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Urban Forestry & Urban Greening j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / u f u g Quercus ilex L. as bioaccumulator for heavy metals in urban areas: Effectiveness a b s t r a c t In recent years the use of plants as bioaccumulators or bioindicators has increased because enable the prediction of pollution for monitoring purposes, even in urban environments where traffic is a major source of heavy metals pollution. In this study we hypothesized holm oak (Quercus ilex L.) a valid trapping species for heavy metals. We also hypothesized that metals capture capacity by deposition on the crown is connected to the surrounding environmental characteristics and the distance of trees from the source of pollution. The study was conducted in the city of Florence. Holm oaks were selected in different sites near to heavy traffic roads. Concentrations of Zn, Pb, Cd, Cu, Fe, Mn, Cr, and Ba were analyzed through two methods: leaf washing with distilled water and leaf unwashing. One-year-old leaves (new leaves) were also compared with previous-year leaves (old leaves). Our results demonstrated the good capacity of this species to capture heavy metals (Pb, Fe, Mn, Cr, and Ba), particularly due to the presence of old leaves, which enhance the crown deposition surface. Washing was effective and it allowed testing the behaviour with regard to microelements: new leaves showed high Cu concentration, while old leaves had high Pb concentration. The dispersion of metals through the atmosphere was assessed through regression analysis, in two comparable gardens: leaves at farther distance from the traffic were richer in Zn, Pb, Mn, and Ba. The physical context of the surrounding environment was probably altering the distribution of heavy metals as barriers to dispersion, which can reach tens of metres from the source of pollution. Therefore, this work suggests that wind modelling and trees distribution and characteristics should be taken into consideration to evaluate the pollutants dispersion, especially for planning of recreational urban green areas.
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Urban Forestry & Urban Greening 12 (2013) 576–584
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Urban Forestry & Urban Greening
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Quercus ilex L. as bioaccumulator for heavy metals in urban areas:
Effectiveness of leaf washing with distilled water and considerations
on the trees distance from traffic
Francesca Ugolinia,, Roberto Tognettib, Antonio Raschia, Laura Baccia
aInstitute of Biometeorology, National Research Council, Firenze, Italy
bDepartment of Bioscience and Territory, University of Molise, Pesche, Italy
article info
Heavy metals
Holm oak
Internal concentration
Traffic pollution
Urban environment
In recent years the use of plants as bioaccumulators or bioindicators has increased because enable the
prediction of pollution for monitoring purposes, even in urban environments where traffic is a major
source of heavy metals pollution. In this study we hypothesized holm oak (Quercus ilex L.) a valid trapping
species for heavy metals. We also hypothesized that metals capture capacity by deposition on the crown
is connected to the surrounding environmental characteristics and the distance of trees from the source
of pollution. The study was conducted in the city of Florence. Holm oaks were selected in different sites
near to heavy traffic roads. Concentrations of Zn, Pb, Cd, Cu, Fe, Mn, Cr, and Ba were analyzed through
two methods: leaf washing with distilled water and leaf unwashing.
One-year-old leaves (new leaves) were also compared with previous-year leaves (old leaves). Our
results demonstrated the good capacity of this species to capture heavy metals (Pb, Fe, Mn, Cr, and Ba),
particularly due to the presence of old leaves, which enhance the crown deposition surface. Washing
was effective and it allowed testing the behaviour with regard to microelements: new leaves showed
high Cu concentration, while old leaves had high Pb concentration. The dispersion of metals through
the atmosphere was assessed through regression analysis, in two comparable gardens: leaves at farther
distance from the traffic were richer in Zn, Pb, Mn, and Ba. The physical context of the surrounding
environment was probably altering the distribution of heavy metals as barriers to dispersion, which can
reach tens of metres from the source of pollution. Therefore, this work suggests that wind modelling
and trees distribution and characteristics should be taken into consideration to evaluate the pollutants
dispersion, especially for planning of recreational urban green areas.
© 2013 Elsevier GmbH. All rights reserved.
In recent years, the interest for urban air pollution has been
increasing because inextricably linked to human health. The main
anthropogenic sources of atmospheric pollution are industrial
plans, domestic heating and vehicles traffic, which produce dust,
inorganic and organic pollutants, including heavy metals. Even
the nearby metal manufacturing facility, the disposal of municipal
waste (incineration and landfill) and industry, such as petrochem-
ical industries (Nadal et al., 2004), can be sources of harmful
compounds that are naturally transported by resuspension pro-
cesses into the town. By the way, meteorological conditions and
the position of the pollution sources play an important role on
determining the level of such pollutants in atmosphere.
Corresponding author at: Institute of Biometeorology, National Research Coun-
cil, via Giovanni Caproni 8, Firenze, Italy. Tel.: +39 0553033701; fax: +39 055308910.
E-mail address: (F. Ugolini).
In urban environment, heavy metals like Cu, Zn, Cd, and Pb
can be originated by different sources, such as rubber tire wear,
lubricating motor oil and tires (Zn), auto workshops, electroplat-
ing industries, gasoline combustion (also for Mn) (Shi et al., 2012),
although the use of unleaded gasoline, affirmed since nineties,
seems to have decreased Pb (Gratani et al., 2008). Cd and Cu are
also generated by industrial emissions (Charlesworth et al., 2003;
Yin et al., 2011); Ba is mainly used in spark plugs for internal
combustion engines; Cr, which is used in alloys, increases hard-
ness and resistance to mechanical wear (McGrath and Smith, 1990)
and derives also from stationary sources of fossil fuels combustion
(Pacyna and Pacyna, 2001), thus prevailing during heating season.
