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Estuarine, Coastal and Shelf Science 88 (2010) 71e83

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Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Analysis of the spatial variation of heavy metals in the Guadiana Estuarysediments (SW Iberian Peninsula) based on GIS-mapping techniques

J. Delgado a,*, J.M. Nieto a, T. Boski b

aDepartment of Geology, University of Huelva, 21071 Huelva, SpainbCentre for Marine and Environmental Research, University of Algarve, 8005-139 Faro, Portugal

a r t i c l e i n f o

Article history:Received 3 November 2009Accepted 9 March 2010Available online 24 March 2010

Keywords:geochemistryenrichment factorsource contaminationGIS-analysis techniqueGuadiana EstuaryIberian Pyrite Belt

* Corresponding author.E-mail addresses: joaquin.delgado@dgeo.uhu.es (J.

es (J.M. Nieto), tboski@ualg.pt (T. Boski).

0272-7714/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.ecss.2010.03.011

a b s t r a c t

This work reveals the usefulness of the GIS (Geographic Information Systems) mapping techniques toshow the distribution of pollutants along an estuarine environment, as the final stage of a thoroughstudy. In the case of study, the environmental quality of the sediments in the Guadiana river estuary wasdetermined by means of a complete geochemical characterization consisting on the calculation ofenrichment factors for the most important metals and metalloids (compared with the local backgroundof non-contaminated sediments). The obtained results were depicted in “enrichment distribution maps”which evidenced a distribution of the elements in two groups: Group-I, elements with natural origin (Al,Fe, Mn, Co, and Cr) distributed homogeneously along the basin, and Group-II, elements associated toanthropic origin (As, Cd, Cu, Pb, Ni and Zn) with clear punctual sources besides a high concentration allover the estuary. The enrichment factors for the elements of Group-II are indicative of the existence ofa noticeable diffuse historical mining pollution associated with the acid mine drainage generated in theinternal zones of the basin, which could overlap minor pollution inputs from other human activities. Theobtained results lead us to a reconsideration of the traditionally thought “unpolluted” environment whenit was compared to nearby estuaries.

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1. Introduction

Estuaries are sedimentary environments of fluvialemarineinteraction where an important biomass exchange takes place. Asa result, they are environments that favour the accumulation ofpotentially contaminant elements (Sanger et al., 1999; Ridgway andShimmield, 2002; Spencer et al., 2003). In this sense, the water andthe elements discharged into the estuary by the rivers and the seawill determine, according to their physicochemical features, theenvironmental quality of the referred environment.

Taking into account that environmental pollution varies greatlywith time and location (Van Alsenoy et al., 1993), and that thesesystems undergo strong anthropogenic pressure (urban, mining,industrial.) instead of their natural and human importance (Pérezet al., 1991; Birch, 1996), it seems necessary to evaluate how theseimpacts will affect to the system quality.

The Guadiana river estuary is located almost completely in theCentral Domain of the South Portuguese Zone (Fig. 1), crossing thematerials of the Iberian Pyrite Belt (FPI), one of the most important

Delgado), jmnieto@dgeo.uhu.

All rights reserved.

metallogenic polymetallic massive sulphide provinces in the world,with original massive sulphide reserves exceeding 1700 milliontonnes (Sáez et al., 1999). Associated with the exploitation of thesesulphide deposits, a very contaminant acid leachate with highconcentrations of metals and metalloids such as Fe, Cu, Zn, Pb, Cd,Mn, As, etc. and sulphates (SO4

2�) is originated. This, called acidminedrainage (AMD) is usually added to the river network. These leach-ates are responsible of theenvironmental pollutionandwaterqualitydegradation in the southern limit of the Guadiana River basin and,consequently, of themarshlands on its mouth (Delgado et al., 2009).

Unlike the nearby Tinto and Odiel rivers, Guadiana River alsoreceives the acidic discharge from important mines on the Portu-guese (São Domingo mine) and Spanish margins (Herrerías mine),as well as several upstreams pollutants from different sources, dueto its long course.

The spatial evaluation and visualization of pollutants is impor-tant to better understand how the sources of risk, the receptors andthe exposure pathways are distributed in the space (Bien et al.,2004; Poggio and Vr�s�caj, 2009). This is the reason of the recentinterest of using GIS techniques in studies of distribution of envi-ronmental pollution (i.e. Mat�ejí�cek et al., 2006; Zhou et al., 2007).However, these techniques are not successful by themselves, theymust be applied after a proper investigation of the environment.

Fig. 1. Regional setting of the Guadiana River Basin.

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8372

In this sense, an exhaustive geochemical study was developedin the area, considering as references geochemical backgrounds formetals and metalloid the ones determined by Delgado et al.(2008), given that in previous works about the contamination inthe area (i.e. Ruiz, 2001), this background values were not properlydetermined.

The aims of this work are: (1) The characterization of theGuadiana estuary environmental quality in terms of trace metalcontent, based on the specific local geochemical backgroundsestablished by the authors for the river basin (2) the evaluation ofthe possible incidence that some processes associated to humanactivities, such as the acid mine drainage, or urban effluents, couldproduce in this environment, (3) the establishment of zones withhigh values of potentially contaminant elements. In order to fulfillthese objectives, the possibility of integrating the result of the studyin a GIS based system was suggested.

2. Regional setting

Guadiana River is one of the most important rivers of the IberianPeninsula whose total length is 810 km, of which the last 200 kmmake a natural border between Portugal and Spain.

Geologically the drainage basin of 67,000 km2 is heterogeneous.Due to the deep (80 m) incision of the Carboniferous shale andgreywacke substratum, the accumulation of the sediments withinthe area of the present estuary started very early, ca 13,000 cali-brated years BP (Boski et al., 2008). Until 7500 cal BP, the sea levelrise of ca 7 mm/yr led to the fast accumulation of mainly clayeysediments. Since then, the pace of the sea level rise slowed to ca1 mm/yr and the estuary begun to accommodate in greater

proportion coarser sediments, partially introduced from thecontinental shelf (Boski et al., 2002). In this period, the lagoonalbodies in the vicinity of the estuary have been enclosed behind thesand spits. The relative stabilization of the mean sea level led to thedevelopment of vast salt-marshes in the intertidal zones covered byhalophytic plants such as Spartina densiflora, Spartina marítima,Atriplex spp. and Salicornia patula.

The hydrologic regime of the Guadiana river is characterized bya very irregular discharge, from almost zero during summer periodsup to a reported 11,000 m3/s for the winter peak of 1876 (Rocha &Correia, 1994), i.e. half a century before the installation of theriver dams. For the low, (1�10 m3 s�1) summer discharges, themesotidal Guadiana Estuary is well mixed, but it is verticallystratified in winter when the flow value are in the order of1�103 m3 s�1). The maximumwidth of the water body is of 550 mand depths are varying between 5 m and 17 m. The mixing zone iswithin the first 10 km from the mouth but brackish conditions mayextend 40 km inland. Since 2002 (closure of the PortugueseAlqueva dam) this flux was reduced drastically.

