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Environmental Pollution 201 (2015) 141e149

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Environmental Pollution

journal homepage: www.elsevier .com/locate/envpol

Quantifying the effect of vegetation on near-road air quality usingbrief campaigns

Zheming Tong a, Thomas H. Whitlow b, *, Patrick F. MacRae b, Andrew J. Landers c,Yoshiki Harada b

a Department of Mechanical and Aerospace Engineering, Cornell University, Gruman Hall, Ithaca, NY, USAb Section of Horticulture, School of Integrative Plant Science, Cornell University, Room 23 Plant Science Building, Ithaca, NY 14853, USAc New York State Agricultural Experiment Station, 630 West North Street, Geneva, NY 14456, USA

a r t i c l e i n f o

Article history:Received 18 August 2014Received in revised form18 February 2015Accepted 20 February 2015Available online 20 March 2015

Keywords:Near-road air pollutionTreesDispersionAerodynamicsPM2.5

* Corresponding author.E-mail address: thw2@cornell.edu (T.H. Whitlow).

http://dx.doi.org/10.1016/j.envpol.2015.02.0260269-7491/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

Many reports of trees' impacts on urban air quality neglect pattern and process at the landscape scale.Here, we describe brief campaigns to quantify the effect of trees on the dispersion of airborne particu-lates using high time resolution measurements along short transects away from roads. Campaigns nearmajor highways in Queens, NY showed frequent, stochastic spikes in PM2.5. The polydisperse PM2.5class poorly represented the behavior of discrete classes. A transect across a lawn with trees had fewerspikes in PM2.5 concentration but decreased more gradually than a transect crossing a treeless lawn. Thiscoincided with decreased Turbulence Kinetic Energy downwind of trees, indicating recirculation, longerresidence times and decreased dispersion. Simply planting trees can increase local pollution concen-trations, which is a special concern if the intent is to protect vulnerable populations. Emphasizingdeposition to leaf surfaces obscures the dominant impact of aerodynamics on local concentration.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

There is a general consensus that proximity to major highwaysincreases the risk of adverse health effects caused by exposure to airpollution (HEI, 2010). Roadside barriers, including vegetation, havebeen shown to alter the dispersion of traffic emissions. If thevegetative barriers consistently lower ground-level air pollutionconcentrations in the near-road environment, they may be apractical tool for reducing human exposure to air pollution alongpopulated roadways.

It is widely reported that trees intercept airborne particleswhich are subsequently removed from the canopy by re-suspension, by rain and leaf abscission (Dochinger, 1980; Freer-Smith et al., 2004; Nowak, 2002; Nowak et al., 2013). Usingempirical estimates of deposition velocities, these reports estimatethe total particulate removed by trees (typically PM10) at either citywide or local scales. Calculations like these are often used toadvocate tree planting policies like the numerous million-treeprograms across the US. However laudable these programs are,the approach ignores the effects of distance from source and the

local aerodynamics around trees, how these affect dispersion andultimately local PM concentration, and provide no guidance for therational design of landscapes to improve local air quality. For thispurpose, a mechanistic approach based on fluid dynamics ofdifferent particle sizes and the local turbulent flow field caused byroad-canopy configurations is needed.

Aerosol science has long known that particle dry depositionvelocity varies as a function of particle size, and ranges over threeorders of magnitude (Sehmel, 1980; Seinfeld and Pandis, 2006;Slinn et al., 1978). This is because particles <0.001 mm behavemore like gases, diffusing along concentration gradients to depositon surfaces, unlike particles >10 mm whose deposition rates de-pends on inertial impaction and gravitational settling. The localturbulent flow field also plays a significant role in particle disper-sion. A tree canopy consists of numerous elements such as leaves,branches and trunks. When these elements interact with airflow,the flow momentum is absorbed by both form and skin-frictiondrag on the canopy, reducing mean flow velocity (Raupach andThom, 1981). Larger scale turbulent eddies introduced by trafficand the background atmosphere are broken down to small scaleeddies by a tree canopy, causing a recirculation zone behind thevegetation with elevated concentrations (Steffens et al., 2012; Tonget al., 2011; Wang and Zhang, 2009).

