heavy metals variations in some conifers in valle d'aosta (western italian alps) from 1930 to...

Microchemical Journal 73(2002) 237–244

0026-265X/02/$ - see front matter� 2002 Elsevier Science B.V. All rights reserved.PII: S0026-265XŽ02.00068-1

Heavy metals variations in some conifers in Valle d’Aosta(Western Italian Alps) from 1930 to 2000

Marco Orlandi *, Manuela Pelfini , Manuela Pavan , Maurizio Santilli ,a, b a b

Maria Perla Colombinia

aDipartimento di Scienze dell’ Ambiente e del Territorio, Universita di Milano-Bicocca, Piazza della Scienza 1, 20126 Milano,`Italy

bDipartimento di Scienze della Terra. Universita degli Studi di Milano, via Mangiagalli 3, 20133 Milano, Italy`

Received 27 February 2002; accepted 8 March 2002

Abstract

The high mountain environment is very sensitive to the climatic and ecological variations that are registered inseveral natural archives as glaciers or plants. Trees, in particular, modify their growth, habitus, ring features andspatial distribution in relation with climate and environmental changes. Heavy metals variation in tree rings ofLarixdecidua have been determined to assess whether this arboreal species can be used as bio-geochemical tracers ofheavy metal pollution to the alpine environment� 2002 Elsevier Science B.V. All rights reserved.

Keywords: Dendrochemical; Heavy metal pollution; Atomic absorption spectrometry; Multidimensional scaling analysis

1. Introduction

The identification, quantification and temporalvariations of heavy metals in alpine conifers rep-resent an important and actual research topic. Infact, the alpine environment is in continuous andrapid evolution both from a natural and anthropicpoint of view: the warming involves a drasticcontraction of alpine glacier, an elevation of tim-berline forests and a general increase in annual

*Corresponding author. Tel.:q390-2644-82812; fax:q390-2644-82890.

E-mail address: [email protected](M. Orlandi).

production of wood. For these reasons, it is nec-essary to measure how the progressive anthropi-zation and the more intense tourism have asignificant impact on the natural environment. Asa matter of fact, the atmospheric pollution fromfossil fuel pollution has increased dramaticallyduring this centuryw1,2x. In particular, for a longtime alkyllead compounds coming from carexhausts have been recognized as an additionaldangerous pollution to the earth’s ecosystem on aglobal scale.In Europe, the historical changes in the compo-

sition of atmospheric heavy metals on a regional

238 M. Orlandi et al. / Microchemical Journal 73 (2002) 237–244

Table 1Experimental conditions used in GFAAS for each analysed metal

Element lynm Ashing Atomisation MatrixTy8C Ty8C

Cd 228.8 500 1400 0.05 mg NH H POq0.003 mg Mg(NO )4 2 4 3 2

Cr 357.9 1500 2300 0.015 mg Mg(NO )3 2

Cu 324.8 1200 1900 0.005 mg Pdq0.003 mg Mg(NO )3 2

Ni 232.0 1100 2300 –Pb 283.3 850 1600 0.050 mg NH H POq0.003 mg Mg(NO )4 2 4 3 2

Table 2Experimental conditions for the drying and cleaning steps

Step Ty8C Ramp time(s) Duration time(s)

