Evaluation of impacts of trees on PM2.5 dispersion in urban streets

Atmospheric Environment 99 (2014) 277e287

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Evaluation of impacts of trees on PM2.5 dispersion in urban streets Sijia Jin a, b, Jiankang Guo a, Stephen Wheeler b, Liyan Kan a, Shengquan Che a, * a b

Department of Landscape Architecture, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China Department of Human Ecology, University of California, Davis, CA 95616, USA

h i g h l i g h t s
PM2.5 concentrations in urban street canyons with or without trees were compared.
The decrease of PM2.5 reduction rates was quantified.
Canopy density and leaf area index were identified as key predictors for PM2.5.
An optimized tree-planting strategy was suggested for a minimum PM2.5 accumulation.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 May 2014 Received in revised form 28 September 2014 Accepted 2 October 2014 Available online 2 October 2014

Reducing airborne particulate matter (PM), especially PM2.5 (PM with aerodynamic diameters of 2.5 mm or less), in urban street canyons is critical to the health of central city population. Tree-planting in urban street canyons is a double-edged sword, providing landscape benefits while inevitably resulting in PM2.5 concentrating at street level, thus showing negative environmental effects. Thereby, it is necessary to quantify the impact of trees on PM2.5 dispersion and obtain the optimum structure of street trees for minimizing the PM2.5 concentration in street canyons. However, most of the previous findings in this field were derived from wind tunnel or numerical simulation rather than on-site measuring data. In this study, a seasonal investigation was performed in six typical street canyons in the residential area of central Shanghai, which has been suffering from haze pollution while having large numbers of green streets. We monitored and measured PM2.5 concentrations at five heights, structural parameters of street trees and weather. For tree-free street canyons, declining PM2.5 concentrations were found with increasing height. However, in presence of trees the reduction rate of PM2.5 concentrations was less pronounced, and for some cases, the concentrations even increased at the top of street canyons, indicating tree canopies are trapping PM2.5. To quantify the decrease of PM2.5 reduction rate, we developed the attenuation coefficient of PM2.5 (PMAC). The wind speed was significantly lower in street canyons with trees than in tree-free ones. A mixed-effects model indicated that canopy density (CD), leaf area index (LAI), rate of change of wind speed were the most significant predictors influencing PMAC. Further regression analysis showed that in order to balance both environmental and landscape benefits of green streets, the optimum range of CD and LAI was 50%e60% and 1.5e2.0 respectively. We concluded by suggesting an optimized tree-planting pattern and discussing strategies for a better green streets planning and pruning. © 2014 Elsevier Ltd. All rights reserved.

Keywords: PM2.5 Air pollution Urban street canyon Canopy density Leaf area index Wind speed Green street planning

1. Introduction Traffic emissions generally constitute the major source for air pollution in urban areas. Particulate matter (PM) is one of the most important components in traffic emissions. Resulting from

* Corresponding author. 800# Dongchuan Road, Shanghai 200240, PR China. E-mail address: [email protected] (S. Che). http://dx.doi.org/10.1016/j.atmosenv.2014.10.002 1352-2310/© 2014 Elsevier Ltd. All rights reserved.

combustion process of fossil fuels in vehicles and road dust resuspension, particulates contribute to the deterioration of air quality, and have a large adverse impact on the health of central city population. Recently epidemiology studies have found that PM, especially PM2.5 (PM with aerodynamic diameters of 2.5 mm or less), can induce respiratory and lung disease, immune-system problems and even premature death (Heal et al., 2012; Zhang et al., 2012) and affects more people than any other airborne pollutants.

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Critical situations usually emerge in densely built-up city areas with street canyons suffering from poor ventilation and high pollutant concentrations. Particularly in urban street canyons with avenues of trees, large crowns occupy considerable space of street canyons and separate the lower street level from the upper roof level (Gromke et al., 2008). Therefore, tree planting may hinder the ambient air exchange and vehicle exhaust dispersion, and increase PM2.5 concentration in the lower region. So research on the influence of tree canopy on PM2.5 concentration has great significance in terms of environmental condition in urban street canyon. As an international metropolis, Shanghai has large population and intensive urban land use. Green space is fairly limited in central city area. To increase the green area, the municipal government has pursued a “green streets” program for several decades. In this context, “green streets” refer to urban secondary roads or minor roads with a street tree canopy density (CD, ratio of the projected ground area of tree crowns to the street canyon ground area, %) greater than approximate 50% (Zhang, 2012). The data of 50% is mainly based on the average canopy density of the existing green streets in summer in central Shanghai. In addition, designers consider “green streets” to be an integrated system consisting of road, street trees, architecture, and public facilities, typically in the form of high-density street canyons. Thus, besides transportation, ecological benefits, the green street also provides important aesthetic and recreational functions. However, since tree canopies intensify the accumulation of airborne PM2.5 and increase the health risk of pedestrians and residents in street canyons, it is important to study the dispersion of PM2.5 in street canyons with different street tree structures to find optimal tree-planting strategies, and to maximize the comprehensive benefits of the green street. A great deal of literature exists on atmospheric quality in treefree street canyons. Air flow field and turbulence in street canyons with different geometrical characteristics, traffic exhaust concentrations and temporal-spatial distribution, as well as trafficinduced turbulence, model studies and photochemical transformation of air pollutants have been investigated in field studies(Bady et al., 2011; Chan and Kwok, 2000; Kourtidis et al., 2002; Kukkonen et al., 2001; Richmond-Bryant and Reff, 2012; Weber et al., 2006; Wehner et al., 2002; Xie et al., 2003), laboratory windtunnel (Carpentieri et al., 2012; Kastner-Klein and Plate, 1999; Pavageau and Schatzmann, 1999; Uehara et al., 2000) and numerical stimulations (Gidhagen et al., 2004; Gousseau et al., 2011; Karakitsios et al., 2006; Nikolova et al., 2011; Solazzo et al., 2009; Xie and Castro, 2009; Zhang et al., 2011b). Further comprehensive overviews on these topics are given in reviews of Vardoulakis et al. (Vardoulakis et al., 2003), Ahmad et al.(Ahmad et al., 2005), Holmes (Holmes and Morawska, 2006) and Carpentieri et al. (Carpentieri et al., 2011). Moreover, PM2.5 measurement and pedestrian exposure to air pollutants by means of field investigation have been addressed in quite a few articles (Chan and Kwok, 2000; Foster and Kumar, 2011; Huang et al., 2007; Kaur et al., 2005; Kristensson et al., 2004; Li et al., 2007; Ye et al., 2003a,b; Zhang et al., 2006). In the last few years, flow and pollutants dispersion in street canyons with trees have been becoming new interests of air quality studies. By contrast, relatively little research can be found in literature. Gromke et al. (Gromke et al., 2008; Gromke and Ruck, 2007, 2009, 2012) investigated the flow and concentration of pollutants in an isolated street canyon with avenues of trees by means of wind tunnel measurement and CFD simulation. They found a decreasing pollutant concentration with increasing height at the facades in the middle of street canyon. They also found that increasing crown diameters and decreasing the tree spacing both led to a noticeable concentration increase at the street canyon wall. For some cases, a variation of trunk heights resulted in a modification of the

