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
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Abstract
Description
技术领域technical field
本发明属于城市街谷绿化设计技术领域,具体涉及一种面向空气质量提升的城市街谷行道树树种选择方法。The invention belongs to the technical field of urban street valley greening design, and in particular relates to a method for selecting tree species on street valleys in an urban street valley for air quality improvement.
背景技术Background technique
城市街道峡谷(街谷)是城市下垫面重要组成部分,承载着城市居民交通出行与日常生活的重要功能。随机动车保有量的激增,交通排放已成为城市空气污染的主要来源,其排放产生的PM2.5、PM10、NOx等空气污染物可诱发心血管和呼吸系统疾病,对城市居民健康产生严重威胁,尤其是路边行人和在道路附近居住、工作的人。随着大都市区建筑群向高层化、高密度发展,街谷内部自然通风和空气流通性显著下降,空气污染防治日益紧迫。Urban street canyons (street valleys) are an important part of the urban underlying surface and carry the important functions of urban residents' transportation and daily life. With the surge in the number of motor vehicles, traffic emissions have become the main source of urban air pollution. Air pollutants such as PM2.5, PM10, and NOx generated by their emissions can induce cardiovascular and respiratory diseases, posing a serious threat to the health of urban residents. Especially roadside pedestrians and people who live and work near the road. With the development of high-rise and high-density buildings in metropolitan areas, the natural ventilation and air circulation in street valleys have declined significantly, and air pollution control has become increasingly urgent.
在既有的城市建成环境中,街谷空间要素无法轻易改变。除了通过控制机动车保有量、降低单位车辆排放强度、推广使用新能源汽车等措施外,街谷绿化因其能够影响空气污染物的扩散与沉降且具有较强的可操作性,被认为是一种应对机动车尾气排放污染有效且经济地方式。街谷绿化植物中行道树叶片及其枝干的吸附沉降能有效降低污染物浓度,但树冠也会阻碍气流的运动,减少街谷内部与上方大气的空气交换。大多数环境条件下行道树空气动力学效应对街谷污染物扩散稀释产生的负面影响远比沉降效应的积极影响更强,不恰当的树种选择与树木种植存在加剧街谷空气污染物积聚和浓度上升风险。现有技术中,忽略多种树木形态特征对空气污染物扩散与沉降的影响、高密度种植行道树的绿化模式在缓解街谷空气污染过程中显然是不适用的,而最合适的方法是结合树木形态对空气污染物的调控机制“在不同的街谷种植正确的树”。In the existing urban built environment, the spatial elements of street valleys cannot be easily changed. In addition to measures such as controlling the number of motor vehicles, reducing the emission intensity per unit of vehicle, and promoting the use of new energy vehicles, street valley greening is considered to be one of the most important aspects of the greening because it can affect the diffusion and settlement of air pollutants and has strong operability. An effective and economical way to deal with vehicle exhaust pollution. The adsorption and deposition of the leaves and branches of the street trees in the green plants in the street valley can effectively reduce the concentration of pollutants, but the canopy can also hinder the movement of airflow, reducing the air exchange between the interior of the street valley and the atmosphere above. Under most environmental conditions, the aerodynamic effects of road trees have a far stronger negative impact on the diffusion and dilution of pollutants in street valleys than the positive effects of sedimentation effects. Inappropriate tree species selection and tree planting exacerbate the accumulation and concentration of air pollutants in street valleys. risk. In the prior art, ignoring the influence of various tree morphological characteristics on the diffusion and deposition of air pollutants, the greening model of high-density planting of street trees is obviously inapplicable in the process of alleviating air pollution in street valleys, and the most suitable method is to combine trees. The regulation mechanism of morphology on air pollutants "planting the right trees in different street valleys".
因此,为了更好地发挥行道树绿化缓解街谷空气污染的生态效益,亟需对面向街谷空气质量提升的行道树树种选择方法进行优化,通过综合考虑建成环境、气象条件、行道树配置三者对空气污染物的协同影响,科学制定与特定街谷环境相适应的行道树树种选择和整形修剪策略。在满足景观美化和热舒适改善需求的同时,提升街谷空气品质。Therefore, in order to better exert the ecological benefits of street tree greening in alleviating air pollution in street valleys, it is urgent to optimize the selection method of street tree species for the improvement of street valley air quality. Synergistic effects of pollutants, scientifically formulate street tree species selection and shaping and pruning strategies that are suitable for specific street valley environments. Improve street valley air quality while meeting the needs of landscaping and thermal comfort improvement.