Fe is the most widespread metal, used as building material, but
in particular for the production of automobiles and load-bearing
Regardless source of production or typology, trace metals from
urban sources are primarily released via atmospheric emissions
(Nriagu and Pacyna, 1988; Kubin and Lippo, 1996), they tend to
adhere to particulate matter to form fine particulates and dust
1618-8667/$ – see front matter © 2013 Elsevier GmbH. All rights reserved.
F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584 577
(Vesper and White, 2003) and finally they are deposited on land,
thus also on the trees. The dispersion and distribution of trace
et al., 2000), the size of the particles (Tomasevi´
c et al., 2005) and sur-
face properties of the substrate on which the metals are deposited.
For instance, regarding vegetal surfaces, specific leaf traits such as
trichomes, roughness, epicuticular waxes, specific leaf area, and
stomatal density may have an influence on particle deposition
(Rossini Oliva and Mingorance, 2006; Ataabadi et al., 2011). More-
over, those deposited on the ground can be readily relocated and
dispersed by wind, rain and surface runoff (Callender and Rice,
Among metals, Pb and Cd have high capacity to accumulate in
the environment (Newman and Clements, 2008), while other ele-
ments (Zn, Fe, and Cu) are essential micronutrients to plants and
humans though dangerous at high exposure levels (Nadal et al.,
2004). In the last decades, several studies have evidenced the possi-
bility to use vegetal organisms as bioaccumulators or bioindicators
in pollution monitoring protocols (Bargagli et al., 1997; Monaci and
Bargagli, 1997; Bargagli, 1998a; Odukoya et al., 2000; Oliva Rossini
and Valdés, 2004; Gjorgieva et al., 2010) of heavy metals and also
polycyclic aromatic hydrocarbons (PAHs) (Alfani et al., 2001; De
Nicola et al., 2008; Lancellotti et al., 2006).
The capacity of foliage accumulation through dry or wet depo-
sition or absorption, strictly depends on the spatial distribution of
the trees, duration of exposure and climate, but also on the species
features, such as leaf area (single leaf and whole foliage), surface
texture (roughness and pubescence), plant habitus (evergreen or
deciduous), and gas exchange (rate between leaf and atmosphere,
multiple stress responses) (Alfani et al., 1996b; Beckett et al., 2000;
Liu et al., 2012; Cocozza et al., 2013). Heavy metals deposited on
the leaf can remain on the surface or enter the leaf tissues (Kabata-
Pendias and Pendias, 1992), although trace metals detected in the
leaves can also come from the soil, via active or passive uptake
by plant roots (Tangahu et al., 2011) and be translocated through
the xylem. The analysis of pollutants concentration can be done
in different ways, depending on the purpose of the study. Analysis
of unwashed samples allows quantifying the deposition of metals
over the surfaces, on the other hand, sample cleaning allows dis-
tinguishing the composition within internal tissues (McCrimmon,
1994; Alfani et al., 2000) due to the translocation from soil to foliage
and incorporated into the tissues. Washing techniques are several
and various (Oliva Rossini and Raitio, 2003) such as mechanical
cleaning (Cercasov, 1985; Krivan et al., 1987), washing through
solvents (Lehndorff and Schwark, 2004), weak acid solutions (Rea
et al., 2000), but also sample washing with distilled water (Alfani
et al., 1996a; Bargagli, 1998a; Monni et al., 2000). Plants in urban
context have a key role since increase the surface on which par-
ticles deposit and absorb pollutants from the soil; therefore, trees
are particularly important especially if extensively distributed. A
variety of species has been used as metal deposition indicators and
bioaccumulators of aerial pollution, including broadleaved species
like chestnut (Nicholas and Fergusson, 1994) and holm oak (Alfani
et al., 1996a, 1997; Gratani et al., 2000), and coniferous species
like Scots pine (Dmuchowski and Bytnerowicz, 1995), as well as
ornamental plants (Oliva Rossini and Valdés, 2004).
This study was conducted during summer 2007 in Florence,
Italy, aiming to assess the capacity of holm oak leaves to hold heavy
metals. The species Quercus ilex L. was chosen because widely used
in Mediterranean urban green areas, due to its attractive shape
and deep shade, as evergreen, able also to withstand detrimental
urban conditions without evidencing marked physiological stress
(Ugolini et al., 2012); in addition, leaves shed when they are 4–5
years old, thus conferring to this species a broad leaf deposition
surface. Four green areas with holm oak individuals close to highly
traffic roads were considered. The study aimed also to identify
possible differences between leaf ages (new fully developed leaves
and one-year-old leaves) and sample treatments (thoroughly
washed with distilled water compared to unwashed).
Materials and methods
Study area
The study was conducted in the city of Florence, Italy, during
summer 2007. Four green areas counting individuals of holm oak,
nearby intense traffic roads, were selected. The selected trees were
similar in size (about 10 m tall and 20 cm in diameter) and placed
to different distance from roads. The sampling sites included three
private gardens and one street with intense traffic (Fig. 1).
Garden 1 (G1): one of the widest private gardens in Florence
(about 70,000 m2). It has an ancient origin and was restored in
XVIII century in English style by the introduction of rare and exotic
species. It also counts individuals of holm oak. The garden is fenced
by a railing along one side facing the busiest boulevard of Florence.
Buildings surround the remainder sides of the garden. Leaf samples
were taken from holm oaks placed at increasing distances from the
boulevard: 3, 35 and 65 m. Leaf samples are designated as G1-3,
G1-35 and G1-65.
