Maximum likelihood mixture estimation to determine metal background values in estuarine and coastal sediments within the European Water Framework Directive

ent 370 (2006) 278–293www.elsevier.com/locate/scitotenv

Science of the Total Environm

Maximum likelihood mixture estimation to determine metalbackground values in estuarine and coastal sedimentswithin the European Water Framework Directive

J.G. Rodríguez ⁎, I. Tueros, A. Borja, M.J. Belzunce,J. Franco, O. Solaun, V. Valencia, A. Zuazo

AZTI-Tecnalia, Marine Research Division, Herrera Kaia, Portualdea; 20110 Pasaia (Spain)

Received 10 April 2006; received in revised form 21 August 2006; accepted 21 August 2006

Abstract

Some of the recently derived European Directives, such as the Water Framework and Marine Strategy, have, as ultimate aims, toachieve concentrations of hazardous substances in the marine environment near background values. Hence, the determination ofnatural background levels, in marine sediments, is highly relevant. The present study proposes the use of the maximum likelihoodmixture estimation (MLME) to determine regional background levels and upper threshold of metal concentration, with the BasqueCountry as a case study (with a data set of 575 samples, from estuarine and littoral areas, including both intertidal and subtidalsediments). The heuristic procedure is applied with unimodal data distributions (Cd, Cr, Fe and Ni) and the mixture densityestimations, based upon maximum likelihood, are carried out with polypopulational data distributions (As, Cu, Mn, Hg, Pb andZn). The upper limits of the distribution are proposed, as the limits between ‘High Status’ and ‘Good Status’ (according to theWater Framework Directive terminology). The regional upper limits were 0.45 μg g−1 for Cd, 71 μg g−1 for Cr, 53,542 μg g−1 forFe, 57 μg g−1 for Ni, 24 μg g−1 for As, 64 μg g−1 for Cu, 447 μg g−1 for Mn, 0.27 μg g−1 for Hg, 66 μg g−1 for Pb, and 248 μgg−1 for Zn. The results from this study can assist further in the determination of sediment reference conditions, to assess chemicalstatus, within the above-mentioned directives; likewise, it will be studied as a useful methodology in determining regional metalbackgrounds in other European countries.© 2006 Elsevier B.V. All rights reserved.

Keywords: Marine sediments; Metals; Background levels; Mixture estimation; Water Framework Directive; Basque Country

1. Introduction

The European Water Framework Directive (WFD;Directive 2000/60/EC) states the need to achieve ‘agood ecological and chemical status’, by 2015, at all of

⁎ Corresponding author. Tel.: +34 943004800; fax: +34 943004801.E-mail addresses: [email protected] (J.G. Rodríguez),

[email protected] (A. Borja).

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the European water masses, including estuarine andcoastal waters (for details, see Borja et al., 2004a; Borja,2005). Although most of the methods implemented forthe WFD are related to the water column, some debatehas taken place in relation to the controversy of includ-ing different matrices (such as sediments) in the as-sessment of the physico-chemical status and qualityguidelines, within the WFD (Crane, 2003; Borja et al.,2004b; Borja and Heinrich, 2005). Sediments are

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279J.G. Rodríguez et al. / Science of the Total Environment 370 (2006) 278–293

considered as good indicators of anthropogenic impactsto coastal and estuarine environments (Ridgway andShimmield, 2002); this is because of their potentialimpact on biological communities and their property toaccumulate substances and to provide a good integrationover time. Likewise, they form part of integral tools forsediment assessment (Chapman and Wang, 2001) andfor the comprehensive assessment of marine and estua-rine systems.

The ultimate aim of the WFD is to achieve theelimination of priority hazardous substances and con-tribute to achievable concentrations in the marineenvironment, near to the background values for naturallyoccurring substances. This approach would permit themaintenance of the structure and functioning of marinecommunities and ecosystems associated with the waterbodies. Hence, the WFD defines as ‘High Status’, forspecific non-synthetic pollutants, those concentrationsremaining within the range associated normally withundisturbed conditions (background levels). A similarapproach has been adopted by the new European MarineStrategy Directive (EMS), in assessing the ecologicaland chemical status within offshore waters (Borja, 2006).

