Characterization of the spatial distribution of
iron- and manganese oxides in the Sterksel
Formation at the Maalbeek quarry, The
Netherlands
19-10-2012
Nathalie Olivier
Faculty of Geosciences
Utrecht University
Contents
Contents .................................................................................................................................................. 2
Abstract ................................................................................................................................................... 4
Introduction............................................................................................................................................. 6
Geological background .......................................................................................................................... 10
Geostatistical analysis ........................................................................................................................... 14
Frequency histograms ................................................................................................................... 14
Summary statistics......................................................................................................................... 14
Scatterplots ................................................................................................................................... 14
Variograms .................................................................................................................................... 15
Spearman Rank Order Correlation ................................................................................................ 16
Methods ................................................................................................................................................ 18
Sampling methods ............................................................................................................................. 18
CBD method ...................................................................................................................................... 21
Aqua regia extraction ........................................................................................................................ 23
XRF-measurements ........................................................................................................................... 23
Results ................................................................................................................................................... 26
Depth profiles .................................................................................................................................... 26
Frequency histrograms ...................................................................................................................... 34
Descriptive statistics .......................................................................................................................... 35
Spearman rank order correlation ...................................................................................................... 36
Variograms ........................................................................................................................................ 38
Correlation of components measured by ICP-OES in the sediment ................................................. 41
XRF components:............................................................................................................................... 43
Discussion .............................................................................................................................................. 44
Depth profiles .................................................................................................................................... 44
Reactive and non-reactive iron ......................................................................................................... 44
Comparison XRF and aqua regia........................................................................................................ 47
XRF measurements............................................................................................................................ 48
CBD method ...................................................................................................................................... 50
2
Aqua regia ......................................................................................................................................... 51
XRF ..................................................................................................................................................... 52
Frequency histograms ....................................................................................................................... 52
Variograms ........................................................................................................................................ 53
Iron oxide enrichment in the Sterksel Formation ............................................................................. 54
Conclusions............................................................................................................................................ 56
Recommendations ................................................................................................................................ 58
Acknowledgements ............................................................................................................................... 58
References ............................................................................................................................................. 59
Appendixes A to D ................................................................................................................................. 66
3
Abstract
Iron and manganese oxides are common components in subsurface environments. They influence
the groundwater flow, sediment chemistry and contaminant transport. The oxides can form coatings
on the surface of sand particles and thereby provide a reactive interface between mineral grains and
groundwater. Concentrations of iron and manganese oxides can be very high in sediments. Because
of their adsorption capacity, naturally occurring iron- and manganese-bearing phases are known to
strongly influence the transfer of inorganic species and ionizable organic compounds.
This project has focused on the spatial distribution and concentration of iron oxides and total iron in
the Sterksel Formation. This formation was deposited by the Rhine during the colder intervals in the
middle Pleistocene. The studied site is the Maalbeek quarry in Limburg. At this site, the formation
consists mostly of coarse sand and pebbles, and has a highly heterogeneous distribution of iron and
manganese. This study is aimed to characterize the spatial distribution of iron and manganese within
the sediment at this quarry. Three different methods have been used to quantify the concentration
of iron and other components. The Citrate- Bicarbonate Dithionite method was used to extract the
reactive iron from the sediment. Ingestion with aqua regia was used to extract acid soluble iron,
manganese and other metals from the sediment. Measurements with XRF were done to get a view
on the total composition of the sediment. The outer layer of coating of the grains was mainly
measured.
In general, enrichment in oxides is in the lower parts of the formation. Manganese oxides are very
locally enriched in thin layers and are in a range of 5.42 ppm to 589 ppm manganese. Reactive iron
concentrations were measured in the range from 139 to 3000 ppm, and total iron concentrations
range from 515 to 515 to 71000 ppm. It is argued that the actual reactive iron concentration should
in many cases be higher than measured here. Possible reasons for the low concentrations are
shortage of reagents, incomplete dissolution due to crystallinity of oxides and degradation of
dithionite. The iron measured by XRF is in the range of 1335 to 12406 ppm. This is a higher value than
the aqua regia measured iron because mainly the coating was measured and not the entire sand
grains. An overestimation might occur here because the lighter elements are underestimated relative
to the heavier elements. The total iron distribution has a lognormal distribution and the reactive and
XRF-measured iron has a bimodal distribution.
There is a weak correlation between iron and manganese, a reason could be that manganese has a
very local enrichment compared to iron. There is a strong iron-titanium correlation, suggesting
presence of ilmenite in the sediment. Iron-silicon measured by XRF has a negative correlation; this
4
could be because the iron oxides form coatings around the grains. The distinguish between reactive
and non-reactive iron was estimated with theFe2O3/Al2O3 ratio and by comparing the iron measured
by different methods. This results in an approximate ‘background’ value of iron oxide of 0.26 times
aluminium oxide concentration.
The variograms that were created result in a horizontal correlation range of maximum 5 meters, and
the vertical correlation range was maximum 1meter. Because the iron enrichment has a layered
structure, it was expected that the vertical correlation range would be lower than the horizontal
range. The sampling grid is not dense enough to get a representative view on the spatial distribution,
because the nugget of the variograms was very high. It is recommended to make the sampling
locations much smaller, with minimum sampling distances of 5 cm in the vertical direction and 10 cm
in the horizontal direction. The results of the smaller sampling locations could be extrapolated to the
whole area.
Figure 1 A depth profile in the Maalbeek quarry which shows
heterogeneity of iron oxide distribution in the Sterksel
Formation, Limburg
5
Introduction
In both terrestrial and marine environments, iron and manganese in different phases have been
studied extensively (e.g. Birnie and Paterson, 1991; Jordan and Rippey 2003; Rutten and De Lange
2003). Iron and manganese are abundant components in soils and sediments and play an important
role in the environment, mainly at the oxic/anoxic interface of sediments (Granina, 2004). Iron has
many forms within the sediment: It is found in clays, carbonates (siderite), sulfides (pyrite) and
oxides (goethite, amorphous iron oxides). A part of the iron is non-reactive. This is the iron in clays
and other silicates. The other phases of iron are reactive, these are the oxides, sulfides and
carbonates (Heerdink and Griffioen, 2008). The redox chemistry of iron and manganese is distinct, as
the reduced forms are well soluble and the oxidized species are very insoluble at near-neutral pH
values (Stumm and Morgan, 1996). In shallow aquifers, iron and manganese are mainly present as
oxides and hydroxides with varying crystallinity and structure (Schwertmann and Taylor, 1989). In
general, the total free iron concentrations of sand sediments range from 0.1 to 4 wt % Fe2O3. This
number can increase to 60 wt % (Breeuwsma, 1987). Enrichments in oxides are often associated
with seepage areas and/or organic rich sediments (van der Veer, 2006). The mobility of iron in
sediments depends on pH, redox conditions and the possibility of complexing with organic
substances (Barral and Guitian 1991).
Oxides have a strong tendency to form inner sphere surface complexes with metal ions, oxyanions
and other ions. Sediments contain a range of organic and inorganic ligands, for example oxyanions
such as phosphate, silicate and natural organic matter, which are known to affect ferrous/ferric
redox transformations (Cornell and Schwertmann, 1996). In porous media, oxides form either
coatings on surface minerals or form separate grains, and by this form a reactive interface between
mineral grains and the ground water (Stumm, 1992). Manganese oxides are noted for their high
sorption capacity for major ions as well as trace components (Murray, 1975; McKenzie, 1980;
Balistieri and Murray, 1982 and Catts and Langmuir, 1986).
Anderson (1990) studied the absorption of metals onto oxide surfaces with different pH values, and
found that uptake is fastest under pH values of 7 and higher. The process of absorption is dependent
on both iron and aluminum oxides. Due to their high sorption capacity, manganese oxides often
accumulate significant amounts of heavy metals and could have an impact on the heavy metal
content of groundwater (Larsen and Postma 1997; Fueller et al. 1996). The reason for the high
absorption capacity of oxides is that at typical groundwater conditions, the surface charge of
dominant mineral species such as quartz and feldspar is negative. The oxides, however, are positively
6
charged (Barber II 1992). Metal oxides of iron and manganese are controlling factors of distribution
of many heavy metals (Lee, 1998, Coston 1995)). Adsorption of metals is however a complex process
because it is dependent on many components in the environment. Sorbing ligands may induce
dissolution or adsorption of metal oxides, depending on the type of sites they absorb to (Stumm,
1996). Ligands can increase metal adsorption through changes in the electrostatic properties of the
solid/solution interface (Brooks, 1998). These coupled sorption-dissolution reactions exert a strong
control over the distribution of and availability of metal ions in aqueous systems (Simanova et al.,
2011).
The concentration of organic matter is an important parameter for the redox state of the sediment. If
there are high concentrations of organic matter in the sediment, the oxygen is depleted rapidly with
depth due to aerobic degradation. Iron then becomes the electron acceptor for degradation of
organic matter and is reduced to ferrous iron. This is why organic contaminants in anoxic aquifers
could be cleaned up by the reduction of iron oxides. Also in oxic aquifers organic matter and iron
oxides influence each other. Davis (1980) reported that positively charged mineral surfaces have
strong sorption affinities for organic matter because of surface complexation.