Garden 2 (G2): a large park (68,000 m2) located along part of the
city walls of mediaeval time. The traffic runs along the ancient city
wall, about 6 m tall, which separates the road from the inside of
the garden; also along the road there are tall trees of Celtis australis
L. Buildings on two sides and a quiet street surround the remain-
der sides of the garden. Inside, the park is adorned by exotic and
Mediterranean species among which three individuals holm oak
were selected for sampling. They are placed at varying distances
from the wall: 2, 40 and 54 m. Leaf samples are designated as G2-2,
G2-40 and G2-54.
Garden 3 (G3): a historical Renaissance style garden (20,000 m2
wide) also characterized by woodland with high trees of holm oaks.
The woodland is surrounded by a 3 m tall wall along the traffic road,
which is busy all day long. Even this garden is surrounded by other
buildings on the other sides. In this garden, two holm oaks were
selected. They are located close to the wall at about 10 m from the
traffic. Leaf samples are called G3-10.
Mariti Street (S): individuals of holm oak are directly exposed
to intense daily traffic in this important avenue. The road is 15 m
wide and is separated into two lanes with a row of trees growing
Fig. 1. Sampling garden sites: Garden 1 (G1), Garden 2 (G2), Garden 3 (G3) and the
Street (S). White lines indicate the busiest roads close to the gardens.
578 F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584
in a 50 cm wide median and at a distance of approximately 10 m
to one another. Here, three individuals were selected for the study.
Leaf samples are indicated as S-0.
Sampling was carried out on the 21st of July 2007 after 46 days
of drought. This prevented leaching of heavy metals from the leaf
surfaces. Sampling was done from the lower third of the canopy of
each tree. Sixteen fully expanded leaves (four from each cardinal
points) were taken from the shoot of the year (new leaves, NL)
and sixteen leaves from the shoot of the previous year (old leaves,
OL) as well. The two samples were kept separated in plastic bags
(fresh weight, leaf area (LA, using the Portable Area Meter, Model
Li-3000, Lincoln, Nebraska USA) and dry weight to determine leaf
indexes like Leaf Mass per Area (LMA) and Leaf Dry Mass Content
(LDMC)). Part of the leaves was stored in a cool place until analysis.
Heavy metal analysis
To investigate the leaf capacity to hold heavy metals two sample
treatments were used. Half of the sampled leaves for each age class
were thoroughly washed in distilled water to remove deposited
particles from the surface (Bargagli, 1998a); the remaining leaves
were analyzed unwashed.
To extract heavy metals, organic matter was decomposed by
wet ashing: 0.5 g of sample was dissolved in a solution 1:5 of
hydrogen peroxide and nitric acid (H2O2and HNO3) (adapted from
Novozamsky et al., 1995). The Teflon digestion bomb was used to
mineralize the samples and prevent the volatilization of metals,
such as Cd and Cu. Then the solution was diluted in distilled water
up to 25 ml, and analyzed by Inductively Coupled Plasma Atomic
Emission Spectroscopy (ICP-AES), which induces excited atoms
and ions to emit electromagnetic radiation at wavelengths char-
acteristic of a particular element. The intensity of this emission is
indicative of the concentration of the element within the sample.
Eventually, concentrations of Zn, Cu, Pb, Mn, Cr, Cd, and Ba were
Statistical analysis
Data were elaborated by using the software Statistica. One-way
ANOVA was used to identify significances among the sites and
Tukey test for post hoc comparison of means was used to com-
pare mean values. Confidence interval was set at 99%. A t-test for
independent samples was used to assess the difference on heavy
metals deposition between treatments (washed/unwashed) within
each site. Differences were tested taking into account leaf age. Mean
values and standard deviations are reported in graph. Moreover, G1
and G2 were considered comparable for size and individuals distri-
metal deposition; thus, leaf samples taken from individuals along
a transect were used for the regression analysis.
Sampling on the base of leaf treatment and leaf age has
evidenced significant differences (Table 1) between the sites for
most of heavy metals except Ba. The concentrations relative to each
site and the comparison among them are then given in Table 2.
Overall, Fe and Mn and Ba and Zn were the most abundant
metals (Table 2), either in unwashed or washed leaves, whereas
Cd showed the lowest concentrations.
Site S was the most polluted in Pb, Cu, Mn, and Ba, which reached
of other sites, but also G3 was richer in metals in comparison with
G1 or G2, especially for Pb, Zn, Fe, Mn, and Cr.
Again, in unwashed leaves, higher metal concentrations were
found in older leaves (P< 0.01). As an example, the maximum dif-
ference between old and new leaves was found in G2 for Fe and
Pb, which showed metal concentrations in old leaves about three
times those in new leaves.
Within washed samples, again, S recorded the highest and G2
the lowest concentrations of all metals except for Ba. Also in this
case, old leaves showed metal concentrations higher than new
leaves. Although Cu exhibited a different trend, with higher amount
in new leaves of G2 and G1, and Zn displayed ambiguity in G1,
where it reached the highest concentration in washed leaves.
S showed the highest reduction of all metals after washing.
In order to assess the metal deposition with regard to the posi-
tion of the plant, statistical analysis was executed for all sites at
each selected position from the road. Following, the results for each
metal discerned the metal concentration on the base of the dis-
tance from the road (Fig. 2), taking into consideration the regression
analysis in G1 and G2.
