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' 2000 OPA (Overseas Publishers Association) N.V.
Toxicological and Environmental Chemistry, Vol. 00, pp. 1±9
Reprints available directly from the publisher
Photocopying permitted by license only
Published by license under
the Gordon and Breach Publishing Group.
Printed in Malaysia.
AN IMPROVED INDEX FOR MONITORING
METAL POLLUTANTS IN SURFACE
SEDIMENTS
GIANPIERO ADAMI, PIERLUIGI BARBIERI
and EDOARDO REISENHOFER
Department of Chemical Sciences, University of Trieste, Via Giorgieri 1,
34127 Trieste, Italy
(Received 12 April 2000; Revised
; In final form
)
An approach for defining the quality of surface sediments of limited areas in terms of heavy
metal contents is proposed. Sediments were taken on a bi-dimensional mapping, for checking
possible different sources of pollution in the case study, a harbour zone. Non residual metals
were determined by ICP-AES in cold diluted hydrochloric acid leachates of sediments. An
``enrichment factor'', r, can be computed for each metal: metals with r values exceeding unity
can be considered as indicators of metal pollution. A ``total enrichment factor'', R, was
proposed in order to assess the degree of pollution of sediments for each site. R is an adimensional value that accounts for the presence of metals that exceed threshold values determined by
background concentrations.
Keywords: Polluted sediments; metal enrichment factors; HCl sediment leaching;
harbour of Trieste
INTRODUCTION
The seawaters of the harbour of Trieste (Northern Adriatic) are seriously
exposed to urban and industrial wastes. The shoreline is densely inhabited,
and there are also many industrial and harbour activities: shipbuilding, good
stocking, and so on. All that implies both occasional and steady inputs of
many chemical species by sewage discharges not completely known and
* Corresponding author. Fax: ‡39-040-6763903. E-mail: [email protected]
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G. ADAMI et al.
controlled. Pollutants tend to accumulate in surface sediments, worsening
the benthic environment; in a previous work we have shown a correlation
between the heavy metal contents in surface sediments and some biometric
parameters of Corbula gibba, a bivalve very resistant also in conditions of
environmental stress, and therefore the sole bivalve still present in all sites of
the examined area (Adami et al., 1997). It has been verified that as heavy
metal contents in sediments increase, numerosity, weight and length of these
indicator organisms decrease.
For evaluating the relevance of the heavy metals in conditioning the seabottom life through water-sediment interactions, the metal contents in sediments must be determined. The analytical procedure followed in sediment
attack determines the metal concentration levels that will lead to the assessment of the fraction due to anthropogenic contributions, as well known in
literature (Loring and Rantala, 1992; Rauret, 1998).
In a recent work we have compared five procedures for dissolving sediments, with increasing strength (Adami et al., 1999): we have also observed
that leaching sediment samples with cold diluted hydrochloric acid makes it
possible to discriminate between ``residual'' metals (Chester and Voutsinou,
1981), having natural sources, and ``non-residual'' metals, likely due to
recent inputs in the sediments, mainly from anthropogenic sources. The best
discrimination is obtained by the mildest attack (as for instance by sodium
acetate): but in this case the solutions obtained can be very diluted, unless
the sediments are highly metal polluted and that implies less precise analytical data. The cold diluted hydrochloric acid leaching is a simple procedure,
with a good ``residual metal sensitivity/accuracy'' trade-off; it is often
reported in current literature (see e.g. Agemian and Chau, 1976; Chester
and Voutsinou, 1981; Huerta-Diaz and Morse, 1990; Voutsinou-Taliadouri,
1995) thus permitting intercomparison of results. On the basis of these
considerations, the cold diluted hydrochloric acid leaching technique
has been selected for determining in sediments the metal fraction due to
anthropogenic inputs.
The present work is devoted to define the quality ± in terms of heavy
metal contents ± of the surface sediments in the harbour of Trieste, providing us a case study on which testing a procedure for the assessment of metal
pollution in a limited area (22 km2) that allows the mapping of a novel
integrated index of metal pollution R. On this purpose we have planned a
sediment monitoring on a bi-dimensional grid (Figure 1). In this way,
different sources of pollution distributed along the shoreline can be detected.
Only surface sediments from an area well characterised from the sedimentological point of view (see Brambati and Catani, 1996) have been sampled
AN IMPROVED INDEX FOR MONITORING METAL POLLUTANTS
FIGURE 1 Contour map of sampling sites in Muggia Bay (Trieste), reporting lines of iso-value for R (the integrated enrichment factor
for surface sediments), and defining areas characterised by R < 1 (non-polluted sites), 1 < R < 1.5 (moderately polluted sites, light grey), and
R > 1.5 ( polluted sites, dark grey).