The reactive properties of the sediment has a strong variance over different types of lithological
classes (Helvoort et al., 2007), and within a class there can be large heterogeneities of reactive
potential. Analyses of geochemical parameters that characterize reactivity is laborious and
expensive, the more so because of sediment heterogeneity. Such analyses are therefore not
routinely performed and effective reactivity of the aquifer sediment is deduced in many cases from
the observed patterns in groundwater chemistry only (van Gaans, 2011).
Since 2006, the TNO Geological Survey has been working on developing a geochemical model of the
Dutch subsurface environment. The geology of the Netherlands has been extensively studied and
described and is therefore well known. By linking geochemical properties to lithological classes of
geological formations, a geochemical model of the subsurface environment can be created from the
geological model. Geochemical properties are obtained by sampling geologically homologous areas.
By statistical analysis of the obtained data, a typical range of geochemical compositions can be
assigned to a certain lithological class per geological formation (van Gaans 2011). The geochemical
model of the subsurface environment is created by randomly assigning values of geochemical
variables, such as sediment reactivity caused by iron and manganese oxides, to grid cells. This results
in a heterogeneous geochemical reaction capacity model of the subsurface. This model can be used
in e.g. groundwater transport models or other instruments for groundwater quality management.
7
By assigning random values for sediment reactivity it is assumed that there is no spatial correlation
between two neighboring cells. However, it is likely that there is a certain spatial correlation between
reactivity at short distances, therefore this model could be further improved by finding the maximum
distance of correlation of datapoints.
The research topic of this thesis is the the Sterksel Formation,
this is a sandy porous medium which surfaces in the eastern
parts of the Netherlands, near Venlo. Within this porous
medium there is a strong heterogeneity of both manganese
and iron oxides. The geological features of the Sterksel
Formation have been extensively studied and described by
Westerhoff (2001). Heterogeneities of the iron concentrations
within the Waalre Formation, which is situated just underneath
the Sterksel Formation, has been studied in the Master Thesis
of Vriens (2011). The reason for variability of iron and
manganese oxides in the Sterksel Formation is not yet known.
One possible explanation is that the areas with increased oxide
concentration are accumulation points for leached iron from
upper layers, i.e. former B horizons of paleosoils. Another
possible explanation is that the iron and manganese were
Figure 2 heterogeneity of iron and
brought to this area in reduced form by groundwater upwelling manganese oxides in the Sterksel Formation,
and cross bedding structures. This profiles is
from lower layers.
approximately 20 cm long
In this thesis, three different methods are used to measure the iron content and other components
in sediments from the Sterksel Formation. The CBD (Citrate-Bicarbonate-Dithionite) method is used
to extract the iron oxides from the sediment. The concentration of iron was measured with
colometric analysis. The resulting values for the concentration of iron oxides is a measure for reactive
iron within the soil. The concentration of metals was analyzed with ingestion with aqua regia and
measurements with ICP-OES (Inductive Coupled Plasma Optical Emission Spectrometry). XRF (X-ray
fluorescence) was used to measure the total composition of the sediment. These three methods all
have certain limitations, but when the results are compared to each other this results in a good view
on the soil composition.
This thesis is aimed on the analysis of spatial distribution and spatial dependency of iron and
manganese oxide concentrations in the Sterksel Formation in the Maalbeek quarry in Limburg. By
8
use of statistical analysis the horizontal and vertical spatial variation is determined, which might be
used for building a geochemical model for this particular formation. Both the concentration of iron
oxides and the total iron concentration in the soil is determined by different extraction methods. The
following questions are addressed in this report:

How are iron and manganese oxides spatially distributed in the subsurface environment of
the Sterksel Formation at the Maalbeek pit?

What is the required sampling density in order to characterize the spatial distribution of iron
and manganese oxides?

How do the different analytical methods compare to each other?

Is a hand-held XRF a suitable method of measuring iron and manganese oxides in the field?

Which diagenetic process led to the iron and manganese distribution at this location?
9
Geological background
The Sterksel Formation was deposited by the Rhine Meuse system during the Middle Pleistocene.
The part of the Sterksel Formation that is studied lies in the western part of the Lower Rhine
Embayment. This is a tectonically subsiding area which is bounded in the south-east by the
Palaeozoic rocks of the Rhenish Massif (Geluk, 1994). Periods with relatively high tectonic activity are
correlated with phases of major uplift of the Rhenish Massif during the Late Tertiary and Middle
Pleistocene (Boenigk, 2006). Fluvial sedimentation in the Lower Rhine Embayments starts at the MioPliocene transition. Due to tectonics, many different blocks were formed in the Lower Rhine
Embayment, which were either lifted up or lowered down. Most faults have a southeast-northwest
orientation. The locations of these faults have a large impact on the sedimentation history. The
studied area lies on the so-called Peelhorst or Peel Blocks (Zagwijn & Doppert, 1978). It is bounded
by two major faults, to the west by the Peel Boundary Fault and to the east by the Viersen Fault (van
Rooijen, 1985). During the colder stages of the Cromerian Stage (850 to 475 kya), the Sterksel
Formation was deposited.
Figure 2 shows the parts of the Netherlands where the Sterksel
Formation is deposited (Meijer, 2007). It spreads out to a large
part of the Netherlands, but it only surfaces in the north of
Limburg. In the northwestern part of the Netherlands, the
sediments from this formation are mixed with sediments of the
Peize Formation. Because the Sterksel Formation consists of
mainly coarse, well conducting sands, it is an important
groundwater reservoir in the Netherlands. In the west parts of
the Netherlands, the formation consists of finer sand fractions
and clay is more abundant than in the eastern parts of the
Netherlands. The thickness of the formation varies from a few Figure 3 Extension of the Sterksel Formation.
meters to a maximum of 60 meters, with an average thickness of
15 meters (Zonneveld, 1958).
The sandy sediments of the Sterksel Formation were deposited during the colder intervals of the
Cromerian Stage (850 – 475 ka). Deposits of warmer climate river systems are not found in the
studied area. This is either because they were not deposited in this specific area since the channels of
meandering rivers are not as wide as the channels of braiding rivers, or because the deposited
material was eroded again (Westerhoff, 2008). The braiding character of the channels is mainly
caused by the permanently frozen soil during the glacials (Lapperre 1995). During the interglacials,
10
the temperatures were comparable to the present-day climate in the Netherlands. A tundra-climate
was dominant during the colder periods. Pollen analysis has shown that the Cromerian Stage consists
of three glacials and four interglacials (Berendsen, 2004). The mineral composition (including garnet,
epidote, alterite and green hornblende) shows that the Sterksel Formation holds sediments
deposited by the Rhine. The composition was different for each glacial, which is used as a tool to
distinguish between the different zones of the Formation. They are called the Woensel, Weert and
Rosmalen zone (Boenigk 2002).
The general lithology of the Sterksel Formation is described as semi-coarse to coarse sand with
gravel, with a grain size ranging from 50 to 2000 μm (Lapperre 1995). Calcium carbonate
concentrations are variable in this formation. The dominating color is grey-brown with red
components. There are some blue-grey to grey colored clay layers, which could contain a high
amount of silt. The heavy mineral fraction varies within the layer but is dominated by epidote
(Westerhoff, 2009).The complete heavy mineral composition is shown in Figure 4.
The Sterksel Formation deposits have been correlated to the Upper Terrace (UT) sequence. This
sequence is described extensively by many authors (e.g. Boenigk, 1978a). The UT1 deposits
correspond on the basis of their stratigraphic position and lithological composition to the Sterksel
Formation of the Dutch stratigraphy (Boenigk 2006). A correlation of the UT1 in the Middle and
Lower Rhine areas is evidenced only by the petrographic similarity of the terrace sediments in the
two areas (Hoselmann, 1994). Excellent evidence for correlation is available for the UT2 of the Lower
Rhine embayment and the UT2/3 of the Middle Rhine area (Hoselmann, 1994). The two terraces
form a single morphological unit. Owing to its morphology, and the predominance of saussurite
(alterite) in the heavy-mineral association, the UT2 is considered to correlate with the Weert zone of
the Sterksel Formation in the Netherlands (Boenigk 1978a, 1990; Zagwijn 1985).
11
Figure 4 Heavy mineral composition, grain size and thickness of the Sterksel Formation
Forest
area
Water
Fields
Urban
Figure 5 Location of the Maalbeek quarry. The yellow dashed line marks the border between the upper terrace on the eastern
side and the middle terrace on the western side. The water shown in the upper left corner of the picture is the present-day
flow location of the Meuse. The quarry stretches out further to the north than shown.
12
13
Geostatistical analysis
In order to determine if there is a spatial correlation of the iron concentration in the Sterksel
Formation in the horizontal and/or vertical direction, geostatistical analysis is used on the obtained
dataset. Geostatistics is a tool that allows to take spatial correlation between neighbouring
observations into account (Matheron, 1971; Journel and Huijbregts, 1978; Goovaerts, 1997). When
geostatistics is applied to a dataset, it is assumed that every sample represents a spatial portion
larger than itself and that there exists a spatial dependence between samples, decreasing with
distance (Armony ,2001). The maximum distance at which spatial dependence between samples
occurs can be derived from variograms. The programs Statistica and Isatis were used to perform the
statistical analysis and to make the graphs of the results. These programs are able to make
distribution histograms, variograms and 3D representations of data.
Frequency histograms
Firstly, frequency histograms are made by use of the results of all sections. The actual location of the
samples is ignored for now, and it is assumed that the sections do not differ much from each other.