Since the beginning of mining activities in the mid XVII centuryin São Domingos Mine (Fig. 1), the high discharge winter periodswere used for the opening of mine reservoirs containing acid, metalrich muds. During the operation period of the mine, mining wastesand slurries were discharged from the mine, ca 100 km upstream,directly to the Guadiana River, which transported the particulatesulphides and waterbornemetals to the estuarine zone, and then tothe Atlantic Ocean. The Estuary was thus exposed to severe heavymetal pollution from São Domingo during one century e 1857 to1966 e (Matos et al., 2002). Nowadays mine pollution is stillimportant (although with less intensity) due to the continuous

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 73

leaching of the mine residues that are still spreaded out near themine complex at São Domingos (Alvarez-Valero et al., 2008; Pérez-López et al., 2008).

3. Materials and methods

For this work there have been collected a total of 100 sedimentsamples of the channel margin (Fig. 2) distributed both along themain channel of the Guadiana river estuary and the marshlandareas located in the municipal districts of Castro Marim and VilaReal de Sto. Antonio (Portugal), and in the municipal district ofAyamonte (Spain). Among all the sub-environments that can bedistinguished in an estuarine system, the channel margin, alsocalled low marshland, is located in the area closest to the tidalchannels. For this reason, it undergoes the tidal action (floodedduring high tide) and therefore represents an initial developmentstate of the marshes (Cáceres, 1999).

The samples of sediment were collected with a manual drill onthe channel margins at the selected locations samples were prop-erly kept in air-tight plastic bags and their positions (obtained withGPS, Garmin Etrex Legend) were recorded for their subsequentimplementation in a geographic information system (GIS).

The wet sediments were placed in porcelain containers andcompletely dried in a SELECTA DIGITHEAT 2001245 (150 l) modeloven at a temperature not higher than 40 �C to remove humiditywithout causingmineralogical changes in the samples. The sampleswere sieved with a 2 mm mesh and divided in two sub-samples,one to conduct the grain size study and the other for the chemicalanalysis. The samples for the chemical analysis were grinded byhand in an agate mortar and the 63 microm fraction was retained.

The elements’ concentrations were analyzed by Acme AnalyticalLaboratories Ltd (Vancouver, Canada), accredited under ISO 9002,through its Italian affiliate (ERS srl, Napoli). Major oxides anda number of trace elements evidencing possible environmentalimpact, among which As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb y Zn areoutstanding, were determined by optical (ICP-AES) and mass (ICP-MS) spectrometry. Aqua regia extractable metal andmetalloids (Ag,As, Au, Ba, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Rb, Sc, Sr, Zn and Zr) werealso determined by digestion of 0.5 g of sample with aqua regiaextracts (3:1 HCleHNO3) at 95 �C for 1 h, follow by ICP-AES anal-ysis. This extraction is commonly used to determine the pseudo-total metal content in environmental samples, which is consideredas a measure of the maximum potential hazard that could occur inlong term or in extreme environmental regimes (McGrath andCunliffe, 1985).

Other parameters such as total carbon and sulphur weredetermined by Loss on Ignition (LECO).

Accuracy was calculated on Acme’s in-house reference mate-rials, DS7 and SO-18. Reference materials DS7 and SO-18 werecalibrated to an aqua regia digestion/ICP-MS determination againstpublished values for a concentrated HCl and HNO3 digestion of theCanadian Certified Reference Materials Project (CCRMP) TILL-4 andLKSD-2.

In addition, to check the quality of the analysis, a total of 9replicates were analyzed. From them the Relative PercentageDifference (% RPD) has been calculated as shown in Eq. (1). Theresults for % RPD (Table 1) are reasonably good and the expectedvalue close to zero. Most values are below 1.5%, except for elementssuch as Na 3.43%, Ag 2.2%, Rb 4.7%, Sc 2.0%, Sn 2.6% and Y 3.8%.However these values are never higher than 5% RPD.

% RPD ¼ ðS� DÞ=½ðSþ DÞ=2� � 100 (1)

Where: S¼ determinate value of the samples, D¼ value of theduplicates.

The evaluation of analytical performance of the total concen-tration was also made by analysing the Certified ReferenceMaterials (CRMs) STSD-1 and STSD-2 (Standard from stream sedi-ments). As it can be seen in Table 1 the measured concentrations ofall the twenty-nine analytes overlap or are much closer to therelative certified values for this standard. Exceptional values (Ag,Au) correspond to elements which have very low concentrations, sothe analytical error is usually higher.

Generally, the ranges of recoveries (Eq. (2)) of the elementsunder study (Fe, Al, As, Cd, Co, Cr, Cu, Ni, Pb and Zn) have been from78.5 to 105%, except Cd, that does not present certified referencevalue. As can be seen in Table 1, a good agreement is found betweenour result of analysis and reference values.

% Recovery ¼ ðObtained value=Certified valuesÞ � 100 (2)

The grain size study of the sediment samples was performedusing a particle size analyser Malvern Mastersizer 2000�, withapproximately 20 g of sediment.

Multivariate analysis techniques have been widely used todistinguish the natural and anthropic contribution of elements inthe estuarine system, based on the variables level of association. Inthis way, principal components analysis (PCA) was carried out bymeans of a Pearson’s correlation matrix (0.05 significance level) tothe variables and samples in order to establish possible relationsand input sources among polluting elements.

Enrichment factors (EF), which compare the concentration ofa given element in the environment with the one expected whenexcluding anthropogenic contributions, that is, with the regionallevel (background) have been calculated. For the calculation of EF,the background values, established from the study of the sedimentsin two deep cores located at the estuary flood plains (Delgado et al.,2008) have been used.

EF distribution maps in order to survey sites of environmentalvulnerability in the Guadiana river estuary were generated bymeans of data interpolation using the ordinary kriging technique(Webster and Oliver, 2001). For this purpose, ArcGIS� ESRI� soft-ware was used, at a 50 m resolution grids (raster images) whereeach cell values (pixel) express the EF of the considered element insediments at a specific location. Weights of predicted values arebased not only in the distance between samples and the predictionposition, but also in the whole spatial distribution among themeasured propositions and their values. A maximum searchingradius of 300 m is established for nearby samples (based onsamples location and at least one interpolation point) in order toobtain a high weight in the spatial distribution for surroundingpoints among each other. Moreover, an interpolation barrier wasused (water surfaces) to avoid no-existing interpolations. Of course,there exists some uncertainty like in any interpolation method, sothe interpolation points should be considered as possible expectedvalues, but no necessarily the true values, which could never beobtained (Zhang, 2006).

4. Results and discussion

4.1. Grain size distribution

For a proper determination of the environmental quality ofsediments, characterization of the grain size is important, as themetals responsible for pollution tend to accumulate in thesmallest grain-sized fractions, i.e., mainly in limes and clays. Thisphenomenon is attributed to the high specific surface presentedby the finest fractions which, as a result, favour adsorptionprocesses (Forstner, 1983; Usero Garcia et al., 1997; Thuy et al.,2000).

Fig. 2. Sampling points map showing the different sub-systems present in the Guadiana Estuary.

