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Heavy metal and grain-size distributions in estuarine shallow water sediments of the Cona Marsh (Venice Lagoon, Italy)
the Science of the Total Environment I
Aa I m m m m t * ~~ m v m ~ ~ t~ ~ ~ t m ~
t
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The Science of the Total Environment 151 (1994) 19-28
ELSEVIER
Heavy metal and grain-size distributions in estuarine shallow water sediments of the Cona Marsh (Venice Lagoon, Italy) Roberto
Z o n t a *a, L u c a Z a g g i a a, E m a n u e l e
Argese b
alstituto per 1o Studio della Dinamica delle Grandi Masse, C.N.R., S. Polo 1364, 30125 Venezia, Italy bDipartimento di Scienze Ambientali, Universit?~degli Studi di Venezia, Dorsoduro 2137, 30123 Venezia, Italy
Received 4 February 1993; accepted 3 June 1993
Abstract Anthropogenic heavy metals and grain size were determined in 15 surface sediment samples from the Cona Marsh, an estuarine area of the Venice Lagoon (Italy). The investigation is based on separate analyses of both metal concentrations obtained with two acid extractions and particle-size percentages by laser light scattering. The grain size was also measured in sample aliquots previously submitted to the organic matter removal to disperse mineral-organic aggregates, visualized by scanning electron microscopy. These aggregates apparently shift the particle-size spectrum toward larger diameters, and the intimate association between metals and sediment particles is more evident after their dispersion. The comparison of the two distributions showed a strong correlation between heavy metals and finer particles content in the sediment (d < 7.8 /zm). The investigation finally illustrated a spatial zoning of the marsh into three sectors with different degrees of pollution, in relation to the water circulation determined by river discharge and tidal forcing. Key words: Heavy metal; Sediment; Grain size; Shallow water; Venice Lagoon; Pollution
1. Introduction Fine-grained suspended particles act as efficient scavengers for anthropogenic heavy metals and generally constitute the p r e f e r r e d vehicle for their transport in coastal environments. Variations in water physico-chemical conditions and hydrodynamics, particularly in shallow water estuarine areas, d e t e r m i n e the particle deposition and the m e t a l transfer to the sediment. T h e r e f o r e , the
* Corresponding author.
spatial distribution of anthropogenic heavy metals in surface sediments r e p r o d u c e s the long t e r m effects of pollution from different sources and constitutes a reference for studies on contaminant transfer and accumulation [1,2]. T h e close association between anthropogenic heavy metals with finer particles [3], however, suggests a control by the effect of variable grain size (and mineralogy) of the samples. In fact, because of differences in particle transport, pathways and biogeochemical conditions, sediments from sectors of the same area quite often have different size characteristics. This m a k e s it dif-
0048-9697/94/$07.00 © 1994 Elsevier Science BV. All rights reserved. SSDI 0048-9697(94)3957-4
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R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
ficult to compare concentrations from different samples. FSrstner [4] reported some methods to correct for this grain-size effect, finally proposing the heavy metal analysis in the sieved diameter d < 63 /zm fraction. However, concentrations in the selected diameter ranges do not necessarily reflect the concentration in the whole sediment sample [5]. A reliable methodology should allow the analysis in unfractionated samples and for a wide range of sediment types [6], to establish the metal content independent of the presence of coarse inert materials which may in practice dilute its concentration. This paper describes an investigation on the relationship between anthropogenic heavy metals and grain size in the sediment of the Cona Marsh (Venice Lagoon). The study is based on a comparison of two separately determined spatial distributions, and also addresses the definition of a methodology to account for the grain-size effect. Two distinct acid extractions were performed on samples to solubilize heavy metals. The grain size was determined in the sampling state, but also after removal of the organic matter, to consider its influence on particle size spectrum [7,8]. The presence of organic matter in shallow marine sediments frequently leads to the formation of
particle aggregates - - having various size, shape and density [9] - - that are highly stable in the environment. As a consequence, the grain-size spectrum experiences an apparent shift toward coarser diameters, thus differing from the single grain spectrum. 2. The test-area
The mainland interface of the Venice Lagoon is characterized by tidal channels which delimit shallow water areas and vegetated fiats, where tidal exchange with the Adriatic Sea is retarded and decreased with respect to the rest of the lagoon (Fig. 1). Through this interface, the discharge of agricultural, industrial and municipal untreated wastes by rivers and channels from the drainage basin occurs. Therefore, the fate of pollutants strictly depends on the behaviour of these characteristic estuarine environments, where accumulation takes place. Among these areas, the Cona Marsh (Fig. 2) is the most interesting, because of the complex morphology and the presence of the Dese River, which is one of the main freshwater inputs to the lagoon. The Marsh has a mean tide level of ~ 5 0 cm. The tide enters through the Torcello Channel, involving its southern and central parts, and travels upstream, via
Fig. 1, Map of the Venice Lagoon. The squared area indicates the location of the Cona Marsh.
21
R. Zonta et aL / The Science of the Total Ent~ronment 151 (1994) 19-28
1N HCI Extraction
Sampling
i
8N HNO3
Org. Matter •
Extraction
Elimination
Analysis
Data
Processing Fig. 3. The adopted operative scheme for sample treatment, analysis and data processing.
Fig. 2. Detailedmap of the Cona Marsh indicatingsampling sites and the nomenclatureof the systemof channels. the Siloncello Channel and Ramo Casone branch to the East, and Dese River to the West. Freshwater supply in the North and salt water forcing from the South produce a complex circulation, with marked differentiations in the chemicalphysical variables (particularly salinity and turbidity) in the different sectors [10]. On the basis of a preliminary investigation on heavy metals in the sediment [11], 15 sampling sites were chosen to obtain a representative description of the pollution conditions in the marsh. The sampling scheme includes sites from the surrounding system of channels - - whose mean depth ranges between 2 and 3 m - - and the principal inputs in the marsh. 3. Materials and methods
Surface sediment samples (the upper 5 cm) were collected with a Plexiglas syringe-type corer,
placed in acid-treated PVC containers and stored at 4°C. Each sample was homogenized and then aliquots of it were separately subjected to the procedure schematically represented in Fig. 3. Approximately 2 g (wet wt.) were oven-dried and leached at room temperature with 60 ml of 1 N HC1, agitating for 24 h with a magnetic stirrer; the same amount of oven-dried sample was instead leached with 40 ml of 8 N HNO3, at 80°C for 3 h. The extraction solutions were prepared using Milli-Q water and Merck Suprapur acids (HC1 30%; HNO 3 65%). The suspensions were then centrifuged, filtered through 0.4/xm Nucleopore membranes and analyzed for seven heavy metals (Cr, Cu, Fe, Mn, Ni, Pb, Zn) by a flameless atomic absorption spectrophotometer (Varian SpectrAA 10). Heavy metal concentration was determined through appropriate calibration curves for both acids, after verification of possible matrix effects. The grain size was measured with a Microtrac particle-size analyzer (Leeds and Northrup rood. 7995, USA). The measure is based on the scattering of a laser beam (630 nm) determined by the
R. Zonta et al. / The Science of the Total Enlironment 151 (1994) 19-28
22
continuous flow of sample particles in a bi-distilled water suspension [12]. To remove rare macro shell fragments and plant debris, samples were wet-sieved at 125 /zm. The volumetric percentages spectrum was determined in the sampling state (referred to in the text as SS) and after removal (referred to as OE) of the organic matter. This latter was obtained by treating the sample with hydrogen peroxide (30%), heated in a water bath and maintained at pH = 8.5 with 1 N NaOH [13]. The Microtrac analysis was performed on the 0.7 < d < 125 ~m diameter interval, obtaining particle-size percentages in 15 dimensional classes corresponding to one-half phi interval of the Udden-Wenworth scale (as expressed by Krumbein [14]). A 100-s measurement time was adopted, and the mean of three repeated readings was used. A Cambridge Steroscan 250 TP scanning electron microscope (S.E.M.), operating at 20 kV with a beam current of 0.32 mA, permitted the visual observation of sediment particles. Specimens were prepared by filtering 5 ml of a 20-mg suspension of sample in 100 ml of bi-distilled water with 0.4 /zm Nucleopore membranes, which were subsequently freeze-dried and gold coated.