The distribution of continuous values is typically shown by a histogram with the range of data values
discretized into a specific number of classes with equal width and the relative proportion of data
within each class is expressed by the height of bars (Goovaerts, 1997). These relative proportions
define the class frequencies, hence the histogram depicts the frequency distribution of values for a
given definition of classes. The histograms show the iron concentrations expressed in ppm (parts per
million) or mg kg -1. This is a useful statistical tool, because it shows the type of statistical distribution
of groups of samples. For most types of statistical analysis, it is assumed that populations have a
normal distribution. The frequency histograms show if this is a valid assumption for this dataset.
Summary statistics
Summary statistics shows what the mean, standard deviation, minimum and maximum values are for
the different values found by the different methods of iron analysis. These statistics are compared to
each other to see if there are major differences between different heights or distances. For these
type of statistics, it is assumed that the dataset has a normal distribution.
Scatterplots
Scatterplots show the correlations of components within the sediment plotted against each other.
This is done for different components measured with the same method and the same component,
measured by different methods. For example, the iron-manganese correlation shows if these
components have a high concentration at the same location, which tells something about the
14
distribution pattern of these components. The iron concentration extracted by aqua regia versus the
iron concentration measured by XRF shows if these methods give comparable results. The trendline
within a scatterplot shows how high the average ratio of the components is. The R2 value shows the
goodness-of-fit of the trendline, i.e. how close the datapoints are to the trendline, and thus how
strong the correlation between two components is.
Variograms
The variogram (or semi-variogram) is a graph relating the variance of the difference in value of a
variable at pairs of sample points to the separation distance between those pairs. The model-based
variogram approach is most useful in cases where there is a continuous, gradual increase in variance
with spatial scale, and the aim is to provide a geographically continuous assessment of the precision
of interpolated maps (Spijker, 2005).
There are a number of adequate measures of statistical difference for the purpose of constructing a
variogram, some designed for robustness in the face of skewed data. By far the most common is the
Matheron classical estimator; defined as half the average squared pairwise difference within the lag.
We select values according to their separation distances (e.g. 0-5m, 5-10m, etc), and take the
average statistical variance for each distance range; this helps to filter off the effects of any unusual
data values within the lag. The classical formula for the construction of a variogram is as follows:
(Matheron (1965)):
𝑚(ℎ)
1
𝑦(ℎ)( =
∗ ∑ {𝑧(𝑠𝑖) − 𝑧(𝑠𝑖 + ℎ)}2
2𝑚 (ℎ)
𝑖=1
Where y(h) is the semivariance, z (si) and z (si + h) are the actual values of z at locations (si) and (si+h)
which are separated by distance h. The sum is over m(h) which is the number of pared comparisons
separated by h. By changing h, an ordered set of semivariances is obtained. These semivariances
constitute the experimental or sample variogram (Haining, 2010).
A variogram can be read by analyzing the range, the sill and the nugget (Figure 6) The range is the
distance where the variance reaches the sill value. At this point, the distance between two samples is
too large for the samples to be correlated to each other. The sill is the value at which the variogram
levels off. The nugget is the amount of variance there is between two samples if the distances
approaches zero. At regions with very large heterogeneities in the microstructure, the nugget is
higher than at homogeneous regions. Also sparse data and errors in measurement could lead to a
higher nugget effect .
15
Figure 6 An example of a variogram. The sill (c) is the maximum variance, the range (h) is the maximum
distance of spatial correlation. The nugget (interception with y-axis) is the variance at short distances.
Spearman Rank Order Correlation
There are many ways to statistically interpret a dataset. The method that is used should depend on
the type of dataset and the distribution. If a dataset is not normally distributed, then nonparametric
methods should be used. Non-parametric tests allow for the analysis of categorical and ranked data.
Randomness of the variables is the basic assumption of almost all distribution functions (Siegel 1956,
Kumar et al., 2007). There are a number of nonparametric methods that can be used to find
relationships between variables, such as Spearman R, Kendall Tau and coefficient Gamma (Statistica
Electronic Textbook). Because it is a simple and robust method that requires no assumptions on the
distribution, it was chosen to use Spearman Rank Order Correlation (Spearman R). Spearman's R is a
widely used measure for the strength of association between two random variables (X and Y) (e.g.
Schmid and Schmidt 2007). Spearman rank correlation works by converting each variable to ranks.
Once the two variables are converted to ranks, a correlation analysis is done on the ranks. The
correlation coefficient is calculated for the two columns of ranks, and the significance of this is tested
in the same way as the correlation coefficient for a regular correlation (McDonald, 2009). This
method assumes that the variables under consideration were measured on at least an rank order
scale, that is, that the individual observations can be ranked into two ordered series (Siegel &
Castellan, 1988).The sign of the Spearman correlation indicates the direction of association between
X and Y. If Y tends to increase when X increases, the Spearman correlation coefficient is positive. A
Spearman correlation of zero indicates that there is no tendency for Y to either increase or decrease
when X increases. The Spearman correlation increases in magnitude as X and Y become closer to
being perfect monotone functions of each other. When X and Y are perfectly monotonically related,
the Spearman correlation coefficient becomes 1 (Lehman,2005).
16
17
Methods
In this chapter, it is explained in which way the samples were collected and treated, what types of
measurements were performed on the samples and how the gathered data was analyzed in order to
obtain answers on the research questions.
Sampling methods
The samples were collected in the Maalbeek quarry, Limburg. This quarry has excavated the
sediment from the Sterksel Formation down to just above the upper border of the Waalre formation,
so the Sterksel Formation is completely exposed. From this quarry, 450 samples have been collected
over five different locations, which are labeled locations (capital) A to E. Two of the sampling
locations (A and E) are approximately longitudinal to the flow direction of the braiding river system,
and the other three are approximately perpendicular to the general flow direction.
Figure 7 Overview of the sampling locations of the different sections
The sampling grid is shown schematically in Figure 8. At each sampling column, the vertical distance
of two samples is 50 centimeters for the lower six meters, and two meters above the lower six
meters. The horizontal distance between two sampling columns increases per column. The distance
between columns a and b is 50 cm. Columns c, d, e and f are at of 2.5 m, 7.5 m, 17.5 m and 35.5 m
apart from column a, respectively. In order to measure spatial variation of iron and manganese
concentrations on very short distances, each sampling column included four extra samples, collected
at very short distances. This was done by randomly picking one of the sampled points, then taking 4
extra samples at 5, 12, 22 and 37 cm vertical distance from this sample, respectively. The samples are
18
collected either above or below the starting point, this is also determined randomly. This sampling
strategy results in a broad profile of almost 40 meters, which also includes samples on the
centimeter scale.
Figure 8 sampling grid
A DGPS (Differential Global Positioning System) is used to measure the exact location and height of
the base of each sampling column. The accuracy is approximately 10 cm for the best measurements
to approximately 2 meters. This is quite accurate compared to a ‘normal’ GPS which has an accuracy
of 10 to 15 meters. Measuring tape and rulers were used to double check the locations of sampling.
Figure 7 shows the locations that were measured with the DGPS. For most sections, the base of the
section was measured by DGPS and the measuring tape was used to measure the distance from the
base.
For safety and convenience, samples were collected by an aerial working platform (Figure 9). The
maximum height of sampling was largely determined by the maximum range and stability of the
platform. For the best statistical results it is ideal to randomly determine the sampling locations.
However, due to safety precautions, the number of sampling locations was limited as some locations
were too steep for safe sampling, and were considered unstable. The precise sampling locations
were determined by putting a pin into the sediment at the highest possible point, and hanging a
labeled rope on the pin. This way, the labels on the rope marked the spots where a sample should be
taken. Because the ‘extra’ samples were taken on very short distances, a measuring tape was used.
Before sampling, the outer few centimeters of the sediment are removed with a scoop in order to
make a fresh sample; then a sample was collected by pushing a plastic container into the sediment.
The diameter of this container is approximately 3 cm. This method keeps the sediment and the
sample undisturbed, and allows short sampling distance. When the sampling location contains large
pebbles or clay, a scoop was used to loosen the material.
19
Figure 9 Working platform at sampling location B
Figure 10 Sampling location B
After collection in the field, samples were stored at room temperature. It is expected that presence
of air and an increase in temperature did not change the chemical composition of the samples, as
they were all naturally oxidized in the field and exposed to air. In order to prepare the samples for
the different analysis procedures, all samples were dried in an oven for at least 24 hours in an oven
of 105 oC and sieved to obtain the grain size fraction below 2 millimeters. Although it is often done,
the material has not been grinded to powder in this study. This is because the goal is to obtain the
concentrations from the surface of the grains. The composition of the sand grains itself is of less
interest. Furthermore, one of the goals is to test the feasibility of a hand-held XRF in the field, to the
samples have to be as less altered as possible.
After the sieving and drying of the samples, three types of analysis were done in order to determine
the composition of the samples. Iron oxides are extracted with sodium dithionite, aqua regia
extraction is used to extract all components adsorbed to the sand and X-Ray Fluorescence (XRF) is
used to measure the total composition of the samples. The results of these three methods are
compared, and should provide information on the concentration of iron oxides; the composition of
components adsorbed to the sand and the total composition (including the sand).
20
CBD method
The Citrate-Bicarbonate-Dithionite (CBD) method was designed to extract the reactive components
from the sediment. In this study, the Mehra Jackson method, adjusted by Pansu (2006) is used.
