.053 R 34 0.696 * 0.037 R 34 0.017 0.965 R 45 0.669 * 0.049 R 45 0.424 0.256 Height Under Branch R 12 0.295 0.44 Height Under Branch R 12 − 0.42 0.26 R 23 0.038 0.922 R 23 0.311 0.415 R 34 0.335 0.379 R 34 0.529 0.143 R 45 0.42 0.26 R 45 0.092 0.813 LAI R 12 0.404 0.281 LAI R 12 0.617 0.077 R 23 − 0.304 0.426 R 23 − 0.567 0.112 R 34 − 0.558 0.119 R 34 − 0.8 ** 0.01 R 45 − 0.534 0.139 R 45 − 0.053 0.892 Among the simulated tree species, Fa (tree height of 10.2 m, crown spread of 12.4 m) and Fc (crown spread of 10.8) were not conducive to the diffusion of PM 2.5 due to their physical characteristics (high tree height or large crown spread), which led to an increase in concentration. In contrast, Ma (crown width 5 m), characterized by a narrow canopy, showed the least obstruction of PM 2.5 dispersion across all four roads. This study indicates that PM 2.5 concentrations are significantly influenced by tree height and canopy width Species with large canopy widths are more likely to hinder PM 2.5 dispersion, thereby increasing PM 2.5 concentrations on pedestrian pathways 3.4.3. Effect of Tree Species on the Mean Absolute PM 2.5 Concentration Difference in the Downwind Area Figure 17 illustrates the M values of downwind sections. On R 12, R 23, and R 45 roads, both in summer and winter, M values were greater than 0, indicating an increase in PM 2.5 concentration in pedestrian areas downwind As shown in Figure 17 a, on the R 12 road, the overall PM 2.5 concentrations slightly increased with M values greater than 0. In summer, the maximum and minimum M values were 0.35 µ g/m 3 (Mi) and 0.12 µ g/m 3 (Ma), respectively, while in winter, the maximum and minimum values were 0.24 µ g/m 3 (Fc) and 0.05 µ g/m 3 (Ma), respectively As shown in Figure 17 b, during summer on the R 23 road, planting Fa (0.47 µ g/m 3 ), Fc (0.45 µ g/m 3 ), and Mi (0.43 µ g/m 3 ) significantly increased PM 2.5 concentrations on pedestrian paths, with Ma (0.16 µ g/m 3 ) showing a slight increase. In winter, the overall increases were modest, with Cc contributing relatively more to PM 2.5 concentrations and Fc (0.10 µ g/m 3 ) contributing the least As shown in Figure 17 c, on the R 34 road during summer, Fa caused the highest increase in PM 2.5 , with an M value of 1.13 µ g/m 3 . Ma induced the smallest increase in PM 2.5 , with an M value of 0.4 µ g/m 3 . In winter, street trees generally benefited from the reduction of PM 2.5 concentration on pedestrian paths, where Fa showed the most significant reduction effect, with an M value of − 0.13 µ g/m 3 , and Ma exhibited the smallest reduction, with an M value of − 0.04 µ g/m 3 . The R 34 road aligned with the prevailing direction of winter wind from north to south. This facilitated PM 2.5 dispersion, but street trees mitigated this by restricting PM 2.5 more to the vehicle lane area, thereby reducing PM 2.5 concentration on pedestrian paths. Fa, with its broad canopy, showed the most significant obstruction

Sustainability 2024 , 16 , 8475 19 of 30 to PM 2.5 , whereas Ct, owing to its smaller canopy, showed a less pronounced obstruction to PM 2.5 Sustainability 2024 , 16 , x FOR PEER REVIEW 21 of 33 Figure 17. Mean absolute PM 2.5 concentration di ff erence in the downwind area on R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) roads. 3.5. Impact of Shrubs on Di ff erent Heights Based on our fi ndings in the previous section, we found that the impact of tree species on heat comfort and the concentration of PM 2.5 showed opposite trends. Therefore, we selected a tree species (As) that balanced the thermal comfort and PM 2.5 di ff usion and another tree species (Ma) that is less conducive to thermal comfort but facilitates PM 2.5 diffusion. We then combined these tree species with di ff erent types of shrubs for simulation. 3.5.1. Impact of Shrub Height on PET Value Figure 18 illustrates how the combination of trees and shrubs signi fi cantly reduced PET values compared with the control. Speci fi cally, combinations with Ma and various heights of shrubs (0/1/1.5/2 m) resulted in summer PET reductions of approximately 0.84 °C to 0.86 °C and winter reductions of about 2.58 °C to 2.63 °C. Similarly, combinations with As and shrubs at the same heights led to summer PET reductions of 1.93–2.00 °C and winter reductions of 4.27–4.35 °C. These fi ndings indicate that trees have a greater impact on PET values than shrubs, whereas the height of shrubs has a minimal in fl uence on PET values. Figure 17. Mean absolute PM 2.5 concentration difference in the downwind area on R 12 ( a ), R 23 ( b ), R 34 ( c ), and R 45 ( d ) roads As depicted in Figure 17 d, during both summer and winter seasons on the R 45 road, M values were greater than 0, leading to an increase in PM 2.5 concentration on pedestrian paths. Specifically, the M value significantly increased in summer compared with winter, indicating a notable rise in PM 2.5 concentration on pedestrian paths of R 45, exacerbating PM 2.5 pollution. When Fa was a street tree, M peaked at 2.5 µ g/m 3 . Fc, Cc, and Mi showed notably higher M values than other tree species at 2.31 µ g/m 3 , 2.18 µ g/m 3 , and 2.18 µ g/m 3 , respectively, while Ma exhibited the smallest M value at 1.5 µ g/m 3 . In winter, Fc, Mi, and Bb exhibited higher M values at 0.62 µ g/m 3 , 0.6 µ g/m 3 , and 0.54 µ g/m 3 , respectively Because the R 45 road does not align parallel to the summer and winter wind directions, PM 2.5 easily accumulated on vehicle and pedestrian paths owing to obstruction by street trees. Larger and denser canopies of street trees hindered PM 2.5 , thereby exacerbating PM 2.5 pollution on pedestrian paths 3.5. Impact of Shrubs on Different Heights Based on our findings in the previous section, we found that the impact of tree species on heat comfort and the concentration of PM 2.5 showed opposite trends. Therefore, we selected a tree species (As) that balanced the thermal comfort and PM 2.5 diffusion and another tree species (Ma) that is less conducive to thermal comfort but facilitates PM 2.5 diffusion. We then combined these tree species with different types of shrubs for simulation 3.5.1. Impact of Shrub Height on PET Value Figure 18 illustrates how the combination of trees and shrubs significantly reduced PET values compared with the control. Specifically, combinations with Ma and various heights of shrubs (0/1/1.5/2 m) resulted in summer PET reductions of approximately 0.84 ◦ C to 0.86 ◦ C and winter reductions of about 2.58 ◦ C to 2.63 ◦ C. Similarly, combinations with As and shrubs at the same heights led to summer PET reductions of 1.93–2.00 ◦ C

