A review of the effects of urban vegetation
on air quality
Jules Kerckhoffs
26-04-2013
Toxicology and Environmental Health
Institute for Risk Assessment Sciences
1. Introduction
Outdoor air pollution has been generally accepted to affect human health. The World Health
Organization (WHO) estimates 1.3 million annual deaths worldwide, with an increased risk of
respiratory and cardiovascular diseases (WHO, 2005). With increasing urban density and high
traffic episodes air pollution is expected to rise even more. Current emphasis is mainly on
concentration levels of particulate matter (PM), nitrogen dioxides (NO2) and ozone (O3). Increase in
levels of these pollutants lead to increased hospital admissions and mortality (Katsouyanni et al,
2001). Although this study found that mortality increased by only 0.5% with an increase of 10
ug/m3 of average PM10 concentrations, it is believed that long term exposure to such pollutants are
crucial in assessing the true health-damaging effects of air pollution (WHO, 2005).
In order to reduce air pollution in urban environments vegetation is used, because of their capability
to help clean the air of pollutants. Trees are effective at capturing significant quantities of pollutants
from the air and have the potential to improve air quality (Beckett et al, 2000). Several Latin
American cities are integrating different kinds of vegetation as part of their environmental
improvement programs, policies and measures (Escobedo et al, 2008). It is believed that vegetation
leaves absorb air pollutants through their stomata and catch particles onto their leaves and branches
(Vos et al, 2012). Whether this is an effective control measure depends on several meteorological
and vegetation characteristics. An assessment of the effect of urban vegetation on inner city air
quality has been done by Leung et al (2011). In this review the benefits of trees are limited to the
urban heat island phenomena, sequestering of carbon dioxides and capturing air pollution. These
benefits are weighed and compared to the disadvantages of urban vegetation, being the release of
volatile organic compounds (VOC's), thereby increasing ozone concentrations. The study
concludes, with a warning about the potential of VOC's altering atmospheric chemistry, that urban
tree planting should be encouraged to improve air quality and mitigate global warming.
As cities grow bigger and buildings get taller, street canyons are formed. In these canyons air
pollutants get trapped, limiting dispersion to the atmospheric boundary layer above. Planting trees
in these settings could limit the dispersion of air pollutants even further (Gromke and Ruck, 2006;
Balczó et al, 2009; Vos et al, 2012). These studies are often based on numerical models and wind
tunnel data, lacking field data to verify the results. Though, it is believed that trees have a negative
impact on air quality in street canyons.
This article reviews studies of the effect of urban vegetation on air quality and assesses the
effectiveness of planting vegetation in urban environments, especially in street canyons, where air
pollution is highest.
2. Benefits of urban vegetation in relation to air quality
Trees can remove gaseous air pollution via different kinds of impaction. Particles are removed by
sedimentation, turbulence and diffusion, classified as dry deposition (Beckett et al, 1997). Surfaces
which are moist, rough or electrically charged have the most chance of catching particles from the
air (Pye, 1987), where large particles deposit faster than smaller particles (McDonald et al, 2007).
Some particles are then absorbed inside the leaf, where pollutant gases diffuse into intercellular
spaces and may be absorbed by water films to form acids or react with inner-leaf surfaces (Nowak
et al, 2006), but most particles remain on the plant surface. These intercepted particles are often
resuspended into the atmosphere (Beckett et al, 1998), washed of by rain or dropped to the ground
with leaf and twig fall (Nowak et al, 2006). It is believed that 50% of the deposited particles on
vegetation is resuspended into the atmosphere with normal meteorological conditions and can be
increased to almost 100% at sufficiently high wind speeds, due to bounce-off (Beckett et al, 1998).
2.1 Deposition velocities
Deposition velocities of particulate matter and other pollutants differ among tree and shrub species
and depend on meteorological (residence time, season) and surface characteristics (tree species,
size, health), therefore very variable in space and time (Loveth, 1994; Pugh et al, 2012). Deposition
velocities for PM used in the assessed studies are shown in table 1. The deposition rate is controlled
by three bulk resistances and the concentration gradient between the air and the surface. The three
resistances are the air movement in the tree crowns space, the transfer through the boundary layer
immediately adjacent to the surface and the chemical and biological absorption capacity of the
surfaces themselves (Loveth, 1994).