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149142

Both experimental and numerical simulation studies haveinvestigated the effect of vegetation on PM concentration alongroads unbounded by buildings. Brantley et al. (2014) conducted afield assessment of the effect of roadside vegetation on near-roadblack carbon and particulate matter. They found that particlecounts in the fine and coarse particle size range (0.5e10 mm aero-dynamic diameter) were unaffected by vegetation. Baldauf et al.(2008) found that both a solid noise barrier and vegetation barriercan reduce PM concentrations in their wakes whenwind is from thedirection of the road. In general, these studies have shown decreasedconcentrations of ultrafine and coarse mode PM with limitedreduction measured for PM2.5 mass. Set€al€a et al. (2013) used passivesamplers to study the effect of urban park/forest vegetation on NO2,anthropogenic VOCs and particle deposition in two Finnish cities.They found that pollutant concentrations were often only slightlylower under tree canopies than in adjacent open areas. Maher et al.(2013) examined the impact of a line of young trees on indoor airquality adjacent to a heavy traffic road, and a substantial reduction ofPM10was observed. Cavanagh et al. (2009) conducted a field study toinvestigate the spatial attenuation of PM10. Concentrations werehigher outside the forest than deep within the forest.

Other researchers have used physical models in wind tunnelsand Computational Fluid Dynamics (CFD) models to simulate theimpact of vegetative buffers on roadside plume dispersion. Gromkeand Ruck performed a wind tunnel experiment on dispersionprocesses of traffic exhaust in urban street canyons with andwithout street trees (Gromke, 2011; Gromke and Ruck, 2009). Treesreduced pollutant dispersion, thereby increasing particle residencetime and concentration. In the wind tunnel, street trees causedlocalized concentration increases of 50% at some locations in thecanyon compared with the treeless case. This indicates that trees instreet canyons reduce air exchange with the ambient atmosphere.Buccolieri et al. (2009) conducted both CFD and wind tunnel ex-periments to study the aerodynamic effects of trees on pollutantconcentration in street canyons. Both approaches showed consid-erably greater pollutant concentration near the leeward wall andslightly lower concentration near the windward wall when treeswere present. Another CFD study compared CFD modeled resultsand field measurements explore the effect of a near-road vegeta-tion barrier on ultrafine particles (Steffens et al., 2012). The CFDmodel was evaluated against the roadside measurements, and agood agreement was observed (Hagler et al., 2012). They found thatincreasing leaf area density (LAD) reduced ultrafine particle con-centration, but the response was non-linear. Pugh et al. modeledthe effect of green walls on air quality in a street canyon. Usingdeposition velocities from the literature, they calculated that greenwalls could cause a 40% reduction for NO2 and a 60% reduction forPM10 (Pugh et al., 2012).

This brief review shows that vegetation can either decrease orincrease PM concentration, depending on the road-canopy config-uration, particle size, and local flow field. The goal of this study is toimprove our understanding of the impact of vegetation on PM2.5transport in the near road environment. We focus on PM2.5 becauseit includes the particle sizes with the lowest deposition velocitiesand is more closely linked to human mortality (EPA, 2009; Seinfeldand Pandis, 2006). By strategically deploying multiple particlecounters and sonic anemometers, this approach achieves highspatial and temporal resolution of PM2.5 concentration in discreteparticle size classes, and corresponding turbulence data. This is aunique addition to the existing literature that provides empiricaldata for detailed landscape scale modeling.

We posed 4 initiating hypotheses:

1. PM2.5 concentrations will be reduced below ambient downwindof tree canopies.

2. PM2.5 concentration will decline more sharply along a transectoccupied by trees than an open transect

3. The effect of trees on PM2.5 concentration depends on winddirection.

4. The effect of trees on PM2.5 concentration depends on particlesize.

2. Methods

2.1. Measurement approach

We used an observational approach to conduct a series of short-term field campaigns exploring the spatiotemporal patterns ofparticulate matter dispersion across a large urban open space(Dominici et al., 2014). We used portable monitoring instruments(see below) to conduct a series of brief, intensive campaigns duringa 2 week period, lasting ca. 10 h each day during daylight hours,capturing both morning and evening rush hours. This approachresembles that of Spengler et al. (2011) in their study of ultrafineparticles in a neighborhood adjacent to a toll plaza. In comparisonwith permanently locatedmonitors, brief campaigns can be used inpublic spaces where vehicles are not allowed, make efficient use ofinstrumentation and labor, allow multiple locations to be moni-tored in real time, are suited to addressing the effectiveness ofvegetated buffers at scales relevant to engineering and humanexposure, and permit sampling where permanent samplers cannotbe secured against vandalism. Importantly, small mobile sensors donot impact local dispersion patterns and can monitor near theground where human exposure would occur. The tradeoff is interms of generalizability of the findings over long time periods andvarying air mass conditions.