Sustainability 2024 , 16 , 8475 20 of 30 and winter reductions of 4.27–4.35 ◦ C. These findings indicate that trees have a greater impact on PET values than shrubs, whereas the height of shrubs has a minimal influence on PET values Sustainability 2024 , 16 , x FOR PEER REVIEW 22 of 33 Figure 18. Distribution of PET values at shrub heights of 0, 1, 1.5, and 2 m, compared with the reference group, when As is combined with Ma. 3.5.2. E ff ect of Shrub Height on the Absolute PM 2.5 Concentration Di ff erence From the results presented in Figure 19, it can be seen that in summer, the PM 2.5 concentration in the downwind areas showed an upward trend with the shrub’s height. The C value of the Qiao irrigation combination was signi fi cantly higher than that of the monoculture. Speci fi cally, when the shrub reached 1.5 m and 2 m and combined with the As, the C values were 5.38 µg/m 3 and 5.37 µg/m 3 , respectively. The results showed that higher shrubs had more obstructive e ff ects on wind and PM 2.5 , which were not conducive to the spread of PM 2.5 . The C value of the combination of As and shrubs was signi fi cantly higher than that of the combination of Ma and shrubs; however, Ma was greatly a ff ected by different shrub heights. In winter, the C values corresponding to shrubs of heights 0 m, 1 m, 1.5 m, and 2 m in both the Ma and As combination increased with shrub height. However, this growth trend was not signi fi cant and had a slight impact on the PM 2.5 concentration of the road. In summary, planting shrubs under tree species that are bene fi cial to the spread of PM 2.5 blocks the spread of PM 2.5 , thereby exacerbating air pollution to a certain extent. Increasing the height of the shrubs increased the concentration of PM 2.5 , but this increase was not obvious. Figure 18. Distribution of PET values at shrub heights of 0, 1, 1.5, and 2 m, compared with the reference group, when As is combined with Ma 3.5.2. Effect of Shrub Height on the Absolute PM 2.5 Concentration Difference From the results presented in Figure 19 , it can be seen that in summer, the PM 2.5 concentration in the downwind areas showed an upward trend with the shrub’s height The C value of the Qiao irrigation combination was significantly higher than that of the monoculture. Specifically, when the shrub reached 1.5 m and 2 m and combined with the As, the C values were 5.38 µ g/m 3 and 5.37 µ g/m 3 , respectively. The results showed that higher shrubs had more obstructive effects on wind and PM 2.5 , which were not conducive to the spread of PM 2.5 . The C value of the combination of As and shrubs was significantly higher than that of the combination of Ma and shrubs; however, Ma was greatly affected by different shrub heights. In winter, the C values corresponding to shrubs of heights 0 m, 1 m, 1.5 m, and 2 m in both the Ma and As combination increased with shrub height. However, this growth trend was not significant and had a slight impact on the PM 2.5 concentration of the road.

Sustainability 2024 , 16 , 8475 21 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 23 of 33 Figure 19. Absolute PM 2.5 concentration di ff erence at shrub heights of 0, 1, 1.5, and 2 m, compared with the reference group, when As and Ma are combined. 4. Discussion 4.1. Impact of Plant Spacing on Thermal Comfort and PM 2.5 The results indicate that, in both summer and winter, the cooling e ff ect of tree-planting spacing ranked as follows: 3 m > 6 m > 9 m. Speci fi cally, the impact of varying tree planting distances on R 45, which had the most greenery, showed a signi fi cant di ff erence in PET values, with a maximum variance of 5.29 °C. This fi nding is consistent with that of Zhao et al. [42], who indicated that higher vegetation coverage and greenery enhance microclimate regulation. Along the north–south oriented R 23 and R 34 roads, the reduction in T mrt ranked in the order 3 m > 6 m > 9 m. Conversely, the impact of 6 m spacing on the T mrt level is most pronounced on east–southeast and west–northwest-oriented R 12 and the southwest–northeast-oriented R 45 roads. Huang et al. [43] highlighted that the cooling bene fi ts of roadside trees are highly localized, with greater cooling e ff ects observed along north–south orientations than in east–west con fi gurations. Ian Estacio et al. [13] similarly found that urban canyons oriented east to west experienced the highest levels of heat discomfort. Di ff erent spacing distances exhibited contrasting e ff ects on PM 2.5 concentration and thermal comfort. These fi ndings corroborate previous studies [13]. The results indicated that as the spacing distance increased, PM 2.5 concentration decreased in the order 3 m > 6 m > 9 m, similar to the fi ndings by Li et al. [15], where particle concentrations increased with vegetation density. Along the R 34 road, the presence of roadside trees in winter aided the reduction in PM 2.5 , with slightly higher reductions observed at 3 m compared with those at 6 m and 9 m. Given the north–south orientation of R 34 and the prevailing northern winds in winter, roadside trees acted as barriers against PM 2.5 di ff usion from vehicular tra ffi c, with denser trees o ff ering more signi fi cant protection. Buccolieri et al. [44] found that under conditions where the dominant wind direction was perpendicular to the street orientation, PM 2.5 concentrations increased by 108%, while concentrations decreased by 18% when the wind direction was parallel to the street. Figure 19. Absolute PM 2.5 concentration difference at shrub heights of 0, 1, 1.5, and 2 m, compared with the reference group, when As and Ma are combined In summary, planting shrubs under tree species that are beneficial to the spread of PM 2.5 blocks the spread of PM 2.5 , thereby exacerbating air pollution to a certain extent Increasing the height of the shrubs increased the concentration of PM 2.5 , but this increase was not obvious 4. Discussion 4.1. Impact of Plant