2.2 Effectiveness of capturing pollutants
Beckett et al (1998) investigated the difference between species in effectiveness at capturing and
absorbing air pollutants. They found that effectiveness is increased when leaf and bark surfaces of
vegetation are rough or sticky, later confirmed by Beckett et al (2000). This was already found by
Manning and Feder (1980) who stated that forests were most effective in trapping particles, due to
their leave surface roughness and have higher deposition rates than short vegetation or grasslands
(McDonald et al, 2007). Saebo et al (2012), however, found no correlation between the capture of
particulate matter and leaf surface roughness for their selected tree and shrub species. For this study
they investigated the accumulation of PM on leaves of 22 trees and 25 shrub species in test fields in
Norway and Poland. For each species, four plants were used for leaf sampling in two growing
seasons, some exposed in both environments. This gave also an opportunity to show that species
accumulating much PM in one location also do this under different meteorological conditions at
another location. Less effective species in capturing pollutants in relative high pollution sites also
capture little pollutants in cleaner environments (Saebo et al, 2012).
Beckett et al (1998) also found a significant difference between trees with broad leaves and
conifers. Broadleaved species renew their leaves each year, thereby removing accumulated particles
from the tree and regrow leaves for new accumulation. However, these particles can cause harm
when they fall onto the ground and accumulate in the soil (McDonald et al, 2007). Conifers on the
other hand keep their foliage the entire year and continue to transpire and accumulate air pollutants,
making conifers more efficient at improving air quality (Beckett et al, 1998). In the study of Tallis
et al (2011) estimating the removal of atmospheric particle pollution in London they also found that
coniferous trees are more effective in PM capture than broadleaved tree species. To determine the
relationship between PM capture and the amount and type of tree species they used two different
models. The first was the pollution/deposition flux model, also called UFORE model and used in
other studies (Nowak et al, 1998; Escobedo, 2008). The second approach is pollution flux model,
Table 1: Deposition velocities for particulate matter.
Reference
Where
Pollutants Deposition velocity in cm/s
McDonald et al, 2007 Tree plantings in West PM10
Midlands and Glasgow (UK)
Between 0.003 and 0.2
Nowak et al, 1998
Modelling urban vegetation All
in Philadelphia
0.64 for in-leaf season,
0.14 for off-leaf season
Tallis et al, 2011
Tree canopy in London
0.64 for Summer, 0.14 for Winter
0.13 for Broadleaved trees,
0.0046 for Conifers
Loveth, 1994
Deposition of pollutants in All
North America
Large particles = 0.5-2
Fine particles = < 0.5
Yang et al, 2008
Green roofs in Chicago
0.10 for Short grass to 0.36 for
deciduous trees*
PM10
All
* Deposition velocities were highest in May and lowest in February.
which uses tree specific deposition velocities, referred to as the 'Tiwary method' and also used data
from London to assess the ability of PM capture in trees (Tiwary et al, 2009). The Tiwary method
estimated a 10-fold difference between broadleaved species and coniferous species, whereas the
pollution/deposition flux method estimated PM capture only doubled of conifers compared to
broadleaved tree species. Next to the fact that they keep their foliage throughout the year, they also
have very high surface areas which gives the greatest potential to capture particulate matter
(McDonald et al, 2007), are more efficient scavengers of particulate lead and their speed of
establishment is very high (Beckett et al, 1998).
Of the 47 woody species Saebo et al (2012) investigated they found up to a 20-fold difference
between species, with trees trapping significantly more pollutants than shrubs and grasses.
McDonald et al (2007) also found that particles deposit more slowly on short vegetation, therefore
deposition rates to trees are significantly larger. The difference in removal rates used by Escobedo et
al (2008) and Yang et al (2008) are less significant than Saebo et al. Difference between removal
rates of trees and shrubs used by Escobedo et al. is only around 8%, whereas the removal rate by
trees is double the rate for grasses in Yang et al (2008). A list of removal rates given in the literature
is shown in table 2. Saebo et al found that tree species which had high removal rates for large
particulate matter where not obvious to be strong at capturing fine particles, possibly explaining the
large differences between removal rates stated in the literature. Therefore, it could be necessary to
divide efficient vegetation species for large (PM10), coarse (PM2.5) and small particles (PM0.2)
(Saebo et al, 2012), also because different PM sizes have different health effects. Finer particles
seem to be more damaging to health, due to their ability to penetrate deeper into the lung (Beckett et
al, 2000). However, Beckett et al (2000) found no differences in the efficiency of all trees tested in
capturing fine, coarse and large particles at both rural and urban locations. Though, they found that
trees planted close to roads capture PM sizes mainly from the larger fraction (Beckett et al, 2000),
due to fact that large particles deposit faster than fine particles (McDonald et al, 2007). In addition,
looking at the removal rates, trees are most effective when planted as close to the source of
pollution as possible (Beckett et al, 1998). Differences between coarse and fine particles are also
shown by Becket et al (1998), who stated that for coarser particles increased stickiness of the
surface increased pollutant capture, whereas for finer particles the roughness of the surface seemed
to be the predominant factor.