4. Results and discussion
4.1. Heavy metal distribution Concentrations obtained with the two extractions are reported in Table 1. HCl-extracted values - - which are in agreement with the previous investigation [11] - - are lower than the corresponding H N O 3 figures. The strong oxidizing effect and the higher extraction temperature and concentration of HNO 3 permits a rapid solubilization of the anthropogenic heavy metals content [15], but determine a greater dissolution of silicate lattices with respect to HC1 [16]. On the other hand, 1 N HC1 is not fully efficient in the solubilization of more resistant metal phases such as authigenic iron sulfides [17,18]. Both these occurrences justify the higher concentrations obtained with HNO 3 acid. Nevertheless, the two extractions substantially show the same heavy metal spatial distribution in the marsh, which correspond to the same distinction between polluted and relatively unpolluted areas. In fact, a linear regression analysis between concentrations obtained for each metal with the two acids, gives a strong degree of correlation with the high r coefficient values reported in
Table 1 Heavy metal concentrations (ppm, d.w.) resulting from the two acid extractions Site
Cr
Cu
Mn
Fe
Ni
Pb
Zn
HC1
HNO 3
HCI
HNO 3
HCI
HNO 3
HCI
HNO 3
HCI
HNO 3
HC1
HNO 3
HCI
HNO 3
1 2 3 4
22.2 24.8 35.4 25.4
27.6 29.6 44.7 27.9
42.5 29.8 56.6 40.0
53.1 44.9 66.0 54.5
12562 11925 15 799 10 902
19818 19635 24 289 21456
375.6 268.3 283.7 291.1
463.2 404.6 392.5 429.7
24.3 24.3 31.3 24.1
33.4 31.7 37.9 29.3
60.5 58.5 80.1 58.5
69.0 65.1 80.8 63.3
225.1 199.0 294.3 176.6
342.6 275.6 363.2 244.0
5 6 7 8
17.5 14.8 13.6 9.3
26.6 27.1 23.1 15.7
59.9 35.2 33.5 14.6
76.3 49.7 39.1 20.9
14295 11 940 11582 6674
25 921 22628 22792 11058
240.9 368.5 152.1 243.0
332.7 391.6 327.3 250.0
25.5 25.4 21.3 17.1
35.6 31.4 27.9 22.2
52.3 54.8 50.7 38.2
58.0 62.0 55.3 38.6
207.5 150.3 170.7 83.8
296.7 311.4 186.5 120.7
9 10 11 12
8.9 9.6 12.1 13.0
20.2 17.5 20.6 21.1
14.1 16.0 18.0 22.0
18.0 20.0 26.0 28.0
5332 5997 7998 6604
10411 11801 13 396 14211
214.8 203.7 238.5 211.2
234.6 239.5 267.5 267.0
16.3 16.3 20.1 18.2
22.5 22.5 26.6 24.9
36.4 38.4 41.6 43.2
37.4 38.2 43.0 44.4
113.5 75.6 128.5 91.2
121.7 111.2 148.0 161.5
13 14 15
12.4 17.4 17.3
18.0 22.3 22.1
22.0 36.0 34.6
32.0 48.0 40.0
6100 9099 9509
13 401 19398 19659
166.7 176.0 342.3
236.4 279,1 354,8
18.2 20.8 21.2
22.0 34.5 31.9
33.6 63.8 58.7
36.8 71.2 59.0
99.6 180.8 145.2
137.0 197.0 222.2
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
extraction, may be due to the presence in the sediment of resistant Mn-bearing phases which introduce a variability in the HC1 efficiency. In order to evaluate the extent of heavy metal pollution, it is opportune to subtract from concentrations measured in the various sites their pre-industrial level (or the 'baseline'). This would correspond to identifying a sediment inside the area which is free of heavy metal contamination and has the same origin, mineralogy and grain size of sampling [1]. In practice this task is difficult to achieve and it is preferable to choose reference values accordingly with less restrictive criteria, which are thought to be more useful and appropriate to the specific test-area [2,4]. For the baseline values we assume concentrations measured in the southern sector of the Marsh, since these are the lowest figures also in the previous investigation [11]. Based on this assumption, a percentage enrichment factor (EF%) was simply defined as:
Table 2 Regression coefficients between concentrations obtained for each metal with the two acid extractions x
y
r
C r HCl
CrHNO3
0.929 a
C u HCI
CuHNO3
0.981 a
F e HCl
FeHNO3
0.933 a
MnHCl
MnHNO~
0.752 b
NiHCJ
NiHNO3
0.881 a
p b Hcl
pbHNO3
0.980 a
Z n ncl
ZnHN03
0.886 a
a.
a S i g n i f i c a n c e level, 0.001 > 6 S i g n i f i c a n c e level, 0.01
>
a
23
> 0.001.
Table 2. Some anomalies affect the Mn regression, which produces a lower r coefficient. At the same time, the correlation matrices computed for HC1 (Table 3) and HNO 3 (Table 4) extracted concentrations, analogously show a strong degree of correlation for each metal with the others. This suggests that the same transport and accumulation processes govern the behaviour of the analyzed metals in the marsh. Again, the HCl-extracted Mn does not show significant correlations. The Mn spatial distribution, as resulted from this
C -
EF%
C min
c'nax _ Cram
Table 3 Correlation matrix for concentrations obtained with the HCI extraction
CuHCl
FenCl
C r HCl
0.756 b
CuHC~
__
FerfCl
__
MnHCl
__
NiHCJ pbHCl
. .
S i g n i f i c a n c e level: a0.001
MnnO
Ni n o
0,800 a
0,431 d
0.919 a
0.374 d
__ __ .
. .
>
>
a
ZnHCl
0.884 a
0.897 a
0.892 a
0.882 a
0.801 a
0.873 a
0.471 d
0.957 a
0.835 a
0,922 a
__
0.528 c
0.472 d
0.384 d
0.873 a
0.921 a 0.900 ~
. .
a ; b0.01
pbncl
.
> 0.001; c0.05 >
. a
> 0.01; aa > 0.05.
Table 4 C o r r e l a t i o n m a t r i x f o r c o n c e n t r a t i o n s o b t a i n e d w i t h the H N O 3 e x t r a c t i o n
CuHNO3
FeHNO3
MnHNO3
NiHNO3
pbHNO3
znnN03
C r rtNo3
0.751 b
0.721 b
0.709 b
0.785 a
0.831 a
0.856 a
CuHNO3
__
0.927 a
0.712 b
0.896 a
0.818 a
0.879 a
F e nN°3
--
--
0.746 b
0.875"
0.833 a
0.845 a
MnHNO3
__
__
__
0.718 b
0.805 a
0.884 a
NiHNO3
__
--
__
0.939 a
0.884 a
pbHNO3
.
S i g n i f i c a n c e level: a0.001
. >
or; b0.01 >
. a
.
> 0.001; c0.05 >
. a
> 0.01; % > 0.05.
0.877 a
24
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
in which, for given extraction and metal, C is the concentration measured at the site, C min and C max are, respectively, the minimum and maximum concentrations detected. EF% values for the HC1 extraction are plotted in Fig. 4, providing evidence of the overall trend for heavy metal concentrations in the Marsh, and also the anomalous distribution of Mn. The heavy metal distribution reflects either the role of the Dese River as a source of contaminants or the tidal effect on water circulation. The north-western sector (sites 1-7, 14 and 15) appears more polluted, with 2-4-fold the concentrations measured in the southern part (sites 8 and 9), where tidal exchange actively occurs. The branch from Bocca Carozza to Dese River (sites 1-5) gives the greatest concentration values (particularly site 3) because of the cyclic freshwater standing due to the tide [19], which also affects
the eastern branch of the S. Maria Channel (sites 14 and 15). The Dese discharge also determines the heavy metal accumulation in its lower reach, both in the river-bed (site 6) and in the shallow lateral branch debouching inside the Marsh (site 7). Concentrations in the eastern sector of the Marsh (sites 10-13) are instead significantly lower. 4.2. Grain-size distribution Sediment grain-size analysis emphasizes a subdivision of the Cona Marsh into three sectors with homogeneous characteristics, which correspond to the spatial zoning identified by the heavy metal distribution. For the three sectors, the mean values of particle-size percentage according to the Wenworth scale are given in Fig. 5, before and after removal of organic matter. If compared with the distribution in the north-western sector, sediment from sites of the southern sector contains a
100
C r Cu Fe Mn N i Pb Zn
mn
,o
l nmmm
60 c
0
1
2
3
/,
5
G
7
8
9
10
11
12
13
lg,
15
Site Fig. 4.
Histograms represent values of the percentage enrichment factor EF% for metals analyzed at each site.
