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Journal of Geo-information Science >

2018 , Vol. 20 >Issue 9: 1235 - 1243

DOI: https://doi.org/10.12082/dqxxkx.2018.180064

Effect of Spatial Distribution of Trees on the Airflow at Pedestrian Breath Height in the Typical Deep Street Canyon

  • LIN Ding , 1, * ,
  • SHEN Xiaoyun 1 ,
  • ZHU Yongbing 2 ,
  • CHEN Chongcheng 1
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  • 1. Key Laboratory of Spatial Data Mining & Information Sharing of MOE, Spatial Information Research Center of Fujian, Fuzhou University, Fuzhou 350116, China;
  • 2. State Key Laboratory for Civilian Nuclear, Biological and Chemical Defence, Beijing 102205, China
*Corresponding author: LIN Ding, E-mail:

Received date: 2018-01-21

  Request revised date: 2018-03-29

  Online published: 2018-09-25

Supported by

National Natural Science Foundation of China, No.31200430

Science and Technology Guidance Project of Fujian Province, No.2016Y0058

Equipment Rehearsal Project , No.40407020602.

Copyright

《地球信息科学学报》编辑部 所有
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Abstract

In order to show the degree of change of the airflow at pedestrian breath height due to trees, trees with four different spatial distribution inside the ideal deep street canyon (H/W = 2) were simulated by CFD from the aerodynamic point of view. Tree canopy was treated as uniform porous media, and an additional source term is integrated to account for additional dissipation due to trees. Our results show that the effect of different spatial configuration of trees on the airflow varies greatly with trees' spatial distribution pattern: (1) Within the street canyon, uniform planted trees hinder the pedestrian airflow while non-uniform planted trees increase its rate. The effects of the on pedestrian airflow are very different under the four spatial distributions. The order of the obstruction effect of trees on airflow from the largest to the smallest is evenly spaced 8m (Spa8m) > evenly spaced 6 m (Spa6m) > evenly spaced 20 m (Spa20m) > not uniformly planted (Non-uniform). The corresponding average airflow enhancement index sequence is$\bar{D}_{spa8}$（-19.31%）<$\bar{D}_{spa6}$（-16.14%）<$\bar{D}_{spa20}$（-10.73%）<$\bar{D}_{non-uniform}$（1.25%）. (2) The pedestrian airflow win the street canyon with uneven-planting was 106.49% higher than that in the control case (uniform tree Planting, Spa8m). Uneven-planting scheme is the case that trees are planted with the sufficient free space at both ends of the street and no trees in the middle. It can allow the corner vortex to infiltrate into the middle of the street valley, promote the vertical vortex inside the street valley and the horizontal vortex movement at both ends, enhance turbulence and vertical exchange, effectively reduce the “tuyere effect” at both ends of the street and the “calm wind effect” in the middle of the street. It improves the wind environment of the entire street valley at the pedestrian breathing plane. (3) Trees with reasonable spatial distribution can improve the street pedestrian wind environment. The airflow at the breath height of pedestrians in the street valley is very sensitive to the local conditions, and the configuration (spatial cluster and density) of the trees will cause a strong spatial change of it. These results point out the importance of trees' spatial distribution in urban greening measures under existing urban building layout with the goal of improving the pedestrian wind environment, alleviating the spread of pollution and the disease by careful landscape design.

Key words： tree; street canyon; airflow; numerical simulation; pedestrian flow

Cite this article

LIN Ding , SHEN Xiaoyun , ZHU Yongbing , CHEN Chongcheng . Effect of Spatial Distribution of Trees on the Airflow at Pedestrian Breath Height in the Typical Deep Street Canyon[J]. Journal of Geo-information Science, 2018 , 20(9) : 1235 -1243 . DOI: 10.12082/dqxxkx.2018.180064

