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The geographic distribution of strontium
isotopes in Danish surface waters – A base forprovenance studies in archaeology, hydrology
and agriculture
Article in Applied Geochemistry · March 2011
Impact Factor: 2.27 · DOI: 10.1016/j.apgeochem.2010.12.006

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Karin Margarita Frei

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The National Museum of Denmark

University of Copenhagen

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Applied Geochemistry 26 (2011) 326–340

Contents lists available at ScienceDirect

Applied Geochemistryjournal homepage: www.elsevier.com/locate/apgeochem

The geographic distribution of strontium isotopes in Danish surface waters – Abase for provenance studies in archaeology, hydrology and agriculture
Karin M. Frei a,⇑, Robert Frei bab

The Danish National Research Foundation Centre for Textile Research, SAXO Institute, University of Copenhagen, Njalsgade 80, DK-2300 Copenhagen, Denmark
Institute of Geography and Geology and Nordic Center for Earth Evolution (NordCEE), University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmarka r t i c l e

i n f oArticle history:
Received 28 September 2010
Accepted 17 December 2010
Available online 22 December 2010
Editorial handling by R. Fugea b s t r a c t
In this paper Sr isotope signatures are reported for 192 surface water (lakes/ponds and rivers/creeks)samples from within Denmark and an isotope distribution map is presented that may serve as a basefor provenance applications, including archaeological migration studies, ground water – surface water– seawater interaction/contamination monitoring, and potentially for agricultural applications, including
cases of authenticity proof for particular food products. The Sr isotopic compositions of surface waters
range from 87Sr/86Sr = 0.7078 to 0.7125 (average 0.7096 ± 0.0016; 2r). This average value lies above
the range of 87Sr/86Sr values between 0.7078 and 0.7082 expected from Late Cretaceous to Early Tertiary(Oligocene) limestones which form the dominant bedrock type in a NW–SE trending belt in Denmark. The
elevated 87Sr/86Sr signatures >0.7095 are explained by additions to the surface waters of radiogenic Sr
predominantly derived from the near-surface weathering and wash-out of Quarternary glaciogenic tills
and soils deposited and formed during and after the last two ice age stages (Saale and Weichsel). The
Sr isotopic compositions and concentrations of the surface waters can, therefore, best be modeled by a
two-component mixing involving carbonaceous bedrock and glaciogenic cover sediments as the two predominant Sr sources. A feasibility study for using Sr isotopic compositions of surface waters as a proxy forbio-available Sr signatures was conducted in a representative test area on Zealand (Land of Legends,
Lejre) where there is no use and application of commercial fertilizers. It is demonstrated that the Sr isotopic signatures of lake waters from within this area are slightly higher (but statistically still indistinguishable) from the average value deﬁned by snail shells and soil leachates considered to characterize
the true bio-available Sr. In combination with results from other studies, this is interpreted to reﬂect
the wash-out of Sr with a higher 87Sr/86Sr signature released by weathering in the topsoils into the saturated water tables, a component which is consequently not readily transferred into the shallow-rooting
plants and into small herbivores feeding on them. Since drinking water is a likely important source of Sr
uptake of humans and larger animals, the contention is that a surface water isotopic composition canpotentially characterize the bio-available component relevant for human and carnivorous large animals.
Spline functions and ordinary linear kriging were used for modeling the geographic distribution of bioavailable Sr isotopes over territorial Denmark. As expected, based on the two-source mixing scenario,
the water 87Sr/86Sr ratio contour maps (with some notable exceptions) neither mirror the pre-Quarternary bedrock geology nor a (Pleistocene) soil-type distribution map of Denmark.
As a conservative suggestion, the use of the average 87Sr/86Sr ratio of 0.7096 ± 0.0015 (2r) is recommended as an average for bio-available Sr from within Denmark (Bornholm excluded) for human and larger animals and as a conﬁdence band for distinguishing ‘‘local’’ from ‘‘non-local’’ signatures inarchaeological provenance studies. The study also reveals that the average 87Sr/86Sr ratio of 0.7088
deﬁned from soil extracts and small herbivores lies approximately 0.15% lower than that deﬁned by
the surface waters. The authors recommend using this lower value as a ‘‘local’’–‘‘non-local’’ discriminator
for food and plant authenticity control in agricultural applications.
Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction
The Sr isotope compositions of surface waters have thepotential to mirror the average composition of bio-available Sr of⇑ Corresponding author. Tel.: +45 39665423.
E-mail address: kmfrei@hum.ku.dk (K.M. Frei).
0883-2927/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.apgeochem.2010.12.006

a speciﬁc area (catchment) and the relatively simple analytical protocols of water–Sr analysis have recently led to a number of attempts to produce extensive regional distribution maps meant to
be used as bases for archaeological, geological, hydrological andagricultural provenance studies (Beard and Johnson, 2000; Evanset al., 2009, 2010; Voerkelius et al., 2010). In the ﬁeld of archaeology, for example, Sr isotope signatures of human bone tissues

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340(particularly of tooth enamel) can offer substantial evidence forcharacterizing prehistoric migrations (Bentley, 2006; Montgomery,
2010; Price et al., 2002). By comparing the isotope values inarchaeological human tooth enamel to bio-available signaturescharacterizing the region, one would hope to identify the geographical area where the diet of a person was obtained duringchildhood. Unlike C, N and O, Sr isotopes are transferred, without
the 87Sr/86Sr ratio measurably changing, from the weathering ofrock minerals (with, importantly, different weathering rates andSr concentrations) through soils and interstitial soil water into
the biosphere and food chains. In mammalian tooth enamel, Sr
substitutes for Ca in hydroxyapatite. Because 87Sr/86Sr values inrocks and minerals vary, depending on their Rb/Sr concentration
ratio and their geological age (as 87Rb radioactively decays to87
Sr with a half-life of 48 billion years), Sr isotopes can, therefore,
be used as a geological, and hence geographical, tracer withinarchaeological skeletons. Clearly, a regional map of biologicallyavailable Sr isotope values is indispensable for interpreting isotope
signatures in archaeological skeletons.
Numerous recent applications of the Sr isotope system include
migration and dispersal studies of birds (Sellick et al., 2009), proofof origin authenticity of certain food products (Crittenden et al.,2007; Horn et al., 1993; Rossmann et al., 2000), plants (Evanset al., 2010; Kelly et al., 2002; West et al., 2009), mineral waters(Montgomery et al., 2006; Voerkelius et al., 2010), and also involve
forensic investigations aiming at origin assignments of unidenti-

327

ﬁed corpses (Rauch et al., 2007). All these studies rely on geographical reference data bases which allow mapping of the bio-available
Sr isotope signatures over potential local and regional target areas.
Because minerals with different Sr concentrations and 87Sr/86Sr ratios weather at different rates, a geological map of 87Sr/86Sr variations in bedrocks, as initially proposed by Beard and Johnson(2000) is not always sufﬁcient to predict the 87Sr/86Sr entering
the environmental Sr cycle.
One way to map regional bio-available 87Sr/86Sr ratios is to analyze hundreds of soil leachate, water and plant samples, as Hodell
et al. (2004) have impressively done for Mesoamerica. An alternative way, applied in archaeological studies, is a more direct measurement of biologically available isotope signatures which relieson the analyses of 87Sr/86Sr ratios directly in archaeological tooth
enamel of a low-mobility animal species from different excavationsites (Bentley et al., 2004; Price et al., 2002). Through a lifetime offeeding, herbivores seem to obtain a remarkably consistent average 87Sr/86Sr ratio that is representative of their catchment area
(Blum et al., 2001; Burton et al., 1999; Price et al., 2002). The reason why archaeologists proposed sampling archaeological rather
than modern material is that the modern environment can potentially be contaminated with exogenous Sr from human sources,
such as for example from fertilizers (e.g., Böhlke and Horan,
2000; Hosono et al., 2007; Probst et al., 2000; Vitoria et al.,2004). These additional, non-geologic sources of Sr in the biosphere can be a threat to the successful use of the Sr isotopic tracer

Fig. 1. Map of Denmark with spatial distribution of Saalian and Weichsealian glacier deposits and related landscape features (from Smed, 1993; with modiﬁcations). Names
and localities indicated refer to those used in the text.

328

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

system, and need to be taken into consideration when for example
using soil extracts or water analyses as monitors for the biologically-available signature at a suspected location of origin.
In this contribution, the authors advocate the use of Sr isotopic
compositions of surface waters (rivers/creeks and lakes/ponds)
from within Denmark to obtain a picture of the geographic distribution of biologically-available Sr, primarily with the aim of constructing a reference baseline map for archaeometric studies ofgeographic mobility of humans and animals, for investigation offood authentication, and potentially for forensic purposes. In orderto validate the methodology, and to circumvent discussion regarding the potential disturbances/contamination of surface waters byanthropogenic additions of exotic Sr, mainly by fertilization of agricultural areas, results are presented from a detailed study performed in an ecological test area (‘‘Land of Legends’’, Lejre
experimental centre; http://www.sagnlandet.dk/) on the island of
Zealand. This study provides supportive evidence for the Sr isotope
agreement between surface waters and animal hair tissues, therefore, constraining the direct link between Sr in water and the foodchain, and validates the suitability and accuracy of the approach.
2. Tertiary–Quarternary geology and landscapes of Denmark
The landscapes of Denmark were shaped by the middle to late
Weichselian glaciers and their associated glaciogenic deposits(Fig. 1). Environmental changes between 40 ka and 15 ka BP in
SW Scandinavia can be described in terms of three major progressive stages beginning with an interstadial sequence followed by
the Last Glacial Maximum (LGM) sequence and terminating withthe deglaciation sequence (Houmark-Nielsen and Kjær, 2003).
These authors comprehensively illustrate the evolution througheach sequence by four consecutive palaeogeographical maps (theirFigs. 3 and 4) which are based on the distribution of tills and intertill sediments. The scope of this article does not permit going into
detail of the Quarternary geology, and, therefore, the reader is referred to the information presented in the work of Houmark-Nielsen and Kjær (2003). The following is merely a short summary of
the main evolutionary stages.
2.1. The interstadial sequence 40–30 ka BP
Although the coastal regions of Norway were kept free of glacierice, the Scandinavian Ice Sheet (SIS) eventually began to grow eastwards over the central Swedish lowlands and into the Baltic
depression, where glaciers advanced through proglacial lakes towards the south between 40 and 36 ka BP. Evidence from Møn(SE Zealand; Fig. 1) shows that a Baltic glacier invaded eastern
Denmark about 36–33 ka BP and deposited the Klintholm Till on
periglacially disturbed strata overlain by sand and glaciolacustrine
mud. The ice distribution towards the west is still poorly understood. However, it must have covered the eastern part of Denmark
because the Klintholm Till overlies interstadial sediments at Sejerø(NW Zealand, Fig. 1).
2.2. Last Glacial Maximum sequence 30–20 kyr BP
Ice growth in the Norwegian mountains caused glacier ice toadvance southwards from the Oslo Fjord at about 29 ka BP as anoutlet surge. This developed into an ice stream from the southernpart of Norway that spread out along the Kattegat Ice Lake ﬂoor
and deposited the Kattegat Till characterized by distinctive Osloerratics in northern Denmark (Houmark-Nielsen, 2003). Ice

Fig. 2. (A) Map of Denmark with localities of surface water samples plotted on the pre-Quaternary geological background. (B) Map of Denmark with localities of surface water
samples plotted on the Pleistocene (glaciogenic) soil-type geological background.

