ORIGINAL PAPER

Iodine concentrations in Danish groundwater: historicaldata assessment 1933–2011

Denitza Dimitrova Voutchkova • Søren Munch Kristiansen •

Birgitte Hansen • Vibeke Ernstsen • Brian Lyngby Sørensen •

Kim H. Esbensen

Received: 20 August 2013 / Accepted: 13 May 2014

� Springer Science+Business Media Dordrecht 2014

Abstract In areas where water is a major source of

dietary iodine (I), the I concentration in drinking water

is an important factor for public health and epidemi-

ological understandings. In Denmark, almost all of the

drinking water is originating from groundwater.

Therefore, understanding the I variation in groundwa-

ter and governing factors and processes are crucial. In

this study, we perform uni- and multivariate analyses

of all available historical Danish I groundwater data

from 1933 to 2011 (n = 2,562) to give an overview on

the I variability for first time and to discover possible

geochemical associations between I and twenty other

elements and parameters. Special attention is paid on

the description and the quality assurance of this

complex compilation of historical data. The high

variability of I in Danish groundwater (\d.l. to

1,220 lg/l) is characterised by both small-scale het-

erogeneity and large-scale spatial trends, e.g. higher

concentrations observed in the eastern part of Den-

mark. Significant trends are observed also with respect

to the depth of extraction and geology, indicating the

importance of older marine limestone and chalk

deposits. A principal component analysis on centred

log-ratio-transformed data (clr) revealed associations

between I, Li, B, Ba, Br implying saline water

influence. High I is also associated with reduced and

alkaline groundwaters for this data set, dominated by

Ca–HCO3 water type.

Keywords Iodine � Groundwater � Denmark �Multivariate data analysis � Compositional data

Introduction

Iodine is an essential component of human thyroid

hormones regulating the metabolic processes in cells

and playing role in the early development of most

organs. Too low I intake, i.e.\150 lg/day for adults,

can result in a variety of iodine disorder diseases (IDD)

as mental retardation, goitre, and hypothyroidism, while

chronically high intake can also cause health problems,

such as I induced hyperthyroidism, elevated goitre, and

subclinical hypothyroidism (WHO 2007).

Electronic supplementary material The online version ofthis article (doi:10.1007/s10653-014-9625-4) contains supple-mentary material, which is available to authorized users.

D. D. Voutchkova (&) � S. M. Kristiansen

Department of Geoscience, Aarhus University,

Høegh-Guldbergs Gade 2, 8000 Aarhus C, Denmark

e-mail: ddv@geo.au.dk

D. D. Voutchkova � B. Hansen � B. L. Sørensen

Geological Survey of Denmark and Greenland (GEUS),

Lyseng. Alle 1, 8270 Højbjerg, Denmark

V. Ernstsen � K. H. Esbensen

Geological Survey of Denmark and Greenland (GEUS),

Øster Voldgade 10, 1350 Copenhagen K, Denmark

K. H. Esbensen

ACABS Research Group, Aalborg University, Campus

Esbjerg, 6700 Esbjerg, Denmark

123

Environ Geochem Health

DOI 10.1007/s10653-014-9625-4

Iodine in the human body originates mainly through

dietary intake including water or through inhalation of

atmospheric I (minor contribution). The air I concen-

tration is low: 10–20 ng/m3 (Hou 2009). Drinking

water is generally not considered a significant major

contributor to the dietary I intake as it usually provides

close to 10 % only (Fuge 2005). However, in

Denmark, where drinking water originates from

groundwaters, 25 % of the I intake in the average diet

was derived from drinking water and other beverages

(some containing high proportions of processed

groundwater) prior to recent mandatory I fortification

of table salt (Rasmussen et al. 2000). In 1998, a

voluntary programme for using iodised salt started in

Denmark (Rasmussen et al. 2002; Laurberg et al.

2006) aiming to increase the average dietary I intake

with 50 lg/day (Laurberg et al. 2006). Two years

later, the voluntary programme was found insufficient

and changed to mandatory in 2001. Since then, I is

added to the salt used in households and in production

of cakes and bread. A more recent study on the dietary

habits in Denmark (n = 4,431) shows that after the

fortification, I from drinking water and other bever-

ages represents 14 % of the dietary intake (Pedersen

et al. 2010). In such areas (as Denmark) where

groundwater is a major dietary source, a thorough

understanding of I variation in groundwater is of

importance for understanding epidemiology.