concentration pattern on the walls. In another study, Buccolieri et al.(Buccolieri et al., 2011) showed the combined influence of building morphology and vegetation on flow and dispersion and assessed the effect of vegetation on local concentration levels. It was believed that for tree-free street canyons under inclined wind directions the larger the aspect ratio the lower the street-level concentration. However, in presence of trees the expected reduction of street-level concentration with aspect ratio was less pronounced. Balczo et al. performed numerical simulations of the impact of tree planting on pollutant dispersion in street canyons by using the CFD code MISKAM. They found the presence of trees lead to increased pollutant concentration inside the canyon, especially  , 2009). Similar results were obtained in the upstream side (Balczo several other pieces of researches (Gu et al., 2010; Salim et al., 2011; Wania et al., 2012). The above findings were mostly derived from wind tunnel or numerical simulation rather than field data. Due to various factors that may influence pollutant dispersion, wind tunnel or numerical simulation could not reliably reproduce real processes occurred in street canyons with trees. Only Salmond et al. presented data from a field study undertaken in New Zealand to determine the local impact of deciduous tree canopies on the distribution of the oxides of nitrogen within a street canyon. An increase in concentrations on the leeward side was observed during leaf-on relative to leaf-off conditions (Salmond, 2013). Hofman et al. (Hofman et al., 2012) demonstrated that biomagnetic leaf monitoring of crown deposited particles could be used to estimate ambient PM concentration and assess its spatial variations. In this context, the impact of treeplanting on the spatial PM2.5 dispersion was seldom quantified by field experiments. The overall goal of this paper is to clarify the temporal-spatial distribution of PM2.5 concentration in street canyons, and the impact of street trees on PM2.5 concentration variation compared with street canyons without trees through direct measurement. For this reason, we performed a seasonal study in six typical street canyons with trees in the residential area of central Shanghai. We monitored the concentrations of PM2.5 at five different heights during wind direction perpendicular to street canyons. In addition, structural characteristics of street trees, instantaneous wind velocity, temperature and humidity were also measured to identify the crucial predictor that may affect pollutant contents. The practical intention of all these investigations is to provide city planners and designers with guidelines on how to plant trees in urban street canyons in regard to air quality management. Developing the optimum strategy is of great importance for the green street planning and management in cities in China and many other parts of the world where haze pollution has become a serious problem. 2. Materials and methods 2.1. Study area Shanghai, 31120 north latitude, 120 300 east longitude, has a subtropical monsoon climate and experiences four distinct seasons. The prevailing wind direction is southeast in spring and summer, northwest in autumn and winter. Annual mean wind speed in central Shanghai is 2.8 m/s (at 10 m above ground level). Shanghai covers a total area of 6340.5 km2, of which 289 km2 is central area. The population was about 23.5 million including 7million within central area at the end of 2011. 2.2. Sampling sites We conducted a preliminary field campaign (MarcheOctober 2011) to get basic information about the green streets in the

S. Jin et al. / Atmospheric Environment 99 (2014) 277e287

residential area of central Shanghai and to select proper sampling sites for the street canyon experiment. Results showed that Platanus acerifolia (London plane tree) and Cinnamomum camphora (Camphor tree) are the most common street tree species, accounting for 85.9% of the total investigated street trees. In addition, the most common green street design in central Shanghai consists of two traffic lanes with one row of street trees at each curbside. We chose two streets with the above-mentioned structure for street canyon field experiments (Fig. 1). One is street with Cinnamomum camphora and the other is with Platanus acerifolia. Both streets run in a northeast-southwest direction and do not have industrial facilities located within 3 km (ensuring that local pollution from nearby facilities does not affect measurements). Three street segments were selected for each sampling street (Fig. 1). The six street segments are symmetric street canyons, and have similar length, aspect ratio H/W (building height H to street width W) and architecture form (residential buildings with 4e5 floors for both sides, commonly built in 1980e2000 in Shanghai), as shown in Table 1. All the segments were continuous with no access to other place. The street trees were treated with different pruning intensities (strong, weak and null) for every segment in early spring 2012 (Table 1). Various pruning treatments would lead to different canopy coverage in four seasons. For each sampling street, we used one additional street segment without any trees as control (Table 1). The two control segments were located nearby the test segments and featured similar conditions, but with no tree canopies. 2.3. PM monitoring: instruments, sampling points and methods The Aerocet 531(Met One Instrument Inc. 2003), a photometric sampler, was used to collect PM2.5 data. It is an automatic instrument that can monitor the PM mass of particles 1, 2.5, 7, 10 and 100 mm in aerodynamic diameter (PM1, PM2.5, PM7, PM10 and TSP). The source light travels at a right angle to the collection system and detector, and the instrument uses the information from the scattered particles to calculate a mass per unit volume. A mean

