UNI LAB Award 2023: DaWei Chen

Exploring the Relationship Between Green Spaces and Microclimate in Beijing's Historic and Cultural Districts

DaWei Chen

1. ABSTRACT Microclimate significantly contributes to enhancing the livability of historical and cultural districts within Chinese cities, particularly with respect to accommodating public activities and preserving buildings. Consequently, the manipulation of microclimates through the integration of various green elements has emerged as a pivotal research focus in this context. This study emphasizes a quantitative investigation of the precise correlation mechanism between various forms of greenery and microclimatic conditions. It comprehensively employs field surveys, 3D data collection, microclimate simulation through ENVI-met, and quantitative model analysis. By comparing the simulated microclimatic data with the observed data, both qualitative and quantitative analyses were conducted to derive the specific mechanisms and relevant parameters influencing microclimatic conditions through different green elements.

This study aims to furnish a scientific foundation for the revitalization of historic and cultural districts and the regulation of their microclimates.

The research project is divided into five parts. In Part 1, the background of the study on the relationship between green forms and microclimate is introduced, and the research content of this project is defined. Part 2 selects 15 typical streets in the historical and cultural blocks of Beijing Old City as the research samples, defines, quantifies, collects, and forms the independent and dependent variable data sets of the selected blocks. Part 3 uses the data set collected above to simulate the microclimate of selected blocks based on ENVI-met software and compares the simulated results with the measured data. In Part 4, three kinds of greening renewal strategies are put forward to satisfy the comfort requirement of human body by using the specific influence mechanism and parameters of different greening morphological parameters on microclimate, and 15 selected blocks were designed for greening renewal. Part 5 summarizes the research conclusions of each part and puts forward the shortcomings and prospects of this project and provides feasible research suggestions for the research area.

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2. DESIGN OBJECTIVE By analyzing the correlation mechanism between greening and microclimate in the historical and cultural blocks of Beijing, a scientific basis is provided for greening planning.

By exploring precise and universal methods and techniques for quantifying the impact of greenery on microclimate, this provides reference for the protection of other historical and cultural blocks in China.

By analyzing the specific impact and mechanism of greening in microclimate regulation and environmental governance, a parameter library and module menu are formed, and optimization plans for the selection and layout of greening in street environmental governance are proposed.

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3. METHODOLOGY 3.1 3D point cloud data collection and processing methods This study used a FARO 3D laser scanner (Figure 1) suitable for short distance point cloud information collection to collect three-dimensional morphological data of the block, including greening information.

The data collection and processing steps are as follows: 1. Selecting typical streets in the historical and cultural blocks of the old city of Beijing 2. Selecting measurement points based on the actual situation of the streets and conducting actual measurement and information collection on the greening forms of the blocks.

3. Cut and process the collected 3D point cloud data using Autodesk Recap software and delete and retain the measured 3D data according to research needs.

4. According to the accuracy requirements of the subsequent simulation software, the processed green point cloud data will be utilized with the Tarsier plugin on the Rhino Grasshopper platform, and then，1 × 1 × 1m sized block is subjected to point cloud voxelization generation and modeling, resulting in a block greening form model based on 3D point cloud data collection.

(a)

(b)

Figure 1: The FARO 3D laser scanner used in this study (a: scanner interface, b: 3D point cloud information collection results, image source: self-drawn by the author)

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3.2 Mobile Microclimate Data Collection Method This study used a handheld mobile microclimate data measuring instrument (Figure 2) to measure the microclimate data of selected historical and cultural street samples. The data collection steps are as follows: 1. for each selected street, based on the terrain map data and the actual environmental conditions, 20-30 data observation points are determined at 5-7m intervals.

2. At an observation time interval of 30s-60s, hold a microclimate data measuring instrument and walk slowly and uniformly along the selected street.

