e-Terra Geochemical characterisation of sediments from

e-Terra
http://e-terra.geopor.pt
ISSN 1645-0388
Volume 5 – nº 6
2008
Revista Electrónica de Ciências da Terra
Geosciences On-line Journal
GEOTIC – Sociedade Geológica de Portugal
_________________________________________________________
Geochemical characterisation of sediments
environments of Lima Estuary (NW Portugal)
from
marginal
RAQUEL CARDOSO – rcardoso@itn.pt (Instituto Tecnológico e Nuclear, Departamento de Química, Grupo de
Química Analítica e Ambiente, Estrada Nacional 10, 2686-953 Sacavém; & Universidade de Lisboa, Faculdade de
Ciências, Centro de Geologia, Campo Grande, ed. C6, 3º piso, Campo Grande, 1749-016 Lisboa, Portugal)
MARIA DE FÁTIMA ARAÚJO – faraujo@itn.pt (Instituto Tecnológico e Nuclear, Departamento de Química,
Grupo de Química Analítica e Ambiente, Estrada Nacional 10, 2686-953 Sacavém, Portugal)
MARIA DA CONCEIÇÃO FREITAS – cfreitas@fc.ul.pt (Universidade de Lisboa, Faculdade de Ciências,
Departamento e Centro de Geologia, Campo Grande, ed. C6, 3º piso, Campo Grande, 1749-016 Lisboa, Portugal)
FRANCISCO FATELA – ffatela@fc.ul.pt (same address as M.C. Freitas)
ABSTRACT: Salt marshes are transitional zones between terrestrial and aquatic environments which occur
along the estuarine upper intertidal zone, usually over a mud substrate and covered by halophyte
vegetation. Characterized by low hydrodynamic conditions, salt marshes show a dynamic balance
between erosion, transport and deposition of sediments, along with vegetation. Here, heavy metals play a
special role, as their presence may affect this sensitive equilibrium. Lima estuary has several salt marsh
zones which are showing reasonable environmental degradation, since the estuary surrounding area is
quite populated and industrialised. To evaluate the importance of Lima estuary salt marsh environments
as traps for trace metals and to establish its environmental assessment, a total of 28 surface sediment
samples were collected in April of 2006 along 3 transects located in salt marshes of Lima lower estuary.
Sedimentological and geochemical analysis were performed in order to determine grain size distribution,
organic matter content and chemical composition for 21 elements (Mg, Al, Si, S, Cl, K, Ca, Ti, Cr, Mn,
Fe, Ni, Cu, Zn, Br, Rb, Sr, Y, Zr, Nb and Pb) by energy-dispersive X-ray fluorescence spectrometry
(EDXRF). The trend of metal contamination was evaluated, taking into account the location of samples
along salt marsh transects, as well as the location of these transects in the lower estuary. The results were
then compared to reference values for non-polluted soils, such as Soil (Ure & Berrow, 1982), enrichment
factors were calculated and inter-parameter correlations were defined. From this, it can be said that the
salt marshes of Lima estuary show increasing metal concentrations from low marsh to high marsh areas,
and also downstream heavy metal enrichment.
KEYWORDS: Tidal flat, Salt marsh, Estuary, Geochemistry, EDXRF, Environmental assessment.
1. INTRODUCTION
Estuaries are attractive regions for the establishment and development of human populations
and all associated infrastructures, due to privileged coastal location. This occupation leads to
degradation of water and sediment quality, along with habitat destruction, which unquestionably
interferes with the rich and diverse ecosystems of recognized importance that exist in these areas.
Additionally, estuaries are recognized as playing a key role in the supply and removal of heavy
metals to coastal areas due to their chemical and physical dynamics (Araújo et al., 2002; Caeiro
et al., 2005). Therefore, the fate and flux of heavy metals discharged from continent to ocean is
determined to a large extent by biogeochemical and sedimentological processes occurring in
estuaries (Monbet, 2006). Here metals are removed from the water column and accumulate in
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bottom sediments by several types of reactions: flocculation, coagulation, formation of
particulate Fe and Mn oxyhydroxides and absorption on suspended matter (mineral or organic)
(Sholkovitz, 1976; Zwolsman et al., 1997). Thus, studies on the distribution of heavy metals in
sediments are of great importance as a reliable tool in the context of environmental pollution
(Förstner & Wittmann, 1983).