Within this context, the determination of naturalbackground in marine habitats, as a relative measure, todistinguish between natural element concentrations andanthropogenically influenced concentrations, is highlyrelevant. This approach is due mainly to the fact that thedegree of contamination of a habitat can be assessed,only if natural levels are known (e.g., Fowler, 1990;Wedepohl, 1995; Carballeira et al., 2000; Baize andSterckeman 2001). The determination of backgroundlevels has been applied also to other environmentallegislation, developed in industrialized countries (Sal-minen and Tarvainen, 1997).

The first studies within this field were undertaken byTurekian and Wedephol (1961), who defined the naturalconcentration of diverse elements, for different geolog-ical substrates. However, due to the geological variety ofthe different regions, later studies have shown the con-venience of deriving local or regional background lev-els, especially if they are necessary for environmentalassessments (e.g., Carral et al., 1995a). In fact, for largeareas, the concept of being able to define just one‘background’ is illusive (Reimann et al., 2005).

There is not a standard procedure to determine theregional background (for detailed discussions of thistopic, see Carballeira et al., 2000; Reimann and deCaritat, 2000 and Reimann et al., 2005). At the presenttime, three main approaches are used, which have bothadvantages and disadvantages (Loring and Rantala,1992). These approaches are based upon (i) sampling of

pristine areas (see review by Carballeira et al., 2000); (ii)sampling with datable long sediment cores which canreach pre-industrial sediments; or (iii) sampling at alarge number of sites. In terms of the first approach, it isdifficult to establish uncontaminated areas, with similarcharacteristics to those of the contaminated sites. Thesecond approach is suitable only when there have beenno major post-depositional movements (Luoma, 1990);likewise, it is technically difficult. The third approachhas the advantage that it can be undertaken everywhere;however, there is no general agreement on the statisticalmethodologies to be applied, within most of them beingfocused upon evaluation of the graphical inspection ofthe empirical data distribution and, more recently, withgeographical displays (see reviews presented byMatschullat et al., 2000; Reimann and Garret, 2005;Filzmoser et al., 2005; Reimann et al., 2005). Moreover,when pristine and contaminated areas are sampled si-multaneously, it can result in polymodal distributions(e.g., Carral et al 1992). Nevertheless, it should be notedthat polymodal distributions can be also due to naturalprocesses. There are several statistical approaches todecompose polymodal distributions into their compo-nents (see reviews by Titterington et al, 1985; McLa-chlan and Peel, 2000). Carral et al. (1992, 1995a)proposed the use of maximum likelihood mixture esti-mation (MLME) to establish the background distribu-tion in marine sediments. These authors referred to thisstatistical approach as ‘modal analysis’ (a term usedextensively in population ecology). MLME has beenshown to be useful in determining the backgroundvalues in sediment and biota (Carral et al., 1995a,b;Carballeira and López, 1997; Carballeira et al., 2002;Villares et al., 2002; Aboal et al., 2004). However,studies using this methodology in marine habitats arescarce; this is due probably to high data requirements.

The aim of this contribution is to apply the MLME, todetermine regional background levels of metals (usingthe Basque Country, northern Spain, as a case study);this can assist further in the determination of sedimentreference conditions, to assess chemical status, withinthe WFD and the EMS Directive.

2. Materials and methods

2.1. Study area and sediment sampling

The Basque Country (Fig. 1) is a coastal mountain-ous region, dominated by rocky shores and estuaries.The lithology is characterised by materials ranging fromthe Palaeozoic to the Quaternary, with an absence ofOligocene materials (Pascual et al., 2004). The area is

Fig. 1. Geological map of the Basque Country (Pascual et al., 2004), together with the locations sampled in this study. Note that (i) sites terminatingwith L are littoral areas; (ii) between brackets number of stations with b63 μm fraction samples higher than 10%.