1 引言

2011年,全球有约35亿人（51%）居住在城市,预计2030年将达到约50亿（60%）[1,2]。地球上大多数人所处的环境是从地面到建筑高度的城市覆盖层（Urban Canopy Layer,UCL）,属于大气边界层的粗糙子层,其流动统计特性强烈依赖于建筑物等障碍物的实际形状和布局,形成城市街道峡谷的局地小气候现象[3]。世界范围内持续的城市化和城市车辆排放的增加,引发了广泛的城市热岛、大气污染、疾病传染等对公众健康不利的问题。许多城市采用树木等植被提供遮荫,缓解热压力,净化空气,缓解当地空气污染等。然而,最近的研究出现了相互矛盾的结果和评论：用植被缓解当地空气污染的措施并不可行,树木等植被的空气动力学效应远比其污染物去除能力强得多[4]。关于建筑物对城市大气污染扩散的影响,国内外学者分别从整个城市[5,6,7]、多个街区[8,9,10,11]、单个街谷[12,13,14,15,16,17,18,19,20]3个尺度开展广泛研究;许多文献还探讨了在局地排放源下树木对城区街谷内空气质量的影响,包括风洞实验室实验[21,22,23]和污染扩散与颗粒物沉降的CFD模拟实验[24,25,26,27,28,29];这些研究指出树木等绿化设施对局地空气质量既有正面的也有负面的影响,取决于街谷和植被特征[30]。大多数研究表明,与无树木种植的情况相比,街谷中树木的存在,使不同污染物的浓度平均增加20%~96%,并在他们的报告中给出了各种纵横比和风向下污染物浓度的详细百分比变化[30,31,32]。然而,这些污染物浓度变化代表了局地条件,是树木和建筑物对空间平均流量的综合影响[33]。鉴于小尺度的流和湍流特征对街道及其周围环境的几何特征相当敏感[33,34,35,36],除了COST732以外,许多研究主要集中在污染物浓度结果的适当性上,而不是在底层流场上;如果底层湍流场的预测不充分,就不能指望得到的扩散特性是准确的[35,36,37]。树木对城市局地空气质量的影响比较复杂,目前人们还不了解树木影响城市局地空气流动的所有参数（如,形态、叶密度、空间配置等）以及这些参数的各自影响程度和相互关联关系[30,33]。
作为城市的基本单位,街道是了解当地大气环流和其他建筑环境相关过程的重要平台。本文以城市中心典型深街谷为参照,开展树木的不同空间配置（均匀/不均匀、密植/疏植）对街谷内行人呼吸高度处（1.5 m）气流流动的影响,力图从机理上解释树木的空间配置对城市局地小气候的影响细节。研究结果对于改善行人呼吸高度的风环境,缓解公共卫生等问题,确定城市规划中绿化策略,具有重要意义。

2 研究方法

目前研究街谷内气流扩散规律的方法主要有实地测量、物理风洞试验和数值模拟。实地测量获得的数据较为真实可信,但易受到测量方法和天气等因素的影响而造成误差。物理风洞试验可控制实验条件有针对性地研究各个因素对街谷气流的影响,但其成本高。二者结果依赖于设计的采样点数量,均无法获得街谷内部气流详细细节数据。随着计算机技术和数值理论的发展,计算能力不断加强,数值模拟法具有实验周期短、费用低,可获得详细细节数据的优点,已经成为研究街谷内气流扩散的重要手段[38]。
本文采用数值模拟法分别对无树木种植的空街谷和不同空间配置下树木绿化的街谷及其街谷内部的气体流动进行计算,并分析树木的空间配置对行人呼吸高度处（离地面1.5 m）水平气流的影响。

2.1 实验模型

参考Gromke等[39]在德国Karlsruhe大学进行的大气边界层风洞试验、欧盟跨国合作长期框架下的微尺度气象模型的质量保证与改进行动计划（COST ACTION 732）所推荐的城市环境中大气流动CFD模拟的最佳实践指南[40]和日本建筑学会建议的CFD模拟建筑物周围行人风环境的实施指南（Architectural Institute of Japan,AIJ）[41],确定如图1所示的实验模型,A和B为建筑物,建筑物高度H=36 m,街谷宽度W=18 m,街谷长L=180 m,形成 H/W=2,L/W=10的典型街谷几何,风向垂直于街谷轴,上游边界到第一栋建筑物距离为144 m,下游边界到第二栋建筑物距离为540 m,计算域顶部边界到建筑物顶部距离为126 m,侧面到建筑物距离为 90 m。街谷中树木几何形态简化为长方体树冠,树冠尺寸（厚6 m、宽9 m、高12 m）、朝向和到两侧建筑物的距离如图2所示,树冠到地面距离为6 m,树冠边缘到两侧建筑物距离为4.5 m。
Fig.1 Computational domain