发明内容SUMMARY OF THE INVENTION
本发明所要解决的技术问题在于针对上述现有技术的不足,提供了一种面向空气质量提升的城市街谷行道树树种选择方法,该方法综合考虑地域气候特征与街谷空间形态特征,基于树木形态对树种进行分类并构建具有不同形态特征的典型行道树模型,利用数值模拟的方法评估具有不同形态特征的行道树对街谷行人空气污染暴露的影响,进而选择有利于缓解街谷空气污染的行道树树种,制定树木整形修剪策略,优化行道树改善街谷空气品质的生态效益。The technical problem to be solved by the present invention is to provide a method for selecting tree species in urban street valleys for air quality improvement. The tree species are classified and typical street tree models with different morphological characteristics are constructed, and the impact of street trees with different morphological characteristics on the air pollution exposure of pedestrians in street valleys is evaluated by numerical simulation, and then street tree species that are conducive to alleviating air pollution in street valleys are selected. Develop tree shaping and pruning strategies to optimize the ecological benefits of street trees to improve air quality in street valleys.
为解决上述技术问题,本发明采用的技术方案是:一种面向空气质量提升的城市街谷行道树树种选择方法,其特征在于,该方法包括:In order to solve the above-mentioned technical problems, the technical scheme adopted in the present invention is: a method for selecting tree species on the street in urban street valleys for air quality improvement, characterized in that the method includes:
S1、前期分析阶段:收集目标区域的气象参数、街道峡谷的形态参数、常见行道树的个体形态参数、并进行整理分析,获取目标区域的典型气象日气象参数的日变化数据、典型街谷空间形态类型、常用行道树种类和常用行道树形态特征,以及目标区域的相应类型街谷中机动车排放空气污染物的逐小时排放速率;所述街道峡谷的形态参数包括建筑高度、建筑排布、道路宽度、建筑材质和道路材质;S1. Pre-analysis stage: collect the meteorological parameters of the target area, the morphological parameters of street canyons, and the individual morphological parameters of common street trees, and conduct sorting and analysis to obtain the daily variation data of typical meteorological parameters of the target area, and the spatial form of typical street canyons. Types, types of commonly used street trees and morphological characteristics of commonly used street trees, as well as the hourly emission rate of air pollutants emitted by motor vehicles in corresponding types of street canyons in the target area; the morphological parameters of the street canyons include building height, building arrangement, road width, building material and road material;
S2、数值模拟阶段:基于所述街道峡谷的形态参数和常见行道树的个体形态参数构建多种典型街谷模型,并逐一开展数值模拟;S2. Numerical simulation stage: build a variety of typical street canyon models based on the morphological parameters of the street canyons and the individual morphological parameters of common street trees, and carry out numerical simulations one by one;
S3、分析评估阶段:结合数据处理方法对S2中构建的多种典型街谷模型的数值模拟结果进行统计分析,并对统计结果进行评估,评估时以街谷中居民暴露风险分数的相对变化率为指标;S3. Analysis and evaluation stage: carry out statistical analysis on the numerical simulation results of various typical street canyon models constructed in S2 in combination with data processing methods, and evaluate the statistical results. During the evaluation, the relative change rate of the residents’ exposure risk scores in the street canyon is used. index;
S4、方案生成阶段:根据评估结果,选择居民暴露风险分数的相对变化率相对低的树木形态所对应的树种为相应类型街谷中最优的行道树树种,并参考该树木形态制定该类街谷行道树整形修剪策略。S4. Plan generation stage: According to the evaluation results, select the tree species corresponding to the tree form with a relatively low relative change rate of the residents' exposure risk score as the optimal street tree species in the corresponding type of street valley, and refer to the tree shape to formulate this type of street valley street tree species Shaping and pruning strategies.
