zechmeister1995

ELSEVIER
Environmental Pollution 89 (1995) 73-80
Elsevier Science Limited
Printed in Great Britain
0269-7491/95/$09.50
0269-7491(94)00042-5
CORRELATION BETWEEN ALTITUDE AND HEAVY METAL
D E P O S I T I O N IN THE ALPS
Harald Gustav Zechmeister
Department of Vegetation Ecology & Biological Conservation, University of Vienna, Althanstrafle 14,
A-1091 Vienna, Austria
(Received 20 December 1993; accepted 3 June 1994)
Abstract
Mosses have been collected from transects along altitudinal gradients on five mountain ranges within the northern
and eastern Alps. The location of the sampling points on
steep slopes provides results depending on altitude rather
than on horizontal distance. As, Cd, Co, Cr, Cu, Fe, Hg,
Ni, Pb, V, Zn and S concentrations in moss shoots for
198~ 1991 have been determined The results of several
multivariate and regression analyses show a remarkable
increase of Pb, Cd, Zn and S concentrations with rising
altitude. High levels of precipitation are strongly correlated
with heavy metal deposition, and seem to be the main
source of heavy metal fallout at higher altitudes. Larger
amounts of wind blown, indigenous particles must also be
considered for several heavy metals (e.g. V). There are
only a few local pollutors situated throughout the Alps,
and the investigation shows that pollution by heavy metals
in alpine regions is caused mainly by long range transport.
a very distinct geomorphology, these maps and the
conclusions deduced from them should be viewed with
suspicion. As this study formed part of an international
'survey of atmospheric heavy metal deposition using
bryophytes as bioindicators' (Rtihling, 1994), investigations on differences in heavy metal deposition in
respect to different altitudes seemed to be necessary.
Generally it is known that there is a strong correlation
between orography, the amount of wet deposition and
rainfall composition (e.g. Fowler et al., 1988; Dore et
al., 1990; Weston & Fowler, 1991; Fowler et al., 1993).
Nevertheless there are only a few investigations on this
topic concerning heavy metals and these studies show
very controversial results (e.g. Groet, 1976; Herman &
Stefan, 1992; Solt6s, 1992). None of these previous investigations, regardless of the results, have been done
by surveying a well orientated transect along an altitudinal gradient. In this investigation the location of the
sampling points on rather steep slopes provided results
that show a distinct relation to altitude rather than to
horizontal distance along transects. Two aims are set
up for this paper: (i) the correlation of heavy metal
deposition in relation to altitude; and (ii) possible
reasons for such a correlation.
Keywords: Heavy metals, altitudinal transect, mosses,
precipitation, Alps.
INTRODUCTION
Mosses have been used for monitoring heavy metal
deposition all over Europe (e.g. Riahling & Tyler, 1971;
Ellison et al., 1976; Pakarinen & Tolonen, 1976;
Steinnes, 1977; Pilegaard et al., 1979; Gydesen et al.,
1983; Rt~hling et al., 1992) and North America (e.g.
Glooschenko, 1989) for a long time. The methods for
sampling, preparation of the samples, and analyses
have evolved in that time and have been improved in
many cases. The literature on this topic is extensive,
and surveys are given for example by Little and Martin
(1974), Martin and Coughtrey (1982), Maschke (1981),
Brown (1984), Rao (1982), Tyler (1990) or Zechmeister
(1994a).
As the method is rather easy to handle and of fairly
low cost, it is frequently used nowadays for monitoring
large areas, even nations, thus providing deposition
maps of heavy metal pollution of an area (e.g. Ellison
et al., 1976; Riahling et al., 1987).