图1 计算域

Fig. 2 Size of rectangle crown

图2 长方体树冠尺寸

根据树木株距（Spacing,树干间距离）不同,设计2种树木空间配置方案：空间均匀和不均匀种植;其中空间均匀种植又从疏到密设置3种株距,分别是等间距20 m（Spa20m）、等间距8 m（Spa8m）和等间距6 m（Spa6m）;由株距8 m的均匀种植抽稀形成不均匀种植,如图3所示。20 m株距的均匀种植下,街谷两端的树木距街两端边缘7 m（图3（a））; 8 m株距的均匀和非均匀种植下,街谷两端的树木距街两端边缘3 m（图3（b）和（d））;6 m株距均匀种植绿化带与街谷两端齐平（图3（c））。
Fig. 3 Schematic diagram of the model

图3 模型示意图

2.2 控制方程和边界条件

假定气流为不可压缩流体,湍流模型采用标准k-ε两方程模型,采用附加源项法[42]表示树木对气流的影响。连续性方程、动量方程、k方程和ε方程的控制方程如式（1）-（4）[43]所示。
∂ u i ∂ x i = 0 （1）
u j ∂ u i ∂ x j = - 1 ρ ∂ p ∂ x i + ∂ ∂ x j υ + υ t ∂ u i ∂ x j + S i （2）
u j ∂ k ∂ x j = ∂ ∂ x j υ t σ k ∂ k ∂ x j + υ t ∂ u i ∂ x j + ∂ u j ∂ x i ∂ u i ∂ x j - ε （3）
式中：i和j为方向角标;分别代表x、y和z方向; ρ 为空气密度/（kg/m3）;ui为i方向上速度分量/（m/s）;p为流体压力/Pa; υ 运动粘度; υ t 为湍流粘度/（m2/s）; υ t =Cμ(k2/ ε );Si为i方向上动量源项;k为湍动能/（m2/s2）; ε 为湍流耗散率/（m2/s3）; C ε 1 和 C ε 2 为经验常数,分别取值1.44和1.92; σ k 为湍动能k对应的Prandtl数,取值1.0; σ ε 为耗散率 ε 对应的Prandtl数,取值1.3。
树木所占空间区域视为均匀多孔介质,它对空气的阻碍作用采用压力损失系数 λ （m-1）来表示,即强制流动条件下气流流经树木之后,上游静压与下游静压的差值,如式（5）所示。
λ = Δ p stat p dyn d = p windward - p leeward 1 2 ρ u 2 d （5）
式中：Δpstat为多孔障碍物的迎风面和背风面的静态压力差/Pa;pdyn为动压/Pa;d为多孔介质沿流动方向的厚度/m;u为平均流动速度/（m/s）。
在动量方程中加入附加源项考虑树木对气流的阻碍作用,如式（6）所示。
S i = - Δ p stat Δ x = - λ 1 2 ρv v i （6）
式中： λ 为多孔介质压力损失系数/m-1; ρ 是空气密度/（kg/m3）;v是气流速度/（m/s）;vi是在i方向上的气流速度分量/m/s;i代表x、y和z方向。
采用指数律入口风速,进口处湍流采用k和ε来表示,如式（7）-（9）所示。
U z = U z ref z z ref α （7）
k = u * 2 C μ 1 - z δ （8）
ε = u * 3 κz 1 - z δ （9）
式中：zref为参考高度,取值18 m,对应的风速为 U(zref)=4.65 m/s;a取0.3。k为湍动能/（m2/s2）; ε 为湍流耗散率/（m2/s3）;u*为摩擦速度,取值0.81 m/s; C μ 为无量纲常数,取0.09; δ 为边界层厚度,取 1.44 m; κ 为冯卡曼常数,取0.40。
进口边界定义为速度进口,Fluent中采用UDF函数模拟进口风速和湍流;出口边界为压力出口;计算域顶部和侧面为对称边界条件;计算域底部和建筑物墙壁采用无滑移壁面边界条件。对控制方程采用二阶迎风离散格式进行离散,压力与速度耦合采用SIMPLE算法。对CFD计算获得的气流流速u,用参考高度流速U（zref）进行归一化处理,生成归一化流速U+。然后,以行人呼吸高度处水平面内的气流为重点分析对象,分别对无树木的空街谷（Treeless）、20 m等间距植树（Spa20m）、8 m等间距植树（Spa8m）、6 m等间距植树（Spa6m）和不均匀植树 （8 m抽稀的Non-uniform）5个场景下街谷内行人呼吸高度水平面进行采样,得到Utreeless、Uspa20、Uspa8、Uspa6和Unon-uniform。以无树木空街谷场景Utreeless为参照,计算各个种植配置下树木阻碍/促进行人呼吸高度处气流流速的大小,如式（10）所示,树木导致局地归一化流速变化的空间分布模式如图6（a）所示。
U Δ 20 = U spa 2 0 - U treeless U Δ 8 = U spa 8 - U treeless U Δ 6 = U spa 6 - U treeless U Δnon - uniform = U non - uniform - U treeless （10）
计算在不同空间配植下树木对行人呼吸高度处局地气流影响程度,如式（11）所示,树木导致局地归一化流速变化强弱的空间分布模式如图6（b）所示。
D spa 20 = U Δ 20 U treeless × 100 % D spa 8 = U Δ 8 U tree less × 100 % D spa 6 = U Δ 6 U treeless × 100 % D non - uniform = U Δnon - uniform U treeless × 100 % （11）
根据采样数据,统计不同树木空间配置对行人呼吸高度处气流的平均影响程度,即流速变化强弱的空间平均值 D ¯ i ,如式（12）所示。
D ̅ i = ∑ j = 1 N D i j N （12）
式中：j为样本点;N为街谷内行人呼吸平面的样本数,即对应的网格数据的样本数,为134 144;i为种植配置方案,i取Spa20m、Spa8m、Spa6m和non-uniform。