Saebo et al also found positive relationships for the capture of particles and the hair density on the
leaves and quantity of leaf waxes. Yet, they found that Pine species had the highest accumulation of
pollutants, in spite the fact their leaves are without hairs or rough surfaces. It seems that long
narrow needles are more easily hit by particles, especially in the stomatal regions deposition was
Table 2: Removal rates for all pollutants.
Reference
Where/what
Pollutants Deposition velocity in cm/s
Escobedo et al, 2008
Cost-effective analysis of PM10
urban forests in Santiago,
Chile
Trees = 7.4 – 8.0 g/m2/yr.
Shrubs = 5.8 – 8.5 g/m2/yr.
Grass = 1.3 – 1.8 g/m2/yr.
Total = 15.6 – 17.3 g/m2/yr.
Nowak et a, 1998
Modelling urban vegetation All PM
in Philadelphia
10.8 g/m2/yr.
McPherson et al, 1994 Urban forest in Chicago
All PM
4.9 – 5.6 g/m2/yr.
Saebo et al, 2012
Plant species differences
PM10
Pine = 24 – 55 g/m2/yr.
(highest)
Yang et al, 2008
Green roofs in Chicago
All PM
9.7 g/m2/yr.
Effectiveness
of
green NO2 and 4.0 – 43.4 g/m2/yr.
infrastructure
PM10
Table 3: Deposition velocities (Mitchell et al, 2010) and removal rates (Saebo et al, 2012) per tree
Pugh et al, 2011
species.
Tree species
Deposition velocity Removal rate
Willow
0.5 cm/s
Elder
0.8 cm/s
Sycamore
1.3 cm/s
Around 9 ug/cm2/yr
Maple
1.9 cm/s
Around 8 – 19 ug/cm2/yr
Beech
3.0 cm/s
Birch
4.6 cm/s
Pine
Around 24 ug/cm2/yr
38.4 ug/cm2/yr
24 – 55 ug/cm2/yr
much greater as stated by Becket et al (1998). Saebo et al (2005) and Beckett et al (2000) therefore
conclude that Pine species should be used more in urban areas, as they are most effective in
capturing particles. Among the broadleaved species, White beam was best, because of their rough
and hairy leaf surface (Becket et al, 2000). Mitchell et al (2010) also assessed the differences
between tree species in capturing particles using magnetic biomonitoring. They found that trees
with ridged, hairy leaves showed the highest deposition velocities, concluding that surface
morphology is the most important factor in particle deposition. Birch and Beech tree species
appeared to have the highest deposition velocities (table 3), due to their ridged and hairy surface.
The Elder and Willow tree species were amongst the lowest accumulation rates, because their
leaves are much smoother and waxy (Mitchell et al, 2010), which seems to oppose the conclusion of
Beckett et al (2000), but in the study of Mitchell et al, only deciduous tree species were used. Other
aspects to be taken into account in choosing urban vegetation is tolerance of tree species (Saebo et
al, 2012) as some trees are better capable of avoiding damage from particles (Becket et al, 1998).