18 drying 110 1 3028 drying 130 15 30Cleaning 2500 1 5

scale were studied analyzing metals into ice corefrom the Alps glaciersw3x; similar informationcould also be achieved using dendrochemical data.Changes in trace element distribution of severaltree species have been shown to reflect changes inmetal deposition and increased urbanization of soilw4,5x. Wood is composed of secondary xylem,formed by the vascular tissues of cambiumw6x.Each growth increment can be divided into alter-nating layers of earlywood and latewood, the latterbeing more dark and dense because of the domi-nance of narrow cells with thickened walls. How-ever, care must be taken when interpretingdendrochemical data. Natural spatium trends inelement composition from pith to cambium or viceversa may occur that differ between treesw7x.Peaks in element concentration at the heartwood–sapwood boundary have been shown to occurw8xand lateral migration of elements is another factthat must be consideredw9x. For these reasons, inorder to reduce these problems, it has been decidedto analyze samples covering a period of 10 yearsand to apply a multidimensional scaling. MetricMultidimensional Scaling(Metric MDS) w10,11xis a widely used multivariate technique for explor-ative data analysis. This method works on thedistance matrixD obtained from the original mul-tidimensional data matrixX, using the Euclideandistance; starting from a scaling of the objects infull-dimensional space it attempts to obtain arepresentation in a Cartesian coordinate system ofa set of objects whose relationships are measuredby a dissimilarity coefficient, i.e. the selecteddistance. The principal coordinates are functionsof the original variables, mediated through thesimilarity or distance function used and explainingthe largest percentage of the total variance.

The aim of the present study is to apply a simplemethodology for measuring the amount of heavymetals Cd, Cr, Cu, Ni, Pb by graphite furnaceatomic absorption spectroscopy(GFAAS) in tree-ring of Larix decidua. These elements were chosenas they are known to have different radial trendsso that the discussion points out to examine thepeculiar metal behavior in comparison with thechronologies and with the use of fossil fuels.

2. Experimental

2.1. Reagents

Sulfuric and nitric acids were pesticide gradefrom Fluka (Milano, Italy). Standard solution ofmetals were 1000 ppm in HNO purchased by3

BDH Chemicals. Distilled deionized water(dd-H O) was used throughout.2

2.2. Apparatus

A Perkin Elmer SIMAA 6000 graphite furnacewas used for all the measurements. The experi-mental conditions used in GFAAS analysis arereported in Tables 1 and 2.

2.3. Sample collection and storage

Twenty-five healthy and dominant specimens ofLarches (Larix decidua), with regular growth,