Washing vs. unwashing. In new leaves (Fig. 2A), washing
revealed the lower concentration of Zn in G1-35, G3-10, G2-54 and
S-0 with respect to unwashed leaves but in two cases (G1-3 and
G2-2) Zn was higher in washed leaves in comparison to unwashed
In old leaves washing revealed lower concentrations than in
unwashed leaves in all sites. The lowest values for both cases were
found in G1-3 while the highest in S-0 but also in G3-10 (Fig. 2B).
Distance from the source inside the gardens G1 and G2: In new
leaves, the regression between Zn concentration and the distance
from the road did not show significant relationships.
In old leaves the regression was positive and strong: the far-
ther the positions the higher the concentrations, either in washed
(P< 0.001; R2= 0.57) or unwashed samples (P< 0.001; R2= 0.45).
In general, old leaves also showed higher Zn than new leaves,
except in G1-3 and G2-54.
Washing vs. unwashing. In new leaves (Fig. 2C), significant dif-
ference between treatments was found only in S-0 with unwashed
leaves richer in Pb. Old leaves (Fig. 2D) recorded the highest val-
ues, and within these, again, unwashed samples were richer in Pb
(P< 0.01).
Distance from the source inside the gardens G1 and G2: The
regression analysis in G2 and G1 resulted in a significant positive
correlation between Pb concentration and distance from the road
only for washed old leaves (P< 0.01; R2= 0.93).
Washing vs. unwashing. In new leaves, unwashed samples
showed higher Cu concentrations (P< 0.01) than washed samples,
except in G2-10 (Fig. 2E).
In old leaves, again, all unwashed samples showed higher Cu
content (P< 0.01) (Fig. 2F).
Distance from the source inside the gardens G1 and G2: In new
leaves a positive and significant regression was found between Cu
and distance from the road in unwashed (P< 0.01; R2= 0.72) and
washed samples (P< 0.01; R2= 0.89).
In old leaves, the regression analysis evidenced a weak rela-
tion (P< 0.01; R2= 0.47) between metal deposition in old leaves and
distance from the road.
F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584 579
Table 1
Mean values ±s.d. for each site. The treatments are kept distinct. Letters indicate the significant differences between the sites within the samples (OL = old leaves and NL =new
leaves). ANOVA was followed by Tukey test for post hoc comparison of means.
Metals (mg kg1)
Leaf age Zn Pb Cu Cd Fe Mn Cr Ba
G1 23.2 ±0.12b 2.3 ±0.12c 7.8 ±0.08c 0.08 ±0.01 246.4 ±2.1c 214.7 ±1.1b 1.1±0.02b 30. 9 ±0.3
G2 25.8 ±0.28b 1.7 ±0.13d 7.5 ±0.04c 0.05 ±0.01 263.9 ±1.6c 153 ±0.7b 1.2 ±0.02b 48.6 ±0.4
G3 48 ±0.65a 3.1 ±0.02b 9.3 ±0.09b 0.08 ±0.01 346.2±1.3b 397.2 ±4.8ab 1.4 ±0.03b 48.9 ±0.8
S0 42.8 ±0.50a 3.7 ±0.15a 19.8 ±0.16a 0.12 ±0.01 534.5 ±6.5a 405.4±1.6a 3.1 ±0.03a 34.5 ±0.4
P< 0.001 P< 0.001 P< 0.01 n.s. P< 0.001 and
P< 0.05
between G1
and G3
P< 0.01 and
P< 0.05
between G1
and S0
P< 0.001 n.s.
G1 21.6 ±0.27b 1.2 ±0.11a 7.2 ±0.08b 0.08 ±0.01a 120.6 ±0.9b 95 ±0.6 0.5 ±0.01b 17.3 ±0.1
G2 22.5 ±0.36b 0.7 ±0.14b 5.9 ±0.01c 0.04 ±0.01b 85.8 ±0.2b 68.4 ±0.6 0.3 ±0.02b 29 ±0.3
G3 26.3 ±0.57ab 1.5 ±0.08a 6.8 ±0.14b 0.07 ±0.01a 112.4 ±1.5b 142.9 ±2.1 0.6 ±0.07b 27.2 ±0.5
S0 29.3 ±0.29a 1.4 ±0.09a 12.2 ±0.08a 0.07 ±0.01a 218.4 ±2.9a 87.8 ±1.2 1.3±0.03a 15.4 ±0.3
P< 0.05 P< 0.05 P< 0.001 P< 0.05;
P< 0.001
between G2
and G1
P< 0.01 n.s. P< 0.001 n.s.
G1 25.6 ±1.68b 1.7 ±0.1b 5.6 ±0.09b 0.1 ±0.002a 111.9 ±0.7b 235 ±0.8ab 0.5 ±0.03b 29.8 ±0.2
G2 22.8 ±0.28c 0.8 ±0.11c 5.3 ±0.04b 0.06 ±0.01b 79.5 ±0.7c 140.9 ±1.3b 0.4 ±0.01b 45.1 ±0.2
G3 42.5 ±0.59a 2 ±0.17b 6.3 ±0.09b 0.09 ±0.005a 150 ±1.8b 332 ±2.3a 0.5 ±0.01b 45.4 ±1.1
S0 40.8 ±0.43a 3.3 ±0.04a 15.3 ±0.22a 0.09 ±0.01a 376.8 ±3.7a 333.9 ±4.7a 2.2 ±0.02a 30.8 ±0.4
P< 0.001 P< 0.001 P< 0.001 P< 0.001 P< 0.001 P< 0.05 P< 0.001b n.s.
