CN115203948A - Urban street valley street tree species selection method for improving air quality - Google Patents
Urban street valley street tree species selection method for improving air quality Download PDFInfo
- Publication number
- CN115203948A CN115203948A CN202210837884.0A CN202210837884A CN115203948A CN 115203948 A CN115203948 A CN 115203948A CN 202210837884 A CN202210837884 A CN 202210837884A CN 115203948 A CN115203948 A CN 115203948A
- Authority
- CN
- China
- Prior art keywords
- street
- tree
- valley
- numerical simulation
- height
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Operations Research (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an air quality improvement-oriented tree species selection method for urban street valley street trees, which comprises the following steps: acquiring daily change data of typical weather parameters of a target area, typical street space form types, common street tree form state characteristics and hourly discharge rate of air pollutants discharged by a motor vehicle; constructing various typical street valley models, carrying out numerical simulation one by one, and then carrying out statistical analysis and evaluation, wherein the relative change rate of the exposure risk scores of residents in the street valleys is used as an index during evaluation; according to the evaluation result, selecting the tree species corresponding to the tree form with the relatively low resident exposure risk score relative change rate as the optimal street tree species in the street valley, and making a street tree shaping and trimming strategy by referring to the tree form. The method quantitatively evaluates the influence of the planting schemes of the street trees in different forms on the exposure risk of street valley residents, and provides accurate basis for the selection of the types of the street trees and the shaping and trimming.
Description
Technical Field
The invention belongs to the technical field of urban valley greening design, and particularly relates to an urban valley street tree species selection method for improving air quality.
Background
Urban street canyons (street canyons) are important components of urban underlying surfaces and bear important functions of urban residents in transportation travel and daily life. The amount of random motor cars is increased rapidly, traffic emission becomes a main source of urban air pollution, and air pollutants such as PM2.5, PM10, NOx and the like generated by the emission can induce cardiovascular and respiratory system diseases, so that serious threats are generated to the health of urban residents, especially roadside pedestrians and people living and working nearby roads. With the development of high-rise and high-density building groups in major urban areas, the natural ventilation and air circulation inside the street valleys are remarkably reduced, and the prevention and control of air pollution is increasingly urgent.
In the existing urban construction environment, the street valley space elements cannot be changed easily. Besides measures of controlling the quantity of the motor vehicles, reducing the emission intensity of unit vehicles, popularizing and using new energy vehicles and the like, the street valley greening can influence the diffusion and sedimentation of air pollutants and has strong operability, so that the street valley greening is considered to be an effective and economic way for dealing with the emission pollution of motor vehicle tail gas. The adsorption and sedimentation of the blades and the branches of the street trees in the street valley greening plants can effectively reduce the concentration of pollutants, but the crowns also can block the movement of airflow, and the air exchange between the interior of the street valley and the atmosphere above the street valley is reduced. The negative influence of the aerodynamic effect of the street trees on the diffusion and dilution of the street valley pollutants under most environmental conditions is far stronger than the positive influence of the settlement effect, and the risk of aggravating the accumulation and concentration rise of the street valley air pollutants exists by improper tree species selection and tree planting. In the prior art, the influence of various tree morphological characteristics on the diffusion and sedimentation of air pollutants is ignored, and a greening mode for planting street trees in high density is obviously inapplicable in the process of relieving the air pollution of the street valleys, and the most appropriate method is to plant correct trees in different street valleys by combining a regulation and control mechanism of the tree morphologies on the air pollutants.