279

Table 1 Species, pruning treatments and street canyon geometry of eight street segments. Street Species segment

H/ Pruning Length Width Building intensity (m) (m) average height W (m)

1

strong

223

17.0

12.5

0.74

weak

220

16.6

12.0

0.72

none

212

17.5

13.5

0.77

strong weak none

205 214 210 160

15.4 15.2 16.0 17.0

14.5 14.0 15.5 13.0

0.94 0.92 0.97 0.76

165

15.5

14.0

0.90

2 3 4 5 6

Cinnamomum camphora Cinnamomum camphora Cinnamomum camphora Platanus acerifolia Platanus acerifolia Platanus acerifolia Null, reference for street segment 1,2,3 Null, reference for street segment 4,5,6

particle diameter is calculated for each of five different sizes. This mean particle diameter is used to calculate a volume (m3), which is then multiplied by the number of particles and then a generic density (mg m3) that is a conglomeration of typical aerosols. The resulting mass is divided by the volume of air sampled for a mass per unit volume measurement (mg m3). Although the sensor is factory calibrated using PLS (polystyrene latex) calibration particles, the concentration measurements of any light scattering instrument can be influenced by an increase in relative humidity (McMurry, 2000). Therefore, a re-calibration was performed by using an experimental relationship among relative humidity, gravimetric and photometric measurement. PM2.5 data were calibrated as C/C0 ¼ 1 þ 0.25 RH2/(1RH); where C and C0 are the average gravimetric and photometric mass concentration respectively, and RH is relative humidity (Foster and Kumar, 2011; Lowenthal et al., 1995; Ramachandran et al., 2003). Moreover, a ball flow meter (Part number 9801) was used to calibrate the flow rate (2.83 l/min ± 5%) of Aerocet 531.

Fig. 1. Location of two sampling streets and six streets segments in Shanghai. The number 1 to 6 represent six street segments in sampling streets. The five-pointed star marks the sampling section which is the center cross section of each street segment.

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Fig. 2. (a) Schematic of the street segments, and sampling point distribution. (b) Image of street segment 2 in spring. (c) Image of street segment 6 in spring.

We placed PM2.5 sampling points at the center cross section of each street segment to avoid edge effects (Hofman et al., 2012; Wania et al., 2012). Since the bulk of traffic related pollutants are accumulated on the leeward side of street canyons (Chan et al., 2002; Gromke et al., 2008), and tree planting obviously increases concentrations on the leeward side and slightly lowers concentration on the windward side (Gromke and Ruck, 2009), we set sampling points at the leeward sidewalk. To be specific, four sampling points were chosen at varying vertical heights (1.5 m, onset of tree crown, middle of tree crown, top of tree crown) for each cross section. An additional sampling point was located at 0.3 m above the ground in the middle of the road (Fig. 2). So in all there were forty sampling points in eight street segments (six segments with trees and two reference segments). PM2.5 was monitored in four months/seasons (January, April, July and October 2012). We selected clear days (wind speed < 2 m/ s) with similar weather conditions in each month to conduct the

measurements. In view of the prevailing wind direction in Shanghai, sampling dates with southeast wind direction were chosen for spring and summer campaigns, while during the autumn and winter campaigns the wind direction was northwest. Thus, the four sampling points were on the south side of the road in spring and summer, and north curbside in autumn and winter. The measurement for both experimental street segments and their corresponding reference segments started at 7:00 a.m., ended at 19:00 p.m. and was made at two-hourly intervals during this time period. We used six instruments (Aerocet 531) to guarantee the measurement for ten sampling points within two hours. One sampling event took two minutes to complete in mass concentration mode. PM2.5 data were monitored once every six minutes for one sampling point, and were repeated 15 times during two hours to acquire the average values. Meanwhile, we randomly recorded 30 instantaneous wind speeds by using an anemograph (testo 405V1)within every 2-min continuous sampling at the same location

Table 2 Seasonal structures of street tree in six plots.

Spring 1 Spring 2 Spring 3 Spring 4 Spring 5 Spring 6 Summer 1 Summer 2 Summer 3 Summer 4 Summer 5 Summer 6 Autumn 1 Autumn 2 Autumn 3 Autumn 4 Autumn 5 Autumn 6 Winter 1 Winter 2 Winter 3 Winter 4 Winter 5 Winter 6

Species

DBH(cm)

H(m)

CW(m)

CBH(m)

HLL(m)

TS(m)

LAI

CD(%)

1 1 1 2 2 2 1 1 1 2 2 2 1 1 1 2 2 2 1 1 1 2 2 2

23.50 23.80 25.64 33.29 36.16 33.39 23.54 23.82 25.67 33.33 36.36 33.45 23.57 23.88 25.71 33.38 36.76 33.49 23.59 23.89 25.72 33.39 36.86 33.49