None of these maps take into account the topography of a country, possibly because this is of minor importance in the hitherto investigated areas. However, in
countries or areas dominated by high mountains (e.g.
the Alps, the Pyrenees or the Tatra) and therefore with
MATERIALS AND METHODS
Moss sampling
These investigations were carried out on five mountain
ranges, which are representative of the different
climatic conditions and geological subsoils of the
northern and eastern Alps. On each of these ranges
sampling points were established at nearly every 200 m
(in altitude), starting at the valley bottom up to the
peak of the mountains. Between four and eight sampling plots were arranged on a single mountain representing differences in altitude (between 750 m and
1 600 m). More data on the sampling sites as well as
their locations can be seen in Tables 1 and 2. Further
details on the sampling plots can be given on request.
Sampling of mosses has been done mainly by following
RfJhling et al. (1987). Below the treeline, the sampling
plots were situated in clearings within forests, with a
distance of at least 5 m to the next tree, and no cover
of any shrubs or tall herbs was accepted. In the valley
bottoms, mosses in grasslands which had been cut
twice a year without the use of fertilisers were also
73
H. G. Zechmeister
74
Table 1. Data on sampling sites
No.
1
2
3
4
5
Name
Coordinates
Altitude (m)
Plots"
Species
Achenkirch
Zillertal
Gasteinertal
Schoberstein
Stuhleck
47°34'/11°38'
47° 14'/11°50'
47°09'/13°04
'
47055'/14020'
47035'/15046
'
900-1 660
650-2 260
1 000-2 150
350-1 250
1 000-1 760
4
8
7
4
5
Hylocomium splendens, Ctenidium molluscum
Pleurozium schreberi
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
~Plots means number of sampling points at a single location.
taken in some cases. Above the treeline, mosses were
collected from open sites with no shelter from bigger
rocks or dwarf-shrubs. Small terraces on steep slopes
were accepted as sampling points in this study, in
contrast to the regulations set up for the international
survey of atmospheric heavy metal deposition (Rtihling,
1994). Nevertheless, dripping wet ground was avoided
and therefore ground water effects can be excluded at any
sites. There were distances of at least 100 m from houses
or roads. The samples at each point represent the sum of
at least five subsamples within 50 m 2, to avoid differences
caused mainly in micro habitats.
The collected amount was 0.5 litre of moss at each point
(if possible) and plastic gloves were used while sampling.
The mosses were stored in paper bags and were dried
immediately after collection. Most of the samplings
took place in September 1991 but on one mountain
range (Achenkirch) there were three collections (July
1991, 1992, and September 1991) to investigate the possible dependency of heavy metal accumulation on the
season (Zechmeister, 1994b). In this paper only results
from the collection in September 1991 are reported.
The mosses used for this investigation were Hylocomium splendens, Pleurozium schreberi, Hypnum
cupressiforme and Ctenidium molluscum. Each of these
mosses receive water and nutrients only by atmospheric
deposition and uptake from soils or litter can be
excluded (e.g. Tamm, 1953; Rtihling et al., 1987). In
most cases only a single moss species was used for each
profile. If this was not possible conversions of concentration data from one moss to another have been
made, following conversion data given by Zechmeister
(1994a). Table 2 shows the converted data. The nomenclature of the mosses follows Frahm and Frey (1987).
Precipitation data
Precipitation data are given for the period of sampling
heavy metal deposition (1989-1991). The data on the
precipitation at different sites were provided by the
'Zentralanstalt for Meteorologie und Geodynamik',
Hohe Warte, Vienna. Data on the increase in precipitation with altitude at various sampling points have
mostly been calculated by use of the precipitation map
(1:50000; Hydrographisches Zentralbtiro des BM ftir
Land- und Forstwirtschaft).
Preparation of the mosses
The mosses were reduced by hand to the shoots which
had been growing during the period from 1989-1991,
so that the determined concentrations were related to
the heavy metal deposition in that period. Following
the works of Tamm (1953), Longton and Green (1969,
1979), Rtihling et aL (1987) or Zechmeister (1994a) this
could be done easily with a very small error rate. After
this, the shoots were cleaned from obvious soil particles. Following the advise of A. Riihling and L. ThSni
(personal communications), who had done investigations on this topic, the samples were not washed. The
unwashed samples were dried at 40°C.