R. Zonta et al. / The Science of the Total Ent~ronment 151 (1994) 19 28
5O
-
•
SS
OE
SS
OE
m
25
125~2
62-16 ,urn SS
OE
4O ID
~ 30 e" 0
u 20
10
0
I NW
E
S
Fig. 5. Mean percentages of the particle size in selected diameter ranges (according to the Wentworth scale) in the three different sectors of the Marsh: north-western (NW), eastern (E), southern (S).
greater amount of fine sand and coarse silt (16 < d < 125/xm), which are partially littoral material from the Adriatic Sea transported through the Torcello Channel. Samples from the eastern sec-
tor (sites 10-13) show instead an intermediate distribution. S.E.M. images provided visual evidence of mineral particles aggregation in the SS samples.
Fig. 6. (a) S.E.M. image of typical organic-mineral aggregates in the sediment of the Cona Marsh (SS sample); (b) enlargement of the surface structure of the upper portion of the larger aggregate in (a). Clay minerals are the main constituent of the aggregate.
R. Zonta et al. / The Science of the Total Ent~ronment 151 (1994) 19-28
26
Coarse aggregates ranging in size from 15-100 /zm (Fig. 6a) were observed mainly in the sediment from the north-western sector. Individual clay crystallites ranging from < 1 to a few microns (Fig. 6b), which are the main constituents of the aggregates, result bounded together by organic coatings, appearing as irregular and opaque on the aggregate surface. On the basis of the grain size analyzed in samples subjected to organic matter removal, the zoning of the Marsh is more evident. Samples from the southern and the eastern sectors show no significant variations, confirming the minor influence of the organic matter on particle aggregation. On the contrary, OE samples from the north-western sector show a net increase in the percentage of finer particles with respect to the corresponding SS spectra, due to the dispersion of the mineral-organic aggregates. Their origin is related to the high adsorption capacity of the silt to clay fraction versus the numerous reactive functional groups of organic molecules [7]. Particles aggregation may be initiated in the water column and is then continued in the sediment, with the intervention of both early diagenetic transformation of the organic matter and biological reworking of inorganic and organic detritus by sediment feeders [20].
For OE samples, it should also be noted that the percentage of particles in the range 7.8 < d < 31 /zm remains practically constant for all the sites, with a mean percentage equal to 32.3% (S.D. = 3.7). This implies that the different sectors of the Marsh may be essentially distinguished on the sole basis of the percentage of particles finer than 7.8 /zm in the 'organic free' samples.
4.3. Comparison between grain-size and heavy metal distributions To investigate their relationship in the sediment, a regression analysis was made between particle-size percentage and heavy metal concentration measured at the 15 sites. Tables 5 and 6 report, for a selected set of representative diameter ranges and for the two extractions, the linear regression coefficient r obtained, respectively, for SS and OE samples. Since the sum of particle-size percentages is 100, the r value for a given diameter range corresponds to a r opposite in sign for the complement of the range to the whole interval 0.7 < d < 125/zm. For SS samples, the percentage of fine silt (3.9 < d < 16/zm) produces the highest r values for both the extractions, but they do not seem to be sufficiently significant. On the contrary, the 'organic free' grain size shows a higher degree of
Table 5 R e g r e s s i o n c o e f f i c i e n t v a l u e s b e t w e e n s e l e c t e d d i a m e t e r r a n g e s a n d h e a v y m e t a l c o n c e n t r a t i o n s - - SS s a m p l e s Dimensional
Cr
range (/zm)
HCI
Cu HNO 3 HCI
Fe
Mn
HNO 3 HCI
HNO 3 HCI
Pb
Ni HNO 3 HCi
HNO 3 HC!
Zn HNO 3 HCI
HNO 3
0.7 0.7 0.7 0.7
< < < <
d d d d
< < < <
2.8 3.9 5.5 7.8
0.35 d 0.410 0.47 d 0.52 c
0.26 d 0.29 d 0.34 d 0.38`1
0.33 d 0.43 d 0.49`1 0.54 c
0.38 d 0.48 a 0.56 e 0.60 c
0.29 d 0.40 d 0.48`1 0.54 c
0.42 d 0.53 c 0.60 c 0.66 b
0.22 d 0.29 d 0.38 d 0.42 d
0.36 d 0.48 d 0.58 c 0.65 b
0.32 d 0.41 d 0.49`1 0.54 c
0.24 d 0.35 d 0.46 d 0.53 c
0.21 a 0.30 d 0.40 d 0.49`1
0.20 d 0.31 d 0.43`1 0.52 c
0.14 d 0.22 d 0.30 'l 0.37 't
0.28 d 0.39 d 0.49 a 0.55 c
0.7 1.4 1.4 3.9 7.8
< < < < <
d d d d d
< < < < <
16 3.9 16 16 31
0.55 c 0.460 0.56 c 0.56 c 0.410
0.400 0.340 0.410 0.41`1 0.300
0.62 ¢ 0.50 d 0.64 c 0.65 b 0.57 c
0.68 b 0.54 c 0.69 b 0.70 b 0.60 c
0.61 c 0.480 0.63 c 0.64 b 0.54 c
0.74 b 0.60 c 0.75 b 0.76 b 0.65 b
0.44 d 0.320 0.440 0.460 0.300
0.72 b 0.55 c 0.74 b 0.76 b 0.58 c
0.59 c 0.46 d 0.60 c 0.61 c 0.500
0.62 ¢ 0.43`1 0.64 b 0.67 b 0.62 c
0.56 c 0.380 0.58 c 0.61 c 0.53 c
0.62 ¢ 0.390 0.64 b 0.69 b 0.62 c
0.470 0.30`1 0.49 '1 0.52 c 0.46`1
0.64 c 0.45`1 0.65 b 0.68 b 0.56 c
11 16 16 62
< < < <
d d d d
< < < <
44 0.08`1 0.07 d 0.22 d 0.22 d 0.17 d 0.240 0.01 d 0.090 0.18`1 0.340 0.250 0.300 0.17`1 0.21 d 44 - 0 . 4 1 d - 0 . 3 1 0 - 0 . 3 2 0 -0.37`1 - 0 . 3 7 0 -0.37`1 - 0 . 3 7 d - 0 . 5 5 c - 0 . 3 4 d - 0 . 2 3 0 - 0 . 2 8 0 - 0 . 3 1 0 - 0 . 3 1 0 -0.38`1 62 - 0 . 6 2 c - 0 . 4 7 0 - 0 . 6 5 b - 0 . 7 1 b - 0 . 6 4 0 - 0 . 7 4 b - 0 . 4 8 d - 0 . 7 8 a - 0 . 6 2 c - 0 . 6 0 c - 0 . 5 9 c - 0 . 6 3 c - 0 . 5 1 d - 0 . 6 7 b 125 - 0 . 4 3 0 - 0 . 3 1 d - 0 . 5 3 c - 0 . 5 8 c - 0 . 5 2 c - 0 . 6 5 b - 0 . 3 5 d - 0 . 6 0 c - 0 . 5 0 d - 0.57 c - 0.470 - 0 . 5 4 c - 0 . 3 8 d - 0 . 5 4 c
S i g n i f i c a n c e level: aO.O01 > a ; bo.o1 > o~ > 0.001; c0.05 > ot > 0.01; dot > 0.05.
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
correlation between finer particles and concentrations obtained with both acids (except for the HCl-extracted Mn). In particular, the most significant set of regression coefficients results for the 0.7 < d < 5.5 ~m range. The 7.8 < d < 31 /~m fraction in the OE samples - - that is almost constant in the Marsh sediment - - data are uncorrelated to heavy metal concentrations and from this standpoint it seems like a 'neutral' material subdividing polluted finer particles from non-polluted coarse ones. Removal of the organic matter permits an outline of how heavy metals in the Marsh sediment are substantially associated to fine-grained particles, which at the same time are the principal vehicle for their transport in the Dese River [10,19].
27
tion. A comparison between anthropogenic heavy metal and grain-size distributions shows a strong degree of correlation between concentrations and finer particles in the 'organic free' size spectra. These reproduce better the spectrum of riverborne suspended particles, and are more directly related to transport and deposition mechanisms which are responsible for the heavy metals accumulation in some sectors of this estuarine area. The removal of organic matter provides a clearer distinction between different sectors of the area investigated and provides more precise information on the association between heavy metal and particle size. The methodology described for the comparison of grain-size and heavy metal distributions, seems to be useful in the evaluation of pollution effects in complex shallow water ecosystems, such as the Cona Marsh. Since it does not involve sample fractionation and concentration analysis in several particle-size ranges, the methodology also avoids the proliferation of chemical analyses and reduces the incidence of possible artifacts due to the sample handling.