11.09 11.56 11.41 12.42 11.19 15.04 11.11 11.70 11.52 12.72 11.39 15.20 11.14 11.69 11.57 12.65 11.31 15.24 11.17 11.76 11.61 12.72 11.19 15.24

6.49 6.67 7.01 7.14 8.04 10.32 6.55 6.78 7.22 7.68 8.52 10.72 6.60 6.90 7.15 7.93 8.77 10.85 6.69 6.90 7.01 7.92 8.73 10.80

3.08 3.14 3.32 3.08 3.53 3.57 3.08 3.14 3.32 3.08 3.53 3.57 3.08 3.14 3.32 3.08 3.53 3.57 3.08 3.14 3.32 3.08 3.53 3.57

3.62 3.80 4.13 4.01 4.18 4.25 3.55 3.75 4.03 3.88 4.00 4.04 3.56 3.72 4.02 3.95 4.03 4.10 3.61 3.89 4.13 4.08 4.43 4.35

6.48 6.12 4.95 6.67 5.95 6.46 6.48 6.12 4.95 6.67 5.95 6.46 6.48 6.12 4.95 6.67 5.95 6.46 6.48 6.12 4.95 6.67 5.95 6.46

1.09 2.51 3.60 1.23 1.83 2.44 1.46 2.94 5.01 2.02 3.16 5.22 1.14 2.72 3.95 1.53 2.40 3.34 1.37 2.82 4.06 0.02 0 0.01

42.01 67.89 82.14 35.08 46.24 60.11 51.3 75.01 89.78 60.12 79.04 93.79 45.02 70.99 86.03 50.10 70.69 82.69 47.21 73.01 87.91 1.02 0.99 1.89

1eCinnamomum camphora; 2ePlatanus acerifolia; DBHediameter at breast height; Heheight; CWecrown width; CBHeclear bole height; HLLeheight of lowest leaves; TSetree spacing; LAIeleaf area index; CDecanopy density.

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as every PM2.5 sampling. The average readings of 15 times samplings were computed as the average wind speeds at the two-hour intervals. In addition, we simultaneously measured temperature and relative humidity with a thermo-hygrometer (testo 610) at each location. It took 90 min to complete one set at all the ten sampling points. Six sets of the measurements (six time periods) were performed for each day, and two days of all the measurements were conducted to obtain the average values for one street segment. So in every season, two days of measurements were done for each street segment with trees, and six days of measurements were done for each reference street segment.

in Table 1. The CBH and HLL values range from 3.08 to 4.43 m. Therefore, the four sampling heights at the sidewalk were uniformly set at 1.5 m, 4 m, 8 m and 12 m, respectively. The obtained LAI and CD vary greatly among different plots and seasons due to different pruning treatments in early spring, 2012. LAI and CD of the last three rows (Winter 4, Winter 5, Winter 6) are both close to zero because Platanus acerifolia is a deciduous species.

2.4. Street trees survey

Fig. 3 demonstrates the vertical variation of PM2.5 from 0.3 m above the ground to the top of tree crown in four seasons. Since measurements were done in each reference site three times (six days) in every season, as described before, we got six groups of reference data. Basically, the diurnal average PM2.5 decreases with increasing height in reference sites, which is in agreement with much former research (Gromke and Ruck, 2007; Hofman et al., 2012; Li et al., 2007; Vardoulakis et al., 2002; Weber et al., 2006). The average attenuation rate is 21.5% ± 4.32% (standard deviation) among all reference sites. The attenuation rates of sampling sites (street canyons with trees) are generally lower than their corresponding reference streets (without trees) during spring and summer. In autumn and winter, there is no obvious attenuation trend, by contrast, the diurnal average PM2.5 increases with the increment of height in the sampling sites of Autumn 2, 3, 5, 6 and Winter 2, 3. As shown in Fig. 3, the diurnal average PM2.5 is higher in sampling sites than in reference sites at identical heights for most cases. The average PM2.5 values and standard deviations of six sections for each season have been calculated and presented in Table 3. The seasonal average PM2.5 at 12 m height varies from 30 mg m3 to 70 mg m3 in this study, which is in the same range as the data published by Shanghai Environmental Monitoring Center during the clear days in 2012 (Most of their monitoring stations are located on the top of buildings with 4e6 floor). In addition, the PM2.5 is also in the similar range with data in the previous studies (Feng, 2009; Ye et al., 2003a,b). Paired-samples T tests were performed between sampling sites and reference sites at the same sampling height, and the significant level values (p) of T test are listed in Table 3. This analysis shows that in most cases (4 of 5) there is no marked difference of the concentrations at 0.3 m height between sampling sites and reference sites, which means the reference sites could serve as a control since they have a statistically similar initial traffic and air quality condition. The test also shows significant differences of PM2.5 between the sampling sites and reference sites at the rest four heights in most instances (15 of 20). The results imply the diurnal average PM2.5 is statistically higher in street canyons with trees planted than tree-free street canyons, especially at relatively higher positions (8 m and 12 m, p < 0.001). However, three street segments (Winter 4, 5, 6) are exceptions since as deciduous species, Platanus acerifolia could not impact the dispersion of air pollutants in winter. The PM2.5 concentrations are mostly higher in winter and spring while get lower in summer and autumn in Table 3. Concentration variation in different seasons is probably attributed to the fact that decreasing temperature results in higher pollutant concentrations, which had been proved by former studies (Gehrig and Buchmann, 2003; Kourtidis et al., 2002; Ye et al., 2003a,b; Zhang et al., 2011a). Relatively higher concentration in winter is mainly due to high air pressure, relatively stable atmospheric conditions and high air humidity. Residents in Shanghai don't have domestic heating in winter.