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8374

Table 1Evaluation of the analysis performance by calculating the % RPD and comparison with certified values.

% RPD CAN STSD-1 CAN STSD-2

Obtained value Certified value % Recovered Obtained value Certified value % Recovered

Al2O3 1.49 8.67 9.00 96.3 16.0 16.1 99.4Fe2O3 0.34 6.28 6.50 96.6 7.35 7.50 98.0MgO 0.12 2.16 2.20 98.2 3.15 3.10 102CaO 0.66 3.66 3.60 102 4.18 4.00 105Na2O 3.43 1.73 1.80 96.1 1.70 1.70 100K2O 1.31 1.24 1.20 103 2.13 2.10 101TiO2 0.45 0.64 0.80 80.0 0.73 0.80 91.3MnO 0.06 0.47 0.50 94.0 0.13 0.10 130TOT/C 0.03 13.2 12.3 108 1.69 1.60 106Ag 2.22 0.30 <0,5 e 0.40 0.50 80.0As 0.60 19.8 23.0 86.1 33.3 42.0 79.3Au* 0.20 0.01 0.01 67.5 0.0015 0.003 50.00Ba 0.60 585 630 92.9 515 540 95Be 1.40 1.00 1.60 62.5 4.00 5.20 76.9Cd 0.10 0.90 Not data e 0.80 Not data e

Co 0.40 16.3 17.0 95.9 20.0 19.0 105Cs 1.30 1.50 1.80 83.3 10.8 12.0 90.0Cu 0.90 34.6 36.0 96.1 41.2 47.0 87.7Mo 1.10 0.90 <5 e 10.3 13.0 79.2Ni 0.50 19.3 24.0 80.4 49.2 53.0 92.8Pb 0.50 37.4 35.0 107 68.5 66.0 104Rb 4.70 e 3.00 e 95.7 104 92Sc 2.00 12.0 14.0 85.7 14.0 16.0 87.5Sn 2.60 3.00 4.00 75.0 5.00 5.00 100Sr 0.90 193 170 114 455 400 114V 1.20 89.0 98.0 90.8 100 101.00 99.0Y 3.80 36.2 42.0 86.2 35.6 37.0 96.2Zn 0.90 150 178 84.3 193 246 78.5Zr 0.00 221 218 101 183 185 99.1

Mayor in %, trace in ppm (* ppb). % RPD (relative percentage difference).

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 75

The sediments have been classified according to the subdivisionestablished by Wentworth (1922) for the finest fractions (sand,mud and clay) and the percentages of the respective grain sizeshave been projected on the triangular diagram suggested by Folk(1954). The grain size data have allowed determining that 97.5%of the surface samples collected in the estuary are classified aslimes or sandy limes, whereas the rest (RG-1, RG-2, S-00-14, ES-7,S-5 and S-6), of sand or slightly lime sand-sized grain, correspondto a sandy bar located in the main channel near Vila Real (Fig. 3).

No big differences in the grain size distribution have beenobserved for the different sub-environments studied. Usually, thesamples from the estuary inner areas (tidal channels and floodplains) show higher contents in clays and limes, while the sandyfraction is more abundant as we approach the source area (mainestuarine channel).

In general, the sediments corresponding to the main estuarychannel show slightly larger grain sizes when compared with thesediments sampled in the secondary tidal channels (Fig. 3). Thepercentage of average grain sizes for clay, lime and sand fractions is15.03, 61.62 and 23.35%, respectively, for the samples of the mainchannel, as opposed to 15.91, 72.46 and 11.63% for the samples ofthe secondary tidal channels, which indicates that lime sedimentsare more abundant in tidal channels, and that the sand percentageis lower with respect to the main channel. As would be expected forthis kind of environments, there is a gradual decrease in the sandrate in the inner areas of the tidal channels at the same time thatpreferably lime and clay rates increase.

Fig. 3. Representation of the surface sediment samples in ternary classificationdiagram suggested by Folk (1954), from the fractions lower than 2 mm obtained byMalvern Mastersizer. (S: sand, Z: silt, sZ: sandy silt, zS: silty sand).

4.2. Geochemical characterization

4.2.1. Major oxidesTotal concentrations of 9 major oxides (SiO2, Al2O3, Fe2O3, MgO,

CaO, Na2O, K2O, TiO2, P2O5, MnO), along with other parameterssuch as LOI, TOT/C (total carbon) and TOT/S (total sulphur) have

been analyzed for the 100 samples of surface sediments (Table 2).Due to their siliciclastic nature, the sediments are mainly composedof SiO2, Al2O3, with amount of 74%, presentingmean concentrationsof 60.4% and 14.9% respectively. These are also followed by Fe2O3(5.71%), Na2O (2.45%) and K2O (2.37%). The rest of the analyzedelements are mean concentrations lower than 2%, reaching valuesof 0.07% in the case of MnO (Table 2). In general, the distribution ofmajor oxides is very homogeneous for the whole estuary. Thevalues that are clearly deviated from the mean belong to the

Table 2Descriptive statistics of the major and trace elements of the Guadiana estuarysediments. Major in %, trace in ppm except for Au (ppb). SD represents standarddeviation of the mean and Bk the background levels established by Delgado et al.(2008).

Element n Min Max Mean Median SD Bk

SiO2 100 42.4 96.6 60.4 58.6 0.92Al2O3 100 1.06 18.4 14.9 15.8 0.33 17.4Fe2O3 100 0.27 7.50 5.71 6.09 0.14 6.40MgO 100 0.05 2.04 1.61 1.70 0.04CaO 100 0.15 2.69 0.97 0.89 0.04Na2O 100 0.25 3.71 2.45 2.48 0.05K2O 100 0.56 2.96 2.37 2.43 0.04TiO2 100 0.07 1.12 0.91 0.96 0.02 0.89P2O5 100 0.02 0.24 0.15 0.15 0.00MnO 100 0.01 0.19 0.07 0.05 0.00 0.06LOI 100 0.90 25.2 10.4 10.7 0.39TOT/C 100 0.12 7.23 1.82 1.69 0.09TOT/S 100 0.01 1.69 0.21 0.17 0.02

Ag 100 0.05 0.30 0.10 0.12 0.01As 100 1.30 81.8 25.5 26.4 1.21 15.8Au* 100 0.02 128 4.00 5.55 1.27Ba 100 58.9 488 380 364 6.73Cd 100 0.05 1.40 0.20 0.23 0.02 0.09Co 100 0.60 24.8 17.1 16.1 0.45 16.4Cr 100 1.60 24.0 19.2 17.6 0.46 21.7Cu 100 1.20 73.0 50.0 47.6 1.76 28.2Hg 100 0.01 4.43 0.34 0.56 0.08Mo 100 0.10 3.80 1.00 1.28 0.09Ni 100 0.70 41.6 27.8 26.1 0.75 32.4Pb 100 1.30 62.5 32.9 32.0 1.17 18.6Rb 100 19.4 144 110 105 2.41 134Sc 100 0.10 15.0 3.90 4.45 0.27Sr 100 15.4 168 131 129 1.97 123Zn 100 4.00 483 168 162 7.10 75.4Zr 100 23.7 416 231 224 5.87 224

Major in %, trace in ppm (* ppb). n (number of samples). SD (standard deviation), Bk(background levels by Delgado et al., 2008).

