A method for selecting street tree species in urban street valleys to improve air quality

Abstract

This invention proposes a method for selecting street tree species in urban street valleys to improve air quality. The method comprises: obtaining daily variation data on meteorological parameters on typical days in a target area, typical street valley spatial morphological types, commonly used street tree species and their morphological characteristics, and hourly emission rates of air pollutants from motor vehicles; constructing multiple typical street valley models, conducting numerical simulations for each, and then conducting statistical analysis and evaluation, using the relative rate of change in resident exposure risk scores within the street valley as an indicator. Based on the evaluation results, tree species corresponding to tree morphologies with relatively low relative rates of change in resident exposure risk scores are selected as the optimal street tree species for the corresponding street valley type, and a street tree shaping and pruning strategy for that street valley is formulated based on these tree morphologies. This method quantitatively evaluates the impact of different street tree planting schemes on resident exposure risk in the street valley, providing an accurate basis for street tree species selection and shaping and pruning.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban street valley greening design, and particularly relates to a method for selecting street tree species in urban street valleys for improving air quality. Background Art

[0002] Urban street canyons are a crucial component of the urban surface, supporting transportation and daily life for urban residents. With the surge in motor vehicle ownership, traffic emissions have become a major source of urban air pollution. The resulting air pollutants, such as PM2.5, PM10, and NOx, can induce cardiovascular and respiratory diseases, posing a serious health threat to urban residents, particularly pedestrians and those living and working near roads. As metropolitan areas develop toward higher-rise and higher-density buildings, natural ventilation and air circulation within street canyons have significantly decreased, making air pollution prevention and control increasingly urgent.

[0003] In the existing urban built environment, street valley spatial elements cannot be easily altered. Besides measures such as controlling vehicle ownership, reducing unit vehicle emissions, and promoting the use of new energy vehicles, street valley greening is considered an effective and economical way to address vehicle exhaust pollution due to its ability to influence the diffusion and deposition of air pollutants and its high maneuverability. While the adsorption and deposition of street tree leaves and branches within street valley greening can effectively reduce pollutant concentrations, the tree canopy also obstructs airflow, reducing air exchange between the street valley and the atmosphere above. Under most environmental conditions, the negative impact of street tree aerodynamics on the diffusion and dilution of street valley pollutants far outweighs their positive effects on deposition. Inappropriate tree species selection and planting risk exacerbating the accumulation and concentration of air pollutants in street valleys. Existing greening models that ignore the influence of various tree morphological characteristics on the diffusion and deposition of air pollutants and plant high-density street trees are clearly inappropriate for alleviating street valley air pollution. The most appropriate approach is to integrate the regulatory mechanisms of tree morphology on air pollutants and "plant the right trees for different street valleys."

[0004] Therefore, to better leverage the ecological benefits of street tree planting in mitigating street canyon air pollution, it is urgent to optimize street tree species selection methods for improving street canyon air quality. By comprehensively considering the synergistic effects of the built environment, meteorological conditions, and street tree configuration on air pollutants, scientific street tree species selection and shaping and pruning strategies adapted to specific street canyon environments can be developed. This will improve street canyon air quality while meeting the needs of landscape beautification and improving thermal comfort. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the above-mentioned existing technologies and provide a method for selecting street tree species in urban street canyons for improving air quality. This method comprehensively considers regional climate characteristics and street canyon spatial morphological characteristics, classifies tree species based on tree morphology, and constructs typical street tree models with different morphological characteristics. Numerical simulation methods are used to evaluate the impact of street trees with different morphological characteristics on the air pollution exposure of pedestrians in street canyons. In this way, street tree species that are conducive to alleviating street canyon air pollution are selected, tree shaping and pruning strategies are formulated, and the ecological benefits of street trees in improving street canyon air quality are optimized.