The I geochemical cycle is a spatially and tempo-

rally dynamic system, where different biotic and

abiotic processes are taking a part. In the hydrogeo-

chemical cycle, I is predominantly in oxidation state

-1 (iodide, I-) and ?5 (iodate, IO3-); and, next to

these inorganic forms, I is also found in different

organic species, which can also contribute a significant

fraction of the total I (Hu et al. 2009).

Iodine is one of the most abundant micronutrients in

sea water, with total concentration of 50–60 lg/l (Ito

and Hirokawa 2009) while I for coastal waters around

Denmark was found to vary seasonally with averages

from 32 to 89 lg/l total I (Truesdale et al. 2003).

Fuge (2005) found 2 lg/l total I in the precipitation

from the interior of the UK with up to 5 lg/l for coastal

precipitation. Similar values are found in rain water

collected in Germany, averaging 2.2 ± 0.8 lg/l

(Gilfedder et al. 2009) and in Denmark, range

0.78–2.70 lg/l (Hou et al. 2009).

Contents of I in Danish aquifer sediments have not

been studied but marine deposits are generally found

to be enriched in I relative to terrestrial sediments,

i.e. I content decreases in the order: deep-sea clays

(3.9 mg/kg) [ organic C-rich shales (near shore,

0.2–6.2 mg/kg) [ limestone (near shore, 2.5 mg/kg,

which is 1/10th of the I found in deep-sea carbonates,

(Muramatsu and Wedepohl 1998)) [ sandstone

(123 lg/kg) [ magmatic rocks (4–8 lg/kg), based

on Muramatsu and Wedepohl (1998) measurements.

From the study on 884 European bottled spring and

mineral waters, I concentrations were found to cover

three to four orders of magnitude, from \0.2 to

4,030 lg/l with median 4.78 lg/l (Reimann and Birke

2010). Many of the high values were traced to deep

formation waters, while one I rich bottled water from

Norway was influenced by water from a recent marine

clay deposit (Reimann and Birke 2010).

Existing data on I in Danish drinking water (close to

100 % originating from groundwater) are limited to

nationwide studies based on very few sampling

locations: tap water from 55 sites (K. M. Pedersen

et al. 1999), 40 sites (Rasmussen et al. 2000), and 47

sites (Saxholt et al. 2008), and drinking water from 22

waterworks (Andersen et al. 2002). This contemporary

data set shows that I concentration in the drinking

water in Denmark varies from 0.7 to 140 lg/l.

However, there is one order of magnitude difference

between the highest values reported by K. M. Pedersen

et al. (1999), Andersen et al. (2002), Saxholt et al.

(2008) and the one found by Rasmussen et al. (2000).

According to Andersen and Lauberg (2009), there is a

distinguishable difference between East and West

Denmark with respect to I content in drinking water.

Even though such regional variations of I content in

the Danish drinking water are known, there have been

no detailed geochemical studies related to I.

In the review of Whitehead (1984), the I concen-

tration in groundwaters from limestone and chalk

aquifers in UK was below 5 lg/l; however, some

iodine-enriched groundwaters ([50 lg/l) were also

present and attributed to possible recent sea water

intrusion, old saline water, or thermal water influence.

Some of the highest I concentrations in groundwaters

reported globally with 129 ± 3 mg/l, 2000 times

higher than the sea water concentration level, are

found in Japanese wells influenced by brine (Mura-

matsu et al. 2001). As far as we know, there are no

published studies on I in the Danish groundwater.

However, historical data on I in the groundwater can

be found in the Danish nationwide geological and

Environ Geochem Health

123

hydrological database (Jupiter). Therefore, the aim of

the present study of I in Danish groundwater was 1) to

give for the first time an overview on the existing

groundwater I data with focus on the spatial variation,

geological setting, and depth of extraction and 2) by

using a multivariate analysis to identify geochemical

associations between I and other variables in order to

elucidate the governing factor(s) for the spatial

variability.