3. Stay at the selected data observation points and observe and record the microclimate data at a height of 1.5m at each observation point in the corresponding time. The measured microclimate data include environmental temperature, relative humidity, environmental CO2 concentration, environmental PM2.5 and PM10 concentration, wind speed and direction, and light intensity.

Correspondingly to the narrow category of "microclimate" discussed in this study, subsequent research mainly refers to the measured environmental temperature, relative humidity, wind speed, wind direction, and light intensity data.

Figures 2: The handheld mobile microclimate data measuring instrument used in this study (Image source: self-drawn by the author)

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3.3 ENVI-met Microclimate Simulation Method Combined with Rhino Modeling This study used Rhino software to model the buildings and other elements of the selected historical and cultural blocks and presented them on the Rhino Grasshopper platform. At the same time, the point cloud voxelization model of the green form of the blocks was added to the model based on the actual layout situation of the current situation. The plug-in was then used to process the block model and import it into ENVI-met software. ENVI-met's plant tool was used to set the relevant parameters of the plant model based on the actual information obtained through research, further carry out microclimate simulation. Based on the need for comparison between actual measurement and simulation results and simulation needs, the location, date, time, simulation time length, and related microclimate parameters of the block will be set, and boundary conditions adjusted on the ENVI-met platform, ultimately achieving dynamic simulation of the microclimate of the block. The ENVI-met simulation software used in this study is the professional version.

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4. RESEARCH FRAMEWORK

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5. DATE PROCESSING (1) On the Autodesk Recap software platform, the block point cloud data obtained from the actual measurement will be scanned using FARO 3D laser scanner and imported, and the point cloud data will be cut and processed to obtain the morphological point cloud data of individual green plants one by one (Figure 3).

(2) Use the Tarsier plugin on the Rhino Grasshopper platform, 1m × 1m ×1m grid size is used to voxel model the point cloud data of the greening form, and the center point coordinates of each grid unit are exported according to the measured distribution positions of the greening (Figure 4); (3) On the ENVI-met simulation software platform, based on the green distribution coordinates in step 2, call the Alberto plant tool to model the morphology of green plants one by one. At the same time, based on the actual measurement results, relevant parameters such as tree height, crown width, and leaf area density (LAD) were set for greening attributes, and a three-dimensional greening model (Figure 5) with precise replication of morphology and attributes was ultimately obtained as the basis for microclimate simulation.

(a)

(b)

(c)

Figure 3: Cutting process of 3D point cloud data of green form based on Autodesk Recap software (a: block point cloud data obtained from FARO 3D laser scanner scanning and measurement, b, c: single plant green form points cloud data obtained from cutting on the Autodesk Recap software platform, image source: self-drawn by the author)

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Figure 4: Voxel Modeling of Greening Form Point Cloud Data Based on Rhino Grasshopper Platform - Taking Single Tree Greening in a Block as an Example (Image Source: Author's Self drawn)

Figure 5: 3D Modeling of Greening Based on ENVI met Simulation Software Alberto Plant Tool - Taking Two Single Tree Greening Sites in a Block as an Example (Image Source: Author's Self drawn)

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6. SELECTED BLOCK

Figure 6: The selected blocks and typical street samples for this study (Image Source: Author's Self drawn)

Select a street within the block with a length of approximately 175m in the east-west direction and a width of approximately 25m in the north-south direction as the specific research scope for the correlation mechanism between greening and microclimate. Widen the selected research scope to approximately equal distances on all four sides, with a depth of 416m × 332m serves as the simulation range for microclimate in the block. The corresponding green form parameters and green distribution obtained from actual measurement.

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Greening form parameters

Measured data

height

11-15m

Crown diameter

7-10 m

Height of the lowest obvious branching point winter LAI summer

5-8m (Winter deciduous trees) 4.8

Table 1: Measured Results of Greening Form Parameters in Xianyukou Street (Table Source: Author's Self drawn)

Figure 7: Greening Types and Distribution of Xianyukou Street (Image Source: Author's Self drawn) Within the research area of the street, which is approximately 175m long in the east-west direction and 25m wide in the north-south direction, 30 data observation points were determined (see Figure 7 for the selection of observation point positions). The brief microclimate parameters measured in March and May are shown in the Tables below.