[0006] To solve the above technical problems, the present invention adopts a technical solution: a method for selecting street tree species in urban street valleys for improving air quality, characterized in that the method comprises:

[0007] S1. Preliminary Analysis Stage: Meteorological parameters, street canyon morphological parameters, and individual morphological parameters of common street trees in the target area are collected and analyzed. Daily variation data on meteorological parameters on typical days in the target area, typical street canyon spatial morphological types, common street tree species and morphological characteristics of common street trees, and hourly emission rates of air pollutants from motor vehicles in corresponding street canyons in the target area are obtained. The street canyon morphological parameters include building height, building layout, road width, building material, and road material.

[0008] S2, numerical simulation stage: Based on the morphological parameters of the street canyon and the individual morphological parameters of common street trees, a variety of typical street canyon models are constructed, and numerical simulations are carried out one by one;

[0009] S3, Analysis and Evaluation Phase: Combined with data processing methods, statistical analysis is performed on the numerical simulation results of various typical street valley models constructed in S2, and the statistical results are evaluated, with the relative change rate of the exposure risk score of residents in the street valley as an indicator;

[0010] S4. Plan generation stage: Based on the assessment results, tree species corresponding to tree morphologies with relatively low relative changes in resident exposure risk scores are selected as the optimal street tree species for the corresponding street canyon type. A shaping and pruning strategy for street trees in this type of street canyon is formulated with reference to these tree morphologies.

[0011] Preferably, in S2, a typical street canyon model is constructed based on the morphological parameters of the street canyon and the individual morphological parameters of common roadside trees, and numerical simulation is performed, specifically including:

[0012] Step 201: Select ENVI-met microclimate simulation software as a numerical simulation tool;

[0013] Step 202: Setting the traffic pollutant emission sources in the target area in the software database, determining the emission height, emission mode, and emission rate of the traffic pollutant emission sources, and freely combining the set values ​​of the individual morphological parameters of the common roadside trees to determine the tree morphological type, and customizing the roadside tree model corresponding to each tree morphological type in the target area;

[0014] Step 203: Based on the traffic pollutant emission sources set in step 202 and the street canyon morphological parameters described in S1, combined with the street tree model described in step 202 and the layout pattern of street canyon trees, multiple typical street canyon models corresponding to various types of street trees are constructed, wherein each street tree model of each tree morphological type corresponds to a typical street canyon model;

[0015] Step 204: For the multiple typical street valley models constructed in step 203, using the daily variation data of meteorological parameters for a typical day in the target area as boundary conditions, set the incoming wind speed, wind direction, air temperature, relative humidity, and urban roughness, and conduct a 24-hour simulation under these background environmental conditions to obtain hourly street valley air pollutant concentration data for each typical street valley model.

[0016] Preferably, the street canyon aspect ratio is calculated by the ratio of the building height to the road width. The street canyon aspect ratio is set in the microclimate simulation software according to the following rules:

[0017] The street canyon morphology is classified according to the street canyon aspect ratio. The specific classification is as follows: when the street canyon aspect ratio is less than or equal to 0.5, the value is set to 0.5 in the numerical simulation; when the street canyon aspect ratio is greater than 0.5 and less than 1.5, the value is set to 1 in the numerical simulation; when the street canyon aspect ratio is greater than 1.5 and less than 2.5, the value is set to 2 in the numerical simulation; when the street canyon aspect ratio is greater than or equal to 2.5, the value is set to 3 in the numerical simulation;

[0018] The rules for setting the values ​​of individual morphological parameters of common street trees in microclimate simulation software are as follows:

[0019] The tree morphology is classified according to the individual morphological parameters of common street trees, and the values ​​used in numerical simulation are set. The individual morphological parameters of common street trees include leaf area density, tree height, height under branches, and crown width. Specifically, they are:

[0020] When the leaf area density is less than or equal to 1, the leaf area density is set to 1 during the numerical simulation; when the leaf area density is greater than 1 and less than 2.5, the leaf area density is set to 1.5 during the numerical simulation; when the leaf area density is greater than or equal to 2.5, the leaf area density is set to 3 during the numerical simulation;