3 结果与分析

3.1 实验结果

如图4所示,（a）和（b）分别为无树木场景下 y/H=0平面流线图与街谷三维流场。图4（a）表明街谷中部存在一个明显的顺时针的涡流,涡流中心在水平方向上偏向于迎风侧墙面,在垂直方向上位于街谷内上部。图4（b）表明：① 街谷两端存在角涡,角涡占据街谷两端入口且离地面越近强度越大,仅少量气流从街两端迎风建筑A的背风墙侧的上部倾斜渗入街谷;② 受建筑物A阻碍而绕行的空气在街道两端的屋顶处形成较强的流动,这些流动遇迎风墙而下洗,一部分渗流出街道两端汇入角涡;另一部分沿y方向朝背风侧墙面渗入街谷内部并爬升,从街两端渗入的这部分涡流在街谷中部相遇（相遇于图5中Treeless中的A区）,期间和街谷内部顺着迎风墙面的下沉气流交互作用,并在街谷中部形成中心偏向于迎风侧墙面的顺时针涡流。
Fig. 4 The streamlines of y/H = 0 plane and flow field inside the treeless street canyon

图4 无树木场景下y/H=0平面流线图和街谷三维流场

Fig. 5 Normalizated contour values U+ of the airflow at pedestrian breath height

图5 行人呼吸高度归一化流速云图U+

Fig. 6 The difference of normalized contour values on velocity and strength of airflow at pedestrian breath height in the street canyon with/without tree（U△20 U△8 U△6 U△non-uniform & Dspa20 Dspa8 Dspa6 Dnon-uniform）

图6 不同空间配置的植树街谷与无树空街谷内行人呼吸高度处归一化流速差异及其强度云图（U△20 U△8 U△6 U△non-uniform和Dspa20 Dspa8 Dspa6 Dnon-uniform）