优选地,S2中基于所述街道峡谷的形态参数和常见行道树的个体形态参数构建典型街谷模型,开展数值模拟,具体包括: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 street trees, and numerical simulation is carried out, including:
步骤201:选择ENVI-met微气候模拟软件为数值模拟工具;Step 201: Select ENVI-met microclimate simulation software as the numerical simulation tool;
步骤202:在软件数据库设置目标区域的交通污染物排放源,确定所述交通污染物排放源的排放高度、排放方式和排放速率,同时结合所述常见行道树的个体形态参数的设定取值进行自由组合确定树木形态类型,并自定义目标区域每种树木形态类型对应的行道树模型;Step 202: Set the traffic pollutant discharge source in the target area in the software database, determine the discharge height, discharge method and discharge rate of the traffic pollutant discharge source, and carry out the process in combination with the set values of the individual morphological parameters of the common street tree. Determine tree morphological types by free combination, and customize the street tree model corresponding to each tree morphological type in the target area;
步骤203:根据步骤202中设置的交通污染物排放源和S1中所述街道峡谷的形态参数,结合步骤202中所述行道树模型以及街谷行道树的布局模式,构建与多种类型行道树对应的多个典型街谷模型,其中每一种树木形态类型的行道树模型对应一种典型街谷模型;Step 203: According to the traffic pollutant emission source set in step 202 and the morphological parameters of the street canyon described in S1, combined with the street tree model described in step 202 and the layout pattern of street canyons, construct multiple types of street trees corresponding to various types of street trees. A typical street canyon model, in which the street tree model of each tree form type corresponds to a typical street canyon model;
步骤204:在步骤203中构建的多个典型街谷模型中,以目标区域典型气象日气象参数的日变化数据作为边界条件,设定来流风速、风向、空气温度、相对湿度以及城市粗糙度,在该背景环境条件下开展24小时模拟,得到每个典型街谷模型下逐小时的街谷空气污染物浓度数据。Step 204: In the multiple typical street valley models constructed in step 203, the daily variation data of the typical meteorological parameters of the target area are used as boundary conditions, and the incoming wind speed, wind direction, air temperature, relative humidity and urban roughness are set. , carry out 24-hour simulation under this background environment, and obtain hourly air pollutant concentration data in street valleys under each typical street valley model.
优选地,通过所述建筑高度和道路宽度的比值计算街道峡谷高宽比,所述街道峡谷高宽比在微气候模拟软件中的设定取值的规则为:Preferably, the street canyon aspect ratio is calculated by the ratio between the building height and the road width, and the rules for setting the street canyon aspect ratio in the microclimate simulation software are:
根据街道峡谷高宽比对街谷形态进行分类,具体分类为:当街道峡谷高宽比小于等于0.5时,数值模拟时设定取值为0.5;当街道峡谷高宽比大于0.5且小于1.5时,数值模拟时设定取值为1;当街道峡谷高宽比大于1.5且小于2.5时,数值模拟时设定取值为2;当街道峡谷高宽比大于等于2.5时,数值模拟时设定取值为3;The street canyon shape is classified according to the aspect ratio of the street canyon. The specific classification is as follows: when the aspect ratio of the street canyon is less than or equal to 0.5, the value is set to 0.5 in the numerical simulation; when the aspect ratio of the street canyon is greater than 0.5 and less than 1.5 , the value is set to 1 during the numerical simulation; when the aspect ratio of the street canyon is greater than 1.5 and less than 2.5, the value is set to 2 during the numerical simulation; when the aspect ratio of the street canyon is greater than or equal to 2.5, the value is set during the numerical simulation The value is 3;
常见行道树的个体形态参数在微气候模拟软件中的设定取值的规则为:The rules for setting values of individual morphological parameters of common street trees in the microclimate simulation software are:
根据常见行道树的个体形态参数对树木形态进行分类,并设定数值模拟时的取值;常见行道树的个体形态参数包括行道树叶面积密度、树高、枝下高、冠幅;具体为:The tree morphology is classified according to the individual morphological parameters of common street trees, and the values for numerical simulation are set; the individual morphological parameters of common street trees include the area density of street leaves, tree height, height under branches, and crown width; specifically:
当叶面积密度小于等于1时,数值模拟时设定叶面积密度取值为1;当叶面积密度大于1且小于2.5时,数值模拟时设定叶面积密度取值为1.5;当叶面积密度大于等于2.5时,数值模拟时设定叶面积密度取值为3;When the leaf area density is less than or equal to 1, the leaf area density is set to be 1 in the numerical simulation; when the leaf area density is greater than 1 and less than 2.5, the leaf area density is set to be 1.5 in the numerical simulation; When it is greater than or equal to 2.5, the leaf area density is set to be 3 during numerical simulation;
当树高小于等于8时,数值模拟时设定树高取值为6;当树高大于8且小于12时,数值模拟时设定树高取值为10;当树高大于等于12时,数值模拟时设定树高取值为14;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, During numerical simulation, the tree height is set to be 14;
当枝下高小于等于3时,数值模拟时设定枝下高取值为3;当枝下高大于3时,数值模拟时设定枝下高取值为5;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;
当冠幅小于等于4时,数值模拟时设定冠幅取值为3;当冠幅大于4且小于8时,数值模拟时设定冠幅取值为6;当冠幅大于等于8时,数值模拟时设定冠幅取值为9。When the crown width is less than or equal to 4, the crown width is set as 3 in numerical simulation; when the crown width is greater than 4 and less than 8, the crown width is set as 6 in numerical simulation; when the crown width is greater than or equal to 8, In the numerical simulation, the crown amplitude is set to be 9.
