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

Urban Forestry & Urban Greening 12 (2013) 576–584

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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 Ugolini a,∗ , Roberto Tognetti b , Antonio Raschi a , Laura Bacci a a b

Institute of Biometeorology, National Research Council, Firenze, Italy Department of Bioscience and Territory, University of Molise, Pesche, Italy

a r t i c l e

i n f o

Keywords: Deposition Heavy metals Holm oak Internal concentration Traffic pollution Urban environment

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. © 2013 Elsevier GmbH. All rights reserved.

Introduction 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 petrochemical industries (Nadal et al., 2004), can be sources of harmful compounds that are naturally transported by resuspension processes 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 Council, via Giovanni Caproni 8, Firenze, Italy. Tel.: +39 0553033701; fax: +39 055308910. E-mail address: [email protected] (F. Ugolini). 1618-8667/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.ufug.2013.05.007

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, electroplating 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 hardness 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 elements. 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

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(Vesper and White, 2003) and finally they are deposited on land, thus also on the trees. The dispersion and distribution of trace metals depend on the site characteristics and wind velocity (Beckett et al., 2000), the size of the particles (Tomasevic´ et al., 2005) and surface 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). Moreover, those deposited on the ground can be readily relocated and dispersed by wind, rain and surface runoff (Callender and Rice, 2000). Among metals, Pb and Cd have high capacity to accumulate in the environment (Newman and Clements, 2008), while other elements (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 possibility 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 deposition 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 (KabataPendias 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 distinguishing 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 particles 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

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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 remainder 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.

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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 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 and quickly brought to laboratory for morphometric measurements (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

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 difference 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 position 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 distance from the road (Fig. 2), taking into consideration the regression analysis in G1 and G2.

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 (H2 O2 and 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 characteristic 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 detected.

Zn

Statistical analysis

Pb

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 compare 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 distribution, in order to assess the influence of the distance from traffic on metal deposition; thus, leaf samples taken from individuals along a transect were used for the regression analysis.

Washing vs. unwashing. In new leaves (Fig. 2C), significant difference between treatments was found only in S-0 with unwashed leaves richer in Pb. Old leaves (Fig. 2D) recorded the highest values, 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).

Results 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 concentrations (data from unwashed sample) up to four times those

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 leaves. 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 farther 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.

Cu 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 relation (P < 0.01; R2 = 0.47) between metal deposition in old leaves and distance from the road.

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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 kg−1 ) Leaf age

OL

Zn

Pb

Cu

Cd

Fe

Mn

Cr

Ba

G1 G2 G3 S0

23.2 ± 0.12b 25.8 ± 0.28b 48 ± 0.65a 42.8 ± 0.50a P < 0.001

2.3 ± 0.12c 1.7 ± 0.13d 3.1 ± 0.02b 3.7 ± 0.15a P < 0.001

7.8 ± 0.08c 7.5 ± 0.04c 9.3 ± 0.09b 19.8 ± 0.16a P < 0.01

0.08 ± 0.01 0.05 ± 0.01 0.08 ± 0.01 0.12 ± 0.01 n.s.

30. 9 ± 0.3 48.6 ± 0.4 48.9 ± 0.8 34.5 ± 0.4 n.s.

21.6 ± 0.27b 22.5 ± 0.36b 26.3 ± 0.57ab 29.3 ± 0.29a P < 0.05

1.2 ± 0.11a 0.7 ± 0.14b 1.5 ± 0.08a 1.4 ± 0.09a P < 0.05

7.2 ± 0.08b 5.9 ± 0.01c 6.8 ± 0.14b 12.2 ± 0.08a P < 0.001

0.08 ± 0.01a 0.04 ± 0.01b 0.07 ± 0.01a 0.07 ± 0.01a P < 0.05; P < 0.001 between G2 and G1

214.7 ± 1.1b 153 ± 0.7b 397.2 ± 4.8ab 405.4 ± 1.6a P < 0.01 and P < 0.05 between G1 and S0 95 ± 0.6 68.4 ± 0.6 142.9 ± 2.1 87.8 ± 1.2 n.s.

1.1 ± 0.02b 1.2 ± 0.02b 1.4 ± 0.03b 3.1 ± 0.03a P < 0.001

G1 G2 G3 S0

246.4 ± 2.1c 263.9 ± 1.6c 346.2 ± 1.3b 534.5 ± 6.5a P < 0.001 and P < 0.05 between G1 and G3 120.6 ± 0.9b 85.8 ± 0.2b 112.4 ± 1.5b 218.4 ± 2.9a P < 0.01

0.5 ± 0.01b 0.3 ± 0.02b 0.6 ± 0.07b 1.3 ± 0.03a P < 0.001

17.3 ± 0.1 29 ± 0.3 27.2 ± 0.5 15.4 ± 0.3 n.s.