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

329

Fig. 3. (A) Contoured distribution of 87Sr/86Sr values of surface waters (sample localities marked with green-ﬁlled circles) from within Denmark. Contouring relies on theapplication of a spline function to the interpolated data grid (see text for details). (B) Contoured distribution of 87Sr/86Sr values of surface water samples, building on a data
grid produced by ordinary linear kriging (see text for details).

drainage through the Norwegian Channel probably led to stagnation and starvation of the ice stream in Kattegat, eventually causing deglaciation in northern Denmark around 26 ka BP. The
Scandinavian Ice Sheet reached its maximum distribution in Denmark about 22 ka BP. The glacier over Denmark and Skåne (southern Sweden), which traditionally is termed the northeast advance,
0.71100.710587

Sr/ 86Sr

0.71000.70950.70900.70850.70800.7075

R2 = 0.63

0

1

2

3

4

5

6

7

8

-1

1/Sr (mg/L )Fig. 4. Mixing diagram with data for 20 randomly selected surface waters. A linear
relationship in such diagrams indicates two-source mixing to explain the scatter ofdata points. The data for the 20 water samples correlate fairly well (correlation
coefﬁcient r2 = 0.63) and imply the predominance of a source with low 87Sr/86Sr
ratios and high Sr concentrations and one with elevated 87Sr/86Sr values and low
concentrations of Sr. These sources are equated with Sr derived from preQuarternary (Late Cretaceous–Paleocene) limestone basement (with respective
seawater signature between 0.7078 and 0.7082; range indicated by gray-ﬁlled bar)and by Sr derived from (rain) water percolating through the various soils downward
into the water-saturated zones of the catchment areas (details see text).

deposited the Mid-Danish Till characterized by central and southern Swedish erratics (Houmark-Nielsen, 1987; Smed, 1993). This
event took place after ca. 25 ka BP in northern and eastern Denmark and slightly before 22 ka BP in central Jutland.
2.3. The deglaciation sequence 20–15 ka BP
By 19 ka BP, deglaciation and the general retreat of the western
margin of the Scandinavian Ice Shield in SW Scandinavia was wellunderway. Marine drawdown continuously reduced the Norwegian Channel Ice Stream and forced the pattern of ﬂow in the Skagerrak towards a more north–south direction as the ice-front
retreated towards the mouth of the Oslo Fjord (Rise et al., 1997).
Ice ﬂow from the mountainous hinterland was restored along thecoast of southern Norway (Larsen et al., 2000), and the thinningof the ice cover caused isostatic rebound. Around 18 ka BP marine
inundation reached northern Denmark leading to the developmentof an archipelago in Vendsyssel (Richardt, 1996), and to rapid
deglaciation and deposition of muddy sediments in a meltwaterdominated fjord system further south. In central Denmark the icefrom Sweden steadily retreated, which caused the opening of the
Kattegat depression and transgression of the Arctic Ocean. Shortly
after 18 ka BP a forced regression caused by glacio-isostatic rebound is registered in Vendsyssel, Kattegat and northern Øresund(Richardt, 1996; Sandgren et al., 1999). During the general deglaciation, an ice stream from the Baltic moved westward and reachedthe East Jutland margin about the same time as the ﬁrst marine
invasion in Vendsyssel (Houmark-Nielsen, 1987). The Young Baltic
Ice advanced over large areas of dead ice. The three lobes of this ice
stream, which developed over Denmark, represent individual

330

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

time-transgressive surge-like advances that were guided by topographic depressions now occupied by the Danish straits; the Lillebælt lobe is the oldest and the lobe through Øresund is the
youngest (Kjær et al., 2003). At ca. 17 ka BP the ice margin had retreated to the Halland coastal moraines along the Swedish west
coast (Lagerlund and Houmark-Nielsen, 1993).
2.4. Paleogene and Neogene sediments
The Paleogene sediments below the Pleistocene glaciogenic
cover show a general younging from NE to SW (Late Cretaceousand Paleocene limestones in Northern Jutland and Zealand; Eocene
and Oligocene volcanic and marine clastic sediments in central Jutland, Funen and parts of western Zealand; Miocene marine clastic
sediments in central and southwestern Jutland). This zonation resulted from increased uplift of the northeastern parts of Denmark
relative to the southwestern parts during post-glacial times. Marine deep water sequences belonging to the Younger Yoldia stage
have been exposed above sea level and created ﬂat plains typicalof the Vendsyssel area (northwesternmost part of Jutland). This uplift during the late Neogene caused Paleogene sediments to be preferentially eroded in the NE relative to the SW, bringing them close
to the surface in a belt through Jutland, Funen and most of Zealand(Fig. 2A). Differential thrusting and faulting of the Precambrianbasement, and reactivation of faults throughout the Paleogene
and Neogene, along a NW–SE trending belt known as the Sorgenfrei-Tornquist zone, caused the Pleistocene layers to be relatively
thin in a belt from northwestern Jutland through Djursland and
Mid-Zealand to the island of Møn. Consequently, the Late Cretaceous–Early Paleocene limestones lie near the surface soils in thisbelt.
3. Samples and analytical techniquesRiver and lake samples were largely obtained as aliquots fromroutine surveillance sampling performed by six Danish Environmental Centers (Miljøcenters of Roskilde, Århus, Odense, Ribe, Aalborg and Ringkøbing) and supplemented by samples fromcolleagues and by the authors’ collection. Sample localities(excluding samples from Bornholm) are plotted on top of the
pre-Quarternary (Late Cretaceous–Miocene) bedrock geology inthe map of Fig. 2A and on top of the Pleistocene soil-and glaciogenic deposit-type distribution map of Fig. 2B.
Water aliquots were ﬁltered with disposable syringe ﬁlters(45 lm) and between 2 and 6 mL aliquots were transferred to
7 mL Teﬂon (Savillex™) vials. The aliquots were evaporated down
on a hot plate at 80 °C overnight. Aliquots for the determination of
Sr by isotopic dilution were spiked with an amount of a 84Sr enriched (abundance 84Sr = 96.3%) tracer prior to evaporation.
Four atmospheric samples from two monitoring stations within
Denmark (Lille Valby and Keldsnor; cf. Fig. 1) were analyzed to obtain information regarding concentration and isotopic compositionof ﬁne airborne particles. The particles collected are ‘‘PM10’’ (i.e.
particles with an aerodynamic diameter <10 lm). Filters with particles collected over 2 days in 2002 (August 2) and 2006 (May 5)
from the two locations were selected more or less randomly. The
total Sr amounts contained in the ﬁlters are slightly above theaverage values usually recovered from ﬁlters collected within Denmark and the differences between 2002 and 2006 are probably
mainly due to different meteorological conditions. The exposure
time of the cellulose ester ﬁlters was 24 h, the collection air volume was approximately 22 m3, and the exposed ﬁlter area was
4 cm2.
Small pieces of snail shells (weighing 20 mg) from Lejre (Zealand) were dissolved in a 1:1 mixture of 6 N HCl and 30% H2O2 on a

hotplate at 80 °C over night. Subsequently the solutions were evaporated down.
Soil extracts from Lejre (Zealand) were produced by adding 5 mLof a 0.05 N HNO3 solution to 1 g of soil in a 10 mL centrifuge tube.
After shaking the slurry for 15 min at room temperature, and after
centrifugation, 2 mL of the supernatant were pipetted off, transferred into a 7 mL Teﬂon beaker and evaporated down overnight.
The atmospheric particle ﬁlters, together with the particles,
were dissolved in 2 mL of a 1:1 mixture of concentrated aqua regia
and concentrated HF in a 7 mL Teﬂon beaker for 24 h. After evaporation, the sample was treated with 0.5 mL of a 1:1 mixture of 8 N
HCl and H2O2 for 2 h on a hotplate at 80 °C, after which the solution
was evaporated down again.
3.1. Ion chromatographic procedures
Samples (water evaporates, soil extracts, dissolved snail shells,
atmospheric ﬁlters) were taken up in a few drops of 3 M HNO3 andthen loaded on commercially available (Eppendorf ™) 1 mL pipettetips with a pressed-in ﬁlter designed to be used as an extraction column. These ‘‘columns’’ were charged with 200 lL of an intensively
pre-cleaned mesh 50–100 SrSpec™ (Eichrome Inc./Tristchem) resin.
The elution recipe essentially followed that of Horowitz et al. (1992),
scaled to needs. Strontium was eluted/stripped by pure deionizedwater and then the eluate was dried on a hotplate.
3.2. Thermal ionization mass spectrometry
Samples were dissolved in 2.5 lL of a Ta2O5–H3PO4–HF activator solution and loaded directly onto previously outgassed
99.98% single Re ﬁlaments. Samples were measured at 1250–
1300 °C in dynamic multi-collection mode on a VG Sector 54 IT
mass spectrometer equipped with eight Faraday detectors(Institute of Geography and Geology, University of Copenhagen).
Five ng loads of the NBS 987 Sr standard gave 87Sr/86Sr =0.710236 ± 0.000010 (n = 14, 2r). The 87Sr/86Sr values of thesamples were corrected for the offset relative to the certiﬁed NIST
SRM 987 value of 0.710250. The errors reported in Tables 1 and 2
are within-run (2rm) precisions of the individual runs.
3.3. Reagents and blanks
Ultrapure acids (Seastar™ and dilutions thereof) and waterfrom a Milli-Rho-Milli-Q (Millipore) system were systematicallyused in the ion chromatographic procedures for the water samples.
All procedures were performed inside better than Class 100 Hepa
ﬁlter equipped chemical workstations, located inside a suite ofClass 1000 overpressured clean rooms. Total procedure blanks
amounted to <65 pg of Sr. One blank Sr composition measured during the analytical period yielded a 87Sr/86Sr ratio of 0.708. The
amount of blank Sr (<65 pg) is insigniﬁcant relative to the amountof sample Sr (200 ng), and, therefore, the measured Sr isotopic
compositions of the waters were insensitive (in the critical ﬁrst ﬁve
digits) to a blank correction.
4. Results
4.1. Country-wide distribution patterns
Strontium isotopic compositions of 192 water samples fromwithin Denmark (Jutland, Funen, Zealand and Langeland), twofrom northern Germany, and three from Bornholm, are listed in Table 1 together with their coordinates (latitude, longitude readings
from a handheld GPS instrument). Results from 20 aliquots forwhich Sr concentrations were measured are listed in Table 2.