2.2. Sample location

We selected Flushing Meadows-Corona Park, a 3.63 km2 com-plex in Queens, New York City, USA (Map is shown in the Supple-mentary Material (SM1), and relevant features are described inTable 1). The park is surrounded by the heavily trafficked VanWyckand Long Island Expressways (LIE), allowing us to select samplelocations to control for prevailing wind direction on any givensampling day. Annual Average Daily Traffic (AADT) is 84,601 vehi-cles/day on the VanWyck and 138,406 vehicles/day on the LIE. Overa 2-week mid-summer period when trees were in full leaf, wesampled at three locations in the park when weather and winddirection were suitable for testing our hypotheses. None of thesesites was deliberately designed to modify airflow or capture par-ticles, yet each represents a landscape common in urban centers inthe eastern US, consisting of trees, lawns, and playing fields near ahighway. The park is separated from the highway right-of-way by acontinuous 2.4 m high chain link fence deliberately kept free ofvegetation, thus having essentially no impact on wind and particlemovement at the scale of our measurements (Details of the vege-tation at each site are presented in Table 1 and SM2).

2.2.1. Hypotheses 1,2 and 4Northeast of the Van Wyck Expwy, we sampled 2 parallel

transects along distance gradients from the road to test for differ-ences in PM2.5 transport across a lawn with scattered treescompared with an adjacent open lawn (Fig. 1A). One particlecounter was located next to the highway while the other counterswere rotated among 3 points along the 2 transects every 15 min,yielding a 45 min cycle. This was repeated throughout the day(Table 2).

Table 1Site description; a) Measurements were taken on days when weather permitted and wind direction was appropriate for testing the 4 hypotheses; b) The unit of latitude andlongitude is in decimal degrees. c) Canopy porosity was determined from hemispherical images taken beneath the canopy. More details are provided in the SupplementaryMaterial (SM2). d) Tree cover percentage was measured with a line intercept method from aerial photographs. e) Grass, bare soil, and pavement cover were measured byquadrat method. Percentage of road pavement is not presented in this table, but can be found in SM2. A list of tree species is also provided in SM2.

Datea Latitudeb Longitudeb Porosityc % Treesd % Bare soil % Grasse

Van Wyck East Jun 7, 2011 40.723 �73.838 15.7% 44.1%(w/trees)0%(no trees)

10.5%(w/trees)3.5%(no trees)

89.5%(w/trees)96.5%(no trees)

LIE South Jul 13/14/15, 2011 40.741 �73.841 9.8% 4.3% 3.1% 66.7%LIE North Jul 12, 2011 40.743 �73.841 21.9% 82.5%(w/trees)

6.3%(no trees)17.0%(w/trees)22.5%(no trees)

71.9%(w/trees)64.7%(no trees)

Fig. 1. Details of the sample points at the 3 sites. A) Van Wyck East, stations and 2,3,4represent the vegetated transect, and 5, 6, 7 represent the open transect. Station 1beside the road serves as a common reference point for both transects; B) LIE (LongIsland Expressway) South; C) LIE North; Wind roses are based on daily on site mea-surements. The numbers on each figure indicate the sampling points. In the text, thesites are referred to as Van Wyck East, LIE South, and LIE North.

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149 143

2.2.2. Hypotheses 1 and 4The 2 LIE sites were selected because the landscape on both

sides of the highway has roadside trees, open lawn and sportsfields, and in the case of LIE North, a patch of closed canopy forest.On the south side of LIE (Fig. 1B), we located particle counters at 3fixed points: at the highway edge, 12 m downwind of a line of trees,and 52 m downwind of the trees in an athletic field. Measurementswere fully synchronized in time.

2.2.3. Hypotheses 3North of the LIE, we sampled at 3 static locations: adjacent to the

highway shoulder, in a grassy field and under a forest canopy(Fig. 1C). On the day we sampled, wind was from the north, upwindof the highway. The three sites are referred to as VanWyck East, LIESouth, and LIE North in the text.