2.3 Pollutant removal by urban vegetation
The total removal of particulate matter and pollutants for entire cities has also been calculated
mainly in the United States (Nowak et al, 2006; McPherson et al, 1994) and the United Kingdom
(McDonald et al, 2007; Tallis et al, 2011; Bealey et al, 2006; Tiwary et al, 2009). Results from the
literature are shown in table 4. Current tree cover has maximum removal percentages of no more
than 4% (McDonald et al, 2007). Nowak et al (2006) analysed 55 cities in the United States with
standardised removal rates multiplied by average pollutant concentration and total amount of tree
cover, to find that average per cent air quality improvement in US cities during daytime of the
vegetation in-leaf season were less than 1%. Removal percentages were already found using the
UFORE model and were 0.72% for PM10, 0.29% for O3 and SO2, 0.2% for NO2 and 0.002% for CO
(Nowak et al, 1998). The UFORE model uses deposition velocities, calculated by the three bulk
resistances, and the pollutant concentration. For a detailed explanation of the model see Nowak et al
(1998). Though, urban trees remove tons of air pollutants each year and combining the total effects
of trees on air pollutants are significant enough that urban tree management could improve air
quality and help meet clean air standards in the United States (Nowak et al, 2006). Escobedo et al
(2008) also used the UFORE model to conclude that management of street trees in Santiago, Chile,
are cost-effective in removing PM10 concentrations. Removal rates estimated by UFORE were
based on field measurements and real-time meteorological and pollution data. The study indicated
that the use of street trees could be a helpful part in the existing environmental and economic
policies in Chile (Escobedo et al, 2008). McDonald et al (2007) used the FRAME model, which is
based on statistical meteorology to calculate wet and dry deposition and uses five different land
cover types. For a detailed description of the model see Singles et al (1998). The model has been
carefully validated against field measurements on national scale in the United Kingdom (McDonald
et al, 2007). For this study sites were selected in the West Midlands (120 x 80km) and Glasgow (60
x 50km). The model estimates that current tree cover in the West Midlands removes 7% of the
primary particulates pollution, equivalent to a reduction of 4% of primary PM10 concentrations.
Table 4: Removal of pollutants by urban trees under current conditions, 100% tree cover and an
increased 25% tree cover.
Reference Pollutants Based on? Removal by urban Percentage
Percentage
trees
removal
with removal
with
100% tree cover realistic
25%
increased
tree
cover
Nowak et al, O3, SO2, Deposition Less than 1%
2006
NO2, CO rates and 108 kg/ha/yr.
and PM10 LAD
Escobedo et PM10
al, 2008
Removal
66 – 265 kg/ha/yr.
rates
calculated
by UFORE
McDonald
et al, 2007
FRAME
model
PM10
7%
(3.7%
tree
cover). Current tree
cover
reduces
concentrations by 4
and 3%
About 2%
18% – 35% 2 – 19% increase
deposition
in deposition.
increase.
Concentrations
reduced by 7 –
26%.
McPherson, PM10
1994
among
others
21% increase of
PM10 deposition
(from 19.4% to
23,5% tree cover)
Nowak et al, PM10
1998
among
others
UFORE
Freiman
al, 2006
Field
measureme
nts
Up to 20% less
pollutant
concentration
Pollution/d 29.6 kg/ha/year
eposition
63.4 kg/ha/year
flux
method and
Tiwary
method
18%
increased
capture (with 30%
additional
tree
cover)
et PM10
Tallis et al,
2011
PM10
Bealey et al, PM10
2006
FRAME
Tiwary
UFORE
PM10
2.4 Pollution removal models
0,72% for PM10, O3 13% overall
and
SO2=0,29%,
NO2=0,2%,
CO=0,002%
138 kg/ha/yr.
7 – 20
pollutant
reductions
% 2.5 – 7 % pollutant
reductions
9
kg/ha/yr.
increase with 5.5%
additional
tree
cover
Deposition velocities and removal rates used in the literature are estimated based on models. The
mostly used models are the UFORE (Urban Forest Effects model) and the FRAME (Fine
Resolution Atmospheric Multi-species Exchange) model. Both models use statistical information
about the weather and pollution concentration data to estimate pollutant removal. The FRAME
model also uses land cover data sets (Bealey et al, 2007), whereas the UFORE model is only based
on tree cover data and does not take into account occult or wet deposition, therefore likely to
underestimate the total deposition (Tiwary et al, 2009). The FRAME model uses five different land
cover types: arable, grass/moor, urban, forest and water (McDonald et al, 2007) and has a grid of
5km x 5km, but can be rescaled to 1km x 1km (Singles et al, 1998) The land cover data used in the
model are derived from the CEH (Centre for Ecology and Hydrology) land cover map 2000 and is
aggregated into the FRAME types from the 27 original land cover types available (McDonald et al,
2007). UFORE only calculates the dry deposition to tree canopies and is based on an urban tree leaf
area index of 6 and a distribution of 90% deciduous and 10% coniferous leaf surface area. Local
leaf-on and leaf-off date are input in the model so that deciduous tree transpiration and related
pollution deposition are limited to the leaf-on period (Nowak et al, 2006). These deposition rates are
calculated for all pollutants (ozone, sulphur dioxide, nitrogen dioxide, carbon monoxide and PM10),
whereas the FRAME model only estimates primary PM10 removal. Another difference between the
models is that the UFORE model generates deposition values for trees, due to a lack of empirical
deposition data for specific species and for different wind speeds (Tiwary et al, 2009) and the
FRAME model uses a vertical concentration gradient with 33 layers making is possible to examine
spatial variability at a finer scale than models assuming instantaneous mixing of emissions, thereby
avoiding the need for air concentration and deposition correction factors (Singles et al, 1998). There
are some other underestimates in both models. The FRAME model only uses primary PM10
concentrations, but the proportion of secondary particles is much higher in air (Bealey et al, 2007).