5. Conclusions Both hydrochloric and nitric acid extractions are effective agents for the description of the heavy metal distribution in the sediment of the Cona Marsh, emphasizing the influence of the Dese River as a source of contaminants and sectors where accumulation occurs. S.E.M. analysis and differences in the grain size observed after the dispersion of mineral-organic aggregates show that organic matter has a fundamental role in the process of particles aggrega-
Acknowledgements The authors thank Dr. R. Pini (C.N.R. Pisa, Italy) and A. Rizzi (C.N.R. Milano, Italy) for their
Table 6 Regression coefficient values between selected d i a m e t e r ranges and heavy metal concentrations -- OE samples Dimensional
Cr
range (/zm)
HCI
Cu
HNO 3 HCI
Fe
Mn
HNO 3 HCI
HNO 3 HCI
Ni
Pb
HNO 3 HCI
Zn
H N O 3 HC1
HNO 3 HCI
HNO 3
0.7 < d < 2.8
0.73 b
0.63 c
0.648
0.62 c
0.65 b
0.708
0.31 o
0.56 c
0.67 b
0.78 a
0.84 a
0.80 a
0.67 b
0.7 < d < 3.9
0.80 a
0.748
0.79 a
0.78 a
0.84 a
0.88 a
0.41 d
0.78 a
0.83 a
0.86 a
0.89 a
0.90 a
0.80 a
0.80 a
0.7 < d < 5.5
0.78 a
0.73 b
0.83 a
0.82 a
0.87 a
0.91 a
0.46 d
0.82 a
0.83 a
0.88 a
0.88 a
0.9l a
0.82 a
0.84 a
0.7 < d < 7.8
0.77 a
0.70 b
0.83 a
0.82 a
0.84 a
0.90 a
0.43 d
0.79 a
0.80 a
0.87 a
0.87 a
0.89 a
0.79 a
0.81 a
0.7 < d <
0.62 c
16
0.76 ~
0,69 b
0.82 a
0.82 ~
0.83 a
0.90 a
0.40 d
0.80 a
0.80 a
0.85 a
0.84 a
0.87 a
0.79 a
0.81 a
1.4 < d < 3.9
0.76 b
0.748
0.80"
0.82 a
0.89"
0.92 a
0.44 d
0.84"
0.84 a
0.82 a
0.81 a
0.85 a
0.80 a
0.85 a
1.4 < d < 16 3.9 < d < 16
0.728 0.60 c
0.68 b 0.52 c
0.82 a 0.77 ~
0.84 a 0.78 a
0.85 a 0.718
0.92 a 0.81 a
0.41 a 0.33 a
0.83 a 0.738
0.79 ~ 0.648
0.82 a 0.728
0.77 a 0.658
0.83 a 0.72 b
0.78 a 0.678
0.83 a 0.72 b
7.8
-0.40 d -0.45 a -0.35 d -0.290
-0.42 a -0.32 d -0.390
-0.380
-0.39 a -0.390
11 < d < 44
31
-0.748
-0.82 a -0.81 a -0.500
-0.8P
-0.768
16 < d < 44
-0.77 a -0.73 b -0.80 a -0.78"
-0.718
-0.77 a -0.748
16 < d < 62 -0.79" -0.728 -0.82 a -0.81" 62 < d < 125 - 0 . 5 7 c - 0 . 5 2 c - 0 . 6 8 8 - 0 . 7 0 8 S i g n i f i c a n c e level: ao.oo]
> t~; b0.01
>
-0.51 d -0.440
--0.46 a - 0 . 3 8 a
- 0 . 7 7 a - 0 . 8 1 a - 0 . 8 2 a --0.80 a - 0 . 7 8 a
-0.85 a -0.85 a -0.48 a -0.84 a -0.79 a -0.81 a -0.84 a -0.86"
--0.84 a - 0 . 8 2 a
- 0 . 8 5 a - 0 . 8 8 a - 0 . 4 4 0 - 0 . 8 4 a - 0 . 8 0 a - 0 . 8 4 a - 0 . 8 6 ~ - 0 . 8 9 a --0.83" - 0 . 8 2 a - 0 . 6 6 b - 0 . 7 7 a - 0 . 2 6 d - 0 . 5 9 c - 0 . 6 5 b - 0 . 7 2 8 - 0.64 c - 0 . 6 9 8 - - 0 . 5 8 c - 0 . 6 5 8
ot > 0.001; c0.05 >
ot > 0.01; dot > 0.05.
28
R. Zonta et aL / The Science of the Total Environment 151 (1994) 19-28
precious contribution, respectively, in the grainsize analysis and in the S.E.M. observation. Further, thanks to G. Cogoni, F. Costa, M. Magris, M. Meneghin, R. Ruggeri and F. Simionato (C.N.R. Venezia, Italy) for collection and treatment of sediment samples. References 1 U. FiSrstner and G.T.W. Wittmann, Metal Pollution in the Aquatic Environment, Springer-Verlag, Berlin, 1981, p. 473. 2 R. Chester and F.G. Voutsinou, The initial assessment of trace metal pollution in coastal sediments, Mar. PoUut. Bull., 12 (1981) 84-91. 3 W. Salomons, Sediments and water quality, Environ. Technol. Lett., 6 (1985) 315-326. 4 U. F/Srstner, in I. Thornton (Ed.), Applied environmental geochemistry, Academic Press, London, 1983, Chapt. 13, p. 395. 5 H.L. Windom, S.J. Schropp, F.D. Calder, J.D. Ryan, R.G. Smith Jr., L.C. Burney, F.G. Lewis and C.H. Rawlinson, Natural trace metal concentrations in estuarine and coastal marine sediments of the Southeastern United States, Environ. Sci. Technol., 23 (1989) 314-320. 6 J.H. Rule, Assessment of trace element geochemistry of Hampton Roads Harbor and lower Chesapeake Bay area sediments, Environ. Geol. Water Sci., 8 (1986) 209-219. 7 M.A. Rashid, Geochemistry of Marine Humic Compounds, Springer-Verlag, New York, 1985, p. 300. 8 W. Salomons and U. F/Srstner, Metals in the Hydrocycle, Springer-Verlag, Berlin, 1984, p. 349. 9 B. Carson, K.F. Carney and A.J. Meglis, Sediment aggregation in a salt-marsh complex, Great Sound, New Jersey, Mar. Geol., 82 (1988) 83-96.
10 G. Ghermandi, D. Campolieti, R. Cecchi, F. Costa, L. Zaggia and R. Zonta, Trace metals behaviour during salt and fresh water mixing in the Venice Lagoon, Nucl. Instr. Meth., B75 (1993) 330-333. 11 S. Bernardi, R. Cecchi, F. Costa, G. Ghermandi, S. Vazzoler and R. Zonta, A preliminary investigation on the distribution of heavy metals in surface sediments of the Cona tidal marsh (Venice Lagoon), I1 Nuovo Cimento, l l C (1988) 667-678. 12 P.E. Plantz, in H.G. Barth (Ed.), Modern Methods of Particle Size Analysis, J. Wiley & Sons, New York, 1984, Chapt. 6, p. 173. 13 A. Riviere, Methodes Granulometrique (Techniques et interpretation), Masson, Paris, 1977, p. 170. 14 W.C. Krumbein, Size frequency distribution of sediments, J. Sed. Petrol., 4 (1934) 65-77. 15 D.J. Carmody, J.B. Pearce and W.E. Yasso, Trace metals in sediments of New York Bight, Mar. Pollut. Bull., 4 (1973) 132-135. 16 H. Agemian and A.S. Chau, Evaluation of extraction techniques for the determination of metals in aquatic sediments, The Analyst, 101 (1976) 138-146. 17 M.A. Huerta-Diaz and J.W. Morse, Pyritization of trace metals in anoxic marine sediments, Geochim. Cosmochim. Acta, 56 (1992) 2681-2702. 18 L. Zaggia, R. Zonta and E. Argese, Extraction of anthropogenic heavy metals from the reduced sediments of the Cona tidal marsh (Venice Lagoon): indeterminations due to authigenic sulfides and clay minerals, (submitted for publication). 19 G. Germandi, R. Cecchi, F. Costa and R. Zonta, Trace metal distribution in aquatic systems as studied by PIXE analysis of water and sediment, Nucl. Instr. and Meth., B49 (1991) 283-287. 20 R.G. Johnson, Particulate matter at the sediment-water interface in coastal environments, J. Mar. Res., 32 (1974) 313-330.