2.3. Instrumentation

2.3.1. Particle countersWe measured atmospheric particulates using 3 Grimm Aerosol

Spectrometers (Model 1.108) equipped with isokinetic probes toreduce the effect of variation in wind speed. We monitored 15 sizeclasses between 0.3 and 20 mm every 6 s. This approximates ahuman resting inhalation rate and also allows us to observe con-ditions corresponding to spikes in PM2.5 concentration. In-struments had been factory calibrated just prior to the summercampaign. In addition, all 3 instruments were co-located for 60mineach sampling day and readings from each instrument wereregressed against their average. These empirical equations wereused to adjust readings to compensate for small variations amongthe instruments. We approximated fine particulate matter (PM2.5)as sum of all sizes from 0.3 mm to 3.0 mm, and particle counts areconverted to mass by assuming that the particles are spherical andusing the conversion factor 1.4 g/cm3 (Armbruster et al., 1984;Murakami et al., 2005). This underestimated the regulatory defi-nition of PM2.5 because it excludes particles below the detection

Table 2Average concentration and standard deviation (shown in parentheses) of PM2.5 atvarious sampling stations for Van Wyck East and LIE South; Locations are indicatedby distances from the road. The averaging period for Van Wyck East site is the sameas the one used in the decay curves. The averaging period for LIE South is from 12:10to 1:30 PM where the traffic and wind condition is most steady.

Open transect at Van Wyck East Roadside 10 m 23 m 40 m

Average concentration [mg/m3] 4.92(1.84) 3.90(1.33) 3.81(1.19) 3.75(1.27)

Vegetated transect at VanWyck East

Roadside 7 m 15 m 51 m

Average concentration [mg/m3] 4.92(1.84) 4.36(0.94) 4.46(1.11) 4.17(0.81)

Vegetated Transect at LIE South Roadside 12 m 52 m

Average concentration [mg/m3] 1.96(0.84) 1.77(0.74) 1.65(0.64)

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149144

limit of the instruments (0.3 mm). It does, however, include therange of particle diameters in so-called accumulation mode wheredeposition is lowest.

2.3.2. Instantaneous wind speed/directionWe used four 3-D Gill sonic anemometers to measure the

instantaneous wind speed and direction at 1 Hz. These data wereused to generate wind roses for the sampling days and also tur-bulent kinetic energy (TKE, see below and SM4).

2.3.3. Hemispherical canopy porosity and tree coverPorosity is a property of vegetation that correlates well with

downwind velocity, turbulence and particle deposition (Heisler andDewalle, 1988; Li et al., 2010; Loeffler et al., 1992; Raupach et al.,2001). We estimated porosity to characterize the canopy densityof the trees closest to our downwindmonitoring stations for each ofthe three sites using a technique modified from Kenney (1987). Weused a digital camera (5 megapixel resolution; Nikon Coolpix 5700)equipped with a fisheye lens (Nikon FC-E9) to take hemisphericalimages beneath the canopy. Images were rendered in high contrastblack and white in Photoshop®, white and black pixels were tallied,and porosity was calculated as the % white pixels on the image. Anexample of the hemispherical image is provided in the Supple-mentary Material. We also estimated tree cover using line in-tercepts perpendicular to the highway and tabulating the distancebelow the drip lines of the trees as a percent of the total distancebetween the highway and the particle counters.

2.4. Data analysis

2.4.1. Temporal variationKoniographs, analogous to hydrographs used by hydrologists,

were used to show fine scale temporal variation in concentration atthe 6-s sampling frequency of the aerosol spectrometers (Whitlowet al., 2011). This sampling rate approximates the human inhalationrate, hence exposure to short term concentration spikes.

2.4.2. Return periodPM concentrations averaged over periods ranging from days-

years, while useful for regulatory purposes, eliminate fine scalepatterns that are useful for quantifying human exposure risk attemporal scales relevant to daily activities, especially physical ex-ercise. Risk is probabilistic and contingent on many environmentalfactors, many of which are beyond our control. Recognizing that airpollution events are stochastic over time, resembling flood events,we used the Gumbel Method (Gumbel, 1941; Whitlow et al., 2011)to calculate return period of PM2.5 events of any observed magni-tude during each day's set of observations. Return period estimatesthe magnitude of the highest concentration occurring during agiven period. This statistic is analogous to the familiar 100-yearflood, which expresses the probability of a flood of an observedmagnitude occurring in any given year. In our usage here, the timescale is in minutes instead of years. Further, like the 100-year floodplain, it also characterizes a specific location. The calculation ofreturn period is provided in the Supplementary Material (SM3).