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G. ADAMI et al.
and analysed, taking into account the environmental aim of this work, that
is the assessment of recent anthropogenic impact on benthic life. Some
relevant heavy metals were determined by a proven technique: inductively
coupled plasma-atomic emission spectroscopy (ICP-AES).
The analytical results obtained in this polluted area are then used for
evaluating a ``total enrichment factor'', R, that makes it possible to quantify
in a simple way the degree of pollution, in terms of heavy metals, of each
examined sediment, and consequently to individuate the sea-bottom areas
more exposed to anthropogenic stress.
In a recent work (Voutsinou-Taliadouri, 1995) a different enrichment
factor was proposed by other authors for discriminating various nonpolluted areas: such factor was calculated averaging concentrations of a metal
in a particular area, and dividing by the mean concentration of the same
metal in all the areas. Our approach is applied instead to a limited area,
suspected of pollution, and considers particularly the anthropogenic metals,
making use of background sediment, relatively nonpolluted. Both reference
sediment and polluted ones constitute a set of homogeneous samples, all
belonging to an unique sedimentological province, with very similar grain
size, pelitic/clay texture, mineral composition (see Brambati and Catani,
1996; Covelli and Fontolan, 1997).
EXPERIMENTAL
Sampling
Surface layers of sediments were collected in June 1997; a Van Veen grab
sampler was used, taking samples of about 0.2 m2 and penetrating up to a
depth of about 15 cm. A bi-dimensional mapping was planned; the 29
sampling sites are situated on an area of about 3 8 Km (see Figure 1).
The samples were cooled (4 C) for transport in laboratory, where the
coarse material (> 2 mm) was removed. The freeze-dried remaining material
was homogenised and sieved: the fraction < 200 mm was used for analysis.
All the steps preceding the chemical analysis were detailed in previous works
(Adami et al., 1997; Barbieri et al., 1999; Adami et al., 1999).
Sediment Leaching Procedure
About 2 g of dry sediment were suspended in 20 mL of HCl 0.5 M (ultrapure
grade) and stirred at room temperature for 16 hours. The so obtained
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AN IMPROVED INDEX FOR MONITORING METAL POLLUTANTS
5
suspensions were centrifuged, the decanted solutions were filtered and made
up to 50 mL with ultrapure MilliQ water, and then transferred in polyethylene bottles for storage before analysis.
Analytical Measurements
All solutions were analysed by inductively coupled plasma-atomic emission
spectroscopy (ICP-AES) using a Spectroflame Modula E instrument by
SPECTRO1 (Germany).
The metal concentrations of chromium, manganese, cobalt, nickel, copper, zinc, lead and vanadium were evaluated using calibration curves
obtained by dilution of SPECTRASCAN1 multi-element standard solutions (for ICP-AES analyses). A decomposition blank, constituted by the
same reagents used through leaching procedure and the major elements (i.e.,
Fe, Al, Mg, Ca, Na and K), was used for preparing the working standards,
in order to compensate the matrix effects.
The repeatability of the procedure was calculated by 6 replicates on 6
different samples (see Table I), and ranged from 2 to 10% (as RSD%,
Relative Standard Deviation expressed as percentage).
Limits of detection (LOD) were calculated following the IUPAC rules
(Long and Winefordner, 1983), and ranged from 0.1 to 0.8 mg g 1.
Calibration curves obtained by means of 5 standard solutions had correlation coefficients higher than 0.999 for all metals, showing a satisfactory
linearity.
RESULTS AND DISCUSSION
We have determined by ICP-AES eight heavy metals (Cr, Mn, Co, Ni, Cu,
Zn, Pb and V) extracted by cold diluted hydrochloric acid from 29 samples
of surface sediments. The metal contents (mg g 1 of dry sediment) are
reported in Table I, together with some analytical parameters as the limits
of detection (LOD) for each metal, and the mean values of relative standard
deviation (RSD%). Reference data of sediments sampled in non-contaminated areas (Chester and Voutsinou, 1981; and Voutsinou-Taliadouri, 1995)
are also reported in the same table; such data were obtained by similar
procedure to the present work, and make it possible a comparison with
our results (keeping in mind that different grain size and of sediments could
play a role). In particular, we note that metal contents of zinc and lead in all
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G. ADAMI et al.