5种场景下行人呼吸高度（1.5 m）的归一化流速U+云图如图5所示,本文设计的种植方案中,不同的空间配置对街谷内行人呼吸高度处的气体流动的影响程度在空间分布上差异悬殊,大致具有以下规律：① 在y/H=±1范围内：迎风侧的流速小于背风侧的流速;均匀种植树木时,流速随着树木株距的降低而减小,归一化流速≤0.05的分布范围随之逐渐增大;而非均匀种植（Non-uniform）下,归一化流速≤0.05的分布范围反而减小,归一化流速为0.1-0.15的分布范围增大;② 在y/H=±（1-2.5）范围内：均匀种植树木时,流速随着树木株距变化而变化的程度比较微小,但在靠近街谷两端的y/H=±2附近出现流速随株距减小而增强的区域（图5中的B区）,这在Spa8m和Spa6m场景下,表现的更突出;而非均匀种植（Non-uniform）下,气流趋势和无树木（Treeless）相同,归一化流速≥0.3分布范围略有减小。

3.2 实验分析

如图6所示,（a）和（b）分别是不同空间配置的植树街谷与无树空街谷内行人呼吸高度处的归一化流速差异分布图U△20、U△8、U△6、U△non-uniform 及其差异强弱分布图Dspa20、Dspa8、Dspa6、Dnon-uniform。
由图6（a）可知：均匀种植方案下,随着株距降低,流速增加/降低的波动范围增大;而非均匀种植（Non-uniform）引起的流速增加/降低波动范围是最低的,且在街两端和中部的流速增加区域也是最大的。此外,最大流速变化区域具有以下特点：① 最大流速减少区域,在Spa6m和Spa8m场景下分别位于街两端和中部附近的背风侧,即（y/H=±2.4, x/W=-0.48）和（y/H=±0.25,x/W=-0.48）;Spa20 m场景下位于街中部（y/H=0,x/W=±0.2）;非均匀种植场景（Non-uniform）下位于街中部附近,即（y/H=-0.1,x/W=0）和（y/H=1.4,x/W=0.25）。② 最大流速增加区域,在Spa6m和Spa8m场景下分别位于街两端附近且偏向迎风侧,即（y/H=±2.1,x/W=0.2）和（y/H=±2.5,x/W=0.1）,Spa20m场景下位于街两端附近（y/H=±2.1, x/W=0.1）,非均匀种植场景（Non-uniform）下位于街中部附近（y/H=-0.25,x/W=0.4）和（y/H=-0.7,x/W=0.25）。
观察图6（b）可知：流速减少变化最强区域都位于街中心,在Spa6m和Spa8m场景下偏向背风侧（y/H=0,x/W=-0.25）;Spa20m和Non-uniform场景下偏向迎风侧（y/H=0,x/W=0.25）。流速增加变化最强区域都位于靠近街中部的（y/H=±0.6,x/W=0.25）,但是,均匀种树的Spa6m和Spa8m还在街两端的（y/H=±2.4,x/H=0.1）附近另有流速的高增强区。结合图5和图6可见,非均匀种树（Non-uniform）同时减少了街道两端“风口效应”和街道中部“风影效应”的区域,改善了整个街谷区域的风环境。这个结果的可能的原因是,对于无树木的街谷,受两侧建筑物的阻碍,形成以内部顺时针漩涡系统和角落漩涡为主的街谷气流模式。在街谷内部,树木的增加使得气流流向波动增大,树木降低了风速并增加了湍流;随着树木间距的缩小,气流被压制透过树冠间的细小自由空间,形成更多小的涡流,幅度逐渐衰减,使得街谷内部的风速进一步减小。在街谷两端,角涡形成强烈的湍流场渗入街谷,角涡渗流受树木阻碍而更具间歇性,使得气流在街谷两端形成激烈的窜效应。本文实验的非均匀种树案例,在街谷两端配置树木,街谷中部不种树,而且街谷两端预留足够的自由空间让角涡渗入街谷中部,促使街谷内部的垂直漩涡和两端的水平角涡充分发展,增强湍流和垂直交换,改善了整个街谷区域的风环境。
图7为5种场景下行人呼吸高度的流线图,在Spa6m和Spa8m场景下,树木减弱两端渗入的气流,在街道中部,两端渗入的气流不再有足够的动能从迎风墙涡旋至背风墙,汇入从迎风侧下沉的屋顶气流,然后流向背风侧再爬升,使街道中部的迎风侧形成流速低值区;无树的空街谷和稀疏种树的Spa20m场景下,街谷两端渗入的气流在街谷中部相遇时仍具有较大的动能,影响从迎风侧下沉的屋顶气流并形成小风涡。不均匀种植的树木对气流的影响则介于二者之间。
Fig. 7 The streamlines of pedestrian airflow with different spatial-distribution tree planting