Aa I m m m m t * ~~ m v m ~ ~ t~ ~ ~ t m ~
t
II~mm* ~ Warn
The Science of the Total Environment 151 (1994) 19-28
ELSEVIER
Heavy metal and grain-size distributions in estuarine shallow water sediments of the Cona Marsh (Venice Lagoon, Italy) Roberto
Z o n t a *a, L u c a Z a g g i a a, E m a n u e l e
Argese b
alstituto per 1o Studio della Dinamica delle Grandi Masse, C.N.R., S. Polo 1364, 30125 Venezia, Italy bDipartimento di Scienze Ambientali, Universit?~degli Studi di Venezia, Dorsoduro 2137, 30123 Venezia, Italy
Received 4 February 1993; accepted 3 June 1993
Abstract Anthropogenic heavy metals and grain size were determined in 15 surface sediment samples from the Cona Marsh, an estuarine area of the Venice Lagoon (Italy). The investigation is based on separate analyses of both metal concentrations obtained with two acid extractions and particle-size percentages by laser light scattering. The grain size was also measured in sample aliquots previously submitted to the organic matter removal to disperse mineral-organic aggregates, visualized by scanning electron microscopy. These aggregates apparently shift the particle-size spectrum toward larger diameters, and the intimate association between metals and sediment particles is more evident after their dispersion. The comparison of the two distributions showed a strong correlation between heavy metals and finer particles content in the sediment (d < 7.8 /zm). The investigation finally illustrated a spatial zoning of the marsh into three sectors with different degrees of pollution, in relation to the water circulation determined by river discharge and tidal forcing. Key words: Heavy metal; Sediment; Grain size; Shallow water; Venice Lagoon; Pollution
1. Introduction Fine-grained suspended particles act as efficient scavengers for anthropogenic heavy metals and generally constitute the p r e f e r r e d vehicle for their transport in coastal environments. Variations in water physico-chemical conditions and hydrodynamics, particularly in shallow water estuarine areas, d e t e r m i n e the particle deposition and the m e t a l transfer to the sediment. T h e r e f o r e , the
* Corresponding author.
spatial distribution of anthropogenic heavy metals in surface sediments r e p r o d u c e s the long t e r m effects of pollution from different sources and constitutes a reference for studies on contaminant transfer and accumulation [1,2]. T h e close association between anthropogenic heavy metals with finer particles [3], however, suggests a control by the effect of variable grain size (and mineralogy) of the samples. In fact, because of differences in particle transport, pathways and biogeochemical conditions, sediments from sectors of the same area quite often have different size characteristics. This m a k e s it dif-
0048-9697/94/$07.00 © 1994 Elsevier Science BV. All rights reserved. SSDI 0048-9697(94)3957-4
20
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
ficult to compare concentrations from different samples. FSrstner [4] reported some methods to correct for this grain-size effect, finally proposing the heavy metal analysis in the sieved diameter d < 63 /zm fraction. However, concentrations in the selected diameter ranges do not necessarily reflect the concentration in the whole sediment sample [5]. A reliable methodology should allow the analysis in unfractionated samples and for a wide range of sediment types [6], to establish the metal content independent of the presence of coarse inert materials which may in practice dilute its concentration. This paper describes an investigation on the relationship between anthropogenic heavy metals and grain size in the sediment of the Cona Marsh (Venice Lagoon). The study is based on a comparison of two separately determined spatial distributions, and also addresses the definition of a methodology to account for the grain-size effect. Two distinct acid extractions were performed on samples to solubilize heavy metals. The grain size was determined in the sampling state, but also after removal of the organic matter, to consider its influence on particle size spectrum [7,8]. The presence of organic matter in shallow marine sediments frequently leads to the formation of
particle aggregates - - having various size, shape and density [9] - - that are highly stable in the environment. As a consequence, the grain-size spectrum experiences an apparent shift toward coarser diameters, thus differing from the single grain spectrum. 2. The test-area
The mainland interface of the Venice Lagoon is characterized by tidal channels which delimit shallow water areas and vegetated fiats, where tidal exchange with the Adriatic Sea is retarded and decreased with respect to the rest of the lagoon (Fig. 1). Through this interface, the discharge of agricultural, industrial and municipal untreated wastes by rivers and channels from the drainage basin occurs. Therefore, the fate of pollutants strictly depends on the behaviour of these characteristic estuarine environments, where accumulation takes place. Among these areas, the Cona Marsh (Fig. 2) is the most interesting, because of the complex morphology and the presence of the Dese River, which is one of the main freshwater inputs to the lagoon. The Marsh has a mean tide level of ~ 5 0 cm. The tide enters through the Torcello Channel, involving its southern and central parts, and travels upstream, via
Fig. 1, Map of the Venice Lagoon. The squared area indicates the location of the Cona Marsh.
21
R. Zonta et aL / The Science of the Total Ent~ronment 151 (1994) 19-28
1N HCI Extraction
Sampling
i
8N HNO3
Org. Matter •
Extraction
Elimination
Analysis
Data
Processing Fig. 3. The adopted operative scheme for sample treatment, analysis and data processing.
Fig. 2. Detailedmap of the Cona Marsh indicatingsampling sites and the nomenclatureof the systemof channels. the Siloncello Channel and Ramo Casone branch to the East, and Dese River to the West. Freshwater supply in the North and salt water forcing from the South produce a complex circulation, with marked differentiations in the chemicalphysical variables (particularly salinity and turbidity) in the different sectors [10]. On the basis of a preliminary investigation on heavy metals in the sediment [11], 15 sampling sites were chosen to obtain a representative description of the pollution conditions in the marsh. The sampling scheme includes sites from the surrounding system of channels - - whose mean depth ranges between 2 and 3 m - - and the principal inputs in the marsh. 3. Materials and methods
Surface sediment samples (the upper 5 cm) were collected with a Plexiglas syringe-type corer,
placed in acid-treated PVC containers and stored at 4°C. Each sample was homogenized and then aliquots of it were separately subjected to the procedure schematically represented in Fig. 3. Approximately 2 g (wet wt.) were oven-dried and leached at room temperature with 60 ml of 1 N HC1, agitating for 24 h with a magnetic stirrer; the same amount of oven-dried sample was instead leached with 40 ml of 8 N HNO3, at 80°C for 3 h. The extraction solutions were prepared using Milli-Q water and Merck Suprapur acids (HC1 30%; HNO 3 65%). The suspensions were then centrifuged, filtered through 0.4/xm Nucleopore membranes and analyzed for seven heavy metals (Cr, Cu, Fe, Mn, Ni, Pb, Zn) by a flameless atomic absorption spectrophotometer (Varian SpectrAA 10). Heavy metal concentration was determined through appropriate calibration curves for both acids, after verification of possible matrix effects. The grain size was measured with a Microtrac particle-size analyzer (Leeds and Northrup rood. 7995, USA). The measure is based on the scattering of a laser beam (630 nm) determined by the
R. Zonta et al. / The Science of the Total Enlironment 151 (1994) 19-28
22
continuous flow of sample particles in a bi-distilled water suspension [12]. To remove rare macro shell fragments and plant debris, samples were wet-sieved at 125 /zm. The volumetric percentages spectrum was determined in the sampling state (referred to in the text as SS) and after removal (referred to as OE) of the organic matter. This latter was obtained by treating the sample with hydrogen peroxide (30%), heated in a water bath and maintained at pH = 8.5 with 1 N NaOH [13]. The Microtrac analysis was performed on the 0.7 < d < 125 ~m diameter interval, obtaining particle-size percentages in 15 dimensional classes corresponding to one-half phi interval of the Udden-Wenworth scale (as expressed by Krumbein [14]). A 100-s measurement time was adopted, and the mean of three repeated readings was used. A Cambridge Steroscan 250 TP scanning electron microscope (S.E.M.), operating at 20 kV with a beam current of 0.32 mA, permitted the visual observation of sediment particles. Specimens were prepared by filtering 5 ml of a 20-mg suspension of sample in 100 ml of bi-distilled water with 0.4 /zm Nucleopore membranes, which were subsequently freeze-dried and gold coated.