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8376

samples from the river sandy bar located in the main channel. Inthe secondary tidal channels, the pattern is typical of these envi-ronments, with a decrease in the rate of SiO2 and an increase inAl2O3 and Fe2O3 as we move away from the main channel. Thisevolution of major oxides is in agreement with the grain sizedistribution described above.

The ratio SiO2/Al2O3 evidences the textural maturity of thesediments. This is due to the relative enrichment of Al-rich phil-osilicates dependent of Si-rich phases that are produced in fine-grained sediments (Weltje and Eynatten, 2004).

In this way, a decrease in the rate SiO2/Al2O3 is related toa decrease in grain size and, as a result, with a decrease in texturalmaturity. The sediments of the Guadiana estuary show a lowtextural maturity (SiO2/Al2O3< 5), except for the samples located inthe levee (river bank) located of the main channel, where rates of44 and 98 for samples RG-1 and RG-2, respectively, are found. Thesehigh values evidence a high textural maturity and imply that thesesediments have undergone a very long sedimentary cycle (López-González et al., 2006).

4.2.2. Trace elementsTrace element concentrations such as Ag, As, Au, Ba, Cd, Co, Cr,

Cu, Hg, Mo, Ni, Pb, Rb, Sc, Sr, Zn and Zr have been analyzed (Table 2).The highest concentrations are presented by elements such as

Ba, Zr, Zn, Sr, Rb, whose mean values exceed 100 ppm. These highvalues are associated to shell fragments for the case of Sr and Rb, orto heavy minerals such as zircon for the case of Zr, very abundant inthe nearby coastal sediments (Fernández-Caliani et al., 1997).

Zn shows very fluctuating values between 4.00 and 483 ppm,with a mean of 168 ppm.

Similarly, the results obtained for Cu are also outstanding,ranging from 1.2 to 73.0 ppm, with amean value of 50.0 ppm. Otherelements follow this decreasing order: Pb, Ni, As, Cr and Co withmean values of 32.9, 27.8, 25.5, 19.2 and 17.1 ppm, respectively.Concentrations registered for Cd are also significant, ranging from0.5 to 1.4 ppm, showing a mean value of 0.2 ppm, which must betaken into account due to the high toxicity of this element.

Taking into account the variability of total concentrations(Table 2) of the trace elements, two large groups with differentbehaviours can be roughly distinguished: (1) elements such as Ba,Co, Cr, Ni, Rb, Sr and Zr, which tend to be homogeneously distrib-uted; and (2) a group composed mainly of As, Cd, Pb and Zn whichshow a very heterogeneous variability.

4.3. Relationship between variables: PCA

To complete a proper geochemical characterization of thesediments, a Principal Component Analysis (PCA) has been con-ducted using Pearson’s correlation coefficients. The coefficientsobtained have been represented in Table 3.

The data used for the analysis were Si, Al, Fe and Mn among themajor oxides, and As, Cd, Co, Cr, Cu, Ni, Pb and Zn among the metalsand metalloids analyzed, since they have been described innumerous works as constituent elements of acid mine drainageassociated with the polymetallic sulphurs of the Iberian Pyrite Belt(e.g. Fernández-Caliani et al., 2009). Also, the clay content of thesamples has been included, as it plays a key role in the accumula-tion of contaminant metals in the sediments (Fernández-Calianiet al., 1997).

The results show high correlation coefficients between Al, Fe,Co, Cr and Ni, along with very high values between As, Zn, Cd andCuePb. Similarly, the existence of high inverse correlations SieAland SieFe has been checked.

The variables analyzed, which represent 81% of the total ofsample variability, have been represented in Fig. 4. In this figure, theprojection factors for axes X (F1) and Y (F2) represent 68% and 13%of the variance, respectively. Factor F1 explains very clearly thedifferences in the composition of the sediments based on Siconcentration, as stated by Morales (1993), which can be distin-guished because they are part of the detritic fraction of sediments(range between gravels and medium-sized limes). This fact issupported by the high inverse correlation coefficients (Table 3)with respect to Fe (�0.95) and Al (�0.94), which are representedsymmetrically opposed to Si in the diagram. The clay fraction tendsto groupwith Fe and Al, including also Mn and other metals such asCo, Cr and Ni (all of them show high correlations between eachother), and which therefore tend to accumulate in the finest frac-tions of the sediments (Table 3). This association (Group-I) may beconsidered in principle as of natural origin, since Co, Cr and Ni arecommonly part of the phylosilicates (Borrego et al., 2004), that bysurface alteration processes are formed in the basin, mobilized andtransported to the estuary, where they tend to accumulate in theclay fraction of sediments. This one, with a different behaviour fromthe rest of the metals studied, has been similarly described by otherauthors such as Morillo et al. (2004) in a study of the sediments ofthe platform in the littoral of Huelva. In addition, in the materialsthat form the low basin of the Guadiana River, numerous forma-tions of basic rocks (constituents of the unit “Complex volcanicsedimentary”, CVS) are found. These are a potential source ofelements such as Co, Cr and Ni, among which, for example, the socalled “Paymogo Volcanic Axis” can be mentioned (Donaire et al.,1998).

The behaviour of elements such as As, Cd, Cu, Pb and Zn isrepresented by factor F2. These elements tend to form a new group,so that they may show a different origin from that already

Table 3Pearson correlation matrix for the parameters analyzed in the surface sediment samples of the Guadiana estuary. Alpha significance level 0.05.

Clay SiO2 Al2O3 Fe2O3 MnO As Cd Co Cr Cu Ni Pb Zn

Clay 1SiO2 L0.70 1Al2O3 0.78 L0.95 1Fe2O3 0.77 L0.94 0.96 1MnO 0.27 �0.39 0.41 0.46 1As 0.44 L0.54 0.54 0.64 0.12 1Cd 0.37 �0.20 0.28 0.31 �0.02 0.55 1Co 0.77 L0.81 0.89 0.90 0.55 0.52 0.44 1Cr 0.78 L0.89 0.88 0.88 0.40 0.48 0.22 0.82 1Cu 0.64 L0.82 0.82 0.83 0.19 0.72 0.44 0.74 0.77 1Ni 0.80 L0.91 0.94 0.94 0.51 0.55 0.28 0.90 0.92 0.78 1Pb 0.63 L0.79 0.78 0.77 0.02 0.63 0.28 0.65 0.73 0.89 0.72 1Zn 0.49 L0.55 0.59 0.62 0.04 0.80 0.74 0.61 0.49 0.80 0.56 0.71 1

In bold, significant values (except diagonal) at the level of significance alpha¼ 0.05 (two-tailed test).