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Measurin g point

temper ature （℃）

humi dity （% ）

wind direc tion （°）

win d spe ed （m/ s）

Measurin g point

temper ature （℃）

humi dity （% ）

wind direc tion （°）

win d spe ed （m/ s）

1

18.7

41.7

36

0.63

16

18.1

44.2

65

0.42

2

18.8

41.8

24

0.53

17

18

44.5

36

0.5

3

18.8

42.3

0

0.47

18

18

44.4

42

0.63

4

18.8

42.5

45

0.59

19

18

44.6

59

0.57

5

18.8

42.6

71

0.77

20

18

45.2

47

1.31

6

18.8

42.6

43

0.95

21

17.9

45.3

59

0.95

7

18.7

42.7

33

1.26

22

18

45.4

68

0.82

8

18.7

42.6

39

0.85

23

18

45.8

57

0.79

9

18.6

42.5

42

0.72

24

18

45.5

46

0.65

10

18.5

42.7

28

0.53

25

18

45.1

29

0.23

11

18.4

42.9

39

0.63

26

18

44.8

35

0.39

12

18.4

43.2

47

0.76

27

18

44.5

44

0.44

13

18.3

44

48

0.59

28

17.9

44

69

0.68

14

18.2

43.9

24

0.53

29

17.9

44.4

39

0.67

15

18.2

43.8

37

0.69

30

17.9

44.5

55

0.27

Table 2: Actual Measurement Results of Microclimate Parameters in Xianyukou Street in March (Table Source: Author's Self drawn)

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Measurin g point

temper ature （℃）

humi dity （% ）

wind direc tion （°）

win d spe ed （m/ s）

Measurin g point

temper ature （℃）

humi dity （% ）

wind direc tion （°）

win d spe ed （m/ s）

1

24.2

25.9

41

0.59

16

23.5

24.7

22

0.79

2

24.1

26.2

39

0.62

17

23.5

24.1

57

0.57

3

24

25.7

41

0.68

18

23.4

24.3

59

0.58

4

24

25.2

60

0.71

19

23.4

24.6

41

0.58

5

23.9

25.2

47

0.63

20

23.4

24.1

53

0.57

6

23.8

25

47

0.59

21

23.3

23.9

47

0.6

7

23.8

24.9

57

0.57

22

23.3

23.7

45

0.63

8

23.7

25.1

60

0.79

23

23.2

24.5

42

0.64

9

23.6

25.7

68

0.56

24

23.2

25.1

66

0.65

10

23.6

26.2

37

0.49

25

23.2

24.9

44

0.72

11

23.6

27.2

48

0.57

26

23.2

24.6

40

0.81

12

23.6

26.9

55

0.63

27

23.2

25

45

0.57

13

23.6

27.2

58

0.58

28

23.2

25.2

44

0.65

14

23.5

26.3

49

0.48

29

23.3

24.9

43

0.89

15

23.5

25.5

46

0.57

30

23.3

25.2

47

1.45

Table 3: Actual Measurement Results of Microclimate Parameters in Xianyukou Street in May (Table Source: Author's Self drawn)

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6.1 Calibration and simulation of numerical models for microclimate in residential areas Block model establishment

Figure 8: Using the INX plugin to preprocess the block model on Sketchup platform - Taking one of the block samples as an example (Image source: self-drawn by the author)

Figure 9: Block Modeling Based on Greening Reproduction - Taking One of the Block Samples as an Example (Image Source: Author's Self drawn)

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6.2 Comparison of microclimate simulation and measured results in blocks— Dongjiaomin Lane (Taking One of the Block Samples as Base Case) Based on the measured data corresponding to time and date, a microclimate simulation was conducted on Dongjiaomin Lane using the ENVI-met software platform for a total of one hour.

