Arboriculture & Urban Forestry 42(5): September 2016 seasonal variability observed across samplings in this study. In fact, the secretory activity may be considered independent from meteorological conditions, which on the other hand, affect the leaf depositions of metals from the PM in the air. The results of whole-plant leaf deposition sug- gest that total leaf area had a greater effect on the interception capacity of shrubs than the morpho- anatomical traits of individual leaves (Table 6). Rapid-growing species with large total leaf area, and high LAI, crown diameters, and height, have been reported to adsorb more pollutants than slower- growing species (Bunzl et al. 1989; Beckett et al. 2000; Fowler et al. 2003; Freer-Smith et al. 2005). The large variation in metal deposition during samplings confirmed that leaves are a temporary retention site for pollutants (Nowak et al. 2006). The results of partial least square regression, especially for Cu and Zn (r2 above 50), make it possible to predict the deposition of metals with moderate accuracy by using climatic parameters (Table 4). The negative relationship between metal deposition and total rainfall indicates that metals on leaves decreased as rain increased, con- firming the washout effect that rain quickly plays on pollutants adsorbed on leaf surfaces (Nowak et al. 2006). The inverse relationship between metal deposition and RH% may be considered more a consequence of the strong relationship between rain and RH% than a direct effect of RH% on metal deposition. Despite this, it is reported that RH% causes an increase in PM diameter and that when PM diameters are greater than 0.1 µm, an increase in diameter leads to an increase of depo- sition velocities (Fowler et al. 2003; Litschke and Kuttler 2008). The positive relationship between metals and wind speed could be due to the fact that increasing wind speed leads to increasing deposition velocities of PM (Beckett et al. 2000). 341 Temperature was also found to have a positive relationship with the deposition of pollutants, as previously reported (Cavanagh et al. 2009). Washout by Rain of Metals from the Canopy The protocol used for rainwater collection does not allow exclusion of possible contamination of samples because of bird droppings, insects, or other undesired animals. Therefore, researches focused mainly on differences between the presence and absence of plants, rather than on differences among species. The greater quantities of metals in containers under shrubs is further demonstration of the role of shrubs in intercepting air pollution (Nowak et al. 2000; Bealey et al. 2007). In addition, the effect of washout caused by rain, and the con- sequent deposition on the ground of metals, helps reduce the resuspension of pollutants in the air. Metal Source Identification The results of correlation, FA, and CA were com- pared in order to identify the possible source(s) of different metals. Cu, Ni, and Zn have high load- ings in factor 1 of FA and are all strongly corre- lated (Table 5). Meanwhile CA clustered Cu and Ni in a different group compared to Zn; Zn was then excluded from the analysis. The relationship between Cu and Ni is then supported by three dif- ferent statistical methods. Cd and Pb are dominant in factor 2 of FA and are significantly correlated. Moreover, in CA, they are clustered together in Group 2. Thus, the relationship between Cd and Pb is confirmed by three different methods. Con- sidering these results, two different groups were created: Group 1 (Cu and Ni) and Group 2 (Pb and Cd). All four of these metals have previous- ly been attributed to traffic emissions (Pant and Harrison, 2013; Valotto et al. 2015). This division Table 6. Leaf area of the whole plant (LA), leaf area index (LAI), average lamina size (LS), number of leaves per plant (N°L), crown diameter (CD), and height of plants (HP), of six shrub species planted in a field near a heavily polluted road (Exp. 3). Values are means ± SD. ANOVA tests (A) of differences between species are also included. V. lucidum LA (m2 LAI ) LS (cm2 N°L CD HP ) 5.43 31.9 0.67 bc 1.12 c 5.66 c 7.06 d 16.8 b A. unedo 3.99 3.9 10.5 1608 265 ab 102 146 0.39 ab 0.97 a 2.68 a 6.45 c 11.5 a P. × fraseri Mean SD A Mean SD A Mean SD A 5.01 5.58 5.32 19.5 3967 898 d 93.1 136 1.58 c 0.88 c 1.45 b 2876 226 c 102 194 13.2 d 28.7 e L. nobilis 4.47 20.8 1443 71.6 166 0.24 a 1.26 b 4.17 b 271 a 12.2 a 9.99 cd E. × ebbingei 9.43 6.9 12.6 0.68 d 1.14 d 1.49 a 7611 1045 e 108 170 11.7 d 31.8 d L. japonicum Mean SD A Mean SD A Mean SD A F 2.9 3.87 4.15 17.3 2268 284 bc 81.5 162 14.4 b 24.9 c P 0.56 ab 24.3 1.08 ab 17.6 2.04 b 31.6 88.1 25.2 13.4 0.00 0.00 0.00 0.00 0.00 0.00 ©2016 International Society of Arboriculture
September 2016
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