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 77

mentioned for group-I. It is well known that elements such as As,Cd, Cu, Pb and Zn are associated to acid mine drainage processes ofthe IPB polymetallic sulphurs (Fernández-Caliani et al., 1997; Olíaset al., 2006). As a result, it could be expected that the maincontributions of these elements to the environment were producedby this process, which has been contrasted in the inner zones of theGuadiana river low basin (Grande et al., 2005; Delgado et al., 2009).Thus, the negative values of F2 seem to gather the group ofelements with a natural origin derived from erosion, transport andsedimentation processes typical of the drainage basin, while thevalues grouped according to the positive variation of F2 wouldrepresent those elements mainly related to historical miningactivity (Group-II).

4.4. Enrichment factors

4.4.1. Normalization of the geochemical dataThe variability of trace metals in sediments can be natural or

influenced to some degree by anthropogenic sources. Since themetals coming from these sources usually accumulate in the finefraction of the sediment, the study of enrichment factors requiresprevious compensation or normalization of grain size on the metalvariability in different textural samples (Aloupi and Angelidis,

Fig. 4. Representation of the principal component analysis (PCA) for the total ofsamples analyzed in the Guadiana river estuary. Alpha significance level 0.05.

2001). Numerous conservative and normalising elements havebeen suggested in the bibliography such as Al, Cs and Fe: Al (Hirst,1962a, 1962b; Bruland et al., 1974; Bertine and Goldberg, 1977;Sharman et al., 1984; Carral et al., 1995), Li (Loring, 1991), Cs(Ackermann, 1980; Grousset et al., 1995) and Fe (Piper, 1971;Blomqvist et al., 1992; Herut et al., 1993).

Due to the existence of local geochemical background valuesestablished for the Guadiana estuary by Delgado et al. (2008),where the best approaches to sediment textural variability areproved to be obtained by normalising with Al, in the present studyAl has been chosen as the normalising element, so that the data canbe comparable. The relationships found between Al and the metalsresponsible for contamination, as well as between Al, Si and theclay fraction, have been represented in Fig. 5. As show in Fig. 5,linear correlation coefficients higher than R2¼ 0.44 (Al2O3 vs Clay)have been obtained in all cases, reaching values up to R2¼ 0.81 forthe case of Al2O3 vs Ni.

4.4.2. Calculation of enrichment factorsAnother factor to be taken into account in the establishment of

enrichment factors for studies on contaminant elements in estuarineand/or marine sediments is the geochemical background. This valuewill be fundamental to distinguish whether an element present inthe environment appears naturally or, on the contrary, it is influ-enced by anthropogenic activities (Daskalakis and O’Connor, 1995;Liu et al., 2003). As a result, the local geochemical backgroundvalues established by Delgado et al. (2008) were chosen when con-ducting this study, so that the work could drive to proper interpre-tations on the environmental quality of the estuary sediments.

Quantification of the environmental quality of an estuary can beestablished by calculating the enrichment factor using thefollowing expression (Eq. (3)).

FE ¼ ð½M�=½N�Þsample=ð½M�=½N�Þbackground (3)

Where: [M]sample¼metal concentration for the studied sample,[M]background¼ regional Background, [N]sample¼ concentration ofthe normalising element for each sample, [N]background¼ value ofthe normalising element in the background.

Analysing the meaning of the enrichment factor, an EF value of 1indicates a predominantly natural origin for that element in thesediment, whereas higher values indicate enrichment due to eithernatural causes (biotic input) or anthropic influence. EF values lowerthan 1 can reflect remobilisation and loss of that element in relationto the normalising element, or overestimation of the contents of thereference metals (Mil-Homens et al., 2006).

Based on the observation of the enrichment mean values (Fig. 6)obtained for Fe, Mn, As, Cd, Co, Cr, Cu, Ni, Pb and Zn (main evidence

Fig. 5. Examples of linear relationship between Aluminium, Silica, clay and some metals in surface samples of sediments in the Guadiana river estuary.

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8378

of pollution in estuarine environments), the elements have beendivided into 3 groups:

(1) Impoverished or poorly enriched element (FEz 1): Fe, Mn, Co,Cr and Ni.

(2) Enriched element (1 < FE< 2): As, Cd, Cu and Pb.(3) Strongly enriched element (FE� 2): Zn.

In the first group, corresponding to impoverished or poorlyenriched elements, the elements that present a natural origin in theGuadiana estuary have been associated. This origin has beendeduced from the relationships found between those elements andAl and the percentage of clay in the samples. The elements Fe, Mn,Co, Cr and Ni in most of the samples present values very close to 1,which supports that these elements do not seem to be influencedby anthropic activities in the study area. This association was

1.00 1.02

1.77 1.94

1.08 0.96

1.86

0.92

1.84

2.47

0

1

2

3

Fe Mn As Cd Co Cr Cu Ni Pb Zn

EF

Fig. 6. Mean EF for the studied elements in the Guadiana estuary (values on a loga-rithmic scale).

highlighted in this coastal area by authors like Morillo et al. (2004)related preferably with the residual fraction and therefore associ-ated with the bedrock of the study area.

In the last two groups of elements heavy metals (As, Cd, Cu, Pband Zn) associated to sulphide deposits are grouped in accordancewith numerousworks on contamination by heavymetals in the IPB.These metals are characterized by a fairly homogeneous distribu-tion of concentration and have been associated in some studies toprocesses related to leachate and surface alteration of polymetallicsulphides in the IPB (Ruiz, 2001) and inner zones of the Guadianariver low basin (Delgado et al., 2009), and to mine-relatedprocesses which have been developed in the IPB for approximately4500 years (Leblanc et al., 2000; Nocete et al., 2005).

This would result in a fairly homogeneous distribution thatcould be called “diffuse”, but with high enrichment values withrespect to background. Nevertheless, some anomalies withmaximum EF values for As, Cd Cu and Zn have been detected,probably as a result of other anthropic activities in the study areamostly urban wastes (discharging without purify) and minorityport activities and industrial effluents.

Median EF which allow to classified elements in three groups,are representative of the current state of contamination in theGuadiana river estuary. They can be useful to identify input ofcontaminant elements of anthropic origin into the system, either asa result of an historical mining activity in the IPB, or as a result ofother anthropic activities in the study area, mostly urban wastes(discharging without purify) and minority port activities andindustrial effluents.

4.5. Sites of environmental vulnerability in the Estuary

For a better understanding of the problems entailed by heavymetal contamination in coastal ecosystems, EF maps of potentially

Fig. 7. Mapping by Kriging’s method of the EF of As, Cd, Co, Cr, Cu, Ni, Pb and Zn.

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 79

Fig. 7. (continued).

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8380

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 81

contaminant elements in the estuary have been drawn using GIS.Also, it allows the development of a tool to make monitoring of thecoastal system easier (Mattakalli et al., 1996; Lathrop et al., 2001;Chica-Olmo et al., 2004), locating zones of greater environmentalvulnerability.

Arsenic shows EF values generally raging from 1 to 2 witha mean value close to 2, which suggests that, this element isenriched along the whole study sector (Fig. 7a). This homogeneousdistribution, that could be attributed to a constant input of thiselement from acid mine drainage, contrasts with a positiveanomaly detected in the proximities of the harbour and yatch clubof Ayamonte (EF> 6), which evidences the presence of a punctualinput of anthropic origin, possibly related to port activity or urbanwastes (Fig. 7a).