Fig. 2. Koniographs at Van Wyck East at 6 s sampling resolution. a) station 1 atroadside; b) roving among station 2, 3, and 4 (7 m, 15 m, 51 m away from the road)along the vegetated transect, c) roving among station 5, 6, and 7 (10 m, 23 m, 40 maway from the road) along the open transect.

2.4.3. Turbulence Kinetic Energy (TKE)We calculated TKE for time intervals when the wind direction

and speed were relatively steady for each day. The sampling fre-quency of the sonic anemometer is 1 Hz, which though not rapidenough to capture turbulence in the dissipation range, does capturemost of the energy containing eddies. The calculation of TKE isprovided in the Supplementary Material (SM4).

3. Results and discussion

3.1. Experiment 1: Van Wyck East

The high resolution of the 6-s sampling frequency shows thenearly instantaneous stochastic variation of PM2.5 concentration inthe roadside environment. Sampling location 1 in Van Wyck Eastsite adjacent to the highway displays most variable PM2.5 concen-tration data (Fig. 2a), showing frequent spikes above background,corresponding to passage of especially “dirty” vehicles. Because ourspectrometers cannot detect particles <0.3 mm, these spikes are notcaused by primary tailpipe emissions but are either secondaryparticles or particles re-suspended from the road surface and loftedby the turbulent wakes of vehicles (Fig. 3a). Observations across thevegetated transect showed far less variation, higher mean con-centrations, and large spikes in concentrationwere absent (Fig. 2b),while the open transect had concentration spikes (Fig. 2c).

Return period plots reveal that along the open transect, samplesat 10, 23 and 40 m from the road are virtually indistinguishablefrom each other (Fig. 3a). In comparison, samples on the transectwith trees show differences in magnitude of events at frequenciesexceeding ca. 1 min (Fig. 3b).

While trees attenuated concentration spikes, the transect withtrees had higher average PM2.5 concentrations (SM5, Table 2).Koniographs of the 15 min average concentrations correspondingto the sampling intervals at the different points on the transectshow the pattern more clearly than the 6-sec koniographs (SM5).Roadside PM2.5 varied over the course of the day in relation totraffic conditions. Noticeably greater concentration was observedduring themorning and afternoon rush hours, when traffic was lowspeed, stop and go (Tong et al., 2000). This flow pattern is typicallymore polluting than the steady-speed driving modes, and gener-ates more total emissions (Tong et al., 2011).

Fig. 3. Event return period plot at the Van Wyck East site, a) open transect, station 5, 6, 7; b) vegetated transect, station 1, 2, 3, 4; Monitoring stations are shown by distances fromthe road.

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149 145

All else being equal, concentration should decrease exponen-tially with distance from source. To de-confound the effect of dis-tance from the effect of trees, we fit exponential curves to data froma period of relatively constant traffic conditions and roadside PM2.5concentration between 11:30 AM to 3:30 PM (Fig. 4 Top, SM6). Eachpoint is the average of ca. 800 observations. The polydisperse PM2.5size class shows the expected exponential decline, with the opentransect showing a steeper decline than the transect with trees(Fig. 4 Top). Two parameters quantify the difference in decay rates.The decay constants in the equations fitted to the data are 0.029(with trees) and 0.204 (open), a 7-fold difference. The half distances(the distance required to attain half of the initial concentration) are12.74 m (with trees) and 3.46 m (open) (Table 2). Both parametersindicate that particle concentration decreases at a substantiallyhigher rate along the open transect.

Note that the R2 for PM2.5 along transect with trees was muchlower than that for the open transect, indicating a poorer fit (SM6).To explore the possible reasons for this, we fitted exponentialcurves to the largest and smallest, more nearly monodisperseparticle sizes included in our estimates of PM2.5, namely0.3e0.4 mm and 2.0e3.0 mm (Fig. 4 Bottom). As before, concentra-tions of both size classes declined more steeply across the opentransect than across the transect with trees. In contrast with0.3e0.4 mmclass, 2.0e3.0 mm class for both transects declinedmoresharply due to greater settling velocity. These findings reaffirm thewell-known fact that larger particles within the polydisperse PM2.5

class behave differently than smaller ones even though they arelumped for regulatory purposes (Seinfeld and Pandis, 2006).