280 J.G. Rodríguez et al. / Science of the Total Environment 370 (2006) 278–293

characterised by sedimentary rocks, with a higherproportion of sandstones and lutites in the eastern partof the region and more marls and limestones towards thewest (Pascual et al., 2004). Most of the estuaries andcoastal areas are affected, to some degree, by urban,industrial wastewaters and/or mining, with special rele-vance to those of Zn, Pb and Fe (Cearreta et al., 2000,2002, 2004; Belzunce et al., 2001, 2004). Hence, thearea has a high number of pressures and impacts (Borjaet al., 2006).

A total of 899 sediment samples were obtained, from23 estuarine and littoral locations along the Basquecoast (Fig. 1), between 1995 and 2005, including bothintertidal and subtidal (from 0 to 65 m water depth)samples. Intertidal sediments were collected by hand,whereas subtidal samples were collected using Day orVan Veen grabs. In both cases, the upper 10 cm ofsediments were collected. Sediment samples wereretained in plastic bottles, transported to the laboratoryand stored at 4 °C until analysis.

2.2. Data normalization and sediment analysis

Since metal concentrations vary in relation to sedi-ment characteristics, a wide variety of normalisationmethods have been used in background studies (seeLoring, 1991; Kersten and Smedes, 2002, for detailedreviews). In this study, two criteria based upon texturalcharacteristics were used, in order to normalise the data:(i) metal concentrations were measured only in samples

containing more than 10% of the dry weight of finefraction (i.e.,b63 μm); and (ii) metal concentrationswere analysed in the b63 μm fraction (Luoma 1990;Förstner and Salomons, 1980; Loring and Rantala,1992). The b63 μm fraction was obtained by the drysieving of samples, previously oven-dried at 60 °C.After these normalisation procedures, a set of 588samples was retained, for analysing the metal concen-trations. Nevertheless, not all the metals were analysedin all the samples.

Finally, As (519 samples), Cd (433 samples), Cr (543samples), Cu (572 samples), Fe (436 samples), Hg (514samples), Mn (440 samples), Ni (575 samples), Pb (568samples) and Zn (570 samples) were analysed, on theb63 μm sediment fraction. All of the samples weredigested in a microwave digestion system. About 1.0 gof dry sediment was accurately weighed and transferredinto the PTFE extraction vessel with the acid mixture(4 mL HCl and 2 mL HNO3) for 20 min, following apressure ramp programme at a maximum power of1400 W. Once the extraction period was completed, thesolid phase was separated from the acid extract, bycentrifugation at 2500 rpm for 10 min. The solid wasrinsed twice with double-deionised water and the rinseswere added to the extract, then diluted finally to 50 mL.All of the glassware was acid-washed (HNO3 10%).

The analysis of metals in the extracts was carried outusing atomic absorption spectrometry (AAS). Cu, Pb,Ni, Cr, Zn, Mn and Fe were determined in an air–acetylene flame. Cadmium content was analysed by

Table 1Results for the analysis of the PACS-2 certified reference marine sediment given in μg g−1 dry sediment (average±S.D.)

As Cd Cu Cr Fe Mn Hg Ni Pb Zn

References 26.2±1.5 2.11±0.15 310±12 90.7±4.6 40,900±600 440±19 3.40±0.2 39.5±2.3 183±8 364±23Measured values 20.5±1.3 1.93±0.2 268±15 57±11 39,899±3406 280±31 2.9±0.4 39±5 183±23 320±26Recovery (%) 78.3 91.9 86.5 63.1 97.6 63.7 95.0 98.8 100.4 87.8


THGA graphite furnace, using Zeeman backgroundcorrection; finally, As and total Hg were determined byquartz furnace atomic absorption spectrometry, afterhydride generation of the sample in a FIA system. Thedata were presented in terms of μg g−1 of sample dryweight. The accuracy of the analytical proceduresemployed for the analysis of metals in sediment sampleswas checked using the PACS-2 (NRC, Canada) certifiedreference material (Table 1). The number of samplesbelow the detection limits (DL) is shown in Table 2.Data below DL were also included in the analysis takingthe DL as the real value.