Cadmium is the element with the highest enrichment in thewhole estuary, with mean values as high as 1.94. In general, mostof the data present an EF variation rate between 1 and 4, whichleads to assume a similar behaviour to that described for As.Likewise, eventual sources of element accumulation in the estua-rine sector have been detected. They are located between thetowns of Vila Real de Sto. Antonio and Ayamonte and at theinternal zone of the Castro Marim tidal channel (EF> 7), as shownin Fig. 7b.

Cobalt and Cr (Fig. 7c, d) are elements that evidence a homoge-neous natural origin, with EF mean values of 1.08 and 0.96respectively. Both elements present EF values very close to 1 in thewhole estuary, with small fluctuations mostly related to floccula-tion/precipitation processes in water mixing zones. Although Cr EFare slightly lower, its relationship with the residual fraction of thesediments was demonstrated (Morillo et al., 2004).

Cooper EF map (Fig. 7e) shows values ranging between 1 and 2.Thus, these values, along with such a homogeneous distribution,highlight a “diffuse historical” contamination of mining origin inmost of the estuary, as happens for other elements such as As andCd.

Analysing Ni distribution (Fig. 7f), it could be presumed that thepresence of this element in the estuary is due to geogenic contri-butions. It is true for most of the estuary, but when studyingpunctual values in detail, it can be observed that, in spite of thatpractically the whole estuary presents EF very close to 1, there existextremely high EF values located in the junction of the tidal channelof Castro Marim and Carraqueira with the main estuarine channel.This locally strong Ni EF values appear to be related to the urbanwastes and aquiculture farms.

Lead and Zn present EF ranging from 1 to 3 for almost all studyarea (Fig. 7g, h), with a mean value of 1.8 for Pb and 2.5 for Zn.

Higher EF (2< EF< 4) for Pb were determined in zones near theInternational Bridge (border between Spain and Portugal) anddownstreams. This phenomenonmay be related to the Pb pollution(leaded gasoline) associated to the traffic on the bridge. Upstreams,the EF maintain values in the same range, followed by a dilutioneffect of seawater reaching values even lower than 1 at the rivermouth.

The general distribution of Pb is similarly consistent with thatdescribed for As, Cd and Cu. In this sense, samples located upwatersof the International Bridge present EF values higher than 1, forexample: S-00-19 (EFPb¼ 1.47) 11 km upstream from the sampleRG-20 (EFPb¼ 1.15), RG-17 (EFPb¼ 1.58), RG-11 (EFPb¼ 1.45), RG-10(EFPb¼ 1.79) and S-00-12 (EFPb¼ 1.83). For detail information on EFvalues, see Annex 1 in the supplementary information. Thus,a general range of variation 1< EF< 2 is established along theestuary, which again reveals a “diffuse historical” contamination ofmining source. A recent study of suspended particulate matter(SPMs) in the Guadiana estuary (Caetano et al., 2006) has shownthat during periods of moderate flow (i.e. winter rains) the SPMs

comprise iron-rich particles containing 3e12 wt.% Fe, 20e80 ppmPb and higher levels of Cu and Zn. Similarly, recent studies of Pbisotopes as tracers of mining pollution in molluscs of the GuadianaEstuary (Company et al., 2008), confirm that the isotopic signatureof Pb in the Estuary is strongly linked to anthropogenic Pb mine-related pollution from “Mina de São Domingos” in Portugal and“Las Herrerías” in Spain, and that geogenic Pb derived from theerosion of the Devonian or Carboniferous rocks, if present, is verysmall (Company et al., 2008). Thus, given that the methodologyapplied eliminates possible geochemical alterations produced bythe grain size, and EF values have been obtained from Backgroundnormalized data, the results are consistent with the available leadisotopic data.

Zn has a similar general distribution but EF values are higherthan Pb (S-00-19 (EFZn¼ 4.11), RG-20 (EFZn¼ 1.33), RG-17(EFZn¼ 2.17), RG-11 (EFZn¼ 1.43), RG-10 (EFZn¼ 2.71) and S-00-12(EFZn¼ 2.64)). This element presents EF much higher than themean value in the proximities of the harbour of Ayamonte(4< EF< 5), as well as in the middle zone of the Castro Marim tidalchannel, with values exceeding up to 6 times the local background(Table X). These high EF values can probably be related to theexistence of an additional input source of anthropic origin in theseareas, the first related to the harbour activities, and the secondprobably related to the aquiculture or urban washes.

5. Conclusions

This study has allowed us to show the existence of “diffusehistorical” contamination in the sediments of the Guadiana estuaryrelated with AMD generated in the inner zones of the basin, and toprove that EF calculation along with a GIS based spatial analysis byArcGIS� is a great tool to assess the environmental quality ofestuarine systems.

PCA analysis allowed to distinguish two main association ofelements characterized by common behaviours and origin: 1) As,Cd, Cu, Pb and Zn mostly of anthropic origin, mainly derived fromAMD processes generated in inner zones of the basin and fromsources of contamination located in the proximities of the towns; 2)Fe, Mn, Co, Cr and Ni are associated with the clay fraction of sedi-ments probably of natural origin (except for some punctualanomalies of Ni).

Enrichment factor maps allowed to define three groups ofelements characterized by different input sources: (1) Impov-erished or poorly enriched element: Fe, Mn, Co, Cr and Ni, (2)Enriched element: As, Cd, Cu and Pb, (3) strongly enrichedelement: Zn.

From the EF distribution maps, zones of environmentalvulnerability have been established in the Guadiana estuary, whererelevant geochemical anomalies of trace elements have been found.These zones are: 1) harbour and town of Ayamonte, As anomaly-related zone; 2) Outskirts of the towns of Ayamonte and Vila Real,with high Cd enrichment factors; 3) Castro Marim tidal channelrepresents a Ni contamination source for the main estuarinechannel; and 4) Harbour of Ayamonte, Castro Marim tidal channeland International Bridge, where important Zn and Pb concentra-tions are registered.

GIS based spatial analysis and Kriging interpolation method,have been proven to be a powerful tool to represent the distributionof enrichment factor (EF) of elements in the study area. The EFmapshelped in the identification of both acidic mine waters and somehuman activities as possible source of contaminants in the sedi-ments of the Guadiana Estuary.

Finally these results show the pollution risk of the ecosystemand underline the requirement of environmental attention to avoidpossible future incidences.

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e8382

Acknowledgements

This study has been developed in the framework of the projectINTERREG IIIA MEGASIG: “Monitoring and environmentalmanagement of the Guadiana estuary wetlands”. JDR alsoacknowledged a Ph.D. grant of the Huelva University.

Appendix. Supplementary data

Supplementary data associated with this article can be found inthe online version, at doi:10.1016/j.ecss.2010.03.011.

References

Ackermann, F., 1980. A procedure for correcting the grain size effect in heavy metalanalyses of estuarine and coastal sediments. Environmental Technology Letters1, 518e527.