Therefore, in order to better exert the ecological benefits of street tree greening and street valley air pollution alleviation, a street tree species selection method for street valley air quality improvement needs to be optimized urgently, and a street tree species selection and shaping trimming strategy adaptive to a specific street valley environment is scientifically formulated by comprehensively considering the synergistic influence of a building environment, meteorological conditions and street tree configuration on air pollutants. The air quality of the street grain is improved while the requirements of landscaping and thermal comfort improvement are met.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a city street valley street tree species selection method oriented to air quality improvement aiming at the defects of the prior art, the method comprehensively considers regional climate characteristics and street valley space morphological characteristics, classifies the tree species based on the tree morphology, constructs typical street tree models with different morphological characteristics, evaluates the influence of street trees with different morphological characteristics on street valley pedestrian air pollution exposure by using a numerical simulation method, further selects the street tree species beneficial to relieving the street valley air pollution, makes a tree shaping and trimming strategy, optimizes the street trees and improves the ecological benefit of street valley air quality.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for selecting tree species of urban street valley street trees for improving air quality is characterized by comprising the following steps:
s1, early analysis stage: collecting meteorological parameters of a target area, morphological parameters of street canyons and individual morphological parameters of common street trees, and performing sorting analysis to obtain daily variation data of typical meteorological weather meteorological parameters of the target area, space morphological types of typical street canyons, types of common street trees and tree morphological characteristics of the common street trees, and hourly discharge rate of air pollutants discharged by motor vehicles in the corresponding types of street canyons of the target area; the morphological parameters of the street canyon comprise building height, building arrangement, road width, building materials and road materials;
s2, a numerical simulation stage: constructing various typical street valley models based on the morphological parameters of the street canyons and the individual morphological parameters of the common street trees, and developing numerical simulation one by one;
s3, analysis and evaluation stage: carrying out statistical analysis on the numerical simulation results of the various typical street valley models constructed in the S2 by combining a data processing method, and evaluating the statistical results, wherein the relative change rate of the exposure risk scores of residents in the street valleys is used as an index during evaluation;
s4, a scheme generation stage: and selecting the tree species corresponding to the tree species with relatively low relative change rate of the resident exposure risk scores as the optimal street tree species in the street valleys according to the evaluation results, and making a street tree shaping and trimming strategy by referring to the tree species.
Preferably, in S2, a typical street valley model is constructed based on the morphological parameters of the street canyon and the individual morphological parameters of the common street tree, and a numerical simulation is developed, which specifically includes:
step 201: selecting ENVI-met microclimate simulation software as a numerical simulation tool;
step 202: setting a traffic pollutant emission source of a target area in a software database, determining the emission height, the emission mode and the emission rate of the traffic pollutant emission source, simultaneously combining the set values of the individual morphological parameters of the common street trees to freely combine to determine tree morphological types, and customizing a street tree model corresponding to each tree morphological type of the target area;
step 203: according to the traffic pollutant emission source set in the step 202 and the form parameters of the street canyon in the S1, in combination with the street tree model in the step 202 and the layout mode of street valley street trees, constructing a plurality of typical street valley models corresponding to various types of street trees, wherein each street tree model of the tree form type corresponds to one typical street valley model;
step 204: <xnotran> 203 , , , , , , 24 , . </xnotran>
Preferably, the street canyon aspect ratio is calculated by the ratio of the building height to the road width, and the rule of the street canyon aspect ratio in the microclimate simulation software for setting the value is as follows:
street valley morphologies are classified according to the street valley height-width ratio, and the specific classification is as follows: when the street canyon height-to-width ratio is less than or equal to 0.5, setting the value to be 0.5 during numerical simulation; when the street canyon height-to-width ratio is more than 0.5 and less than 1.5, setting the value to be 1 during numerical simulation; when the street canyon height-to-width ratio is more than 1.5 and less than 2.5, setting the value to be 2 during numerical simulation; when the street canyon height-to-width ratio is more than or equal to 2.5, setting the value to be 3 during numerical simulation;
the rule of setting the value of the individual morphological parameters of the common street trees in the microclimate simulation software is as follows:
classifying tree forms according to individual form parameters of common street trees, and setting values during numerical simulation; the individual morphological parameters of the common street trees comprise the area density of the leaves of the street trees, the height of the trees, the height under the branches and the crown width; the method specifically comprises the following steps:
when the leaf area density is less than or equal to 1, setting the leaf area density to be 1 during numerical simulation; when the leaf area density is larger than 1 and smaller than 2.5, setting the leaf area density value to be 1.5 during numerical simulation; when the leaf area density is more than or equal to 2.5, setting the leaf area density to be 3 during numerical simulation;
when the tree height is less than or equal to 8, setting the tree height value to be 6 during numerical simulation; when the tree height is larger than 8 and smaller than 12, setting the tree height to be 10 during numerical simulation; when the tree height is greater than or equal to 12, setting the tree height value to be 14 during numerical simulation;
when the under-branch height is less than or equal to 3, setting the under-branch height value to be 3 during numerical simulation; when the under-branch height is larger than 3, setting the under-branch height value to be 5 during numerical simulation;
when the crown width is less than or equal to 4, setting the crown width value to be 3 during numerical simulation; when the crown width is larger than 4 and smaller than 8, setting the crown width value to be 6 during numerical simulation; and when the crown width is more than or equal to 8, setting the crown width to be 9 during numerical simulation.