Sodium dithionite (Na2S2O4) is a strong reducing component that reduces and solubilizes secondary
iron oxides, including goethite, hematite and magnetite (Mehra and Jackson, 1960). The optimum pH
for reduction is between 7 and 8. If the pH drops below pH 6.5, sulfur can precipitate, which results
in a suspension in the extracts that prevents measurement by absorption spectrometry and above
pH of 9, the reducing capacity of the dithionite is decreased (Deb, 1950). This method dissolves
crystalline iron oxides, non-crystalline iron oxides and iron and aluminum organic complexes as well
as exchangeable iron and manganese oxides and some crystalline compounds with a SiO2 to Al2O3
ratio less than 0.5. Clays are not affected by the citrate-bicarbonate solution (Pansu, 2006). Once
reduced, the Fe2+ forms a complex with the citrate (Hunt, 1994). The reaction of iron reduction in
citric complexing solution is as follows:
S2O42- +Fe2O3 + 2 HOC(COO)33- + 2 H+  2 SO32- + 2 FeHOC(COO)3- + H2O
(1)
The citrate bicarbonate buffer was made by dissolving 79.4 g L-1
trisodium citrate (C6H5Na3O7 * 2H2O) with 9.24 g L-1 of sodium
bicarbonate (NaHCO3). The pH of the buffer is adjusted to 7.5.
Figure 11 shows the scheme of the CBD method. From each
sample, between 1 and 2.5 gram is mixed with 22.5 ml of buffer.
The iron contents in a sample should not exceed 0.5 grams of
Fe2O3 in order to obtain an excess of reducer and complexant.
The amount of sample is dependent on the estimated
concentration of iron. The sample with the buffer is allowed to
heat up in a water bath of 75 oC. The elevated temperature
increases the reaction rate. Approximately 0.5 grams of
dithionite powder is added, this is mixed and allowed to react for
10 minutes. Another 0.5 gram of dithionite powder is added and
allowed to react for 10 minutes. The dithionite is added in two
steps because the component is unstable and will degrade after
a few minutes. After 20 minutes of digestion, the tubes are
Figure 11 Scheme of the CBD method
centrifuged by 2500 g for 5 minutes, the liquid phase is decanted
in a new Greiner tube. In order to remove all dissolved iron, the solid phase is washed twice by resuspending the sample with 10 ml of buffer; stirring, centrifuging and decanting in the same tube as
21
the previous step. The tubes were weighted afterward in order to determine the dissolution. One
blank and one standard (ISE-912) were included in each series.
The concentrations of the iron extracted by the CBD method were determined using
spectrophotometric analysis. The calibration curves were made with 10 standards with
concentrations between 0 and 1 mmol L-1 iron. Ferrozine ( 3-[2- pyridyl]-5,6-bis[4-phenylsulfonic
acid]- ,2,4-triazine) is used as a reagent for ferrous iron because it forms a stable, intensely magentacolored component with the ferrous iron ion. It is soluble in water and may be used for the direct
determination for iron in water. All iron in solution is reduced to ferrous iron by a reducing agent,
then a buffer is added in order to obtain a near-neutral pH, as the absorbance is highest at pH 7 (To
et al., 1999). The absorption spectrum of the ferrous complex of ferrozine exhibits a single sharp
peak with maximum absorbance at 562 nm (Stookey, 1970). The absorbance of each sample is linked
to the concentration in mmol/L by the calibration curve.
A lot of authors argue that dithionite is a highly effective method reagent for dissolution of iron
oxides. It is almost unique because it is effective at pH > 5 while most reagents need an acid
environment in order to work (Blesa, 1992). It dissolves iron reductively and form inner sphere
complexes with surface Fe3+ ions that later evolve through charge transfer from the reductant
complexing anion or from the bridged reduced metal ion. The method from Deb (1950) was modified
by Aguilera and Jackson (1953) and later by Mehra and Jackson (1960). Blesa (1992) has tried to
figure out the process and limitations of the dithionite method. They propose a lower temperature
for the dithionite method (40 oC) since at higher temperatures, the rate of S2O4- decomposition is too
high. Also, they propose a lower pH (5.5) for the reductants to be effective and bubbling with N2
during the extraction is deemed necessary in order to protect the environment from air. In the same
study, Blesa (1992) mentions that iron can reprecipitate when not all oxides are dissolved. In this
study, the process of dissolution of iron takes up to 180 minutes. At high temperatures, more
addition of dithionite is required as it degrades quickly.
The extracts turned yellow after a few days, which is an indication of the dithionite to degrade. It is
unknown if the spectrophotometric measurement of the iron concentration was influenced by the
discoloring, but it was observed the extracts with the higher iron concentration had a more
pronounced yellow color.
22
Aqua regia extraction
The aqua regia extraction is designed to dissolve all components of the sample, except for quartz.
This results in a total spectrum of elements, with concentrations in ppm (mg kg-1) . Aqua regia or
nitro-hydrochloric acid is a highly corrosive mixture of acids, a fuming yellow or red solution. The
mixture is formed by mixing 65% nitric acid and 35% hydrochloric acid, usually in a volume ratio of
1:3 (Nieuwenhuize et al., 1991). For the extraction, 125 mg of sample is pre-weighed in a Teflon vial.
To the vial, 1.5 ml of 63 % HNO3 and 4.5 ml of 67% HCl was added. The components are mixed and
are allowed to react overnight at 90 0C. Lids are put on the vials to prevent evaporation. The next
day, the vials were allowed to cool, the lids were taken off and the temperature is increased to 160
0
C. This causes the liquids to evaporate, until a gel-like substance remains. To the residue, 20 ml of 5
% HNO3 is added to the vials, and the lids are put back on and the samples are re-heated to 90 0C for
(at least) two hours. Then the vials are re-weighed to determine the weight of the acid, which is
needed to calculate the dilution. For analysis of the aqua regia extracted components, ICP-OES
(Inductively Coupled Plasma- Optical Emission Spectrometry ) is used.
XRF-measurements
XRF (X-Ray Fluorescence) is a method that measures the chemical composition of samples, by
sending an X-ray to the surface of the samples and analyzing the backscattered X-ray. This is a fast,
non-destructive method which could be applied in the field, without working with chemicals. There
are different types of XRF measuring devices, in this case a handheld silicon based XRF was used.
The XRF works by exciting the sample material
with an X-ray beam. An inner electron is ejected
from its shell by the beam. The empty shell is then
filled with a higher energy electron from an outer
shell. The energy difference between the shells is
emitted as a characteristic radiation with a specific
wavelength (Kaiser and Wright, 2008). This process
is schematically shown in Error! Reference source
not found.. The intensity of the radiation is
proportional to the concentration of an element in
the sample material. The elemental concentrations
Figure 12 Exciting of inner shell electron by XRF radiation
are obtained by calculating the peak area, and
subtracting elemental interference corrections as well as applying calibration factors.
23
Advantages of this method are that it is non-destructive, it requires no chemicals, it provides total
element concentrations of all element forms, the device is small and light weight, the method is fast
and can be taken into the field. This saves time and can help select samples for further research,
which saves time in the lab. The only disadvantages are that the calibration for the light elements is
still problematic, and the instrument is expensive. Magnesium and aluminum belong to the lighter
elements, they are difficult to measure. Flushing with helium is required for precise measurements,
especially for the lighter elements (De Smeth 2011). The X-rays travel easier through helium than air,
so the scatter is decreased, which increases the accuracy of the measurement.
The mining mode has been used to measure the samples of wall B. This mode was chosen because
the elements of interest are oxides, which fall within the main spectrum of this mode (Figure 13). The
results for magnesium, aluminum and silica are less reliable because they are light elements.
Figure 13 Detectable elements at the mining mode of a portable XRF
In order to test how reproducible the results are, two of the samples were appointed as standards.
These samples are measured at least twelve times during the measuring campaign. These results are
also used as a check if the results are changing, for example when the helium flow was not constant.
The same standards as for the extraction methods were not used, because this is in powder form and
would result in a false accuracy.
In order to make a proper comparison between this method and the two extraction methods, the
same (dried and sieved) material is used for the XRF measurements. Because this method is meant to
be used in the field, also a few of the samples that have not been dried and sieved were measured.
When used on wet samples, the XRF could give different results because the water film around a
sample adsorbs a part of the X-rays. However, when the water content is less than 20%, the error is
small. (Kalnicky, 2001).
24
25
Results
Samples were composed of brown to orange, white-brown and in some cases partially black sand
and gravel. A few samples held silty material. After the extractions and measurements, depth profiles
were constructed and statistical analysis was done on the dataset. This chapter is subdivided by
results of iron and manganese measurements, the depth profiles of iron and the correlation between
the different measured components.
Depth profiles
Figure 14 shows the depth profiles from all sections. The x-axis represents the concentration iron in
ppm and the y-axis represents the height in meters above the bottom of the Sterksel Formation. The
height above the bottom of the Sterksel Formation was known because locations C and D start at the
bottom, and with GPS the height was measured. It is assumed that the height of the border of the
Sterksel Formation does not change significantly in this area quarry. In the figure, the red lines
represent the total iron concentration, extracted by ingestion with aqua regia and measured with
ICP-OES. The blue lines represent the reactive iron, extracted by the CBD method and measured
spectrophotometrically. The samples from section B are also measured with XRF, the associated
depth profiles are plotted with green lines.