The procedure described above is a common methodfor the determination of metals in sediments, inmonitoring programs for environmental purposes. Themethod provides the acid-extractable metal concentra-tion (see, e.g., Förstner, 1987).

3. Results

3.1. Background determination

Cluster analyses (group average method; squaredEuclidean distance) were carried out, on standardisedand non-standardised data, before the calculation of thebackground values. These cluster analyses did not dif-ferentiate between the different areas (i.e., estuaries orsub-geographical areas), or between different habitats(i.e., intertidal, subtidal, estuarine or littoral). MoreoverBox–Whisker plots of element concentration wererepresented to evaluate the variation in the 23 sub-areas sampled (Figs. 2 and 3).

Since the upper 10 cm of sediments were sampled,the results can reflect relatively recent contaminationevents. It is thus necessary to remove data from highlycontaminated areas before calculating the background.Therefore, for each metal, a subset of data was truncated(based, partially, on the heuristic procedure proposed byReimann et al., 2005), as shown below.

Table 2Detection limits of the analysed metals and number of the samples below th

As Cd Cu

Detection limit (μg g−1) 0.05 0.04 1Number of samples below the detection limit 0 6 0

(i) Empirical cumulative distribution functions(ECDF), with different scales (linear, logarithmi-cal and probability), were displayed to evaluatethe presence of extremely high or low values,separated widely from the main mass of the data(anomalous values, AV) (Fig. 4). AVs were repre-sented in maps to seek an explanation for theirpresence and, generally, removed from the data.

(ii) New ECDFs (Fig. 5) and box plots (Fig. 6) weredisplayed and the statistics of data were calculatedto evaluate if the global distribution was normal orlog-normal (see Reimann et al., 2005, for moredetails). When the concentration data distributionof a metal was log-normal, it was log-transformedin order to increase the symmetry.

(iii) Upper inner fence (UIF) was calculated on the basisof the 75th percentile+1.5⁎ (75th percentile−25thpercentile). The far upper inner fence (FUIF) wascalculated as the 75th percentile+3⁎ (75th percen-tile−25th percentile). The lower inner fence (LIF)was calculated as the 25th percentile−1.5⁎ (75thpercentile−25th percentile). Values higher than theFUIF were considered as far outliers. Values be-tween UIF and FUIF (or lower than LIF) wereconsidered as outliers. For eachmetal, areas (Fig. 1)with more than 10% of far outliers, or more than25% of outliers, were removed.

(iv) New ECDFs (Fig. 7), histograms with density traceand ntigrams (equal-area histograms) were dis-played, in order to evaluate if the global distributionwas normal or log-normal and to evaluate thepresence of polymodality. Ntigrams were estab-lished with software Fathom (KCP Technologies,2000). Density traces were made, using the cosinemethod, with Statgraphics software (STSC, 1991).

The metal concentrations of the b63 μm fraction,measured in the truncated subsets of data, ranged 0.65–65 μg g−1 for As, 0.05–2.44 μg g−1 for Cd, 0.42–337 μg

ose limits

Cr Fe Mn Hg Ni Pb Zn

0.4 5 0.5 0.03 1 0.05 0.50 0 0 1 0 2 0

Fig. 2. Box–Whisker plots of As, Cd, Cr, Cu and Fe concentrations in b63 μm fraction samples of surficial sediments.


g−1 for Cr, 1.2–134 μg g−1 for Cu, 493–118,975 μg g−1

for Fe,b0.03–1.03μg g−1 for Hg, 61–525 μg g−1 forMn,2.1–104 μg g−1 for Ni,b0.05–97 μg g−1 for Pb and 62–410 μg g−1 for Zn.

The truncated data distribution showed unimodaldistributions for Cd, Cr, Fe and Ni (Fig. 8), togetherwith polymodal distributions for As, Cu, Mn, Hg, Pb andZn (Fig. 9). Chi-square goodness-of-fit test, carried out on

Fig. 3. Box–Whisker plots of Hg, Mn, Ni, Pb and Zn concentrations in b63 μm fraction samples of surficial sediments.


data after removing outliers, showed normal distributionsin Cd, Cr, Fe and Ni, at 90% confidence level (Table 3).