Methods and materials

Data

The groundwater data used in this study are taken

from the Danish public nationwide geological and

hydrological database, Jupiter (GEUS 2011). All

groundwater samples analysed for I together with

other supplementary information were downloaded

from Jupiter on 24th of November 2011. The supple-

mentary information consisted of: location (ground-

water sampling screen, i.e. well ID number and

geographical coordinates, screen number, screen

depth—top and bottom); when the samples were

collected (date); dominating geological setting at the

screen (given with % from the length of the screen and

type of geological setting, see Supplementary materi-

als 5); used analytical method (for each geochemical

element); samples with concentrations below detec-

tion limits (assigned an attribute ‘\x’, where x is a

specific detection limit); and objective of analytical

determination.

For the purpose of a multivariate study, all available

data were used not only for I, but also for possible co-

explanatory geochemically and geologically relevant

major and trace elements and parameters. Figure 1

presents the number of available samples analysed for I,

and the following relevant variables: free CO2 (called

further Agg.CO2), B, Ba, Br, Ca, CH4, Cl, Fe, F, H2S,

HCO3, I, K, Li, Mg, Mn, Na, NO3, O2, PO4, SO4, Sr,

conductivity, NVOC, pH, and redox values. The

charges are not included as not for all listed analytes

the exact speciation is available in the Jupiter database.

Two variables used in the multivariate analysis are

not provided by Jupiter: ‘distance to coast line’ and

‘distance to major faults’. The ‘Near 3D’ tool of

ArcMap10.0 (ESRI 1999–2010) was used to calculate

the horizontal distance from each sample location

(well X, Y coordinates provided from Jupiter data set)

to both the coastline and/or to the nearest major

geological fault lines. The distances to the present

coast line are calculated as distances to the closest sea

or fjord, after a specific data control was made, and all

derived distance values were manually checked for

calculation mistakes due to complex coastline shapes.

The distance to faults was measured as distance to the

closest of the major fault lines described by Hakansson

and Pedersen (1992).

Data sets preparation and pre-treatment

Master data set and quality assessment

After the raw data were extracted from the database, a

master data set (MDS) was prepared which consisted

Fig. 1 Number of samples analysed for iodine and 25 co-joint

variables in the master data set based on data extracted from the

Jupiter database (variables excluded in the preparation of the

master data set for multivariate analysis are also indicated;

r-MDS stands for reduced master data set)

Environ Geochem Health

123

of 28 variables (I, 25 geochemical elements and

parameters, distance to the coast, and distance to major

faults) for a grand total of 2,562 objects. The data set is

characterised by many missing values because far

from all groundwater samples have been analysed for

all of the analytes (Fig. 1). The supplementary infor-

mation (Id well number, sample number, screen

number, X and Y coordinates, laboratory methods,

sampling date, geology, detection limits, and purpose,

etc.) was used for a thorough initial data quality

evaluation. The highly significant diversity of I data

quality due to many different analytical methods, as

well the possible errors in the database entries with

respect to I speciation, is discussed in full detail in

Supplementary materials 1. The MDS was first

checked for gross outliers by manual exploration for

unusually high values. As a result, a few additional

unit errors were also eliminated (Supplementary

materials 2). Values below detection limit were

substituted with 0.75 of the detection limit, following

standard geochemical traditions. Details concerning

different detection limits for I and all other analytes, as

well as the proportions of censored values and missing

data are given in Supplementary materials 3. The

Fig. 2 Spatial distribution

of the master data set (MDS)

samples/objects and the

reduced master data set (r-

MDS) samples; in the upper

right corner, the Danish

administrative regions are

given (to facilitate the

understanding of the Results

part); in the lower part of the

figure are given: (left)—

probability plot of the I

concentrations from the

r-MDS; (right)—probability

plot of all I data in the MDS,

with samples grouped by

geographical region (East

vs. West)

Environ Geochem Health

123

spatial distribution of the MDS objects (samples) can

be seen on Fig. 2.

Preparation and pre-treatment of the data

for multivariate analysis

Two different operations were needed in order to

prepare the data for multivariate analysis. First, the

size of the MDS was reduced due to the many missing

values in the matrix, and second, the closed array

problem was addressed by applying the centred log-

ratio (clr) transformation of the compositional data

(see further below for details).