[0021] When the tree height is less than or equal to 8, the tree height is set to 6 during the numerical simulation; when the tree height is greater than 8 and less than 12, the tree height is set to 10 during the numerical simulation; when the tree height is greater than or equal to 12, the tree height is set to 14 during the numerical simulation;

[0022] When the height under the branch is less than or equal to 3, the value of the height under the branch is set to 3 during the numerical simulation; when the height under the branch is greater than 3, the value of the height under the branch is set to 5 during the numerical simulation;

[0023] When the crown width is less than or equal to 4, the crown width is set to 3 during numerical simulation; when the crown width is greater than 4 and less than 8, the crown width is set to 6 during numerical simulation; when the crown width is greater than or equal to 8, the crown width is set to 9 during numerical simulation.

[0024] Preferably, S3 combines data processing methods to statistically analyze the numerical simulation results of the various typical street valley models constructed in S2, and evaluates the statistical results, using the relative change rate of the exposure risk score of residents in the street valley as an indicator; specifically, including:

[0025] Step 301: Use software to visualize the numerical simulation results and perform mathematical statistics on the simulation data to obtain the average value of the air pollutant concentration on the sidewalks on both sides of the street canyon throughout the day;

[0026] Step 302: Based on the exposure time, respiratory rate, and sensitivity to traffic emission pollutants of different types of people in the city, calculate the exposure risk scores of residents in street valley pedestrian areas near motor vehicle emission sources under different scenarios. The calculation formula is as follows:

[0027]

[0028]

[0029] Where, ERF is the residents’ exposure risk score; P i is the total number of people in category i; RT i is the average respiratory rate of the i-th group of people, in m 3 / s;ET i is the average exposure time of the i-th group of people, in h / d, Q i is the sensitivity coefficient of the i-th group of people to traffic emission pollutants; C is the average air pollutant concentration at a height of 1.5m on the sidewalk, in kg / m 3 ; E is the total pollutant emissions during the period under consideration, in kg; the population is divided into three categories, with n being 1, 2, and 3, including the elderly, adults, and children. The first category refers to the elderly, the second category refers to adults, and the third category refers to children. Their respiratory rates and exposure times are shown in the following table:

[0030] Crowd category Elderly people Adults Child <![CDATA[Respiration rate (m 3 / d)]]> 10-15 15-20 10-14 Exposure time (h / d) 0.8-1.5 1.5-3 1-1.5

[0031] Step 303: Calculate the relative changes in residents' exposure risk scores between tree-planted street valleys and treeless street valleys in multiple typical street valley models. The calculation formula is as follows:

[0032]

[0033] Where ΔERF is the relative rate of change of residents’ exposure risk score; ERF tree Exposure risk scores for residents of Tree Street Valley; ERF tree-free Exposure risk scores for residents of treeless street valleys;

[0034] If the relative change rate of the residents' exposure risk score in the typical street valley model is less than 0, the street tree species with the corresponding tree morphology will be selected as the tree species to be planted;

[0035] If the relative change in the resident exposure risk score in the typical street valley model is greater than 0, the tree species corresponding to the tree morphology with a smaller relative change rate of the resident exposure risk score is selected.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. This paper combines street canyon morphological parameters with tree morphological parameters to classify urban street canyons and street trees, constructing corresponding models. Using numerical simulation, this paper screens street tree morphologies and corresponding tree species that are beneficial for improving street canyon air quality. This allows for the planting of the right trees in the right street canyon locations, thereby optimizing the ecological benefits of street trees and improving street canyon air quality. This approach has important theoretical and practical implications for the development of healthy cities and sustainable development.

[0038] 2. This study combines the impact of street trees on the diffusion and adsorption of air pollutants, comprehensively considering the synergistic effects of meteorological conditions, street canyon morphology, and other factors, to quantitatively assess the impact of street trees with different morphological characteristics on pedestrian air pollution exposure in street canyons. This provides a basis for accurate tree species selection and shaping and pruning for street tree planting aimed at improving street canyon air quality. Through scientific methods, the microclimate benefits of street trees are optimized and their potential negative impact on air quality is reduced.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is the operational process of the method for selecting urban street valley roadside tree species for improving air quality disclosed in Example 1 of the present invention.