The values used to determine the limits of the re-gional background for Cd, Cr, Fe and Ni were the upper

and the lower whiskers shown in Tukey boxblots(Fig. 8). The upper whisker (threshold) value was de-termined, for each metal, as the highest observed valuebelow the UIF. Similarly, the lower whisker value was

Fig. 4. Empirical cumulative distribution plots of the metal concentration data in b63 μm fraction samples of surficial sediments.


determined as the lowest observed value, above theLIF.

When the presence of polymodality was observed,an MLME was carried out using the softwareNORMSEP (Gayanilo et al., 1996). MLME permitsthe identification of discrete Gaussian sub-populationswithin a data set (for detailed explanations, on thedecomposition of a mixture into normal or log-normalcomponents, see Behboodian, 1975; Holgersson andJorner, 1978; Titterington et al., 1985; Bilmes, 1997;

McLachlan and Peel, 2000). The software NORMSEPrequires initial values of the mean of each Gaussiansubgroup, estimated from the ECDFs, together withhistograms with density trace and ntigrams displayedpreviously.

Metal data with a polymodal distribution were sub-divided into discrete Gaussian sub-populations, with anMLME (Fig. 9). The number of sub-groups identified inthe data of Mn, Pb and Zn was 3; this was higher for As,Cu and Hg (5, 4 and 6, respectively). In order to

Fig. 5. Cumulative distribution plots of the metal concentration data in b63 μm fraction samples of surficial sediments, after removing anomalous values.


evaluate the agreement between the predicted values(i.e., the values resulting from the sum of all sub-groupsestimated for each of the central values of the histogramintervals) and the real values (i.e., the observed values ofeach histogram channel of Fig. 9), an r2 value (Pearsoncorrelation coefficient) was calculated for each of themetals. This value should be taken into account care-fully, since parametric requirements cannot be assumed;however, higher values reflect a better adjustment of the

MLME. The highest r2 values were found in Hg, As, Cuand Zn (0.96, 0.96, 0.94 and 0.91, respectively), whilstthe lowest were those in Pb and Mn (0.89 and 0.79,respectively). For each metal, the subgroup with thelowest mean is assumed as the regional background; theirlimits were calculated as the range given by mean±2.698⁎standard deviation (of the assumed backgroundsubgroup). These values are equal to the fencescalculated for the unimodal distributions (see above).

Fig. 6. Tukey boxplots for metal concentration data, in b63 μm fraction samples of surficial sediments, after removing anomalous values.


The regional background metal concentrations,within the Basque Country, are listed in Table 4. Thevalues indicated as upper limits in Table 4 are proposedas the limits between ‘High Status’ and ‘Good Status’,according to the WFD (see Discussion).

4. Discussion

4.1. Methodological considerations

Although the term “background level” has receivedseveral definitions, in environmental studies, it is usedusually to refer to pre-industrial levels or natural levels,from pristine areas (see reviews of Carballeira et al.,2000; Matschullat et al., 2000). Although some authorsconsider that ‘background’ should not be expressed as asingle value (see, e.g., Reimann et al., 2005), it isdefined usually as the central value (mean, median or95% confidence of the mean), or the upper limit value ofthe range (called usually threshold). This approach isuseful for environmental purposes, because it allows thecalculation of contamination factors, or enrichmentfactors, especially if the background values are calculat-ed locally, for an area with low lithological variations(e.g., Häkanson 1980).

Either the approach used for the metals with uni-modal distributions (based upon Reimann et al., 2005),or the approach used for the metals with the polymodaldistributions (based upon Carral et al., 1992, 1995a),permits the determination of the range of variation of thebackground concentration and characterises it as a sin-gle value, if necessary. Hence, the more usual cumula-tive frequency plot approach (CFPA) (e.g., Lepeltier,

1969), to calculate background levels, cannot determineits range without a certain degree of subjectivity (e.g.,Förstner and Wittmann, 1981; Reimann et al., 2005).