3.2. Experiment 2: LIE south

This experiment investigated the effect of a line of trees near theroad. Observations were made using instruments at static locationsand were synchronized, thereby accounting for fine scale differ-ences in emission sources and weather. It provides valuable infor-mation to study instantaneous spike attenuation and turbulencedissipation along the transect. There was no open transect forcomparison at the site.

Although many concentration spikes at the roadside wereclosely correlated with spikes at stations distant from the road,non-synchronous spikes were also observed at these distant loca-tions (Fig. 5). Further analysis shows that particles in the1.6e2.0 mm and 2e3 mm size classes accounted for 63% of the totalmass of particles comprising the spikes in contrast with the average

proportion for the entire period of 36%. Taken together, these ob-servations indicate that the road was not the only source of PM2.5and that different sources have characteristically different size classsignatures. Variation in PM2.5 concentration was lower at station 2behind the vegetation 12 m from the roadway (Table 2). A com-parison of decay curves for PM2.5, 0.3e0.4 mm and 2e3 mm (Fig. 6,SM7) shows that the concentration of 2e3 mm particle declinedmore sharply than 0.3e0.4 mm particles. The average concentrationof 0.3e0.4 mm particles downwind of the line of trees was essen-tially the same as beside the road despite being 12 m more distant.

Return period plots for the three different size classes illustratethe importance of both landscape location and particle size indetermining the exposure risk (SM8). For events with return pe-riods <4 min, PM2.5 behaves as might be expected: more distantlocations experience lower concentrations. For events recurringless frequently than 4 min, however, the field station had higherconcentrations than the station behind the trees (station 2). Inter-estingly, for very frequent events (<0.3 min), concentrations ofparticles between 0.3 and 0.4 mmwere highest at the 12 m station,downwind of trees.

3.2.1. Recirculation zoneWind measurements upwind and downwind of trees provides

insight into the local turbulent flow field. At the station downwindof a tree, reduced TKE was observed at both Van Wyck East and LIESouth (Fig. 7a). TKE followed the same general pattern at both sites.It was lowest immediately downwind of trees and greater furtherdownwind and in the open for all three days and exceeded the TKEat the roadside station for two out of three days. Large scale back-ground turbulence due to atmospheric instability was the likelycause. This finding agrees with many other studies showing thatporous vegetation barriers reduce mean wind speed, break downlarger scale upwind turbulent eddies, and create a recirculationzone downwind (Kaimal and Finnigan, 1994; Steffens et al., 2012).The particle data shows that the PM2.5 concentration along thevegetated transects decays considerably slower and has less vari-ation than across the open transect. The recirculation zone slowsparticle dispersion and sometimes results in higher local concen-tration than the open transect (Figs. 4 and 6). In contrast, solidbarrier generally reduces concentration immediately downwind bydeflecting the flow field upward (Baldauf et al., 2008; Hagler et al.,2012). A barrier comprised of treeswith lowhemispherical porosityand a canopy extending to the ground might have a similar effect.Our estimates of porosity beneath tree canopies range from 10 to

Fig. 4. Top) Normalized decay curves of PM2.5 concentration from the edge of the highway to the field based on 4 measurement locations from a period where the traffic conditionis steady (11:30 AM to 3:30 PM) at Van Wyck East site. Concentrations are normalized by the average concentration at station 1 (roadside). Bottom) Normalized decay curve for0.3e0.4 mm and 2e3 mm size fractions along the open and vegetated transects at Van Wyck East site. Curves for both the open and vegetated transects were forced to decay to thesame ambient background at station 7 (Van Wyck East) along the open transect.

Fig. 5. Koniograph of PM2.5 concentration for LIE South at 6 s sampling resolution. Measurements at three static stations are shown, a) roadside, station 1; b) 12 m, station 2; c)52 m, station 3; Traffic and wind conditions were most steady during the selected period from 12:10 to 1:30 PM.

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149146

22%. Wind breaks of this density have been found to have a recir-culation zone extending downwind between 8 and 10 times theirheight (Heisler and Dewalle, 1988).