In the study of McDonald et al (2007) a correlation between the modelled primary PM10
concentrations and the measured total primary and secondary PM10 concentrations were assumed.
The UFORE model was based on field measurements in Chicago, where they compared the
modelled data to deposition to A.pseudoplatanus only during the leaf-on period, but there will be
deposition onto woody surfaces during the winter (Tiwary et al, 2009).
2.5 Effects of increased tree cover
The model of McDonald et al (2007) also calculates scenarios when 100% of the available land
would be covered in vegetation, creating the maximum removal increase of pollutants in urban
areas (table 4). For the West Midlands this would result in 35% increase in particle deposition,
which relates to 7% decrease in total PM10 concentrations. Glasgow, which has more urban density
than the West Midlands, also removes 7% of the primary particulate deposition, but a 100% tree
cover on available land would only decrease total PM10 concentrations by 1.2% (McDonald et al,
2007). Decreased pollution concentration under 100% tree cover on available land was also
estimated by Nowak et al (2006). Average decrease in particles over 55 US cities was 2%, which is
comparable to the results from McDonald et al. Nowak et al (1998) estimated increased removal of
particulate pollution in the city of Philadelphia and found 13% increase in deposition could be
achieved with 100% tree cover. It should be noted that there is great difference in the estimated
percentile increased deposition of particles and the percentile total concentration decrease. For
example the deposition increase of particles in Glasgow with 100% tree cover is 18%, whereas
primary PM10 concentrations are reduced by 7% and total PM10 concentrations by only 1.2%
(McDonald et al, 2007). Freiman et al (2006) determined differences in PM capture differently.
Instead of using the same site and model particulate deposition, they used different sites in Israel
with different percentile tree cover. Selected urban sites had a per cent tree cover ranging from 36 to
61% and paired sites differed in mean summer pollution concentration of around 18%. This number
seems high, because it concerns pollution concentration and no removal percentage. Several studies
have estimated the percentile removal decrease when tree cover would be increased with a more
realistic 25%. McDonald et al (2007) estimated a 19% increase in deposition of particles for the
West Midlands and Glasgow. These results are comparable to the 21% increased removal of PM 10
in the study of McPherson (1994), with increased tree cover of 21% and the 18% increased capture
of PM10 and by Tallis et al (2011) with 30% additional tree cover. Total concentration levels of
particles will be less changed by the 25% addition tree cover, but could play an important role in
reducing PM concentration is the future. Bealey et al (2006) estimated the reduction in particulate
pollution concentration when tree cover would be increased with 25%. They used the FRAME
model to calculate a 2.5 – 7% reductions in particulate concentrations are possible in the city of
Wolverhampton. Furthermore, a realistic case study in London found an increase in particulate
deposition with 9 kg/ha/yr. with an increase 5.5% additional tree cover, based on 75% grass, 20%
Sycamore maple and 5% Douglas Fir (Tiwary et al, 2009). Tree planting schemes can therefore
contribute to a better air quality and benefits to human health.
2.6 Additional benefits of urban vegetation
Besides the removal of air pollutants, urban vegetation improves environmental sustainability in
other ways. In the first place, they contribute to scenic beauty (Leung et al, 2011). Trees in urban
environments are primary planted for their aesthetic value. Another advantage of urban vegetation is
the reduced energy consumption. In warmer climates this is due to the cooling from shade and
evapotranspiration processes (Salmond et al, 2013). The cooling effect of increased vegetative
coverage in urban areas would help offset, at least partially, the undesirable urban heat island effect
caused by the heat trapped in building materials (Leung et al, 2011). On the other hand, in cooler
areas it has positive effects on urban climate such as increased temperature and humidity (Balzco et
al, 2009) and reduces the need for space heating in houses, thereby promoting energy conservation
(Oke, 1988). These probably outweigh the added disadvantages, such as heat stress in the summer.