The approach proposed by Reimann et al. (2005) isbased upon graphical inspection, using statistical andgeographical displays. This approach can be consideredmore useful than the CFPA, because it permits theremoval of data from polluted areas. Although a certaindegree of subjectivity is provided due to the defining ofcriteria to include or not include areas, similar results(with different percentages of outliers allowed in anarea) are found in this contribution; this is due probablyto the large number of data.

The approach proposed by Carral et al. (1992, 1995a)is based upon the decomposition of the observeddistributions, into their components, when polymodalityis observed; it was used in this study, in combination tothe heuristic method proposed by Reimann et al. (2005).There exist several methods to carry out a mixtureanalysis (see, e.g., Everitt, 1984). Amongst them, thatproposed by Carral et al. (1992, 1995a), i.e., MLME,has the advantage that can be used with a wide numberof types of data distribution (depending upon the soft-ware used). Nevertheless, it has the disadvantages of (i)a large number of data are required to be performed (see,e.g., Mendell et al., 1991); (ii) although there are several‘goodness-fit’ tests for mixtures distributions (Yantiset al., 1991; Agha and Branker, 1997), there is not astandard procedure available to measure the agreementbetween the real and the predicted data; (iii) there is nota standard criteria to determine the number of subgroups(Hsu et al., 1986; Richardson and Green, 1997; Sahuand Cheng, 2003); and (iv) the software to perform the

Fig. 7. Cumulative distribution plots of the data after removing anomalous values, and sampling areas with more than 10% of far outliers or more than25% of outliers.


MLME requires usually histogram data, whose shapedepends upon the number and width of the classintervals. In relation to these points in this contribution,the following criteria were used: (i) if most of the his-togram intervals have counts of 5 or greater, an MLMEcan be performed (Du, 2002); (ii) an r2 value from thePearson correlation coefficient can be calculated toestimate the agreement (see Results); if a significancelevel is required, non-parametric tests can be used (seereferences, above); (iii) the number of sub-groups can be

given by the number of modes shown by the histogramsand ntigrams (Carral et al., 1995a, 1995b); and (iv) thenumber and width of the intervals should be selected, inorder to establish a histogram with similar modes andshape, as shown by the ntigrams and density tracers(note that transformation to a histogram is, effectively,smoothing of the data). Other approaches, based else-where on the nonlinear least-square decomposition ofmixture distributions, were also proposed to geochem-ical exploration when polymodal distributions occur

Fig. 8. Histogram, density trace, one-dimensional scattergram and Tukey boxplot for truncated metal concentrations, after removing sampling areaswith more than 10% of far outliers or more than 25% of outliers. See Reimann et al. (2005) for methodological details.

Fig. 9. Gaussian sub-populations identified byMLME, on the basis of data of metal concentration, in estuarine and marine sediments of the Basque Country(top 10 cm), for theb63μmfraction.Gaussian sub-populations are represented by a fine line. The sumof all the sub-populations is represented by a coarse line.


Table 3Results of Kolmogorov–Smirnov tests for normality, for Cd, Cr, Feand Ni, after removing outliers

Cd Cr Fe Ni

n (without outliers) 38 238 391 370Chi-square goodness of fit 11.68 37.42 40.29 47.98P-value 0.55 0.20 0.37 0.13


(e.g., Rantitsch, 2004). In our data, As, Cu, Mn, Hg, Pband Zn showed the best adjustment disaggregating thedata into normal distributions, but this cannot beconsidered as generality in geochemical data (seediscussion and references in Reimann and Filzmoser,2000). On the other hand, it should be taken into accountthat multimodal distributions can be due to other pro-cesses not related to contamination. It is thus necessaryto combine the mixture analysis with the graphical in-spection as proposed by Reimann et al. (2005).

4.2. Background values

There are several previous studies witch have beenundertaken in the Basque Country, for determiningmetal background values; however, the comparison isdifficult due to differences in the analytical and statisti-cal approaches. Nonetheless, the mean value of thebackground ranges found in this study, for As, Cd, Cr,Mn, Hg and Zn, fits well with the previous valuesprovided for the Basque Country (Table 5).