优选地,S3中结合数据处理方法对S2中构建的多种典型街谷模型的数值模拟结果进行统计分析,并对统计结果进行评估,评估时以街谷中居民暴露风险分数的相对变化率为指标;具体包括:Preferably, the numerical simulation results of various typical street canyon models constructed in S2 are statistically analyzed in combination with the data processing method in S3, and the statistical results are evaluated, and the relative change rate of the residents' exposure risk scores in the street canyon is used as an indicator during the evaluation. ; specifically:
步骤301:利用软件对数值模拟结果进行可视化,并对模拟数据进行数理统计,获取一天中街谷内部两侧人行道空气污染物浓度的平均值;Step 301: Use software to visualize the numerical simulation results, and perform mathematical statistics on the simulation data to obtain the average value of air pollutant concentrations on the sidewalks on both sides of the street valley in a day;
步骤302:结合城市不同类型人群的暴露时间、呼吸速率以及对交通排放污染物暴露的敏感性,计算不同情景下临近机动车排放源附近街谷行人区域的居民暴露风险分数,计算公式如下:Step 302: Combine the exposure time, respiration rate and sensitivity to traffic emission pollutants exposure of different types of people in the city, calculate the exposure risk scores of residents in pedestrian areas in street valleys near motor vehicle emission sources under different scenarios, and the calculation formula is as follows:
式中,ERF为居民暴露风险分数;Pi为第i类人群的总人数;RTi为第i类人群的人均呼吸速率,单位是m3/s;ETi为为第i类人群的人均暴露时间,单位是h/d,Qi为第i类人群对交通排放污染物暴露的敏感系数;C为人行道1.5m高度的平均空气污染物浓度,单位是kg/m3;E是考虑期间的总污染物排放量,单位是kg;所述人群划分为三类,n取1,2,3,包括老年人、成年人、小孩,第1类人群指老年人,第2类人群指成年人,第3类人群指小孩,其呼吸速率与暴露时间如下表所示:In the formula, ERF is the resident exposure risk score; Pi is the total number of people in category i; RT i is the per capita respiratory rate of group i, in m 3 /s; ET i is the per capita population in category i Exposure time, the unit is h/d, Qi is the sensitivity coefficient of the i -th population to the traffic discharge pollutant exposure; C is the average air pollutant concentration at the height of 1.5m on the sidewalk, the unit is kg/m 3 ; E is the consideration period The total emission of pollutants, the unit is kg; the population is divided into three categories, n is 1, 2, 3, including the elderly, adults, children, the first category of people refers to the elderly, the second category of people refers to adults Humans, group 3 refers to children, whose breathing rate and exposure time are shown in the table below:
步骤303:计算多个典型街谷模型中植树街谷与无树街谷中居民暴露风险分数的相对变化,计算公式如下:Step 303: Calculate the relative change of the exposure risk scores of residents in the tree-planted street valley and the tree-free street valley in multiple typical street valley models. The calculation formula is as follows:
式中,ΔERF为居民暴露风险分数的相对变化率;ERFtree为植树街谷的居民暴露风险分数;ERFtree-free为无树街谷的居民暴露风险分数;In the formula, ΔERF is the relative change rate of the residents' exposure risk score; ERF tree is the residents' exposure risk score in the tree-planting street valley; ERF tree-free is the residents' exposure risk score in the tree-free street valley;
若典型街谷模型中的居民暴露风险分数的相对变化率小于0,则得出相应树木形态的行道树树种作为选择种植的树种;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 form is obtained as the tree species selected for planting;
若典型街谷模型中的居民暴露风险分数的相对变化大于0,则选择居民暴露风险分数相对变化率较小的树木形态所对应的树种。If the relative change of the resident exposure risk score in the typical street valley model is greater than 0, the tree species corresponding to the tree form with a small relative change rate of the resident exposure risk score is selected.