We investigated the structure of street tree populations for each experimental street segment in four seasons. Eight main variables were collected: tree species, number, diameter at breast height (DBH, cm), height (H, m), crown width (CW, m), clear bole height (CBH, m), height of lowest leaves (HLL, m), tree spacing (TS, m). In addition, the LAI-2200 Plant Canopy Analyzer (LI-COR Inc., Lincoln, Nebraska) was used to estimate the leaf area index (LAI) and canopy density (CD). In this study, we took all street trees in one street canyon segment as a plant community. Thus, LAI refers to the value of the plant community, and equals to the one-sided leaf area of all street trees divided by the ground surface area of street canyon segment, m2/m2. Similarly, CD equals to the projected ground area of tree crowns divided by the ground surface area of street canyon segment, %. 2.5. Data analysis We used a mixed-effects model to analyze the influence of three groups of factors on PM2.5 dispersion. The model gives a single overall test of the usefulness of the given explanatory variables, without focusing on individual levels. In addition, this technique allows for analysis of multidimensional data with both fixed and random effects (Zuur et al., 2009). The PM2.5 concentration at 0.3 m heights was used as initial pollution factor. The weather factors consisted of temperature, relative humidity and rate of change of wind speed. The vegetation factors included species, H, CW, LAI, CD and pruning intensity. So there are 10 variables within 3 groups of factors that need to be evaluated whether it significantly accounted for the PM2.5 dispersion. The 2-h average values of all the 10 variables were used in this model. The sample size is 288 in all the different sampling time, street segments and seasons. We converted all data to a normalized formation by using STANDARDIZE function in Excel, and ran mixed-effects model on SPSS 20.0 (Pallant, 2010). Street segment was included as the random effect; variables of three groups of factors were designated as the fixed effects; a created variable “PMAC” indicating PM2.5 dispersion pattern was chosen as dependent variable. All fixed effects were fitted to the data and the statistical significance of the parameters was evaluated with likelihood ratio tests; nonsignificant effects were sequentially removed from the model until all effects were significant (Cavanagh et al., 2009). In addition, correlations analyses were performed on SPSS 20.0 (Pallant, 2010). 3. Results 3.1. Structures of street trees during four seasons The structure characteristics of street trees in six experimental plots are listed in Table 2. The height of street tree vary from 11.09 to 15.24 m, which is in the same range with building height, as seen

3.2. Vertical distribution of PM2.5 in sampling sites and reference sites

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Fig. 3. Vertical distribution of PM2.5 in sampling sites and reference sites. The number 1 to 6 represent six street segments in sampling streets.

Since vertical attenuation of PM2.5 in these sites are found (Fig. 3), in order to quantify the impact of trees and other factors on PM2.5 vertical dispersion, the attenuation coefficient (PMAC) at each street section was calculated as follows:

PMAC ¼

0 C00  C12 C  C12  100%  0  100% 0 c0 c0

PMAC e attenuation coefficient, means the impact of three groups of factors on PM2.5 dispersion rate C0 e PM2.5 concentration at 0.3 m in the sampling site C12 e PM2.5 concentration at 12 m in the sampling site C00 e PM2.5 concentration at 0.3 m in the reference site C120 e PM2.5 concentration at 12 m in the reference site

Fig. 4 shows that PMAC seems to vary from street segments, species and seasons. PMAC generally differs in the order of 1 < 2 < 3 for Cinnamomum camphora street segments, and 4 < 5 < 6 for Platanus acerifolia street segments, except for Winter 4, 5, 6 where no significant differences between sampling sites and reference sites are found. This result shows that pruning intensity of tree crown is responsible for the PMAC variation among different street segments. Correlation analysis on SPSS shows a highly significant negative correlation between pruning intensity and PMAC (Pearson correlation 0.580, 2-tailed Sig. 0.003). So we can come to a conclusion that severer pruning strategy will lead to lower PMAC in urban street canyons. In terms of species, the PMAC of Cinnamomum camphora street segments was relatively higher than those of Platanus acerifolia street segments, especially for street segment 1, 2 and 3 in winter. In contrast, the deciduous trees in 4, 5 and 6 lost most of their

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Table 3 PM2.5 concentration (average ± standard deviation) and significant level value (p) of T test between the sampling sites and reference sites.

C0.3m(mg m3) C1.5m(mg m3) C4m(mg m3) C8m(mg m3) C12m(mg m3) C0 0.3m(mg m3) C0 1.5m(mg m3) C0 4m(mg m3) C0 8m(mg m3) C0 12m(mg m3) P1 p2 p3 P4 p5

Spring

Summer

Autumn

Winter1,2,3

Winter4,5,6

52.15 ± 10.31 49.66 ± 10.44 48.76 ± 10.43 46.87 ± 10.27 44.61 ± 9.90 49.69 ± 12.38 46.71 ± 11.26 44.92 ± 11.16 42.09 ± 9.94 38.65 ± 8.99 0.005* 0.002* <0.001* <0.001* <0.001*

39.62 ± 9.59 38.21 ± 9.24 38.20 ± 9.54 37.27 ± 9.33 35.15 ± 9.64 38.81 ± 9.98 36.10 ± 9.00 35.33 ± 8.61 32.76 ± 8.27 28.52 ± 7.75 0.562 0.116 0.032* <0.001* <0.001*

40.98 ± 13.12 39.75 ± 12.36 39.50 ± 12.10 39.16 ± 11.94 40.03 ± 12.72 39.52 ± 13.36 36.61 ± 12.72 35.66 ± 12.67 33.01 ± 12.77 31.63 ± 10.78 0.144 0.002* 0.002* <0.001* <0.001*