4. Results and discussion
4.1. Heavy metal distribution Concentrations obtained with the two extractions are reported in Table 1. HCl-extracted values - - which are in agreement with the previous investigation [11] - - are lower than the corresponding H N O 3 figures. The strong oxidizing effect and the higher extraction temperature and concentration of HNO 3 permits a rapid solubilization of the anthropogenic heavy metals content [15], but determine a greater dissolution of silicate lattices with respect to HC1 [16]. On the other hand, 1 N HC1 is not fully efficient in the solubilization of more resistant metal phases such as authigenic iron sulfides [17,18]. Both these occurrences justify the higher concentrations obtained with HNO 3 acid. Nevertheless, the two extractions substantially show the same heavy metal spatial distribution in the marsh, which correspond to the same distinction between polluted and relatively unpolluted areas. In fact, a linear regression analysis between concentrations obtained for each metal with the two acids, gives a strong degree of correlation with the high r coefficient values reported in
Table 1 Heavy metal concentrations (ppm, d.w.) resulting from the two acid extractions Site
Cr
Cu
Mn
Fe
Ni
Pb
Zn
HC1
HNO 3
HCI
HNO 3
HCI
HNO 3
HCI
HNO 3
HCI
HNO 3
HC1
HNO 3
HCI
HNO 3
1 2 3 4
22.2 24.8 35.4 25.4
27.6 29.6 44.7 27.9
42.5 29.8 56.6 40.0
53.1 44.9 66.0 54.5
12562 11925 15 799 10 902
19818 19635 24 289 21456
375.6 268.3 283.7 291.1
463.2 404.6 392.5 429.7
24.3 24.3 31.3 24.1
33.4 31.7 37.9 29.3
60.5 58.5 80.1 58.5
69.0 65.1 80.8 63.3
225.1 199.0 294.3 176.6
342.6 275.6 363.2 244.0
5 6 7 8
17.5 14.8 13.6 9.3
26.6 27.1 23.1 15.7
59.9 35.2 33.5 14.6
76.3 49.7 39.1 20.9
14295 11 940 11582 6674
25 921 22628 22792 11058
240.9 368.5 152.1 243.0
332.7 391.6 327.3 250.0
25.5 25.4 21.3 17.1
35.6 31.4 27.9 22.2
52.3 54.8 50.7 38.2
58.0 62.0 55.3 38.6
207.5 150.3 170.7 83.8
296.7 311.4 186.5 120.7
9 10 11 12
8.9 9.6 12.1 13.0
20.2 17.5 20.6 21.1
14.1 16.0 18.0 22.0
18.0 20.0 26.0 28.0
5332 5997 7998 6604
10411 11801 13 396 14211
214.8 203.7 238.5 211.2
234.6 239.5 267.5 267.0
16.3 16.3 20.1 18.2
22.5 22.5 26.6 24.9
36.4 38.4 41.6 43.2
37.4 38.2 43.0 44.4
113.5 75.6 128.5 91.2
121.7 111.2 148.0 161.5
13 14 15
12.4 17.4 17.3
18.0 22.3 22.1
22.0 36.0 34.6
32.0 48.0 40.0
6100 9099 9509
13 401 19398 19659
166.7 176.0 342.3
236.4 279,1 354,8
18.2 20.8 21.2
22.0 34.5 31.9
33.6 63.8 58.7
36.8 71.2 59.0
99.6 180.8 145.2
137.0 197.0 222.2
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
extraction, may be due to the presence in the sediment of resistant Mn-bearing phases which introduce a variability in the HC1 efficiency. In order to evaluate the extent of heavy metal pollution, it is opportune to subtract from concentrations measured in the various sites their pre-industrial level (or the 'baseline'). This would correspond to identifying a sediment inside the area which is free of heavy metal contamination and has the same origin, mineralogy and grain size of sampling [1]. In practice this task is difficult to achieve and it is preferable to choose reference values accordingly with less restrictive criteria, which are thought to be more useful and appropriate to the specific test-area [2,4]. For the baseline values we assume concentrations measured in the southern sector of the Marsh, since these are the lowest figures also in the previous investigation [11]. Based on this assumption, a percentage enrichment factor (EF%) was simply defined as:
Table 2 Regression coefficients between concentrations obtained for each metal with the two acid extractions x
y
r
C r HCl
CrHNO3
0.929 a
C u HCI
CuHNO3
0.981 a
F e HCl
FeHNO3
0.933 a
MnHCl
MnHNO~
0.752 b
NiHCJ
NiHNO3
0.881 a
p b Hcl
pbHNO3
0.980 a
Z n ncl
ZnHN03
0.886 a
a.
a S i g n i f i c a n c e level, 0.001 > 6 S i g n i f i c a n c e level, 0.01
>
a
23
> 0.001.
Table 2. Some anomalies affect the Mn regression, which produces a lower r coefficient. At the same time, the correlation matrices computed for HC1 (Table 3) and HNO 3 (Table 4) extracted concentrations, analogously show a strong degree of correlation for each metal with the others. This suggests that the same transport and accumulation processes govern the behaviour of the analyzed metals in the marsh. Again, the HCl-extracted Mn does not show significant correlations. The Mn spatial distribution, as resulted from this
C -
EF%
C min
c'nax _ Cram
Table 3 Correlation matrix for concentrations obtained with the HCI extraction
CuHCl
FenCl
C r HCl
0.756 b
CuHC~
__
FerfCl
__
MnHCl
__
NiHCJ pbHCl
. .
S i g n i f i c a n c e level: a0.001
MnnO
Ni n o
0,800 a
0,431 d
0.919 a
0.374 d
__ __ .
. .
>
>
a
ZnHCl
0.884 a
0.897 a
0.892 a
0.882 a
0.801 a
0.873 a
0.471 d
0.957 a
0.835 a
0,922 a
__
0.528 c
0.472 d
0.384 d
0.873 a
0.921 a 0.900 ~
. .
a ; b0.01
pbncl
.
> 0.001; c0.05 >
. a
> 0.01; aa > 0.05.
Table 4 C o r r e l a t i o n m a t r i x f o r c o n c e n t r a t i o n s o b t a i n e d w i t h the H N O 3 e x t r a c t i o n
CuHNO3
FeHNO3
MnHNO3
NiHNO3
pbHNO3
znnN03
C r rtNo3
0.751 b
0.721 b
0.709 b
0.785 a
0.831 a
0.856 a
CuHNO3
__
0.927 a
0.712 b
0.896 a
0.818 a
0.879 a
F e nN°3
--
--
0.746 b
0.875"
0.833 a
0.845 a
MnHNO3
__
__
__
0.718 b
0.805 a
0.884 a
NiHNO3
__
--
__
0.939 a
0.884 a
pbHNO3
.
S i g n i f i c a n c e level: a0.001
. >
or; b0.01 >
. a
.
> 0.001; c0.05 >
. a
> 0.01; % > 0.05.
0.877 a
24
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
in which, for given extraction and metal, C is the concentration measured at the site, C min and C max are, respectively, the minimum and maximum concentrations detected. EF% values for the HC1 extraction are plotted in Fig. 4, providing evidence of the overall trend for heavy metal concentrations in the Marsh, and also the anomalous distribution of Mn. The heavy metal distribution reflects either the role of the Dese River as a source of contaminants or the tidal effect on water circulation. The north-western sector (sites 1-7, 14 and 15) appears more polluted, with 2-4-fold the concentrations measured in the southern part (sites 8 and 9), where tidal exchange actively occurs. The branch from Bocca Carozza to Dese River (sites 1-5) gives the greatest concentration values (particularly site 3) because of the cyclic freshwater standing due to the tide [19], which also affects
the eastern branch of the S. Maria Channel (sites 14 and 15). The Dese discharge also determines the heavy metal accumulation in its lower reach, both in the river-bed (site 6) and in the shallow lateral branch debouching inside the Marsh (site 7). Concentrations in the eastern sector of the Marsh (sites 10-13) are instead significantly lower. 4.2. Grain-size distribution Sediment grain-size analysis emphasizes a subdivision of the Cona Marsh into three sectors with homogeneous characteristics, which correspond to the spatial zoning identified by the heavy metal distribution. For the three sectors, the mean values of particle-size percentage according to the Wenworth scale are given in Fig. 5, before and after removal of organic matter. If compared with the distribution in the north-western sector, sediment from sites of the southern sector contains a
100
C r Cu Fe Mn N i Pb Zn
mn
,o
l nmmm
60 c
0
1
2
3
/,
5
G
7
8
9
10
11
12
13
lg,
15
Site Fig. 4.
Histograms represent values of the percentage enrichment factor EF% for metals analyzed at each site.