Chemical analyses
The following 11 heavy metals were determined: As,
Cd, Co, Cr, Cu, Fe, Hg, Ni, Pb, V and Zn. As sulphur
(S) is a good indicator for the burning of fossil fuels,
and is itself an important pollutant, it was also determined.
Before the analyses were done, all samples were dried
once again at 70 ° over 12 h. Then they were pulverized
by a Retsch-Titan-mill and 2 g were mixed with a mixture (5 to l) of nitric acid and perchloric acid. The decomposition was done in two ways: (i) by heating block
with open vessels; (ii) by heating block with reflux system. The extracts were made up to a volume of 100 ml.
Variant (ii) was used for volatile elements such as Hg
or S. All other elements were determined by using
method (i). S, Fe, Zn, Cu, Pb, V were detected on a
plasma emission spectrometer (Plasma II, PerkinElmer). Cr, Ni, Cd and Co were determined by an
atomic absorption spectrometer with a graphite furnace
(Perkin-Elmer 5100 Zeeman-PC). Hg and As were detected by atomic absorption spectrometry (Perkin-Elmer
5000) using a Mercury-Hydride-System MHS 20.
Several standard reference materials were used to
check the accuracy of the methods (e.g. material from
the International Plant Analytical Exchange IPE, Dept.
Soil Science and Plant Nutrition, Wageningen; or reference samples (mosses) from the Dept. Vegetation Ecology, University of Lund).
Statistics
Regression analysis was performed to relate the heavy
metal content of the samples with the independent variables, altitude and precipitation. Regressions were calculated for each site. For Sites 2 and 3, calculations
have been done both with and without rejecting outliers. Outliers were defined by altitude. Sampling points
up to 500 m above the valley bottom were rejected, as
they are strongly influenced by local emission sources
12.6
12.1
15.1
13.0
10.8
15.4
17.9
19.9
15-2
6-9
11-5
8.6
12.0
13.2
18.8
17-2
23-0
16.9
22.8
14-4
3.1"
3.2*
3.3*
3.4
3.5
3.6
3.7
3.8
3.9
4.1
4.2
4.3
4.4
4.5
5.1
5.2
5.3
5.4
5.5
5.6
0.8
1.0
0.5
1.4
0.6
1-0
1.8
1.7
1.2
1.4
5-9
5.4
2.4
2.1
2.5
1.6
1.7
1.8
1.7
3.1
1.3
1.4
2.8
3-7
5-2
6.5
1.4
1.6
0-6
0.7
0.9
1.4
0.9
1.6
V
1 147
1074
914
880
783
887
970
1086
988
922
990
1216
1099
1237
1011
1096
1023
1080
1 157
899
833
832
1 169
1211
1304
1317
1018
1071
1 125
1004
981
1044
1033
1 149
S
29-9
33.7
27.4
26.8
25.6
24.5
39.1
36.9
35.1
28.1
64.0
35.3
33.0
39.6
38.0
40.1
41.6
38-2
38.9
29.9
29.5
25.0
36.0
51.5
42.2
64.2
37.6
41.4
29.4
38.1
36-0
30.5
32.4
70.5
Zn
672
503
288
391
369
336
717
613
609
368
2087
1248
1 144
392
470
477
527
466
355
1970
215
235
453
479
931
1239
844
952
230
394
273
364
322
521
Fe
Cr
Ni
5.4
5.6
5-4
6.3
5.2
5.8
5-9
6.6
6.5
4.4
7.7
6.2
6.9
6.3
6.3
4.9
7.4
5.9
7.3
4.8
5.3
5.2
6.0
5.2
4.7
6.6
5.6
6-1
5.6
5.8
5.6
5.9
5.4
5.9
3.0
1-7
1.5
1.6
1.8
1.5
3.4
2.6
1.2
2.3
5-3
4.6
2.7
1-5
1.8
1-3
1.9
1.7
1.4
6.2
0.9
0.4
1.6
1.5
2.4
2.7
1.5
1.7
0-3
0.5
0.8
0.9
1.0
1.0
2.3
3.8
2-1
1.9
3.3
2.1
2.6
2-1
3-0
2-2
6.9
7.0
4-0
1.8
1.9
1.4
2.2
1.9
1.9
4.6
1.1
0.3
2.2
1.7
2.4
2-0
2-2
2.7
0.7
1.4
1.4
1-3
1-5
3.2
(/zg g i dry weight) b
Cu
*Indicates sampling points excluded by outlier rejection analysis.