As seen from Fig. 1, the number of analyses for the

potentially co-varying other 25 elements varies from

65 (O2) to 2,131 (B). The requirement for proper

multivariate data analysis (MVDA) is that the pro-

portion of samples that does not contain missing

information for all (or nearly all) variables should be

as high as possible. Thus, the original MDS had to be

sensibly reduced so as to present a suitable low

proportion of missing values throughout, which

should be as randomly distributed over the data matrix

as possible. While this configuration is desirable, in

practice, it is very much the existing variable coverage

in the database that brackets such endeavours. Thus,

reduction of the MDS was carried out in two steps: (1)

reduction in the number of variables (analytes), i.e.

deletion of variables with a too high proportion of

adversely distributed missing values (major, trace

elements, and field measurements) followed by (2)

reduction of remaining objects (samples) still with a

too high proportion of missing values. In the first step,

eight variables were excluded (Agg.CO2, CH4, F, H2S,

NVOC, O2, PO4, redox value), based on the insuffi-

cient number of co-existing analyses, Fig. 1. The

choice of variables (and the exclusion of variables)

can limit interpretation of the MVDA results by failing

to recognise potentially important processes or by

lacking sufficient data evidence due to omitted

variables, necessitating a balance between the con-

trasting requirements. After removing eight variables,

there were still too many missing values in the data

matrix, so further reduction of the objects was also

necessary, with the primary objective of keeping as

many as possible of high(er) I values. All MDS

reductions were made according to well-established

general chemometric rules and procedures (Esbensen

2010).

This clean-up resulted in a reduced MDS (r-MDS),

which consists of 20 variables (B, Ba, Br, Ca, Cl, Fe,

HCO3, I, K, Li, Mg, Mn, Na, NO3, SO4, Sr,

conductivity, distance to coastline, distance to faults,

and pH) and 506 objects, representing 20 % of the

original number of objects. Although an apparently

dramatic reduction in the number of analyses

(2,562 ? 506) available for MVDA, it is a necessary

reflection of the very disparate analytical coverage

displayed in the database and the resulting covariance

matrix coverage, Fig. 1. For the purpose of a first

overview of the fullest multivariate data structure,

however, r-MDS is the optimal co-joint data set that

can be obtained from the Jupiter database. The spatial

distribution of the r-MDS objects (samples) can be

seen on Fig. 2. Most of them are located in Jutland and

part of Zealand. An overview on the number of

censored data (\d.l.) as well as missing values per

variable in the r-MDS is given in the Supplementary

materials 3. The missing values were substituted with

the arithmetic mean of the respective variable columns

in the matrix (neutral with respect to PCA) in order to

be able to address the closure problem of the

compositional data in a rational fashion.

Compositional data are multivariate data in which

components (variables, analytes) are part of a whole,

i.e. variables sum to a constant (often 100 %) (Paw-

lowsky-Glahn and Egozcue 2006). The main charac-

teristic of compositional data is that the variables are

not free to range from ‘-’ infinity to ‘?’ infinity

within the data analytical Euclidian space. They

instead occupy a restricted space, called a ‘simplex’,

having its own metric, which follows other rules than

Euclidian geometry (Buccianti and Pawlowsky-Glahn

2005). Two characteristics of compositional data are

especially important from the multivariate data ana-

lytical and interpretational (practical) point of view:

Closed variables are not free to vary independently

because of the constant sum constraint, and only their

relative magnitudes and variations (not absolute

values/concentrations) are to be used for data analysis

and interpretations. In order to overcome this ‘closure’

problem, three different log-ratio transformations

(additive, centred, and isometric) have been intro-

duced in geochemistry and data analysis over the last

two decades (see review by Pawlowsky-Glahn and

Egozcue (2006)). Buccianti and Pawlowsky-Glahn

(2005) demonstrated that the centred log-ratio (clr) is

an appropriate method for water chemistry data, and

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123

consequently the clr transformation was chosen for our

study. In order to transform the data, for the n-part

composition x (x is one sample/object, analysed for n

compounds), the clr(x) coefficient/value, written as

vector with n parts, is given as:

clr xð Þ ¼ clr x1; x2; . . .; xn½ �¼ ln x1=gð Þ; ln x2=gð Þ; . . .; ln xn=gð Þ½ �;

where g is the geometric mean of the sample

composition g = (x1, x2, …,xn)1/x.