Salt marshes, located on estuary margins, are generally characterised by an aluvionar
substrate periodically inundated by tides, and this feature distinguishes these environments from
terrestrial or aquatic. Its gentle topography divides the salt marsh in two domains, low marsh and
high marsh, according to the periodicity and duration of flooding. Submersion time also controls
the type of halophyte vegetation, which covers the substrate, and the distribution of all living
communities along the tidal marsh. Besides low hydrodynamic conditions that prevail in these
zones, which favour particle settling, salt marshes can act as filters and accumulate a wide
variety of pollutants, including heavy metals (Cundy et al., 2005; Reboreda & Caçador, 2007)
due to specific plant-sediment dynamics. Salt marsh filtering and enhancing capabilities for
water quality are also well known (Boorman, 1999). The dynamic balance between sediment
erosion, transport, settling and plant cover make salt marshes important contaminant sinks, and
its enrichment in these environments is a strong indicator of estuarine contamination.
The aim of this work is to establish the distribution of heavy metals, grain size and organic
matter along salt marsh transects, determine relationships between these parameters, observe
their variability among different salt marsh regions, as well as assessing if these zones are being
affected by anthropogenic heavy metal inputs.
2. STUDY AREA
The Lima river flows along 135 km from the Orense province (Spain) towards the city of
Viana do Castelo where the estuary develops, in the north-western coast of Portugal (figure 1).
Figure 1 – Location of Lima estuary and sampling sites, adapted from Google earth (http://earth.google.com) and
from Portuguese Military Chart, M888 series, sheet 040, scale 1:25000 (Instituto Cartográfico do Exército, 1997).
Lima River watershed has an area of 2535 km2, 47% of which are in Portuguese territory. It
develops on Hercynian granites and, to a less extent, on Ordovician and Silurian schists and
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greywackes. The Lima estuary has a semidiurnal high-mesotidal regime, which effects can be
felt 20km upstream (Alves, 2003; Ramos et al., 2006). The astronomical spring tide reaches a
maximum of 4m high (IH, 2006) but amplification by storm surge must be considered (Taborda
& Dias, 1991). Tidal flat and salt marsh areas are present on estuary’s both margins, as well as
on estuarine islets.
3. SAMPLING AND ANALYTICAL PROCEDURES
To assess the local variation patterns in terms of both sedimentary and geochemical
parameters, 28 samples were collected along 3 transects in estuarine margins, near the locations
of Barco do Porto (BRP-L, 9 samples), Darque (DAR-L, 9 samples) and Nossa Senhora das
Areias (NSR-L, 10 samples). These include sampling in different domains, namely tidal flats,
low marsh and high marsh sectors, as indicated in table 1.
Table 1 – Samples collected in tidal flat, low and high marsh domains in the transects BRP-L, DAR-L and NSR-L.
Sediment sampling was performed in April of 2006. All samples were deep frozen and then
freeze-dried, prior to analysis. Sediments were prepared for geochemical analysis, first by
passing samples through a plastic sieve to remove the grain fraction larger than 2 mm, followed
by mechanical grounding of the left sample in an agate mill. A binding agent (Chemplex®
Liquid Binder) was added to 2 g of powdered sample, which was then homogenized, dried and
compacted into pressed pellets of 1”1/4 diameter.
Sediment pellets were analysed in batches of 14 samples by EDXRF using a Kevex 771 XRF
analyst system, where spectral data were acquired using three different excitation modes under
vacuum conditions: direct radiation from the Rh anode (4 kV, 0.18 mA) to quantify Mg, Al, Si
and S; characteristic radiation from a Ge target (15 kV, 2.0 mA) to quantify Cl, K, Ca, Ti, Cr,
Mn, Fe, Ni, Cu and Zn; and characteristic radiation from a Ag target (35 kV, 1.20 mA) to
quantify Br, Rb, Sr, Y, Zr, Nb and Pb. Once elemental concentrations are proportional to each
peak area they can be determined, after proper spectral adjustment, using a specific computer
software, EXACT (Energy dispersive X-ray Analysis Computation Technique), which calculates
calibration coefficients for the detected elements.