90.86 ± 21.67 90.26 ± 21.37 90.96 ± 22.24 91.15 ± 23.55 95.79 ± 27.62 89.38 ± 20.32 82.87 ± 18.19 80.61 ± 17.74 77.88 ± 18.13 71.80 ± 17.56 0.401 <0.001* <0.001* <0.001* <0.001*

80.99 76.51 76.06 74.68 70.28 80.24 76.96 76.10 73.76 68.09 0.611 0.827 0.987 0.714 0.456

± ± ± ± ± ± ± ± ± ±

19.23 17.21 16.89 14.77 15.79 14.21 12.53 14.22 13.38 13.29

C0.3m, C1.5m, C4m, C8m, C12mePM2.5 concentration at the height of 0.3 m, 1.5 m, 4 m, 8 m, 12 m in sampling sites. C0 0.3m, C0 1.5m, C0 4m, C0 8m, C0 12mePM2.5 concentration at the height of 0.3 m, 1.5 m, 4 m, 8 m, 12 m in reference sites. Winter1,2,3eAverage PM2.5 concentration for street segments 1, 2, 3 in winter. Winter4,5,6eAverage PM2.5 concentration for street segments 4, 5, 6 in winter. p1, p2, p3, p4, p5eSignificant level value of T test between the sampling sites and reference sites at the height of 0.3 m,1.5 m, 4 m, 8 m, 12 m, respectively. *There is marked difference between the groups under the condition of p < 0.05 (2-tailed).

leaves during winter and resulted in much lower PMAC. It indicates that deciduous trees could hardly hinder the dispersion process of PM2.5 emitted in canyons in winter, which is the most polluted season. One conclusion is that more deciduous trees instead of evergreen trees should be used for green streets in Shanghai in order to mitigate winter pollution problems. The seasonal variation of PMAC differs between species (Fig. 4). In the Cinnamomum camphora street segments (Street No. 1, 2 and 3), the highest coefficients for each street segment consistently occur in winter instead of summer when trees are flourishing and canopy densities reach to the maximum level to block the dispersion of PM2.5 (Fig. 4). For each Cinnamomum camphora street segment, compared with the one in winter, the increased canopy in summer didn't lead to a corresponding increment of PMAC. This result is hard to explain since comprehensive physical and chemical processes could affect the PM2.5 dispersion. There might be two mechanisms. On the one hand, the slowdown of air flow in/under the denser canopy results in a higher residence time and thus increases concentrations in summer. On the other hand, there might be increased deposition in the presence of more leaf surface in summer. However, which effect prevails is not clear without further field study. Besides, low initial concentrations (PM2.5 concentrations at 0.3 m height) may have correlation with relatively low PMAC in summer. Further data analysis supporting this point can be seen in discussion section. In terms of each Platanus acerifolia street segment (Street No. 1, 2 or 3), the PMAC variations among spring, summer and autumn are similar with Cinnamomum camphora street segments. However, for each street segment, the PMAC in winter is close to zero, which again proves that deciduous trees are helpful to urban air environment in winter. 3.3. Wind speed in sampling sites and reference sites The diurnal average wind speed for each sampling point was calculated and presented in Fig. 5. Generally speaking, the wind speeds in sampling sites (street canyons with trees) are lower than in their corresponding reference streets (without trees). Gromke proved this result by wind tunnel experiments (Gromke et al., 2008). The average wind speed in sampling sites (average value of the 120 data marked by red points) is 0.50 ± 0.16 m/s (standard deviation). The average wind speed in reference sites (average value of the 120 data marked by black points) is 0.67 ± 0.22 m/s (standard deviation). Paired-samples T test shows there is a highly

significant difference between the two groups of data (p < 0.01). Thus, the wind speeds in streets with trees are statistically lower than the ones in streets without tree, which means the presence of tree crowns slows down the major air circulation in street canyons. We found obvious trend of the vertical distribution of the wind speed for neither street canyons with trees nor the tree-free ones. However, there is one repeatedly occurred feature for the street canyons with trees. The lowest wind speed frequently happened at 8 m height (sampling points in the middle of the tree crowns). The frequency is 15 of 24 (6 street segments, 4 seasons). This result indicates the air circulation declined to the lowest level inside of the tree crowns among the five sampling points with different heights. The result of correlation analysis showed negative correlation between the diurnal average wind speed and the diurnal average PM2.5. The Pearson correlation and 2-tailed Sig. are 0.157 and 0.087, respectively. Thus, the correlation is not statistically significant. In order to deeply analyze the relationship between wind speed and PM2.5 dispersion, the average wind speed of the five sampling points in each street segment was calculated, then rate of change of wind speed (the average wind speed difference between reference site and sampling site divided by the average wind speed in reference site) was calculated and presented in Fig. 6. As can be seen in Fig. 6, the rates of change of wind speed are positive in most cases, which means the average wind speeds are generally higher in reference sites than in sampling sites. However, the rates of change of wind speed are close to zero in street segment 4, 5 and 6 in winter, which appears to mean the deciduous trees didn't change the air circulation in the street canyons in winter. This finding explains well the hardly monitored PMAC in street segment 4, 5 and 6 in winter (Fig. 4). Correlation analysis between the rate of change of wind speed and pruning intensity shows a significant negative correlation (Pearson correlation 0.406; 2-tailed Sig. 0.049). Thus, strong pruning intensity generally leads to lower rate of change of wind speed for street segments with the same species. Through the above data analysis, a reasonably logical process can be completed. Pruning intensity influences the extent of the tree canopy, which in turn affects rate of change of wind speed and PMAC.