239M. Orlandi et al. / Microchemical Journal 73 (2002) 237–244

Table 3The ppm of metalydry weight of wood

Sample Years

1999–1990 1989–1980 1979–1970 1969–1960 1959–1950 1949–1940 1939–1930

ma–Cu 18.517 16.822 8.543 7.076 6.749 4.784 2.336ma–Ni 20.377 15.801 15.959 6.517 6.973 5.680 1.634ma–Cd 3.566 3.289 3.365 0.003 0.646 0.318 0.800ma–Pb 7.443 5.829 5.400 1.481 2.067 1.239 1.320ma–Cr 19.394 20.754 29.289 6.127 5.564 6.176 1.836mb–Cu 12.151 8.173 24.340 14.160 5.750 48.358 9.841mb–Cd 5.689 4.513 6.594 2.086 2.751 2.764 3.915mb–Ni 6.495 5.157 13.440 1.793 1.457 2.642 4.271mb–Pb 7.497 7.118 10.416 4.164 3.059 7.204 5.649mb–Cr 4.500 32.783 11.130 6.425 5.023 10.654 8.959mc–Cu 25.639 12.580 19.348 29.260 6.344 5.867 31.895mc–Ni 6.164 1.431 3.364 4.899 2.178 1.299 0.647mc–Cd 15.825 13.145 12.928 9.020 3.962 3.008 3.371mc–Pb 17.968 10.225 11.641 16.207 3.622 3.881 6.759mc–Cr 25.782 21.250 27.115 18.724 8.086 6.126 8.648md–Cu 129.426 28.331 13.698 21.244 10.324 5.490 5.722md–Cd 0.040 1.169 0.900 0.642 1.765 0.797 2.574md–Ni 6.196 3.608 13.014 8.293 5.693 2.567 6.260md–Pb 18.198 4.458 1.622 3.767 7.670 1.033 2.805md–Cr 26.272 16.698 19.976 10.234 7.500 4.167 7.072me–Cu 10.953 6.848 6.605 2.447 1.850 4.124 6.348me–Pb 13.851 3.689 2.785 1.822 1.322 2.581 2.849me–Ni 38.275 9.307 5.246 5.130 0.228 9.456 6.821me–Cd 12.508 3.008 1.465 1.328 0.973 2.117 1.090me–Cr 28.442 11.024 6.579 5.960 5.205 6.681 12.899mf–Cu 15.152 6.920 13.266 4.137 5.399 6.179 11.377mf–Ni 16.567 13.002 17.639 7.499 7.616 6.817 14.641mf–Cd 0.655 3.451 0.799 0.701 1.807 0.798 1.857mf–Pb 4.269 3.768 1.034 0.270 2.842 1.106 1.703mf–Cr 15.903 14.389 17.002 9.777 12.008 8.228 14.688mg–Cu 18.576 20.484 41.854 5.334 5.125 3.352 3.532mg–Cd 1.586 2.754 3.922 1.706 1.722 0.182 0.391mg–Ni 23.836 21.901 18.044 6.138 6.295 3.467 2.792mg–Pb 6.463 5.112 6.785 2.509 0.551 0.361 0.313mg–Cr 23.824 23.284 18.981 9.247 10.075 4.934 4.254mh–Cu 8.384 7.776 7.595 5.831 9.955 7.312 6.011mh–Ni 13.940 10.918 9.827 8.466 8.559 9.741 6.706mh–Cd 4.308 1.232 2.633 0.852 0.142 1.059 1.137mh–Pb 2.884 0.791 1.288 0.027 0.708 0.819 0.412mh–Cr 18.377 13.971 14.178 11.293 12.071 10.151 9.377mi–Cu 10.053 5.527 5.844 9.226 9.490 5.320 5.202mi–Ni 11.655 13.841 11.141 11.525 10.842 6.035 5.100mi–Cd 3.134 3.388 2.556 3.847 3.450 1.721 1.611mi–Pb 2.058 0.910 0.067 0.426 0.780 0.471 0.433mi–Cr 18.155 23.480 18.579 20.564 20.373 10.019 9.187ml–Cu 15.380 6.917 9.412 4.730 4.002 5.387 8.774ml–Ni 18.723 5.847 14.325 4.761 3.228 4.853 11.831ml–Cd 1.867 0.952 0.187 1.473 0.460 0.155 1.338ml–Pb 0.890 0.265 0.493 0.020 0.018 0.021 0.027ml–Cr 21.110 9.768 9.874 6.503 5.369 6.751 12.620Mm–Cu 8.504 11.944 10.966 5.148 6.444 6.423 13.712Mm–Ni 18.784 20.266 12.530 9.363 8.670 10.624 5.783


Table 3(Continued)