The microclimate simulation values such as outdoor atmospheric temperature, relative humidity, wind speed, wind direction, physiological equivalent temperature, etc. at a height of 1.4m at each observation point during the simulation period were obtained (Figure 10 to Figure 17).

Export the obtained microclimate data, select the same observation point location, compare the simulated microclimate values with the measured values to verify the accuracy of the simulation, and calibrate the model (Figure 18).

Figure 10: Simulation of Atmospheric Temperature in Dongjiaomin Lane (March) and Visual Expression (Image Source: Author's Self drawn)

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Figure 11: Relative Humidity in Dongjiaomin Lane (March) and Visual Expression (Image Source: Author's Self drawn)

Figure 12: Simulation of Wind Speed and Direction in Dongjiaomin Lane (March) and Visual Expression (Image Source: Author's Self drawn)

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Figure 13: Simulation of Physiological Equivalent Temperature in Dongjiaomin Lane (March) and Visual Expression (Image Source: Author's Self drawn)

Figure 14: Simulation of Atmospheric Temperature in Dongjiaomin Lane (May) and Visual Expression (Image Source: Author's Self drawn)

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Figure 15: Simulation of Relative Humidity in Dongjiaomin Lane (May) and Visual Expression (Image Source: Author's Self drawn)

Figure 16: Simulation of Wind Speed and Direction in Dongjiaomin Lane (May) and Visual Expression (Image Source: Author's Self drawn)

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Figure 17: Simulation of Mean Radiant Temperature in Dongjiaomin Lane (May) and Visual Expression (Image Source: Author's Self drawn)

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Figure 18: Comparison of Microclimate Simulation and Measured Data at Various Observation Points in Dongjiaominxiang (Image Source: Author's Self drawn)

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6.3 Calibration of numerical models for microclimate in street Based on comparing simulated and measured data, this study further calibrated and optimized the ENVI met numerical models of all street samples to further improve the accuracy of microclimate simulation. Including: Considering simulation accuracy and efficiency, determine the grid size is 1m×1m×1m, for voxel modeling and microclimate simulation of subsequent green form point cloud data using ENVI met software; Improved the modeling accuracy of blocks such as Dongjiaominxiang; Debugging ENVI-met software to set relevant environmental parameters and boundary conditions such as atmospheric temperature, relative humidity, wind speed and direction, carbon dioxide concentration, PM concentration, etc. during the microclimate simulation process; On the premise of meeting the simulation requirements, the particle size of the simulation time should be appropriately changed to meet the actual needs of large-scale multi condition simulation.

Conclusion of Cluster Analysis - Extraction of Green Form Features

Classification of green form attributes

form feature

Feature extraction

Deciduous/evergreen plants, height, height of the lowest obvious branching point, crown width, trunk diameter, crown shape Crown height, crown coverage, crown line characteristics, crown transmittance, etc.

Canopy feature

LAI, LAD, Total Leaf Area, etc.

Leaf feature

Blade width, blade shape, blade area, etc.

environmental factor

Planting spacing, planting location, tree species combination, planting structure, green coverage rate, green plot ratio, etc.

Table 4: Extraction of Green Form Features (Table Source: Author's Self drawn)

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6.4 Multi working condition simulation.

Numerical model construction and boundary condition formulation

boundary condition

Specific values

Maximum temperature and time

29.5℃（15:00）

Minimum temperature and time

14.2℃（3:00）

Maximum humidity and time

50.7%（21:00）

Minimum humidity and time

11.4%（14:00）

Average wind speed (m/s)

1

wind direction

Northeast wind（45°）

Environmental CO2 concentration (ppm)

470

Environmental PM10 concentration (ppm)

14

Environmental PM2.5 concentration (ppm)

13

Table 5: Determination of Model Boundary Conditions (Table Source: Author's Self drawn)

Figure 19: Construction of ENVI-met Microclimate Simulation Numerical Model (Image Source: Author's Self drawn)

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6.5 Analysis of the Correlation Mechanism between Greening Forms and Microclimate Due to research conditions and space limitations, detailed explanations will be provided on five elements: green crown width, height of the lowest obvious branching point, LAD, layout spacing, and shrub planting spacing.