In general, the concentration depth profiles of the reactive iron follow the profiles of the total iron,
but the concentrations are lower. Section A consists of two locations which are approximately 63
meters apart. Sections Ab and Ac are 50 cm and 2.5 m apart from section Aa, respectively and
sections Ad and Ae are 2.5 m and 50 cm apart from section Af, respectively. Unlike the other
sections, the profiles Aa to Ac are not near- vertical ‘walls’ but a slope with an angle of approximately
30 degrees. All sampling spots are registered with GPS so the horizontal and vertical distances are
known. Only the vertical distances have been taken into account for making these depth profiles. The
horizontal distances were assumed to be negligible. Some layers within sections Aa, Ab and Ac
seemed quite fine grained an thin lenses of silt or clay were found. This suggests that the sediments
at this location were deposited by low energy river channels. Locations Aa to Ac show a strongly
fluctuating concentration profile, with the highest concentrations between 10 and 11 meters height.
The depth profile of the total iron of section Aa shows a peak at 10.5 meters height. The sediment
was described as medium coarse sand, with estimated moderate to high concentration of iron,
judged by color. The sample at 10.5 meter height of profile Ac was described as silty material and a
moderate iron concentration. Also at 11 meters height the sample contained silty material. Sections
Ad to Af are located at 63; 64.5 and 65 meters distance from Aa, respectively. These sections have a
26
strong variation in grain size and color (Figure 1 is a picture from this section). The peak of total iron
concentration is at a transition of fine grained to medium coarse grained sand. There is no clear
difference in color of the sediment. The distinct peak at 8 meters height is not at a clear transition of
grain size or color.
The samples from section B have been measured with XRF as well as the other two methods.
Although the ICP-OES and XRF methods both result in a measure for total iron within the sample, the
values for XRF are higher than the values for ICP-OES with the exception of only a few samples. The
overall trends of the depth profiles are similar to the profiles of the other two methods. Depth profile
Bc shows a peak for the XRF measured iron at 9 meters height. At this point, the sediment is poorly
sorted with some silty material. Profile Bd was measured at greater depth because a hole was dug in
front of the section. At 5 meters height, a transition in grain size is described from coarse sand with
low iron content, to fine grained sand, which is cemented and brown colored. This profile shows that
between 2 and 4.5 meters depth the iron concentration is low. It is estimated that the direction of
the profiles are approximately perpendicular to the former direction of the river flow.
Sections C and D are located close to each other and are oriented in the same direction as section B,
approximately perpendicular to the former river flow. The distance between C and D is small, so it is
assumed that the profiles found in C continue in section D. Sections C and D are the only ones which
start directly at the bottom of the Sterksel Formation. In general, the lower 3.5 meters of the
sections all have low iron content, and there is a pronounced peak which shifts upward from 3.5 to
4.5 meters height over the section. Above 5 meters height, the concentration decreases to values
similar to the lower part of the section. In depth profile Ca, the lower part of the profile has a low
iron content, which is confirmed by the and coarse grain size. At 3 meters height, there is a sharp
transition to high iron content, the sediment here has an orange brown color and contains silty
material. The upper two samples are described as poorly sorted sediment, with both fine grained
sediment and pebbles. The lowermost sample of depth profile Cb was described as fine grained sand,
with low iron content. The actual iron content is however relatively high (50000 ppm). Because this
sample also had a high water content, it is possible that there was some iron in reduced form here.
At 2.5 meters height the iron oxide content gradually increases. The sediment color here suggests
high iron oxide color. The material here is coarse. At the top of this profile, manganese oxides are
present. Depth profile Cc has a similar depth profile to Cb, with a seemingly low iron for the
lowermost few meters. At 4 meters height, according to the sediment description, a sharp transition
occurs to very high iron content and very coarse material. In the depth profile, the peak is not
extremely high, perhaps because the sediment is coarse, which results in a low surface area. This
decreases the amount of iron that can adsorb to the sediment. The peak in profile Cd 4.5 meter
27
height is at a location where the sediment is poorly sorted, with both coarse and silty material. The
peaks in depth profile Ce are also associated with high oxide content and poorly sorted sediment.
The depth profile of Cf has a different scale for iron concentration than sections Ca to Ce, because
the concentration is much lower. The peak in concentration at 3.5 meters height is at a location with
poor grain size sorting.
Profile Df from section D is closest to Cf of the C section, and lies at approximately 13.6 meters
distance from Cf. Both section consists mainly of coarse grained sediment, but the lower five meters
of the depth profiles of section D contain reactive iron, while the profiles of section C only have high
reactive iron between 3 and 5 meters height. Profile Da contains mostly coarse and very coarse
material. The lowermost sample might contain some reduced iron because the iron content could
not be guessed from the color. Between 2 and 4 meters height, the material is very coarse and
moderately sorted. The reactive iron content is high here, which correlates with the description of
cemented material. In depth profile Db, the lowermost sample has an extremely high iron
concentration (71342 ppm). This sample is taken from a crust on the transition of sand and clay. The
two samples above this one contain a lot of water and might contain reduced iron. The distinct peak
at 5 meters high could not directly be explained from the grain size and sorting. Although it was
described as high iron content, coarse sand, it is uncertain why the values are so extreme, but the
sample right above is well sorted medium coarse sand with low to medium iron. Depth profile Dc is
similar to Db. The lowermost sample contains cemented sediment with a high iron content. From 4
meters height the total iron is increased. At 5 meters height there is a small peak in manganese and a
large peak in iron. Judged by color this part of the profile contains many iron oxides. The iron
concentration decreases above 5 meters to very small concentrations. At profile Dd, peak at top of
profile was not expected because this sample contains no color that suggests iron oxides. The iron
concentration peaks at 1.5 m and 5 m of profile De are associated with coarse, well sorted sediment
and an orange color. The same counts for the peak at depth profile Df. The depth profile of Df has a
peak in iron concentration at 4 meters height. +
The profiles from the E section are only 3.5 meters high, the concentration of both reactive iron and
total iron are quite constant with depth, and the concentrations are not very high. The concentration
scale is different for this location than for the other locations, as it only goes to 4000 ppm instead of
10,000 ppm. The only exception is the highest point of depth profile E, the sediment of this sample is
poorly sorted and seemed to contain few iron oxides.
28
30
31
32
Figure
14
Depth
profiles
of
reactive
iron
and
total
iron,
and
XRF
measured
iron
of
section
B
33
Frequency histograms
Figure 15 shows the frequency histograms of the iron concentrations that were found by the CBD
method (left) and the iron concentrations found by the aqua regia method (right). This graph shows
that the distribution of reactive iron is bimodal, with a peak between 300 and 600 ppm, and one
between 900 and 1500 ppm. The total iron has a lognormal distribution, with many observations of
lower concentrations and a ‘tail’ with few observations of high concentrations. Most concentration
observations are between 1000 and 3000 ppm, and maximum values are around 10,000 ppm. If the
natural logarithm is calculated of all values, this results in a normal distribution (see Appendix C).
Figure 16 shows the distribution of the total iron and XRF-measured iron. These datasets both have a
bimodal distribution. Total iron is highest between 750 and 1750 ppm, and 2250 and 3000 ppm. For
XRF measured iron, the peaks in distribution is between 1750 and 3500 ppm and 5500 and 7500
ppm.
Figure 15 Histograms of reactive iron, measured colorimetrically (left) and histogram of total iron, measured with ICP-OES (right)
Figure 16 Frequency histograms of total iron (left) and XRF iron (right) for samples of section B.
Descriptive statistics
Table 1 shows the basis statistics of the acquired dataset, with respect to iron and manganese. The
average reactive iron measured spectrophotometrically, is 1130 ppm, the average total iron
measured by ICP-OES is 3468 ppm. The iron measured by XRF is on average 43 % higher than the iron
measured by ICP-OES. Manganese measured by XRF gave many values below detection limit, and
was therefore not usable for statistical analysis. Only the values of iron measured by ICP-OES are
used.
From the mean and the standard deviation, the expected range of values is calculated. Because the
distribution of iron and manganese in this dataset is lognormal, the standard deviation of the Lnvalues are calculated. The chance that a value falls within the range of twice the standard deviation
above and two times the standard deviation below the mean is 95%. The ranges of the 95 percentile
distribution are calculated and changed back into real values. This method shows which data is likely
to hold errors, but also the data of the samples that actually have an extreme concentration of iron
or manganese. Because it was observed that enrichment of iron and manganese in this formation can
be on very local scale, it is possible that some samples contain extreme amounts of iron or
manganese. There are some data points that have high values for both iron and manganese, it is
possible that the extreme values are correct for these samples.
Standard
95%
Range
Descriptive statistics
Valid N
Mean
Minimum
Maximum
deviation
max)
Reactive iron (CBD)
457
1130
139
3000
653
0-2436
Total iron (ICP-OES)
449
3468
515
71342
4576
0-12620
Total iron (XRF)
89
4993
1335
12406
2509
0-10011
Manganese (ICP-OES)
361
39.9
5.42
589
51.4
0-142.7
Table 1 Statics of iron and manganese data, all values are in ppm
35
(min-
Spearman rank order correlation
Because there is not a normal distribution of the oxides but a lognormal and bimodal distribution,
there is a limitation on the further statistical methods that can be used. The Spearman rank order
correlation does not assume one type of distribution and thus can be used to find correlations
between the different methods of analysis. The Spearman correlations of the methods used on the
samples of section B is shown in Table 2. There is a weak correlation between aqua regia and
dithionite, and a very weak correlation between XRF and dithionite. The correlation between XRF and
aqua regia is significant (0.635). Table 3 shows the Spearman rank order correlation of the whole
dataset. The correlations of XRF with the two extraction methods are the same, but the dithioniteaqua regia correlation is much higher (0.508) than for the samples of only section B.