aSampling point according to Site no. in Table 1.
bSum of 1989-1991.
11.1
9.8
15.2
31.3
14-5
29.7
12-0
7.8
8.1
18.9
12-6
24.3
18.7
24.5
Pb
1.1
1.2
1.4
1.2
1.3
1.4
2.1"
2.2*
2.3
2.4
2.5
2.6
2.7
2.8
Sampling point a
0.2
0-3
0.2
0.2
0.2
0.2
0-2
0-2
0-3
0.3
0.4
0.3
0.4
0.4
0.3
0.5
0.4
0.5
0.2
0-3
0-3
0.3
0.5
0.6
0-5
1.0
0.4
0.5
0.4
0.4
0.5
0.3
0.4
0.7
Cd
0.6
0.5
0.3
0-4
0.5
0.3
0.6
0-4
0.5
0.1
1.t
1.1
0.9
0-2
0.3
0.5
0-2
0.3
0.1
1.1
0-2
0.1
0.2
0-2
0.4
0.5
0.8
0.5
0.2
0.2
0.2
0.2
0-3
0.4
Co
0.03
0.02
0.02
0.02
0.02
0.02
0.04
0.03
0.04
0.03
0.05
0.06
0-04
0-04
0.04
0.03
0.05
0.04
0.04
0.04
0.04
0.04
0.05
0.06
0.09
0.11
0.09
0.09
0.05
0.05
0.04
0.04
0.04
0.05
Hg
1.33
0.59
0.39
0.63
0.66
0.29
0.64
0.75
0.80
0.21
0.84
0-82
0.69
0.48
0.40
0.36
0.52
0.39
0.77
1.19
0.30
0.23
0.46
0.54
0.71
0.81
0.94
0.95
0.34
0.42
0.29
0.34
0.24
0.54
As
Table 2. Heavy metal concentration in mosses
1000
1250
1 550
1450
1650
1 850
2100
2050
2150
350
550
750
1 100
1250
1000
1200
1300
1440
1600
1 760
900
1 100
1660
1 100
1400
1660
650
1000
1250
1550
1750
1950
2130
2260
Altitude (m)
3555
4000
4500
4360
4 720
5080
5530
5440
5620
4263
4500
4800
5400
5 500
2681
3050
3250
3600
3750
4120
3855
4200
5 100
4200
4800
5100
3060
3 560
4010
4490
4850
5 210
5540
5900
Precipitation (mm) b
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Ctenidium molluscum
Ctenidium rnolluscum
Ctenidium molluscum
Hypnum cupressiforme
Hypnum cupressiforme
Pleurozium schreberi
Pleurozium schreberi
Pleurozium schreberi
Pleurozium schreberi
Pleurozium schreberi
Pleurozium schreberi
Pleurozium schreberi
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Pleurozium schreberi
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Hylocomium splendens
Pleurozium schreberi
Hylocomium splendens
Hylocomium splendens
Species
e~
E"
H. G. Zechmeister
76
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900
1200
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(b)
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1800
2100
2400
=Itit~de
Fig. 1. Outlier rejection for (a) Zn (Gasteinertal) and (b) V (Zillertal). Altitude in m; concentrations in/~g g 1 dry weight. (a) Correlation factor -- 0.82008; points deleted = 1,2,3; (b) correlation factor -- 0.80447; points deleted = 1,2. Dotted lines represent the
regression-line without outlier rejection.
a n d a t m o s p h e r i c i n v e r s i o n s (see below). T h e p l o t s (Fig.