331

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340
Table 1
Sr isotope compositions of surface (lake and river) water samples from within Denmark and from northern Germany.
Location

Region87

Sr/86Sr±2SE

LatitudeLongitude

Arreskov SøEskelund Mose
HovlungNørresøØstersjø, Lyø
Søbo Sø
Søholm SøStore Øresø
Vestersjø, Lyø
Davinde SøGeelså
OdenseÅ
Ulrikshom
Vejrup Å
Hedeby Hostrup SøTreene Friedrichstadt
Aﬂøb Årslev Engsø (Århus Å)
Aﬂøb Bryrup Langsø (Lystrup Å)
Aﬂøb Hinge Sø (Alling Å)Aﬂøb Ravn Sø (Knud Å)
Aﬂøb Silkeborg Langsø (Gudenå)Aﬂøb Stillig Solbjerg sø (Århus Å)
Gudenå ved Ry
Bjerndrup Mølle ÅGelsbro Gelså
Lindskov Mølle
Rudbøl SøVibæk Mølle
Barnsø
Bassin kilde Trindeborg hus
Dons NørresøDons Søndesø
Dybuad ÅEngelsholm SøEngsig Bæk BrattenFårup Sø
Førby Sø
Harring Å Harring Hedegård
Hastrup SøHolm SøKær Vig
Karlsgårde SøKetting Nor
Kjellerup SøKloster Eng Lergrav Nord
Kløv Å
Lille Søgård Sø 1
Lille Søgård Sø 2
Lindholm ÅMors Aﬂøb Legind SøNordborg SøOldenorRåbjerg Mik Sø øst
Rands Fjord
Saby Å Sabygård
Skærsø
Skårup Møllebæk
Stallerup SøStevning Dam
Store Søgård SøStubberup BækTranekær Sø
Vandet SøVarnæs SkovsøVillestrup Å 1
Villestrup Å 2
SjørupFlyndersøHærup SøKås Sø
Movsø
Skalle Sø 9-9Skån Sø
Søby Sø

Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Funen
Northern Germany
Northern Germany
JutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutland0.709800.710470.71000
0.709960.709180.709880.709850.710100.709220.710020.70946
0.70959
0.710080.70949
0.70873
0.709310.709390.710030.70959
0.709530.709260.71000
0.709120.70987
0.70899
0.708910.709190.70949
0.709110.70787
0.709410.709710.70789
0.709020.709510.70933
0.709090.70852
0.709640.710410.710360.709930.709480.709070.708400.709030.709410.709800.708710.708830.710200.70957
0.712820.709280.710760.710490.71066
0.70946
0.70955
0.710250.707990.709160.70798
0.709440.707900.70801
0.70826
0.708540.70830
0.709220.709090.708750.71058
0.709250.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.000020.00001
0.000020.000020.000020.00001
0.0000255.160093
55.495190
55.275452
55.146897
55.041677
55.208307
55.273976
55.119342
55.055040
55.333301
55.405691
55.393941
55.418305
55.433359
54.495456
54.379945
56.147719
56.021347
56.264869
56.103707
56.173885
56.064596
56.092050
54.943135
55.300424
54.961850
54.896242
54.894680
55.645380
56.900096
55.557373
55.545222
56.964514
55.719393
57.520742
55.732914
56.932656
56.859302
55.939950
55.645339
54.941258
55.648568
54.970435
56.600268
57.044806
57.067797
55.429451
54.940436
57.083545
56.764734
55.059722
55.040688
57.641682
55.610759
57.337697
55.582453
57.440129
55.525046
55.242338
54.935562
56.728780
55.085434
57.018399
55.031598
56.739338
56.739338
56.785074
56.500041
56.564332
56.615647
56.425036
56.497950
56.509320
56.049483

10.307437
10.137537
10.361427
10.379469
10.174636
10.208265
10.131111
10.296870
10.130096
10.542663
10.559034
10.390301
10.560005
10.480465
9.566961
9.087637
10.087363
9.516900
9.528542
9.824005
9.600367
10.085214
9.752795
9.453999
8.926368
8.802689
8.752799
9.953901
8.267157
9.929576
9.424598
9.427732
9.408516
9.320302
10.450442
9.403442
8.410850
8.519701
9.252432
8.207144
9.769950
8.583442
9.874018
9.9961009.861029
8.766629
9.328443
9.457581
9.892994
8.834208
9.753857
9.722937
10.397950
9.709734
10.478558
9.276419
10.313665
9.420749
9.368656
9.446366
9.963261
9.304908
8.562714
9.597845
9.958513
9.958513
9.566762
8.895224
9.592152
8.704217
9.223308
8.869160
8.828857
9.066979(continued on next page)

332

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

Table 1 (continued)
Location

Region87

Sr/86Sr±2SE

LatitudeLongitude

Søbylejet v. 31
Sønder Lem Vig
Stubbergård SøTranemose, Ringkøbing
Vedsø, Ringkøbing
Bæk vest for Svendstrup
Bredkær Bæk
Dybdalbæk 1
Dybdalbæk 2
Gryde ÅHellegård Å
Jordbro Å, Jordbromølle
Karup Å – Hagebro
Karup Å 100 m ns Sejbæk
Karup å, Nørkærbro
Lånum Bæk, Bækgård
Lille ÅOmme Å v/Sdr. skov bro
Savstrup ÅSejbæk, Sjørup plantage øst
Skjern Å NS Jernbanebro
Skjern Å v/Ahlergårde
Skjern Å v/Gjaldbæk bro
Skjern Å v/Kanoplads
Skjern Å v/Tarp broStorå v/GrydholtStorå v/Vemb ‘‘Skærumbro’’
Vium Mølle ÅHampensøRævsø
Draved Mose
Kindersee in Uge
Løvenholm Slosø
MørkesøØjesø
Rampensø
Vejensø
Tryggelev Nor, LangelandÆbelholt ÅÅmose ÅArresø kanal
Borup BækDamhusåen
Dumpedals-renden
Esrum åFiskebæk
Græse åHalleby Å
Havelse å
Højbro Å
Hove Å
Hove Å
Jonstrup Å
Kighanerenden
Køge Å
Ladegårdsåen
Lammefjord Søkanal
Langvad Å
Li. Vejle ÅLyngby å
Mademose åMaglemose Å
Mølleåen
Mølleåen
NivåØsterbæk
Pøle å
Søborg kanalStore Vejle ÅTryggevælde ÅTuse Å
Usserød ÅVærebro Å
Vejlesø Kanal
Bavelse SøJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandLangeland
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand0.709650.70874
0.708420.710480.70887
0.708620.709810.709630.71049
0.70956
0.70875
0.708420.70966
0.70968
0.70966
0.709180.70967
0.710180.709650.709070.71097
0.709900.71001
0.709370.709880.709420.709240.70872
0.71077
0.711750.71057
0.709230.71032
0.709890.71006
0.70998
0.71057
0.70961
0.70986
0.70986
0.709850.710110.709400.71032
0.70866
0.709510.70935
0.709930.70991
0.71167
0.709510.709190.70889
0.709240.709780.70974
0.709280.709090.710290.709840.710480.709470.709310.709280.709210.710230.70967
0.709420.70883
0.70959
0.70957
0.708820.709070.70932
0.709780.000020.000020.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.000020.00001
0.00001
0.000020.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001

56.017239
56.562848
56.439061
56.519200
56.403781
56.468787
56.480422
55.923361
55.923361
56.352824
56.471832
56.532367
56.412120
56.407546
56.514126
56.460962
56.324766
55.924458
56.358878
56.412723
55.929602
55.956770
55.938137
55.973793
55.952314
56.342772
56.334755
56.668473
56.018653
56.024956
55.011876
54.964593
56.450744
56.338178
56.291441
56.438609
55.481056
54.796125
55.948530
55.611389
55.977964
55.508868
55.662174
55.830005
56.095178
55.804486
55.847280
55.567400
55.901585
56.086174
55.727523
55.727151
55.772791
55.841472
55.468977
55.691516
55.772131
55.625914
55.610513
55.936411
55.802716
55.693107
55.807128
55.780883
55.932742
56.082260
55.988845
56.118697
55.619850
55.334485
55.699917
55.929707
55.742730
55.810387
55.334645

9.102887
8.787889
8.913816
8.840303
9.395620
8.825485
8.523702
9.391144
9.391144
8.509759
8.725464
9.205405
9.000324
9.019867
8.982547
9.139363
8.405239
8.589929
8.736958
9.093142
8.507769
8.706842
8.627693
9.070261
8.832075
8.747099
8.344414
8.9491089.378453
9.443000
8.950443
9.288924
10.442640
10.344505
10.609395
10.629129
9.178738
10.667304
12.189745
11.387309
12.034109
11.949831
12.479286
12.447944
12.375492
12.380680
12.126369
11.263454
12.152491
12.155794
12.214047
12.136257
12.293426
12.555578
12.101673
12.587324
11.624853
11.980296
12.315761
12.085040
12.031977
12.138256
12.550192
12.446121
12.485936
12.469003
12.222322
12.304391
12.373367
12.205122
11.586790
12.494969
12.227857
12.460977
11.636098

333

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340
Table 1 (continued)
Location