Variable
Dithionite
Aqua regia
XRF
Dithionite
1
0.126
0.0347
Aqua regia
0.1236
1
0.635
XRF
0.0347
0.635
1
Table 2 Spearman rank order correlation of section B
Variable
Dithionite
Aqua regia
XRF
Dithionite
1
0.508
0.0347
Aqua regia
0.508
1
0.635
XRF
0.0347
0.635
1
Table 3 Spearman rank order correlation of whole section
36
The different analyzed components can also be correlated with this method. Table 4 shows the
Spearman R for the reactive iron (analyzed by the CBD method), and the iron, manganese,
magnesium and aluminum found by the ICP-OES after ingestion with aqua regia. There is a relatively
strong correlation between the reactive iron and total iron. The other components do not show a
clear correlation. There is a quite strong positive correlation between the total iron and the other
components. Aluminum, manganese and magnesium also correlate strongly with each other. The
reason for these differences could be that magnesium and aluminum are mainly found on the nonreactive part of the sediment. This is why they correlate well with each other but not with the
reactive iron.
Variable
Reactive iron
Total iron
Manganese
Magnesium
Aluminum
Reactive iron
1
0.505
0.2244
-0.0803
0.129
Total iron
0.505
1
0.6702
0.622
0.621
Manganese
0.225
0.672
1
0.668
0.726
Magnesium
-0.0803
0.622
0.668
1
0.847
Aluminum
0.129
0.621
0.726
0.847
1
Table 4 Spearman rank order correlation of aqua regia extracted components and CBD extracted iron
37
Variograms
The variograms have been constructed in order to see at what the maximum distance is at which
concentrations of data points correlate to each other. Figure 17 to Figure 21 show the variograms
that have been constructed for this study. In each variogram, the thin line shows the experimental
variogram as constructed by calculating the variance of the different data points. The thick, smooth
line shows the best-fit of the variogram, plotted by the program Isatis. The fitted variogram can be
used to get a quantitative interpretation of the range of spatial correlation, and in combination with
the experimental variogram, a qualitative interpretation on the accuracy of this range can be argued.
Figure 17 shows the variograms for the horizontal correlation of the total iron and the reactive iron
of section A. The experimental variograms have a large fluctuation with peaks at 5 meters, but the
fitted variograms have a range of 12 meters and 8 meters for the total iron (left figure) and reactive
iron (right figure), respectively. The horizontal-distance variograms of location A are the only
locations at which the range is so high. The nugget of the total iron is larger than the nugget of the
reactive iron. Figure 18 show the variograms for section A of the vertical correlation range. As
expected, the range of these variograms is much shorter than the horizontal range. For the total iron,
the fitted variogram does not show a clear sill but from the experimental variogram it can be
estimated that the sill should not be far from 2 meters. The variance of the reactive iron has a peak
at 1 m and decreases at larger distance. The fitted variogram of the reactive iron shows a range of 1
m. Both the reactive iron and the total iron of the vertical-distance variograms have large nuggets,
which suggest that the sampling density should be increased to get a more accurate variogram.
Figure 19 shows the variograms of horizontal correlation at section B, for the total and reactive iron.
According to the figure, there is no spatial horizontal correlation for this section. For the reactive
iron, the correlation is plotted at approximately 2 meters. Figure 20 shows the horizontal and vertical
range of correlation of iron measured by the XRF. The horizontal range is 75 cm, the vertical range is
not precisely determined from this variogram but is interpreted as (almost) non existing. The vertical
range for section B of total iron is less than 1 meter, with a large nugget. The range of the reactive
iron is 0 meters. Because this section consists of a near-vertical wall, all samples of one column are
at the same horizontal distance of the starting point. This means that the variogram is constructed
from only a few points, which are the averaged values of the different sampling columns. The
variograms of vertical correlation of section B are shown in Figure 21. The fitted variogram of the
total iron in vertical distance has a range of 0.8 meters. The reactive iron does not have a correlation
because there is a high amount of fluctuation. The XRF measured iron has an increasing value for the
variance but does not show a sill. The nugget is large for all variograms.
38
The variograms of the other sections (C, D and E) are shown in Appendix C. These variograms have in
common that both the horizontal and vertical distance have a high nugget and a highly fluctuating
variance. The range, if any, is at very short distance, approximately 1 meter for the horizontal
distance and 20 to 50 cm for the vertical distance.
Figure 17 horizontal correlation for total iron (left) and reactive iron (right) of section A
Figure 18 Variograms of vertical distance correlation for total iron (left) and reactive iron (right) of section A
39
Figure 19 Variograms of horizontal distance of section B for total iron (left) and reactive iron (right)
Figure 20 Variograms of section B of horizontal distance (left) and vertical distance (right) of XRF-measured
samples
Figure 21 Variograms of the vertical correlation of section B for total iron (left) and reactive iron(right)
40
Correlation of components measured by ICP-OES in the sediment
The aqua regia extraction and ICP-OES measurements results in concentration values of many
components, mainly metals. The concentrations and ratios of components can provide detailed
information on the sediment composition and the soil processes after deposition.
There is a high positive correlation between magnesium and aluminum. The oxides are calculated
from the total elemental concentration and the molar mass of the oxides. The average MgO/Al2O3
ratio is approximately 0.0532. This is lower than what Huisman (1998) showed for other Dutch
MgO (ppm)
Thousands
formations that consist of riverine sediments; there ratios at or below 0.1 are reported.
8
y = 0.0532x
R² = 0.8654
6
4
2
0
0
20
40
60
80
Al2O3 (ppm)
100
120
140
Thousands
Figure 22 Correlation MgO/Al2O3
There is a very weak correlation between iron and manganese for the aqua regia extracted samples.
This means that iron and manganese do not precipitate at the same locations within the sediment.
There is a strong positive correlation between iron- zinc, iron-copper and iron-titanium: R2 = 0.815,
0.602 and 0.511, respectively. The strong iron-titanium correlation suggests that there could be
small concentrations of the mineral ilmenite (FeTiO3) present in the sediment. It was expected that
there is a high correlation between iron and other metals, because metals have a tendency to absorb
to the surface of iron oxides. As mentioned before, presence of reactive iron oxides could have a high
adsorption rate of metals. This strong positive correlation is a geochemical indicator of variability of
adsorptive reactivity of subsurface sediments (Davis, 1982). Copper and zinc are often adsorbed to
organic matter and carbonates instead of to iron oxides (Yu, 2001) but since organic matter and
carbonates are scarce in this sediment, all metals are adsorbed to the iron. Although it is expected
that the metals would adsorb to the oxides, the correlation between iron from the CBD method
(reactive iron) and metals is much less pronounced (R2 is around 0.1) than the correlation between
total iron and metals.
41
Thousands
20
y = 10.979x + 2702.5
R² = 0.0944
15
Fe (ppm)
10
5
0
Fe (ppm)
Thousands
0
100
200
300
Mn(ppm)
400
500
600
20
15
10
y = 212.88x + 352.57
R² = 0.8153
5
0
Thousands
0
20
40
Zn(ppm)
60
80
100
30
25
20
Fe (ppm)
15
10
y = 1169.4x - 2708.9
R² = 0.2958
5
0
Thousands
0
5
10
15
Cu (ppm)
20
25
30
20
15
Fe(ppm)
10
y = 15.096x + 1359.6
R² = 0.5112
5
0
0
200
400
Figure 23 Correlation between iron and other metals
42
Ti (ppm)
600
800
1000
XRF components:
The elements with notable correlations are shown in Figure 24. There is a negative correlation
between the concentration if silica and iron (R2 = 0.653). This could the result of the coating of iron
oxides around the quartz grains. The signal of the silica (which is a light element) might be absorbed
by the heavier iron molecules. There is a strong positive correlation between iron and titanium
measured by the XRF. There is a significant correlation between titanium and iron (0.492). Similar
correlations have been reported by other studies (Chen 2008). Similar to the ICP-OES results, and a
poor correlation between iron and manganese (R2 =0.0819). When the high values of manganese in
Fe (ppm)
Thousands
this figure are deleted, the correlation does not improve significantly.