Other benefits of urban vegetation are the increased local biodiversity in urban areas, decreased
noise hindrance, minimise storm water run-off, provide opportunities for outdoor recreation and
trees store carbon dioxide in their biomass (Leung et al, 2011; Salmond et al, 2013; Vos et al, 2012).
3. Adverse effects of urban vegetation
3.1 Street canyons
Several studies investigated the effect when trees are placed as close to pollution source as possible,
especially in street canyons, see table 5. Street canyons are streets were the height of the
surrounding buildings is relatively great in proportion with the width of the street. Wind entering on
the windward wall (the wall opposed to the wind direction) is relatively clean air near the top, but
becomes more polluted towards the floor. Then the flow traverses the pollution source of the
vehicles towards the leeward wall. Concentrations are highest at the base of the leeward wall and
decrease with height (Oke, 1988; Gromke and Ruck, 2007). Wind flowing back from the leeward
wall to windward wall results in higher pollutants concentrations in front of the windward wall,
especially on pedestrian level, before it gets mixed with clean air from above (Gromke and Ruck,
2007). They found that pollutant concentrations on the leeward side of the canyon were 2.5 times
higher than the windward wall, using wind tunnel data. This was confirmed by field measurements
conducted by Salmond et al (2013), finding pollutant concentrations on the leeward wall 2 – 3 times
higher than the windward side of the street.
Towards the street corners, a decrease of pollution levels is observable for both walls, due to
additional ventilation induced by corner eddies. Oke (1988) concluded that street canyons were
optimal if they had a height to width ration of 0.4 – 0.6. This ratio was determined by the fact that a
busy street in cities should try to maximise shelter, maximise urban warmth and maximise solar
access in addition to maximising the dispersion of pollutants. The lower limit of about 0.4 is to
provide enough shelter and to retain a reasonable proportion of the heat island warmth. The upper
Table 5: Dispersion models in vegetated street canyons
Reference
Model
Which
pollutants
Balczo et al. MISKAM Passive scalar
and wind
tunnel
Trees
Ries
and MISKAM
Eichhorn
Trees 2D
Salmond
al
Trees
Concentration
Leeward wall
Concentration
windward wall
Overall
100%
(wind 40% (wind tunnel) 20-40% increase with
tunnel)
60% (MISKAM) dense vegetation.
140%
(MISKAM)
Passive scalar Pollutant
concentrations
are
slightly
higher
(max
12%)
et Field study NO, NO2
chemilumi
nescence
monitors
Gromke and Wind
Sulphur
Ruck
tunnel data hexafluoride
Trees
as tracer gas
Pollutant
concentrations
are 2-3 times
higher
than
windward.
Average
concentration 2.5
times higher than
windward.
Trees lead to an increase
in
near
surface
concentrations.
Small decrease in
pollution
with
increasing crown
diameters.
Decrease in flow rate
with increased blockade
effect of larger tree
crowns.
limit of 0.6 ensures that enough solar access reaches the pedestrian level and atmospheric dispersion
is maintained (Oke, 1988).
3.2 Vegetated street canyons
One of the first studies to the inclusion of vegetation in dispersion models was done by Ries and
Eichhorn (2001). They developed a numerical model consisting of Cartesian components of the
equation of motion (MISCAM) and included vegetation by adding the stand density and leaf area
density of trees to the model. They found that wind speed is reduced within the tree crowns and
pollutants concentrations at the leeward wall are therefore slightly higher in case of a vegetated
street canyon (Ries and Eichhorn, 2001). However, their model did not account for threedimensional effects such as the influence of corner eddies (Gromke and Ruck, 2007).
Balczo et al (2009) also used the MISCAM model to assess flow and pollutant dispersion in street
canyons with tree planting and compared this with wind tunnel data. They concluded that MISCAM
was able to qualitatively reproduce the essential flow and pollutant patterns in isolated street
canyons with and without tree planting. Both models showed an increase of 20 – 40% in average
pollutant concentration compared to the treeless case (Balczo et al, 2009). Changes in pollutant
concentrations differ on the leeward side and windward side of the road and depend on several other
factors. Differences between both walls are best shown in Balczo et al (2009) and Gromke and
Ruck (2007), were they found 80 – 140% increase in concentration on the middle of the leeward
side of the street and a decrease of 30 – 60% on the windward side of the canyon, see table 5 and 6.