G1 23.72 ±1.36 0.9 ±0.08a 6.1 ±0.10 0.11 ±0.002a 68.9 ±0.6b 100 ±0.3ab 0.3 ±0.01b 16.6 ±0.1
G2 23.22 ±0.22 0.6 ±0.15b 6.4 ±0.08 0.04 ±0.01b 57.05 ±0.6b 66.18 ±0.7b 0.2 ±0.02b 29.8 ±0.2
G3 5.36 ±0.43 0.9 ±0.02b 6 ±0.11 0.08±0.01ab 69.02 ±0.9b 156.6 ±0.4a 0.2 ±0.01b 28.9 ±0.4
S0 0.95 ±0.28 1.2 ±0.08a 7.30 ±0.07 0.07 ±0.01ab 114.18 ±1a 70.98 ±1.1b 0.62 ±0.01a 11. 6 ±0.1
n.s. P< 0.001 n.s. P< 0.001 P< 0.001 P< 0.05 P< 0.001 n.s.
Washing vs. unwashing. In new leaves (Fig. 2G), significant dif-
ferences were found only at G1-65 (P< 0.01) and G2-2 (P< 0.05)
with higher concentrations in washed leaves while in most sites dif-
ferences were not significant. In old leaves (Fig. 2H), three locations
(G1-35, G1-3, G3-10) showed higher concentrations in washed
with respect to unwashed samples, while the contrary in G1-65
and S-0.
Distance from the source inside the gardens G1 and G2:
For both new and old leaves not significant regression was
Washing vs. unwashing. In new leaves (Fig. 2I), unwashed
leaves showed significantly higher (P< 0.01) Fe concentration than
samples showed the highest values and maximum concentrations
were found in S-0, which almost doubled the values of other loca-
tions in both treatments.
Old leaves also showed Fe concentration much higher than in
new leaves (P< 0.01).
Distance from the source inside the gardens G1 and G2: The
regression analysis between Fe concentration and distance from
Table 2
Mean values ±s.d. are given for each treatment (unwashed/washed leaves) and leaf age. New leaves (NL) were compared to old leaves (OL) through the t-test for independent
paired samples.
Metals (mg kg1)
Zn Pb Cu Cd Fe Mn Cr Ba
G1 OL 23.2 ±0.12** 2.3 ±0.12** 7.8 ±0.08** 0.08 ±0.01 246.4 ±2.1** 214.7 ±1.1** 1.1 ±0.02** 30. 9 ±0.3**
NL 21.6 ±0.27** 1.2 ±0.11** 7.2 ±0.08** 0.08 ±0.01 120.6 ±0.9** 95 ±0.6** 0.5 ±0.01** 17.3 ±0.1**
G2 OL 25.8 ±0.28** 1.7 ±0.13** 7.5 ±0.04** 0.05 ±0.01 263.9 ±1.6** 153 ±0.7** 1.2 ±0.02** 48.6 ±0.4**
NL 22.5 ±0.36** 0.7 ±0.14** 5.9 ±0.01** 0.04 ±0.01 85.8 ±0.2** 68.4 ±0.6** 0.3 ±0.02** 29 ±0.3**
G3 OL 48 ±0.65** 3.1 ±0.02** 9.3 ±0.09 ** 0.08 ±0.01 346.2 ±1.3** 397.2 ±4. 8** 1.4 ±0.03** 48.9 ±0.8**
NL 26.3 ±0.57** 1.5 ±0.08** 6.8 ±0.14** 0.07 ±0.01 112.4 ±1.5** 142.9 ±2.1** 0.6 ±0.07** 27.2 ±0.5**
AOL 42.8 ±0.50** 3.7 ±0.15** 19.8 ±0.16** 0.12 ±0.01** 534.5 ±6.5** 405.4 ±1.6** 3.1 ±0.03** 34.5 ±0.4**
NL 29.3 ±0.29** 1.4 ±0.09** 12.2 ±0.08** 0.07 ±0.01** 218.4 ±2.9** 87.8 ±1.2** 1.3 ±0.03** 15.4 ±0.3**
G1 OL 25.6 ±1.68 1.7 ±0.1** 5.6 ±0.09** 0.10 ±0.002 111.9 ±0.7 ** 235 ±0.8 ** 0.5 ±0.03** 29.8 ±0.2**
NL 23.72 ±1.36 0.9 ±0.08** 6.1 ±0.10** 0.11 ±0.002 68.9 ±0.6** 100 ±0.3** 0.3 ±0.01** 16.6 ±0.1**
G2 OL 22.8 ±0.28 0.8 ±0.11** 5.3 ±0.04** 0.06 ±0.01 79.5 ±0.7** 140.9 ±1.3** 0.4 ±0.01** 45.1 ±0.2 **
NL 23.22 ±0.22 0.6 ±0.15** 6.4 ±0.08** 0.04 ±0.01 57.05 ±0.6** 66.18 ±0.7** 0.2 ±0.02** 29.8 ±0.2**
G3 OL 42.5 ±0.59** 2±0.17** 6.3 ±0.09** 0.09 ±0.005 150 ±1.8 ** 332 ±2.3** 0.5 ±0.01** 45.4 ±1.1**
NL 5.36 ±0.43** 0.9 ±0.02** 6±0.11** 0.08 ±0.01 69.02 ±0.9 156.6 ±0.42** 0.2 ±0.01** 28.9 ±0.4**
AOL 40.8 ±0.43** 3.3 ±0.04** 15.3 ±0.22** 0.09 ±0.01 376.8 ±3.7 ** 333.9 ±4.7 ** 2.2 ±0.02** 30.8 ±0.4**
NL 0.95 ±0.28** 1.2 ±0.08** 7.30 ±0.07** 0.07 ±0.01 114.18 ±1** 70.98 ±1.1** 0.62 ±0.01** 11. 6 ±0.1**
** Significances at P< 0.01.
580 F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584
Fig. 2. Heavy metal concentrations in new leaves (NL) and old leaves (OL) identified by the treatments: washed samples (grey bars) and unwashed samples (white bars).