There are few challenges associated with applying

this type of transformation to non-complete data sets

representing compilation of historical data (see

Results and discussion). The clr transformation was

performed only on the compositional variables (B,

Ba, Br, Ca, Cl, Fe, HCO3, I, K, Li, Mg, Mn, Na, NO3,

SO4, and Sr). Prior to the clr transformation, all lg/l

were recalculated to mg/l (the actual sample densities

are unknown, so density of 1 kg/l is assumed). The

four additional non-compositional variables (con-

ductivity, distance to coastline, distance to faults, and

pH) were centred with their arithmetic means and

weighted by the factor 1/STD before the PCA, known

as auto-scaling in multivariate data analysis (Esben-

sen 2010).

Data analysis

The univariate data analysis (based on means, per-

centiles, probability plots, and box plots) was done on

all iodine samples (n = 2,562) from the MDS after the

substitution of the values below detection limit (see

previous part).

The compositional variables were clr transformed

with the open source software CoDaPack (Comas-

Cufı and Thio-Henestrosa 2011); the multivariate data

analysis (PCA) was performed on the pre-treated

r-MDS (506 9 20) (see previous part) using the

Unscrambler 10.1 software package (CAMO 2011).

Principal component analysis (PCA) is used for its

suitability for simultaneous multivariate data descrip-

tion and structure exploration, notably for the possi-

bility of discovering grouping of samples and/or

variables which may otherwise be swamped in indi-

vidual sampling and analytical errors if only studied

univariately, the so-called full-spectrum advantage

(Esbensen 2010). The objective of the present PCA

was to explore the overall data structure and to

identify geochemical associations between iodine and

other major and minor ions. The nonlinear iterative

projections by alternating least-squares (NIPALS)

algorithm is used. For detailed explanation on the

methods NIPALS and PCA, see (Esbensen 2010) and

further background references herein.

Results and discussion

Iodine concentration and spatial variation

in Danish groundwater

The groundwater data included in this master data set

contain samples analysed for I (n = 2,562) obtained

from year 1933 to year 2011. Iodine has been analysed

irregularly: Less than 2 % of the samples are taken

before 1990 and the year with the most analyses is

2005 (n = 922), representing more than 35 % of all I

analyses for the studied period. About half of the

samples (50.11 %) are taken as part of the Danish

Groundwater Monitoring Programme (GRUMO). The

purpose of GRUMO is to document quantitatively and

qualitatively the groundwater status and trends so the

effect of the national action plans on the aquatic

environment can be evaluated (Hansen et al. 2011).

Until 2011, iodine was not part of the analytical

programme of GRUMO. A total of 74 clustered

catchments with area 5–50 km2 each containing 25

wells are included in the GRUMO monitoring

programme (Hansen et al. 2011), and this is reflected

by the spatial distribution of the samples part of our

data set, which are from 975 wells distributed

throughout Denmark (see Fig. 2). At some of these

wells, more than one well screen is available, so the

samples taken at different depths might be represent-

ing different aquifers too. Based on the screen lengths

which vary substantially—from less than 1 m length

to 110 m—can be concluded that the well design and

the screen types differ too. Close to 2/3 of the samples

are extracted from screens shorter than 2 m.

The I concentration varies from below detection

limit (the lowest detection limit is 0.4 lg/l) to

1,220 lg/l. For 90 % of the samples, I concentration

is below 20 lg/l and only 11 samples have I above

200 lg/l. The median value is 5.4 lg/l, and the

average I concentration in the groundwater is

13.83 lg/l.

It is interesting to check if the East–West trend for

the drinking water I (high–low I concentration,

Environ Geochem Health

123

respectively) found by Andersen and Lauberg (2009)

can be observed also in the groundwater data.

Even though 100 % of the Danish drinking water is

with groundwater origin, only around 16 % of the

samples in this master data set are extracted from

wells belonging to waterworks (very few of them are

actual production wells). Indeed, even though both

high and low concentration are clustered together, the

average mean I concentration for the administrative

Capital Region is the highest (26.81 lg/l) followed

by Region Zealand (14.06 lg/l), whereas the lowest

average mean is calculated for the region of Central

Denmark (7.6 lg/l). This trend is also present for the

25th, 50th, 75th, 90th, and 95th percentiles (see

Supplementary materials 4). The probability plot for

I concentrations in West Denmark (Jutland peninsula

and the island of Funen) and East Denmark (Zealand

and Bornholm) clearly visualises the East–West

trend (Fig. 2, see also Supplementary materials 4).