Sample Years

1999–1990 1989–1980 1979–1970 1969–1960 1959–1950 1949–1940 1939–1930

Mm–Cd 0.170 0.444 1.806 0.510 0.808 0.227 0.004Mm–Pb 4.817 2.449 2.975 1.158 1.427 3.508 2.451Mm–Cr 19.086 29.037 16.797 11.300 11.084 9.332 7.797pe–Cu 14.075 14.984 8.068 2.981 12.150 5.322 7.111pe–Ni 11.732 8.395 6.884 12.298 39.615 7.559 13.800pe–Pb 83.261 80.998 58.290 30.893 83.378 52.140 36.228pe–Cr 2.497 5.593 1.222 0.052 1.030 0.015 3.485pe–Cd 12.668 13.517 10.500 4.404 9.199 11.779 8.374pd–Cu 1.883 2.517 2.848 2.245 1.743 2.319 3.026pd–Ni 6.381 10.812 15.073 67.552 13.067 20.208 11.475pd–Cd 3.653 6.715 7.125 4.703 4.479 3.613 3.629pd–Pb 16.684 22.655 32.384 21.510 16.821 19.601 16.971pd–Cr 0.226 0.185 0.053 0.177 0.164 0.128 0.203s1b–Cu 1.552 0.364 0.475 0.472 1.031 0.753 1.938s1b–Ni 1.576 1.693 1.021 1.082 1.472 1.724 0.794s1b–Cd 2.011 1.469 1.652 1.671 0.741 1.408 0.717s1b–Pb 2.839 1.761 2.114 2.117 2.368 3.016 1.370s1b–Cr 1.114 0.672 3.929 0.313 1.922 0.717 0.416s2b–Cu 12.152 23.915 18.451 1.400 1.275 1.004 1.295s2b–Ni 11.585 20.434 15.322 6.631 3.179 1.483 1.580s2b–Cd 1.943 1.397 1.278 2.422 0.977 1.331 0.008s2b–Pb 2.549 3.152 2.074 5.874 3.886 0.371 1.261s2b–Cr 12.851 19.590 14.850 3.925 0.036 0.430 0.618s2a–Cu 13.868 17.757 4.804 4.840 1.748 4.727 11.075s2a–Ni 14.008 22.019 6.484 12.592 2.901 33.939 7.643s2a–Cd 0.029 0.047 0.015 0.017 0.447 0.085 1.796s2a–Pb 1.263 2.826 0.592 0.630 0.117 3.821 1.678s2a–Cr 3.829 20.110 3.764 10.486 0.754 33.963 11.736pa–Cu 16.925 13.369 17.092 12.890 10.223 6.562 5.740pa–Ni 6.471 2.763 2.750 2.199 3.298 0.565 0.728pa–Cd 0.797 1.514 1.929 1.483 1.392 0.963 0.437pa–Pb 2.205 0.788 2.447 1.032 1.066 0.022 0.386pa–Cr 8.553 7.640 9.232 6.095 5.718 3.257 2.203pb–Cu 5.022 3.320 3.437 3.190 5.589 2.416 2.005pb–Ni 4.797 3.346 3.744 4.464 3.916 1.380 1.388pb–Cd 1.275 0.949 1.124 1.145 1.230 0.649 0.395pb–Pb 10.884 8.278 6.827 9.917 11.566 5.720 4.273pb–Cr 1.115 0.974 0.900 0.674 1.440 0.466 0.433pc–Cu 41.599 5.003 7.981 2.956 2.504 2.411 3.174pc–Ni 3.228 13.652 49.297 3.640 3.984 0.881 1.847pc–Cd 0.463 0.397 0.741 0.189 0.152 0.095 0.220pc–Pb 11.982 7.744 13.108 3.785 3.644 3.313 3.732pc–Cr 0.596 0.819 3.531 0.746 0.503 0.215 0.357p4b–Cu 4.544 3.253 2.873 3.854 52.279 4.530 3.355p4b–Ni 4.650 1.828 2.373 2.832 3.560 4.242 1.708p4b–Cd 0.000 0.611 0.562 2.326 0.907 0.542 0.282p4b–Pb 3.192 2.440 2.508 4.369 6.618 3.159 1.727p4b–Cr 4.588 1.713 0.972 1.462 1.128 1.418 0.993


Fig. 1. Cu, Ni, Cr mean concentration in tree rings ofLarix decidua from 1930 to 1999.

Fig. 2. Pb, Cd mean concentration in tree rings ofLarix decidua from 1930 to 1999.

were sampled in June of 2000 in Gressoney Valleynear Lys glacier(Valdaosta Western Italian Alps)at an altitude of 1800–2000 m.a.s. The sampleweight was between 0.078–0.68 g. The area was

chosen due to its strategic position for the pollutantatmospheric fluxes. Three thin cores, 5 mm indiameter and 40 cm in length, for each tree weretaken at the breast height(1.5 m) using an acid


Fig. 3. Metric Multidimensional Scaling.

washed (10% H SO) stainless steel increment2 4

borer. After each coring, the borer was washedthree times with acetone and three times with dd-H O. Samples were immediately sealed in dry2

plastic straws for storage and transport to thelaboratory and were stored aty10 8C prior toanalysis.One core was used for ring width and two for

chemical analysis. From each tree, the core wasdivided into ten rings formed after 1930. Ten-yearincrements were separated using a stainless steelknife washed in 10% HNO and rinsed with dd-3

H O.2

2.4. Analytical procedure

Samples were placed in pre-washed(15%H SO ) 50 ml borosilicate glass tube, dried at 702 4

8C for 48 h and weighed. Samples were suspendedin 10 ml of 70% HNO and digested under reflux3

at 80 8C for 24 h. The cold solution was filteredthrough acid washed(10% HNO ) Whatman No.3

42 filter paper and diluted to 50 ml with dd-H O.2

Solutions were analyzed for Cd, Cr, Cu, Ni, Pb byGFAAS in the experimental conditions outlinedabove.The present procedure is able to quantitatively

recover each metal analysed with a detection limitof 50 mgyl.