Region87±2SE

LatitudeLongitude

Kalø Grå
Maribo SøndersøNørremose
Røgbølle Sø
Sø ved Korsør Nor
Svenstrup Lergrav
Tystrup Sø
Ved sø
Vedde Sø
Vesterborg SøArresø
Bøgeholm SøBondedamDybesø
Furesø 1
Gudmindrup Mose
Gundsømagle SøHøholmsø
Højby SøKobberdam
Langebjerg Gravsø
MaglesøPeblingesøPorsemoseSjælsø
SøndersøSortedam Sø nord
TissøVallensbæk Sø
Uledige SøUlse SøHillerød Slotssø
Krebsedam, Tokkekøb Hegn, Allerød
Lejre Forsøgscenter, lower Kanu-lake
Lejre Forsøgscenter, upper Viking settlement
Midtmosen, GladsaxeØstmosen, Gladsaxe
Store Donsedam, Tokkekøb Hegn, Allerød
Teglværksø, Dæmpegård, Tokkekøb Hegn, AZlleearløadnd
Tudeå, Trelleborg, Slægelse
Furesø 2

Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand
Zealand0.70901
0.71079
0.71079
0.710230.70967
0.71005
0.709130.71000
0.71094
0.70967
0.70978
0.711510.710990.71066
0.709340.710190.709400.709480.711500.709530.709370.709780.709210.709160.709540.708990.709140.709930.709080.709900.709240.710430.710540.710530.709270.708960.709030.710640.708930.70977
0.709380.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.000020.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.000020.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.0000154.854562
54.758431
55.509372
54.711242
55.337658
55.356213
55.367478
55.455458
55.529088
54.866447
55.992131
56.061990
56.061629
55.963715
55.800659
55.905603
55.727420
55.696780
55.917774
56.059038
55.871586
55.471244
55.683677
55.693051
55.866842
55.775616
55.695108
55.571687
55.636859
55.066372
55.297534
55.932193
55.890090
55.617813
55.615269
55.715804
55.719903
55.894234
55.894476
55.394329
55.798917

11.642074
11.508339
11.535307
11.572188
11.213651
11.181555
11.571993
11.490720
11.538161
11.273467
12.108888
12.531578
12.546893
11.746180
12.415210
11.568806
12.190173
12.287185
11.603784
12.558739
12.230105
11.507793
12.561761
12.295467
12.440289
12.352271
12.575841
11.290510
12.345344
12.015087
11.994510
12.300519
12.404741
11.935043
11.939141
12.503080
12.526976
12.406879
12.386915
11.267464
12.364541

Average
Median0.70960
0.709540.00149

Average Zealand
Average Funen
Average Jutland

0.70973
0.70979
0.709480.00132
0.000730.001670.72063
0.70972
0.715990.00001
0.00001
0.0000155.126762

14.945160

55.053610

14.888326

0.715450.01094

Bastemose
Stampen ÅLæsåBornholm
Bornholm
Bornholm

Average Bornholm

87
Sr/86Sr values from within Denmark (including the two German
locations; excluding those from Bornholm) range from 0.7079 to
0.7128 and average 87Sr/86Sr = 0.7096 ± 0.0015 (2r; n = 194). Statistically there is no difference in the average surface water values
between Zealand (87Sr/86Sr = 0.7098 ± 0.0013; n = 82), Jutland(87Sr/86Sr = 0.7095 ± 0.0017; n = 96) and Funen (including Langeland) (87Sr/86Sr = 0.7098 ± 0.0007; n = 14), and the average composition of lake/pond waters (87Sr /86Sr = 0.7097 ± 0.0017; 2r) cannot
be distinguished statistically from that of river/creek waters(87Sr/86Sr = 0.7095 ± 0.0013; 2r). Strontium isotopic compositionsof water samples from Bornholm vary to a much larger degree,
from 0.7097 to 0.7206 (Table 1) and reﬂect the elevated 87Sr/86Sr
ratios derived from the Precambrian basement exposed on this island. Fig. 3A and B are contour maps which illustrate the geographical distribution of 87Sr/86Sr ratios. The contouring was performed
on a grid data base which was produced by spline function algorithms (Fig. 3A) and kriging (Fig. 3B) applied to the x (latitude), y(longitude) and z (87Sr/86Sr ratio) three-dimensional sample distribution of the surface waters.

Sr/86Sr

Spline interpolation features provided by ArcGIS Geographical
Information Systems (GIS) were used to construct the contoured
base map in Fig. 3A. The spline method is an interpolation methodthat estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface thatpasses exactly through the input points. Conceptually, the sample
points are extruded to the height of their magnitude; spline bends
a sheet of rubber that passes through the input points while minimizing the total curvature of the surface. It ﬁts a mathematical
function to a speciﬁed number of nearest input points while passing through the sample points. This method is best for generating
gently varying surfaces such as elevation, water table heights, or
pollution concentrations. There are two spline methods: regularized and tension. The regularized method creates a smooth, gradually changing surface with values that may lie outside the sample
data range. The tension method controls the stiffness of the surface
according to the character of the modeled phenomenon. It createsa less smooth surface with values more closely constrained by the
sample data range. The tension feature has been used to produce

334

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

the splined map, and the output raster was calculated on the basisof taking a maximum of ﬁve nearest neighbor points into thealgorithm.
For comparative purposes, ordinary linear kriging was used asanother grid interpolation method. Kriging is a method of interpolation named after the South African mining engineer D.G. Krige
who developed the technique in an attempt to more accurately
predict ore reserves. Over the past several decades kriging has become a fundamental tool in the ﬁeld of geostatistics. The kriging
routines implemented in ArcGIS are based on the Geostatistical
Software Library (GSLIB) routines published by Deutsch and Jour-

Table 2
Sr concentrations and isotopic compositions of randomly selected surface water
samples.
SampleRegion[Sr] (mg/L)

87

Sr/86Sr

2SE (abs.)

Eskelund Mose
Nørresø
Søholm SøStore Øresø
Dybdalbæk
Flyndersø
Kjellerup SøSaby Å Sabygård
Skalle Sø 9-9
Skjern Å v/Tarp bro
Skjern Å v/Tarp bro
Trindbor Hus kilde
Vandet SøVibæk Mølle
Villestrup ÅFuresø 11-11
Furesø 17-2
Langvad Å
Tissø
W1 Lejre

Funen
Funen
Funen
Funen
JutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutlandJutland
Zealand
Zealand
Zealand
Zealand
Zealand

0.19
0.21
0.16
0.15
0.14
0.28
0.19
0.14
0.24
0.14
0.17
1.08
0.71
0.35
1.01
0.26
0.20
0.47
0.32
0.25

0.710400.70998
0.710040.709540.70986
0.708620.709100.71074
0.70872
0.70957
0.709860.70787
0.70798
0.70875
0.70794
0.709360.70930
0.70905
0.70994
0.709270.00001
0.00001
0.000020.00001
0.00001
0.000020.000020.00001
0.00001
0.00001
0.000020.00001
0.000020.00001
0.000020.00001
0.00001
0.000020.00001
0.00001

SE = Standard error (analytical precision); abs. = absolutenel (1992). Since kriging is a rather complex interpolation technique and includes numerous options, a complete description of
kriging is beyond the scope of this article. The experimental variogram from the surface water scatter point set (the base for theinterpolation) is not shown herein. ArcGIS produces a model variogram which in this case makes use of the sill and nugget values
indicated by the variogram to calculate the weights assigned toeach scatter point. The default values provided by ArcGIS wereused for search parameters and produced a prediction raster of1  1 km.
While similar in the overall interpolated 87Sr/86Sr value distribution pattern of surface waters, the output of the spline routine(Fig. 3A) reveals a more detailed and higher resolved map than thatprovided by kriging (Fig. 3B). Most of Zealand, Funen and central
Jutland are characterized by 87Sr/86Sr values between slightly higher than 0.710 and 0.709 (in the spline map of Fig. 3A), whereas the
kriging map (Fig. 3B) depicts a more smoothed out and homogeneous distribution pattern with 87Sr/86Sr values between 0.7095
and 0.7100. Both interpolation routines agree in the characterization of northern Jutland, where a belt including the Limfjord regionand stretching across to the coast north of Djursland is characterized by signiﬁcantly lower 87Sr/86Sr values between <0.7080 and
0.7085. Northernmost Jutland (Vendsyssel), and a number of areas
in mid and southern Jutland, Funen and Zealand, on the contrary,are characterized by higher 87Sr/86Sr values >0.710 (particularly
shown in the spline map of Fig. 3A).
4.2. Relationship between 87Sr/86Sr signatures and Sr concentrations instream and lake waters
Strontium isotopes and Sr concentrations of 20 randomly selected surface water samples are listed in Table 2. The isotopic signatures are plotted against 1/[Sr] in Fig. 4. Linear correlations insuch a diagram are the result of a two-endmember mixing relationship. The 20 data points are fairly well correlated (correlation

Table 3
Strontium isotopic compositions of soil extracts, snail shells, lake waters and average sheep wool from Lejre.
SampleType

Treatment87

Sr/86Sr

2SE (abs.)

Reference

Soil 0
Soil 1
Soil 2
Soil 3
S-1
S-2
S-3
S-4
S-5
S-6

Topsoil

0.05 M HNO3

0.70761
0.70950
0.708540.70961
0.708500.70776
0.70946
0.709020.708800.709400.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001

Frei et al. (2009)
Frei et al. (2009)
Frei et al. (2009)
Frei et al. (2009)This study
This study
This study
This study
This study
This study

0.708820.00144

Snail shell

6 M HCl–H2O2

0.708990.707490.710120.70857
0.70837
0.70849
0.709100.70859
0.70830
0.708820.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001
0.00001

0.708690.00134

Average
SN-0
SN-1
SN-2
SN-3
SN-4
SN-5
SN-6
SN-7
SN-8
SN-9
Average
Sheep wool average
W-1
W-2
W-3

Lake water

One milliliter direct

Average
SE = Standard error (analytical precision); abs. = absolute.