Significant differences between treatments at P< 0.01 do not have signs in graphs; significant differences at P<0.05 are indicated with *; not significant results are indicated
with n.s. notation.
the source of pollution was not significant for both sample treat-
ments, per leaf age.
Washing vs. unwashing. For new leaves, G1-3, G3-10, G2-40
recorded higher concentrations of Mn in washed samples while in
other locations (G1-65, G2-54, S-0) the higher concentrations were
found in unwashed samples (Fig. 2K). In old leaves (Fig. 2L) higher
values were found in unwashed samples. S-0 was the most polluted
Distance from the source inside the gardens G1 and G2: In new
leaves, regardless sample treatments, the regression found out a
positive and strong relation between metal concentration and dis-
tance from the traffic for unwashed (P< 0.01; R2> 0.88) and washed
samples (P< 0.01; R2> 0.92).
F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584 581
For old leaves, in G1and G2, the regression analysis evidenced
a positive and strong relation between Mn concentration and dis-
tance from the traffic (for unwashed samples: P< 0.01; R2> 0.90 and
for washed samples P< 0.01; R2> 0.93), as found in new leaves.
In general, regardless of the method of detection, new leaves
showed lower concentrations of Mn than old leaves (P< 0.01)
Washing vs. unwashing. Both in new and old leaves (Fig. 2M and
N), unwashed samples showed higher concentrations than washed
leaves (P< 0.01). The highest values were found in S-0 in both treat-
ments, with Cr concentrations almost doubling those of other sites.
Distance from the source inside the gardens G1 and G2: No sig-
nificant correlation was found between Cr and distance from the
traffic in G1 and G2. In general, new leaves showed very low values
in comparison to old leaves
G2-54 was the site with the highest concentration of Ba (Fig. 2O
and P), whereas G1-3 and G2-2 were those with minimum concen-
trations. Moreover, old leaves showed higher concentrations than
new leaves.
Washing vs. unwashing. In new leaves (Fig. 2O), only some
locations recorded higher values in unwashed sample. This was
observed in G1-65, S-0 and G2-54, while other (G1-35, G3-10, G240,
G2-2) showed higher values in the washed samples. On the other
hand, in old leaves (Fig. 2P), Ba concentrations showed higher val-
ues in unwashed samples in all sites.
Distance from the source inside the gardens G1 and G2: A pos-
itive and rather strong relation between Ba concentration and
distance from the traffic was found in new leaves either unwashed
(P< 0.01; R2>0.69) or washed (P< 0.01; R2> 0.75), but also in old
leaves either unwashed (P< 0.01; R2> 0.68) or washed (P< 0.01;
R2> 0.68).
Multiple regression analysis between metal accumulation
within the leaves (thoroughly washed samples) and biomass
indices (LMA, LDMC) was also tested. All metals showed a signifi-
cant, though weak, positive relation with LDMC, regardless of leaf
age (data not shown).
Trees in urban areas play an important role not only for shading
and the mitigation of microclimate conditions (Petralli et al., 2009),
but also for the capacity through foliage and stem surfaces to
adsorb particulate matter. In general, accumulation of pollutants
in plant tissues can be considered an indicator of air pollution and
used in monitoring protocols. Leaves and stems are rough sur-
faces on which particles deposit, for this reason they allow low
cost monitoring of heavy metals (Bargagli, 1998b; Odukoya et al.,
2000; Oliva Rossini and Valdés, 2004). Many studies have evidenced
that atmospheric pollution by heavy metals can be estimated
through the analysis of leaf samples, regardless of the preliminary
treatment of sample material. In particular, sample washing after
sampling, may decrease the element contents of about 10–30% in
comparison with unwashed samples (Ward et al., 1977). Routine
analysis uses washed as well as unwashed leaves, and washing
can be done also without using solvents (Lehndorff and Schwark,
2004). Metals from anthropogenic sources are mainly in water-
soluble forms (Fernandez Espinosa et al., 2002). Sample cleaning
is absolutely essential if the purpose is to distinguish between
pollutants deposited on the surface of leaves and pollutants accu-
mulated within the internal tissues (McCrimmon, 1994; Alfani
et al., 2000). Atmospheric contamination in the city of Florence is
mostly ascribed to vehicular traffic and heating and, therefore, the
highest attention was given to heavy traffic roads, which were con-
sidered the main source of metal pollution. The assessment of the
capacity of holm oak individuals placed in different urban contexts
near to high-density roads to capture heavy metals was conducted,
comparing two leaf ages and two sample treatments.
However, it is very difficult to know if metal concentration
within internal tissues origins from the uptake from the leaf surface
or the absorption from the soil by the roots. Indeed, the concen-
tration of contaminants within leaf samples could depend on the
mobility of the metal within the soil-plant system through the tran-
spiration stream and phloem flux.
The analysis of unwashed leaves indicated the background lev-
els of environmental contaminations, though rain may flush a
certain amount of contaminants. We found that Fe and Mn are
the most represented metals on leaves despite their lower mass
in comparison with other metals.