However, the I concentrations are far from homoge-

neous throughout the administrative and geographi-

cal regions—both high and low concentrations

cluster together. From a geochemical point of view,

it is interesting to find an explanation for both small-

scale variations as well as for the large-scale trends.

The general trend of I concentration distribution

with depth is that the relatively higher iodine

concentrations start to be observed very sharply at

extraction depths around 40 m (to 80 m) below terrain

(Fig. 3). This difference could be explained either by a

shift in the geological settings (e.g. the limestone/

chalk vs. glacial deposits) or in the groundwater

chemistry (e.g. reflecting different water types) around

that depth.

Most Danish aquifers consist of Quaternary or

Miocene sand and/or gravel, Palaeocene to Late

Cretaceous chalk/limestone (Kelstrup et al. 1982).

Only the dominating geological setting (proportional

to the screen length) is known for about 70 % of the

well screens. There are more than 40 different

geological settings, representing either the aquifer or

an aquitard dominating at the screen length (see

Supplementary materials 5). The highest 75th, 90th,

and 95th percentiles are found for the samples

representing groundwater from Palaeocene to Late

Cretaceous chalk/limestone (Fig. 4 and Supplemen-

tary materials 5). The lowest median (2.8 lg/l) is for

the Palaeogene settings (w/o limestone); the median

value for samples representing glacial meltwater

settings (7 lg/l) is as high as for the samples

representing the Palaeocene/Cretaceous limestone/

chalk (Fig. 4). In Quaternary sediments above marine

calcareous deposits, I concentrations are generally

elevated according to Fuge (2005), which might

Fig. 3 Iodine concentrations in 10/20 m depth intervals of: (a) the top screen and (b) the bottom screen depths below terrain level

Environ Geochem Health

123

explain the lack of more pronounced difference in

Fig. 4.

If we look at the types of dominating setting at

extraction depths of 30–40 m and compare to the ones

at 40–50 m, it can be seen that there are almost equal

amounts of samples representing glacial meltwater

settings (37 vs. 35 %, respectively). However, the

samples representing a limestone/chalk aquifer are

twice as many at 40–50 m depth (26 %) than at

30–40 m (13 %), which could explain the sharp

difference in the I concentrations below and above

40 m below terrain, Fig. 3 (see also Supplementary

materials 5).

The univariate data analysis allows only a descrip-

tion of the variability of the concentrations with

respect to geographical location, depth, or geology.

However, for pointing at a specific source or process,

responsible for the I variation in Danish groundwater,

a multivariate approach is needed.

Identifying governing sources and processes

by using multivariate data analysis

The PCA model revealed a strong data structure, as

only two principal components (PCs) were needed to

explain 93 % of the total variance (PC1: 87 %, PC2:

6 %). No clear multivariate outliers were identified;

thus, the PCA model is representing all 506 samples of

the r-MDS. Descriptive statistics of the r-MDS (incl.

clr-transformed data) is given in Supplementary mate-

rials 6; summary of the PCA model characteristics are

presented in Supplementary materials 7 (incl. PC1 and

PC2 loadings and descriptive statistics of PC1 and PC2

scores). Information about the relative variability in the

data set can be obtained by analysing the position of the

variables in the loading plot, as explained by Daunis-I-

Estadella et al. (2006) (interpretation of bi-plots).

Buccianti and Pawlowsky-Glahn (2005) also demon-

strated a hydrogeochemical interpretation based on clr-

transformed data. PC1–PC2 loading and score plot are

here used (Fig. 5e) to identify geochemical associations

between I and other major and minor ions and to further

elucidate the potential governing sources or processes.

It is interesting to note that the minor and trace

elements (Li, I, B, Ba, Br, Sr) are all located on the

positive side of the PC1, whereas the major elements

(HCO3, Ca, SO4, Cl, Na, Mg, K, NO3) are on the

opposite side (except Fe and Mn, which are also on the

positive side of PC1). The grouping of the variables on

the loading plot resembles very closely the differ-

ences/similarities in the variability shown on the box

plot of the clr-transformed data (Fig. 6j) and the raw

data (Fig. 7j).