本发明与现有技术相比具有以下优点:Compared with the prior art, the present invention has the following advantages:
1、本发明结合街谷形态参数与树木形态参数,对城市街谷与行道树进行分类,并构建相应的模型,利用数值模拟的方法筛选有利于改善街谷空气质量的行道树形态及其对应的树种,实现在街谷正确的位置种植正确的树,从而优化行道树的生态效益,提升街谷空气品质,对健康城市的建设和可持续发展具有重要的理论意义与实践意义。1. The present invention combines street valley morphological parameters and tree morphological parameters to classify urban street valleys and street trees, build corresponding models, and use the method of numerical simulation to screen street tree shapes and their corresponding tree species that are conducive to improving the air quality of street valleys , to plant the correct trees in the correct location in the street valley, so as to optimize the ecological benefits of street trees and improve the air quality of the street valley, which has important theoretical and practical significance for the construction and sustainable development of a healthy city.
2、本发明结合行道树对空气污染物扩散与吸附沉降的影响,综合考虑了气象条件、街谷形态等因素的协同作用,量化评估具有不同形态特征的行道树对街谷行人空气污染暴露的影响,为面向街谷空气质量提升的行道树种植提供了准确的树种选择和整形修剪依据。通过科学的方法优化行道树的微气候效益、减少其对空气质量潜在的负面后果。2. The present invention combines the influence of street trees on the diffusion and adsorption of air pollutants, and comprehensively considers the synergistic effect of meteorological conditions, street valley morphology and other factors, and quantitatively evaluates the impact of street trees with different morphological characteristics on the air pollution exposure of street valley pedestrians, It provides accurate tree species selection and shaping and pruning basis for street tree planting for the improvement of air quality in street valleys. A scientific approach to optimizing the microclimate benefits of street trees and reducing their potential negative consequences for air quality.
下面通过附图和实施例对本发明的技术方案作进一步的详细说明。The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.
附图说明Description of drawings
图1是本发明实施例1公开的面向空气质量提升的城市街谷行道树树种选择方法的操作流程。FIG. 1 is an operation flow of a method for selecting tree species for urban street valleys for air quality improvement disclosed in
图2是按照本发明实施例1所构建的树木形态类型模型图,图2-(1)是高叶密度、中等树高、低枝下高、中等冠幅(DMSM)的行道树,对应树种为悬铃木,图2-(2)中等叶密度、低树高、低枝下高、窄冠幅(MSLN)的行道树,对应树种为银杏。Fig. 2 is a tree morphological type model diagram constructed according to Example 1 of the present invention, Fig. 2-(1) is a street tree with high leaf density, medium tree height, low branch height, medium crown width (DMSM), and the corresponding tree species is Platanus, Figure 2-(2) A street tree with medium leaf density, low tree height, low branch height and narrow crown width (MSLN), the corresponding tree species is Ginkgo biloba.
图3是按照本发明实施例1所构建的街谷模型,图3-(1)是树木形态为DMSM的街谷模型三维示意,图3-(2)是树木形态为MSLN的街谷模型三维示意。Fig. 3 is a street valley model constructed according to Example 1 of the present invention, Fig. 3-(1) is a three-dimensional schematic diagram of a street valley model with a tree shape of DMSM, Fig. 3-(2) is a three-dimensional street valley model with a tree shape of MSLN signal.
图4是按照本发明实施例1所模拟出的街谷污染物浓度差值图,图4-(1)是树木形态为DMSM的植树街谷与无树街谷的污染物浓度差值,图4-(2)是树木形态为MSLN的植树街谷与无树街谷的污染物浓度差值。Fig. 4 is a graph of the concentration difference of pollutants in a street valley simulated according to Example 1 of the present invention, Fig. 4-(1) is a graph of the concentration difference of pollutants between a tree-planting street valley and a tree-free street valley where the tree form is DMSM, Fig. 4-(2) is the difference in pollutant concentration between the tree-planted street valley with MSLN and the tree-free street valley.