Two certified reference materials (SRM 2740 - Buffalo River Sediment and SRM 1646 Estuarine Sediment, National Institute of Standards and Technology, New York, U.S.A) were
also analysed with each batch of samples in order to determine the accuracy and precision of
measured elemental concentrations.
The sedimentological procedures included determinations of organic matter content through
loss on ignition (LOI) method, where 2.00 g of sample are placed in a furnace for 2 hours and
then weighted again. Organic matter content is given by the difference between initial and final
weights. Separations of fine and coarse fractions of sediment by wet sieving were also performed
using a 63 µm sieve. Textural classification of sediments follows Flemming (2000).
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4. RESULTS AND DISCUSSION
The distribution of chemical elements in sediments is usually controlled by its texture and
mineralogical composition. There are many studies concerning the relationships between grain
size and chemical composition of sediments, especially heavy metal content, as those referenced
by Förstner & Wittmann (1983). Heavy metals show strong affinity to silt and specially clay
fractions of sediment (<63 µm), due to its high reactivity and specific surface which promotes
metal retention. Additionally, Si and Al behaviour tend to be inverse, given that Al is essentially
associated to the clay fraction (i.e. aluminossilicates), whereas Si concentration is substantially
higher in samples with high silt and sand content (i.e. quartz particles). Otherwise, in strictly clay
dominated samples, Al and Si have similar behaviour, and are subsequently positively
correlated; however, this situation is not observed for the studied samples since quartz is present
in significant amounts. Since Al is a non-reactive, conservative element usually not associated
with an anthropogenic origin, it is widely used in geochemical normalization in order to
distinguish between natural metal concentrations and those influenced by anthropogenic inputs.
Along with the calculation of enrichment factors and the establishment of correlations between
sedimentological and geochemical parameters, these proceedings allow an assessment of
contamination patterns in samples from different locations.
4.1. Sedimentological characterization
Materials sampled in the three salt marshes of Lima estuary show wide variations in terms of
grain-size distributions and organic matter content, as shown in figure 2.
Sedimentological variations along the estuary show that samples are in general coarser
(mainly muddy/slightly muddy sands) and contain less organic matter (LOI < 10%) in the marsh
closer to the estuarine mouth (NSR-L); in this area, no significant differences are found along the
transect. This might be explained by the presence of a dredged sand processing facility in the
marsh vicinities, which causes resuspension of large quantities of sediment which affects the
marsh area. In DAR-L transect, fine sediments (sandy muds) are found in P1 to P5 samples
whereas the coarse fraction is dominant (muddy sands) from P6 to P9. Along with the decrease
in fine fraction content, organic matter also decreases significantly. BRP-L transect (upstream) is
mostly constituted by sandy/slightly sandy muds in the tidal flat and marsh sectors and by muddy
sands in part of the high marsh (samples P7 and P8). Organic matter content is slightly higher in
the marsh comparatively to tidal flat.
4.2. Geochemical characterization
The elemental concentrations for 21 elements determined by EDXRF for the 28 surface
sediment samples are resumed in table 2.
Al and Si concentration values vary as a result of sediment textural differences. This is shown
by strong positive correlation between Al and the fraction below 63µm (in special for NSR-L
transect, R2=0.87, p<0.05), as well as negative correlation between Al and Si (in special for
BRP-L, R2=0.76). This is due to the affinity between Si and the coarse fraction of these samples,
mainly constituted by quartz grains, and between Al and the fraction below 63µm.
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Figure 2 – Distribution of sediment fraction below 63µm (silt + clay) and organic matter content for BRP-L, DAR-L
and NSR-L transects (TF = Tidal Flat, LM = Low Marsh, HM = High Marsh).
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Table 2 – Maximum, minimum and arithmetic average values for elemental concentrations determined for BRP-L,
DAR-L and NSR-L transects (n.d. = not detected).