Preferably, in the step S3, the numerical simulation results of the various typical street valley models constructed in the step S2 are statistically analyzed by combining a data processing method, and the statistical results are evaluated, wherein the evaluation takes the relative change rate of the exposure risk scores of the residents in the street valleys as an index; the method specifically comprises the following steps:
step 301: visualizing the numerical simulation result by using software, carrying out mathematical statistics on the simulation data, and obtaining the average value of the concentration of air pollutants of sidewalks at two sides inside a street valley in one day;
step 302: the method comprises the following steps of calculating the resident exposure risk score of a street valley pedestrian area close to a motor vehicle emission source under different situations by combining the exposure time, the breathing rate and the sensitivity to the traffic emission pollutant exposure of different types of urban crowds, wherein the calculation formula is as follows:
wherein ERF is the resident exposure score; p i Total population for the ith population; RT (reverse transcription) i Is the average human respiratory rate of the ith population in m 3 /s；ET i Is the per-capita exposure time of the ith population in units of h/d, Q i A sensitivity coefficient for exposure of the i-th group of people to traffic emission pollutants; c is the average air pollutant concentration of the pedestrian path at the height of 1.5m, and the unit is kg/m 3 (ii) a E is the total pollutant emission in kg during consideration; the population is divided into three groups, wherein n is 1,2,3 and comprises the elderly, adults and children, the 1 st group refers to the elderly, the 2 nd group refers to the adults and the 3 rd group refers to the childrenA child whose breathing rate and exposure time are shown in the following table:
| group of people | Old people | Adults | Children's toy |
| 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 |
Step 303: calculating the relative change of the exposure risk scores of residents in the tree-planted street valley and the non-tree street valley in a plurality of typical street valley models, wherein the calculation formula is as follows:
where Δ ERF is the relative rate of change of the resident exposure score; ERF tree Is the exposure risk score of residents in the tree planting street valley; ERF tree-free Is the resident exposure risk score of the treeless street valley;
if the relative change rate of the resident exposure risk scores in the typical street valley model is less than 0, obtaining the street tree species with corresponding tree forms as the tree species to be selectively planted;
and if the relative change of the resident exposure risk scores in the typical street valley model is larger than 0, selecting tree species corresponding to the tree forms with small relative change rates of the resident exposure risk scores.
Compared with the prior art, the invention has the following advantages:
1. the method combines the street valley form parameters and the tree form parameters to classify the urban street valleys and the street trees, constructs a corresponding model, screens the street tree form which is beneficial to improving the street valley air quality and the corresponding tree species by using a numerical simulation method, realizes the planting of correct trees at the correct positions of the street valleys, optimizes the ecological benefits of the street trees, improves the street valley air quality, and has important theoretical significance and practical significance for the construction and sustainable development of healthy cities.
2. The method combines the influence of the street trees on the diffusion and adsorption sedimentation of air pollutants, comprehensively considers the synergistic effect of factors such as meteorological conditions, street valley forms and the like, quantificationally evaluates the influence of the street trees with different form characteristics on the air pollution exposure of street valley pedestrians, and provides accurate tree species selection and shaping and trimming basis for planting the street trees for improving the street valley air quality. The microclimate benefit of the street tree is optimized through a scientific method, and the potential negative consequences on air quality are reduced.