图7 不同种植方案下行人呼吸高度的流线图

统计不同空间配置下树木对行人呼吸高度处气流的平均影响程度 D ¯ i ,即,流速变化强弱的空间平均值,结果如表1所示。按照式（13）计算不均匀植树流速比其对照案例（均匀植树Spa8m方案）的整体增强/减弱程度。
Tab.1 Average enhancement D¯i of pedestrian airflow with different spatial-distribution-tree planting

表1 不同树木种植方案的行人高度平均气流增强值D¯i

种植方案 平均气流增强值D¯i/%
Spa20m -10.73
Spa8m -19.31
Spa6m -16.14
Non-uniform 1.25
D ̅ non - uniform - D ̅ spa 8 D ̅ spa 8 × 100 % （13）
结果如下：① 均匀植树的3种情景都导致了行人呼吸高度处流速整体降低, D ¯ i 为负数,树木对气流主要起阻碍作用。而不均匀植树时, D ¯ i 为正数,表明树木提升了行人呼吸高度处的整体流速。不同空间配置下的树木对行人呼吸高度处气流的影响强度不同,阻碍作用从大到小的顺序为Spa8m>Spa6m>Spa20m>Non-uniform;对应的平均气流增强指标顺序为 D ¯ spa 8 （-19.31%）< D ¯ spa 6 （-16.14%）< D ¯ spa 20 （-10.73%）< D ¯ non - uniform （1.25%）。② 对比不均匀和均匀的种植方案,不均匀植树下树木对街谷内行人呼吸平面的气流有积极促进作用,它有效减少了街道两端“风口效应”和街道中部“风影效应”的区域,改善了整个街谷区域的风环境,流速比其对照案例（均匀植树Spa8m方案）整体增强了106.49%。

4 结论

街谷是组成城市的基本单元,大多数研究表明,与无树木种植的情况相比,街谷中树木的存在,对局地空气质量带来负面影响[30,31,32];然而,街谷尺度的城市大气环境对局地条件非常敏感,周围粗糙元素（建筑物和树木等）的局部配置（几何形态、空间簇集、密度）将引起局地流动和湍流在垂直和水平方向上表现出强烈的空间变化[11,30,44],本文模拟的树木不同空间配置对街谷行人呼吸高度气流影响的实验结果说明证实了现有文献所报告的结论。
虽然实际街谷的配置更加复杂且不易提供通用建议[30,45],通过谨慎的景观设计,对街谷四周粗糙元素的匹配和平衡有可能改变污染物运输和扩散模式并改善建筑环境中的空气质量[30,44]。因此,掌握街谷内空气流动模式对于研究街谷内污染物扩散规律、评估行人高度风环境、改善居住条件以及节能减排具有重要意义。本文从空气动力学角度,应用数值模拟方法研究H/W=2的典型深街谷几何内,树木的不同空间配植方案对行人呼吸高度气流的影响,从实验结果和分析得如下结论：
（1）均匀树木种植对街谷内行人呼吸高度的气流起到阻碍作用,不均匀种植则有效提升整体流速。四种空间配植方案下树木对气流的影响程度不同,阻碍作用从大到小的顺序为均匀间距8 m（Spa8m）>均匀间距6 m （Spa6m）>均匀间距20 m （Spa20m）>不均匀配植（Non-uniform）;对应的平均气流增强指标顺序为 D ¯ spa 8 （-19.31%）< D ¯ spa 6 （-16.14%）< D ¯ spa 20 （-10.73%）< D ¯ non - uniform （1.25%）。
（2）对比不均匀和均匀的种植方案,不均匀植树下树木对街谷内行人呼吸平面的气流有积极促进作用,它有效减少了街道两端“风口效应”和街道中部“风影效应”的区域,改善了整个街谷区域的风环境,流速比其对照案例（均匀植树Spa8m方案）整体增强了106.49%。
（3）不同空间配置下树木对街谷内行人呼吸高度处局地气流的影响强弱在空间分布模式上差异悬殊,合理空间配置的树木能够改善街谷内部的行人风环境,在既有城市建筑布局条件下,利用树木等城市绿化措施能够经济且有效地改善城市的行人风环境,对缓解污染扩散、疾病传播等问题具有参考意义。
本文分析树木的空间配植差异对典型深街谷几何内行人风环境的影响,然而,街谷内的气流运动还包括受到太阳辐射引起热对流,真实城市建筑高低错落布局多样,这些都强烈影响着城市行人风环境,后续将开展相关方面的研究。