具体实施方式Detailed ways
实施例1Example 1
如图1所示,本发明实施例的一种面向空气质量提升的城市街谷行道树树种选择方法,该方法包括:As shown in FIG. 1, an embodiment of the present invention is a method for selecting street tree species in urban street valleys for air quality improvement, the method includes:
S1、前期分析阶段:获取目标区域的典型气象日气象参数的日变化数据、典型街谷空间形态类型、常用行道树种类和常用行道树形态特征,以及目标区域的相应类型街谷中机动车排放空气污染物的逐小时排放速率;具体包括:S1. Preliminary analysis stage: obtain the daily variation data of typical meteorological parameters in the target area, the spatial form types of typical street valleys, the types of commonly used street trees and the morphological characteristics of commonly used street trees, and the corresponding types of street valleys in the target area. Motor vehicles emit air pollutants hourly emission rate; specifically includes:
确定研究城市区域,以某假想小区域城市为例,收集目标区域的气象参数,所述气象参数包括空气温度、相对湿度、风速、风向,并整理得到典型气象日气象参数的日变化数据;Determine the research city area, take an imaginary small-area city as an example, collect the meteorological parameters of the target area, the meteorological parameters include air temperature, relative humidity, wind speed, wind direction, and organize the daily variation data of typical meteorological parameters;
对该区域的建筑高度、建筑材质、建筑排布、建筑形态、道路宽度、道路材质、街道峡谷高宽比等街谷环境信息开展实地调研,结合获取街谷的基本信息,根据表1的分类方法确定典型街道峡谷空间形态类型;所述街道峡谷高宽比为建筑高度与道路宽度的比值;Conduct on-the-spot investigation on the street valley environmental information such as building height, building material, building arrangement, building form, road width, road material, street canyon aspect ratio, etc., and obtain the basic information of street valley according to the classification in Table 1. The method determines the spatial form type of typical street canyons; the street canyon aspect ratio is the ratio of building height to road width;
实地测量目标区域内常见行道树特征参数,记录树种、叶面积指数(Leaf areaindex,LAI)或叶面积密度(Leaf area density,LAD)、树高、冠幅、树形等数据信息,并表2中提出的树木形态分类标准进行数据整理,并确定每种树木形态所对应的树种;Measure the characteristic parameters of common street trees in the target area on the spot, record the tree species, leaf area index (LAI) or leaf area density (LAD), tree height, crown width, tree shape and other data information, and table 2 The proposed tree morphological classification standard is used to organize the data, and determine the tree species corresponding to each tree morphological;
查找目标区域典型街谷空间形态类型所对应道路的交通流量数据,统计逐小时机动车车流量,同时查询机动车排放因子,计算得出相应道路机动车产生的逐小时污染物排放速率,并调查相应街谷周边5km范围内是否存在其他污染物排放源,如果存在,测量非机动车区域的污染物浓度作为背景浓度。Find the traffic flow data of the road corresponding to the typical street valley spatial form type in the target area, count the hourly motor vehicle traffic flow, and query the motor vehicle emission factor at the same time, calculate the hourly pollutant emission rate generated by the corresponding road vehicles, and investigate Whether there are other pollutant emission sources within 5km around the corresponding street valley, if so, measure the pollutant concentration in the non-motorized area as the background concentration.
S2、数值模拟阶段:基于所述街道峡谷的形态参数和常见行道树的个体形态参数构建多种典型街谷模型,并逐一开展数值模拟;具体通过以下步骤实现:S2. Numerical simulation stage: Based on the morphological parameters of the street canyons 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; the specific implementation is achieved through the following steps:
步骤201:选择ENVI-met微气候模拟软件为数值模拟工具,ENVI-met主要包括Space、ENVI-guide、ENVI-core、Leonardo等模块,能够输入基本参数、展开模拟、并模拟结果进行可视化;Step 201: Select ENVI-met microclimate simulation software as a numerical simulation tool. ENVI-met mainly includes modules such as Space, ENVI-guide, ENVI-core, Leonardo, etc., which can input basic parameters, conduct simulation, and visualize simulation results;
步骤202:在软件数据库设置目标区域的交通污染物排放源,确定所述交通污染物排放源的排放高度、排放方式和排放速率,同时结合所述常见行道树的个体形态参数的设定取值进行自由组合确定树木形态类型,并自定义目标区域每种树木形态类型对应的行道树模型;不同形态类型的行道树如图2所示3D模型;Step 202: Set the traffic pollutant discharge source in the target area in the software database, determine the discharge height, discharge method and discharge rate of the traffic pollutant discharge source, and carry out the process in combination with the set values of the individual morphological parameters of the common street tree. Determine the tree morphological types by free combination, and customize the street tree model corresponding to each tree morphological type in the target area; the 3D models of street trees of different morphological types are shown in Figure 2;
步骤203:根据步骤202中设置的交通污染物排放源和S1中所述街道峡谷的形态参数,结合步骤202中所述行道树模型街谷行道树的布局模式,构建与多种类型行道树对应的多个典型街谷模型,其中每一种树木形态类型的行道树模型对应一种典型街谷模型;典型街谷模型如图3所示;Step 203: According to the traffic pollutant emission source set in step 202 and the morphological parameters of the street canyon described in S1, combined with the layout pattern of the street tree model street valley street tree described in step 202, construct a plurality of street trees corresponding to various types of street trees. Typical street valley model, in which the street tree model of each tree type corresponds to a typical street valley model; the typical street valley model is shown in Figure 3;
通过所述建筑高度和道路宽度的比值计算街道峡谷高宽比,所述街道峡谷高宽比在微气候模拟软件中的设定取值的规则为:The street canyon aspect ratio is calculated by the ratio between the building height and the road width. The rules for setting the street canyon aspect ratio in the microclimate simulation software are:
根据街道峡谷高宽比对街谷形态进行分类,具体分类如表1所示。The street canyon shape is classified according to the height-width ratio of the street canyon, and the specific classification is shown in Table 1.