Frei et al. (2009)This study
This study
This study
This study
This study
This study
This study
This study
This study

0.709690.00066

Frei et al. (2009)

0.709270.710530.709520.00001
0.00001
0.00001

Frei et al. (2009)
Frei et al. (2009)This study

0.709780.00133

335

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

coefﬁcient r2 = 0.68) and imply the existence of two major sources,
each with their own Sr isotopic signature and Sr concentration. The
ﬁrst endmember can be equated with Sr derived from pre-Quarternary (Late Cretaceous–Paleocene) limestones (87Sr/86Sr = 0.7078–
0.7082; Frei and Frei, 2002; McArthur et al., 2001) underlying theglaciogenic sediments in extended areas, particularly in the northeastern (Zealand, north and central Jutland) parts of Denmark (cf.
Fig. 2A). The radiogenic endmember, characterized by waters withlower Sr concentrations and elevated 87Sr/86Sr signatures, is interpreted as surface water obtaining a radiogenic signature from labile Sr in the silicate-rich till-topsoils (with Precambrian
granitoid components; see discussion below).
4.3. Results from the Lejre test area
Strontium isotopic ratios of soil extracts, snail shells, sheepwool and lake waters from within the Lejre experimental center(Land of Legends; http://www.sagnlandet.dk) are presented inTable 3. Data are plotted in Fig. 5. The test area is representativeof large regions within Denmark where the landscape is dominatedby glaciogenic deposits (moraines) and sediments (tills), and postglaciogenic erosion features. The area is maintained ecologicallyand pastures have never been fertilized with industrially produced
0.712

87

Sr/ 86Sr

0.711

Average soil extracts
Soil extracts
Snail shells
Average snail shells
Average lake waters
Lake waters
Average soil extracts
Average sheep hair
Average snail shells
Average lake waters
Average sheep hair

0.710

0.709

fertilizers. Soil extract data and snail shell values show an identicalscatter of 87Sr/86Sr ratios each with two outliers plotting outside
the range of ‘‘local’’ Danish signatures determined by the surface
water samples presented herein (87Sr/86Sr = 0.7096 ± 0.0015; 2r;
Fig. 5). Statistically, the data from soil extracts and snail shells cannot be distinguished from those of wool (Frei et al., 2009) fromsheep that were fed on the test center’s pastures and from thecompositions of three lake water samples from within the test area
which are interpreted to consist of a smoothed-out average of thebio-available Sr characteristic of the tested area (Table 3 andFig. 5). It is noted, however, that there is a better coincidence of
sheep wool with water data, and that there is a tendency for water
samples to have slightly elevated (on average 0.001 higher)87
Sr/86Sr ratios compared to snail shells and soil extracts.
4.4. Atmospheric strontium
The focus of this article lies outside a detailed investigation on
the relative contribution of atmospheric Sr input into the surface
waters sampled herein. However, in order to obtain an idea of
the importance of atmospheric Sr deposition in Denmark, the results from atmospheric particle analyses from two locations, collected at different times, are reported here (Lille Valby, Zealand;
http://www2.dmu.dk/1_viden/2_Miljoe-tilstand/3_luft/4_Maalinger/5_stationer/lillevalby.asp and Keldsnor, Langeland; http://
www2.dmu.dk/1_viden/2_Miljoe-tilstand/3_luft/4_Maalinger/
5_stationer/keldsnor.asp). Furthermore, the Sr isotope compositionand concentration of one rainwater sample was determined(Søborg, Zealand). Data are listed in Table 4. The average airborne
particulate matter is characterized by a 87Sr/86Sr ratio of
0.7107 ± 0.0009, whereas rainwater, as expected, has an isotopic
composition that lies close to 87Sr/86Sr = 0.7092, which is the composition of modern seawater (McArthur et al., 2001).
5. Discussion

0.708

0.707

Fig. 5. Strontium isotope data of soil extracts, snail shells and lake waters from the
Lejre experimental center on Zealand. While statistically overlapping, the 87Sr/86Sr
signatures of snail shells and soil extracts are slightly lower than those of the lakewaters, whereas the latter agree with the 87Sr/86Sr signatures of sheep wool (Freiet al., 2009) from sheep grazing on pastures within the center area. The offset
between snail shell/soil extract – and surface water/sheep wool data is explained by
preferential uptake by small herbivores of Sr with a slightly lower 87Sr/86Sr
signature from equilibrated soil waters within the washed-out topsoils, relative to
Sr with an elevated 87Sr/86Sr signature from the water saturated levels deeper down
which is reﬂected by the surface waters. These waters, in turn, are likely to be adominant source of Sr – via drinking uptake – entering larger animals (details intext).

In order to understand and interpret the Sr isotopic ratios measured in the surface waters it would be essential to constrain the
cation ﬂuxes and the identity and magnitude of cation sources
and sinks that are relevant for the total mass budget. Elements
present in the soil, water and plants of an ecosystem are ultimately
sourced from a combination of atmospheric deposition (wet anddry precipitation) and weathering of soil parent rock material. Subsequent to these primary inputs, elements may undergo transfer
and recycling between soil solutions, plants, soil organic matter,secondary minerals, and the so-called soil cation exchange complex. In addition, the soil–plant–water system can be modiﬁed by
input of elements as irrigation water solutes, fertilizers and otheragricultural additions. There are numerous studies that have used

Table 4
Air ﬁlter and rain water analyses.
SampleLocation

Collection date

Filtrate (lg)

Keldsnor
Keldsnor
Lille Valby
Lille Valby

May 5, 2006
August 2, 2006
May 5, 2006
August 2, 2006

1200
550
1590.045087

Sr/86Sr

2SE (abs.)

0.71135
0.70972
0.71159
0.710310.000030.00006
0.000160.00012

Average
0.71074

2SD (abs.)
0.000880.709310.00008

Air ﬁlters
400856365251402139364236Sr (lg)
0.08
0.04
0.17
0.03

Rain water
ANV

SøborgApril 5, 2010

SE = Standard error (analytical precision); SD = Standard deviation; abs. = absolute.

Sr (lg/L)
12

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K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

the Sr isotope system to trace and quantify the sources and ﬂuxes
of base cations in soil, water and plant materials (Åberg et al., 1989,
1990; Bailey et al., 1996; Hornbeck et al., 1997; Kennedy et al.,
1998; Nakano et al., 2001). The key to the monitoring of short-term
elemental mobilization is the analysis of the soil cation exchange
complex which consists of base cations, primarily Na+, K+, Mg2+
and Ca2+, with which Sr shows a strong geochemical afﬁnity. It is
held within the soil matrix by electrostatic attraction to negatively
charged surfaces of clay particles and organic matter. The soil exchange complex cations are in dynamic equilibrium with cationsin the soil solution which is the main component of a bio-available
fraction. Collectively the cations in the soil solution and the soil
cation exchange complex are commonly referred to as labile soil
cations (Capo et al., 1998; Stewart et al., 1998). Studies using Sr
isotopes have shown that amounts of precipitation, rock texture
and substrate age are parameters which control the release of cations from soil pools and the relative contributions to biologicallyavailable Sr of atmospheric and weathering sources. Althoughchemical weathering is known to be an important control on cation
concentrations of stream water (Berner et al., 1990), detailed studies addressing the bedrock or atmospheric derived contributions tostream water and the geochemical link between stream and soil
water are rare. In one of the few studies, Nakano et al. (2001) demonstrated that 87Sr/86Sr ratios in surface soil waters in the Kawakami volcanic watershed (central Japan) were almost identical to the87
Sr/86Sr ratio of exchangeable soil and existing plants with different rooting zones. The agreement of Sr isotope signatures of bedrock-derived secondary minerals with high cation exchange
capacity and wide distribution in lower soil horizons and watersaturated zones with the signatures of stream waters led theseauthors to conclude that Sr in stream waters is controlled by exchange reactions at depth, with a minor (up to 10%) contribution
from atmospherically-derived cations (rainwater). In contrast,
according to Nakano et al. (2001), 87Sr/86Sr ratios of soil water inthe surface soil vary widely (due to the contribution of atmospherically-derived cations to the biologically available, Ca and Sr deﬁcient weathered surface soil pool) and differ signiﬁcantly fromthose of soil minerals at depth.
While an in depth study of the surface water–soil–plant system
exceeds the scope of this article, certain issues of the above relationship are discussed because of their importance for the deﬁnition of local bio-available Sr signatures that will tentatively be
used as a base for future migration, authenticity and provenancestudies in Denmark.
5.1. Importance of fertilizers
Fertilizers are used in agriculture to increase yield. Artiﬁcial fertilizers used in Denmark supply three main nutrients: N, P and K.
The fertilizers are delivered as straight fertilizers (N, P or K fertilizers), or mixed fertilizers (e.g. PK and NPK fertilizers) as industrial
products. Fertilizer consumption is decreasing in Europe in recent
years as a result of decreasing production in agriculture andincreasing efﬁciency of fertilizer application. The most frequently
used fertilizers in Denmark are straight fertilizers: calcium ammonium nitrate (N-fertilizer), triplesuperphosphate (P-fertilizer) andK2O (K-fertilizer). Only restricted information is available on the
Sr isotopic composition of compound N (NO3)–P–K fertilizers usedin Europe, and there are not many studies accessible from which to
extract information regarding the Sr isotopic composition of fertilizers in general, and of compound and straight fertilizers in particular. In a recent study Vitoria et al. (2004) presented a detailed
isotopic characterization of commercial fertilizers used in Spainand referred to additional literature cited in their work. In Table
1 of Vitoria et al. (2004) 87Sr/86Sr ratios of a range of different fertilizers are listed. The 87Sr/86Sr values of these fertilizer range from0.7034 (a NPK fertilizer) to 0.7152 (a straight K fertilizer), but the
majority of fertilizers analyzed had 87Sr/86Sr signatures between
0.708 and 0.709 which the authors, based on a literature compilation, claimed to be representative of worldwide suppliers.
In order to estimate the addition of fertilizer Sr to surface
water, information is needed on the Sr concentrations of surface
waters and in the applied fertilizer, besides each of their 87Sr/86Sr
values. For the fertilizer endmember, the concentrations determined by Hosono et al. (2007), who dissolved an appropriate
amount of fertilizer in water so that the amount reproduces theconditions during application of the fertilizer in the ﬁeld, areused. The Sr concentrations of dissolved basal and supplemental
fertilizers studied by Hosono et al. (2007) range from 17 to
84 lg/L (their Table 2). For the surface water, the data for the
20 randomly selected lake and river waters from within Denmark(see Table 2, discussed later on) are used. The Sr concentrations
determined in these 20 lake and stream waters from within Denmark range from 0.14 to 1.08 mg/L (Table 2). Results from two
mixing scenarios relevant for Danish surface waters are presented
in Fig. 6A and B. The mixing calculations are based on a worst
case scenario which takes the lowest surface water Sr concentration (140 lg/L; Table 2; most susceptible to contamination) into
consideration and rely on two cases: (1) the use of an average87
Sr/86Sr fertilizer value of 0.7085 (deduced from the study of
Hosono et al. (2007)) and (2) the use of an extreme fertilizer with
a 87Sr/86Sr value of 0.715 (the highest value reported for a straightfertilizer by these authors). An applied fertilizer Sr concentrationof 50 lg/L was used (the approximate average of the data reported by Hosono et al. (2007); their Table 2). The mixing relationships are based on the two equations (Eqs. (1) and (2)),
which enables calculation of the hypothetical concentration andisotopic composition of a pristine (uncontaminated) surface water(rp) from a measured, assumed fertilizer (f)–contaminated water(rc).