K shows regular concentration values in all transects (~2-3%) and presents high positive
correlations with Rb especially for BRP-L (R2=0.84), as they present similar chemical behaviour.
The high concentrations for these two elements observed here, when compared to other
Portuguese estuaries (Cardoso, 2007), can be interpreted as the presence of K feldspar-rich rocks
(granites) in Lima watershed, as well as their weathering materials.
The higher Ca concentrations for NSR-L sediments are explained by the presence of biogenic
marine particles, essentially shell fragments. Ca and Sr present typical positive correlation
(R2=0.98) as they have similar chemical behaviour.
Mn and Fe are strongly positive correlated in NSR-L (R2=0.94), and also correlated to Mg,
Al, Ti and to the <63µm size fraction (R2 between 0.72 and 0.95). This indicates their affinity to
the fine fraction, and their probable presence as iron oxides / hydroxides, as explained later. For
BRP-L and DAR-L, these correlations are not as evident as for NSR-L.
Mn presents good correlation with organic matter content for BRP-L and DAR-L (R2max=0.72
and 0.79, respectively). Mn is an essential trace element in life, as it is present in several
metalloenzymes responsible for changing oxygen states in photosynthesis (e.g., from H2O to O2)
(Cox, 1995). By this, Mn accumulates in plants, and is consequently correlated to organic matter
content as they vary proportionally in the sediment.
S and Cl present similar variations in all salt marshes and are positively correlated (max.
DAR-L, R2=0.95). Here, S and Cl are also negatively correlated to elements such as K (R2=0.91
and 0.97, respectively) and Si (R2=0.79 and 0.83, respectively), what points a different source:
while the latter are essentially lithogenic, S and Cl are related to salinity. In addition, Cl, Br and
organic matter content show strong positive correlations to each other (R2Cl-Br=0.82;
R2Cl-O.M.=0.87; R2Br-O.M.=0.97), what can be an indicator of their affinity to organic matter or, by
the other hand, derived from the longer emersion periods and consequent concentration that
occur in these sectors, which are also enriched in organic matter in most cases. In fact, Br and Cl
have the highest concentrations for the highest organic matter content samples (DAR-L P1 and
P2).
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Other interesting positive correlations are those among elements such as Mg, Ti, Cr, Mn, Fe,
Ni, Cu, Zn and Pb, and also among some of them with Al and the <63µm size fraction. This can
be explained by their occurrence adsorbed on fine particles, due to larger specific surface and
exchange capability. The high correlation between Fe, and Zn and Ni (R2=0.93 and 0.94)
suggests that these metals are retained in the sediments as iron oxides/hydroxides.
4.3. Enrichment factors
To assess heavy metal enrichment, metal concentrations were first normalised for Al, in order
to minimize textural effects. In order to distinguish between natural and anthropogenic inputs,
these normalised values were used to calculate enrichment factors (E.F.) relatively to soil
reference values (Ure & Berrow, 1982), which represent the average composition of
uncontaminated soils. Therefore, Al normalization and calculation of enrichment factors can be
resumed in equation 1:
, where
.
(1)
An E.F. close to 1 means that the sample metal content is analogous to the reference value,
indicating an essentially natural origin for that element. E.F. over 1.5 point to an enrichment that
can be natural or anthropogenic. Nevertheless, E.F. over 2 are generally associated to
anthropogenic inputs. E.F. below 0.5 may express either a metal depletion relative to Al, or an
over-estimation of the reference value for that metal (Feng et al., 2004; Zhang, 1995).
The results of E.F. calculations are shown in figure 3.
All samples are enriched in Zn, with E.F. ranging from 1.26 (NSR-L) to 2.58 (DAR-L); an
outlier value of 9.41 has been obtained in P10 sample of NSR-L, which will be discussed later.
Ni is the only metal which is depleted in all samples.
For BRP-L, E.F. have an analogous distribution pattern along the salt marsh, with depletion
for Cr and Ni and enrichments for Cu, Pb and Zn, relatively to soil reference values. Zn has an
E.F.>1.5 for P1 and P2 (tidal flat), P3, P4 and P5 (low marsh) samples. These highest E.F. can be
explained by the presence of a large cellulose transformation plant about 2.5 km upstream, which
effluents might influence the river sediment load composition, affecting the topographically
lower sectors. Cu and Pb are only slightly enriched in most samples, with a maximum E.F. of
1.51.