[0041] Figure 2is a tree morphology model diagram constructed according to Example 1 of the present invention, Figure 2 -(1) is a street tree with high leaf density, medium tree height, low branch height, and medium crown width (DMSM), and the corresponding tree species is Platanus chinensis. Figure 2 -(2) Street trees with medium leaf density, low tree height, low branch height, and narrow crown width (MSLN), the corresponding tree species is Ginkgo biloba.

[0042] Figure 3 is a street valley model constructed according to Example 1 of the present invention, Figure 3 -(1) is a 3D schematic diagram of a street valley model with DMSM tree morphology. Figure 3 -(2) is a three-dimensional diagram of a street valley model with tree forms of MSLN.

[0043] Figure 4 This is a street valley pollutant concentration difference diagram simulated according to Example 1 of the present invention. Figure 4 -(1) is the difference in pollutant concentration between the tree-planted street valley with DMSM tree morphology and the treeless street valley, Figure 4 -(2) is the difference in pollutant concentration between the tree-planted street valley with MSLN tree morphology and the treeless street valley. DETAILED DESCRIPTION

[0044] Example 1

[0045] like Figure 1 As shown, an embodiment of the present invention provides a method for selecting street tree species in urban street valleys for improving air quality, the method comprising:

[0046] S1. Preliminary analysis phase: Obtain daily variation data on meteorological parameters on typical days in the target area, typical street canyon spatial morphological types, common street tree species and common street tree morphological characteristics, and hourly emission rates of air pollutants emitted by motor vehicles in the corresponding street canyons in the target area; specifically, the following:

[0047] Determine the urban area to be studied. Taking a hypothetical small urban area as an example, collect meteorological parameters of the target area, including air temperature, relative humidity, wind speed, and wind direction, and compile daily variation data of meteorological parameters on a typical meteorological day;

[0048] Field research was conducted to determine the street canyon environmental information in the area, including building height, building materials, building layout, building form, road width, road materials, and street canyon aspect ratio. Based on this basic street canyon information, the typical street canyon spatial morphology type was determined using the classification method in Table 1. The street canyon aspect ratio is the ratio of building height to road width.

[0049] Measure the characteristic parameters of common street trees in the target area on site, recording data such as tree species, leaf area index (LAI) or leaf area density (LAD), tree height, crown width, and tree shape. The data were collated using the tree morphology classification standards proposed in Table 2, and the tree species corresponding to each tree morphology were determined.

[0050] Find traffic flow data for roads corresponding to typical street valley spatial morphology types in the target area, count the hourly motor vehicle flow, and simultaneously query the motor vehicle emission factor to calculate the hourly pollutant emission rate generated by motor vehicles on the corresponding roads. In addition, investigate whether there are other pollutant emission sources within a 5km radius around the corresponding street valley. If so, measure the pollutant concentration in the non-motor vehicle area as the background concentration.

[0051] S2, numerical simulation stage: Based on the morphological parameters of the street canyon and the individual morphological parameters of common street trees, a variety of typical street canyon models are constructed and numerical simulations are performed one by one. This is achieved through the following steps:

[0052] Step 201: Select ENVI-met microclimate simulation software as the numerical simulation tool. ENVI-met mainly includes modules such as Space, ENVI-guide, ENVI-core, and Leonardo, which can input basic parameters, carry out simulation, and visualize simulation results;

[0053] Step 202: Set the traffic pollutant emission sources in the target area in the software database, determine the emission height, emission mode and emission rate of the traffic pollutant emission sources, and freely combine the set values ​​of the individual morphological parameters of the common roadside trees to determine the tree morphological type, and customize the roadside tree model corresponding to each tree morphological type in the target area; different morphological types of roadside trees are as follows; Figure 2 3D model shown;