6.5.1 The Correlation Mechanism between Greening Crown Width and Microclimate Define the minimum model units from west to east as (a), (b), and (c) three blocks, with corresponding green crown parameters of 19m, 15m, and 11m for each block. Set the default adjacent interval for each group of green layouts to 12m, and the other green form parameters are the same; Obtain the comparison results of microclimate parameters such as atmospheric temperature, relative humidity, and wind speed and direction at a height of 1.4m for (a), (b), and (c) three blocks; Select 12 data observation points with consistent relative positions at intervals of 5m along each block, export microclimate data from the three blocks, and conduct comparative analysis to study the impact of changes in green canopy on microclimate. The research on the correlation mechanism between other green forms and microclimate is the same.

Figure 20: Block (a), (b), and (c) Microclimate Numerical Model under Changing Crown Size (Image Source: Author's Self drawn)

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Figure 21: Simulation and visual expression of atmospheric temperature under changes in crown size (Image source: self-drawn by the author)

Figure 22: Simulation and Visual Expression of Relative Humidity under Changing Crown Size (Image Source: Author's Self drawn)

Figure 23: Simulation and visualization of wind speed and direction under changing crown diameter (Image source: self-drawn by the author)

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Figure 24: Comparison of Microclimate Simulation Data in Neighborhoods with Changes in Crown Size (a), (b), and (c) (Image Source: Author's Self drawn)

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6.5.2 The correlation mechanism between the height of the lowest obvious branching point of greening and microclimate The minimum obvious branching points corresponding to each block have heights of 7m, 4m, and 1m. The default adjacent spacing for each group of green layouts is 12m, and other green form parameters are the same;

Figure 25: Block (a), (b), (c) Microclimate Numerical Model with Height Change of the Lowest Significant Branch Point (Image Source: Author's Self drawn)

Figure 26: Atmospheric temperature simulation and visual expression under the condition of changing the height of the lowest obvious branching point (Image source: self-drawn by the author)

Figure 27: Simulation and visual expression of relative humidity under the condition of changing the height of the lowest obvious branching point (Image source: self-drawn by the author)

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Figure 28: Simulation and visualization of wind speed and direction under the condition of changing the height of the lowest obvious branching point (Image source: self-drawn by the author)

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Figure 29: Comparison of microclimate simulation data for blocks (a, b, c) with changes in the height of the lowest significant branching point (Image source: self-drawn by the author)

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6.5.3 The correlation mechanism between LAD and microclimate The LAD corresponding to each block is 2, 1.2, and 0.4, and the default adjacent interval for each green layout is set to 12m. Other green form parameters are the same;

Figure 30: Block (a), (b), (c) Microclimate Numerical Model under LAD Change (Image Source: Author's Self drawn)

Figure 31: Simulation and visualization of atmospheric temperature under changes in LAD (Image source: selfdrawn by the author)

Figure 32: Simulation and Visual Expression of Relative Humidity under LAD Changes (Image Source: Author's Self drawn)

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Figure 33: Simulation and visualization of wind speed and direction under the condition of LAD change (Image source: self-drawn by the author)

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Figure 34: Comparison of Microclimate Simulation Data in Neighborhoods (a), (b), and (c) with LAD Changes (Image Source: Author's Self drawn)

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6.5.4 The correlation mechanism between green layout spacing and microclimate.

