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Research ArticleArticles

Carbon Uptake and Air Pollution Mitigation of Different Evergreen Shrub Species

Jacopo Mori, Alessio Fini, Gianluca Burchi and Francesco Ferrini
Arboriculture & Urban Forestry (AUF) September 2016, 42 (5) 329-345; DOI: https://doi.org/10.48044/jauf.2016.029
Jacopo Mori
Jacopo Mori (corresponding author), Dipartimento di Scienze delle Produzioni Agroalimentari e, dell’Ambiente, DiSPAA, Università degli Studi di Firenze, Viale delle Idee 30, 50019, Sesto Fiorentino (FI), Italy, Consiglio per la ricerca in agricoltura e l’analisi dell’economia, agraria CREA, Via dei Fiori 8, 51012, Pescia (PT), Italy,
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  • For correspondence: [email protected]
Alessio Fini
Alessio Fini, Dipartimento di Scienze delle Produzioni Agroalimentari e, dell’Ambiente, DiSPAA, Università degli Studi di Firenze, Viale delle Idee 30, 50019, Sesto Fiorentino (FI), Italy
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Gianluca Burchi
Gianluca Burchi, Consiglio per la ricerca in agricoltura e l’analisi dell’economia, agraria CREA, Via dei Fiori 8, 51012, Pescia (PT), Italy
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Francesco Ferrini
Francesco Ferrini, Dipartimento di Scienze delle Produzioni Agroalimentari e, dell’Ambiente, DiSPAA, Università degli Studi di Firenze, Viale delle Idee 30, 50019, Sesto Fiorentino (FI), Italy
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  • Figure 1.
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    Figure 1.

    Arial photo of the area surrounding the experimental site. The orientation of the two vegetation belts (indicated with dark rectangle) and the main land use areas (agricultural and residential) are shown. Black arrow indicates north.

  • Figure 2.
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    Figure 2.

    A) Daily trend of net photosynthesis expressed as μmol (CO2) m−2 (leaf area) s−1; B) daily CO2 uptake of the whole plant expressed as g (CO2 assimilated) day−1; C) relative growth rate (RGR); and D) relation between RGR and whole-plant daily CO2 assimilation of seven different shrubs in 2011, under optimal water availability. Data of CO2 uptake are the average of 12 daily measurements of net photosynthesis. Double asterisk (**) indicates significant differences among species at P < 0.01, different letters indicate different homogeneous groups using Duncan’s MRT.

  • Figure 3.
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    Figure 3.

    Carbon allocation to different plant organs in seven shrub species in 2011 under optimal water availability. Different letters within an organ (i.e., leaves, stem, and roots) indicate significant differences among species at P < 0.01 using Duncan’s MRT.

  • Figure 4.
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    Figure 4.

    A) Irrigation scheduling and substrate moisture (expressed as percentage of the water holding capacity of the container), for plants under drought stress (WS); and B) Whole plant carbon uptake in six shrubs grown under water stress in 2012 expressed as g (CO2 assimilated) per h−1. Data of substrate moisture are the average 12 plants per species for six species. T indicates when physiological measurements were carried out. Different letters within the same sampling date indicate significant differences among species at P > 0.01 using Duncan’s MRT.

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    Figure 5.

    Maximum quantum yield of PSII photochemistry during the “drought phase 1” in six shrubs grown under either well watered (WW) or water stress (WS) conditions. Double asterisk (**) indicates significant differences between WW and WS plants of a species for P > 0.01 using Duncan’s MRT. The normalized index of variation (NIV) was calculated as: [(Fv/Fm)WS − (Fv/Fm)WW] / [(Fv/Fm)WS + (Fv/Fm)WW] (Tattini et al. 2006).

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    Figure 6.

    Metal depositions on leaf surface, expressed as μg (metal) cm−2 (leaf area), in the different periods during growth season of 2011: 21 June 2011 (I); 03 August 2011 (II); 04 October 2011 (III); 30 August values are means of all species (μg cm−2). Different letters within the same sampling date indicate significant differences among species at P > 0.01 using Duncan’s MRT.

  • Figure 7.
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    Figure 7.

    Environmental scanning electron microscope (ESEM) micrographies: A) adaxial and B) abaxial leaf surface of (1) Viburnum lucidum, (2) Arbutus unedo, (3) Photinia × fraseri, (4) Laurus nobilis, (5) Elaeagnus × ebbingei, and (6) Ligustrum japonicum. Images were taken at different magnifications as reported on the images.

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    Figure 8.

    A) Accumulation of Cu and Zn; B) Ni and Pb; C) and Cd expressed as mg (metal) / plant, from June to October 2011 in the water collected at the base of Viburnum lucidum, Arbutus unedo, Photinia × fraseri, Laurus nobilis, Elaeagnus × ebbingei, Ligustrum japonicum, and control. Values are means (mg/plant). Different letters within the same sampling date indicate significant differences among species at P < 0.05 (*) using Duncan’s MRT.

Tables

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    Table 1.

    Meteorological data for the experimental area of the three periods prior to the 2011 samplings.

    Sampling periods19 May – 21 June21 June – 03 August03 August – 04 October
    Precipitation (mm)  10.80  11.20  80,00
    Wind directionSWSWWSW
    Wind speed (m s−1)    0.80    0.80    0.62
    Temperature (°C)  20.78  23.33  23.24
    Relative humidity (RH%)  71.45  63.94  61.33
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    Table 2.

    Leaf area (m2) of the six shrub species used for trace metals analysis at planting date (autumn 2010).