In DAR-L, all samples show enrichment in Cu, Zn and Pb, and depletion in Ni and Cr,
relatively to reference values. Pb has the highest E.F. for P9 sample (2.89) and Zn for P1 sample
(2.58), both corresponding to high marsh zones. Zn shows strong positive correlation to Ni and
Cu (R2= 0.78 and 0.80, respectively), what also happens for Ni and Cr (R2= 0.81).
In NSR-L, Cr and Ni show the lowest E.F. for all samples (max. 1.12), and E.F. for Pb vary
between 0.78 and 1.41 for P1 to P8 samples, showing slight enrichment (E.F.>1.5) for P9 and
P10 samples. Observing the particular behaviour of Zn and Cu along this transect, it is seen that
Zn always shows enrichment in samples P1 to P9, while Cu is not enriched in samples P1 to P7
and is slightly enriched in P8 (1.87) and P9 (2.25). In P10 sample, there is an abrupt E.F.
increase for Cu, reaching a value of 22.19, and also for Zn, with an E.F. of 9.41. This enrichment
might be due to the presence of a landfill in the upper border of the marsh. Samples latter
collected in this site were subsequently analysed and their contamination in Zn and Cu was
confirmed (Cardoso, 2007).
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Figure 3 – Enrichment factors calculated relative to Soil reference values (Ure & Berrow, 1982) for BRP-L, DAR-L
and NSR-L transects (TF=Tidal Flat, LM=Low Marsh, HM=High Marsh).
5. CONCLUSIONS
Our results demonstrate that the highest concentrations of metals tend to occur in the high
marsh samples. This could be due to two reasons: 1) more densely vegetated salt marsh zones
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tend do act as a filter for trace metals transported with fine sediments, along with less sediment
remobilization by more sparse flooding periods; 2) these zones are associated to specific physical
and chemical conditions, promoting the accumulation of metals via biogeochemical processes
such as metal-rich concretions around roots and accumulation in biological tissues which will
constitute organic matter present in sediments (Cundy et al., 2005; Reboreda & Caçador, 2007;
Sundby et al., 1998). So, salt marsh plants play a special role in trapping and/or accumulating
heavy metals. Exceptions for this are BRP-L P1 to P5 samples (tidal flat and low marsh), in
which the high metal contents are probably controlled by local anthropogenic influences, such as
riverine polluted sediments that affect the tidal flat and low marsh sectors. Additionally, the E.F.
for the high marsh sediments of BRP-L is slightly lower than the value for DAR-L and NSR-L,
what can be explained by a stronger anthropogenic influence downstream.
The exceptional E.F. values for NSR-L P10 sample is related to its proximity to the landfill,
responsible for the Cu and Zn inputs to the high marsh. All other samples show regular E.F.
pattern along the transect indicating that the influence of the landfill decreases considerably with
the distance from upper high marsh limit.
Comparing the three salt marshes, heavy metals such as Cr, Cu, Zn and Pb generally show a
downstream enrichment trend (table 3). This can be explained both by the cumulative effect of
the effluents discharged in the river course as we approach the coastal region (including
industrial and urban wastewaters), and also by the marine water influence which cause metals
flocculation (Sholkovitz, 1976), and its consequent accumulation in sediments.
Table 3 – Maximum enrichment factors for Cr, Ni, Cu, Zn and Pb found for BRP-L, DAR-L and NSR-L transects.
Heavy metals are generally correlated to the fraction below 63µm, and their enrichment
factors (>1.5) suggest anthropogenic origin, especially for Zn, Pb and Cu. These anthropogenic
inputs are clearer for the intermediate and downstream salt marshes, as they are affected by
several pollutant sources.
Acknowledgements
This work was partially supported by project MicroDyn, under the FCT research contract
POCTI/CTA/45185/2002, and by a PEPAP grant under POAP programme.
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Received 17 January 2008
Revised 7 February 2008
Published 6 May 2008
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