The clr(I) and clr(Li) variables are clearly

positively associated with PC1 (87 % explained

variance) (Fig. 5e). The nearly coincident clr(I) and

clr(Li) are showing that the variance of ln(I/Li) is

close to 0, so I/Li ratio is constant or almost

constant for the data set. This is also supported by

the similarity in the clr variation: for clr(Li) from

-9.24 to -3.07, and for clr(I) from -7.04 to -1.5,

mapped on the PC1–PC2 score plot (Fig. 5a, c).

The raw I and Li concentrations are also mapped on

the PC1–PC2 score plot (note that the scores are

Fig. 4 Box plot of iodine

concentrations grouped

according to the dominating

geological setting at the

screen length (see also

Supplementary materials 5)

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123

based on clr-transformed data) for comparison and

orientation (Fig. 5b, d); the Li concentrations in

r-MDS vary from 0.0375 to 51.4 lg/l, with a mean:

7.89 lg/l and median 6.1 lg/l, whereas I concen-

trations vary from 0.75 to 700 lg/l (see Supple-

mentary materials 6 for descriptive statistics of

r-MDS). The Li median of our data set is lower

than the median of the European bottled water,

10 lg/l (Reimann and Birke 2010). The clr(I) is

also close to other clustered variables—clr(Ba),

clr(Br), and cl(B). Ba content in groundwater is

mainly caused by dissolution of Ba-holding miner-

als, e.g. barite (BaSO4) or witherite (BaCO3)

(Mokrik et al. 2009); however, for aquifers where

groundwater recharge is mixing with sea water,

Santos et al. (2011) discusses that no single process

can explain Ba distribution. Four processes are

pointed to be of major interest: mixing of sea- and

Fig. 5 Principal

component analysis plots,

based on the reduced master

data set: A, B, C, D ? PC1–

PC2 score plot with mapped:

clr(I) (a); raw I

concentrations (b); clr(Li)

(c); and raw Li

concentrations (d); E ?PC1–PC2 loading plot

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freshwater, ion exchange-driven sorption/desorption

to/from sediments, marine organic matter minerali-

sation, and Fe and Mn oxide cycling (Santos et al.

2011). The B, Br, and Li have been used for

studying the origin of groundwater salinity in order

to distinguish between marine, geothermal, and

evaporite influences (Sanchez-Martos et al. 2002).

High Li content can be found in thermal waters; it

is used also for tracing the mixing of brines

produced by sea water evaporation (see overview

by Sanchez-Martos et al. (2002)). The grouping of

the clr variables I, Li, B, Ba, and Br and their

similar variability in the data set suggest for

influence of similar processes or sources.

At the negative side of PC1, clr(HCO3), and

clr(Ca) are the most influential variables, followed by

the cluster—clr(Cl), clr(SO4), clr(Na), clr (Mg). The

clr-transformed values mapped on the PC1–PC2

score plot (Fig. 6a–f) are visualising the similarity

between the variables from these two groups (for

orientation, the raw concentrations are mapped and

also shown on Fig. 7a–f). The smallest distance

between two variables on the loading plot is the one

between clr(Cl) and clr(SO4), similarly to I/Li ratio,

this means that the Cl/SO4 ratio is also constant, or

almost constant, for this data set. The clustering of

the four variables (Cl, SO4, Na, and Mg) can be

explained by their primarily marine origin. The Ca–

HCO3 pair is pointing at the carbonate system and its

importance. This is also supported by the Ca–HCO3

water type prevalence in our data set (see Piper

Diagram in Supplementary Materials 8). Not sur-

prisingly, the samples characterised by low pH

(Fig. 6i) are also plotting in the upper part of PC1

where the low Ca–HCO3 are found.

PC2 (explaining only 6 % of the variance) is

influenced strongly by only two of the clr vari-

ables—NO3 and Fe. The clr(NO3) is on the positive

side of PC2, whereas clr(Fe) is on the negative side,

followed by clr(Mn). The position of the clr(NO3)

and clr(Fe) on the loading plot results in almost

clear separation of the samples in two groups on the

PC1–PC2 score plot which is clearly visualised by

the trends shown on Fig. 6g, h). The Fe-NO3

grouping of the samples can be interpreted as

representing the different redox state of the ground-

water—oxidised versus reduced (Hansen et al.