1) s h o w the fitted line for r e g r e s s i o n s b o t h w i t h a n d
w i t h o u t outliers. T h e c o r r e l a t i o n coefficients c a l c u l a t e d
b y r e g r e s s i o n a n a l y s i s were tested for significance (p =
0.05).
Correlation analysis and principal components analysis ( P C A ) are m u l t i v a r i a t e m e t h o d s w h i c h r e d u c e d a t a
d i m e n s i o n a l i t y b y f o r m i n g l i n e a r c o m b i n a t i o n s o f the
i
0.62
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...............................................................................
o r i g i n a l v a r i a b l e s . T h e use o f b o t h P C A a n d P e a r s o n s
c o r r e l a t i o n coefficient a s s u m e s t h a t a l i n e a r r e l a t i o n s h i p
exists, d i s t o r t i o n s (Orloci, 1974) c a n n o t be i n t e r p r e t e d
b y these m e t h o d s . F o r e a c h p r o c e d u r e , the d a t a m a t r i x
c o n t a i n e d the h e a v y m e t a l d a t a o f all s a m p l e s o f Hylocomium splendens a n d Pleurozium schreberi a n d the additional variables on altitude and precipitation at each
sampling point.
i
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5.3
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Comp.... t 1
Fig. 2. Principal components analysis: (a) plot and (b) biplot for the first two components; Component 1 (37-5%), Component
2 (18.6%); data matrix according to all samples of Hylocomium splendens and Pleurozium schreberi including altitude (SL) and
precipitation (PR) at each sampling point.
Correlation between altitude and heavy metal deposition in the Alps
Table 3. Significant regressions (p = 0.05) between altitude and
heavy metal concentrations (including sulphur) at different sites
Site
1
2
3
4
5
As Cd Co Cr Cu
Fe Hg Ni Pb
X*
X* X* X*
X
X
X
X
S
V
Zn
X
X
X* X*
X
X indicates significant regression analysis; X* indicates significant regression after elimination of sampling points at the
valley bottom (using outlier rejection).
Data and moss species used for calculations are given in
Table 2. For Site 1 only Hylocomium splendens has been
taken
Correlation analysis was performed by calculating
the Pearson product-moment correlation coefficient.
This has a range from -1 (perfect negative correlation)
to +1 (perfect positive correlation). The calculated
coefficients were tested for significance (Student's ttest).
The eigenvalues calculated by PCA which correspond to the axis are given in the text. In Fig. 2 a plot
of the component weights and a biplot are given. The
ordination of the variables within the axes (plot of
component weights) as the length of the vectors and
the angle between them (biplot) give information on
the contribution of the variables to the principal components, as well as on the correlation between them. In
factor analysis, factor weights are scaled, rotation of
the matrix was performed by using Varimax rotation.
All statistical methods involved were performed by
using the statistical package 'Statgraphics' on a PC system at t h e University of Vienna, Institute for Plant
Physiology.
R E S U L T S AND D I S C U S S I O N
Data on the concentrations of heavy metals in mosses
(/~g g ~ dry weight) at each sampling point are given in
Table 2, They represent the concentration in the moss
shoots which grew in the years 1989-1991 (total of 3
years).
Distribution patterns
At Site I (Achenkirch) the heavy metal concentrations
show a clear increase with altitude in most metals. In
Hylocomium splendens a remarkable difference in nearly
all heavy metal concentrations was found between
Sampling Point 1.1 and Sampling Point 1.4 and an
even ~;tronger difference in concentration was found between Sites 1.2 and 1.4. The slight decrease of heavy
metal concentration from Sampling Point 1.1 to Point
1.2 was probably due to the more sheltered habitat of
Plot t.2, and to Site 1.1 being slightly influenced by a
nearby main road. The results given for the moss
CtenMium molluscum are very similar, even though the
tested sampling points are not exactly the same. The
increasing rates are small in correspondence to the
77
increases in altitude. Similar results were found on
other sampling times (Zechmeister, 1994b).