In this study, washing with distilled water had significant effect
on the reduction of contaminant concentration in Zn, Cu, Fe, and
Cr, and more specifically in old leaves for Zn, Cu, Fe, Cr, Pb, and Ba.
Nevertheless, in old leaves, countering results have been
observed especially for Cd and Mn, which showed higher concen-
trations in washed leaves of some sites (Cd in garden 1 at 35 and 3 m
and in garden 3 at 10 m and in garden 2 at 54 m from the road; Mn
in garden 1 at 65 and 3 m from the road). These conflicting results
were also observed in new leaves (Cd in garden 1 at 65 m and gar-
den 2 at 2 m, Mn and Ba in garden 1 at 35 m, garden 2 at 40m and
garden 3).
Trees of the genus Quercus are known as accumulators of Mn in
leaves (Bargagli, 1998b). However, Mn concentration in both sam-
ple treatments fell into the range considered as normal in plant
tissues: 17–600 mg kg1for woody angiosperms (Bowen, 1979),
with lower concentrations in new leaves with respect to old leaves.
Cd, even in unwashed samples, showed low concentrations, far
from the phytotoxic level of 5 mg kg1(Kabata-Pendias and Pen-
dias, 1992), without significant differences between the two leaf
ages (comparing the mean values in each site). Moreover, in new
leaves, the sample treatments did not elicit significant differences
for Cd concentration for six locations out of eight, suggesting a
stock capacity, with the exception of G1-65 and G2-2 which showed
higher concentrations in washed leaves, as mentioned above. The
same was found in old washed leaves samples but in different loca-
tions (G1-35, G1-3 and G3-10).
Internal Cd might come from soil although other elements, like
Zn, Cu and other trace elements, interact with Cd and reduce Cd
uptake from the soil (Kabata-Pendias and Pendias, 1992). Never-
theless, Cd absorption by the plant roots (Cocozza et al., 2008, 2011)
cannot be ruled out, and holm oak has been considered highly tol-
erant to Cd. However, Cd seems to be stored mainly at root level
(Domínguez et al., 2011). Then, only part of this can be translo-
cated towards the leaves, depending on the extraction capacity
and the hydraulic conductance (Cocozza et al., 2013). Neverthe-
less, regardless of leaf age, washing with distilled water would be
not recommendable for Cd, as found in other species (Oliva Rossini
and Valdés, 2004) because of the similarity of results between the
two treatments in the majority of sites.
Cd can accumulate at long distances from the source of pollution
due to the small size of particles, which permits a great aerial dis-
persion (De Nicola et al., 2008), though no significance was found
in the regression analysis with the distance from traffic. In garden
2, the concentrations were lower than other sites as if the presence
of the artefacts like the wall limited the dispersion.
Some of the metals analyzed, such as Zn (Dmuchowski and
Bytnerowicz, 1995), Fe and Cu (Shuman, 2004), are important
elements for plant physiology, playing an important role in biosyn-
thesis of enzymes, phytohormones and proteins.
582 F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584
Zn in washed leaves achieved concentrations in the range from
10 to 100 ppm, which is considered normal for plants (Rahimi
and Bussler, 1978), and, even for this metal, we observed two
sites (G1-3 and G2-2) where washed new leaves had even higher
metal concentrations in comparison with unwashed samples. Also
Cu in washed samples was in the range considered as normal
(6–14 mg kg1) for woody angiosperms (Bowen, 1979), though the
concentration of metal in the samples taken from the gardens was
relatively lower. Its concentration in new leaves was higher than
in old leaves. Some authors have observed the remobilization of
Cu to non-senescent parts before shedding (Aznar et al., 2009),
thus new leaves are able to accumulate Cu through translocation
from old leaves. This might explain the higher Cu concentration in
washed new leaves, as observed in Aesculus hippocastanum (Kim
and Fergusson, 1994) and Tilia spp. (Aniˇ
c et al., 2011).
The counter findings are difficultly explained. Investigating a
larger sample size, physiological and anatomical barriers to metal
distribution within leaves, and the whole nutrient status of plants
would have likely brought to clearer results; nevertheless, within
the same washed samples only a few metals showed higher con-
centrations with respect to the unwashed ones, therefore, we may
also exclude the possibility of human mistakes. Schreck et al. (2012)
found that some compounds are internalized in their primary form
underneath an organic layer and that internalization through the
cuticle or penetration through stomata openings are the two major
mechanisms involved in foliar uptake of particulate matter, though
this does not clarify the higher presence of metals inside the leaves
with respect to the unwashed samples.
Compared to new leaves, old leaves were richer in metals in
both sample treatments with a maximum difference for Fe and Pb
in garden 2. Here, in unwashed old leaves, these metals were about
three times and two times respectively more than in new leaves. In
addition, Fe, another important microelement, was highly present
in unwashed leaves, as obtained by De Nicola et al. (2008), with
maximum values in the street (S-0).
According to C¸ elik et al. (2005), Cu, Zn and Pb are directly
related to the traffic density and the concentrations found in site
S-0 would confirm this hypothesis. The nonessential metal Pb is
sequestrated passively in senescing foliage through a detoxification
process (Aznar et al., 2009). This would explain the high concen-
tration in washed old leaves. Moreover, if Pb comes mostly from
leaf atmospheric uptake (Hovmand et al., 2009), its content would
indicate atmospheric Pb (Aniˇ
c et al., 2011). Pb origins from com-
bustion of gasoline, though the affirmed use of unleaded gasoline
has been reducing Pb values. Overall, Pb concentrations were below
the thresholds set at <10 ppm for plant biology (Allaway, 1968)
in all sampling sites. Cr has also a good capacity to disperse and
deposit on leaves; indeed, its concentration was particularly high
in unwashed leaves. Also Ba was highly accumulated on leaf sur-
faces of unwashed old leaves and at farther distances from the road,
as found in garden 1 (G1); whereas the lowest values were recorded
in S-0 and closer to the city wall in G2.