2009). On Fig. 5b, it can be seen that the higher I

concentrations (and clr(I)) mainly lie in the lower

part of the PC1–PC2 score plot, where the influence

of clr(Fe) is higher, implying that the higher I

concentrations are found in reduced groundwaters

then in oxidised.

The link between clr(NO3) and clr(Fe) is almost

perpendicular to the link between clr(I) and

clr(HCO3), which means that there is near zero

correlation between the two pairs and they can

consequently be considered to be independent.

Based on historical data from the Jupiter database,

it is possible to derive a first overview of Danish

groundwater with respect to I and its affiliation to other

chemical compounds and parameters by multivariate

analysis. It is emphasised that the quality of the input

data (discussed in Supplementary materials 1, 2, and

3) is a crucial success factor for the data analysis and

for the reliability of the possible geochemical inter-

pretations. While gathering historical hydro-geochem-

ical data from the public Danish database, Jupiter, is

relatively straightforward, the individual co-joint data

possibilities are of highly variable individual validity,

and far from all of a sufficient quality for strong

multivariate analysis. Comprehensive scrutiny of the

Jupiter database resulted in substantial reductions of

the data set when questioned from multivariate data

quality point of view.

Additionally to the above issues, there were the

challenges associated with ‘proper’ clr data transfor-

mation: (1) proper treatment of missing and censored

values; (2) weight % recalculation; (3) data modelling

of mixed compositional ? not compositional data. All

these issues were handled in appropriate fashions (see

Materials and Methods) in our analysis; however,

some uncertainties might be introduced because of the

made assumptions. As the scope of our study is to

present a first overview on existing historical data on I

rather than developing and testing alternative data

analytical approaches, we have restricted ourselves to

as simple analysis as possible, in which the limitations

and the assumptions we have made along the process

should be kept in mind.

Fig. 6 Principal component analysis plots, based on the

reduced master data set: A, B, C, D, E, F, G, H, I ? PC1–

PC2 score plots with mapped: clr(HCO3) (a); clr (Ca) (b);

clr(SO4) (c); clr(Na) (d); clr(Mg) (e); clr(Cl) (f); clr(NO3) (g);

clr(Fe) (h); pH (i); box plot of the clr-transformed variables with

whiskers ±1.5xIQR (j)

b

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Conclusions

The main two study objectives have been fulfilled as

much as the data and the chosen methodology allowed

for. The overview on the existing I groundwater with

focus on the spatial variability, geology, and depth of

extraction was made. The spatial variation of I

concentrations in Danish groundwaters is character-

ised both by small-scale variation (both high and low I

samples clustered together) and by a large-scale East

versus West trend with higher I concentrations

observed in the east part of Denmark. The highest I

concentrations are found in samples from aquifers

dominated by Palaeocene to Cretaceous limestone/

chalk deposits and clear shift in the I concentration is

observed between 30–40 m and 40–50 m extraction

depth reflecting the geology in the setting dominated

by limestone/chalk deposits. From the multivariate

analysis, associations between I and other constituents

were identified, and an attempt to identify governing

sources and processes was made. Iodine, Li, B, Ba, Br

are exhibiting similar variability (clustered on the

PC1–PC2 loading plot) which suggests common

source or governing process. Saline water influence

is implied; however, further studies are needed in

order to point the specific process/source, which most

probably varies in different areas. The redox state of

the water was found to be an important characteristic

of the r-MDS, and most of the high I concentrations (as

well as Li) are found at the reduced waters’ side of the

PC1–PC2 score plot. The groundwater samples in this

data set were found to be dominated by Ca–HCO3

water type, and high I concentrations are associated

also with the high pH (alkaline) side of the PC1–PC2

score plot.

Based on the historical data from the Jupiter

database, it was possible to get for first time an

overview on the I concentrations in Danish ground-

water. This work should be seen as a pilot study—an

investigation to clear the way for future geochemical

studies on I variability (both in concentration and

speciation) resulting in delineation of geographical

(and geological) areas influenced by different factors

responsible for the observed variations.

Acknowledgments This paper is part of the Ph. D. study of the

first author; the Ph. D. project was funded by GEOCENTER

Denmark. We gratefully acknowledge the financial support by

the Geological Survey of Denmark and Greenland (GEUS) and

Aarhus University.

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