3.3. Experiment 3: LIE north

The third experiment showed the impact of wind directionrelative to the highway emission source. Though wewere unable to

collect continuous measurements for all 3 locations during theentire period due to equipment problems, when all 3 instrumentswere operating, extreme spikes were absent beneath the treecanopy and in the open field (Fig. 8, SM9). North winds, that isnorth from the highway, had an overriding effect regardless of treecover. However, random spikes were still observed beside the road,indicating that vehicle-induced turbulence can cause extremeevents (Wang and Zhang, 2009). Beneath the canopy there were no

Fig. 6. Normalized decay curves for PM2.5 concentration, 0.3e0.4 mm particle and 2e3 mm particles as a function of distance from the edge of highway for the corresponding periodat LIE South site. The concentration of large particles declines more steeply with distance than does the concentration of small particles. The polydisperse class PM2.5 shows anintermediate decay rate. The poorer fit for the small particles corresponds to the relatively higher concentration observed downwind of trees.

Fig. 7. Average turbulent kinetic energy (TKE) calculated as a function of distance away from the road at a) Van Wyck East, b) LIE South. At Van Wyck East, measurements weretaken from a station upwind of vegetation (4 m from road), and a station downwind of vegetation (53 m from the road) at two heights (2 m and 3 m above the ground). At LIE South,three stations are shown as, roadside, downwind of vegetation barrier (12 m from road), and far field (52 m from road).

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149 147

concentration spikes (Fig. 8). The proportion of the PM populationfalling in different size classes was affected by upwind sources andhence wind direction plays a big role. Whenwind passed over openpark space upwind of the highway, the proportions of each particle

Fig. 8. Konigragh for LIE North site. “Roadside” is station 1. “Inside canopy” is location 2. Tproblem with the monitor in the open field prevented data collection from the entire day.

size were similar among all sample locations (Fig. 9 a,b,c). The0.3e0.4 mm and 0.4e0.5 mm classes accounted for nearly 75% ofPM2.5 (Fig. 9 a,b,c). In contrast, south of the LIE with wind from thenorth, the percentage of these size fractions dropped to ca. 40% of

he Open Field is location 3. The wind direction was from the park to the highway. A

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149148

PM2.5 (Fig. 9 d,e,f).This is approximately equal to the contribution of larger parti-

cles (2e3 mm and 1.6e2 mm). It is informative to compare theparticle size distribution during concentration spikes with theaverage size distribution excluding spikes. All spikes exceeding twostandard deviations above the average are analyzed for the LIENorth and South sites. The pie chart displays a very different picturein contrast with the proportion computed over the entire period atroadside station (SM10). At LIE North (upwind of highway), greaterproportion of large particles (1.6e3 mm) was observed, which isprobably due to wear products that are re-suspended from thepavement by vehicle-induced turbulence (Wang and Zhang, 2009).At LIE South site (downwind of highway), spikes monitored had ahigher proportion of small particles (0.3e0.4 mm) when the windblew across the road. These particles are likely to include secondaryparticles aggregating from tailpipe emissions as well as particles re-suspended from the road.

4. Conclusions

Brief monitoring campaigns investigated the dispersion of par-ticulates with high temporal and spatial resolution next to highwaysources. Only 2 of our hypotheses were supported by our findings,namely the wind direction in relation to source and particle sizeaffect PM2.5 concentration. The remaining hypotheses dealt directlywith the positive effects that trees are expected to have on local airquality. Neither was supported by the evidence: concentrationswere higher downwind of trees, leading to less steep decay rates.Decomposing PM2.5 into discrete size classes ranging from 0.3 mmto 3 mm showed that the physical behaviors of each size class arevery different along both open and vegetated transect. Fitting PM2.5concentration to exponential decay curves reveals that small par-ticles (0.3e0.4 mm) have lower extinction coefficients and longerhalf distances in the air column than do larger particles (2e3 mm).Although the deposition behavior of different sizes of particles arewell known in the atmospheric science, this is typically ignored oroverlooked in the planning and design of green space intended tomitigate air pollution.