TABLE I Sediment metal contents (mg g 1 of dry sediment), LODs, mean values of relative
standard deviation (RSD%) and literature data of sediments sampled in nonpolluted areas
Site
Cr
Mn
Co
Ni
Cu
Zn
Pb
V
0
1
2
3
4
5
6
7
8
9
10
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
6.2
12.2
15.2
16.7
11.6
9.6
14.5
16.7
14.0
15.0
11.0
11.4
10.1
10.5
10.6
14.1
13.6
13.6
10.2
10.0
11.2
7.9
11.0
11.1
4.1
10.3
13.8
9.8
10.6
240
207
269
238
222
222
316
317
299
300
296
319
262
289
280
295
292
310
290
349
315
363
288
266
279
232
314
292
297
3.9
4.0
6.4
10.2
6.0
5.5
8.1
8.1
6.7
6.7
5.1
5.5
4.9
6.5
5.1
6.1
6.7
8.0
4.4
6.0
5.5
3.5
4.2
4.5
5.0
9.1
6.2
4.7
5.8
15.7
16.3
20.3
30.1
16.7
16.0
26.6
23.6
17.7
23.0
16.6
16.4
18.1
16.6
15.3
19.9
20.6
22.4
14.5
19.6
15.5
12.7
18.4
18.5
10.7
26.3
21.7
18.8
18.0
15.8
43.7
38.8
26.5
25.7
24.6
32.8
29.0
25.7
20.7
29.3
30.6
23.2
23.7
29.5
32.4
29.4
42.0
38.7
27.4
27.4
61.9
55.9
37.4
33.4
62.3
44.4
37.5
45.4
81.3
146
127
97.9
100
86.2
187
127
117
89.1
148
300
171
135
176
240
170
216
204
189
147
321
312
191
213
231
232
222
247
59.8
59.6
71.0
57.8
67.1
55.9
134
78.4
77.3
58.0
92.5
181
108
92.3
125
150
123
126
145
143
106
229
213
142
168
234
165
171
191
13.7
13.1
23.0
26.3
27.7
20.1
31.0
28.1
29.2
28.0
25.6
27.2
25.8
29.9
28.9
30.4
29.8
29.2
30.4
26.7
32.8
30.2
41.8
37.6
34.8
30.6
37.8
35.6
37.9
LOD
mean RSD%
0.1
10.0
0.5
4.0
0.5
6.0
0.8
8.0
0.5
6.0
0.2
4.0
0.2
3.0
0.8
2.0
14±180
170±980
3±20
12±207
3±35
17±72
5±32
Ð
26
754
9.5
38
9.5
21
20
Ð
Voutsinou-Taliadouri,
1995
Chester and Voutsinou,
1981
our sediments are up to ten times higher than the upper limits observed in
nonpolluted sediments.
We can use the analytical data corresponding to each sampling site for
the computation of an ``enrichment factor'', r, defined as the ratio:
r ˆ …Csed
Cback †=Cback
where Csed is the concentration of a particular metal in each sediment, while
Cback is the concentration of this metal determined in a ``background sediment'', i.e. a sediment that can be considered as relatively nonpolluted. In
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AN IMPROVED INDEX FOR MONITORING METAL POLLUTANTS
7
TABLE II Values of r for the 29 sites (r > 1 are boldfaced). Averaged values of r for each
metal are reported in last line
Site
1
2
3
4
5
6
7
8
9
10
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
average
Cr
Mn
Co
Ni
Cu
Zn
Pb
V
0.95
1.43
1.68
0.87
0.54
1.32
1.68
1.24
1.41
0.76
0.83
0.62
0.69
0.70
1.26
1.17
1.17
0.63
0.60
0.79
0.27
0.77
0.78
<0.01
0.65
1.21
0.57
0.70
<0.01
0.12
<0.01
<0.01
<0.01
0.32
0.32
0.24
0.25
0.23
0.33
0.09
0.20
0.16
0.23
0.22
0.29
0.20
0.45
0.31
0.51
0.20
0.11
0.16
<0.01
0.31
0.21
0.24
0.02
0.65
1.60
0.54
0.40
1.07
1.06
0.70
0.72
0.31
0.42
0.26
0.68
0.29
0.56
0.72
1.04
0.13
0.54
0.41
<0.01
0.08
0.16
0.28
1.33
0.60
0.21
0.48
0.04
0.29
0.92
0.07
0.02
0.70
0.51
0.13
0.47
0.06
0.05
0.16
0.06
<0.01
0.27
0.32
0.43
<0.01
0.25
<0.01
<0.01
0.18
0.18
<0.01
0.68
0.38
0.20
0.15
1.76
1.45
0.68
0.63
0.55
1.07
0.84
0.63
0.31
0.86
0.93
0.47
0.50
0.87
1.05
0.86
1.66
1.45
0.73
0.74
2.92
2.54
1.36
1.12
2.94
1.81
1.37
1.87
0.79
0.56
0.21
0.24
0.06
1.31
0.57
0.44
0.10
0.82
2.69
1.11
0.66
1.17
1.95
1.09
1.66
1.51
1.32
0.81
2.96
2.84
1.35
1.62
1.84
1.85
1.73
2.04
<0.01
0.19
<0.01
0.12
<0.01
1.24
0.31
0.29
<0.01
0.55
2.03
0.81
0.54
1.09
1.51
1.06
1.11
1.43
1.39
0.77
2.83
2.55
1.37
1.81
2.91
1.77
1.86
2.19
<0.01
0.68
0.92
1.03
0.47
1.26
1.05
1.13
1.05
0.87
0.99
0.88
1.18
1.11
1.22
1.18
1.13
1.22
0.95
1.40
1.21
2.06
1.75
1.54
1.24
1.77
1.60
1.77
0.90
0.21
0.54
0.23
1.21
1.26
1.13
1.17
the present case, a sample taken just beyond the dam-line (site 0) has been
chosen as background sediment, since the site is not directly exposed to
pollutant inputs from the shoreline, and grain size and mineral composition
are similar to the other sediments here examined.