[0054] Step 203: Based on the traffic pollutant emission sources set in step 202 and the street canyon morphological parameters described in S1, and in combination with the layout pattern of the street trees in the street canyon model described in step 202, multiple typical street canyon models corresponding to various types of street trees are constructed, wherein each street tree model of a tree morphological type corresponds to a typical street canyon model; the typical street canyon model is as follows: Figure 3 As shown;

[0055] The street canyon aspect ratio is calculated by the ratio of the building height to the road width. The street canyon aspect ratio is set in the microclimate simulation software as follows:

[0056] The street canyon morphology is classified according to the height-to-width ratio of the street canyon. The specific classification is shown in Table 1.

[0057] Table 1 Typical street valley morphology classification

[0058] type Aspect ratio (H / W) range Application Value Category I H / W≤0.5 0.5 Category II 0.5<H / W<1.5 1 Category III 1.5<H / W<2.5 2 Category IV H / W≥2.5 3

[0059] The rules for setting the values ​​of the individual morphological parameters of common street trees in the microclimate simulation software are as follows: tree morphology is classified according to the individual morphological parameters of common street trees, and the values ​​used in numerical simulation are set. The individual morphological parameters of common street trees include leaf area density, tree height, height under branches, and crown width. The specific classification is shown in Table 2.

[0060] Table 2. Tree morphology classification based on individual morphological parameters of common street trees

[0061]

[0062]

[0063] Step 204: For the multiple typical street valley models constructed in step 203, set the daily variation data of meteorological parameters for typical weather days as boundary conditions, set the incoming wind speed, wind direction, air temperature, relative humidity, and urban roughness, and conduct a 24-hour simulation under these background environmental conditions to obtain hourly street valley air pollutant concentration data for each typical street valley model. Specifically, set the boundary conditions in the ENVI-guide module.

[0064] S3, Analysis and Evaluation Phase: Statistical analysis is performed on the numerical simulation results of various typical street canyon models constructed in S2 using data processing methods. The statistical results are then evaluated using the relative rate of change in the exposure risk scores of residents in the street canyons as an indicator. This is achieved through the following steps:

[0065] Step 301: Use the Leonardo module to visualize the numerical simulation results. The difference in pollutant concentration at pedestrian height in two typical street canyon models is as follows: Figure 4 As shown, mathematical statistics were further performed on the simulated data to obtain the average value of the air pollutant concentration on the sidewalks on both sides of the street canyon during the day;

[0066] Step 302: Based on the exposure time, respiratory rate, and sensitivity to traffic emission pollutants of different types of people in the city, calculate the exposure risk scores of residents in street valley pedestrian areas near motor vehicle emission sources under different scenarios. The calculation formula is as follows:

[0067]

[0068]

[0069] Where, ERF is the residents’ exposure risk score; Pi is the total number of people in category i; RT i is the average respiratory rate of the i-th group of people, in m 3 / s;ET i is the average exposure time of the i-th group of people, in h / d, Q i is the sensitivity coefficient of the i-th group of people to traffic emission pollutants; C is the average air pollutant concentration at a height of 1.5m on the sidewalk, in kg / m 3 ; E is the total pollutant emissions during the period under consideration, in kg; the population is divided into three categories, with n being 1, 2, and 3, including the elderly, adults, and children. The first category refers to the elderly, the second category refers to adults, and the third category refers to children. Their respiratory rates and exposure times are shown in the following table:

[0070] Crowd category Elderly people Adults Child <![CDATA[Respiration rate (m 3 / d)]]> 10-15 15-20 10-14 Exposure time (h / d) 0.8-1.5 1.5-3 1-1.5

[0071] Step 303: Calculate the relative changes in residents' exposure risk scores between tree-planted street valleys and treeless street valleys in multiple typical street valley models. The calculation formula is as follows:

[0072]