Aloupi, M., Angelidis, M.O., 2001. Geochemistry of natural and anthropogenicmetals in the coastal sediments of the island of Lesvos, Aegean Sea. Environ-mental Pollution 113, 211e219.

Alvarez-Valero, A.M., Pérez-López, R., Matos, J., Capitán, M.A., Nieto, J.M., Sáez, R.,Delgado, J., Caraballo, M., 2008. Potential environmental impact at São Domi-ngos mining district (Iberian Pyrite Belt, SW Iberian Peninsula): evidence froma chemical and mineralogical characterization. Environmental Geology 55,1797e1809.

Bertine, K.K., Goldberg, E.D., 1977. History of heavy metal pollution in southernCalifornia coastal zone e reprise. Environmental Science and Technology 1,297e299.

Bien, J.D., ter Meer, J., Rulkens, W.H., Rijnaarts, H.H.M., 2004. A GIS-based approachfor the long-term prediction of human health risks at contaminated sites.Environmental Modeling Assessment 9, 221e226.

Birch, G.F., 1996. Sediment-bound metallic contaminants in Sydney’s Estuaries andadjacent offshore, Australia. Estuarine, Coastal and Shelf Science 42, 31e44.

Blomqvist, S., Larsson, U., Borg, H., 1992. Heavy metal decrease in the sediments ofa Baltic bay following tertiary sewage treatment. Marine Pollution Bulletin 24,258e266.

Borrego, J., López-González, N., Carro, B., 2004. Geochemical signature as paleo-environmental markers in Holocene sediments of the Tinto River estuary(Southwestern Spain). Estuarine, Coastal and Shelf Science 61, 631e641.

Boski, T., Moura, D., Veiga-Pires, C., Camacho, S., Duarte, D., Scott, D.B., Fernandes, S.G., 2002. Postglacial sea-level rise and sedimentary response in the GuadianaEstuary, Portugal/Spain border. Sedimentary Geology 150, 103e122.

Boski, T., Camacho, S., Moura, D., Fletcher, W., Wilamowski, A., Veiga-Pires, C.,Correia, V., Loureiro, C., Santana, P., 2008. Chronology of the sedimentaryprocesses during the postglacial sea level rise in two estuaries of the Algarvecoast, Southern Portugal. Estuarine, Coastal and Shelf Science 77, 230e244.

Bruland, K.W., Bertine, K., Koide, M., Goldberg, E.D., 1974. History of metal pollutionin Southern California Coastal zone. Environmental Science and Technology 8,425e432.

Cáceres, L.M., 1999. Geomorfología del sector occidental de la Depresión del Gua-dalquivir (Huelva). Public. Serv. Huelva University, 255 pp.

Caetano, M., Vale, C., Falcão, M., 2006. Particulate metal distribution in Guadianaestuary punctuated by flood episodes. Estuaries Coast and Shelf Science 70,109e116.

Carral, E., Villares, R., Puente, X., Carballeira, A., 1995. Influence of watershedlithology on heavy metal levels in estuarine sediments and organisms in Galicia(north-west Spain). Marine Pollution Bulletin 30, 604e608.

Chica-Olmo, M., Rodriguez, F., Abarca, F., Rigol-Sanchez, J.P., DeMiguel, E., Gomez, J.A., Fernandez-Palacios, A., 2004. Integrated remote sensing and GIS techniquesfor biogeochemical characterization of the Tinto-Odiel estuary system, SWSpain. Environmental Geology 45, 834e842.

Company, R., Serafim, A., Lopes, B., Cravo, A., Shepherd, T.J., Pearson, G.,Bebianno, M.J., 2008. Using biochemical and isotope geochemistry to under-stand the environmental and public health implications of lead pollution in thelower Guadiana River, Iberia: a freshwater bivalve study. Science of the TotalEnvironment 405, 109e119.

Daskalakis, K.D., O’Connor, T.P., 1995. Normalization and elemental sedimentcontamination in the coastal United States. Environmental Science and Tech-nology 29, 470e477.

Delgado, J., Nieto, J.M., Boski, T., Alabardeiro, L., 2008. Determinación de los valoresde fondo regional en sedimentos Holocenos del estuario del río Guadiana (SWde España). Geogaceta 44, 235e238.

Delgado, J., Sarmiento, A.M., Condesso de Melo, M.T., Nieto, J.M., 2009. Environ-mental impact of mining activities in the southern sector of the Guadiana basin(South-west of the Iberian Peninsula). Air, Water and Soil Pollution 199,323e341.

Donaire, T., Sáez, R., Pascual, E., 1998. Geología y evolución magmática del ejevolcánico de Paymogo, Faja Pirítica Ibérica. Geogaceta 24, 115e118.

Fernández-Caliani, J.C., Munoz, F.R., Galan, E., 1997. Clay mineral and heavy metaldistributions in the lower estuary of Huelva and adjacent Atlantic shelf, SWSpain. Science of The Total Environment 198, 181e200.

Fernández-Caliani, J.C., Barba-Brioso, C., González, I., Galan, E., 2009. Heavy metalpollution in soils around the abandoned mine sites of the Iberiam Pirite Belt(Southwest Spain). Water Air and Soil Pollution 200, 211e226.

Folk, R., 1954. The distinction between grain size and composition in sedimentaryrock nomenclature. Journal of Geology 62, 344e359.

Forstner, U., 1983. Assessment of metal pollution in rivers and estuaries. AppliedEnvironmental Geochemistry, 395e423.

Grande, J.A., Beltrán, R., Sáinz, A., Santos, J.C., de la Torre, M.L., Borrego, J., 2005. Acidmine drainage and acid rock drainage processes in the environment of Herre-rías Mine (Iberian Pyrite Belt, Huelva-Spain) and impact on the Andévalo Dam.Environmental Geology 47, 185e196.

Grousset, F.E., Quetel, C.R., Thomas, B., Donard, O.F., Lambert, C.E., Guillard, F.,Monaco, A., 1995. Anthropogenic vs. lithogenic origins of trace elements (As, Cd,Pb, Rb, Sb, Sc, Sn, Zn) in water column particles: northwestern MediterraneanSea. Marine Chemistry 48, 291e310.

Herut, B., Hornung, H., Krom, M.D., Kress, N., Cohen, Y., 1993. Trace metals inshallow sediments from the Mediterranean coastal region of Israel. MarinePollution Bulletin 26 (12), 675e682.

Hirst, D.M., 1962a. The geochemistry of modern sediments from the gulf of Paria e I.Geochimica et Cosmochimica Acta 26, 309e344.

Hirst, D.M., 1962b. The geochemistry of modern sediments from the gulf of Paria eII. Geochimica et Cosmochimica Acta 26, 1147e1187.

Lathrop, R.G., Styles, R.M., Seitzinger, S.P., Bognar, J.A., 2001. Use of GIS mapping andmodeling approaches to examine the spatial distribution of seagrasses in Bar-negat Bay, New Jersey. Estuaries 24, 904e916.