14
12
10
8
6
4
2
0
y = -0.0615x + 25728
R² = 0.6534
250
270
290
310
330
350
370
Fe(ppm)
Thousands
Si (ppm)
390
410
Thousands
y = 5.7812x + 941.23
R² = 0.4916
14
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
1400
1600
1800
Fe (ppm)
Thousands
Ti (ppm)
14
y = 4.7781x + 4792.1
R² = 0.0819
12
10
8
6
4
2
0
0
200
400
600
Mn (ppm)
800
1000
1200
Figure 24 Ratios of components measured by XRF
43
Discussion
Depth profiles
The grain size of all samples was only estimated qualitatively, by use of a ‘sand ruler’ which allows to
give rough estimates on the dominant grain size (fine, medium coarse, coarse or very coarse sand)
and sorting (well, moderate, poor). The color was also logged, but this could be dependent on moist
content. In general, the peaks in iron concentrations measured by aqua regia occur mostly in the
relative fine grained (silty) and the poorly sorted sediment. Especially at sharp transitions of coarse to
fine grained material the iron concentrations are high. The sediment with an orange or brown color is
expected to have high iron oxide content. Many samples with high concentrations contained cementlike material. This is expected that this ‘cement’ is iron oxide which forms separate grains. In order to
conform this, electron microscope should be used to look at the sediment structure. For many
samples, there are distinct peaks for the total iron concentration but not as strongly for the reactive
iron. However, for iron oxides it is expected that the elevation in iron is reactive. It is likely that the
CBD method gives an underestimation of the actual reactive iron content. Because mainly the
highest peaks of reactive iron are ‘missing’, it is possible that there were not enough reagents used
during the CBD method, and extraction of reactive iron was incomplete. Another possibility is that
the dithionite degraded before measurement and the iron became re-oxidized. It is also possible that
the sediment with high iron concentrations has a higher amount of crystalline oxides, which are
harder to dissolve.
Reactive and non-reactive iron
The distinction between reactive and non-reactive (or ‘background’) iron is an important factor for
the sediment reactivity, as mainly the reactive iron in the sediment is responsible for reactive
processes in the sediment. In general, iron oxides, iron carbonates and iron sulfides are all important
reactive iron phases, but it is assumed that the sediment in this study only contains oxides. Anoxic
environments are needed to form carbonates and sulfides, and it is not assumed that the sediments
of this formation has been anoxic. Therefore, the reactive iron consists of the amorphous and weak
crystalline iron oxides, and the non- reactive iron consists of iron from clay minerals and other
silicates. There are different ways of distinguishing between background and reactive iron. One way
is by making a Fe2O3 /Al2O3 scatterplot of the data points, and constructing a baseline of minimum
ratio. The theory behind this analysis is that the silicate minerals have a set ratio of iron and
aluminum. A linear relation can show this ratio. A standard linear regression will not work, because
the baseline must not be drawn through all data points but just the ones without reactive iron
(Heerdink and Griffioen, 2008). Instead, the average of the lowest 25% of the ratios of Al2O3 and
44
Fe2O3 is found. With the molar mass of iron, aluminum and oxygen, the concentration of iron and
aluminum are converted to iron oxides and aluminum oxides. From the found number, a line is
drawn through the scatterplot of Fe2O3 over Al2O3. The result of this method is that the 12.5% of the
lowest ratio is assumed to have no enrichment in reactive iron. The assumption for this methods is
that the dataset holds samples which contain only background iron and no reactive iron. All data
points above this baseline are samples which contain both background and reactive iron.
Huisman (1998) has constructed baselines according to this method, and proposed that the ratio iron
aluminum is: Fe2O3 = 0.25 * Al2O3. This is however only a rough approximation, it turns out that the
ratio iron/aluminum is different per geological formation, and even within formations the ratio could
differ (Bakker 2007). The average difference between the total iron concentration and the reactive
iron concentration results in an iron/aluminum ratio of 0.2649. This ratio is quite similar to the ratio
found by the formula of Huisman (1998). Figure 25 shows the baseline of the Fe/Al ratio constructed
by extracting the reactive iron concentration from the total iron concentration. A disadvantage of
this method is that constructing such a baseline needs the assumption that there are samples within
the dataset which do not contain reactive iron. Further analysis of the sediment is required to show if
this assumption is valid. Another disadvantage of this method is that the fitting of a baseline within a
Fe/Al scatterplot is that this process is subjective. Lines could be fitted in the wrong manner if there
Fe2O3 (ppm)
Thousands
are not many data points with a low amount of reactive iron.
100
90
80
70
60
50
40
30
20
10
0
y = 0.2649x
0
20
40
60
80
Al2O3(ppm)
100
120
140
Thousands
Figure 25 Fe/Al – baseline for reactive and background iron
45
Another way of distinguishing the background iron from the reactive iron within the sediment is with
comparison of the results from the CBD method and the results of the aqua regia method. Because
the CBD method only extract the reactive iron and the aqua regia method extracts all iron phases,
the difference between the found concentrations can be assumed to be the concentration of
background iron. The value of background iron was converted Fe to Fe2O3 with the molar masses.
Then the ratio Fe2O3 (background) to Al2O3 (measured by ICP-OES) is calculated and plotted. The
average ratio is 0.338 (Figure 26). This is much higher than the ratio found by the baseline- method.
The correlation between the background iron oxides found by this method over aluminum oxides is
not very high. The ratio of background iron –aluminum was expected to be lower, so the
concentration of reactive iron was expected to be higher. This is in line with the previous observation
Thousands
background Fe2O3 (ppm)
that the CBD does not extract all the reactive phases of iron.
50
y = 0.3378x
R² = 0.1816
40
30
20
10
0
0
20
40
60
80
Al2O3 (ppm)
100
120
140
Thousands
Figure 26 Total iron oxides - reactive iron oxides (background) vs aluminum oxides
Because the manganese and aluminum have a low correlation value, it is assumed that manganese,
similar to iron, has an enrichment for some samples and a background value for other samples. The
baseline is shown in Figure 27 shows the baseline, the resulting ratio is 0.0036, which means that the
background value is 3.6 mg per g aluminum.
Mn2O3 (ppm)
1000
y = 0.0028x
800
600
400
200
0
0
20
40
60
80
Al2O3(ppm)
Figure 27 Fe/Al baseline for reactive and background manganese
46
100
120
140
Thousands
Comparison XRF and aqua regia
Both aqua regia with ICP-OES and XRF measures the total concentration of many components. The
concentration values are different for many components ( Figure 28). This is partially due to the fact
that the digestion with aqua regia is incomplete for silicate minerals. The XRF is a reliable method for
heavier components such as iron and manganese, but for the lighter elements the concentrations are
underestimated. On average, the measured concentration for iron is 1.22 times higher when
measured by XRF than measured by ICP-OES. This difference is much larger for aluminum (2.7 times)
and potassium (4.8 times). The most likely explanation is that aqua regia methods do not constitute a
complete digestion because the least acid-soluble components such as silicates, are not wholly
digested (Killbride, 2005). The data gathered by XRF are derived from all matrix materials and thus
represent the total composition of the sediment (Potts, 1995). However, the XRF measurements are
less reliable for lighter elements such as aluminum and magnesium. This explains why the difference
for potassium is much larger than for aluminum. Another reason why the results of the XRF
measurements might contain errors is that loose particles were measured instead of a flat surface.
This causes an increased scattering of the X-ray beam, which decreases the accuracy of the results
Thousands
(Argyraki et al., 1997).
Aluminum
80
y = 2.7381x + 22067
R² = 0.6073
60
XRF
40
20
0
XRF
Thousands
0
2
4
6
8
aqua regia
10
12
16
Thousands
Potassium
20
14
y = 4.8014x + 7988.6
R² = 0.1759
15
10
5
0
0
200
400
600
800
aqua regia
1000
1200
1400
1600
Figure 28 Correlations between concentration measurements of aluminum and potassium for XRF and ICPOES
47
XRF measurements
The hand-held XRF measurements were used to make an estimate if the measurements of a handheld XRF in the field would create results which are comparable to results of extraction methods. If
so, the measurements can in the future be used as a replacement for the slower and more expensive
extraction methods. Because no representative standard with a known composition was available,
two samples were chosen as a standard in order to track the reproducibility and to see if there is any
change in values during the XRF measurements. One of the standards had a low iron content and one
had a high iron content. The concentrations according to the XRF were 2281 ppm for the low-iron
standard and 8796 ppm for the high-iron standard. The standard deviations were 367.5 and 371.9
ppm, respectively. These are deviations of 16 % and 4.2%, respectively.
One of the disadvantages that were discovered during the measuring of the sediment is that the
detection limit for the XRF device is quite high. Especially for the results of the manganese
concentrations there were a lot of samples for which values below detection limit were found. This is
a problem, because also low amounts of manganese oxides in the field are significant. Less than a
third of the samples had a concentration which was larger than the detection limit. This is where the
aqua regia extraction has a clear advantage, because almost all results were values above the
detection limit. Figure 29 shows the relation between the iron measured by XRF and by ICP-OES.
The XRF measurements give higher concentrations than the ICP-OES. This might be explained by the
fact that the digestion with aqua regia is incomplete, and iron containing feldspars is not extracted by
this method. A different explanation could be that due to the matrix effect of the grains, the iron
concentration is too high. Figure 30 shows the relation between the manganese measured by XRF
and by ICP-OES. There is a very low correlation between the different measurement methods. This is
partially explained by the fact that the XRF results contained many values of 1, which means that the
concentration is below detection limit. The detection limit for the ICP is much lower, so real values
were created. However, some values measured by the XRF are much higher than the ICP-OES values.
This could be caused by heterogeneities of oxide coatings within samples.
48
XRF
y = 1.8586x
R² = 0.2244
14000
12000
10000
8000
6000
4000
2000
0
0
1000
2000
3000
4000
aqua regia
5000
6000
7000
Figure 29 Comparison of iron concentration for XRF and aqua regia measurements, in ppm.
y = 1.5322x - 11.57
R² = 0.0981
200
XRF
150
100
50
0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
aqua regia
Figure 30 Comparison of manganese concentration for XRF and aqua regia measurements, in ppm
49
CBD method
The CBD method was tested beforehand with different types of iron oxides (hematite, goethite and
lepidocrocite). This test showed that by three subsequent treatments of the samples, there was still a
significant amount of iron oxide remaining within the sample (data not shown). There was also a
residual coloring (orange, red or brown) which showed the presence of the oxides in the sediments.