The most polluted site was S-0, where Pb, Cu, Fe, Mn, and Ba
concentrations were up to four times those in other sites, and G3
where Pb, Zn, Fe, Mn, and Cr deposited preferentially in compari-
son with G1 or G2. The positions of trees with respect to the traffic
source had an important role in the amount of metals deposited
on leaves. In particular, old leaves showed a clear trend towards
higher depositions of Zn, Pb, Mn, and Ba at farther distances from
the traffic. This confirms the great capacity of heavy metals to dis-
perse in the atmosphere up to tens of metres from the road, and
the potential high capacity of holm oak to capture pollutants due
to its evergreen habitus and wide crown (Gratani and Varone, 2007).
This study confirms that on leaf surfaces, Cu, Pb and Fe deposit con-
spicuously, especially the Pb content was higher in the leaf surface
deposit than in the leaf as found by Alfani et al. (1996b). However,
the position of old leaves partially sheltered by the new ones, might
reduce the interception of particles, although they are exposed to
pollution for longer time. Many authors have studied the effective-
ness of different species in particles capturing in relation to leaf
size and features (Alfani et al., 1996b; Beckett et al., 2000; Liu et al.,
2012; Speak et al., 2012).
Particle dispersion at farther distances from the source of pol-
lution is also linked to intensity of traffic in closest roads but also
to the shape and size of the roads, which may affect wind circula-
tion, but also on the wind speed and direction. Street features but
also the context outside of the streets, influence the wind dynamics
and turbulence and, therefore, particles distribution (Carpentieri
and Robins, 2009; Kumar et al., 2011). The use of wind flow mod-
elling would be of add even if flow regimes depend on geometries of
canyons as well as of buildings (Hussain and Lee, 1980), therefore
the real characteristics are difficult to standardize. Bowker et al.
(2007) found higher air pollutant concentrations near the road,
in open terrain situations with no barriers present, moreover, the
presence of a barrier or vegetation resulted in a lower downwind
pollutant concentrations.
In our contexts, the presence of the wall and the trees of C. aus-
tralis within the street canyon (in G2) and trees along the avenue
(in G1), but also the roughness (due to the distribution of further
tall trees) of the gardens behind should be influent.
These ‘obstacles’ might interfere with winds perpendicular to
the road axis, promoting vortexes inside the gardens as described
by the ‘wake interference flow’ for buildings (Oke, 1988). The
downwind flow at level of distant trees might explain the results
concerning the higher deposition of several metals (Zn, Pb, Cu, Ba,
Mn, and Ba especially in the case of old leaves).
We did not find significant relationships between biomass
indices and metal concentrations, although heavy metals are
known to affect photosynthesis and productivity. Marques et al.
(2011) studied the effects of Cd on physiological and anatomical
features of eucalyptus seedlings and found that mesophyll and
leaf blade thickness decreased. Cd, Cu and Zn have been found to
indirectly affect LA and the LMA, and several tree species show
anatomical plasticity with leaf traits similar to xerophytes (Shi and
Cai, 2009; Pandey and Tripathi, 2011). In our study, though weakly,
only LDMC was influenced by the metal accumulation regardless
of the metal or leaf age. This index is involved in a fundamental
trade-off between rapid production of biomass and an efficient con-
servation of nutrients (Grime et al., 1997; Ryser and Urbas, 2000)
and the relationship confirms that holm oak is rather tolerant to
urban environment (Ugolini et al., 2012). To conclude, holm oak
played an important role in intercepting pollutants, especially for
the presence of old leaves, which are exposed to pollutants for
longer time. Old leaves of holm oak were particularly efficient in
adsorbing heavy metals (Pb, Fe, Mn, Cr, and Ba). Washing of sam-
ples with distilled water allowed also testing the capability of leaves
to accumulate metals within internal tissues, but counter results,
for which a plausible explanation warrants further studies, were
obtained for metals like Cd in old leaves and Mn and Ba in new
leaves. Washing allowed also examining the behaviour of some
important microelements. For instance, Cu was translocated to new
leaves and Pb to old leaves before senescence. The dispersion of
metals through the atmosphere was assessed in two similar gar-
dens, G1 and G2, where leaves of trees at farther distance from the
traffic were richer in Zn, Pb, Mn, and Ba. Although the physical con-
text of the site may alter the distribution of heavy metals (e.g., as
barrier to dispersion, further trees inside the gardens), locations
may vary the amount of intercepted metals also depending on the
specificity of compounds, and physical characteristics of the sites,
rather than only on the distance from the main source of pollut-
ants. However, we may also conclude that several aspects should
be considered in planning the urban green areas with recreational
F. Ugolini et al. / Urban Forestry & Urban Greening 12 (2013) 576–584 583
purposes. Wind circulation and position of trees and artefacts
should be very carefully considered in order to avoid misunder-
standings about the healthier position from the source of pollution.
We thank Prof. L. Sanità di Toppi and Prof. C. Mucchino of the
University of Parma for their support in laboratory analysis.
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