3. Results

The metals under study were detected in all thesamples, except in five for which unreliable resultswere obtained. In a way to obtain a comparableseries of data, the metal concentrations of the 25


Fig. 4. Metal mean concentrations in tree rings ofLarix decidua from 1930 to 1999.

tree samples were normalized and expressed asppm of metalydry weight of wood, using thefollowing formula:

CsC9VyP

C (ppm)s mg of metalyg of dried wood; C9

(ppb)sconcentration of metal in a final volumeof 50 ml; Vs50 mly1000 ml; Psdry weight ofsample(g)The results obtained are reported in Table 3.

From these data it was possible to calculate themean concentration of metals in ten-year rings ofLarix decidua during the period 1930–2000 Theresults are reported in Figs. 1 and 2.A data exploration was performed by applying

Metric MDS. By the multivariate approach, theobjects are represented in a space of reduceddimensionality relative to the original data set,while preserving their distance relationships aswell as possible: dissimilar objects are plotted farapart in the ordination space and similar objectsclose to one other.The two-dimensional representation of Fig. 3

explaining the 71.9% of the total variance, high-

lights a cluster, hoped with the red line, of objectsvery similar to each other and reveals the presenceof 6 outliers, i.e. objects significantly differentfrom the others: that is, pe, md, pd, mc, mb, s2a.

1. The object pe–Pb is far from all the othersbecause of its lead concentration, which is muchhigher than the others for all the years studied.

2. The high concentration of copper in the samplemd in the years from 1980 to 1999 is responsiblefor its location out of the cluster.

3. The sample pd is separated from all the othersbecause of its concentration of nickel(pd–Ni),particularly high in the years from 1940 to 1969and of lead (pd–Pb), high in all the yearsstudied.

4. The sample mc is characterised by a highconcentration of copper(mc–Cu) in the years1930–1939 and 1960–1969.

5. The high concentration of copper in the samplemb (mb–Cu) in the years 1940–1949 is thereason for its location out of the cluster.

6. The sample s2a shows a high concentration both


of chromium(s2a–Cr) and nickel(s2a–Ni) inthe years 1940–1949.

In order to have a homogeneous dataset, thesesamples have been excluded from the analysis ofthe time-trend of the metals shown in Fig. 4.

4. Discussion

This reconnaissance study was designed(i) toestablish a simple analytical procedure able toproduce high quality data about the presence ofheavy metal in arboreal species and(ii) to assesswhether Larix decidua can be used as bio-geo-chemical tracers of heavy metal pollution.The direct comparison of our heavy metal con-

centration data with other data previously reportedin other vascular plantsw12,13x is difficult becausethe exact mechanism of metal incorporation in treerings is not fully understoodw14x and differentspecies can have different adsorption coefficientsfor heavy metalsw15x, but the analysis of the time-trend of the metals is in agreement with theliterature results. In particular, it is interesting tonote the increasing of heavy metal concentrationin 1990–1999 tree ring, especially lead. The resultis perhaps unexpected given the introduction inthe early 1990s of ‘unleaded’ gasoline(PbF0.02gyl) w16x but it is in agreement with the resultsreported by Tommasini et al.w13x and it is indic-ative of an increase in automobile traffic in thelast decade

The data collected show that tree rings ofLarixdecidua are potentially a powerful bio-geochemicaltracer for monitoring heavy metal pollution historydue to human activity.

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heavy metals variations in some conifers in valle d'aosta (western italian alps) from 1930 to...

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