    MeanSD
    Viburnum lucidum0.410.04
    Arbutus unedo0.300.01
    Photinia × fraseri0.350.05
    Laurus nobilis0.440.09
    Elaeagnus × ebbingei0.500.06
    Ligustrum japonicum0.200.05
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    Table 3.

    Foliar deposition, per unit leaf area and per the whole canopy, of six shrub species of five trace metals (Cd, Cu, Ni, Pb, and Zn). Data are the mean of three samplings performed in June, August, and October. Data are means ± SD. ANOVA tests (A) of differences between species are also included. Values as 0.000 are less than 0.001.

    V. lucidumA. unedoP. × fraseriL. nobilisE. × ebbingeiL. japonicum
    Depositions per unit leaf area (μg cm−2 *1000)
    MeanSDAMeanSDAMeanSDAMeanSDAMeanSDAMeanSDAFP
    Cd0.050.06a0.040.05a0.030.04a0.040.06a0.030.05a0.030.05a0.310.90
    Cu81.483.7a96.4109a70.165.6a116158a108123a69.656.2a0.830.55
    Ni14.817.7a17.117.5a11.813.1a25.131.7a24.426.6a18.014.9a1.020.44
    Pb2.371.35ab1.621.25bc1.890.96abc1.251.03c2.090.72ab2.550.75a3.710.02
    Zn92.382.4a140131a114127a182201a164155a146133a2.330.09
    Depositions on the whole plant (mg)
    MeanSDAMeanSDAMeanSDAMeanSDAMeanSDAMeanSDAFP
    Cd0.000.00a0.000.00a0.000.00a0.000.00a0.010.01a0.000.00a0.440.82
    Cu4.084.20b3.844.35b3.913.66b3.064.48b10.111.6a2.452.22b2.460.04
    Ni0.740.89b0.680.70b0.660.73b0.730.92b2.302.51a0.570.59b3.160.01
    Pb0.110.07ab0.060.05bc0.110.05ab0.030.03c0.160.10a0.100.03b5.530.00
    Zn4.624.13b5.605.23b6.377.06b5.305.85b15.514.6a5.135.17b3.080.02
    • View popup
    Table 4.

    Partial least square regression analysis (PLSR) coefficients showing how deposition of different metals depends on wind velocity (Wind), air temperature (Air T°), relative humidity (RH%), and precipitation (Prec.). Values as 0.000 are not equal to 0 but less than 0.001.

    FPr2Const.WindAir T°RH%Prec.
    Cd10.740.00215−0.0000.0000.000−0.000−0.000
    Cu34.410.00053−0.7320.4860.048−0.009−0.001
    Ni19.980.00040−0.1210.0860.007−0.001−0.000
    Pb3.240.0775−0.0010.0020.000−0.000−0.000
    Zn128.180.00067−1.1590.8010.071−0.012−0.002
    • View popup
    Table 5.

    Clusters derived from cluster analysis and factor loadings of two factors identified from factor analysis for metals on leaves during the entire sampling period. Varimax rotation was applied to factor analysis.

    ClusterFactor 1Factor 2
    Cd20.250.83
    Cu10.970.09
    Ni10.960.01
    Pb2−0.020.89
    Zn20.880.28
    • View popup
    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. lucidumA. unedoP. × fraseriL. nobilisE. × ebbingeiL. japonicumFP
    MeanSDAMeanSDAMeanSDAMeanSDAMeanSDAMeanSDA
    LA (m2)5.010.67bc3.990.39ab5.581.58c2.90.24a9.430.68d3.870.56ab24.30.00
    LAI5.431.12c3.90.97a5.320.88c4.471.26b6.91.14d4.151.08ab17.60.00
    LS (cm2)31.95.66c10.52.68a19.51.45b20.84.17b12.61.49a17.32.04b31.60.00
    N°L1608265ab3967898d2876226c1443271a76111045e2268284bc88.10.00
    CD1027.06d93.16.45c10213.2d71.612.2a10811.7d81.514.4b25.20.00
    HP14616.8b13611.5a19428.7e1669.99cd17031.8d16224.9c13.40.00
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Arboriculture & Urban Forestry (AUF): 42 (5)
Arboriculture & Urban Forestry (AUF)
Vol. 42, Issue 5
September 2016
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Carbon Uptake and Air Pollution Mitigation of Different Evergreen Shrub Species
Jacopo Mori, Alessio Fini, Gianluca Burchi, Francesco Ferrini
Arboriculture & Urban Forestry (AUF) Sep 2016, 42 (5) 329-345; DOI: 10.48044/jauf.2016.029

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Carbon Uptake and Air Pollution Mitigation of Different Evergreen Shrub Species
Jacopo Mori, Alessio Fini, Gianluca Burchi, Francesco Ferrini
Arboriculture & Urban Forestry (AUF) Sep 2016, 42 (5) 329-345; DOI: 10.48044/jauf.2016.029
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Keywords

  • Arbutus unedo L
  • CO2 Assimilation
  • drought
  • Elaeagnus × ebbingei L
  • Italy
  • Laurus nobilis L
  • Leaf Deposition
  • Ligustrum japonicum Thunb
  • Meteorological Parameters
  • Photinia × fraseri Dress
  • Relative Growth Rate
  • Seasonal Trend
  • Shrub
  • Trace Metals
  • Traffic Pollution
  • Viburnum tinus subsp. lucidum L
  • Viburnum tinus subsp. tinus L

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