[0073] Where ΔERF is the relative rate of change of residents’ exposure risk score; ERF tree Exposure risk scores for residents of Tree Street Valley; ERF tree-free Exposure risk scores for residents of treeless street valleys;

[0074] Based on the calculated relative change rates of resident exposure risk scores across multiple typical street valley models, if the relative change rate of the resident exposure risk score in a typical street valley model is less than 0, street tree species with the corresponding tree morphology are selected as candidate planting species. If the relative change in the resident exposure risk score in a typical street valley model is greater than 0, tree species corresponding to tree morphologies with smaller relative change rates are selected as candidate planting species. Finally, the selected tree species are determined based on the actual conditions of the target area. Calculations show that the average ΔERF on both sides of street valleys with DMSM tree morphology is 8.00%, while the average ΔERF on both sides of street valleys with MSLN tree morphology is 3.70%.

[0075] S4. Plan generation phase: Based on the assessment results, ginkgo biloba, corresponding to the tree morphology MSLN with a relatively low relative rate of change in resident exposure risk scores, was selected as the optimal street tree species for the corresponding street canyon type. A street tree shaping and pruning strategy was developed based on this tree morphology, and existing street trees were shaped and pruned to reduce their negative impact on the diffusion of air pollutants.

[0076] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent variation made to the above embodiment based on the essence of the invention technology shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for selecting street tree species in urban street valleys for improving air quality, characterized in that: The method includes: S1. Preliminary Analysis Stage: Collect and analyze meteorological parameters, street canyon morphological parameters, and individual morphological parameters of common street trees in the target area. This includes daily variation data on meteorological parameters on typical days in the target area, typical street canyon spatial morphological types, common street tree species and morphological characteristics, and hourly emission rates of air pollutants from motor vehicles in corresponding street canyons in the target area. The street canyon morphological parameters include building height, building layout, road width, building materials, and road materials. S2, numerical simulation stage: Based on the morphological parameters of the street canyon and the individual morphological parameters of common street trees, a variety of typical street canyon models are constructed, and numerical simulations are carried out one by one; S3, Analysis and Evaluation Phase: Statistical analysis of the numerical simulation results of various typical street valley models constructed in S2 is conducted using data processing methods. The statistical results are then evaluated using the relative rate of change in the exposure risk scores of residents in the street valley as an indicator. Specifically, the following steps are involved: Step 301: Use software to visualize the numerical simulation results and perform mathematical statistics on the simulation data to obtain the average value of the air pollutant concentration on the sidewalks on both sides of the street canyon throughout the day; Step 302: Based on the exposure time, respiratory rate, and sensitivity to traffic emission pollutants of different types of people in the city, calculate the exposure risk scores of residents in street valley pedestrian areas near motor vehicle emission sources under different scenarios. The calculation formula is as follows: ； ； Where, ERF Exposure risk scores for residents; P i For the i The total number of people in the category; RT i For the i The average respiratory rate of the population, in m 3 / s; ET i For the i The average exposure time per person in the population, in h / d; Q i For the i The sensitivity coefficient of the population to traffic emission pollutants; C is the average air pollutant concentration at a height of 1.5 m from the sidewalk, in kg / m 3 ; E is the total pollutant emissions during the assessment period, in kg; the population is divided into three categories: Category 1 refers to the elderly, Category 2 refers to adults, and Category 3 refers to children; Step 303: Calculate the relative change rate of residents' exposure risk scores in tree-planted street valleys and treeless street valleys in multiple typical street valley models. The calculation formula is as follows: ； Where, is the relative rate of change of residents’ exposure risk scores; Exposure risk scores for residents of Tree Street Valley; is the resident exposure risk score of the treeless street valley; S4, plan generation stage: based on the assessment results, select the tree species corresponding to the tree morphology with a relatively low relative change rate of the resident exposure risk score as the optimal street tree species in the corresponding type of street valley, and formulate the shaping and pruning strategy of the street trees in this type of street valley with reference to the tree morphology.