Srrc ¼ Srf f þ Srrp ð1  f Þ

ð1Þ

and

ð87 Sr=86 SrÞrc ¼ ð87 Sr=86 SrÞf ðSrf =Srrc Þf þ ð87 Sr=86 SrÞrp ðSrrp =Srrc Þð1  f Þ
ð2Þ
where f denotes the proportion (in%) of fertilizer-derived Sr. The
calculations demonstrate that, in case 1, using an average87
Sr/86Sr = 0.7085 (Vitoria et al., 2004) and an average Sr concentration of 50 lg/L representative of applied solute fertilizer (Hosono
et al., 2007), the Sr isotopic data of the calculated uncontaminatedsurface water is completely insensitive to fertilizer addition (morethan 85% fertilizer Sr would be necessary as a mixed-in component
to change the Danish surface waters beyond the statistically deﬁned
variation of 87Sr/86Sr = 0.7096 ± 0.0015; see above; Fig. 6A). In case2, which uses an extreme fertilizer with respect to its Sr isotopic signature (87Sr/86Sr = 0.715), a minimum of 60% of fertilizer Sr would
be needed to change a pristine surface water beyond the statisticalvariation (Fig. 6B). In both calculations, therefore, unrealisticallyhigh amounts of applied solute fertilizer would be necessary to signiﬁcantly change the Sr isotopic compositions of the stream andlake waters.
Based on the above calculations, the importance and potential
signiﬁcance of fertilizer Sr contamination of Danish surface waters
is deemed to be minimal and consequently it is concluded that themeasured 87Sr/86Sr values in these waters reﬂect the natural variation of labile Sr (plus atmospheric additions?) on land.
5.2. Importance of atmospheric contamination
The 87Sr/86Sr ratio in soils is generally dependent on soil depth
because, as depth increases, bedrock weathering becomes more

337

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340
0.7103A

0.710287

Sr/86Sr pr

0.71010.71000.70990.70980.70970.70960.7095
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

f value

0.70950.70850.708087

Sr/86Sr pr

0.70900.70750.7070B

0.7065
0

0.1

0.2

0.3

0.4

0.5

f -valueFig. 6. Mixing diagrams illustrating the 87Sr/86Sr compositions of originally pristine (uncontaminated) surface waters (Srrp) that would have pre-existed if a measured,assumed fertilizer-contaminated river water (Srrc) with a 87Sr/86Sr = 0.7096 (the average of Danish surface water samples analyzed herein) and Sr concentration of 140 lg/L(the lowest of the 20 analyzed water concentrations; Table 2) was variably contaminated by fertilizer solutions containing 50 lg/L of Sr (Hosono et al., 2007) with 87Sr/86Sr
values of (A) 0.7085 (average of worldwide fertilizer signatures reported by Vitoria et al. (2004)), and (B) 0.715 (fertilizer with the highest 87Sr/86Sr reported by Vitoria et al.
(2004)). The f value denotes the% fraction of fertilizer in the assumed contaminated water. Only if the surface waters contained fertilizer contamination levels of >85% and>60% (marked by dotted gray line), respectively, the 87Sr/86Sr signature of an originally pristine surface water would come to lie outside the statistical (2r) variation of themeasured surface waters (indicated by the gray-shaded boxes). Such levels of volume contamination are unrealistic and, therefore, the potential pollution of surface waters
by fertilizers are deemed negligible.

important relative to atmospheric sources (Probst et al., 2000). Analyzing coniferous trees, Poszwa et al. (2004) found that 87Sr/86Sr ratios of bio-available Sr generally increased in soils in relation todepth, due to minerals being weathered. This is an important observation which will be taken up later on when discussing the datafrom the test area in Lejre. Different species such as spruce and pinefrom the same area can have different 87Sr/86Sr ratios. Spruce trees,for example, recycle Sr from the litterfall up to 12 times more thanpine trees do (Poszwa et al., 2004). These differences are probably
controlled by the differences in the soil parent material where thetrees have their roots (Dijkstra et al., 2003). In a tropical rainforest(in French Guyana) with extreme chemical weathering conditions,Poszwa et al. (2002) found that tree 87Sr/86Sr ratios were similar
for all species studied, and close to 87Sr/86Sr values of litter andnear-surface roots, but that there was a gradient of 87Sr/86Sr in
the tree roots with depth, indicating that the litter and upper soil
layers retained the Sr deposited by rain.
Both airborne particulate (dust) and rainwater Sr isotopic signatures determined herein (Table 4) lie within the statistical limits
deﬁned by the surface water samples analyzed during this study(Table 1). It is impossible to calculate mass budget inﬂuences without knowing the proper ﬂuxes from the atmosphere into the soils
and onwards to the stream and lake waters. All that can be said
here is that airborne particulate matter, if signiﬁcant in the mass
budget of the labile upper soil horizons, would shift the bio-available Sr to slightly higher isotopic compositions in waters, whereas
rainwater would statistically not severely alter the isotopic composition of the surface waters, with the assumption that Sr in thestream and lake waters is derived essentially from mobilizationof labile Sr in shallow soil horizons. The scenario by which the labile Sr in soil water is dominated by atmospheric contamination isa rather unlikely one, as studies have shown that rock weathering
is still the major contributor of Sr to the bio-available fraction(Bailey et al., 1996). A study by Probst et al. (2000) has shown that,even with modern polluted precipitation (87Sr/86Sr < 0.710) supplying half the Sr available to modern plants in the Vosges Mountains (France), Sr available to modern plants in the soil solutions
there nevertheless has a substantially radiogenic Sr content(87Sr/86Sr  0.724) due to weathering of the bedrock endmember(87Sr/86Sr = 0.735–0.740).

338

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

5.3. Deﬁning the ‘‘local’’ signature
Factors such as the physical and hydrologic characteristics ofsoil and atmospheric deposition are likely to be relatively similar
between the various watersheds within Denmark and differences
in base cation ﬂuxes to streams most likely reﬂect differences inchemical weathering. However, predicting the role of rock weathering is complex for glaciated terrains such as prevail in Denmark.
Most estimates of base cation potentials are based on bedrock
maps (e.g., Beard and Johnson, 2000; Evans et al., 2009, 2010).
However, superﬁcial till deposits, usually derived from off-site,
can be more inﬂuential than underlying bedrock as sources of cations for streams and lakes in general. Pleistocene glaciation removed essentially all pre-existing soils and surﬁcial deposits
from within Denmark. Present day soils are developed on till, icecontact, and associated outwash deposits left by the last and/or
second last stages of the Saale-Weichsel ice sheets. Because soils
are developed on mixtures of local and non-local transported
materials, the chemical and mineralogical composition of the soil
parent material reﬂects a variety of lithogenic sources erodedand deposited by the glaciers. This complexity makes it difﬁcult
to elaborate on the source and nature of 87Sr/86Sr values in Danish
surface waters exceeding 0.7092 (the composition of modern
seawater; McArthur et al., 2001). One can speculate that labile Sr
fractions with elevated isotopic signatures are leached from glaciogenic tills (with predominantly Precambrian granitoid components from northern Scandinavia) and mixed with nonradiogenic components derived from chalk-rich matrix in the soils
and/or rainwater. The dampening effect of the geologically derived
values by rainwater saturation of the soils on the 87Sr/86Sr biosphere budget in western Britain has been described by Evanset al. (2010) and for Australian basalt plains by Raiber et al.
(2009). An interesting aspect is reﬂected by the data from the Lejre
test area: there it is observed that, while the average of Sr isotope
compositions from snail shells and soil extracts agree with eachother (Table 3 and Fig. 5), the lake waters show slightly elevatedsignatures. A recent study which reports the Sr isotopic compositions of wheat and honey samples from Jutland and compares
them to respective surface water compositions (Voerkelius et al.,2010), seems to conﬁrm the observation. The data shows a slightoffset (magnitude of this offset D87Sr/86Sr  0.001) of stream andspring water values (87Sr/86Sr  0.7095) compared to data on
wheat and honey (87Sr/86Sr  0.7085; in the present authors’ view
potentially averaging the composition of grasses and bushes within a bee’s collection area; their Fig. 2). This offset deserves to be
noted and kept in mind, particularly when these biosphere ranges
are used as reference values in future provenance studies. The difference between the surface water pool and the fauna pool (hererepresented by snails), and by analogy-based on the study of
Voerkelius et al. (2010) – the ﬂora pool, can tentatively be explained by the possibility that small herbivores obtain their Sr fromsurface-near, upper soil horizons (through shallow-rooted plants),
whereas stream and lake waters obtain their signatures predominantly from water saturated deeper soil levels where accumulation(adsorption) of radiogenic Sr derived from near-surface soil leaching/weathering occurs. Unlike the study of Drouet et al. (2007),
who observed a correlated Ca–87Sr/86Sr decrease in loess proﬁlesfrom top to bottom which they explained as resulting from thepreferential and selective weathering of Ca-rich minerals with alow 87Sr/86Sr (carbonates, Ca-plagioclase), the present authors instead argue for a preferential release of radiogenic Sr from either
K-rich silicates and/or from old, Precambrian Sr-bearing mineralssuch as plagioclase within the topsoils to the water saturated levels. This argument is supported by the correlation between
87
Sr/86Sr values and Sr concentrations in the 20 randomly selected
stream and lake water samples from Denmark (Table 2 and Fig. 4).