Spacing on Thermal Comfort and PM 2.5 The results indicate that, in both summer and winter, the cooling effect of tree-planting spacing ranked as follows: 3 m > 6 m > 9 m. Specifically, the impact of varying tree planting distances on R 45, which had the most greenery, showed a significant difference in PET values, with a maximum variance of 5.29 ◦ C. This finding is consistent with that of Zhao et al. [ 42 ], who indicated that higher vegetation coverage and greenery enhance microclimate regulation. Along the north–south oriented R 23 and R 34 roads, the reduction in T mrt ranked in the order 3 m > 6 m > 9 m. Conversely, the impact of 6 m spacing on the T mrt level is most pronounced on east–southeast and west–northwest-oriented R 12 and the southwest–northeast-oriented R 45 roads. Huang et al. [ 43 ] highlighted that the cooling benefits of roadside trees are highly localized, with greater cooling effects observed along north–south orientations than in east–west configurations. Ian Estacio et al. [ 13 ] similarly found that urban canyons oriented east to west experienced the highest levels of heat discomfort Different spacing distances exhibited contrasting effects on PM 2.5 concentration and thermal comfort. These findings corroborate previous studies [ 13 ]. The results indicated that as the spacing distance increased, PM 2.5 concentration decreased in the order 3 m > 6 m > 9 m, similar to the findings by Li et al. [ 15 ], where particle concentrations increased with vegetation density. Along the R 34 road, the presence of roadside trees in winter aided the reduction in PM 2.5 , with slightly higher reductions observed at 3 m compared with those at 6 m and 9 m. Given the north–south orientation of R 34 and the prevailing northern winds in winter, roadside trees acted as barriers against PM 2.5 diffusion from vehicular traffic, with denser trees offering more significant protection. Buccolieri et al. [ 44 ] found that under conditions where the dominant wind direction was perpendicu-

Sustainability 2024 , 16 , 8475 22 of 30 lar to the street orientation, PM 2.5 concentrations increased by 108%, while concentrations decreased by 18% when the wind direction was parallel to the street 4.2. Effect of Tree Species on Heat Comfort and PM 2.5 Planting trees significantly reduced the PET values in pedestrian spaces; however, certain species, such as Ma, may increase the PET values during specific times. LAI, crown width, and height were the primary factors influencing vegetation cooling and ventilation [ 45 ]. The results showed that Fa and Cc effectively reduced PET values, whereas Ma and Ct showed relatively limited cooling effects. The narrow crown and high canopy base [ 46 ] of Ma inadequately shielded pedestrian areas from solar radiation [ 47 ] and hindered airflow to some extent, thereby increasing the T mrt values. Notably, tree planting increased PET values in winter along R 12 roadsides near buildings. This effect could be attributed to the orientation (southeast–northwest) of R 12, which predominantly exposed these areas to north winds during winter. Additionally, the narrow crowns of the trees failed to provide effective shading by 2 PM, exacerbating heat absorption and prolonged release from nearby buildings [ 13 ]. Solar radiation is a primary factor influencing thermal comfort [ 41 ]. Huang et al. [ 43 ] found that trees may adversely affect wind speeds or solar exposure depending on their relationship with buildings or canopy structures. Fa effectively mitigated winter afternoon solar radiation with its broad crown and dense canopy, lowering T mrt through tree-induced cooling The effect of tree species on PM 2.5 concentration and thermal comfort showed an opposite trend, with different tree species having different impacts on PM 2.5 . The physical characteristics of trees, such as crown width, tree height, and LAI, significantly affected the concentration of PM 2.5 . Among them, crown width and tree height had more significant effects. On the other hand, tree species such as Fa and Fc were unfavorable for PM 2.5 diffusion due to their physical characteristics. The small crown of Baa had the least effect on PM 2.5 concentrations in pedestrian areas. Yang et al. [ 48 ] found that higher tree heights increased concentrations. He et al. [ 31 ] also found that PM 2.5 concentrations were significantly affected by factors such as tree crown width. Excessively wide crowns and tall trees can exacerbate PM 2.5 pollution, and an increase in under-branch heights can favor PM 2.5 diffusion in the vertical direction. However, it has also been shown that the effect of trunk height on concentration changes is negligible [ 49 ]. The results of this study show that the impact of under-branch height on PM 2.5 concentration is small, which may be because this study mainly focuses on the horizontal distribution of PM 2.5 at pedestrian heights. At the same time, the tree species morphology indicator variables may not be comprehensive enough, limiting the analysis of under-branch height’s effect. The study’s roadway environment differed from the other studies’ climatic conditions, which led to different results These findings underscore the significant impacts of crown width, tree height, and LAI on thermal comfort and PM 2.5 concentrations. Therefore, roadside tree species should be considered when selecting them, considering their combined effects on the thermal environment and PM 2.5 4.3. Effect of Shrub Height on Thermal Comfort and PM 2.5 Based on the research findings, combining trees and shrubs significantly reduced PET values, with minimal impact observed from shrub height variations. Yang et al. [ 19 ] confirmed that trees markedly improved outdoor thermal comfort, whereas shrubs and ground-cover plants showed less pronounced improvements. Therefore, despite the variations in tree species, the PET values for the tree–shrub combinations were similar. In this study, planting shrubs under tree species effectively obstructed PM 2.5 dispersion, thereby potentially exacerbating air pollution to some extent. As shrub height increased, there was a slight upward trend in the downwind PM 2.5 . Although shrubs obstructed PM 2.5 , the increase in shrub height did not significantly increase PM 2.5 .