Sites 2 and 3 can be treated as one, as they show
rather similar patterns in the distribution of heavy
metal concentrations. At both sites there were fairly
high concentrations in mosses collected at the valley
bottom (2.1, 2.2 and 3.1, 3.2, 3.3). This is probably due
to intensive traffic and local house fires (Riicker & Peer,
1988; Bolhar-Nordenkampf, 1989). These influences
reach up to 500 m above the valley bottom. Up to this
level the vegetation is also influenced by atmospheric
inversions, which are fairly common in many parts of
the Alps (Kaiser, 1992). Above this line a slight decrease in concentration was found, followed by a constant increase in concentration with ascending altitude.
Concentrations in the highest areas were mostly as high
as or even much higher than in the valley bottom.
Using regression analysis with outlier rejection and
therefore excluding the sampling points at the valley
bottom, a very distinct, and for many cases significant,
increase of heavy rnetal concentration with ascending
altitude could be seen (see also Table 3 and Fig. 1).
Figure 1 gives two characteristic examples (Zn, V) and
shows significant regressions, which might not have
been that clear without outlier rejection. More plots of
these regressions are available on request.
The concentrations at Site 4 show a very typical distribution pattern for nearly each heavy metal. There
was a sharp rise in heavy metal concentration at Point
4.2 followed by a more or less constant decrease. The
increase of heavy metal concentration is mainly due to
a small scale local metal processing industry. Only Cd,
Pb and S show higher concentrations at the top of the
mountain, probably depending on long range transport
from not too distant steelworks, which is also confirmed by the results of multivariate methods (see
below).
Site 5 is also in the vicinity of an extensive area of
local steelworks. Nevertheless, the highest As, Co, Cr,
Fe, Ni and V concentrations could be found on the top
of the mountain, which is the sampling point furthest
from the steelworks. Maybe this was a result of the
emission by high chimneys, as well as of long range
transport. Apart from the peak in concentration at the
top of the mountain no obvious deposition pattern
could be found.
Correlation of heavy metal concentration and precipitation
The obvious increase of heavy metal concentration
with ascending altitude in many cases could be put
down to various factors. In alpine meadows there is, of
course, a higher amount of wind blown particles arising
from indigenous soils, as the cover of vegetation is
sparse. According to Chamberlain (1966) or Clough
(1975) heavy metals spread as small particles and show
a deposition closely related to windspeed, which increases
above the tree line. In predominantly dry areas, vegetation
cover and windspeed might then be major factors in
the deposition of heavy metals (e.g. Bourcier et al.,
1980). With the high amounts of precipitation in the
H. G. Zechmeister
78
Table 4. Correlation coefficients (Pearson product--moment correlation) between heavy metals (including sulphur), altitude and precipitation
Precipitation
As
-0.13
Cd
0.02
Co
0.02
Cr
0.01
Cu
0.11
Fe
4).01
Hg
4).22
Ni
0.16
Pb
0.16
S
0.08
V
0.04
Zn
0.12
Altitude
0.6*
Precipitation 1.00
Altitude
Zn
4).11
0.00
4).23
4).22
0.03
4).26
4).30
4).23
0.64*
4).02
0.36*
0.04
1.00
0-17
0.63*
0-24
0-18
0-48*
0.38*
0-39*
0-39*
0-3*
0.42*
0.45*
1-00
V
S
0.33 0.21
0.12 0.41"
0.65* 0.08
0.741"0.03
0.45* 0.36*
0.77* 0.08
0.29 0.33
0.78* 0.16
4).13 0.21
0.28 1.00
1-00
Pb
Ni
Hg
Fe
4).12 0.55* 0.29 0.67*
0.11 4).01 0.51" 0.06
4).27 0.84* 0.25 0.89*
4).18 0.81" 0.04 0.89*
0.34 0.33 0.21 0.28
4).19 0.83* 0.32 1.00
4).13 0 . 1 4 1.00
4).22
1.00
1.00
Cu
Cr
Co
Cd
0.23 0.64* 0.7* 4).19
0.02 4).21 4).03 1.00
0.15 0-82* 1.00
0-16
1.00
1.00
As
1.00
*Indicates significance.