This correlation can best be explained by a mixing of two sources,
i.e. mixtures of Ca- (and by analogy Sr-) rich waters at depth where
they obtained their signature mainly by dissolution of Late Cretaceous–Early Tertiary limestones with downward percolatingwaters which obtained a slightly more radiogenic 87Sr/86Sr signature from labile Sr in the silicate-rich till-topsoils.
While this suggestion is far from being tested in detail (morethorough studies are necessary and will be conducted in the future
to verify this), the result has implications for the deﬁnition of thebio-available signature to be used for discrimination between ‘‘local’’ and ‘‘non-local’’ sources in migration and other authentication
studies. Everything depends on the relative uptake of Sr via plants
and water into the bone tissues of animals and humans. In humans
and larger animals, Sr uptake is potentially dominated by drinking
water (represented by stream and lake waters), whereas smaller
animals (such as snails and potentially other small herbivores)
might obtain their Sr predominantly from plants, preferentiallyshallow rooted ones, which obtain their signatures from the uppermost leached soil horizons.
Consequently, at least for the Danish biosphere characterization, the local ranges have to be adapted towards the material fromwhich one would like to obtain geographical information. Here it is
proposed to use the Sr isotopic signatures of stream/lake waters asa biosphere proxy for larger animals and humans, and small faunaand shallow-rooted plants for plant and food authenticity tracing.
5.4. The biospheric 87Sr/86Sr distribution and the geologicalbackground
It has already mentioned that there is no clear-cut apparent link
between the contoured 87Sr/86Sr distribution (Fig. 3A and B) withthe pre-Quarternary (Late Cretaceous–Miocene) bedrock ‘‘basement’’ below the Pleistocene glaciogenic sediments in Denmark.
If the surface waters were signiﬁcantly inﬂuenced by weatheringof this basement, one would expect some kind of a diagonally split
distribution across Denmark, with low 87Sr/86Sr values in thenorthern and northwestern parts of Denmark (where late Cretaceous-Paleocene limestones predominate), and elevated signaturesin the southwestern parts (where clastic marine sediments deposited during the Miocene predominate). The comparison of the87
Sr/86Sr distribution maps in Fig. 3A and B with the pre-Quarternary bedrock map reveals that this is not the case.
The region with particularly low 87Sr/86Sr signatures in surface
water deserves some more detailed considerations and thoughts.
In this region widespread Early Eocene volcanic ash beds occur. A
scenario is favored whereby Sr released by weathering of theseabundant Early Eocene volcanic ash layers (possibly pertaining to
the large Tertiary Faeroes–East Greenland volcanic province), contributes signiﬁcantly to the Sr budget of the water tables in thisparticular region. Low 87Sr/86Sr signatures of 0.7042 were forexample measured on Early Eocene ash beds on the Island of Fur(Morton and Evans (1988); Fig. 1) located within the ‘‘anomalous’’
zone (Fig. 3A and B). and a range of 87Sr/86Sr ratios between 0.7038
and 0.7052 was reported for Paleogene volcanic ash layers from the
Limfjord area by Larsen et al. (2003).
A comparison of Fig. 1B, a map displaying the distribution ofdifferent types of Pleistocene glaciogenic, marine and freshwater
sediments, with the contoured surface water 87Sr/86Sr ratio distribution in Fig. 3A and B reveals that, with the sample density
achieved so far, a link between the type of Pleistocene sedimentand the 87Sr/86Sr distribution pattern cannot be established immediately. An exception to this is the area of Vendsyssel (northernmost Jutland), where elevated 87Sr/86Sr ratios in surface waters(Fig. 3A and B) can be correlated with the occurrence of predominant marine clays deposited in the so-called ‘‘Yoldia Sea’’ at theend of the Weichsel ice age stage. There it is conceivable that

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

surface waters obtain a comparably larger input of radiogenic Sr
from the weathering of these marine sediments with clastic material derived from the old Scandinavian Shield.
The general lack of a correlation between 87Sr/86Sr ratios of surface waters and the Pleistocene sediment cover in most areas ofDenmark is conformable with the results which predict that, in order to explain the Sr isotopic signatures and concentrations in surface waters from within Denmark, input from two predominantsources is required (Fig. 4). A larger surface water sample density
perhaps could eventually establish the link between the two geological background maps (Fig. 2A and B). Each of these maps potentially reﬂects the geographic distribution of one of the two major Srsources mixed in Danish surface waters. Information from thesemaps, combined with enhanced surface water data sets, has thepotential to delineate and more precisely differentiate between
isotopically district biosphere areas within Denmark, in such way
as successfully demonstrated by Beard and Johnson (2000),
Voerkelius et al. (2010) and by Evans et al. (2010) in their studies
and attempts to produce regional maps for provenance purposes.

6. Conclusions
This study presents results of a large-scale investigation with ahigh number of analyzed surface water samples which illustrate
the potential of Sr isotope analysis to provide a proxy of the biosphere signatures necessary for provenance/migration studies
and for food authenticity control. The conclusions can be summarized as follows:
1. 87Sr/86Sr ratios of stream and lake waters within Denmark(excluding Bornholm) span a range from 0.7079 to 0.7128 andhave an average value of 0.7096 ± 0.0015 (n = 192; 2r).
2. The composition of the surface waters in Denmark can be
explained by contribution/mixing of two major sources of Sr.
These are a) waters with Sr derived from pre-Quarternary carbonaceous sediments (87Sr/86Sr = 0.7078–0.7082) and b) waterswhich obtained a radiogenic component by interaction and percolation of atmospheric (mostly rain) water through Pleistocene
glaciogenic soils with Precambrian granitoid components.
3. The average 87Sr/86Sr value of the stream and lake waters seems
to have an offset relative to the bio-available Sr fraction determined on faunal samples of D87Sr/86Sr  0.001. This offset isapparent in the present own data from a test area within the
Lejre experimental center on Zealand, where soil extracts andsnail shells exhibit, on average, a lower 87Sr/86Sr value than lakewaters from the test area. This apparent discrepancy is consistent with the offset inherent in the honey/wheat/surface water
data of Voerkelius et al. (2010) valid for Jutland. This offset is
tentatively explained by downwards migration of radiogenic(high 87Sr/86Sr) Sr from the tillite-rich soils into the watertables, leaving behind a comparatively leached (low 87Sr/86Sr)
topsoil in which shallow-rooting plants extract the bio-available Sr.
4. As water (similar to food) is expected to be an important sourceof nutritional Sr, entering larger carnivorous animals andhumans by absorptive uptake, it is argued that the average87
Sr/86Sr value of 0.7096 ± 0.0015 deﬁned by the Danish surface
waters likely represents a bio-available fraction necessary forinvestigating and delineating prehistoric human and animal
mobility. This value should discriminate between ‘‘local’’ and‘‘non-local’’ signatures determined in target materials studied
for their provenance. It is proposed that the band width of87
Sr/86Sr = 0.7081–0.7111 be used conservatively to characterize a ‘‘local’’ Danish signature. On the other hand, the isotopic
signature of bio-available Sr deduced from the analyses of soil

339

extracts, shallow-rooted plants and small herbivorous animals
can potentially be used for cases such as authenticity proof
for food and plants in small restricted areas.
5. The sampling density of stream and lake waters performed inthis study allowed for the construction of a contoured 87Sr/86Sr
distribution map over Denmark which can be used as a reference/proxy map for archaeological, geological and agricultural
studies aimed at tracing the origin of a target material, within
Denmark. A direct link between water signature and the preQuarternary basement rocks seems not to be obvious, given
the fact that the surface waters are composed of at least two
major sources of Sr. Likewise, there is no obvious dependenceof the 87Sr/86Sr values of the waters from the Pleistocene glaciogenic deposits (e.g. moraines) and sediments (tills with extraneous boulders) left behind from the last two (Saale andWeichsel) ice age stages.
6. The area around Limfjord in central-north Jutland is characterized by exceptionally low surface water values (87Sr/86Sr <0.7085). The low values could be explained by increased input
of non-radiogenic Sr to the surface water derived from theweathering of Early Eocene volcanic ash beds which are widespread in this particular area.
The applicability and usefulness of the map of Sr isotope ratiosof surface waters from Denmark, e.g. for archaeological tracing
studies (using tooth enamel of ancient humans and animals) andfor agricultural products and food commodities, will be subjected
to future tests and will hopefully prove promising as an alternative
to mapping bio-available Sr isotopic fractions and substantiate that
the Sr isotope system can signiﬁcantly contribute to resolving
questions of geographical origin.
Acknowledgments
We thank the many people from the various Danish Environmental Centers who kindly provided us with water samples: Heine
Guesling, Jette Christiansen, Ruth Sthen Hansen, Karen Rom, Torben Wiis, Arne Ryge Pedersen, and Karsten Fugl. In addition, Sigrid
Schmidt, Peter Engesgard, Tod Waight and Mette-Marie and Thomas Elvensø are thanked for additional samples which they collected during their freetime. Kåre Kemp and Peter Wåhlin havekindly provided the atmospheric ﬁlter samples. Toni Larsen andChristina Nora Jensen helped with ion chromatographic separations in the clean labs and Toby Leeper with mass spectrometricalanalyses. Mikael Pedersen is thanked for his kind help in GIS-related issues, and for constructing the contour maps presented
herein. John Bailey helped with linguistic improvements, and T.
Douglas Price and James Burton contributed with fruitful discussions. We thank J. Evans, an anonymous reviewer, and the editor
R. Fuge, for their constructive comments and suggestions for
improvement of the manuscript.
ReferencesÅberg, G., Jacks, G., Hamilton, P.J., 1989. Weathering rates and 87Sr/86Sr ratios – anisotopic approach. J. Hydrol. 109, 65–78.
Åberg, G., Jacks, G., Wickman, T., Hamilton, P.J., 1990. Strontium isotopes in trees as
an indicator for calcium availability. Catena 17, 1–11.
Bailey, S.W., Hornbeck, J.W., Driscoll, C.T., Gaudette, H.E., 1996. Calcium inputs andtransport in a base-poor forest ecosystem as interpreted by Sr isotopes. Water
Resour. Res. 32, 707–719.
Beard, B.L., Johnson, C.M., 2000. Strontium isotope composition of skeletal material
can determine the birth place and geographic mobility of humans and animals.
J. Forensic Sci. 45, 1049–1061.
Bentley, R.A., 2006. Strontium isotopes from the earth to the archaeologicalskeleton: a review. J. Archaeol. Method Theory 13, 135–187.
Bentley, R.A., Price, T.D., Stephan, E., 2004. Determining the ‘local’ 87Sr/86Sr range forarchaeological skeletons: a case study from Neolithic Europe. J. Archaeol. Sci.
31, 365–375.