The mean background values of Cu, Fe and Ni,determined in this study, are higher than reported previ-ously within the same region (Table 5). The differencesfound in Cu, Fe, Ni and Pb with those reported byLegorburu et al. (1989), Sola et al. (1990) and Cearretaet al. (2000, 2002) are because, probably, that they

Table 4Statistical descriptors of the background concentrations (μg g−1), of metals(b63 μm fraction)

Distribution Metal Lower limit Upper li

Unimodal Cd 0.05 0.4Cr b0.4 71Fe 11,000 53,542Ni 2 57

Polymodal As b0.05 24Cu 2 64Mn 32 447Hg b0.03 0.2Pb b0.05 66Zn 46 248

Mean, median and standard deviation (S.D.), for metals with unimodal distribshould be interpreted with care, since their distributions were not well adjus

calculated values on the bulk sediment, instead of theb63 μm fraction.

The differences found for Cu, Fe and Ni between thisstudy and that of Borja et al. (1995), who analysed theb63 μm fraction, could be related to the fact that theiroriginal results were based upon the CFPA method.Nevertheless, the values presented in this study, for thesemetals, fit within the range found using a similarmethodology (MLME carried out on the b63μm fraction)by Carballeira et al. (2000) in NW Spain; this is despitelithological differences between the areas studied.

4.3. Using background values in implementingEuropean directives

TheWFD and EMS define the chemical ‘High Status’,when concentrations of specific non-synthetic pollutants,such as metals, remain within the range normallyassociated with undisturbed conditions (i.e., below thethreshold) (Fig. 10). Hence, this study contributes to theassessment of regional undisturbed sediment conditions,based upon the methodologies which determine the metalbackground levels.

Conversely, the WFD (Article 16) states that ‘theCommission shall submit proposals for quality standardsapplicable to the concentrations of the priority sub-stances in surface water, sediments or biota’. Moreover,Article 2 defines an Environmental Quality Standard(EQS) as ‘the concentration of a particular pollutant orgroup of pollutants in water, sediment or biota thatshould not be exceeded in order to protect human healthand the environment’. The concentrations between back-ground levels and EQS are in accordance with the WFD‘Good Status’; as such, they can be considered as those inwhich human impacts are sustainable and reversible andall targets aremet (after the EMS, see Anonymous, 2004)

in estuarine and coastal surface sediments within the Basque Country

mit Mean Median S.D.

5 0.24 0.24 0.1026 25 15

31,784 31,067 769829 29 1012 433 12240 77

7 0.13 0.0531 13174 37

ution, were calculated excluding outliers. Values given for Mn and Pbted by the maximum likelihood mixture (see text).

Table 5Central values (within the DLC, CPA, MLME approaches) or upper threshold (within the CFP approach) of metal background ranges, as reported in previous studies (μg g−1) from the Basque Country(BC) and the Iberian Peninsula

Authors Area Statisticalapproach

Analyticalmethodology

Sediment As Cd Cu Cr Fe Mn Hg Ni Pb Zn

Cearreta et al.,2000, 2002

Nervion estuary(western BC)

DLC XRF bulk 16 19–20 76–85 25,000 300–400 20–23 21–23 60–63

Legorburu et al.,1989

Gipuzkoa(eastern BC)

CFP AAS bulk 0.20 21 26 21,600 289 17 16 56

Sola et al.,1990

Gipuzkoa(eastern BC)

CFP AAS bulk 0.24 15 23 9100 232 12 23

Borja et al.,1996

Basque Country CFP AAS b63 μm fraction 0.32 18 11 19,000 175 0.14 12 34 175

This study Basque Country MLME+GD AAS b63 μm fraction 12 0.24 33 26 31,784 240 0.13 29 31 147Carballeira et al.,

2000Galicia(NW Spain)

MLME AAS b63 μm fraction 20–35 30–54 29,000–33,000 248–395 31–38 50–78 120–136

Cobelo-García andPrego; 2003

Ferrol Ria(NW Spain)

DLC AAS b63 μm fraction 12 63 24,000 26 27 55

Rubio et al.,2000

Vigo Ria(NW)

CPA ICP/AES bulk 29 34 35,100 244 30 51 105

Riba et al.,2002

Guadalquivir estuary(SW Spain)

DLC AAS/DPASV bulk 0.16–0.18 12–23 14,200–14,300 234–433 24 123–156

Blasco et al.,2000

Barbate estuary andBay of Cádiz (S Spain)

DLC AAS b1000 μm fraction 15–21 60–97 22,500–42,200 278–403

Statistical approach: DLC=dated long cores/vertical profiles; CFP=cumulative frequency plots; CPA=component principal analysis; MLME=maximum likelihood mixture estimation; andGD=geographical display. Analytical methodology: XRF=X-ray fluorescence spectroscopy; AAS=atomic absorption spectroscopy; voltammetry. ICP/AES=inductively coupled plasma–atomicemission spectroscopy; and DPASV=differential pulse anodic stripping.

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Fig. 10. Relationships between increasing disturbance, due to metal contamination, and chemical status, according to the WFD and EMS terminology(modified and adapted from Anonymous, 2004). Note: EQS=Environmental Quality Standards (for explanation, see text).


(Fig. 10). Nevertheless, the WFD does not specify howbackground levels should be taken into account. In thisstudy, values below the upper limit of the natural range ofthe background (i.e., upper threshold value) have beenconsidered as in ‘High Status’. In other words, theboundary between ‘High status’ and ‘Good status’, asshown in Fig. 10, should be considered as the upper limitof the natural range.

Some methods for the derivation of EQS (see reviewsby Lepper, 2000; ICES, 2003, together with the detaileddiscussion of Crane, 2003) have been proposed. Some ofthese adoptions include measures of (i) sedimentchemistry; (ii) toxicity; (iii) biomagnification and bio-availability; (iv) benthic community status (Chapman,1996; Chapman et al., 2002); and (v) species sensitivitydistributions (Leung et al., 2005). However, the WFDdevelops the procedure for the setting of chemical qualitystandards, by Member States, including a maximumannual average concentration, including (i) appropriatesafety factors (chronic and acute), consistent with thenature and quality of the available data; (ii) data onpersistence and bioaccumulation; (iii) that the standardderived should be compared with any evidence from fieldstudies; and (iv) that the standard derived shall be subjectto peer review and public consultation, includ-ing allowance for the calculation a more precise safetyfactor.

‘Moderate Status’ can be considered when concentra-tions range from EQS and precautionary limits, indicat-ing that the ecosystem will be subject to serious orirreversible harm, or that society has driven the eco-system to an unstable state(Anonymous, 2004) (Fig. 10).In exceedance of these limits, the status can be con-sidered as ‘Poor’ or ‘Bad’, with the ecosystem beingseverely disturbed.

5. Conclusions

The MLME approach, combined with the heuristicmethod, is useful in determining the regional back-

ground metal levels, with a polymodal empirical datadistribution. Such methodology can provide assistanceto other regions and countries, in establishing back-ground levels and accomplishing (within the require-ments of the WFD and EMS) a further assessment of thechemical status. In this way, more investigation must beundertaken in determining EQS, together with bound-aries for the different levels of the status.

Acknowledgements

Data used in this contribution were obtained fromseveral studies supported by different conventions be-tween AZTI-Tecnalia and several organizations and insti-tutions, such as the Departamento de Medio Ambiente yOrdenación del Territorio (Basque Government); Con-sorcio de Aguas Bilbao-Bizkaia; Diputación Foral deGipuzkoa; and Aguas del Añarbe. We thank alsoProfessor Michael Collins (School of Ocean and EarthScience, University of Southampton, UK, and AZTI-Tecnalia, Marine Research Division) for advising us onsome details of this paper and Agurtzane Urtizberea(AZTI-Tecnalia, Marine Research Division), for hermathematical assessment.

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Maximum likelihood mixture estimation to determine metal background values in estuarine and coastal sediments within the European Water Framework Directive