The technical solution of the present invention is further described in detail by the accompanying drawings and examples.
Drawings
Fig. 1 is an operation flow of the method for selecting tree species of urban street valley street trees for improving air quality, disclosed in embodiment 1 of the present invention.
FIG. 2 is a diagram of a tree morphology type model constructed according to example 1 of the present invention, FIG. 2- (1) is a street tree of high leaf density, medium tree height, low branch height, and medium crown (DMSM) corresponding to the species plane, mitsubishi, FIG. 2- (2) is a street tree of medium leaf density, low tree height, low branch height, and narrow crown (MSLN) corresponding to the species plane, ginkgo biloba.
Fig. 3 is a street valley model constructed according to embodiment 1 of the present invention, fig. 3- (1) is a three-dimensional schematic of the street valley model with the tree form DMSM, and fig. 3- (2) is a three-dimensional schematic of the street valley model with the tree form MSLN.
Fig. 4 is a graph of the concentration difference of contaminants in street valleys simulated in example 1 of the present invention, fig. 4- (1) is the concentration difference of contaminants in the street valleys with DMSM planted in the tree and the street valleys without trees, and fig. 4- (2) is the concentration difference of contaminants in the street valleys with MSLN planted in the tree and the street valleys without trees.
Detailed Description
Example 1
As shown in fig. 1, a method for selecting tree species of an urban street valley street tree for improving air quality in an embodiment of the present invention includes:
s1, early analysis stage: acquiring daily change data of typical weather parameters of a target area, space form types of typical street valleys, types of common street trees, tree form characteristics of common street valleys and hourly discharge rate of air pollutants discharged by motor vehicles in the street valleys of the corresponding type of the target area; the method specifically comprises the following steps:
determining a research city area, taking a certain supposed small-area city as an example, collecting meteorological parameters of a target area, wherein the meteorological parameters comprise air temperature, relative humidity, wind speed and wind direction, and obtaining daily variation data of typical meteorological daily meteorological parameters through arrangement;
carrying out on-site research on street canyon environment information such as building height, building materials, building arrangement, building form, road width, road materials, street canyon height-width ratio and the like in the region, and determining the space form type of a typical street canyon according to the classification method of the table 1 by combining the basic information of the street canyons; the street canyon aspect ratio is the ratio of the building height to the road width;
measuring characteristic parameters of common street trees in a target area on site, recording data information such as tree species, leaf Area Index (LAI) or Leaf Area Density (LAD), tree height, crown breadth, tree shape and the like, performing data sorting according to tree form classification standards provided in a table 2, and determining a tree species corresponding to each tree form;
searching traffic flow data of roads corresponding to typical street valley space form types of a target area, counting hourly motor vehicle flow, simultaneously inquiring motor vehicle emission factors, calculating hourly pollutant emission rates generated by motor vehicles of corresponding roads, investigating whether other pollutant emission sources exist in a range of 5km around the corresponding street valley or not, and measuring pollutant concentration of a non-motor vehicle area as background concentration if the pollutant emission sources exist.
S2, a numerical simulation stage: constructing various typical street valley models based on the morphological parameters of the street canyons and the individual morphological parameters of the common street trees, and developing numerical simulation one by one; the method is realized by the following steps:
step 201: selecting ENVI-met microclimate simulation software as a numerical simulation tool, wherein the ENVI-met mainly comprises a Space module, an ENVI-guide module, an ENVI-core module, a Leonardo module and other modules, and can input basic parameters, expand simulation and simulate results for visualization;
step 202: setting a traffic pollutant emission source of a target area in a software database, determining the emission height, the emission mode and the emission rate of the traffic pollutant emission source, simultaneously, carrying out free combination by combining the set values of the individual form parameters of the common street trees to determine tree form types, and customizing a street tree model corresponding to each tree form type of the target area; the street trees with different morphological types are shown as a 3D model in FIG. 2;
step 203: according to the traffic pollutant emission source set in step 202 and the morphological parameters of the street canyon in S1, in combination with the layout pattern of the street tree model street canyon street tree in step 202, constructing a plurality of typical street valley models corresponding to a plurality of types of street trees, wherein the street tree model of each tree form type corresponds to one typical street valley model; a typical street valley model is shown in fig. 3;
and calculating the street canyon height-to-width ratio according to the ratio of the building height to the road width, wherein the rule of the street canyon height-to-width ratio set in the microclimate simulation software is as follows:
street valley morphologies were classified according to street canyon height-width ratio, and the specific classification is shown in table 1.