The authors have declared that no competing interests exist.

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Abhijith K V, Kumar P, Gallagher J, et al.Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments: A review[J]. Atmospheric Environment, 2017,162:71-86.

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[31]
Janhäll S.Review on urban vegetation and particle air pollution: Deposition and dispersion[J]. Atmospheric Environment, 2015,105:130-137.61Combining deposition and dispersion helps designing urban vegetation related to air quality.61The dilution of emissions with clean air from aloft is crucial; limit high urban vegetation.61High concentrations of air pollutants increase deposition; vegetation should be close to the source.61Air floating above, and not through, vegetation barriers is not filtered; decides barrier porosity.61Differently designed vegetation catch different particle sizes.

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[32]
Vardoulakis S, Fisher B E A, Pericleous K, et al. Modelling air quality in street canyons: A review[J]. Atmospheric Environment, 2003,37(2):155-182.High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air quality and support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition esuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models. A range of monitoring techniques is used to measure pollutant concentrations in urban streets. Point measurement methods (continuous monitoring, passive and active pre-concentration sampling, grab sampling) are available for gaseous pollutants. A number of sampling techniques (mainly based on filtration and impaction) can be used to obtain mass concentration, size distribution and chemical composition of particles. A combination of different sampling/monitoring techniques is often adopted in experimental studies. Relatively simple mathematical models have usually been used in association with field measurements to obtain and interpret time series of pollutant concentrations at a limited number of receptor locations in street canyons. On the other hand, advanced numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy.

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[33]
Krayenhoff E S, Santiago J L, Martilli A, et al.Parametrization of drag and turbulence for urban neighbourhoods with trees[J]. Boundary-Layer Meteorology, 2015,156(2):157-189.Urban canopy parametrizations designed to be coupled with mesoscale models must predict the integrated effect of urban obstacles on the flow at each height in the canopy. To assess these neighbourhood-scale effects, results of microscale simulations may be horizontally-averaged. Obstacle-resolving computational fluid dynamics (CFD) simulations of neutrally-stratified flow through canopies of blocks (buildings) with varying distributions and densities of porous media (tree foliage) are conducted, and the spatially-averaged impacts on the flow of these building-tree combinations are assessed. The accuracy with which a one-dimensional (column) model with a one-equation ( \(k\) \(l\) ) turbulence scheme represents spatially-averaged CFD results is evaluated. Individual physical mechanisms by which trees and buildings affect flow in the column model are evaluated in terms of relative importance. For the treed urban configurations considered, effects of buildings and trees may be considered independently. Building drag coefficients and length scale effects need not be altered due to the presence of tree foliage; therefore, parametrization of spatially-averaged flow through urban neighbourhoods with trees is greatly simplified. The new parametrization includes only source and sink terms significant for the prediction of spatially-averaged flow profiles: momentum drag due to buildings and trees (and the associated wake production of turbulent kinetic energy), modification of length scales by buildings, and enhanced dissipation of turbulent kinetic energy due to the small scale of tree foliage elements. Coefficients for the Santiago and Martilli (Boundary-Layer Meteorol 137: 417 439, 2010 ) parametrization of building drag coefficients and length scales are revised. Inclusion of foliage terms from the new parametrization in addition to the Santiago and Martilli building terms reduces root-mean-square difference (RMSD) of the column model streamwise velocity component and turbulent kinetic energy relative to the CFD model by 89 % in the canopy and 71 % above the canopy on average for the highest leaf area density scenarios tested: \(0.50\hbox { m}^{2}~\hbox {m}^{-3}\) . RMSD values with the new parametrization are less than 20 % of mean layer magnitude for the streamwise velocity component within and above the canopy, and for above-canopy turbulent kinetic energy; RMSD values for within-canopy turbulent kinetic energy are negligible for most scenarios. The foliage-related portion of the new parametrization is required for scenarios with tree foliage of equal or greater height than the buildings, and for scenarios with foliage below roof height for building plan area densities less than approximately 0.25.