Concentration levels also depend on the crown permeability (leaf area density), the branch free
trunk height, tree spacing and the crown volume. Gromke and Ruck (2007) found no differences in
concentration when crown permeability was changed, but Wania et al (2012) found an increase in
concentration with densely foliated trees. Differences between high and medium leaf area density
were not detected by Balczo et al (2009), but concluded that tree species with low leaf area density
are favourable.
Salmond et al (2013) conducted a field study to the influence of street trees in canyons, using three
chemiluminescence monitors, measuring concentrations of NO and NO2 at different heights and
comparing leaf-on periods (high leaf area density) with leaf-off periods (low leaf area density).
Table 6: Differences in pollutant concentrations with increasing crown diameter (Gromke and Ruck, 2007).
Two-way traffic
Trees with spherical impermeable Trees with spherical impermeable
crowns of 9m diameter1
crowns of 15m diameter2
Standing Traffic
traffic
turbulence
enhances mixing
of
vehicle
exhausts
and
provides more
homogeneous air
in the canyon.
Concentration level in the middle of
leeward wall is hardly affected by
tree planting. Towards the corners a
steady increase is found (30%). For
the windward wall a decrease in the
middle is found (-20%). Towards the
canyon ends an increase is
observable (30%).
Significant increase at leeward side in
the centre, indicating canyon vortex
strength has been reduced (20%).
Strong increase in concentration at the
edges of canyon for both leeward (up to
80%) and windward side (up to 50%),
because corner eddies are hindered.
Shows important role of corner eddies
in pollutant removal and dilution at the
street
corners.
Decrease
in
concentration in the middle of
windward wall (-30%).
Two-way 40 km/h
traffic
Traffic induced turbulence leads to
better mixing of concentrations.
Leeward wall no differences.
For the windward wall a more
pronounced
increase
in
concentration is observed (10%).
This indicates that the tree crowns
hinder the removal of pollutants.
Reduction in concentration in the
middle of the windward wall (-30%).
No significant changes with standing
traffic.
1
There is gap of 6m between tree crowns and 3m between tree and wall. So sufficient free space remains to form smaller
canyon vortices.
2
Crown volume is 39.4 %, tree spacing unchanged with 15m. Trunk height is 4.5m and 1.5m overtopping the rooftop.
During leaf-on periods, average concentrations were higher below the tree top, whereas no
differences were observed during the leaf-off period. These results suggest that the presence of
leaves reduces upward transport of pollutants and reduces penetration of clean air downwards from
above the tree top, thereby keeping the pollution in the canyon (Salmond et al, 2013).
There is more consensus about the effect of tree spacing in street canyons. Larger tree spacing
ensures better natural ventilation (Wania et al, 2012) and pollutant concentrations are decreased
(Vos et al, 2012). If tree spacing is increased by 33%, pollutant concentrations would decrease by
25.8% at the leeward side of the canyon and be distributed more homogeneously (Gromke and
Ruck, 2007). The influence of crown volume seems to be of great importance if the crowns exceed
rooftop level. Increase of pollutant concentrations were observed with increasing crown volume at
the leeward wall, especially when the crown height exceeded rooftop level (Gromke and Ruck,
2007). Oke (1988) stated that canyons were favourable with height to width ratios between 0.4 and
0.6 and Wania et al (2012) adds that in canyons with ratios higher than 0.5 crown closure should be
avoided. The higher the surrounding buildings, the bigger the negative impact of trees (Vos et al,
2012).
3.3 Other disadvantages of urban vegetation
There are several other aspects to consider about the decision to plant trees or not. One of the
biggest disadvantages of vegetation is the fact that they release volatile organic compounds (VOC)
into the air (McDonald et al, 2007; Tallis et al, 20110. VOC's, both natural and man-made, react
with various components in the atmosphere that modify the atmospheric chemistry. They can react
with nitrogen oxides to form ozone (Leung et al, 2011). VOC's emitted from plants can also
continue to grow in size and agglomerate into particles which eventually become large enough to be
precipitated with rain and snow. VOC emissions from vegetation may even outweigh their benefits
(Leung et al, 2012), but this can be reduced by carefully selecting trees that have less VOC
emission (Saebo et al, 2012; Leung et al, 2012).
Another characteristic of vegetation to keep in mind when designing urban vegetation is the pollen
production of plants (Saebo et al, 2012) which can have severe health effects in the form of hayfever (Becket et al, 1998). Although they account for only a small proportion of airborne particles in
the atmosphere, they have been found to trigger asthma and other allergic responses in humans,
particularly children (Leung et al, 2011).