Define the minimum model unit from west to east as (a), (b), and (c) three blocks, with corresponding green layout spacing of 6m, 12m, and 18m for each block. Other green form parameters are the same;

Figure 35: Block (a), (b), (c) Microclimate Numerical Model with Changing Greening Layout Spacing (Image Source: Author's Self drawn)

Figure 36: Atmospheric temperature simulation and visualization expression under the condition of changing the spacing of green layout (Image source: self-drawn by the author)

Figure 37: Simulation and Visual Expression of Relative Humidity under Changing Spacing of Greening Layout (Image Source: Author's Self drawn)

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Figure 38: Simulation and visualization of wind speed and direction under the condition of changing the spacing of green layout (Image source: self-drawn by the author)

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Figure 39: Comparison of Microclimate Simulation Data for Blocks (a), (b), and (c) with Changes in Greening Layout Spacing (Image Source: Author's Self drawn)

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6.5.5 The correlation mechanism between shrub planting spacing and microclimate.

The shrub planting spacing corresponding to each block is 0m, 1m, 2m, and the default adjacent spacing for each group of green layouts is 12m. Other green form parameters are the same;

Figure 40: Block (a), (b), (c) Microclimate Numerical Model with Changing Shrub Planting Spacing (Image Source: Author's Self drawn)

Figure 41: Simulation and visualization of atmospheric temperature under the condition of changing shrub planting spacing (Image source: self-drawn by the author)

Figure 42: Simulation and visualization of relative humidity under changing shrub planting spacing (Image source: self-drawn by the author)

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Figure 43: Simulation and visual expression of wind speed and direction under changing shrub planting spacing (Image source: self-drawn by the author)

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Figure 44: Comparison of Microclimate Simulation Data in Neighborhoods (a), (b), and (c) with Changes in Shrub Planting Spacing (Image Source: Author's Self drawn)

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7. IDENTIFICATION OF KEY FEATURES OF GREENING FORM The canopy width, LAI, height of the lowest obvious branching point, layout spacing, and shrub planting spacing of greening are important factors that affect the microclimate conditions of the block, among which LAI and LAD have a certain degree of positive proportional relationship.

Replace LAI with LAD for subsequent research.

condition

Crown diameter （m）

Height of the lowest obvious branching point（m）

Maximum value

19

minimum value Parameter selection

LAD

Greening layout spacing （m）

Shrub planting spacing （m）

7

2

18

2

11

1

0.4

6

0

11、15、19

1、4、7

0.4、1.2、2

6、12、18

0、1、2

boundary

Table 6: Parameter Selection and Value Range of Each Greening Form Feature Element (Table Source: Author's Self drawn)

Elements of greening form

Crown diameter

Changes in parameters of green form elements (within the range of values)

point obvious height

(reduced by 3m)

Shrub planting spacing

Change in average wind speed （m/s）

-0.14

+0.25

-0.21

-0.07

+0.64

-0.07

-0.09

+0.24

-0.19

-0.19

+0.53

-0.13

-0.17

+0.16

-0.02

(increase by 4m) 0.5 units

Greening layout spacing

Change in average relative humidity （%）

0.5 units

Lowest obvious branching

LAD

Average atmospheric temperature change （℃）

0.5 units (increase by 0.8) 0.5 units (reduced by 6m) 0.5 units (reduced by 1m)

Table 7: Quantitative Analysis of the Impact of Various Greening Form Elements on Microclimate Values (Table Source: Author's Self drawn)

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According to Table 6-7, the quantitative analysis shows that for every 0.5 unit change in the value range of each green form characteristic element, the degree of impact on microclimate changes. The following conclusions are drawn: (1) In terms of atmospheric temperature regulation, the order of influence of various green form characteristic elements from high to low is as follows: green layout spacing (reduced by 3m)>shrub planting spacing (reduced by 1m)>crown width (increased by 1m)>LAD (increased by 0.8)>height of the lowest obvious branch point (reduced by 3m), among which reducing green layout spacing and shrub planting spacing Increasing the crown size is the main feasible way to regulate atmospheric temperature in the historical and cultural districts of the old city of Beijing.