TABLE 1 typical street-valley morphology classification chart
| Types of | Range of aspect ratio (H/W) | Application value |
| Class I | H/W≤0.5 | 0.5 |
| Class II | 0.5＜H/W＜1.5 | 1 |
| Class III | 1.5＜H/W＜2.5 | 2 |
| Class IV | H/W≥2.5 | 3 |
The rule of setting the value of the individual morphological parameters of the common street trees in the microclimate simulation software is as follows: classifying tree forms according to individual form parameters of common street trees, and setting values during numerical simulation; the individual morphological parameters of the common street trees comprise the area density of leaves, the height of the tree, the height under branches and the crown width of the street tree; the specific classification is shown in table 2.
TABLE 2 Classification of individual morphological parameters of common street trees to tree morphology
Step 204: setting incoming flow wind speed, wind direction, air temperature, relative humidity and city roughness in the plurality of typical street valley models constructed in step 203 according to the daily variation data of typical weather parameters as boundary conditions, and carrying out 24-hour simulation under the background environment condition to obtain the hourly street valley air pollutant concentration data of each typical street valley model. Specifically, setting boundary conditions in an ENVI-guide module;
s3, an analysis and evaluation stage: carrying out statistical analysis on the numerical simulation results of the various typical street valley models constructed in the S2 by combining a data processing method, and evaluating the statistical results, wherein the relative change rate of the exposure risk scores of residents in the street valleys is used as an index during evaluation; the method is realized by the following steps:
step 301: visualizing the numerical simulation result in a Leonardo module, wherein the difference value of the concentrations of the pollutants at the heights of the pedestrians in the two typical street valley models is shown in figure 4, and further carrying out mathematical statistics on the simulation data to obtain the average value of the concentrations of the pollutants in the sidewalks at two sides inside the street valley in one day;
step 302: by combining the exposure time, the breathing rate and the sensitivity to the exposure of the traffic emission pollutants of different types of people in cities, the resident exposure risk score of the pedestrian area near the street valley near the motor vehicle emission source under different scenes is calculated, and the calculation formula is as follows:
wherein ERF is the resident exposure score; p i Total number of people who are the ith group; RT (reverse transcription) i The average human respiratory rate of the ith population is m 3 /s；ET i Is the average human exposure time of the ith population in h/d, Q i A sensitivity coefficient for exposure of the ith group to traffic emission pollutants; c is the average air pollutant concentration of the sidewalk with the height of 1.5m, and the unit is kg/m 3 (ii) a E is the total pollutant emission in kg during consideration; the population is divided into three groups, wherein n is 1,2,3 and comprises the elderly, adults and children, the 1 st group refers to the elderly, the 2 nd group refers to the adults and the 3 rd group refers to the children, and the breathing rate and the exposure time are shown in the following table:
| group of people category | Old people | Adults | For children |
| 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 |
Step 303: calculating the relative change of the exposure risk scores of residents in the tree-planted valley and the non-tree valley in a plurality of typical valley models, wherein the calculation formula is as follows:
where Δ ERF is the relative rate of change of the resident exposure score; ERF tree Is the exposure risk score of residents in the tree planting street valley; ERF tree-free Is the resident exposure risk score of the treeless street valley;
according to the calculated relative change rate of the resident exposure risk scores in the typical street valley models, if the relative change rate of the resident exposure risk scores in the typical street valley models is smaller than 0, obtaining the street tree species in the corresponding tree forms as alternative planted tree species; and if the relative change of the resident exposure risk scores in the typical street valley model is larger than 0, selecting the tree species corresponding to the tree form with the smaller relative change rate of the resident exposure risk scores as the alternative planted tree species, and finally determining the planted tree species according to the alternative planted tree species and by combining the actual situation of the target area. Through calculation, the Δ ERF on both sides of the street valley with the tree form DMSM is 8.00% on average, and the Δ ERF on both sides of the street valley with the tree form MSLN is 3.70% on average.