Sustainability 2024 , 16 , 8475 23 of 30 4.4. Study Limitations First, the simulation scenarios in this study had some limitations. In reality, wind speed and direction are constantly changing, whereas the modeled wind speed and direction are fixed, potentially contributing to the differences between simulations and measurements [ 21 , 50 ]. Moreover, this study employed a single pollution source in the model; however, environmental factors such as airflow and dust from vehicles can introduce additional pollutants, reflecting the diversity of real-world pollution sources [ 51 ]. Nevertheless, this study used simulations to explore the trends in PM 2.5 concentration variations influenced by different factors. Second, the research examined the effects of greenery on thermal comfort and PM 2.5 concentration across different types of roads; however, it did not account for factors such as road orientation and lane division that could affect these outcomes. In addition, this study showed that the physical characteristics of trees, such as under-branch height, had a lesser effect on PM 2.5 concentrations, but this may have been due to the lack of a comprehensive range of morphological indicators of tree species, which limited the analysis of the effect of under-branch height. Finally, the study focused solely on the dispersion effects of PM 2.5 and did not consider its deposition effects. Greenery typically has a greater impact on particle dispersion than on deposition [ 52 ]. Future research should consider varying wind speed and direction in simulations to more accurately reflect real-world conditions. Further research should consider the combined effects of environmental factors such as road orientation and traffic separation zones on thermal comfort and PM 2.5 concentrations. In addition, future studies should incorporate more morphological indicators of tree species for a comprehensive analysis 5. Conclusions This study comprehensively analyzed the effects of different plant spacing, tree species, and tree–shrub combinations on the thermal comfort and PM 2.5 concentration of sidewalks under hot and humid climatic conditions in summer and winter. Simulations using the validated ENVI-met model were conducted to analyze the thermal comfort and PM 2.5 concentration at pedestrian heights and provide new perspectives and data support for future related studies. Based on these analyses, the following conclusions are drawn: 1 Tree spacing had contrasting effects on the thermal environment and PM 2.5 . Smaller spacings improved thermal comfort more effectively, with 3 m spacing reducing PET values by 17–20.3 ◦ C in summer and 3.3–12.6 ◦ C in winter. However, smaller spacings increased PM 2.5 concentrations, with maximum C values at 3 m spacing of 5.05 µ g/m 3 (R 45) in summer and maximum M values of 2.13 µ g/m 3 (R 23) in winter This is particularly noticeable on roads with a high number of green belts 2 Trees with wide crowns and high LAIs significantly improved thermal comfort, with reductions of up to 6.5 ◦ C ( Ficus altissima ) in summer and 6.6 ◦ C ( Ficus altissima ) in winter. Conversely, trees with small crowns facilitated PM 2.5 Michelia alba exhibited the highest C and M values at 3.39 µ g/m 3 and 1.5 µ g/m 3 in summer and 1.22 µ g/m 3 and 0.4 µ g/m 3 in winter, respectively. Planting species such as Ficus altissima and Cinnamomum camphora noticeably enhanced thermal comfort, whereas Michelia alba and Chukrasia tabularis were more effective in reducing PM 2.5 3 Combining trees with shrubs improved thermal comfort somewhat; however, increasing shrub height resulted in higher PM 2.5 . When shrub heights reached 1.5 m and 2 m in summer, C values peaked at 5.38 µ g/m 3 and 5.37 µ g/m 3 , respectively Planting trees primarily for summer considerations on streets significantly impacted pedestrian comfort and PM 2.5 levels more than winter tree planting 1 High Traffic and PM 2.5 Emission Roads: Prioritize reducing PM 2.5 pollution on busy urban expressways with dense traffic. We recommend planting Michelia alba and Chukrasia tabularis species with narrow crowns at 9 m spacing without additional shrub planting.

Sustainability 2024 , 16 , 8475 24 of 30 2 Main Urban Roads: Consider both thermal comfort and the impact of PM 2.5 on roads with high pedestrian and vehicle densities. Opt for moderate spacing like 6 m and choose species with moderate tree height, crown widths, and leaf area indices, such as Alstonia scholaris , Bauhinia blakeana , and Dracontomelon duperreanum 3 Minor Urban Roads: Prioritize PET on roads with fewer vehicles and more pedestrians. Opt for closer spacing, such as 3 m or 6 m, and plant species with large crowns and high leaf area indices, such as Ficus altissimo , Ficus concinna , and Cinnamomum camphora Moreover, shrubs could be added for aesthetic purposes 4 Wind direction has a significant effect on PM 2.5 dispersion. For roads that are not parallel to the wind direction, it is recommended that diffusion-friendly tree species be planted at larger intervals, such as 9 m intervals for Michelia alba . If PM 2.5 pollution is more severe in summer on roads parallel to the wind direction, large spacing and diffusion-friendly tree species should be selected. If PM 2.5 pollution is more severe in winter, smaller