northern and eastern Alps, however, the main source of
heavy metal deposition seems to be rain. As in the Alps
the precipitation rises more or less constantly with increasing altitude, a correlation is obvious. To test this
theory, regressions between precipitation and heavy
metal concentration have been calculated for each site
and each metal. Significant correlations (p -- 0.05) can be
seen in Table 3. These results show only the correlation
between the total quantity of rain and concentration,
not considering the fact that there may be also a strong
impact by the frequency or the intensity of a single
rainfall event (e.g. Martin & Coughtry, 1982). Dry
deposition might occur as well, even during periods of
rain.
The dispersion of indigenous heavy metals may be
recognised by higher amounts of Fe, Cr and V. In this
investigation, only V shows a slight increase at higher
altitudes. Ti might be a very good indicator for the
detection of indigenous particles (Brown & Brown,
1990), but it was not analysed in this study.
As the quantity of precipitation is calculated in most
cases as a variable dependent on altitude, there always
has to be a strong correlation between precipitation
and altitude in the data in Table 3. To avoid this side
effect, multivariate methods have been used without
respect to sampling sites. For these analyses only data
from Hylocomium splendens and Pleurozium schreberi
have been used, as they have fairly high and equal
accumulation capacities for heavy metals. Ctenidium
molluscum has a much higher accumulation capacity
and was excluded for this reason. These results are
given in Table 4 and Fig. 2.
In Table 4, the Pearson product-moment correlation
coefficient and significances are given. Pb and V show
a significant correlation to altitude. As this analysis
only reveals relationships between pairs of species, the
effect of other variables (hidden influences) cannot be
detected.
The plots in Fig. 2 show the graphs according to
principal components analysis (PCA). The eigenvalue
for component 1 is 5.25, for component 2 it is 2-56.
Nevertheless component 1 (37.5%) and 2 (18.6%) represent only 56.1% of the total variance. But even the
rotation of the matrix (Varimax) in factor analysis does
not change the results too much, from those given by
the first two components of PCA. The results given by
PCA are also fairly similar to Pearson correlation
coefficients in many respects.
Each PCA component is hardly explained by a single
factor, though there seems to be a strong relation to
altitudinal distribution (Component 1) and common
emission sources (Component 2). Two more or less distinctive groups could be detected. Group 1 (Fe, Ni, Cr,
Co, V, As) which also shows strong internal relations
by calculating Pearson correlation coefficients, could
be released by steelworks, but shows a close relation to
indigenous soils and therefore to windblown particles
too. The contrasting ordination of Group 1 and altitude illustrates the minor correlation between elements
of Group 1 and altitude. This is partly in contrast to
regression analysis and Pearson correlation coefficients.
The ordination of precipitation (PR) shows little relevance to Component 1 and should not be considered in
explaining Component 1. This is in sharp contrast to
other mathematical approaches. Group 2 (Pb, Cd, S,
Zn) shows very similar ordination within Component
2. Elements of this group derive mainly from fossil fuel
combustion, e.g. traffic (Adriano, 1986; Vernet, 1991),
but might not be associated to this source alone. Anyway these elements are mainly released as a consequence of human activities. Nevertheless, the small
particles released from various high temperature processes reach the atmosphere and are subjected to a
wide distribution (Galloway et al., 1982).