(2) In terms of relative humidity regulation, the order of influence degree of each green form feature element from high to low is the lowest obvious branch point height (reduced by 3m)>green layout spacing (reduced by 3m)>crown width (increased by 1m) ≥ LAD (increased by 0.8)>shrub planting spacing (reduced by 1m). Reducing the height of the lowest obvious branching point and reducing the spacing of green layout are the main feasible ways to regulate relative humidity in the historical and cultural blocks of the old city of Beijing. Increasing the crown width and increasing LAD have a relatively low impact on relative humidity and similar effects.

(3) For the adjustment of average wind speed, the order of influence degree of each green form feature element from high to low is crown width (increasing by 1m)>LAD (increasing by 0.8)>green layout spacing (decreasing by 3m)>lowest obvious branch point height (decreasing by 3m)>shrub planting spacing (decreasing by 1m). Increasing the crown width, increasing LAD, and reducing the spacing of green layout are the main feasible ways to regulate the average wind speed in the historical and cultural districts of the old city of Beijing.

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8. GREENING RENEWAL STRATEGY Greening Renewal Strategy under the Adjustment of Greening Form Feature Elements In the greening and updating of street blocks, priority should be given to reducing the spacing of greening layout. Secondly, consideration should be given to replacing tree species and shapes (changing crown width, minimum branch point height, and leaf area density) without affecting the style of historical and cultural blocks, damaging the safety of historical block buildings, and damaging ancient trees and trees. Finally, the choice should be to reduce the spacing of shrub planting or plant shrubs to assist in regulating the microclimate of the street block. According to the priority of selection, it can be roughly divided into the following three schemes: (1) Taking reducing the spacing of green layout as the core - reducing the spacing of green layout, adding the same type of tree species with larger crown width, and planting some shrubs according to the specific situation, to maximize the efficiency of regulating the microclimate situation of the block, while adhering to the principle of protecting the style and appearance of the block;

Figure 45: Before the Greening Renewal of the Block (Scheme 1) (Image Source: Author's Self drawn)

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Figure 46: After the greening and renovation of the block (Scheme 1) (Image source: selfdrawn by the author) (2) Taking changing the original greening tree species in the block as the core - based on the actual situation and protection principles, some greening tree species in the block will be replaced, with priority given to selecting tree species with larger crown width, lower height of the lowest obvious branch point, and higher LAD. The spacing of greening layout will be adjusted according to the situation, and some shrubs will be planted to regulate the microclimate of the block more efficiently;

Figure 47: Before the Greening Renewal of the Block (Scheme 2) (Image Source: Author's Self drawn)

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Figure 48: After the greening and renovation of the block (Scheme 2) (Image source: selfdrawn by the author) (3) Planting shrubs as the core - in cases where the greening form of the street block is greatly restricted and it is difficult to reduce the spacing of greening layout or replace tree species, shrubs can be partially planted according to the actual situation of the street block to supplement and regulate the microclimate.

Figure 49: Before the Greening Renewal of the Block (Scheme 3) (Image Source: Author's Self drawn)

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Figure 50: After the greening and renovation of the block (Scheme 3) (Image source: selfdrawn by the author)

Small scale green landscape design

Figure 51: Greening Landscape Design of Street Sidewalks (Image Source: Author's Self drawn)

Figure 52: Greening Landscape Design of Concentrated Activity Sites in the Block (Image Source: Author's Self drawn)

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9. APPLICATION OF GREENING AND UPDATING STRATEGIES FOR DIFFERENT TYPES OF BLOCK SAMPLES 9.1 Definition of Block Types According to the suitability of different greening and renewal strategies for block applications, the selected historical and cultural blocks in Beijing are roughly divided into three categories in order: (1) Blocks with larger spacing in green layout - mainly by reducing the spacing in green layout and planting similar tree species between adjacent green spaces to increase planting density as the core method; (2) Blocks with smaller spacing in green layout - the core method for microclimate regulation in the block is to appropriately replace some tree species or tree shapes while protecting the style of the block and ancient trees. Tree species with larger crown, lower height of the lowest obvious branch point, and higher LAD should be selected to replace the original tree species in the block; (3) For blocks with smaller spacing in green layout, it is inconvenient to replace tree species due to mature green growth and ancient trees within the block, or the original green cannot be changed due to policy reasons. In this case, the main method is to adjust the microclimate of the street area by planting shrubs in moderation.