spacing and tree species with large crowns, such as 3 m or 6 m spacing, as well as large crowns, such as Ficus altissima , can be selected; these will, to a certain extent, block the diffusion of PM 2.5 to the sidewalks 5 In summary, this study investigated the effects of roadway greening design on thermal comfort and PM 2.5 concentration in hot and humid areas and made optimization recommendations. Although this study provides valuable references, future studies should consider the relevant factors more comprehensively to optimize the greening design of urban roads further to improve environmental quality Author Contributions: M.D.: Writing—original draft, Methodology, Formal analysis, Data curation Y.Z.: Supervision, Methodology, Conceptualization; J.Y.: Supervision, Methodology, Conceptualization; W.W.: Data curation; X.L.: Methodology, Data curation; Z.Z.: Methodology; B.H.: Data curation All authors have read and agreed to the published version of the manuscript Funding: This study was supported by the Hub Platform for Innovation in Critical Infrastructure Security and Intelligent Operation and Maintenance of Guangzhou University (grant no. PT 252022006) Institutional Review Board Statement: Not applicable Informed Consent Statement: Not applicable Data Availability Statement: Data are contained within the article and supplementary materials Acknowledgments: In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments) Conflicts of Interest: The authors declare no conflicts of interest Nomenclatures LAD leaf area density LAIs leaf area indices MAE mean absolute error T mrt mean radiant temperature PM 2.5 particulate matter 2.5 PET pedestrian thermal comfort PMV predicted mean vote RH relative humidity RMSE root mean square error SET standard effective temperature WBGT wet bulb globe temperature T a air temperature Ws wind speed C absolute PM 2.5 concentration difference M mean absolute PM 2.5 concentration difference R 12 one roadbed and two belts

Sustainability 2024 , 16 , 8475 25 of 30 R 23 two roadways and three belts R 34 three roadways and four belts R 45 four roadways and five belts Ma Michelia alba Fa Ficus altissima Bb Bauhinia blakeana Mi Mangifera indica As Alstonia scholaris Ct Chukrasia tabularis Dd Dracontomelon duperreanum Fc Ficus concinna Cc Cinnamomum camphora Appendix A Table A 1. Boundary conditions for the simulation process using the ENVI-Met model Boundary Conditions for the Simulation Process Using the ENVI-Met Model Location Guangzhou (23 ◦ 12 ′ N;113 ◦ 20 ′ E) Simulation date Summer September 20, September 21, September 24, September 25, 2023 Winter January 21, January 22, January 24, January 25, 2024 Simulation time 8:00–10:00, 11:00–14:00,15:00–17:00 Model dimensions R 12 X-Grids: 27 Y-Grids: 101 Z-Grids: 13 R 23 X-Grids: 42 Y-Grids: 102 Z-Grids: 13 R 34 X-Grids: 57 Y-Grids: 104 Z-Grids: 16 R 45 X-Grids: 39 Y-Grids: 101 Z-Grids: 15 Grid cell dx = 3 dy = 3 dz = 3 Grid north 0 Nesting grids 5 Roughness length 0.1 Wind direction (N:0, 180:S) R 12 45 (summer) 0 (winter) R 23 90 (summer) 0 (winter) R 34 135 (summer) 0 (winter) R 45 202.5 (summer) 0 (winter) Wind speed R 12 0.8 (summer) 1.9 (winter) R 23 0.8 (summer) 0.7 (winter) R 34 0.8 (summer) 0.9 (winter) R 45 0.8 (summer) 2 (winter) Air temperature R 12 29–37.45 ◦ C (summer) 5.4–8.3 (winter) R 23 30.77–37.8 ◦ C (summer) 5.5–15.5 (winter) R 34 29.77–38.1 ◦ C (summer) 5.1–15.9 (winter) R 45 30.77–41.8 ◦ C (summer) 13.8–22.6 (winter) Relative humidity R 12 50–72% (summer) 26–33% (winter) R 23 45–75% (summer) 20–31% (winter) R 34 47–74% (summer) 53–64% (winter) R 45 53–77% (summer) 44–66% (winter) PET index calculation Bio-met process Results visualization Leonardo visualization tool

Sustainability 2024 , 16 , 8475 26 of 30 Table A 2. Standard deviation, variance, and coefficient of variation of measured temperature, humidity, and PM 2.5 concentration during summer months Scene Site Season Measured Parameters Mean Variance Standard Deviation (SD) Coefficient of Variation (CV) R 12 1 summer T a ( ◦ C) 33.291 5.333499 2.309437 6.937121 summer RH (%) 65.13 37.36233 6.112474 9.385035 summer PM 2.5 ( µ g/m 3 ) 20.10667 24.83179 4.983151 24.78357 2 summer T a ( ◦ C) 33.965 3.716917 1.927931 5.676227 summer RH (%) 60.44 73.08933 8.54923 14.14499 summer PM 2.5 ( µ g/m 3 ) 20.10667 24.83179 4.983151 24.78357 3 summer T a ( ◦ C) 36.146 8.217538 2.866625 7.930683 summer RH (%) 59.09 54.90767 7.409971 12.54014 summer PM 2.5 ( µ g/m 3 ) 20.55333 22.44425 4.737537 23.04997 R 23 1 summer T a ( ◦ C) 35.305 4.779406 2.186185 6.192282 summer RH (%) 58.04 77.06267 8.778534 15.12497 summer PM 2.5 ( µ g/m 3 ) 26.24 52.61896 7.253893 27.64441 2 summer T a ( ◦ C) 35.074 7.587471 2.754536 7.8535 summer RH (%) 63.21 59.32544 7.702301 12.18526 summer PM 2.5 ( µ g/m 3 ) 26.51 81.63828 9.035391 34.08295 3 summer T a ( ◦ C) 37.36 2.100156 1.449191 3.878992 summer RH (%) 57.28 42.60622 6.527344 11.3955 summer PM 2.5 ( µ g/m 3 ) 24.91333 42.28967 6.503051 26.10269 R 34 1 summer T a ( ◦ C) 34.469 8.515254 2.918091 8.465841 summer RH (%) 63.14 57.20489 7.563391 11.97876 summer PM 2.5 ( µ g/m 3 ) 28.1814 70.57995 8.401188 29.81111 2 summer T a ( ◦ C) 35.844 7.686716 2.772493 7.734886 summer RH (%) 58.79 29.95656 5.473258 9.309846 summer PM 2.5 ( µ g/m 3 ) 27.56552 