CONCLUSIONS
In general, the results show a high correlation between
altitude and heavy metal concentration in mosses for
Pb and to a lesser extent for Cd. These heavy metals
are mainly accumulated and spread by human beings and
have a very low content in rural sites and unpolluted
Correlation between altitude and heavy metal deposition in the Alps
soils or plants (e.g. Galloway et al., 1982;
Glooschenko, 1989; Schmid-Grob et al., 1991). According to multivariate methods and regression analysis, an
altitudinal related increase also occurs for S, Zn and V.
These results are similar to those obtained in an investigation done by Mutsch (1992), who analysed soils
in Austrian woods. He found an increase of heavy
metal concentrations, mainly for Pb and Cd, and put it
down to external long-range transport. Solt6s (1992)
found similar correlations for Pb in the Tatra. Groet
(1976) correlated Zn and Cd contents with increasing
altitudes in the United States. Riacker and Peer (1988)
found higher concentrations of Pb, Cd and Zn in soils
of alpine regions than of woods below. Ross (1990) and
Rfihling et al. (1992) suspect that higher amounts of Zn
are also partly related to indigenous soils.
Correlations between precipitation and heavy metal
concentration in mosses are given by Ross (1990) for
Cd, Cu, Fe, Pb, Zn and V. R0hling and Tyler (1971)
found this relationship only for Pb. Both investigations
have not been performed on an altitudinal transect and
so may be related to other factors as well.
Contrary to this, the results of Herman and Stefan
(1992) and Herman (1992), who analysed soils, spruce
needles and barks in Alpine valleys, did not show any
altitude relationship, nor did Schmid-Grob et al. (1991),
who put it down to the fact that their transects covered
too small a range in altitude.
The results of this work are based on the assumption
that tLg g~ heavy metal (dry weight) is determined
mainly by atmospheric input. Differences resulting
from different growth patterns at various altitudes do
not explain the increase of heavy metal content of the
moss samples.
Despite the fact that the annual biomass of the individual moss stems and leaves decreases with increasing
altitude, the population density increases at higher
altitude (Zechmeister, 1995). Therefore the area covered
by the analysed moss, as well as the biomass which
cover this area is almost constant under various climatic
conditions. R~hling (1985) confirms this assumption
for Swedish populations of Hylocomium splendens (113 g
m 2) as well as Zechmeister (1994a, 1995) for populations of Hylocomium splendens and Pleurozium
schreberi at various regions and altitudes within Austria. Nevertheless, some alpine populations of these two
species which are related to 'forma alpina' show greater
biomass per area than populations with ordinary growth
patterns at lower altitudes. In the current investigation,
two samples were taken from 'forma alpina' species. In
these cases (Sampling Points 3.8 and 2.8) the actual
concentrations ate underestimated compared to the other
samples. In consequence, the increase of heavy metal
concentration is probably higher than shown for example in Fig. 1. But area related data on growth measurements are still too few to calculate conversion
values between these two growth forms.
The increase of heavy metals with altitude can be
observed clearly only in areas with a rather weak local
influence of pollutors. In areas with a strong local
79
pollution these results can not be confirmed for obvious reasons. Although only a few local pollutors are
situated throughout the Alps, the investigation demonstrates that there must be an unrelated high pollution
by heavy metals in alpine regions caused mainly by
long-range transport. Severe disturbances in many of
the sensitive habitats (e.g. alpine lakes) or impact on
the human foodchain (e.g. by traditional alpine agriculture) cannot be excluded.
ACKNOWLEDGEMENTS
This project was supported by the Austrian Ministry of
Science and Research and the Federal Environmental
Agency. The Austrian Research Center Seibersdorf (Dr
O. Horak) is thanked for the analysis of the samples.
The author also wishes to thank Mr Ron Smith (Institute of Terrestrial Ecology, Penicuik, Midlothian) and
two unknown reviewers for their scientific comments
on the manuscript.
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