S4, a scheme generation stage: according to the evaluation result, the ginkgo corresponding to the tree form MSLN with the relatively low relative change rate of the resident exposure risk score is selected as the optimal street tree species in the street valley of the corresponding type, the street tree shaping and trimming strategy is formulated by referring to the tree form, and the planted street tree is shaped and trimmed so as to reduce the negative influence of the planted street tree on the air pollutant diffusion.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way. Any simple modification, change and equivalent changes of the above embodiments according to the technical essence of the invention are still within the protection scope of the technical solution of the invention.
Claims (4)
1. A method for selecting tree species of urban street valley street trees for improving air quality is characterized by comprising the following steps:
s1, a preliminary analysis stage: collecting meteorological parameters of a target area, morphological parameters of street canyons and individual morphological parameters of common street trees for sorting and analyzing, and acquiring daily change data of typical meteorological weather parameters, space morphological types of typical street canyons, types of common street trees and tree morphological characteristics of common street trees of the target area, and hourly discharge rates of air pollutants discharged by motor vehicles in corresponding types of street canyons of the target area; the morphological parameters of the street canyon comprise building height, building arrangement, road width, building materials and road materials;
s2, a numerical simulation stage: constructing various typical street valley models based on the morphological parameters of the street canyons and the individual morphological parameters of the common street trees, and developing numerical simulation one by one;
s3, analysis and evaluation stage: carrying out statistical analysis on the numerical simulation results of the various typical valley models constructed in the S2 by combining a data processing method, and evaluating the statistical results, wherein the relative change rate of the exposure risk scores of residents in the valley is used as an index during evaluation;
s4, a scheme generation stage: and selecting a tree species corresponding to a tree form with a relatively low resident exposure risk score relative change rate as an optimal street tree species in the street valley according to the evaluation result, and making a street tree pruning strategy by referring to the tree form.
2. The method for selecting tree species of street valley street trees for improving air quality as claimed in claim 1, wherein in S2, a typical street valley model is constructed based on morphological parameters of the street canyons and individual morphological parameters of common street trees, and numerical simulation is carried out, specifically comprising:
step 201: selecting ENVI-met microclimate simulation software as a numerical simulation tool;
step 202: setting a traffic pollutant emission source of a target area in a software database, determining the emission height, the emission mode and the emission rate of the traffic pollutant emission source, simultaneously combining the set values of the individual form parameters of the common street trees to freely combine to determine various tree form types, and customizing a street tree model corresponding to each tree form type of the target area;
step 203: according to the traffic pollutant emission source set in the step 202 and the form parameters of the street canyon in the S1, in combination with the street tree model in the step 202 and the layout mode of street valley street trees, constructing a plurality of typical street valley models corresponding to various types of street trees, wherein each street tree model of the tree form type corresponds to one typical street valley model;
step 204: <xnotran> 203 , , , , , , 24 , . </xnotran>
3. The method for selecting the tree species of the urban street valley street trees oriented to the air quality improvement, according to the claim 2, is characterized in that the street canyon aspect ratio is calculated by the ratio of the building height to the road width, and the rule of the street canyon aspect ratio set in the microclimate simulation software is as follows:
the street valley morphology is classified according to the street valley height-width ratio, and the specific classification is as follows: when the street canyon height-to-width ratio is less than or equal to 0.5, setting the value to be 0.5 during numerical simulation; when the street canyon height-to-width ratio is more than 0.5 and less than 1.5, setting the value to be 1 during numerical simulation; when the street canyon height-to-width ratio is more than 1.5 and less than 2.5, setting the value to be 2 during numerical simulation; when the street canyon height-to-width ratio is more than or equal to 2.5, setting the value to be 3 during numerical simulation;