The so obtained r values, relatives to the sampled sites, are reported in
Table II. In last line of the same table are also reported the averaged values
of r for each metal.
We can observe that manganese, cobalt and nickel have all values much
below the unity, only chromium has 0.90; on the contrary, the metals likely
due to anthropogenic activities, i.e. copper, zinc, lead and vanadium, have
all values above the unity (i.e., they have concentrations at least two time
higher than the background sediment). Here we decide to set the threshold
for selecting metals which are to be included in the global R index at the
value r ˆ 1.0.
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G. ADAMI et al.
TABLE III
Site
1
2
3
4
5
6
7
8
9
10
12
13
14
15
Total enrichment factors, R, for each site (R > 1.5 are boldfaced)
R
Site
R
0.64
0.72
0.45
0.50
0.27
1.22
0.69
0.62
0.37
0.77
1.66
0.82
0.72
1.06
16
17
18
19
20
21
22
23
24
25
26
27
28
29
1.43
1.05
1.39
1.40
1.10
0.93
2.48
2.50
1.46
1.52
2.23
1.80
1.64
1.97
Taking r ˆ 1.0 as an operational threshold value, we can so consider Cu,
Zn, Pb and V as ``indicators of the local pattern of anthropogenic metal
pollution'', and use them for evaluating the degree of pollution of surface
sediments. For this purpose, we have computed a ``total enrichment factor''
(R) for each site, averaging the r values of the all (n) indicator-metals (in this
case: Cu, Zn, Pb and V, n ˆ 4):
R ˆ …r†=n
The resulting R values are reported in Table III, providing us with an
integrated index of the local metal pollution patterny. Contour plots can
be used for having a synthetic view of the spatial distribution of this
integrated indicator. We can also choose a criterion for classifying the
examined sites: for instance, those with R values exceeding 1.5, that we
can define as highly polluted, those with R values between 1.5 and 1,
moderately polluted, and those with R values below unity, that we can
consider as less exposed to pollution.
The drawing of the three classes of sediments in Figure 1 makes it evident
that, accordingly to the above reported criterion, the area facing piers V, VI
and VII (sites from 22 to 29) is the most exposed to metal pollution, as well
as site 12 facing a steel mill. Site 6, near the Industrial Harbour, and the area
between pier VII and site 12, facing a warehouse district, present a moderately compromised situation. On the basis of our criterion, all other sites can
be considered as low polluted.
y
The inclusion of Cr in the computation of R yields very similar results.
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AN IMPROVED INDEX FOR MONITORING METAL POLLUTANTS
9
CONCLUSIONS
The analytical determination of metals in solutions obtained by leaching
surface sediments in cold diluted hydrochloric acid produces data useful to
define the quality of the sediments in terms of metal pollution. The analytical procedure used is simple, inexpensive and not time-consuming. The
obtained data allow us to compute an ``enrichment factor'', r, for all metals
and each sampled site, that makes it possible to individualise the anthropogenic metals. Moreover, computing a ``total enrichment factor'', R, for
each site gives a quantitative criterion for classifying the sediments accordingly their metal pollution degree in an unsophisticated, prompt and at the
same time informative way.
Acknowledgements
The Authors are indebted to the Italian Ministry of University and Scientific
Research for its financial support.
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