4. Green walls and green roofs
A solution for the adverse effects of trees in street canyons and keeping the capturing capacity of
vegetation is building green roofs and green walls. Although trees and shrubs remove air pollutants
more effective than green roofs and green walls, intensive green roofs are almost as effective as the
baseline for the removal of PM10 (Currie and Bass, 2005). Removal rates stated in the literature are
given in table 7. While not as effective as trees, due to lower surface roughness and increased
distance from the source, their construction does not require upheaval of the urban environment
(Speak et al, 2012). Yang et al (2008) estimated the potential of air pollution removal by green roofs
in Chicago and states that green roofs can remove large amounts of pollutants from the air. They
used deposition velocities calculated by the UFORE model and saw that uptake of O3 was highest,
followed by NO2, PM10 and SO2. A big drawback of green roofs is its costs. A medium sized tree,
which costs approximately 400$ removes the same as 19m2 extensive green roof in one year, which
would cost around 3000$ (Yang et al, 2008). Deutch et al (2005) also estimated that one entire
hectare of green roof would be equivalent to 138 street trees. Nevertheless, the potential removal of
particulate matter with an increased green roof area of 15.3% in the Manchester area is around 2.3%
of the emitted PM10 in this area. As there are also differences between species in green roofs, larger
quantities could be removed with grass roofs removing PM10 concentrations up to 17.5%
(A.stolonifera and F.rubra) (Speak et al, 2012). Other benefits of green roofs are the reduction of
storm water run-off, saving energy, reducing urban heat islands and extending life span of roofs
(Yang et al, 2008).
Table 7: Removal rates by green roofs and green walls
Reference
Where
Model
Pollutants
Yang et al
2008
Chicago
How much can
be removed
Big-leaf resistance O3, SO2, NO2 and PM10 85 kg/ha/yr.
model
Green roofs
Currie and Bass Toronto
2005
UFORE
O3, SO2, NO2 and PM10 63kg/ha/yr.
UFORE
O3, SO2, NO2 and PM10 83 kg/ha/yr.
Green roofs
Deutch et al
2005
Washington
Green roofs
Pugh et al
2012
Green walls
No specific area CiTTyStreet
PM and NO2
PM = 23%
NO2
=
15%
reduced
concentrations
Another option for reducing pollutant concentrations is the implementation of green walls in urban
street canyons. This alternative was investigated by Pugh et al (2012), using a model of streetcanyon chemistry and deposition, called CiTTy-Street. This model, developed from the CiTTyCAT
model, assigns deposition velocities separately, different for roofs, canyon walls and floors. CiTTyStreet was evaluated against measurements made in and above street canyons in Hanover, Berlin
and Copenhagen (Pugh et al, 2012). Simulating the implementation of green walls across large area
of streets reduced concentrations of NO2 and PM10 by respectively 15% and 23%. These reductions
were strongly dependent on residence time and the fraction of the wall covered in vegetation.
Because green walls act directly to the emitted pollutants, greening of in-canyon surfaces is more
effective than greening of roofs. Greening also reduces surface temperatures and noise pollution
(Pugh et al, 2012).
Removal rates in table 7 are within the range of the removal rates generated by urban tree removal,
shown in table 4. Because models such as UFORE predict that trees and shrubs are more effective
in capturing pollutants than grasslands, it can be said that green walls are an effective addition to
pollutant removal. They are close to main pollutants sources and can directly filter the air, without
altering the dispersion of air.
5. Conclusions
Urban vegetation has the potential to contribute to the reduction of pollutant concentrations in
cities, although current removal percentages are very low. Coniferous trees are better at trapping
pollutant particles than deciduous trees, because they keep their foliage throughout the year and
have very high surface areas. Hairy and rough leave surface seem to help with capturing particles.
A planting scenario with planting trees on 25% of the available land in urban areas could result in
bigger changes, ranging from an increase of 2-21% in deposition and a reduction of 7% of pollutant
concentrations. Although several studies suggest that planting trees closer to the pollution source, in
most cases busy roads, is more effective, more recent studies show a reduction of pollutant
dispersion when too many trees are placed within the street canyon. Overall pollutant
concentrations can be increased up to 40% depending on the crown diameter and volume, tree
spacing, trunk height and crown permeability of leave area density. As increases in pollutants with
increased tree cover due to decreased dispersion will outweigh the positive effects of trees, it seems
clear that street designs are best with less trees. It should be noted that roadside trees are not only
placed for their effect on air quality and that aesthetics probably plays a more important role.
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