84.59892 9.197767 33.36693 3 summer T a ( ◦ C) 37.125 3.710783 1.926339 5.188793 summer RH (%) 63.83 22.05789 4.696583 7.357955 summer PM 2.5 ( µ g/m 3 ) 27.52 79.55388 8.919298 32.41024 R 45 1 summer T a ( ◦ C) 35.233 5.124401 2.263714 6.424982 summer RH (%) 61.45 20.10722 4.484108 7.297165 summer PM 2.5 ( µ g/m 3 ) 25.87 42.16554 6.4935 25.1005 2 summer T a ( ◦ C) 33.257 6.020823 2.453737 7.378106 summer RH (%) 67.34 7.962667 2.82182 4.190407 summer PM 2.5 ( µ g/m 3 ) 27.29444 22.13242 4.704511 17.23615 3 summer T a ( ◦ C) 36.92 5.445156 2.333486 6.320384 summer RH (%) 62.26 17.31822 4.161517 6.684094 summer PM 2.5 ( µ g/m 3 ) 27.26816 34.16276 5.844892 21.43486 Table A 3. Standard deviation, variance, and coefficient of variation of measured temperature, humidity, and PM 2.5 concentrations in winter Scene Site Season Measured Parameters Mean Variance Standard Deviation (SD) Coefficient of Variation (CV) R 12 1 winter T a ( ◦ C) 6.7233 1.820863 1.349394 20.07041 winter RH (%) 45.1866 5.740936 2.396025 5.302512 winter PM 2.5 ( µ g/m 3 ) 35.84 1.711556 1.308264 3.650291 2 winter T a ( ◦ C) 7.437 1.683201 1.297382 17.44497 winter RH (%) 61.16 1.698222 1.303159 2.130737 winter PM 2.5 ( µ g/m 3 ) 36.03 0.793444 0.890755 2.472259 3 winter T a ( ◦ C) 8.859 3.304299 1.817773 20.51894 winter RH (%) 42.68 11.21067 3.348233 7.84497 winter PM 2.5 ( µ g/m 3 ) 36.1 1.073333 1.036018 2.869856 R 23 1 winter T a ( ◦ C) 11.425 10.05647 3.171194 27.75662 winter RH (%) 59.06 2.147111 1.465302 2.48104 winter PM 2.5 ( µ g/m 3 ) 38.14 2.876 1.695877 4.446453 2 winter T a ( ◦ C) 11.482 7.191742 2.681742 23.35606 winter RH (%) 41.7134 19.38863 4.403252 10.55597 winter PM 2.5 ( µ g/m 3 ) 37.06 5.751556 2.39824 6.471236 3 winter T a ( ◦ C) 12.713 17.55949 4.190405 32.96157 winter RH (%) 40.7 33.11333 5.754419 14.13862 winter PM 2.5 ( µ g/m 3 ) 38.62 3.892889 1.973041 5.108857

Sustainability 2024 , 16 , 8475 27 of 30 Table A 3. Cont Scene Site Season Measured Parameters Mean Variance Standard Deviation (SD) Coefficient of Variation (CV) R 34 1 winter T a ( ◦ C) 10.6074 1.268156 1.126125 10.61641 winter RH (%) 73.761 7.155227 2.674926 3.626477 winter PM 2.5 ( µ g/m 3 ) 10.98 140.2329 11.842 107.8506 2 winter T a ( ◦ C) 10.815 1.726806 1.31408 12.15053 winter RH (%) 68.14 1.009333 1.004656 1.4744 winter PM 2.5 ( µ g/m 3 ) 11.79778 141.7642 11.90648 100.9214 3 winter T a ( ◦ C) 10.986 1.715738 1.309862 11.92301 winter RH (%) 64.85 13.91389 3.730133 5.751939 winter PM 2.5 ( µ g/m 3 ) 12.41 144.9921 12.04127 97.02874 R 45 1 winter T a ( ◦ C) 19.388 11.95355 3.457391 17.83263 winter RH (%) 66.03 12.15344 3.486179 5.27969 winter PM 2.5 ( µ g/m 3 ) 29.5 6.302222 2.510423 8.509908 2 winter T a ( ◦ C) 17.3311 8.960809 2.993461 17.27219 winter RH (%) 60.1699 55.81766 7.471121 12.41671 winter PM 2.5 ( µ g/m 3 ) 30.54 5.611556 2.368872 7.756622 3 winter T a ( ◦ C) 20.682 20.526 4.530562 21.90582 winter RH (%) 46.84 65.67378 8.103936 17.30131 winter PM 2.5 ( µ g/m 3 ) 30.39 4.912111 2.216328 7.292953 Sustainability 2024 , 16 , x FOR PEER REVIEW 30 of 33 Figure A 1. E ff ects of the T a species on the T a of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 2. E ff ects of tree species on the T mrt of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 3. E ff ects of tree species on the Ws of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 1. Effects of the T a species on the T a of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ) Sustainability 2024 , 16 , x FOR PEER REVIEW 30 of 33 Figure A 1. E ff ects of the T a species on the T a of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 2. E ff ects of tree species on the T mrt of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 3. E ff ects of tree species on the Ws of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 2. Effects of tree species on the T mrt of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ).

Sustainability 2024 , 16 , 8475 28 of 30 Sustainability 2024 , 16 , x FOR PEER REVIEW 30 of 33 Figure A 1. E ff ects of the T a species on the T a of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 2. E ff ects of tree species on the T mrt of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 3. E ff ects of tree species on the Ws of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ). Figure A 3. Effects of tree species on the Ws of R 12, R 23, R 34, and R 45 during summer ( a – d ) and winter ( e – h ) References 1 Banerjee, S.; Ching, N.Y.G.; Yik, S.K.; Dzyuban, Y.; Crank, P.J.; Yi, R.P.X.; Chow, W.T.L. Analysing impacts of urban morphological variables and density on outdoor microclimate for tropical cities: A review and a framework proposal for future research directions Build. Environ 2022 , 225 , 109646. [ CrossRef ] 2 Xu, T.; Song, Y.; Liu, M.; Cai, X.; Zhang, H.; Guo, J.; Zhu, T. Temperature inversions in severe polluted days derived from radiosonde data in North China from 2011 to 2016 Sci. Total Environ 2019 , 647 , 1011–1020. [ CrossRef ] [ PubMed ] 3 Guo, Y.M.; Gasparrini, A.; Li, S.S.; Sera, F.; Vicedo-Cabrera, A.M.; Coelho, M.; Saldiva, P.H.N.; Lavigne, E.; Tawatsupa, B.; Punnasiri, K.; et al. Quantifying excess deaths related to heatwaves under climate change scenarios: A multicountry time series modelling study PLoS Med 2018 , 15 , e 1002629. [ CrossRef ] [ PubMed ] 4 Dab, W.; S é gala, C.; Dor, F.; Festy, B.; Lameloise, P.; Le Moullec, Y.; Le Tertre, A.; M é dina, S.; Qu é nel, P.; Wallaert, B.; et al. Air pollution and health: Correlation or casuality?: The case of the relationship between particle exposure and deaths from heart and lung disease J. Air Waste Manag. Assoc 2001 , 51 , 203–219. [ CrossRef ] [ PubMed ] 5 Li, C.G.; Lin, T.; Zhang, Z.F.; Xu, D.; Huang, L.; Bai, W.P. Can transportation infrastructure reduce haze pollution in China? Environ. Sci. Pollut. Res 2022 , 29 , 15564–15581. [ CrossRef ] 6 Karagulian, F.; Belis, C.A.; Dora, C.F.C.; Prüss-Ustün, A.M.; Bonjour, S.; Adair-Rohani, H.; Amann, M. Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level Atmos. Environ 2015 , 120 , 475–483. [ CrossRef ] 7 Hill, W.; Lim, E.L.; Weeden, C.E.; Lee, C.; Augustine, M.; Chen, K.; Kuan, F.C.; Marongiu, F.; Evans, E.J.; Moore, D.A.; et al. Lung adenocarcinoma promotion by air pollutants Nature 2023 , 616 , 159–167. [ CrossRef ] 8 Zheng, G.Z.; Zhu, N.; Tian, Z.; Chen, Y.; Sun, B.H. Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments Saf. Sci 2012 , 50 , 228–239. [ CrossRef ] 9 Sæbo, A.; Popek, R.; Nawrot, B.; Hanslin, H.M.; Gawronska, H.; Gawronski, S.W. Plant species differences in particulate matter accumulation on leaf surfaces Sci. Total Environ 2012 , 427 , 347–354. [ CrossRef ] 10 Salim, M.H.; Schlünzen, K.H.; Grawe, D. Including trees in the numerical simulations of the wind flow in urban areas: Should we care? J. Wind Eng. Ind. Aerodyn 2015 , 144 , 84–95. [ CrossRef ] 11 Vos, P.E.J.; Maiheu, B.; Vankerkom, J.; Janssen, S. Improving local air quality in cities: To tree or not to tree? Environ. Pollut 2013 , 183 , 113–122. [ CrossRef ] [ PubMed ] 12 Yang, J.; Zhao, Y.; Guo, T.; Luo, X.; Ji, K.; Zhou, M.; Wan, F. The impact of tree species and planting location on outdoor thermal comfort of a semi-outdoor space Int. J. Biometeorol 2023 , 67 , 1689–1701. [ CrossRef ] [ PubMed ] 13 Estacio, I.; Hadfi, R.; Blanco, A.; Ito, T.; Babaan, J. Optimization of tree positioning to maximize walking in urban outdoor spaces: A modeling and simulation framework Sustain. Cities Soc 2022 , 86 , 104105. [ CrossRef ] 14 Park, C.Y.; Lee, D.K.; Krayenhoff, E.S.; Heo, H.K.; Hyun, J.H.; Oh, K.; Park, T.Y. Variations in pedestrian mean radiant temperature based on the spacing and size of street trees Sustain. Cities Soc 2019 , 48 , 101521. [ CrossRef ] 15 Li, Z.T.; Zhang, H.; Juan, Y.H.; Lee, Y.T.; Wen, C.Y.; Yang, A.S. Effects of urban tree planting on thermal comfort and air quality in the street canyon in a subtropical climate Sustain. Cities Soc 2023 , 91 , 104334. [ CrossRef ] 16 Wania, A.; Bruse, M.; Blond, N.; Weber, C. Analysing the influence of different street vegetation on traffic-induced particle dispersion using microscale simulations J. Environ. Manag 2012 , 94 , 91–101. [ CrossRef ] 17 Yao, Y.B.; Chang, J.; Yang, H.Y.; Jie, B. Current status and development trend of landscape visual environment quality evaluation research J. West Anhui Univ 2021 , 37 , 110–119.

Sustainability 2024 , 16 , 8475 29 of 30 18 Salmond, J.A.; Williams, D.E.; Laing, G.; Kingham, S.; Dirks, K.; Longley, I.; Henshaw, G.S. The influence of vegetation on the horizontal and vertical distribution of pollutants in a street canyon Sci. Total Environ 2013 , 443 , 287–298. [ CrossRef ] 19 Yang, Y.J.; Zhou, D.; Wang, Y.P.; Ma, D.X.; Chen, W.; Xu, D.; Zhu, Z.Z. Economical and outdoor thermal comfort analysis of greening in multistory residential areas in Xi’an Sustain. Cities Soc 2019 , 51 , 101730. [ CrossRef ] 20 Li, J.Y.; Zheng, B.H.; Ouyang, X.; Chen, X.; Bedra, K.B. Does shrub benefit the thermal comfort at pedestrian height in Singapore? Sustain. Cities Soc 2021 , 75 , 103333. [ CrossRef ] 21 Wu, J.S.; Luo, K.Y.; Wang, Y.; Wang, Z.Y. Urban road greenbelt configuration: The perspective of PM 2.5 removal and air quality regulation Environ. Int 2021 , 157 , 106786. [ CrossRef ] [ PubMed ] 22 Chen, X.; Wu, J.; Yang, W.B.; Wang, Z.Y.; Chen, S.T.; Hu, X.S.; Lu, K.F.; Fan, Z.M.; Lin, M.; Chen, P. Measuring and modeling the effects of green barriers on the spatial distribution of fine particulate matter at roadside Urban Clim 2023 , 52 , 101727. [ CrossRef ] 23 Ali-Toudert, F.; Mayer, H. Effects of asymmetry, galleries, overhanging facades and vegetation on thermal comfort in urban street canyons Sol. Energy 2007 , 81 , 742–754. [ CrossRef ] 24 Lin, P.Y.; Song, D.X.; Qin, H. Impact of parking and greening design strategies on summertime outdoor thermal condition in old mid-rise residential estates Urban For. Urban Green 2021 , 63 , 127200. [ CrossRef ] 25 Xiong, Y.Z.; Huang, S.P.; Chen, F.; Ye, H.; Wang, C.P.; Zhu, C.B. The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China Remote Sens 2012 , 4 , 2033–2056. [ CrossRef ] 26 Zhou, K.; Ye, Y.H.; Liu, Q.; Liu, A.J.; Peng, S.L. Evaluation of ambient air quality in Guangzhou, China J. Environ. Sci 2007 , 19 , 432–437. [ CrossRef ] 27 Li, K.M.; Zhang, Y.F.; Zhao, L.H. Outdoor thermal comfort and activities in the urban residential community in a humid subtropical area of China Energy Build 2016 , 133 , 498–511. [ CrossRef ] 28 Yang, X.S.; Zhao, L.H.; Bruse, M.; Meng, Q.L. Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces Build. Environ 2013 , 60 , 93–104. [ CrossRef ] 29 Wang, B. Study on optimization strategy of road greening in the central urban area of Zengcheng District, Guangzhou City, China. Master’s Thesis, South China University of Technology, Guangzhou, China, 2020 30 Järvi, L.; Kurppa, M.; Kuuluvainen, H.; Rönkkö, T.; Karttunen, S.; Balling, A.; Timonen, H.; Niemi, J.; Pirjola, L. Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone Sci Total Environ 2023 , 856 , 158974. [ CrossRef ] 31 He, H.Y.; Zhu, Y.S.; Liu, L.; Du, J.; Liu, L.R.; Liu, J. Effects of roadside trees three-dimensional morphology characteristics on traffic-related PM 2.5 distribution in hot-humid urban blocks Urban Clim 2023 , 49 , 101448. [ CrossRef ] 32 Dai, S.; Bi, X.; Chan, L.Y.; He, J.; Wang, B.; Wang, X.; Peng, P.; Sheng, G.; Fu, J. Chemical and stable carbon isotopic composition of PM 2.5 from on-road vehicle emissions in the PRD region and implications for vehicle emission control policy Atmos. Chem. Phys 2015 , 15 , 3097–3108. [ CrossRef ] 33 Liu, J.Y.; Zheng, B.H. A Simulation Study on the Influence of Street Tree Configuration on Fine Particulate Matter (PM 2.5 ) Concentration in Street Canyons Forests 2023 , 14 , 1550. [ CrossRef ] 34 Liu, Z.; Tan, G.; Zhao, L. Simplification Method for Building Tree Models in ENVI-met: A Case Study of Ficus microcarpa Guangdong Landsc. Arch 2018 , 46 , 83–87 35 Lalic, B.; Mihailovic, D.T. An empirical relation describing leaf-area density inside the forest for environmental modeling J. Appl Meteorol 2004 , 43 , 641–645. [ CrossRef ] 36 Fang, Z.S.; Feng, X.W.; Liu, J.L.; Lin, Z.; Mak, C.M.; Niu, J.L.; Tse, K.T.; Xu, X.N. Investigation into the differences among several outdoor thermal comfort indices against field survey in subtropics Sustain. Cities Soc 2019 , 44 , 676–690. [ CrossRef ] 37 Zhang, L.L.L.; Wei, D.; Hou, Y.Y.; Du, J.F.; Liu, Z.; Zhang, G.M.; Shi, L. Outdoor Thermal Comfort of Urban Park—A Case Study Sustainability 2020 , 12 , 1961. [ CrossRef ] 38 Lin, T.-P.; Matzarakis, A. Tourism climate and thermal comfort in Sun Moon Lake, Taiwan Int. J. Biometeorol 2008 , 52 , 281–290 [ CrossRef ] 39 Fang, Z.; Feng, X.; Xu, X.; Zhou, X.; Lin, Z.; Ji, Y. Investigation into outdoor thermal comfort conditions by different seasonal field surveys in China, Guangzhou Int. J. Biometeorol 2019 , 63 , 1357–1368. [ CrossRef ] 40 Polednik, B.; Piotrowicz, A. Pedestrian exposure to traffic-related particles along a city road in Lublin, Poland Atmos. Pollut. Res 2020 , 11 , 686–692. [ CrossRef ] 41 Jung, S.J.; Yoon, S. Effects of Creating Street Greenery in Urban Pedestrian Roads on Microclimates and Particulate Matter Concentrations Sustainability 2022 , 14 , 7887. [ CrossRef ] 42 Zhao, D.; Lei, Q.H.; Shi, Y.J.; Wang, M.D.; Chen, S.B.; Shah, K.; Ji, W.L. Role of Species and Planting Configuration on Transpiration and Microclimate for Urban Trees Forests 2020 , 11 , 825. [ CrossRef ] 43 Huang, J.M.; Chen, L.C. Synergistic Effects of Roadside Trees and Spatial Geometry on Thermal Environment in Urban Streets: A Case Study in Tropical, Medium-Sized City, Taiwan Buildings 2023 , 13 , 2092. [ CrossRef ] 44 Buccolieri, R.; Jeanjean, A.P.R.; Gatto, E.; Leigh, R.J. The impact of trees on street ventilation, NO x and PM 2.5 concentrations across heights in Marylebone Rd street canyon, central London Sustain. Cities Soc 2018 , 41 , 227–241. [ CrossRef ] 45 Zhang, L.; Zhan, Q.M.; Lan, Y.L. Effects of the tree distribution and species on outdoor environment conditions in a hot summer and cold winter zone: A case study in Wuhan residential quarters Build. Environ 2018 , 130 , 27–39. [ CrossRef ]

Sustainability 2024 , 16 , 8475 30 of 30 46 Zhang, J.; Gou, Z.H. Tree crowns and their associated summertime microclimatic adjustment and thermal comfort improvement in urban parks in a subtropical city of China Urban For. Urban Green 2021 , 59 , 126912. [ CrossRef ] 47 Pace, R.; De Fino, F.; Rahman, M.A.; Pauleit, S.; Nowak, D.J.; Grote, R. A single tree model to consistently simulate cooling, shading, and pollution uptake of urban trees Int. J. Biometeorol 2021 , 65 , 277–289. [ CrossRef ] 48 Yang, H.; Chen, T.; Lin, Y.; Buccolieri, R.; Mattsson, M.; Zhang, M.; Hang, J.; Wang, Q. Integrated impacts of tree planting and street aspect ratios on CO dispersion and personal exposure in full-scale street canyons Build. Environ 2020 , 169 , 106529 [ CrossRef ] 49 Morakinyo, T.E.; Lam, Y.F. Study of traffic-related pollutant removal from street canyon with trees: Dispersion and deposition perspective Environ. Sci. Pollut. Res 2016 , 23 , 21652–21668. [ CrossRef ] 50 Abhijith, K.V.; Kumar, P.; Gallagher, J.; McNabola, A.; Baldauf, R.; Pilla, F.; Broderick, B.; Di Sabatino, S.; Pulvirenti, B. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments—A review Atmos. Environ 2017 , 162 , 71–86. [ CrossRef ] 51 Deng, S.X.; Ma, J.; Zhang, L.L.; Jia, Z.K.; Ma, L.Y. Microclimate simulation and model optimization of the effect of roadway green space on atmospheric particulate matter Environ. Pollut 2019 , 246 , 932–944. [ CrossRef ] 52 Baldauf, R. Roadside vegetation design characteristics that can improve local, near-road air quality Transp. Res. Part D-Transp Environ 2017 , 52 , 354–361. [ CrossRef ] [ PubMed ] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.