the rule of setting the value of the individual morphological parameters of the common street trees in the microclimate simulation software is as follows:
classifying tree forms according to individual form parameters of common street trees, and setting values during numerical simulation; the individual morphological parameters of the common street trees comprise the area density of the leaves of the street trees, the height of the trees, the height under the branches and the crown width; the method specifically comprises the following steps:
when the leaf area density is less than or equal to 1, setting the leaf area density to be 1 during numerical simulation; when the leaf area density is more than 1 and less than 2.5, setting the leaf area density to be 1.5 during numerical simulation; when the leaf area density is more than or equal to 2.5, setting the leaf area density to be 3 during numerical simulation;
when the tree height is less than or equal to 8, setting the tree height value to be 6 during numerical simulation; when the tree height is larger than 8 and smaller than 12, setting the tree height to be 10 during numerical simulation; when the tree height is greater than or equal to 12, setting the tree height value to be 14 during numerical simulation;
when the under-branch height is less than or equal to 3, setting the under-branch height value to be 3 during numerical simulation; when the under-branch height is larger than 3, setting the under-branch height to be 5 during numerical simulation;
when the crown width is less than or equal to 4, setting the crown width value to be 3 during numerical simulation; when the crown width is larger than 4 and smaller than 8, setting the crown width value to be 6 during numerical simulation; and when the crown width is more than or equal to 8, setting the crown width to be 9 during numerical simulation.
4. The method for selecting the tree species of the street valley street trees for improving the air quality as claimed in claim 2, wherein the statistical analysis is performed on the numerical simulation results of the various typical street valley models constructed in the step S2 by combining a data processing method in the step S3, and the statistical results are evaluated, wherein the relative change rate of the exposure risk scores of residents in the street valleys is used as an index during the evaluation; the method specifically comprises the following steps:
step 301: visualizing the numerical simulation result by using software, carrying out mathematical statistics on the simulation data, and obtaining the average value of the concentration of air pollutants of sidewalks at two sides inside a street valley in one day;
step 302: the method comprises the following steps of calculating the resident exposure risk score of a street valley pedestrian area close to a motor vehicle emission source under different situations by combining the exposure time, the breathing rate and the sensitivity to the traffic emission pollutant exposure of different types of urban crowds, wherein the calculation formula is as follows:
wherein ERF is the resident exposure score; p is i Total number of people who are the ith group; RT (reverse transcription) i The average human respiratory rate of the ith population is m 3 /s；ET i Is the per-capita exposure time of the ith population in units of h/d, Q i A sensitivity coefficient for exposure of the ith group to traffic emission pollutants; c is the average air pollutant concentration of the pedestrian path at the height of 1.5m, and the unit is kg/m 3 (ii) a E is the total pollutant emission in kg during consideration; the population is divided into three groups, wherein n is 1,2,3 and comprises the elderly, adults and children, the 1 st group refers to the elderly, the 2 nd group refers to the adults and the 3 rd group refers to the children, and the breathing rate and the exposure time are shown in the following table:
Step 303: calculating the relative change of the exposure risk scores of residents in the tree-planted street valley and the non-tree street valley in a plurality of typical street valley models, wherein the calculation formula is as follows:
where Δ ERF is the relative rate of change of the resident exposure score; ERF tree Is the exposure risk score of residents in the tree planting street valley; ERF tree-free The exposure risk score of residents in the non-tree street valley is obtained;
if the relative change rate of the resident exposure risk scores in the typical street valley model is less than 0, obtaining the street tree species with corresponding tree forms as the tree species to be selectively planted;