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[34]
Ai Z T, Mak C M.CFD simulation of flow in a long street canyon under a perpendicular wind direction: Evaluation of three computational settings[J]. Building and Environment, 2017,114:293-306.

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[35]
Castro I P, Xie Z T, Fuka V, et al.Measurements and computations of flow in an urban street system[J]. Boundary-Layer Meteorology, 2017,162(2):207-230.We present results from laboratory and computational experiments on the turbulent flow over an array of rectangular blocks modelling a typical, asymmetric urban canopy at various orientations to the a

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[36]
Wolf-Grosse T, Esau I, Reuder J.Sensitivity of local air quality to the interplay between small- and large-scale circulations: a Large Eddy Simulation study[J]. Atmospheric Chemistry and Physics, 2017,17(11):7261-7276.

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Li H, Cui G, Zhang Z.A New Scheme for the simulation of microscale flow and dispersion in urban areas by coupling large-eddy simulation with mesoscale models[J]. Boundary-Layer Meteorology, 2018,167(1):145-170.

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[38]
Blocken B, Janssen W D, Hooff T V.CFD simulation for pedestrian wind comfort and wind safety in urban areas: General decision framework and case study for the Eindhoven University campus[J]. Environmental Modelling & Software, 2012,30:15-34.

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CODASE. (2008)Concentration data of street canyon, internet database[ED/OL]. .

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Franke J, Hellsten A, Schlünzen H, et al.Best practice guideline for the CFD simulation of flows in the urban environment[M]. Belgium: Cost Office Brussels, 2007.

[41]
Tominaga Y, Mochida A, Yoshie R, et al.AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2008,96(10-11):1749-1761.Significant improvements of computer facilities and computational fluid dynamics (CFD) software in recent years have enabled prediction and assessment of the pedestrian wind environment around buildings in the design stage. Therefore, guidelines are required that summarize important points in using the CFD technique for this purpose. This paper describes guidelines proposed by the Working Group of the Architectural Institute of Japan (AIJ). The feature of these guidelines is that they are based on cross-comparison between CFD predictions, wind tunnel test results and field measurements for seven test cases used to investigate the influence of many kinds of computational conditions for various flow fields.

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[42]
Gromke C.A vegetation modeling concept for Building and Environmental Aerodynamics wind tunnel tests and its application in pollutant dispersion studies[J]. Environmental Pollution, 2011,159(8-9):2094-2099.

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张兆顺,崔桂香,许春晓.湍流理论与模拟[M].北京:清华大学出版社,2005.

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Giometto M G, Christen A, Egli P E, et al.Effects of trees on mean wind, turbulence and momentum exchange within and above a real urban environment[J]. Advances in Water Resources, 2017,106:154-168.

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Giometto M G, Christen A, Meneveau C, et al.Spatial characteristics of roughness sublayer mean flow and turbulence over a realistic urban surface[J]. Boundary-Layer Meteorology, 2016,160(3):425-452.

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