Marine Pollution Bulletin 66 (2013) 25–38

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Marine Pollution Bulletin

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Source characterization and spatio–temporal evolution of the metal pollutionin the sediments of the Basque estuaries (Bay of Biscay)

Irati Legorburu ⇑, José Germán Rodríguez, Ángel Borja, Iratxe Menchaca, Oihana Solaun,Victoriano Valencia, Ibon Galparsoro, Joana LarretaAZTI-Tecnalia, Marine Research Division, Herrera Kaia, Portualdea z/g, 20110 Pasaia, Spain

a r t i c l e i n f o a b s t r a c t

Keywords:SedimentMetalSpatio–temporal evolutionGISEstuaryBay of Biscay

0025-326X/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.marpolbul.2012.11.016

⇑ Corresponding author. Tel.: +34 667174453; fax:E-mail address: ilegorburu@azti.es (I. Legorburu).

According to Water Framework Directive requirements, Member States must identify and analyze effectsderived from human pressures in aquatic systems. As different kind of pressures can impact water bodiesat different scales, analyses of spatio-temporal evolution of water bodies becomes essential in order tounderstand ecosystem responses. In this investigation, an analysis of spatio-temporal evolution of sedi-mentary metal pollution (Cd, Cr, Cu, Hg, Ni, Pb, Zn) in 12 Basque estuaries (Bay of Biscay) is presented.Data collected in extensive sampling surveys is the basis for the GIS-based statistical approach used.The implementation of pollution abatement measures is reflected in a long-term decontamination pro-cess, mostly evident in estuaries with highest historical sediment pollution levels. Spatial evolution isdetermined by either naturally occurring or human driven processes. Such spatial processes are moreobviously being reflected in estuaries with lower historical sediment pollution levels.

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1. Introduction ensure the socioeconomic objectives of those systems (Brils,

Estuaries are very dynamic systems where, due to their locationbetween marine and land environments, a wide variety of ecolog-ical and physicochemical processes take place (e.g. Ridgway andShimmield, 2002). Estuaries are crucial ecosystems in many as-pects: acting as important sites for primary production (e.g. Brickeret al., 2008); providing habitat for multiple species (e.g. Lotze,2010); regulating material exchange processes between riverineand coastal environments (e.g. Das et al., 2010); and providinggoods and services (e.g. Pinto et al., 2010).

The European Water Framework Directive (WFD, 2000/60/EC)aims to achieve ‘‘Good Ecological and Chemical Status’’ in all theEuropean water bodies, by 2015 (EC, 2000; Borja, 2005; Heringet al., 2010). In this sense, many authors highlight the influenceof sediments in the ecological and physico-chemical processesoccurring in an estuary (Borja et al., 2004a, 2009a; Casper, 2008;Magni et al., 2008; Teixeira et al., 2008; Tueros et al., 2009). As adynamic compartment of estuarine systems, depending on phys-ico-chemical conditions, sediments could act either as a sink forpollutants or be turned into a new pollution source for the watercolumn (e.g. Atkinson et al., 2007). Therefore, the identification,control and removal of pollution sources become essential in orderto maintain the good ecological functioning of the water bodies(EC, 2000, 2008). This will provide mechanisms for an adequatesediment management, allowing at the same time, to protect and

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2008; Apitz, 2012).Such management process requires a carefully designed sedi-

ment sampling scheme, minimizing as much as possible the effectof spatial and sampling variation sources (Pattersen et al., 1999).Sampling variation could be minimized by combining a good selec-tion of sampling sites with adequate sampling practices (Demetri-ades and Volden, 1997). However, considering spatial variability,the implementation of the WFD requires the handling of spatialdata relevant to different spatial scales (Vogt et al., 2002). Addi-tionally, considering the long term view of ecosystem-based man-agement approaches, the temporal trends given by monitoringprogrammes would enable the adaptation of decision making pro-cesses to long-term ecological goals (MacDonald et al., 2009). Nev-ertheless, due to the differences in the scales at which differentkind of pressures are reflected (IMPRESS, 2002), small-scalesources of spatial variance (e.g. pollutant discharge point sources)could overlap the natural large-scale spatial pattern of an estuary(Caeiro et al., 2003). Hence, under large-scale monitoring pro-grammes, there is a risk of failing in detecting such small-scale var-iability sources (Kiersch et al., 2010). Therefore, periodicalextensive surveys, with a substantially higher amount of locationssampled (Belzunce et al., 2001; Dean et al., 2007), will help detect-ing the variability derived from different spatial-scale processes. Inthis sense, the use of Geographical Information System (GIS) toolsbecomes effective, as they allow the integration of informationfrom different sources and spatial scales (Stanbury and Starr,2000), providing a holistic view for an adequate management ofwater resources.

26 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

Hence, the approach used in this contribution, has as mainobjective to carry out a long-term spatio–temporal evolution anal-ysis of the metal pollution affecting the sediments of the Basqueestuaries (Bay of Biscay). For this investigation, extensive samplingsurveys and GIS capabilities have been combined, considering: (i)the effect of pollution abatement measures and human pressuresin the spatio–temporal variability of estuarine metal contamina-tion; and (ii) variations occurred in the metal stocks (understoodas the mass of metal fixed to sediments) during the differentcampaigns.

2. Materials and methods

2.1. Study area

The Basque coast is located in the southeastern Bay of Biscayand has an approximate length of 150 km (Fig. 1). Due to its geo-graphical location, it is considered a high energy environment(González et al., 2004; Galparsoro et al., 2012). It is drained by12 main torrential rivers, which are responsible of supplying1.57 � 106 t yr�1 of suspended material into the Bay of Biscay (Uri-arte et al., 2004a). Basque estuaries are strongly differentiated interms of basin size, hydrological, morphological and dynamic fea-tures (Borja et al., 2006) (see Table A1 in Supplementary material).Although all of them can be classified as mesotidal systems, someof them show some characteristics of macrotidal estuaries (Valen-cia and Franco, 2004).

Historically, industrial concentration and population densityhave been the main drivers producing the most important pres-sures in these systems: water and sediment pollution; intertidallosses; and shoreline reinforcement (Borja et al., 2006). For manyyears, untreated domestic and industrial wastewaters have beendirectly dumped into the estuaries, degrading seriously the envi-ronmental quality of the area (Cearreta et al., 2004; Borja et al.,2006). Nevertheless, an overall improvement has been observedin the quality of these systems over the last years (Borja et al.,2009b, 2010; Pascual et al., 2012). Such improvements are mainlyrelated to: (i) a decrease of the most polluting discharges, followingthe general decay and the changing practices in the heaviest indus-trial activities (Belzunce et al., 2004a,b; Leorri et al., 2008); and (ii)

Fig. 1. (a) Study area and sampling locations in Basque estuaries, within the Bay of Bestuary: (b) sampling points under the Littoral Water Quality Monitoring and Control Netwo

the diversion of wastewater discharges to coastal areas derivedfrom the implementation of water treatment schemes (Francoet al., 2004; Tueros et al., 2009).

2.2. Sample collection and analysis

2.2.1. Sampling strategyIn the late 1990’s the Basque Water Agency established an

extensive monitoring programme to determine the spatial distri-bution of the sediments quality within the Basque estuaries.Hence, two surveys were carried out in a 11-year interval. Barba-dun, Ibaizabal and Lea estuaries were sampled in 1998 and 2009;Butroe, Oka and Artibai in 1999 and 2010; Deba, Urola and Oiart-zun in 2000 and 2011; and Oria, Urumea and Bidasoa in 2001and 2012 (see Fig. 1, for locations). During each survey a total of359 samples were collected and analyzed for sedimentologicalparameters whilst the metal content was determined for 128 ofthem. Additionally, in order to get a more complete spatial cover-age, data from the Littoral Water Quality Monitoring and Control Net-work (Borja et al., 2004b) corresponding to the sampling years ofeach system were also included in the present analysis.

Intertidal sediments were sampled by hand, whilst sampling ofsubtidal samples was performed on board using either Day or VanVeen grabs. In all cases, the upper 10 cm of sediments were col-lected, retained in plastic bottles, transported to the laboratoryand stored at 4 �C until analysis.

2.2.2. Sample analysisTechniques used in the determination of the grain size distribu-

tion of the samples varied depending on the fine content of thesediments. Grain size of samples with low fine sediment content(<10%) was determined by dry sieving following Folk (1974);and, grain size distribution of samples with high contents of finesediments (>10%) was determined by Laser Diffraction Particle SizeAnalyser (LDPSA). The mud content values for those samples mea-sured by LDPSA, were transformed following Rodríguez and Uriarte(2009), due to the underestimation of the finest fraction of the sed-iment given by the LDPSA method (Campbell, 2003; Di Stefanoet al., 2010).

Organic Matter (OM) content of the sediments was determinedby loss of weight on ignition at 450 �C during 6 h, according to

iscay. Examples of different sampling approaches are also shown for the Oiartzunrk (LQM); and (c) sampling points of the intensive sediment characterization survey.

I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38 27

Dean (1974). In addition, redox potential (Eh) was measured with acombined Pt-ring electrode, following Langmuir (1971). Redox po-tential of intertidal samples was measured in situ at the samplinglocation whilst that of subtidal samples was measured on board di-rectly from the grab.

Metal (Cd, Cr, Cu, Hg, Ni, Pb and Zn) concentrations (lg g�1)were measured in triplicate in the extracts of the fine fraction ofthe sediments (<63 lm). Dried sediment (1 g) was digested in anacid mixture (HCl:HNO3; 2:1; v/v) using a high pressure micro-wave digestion system. Once the digestion was completed, solidphases were separated from the extracts by centrifugation. Afterthe solid phases were rinsed twice with double-deionised water,rinses were added to the extracts. Metal concentrations weredetermined by Atomic Absorption Spectrometry (AAS): Cd wasanalyzed by THGA graphite furnace, using Zeeman backgroundcorrection; Cr, Cu, Ni, Pb and Zn were determined in an air–acety-lene flame; finally, total Hg was measured by quartz furnace AASfollowing cold vapor method (using NaBH4 as a reducing agent ina FIA system). Analytical accuracy of the extraction and analysisprocedures was checked using the PACS-2 (NRC, Canada) certifiedreference material). Recoveries for the certified metals were: 85%Cd, 70% Cr, 95% Cu, 90% Hg, 98% Ni, 98% Pb, 95% Zn (for details ofthe methodology see Rodríguez et al., 2006; Tueros et al., 2009).

2.3. Metal source characterization

Due to the close interconnection between pressures and im-pacts, knowledge of pollution sources becomes essential to under-stand ecosystem responses (Islam and Tanaka, 2004). In order todetermine main metal sources, pressures affecting the studiedestuaries were considered. From all the pressures identified byBorja et al. (2006), only those responsible of causing pollution inthe water masses were taken into account. Pressures were groupedas follows: Urban discharges, stormwater discharges, industrialdischarges, agriculture/farm discharges, lixiviation of slag/rubbishstorage activities, pressures derived from maritime transport activ-ities (fishing and leisure ports or commercial harbors) and oilpumps (for additional details see Borja et al. (2006)). Althoughstormwater discharges could be considered as a type of urban dis-charge, they were considered apart, as their effect could be indica-tive of a possible non-point source of pollutants. Pressure effectwas considered in terms of distance between pressure and sample.Therefore, corresponding distances were determined by means of apath distance algorithm implemented in the Spatial Analyst exten-sion of ArcGIS 9.3.1 software (ESRI).

Thus, in the sources characterization sand and fine percentages,organic matter content, redox potential, metal concentration anddistances to the selected pressures of the considered sedimentsamples were considered. After outlier removal following theBox-Plot method (Tukey, 1977), data from a total of 234 sampleswere log transformed or arcsin root transformed. Afterwards,Spearman correlation coefficients were determined to measurethe association strength of the variables involved. Furthermore,in order to determine the relationship between environmentalvariables and the spatial distribution of metals in sediments a par-tial ReDundancy Analysis (pRDA) was applied (Legendre et al.,2005). That analysis would allow the determination of spatial var-iability of metal concentration with (i) distances to consideredpressures, and (ii) sediment properties. Significance of environ-mental variables was tested using the Monte Carlo permutationtest (Legendre et al., 2005). Additionally, a partialling out proce-dure of the explained variance was carried out to determine thepercentage of variance explained by (i) each group of environmen-tal variables by themselves, and (ii) that explained by interactionsbetween both groups (Borcard et al., 1992). Statgraphics Plus 5 andCanoco 4.5 softwares were used to perform the statistical analyses.

Finally, based in Corine Land Cover maps, changes in the extentof the Urban Morphological Zones (UMZs) between 1990 and 2006were determined (Simon et al., 2010; EEA, 2011). Demographicalchanges in the urban areas around the studied estuaries in thesampled years were also considered (INE, 2012).

2.4. Pollution level calculation

Estuaries were classified according to their pollution levels.Measured metal concentrations at each sampling location werecompared with the mean background values calculated for the Bas-que coastal area (Rodríguez et al., 2006), giving as a result theEnrichment Factor (EF) for each metal at each sampling location(Eq. (1)). Tomlinson et al. (1980) defined the Load Pollution Index(LPI) as the geometric mean of the EF values obtained for a givenpollutant (Eq. (2)). Thus, global LPIs corresponding to each metalon each system were calculated. Furthermore, depending on thegeoaccumulation index (Igeo) defined by Müller (1979), sedimentscould be classified into different pollution classes (Eq. (3)). Consid-ering Eqs. (2) and (3), a relationship was established between glo-bal Igeo and LPIs (Eq. (4)). Based on Müller’s (1979) pollutionclassification, estuaries were classified into different pollution lev-els depending on their global LPI value, as follows (Table A2 in Sup-plementary material): Class 0, LPI < 1.5 (non-polluted); Class 1,1.5 < LPI < 3 (non-polluted to moderately polluted); Class 2,3 < LPI < 6 (moderately polluted); Class 3, 6 < LPI < 12 (moderatelyto highly polluted); Class 4, 12 < LPI < 24 (highly polluted); Class 5,24 < LPI < 48 (highly to extremely polluted); Class 6, LPI > 48 (ex-tremely polluted).

EF ¼ Cn=Bn ð1Þ

LPI ¼ ðEF1 � EF2 � . . . � EFnÞ1=n ð2Þ

Igeo ¼ log2ðCn=1:5 � BnÞ ¼ log2ðEF=1:5Þ ð3Þ

Global Igeo ¼ log2ðLPIglobal=1:5Þ ð4Þ

2.5. Stock calculation

Fine sediment (Eq. (5)) and metal (Eq. (6)) stocks at each sam-pling point were calculated as follows:

MiTF ¼ MF � ð1� /Þ � q � Z ð5Þ

MiM ¼ MiTF � CiF ð6Þ

where MITF was the total fine sediment mass (kg m�2); MF was thefine fraction value of the sample (<63 lm); / was the porosity offine sediments, calculated as the fraction of the water content ofmuddy sediments according to Flemming and Delafontaine(2000); q was the density of fine sediments, a value of 2736 kg m�3

was used, calculated as the mean density value of the main speciespresent in the mineralogical composition of the area (Allen, 1985;Uriarte et al., 2004b); Z = 0.1 m, was the sampled sediment depth;MiM was the total metal mass (g m�2) in the sampled point; andCiF was the measured metal concentration in the fine fraction ofthe sample (<63 lm), in g kg�1.

Obtained fine sediment and metal mass values at each samplingpoint, were interpolated for the whole estuarine surfaces, allowingthe determination of the total fine sediment and metal stocks perconsidered year and estuary. Surfer 8 (Golden Software) was usedto create the interpolation grids, which consisted in an Inverse Dis-tance to a Weight (IDW) algorithm supported by a fault trace file.Accuracy of the interpolated surfaces depends on the weightingpower used, which at the same time is associated with the localcoefficient of variation (CV) of the samples. Therefore, local CV val-

28 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

ues for each of the datasets to interpolate were calculated(Table A3 in Supplementary material). This way, a weightingpower of 1 was used for datasets with a CV value lower than 0.8and a weighting power of 4 was used for datasets with a CV valuehigher than 0.8 (Xie et al., 2011). Generated grids were importedinto ArcGis 9.3.1., and maps representing the metal mass variationsalong the estuarine surfaces were created. The sum of all grid cellvalues gave the total fine sediment and metal mass estimation foreach estuary:

SF ¼Z n

i¼1ðAi �MiTFÞ ð7Þ

SM ¼Z n

i¼1ðAi �MiMÞ ð8Þ

where SF was the total fine sediment stock (kg); SM was the totalmetal stock (g); MiTF and MiM (g m�2) were the total fine sedimentand metal masses at each sampling point calculated from Eqs. (5)and (6) respectively; and Ai was the considered estuarine surfaceunit (m2). Thus, SF and SM refer respectively to the fine sedimentand metal stocks in an estuary, taking only into account theuppermost 10 cm of sediment.

Maps representing stock variations along the estuaries were ob-tained calculating the differences in the stock distribution betweenthe former and the latter surveys. In order to provide an easierinterpretation of the maps obtained, negative values in the mapsrepresenting stock variations imply a decrease of the stock duringthe studied time period. Inversely, a positive value would be indic-ative of an increase of the stock considered.

3. Results

3.1. Pollution level variations

Mean pollution levels calculated for each estuary during bothsurveys are shown in Table 1. These results suggest the existenceof two different patterns among the studied estuaries. Ibaizabal,Deba, Urola, Urumea and Oiartzun estuaries showed a general de-crease in their overall pollution levels derived from the studiedmetals (Table 1). In contrast, Barbadun, Butroe, Oka, Lea, Artibai,Oria and Bidasoa estuaries did not show a so clear decrease pat-tern. In fact, some metals show increases in their overall mean con-centration along the estuary, meanwhile others, remained at thesame levels and others have decreased their mean concentration.However, it must be noted that almost all estuaries gathered inthe latter group showed losses on their total metal stocks.

3.2. Fine sediment and metal stocks

Results obtained in the metal and fine sediment stock calcula-tion are shown in Table 1. From the total, 67 stock reductions havebeen found: 3 for fine sediments; 10 for Cd, Hg and Zn; 9 for Cr, Cuand Pb; and 7 for Ni. In turn, 29 episodes of stock increases havebeen observed: 9 for fine sediments; 2 for Cd, Hg and Zn; 3 forCr, Cu, and Pb; and 5 for Ni.

Excepting Urola, Oria and Urumea estuaries, all the studied sys-tems showed an increase on their total fine sediment stock. Finesediment stock increases ranged from 2.4%, for Bidasoa estuary,to 231.3%, for Butroe estuary. Decreases in fine sediment stock ran-ged from 18.9%, for Urumea estuary, to 70.3%, for Oria estuary. Bar-badun estuary showed the lowest fine sediment stock during thefirst survey, whilst for the second survey the lowest value corre-sponded to Urumea estuary. During both surveys Bidasoa estuaryaccumulated the highest quantity of fine sediment.

In general terms, highest metal stock values were found duringthe 1998–2001 surveys. Ibaizabal, Deba, Urola, Oria, Urumea andBidasoa estuaries showed a decrease in their stocks for all metals.Although for almost all metals Oka, Lea and Oiartzun estuariesshowed a decreasing pattern, some increasing trends could be ob-served, probably derived from differences in land use and hydrog-eomorphological features: Cu and Ni in Oka estuary, Cd in Leaestuary and Ni in Oiartzun estuary. In Butroe and Artibai estuariesan increase for many of their metal stocks could be observed, eventhough there were some exceptions: Cd in Butroe estuary and Cd,Cu, Hg and Zn in Artibai estuary. Finally, in Barbadun estuary allthe measured stocks increased. During the first survey the lowestmetal stocks corresponded to: Lea estuary for Cd, Ni, Pb and Zn;Barbadun estuary for Cr and Hg; and Butroe estuary for Cu.

Considering the second survey, lowest stock values were found inDeba estuary for Cd, and Lea estuary for the rest of metals. Regardingthe highest metal stocks, during both surveys Bidasoa showed thehighest values for Cu, Ni, Pb and Zn, whilst Cd and Hg were moreabundant in the Ibaizabal estuary. During the first survey the higheststock for Cr was found in the Ibaizabal estuary, whilst during the sec-ond survey that value corresponded to Bidasoa estuary.

Deba estuary showed the most pronounced losses for all metalsexcepting Cr, that decreased more markedly in Urumea estuary. Cdand Hg increases were more pronounced in the Barbadun estuary,while the same occurred for Cr, Cu, Ni, Pb and Zn in the Butroe estuary.

3.3. Metal source characterization

Obtained stock values and Spearman correlation coefficient val-ues are shown in Tables 1 and 2, respectively. The concentration ofall metals correlated negatively with the sand percentage and re-dox potential of the samples. However, the correlation betweenmetal concentration and the OM and fine sediment content ofthe samples was positive (no correlation was found between Niand fine sediments). A positive correlation was found betweenthe concentrations of all metals. The same occurred for the dis-tances to almost all the pressures considered, excepting the dis-tances to agricultural discharges that showed a negativerelationship with the rest of significant pressures.

According to RDA results (Fig. 2a), environmental variables ex-plained 36.8% of the variability in the metal concentration of thesediments (F-ratio: 23.99; p-value = 0.002). From the total of envi-ronmental variables, redox potential and distances to maritimetransport activities, oil pumps, stormwater and urban dischargesdid not show a significant effect in the global metal concentrationvariability. As the angles formed between variables are representa-tive of the correlation found between them, different patterns havebeen observed (angles close to 90� indicate no correlation, anglesclose to 0� are indicative of a strong positive correlation and anglesclose to 180� indicate a strong negative correlation). All metals cor-related positively with the fine sediment and OM content of thesamples. However, the correlation found between metal concen-tration with either the sand content of the samples or the distancesto industrial discharges and slag/rubbish activities was negative.Finally, excepting Hg, none of the metals showed high correlationwith agricultural discharges (Fig. 2a). After the partialling out pro-cedure of the total variance, it was determined that the percentageof variance explained by pressures affecting the estuaries and sed-iment properties accounted for 18.6% and 7.7% respectively, whilstthat derived from interactions between both groups of variableswas 10.5%.

4. Discussion

The WFD defines the quality of aquatic systems in an integratedway, including either biological or physico-chemical elements.

Table 1Variations between sampling surveys in population density, extent of Urban Morphological Zones (UMZs), mean Organic Matter content (OM%), mean redox potential, finesediment and metal stocks for the studied estuaries. Mean pollution levels of the estuaries according to Müller’s (1979) pollution classification are also given: 0: non-polluted; 1:non-polluted/moderately polluted; 2: moderately polluted; 3: moderately/highly polluted; 4: highly polluted.

Year Barbadun Ibaizabal Butroe Oka Lea Artibai

1998 2009 1998 2009 1999 2010 1999 2010 1998 2009 1999 2010

DPopulation (%) +18.0 �13.0 +23.0 +6.0 +0.1 �12.0DUMZ (%) +9.2 +16.5 +15.4 +20.9 0.0 �1.0OM% 5.5 4.2 8.7 5.2 4.3 2.4 5.7 2.9 6.9 3.9 8.0 7.4Redox (Eh) +143 +228 �53 +17 +191 +154 +139 +187 �51 +187 +35 +84Fine (ton) 115.6 250.6 1235.5 1365.4 148.8 492.9 1400.6 1573.7 199.0 212.0 212.6 328.5DFine (%) +116.8 +10.5 +231.3 +12.4 +6.5 +54.7Cd Pollution class 1 0 3 2 0 0 0 0 0 0 1 0Cd (kg) 0.08 0.11 4.10 0.99 0.19 0.14 0.64 0.56 0.06 0.06 0.36 0.07DCd (%) +49.9 �75.8 �29.7 �12.8 +1.1 �80.7Cr Pollution class 0 0 2 1 0 0 0 0 0 0 0 0Cr (kg) 2.13 6.16 154.47 84.70 4.56 18.94 107.57 72.54 2.34 1.80 3.67 9.78DCr (%) +189.0 �45.2 +315.0 �32.6 �23.3 +166.5Cu Pollution class 1 1 2 1 0 0 0 0 0 0 1 0Cu (kg) 11.80 21.76 143.43 92.35 7.38 20.59 50.86 54.31 8.31 1.79 23.84 18.90DCu (%) +84.5 �35.6 +179.1 +6.8 �78.5 �20.7Hg Pollution class 1 0 3 3 2 0 2 0 0 0 1 0Hg (kg) 0.03 0.06 2.21 1.03 0.07 0.08 1.13 0.38 0.04 0.01 0.05 0.04DHg (%) +76.5 �53.6 +11.9 �66.6 �69.7 �11.5Ni Pollution class 0 1 0 0 0 0 0 0 0 0 0 0Ni (kg) 4.17 15.12 51.90 49.59 3.67 23.23 52.36 58.40 2.30 2.09 6.09 9.58DNi (%) +262.4 �4.4 +533.7 +11.5 �9.2 +57.4Pb Pollution class 1 1 2 2 0 0 1 0 0 0 1 1Pb (kg) 6.8 26.23 238.11 140.46 10.72 42.87 143.84 61.41 6.16 3.09 17.65 19.79DPb (%) +281.5 �41.0 +299.9 �57.3 �49.8 +12.1Zn Pollution class 1 1 2 1 0 0 0 0 0 0 0 0Zn (kg) 28.81 66.79 635.89 257.23 30.75 94.46 226.48 194.13 19.20 6.82 48.84 48.75DZn (%) +131.8 �59.5 +207.2 �14.3 �64.5 �0.2

Year Deba Urola Oria Urumea Oiartzun Bidasoaa

2000 2011 2000 2011 2001 2012 2001 2012 2000 2011 2001 2012

DPopulation (%) +6.0 +12.0 +14.3 +3.3 �1.0 +8.6DUMZ (%) 0.0 +27.1 +22.7 +14.4 +19.4 +20.8OM% 7.4 3.2 9.7 4.3 9.6 4.2 8.1 4.4 15.0 6.7 9.6 7.3Redox (Eh) +147 +131 +159 +77 +25 +137 +169 +326 �128 �78 �82 +110Fine (ton) 116.7 129.6 488.8 331.2 882.4 262.3 115.7 93.8 576.7 600.0 4026.4 4125.0DFine (%) +11.1 �32.2 �70.3 �18.9 +4.0 +2.4Cd pollution class 1 0 2 0 1 0 0 1 3 1 1 1Cd (kg) 0.14 0.03 0.50 0.15 0.33 0.21 0.17 0.06 1.79 0.51 1.70 0.93DCd (%) �81.2 �70.4 �35.2 �67.5 �71.4 �45.6Cr Pollution class 2 1 1 1 1 1 2 1 2 1 1 1Cr (kg) 42.97 6.81 38.33 20.84 35.55 28.04 47.88 5.53 49.04 31.35 116.39 95.15DCr (%) �84.1 �45.6 �21.1 �88.5 �36.1 �18.2Cu Pollution class 2 1 1 0 1 1 2 1 2 2 1 1Cu (kg) 30.68 4.37 35.29 20.97 66.59 34.24 31.15 6.21 109.59 69.10 194.55 99.34DCu (%) �85.8 �40.6 �48.6 �80.0 �37.0 �48.9Hg Pollution class 2 0 2 0 2 1 4 3 3 2 1 0Hg (kg) 0.10 0.01 0.28 0.07 0.32 0.12 0.55 0.10 0.82 0.43 0.79 0.33DHg (%) �86.0 �76.8 �62.2 �82.3 �47.6 �58.8Ni Pollution class 2 1 1 0 0 1 1 0 0 0 0 0Ni (kg) 21.15 6.13 29.67 16.42 28.68 23.80 9.22 4.23 18.34 20.35 83.27 79.48DNi (%) �71.0 �44.6 �17.0 �54.1 +10.9 �4.6Pb Pollution class 2 1 2 1 1 1 3 3 3 2 2 2Pb (kg) 32.58 6.00 85.77 34.91 67.91 32.82 37.95 29.99 172.15 111.76 385.12 274.22DPb (%) �81.6 �59.3 �51.7 �20.9 �35.1 �28.8Zn Pollution class 2 1 2 1 1 0 2 2 2 2 1 1Zn (kg) 212.67 27.36 305.16 128.91 252.49 124.46 125.81 48.17 470.55 413.08 731.01 448.17DZn (%) �87.1 �57.8 �50.8 �61.7 �12.2 �38.7

a Only information from the Gipuzkoa province has been considered in population density and UMZ calculations for Bidasoa estuary. (Data from the French and Navarreparts of the basin were not available.)

I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38 29

That definition requires a good comprehension of the processesoccurring at different ecosystem components (Borja et al.,2009a). Sediments are good indicators of anthropogenic impacts,as they provide time-integrated information about the pollutionof a particular area (e.g. Rubio et al., 2010). This contribution pro-vides an improved state of knowledge about the sedimentary com-partment that can be used for the assessment and achievement oflegislative ecological goals within the WFD, both in humanpressures determination and chemical status assessment.

In this context, as pointed by Landre et al. (2011), the samplingscheme performed in this contribution (replicating surveys carriedout several years earlier) has resulted practical for the determina-tion of spatio–temporal trends of the sedimentary pollution. Itshould be noted that the metal-related parameters used in thiscontribution (differences in metal pollution classes and variationsin the stock distributions) provide different kinds of information.On the one hand, metal pollution classes are directly related withthe metal concentration of sediments (Eqs. (1)–(4)) and could be

Table 2Spearman correlation coefficient values. Statistically significant correlation coefficient values (p < 0.05) are shown in bold. OM: organic matter; AG: distance to agriculturaldischarges; IND: distance to industrial discharges; MT: distance to maritime transport activities; OIL: distance to oil pumps; SLAG: distance to lixiviation of slag/rubbish storageactivities; STWT: distance to stormwater discharges; URB: distance to urban discharges.

Sand % Fine % OM % Redox Cd Cr Cu Hg Ni Pb Zn AG IND MT OIL SLAG STWT

Sand %Fine % �0.966OM % �0.533 0.513Redox 0.450 �0.443 �0.593Cd �0.360 0.372 0.429 �0.418Cr �0.340 0.321 0.357 �0.413 0.662Cu �0.328 0.316 0.456 �0.461 0.770 0.690Hg �0.226 0.222 0.317 �0.324 0.558 0.546 0.548Ni �0.137 0.113 0.163 �0.203 0.492 0.744 0.624 0.229Pb �0.299 0.073 0.434 �0.394 0.738 0.716 0.800 0.623 0.556Zn �0.284 0.269 0.423 �0.429 0.784 0.722 0.875 0.571 0.648 0.877AG �0.199 0.203 0.143 �0.326 0.316 0.230 0.237 0.457 0.011 0.266 0.260IND 0.318 �0.316 �0.380 0.403 �0.522 �0.271 �0.548 �0.334 �0.218 �0.551 �0.597 �0.299MT �0.098 0.102 �0.069 0.064 0.035 0.260 0.108 0.112 0.294 0.089 0.123 �0.186 0.026OIL 0.157 �0.177 �0.336 0.373 �0.430 �0.174 �0.397 �0.098 �0.146 �0.386 �0.373 �0.277 0.493 0.501SLAG 0.280 �0.282 �0.238 0.342 �0.548 �0.404 �0.465 �0.401 �0.212 �0.473 �0.491 �0.436 0.386 0.173 0.385STWT �0.004 0.048 �0.061 0.182 �0.145 �0.047 �0.185 0.054 �0.074 �0.146 �0.190 0.001 0.308 0.120 0.272 0.176URB 0.047 0.012 �0.190 0.271 �0.115 �0.051 �0.147 0.017 �0.059 �0.134 �0.159 0.070 0.218 0.136 0.190 �0.032 0.412

Fig. 2. (a) Redundancy Analysis (RDA) biplot of the relationship between metalconcentration in sediments (full-line arrows) and environmental variables (dashedarrows) (AGR: distance to agricultural discharges; IND: distance to industrialdischarges; SLAG: distance to lixiviates of slag/rubbish storage activities; OM:organic matter content of the samples; FINE %: fine sediment (<63 lm) percentageof the samples; SAND %: sand percentage of the samples). (b) Partition of thevariation explained by two groups of environmental variables obtained by partialRDA; (P) distance to pressures: agricultural discharges, industrial discharges andlixiviates of slag/rubbish storage activities; (S) sediment properties: fine and sandpercentages and organic matter content of the samples.

30 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

considered indicative of the effectiveness of the pollution abate-ment measures adopted. Thus, the analysis of these changes pro-vides information about the temporal evolution of sedimentpollution. On the other, mapping the stock variation distribution(expressed as metal and fine sediment mass changes along theestuarine surfaces, Eqs. (7) and (8)) facilitates the identificationof processes occurring at the spatial scale. Therefore the integratedinterpretation of both metal content-related parameters allows theidentification of processes influencing the spatio–temporal evolu-tion of sedimentary pollution. Moreover, the combined use of mul-tivariate ordination techniques (as it is RDA) and mapping

capabilities, provide improved information for determining thefactors affecting the spatial distribution of pollutants in sediments(Amano et al., 2011). Therefore, the approach used in this contribu-tion enhances analysis skills for spatio–temporal data and canserve as an example for other locations.

It must be noted that pressures considered in this work (mainlydischarges of different origins) are considered sources of small spa-tial-scale variability in sediments (Caeiro et al., 2003; Tao et al.,2007). According to Birch et al. (2001), small spatial-scale variancein the sediment metal concentration increases with ambient en-ergy. Thus, considering the high wave energy acting over the Bas-que coastal area (González et al., 2004; Galparsoro et al., 2012), ahigher small spatial-scale variability must be expected for the mostouter parts of the estuaries. However, such a source of variabilityhas substantially been reduced by analyzing the fine fraction ofthe sediments (<63 lm), minimizing confounding effects of vari-able grain sizes (Birch et al., 2001). Moreover, due to the dilutioneffect caused by the mixing with coarser less contaminated marinesediments, the most outer parts of the Basque estuaries generallyshow lower metal and fine sediment concentration values (Belzun-ce et al., 2004b) (Figs. A1–A12 in Supplementary material).

Metal stock calculation approaches in the sedimentary com-partment are scarce in the literature. Metal stock estimations ob-served in this contribution are several orders of magnitude lowerthan those observed in different water systems of France (Audryet al., 2004; Larrose et al., 2010; Tessier et al., 2011). However, thiscould not be considered a reasonable comparison as the studiedsurface of the Basque water bodies is also several orders of magni-tude lower (Table 3). In any case, all these authors highlight theinfluence of human pressures in sediment pollution. Human drivenpressures and impacts (as well as their subsequent abatementmeasures), although interconnected, do not impact water bodiesat the same scales (IMPRESS, 2002). In this contribution, the effectsof the pressures and impacts derived from human drivers reflectprocesses occurring at different spatio–temporal scales, as dis-cussed below.

4.1. Temporal evolution

The anthropogenic interference level to which a system hasbeen subjected to, determines its recovery capacity (Borja et al.,2010). Therefore, the sedimentary recovery of the studied estuarieswill depend on the historical and present pressures that have af-fected them (Leorri et al., 2008). Spearman correlation matrix

Table 3Metal stock values (kg) as reported in previous studies undertaken in different systems. Surfaces of the systems (km2) are also given.

Authors System Surface Cd Cr Cu Hg Ni Pb Zn

This study Barbadun 0.60 0.08–0.11 2.13–6.16 11.80–21.76 0.03–0.06 4.17–15.12 6.88–26.23 28.81–66.79Ibaizabal 20.70 0.99–4.10 84.70–154.47 92.35–143.43 1.03–2.21 49.59–51.90 140.46–238.11 257.23–635.89Butroe 1.36 0.14–0.19 4.56–18.94 7.38–20.59 0.07–0.08 3.67–23.23 10.72–42.87 30.75–94.46Oka 5.66 0.56–0.64 72.54–107.57 50.86–54.31 0.38–1.13 52.36–58.40 61.41–143.84 194.13–226.48Lea 0.43 0.06 1.80–2.34 1.79–8.31 0.01–0.04 2.09–2.30 3.09–6.16 6.82–19.20Artibai 0.43 0.07–0.36 3.67–9.78 18.90–23.84 0.04–0.05 6.09–9.58 17.64–19.79 48.75–48.84Deba 0.66 0.03–0.14 6.81–42.97 4.37–30.68 0.01–0.10 6.13–21.15 6.00–32.58 27.36–212.67Urola 0.91 0.15–0.50 20.84–38.33 20.97–35.29 0.07–0.28 16.42–29.67 34.91–85.77 128.91–305.16Oria 1.85 0.21–0.33 28.04–35.55 34.24–66.59 0.12–0.32 23.80–28.68 32.82–67.91 124.46–252.49Urumea 0.68 0.06–0.17 5.53–47.88 6.21–31.15 0.10–0.55 4.23–9.22 29.99–37.95 48.17–125.81Oiartzun 0.97 0.51–1.79 31.35–49.04 69.10–109.59 0.43–0.82 18.34–20.35 111.76–172.15 413.08–470.55Bidasoa 7.49 0.93–1.70 95.15–116.39 99.34–194.55 0.33–0.79 79.48–83.27 274.22–385.12 448.17–731.01

Audry et al. (2004) Lot-Garonne 11840 2 � 105 1 � 107

Larrose et al. (2010) Gironde 625 1 � 104 145 � 104 45 � 104 2.5 � 103 59 � 104 91 � 104 33 � 105

Tessier et al. (2011) Toulon Bay 53 88 � 103 2.5 � 103 1 � 105 156 � 103

I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38 31

and RDA analysis (Fig. 2 and Table 2) highlight, on the one hand,the importance of industrial discharges and slag/rubbish disposalactivities as metal sources and, on the other, the importance of sed-iment properties on metal sorption capacity. Additionally, the po-sitive correlation found in RDA between distances to agriculturaldischarges and Hg concentrations, implies that the farther awayfrom this kind of discharges, higher concentrations of Hg will befound. Besides, correlation matrix related positively the concentra-tion of all metals (excepting Ni) and the distance to agriculturaldischarges. Therefore, according to such results, the role of agricul-ture as a metal source could be discarded. These relations betweenvariables must be interpreted into a geographical context: areaswere industrial discharges and slag/rubbish storage activities oc-cur, become indicative of areas characterized by the presence ofpolluted organically enriched fine sediments. However, the imple-mentation of sanitation systems has led to a progressive improve-ment of the water quality parameters within Basque estuaries(Franco et al., 2004; García-Barcina et al., 2006; Pascual et al.,2012). Moreover, considering the increases observed in fine sedi-ment stocks (Table 1, Figs. A1–A12 in Supplementary material),the improvement of water quality parameters seems to be respon-sible for the decreases in either the pollution levels or metal stocksobserved. In fact, such an improvement is reflected in changes inthe sedimentary compartment that are characterized by the de-crease of the sedimentary OM content (Table 1, Fig. 3) and theincreasing trend of redox potential values (Table 1). Whilst thegeneral decay of sedimentary OM provides to sediments a lowermetal binding capacity (Lin and Chen, 1998; Sondi et al., 2008),the increased redox potential values favor metal remobilizationprocesses, enhancing the metal transfer into the water column(Kelderman and Osman, 2007; De Jonge et al., 2012).

Hence, the general metal stock decrease observed in Basqueestuaries must be regarded as a long term decontamination pro-cess. Although some remarks must be done, that temporal processis being more clearly reflected in some of the estuaries with higherhistorical industrial activities and sediment pollution levels, i.e.Ibaizabal, Deba, Urola, Urumea and Oiartzun (Table 1, Fig. 4). Therole played as a new pollution source by transport and redistribu-tion processes of sediments located in the most polluted areas ofan estuary could give, as a result, a slight and continued metal con-centration increase in the less polluted areas of that system (Fdez-Ortiz de Vallejuelo et al., 2010). In the case of the Oiartzun estuary,despite of the decreases in most metal stocks, Cu, Hg, Pb and Zn re-main at moderate pollution levels (Table 1, Fig. A11 in Supplemen-tary material). This estuary accommodates a commercial harbor,and harbor domains with high residence times are more suscepti-ble of suffering from water degradation due to a pollution event(Grifoll et al., 2011). In this context, Montero et al. (2011), attrib-

uted to the lower water renewal rates (35 days, after Borja et al.(2006)) the high concentrations of metals found in the sedimentsof some parts of the Oiartzun estuary. Besides, despite of the abate-ment measures developed (Franco et al., 2004), nowadays still ex-ist important pressures affecting the estuary; among others:shipyarding activities, a coal-fired power plant, commercial harboractivities and high population density (Fig. 4a). Therefore, the highpressure levels added to the low hydrodynamic conditions in thisbasin could be responsible of the deceleration of the sedimentrecovery process. Finally, important metal stock losses have alsobeen observed for Urumea estuary (Fig. A10 in Supplementarymaterial), that are not being reflected in the overall pollution statusdue to the presence of spatially occurring processes (Table 1). Thus,results obtained for this system will be discussed in followingsections.

4.2. Spatial evolution

4.2.1. Large spatial-scale evolution4.2.1.1. Changes in driving forces. Over the last years, all the estuar-ies (excepting Artibai) have suffered from an increase either intheir population density or in the surface extent of their UMZs (Ta-ble 1). Urban environments act as continuous and diffuse source ofparticles and associated pollutants to their adjacent aquatic envi-ronments (Jartun et al., 2008). Thus, such an increase in urbanpressure could be indicative of increasing diffuse particle boundmetal sources derived from urban runoff (Mitchell, 2005; Chalmerset al., 2007). Moreover, distances to stormwater and urban dis-charges, although weakly, appeared inversely correlated with Cu,Pb and Zn concentrations in sediments (Table 2). The correlationbetween these common metals in urban runoff (Ancion et al.,2010), enhances the role of urban environments as diffuse sourcesof particle bound metals.

Studying different basins along the Cantabrian coast, Bruschiet al. (2012) detected a general increase in the sedimentation ratesderived from human driven geomorphic changes. In this contribu-tion, all the estuaries located in the Bizkaia province have in-creased their total fine sediment stock, whilst from those locatedin Gipuzkoa province, Oria, Urola and Urumea have decreased theirtotal fine sediment stock (Table 1, Figs. 5a–d, Figs. A1–A12 in Sup-plementary material). Moreover, although Bidasoa, Oiartzun andDeba increased their fine sediment stock (2.4%, 4.0% and 11.1%respectively), such an increment is generally lower than that ob-served for the estuaries located in Bizkaia, that ranged between6.5% and 116.8% (Table 1). Due to the increase in sediment loadscaused by fast-growing plantation forests, several authors pointedout in the negative effect derived from these exploitations in waterquality (Lara et al., 2009; Garmendia et al., 2012). From the total

Fig. 3. Organic matter content in surficial sediments of different systems. Deba estuary: (a) 2000 and (b) 2011; Oiartzun estuary: (c) 2000 and (d) 2011; and Urola estuary: (e)2000 and (f) 2011.

32 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

surface intended for plantation forests in the Gipuzkoa provincebasins (803 km2), 67.3% is focused on fast growing plantations:0.3% Eucalyptus spp.; 67.0% Pinus radiata (Fig. 5e). In the Bizkaiaprovince basins that value reaches 85.9% of the total land intendedfor plantation forests (1081 km2): 10.0% Eucalyptus spp.; 75.9% P.radiata (Fig. 5e) (data extracted from the geoportal of the Depart-ment of Environment, Land Use, Agriculture and Fisheries of theBasque Government, ftp://ftp.geo.euskadi.net/cartografia). Thus,the differences in land use among both provinces could explainthe differences observed in the fine sediment stock variations.Hydrodynamic conditions maintain a dynamic equilibrium, regu-lating the small variations in water flow and sedimentation byresuspension and resettlement processes (Brils, 2008). Therefore,large variations in the characteristics of the sedimentary compart-ment could be indicative of new equilibrium states derived fromemerging sources of sediments and their associated pollutants.Nevertheless, such an increase in sedimentation rates could favorthe burial of polluted sediments by the continuous deposition ofless contaminated sediments (after the sanitation mentioned be-fore), enhancing the natural recovery process of the superficial sed-iments. In this sense, natural recovery of the sediments derivedfrom sediment burial processes has been described to occur in atime-scale ranging from 3 years to up to 40 years (Smith et al.,2009; Parsons et al., 2010).

Besides, it must be noted that the increases in the metal stocksdo not necessarily imply an increase in the metal pollution levels.This pattern is clearly reflected in the results obtained for the Bar-badun and Butroe estuaries (Table 1). Despite of the overall lowerpressure levels to which they are subjected (Borja et al., 2006),both estuaries have experienced increases in the extent of their

UMZs, in the population density around them, as well as in theirfine sediment stocks. This would imply a change in the drivingforces determining their sediment-associated pollution, which isbeing reflected as a large spatial-scale redistribution process ofpollution sources and accumulation areas (Figs. 6a–b; Figs. A1and A3 in Supplementary material).

4.2.1.2. Geological composition of the basins. Considering the geolog-ical composition of the studied basins, the predominance of theBlenda–galene–pyrite–calcopyrite (BGPC) paragenesis observedin the eastern basins, is progressively substituted westward by ironoxides. Such a mineralogical composition enhances the naturalenrichment of the sediments in Cu, Pb and Zn (Belzunce et al.,2004a). Therefore, the underlying geology of Bidasoa and Oiartzunestuaries appears as an additional factor explaining the high stocksof these metals found in these systems (Table 1).

4.2.2. Small spatial-scale evolution4.2.2.1. Sediment remobilization activities. Land use changes oc-curred in the studied systems over the last years are reflected asriver bank restoration activities, infrastructure building works,maintenance dredging or channeling activities. The reworking ofburied contaminated bottom sediments derived from these actionsenhances the potential contaminant spread into previously unaf-fected areas (Je et al., 2007). This way, the proximity to developingrestoration activities and the subsequent remobilization of buriedmetals determine the metal stock increases (or lower decrease de-grees) observed in the Urumea estuary and the outer reaches of theBarbadun estuary (Figs. 6a and c; Figs. A1 and A10 in Supplemen-tary material). Furthermore, the metal load of reworked buried

Fig. 4. Metal stock variations in surficial sediments: (a) Zn in Oiartzun estuary; (b) Cr in Ibaizabal estuary; (c) Hg in Deba estuary; (d) Cd in Urola estuary; and (e) Cu inUrumea estuary. Solid line indicates the limits between metal stock increase–decrease areas.

I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38 33

sediments could condition the overall assessment of a water body.As an example, Cd stock in the Urumea estuary has felt 67.5% (Ta-ble 1). However, the high Cd load of reworked sediments penalizedthe global LPI, giving as a result a worsening in the pollution statusderived from this metal (Table 1, Fig. 6c).

4.2.2.2. Morphological features. Many of the areas where metalstock increases (or at least, lower metal stock loss rates) have beenobserved show an effect of their hydromorphological environment.Either naturally occurring or human-driven changes in morpholog-ical characteristics and hydrodynamic conditions determine, in agreat extent, the small-spatial scale metal accumulation patterns.Whilst depositional areas are characterized by showing low hydro-dynamic conditions, dynamic areas present erosional features,especially for the finest fractions of the sediments (Birch et al.,2001; Nitsche et al., 2007).

Human-driven alterations of hydromorphological conditionsare reflected in intertidal loss and development of estuarine infra-structures and reinforcements (Borja et al., 2006). In order to beingprotected against wave action or increase their capabilities, portslocated into Basque estuaries have been subjected to a constantdevelopment of their infrastructures (González et al., 2004). Suchinfrastructures alter the hydrodynamic conditions and sedimentdistribution patterns (Lee et al., 2012) and show a direct effect over

the sediment-bound pollutant dispersion and distribution (Azeve-do et al., 2010). Thus, despite of the effectiveness of pollutionabatement measures, hydrodynamic conditions around these fea-tures favor the pollutant accumulation around them.

Results obtained for Bidasoa and Lea estuaries highlight theimportance of naturally occurring hydromorphological alterations.Prior to their mouth these estuaries present several mudflats andmarshes. The lower hydrodynamic conditions over such shelteredareas, added to a contribution of metal release and intake pro-cesses driven by the vegetation present, seems to be responsibleof the metal stock variations observed (Borja et al., 2004c; Rebore-da and Caçador, 2007) (Figs. 7a and b). Additionally, prior to theBidasoa estuary mouth, the main water course widens into Txing-udi Bay, lowering the hydrodynamical conditions over the area,and enhancing the accumulation of metals (Fig. 7a; Fig. A12 in Sup-plementary material). Thus, natural hydromorphological condi-tions appear as a key factor explaining the metal stock increases(or lower decrease levels) found along both estuaries. Moreover,the implementation of sanitation systems, that have diverted thetreated wastewater discharges to coastal waters in the Bidasoaestuary (Franco et al., 2004) and the apparent lack of a change inthe driving forces acting over Lea estuary (Table 1), reinforcesthe role of natural hydromorphological features explaining the dis-tribution of the stocks.

Fig. 5. Fine sediment (<63 lm) stocks for some of the studied systems. Oria estuary: (a) 2000 and (b) 2011; Butroe estuary: (c) 1999 and (d) 2010. (e) Distribution of Pinusradiata and Eucalyptus spp. plantation forests among Bizkaia and Gipuzkoa basins.

34 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

Finally, results obtained for Artibai, Oka and Oria estuaries showa mixed effect of naturally occurring hydromorphological varia-tions as well as those derived from human induced changes. Aspreviously mentioned, the presence of mudflats and marshes en-hances the retention of metals in some areas of these systems. Con-sidering Oka estuary, due to the differences between the outer and

inner parts in terms of hydrodynamic conditions, morphology andhuman pressures, the system is divided into two differentiatedwater bodies (Borja et al., 2006) (Fig. 7c). Morphologically bothparts of the estuary present clear differences. The outer part ischaracterized by showing tidally dominated wide intertidal salt-marshes (Liria et al., 2009), with lower human pressure levels.

Fig. 6. Metal stock variations in surficial sediments: (a) Cr in Barbadun estuary; (b) Ni in Butroe estuary; and (c) Cd in Urumea estuary. Solid line indicates the limits betweenmetal stock increase–decrease areas.

Fig. 7. Metal stock variations in surficial sediments: (a) Pb in Bidasoa estuary; (b) Ni in Lea estuary; and (c) Cu in Oka estuary. Locations of the main marsh areas are alsoshown. Solid line indicates the limits between metal stock increase–decrease areas.

Fig. 8. Metal stock variations in surficial sediments: (a) Zn in Oria estuary; and (b) Hg in Artibai estuary. Solid line indicates the limits between metal stock increase–decreaseareas.

I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38 35

However, the inner part shows a higher riverine influence andhigher human pressure level (Chust et al., 2010). In the outer partof the estuary, the presence of artificial infrastructures in the

village and port of Mundaka derives in metal stock increasesaround them (Fig. 7c, Fig. A4 in Supplementary material). In con-trast, the inner part is formed by a channel shaped stream that

36 I. Legorburu et al. / Marine Pollution Bulletin 66 (2013) 25–38

widens dramatically when gets closer to the outer section. Such asudden increase in the cross section will slow down the river flowand favor the deposition of suspended particles (Nitsche et al.,2007). Therefore, the combined effect of marsh retention withthe deposition of particle bound metals originated upstream (Mon-tero et al., 2012), would explain the stock increases found (Fig. 7c,Fig. A4 in Supplementary material). Finally, the artificial infrastruc-tures located at Artibai and Oria estuaries (a fishing harbor and afish market and a marina respectively), influence the stock distri-bution at their outermost reaches. In addition, metal stock in-creases are observed around the bending forms of their watercourse (Fig. 8; Figs. A6 and A9 in Supplementary material). Bendingforms alter the riverine flow velocity, which, at the same time, hasa direct effect on the sediment accumulation processes occurringaround them (Peakall et al., 2007; Ottevanger et al., 2011), andtherefore, on the sediment bound metal accumulation.

5. Conclusion

The availability of data from monitoring network combinedwith the use of statistical analyses and GIS facilities has resultedin a useful methodology to examine the spatio–temporal evolutionof the metal pollution in the sediments of the Basque estuaries. Dif-ferent behaviors are observed depending on hydromorphologicalaspects and the human interference level to which the estuarieshave been subjected. A close interconnection between main drivers(industrial concentration and population density) and pressures(water/sediment pollution, intertidal loss and shoreline reinforce-ments) has been observed determining the spatio–temporal evolu-tion of the metal stocks. A marked long-term decontaminationprocess has been observed in the historically more polluted estuar-ies, derived from restoration activities and development of sanita-tion plans. However, in estuaries with lower historical pollution ahigher influence of spatial-scale processes has been observed.Whilst changes in population density and land use of the basinshave a crucial importance in large spatial scale processes, coastaldevelopment and hydromorphological variations are clearly re-flected in small-spatial scale metal accumulation processes.

Acknowledgements

Data for this study were obtained from different projectsfunded by the Basque Water Agency (URA) and the Departmentof Environment, Land Use, Agriculture and Fisheries of the BasqueGovernment. Irati Legorburu was supported by a Ph.D. Grant fromthe Iñaki Goenaga Technological Centres Foundation of the BasqueCountry. This paper is contribution number 602 from the MarineResearch Division (AZTI-Tecnalia).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.marpolbul.2012.11.016.

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