2. The method for selecting street tree species in urban street valleys for improving air quality according to claim 1, characterized in that: In S2, a typical street canyon model is constructed based on the morphological parameters of the street canyon and the individual morphological parameters of common street trees, and numerical simulation is carried out, specifically including: Step 201: Select ENVI-met microclimate simulation software as a numerical simulation tool; Step 202: Setting the traffic pollutant emission sources in the target area in the software database, determining the emission height, emission mode, and emission rate of the traffic pollutant emission sources, and freely combining the set values ​​of the individual morphological parameters of the common roadside trees to determine multiple tree morphological types, and customizing the roadside tree model corresponding to each tree morphological type in the target area; Step 203: Based on the traffic pollutant emission sources set in step 202 and the street canyon morphological parameters described in S1, combined with the street tree model described in step 202 and the layout pattern of street canyon trees, multiple typical street canyon models corresponding to various types of street trees are constructed, wherein each street tree model of each tree morphological type corresponds to a typical street canyon model; Step 204: For the multiple typical street valley models constructed in step 203, using the daily variation data of meteorological parameters for a typical day in the target area as boundary conditions, set the incoming wind speed, wind direction, air temperature, relative humidity, and urban roughness, and conduct a 24-hour simulation under these background environmental conditions to obtain hourly street valley air pollutant concentration data for each typical street valley model.

3. The method for selecting street tree species in urban street valleys for improving air quality according to claim 2, characterized in that: The street canyon aspect ratio is calculated by the ratio of the building height to the road width. The street canyon aspect ratio is set in the microclimate simulation software as follows: The street canyon morphology is classified according to the street canyon aspect ratio. The specific classification is as follows: when the street canyon aspect ratio is less than or equal to 0.5, the value is set to 0.5 in the numerical simulation; when the street canyon aspect ratio is greater than 0.5 and less than 1.5, the value is set to 1 in the numerical simulation; when the street canyon aspect ratio is greater than 1.5 and less than 2.5, the value is set to 2 in the numerical simulation; when the street canyon aspect ratio is greater than or equal to 2.5, the value is set to 3 in the numerical simulation; The rules for setting the values ​​of individual morphological parameters of common street trees in microclimate simulation software are as follows: The tree morphology is classified according to the individual morphological parameters of common street trees, and the values ​​used in numerical simulation are set. The individual morphological parameters of common street trees include leaf area density, tree height, height under branches, and crown width. Specifically, they are: When the leaf area density is less than or equal to 1, the leaf area density is set to 1 during the numerical simulation; when the leaf area density is greater than 1 and less than 2.5, the leaf area density is set to 1.5 during the numerical simulation; when the leaf area density is greater than or equal to 2.5, the leaf area density is set to 3 during the numerical simulation; When the tree height is less than or equal to 8, the tree height is set to 6 during the numerical simulation; when the tree height is greater than 8 and less than 12, the tree height is set to 10 during the numerical simulation; when the tree height is greater than or equal to 12, the tree height is set to 14 during the numerical simulation; When the height under the branch is less than or equal to 3, the value of the height under the branch is set to 3 during the numerical simulation; when the height under the branch is greater than 3, the value of the height under the branch is set to 5 during the numerical simulation; When the crown width is less than or equal to 4, the crown width is set to 3 during numerical simulation; when the crown width is greater than 4 and less than 8, the crown width is set to 6 during numerical simulation; when the crown width is greater than or equal to 8, the crown width is set to 9 during numerical simulation.

4. The method for selecting street tree species in urban street valleys for improving air quality according to claim 1, characterized in that: S3 also includes: If the relative change rate of the residents' exposure risk score in the typical street valley model is less than 0, the street tree species with the corresponding tree morphology will be selected as the tree species to be planted; If the relative change rate of the resident exposure risk score in the typical street valley model is greater than 0, the tree species corresponding to the tree morphology with a smaller relative change rate of the resident exposure risk score is selected.

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