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PM2.5 a enua on coefficient(%)

40

Spr.

35

Sum.

Aut.

Win.

30 25 20

15 10 5 0 -5

1

2

3

4

5

6

Street No.

Fig. 4. PM2.5 attenuation coefficients in six street canyons and four seasons. The number 1 to 6 represent six street segments in sampling streets.

4. Discussion 4.1. Factors affecting PMAC The dispersion of airborne pollutants in street canyons is a complex process, which might be affected by various factors, including local atmospheric chemistry, meteorology, and characteristics of street canyons such as architecture and street trees (Gromke and Ruck, 2007; Kastner-Klein and Plate, 1999; Uehara et al., 2000). In this study, the main factors may influence PMAC include weather, initial pollution level, and structure of street trees such as height and canopy density. To be specific, 10 variables (temperature, relative humidity, rate of change of wind speed, PM2.5 concentration at 0.3 m sampling point, species, H, CW, LAI, CD and pruning intensity) within 3 groups of factors (weather, initial concentration, and street tree characteristics) were chosen to

evaluate whether it significantly accounts for the attenuation rate in street canyons. Through the data analysis in the result section, we have found both rate of change of wind speed and pruning intensity have significant correlation with PMAC. However, the correlation analysis only focuses on two variables. In order to perform a single overall test of the usefulness of the 10 variables, we used the mixedeffects model to evaluate their usefulness to PMAC. Table 4 lists the variables significantly affecting PMAC resulted from mixed-effects model. The outcome shows the significant effects of CD, LAI, rate of change of wind speed, pruning intensity and species. PMAC, therefore, becomes more noticeable with increasing canopy density and LAI to some degree, and reducing the pruning intensity. CD and LAI are both indicators describing the coverage of tree crown to street canyon, while pruning intensity would directly and significantly change CD and LAI, as shown in Table 2. Gromke et al.(Gromke and Ruck, 2007) found increasing concentrations at the leeward street canyon wall with increasing crown diameter which is also an index related to crown coverage. As shown in Figs. 5 and 6, this result is mainly caused by a reduced air circulation with the presence of tree, which would lead to the accumulation of particles underneath/within the tree canopy. The significant effect of species might be due to the distinct differences between Cinnamomum camphora street canyons and Platanus acerifolia street canyons in winter. In this case, Platanus acerifolia is more suitable for the green street than Cinnamomum camphora, as mentioned before. Since the prevailing wind direction of Shanghai is northwest in winter, the north side of street is theoretically the leeward. Therefore, deciduous trees are most needed on the north side of street canyons than on the south side in view of the worst environmental conditions in winter. Normally, the traffic condition, air temperature and humidity would affect the dispersion of ambient pollutants to some extent.

Fig. 5. Diurnal average wind speed in sampling sites and reference sites. The number 1 to 6 represent six street segments.

S. Jin et al. / Atmospheric Environment 99 (2014) 277e287 50

Spr.

Sum

Aut.

Win.

Table 5 Pearson correlation coefficients and sig. (2-tailed) of correlation analyses. Initial PM2.5 concentration Temperature Humidity

40

0.451 0.141 0.420 0.174

PMACC Pearson Correlation 0.622* Sig. (2-tailed) 0.031 PMACP Pearson Correlation 0.704* Sig. (2-tailed) 0.011

35 30 25

0.247 0.440 0.007 0.982

PMACC refers to PMAC of Cinnamomum camphora street canyons; PMACP refers to PMAC of Platanus acerifolia street canyons; * significant correlation.

20 15 10

0 1 -5

2

3

4

5

6

Street No.

Fig. 6. Rate of change of wind speed in six street canyons and four seasons. The number 1 to 6 represent six street segments in sampling sites.

But they don't show an obvious impact on PMAC in the mixedeffected model. It is probably because the initial concentration, temperature and humidity are all variables related to seasons, while the PMAC in winter shows totally opposite behaviors between the two species. In order to further analyze the impact of variables on PMAC, we did the correlation analyses by using data of the two species separately. Table 5 lists the Pearson correlation coefficients and significant level values (2-tailed) of the correlation analyses for the two species. There is a statistically significant positive correlation between PMAC and the initial concentration for the Cinnamomum camphora street canyons. Thus, the relatively low PMACC may relate to the low initial concentration in summer. On the contrary, there is a significant negative correlation between PMACP and the initial concentration. This results from the high initial concentrations but close to zero PMAC in the deciduous street canyons in winter. There is no significant correlation between PMAC and temperature and humidity, respectively. However, we can still find the negative correlation between PMACC and temperature (not significant), the positive correlation between PMACC and humidity (not significant). 4.2. Impact of CD and LAI on PMAC Since CD and LAI are the most significant predictors of PMAC in street canyon, regression analysis was used to quantify the relationships between PMAC and CD, LAI, respectively. Figs. 7 and 8 show the results of regression analysis. Basically, PMAC is positively related with both CD and LAI of the plots. As seen in Fig. 7, the PMAC seems to increase unevenly with increasing CD. Specifically, the increment rate of PMAC when CD is above 50% is much higher than that when CD is below 50%. On one hand, if the PMAC should be controlled less than the level of 10% in consideration of air quality, the CD should be no more than approximately

Table 4 Significant variables affecting PMAC (PM2.5 attenuation coefficient), street segment was regarded as a random effect, while the other three groups of factors as fixed effects. Bold values represent significant effects (p < 0.05).