R. Zonta et al. / The Science of the Total Ent~ronment 151 (1994) 19 28
5O
-
•
SS
OE
SS
OE
m
25
125~2
62-16 ,urn SS
OE
4O ID
~ 30 e" 0
u 20
10
0
I NW
E
S
Fig. 5. Mean percentages of the particle size in selected diameter ranges (according to the Wentworth scale) in the three different sectors of the Marsh: north-western (NW), eastern (E), southern (S).
greater amount of fine sand and coarse silt (16 < d < 125/xm), which are partially littoral material from the Adriatic Sea transported through the Torcello Channel. Samples from the eastern sec-
tor (sites 10-13) show instead an intermediate distribution. S.E.M. images provided visual evidence of mineral particles aggregation in the SS samples.
Fig. 6. (a) S.E.M. image of typical organic-mineral aggregates in the sediment of the Cona Marsh (SS sample); (b) enlargement of the surface structure of the upper portion of the larger aggregate in (a). Clay minerals are the main constituent of the aggregate.
R. Zonta et al. / The Science of the Total Ent~ronment 151 (1994) 19-28
26
Coarse aggregates ranging in size from 15-100 /zm (Fig. 6a) were observed mainly in the sediment from the north-western sector. Individual clay crystallites ranging from < 1 to a few microns (Fig. 6b), which are the main constituents of the aggregates, result bounded together by organic coatings, appearing as irregular and opaque on the aggregate surface. On the basis of the grain size analyzed in samples subjected to organic matter removal, the zoning of the Marsh is more evident. Samples from the southern and the eastern sectors show no significant variations, confirming the minor influence of the organic matter on particle aggregation. On the contrary, OE samples from the north-western sector show a net increase in the percentage of finer particles with respect to the corresponding SS spectra, due to the dispersion of the mineral-organic aggregates. Their origin is related to the high adsorption capacity of the silt to clay fraction versus the numerous reactive functional groups of organic molecules [7]. Particles aggregation may be initiated in the water column and is then continued in the sediment, with the intervention of both early diagenetic transformation of the organic matter and biological reworking of inorganic and organic detritus by sediment feeders [20].
For OE samples, it should also be noted that the percentage of particles in the range 7.8 < d < 31 /zm remains practically constant for all the sites, with a mean percentage equal to 32.3% (S.D. = 3.7). This implies that the different sectors of the Marsh may be essentially distinguished on the sole basis of the percentage of particles finer than 7.8 /zm in the 'organic free' samples.
4.3. Comparison between grain-size and heavy metal distributions To investigate their relationship in the sediment, a regression analysis was made between particle-size percentage and heavy metal concentration measured at the 15 sites. Tables 5 and 6 report, for a selected set of representative diameter ranges and for the two extractions, the linear regression coefficient r obtained, respectively, for SS and OE samples. Since the sum of particle-size percentages is 100, the r value for a given diameter range corresponds to a r opposite in sign for the complement of the range to the whole interval 0.7 < d < 125/zm. For SS samples, the percentage of fine silt (3.9 < d < 16/zm) produces the highest r values for both the extractions, but they do not seem to be sufficiently significant. On the contrary, the 'organic free' grain size shows a higher degree of
Table 5 R e g r e s s i o n c o e f f i c i e n t v a l u e s b e t w e e n s e l e c t e d d i a m e t e r r a n g e s a n d h e a v y m e t a l c o n c e n t r a t i o n s - - SS s a m p l e s Dimensional
Cr
range (/zm)
HCI
Cu HNO 3 HCI
Fe
Mn
HNO 3 HCI
HNO 3 HCI
Pb
Ni HNO 3 HCi
HNO 3 HC!
Zn HNO 3 HCI
HNO 3
0.7 0.7 0.7 0.7
< < < <
d d d d
< < < <
2.8 3.9 5.5 7.8
0.35 d 0.410 0.47 d 0.52 c
0.26 d 0.29 d 0.34 d 0.38`1
0.33 d 0.43 d 0.49`1 0.54 c
0.38 d 0.48 a 0.56 e 0.60 c
0.29 d 0.40 d 0.48`1 0.54 c
0.42 d 0.53 c 0.60 c 0.66 b
0.22 d 0.29 d 0.38 d 0.42 d
0.36 d 0.48 d 0.58 c 0.65 b
0.32 d 0.41 d 0.49`1 0.54 c
0.24 d 0.35 d 0.46 d 0.53 c
0.21 a 0.30 d 0.40 d 0.49`1
0.20 d 0.31 d 0.43`1 0.52 c
0.14 d 0.22 d 0.30 'l 0.37 't
0.28 d 0.39 d 0.49 a 0.55 c
0.7 1.4 1.4 3.9 7.8
< < < < <
d d d d d
< < < < <
16 3.9 16 16 31
0.55 c 0.460 0.56 c 0.56 c 0.410
0.400 0.340 0.410 0.41`1 0.300
0.62 ¢ 0.50 d 0.64 c 0.65 b 0.57 c
0.68 b 0.54 c 0.69 b 0.70 b 0.60 c
0.61 c 0.480 0.63 c 0.64 b 0.54 c
0.74 b 0.60 c 0.75 b 0.76 b 0.65 b
0.44 d 0.320 0.440 0.460 0.300
0.72 b 0.55 c 0.74 b 0.76 b 0.58 c
0.59 c 0.46 d 0.60 c 0.61 c 0.500
0.62 ¢ 0.43`1 0.64 b 0.67 b 0.62 c
0.56 c 0.380 0.58 c 0.61 c 0.53 c
0.62 ¢ 0.390 0.64 b 0.69 b 0.62 c
0.470 0.30`1 0.49 '1 0.52 c 0.46`1
0.64 c 0.45`1 0.65 b 0.68 b 0.56 c
11 16 16 62
< < < <
d d d d
< < < <
44 0.08`1 0.07 d 0.22 d 0.22 d 0.17 d 0.240 0.01 d 0.090 0.18`1 0.340 0.250 0.300 0.17`1 0.21 d 44 - 0 . 4 1 d - 0 . 3 1 0 - 0 . 3 2 0 -0.37`1 - 0 . 3 7 0 -0.37`1 - 0 . 3 7 d - 0 . 5 5 c - 0 . 3 4 d - 0 . 2 3 0 - 0 . 2 8 0 - 0 . 3 1 0 - 0 . 3 1 0 -0.38`1 62 - 0 . 6 2 c - 0 . 4 7 0 - 0 . 6 5 b - 0 . 7 1 b - 0 . 6 4 0 - 0 . 7 4 b - 0 . 4 8 d - 0 . 7 8 a - 0 . 6 2 c - 0 . 6 0 c - 0 . 5 9 c - 0 . 6 3 c - 0 . 5 1 d - 0 . 6 7 b 125 - 0 . 4 3 0 - 0 . 3 1 d - 0 . 5 3 c - 0 . 5 8 c - 0 . 5 2 c - 0 . 6 5 b - 0 . 3 5 d - 0 . 6 0 c - 0 . 5 0 d - 0.57 c - 0.470 - 0 . 5 4 c - 0 . 3 8 d - 0 . 5 4 c
S i g n i f i c a n c e level: aO.O01 > a ; bo.o1 > o~ > 0.001; c0.05 > ot > 0.01; dot > 0.05.
R. Zonta et al. / The Science of the Total Environment 151 (1994) 19-28
correlation between finer particles and concentrations obtained with both acids (except for the HCl-extracted Mn). In particular, the most significant set of regression coefficients results for the 0.7 < d < 5.5 ~m range. The 7.8 < d < 31 /~m fraction in the OE samples - - that is almost constant in the Marsh sediment - - data are uncorrelated to heavy metal concentrations and from this standpoint it seems like a 'neutral' material subdividing polluted finer particles from non-polluted coarse ones. Removal of the organic matter permits an outline of how heavy metals in the Marsh sediment are substantially associated to fine-grained particles, which at the same time are the principal vehicle for their transport in the Dese River [10,19].
27
tion. A comparison between anthropogenic heavy metal and grain-size distributions shows a strong degree of correlation between concentrations and finer particles in the 'organic free' size spectra. These reproduce better the spectrum of riverborne suspended particles, and are more directly related to transport and deposition mechanisms which are responsible for the heavy metals accumulation in some sectors of this estuarine area. The removal of organic matter provides a clearer distinction between different sectors of the area investigated and provides more precise information on the association between heavy metal and particle size. The methodology described for the comparison of grain-size and heavy metal distributions, seems to be useful in the evaluation of pollution effects in complex shallow water ecosystems, such as the Cona Marsh. Since it does not involve sample fractionation and concentration analysis in several particle-size ranges, the methodology also avoids the proliferation of chemical analyses and reduces the incidence of possible artifacts due to the sample handling.