An especially interesting finding is that the presence of treesbetween a source and measurement location reduced the fre-quency and intensity of concentration spikes while at the sametime increasing average concentration. The net effect was thatconcentrations along transects with trees declined less sharply

Fig. 9. Mass proportion of each particle size class as shown in the legend. a,b,c) represent s(station 3) respectively. d,e,f) represent stations at LIE south site: roadside (station 1), behi

than along open transects. This accompanied decreased TKEdownwind of trees, indicating recirculation, longer particle resi-dence times and decreased dispersion. In sum, aerodynamics, notsurface area available for deposition, controls local PM2.5 concen-tration. This has direct implications for designing landscapesintended to mitigate air pollution: downwind recirculation zonesmust be dimensioned and we must avoid locating sensitive uses inthese zones. The laws of diffusion state that higher ambient con-centrations result in a steeper concentration gradient, one of the“driving forces” controlling deposition rate. Higher concentrationtherefore means that deposition is enhanced in the downwindrecirculation zones while at the same time increasing human risk.Our empirical findings comply with theory and echo what has beencalled “the green paradox,” underscoring the need for multi-scaleapproaches (Vos et al., 2013).

Differences between transects with and without trees wereapparent only whenwind was from the direction of the road, hencethe effect trees have on particle concentration is contingent onwind direction. The mass proportion of each particle size class fromthe roadside concentration spikes is very different from their pro-portion of the average concentration. The proportion of smallparticles (0.3e0.4 mm) was greater downwind of the highway,probably due to the effects of traffic. Upwind of the highway, theproportions of each particle size were similar across sample loca-tions, showing that wind direction overrides the traffic impact,distance from source and canopy cover.

PM2.5 is often reported as the daily average at a city scale. Tobetter understand health impacts, however, particle size distribu-tions, temporal and spatial resolution of PM2.5 may be moreinformative. Smaller particles in PM2.5 will be inhaled more deeplyand are more toxic than larger particles of the same composition(Ferin, 1994; Ferin et al., 1992). Studies also show that acuteexposure to traffic related PMwas associatedwith elevated levels inexhaled nitrate and nitrite, an early biological response that mayprecede respiratory symptoms (Ezzati et al., 2000; Van Vliet et al.,2013). Establishing links between spatiotemporal variation in PMand human responses warrants far greater emphasis.

Ideally, trees could be located in landscapes to reduce the PMdose humans receive. Dose is probabilistic, contingent on location,timing, duration, traffic, and aerodynamics, among other variableslike regional air mass. If we intend to design landscapes to reduceexposure to air pollution, then a simple “more trees are better”approach is inadequate. Failure to account for the functional

tations at LIE North site: roadside (station 1), inside canopy (station 2), and open fieldnd vegetation barrier (station 2), and open field (station 3).

Z. Tong et al. / Environmental Pollution 201 (2015) 141e149 149

mechanisms may not achieve the desired outcomes and mayactually increases local pollution concentrations, with concomitantnegative effects on human health. For example, PM2.5 concentra-tion downwind of the highway was consistently higher (10% at25 m) along the transect with trees than the open transect (Fig. 4).The magnitude of this local increase is 40e200 times the calculatedreduction in PM2.5 by trees in 10 US cities (Nowak et al., 2013). Howdoes this spatially distributed effect translate into the monetaryvalue of the services attributed to urban trees? Ultimately, all hu-man exposure is local. Designing landscapes to reduce exposure toair pollution should start with operational definitions of biophysicaloutcomes that can be empirically verified. For example, such defi-nitions could include a specific half distance for the decay in con-centration in a specific size class, or the total distance needed to de-couple the concentration from roadside influence. This will requireboth empirical field study and CFD modeling of the pollution gra-dients and flow fields around trees and other landscape features.Attention should shift from the current paradigm based on depo-sition to the overriding influences of landscape aerodynamics anddispersion. Trees define zones of recirculation, which in turn causegreater deposition because they increase local pollution concen-trations, while at the same time increase the dose received byhumans in these zones. This finding is especially important if weare serious about using green infrastructure to improve local airquality and hence, human health. As Nowak has been quoted,“We're not going to be able to plant our way out of this problem”

(Kessler, 2013).

Acknowledgments

We are grateful for the support from the following grants: USDA(CREES) grants 2001-38875-10702 and 2004-38875-02190; USDA(Forest Service) 05-DG-11244225-228CRIS 0190768; and forthoughtful comments from K. Max Zhang, Frederick Cowett, AaronMatch, Hannah George and 2 anonymous reviewers.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.envpol.2015.02.026.

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