340

K.M. Frei, R. Frei / Applied Geochemistry 26 (2011) 326–340

Berner, R.A., Berner, E.K., Schroeder, P.A., Lyons, T.W., 1990. Sodium–calcium ionexchange in the weathering of shales – implications for global weatheringbudgets. Geology 18, 190.
Blum, J.D., Taliaferro, E.H., Holmes, R.T., 2001. Determining the sources of calcium
for migratory songbirds using stable strontium isotopes. Oecologia 126, 569–
574.
Böhlke, J.K., Horan, M., 2000. Strontium isotope geochemistry of groundwaters andstreams affected by agriculture, Locust Grove, MD. Appl. Geochem. 15, 599–609.
Burton, J.H., Price, T.D., Middleton, W.D., 1999. Correlation of bone Ba/Ca and Sr/Ca
due to biological puriﬁcation of calcium. J. Archaeol. Sci. 26, 609–616.
Capo, R.C., Stewart, B.W., Chadwick, O.A., 1998. Strontium isotopes as tracers of
ecosystem processes: theory and methods. Geoderma 82, 197–225.
Crittenden, R.G., Andrew, A.S., LeFournour, M., Young, M.D., Middleton, H.,
Stockmann, R., 2007. Determining the geographic origin of milk in Australasia
using multi-element stable isotope ratio analysis. Int. Dairy J. 17, 421–428.
Deutsch, C., Journel, A.G., 1992. GSLIB, Geostatistical Software Library and User’s
Guide. Oxford University Press, New York.
Dijkstra, F.A., Van Breemen, N., Jongmans, A.G., Davies, G.R., Likens, G.E., 2003.
Calcium weathering in forested soils and the effect of different tree species.
Biogeochemistry 62, 253–275.
Drouet, T., Herbauts, J., Gruber, W., Demaiffe, D., 2007. Natural strontium isotope
composition as a tracer of weathering patterns and exchangeable calcium
sources in acid leached soils developed on loess of central Belgium. Eur. J. Soil
Sci. 58, 302–319.
Evans, J.A., Montgomery, J., Wildman, G., 2009. Isotope domain mapping of Sr-87/
Sr-86 biosphere variation on the Isle of Skye, Scotland. J. Geol. Soc. 166, 617–
631.
Evans, J.A., Montgomery, J., Wildman, G., Bouton, N., 2010. Spatial variations in
biosphere 87Sr/86Sr in Britain. J. Geol. Soc. 167, 1–4.
Frei, K.M., Frei, R., Mannnering, U., Gleba, M., Nosch, M.L., Lyngstrøm, H., 2009.
Provenance of ancient textiles – a pilot study evaluating the strontium isotope
system in wool. Archaeometry 51, 252–276.
Frei, R., Frei, K.M., 2002. A multi-isotopic and trace element investigation of theCretaceous-Tertiary boundary layer at Stevns Klint, Denmark – inferences forthe origin and nature of siderophile and lithophile element geochemical
anomalies. Earth Planet Sci. Lett. 203, 691–708.
Hodell, D.A., Quinn, R.L., Brenner, M., Kamenov, G., 2004. Spatial variation ofstrontium isotopes (87Sr/86Sr) in the Maya region: a tool for tracking ancienthuman migration. J. Archaeol. Sci. 31, 585–601.
Horn, P., Schaaf, P., Holbach, B., Holzl, S., Eschnauer, H., 1993. 87Sr/86Sr from rockand soil into vine and wine. Z. Lebensmittel-Untersuchung-Forschung 196,
407–409.
Hornbeck, J.W., Bailey, S.W., Buso, D.C., Shanley, J.B., 1997. Streamwater chemistry
and nutrient budgets for forested watersheds in New England: variability andmanagement implications. Forest Ecol. Manage. 93, 73–89.
Horowitz, E.P., Chiarizia, R., Dietz, M.L., 1992. A novel strontium-selective extraction
chromatographic resin. Solvent Extract. Ion Exchange 10, 313–336.
Hosono, T., Nakano, T., Igeta, A., Tayasu, I., Tanaka, T., Yachi, S., 2007. Impact of
fertilizer on a small watershed of Lake Biwa: use of sulfur and strontium
isotopes in environmental diagnosis. Sci. Total Environ. 384, 342–354.
Houmark-Nielsen, M., 1987. Pleistocene stratigraphy and glacial history of thecentral part of Denmark. Bull. Geol. Soc. Denmark 36, 1–187.
Houmark-Nielsen, M., 2003. Signature and timing of the Kattegat Ice Stream: onsetof the LGM-sequence in the southwestern part of the Scandinavian Ice Sheet.
Boreas 32, 227–241.
Houmark-Nielsen, M., Kjær, K.H., 2003. Southwest Scandinavia, 40–15 kyr BP:
palaeogeography and environmental change. J. Quatern. Sci. 18, 769–786.
Kelly, S., Baxter, M., Chapman, S., Rhodes, C., Dennis, J., Brereton, P., 2002. The
application of isotopic and elemental analysis to determine the geographicalorigin of premium long grain rice. Eur. Food Res. Technol. 214, 72–78.
Kennedy, M.J., Chadwick, O.A., Vitousek, P.M., Derry, L.A., Hendricks, D.M., 1998.
Changing sources of base cations during ecosystem development, Hawaiian
Islands. Geology 26, 1015–1018.
Kjær, K.H., Houmark-Nielsen, M., Richardt, N., 2003. Ice-ﬂow patterns and dispersalof erratics at the southwestern margin of the last Scandinavian ice sheet:
imprint after palaeo-ice streams. Boreas 32, 130–148.
Lagerlund, E., Houmark-Nielsen, M., 1993. Timing and pattern of the lastdeglaciation in the Kattegat region, southwest Scandinavia. Boreas 22, 337–347.

Larsen, E., Sejrup, H.P., Janocko, J., Landvik, J.Y., Stalsberg, K., Steinsund, P., 2000.
Recurrent interaction between the Norwegian Channel Ice Stream andterrestrial-based ice across southwest Norway. Boreas 29, 185–201.
Larsen, L.M., Fitton, J.G., Pedersen, A.K., 2003. Paleogene volcanic ash layers in theDanish Basin: compositions and source areas in the North Atlantic Igneous
Province. Lithos 71, 47–80.
McArthur, J.M., Howarth, R.J., Bailey, T.R., 2001. Strontium isotope stratigraphy:
LOWESS version 3: best ﬁt to the marine Sr-isotope curve for 0–509 Ma andaccompanying look-up table for deriving numerical age. J. Geol. 109, 155–170.
Montgomery, J., 2010. Passports from the past: investigating human dispersials
using strontium isotope analysis of tooth enamel. Ann. Hum. Biol. 37, 325–346.
Montgomery, J., Evans, J.A., Wildman, G., 2006. Sr-87/Sr-86 isotope composition ofbottled British mineral waters for environmental and forensic purposes. Appl.
Geochem. 21, 1626–1634.
Morton, A.C., Evans, J.A., 1988. Geochemistry of basaltic ash beds from the Fur
Formation, Island of Fur, Denmark. Bull. Geol. Soc. Denmark 37, 1–9.
Nakano, T., Yokoo, Y., Yamanaka, M., 2001. Strontium isotope constraint on theprovenance of basic cations in soil water and stream water in the Kawakami
volcanic watershed, central Japan. Hydrol. Process. 15, 1859–1875.
Poszwa, A., Dambrine, E., Ferry, B., Pollier, B., Loubet, M., 2002. Do deep tree roots
provide nutrients to the tropical rainforest? Biogeochemistry 60, 98–118.
Poszwa, A., Ferry, B., Dambrine, E., Pollier, B., Wickman, T., Loubet, M., Bishop, K.,
2004. Variations of bioavailable Sr concentration and 87Sr/86Sr ratio in boreal
forest ecosystems: role of biocycling, mineral weathering and depth of root
uptake. Biogeochemistry 67, 1–20.
Price, T.D., Burton, J.H., Bentley, R.A., 2002. The characterization of biologicallyavailable strontium isotope ratios for the study of prehistoric migration.
Archaeometry 44, 117–135.
Probst, A., El Gh’mari, A., Aubert, D., Fritz, B., McNutt, R., 2000. Strontium as a tracer
of weathering processes in a silicate catchment polluted by acid atmospheric
inputs, Strengbach, France. Chem. Geol. 170, 203–219.
Raiber, M., Webb, J.A., Bennetts, D.A., 2009. Strontium isotopes as tracers todelineate aquifer interactions and the inﬂuence of rainfall in the basalt plains ofsoutheastern Australia. J. Hydrol. 367, 188–199.
Rauch, E., Rummel, S., Lehn, C., Buttner, A., 2007. Origin assignment of unidentiﬁed
corpses by use of stable isotope ratios of light (bio-) and heavy (geo-) elements– a case report. Forensic Sci. Int. 168, 215–218.
Richardt, N., 1996. Sedimentological examination of the Late Weichselian sea-level
history following deglaciation of northern Denmark. Geol. Soc., London, Spec.
Publ. 111, 261–273.
Rise, L., Longva, O., Ottesen, D., Kuzedforf, H., Svendsen, E., Skilbrei, J.R., Lepland, A.,
1997. The birth of the Skagerrak and the Norwegian Trench—the quaternary
period. In: Longva, O., Thorsnes, T. (Eds.), Skagerrak in the Past and Present.
Norges Geologiske Undersøkelse Special Publication, pp. 19–35.
Rossmann, A., Haberhauer, G., Holzl, S., Horn, P., Pichlmayer, F., Voerkelius, S., 2000.
The potential of multielement stable isotope analysis for regional origin
assignment of butter. Eur. Food Res. Technol. 211, 32–40.
Sandgren, P., Snowball, I., Hammarlund, D., Risberg, J., 1999. Stratigraphic evidence
for a high marine shoreline during the Late Weichselian deglaciation on the
Kullen Peninsula, southern Sweden. J. Quatern. Sci. 14, 223–237.
Sellick, M.J., Kyser, T.K., Wunder, M.B., Chipley, D., Norris, D.R., 2009. Geographic
variation of strontium and hydrogen isotopes in avian tissue: implications for
tracking migration and dispersal. PLoS One 4, e4735.
Smed, P., 1993. Indicator studies: a critical review and a new data-presentation
method. Bull. Geol. Soc. Denmark 40, 332–340.
Stewart, B.W., Capo, R.C., Chadwick, O.A., 1998. Quantitative strontium isotope
models for weathering, pedogenesis and biogeochemical cycling. Geoderma 82,
173–195.
Vitoria, L., Otero, N., Soler, A., Canals, A., 2004. Fertilizer characterization: isotopic
data (N, S, O, C, and Sr). Environ. Sci. Technol. 38, 3254–3262.
Voerkelius, S., Lorenz, G.D., Rummel, S., Quetel, C.R., Heiss, G., Baxter, M., BrachPapa, C., Deters-Itzelsberger, P., Hoelzl, S., Hoogewerff, J., Ponzevera, E., Van
Bocxstaele, M., Ueckermann, H., 2010. Strontium isotopic signatures of naturalmineral waters, the reference to a simple geological map and its potential forauthentication of food. Food Chem. 118, 933–940.
West, J.B., Hurley, J.M., Dudas, F.O., Ehleringer, J.R., 2009. The stable isotope ratios of
Marijuana. II. Strontium isotopes relate to geographic origin. J. Forensic Sci. 54,
1261–1269.