G1 G2 G3 S0

25.6 ± 1.68b 22.8 ± 0.28c 42.5 ± 0.59a 40.8 ± 0.43a P < 0.001 23.72 ± 1.36 23.22 ± 0.22 5.36 ± 0.43 0.95 ± 0.28 n.s.

1.7 ± 0.1b 0.8 ± 0.11c 2 ± 0.17b 3.3 ± 0.04a P < 0.001 0.9 ± 0.08a 0.6 ± 0.15b 0.9 ± 0.02b 1.2 ± 0.08a P < 0.001

5.6 ± 0.09b 5.3 ± 0.04b 6.3 ± 0.09b 15.3 ± 0.22a P < 0.001 6.1 ± 0.10 6.4 ± 0.08 6 ± 0.11 7.30 ± 0.07 n.s.

0.1 ± 0.002a 0.06 ± 0.01b 0.09 ± 0.005a 0.09 ± 0.01a P < 0.001 0.11 ± 0.002a 0.04 ± 0.01b 0.08 ± 0.01ab 0.07 ± 0.01ab P < 0.001

111.9 ± 0.7b 79.5 ± 0.7c 150 ± 1.8b 376.8 ± 3.7a P < 0.001 68.9 ± 0.6b 57.05 ± 0.6b 69.02 ± 0.9b 114.18 ± 1a P < 0.001

235 ± 0.8ab 140.9 ± 1.3b 332 ± 2.3a 333.9 ± 4.7a P < 0.05 100 ± 0.3ab 66.18 ± 0.7b 156.6 ± 0.4a 70.98 ± 1.1b P < 0.05

0.5 ± 0.03b 0.4 ± 0.01b 0.5 ± 0.01b 2.2 ± 0.02a P < 0.001b 0.3 ± 0.01b 0.2 ± 0.02b 0.2 ± 0.01b 0.62 ± 0.01a P < 0.001

29.8 ± 0.2 45.1 ± 0.2 45.4 ± 1.1 30.8 ± 0.4 n.s. 16.6 ± 0.1 29.8 ± 0.2 28.9 ± 0.4 11. 6 ± 0.1 n.s.

Unwashed

NL

OL Washed

G1 G2 G3 S0

NL

Cd

Fe

Washing vs. unwashing. In new leaves (Fig. 2G), significant differences 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 differences 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 observed.

Washing vs. unwashing. In new leaves (Fig. 2I), unwashed leaves showed significantly higher (P < 0.01) Fe concentration than washed leaves (except G2-40). Within old leaves (Fig. 2J), unwashed samples showed the highest values and maximum concentrations were found in S-0, which almost doubled the values of other locations 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 kg−1 ) Leaf age G1 G2 Unwashed G3 A G1 G2 Washed G3 A **