5. Conclusions Both hydrochloric and nitric acid extractions are effective agents for the description of the heavy metal distribution in the sediment of the Cona Marsh, emphasizing the influence of the Dese River as a source of contaminants and sectors where accumulation occurs. S.E.M. analysis and differences in the grain size observed after the dispersion of mineral-organic aggregates show that organic matter has a fundamental role in the process of particles aggrega-
Acknowledgements The authors thank Dr. R. Pini (C.N.R. Pisa, Italy) and A. Rizzi (C.N.R. Milano, Italy) for their
Table 6 Regression coefficient values between selected d i a m e t e r ranges and heavy metal concentrations -- OE samples Dimensional
Cr
range (/zm)
HCI
Cu
HNO 3 HCI
Fe
Mn
HNO 3 HCI
HNO 3 HCI
Ni
Pb
HNO 3 HCI
Zn
H N O 3 HC1
HNO 3 HCI
HNO 3
0.7 < d < 2.8
0.73 b
0.63 c
0.648
0.62 c
0.65 b
0.708
0.31 o
0.56 c
0.67 b
0.78 a
0.84 a
0.80 a
0.67 b
0.7 < d < 3.9
0.80 a
0.748
0.79 a
0.78 a
0.84 a
0.88 a
0.41 d
0.78 a
0.83 a
0.86 a
0.89 a
0.90 a
0.80 a
0.80 a
0.7 < d < 5.5
0.78 a
0.73 b
0.83 a
0.82 a
0.87 a
0.91 a
0.46 d
0.82 a
0.83 a
0.88 a
0.88 a
0.9l a
0.82 a
0.84 a
0.7 < d < 7.8
0.77 a
0.70 b
0.83 a
0.82 a
0.84 a
0.90 a
0.43 d
0.79 a
0.80 a
0.87 a
0.87 a
0.89 a
0.79 a
0.81 a
0.7 < d <
0.62 c
16
0.76 ~
0,69 b
0.82 a
0.82 ~
0.83 a
0.90 a
0.40 d
0.80 a
0.80 a
0.85 a
0.84 a
0.87 a
0.79 a
0.81 a
1.4 < d < 3.9
0.76 b
0.748
0.80"
0.82 a
0.89"
0.92 a
0.44 d
0.84"
0.84 a
0.82 a
0.81 a
0.85 a
0.80 a
0.85 a
1.4 < d < 16 3.9 < d < 16
0.728 0.60 c
0.68 b 0.52 c
0.82 a 0.77 ~
0.84 a 0.78 a
0.85 a 0.718
0.92 a 0.81 a
0.41 a 0.33 a
0.83 a 0.738
0.79 ~ 0.648
0.82 a 0.728
0.77 a 0.658
0.83 a 0.72 b
0.78 a 0.678
0.83 a 0.72 b
7.8
-0.40 d -0.45 a -0.35 d -0.290
-0.42 a -0.32 d -0.390
-0.380
-0.39 a -0.390
11 < d < 44
31
-0.748
-0.82 a -0.81 a -0.500
-0.8P
-0.768
16 < d < 44
-0.77 a -0.73 b -0.80 a -0.78"
-0.718
-0.77 a -0.748
16 < d < 62 -0.79" -0.728 -0.82 a -0.81" 62 < d < 125 - 0 . 5 7 c - 0 . 5 2 c - 0 . 6 8 8 - 0 . 7 0 8 S i g n i f i c a n c e level: ao.oo]
> t~; b0.01
>
-0.51 d -0.440
--0.46 a - 0 . 3 8 a
- 0 . 7 7 a - 0 . 8 1 a - 0 . 8 2 a --0.80 a - 0 . 7 8 a
-0.85 a -0.85 a -0.48 a -0.84 a -0.79 a -0.81 a -0.84 a -0.86"
--0.84 a - 0 . 8 2 a
- 0 . 8 5 a - 0 . 8 8 a - 0 . 4 4 0 - 0 . 8 4 a - 0 . 8 0 a - 0 . 8 4 a - 0 . 8 6 ~ - 0 . 8 9 a --0.83" - 0 . 8 2 a - 0 . 6 6 b - 0 . 7 7 a - 0 . 2 6 d - 0 . 5 9 c - 0 . 6 5 b - 0 . 7 2 8 - 0.64 c - 0 . 6 9 8 - - 0 . 5 8 c - 0 . 6 5 8
ot > 0.001; c0.05 >
ot > 0.01; dot > 0.05.
28
R. Zonta et aL / The Science of the Total Environment 151 (1994) 19-28
precious contribution, respectively, in the grainsize analysis and in the S.E.M. observation. Further, thanks to G. Cogoni, F. Costa, M. Magris, M. Meneghin, R. Ruggeri and F. Simionato (C.N.R. Venezia, Italy) for collection and treatment of sediment samples. References 1 U. FiSrstner and G.T.W. Wittmann, Metal Pollution in the Aquatic Environment, Springer-Verlag, Berlin, 1981, p. 473. 2 R. Chester and F.G. Voutsinou, The initial assessment of trace metal pollution in coastal sediments, Mar. PoUut. Bull., 12 (1981) 84-91. 3 W. Salomons, Sediments and water quality, Environ. Technol. Lett., 6 (1985) 315-326. 4 U. F/Srstner, in I. Thornton (Ed.), Applied environmental geochemistry, Academic Press, London, 1983, Chapt. 13, p. 395. 5 H.L. Windom, S.J. Schropp, F.D. Calder, J.D. Ryan, R.G. Smith Jr., L.C. Burney, F.G. Lewis and C.H. Rawlinson, Natural trace metal concentrations in estuarine and coastal marine sediments of the Southeastern United States, Environ. Sci. Technol., 23 (1989) 314-320. 6 J.H. Rule, Assessment of trace element geochemistry of Hampton Roads Harbor and lower Chesapeake Bay area sediments, Environ. Geol. Water Sci., 8 (1986) 209-219. 7 M.A. Rashid, Geochemistry of Marine Humic Compounds, Springer-Verlag, New York, 1985, p. 300. 8 W. Salomons and U. F/Srstner, Metals in the Hydrocycle, Springer-Verlag, Berlin, 1984, p. 349. 9 B. Carson, K.F. Carney and A.J. Meglis, Sediment aggregation in a salt-marsh complex, Great Sound, New Jersey, Mar. Geol., 82 (1988) 83-96.
10 G. Ghermandi, D. Campolieti, R. Cecchi, F. Costa, L. Zaggia and R. Zonta, Trace metals behaviour during salt and fresh water mixing in the Venice Lagoon, Nucl. Instr. Meth., B75 (1993) 330-333. 11 S. Bernardi, R. Cecchi, F. Costa, G. Ghermandi, S. Vazzoler and R. Zonta, A preliminary investigation on the distribution of heavy metals in surface sediments of the Cona tidal marsh (Venice Lagoon), I1 Nuovo Cimento, l l C (1988) 667-678. 12 P.E. Plantz, in H.G. Barth (Ed.), Modern Methods of Particle Size Analysis, J. Wiley & Sons, New York, 1984, Chapt. 6, p. 173. 13 A. Riviere, Methodes Granulometrique (Techniques et interpretation), Masson, Paris, 1977, p. 170. 14 W.C. Krumbein, Size frequency distribution of sediments, J. Sed. Petrol., 4 (1934) 65-77. 15 D.J. Carmody, J.B. Pearce and W.E. Yasso, Trace metals in sediments of New York Bight, Mar. Pollut. Bull., 4 (1973) 132-135. 16 H. Agemian and A.S. Chau, Evaluation of extraction techniques for the determination of metals in aquatic sediments, The Analyst, 101 (1976) 138-146. 17 M.A. Huerta-Diaz and J.W. Morse, Pyritization of trace metals in anoxic marine sediments, Geochim. Cosmochim. Acta, 56 (1992) 2681-2702. 18 L. Zaggia, R. Zonta and E. Argese, Extraction of anthropogenic heavy metals from the reduced sediments of the Cona tidal marsh (Venice Lagoon): indeterminations due to authigenic sulfides and clay minerals, (submitted for publication). 19 G. Germandi, R. Cecchi, F. Costa and R. Zonta, Trace metal distribution in aquatic systems as studied by PIXE analysis of water and sediment, Nucl. Instr. and Meth., B49 (1991) 283-287. 20 R.G. Johnson, Particulate matter at the sediment-water interface in coastal environments, J. Mar. Res., 32 (1974) 313-330.