and if the relative change of the resident exposure risk scores in the typical street valley model is larger than 0, selecting tree species corresponding to the tree forms with small relative change rates of the resident exposure risk scores.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210837884.0A CN115203948A (en) | 2022-07-16 | 2022-07-16 | Urban street valley street tree species selection method for improving air quality |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210837884.0A CN115203948A (en) | 2022-07-16 | 2022-07-16 | Urban street valley street tree species selection method for improving air quality |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115203948A true CN115203948A (en) | 2022-10-18 |
Family
ID=83582474
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210837884.0A Pending CN115203948A (en) | 2022-07-16 | 2022-07-16 | Urban street valley street tree species selection method for improving air quality |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115203948A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimized tree pollen concentration index |
| CN116011085A (en) * | 2023-02-24 | 2023-04-25 | 北京师范大学 | Urban community landscape greening three-dimensional visual planning method based on ecological benefits |
| CN116882034A (en) * | 2023-09-06 | 2023-10-13 | 武汉大学 | Urban three-dimensional greening distribution method based on three-dimensional simulation |
-
2022
- 2022-07-16 CN CN202210837884.0A patent/CN115203948A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimized tree pollen concentration index |
| CN116011085A (en) * | 2023-02-24 | 2023-04-25 | 北京师范大学 | Urban community landscape greening three-dimensional visual planning method based on ecological benefits |
| CN116011085B (en) * | 2023-02-24 | 2024-04-19 | 北京师范大学 | Urban community landscape greening three-dimensional visual planning method based on ecological benefits |
| CN116882034A (en) * | 2023-09-06 | 2023-10-13 | 武汉大学 | Urban three-dimensional greening distribution method based on three-dimensional simulation |
| CN116882034B (en) * | 2023-09-06 | 2023-11-17 | 武汉大学 | Urban three-dimensional greening distribution method based on three-dimensional simulation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115203948A (en) | Urban street valley street tree species selection method for improving air quality | |
| Wang et al. | Canopy density effects on particulate matter attenuation coefficients in street canyons during summer in the Wuhan metropolitan area | |
| Buccolieri et al. | Review on urban tree modelling in CFD simulations: Aerodynamic, deposition and thermal effects | |
| Forkenbrock et al. | Environmental justice in transportation planning | |
| KR101668753B1 (en) | Air Pollution Management System | |
| Wania et al. | Analysing the influence of different street vegetation on traffic-induced particle dispersion using microscale simulations | |
| Peper et al. | Equations for predicting diameter, height, crown width, and leaf area of San Joaquin Valley street trees | |
| CN111340371B (en) | Health risk assessment method and system for homeland space planning compilation | |
| CN111582580B (en) | Travel path planning method considering population pollutant exposure | |
| Mahdavinejad et al. | An ENVI-met simulation study on influence of urban vegetation congestion on pollution dispersion | |
| Hashad et al. | Impact of roadside conifers vegetation growth on air pollution mitigation | |
| CN116090673B (en) | Traffic source PM2.5 dynamic prediction method and system | |
| Couret et al. | Urban variables for adaptation to global warming in a hot-humid climate. Cuban cities as a case study | |
| CN116011085B (en) | Urban community landscape greening three-dimensional visual planning method based on ecological benefits | |
| Guo et al. | The right tree for the right street canyons: An approach of tree species selection for mitigating air pollution | |
| Jareemit et al. | Modeling the effects of urban form on ventilation patterns and traffic-related PM2. 5 pollution in a central business area of Bangkok | |
| CN116681212A (en) | Urban group county scale vegetation carbon fixation driving contribution analysis method and system | |
| Yan et al. | Mitigating PM2. 5 exposure with vegetation barrier and building designs in urban open-road environments based on numerical simulations | |
| CN116956393A (en) | Urban ventilation gallery calculation method based on GIS | |
| CN113554344A (en) | Comprehensive evaluation method and device for ecological benefits of urban road green land | |
| Di Nicola et al. | A new approach for roughness representation within urban dispersion models | |
| Podhajska et al. | Phytoremediation as an urban paradigm in promoting the health-potential of small green areas. | |
| Lavtižar | Adapting to the urban microclimate–street pollution | |
| Picone | Potential Green Infrastructure Impacts on air quality in Different Local Climates Zones of Brindisi, Italy. | |
| Ding et al. | Spatial and temporal urban air pollution patterns based on limited data of monitoring stations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |