lable at ScienceDirect

Environmental Pollution 158 (2010) 2158e2169

Contents lists avai

Environmental Pollution

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

Quantifying sediment-associated metal dispersal using Pb isotopes: Applicationof binary and multivariate mixing models at the catchment-scale

Graham Bird a,*, Paul A. Brewer a, Mark G. Macklin a, Mariyana Nikolova b, Tsvetan Kotsev b,Mihail Mollov c, Catherine Swain a

aCentre for Catchment and Coastal Research and River Basin Dynamics and Hydrology Research Group, Institute of Geography and Earth Sciences, Aberystwyth University,Aberystwyth SY23 3DB, United Kingdomb Institute of Geography, Bulgarian Academy of Sciences, “Acad. G. Bonchev”, bl.3, 1113 Sofia, Bulgariac Executive Environmental Agency e Ministry of Environment and Waters, “Tzar Boris III” Boulevard, No. 136, Sofia, Bulgaria

Pb isotopic evidence used to quantify sediment-associated metal deliv

ery within a mining-affected river catchment.

a r t i c l e i n f o

Article history:Received 17 November 2009Received in revised form12 February 2010Accepted 27 February 2010

Keywords:Pb isotopesMixing modelsSediment dispersalMetals

* Corresponding author.E-mail address: grb@aber.ac.uk (G. Bird).

0269-7491/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.envpol.2010.02.020

a b s t r a c t

In this study Pb isotope signatures were used to identify the provenance of contaminant metals andestablish patterns of downstream sediment dispersal within the River Maritsa catchment, which isimpacted by the mining of polymetallic ores. A two-fold modelling approach was undertaken to quantifysediment-associated metal delivery to the Maritsa catchment; employing binary mixing models intributary systems and a composite fingerprinting and mixing model approach in the wider Maritsacatchment. Composite fingerprints were determined using Pb isotopic and multi-element geochemicaldata to characterize sediments delivered from tributary catchments. Application of a mixing modelallowed a quantification of the percentage contribution of tributary catchments to the sediment load ofthe River Maritsa. Sediment delivery from tributaries directly affected by mining activity contributes42e63% to the sediment load of the River Maritsa, with best-fit regression relationships indicating thatsediments originating from mining-affected tributaries are being dispersed over 200 km downstream.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

In river catchments affected by historical and on-going metalmining activity, identifying the source of contaminant metals is keyto developing effective remediation and catchment managementstrategies (Hutchinson and Rothwell, 2008; Macklin et al., 2006).This is particularly true in larger river catchments, where contam-inants may be potentially sourced from a variety of sources (Gelinasand Schmit, 1997; Miller et al., 2002). As a result, reliable tracers arerequired to identify the provenance of contaminant metals that arebeing transported through river systems as part of the sedimentload (Ettler et al., 2006; Miller et al., 2007; Monna et al., 2000).

Lead isotopes have become increasingly used as geochemicaltracers in environmental studies (Bi et al., 2009; Emmanuel andErel, 2002; Hao et al., 2008; Komárek et al., 2007; Teutsch et al.,2001), notably in studies of airborne particulate contamination(Ettler et al., 2004; Farmer et al., 2005; Gallon et al., 2005;Mihaljevi�cet al., 2006; Sturges and Barrie, 1987). Lead isotopes are particularly

All rights reserved.

applicable given that Pb in the environment retains the isotopicsignature of the ore it originated from (Ault et al.,1970;Hopper et al.,1991). Of particular relevance to tracing metal dispersal in mining-affected river catchments is that as a consequence, naturally- andanthropogenically-sourced Pb often have different isotopiccompositions (Bacon, 2002; Marcantonio et al., 1999).

Four Pb isotopes have been extensively used as geochemicaltracers: 204Pb, which a stable, and the long-lived radiogenic isotopes206Pb, 207Pb and 208Pb, which are the daughter products of the decayof 238U, 235U and 132Th, respectively (Gobiel et al., 1995). So whilstprimordial Pbhadafixed isotopic composition, the amounts of 206Pb,207Pb and 208Pb that have been added over time, result in thediffering isotopic signatures observed in ore deposits today (Wilsonet al., 2005). The actual increase in radiogenic isotopes, relative to204Pb, depends upon two factors. First, the age of the ore body asupon formation Pb becomes isolated from Th and U causing theisotopic composition to cease changing. The second key factor is therelative concentrations of Pb, Th and U in the original magmaticmixture (Bacon and Dinev, 2005).

Previous studies utilizing Pb isotopes as geochemical tracers influvial environments have been in generally small river catchmentsand very few have sought to use Pb isotopic data to quantify the

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2159

contributions of sediment-associated metals to the sediment loadof mining-affected rivers (but see Brewer et al., 2005; Miller et al.,2007). In this study we have utilized Pb isotope and multi-elementgeochemical data in both binary and multivariate mixing modelsto: 1) provenance contaminant metals in river channel sedimentsof the Maritsa drainage basin, southern Bulgaria, and 2) quantifythe relative contribution of sediment-associated metals from keysources within the catchment.

2. Study area

The Maritsa catchment covers an area of 53 000 km2 and has itsheadwaters in the Rila Mountains of central Bulgaria. The RiverMaritsa flows southeastwards for 275 km in Bulgarian territorybefore crossing Bulgaria's border with Turkey, eventually dis-charging into the Aegean Sea. Geologically, Bulgaria can be dividedinto five structural-metallogenic zones (Fig. 1), which are typifiedby differing metallogeny (Bogdanov, 1982). The Maritsa catchmentdrains the Srednogorie and Rhodope metallogenic zones, whichcontain some of Europe's most productive metal ore deposits.

The Srednogorie metallogenic zone forms part of the Apuse-nieBanateTimokeSrednogorie (ABTS) belt (Neubauer and Heinrich,2003), which comprises an L-shaped structure of Late Cretaceouscalc-alkaline activity within eastern central Europe incorporatingRomania, Bulgaria and Serbia (Ciobanu et al., 2002; von Quadt et al.,2003). The ABTS belt contains major Cu deposits with Ciobanu et al.(2002) identifying at least 50 Cu deposits associated with it. WithintheMaritsa catchment,major CuandCueAudeposits are found in thePanagyurishte ore district, a 4800 km2 area within the Srednogoriemetallogenic zone (Strashimirov et al., 2002) dominated by massivesulphide and porphyry Cu deposits (Bogdanov, 1980), often with

Fig. 1. Map of Bulgaria showing the location of study rivers, river sediment sample sites, oPyasachnik, Stryama and Sazlika represent 10 samples collected from each river.

notable occurrences ofAu (Moritz et al., 2005). vonQuadt et al. (2003)describe how ore body formation in the Panagyurishte district,associatedwith Late Cretaceousmagmatism, developed southwards;starting with the formation of the Elatsite Cu deposit (92.1� 0.3 Ma)and culminated at the border of the Srednogorie and Rhodope met-allogenic zones with the intrusion of the Capitan Dimitrievo pluton(78.54 � 0.15 Ma).

The Rhodope metallogenic zone in southern Bulgaria forms partof the SerbomacedonianeRhodope belt with ore deposits beingassociated with Miocene-age calc-alkaline volcanic rocks (Heinrichand Neubauer, 2002). The Rhodope zone contains Bulgaria's mostimportant Pb and Zn reserves present as hydrothermal vein andmetasomatic replacement deposits (Marchev et al., 2002, 2005).Mining in the RhodopeMassif is believed to have commencedmorethan 2500 years BP (Rice et al., 2007), however, mining activitysignificantly expanded in the latter half of the twentieth century(Vassileva et al., 2005). For example, the 70 individual deposits,including Luku and Madan (Fig. 1), which comprise the centralRhodopean Dome yielded 114 Mt of Pb/Zn ore between 1941 and1995 (Marchev et al., 2005).

In its upper reaches the River Maritsa drains the Srednogoriemetallogenic zone; of particular importance are its north banktributaries, the Rivers Topolnitsa and Luda Yana, which drain thePanagyurishte ore district (Fig. 1). The River Maritsa also drains theRhodope metallogenic zone, principally via the Rivers Chepelarskaand Arda. The River Chepelarska is a south bank tributary of theMaritsa, whilst the River Arda has its headwaters in the RhodopeMassif and flows eastwards for 241 km in Bulgaria, before joiningthe River Maritsa in Greece (Fig. 1). Historic and on-going miningactivity within the Srednogorie and Rhodope Metallogenic Zoneshas left a widespread legacy of contamination within the Maritsa

re deposits and tailings ponds. Sediment sample site locations on the Rivers Vuchar,

G. Bird et al. / Environmental Pollution 158 (2010) 2158e21692160

catchment (Bird et al., 2010; Panayotova, 1997) and has led to thepotential transboundary dispersal of contaminant metals fromBulgaria into Greece and Turkey.

3. Methods

Samples of river channel sediment (95 samples) and mine waste (30 samples)were collected during August 2005. River channel sediments (10 spot samples takenwithin a 5 m radius) were collected with a stainless steel trowel at low river stagefrom exposed bar surfaces. Samples of particulate mine and smelting waste (10 spotsamples takenwithin a 5 m radius) were collected from spoil tips, waste dumps andtailings ponds.

Samples were air-dried, disaggregated and sieved through a 63 mm mesh toisolate the chemically active silt and clay fraction. Samples were digested in 70%HNO3 for 1 h and Cd, Cu, Pb and Zn concentrations were determined by ICP-MS atAberystwyth University. Precision of metal determinations was determined usingblind repeats (10% of total sample number) and found to be 3.2% (Cd), 6.2% (Cu), 2.6%(Pb) and 3.9% (Zn). Accuracy of metal determinations was monitored by analysis ofan in-house reference material (ABS1); a stream sediment with values certified fora HNO3 digest. Accuracies versus ABS1 were found to be 3.4% (Cd), 8.2% (Cu), 7.3%(Pb) and 5.2% (Zn). Whilst digestion with concentrated HNO3 does not providea ‘total’ metal determination, monitoring of metal recoveries using the HNO3 digestversus ‘total’ certified values of the GSD-12 reference material (a stream sediment)found very acceptable recoveries of between 87% and 97%. Lead isotopes 204Pb,206Pb, 207Pb and 208Pb were determined in sediment samples and the NIST981reference material using a Thermo-Finnigan Element2 Magnetic Sector ICP-MS atAberystwyth University. Values for 204Pb were corrected for interference of Hg andisotopic ratios adjusted against the NIST981 data. Analytical precision for thedetermination Pb isotope ratios was found to be 0.26% (206Pb/204Pb), 0.17%(207Pb/206Pb) and 0.14% (208Pb/206Pb). Analytical accuracy versus the NIST981standard was 0.25% (206Pb/204Pb), 0.14% (207Pb/206Pb) and 0.39% (208Pb/206Pb).

4. Results

4.1. Metal concentrations in river sediments

In order to quantify the enrichment of metal concentrations inriver channel sediments in the Maritsa catchment, metal levelshave been compared to US EPA Threshold Effect Concentration(TEC) and Probable Effect Concentration (PEC) guidelines (Table 1)for freshwater sediments (US EPA, 2002). It is apparent that metalconcentrations within river channel sediments of the River Maritsaand key, mining-affected tributaries, are elevated above both TECand PEC values (Fig. 2). All metal levels are highest in the down-stream reach of the River Chepelarska, which drains the Luku Pb/Znore field and it is noticeable that in the River Maritsa itself,concentrations are highest from 150 km channel distance, down-stream of the ChepelarskaeMaritsa confluence, with peak metallevels being elevated above PEC values by between 1.4 and 5 times.Elevated metal concentrations in the River Maritsa immediatelyupstream of the Bulgarian border with Turkey indicate that sedi-ment-associated metals are being dispersed across these interna-tional borders. Effective management of the Maritsa catchment

Table 1Threshold Effect Concentration (TEC) and Probable Effect Concentration (PEC)guidelines (mg kg�1) defined by the US EPA for metals in freshwater sediments andrange and mean metal concentrations (mg kg�1) determined in river channelsediment in the Rivers Topolnitsa, Luda Yana and Chepelarska.

Cd Cu Pb Zn

US EPA guideline valuesTEC value 0.99 31.6 35.8 121PEC value 4.98 149 128 459

Range and mean metal concentrationsTopolnitsa

(n ¼ 8)<0.2e8(2)

25e1300(840)

15e112(57)

27e220(111)

Luda Yana(n ¼ 6)

<0.2e2(0.5)

48e3100(1370)

2.7e170(44)

18e106(65)

Chepelarska(n ¼ 6)

1e257(64)

36e941(349)

160e5100(2096)

132e15 200(4360)

therefore requires the identification of key contaminant sourcesand a quantification of their contributions to the sediment load ofthe River Maritsa.

4.2. Pb isotope signatures in metal ore deposits and river sediments

Within the Maritsa catchment it is possible to differentiatebetween ore deposits based upon the bivariate relationship between208Pb/206Pb and 207Pb/206Pb isotopic ratios (Fig. 3). The younger,Miocene-age deposits of the Rhodope metallogenic zone are char-acterized by generally lower 208Pb/206Pb and 207Pb/206Pb ratios thanthe Srednogorie zone (Table 2), with 208/206Pb ratios in the westRhodope zone (2.0693e2.0771) being lower than those in the east(2.0697e2.0833). Within the Srednogorie metallogenic zone, theUstrem and Lesovo Pb/Zn deposits, which are located in the Tundzhariver catchment, exhibit higher 208Pb/206Pb and 207Pb/206Pb ratiosthan the Cu and CueAu ore deposits of the Panagyurishte ore district.This is potentially explained by the respective ages of mineralization,with the Panagyrishte deposits dating from 92.22 � 0.30 Ma at Che-lopech (Moritz et al., 2005) to 77e78Ma at Radka (Kouzmanov et al.,2002), whereas the Ustrem deposit is believed to much older at240e250 Ma (Amov et al., 1974).

Similar to isotopic signatures found in ore deposits, it is alsopossible to distinguish between the isotopic signatures of minewaste generated from exploitation of the respective ore deposits inthe Maritsa catchment (Fig. 3). 208Pb/206Pb and 207Pb/206Pb signa-tures forminewaste (Table 2) from theMadan, Luku andMadjarovodeposits (Rhodope zone) are again lower than those of the Pan-agyurishte ore district (Radka, Medet and Elatsite deposits), whilstsignatures in mine waste from the Ustrem Pb/Zn deposit beinghigher still with average ratios of 0.8473 (207Pb/206Pb) and 2.0882(208Pb/206Pb). The data in Fig. 3 also indicate that the isotopicsignatures between mine wastes closely match the primary signa-tures of the ore deposits that the material was sourced from; high-lighting that Pb isotope signatures remain unchanged through oreextraction and processing (Ault et al., 1970).

Key patterns in isotopic signatures displayed in ore deposits andassociatedminewaste are also replicated in samples of river channelsediment taken from rivers draining areas of differingmineralization(Fig. 3). The isotopic signatures of river channel sediments directlydraining the Rhodope zone (River Arda) and the Panagyurishte oredistrict (Rivers Topolnitsa and Luda Yana) show statistically signifi-cant separation (Table 3),with the RiverArda sediments,whichdrainthe younger Rhodope deposits, having lower 207Pb/206Pb ratios. Theisotopic signatures for the River Maritsa, which drains deposits inboth theRhodopezoneandPanagyurishtedistrict, fall between thoseof the River Arda and the Rivers Topolnitsa and Luda Yana, indicatingthe influence of ore deposits in both regions on the isotopiccomposition of the River Maritsa. Ratios for 207Pb/206Pb in riverchannel sediments of the Rivers Maritsa and Arda are significantlydifferent (Table 3). The River Iskar, a northward flowing tributary oftheDanube, alsodrains thePanagyurishte oredistrict (principally theElatsite Cu deposit) and isotopic signatures in channel sedimentsclosely match those of the Rivers Topolnitsa and Luda Yana. Theability to differentiate between isotopic signatures of channel sedi-ments in different rivers, influenced by the nature of mineralizationwithin the respectivecatchments, is key to successfullyprovenancingsediment-associated contaminants.

4.3. Binary mixing models

Identification of strong linear trends in bivariate Pb isotope plotshave been previously used to identify the mixing of Pb fromisotopically distinct sources of metals supplied to river channelsediments (Kurkjian et al., 2004; Marcantonio et al., 1999; McGill

Fig. 2. Metal concentrations in river channel sediment of the River Maritsa and tributaries. Values are plotted versus US EPA TEC and PEC guideline values.

Fig. 3. Bivariate plot of 208Pb/206Pb and 207Pb/206Pb isotope ratios measured in metal ore deposits and metal mine waste within the Maritsa river catchment and in river channelsediment from the River Maritsa and tributaries not impacted by metal mining. Linear relationship between Pb isotope ratios measured in the River Maritsa has an r2 coefficient of0.89. Data for metal ore deposits sourced from Stos-Gale et al. (1998) and Amov (1999).

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2161

Table 2Mean Pb isotope ratio values for metal ore deposits, mine and smelting waste and river sediments within the River Maritsa catchment. Number of analyses given inparentheses.

206Pb/207Pb 207Pb/206Pb 208Pb/206Pb 206Pb/204Pb 207Pb/204Pb 208Pb/204Pb

Ore depositsPanagyurishte ore districtElatsite (3)a e 0.843 2.081 19.009 15.644 38.821Chelopech (7)a e 0.843 2.083 18.570 15.658 38.686Medet (3)a e 0.847 2.087 18.474 15.645 38.561Assarel (1)a e 0.846 2.087 18.434 15.600 38.742Radka (14)b e 0.846 2.087 18.490 15.636 38.581Elshitsa (3)c e e e 18.622 15.628 38.769Vlaikov Vruh (2)c e e e 18.649 15.611 38.685

Rhodope metallogenic zoneMadan (45)a,c e 0.837 2.082 18.707 15.681 38.955Luku (30)a,c e 0.838 2.078 18.706 15.673 38.881Madjarovo (27)d e 0.835 2.069 18.770 15.681 38.849Zvezdel (29)a e 0.838 2.078 18.700 15.679 38.857

Mine and smelter wastePanagyurishte ore districtElatsite spoil (4) 1.163 0.844 2.084 18.600 15.650 38.760Benkovski tailings (3) 1.170 0.852 2.094 18.338 15.584 38.533Medet tailings (3) 1.183 0.845 2.092 18.543 15.668 38.795Radka tailings (3) 1.183 0.845 2.083 18.367 15.527 38.250Tsar Assen spoil (4) 1.177 0.845 2.082 18.286 15.534 38.329

Rhodope metallogenic zoneLuku tailings (3) 1.194 0.838 2.081 18.622 15.591 38.676Madan tailings (3) 1.191 0.838 2.081 18.639 15.547 38.852Madjarovo spoil (4) 1.190 0.837 2.078 18.010 15.760 38.571KZM Smelter (4) 1.185 0.848 2.092 18.473 15.727 38.636

River channel sedimentsRiver Maritsa (10) 1.189 0.840 2.076 18.498 15.535 38.282River Topolnitsa (8) 1.185 0.844 2.079 18.430 15.547 38.403River Luda Yana (6) 1.184 0.840 2.068 18.484 15.529 38.226River Chepelarska (6) 1.189 0.847 2.087 18.419 15.589 38.765River Arda (9) 1.192 0.838 2.081 18.578 15.569 38.664

a Amov (1999).b Amov (2000).c von Quadt et al. (2005).d Stos-Gale et al. (1998).

G. Bird et al. / Environmental Pollution 158 (2010) 2158e21692162

et al., 2003). Key sediment/sediment-bound metal sources to theriver are taken to be those that plot at the opposing ends (Milleret al., 2002) and along the trendline in close association withsample ratios (Mackenzie and Pulford, 2002). It is apparent thatthese relationships will be strongest and most readily identifiablein smaller river catchments, where the number of potential sourcesis limited. Examples of this can be seen in the Rivers Topolnitsa andChepelarska (Fig. 4), both tributaries of the River Maritsa, whereratios of 207Pb/206Pb and 208Pb/206Pb in river channel sediments arecharacterized by a strong correlation coefficients (r2 ¼ 0.91e0.98).

Lead concentrations in both rivers, particularly the Chepelarska,are elevated above US EPA guideline values. In the case of the RiverTopolnitsa Cu concentrations (110e1700 mg kg�1) are also highlyenriched reflecting Cu mineralization and mining within the

Table 3P coefficients for non-parametric significant difference analysis for Pb isotope ratios deterand 99% significance limits, respectively.

Arda 207/206Pb Arda 208/206Pb Topolnitsa 2

Maritsa 207/206Pb 0.225* e 0.000**Maritsa 208/206Pb e 0.455 e

Arda 207/206Pb e e 0.000**Arda 208/206Pb e e e

Topolnitsa 207/206Pb e e e

Topolnitsa 208/206Pb e e e

catchment. In the River Chepelarska, Pb isotope signatures in riversediments form a strong linear trend between signatures derived in1) smelter waste deposited on the floodplain, 2) the Luku oredeposit and associated tailings and 3) bedrock within the catch-ment, indicative of background isotopic signatures. In the Top-olnitsa catchment, the linear relationship for isotopic signatures inriver channel sediments falls between those for theMedet ore bodyand associated tailings and catchment bedrock. Signatures for theChelopech deposit and associated Benkovski tailings, which arealso present in upper catchment (Fig. 1), also plot along the lineartrend defined by channel sediments. The relative importance ofthese sources in supplying sediment-associated Pb to the recipientrivers will change downstream reflecting the locations of sourceinputs and the dynamics of sediment dispersal and mixing. It is

mined in river channel sediments. * and ** denotes relationship significant at the 95

07/206Pb Topolnitsa 208/206Pb Iskar 207/206Pb Iskar 208/206Pb

e 0.000** e

0.003** e 0.000**e 0.000** e

0.014* e 0.000**e 0.009** e

e e 0.004**

Fig. 4. Ratios of 208Pb/206Pb and 207Pb/206Pb measured in river channel sediments, metal ores and mine and smelter waste from a) the Topolnitsa and b) Chepelarska rivercatchments. Data for Medet, Chelopech and Luku ores sourced from Stos-Gale et al. (1998) and Amov (1999).

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2163

G. Bird et al. / Environmental Pollution 158 (2010) 2158e21692164

possible to quantify the relative contributions of sediment-associ-ated Pb to the channel load using a binary mixing model:

XA ¼

� 20XPb20XPb

�S�� 20XPb

20XPb

�B� 20XPb

20XPb

�A�� 20XPb

20XPb

�B

� 100 (1)

where XA is the percentage fraction of end-member A and (20X/20XPb)S, (20X/20XPb)A, and (20X/20XPb)B are isotopic ratios (e.g.207Pb/206Pb) in the sample of river channel sediment, end-memberAand end-member B, respectively. In this study themodel was run forboth 208/206Pb and 207/206Pb data and the average contributionderived.

In the River Topolnitsa, application of the binary mixing modelindicates that in the upper reaches of the catchment, the mining ofthe Medet ore body constitutes the source of over 90% of sediment-associated Pb to the sediment load of the river (Fig. 4a). This alsosuggests, therefore, that mining at Medet will also be acting as thekey source of other contaminant metals, notably Cu. The influencesediment and sediment-associated metals from the Medet mineand associated tailings ponds reduces with distance downstream inthe River Topolnitsa; accounting for 30% of sediment-associated Pbafter 107 km channel distance, immediately upstream of the Top-olnitsa/Maritsa confluence. This reduction is likely to be due tomixing with sediment sourced from other tributaries and sourceswithin the catchment. For example at 107 km channel distance theestimated contribution of Pb, and by inference sediment, frombackground sources comprises approximately 70% of the sedimentload (Fig. 4a).

Within the Chepelarska catchment it is possible to identify twokey trends, which form either side of the ‘background’ isotopicsignature for the catchment (Fig. 4b). For river channel sediments inthe upper reaches of the river; the isotopic signatures for the Lukutailings form the lower end-member. Secondly, in the lower Che-pelarska isotopic signatures for the river channel sediments forma linear trend between those of the catchment background andwaste from the KZM Pb/Zn smelter, which contains high levels of Cd(1500 mg kg�1), Cu (3600 mg kg�1), Pb (64 000 mg kg�1) and Zn(148 000 mg kg�1). The presence of two linear trends suggests thatwith increasing distance downstream, there is a shift in theimportance of sediment-associated Pb delivery from two keysources within the catchment. Quantification of sediment-associ-ated Pb delivery between 30 and 52 km channel distance indicates36e89% of Pb originates from sources associatedwith themining ofthe Luku ore deposit (Fig. 4b). In the lower Chepelarska, 75e80% ofthe Pb is believed to originate from smelter waste that is depositedon the floodplain adjacent to the river channel and is being activelyeroded and dispersed as part of the riverine sediment load. Withinthe River Chepelarska, the percentage of sediment-associated Pbnot attributed to these two key sources will represent materialsourced from other sources, notably the weathering of Pb-miner-alised bedrock within the catchment.

4.4. Composite fingerprinting of sediment sources

Pb isotope and metal concentration data have highlighted keysources within tributaries such as the Rivers Topolnitsa and Che-pelarska that will be at least partly responsible for the presence ofelevatedmetal levels within theMaritsa. The isotopic signatures forriver channel sediments in the River Maritsa form a strong lineartrend (r2 ¼ 0.89), however it is possible to identify a wide range ofpotential sources of sediment-associated Pb, and other metals,within the Maritsa catchment (Fig. 3). Lead isotopic signatures for

ore deposits within the Srednogorie and Rhodope metallogeniczones plot along and at the upper end of the linear trend. Tributariesnot directly affected by mining activity plot at the lower end of thelinear trend with generally lower 208Pb/206Pb and 207Pb/206Pbsignatures.

The increased number of potential sources identifiable withina larger catchment, such as theMaritsa, precludes the use of a binarymixing model (Ip et al., 2007), therefore in order to quantify thedelivery of sediments from tributary catchments a compositefingerprinting and multivariate mixing model can be applied(Collins and Walling, 2004, 2007; Collins et al., 1996; Walling et al.,1999, 1993). Tributaries of the River Maritsa were treated as sourcegroups with Pb isotope signatures for 208Pb/206Pb, 207Pb/206Pb,208Pb/207Pb, 206Pb/204Pb, 207Pb/204Pb and 208Pb/204Pb were used inconjunctionwith concentrations of Be, B, Mg, Sc, Ti, V, Cr, Co, Al, Ni,Fe, Zn, Cu, Ga, As, Rb, Sr, Y, Zr, Nb, Mo, Pd, Cd, Sn, Sn, Sb, Sb, Cs, Ba, Ba,La, Ce, Pr, Nd, Sm, Eu, Gd, Gd, Tb, Dy, Dy, Ho, Er, Tm, Yb, Lu, Hg, Tl, Pb,Tl, Pb, Pb, Bi, Th as diagnostic properties (Collins andWalling, 2007;Rowan et al., 2000) in order to establish composite fingerprints forsediments delivered from tributary catchments.

Following non-parametric significant difference analysis (Krus-kalleWallis H-Test) to determine which geochemical propertiesdisplay significant difference between groups (at the 99% confi-dence level), a multivariate discriminant function (MDF) analysisusing the minimisation of Wilks' lambda (L) identifies the fewestnumber of parameters that are able to distinguish between sources:

L ¼PðXDi � XDGÞ2PðXDi � XDT Þ2

(2)

where XDi is the score for the observation i on the discriminantfunction D, XDG is the mean score on the discriminant function D forthe observations in the group of which I is a member, and XDT is themean score on the discriminant function D for all observations(Johnston, 1978).

A multivariate mixing model (c.f. Walling et al., 1999) was thenbe employed to determine the relative contribution of each sourcegroup to samples river channel sediment in the River Maritsa. Alinear equation is generated for each property in the fingerprintand, using the least squares method, the proportion of materialderived from each source group is established by minimising thesum of the squares of the residuals (equation (3)) for all diagnosticproperties included in the model:

Res ¼Xni¼1

Cgi � ðCsiPsÞ

Cgi

!2

Wi (3)

where Res is the sum of the squares of the residuals, Cgi is theconcentration of diagnostic property i in the fluvial sedimentsample, Csi is the mean concentration of tracer property i in sourcegroup s and Ps is the relative proportion from source groups(Walling et al., 1999).

All source groups comprised a minimum of 10 samples (Rowanet al., 2000) and the MDF analysis established the minimumnumber of diagnostic properties that could correctly classify 100%of the sourcematerial correctly. Goodness of fit of themixingmodelresults was tested through comparison of the actual fingerprintproperty value measured in channel sediment with the corre-sponding values predicted by the model based upon percentagecontributions estimated from each source group (c.f. Collins et al.,1997). Relative errors for each diagnostic property within therespective signatures were averaged to provide a mean error ateach sample site. Errors were within 10%, ranging from 5.2% to 9.8%,and suggest that the mixing model provides an acceptableprediction of diagnostic property values in the channel sedimentsamples. Sufficient geochemical data was not available to

Fig. 5. Plots of percentage sediment contribution from tributary catchments to the sediment load of the River Maritsa.

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2165

characterize small number of tributaries within the Maritsacatchment (totalling up to 32% of the catchment area); thereforemixing model data for all characterized source groups was scaledrelative to the proportion of uncharacterized catchment. Unchar-acterized contributions were found to account for only between 3and 29% of sediment contributions.

Table 4r2 values for regression relationships between channel distance along the RiverMaritsa and percentage sediment contributions from tributary catchments. * and **denote relationship significant at the 95 and 99% significance limits, respectively.

Linear Exponential Logarithmic Power

Upper Maritsa (n ¼ 9) 0.71** 0.89** 0.83** 0.95**Topolnitsa (n ¼ 9) 0.70** 0.85** 0.84** 0.93**Luda Yana (n ¼ 8) 0.84** 0.83** 0.91** 0.87**Vuchar (n ¼ 7) 0.56* 0.56* 0.61* 0.60*Pyasachnik (n ¼ 7) 0.96** 0.98** 0.98** 0.97**Chepelarska (n ¼ 5) 0.89** 0.84** 0.84** 0.82**

4.5. Quantifying sediment supply to the River Maritsa

Data from the mixing model indicate that within the upper100 km of the River Maritsa, between 44 and 62% of river sedimentis sourced from the Rivers Topolnitsa and Luda Yana (Fig. 5). Sedi-ment contributions from these two tributaries reducewith distancedownstream to a minimum of 8 and 5%, respectively in the lowerreaches of the River Maritsa. Peak sediment contributions from theRivers Vuchar (18%) and Pyasachnik (17%), occur within samplescollected closest to their confluences with the River Maritsa, but arelower than those for the mining-affected Topolnitsa and Luda Yana.Of particular importance in terms of sediment delivery to the RiverMaritsa and its middle and lower reaches, is material sourced fromthe River Chepelarska, with between 38 and 20% of sediment beingattributed as having originated from the Chepelarska between 154and 260 km channel distance (Fig. 5). At the last two sample sites(230 and 250 km), sediments sourced fromthe River Sazlika accountfor the second highest contribution to the RiverMaritsa (20 and 15%,respectively) and in combination with the Chepelarska account forbetween 44 and 35% of sediment delivery, with Pb isotopic evidencehighlighting smelter waste within the lower Chepelarska catch-ment as key source of sediment-associated contaminants.

Along the Bulgarian reach of the River Maritsa, the Rivers Top-olnitsa, Luda Yana and Chepelarska are tributaries that are directlyaffected by metal mining. Metal levels in river sediments withinthese rivers are highly enriched with contaminant metals (Table 1),

for example, sediment in the lower River Chepelarska has alreadybeen shown to be highly enriched in Cd (260 mg kg�1), Cu(950 mg kg�1), Pb (4300 mg kg�1) and Zn (8700 mg kg�1). Isotopicevidence for the Rivers Topolnitsa and Chepelarska has highlightedthe importance of contaminant sources associated withmining andmine and smelter waste storage. Data from the multivariate mixingmodel indicate that combined delivery from these three tributariesaccounts for 37e63% of sediment contributions to the River Mar-itsa, with an average of 49.5%. This therefore suggests that onaverage, half of the channel sediment load within the River Maritsaoriginates from tributaries that are directly affected by miningactivity.

The implications of sediment delivery from these contaminatedtributaries are highlighted in the elevated metal concentrationswithin the River Maritsa, particularly the lower Maritsa (down-stream of 150 km), where metal levels are elevated above US EPAguideline values. Within this reach of the River Maritsa, sedimentdelivery from the River Chepelarska is of principal concern, with20e38% the sediment load being identified as originating from thistributary. The potential importance of other sources of contaminantmetals within the Maritsa catchment cannot be discounted. Forexample, whilst concentrations in other tributaries are not as high

Fig. 6. Best-fit regression relationships for downstream distance in the River Maritsaand percentage sediment contribution from tributary catchments. Modelled relation-ships allow the footprint of sediment dispersal from tributaries to be quantified.

Fig. 7. Metal concentrations in river channel sediment of the River Arda and tr

G. Bird et al. / Environmental Pollution 158 (2010) 2158e21692166

as those in the Topolnitsa, Luda Yana and Chepelarska (Fig. 2), metallevels in rivers such as the Vuchar and Pyasachnik are elevatedabove US EPA guidelines. Whilst these tributaries are not directlyaffected by present mining activity, these catchment drain miner-alised regions of the Srednogorie and Rhodope metallogenic zonesand may contain metals dispersed from natural bedrock weath-ering and/or unidentified and disused mine workings.

Patterns of downstream decline in percentage sedimentcontributions from studied tributaries are generally best describedby non-linear functions (Table 4). This generally indicates that therates of downstream reductions in the percentage of sediment inthe River Maritsa originating from the respective tributaries reducewith increasing distance downstream. For example, the percentageof sediment originating from the River Topolnitsa reduces by 62%between 67 and 110 km channel distance, however, between 110and 154 km channel distance the reduction is only 33%. Down-stream reductions will represent the influence of sediment mixingwith inputs of sediment from other tributaries that will ‘dilute’ theinfluence of any one sediment source (Miller et al., 2007).

Utilizing the best-fit regression equations for the relationshipsbetween channel distance and sediment contribution it is possible

ibutaries. Values are plotted versus US EPA TEC and PEC guideline values.

Fig. 8. Bivariate plot of 208Pb/208Pb and 207Pb/206Pb isotope ratios measured in samples of river channel sediment, metal ores and mine waste from the River Arda. Data forMadjarovo and Zvezdel ore deposits sourced from Stos-Gale et al. (1998), data for the Luku ore deposit sourced from Amov (1999).

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2167

to model downstream decay patterns for sediment contributionsand therefore quantify the footprint sediments dispersed fromtributary catchments into the River Maritsa. Despite downstreamreduction in contributions due to mixing and physical dilution, it isunrealistic to suggest a zero contribution from a given tributary;therefore the downstream extent of tributary footprints has beenquantified down to 5% sediment contribution (Fig. 6). In the case ofthe upper Maritsa, this falls within the sampled reach, as themixing model predicted a <5% contribution at 207 km channeldistance. With respect to the mining-affected Rivers Topolnitsa,Luda Yana and Chepelarska, footprints extend between 300 and365 km channel distance, suggesting that approximately 5% ofsediment in the River Maritsa will have originated from thesetributary catchments between 230 and 300 km downstream oftheir confluences with the trunk stream. These geochemical foot-prints extend across Bulgaria's border with Greece, and furtherhighlight the transboundary dispersal of metal-rich sediment,sourced from mining-affected catchments in Bulgaria.

Within Greek/Turkish territory patterns of sediment mixing andrelative contribution of the tributary catchments described aboveto the sediment load of the Maritsa will be influenced by inputs ofsediment from two additional key tributaries of the Maritsa thathave their headwaters in Bulgaria. The Rivers Tundzha and Ardacatchments lie to the north and south of, respectively of the RiverMaritsa and drain the Srednogorie and Rhodope metallogeniczones (Fig. 1). The River Arda catchment in particular receivesmetalloadings from the exploitation of Pb/Zn ore deposits at Madan,Zvezdel and Madjarovo. Sediment-associated metal levels in the

River Arda are elevated above US EPA TEC and PEC values along theBulgarian reach, including immediately upstream of the Bulgarian/Greek border (Fig. 7). Lead and zinc concentrations are notablyenriched, and exceed TEC values by up to 97 and 15 times,respectively. Similar to River Maritsa, peak metal concentrationsgenerally occur in the mining-affected tributaries (Fig. 7), notablythe Rivers Elhovska and Madanska.

Isotopic signatures for ore deposits and mine waste within theArda catchment plot closely along a linear trend defined by therelationship between 208Pb/206Pb and 207Pb/206Pb ratios in riverchannel sediments (Fig. 8). This, in conjunction with presence ofelevated metal concentrations throughout the Arda catchment,highlights the importance metal mining as a source of sediment-associated metals to the River Arda. Whilst the isotopic signaturesin river channel sediments form a strong linear trend, the widevariety of potential metal sources within the catchment precludesthe use of a binary mixing model to quantify Pb delivery from thesesources. Unfortunately sufficient geochemical data is not availableto utilize the composite fingerprinting and mixing model approachwithin the Arda catchment, however, the data presented clearlyhighlight the presence of metal-rich sediments within the RiverArda and the potential for mobilization across the Bulgaria/Greeceborder and downstream through the wider Maritsa catchment.

5. Conclusions

Data presented have highlighted how Pb isotope signaturesdetermined in river channel sediments clearly reflect those of ore

G. Bird et al. / Environmental Pollution 158 (2010) 2158e21692168

bodies and sources mining-associate waste in mineralised andmining-affected river catchments. This has allowed Pb isotopes to beused as geochemical tracers of contaminated sediment dispersalwithinmining-affected catchments. In relatively small river systems,with limited numbers of potential Pb sources, isotopic signatures canbe used in binarymixingmodels to quantify the contribution of Pb toriver channel sediments fromkey sources. In larger catchments, suchas the Maritsa system, where contaminated sediments are sourcedfrom large number of potential sources, composite fingerprints canbe determined for tributary catchments using Pb isotope andmulti-element geochemical data. The use of a multivariate mixing modelallows their contribution to the sediment load of the trunk stream tobe quantified and geochemical footprints for the dispersal ofcontaminated sediments to be established.

Identifying key sources of contaminant metals in the RiverMaritsa catchment using Pb isotopic signatures provides respon-sible authorities with a means of targeting the focus of remediationactivities. For example, stabilizing smelter waste stored on thefloodplain of the River Chepelarska must be a primary target forremediation activities in addition to reducing thefluxofmetals fromsources associated with mining of the Medet, Assarel and Luku orebodies. Whilst river sediments in mineralised catchments willconsistently carry a metal load resulting from bedrock weathering,reducing the dispersal of contaminants from the sources high-lighted will begin to reduce the flux of metals through the Maritsacatchment.

Acknowledgements

The authors would like to thank The Royal Society for providingthe funding for this project through a Joint Project Grant.

References

Amov, B., 1999. Lead isotope data for ore deposits from Bulgaria and the possibilityfor their use in archaeometry. Berliner Beiträge zur Archäometrie 16, 5e19.

Amov, B., 2000. Comment on Z.A. Stos-Gale et al. ‘Lead isotope data from the iso-trace laboratory, Oxford: archaeometry database 5, Ores from Bulgaria’.Archaeometry, 40 (1) (1998), 217e226. Archaeometry 42, 237e241.

Amov, B.G., Bogdanov, B., Baldzhieva, T., 1974. Problems of Ore Deposition: FourthSymposium of IAGOD. (Izotopniy sostav svinsta i nekotorie voprosy genezisa ivostrasta orudineniy yougovostochnoy Bolgarii), vol. 2. Bulgarian Academy ofSciences, Varna, Bulgaria, pp. 13e25 (in Russian).

Ault, W.U., Senechal, R.G., Erlebach, W.E., 1970. Isotopic composition as a naturaltracer of lead in the environment. Environmental Science and Technology 4,305e313.

Bacon, J.R., 2002. Isotopic characterisation of lead deposited 1989e2001 at twoupland Scottish locations. Journal of Environmental Monitoring 4, 291e299.

Bacon, J.R., Dinev, N.S., 2005. Isotopic characterisation of lead in contaminated soilsfrom the vicinity of a non-ferrous metal smelter near Plovdiv, Bulgaria. Envi-ronmental Pollution 134, 247e255.

Bi, X., Feng, X., Yang, Y., Li, X.D., Shain, G.P.Y., Li, F., Qiu, G., Li, G., Liu, T., Fu, Z., 2009.Allocation and source attribution of lead and cadmium in maize (Zea mays L.)impacted by smelting emissions. Environmental Pollution 157, 834e839.

Bird, G., Brewer, P.A., Macklin, M.G., Nikolova, M., Kotsev, T., Mollov, M., Swain, C.,2010. Contaminant-metal dispersal in mining-affected river catchments of theDanube and Maritsa drainage basins, Bulgaria. Water, Air, and Soil Pollution206, 105e127.

Bogdanov, B., 1980. Massive sulphide and porphyry copper deposits in the Pan-agyurishte District, Bulgaria. In: Jankovic, S., Sillitoe, R.H. (Eds.), EuropeanCopper Deposits: Proceedings of an International Symposium. BelgradeUniversity, Bor, Yugoslavia, pp. 50e58.

Bogdanov, B., 1982. Bulgaria. In: Dunning, F.W., Mykura, W., Slater, D. (Eds.), MineralDeposits of Europe. Southeast Europe, vol. 2. The Mineralogical Society, London,pp. 215e232.

Brewer, P.A., Nikolova, M., Bird, G., Kotsev, T., Macklin, M.G., Mollov, M., Swain, C.,2005. Source to sink river pollution assessment in northern Bulgaria: prelimi-nary results on contaminant delivery in the lower Danube basin. In: Proceed-ings of the Fifth International SGEM Scientific Conference: ModernManagement of Mine Producing, Geology and Environment Protection, pp.387e396.

Ciobanu, C.L., Cook, N.J., Stein, H., 2002. Regional setting and geochronology of theLate Cretaceous Banatitic Magmatic and Metallogenic Belt. MineraliumDeposita 37, 541e567.

Collins, A.L.,Walling,D.E., 2004.Documentingcatchment suspended sediment sources:problems, approaches and prospects. Progress in Physical Geography 28,159e196.

Collins, A.L., Walling, D.E., 2007. Sources of fine sediment recovered from thechannel bed of lowland groundwater-fed catchments in the UK. Geomor-phology 88, 120e138.

Collins, A.L., Walling, D.E., Leeks, G.J.L., 1996. Composite fingerprinting of the spatialsource of fluvial suspended sediment: a case study of the Exe and Severn riverbasins, United Kingdom. Géomorphologie: Relief, Processus, Environnement 2,41e54.

Collins, A.L., Walling, D.E., Leeks, G.J.L., 1997. Source type ascription for fluvialsuspended sediment based on a quantitative composite fingerprinting tech-nique. Catena 29, 1e27.

Emmanuel, S., Erel, Y., 2002. Implications from concentrations and isotopic data for Pbpartitioning processes in soils. Geochimica et Cosmochimica Acta 66, 2517e2527.

Ettler, V., Mihaljevi�c, M., Komárek, M., 2004. ICP-MS measurements of lead isotopicratios in soils heavily contaminated by lead smelting: tracing the sources ofpollution. Analytical and Bioanalytical Chemistry 378, 311e317.

Ettler, V., Mihaljevi�c, M., �Sebek, O., Molek, M., Grygar, T., Zeman, J., 2006.Geochemical and Pb isotopic evidence for sources and dispersal of metalcontamination in stream sediments from the mining and smelting district ofPribram, Czech Republic. Environmental Pollution 142, 409e417.

Farmer, J.G., Graham, M.C., Bacon, J.R., Dunn, S.M., Vinogradoff, S.I., Mackenzie, A.B.,2005. Isotopic characterisation of the historical lead deposition record atGlensaugh, an organic-rich, upland catchment in N.E. Scotland. Science of theTotal Environment 346, 121e137.

Gallon, C.L., Tessier, A., Gobeil, C., Beaudin, L., 2005. Sources and chronology ofatmospheric lead deposition to a Canadian Shield lake: inferences from Pbisotopes and PAH profiles. Geochimica et Cosmochimica Acta 69, 3199e3210.

Gelinas, Y., Schmit, J.P., 1997. Extending the use of the stable lead isotope ratios asa tracer in bioavailability studies. Environmental Science and Technology 31,1968e1972.

Gobiel, C., Johnson, W.K., MacDonald, R.W., Wong, C.S., 1995. Sources and burden oflead in St. Lawrence Estuary sediments: isotopic evidence. EnvironmentalScience and Technology 29, 193e201.

Hao, Y., Guo, Z., Yang, Z., Fan, D., Fang, M., Li, X., 2008. Tracking historical leadpollution in the coastal area adjacent to the Yangtze River Estuary using leadisotopic compositions. Environmental Pollution 156, 1325e1331.

Heinrich, C.A., Neubauer, F., 2002. CueAuePbeZneAg metallogeny of the Alpi-neeBalkaneCarpathianeDinaride geodynamic province. Mineralium Deposita37, 533e540.

Hopper, J.F., Ross, H.B., Sturges, W.T., Barrie, L.A., 1991. Regional source discrimi-nation of atmospheric aerosols in Europe using the isotopic composition oflead. Tellus 43B, 45e60.

Hutchinson, S.M., Rothwell, J.J., 2008. Mobilization of sediment-associated metalsfrom historical Pb working sites on the River Sheaf, Sheffield, UK. Environ-mental Pollution 155, 61e71.

Ip, C.C.M., Li, X.D., Zhang, G., Wai, O.W.H., Li, Y.S., 2007. Trace metal distribution insediments of the Pearl River Estuary and the surrounding coastal area, SouthChina. Environmental Pollution 147, 311e323.

Johnston, R.J., 1978. Multivariate Statistical Analysis in Geography: a Primer on theGeneral Linear Model. Longman, London.

Komárek, M., Chrastnỳ, V., �Stíchová, J., 2007. Metal/metalloid contamination andisotopic composition of lead in edible mushrooms and forest soils originatingfrom a smelting area. Environment International 33, 677e684.

Kouzmanov, K., Bailly, L., Ramboz, C., Rouer, O., Beny, J.-M., 2002. Morphology, originand infrared microthermometry of fluid inclusions in pyrite from the Radkaepithermal copper deposit, Srednogorie zone, Bulgaria. Mineralium Deposita37, 599e613.

Kurkjian, R., Dunlap, C., Flegal, A.R., 2004. Long-range downstream effects of urbanrunoff and acid mine drainage in the Debed River, Armenia: insights from leadisotope modeling. Applied Geochemistry 19, 1567e1580.

Mackenzie, A.B., Pulford, I.D., 2002. Investigation of contaminant metal dispersalfrom a disused mine site at Tyndrum, Scotland, using concentration gradientsand stable Pb isotopes. Applied Geochemistry 17, 1093.

Macklin, M.G., Brewer, P.A., Hudson-Edwards, K.A., Bird, G., Coulthard, T.J., Dennis, I.,Lechler, P.J., Miller, J.R., Turner, J.N., 2006. A geomorphological approach to themanagement of rivers contaminated by metal mining. Geomorphology 79,423e447.

Marcantonio, F., Flowers, G.C., Templin, N., 1999. Lead contamination in a wetlandwatershed: isotopes as fingerprints of pollution. Environmental Geology 39,1071e1076.

Marchev, P., Downes, H., Thirlwall, M.F., Moritz, R., 2002. Small-scale variations of87Sr/86Sr isotope composition of barite in the Madjarovo low-sulphidationepithermal system, SE Bulgaria: implications for sources of Sr, fluid fluxes andpathways of the ore-forming fluids. Mineralium Deposita 37, 669e677.

Marchev, P., Kaiser-Rohrmeier, M., Heinrich, C.A., Ovtcharova, M., Von Quadt, A.,Raicheva, R., 2005. Hydrothermal ore deposits related to post-orogenic exten-sional magmatism and core complex formation: the Rhodope Massif of Bulgariaand Greece. Ore Geology Reviews 27, 53e89.

McGill, R.A.R., Pearce, J.M., Fortey, N.J., Watt, J., Ault, L., Parrish, R.R., 2003.Contaminant source apportionment by PIMMS lead isotope analysis and SEM-image analysis. Environmental Geochemistry and Health 25, 25e32.

Mihaljevi�c, M., Zuna, M., Ettler, V., �Sebek, O., Strnad, L., Goliá�s, V., 2006. Lead fluxes,isotopic and concentration profiles in a peat deposit near a lead smelter(P�ríbram, Czech Republic). Science of the Total Environment 372, 334e344.

G. Bird et al. / Environmental Pollution 158 (2010) 2158e2169 2169

Miller, J.R., Lechler, P.J., Hudson-Edwards, K.A., Macklin, M.G., 2002. Lead isotopicfingerprinting of heavy metal contamination, Rio Pilcomayo basin, Bolivia.Geochemistry: Exploration, Environment, Analysis 2, 225e233.

Miller, J.R., Lechler, P.J., Mackin, G., Germanoski, D., Villarroel, L.F., 2007. Evaluationof particle dispersal from mining and milling operations using lead isotopicfingerprinting techniques, Rio Pilcomayo Basin, Bolivia. Science of the TotalEnvironment 384, 355e373.

Monna, F., Hamer, K., Leveque, J., Sauer, M., 2000. Pb isotope as a reliable marker ofearly mining and smelting in the Northern Harz province (Lower Saxony,Germany). Journal of Geochemical Exploration 68, 201e210.

Moritz, R., Chambefort, I., Georgieva, S., Jacquat, S., Petrunov, R., 2005. The Chelo-pech high-sulphidation epithermal CueAu deposit. Ore Geology Reviews 27,130e131.

Neubauer, F., Heinrich, C.A., 2003. Late Cretaceous and Tertiary geodynamics andore deposit evolution of the AlpineeBalkaneCarpathianeDinaride orogen. In:Eliopolous, D.G. (Ed.), Mineral Exploration and Sustainable Development.Millpress, Rotterdam, pp. 1133e1136.

Panayotova, M., 1997. Impact of sulphide non-ferrous ore mining and dressingactivities on the environment. Journal of Environmental Science and Health:Part A e Environmental Science and Engineering & Toxic and HazardousSubstance Control 32, 2213e2228.

Rice, C.M., McCloyd, R.J., Boyce, A.J., Marchev, P., 2007. Stable isotope study of themineralization and alteration in the Madjarovo PbeZn district, south-eastBulgaria. Mineralium Deposita 42, 691e713.

Rowan, J.S., Goodwill, P., Franks, S.W., 2000. Uncertainty estimation in finger-printing suspended sediment sources. In: Foster, I.D.L. (Ed.), Tracers inGeomorphology. John Wiley & Sons Ltd, Chichester, pp. 279e290.

Stos-Gale, Z.A., Gale, N.H., Annetts, N., Todorov, T., Lilov, P., Raduncheva, A.,Panayotov, I., 1998. Lead isotope data from the isotrace laboratory, Oxford:archaeometry database 5, ores from Bulgaria. Archaeometry 40, 217e226.

Strashimirov, S., Petrunov, R., Kanazirski, M., 2002. Porphyry-copper mineralisationin the central Srednogorie zone, Bulgaria. Mineralium Deposita 37, 587e598.

Sturges, W.T., Barrie, L.A., 1987. Lead 206/207 isotope ratios in the atmosphere ofNorth America as tracers of US and Canadian emissions. Nature 329, 144e146.

Teutsch, N., Erel, Y., Halicz, L., Banin, A., 2001. Distribution of natural and anthro-pogenic lead in Mediterranean soils. Geochimica et Cosmochimica Acta 65,2853e2864.

US EPA, 2002. A Guidance Manual to Support the Assessment of ContaminatedSediments in Freshwater Ecosystems. US EPA, US EPA-905-B02-001-C, p. 232.

Vassileva, R.D., Bonev, I.K., Marchev, P., Atanassova, R., 2005. PbeZn deposits in theMadan ore field, South Bulgaria. Ore Geology Reviews 27, 90e91.

von Quadt, A., Moritz, R., Peytcheva, I., Heinrich, C.A., 2005. Geochronology andgeodynamics of Late Cretaceous magmatism and CueAu mineralization in thePanagyurishte region of the ApusenieBanateTimokeSrednogorie belt, Bulgaria.Ore Geology Reviews 27, 95e126.

von Quadt, A., Peytcheva, I., Cvetkovic, V., 2003. Geochronology, geochemistry andisotope tracing of the Cretaceous magmatism of east-Serbia and Panagyurishtedistrict (Bulgaria) as part of the ApusenieTimokeSrednogorie metallogenic beltin eastern Europe. In: Eliopolous, D.G. (Ed.), Mineral Exploration and Sustain-able Development. Millpress, Rotterdam, pp. 407e410.

Walling, D.E., Owens, P.N., Leeks, G.J.L., 1999. Fingerprinting suspended sedimentsources in the catchment of the River Ouse, Yorkshire, UK. HydrologicalProcesses 13, 955e975.

Walling, D.E., Woodward, J.C., Nicholas, A.P., 1993. A multi-parameter approach tofingerprinting suspended sediment sources. In: Peters, N.E., Hooehn, E.,Leibundgut, C., Tase, N., Walling, D.E. (Eds.), Tracers in Hydrology. IAHS Publi-cation No. 215, pp. 329e337. Wallingford.

Wilson, C.A., Bacon, J.R., Cresser, M.S., Davidson, D.A., 2005. Lead isotope ratios asa means of sourcing anthropogenic lead in archaeological soils: a pilot study ofan abandoned Shetland Croft. Archaeometry 48, 501e509.