Zn

Pb

Cu

Cd

Fe

Mn

Cr

Ba

OL NL OL NL OL NL OL NL

23.2 21.6 25.8 22.5 48 26.3 42.8 29.3

± ± ± ± ± ± ± ±

0.12** 0.27** 0.28** 0.36** 0.65** 0.57** 0.50** 0.29**

2.3 1.2 1.7 0.7 3.1 1.5 3.7 1.4

± ± ± ± ± ± ± ±

0.12** 0.11** 0.13** 0.14** 0.02** 0.08** 0.15** 0.09**

7.8 7.2 7.5 5.9 9.3 6.8 19.8 12.2

± ± ± ± ± ± ± ±

0.08** 0.08** 0.04** 0.01** 0.09 ** 0.14** 0.16** 0.08**

0.08 0.08 0.05 0.04 0.08 0.07 0.12 0.07

± ± ± ± ± ± ± ±

0.01 0.01 0.01 0.01 0.01 0.01 0.01** 0.01**

246.4 120.6 263.9 85.8 346.2 112.4 534.5 218.4

± ± ± ± ± ± ± ±

2.1** 0.9** 1.6** 0.2** 1.3** 1.5** 6.5** 2.9**

214.7 95 153 68.4 397.2 142.9 405.4 87.8

± ± ± ± ± ± ± ±

1.1** 0.6** 0.7** 0.6** 4. 8** 2.1** 1.6** 1.2**

1.1 0.5 1.2 0.3 1.4 0.6 3.1 1.3

± ± ± ± ± ± ± ±

0.02** 0.01** 0.02** 0.02** 0.03** 0.07** 0.03** 0.03**

30. 9 17.3 48.6 29 48.9 27.2 34.5 15.4

± ± ± ± ± ± ± ±

0.3** 0.1** 0.4** 0.3** 0.8** 0.5** 0.4** 0.3**

OL NL OL NL OL NL OL NL

25.6 23.72 22.8 23.22 42.5 5.36 40.8 0.95

± ± ± ± ± ± ± ±

1.68 1.36 0.28 0.22 0.59** 0.43** 0.43** 0.28**

1.7 0.9 0.8 0.6 2 0.9 3.3 1.2

± ± ± ± ± ± ± ±

0.1** 0.08** 0.11** 0.15** 0.17** 0.02** 0.04** 0.08**

5.6 6.1 5.3 6.4 6.3 6 15.3 7.30

± ± ± ± ± ± ± ±

0.09** 0.10** 0.04** 0.08** 0.09** 0.11** 0.22** 0.07**

0.10 0.11 0.06 0.04 0.09 0.08 0.09 0.07

± ± ± ± ± ± ± ±

0.002 0.002 0.01 0.01 0.005 0.01 0.01 0.01

111.9 68.9 79.5 57.05 150 69.02 376.8 114.18

± ± ± ± ± ± ± ±

0.7 ** 0.6** 0.7** 0.6** 1.8 ** 0.9 3.7 ** 1**

235 100 140.9 66.18 332 156.6 333.9 70.98

± ± ± ± ± ± ± ±

0.8 ** 0.3** 1.3** 0.7** 2.3** 0.42** 4.7 ** 1.1**

0.5 0.3 0.4 0.2 0.5 0.2 2.2 0.62

± ± ± ± ± ± ± ±

0.03** 0.01** 0.01** 0.02** 0.01** 0.01** 0.02** 0.01**

29.8 16.6 45.1 29.8 45.4 28.9 30.8 11. 6

± ± ± ± ± ± ± ±

0.2** 0.1** 0.2 ** 0.2** 1.1** 0.4** 0.4** 0.1**

Significances at P < 0.01.

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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 treatments, per leaf age. Mn 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 site. 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 distance from the traffic for unwashed (P < 0.01; R2 > 0.88) and washed samples (P < 0.01; R2 > 0.92).

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For old leaves, in G1and G2, the regression analysis evidenced a positive and strong relation between Mn concentration and distance 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) Cr 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 treatments, with Cr concentrations almost doubling those of other sites. Distance from the source inside the gardens G1 and G2: No significant 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 Ba 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 concentrations. 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 values in unwashed samples in all sites. Distance from the source inside the gardens G1 and G2: A positive 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 significant, though weak, positive relation with LDMC, regardless of leaf age (data not shown). Discussion 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 surfaces 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 watersoluble 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 accumulated within the internal tissues (McCrimmon, 1994; Alfani et al., 2000). Atmospheric contamination in the city of Florence is

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mostly ascribed to vehicular traffic and heating and, therefore, the highest attention was given to heavy traffic roads, which were considered 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 concentration of contaminants within leaf samples could depend on the mobility of the metal within the soil-plant system through the transpiration stream and phloem flux. The analysis of unwashed leaves indicated the background levels 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 concentrations 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 garden 2 at 2 m, Mn and Ba in garden 1 at 35 m, garden 2 at 40 m and garden 3). Trees of the genus Quercus are known as accumulators of Mn in leaves (Bargagli, 1998b). However, Mn concentration in both sample treatments fell into the range considered as normal in plant tissues: 17–600 mg kg−1 for 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 kg−1 (Kabata-Pendias and Pendias, 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 locations (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). Nevertheless, Cd absorption by the plant roots (Cocozza et al., 2008, 2011) cannot be ruled out, and holm oak has been considered highly tolerant to Cd. However, Cd seems to be stored mainly at root level (Domínguez et al., 2011). Then, only part of this can be translocated towards the leaves, depending on the extraction capacity and the hydraulic conductance (Cocozza et al., 2013). Nevertheless, 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 dispersion (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 biosynthesis of enzymes, phytohormones and proteins.

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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 kg−1 ) 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. (Aniˇcic´ 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 concentrations 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 concentration in washed old leaves. Moreover, if Pb comes mostly from leaf atmospheric uptake (Hovmand et al., 2009), its content would indicate atmospheric Pb (Aniˇcic´ et al., 2011). Pb origins from combustion 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 surfaces 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 comparison 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 disperse 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 conspicuously, 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 effectiveness 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 pollution is also linked to intensity of traffic in closest roads but also to the shape and size of the roads, which may affect wind circulation, 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 modelling 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. australis 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 conservation 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 samples 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 gardens, G1 and G2, where leaves of trees at farther distance from the traffic were richer in Zn, Pb, Mn, and Ba. Although the physical context 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 pollutants. However, we may also conclude that several aspects should be considered in planning the urban green areas with recreational

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