表1典型街谷形态分类表Table 1 Classification of typical street valleys
常见行道树的个体形态参数在微气候模拟软件中的设定取值的规则为:根据常见行道树的个体形态参数对树木形态进行分类,并设定数值模拟时的取值;常见行道树的个体形态参数包括行道树叶面积密度、树高、枝下高、冠幅;具体分类如表2所示。The rules for setting values of the individual morphological parameters of common street trees in the microclimate simulation software are: classify the tree morphology according to the individual morphological parameters of common street trees, and set the values for numerical simulation; the individual morphological parameters of common street trees Including the leaf area density, tree height, under-branch height, and crown width on the sidewalk; the specific classification is shown in Table 2.
表2常见行道树的个体形态参数对树木形态分类表Table 2. Individual morphological parameters of common street trees to tree morphological classification
步骤204:在步骤203中构建的多个典型街谷模型中根据典型气象日气象参数的日变化数据作为边界条件,设定来流风速、风向、空气温度、相对湿度以及城市粗糙度,在该背景环境条件下开展24小时模拟,得到每个典型街谷模型下逐小时的街谷空气污染物浓度数据。具体在ENVI-guide模块置边界条件;Step 204: Set the incoming wind speed, wind direction, air temperature, relative humidity and urban roughness according to the daily variation data of typical meteorological parameters in the multiple typical street valley models constructed in step 203. A 24-hour simulation was carried out under the background environmental conditions to obtain hourly air pollutant concentration data in each typical street valley model. Specifically, set the boundary conditions in the ENVI-guide module;
S3、分析评估阶段:结合数据处理方法对S2中构建的多种典型街谷模型的数值模拟结果进行统计分析,并对统计结果进行评估,评估时以街谷中居民暴露风险分数的相对变化率为指标;具体通过以下步骤实现:S3. Analysis and evaluation stage: carry out statistical analysis on the numerical simulation results of various typical street canyon models constructed in S2 in combination with data processing methods, and evaluate the statistical results. During the evaluation, the relative change rate of the residents’ exposure risk scores in the street canyon is used. Indicator; it is achieved through the following steps:
步骤301:利用在Leonardo模块中对数值模拟结果进行可视化,两种典型街谷模型行人高度的污染物浓度差值如图4所示,并进一步对模拟数据进行数理统计,获取一天中街谷内部两侧人行道空气污染物浓度的平均值;Step 301: Using the Leonardo module to visualize the results of the numerical simulation, the difference in pollutant concentration between the pedestrian heights of the two typical street valley models is shown in Figure 4, and further perform mathematical statistics on the simulated data to obtain the interior of the street valley in one day. The average value of air pollutant concentrations on both sides of the sidewalk;
步骤302:结合城市不同类型人群的暴露时间、呼吸速率以及对交通排放污染物暴露的敏感性,计算不同情景下临近机动车排放源附近街谷行人区域的居民暴露风险分数,计算公式如下:Step 302: Combine the exposure time, respiration rate and sensitivity to traffic emission pollutants exposure of different types of people in the city, calculate the exposure risk scores of residents in pedestrian areas in street valleys near motor vehicle emission sources under different scenarios, and the calculation formula is as follows:
式中,ERF为居民暴露风险分数;Pi为第i类人群的总人数;RTi为第i类人群的人均呼吸速率,单位是m3/s;ETi为为第i类人群的人均暴露时间,单位是h/d,Qi为第i类人群对交通排放污染物暴露的敏感系数;C为人行道1.5m高度的平均空气污染物浓度,单位是kg/m3;E是考虑期间的总污染物排放量,单位是kg;所述人群划分为三类,n取1,2,3,包括老年人、成年人、小孩,第1类人群指老年人,第2类人群指成年人,第3类人群指小孩,其呼吸速率与暴露时间如下表所示:In the formula, ERF is the resident exposure risk score; Pi is the total number of people in category i; RT i is the per capita respiratory rate of group i, in m 3 /s; ET i is the per capita population in category i Exposure time, the unit is h/d, Qi is the sensitivity coefficient of the i -th population to the traffic discharge pollutant exposure; C is the average air pollutant concentration at the height of 1.5m on the sidewalk, the unit is kg/m 3 ; E is the consideration period The total emission of pollutants, the unit is kg; the population is divided into three categories, n is 1, 2, 3, including the elderly, adults, children, the first category of people refers to the elderly, the second category of people refers to adults Humans, group 3 refers to children, whose breathing rate and exposure time are shown in the table below:
步骤303:计算多个典型街谷模型中植树街谷与无树街谷中居民暴露风险分数的相对变化,计算公式如下:Step 303: Calculate the relative change of the exposure risk scores of residents in the tree-planted street valley and the tree-free street valley in multiple typical street valley models. The calculation formula is as follows:
式中,ΔERF为居民暴露风险分数的相对变化率;ERFtree为植树街谷的居民暴露风险分数;ERFtree-free为无树街谷的居民暴露风险分数;In the formula, ΔERF is the relative change rate of the residents' exposure risk score; ERF tree is the residents' exposure risk score in the tree-planting street valley; ERF tree-free is the residents' exposure risk score in the tree-free street valley;
根据计算出的多个典型街谷模型中的居民暴露风险分数的相对变化率,若其中典型街谷模型中的居民暴露风险分数的相对变化率小于0,则得出相应树木形态的行道树树种作为备选的种植的树种;若典型街谷模型中的居民暴露风险分数的相对变化大于0,则选择居民暴露风险分数相对变化率较小的树木形态所对应的树种作为备选的种植的树种,最后根据备选的种植树种,结合目标区域的实际情况,确定种植的树种。通过计算可知,树木形态为DMSM的街谷两侧ΔERF平均为8.00%,树木形态为MSLN的街谷两侧ΔERF平均为3.70%。According to the calculated relative change rates of the residents' exposure risk scores in the multiple typical street valley models, if the relative change rate of the residents' exposure risk scores in the typical street valley models is less than 0, the street tree species with the corresponding tree shape is obtained as the Alternative planting tree species; if the relative change of the resident exposure risk score in the typical street valley model is greater than 0, the tree species corresponding to the tree form with a small relative change rate of the resident exposure risk score is selected as the alternative planting tree species. Finally, according to the alternative planting tree species, combined with the actual situation of the target area, determine the planting tree species. It can be seen from the calculation that the average ΔERF on both sides of the street valley with tree morphology is DMSM is 8.00%, and the average ΔERF on both sides of street valley with tree morphology is 3.70%.
S4、方案生成阶段:根据评估结果可知,选择居民暴露风险分数的相对变化率相对低的树木形态MSLN所对应的银杏为相应类型街谷中最优的行道树树种,并参考该树木形态制定行道树整形修剪策略,对于已种植行道树进行整形修剪以减少其对空气污染物扩散的负面影响。S4. Scheme generation stage: According to the evaluation results, the ginkgo tree corresponding to the tree form MSLN with a relatively low relative change rate of the residents' exposure risk score is selected as the optimal street tree species in the corresponding type of street valley, and the street tree shaping and pruning are formulated with reference to the tree shape. Strategies to shape and prune planted street trees to reduce their negative impact on the spread of air pollutants.
以上所述,仅是本发明的较佳实施例,并非对本发明作任何限制。凡是根据发明技术实质对以上实施例所作的任何简单修改、变更以及等效变化,均仍属于本发明技术方案的保护范围内。The above descriptions are only preferred embodiments of the present invention, and do not limit the present invention in any way. Any simple modifications, changes and equivalent changes made to the above embodiments according to the technical essence of the invention still fall within the protection scope of the technical solutions of the present invention.
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CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimizing 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 |
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2022
- 2022-07-16 CN CN202210837884.0A patent/CN115203948A/en active Pending
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
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CN115935855A (en) * | 2023-01-09 | 2023-04-07 | 北京科技大学 | Urban greening method and device based on optimizing 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 | Three-dimensional visualization planning method of urban community landscape greening 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 |
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