Leblanc, M., Morales, J.A., Borrego, J., Elbaz-Poulichet, F., 2000. 4,500-year-oldmining pollution in southwestern Spain: long-term implications for modernmining pollution. Economic Geology 95, 655e661.

Liu, W.X., Li, X.D., Shen, Z.G., Wang, D.C., Wai, O.W.H., Li, Y.S., 2003. Multivariatestatistical study of heavy metal enrichment in sediments of the Pearl RiverEstuary. Environmental Pollution 121, 377e388.

López-González, N., Borrego, J., Ruiz, F., Carro, B., Lozano-Soria, O., Abad, M.,2006. Geochemical variations in estuarine sediments: provenance and envi-ronmental changes (Southern Spain). Estuarine, Coastal and Shelf Science 67,313e320.

Loring, D.H., 1991. Normalization of heavy-metal data from estuarine and coastalsediments. ICES Journal of Marine Science 48, 101e115.

McGrath, S.P., Cunliffe, C.H., 1985. A simplified method for the extraction of themetals Fe, Zn, Ni, Pb, Cr, Co, Mn from soils and sewage sludges. Journal of theScience of Food and Agriculture 36, 794e798.

Mat�ejí�cek, L., Engst, P., Ja�nour, Z., 2006. A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: a case study ofPrague’s environment extended by LIDAR data. Ecological Modelling 199,261e277.

Matos, J.X., Oliveira, V., Alves, H., Carvalho, R., 2002. Património geológico-mineiro ehistórico da Mina de S. Domingos, Faixa Piritosa Ibérica. In: Actas do CongressoInternacional Sobre Património Geológico e Mineiro, IGM/SEDPGYM, pp.517e535.

Mattakalli, N.M., Devereux, B.J., Richards, K.S., 1996. Prediction of river dischargeand surface water quality using an integrated geographical information sytemapproach. Internacional Journal of Remote Sensing 17, 683e701.

Mil-Homens, M., Stevens, R.L., Abrantes, F., Cato, I., 2006. Heavy metal assessmentfor surface sediments from three areas of the Portuguese continental shelf.Continental Shelf Research 26, 1184e1205.

Morales, J.A., 1993. Sedimentología del Estuario del Río Guadiana (SO España-Portugal). Doctoral tesis, Sevilla University, 300 pp.

Morillo, J., Usero, J., Gracia, I., 2004. Heavy metal distribution in marine sedimentsfrom the southwest coast of Spain. Chemosphere 55, 431e442.

Nocete, F., Álex, E., Nieto, J.M., Sáez, R., Bayona, M.R., 2005. An archaeologicalapproach to regional environmental pollution in the south-western IberianPeninsula related to third millennium BC mining and metallurgy. Journal ofArchaeological Science 32, 1566e1576.

Olías, M., Cánovas, C.R., Nieto, J.M., Sarmiento, A.M., 2006. Evaluation of the dis-solved contaminant load transported by the Tinto and Odiel rivers (South WestSpain). Applied Geochemistry 21, 1733e1749.

Pérez, M., Usero, J., Gracia, I., Cabrera, F., 1991. Trace metals in sediments fromthe “Ría de Huelva”. Toxicological and Environmental Chemistry 31-32,31e44.

Pérez-López, R., Álvarez-Valero, A.M., Nieto, J.M., Sáez, R., Matos, J.X., 2008. Use ofsequential extraction procedure for assessing the environmental impact atregional scale of the São Domingos Mine (Iberian Pyrite Belt). AppliedGeochemistry 23, 3452e3463.

Piper, D.Z., 1971. The distribition of Co, Cr, Cu, Fe, Mn, Ni and Zn in Framveren,a Norwegian anoxic Fjord. Geochimica et Cosmochimica Acta 35, 531e559.

Poggio, L., Vr�s�caj, B., 2009. A GIS-based human health risk assessment for urbangreen space planningdan example from Grugliasco (Italy). Science of TotalEnvironment. doi:10.1016/j.scitotenv.2009.08.026.

Ridgway, J., Shimmield, G., 2002. Estuaries as repositories of historical contamina-tion and their impact on shelf seas. Estuarine, Coastal and Shelf Sciences 55,903e928.

Rocha, J.S., Correia, F.N.,1994.Defence fromfloods andfloodplainmanagement inmiddlesize catchments. In: Gardiner, J., Starosolszky, O., Yevjevich, V. (Eds.), Defence from

J. Delgado et al. / Estuarine, Coastal and Shelf Science 88 (2010) 71e83 83

Floods and FloodplainManagement. NATOASI Series, Series E: Applied Sciences, vol.299. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 395e417.

Ruiz, F., 2001. Trace metals in Estuarine sediments from the southwestern Spanishcoast. Marine Pollution Bulletin 42, 481e489.

Sáez, R., Pascual, E., Toscano, M., Almodóvar, G.R., 1999. The Iberian type ofvolcano-sedimentary massive sulphide deposits. Mineralium Deposita 34,549e570.

Sanger, D.M., Holland, A.F., Scott, G.I., 1999. Tidal creek and salt marsh sediments inSouth Carolina coastal estuaries: I. Distribution of trace metals. Archives ofEnvironmental Contamination and Toxicology 37, 445e457.

Sharman, P., Borole, D.V., Zigde, M.D., 1984. 210 Pb based trace element fluxes in thenearshore and estuarine sediments of Bombay, India. Marine Chemistry 47,227e241.

Spencer, K.L., Cundy, A.B., Croudace, I.W., 2003. Heavy metal distribution and early-diagenesis in salt marsh sediments from the Medway Estuary, Kent, UK. Estu-arine, Coastal and Shelf Science 57, 43e54.

Thuy, H.T.T., Tobschall, H.J., An, P.V., 2000. Trace element distributions in aquaticsedimentsofDanangeHoianarea,Vietnam.EnvironmentalGeology39, 733e740.

Usero Garcia, J., Morillo, A., Gracia Manarillo, I., 1997. Contaminacion por metales ensedimentos acuaticos. I. Metales en los ecosistemas acuaticos (Metal contami-nation in aquatic sediments). 17, 44e50.

Van Alsenoy, V., Bernard, P., VanGrieken, R.,1993. Elemental concentrations and heavymetal pollution in sediments and suspended matter from the Belgian North Seaand the Scheldt estuary. Science of the Total Environment 133, 153e181.

Webster, R., Oliver, M.A., 2001. Geostatistics for Environmental Scientists. JohnWiley & Sons, UK.

Weltje, G.J., Eynatten, H.v, 2004. Quantitative provenance analysis of sediments:review and outlook. Sedimentary Geology 171, 1e11.

Wentworth, C.K., 1922. A scale of grade and clase terms for clastic sediments.Journal of Geology 30, 306e309.

Zhang, C., 2006. Using multivariate analyses and GIS to identify pollutants and theirspatial patterns in urban soils in Galway, Ireland. Environmental Pollution 142,501e511.

Zhou, F., Huaicheng, G., Zejia, H., 2007. Spatial distribution of heavy metals in HongKong’s marine sediments and their human impacts: a GIS-based chemometricapproach. Marine Pollution Bulletin 54, 1372e1384.