PMAC

Variables

F

Significance level p

CD LAI Rate of change of wind speed Pruning intensity Species

7.097 6.577 5.993 5.784 4.389

0.005 0.007 0.018 0.026 0.048

PM2.5 A enua on Coefficient

40

5

y = -5E-06x4 + 0.001x3 - 0.0574x2 + 1.2184x - 1.2436 R² = 0.792

35 30 25 20 15 10 5 0 -5 0

20

40

60

80

100

Canopy Density/CD (%) Fig. 7. Regression analysis between CD and PM2.5 attenuation coefficient.

60%. Otherwise, it might lead to health-threatening air quality conditions. On the other hand, CD should also be higher than 50% taking into account the landscape effect and the definition of the green street in Shanghai. Hence, canopy densities should be kept moderate in order to balance esthetic function and air pollution dispersal needs. The optimum range for the CD of street trees is 50%e60% in this study. (Fig. 7) Fig. 8 shows a progressive relationship between LAI and PMAC. The increment rate of PMAC appears to gradually decrease with increasing LAI, and PMAC reaches the maximum value when LAI increases to around 4.5, which was calculated based on the simulation equation. PMAC probably starts to decrease after this peak point, owing to the hypothesis that the absorption effect of leaves starts to be stronger than the hindrance effect of tree crowns. Likewise, the LAI should be in the range of 1.5e2.0 when comprehensive benefits are considered. The results could be considered as a reference for street tree planning in Shanghai. For example, the CD value of 60% would be a nice year-round threshold for street tree design in east-west street canyons. To meet this requirement, the CD value should be controlled at approximately 40% after pruned in spring according to 40

PM2.5 A enua on Coefficient(%)

Rate of change of wind speed (%)

45

285

y = -0.618x3 + 4.255x2 - 1.132x + 0.825 R² = 0.738

35 30 25 20 15 10 5

0 -5 0

1

2

3

Leaf Area Index (LAI)

4

5

Fig. 8. Regression analysis between LAI and PM2.5 attenuation coefficient.

6

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the data from this investigation. That is to say, if both PM2.5 control and landscape effect are considered, about 24 normal-sized trees (Platanus acerifolia, with 12 m of height and 7 m of crown width) should be planted on each side of a street canyon with typical structure (east-west, 200 m of length, 16 m of width, 12 m of average building height) in central Shanghai. Therefore, the distance between street trees is supposed to be 8 m. In this case the PMAC would be controlled less than 10%. The above results indicate that street segment 4 (Platanus acerifolia, strong pruning intensity) has the most optimal tree structure among these six street segments. (Fig. 8) 5. Conclusions To sum up, a seasonal campaign was conducted to monitor the PM2.5variation in six typical street canyons in the residential area of central Shanghai, China. The results show that the diurnal average PM2.5 statistically decreases with increasing height in tree-free street canyons. The decrease could not be found or is weakened in street canyons with trees due to the fact that the street trees hinder the dispersion of air pollutants. In addition, the attenuation coefficient of PM2.5 (PMAC) is developed as indicator to quantify the decrease of PM2.5 reduction rate. The wind speed is significantly lower in street canyons with trees than in tree-free ones. CD, LAI, rate of change of wind speed, pruning intensity and species are identified as the main factors influencing PMAC. To minimize PM2.5 concentration without sacrificing landscape benefits, the optimum range of CD and LAI would be 50%e60% and 1.5e2.0 respectively. In this case, street segment 4 with deciduous trees and strong pruning intensity might be a paradigm for tree planting in Shanghai. This paper could be regarded as an example of applying theoretical research findings to a practical city planning problem by using field experimental approach. Acknowledgments This work was funded by the National Key Technology R&D Program (2012BAC13B04-05) and the Shanghai Key Projects of Science & Technology Program (11231201002). All authors would like to give thanks to Shanghai Greening & Management Guidance Station for their help on street trees pruning. We would like to thank our team workers Bingqin Yu, Zhigang Li, Changkun Xie, Mingling Chen, Qiangqiang Li, Wenjuan Rui, Tao Chen, Zixin Shen, Hongjian Wei, Yuan Zhang and Yanling Zhao for their hard work on field experiment and data collection. We also thank the professional advices of manuscript by Shan Yin, Jibao Jiang from Shanghai Jiao Tong University, and Lin Jiang from the University of California, Davis. References Ahmad, K., Khare, M., Chaudhry, K.K., 2005. Wind tunnel simulation studies on dispersion at urban street canyons and intersections - a review. J. Wind Eng. Ind. Aerodyn. 93, 697e717. Bady, M., Kato, S., Takahashi, T., Huang, H., 2011. An experimental investigation of the wind environment and air quality within a densely populated urban street canyon. J. Wind Eng. Ind. Aerodyn. 99, 857e867. , M.G., Christof, Ruck, Bodo, 2009. Numerical modeling of flow and pollutant Balczo dispersion in street canyons with tree planting. Meteorol. Z. 18, 10. Buccolieri, R., Salim, S.M., Leo, L.S., Di Sabatino, S., Chan, A., Ielpo, P., de Gennaro, G., Gromke, C., 2011. Analysis of local scale treeeatmosphere interaction on pollutant concentration in idealized street canyons and application to a real urban junction. Atmos. Environ. 45, 1702e1713. Carpentieri, M., Kumar, P., Robins, A., 2011. An overview of experimental results and dispersion modelling of nanoparticles in the wake of moving vehicles. Environ. Pollut. 159, 685e693. Carpentieri, M., Kumar, P., Robins, A., 2012. Wind tunnel measurements for dispersion modelling of vehicle wakes. Atmos. Environ. 62, 9e25.

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