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9.2 Simulation and Application of Greening Renewal Strategy (1) Reduce the spacing of green layout—Taking Liulichang West Street as an example

Figure 53: Current Situation of Greening Forms in Liulichang West Street (Image Source: Author's Self drawn)

Figure 54: After the green form of Liulichang West Street has been updated (Image Source: Author's Self drawn)

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(2) Replace the greening tree species in the block—Taking Xianyukou Street as an example

Figure 55: Current Situation of Greening Forms in Xianyukou Street (Image Source: Author's Self drawn)

Figure 56: Updated Green Form of Xianyukou Street (Image Source: Author's Self drawn)

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(3) Partially planted shrubs—Taking Dongsi 14th Street as an example

Figure 57: Current Situation of Greening Forms in Dongsi 14th Street (Image Source: Author's Self drawn)

Figure 58: Updated Green Form of Dongsi 14th Street (Image Source: Author's Self drawn)

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10. CONCLUSION This study selected 15 typical streets belonging to different neighborhoods from the historical and cultural blocks in the old city of Beijing as the research objects and verified the applicability of ENVI-met software in the study of the "green form microclimate" correlation mechanism in historical and cultural blocks. By comparing simulated and measured data, this study calibrated and optimized the microclimate numerical models of all block samples, quantitatively studied the specific mechanisms and key parameters of different green form feature elements affecting microclimate, and ultimately provided scientific basis for the formulation of block greening renewal strategies under human comfort goals. The main achievements of the paper are as follows: (1) Complete the 3D data collection and voxel modeling of 15 street samples of green forms in the historical and cultural blocks of the old city of Beijing. It has been proven that compared to traditional microclimate simulation, the voxel modeling of green areas based on 3D data collection has higher accuracy and the simulation results are closer to the real situation, which is conducive to the subsequent quantitative analysis of the specific impact mechanism of various green form feature elements on microclimate.

(2) Based on multiple operating conditions simulation, clarify the specific mechanisms by which different green form characteristics affect microclimate, and rank the degree of impact for five important elements. Based on the calibration of the ENVI-met model, simulation and comparison showed that reducing the spacing of green layout, reducing the spacing of shrub planting, and increasing the crown width were the main feasible ways to regulate atmospheric temperature; Reducing the height of the lowest obvious branching point and reducing the spacing of green layout are the main feasible ways to regulate relative humidity. Increasing crown diameter and increasing LAD have a relatively low impact on relative humidity and similar effects; Increasing the crown width, increasing LAD, and reducing the spacing of green layout are the main feasible ways to regulate the average wind speed; Reducing the spacing between greeneries is the best feasible way to comprehensively regulate the microclimate of the neighborhood.

(3) Propose scientific strategies for block greening and renewal, and simulate and apply them to selected blocks, including reducing the spacing of greening layout, replacing block greening tree species, and partially planting shrubs. Select suitable strategies to simulate and apply greening and renewal strategies to block samples one by one, providing scientific reference for future greening planning and renewal design of the block.

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11. FUTURE WORK This study can be further improved from the following two aspects in the future: (1) Expanding the number of street samples, optimizing data collection methods, improving various environmental parameters required for microclimate simulation, and further improving the accuracy and reliability of microclimate simulation analysis; (2) Exploring the microclimate impact modes under the comprehensive context of various green form elements, establishing a more scientific and comprehensive evaluation system, and further exploring and proposing more reasonable strategies for district green renewal.

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