However, pure crystalline iron oxides were used for this test. Crystalline iron oxides are less soluble
than poorly crystalline or amorphous iron oxides. Because the residues of the actual samples
showed no orange, red or brown color after the CBD extraction, it was assumed that this method was
successful in reducing all oxides. Pansu (2006) mentions that the color of the residue gives a good
indication of the effectiveness of the treatment, but the presence of magnetite or ilmenite, which are
not attacked by the CBD treatment, can color the residue black or gray. Titanium was found in these
sediments, with a strong correlation with iron. Therefore it can be assumed that there is a low
amount of ilmenite in this sediment. These minerals are not dissolved by CBD but are dissolved by
aqua regia.
There a number of authors suggesting that the method of Mehra and Jackson(1960) is not sufficient
for the complete dissolution of iron oxides. Many adjustments in pH, temperature and duration of
ingestion have been proposed in order to increase the extracted amount of iron oxides. For example,
Claff (2010) found that the time needed for 100% dissolution of crystalline iron oxides by the
dithionite method is of 4 hours. This is much longer than the ingestion time used in this study. Pansu
(2006) recommends duration of the extraction of 15 minutes because re-oxidation of iron and the
precipitation of iron sulfide can occur after 15 minutes. Conley (1970) proposed that at a
temperature of 85 oC, ingestion of 60 minutes is needed in order to dissolve all iron, and longer
duration might reverse the iron reduction. There are studies suggesting that the dithionite degrades
rapidly in water, especially at high temperatures. The yellow to brown color that many of the
extracts showed after the extraction with the CBD method suggests that iron was re-oxidized as a
result of the degradation of dithionite. It is also possible that iron sulfides have formed. These
processes could result in an underestimation of the amount of extracted iron. Mainly the extracts
with a high iron concentration showed a strong yellow color. An underestimation of iron for the
samples with the higher iron concentrations would be expected, as the aqua regia extraction at some
points shows a very high iron concentration while the CBD method does not. It would make sense
that the enrichment in iron by oxides would form the increased iron concentrations, and not the
background iron. Therefore it is concluded that the CBD method is not a good method to obtain a
total concentration of iron oxides.
50
Aqua regia
Aqua regia extractions with subsequent ICP-OES measurements are a very common method of
elemental analysis of soils and sediments, (Hubner, 2011), therefore it can easily be compared to
results of other sediment research projects.
After the treatment of the samples with aqua regia, a residue of mostly while colored material
remained within the Teflon vials. It is assumed that this residue consists mostly silicate minerals. In
general, these elements are considered less important for estimating the mobility and behavior of
the elements (Niskavaara, 1997). Because silicates do not dissolve in aqua regia, therefore K, Ti, Al
and other feldspar components do not have the correct concentrations in aqua regia measurements.
There are some studies that suggest that the ingestion with aqua regia should last for two weeks in
order to dissolve all components (instead of overnight, as was done in this study). If there are iron
carbonates (siderite) present in these sediments, they would probably not dissolve in aqua regia
overnight. This might be an explanation for the difference between XRF and aqua regia measured
iron, but it is not likely that iron carbonates are present in high concentrations. This should be further
looked at in future studies. There are different methods to carry out the aqua regia extraction. Next
to the hot plate digestion method, there is the microwave digestion method. According to Tighe et
al. (2004) the microwave aqua regia method results in better analytical precision, and higher
absolute recoveries for many components including Fe and Mn than the other methods.
Because only 125 mg of sample is used for the aqua regia extraction, this method could be sensitive
for any heterogeneities within a sample. The dithionite method uses at least one gram of the sample
and is therefore a more representable value for the whole sample. The reproducibility of the samples
is very variable, the difference in concentration value ranges from 3 to 82% with an average of 40%.
The reproducibility of the CBD method is much better, with a 15% difference between two samples.
The reason that the aqua regia method is poorly reproducible is that Iron oxides do not occur as
uniform surface coatings in river sediments. Poulton and Raiswell (2005) showed with electron
microscope observations that iron oxide occurs in different forms, depending on the grain size of the
porous medium. The oxides form either spheres associated with the edges of clay minerals in fine
fractions, or form crystalline oxides infilling the pores of the pores of the medium/coarse fraction.
This means that the finer fractions contain a larger ratio of easily reducible iron than the coarser
fractions. For the aqua regia method, it would have resulted in more uniform results to grind the
sediment to powder.
51
XRF
The main problem of the XRF measurements is that the lighter elements (up to calcium) are harder
to measure with a hand held XRF than the heavier elements. First of all, lighter elements are only
measured at the surface of each sample. This is because the energy level of the backscatter X-ray
beam is lower for the lighter elements, and therefore it gets adsorbed sooner by the sample. This
could result in an underestimation of the lighter elements. The accuracy of the measurements could
be enhanced by flushing the XRF device with helium, but in the field this is not possible. In the lab,
flushing with helium did occur, but this does not completely dismiss the inaccuracy. Because
aluminum is one of the lighter elements, the ratio iron/aluminum should not be used regardless,
since aluminum could easily be underestimated. Both Fe and Mn are assumed to be measured
accurately at the XRF measurements. The penetration depth for iron and manganese is
approximately 3 to 4 mm depth. For light elements such as aluminum, only the outer surface is
measured (approximately 1 mm).
The most accurate method of analysis of elements with a hand-held XRF is when all samples are first
dried, grinded to powder and compressed into a flat surface. Even then there is a matrix effect from
the space between the individual gains, which decreases the accuracy of the results (Ridings 1999).
The handheld XRF could be used in the field, if a flat surface is made and if the device is held very still
while measuring. The measurements take up to 30 seconds per screen, and a four-screen method is
used when sediments are analyzed. Slight movements of the device changes the location that is
measured. This is why it is easier to perform the measurements in the lab, where the device can be
locked in a holder, so there is no movement during analysis. Furthermore, if measurements occur in
the field, the moisture content of the soil cannot be too high. Water adsorbs the X-rays which would
result in a decrease in accuracy of the measured concentrations. It would also be advised to bring
some reference material to the lab and preform a total extraction of the sediment, in order to have a
comparison to the measured values.
Frequency histograms
The frequency histograms showed that there is a lognormal distribution for the total iron and a
bimodal distribution for the reactive iron and the XRF-measured iron. Previous research to metal
content in river sediments has showed that metal content of the sediment strongly correlates with
large surface areas, i.e. the fine fraction of the sediment. Increasing surface area expands the
number of sites and thus contains metal concentrations (Stamoulis, 1995). It is possible that the grain
size distribution also has a bimodal trend, and therefore the absorption of iron coatings is also
bimodal distributed.
52
Variograms
With the exception of section A, the variograms of all sections is close to zero meters. The reason
that section A is the exception is probably because these samples are collected on a slope. This
results that the vertical distances are smaller, and there are much more data points for the horizontal
variance. More data points on a short distance results in a higher range of spatial correlation.
Armstrong(1984) explains that erratic behavior of variograms can partially be explained by a large
differences in number of pairs per distance class. Other reasons are mixed populations and skew
distributions of concentrations. All these factors could have contributed to the
When variograms are constructed by taking the horizontal as well as the vertical distance into
account, this results in a larger range of correlation. It was however the goal of this study to
distinguish between the horizontal and vertical heterogeneities therefore this would not provide any
useful information. For better results of the horizontal and vertical correlation range, more samples
on the centimeter scale should have been taken, for example starting with 5 cm instead of 50, with a
maximum of 10 meters instead of 40. This results in smaller sampling locations but the results could
be extrapolated to the whole area when the small scale heterogeneities are known.
There have been more studies which concluded that variograms are not able to describe realistic
heterogeneity in complex geological environments. Complex geological patterns including
sedimentary structures, multi-facies deposits, structures with large connectivity, curvi-linear
structures, etc. cannot be characterized using only two-point statistics (Koltermann and Gorelick
1996; Journel and Zhang 2006 Huysmans and Dassargues 2009). Because the Sterksel Formation was
built up in different stages of high energy braiding river systems, many cross-bedding occurs in this
area (for example visible in Figure 1). It is known that there are (at least) three stages. It is possible
that erosion of the older sediments occurred when the newer sediments were deposited. This
probably contributes to the heterogeneity of the grain size distribution and therefore probably as
well of the iron distribution.
Because the variograms show a spatial correlation of only a few meters, in both horizontal and
vertical direction, it might be concluded that this particular geological structure is too heterogeneous
and complex to define with the variograms. This finding fits with the observation of the
heterogeneously distributed iron and manganese, especially in the vertical direction. It might be
more feasible to find a different way of characterizing the whole aquifer in this area. One way of
doing this could be sampling in only one location at a very high density, for example 30 samples in
one square meter. With a lot of high density data points, the heterogeneity in horizontal and vertical
direction could be characterized and extrapolated to the whole region.
53
Iron oxide enrichment in the Sterksel Formation
From the observations in the field and the results from the extractions and measurements there
were no arguments found that suggest that the iron enrichment is the result from soil forming
processes. Therefore it is argued that the enrichment is the result of groundwaterflow from the clay
layer of the Waalre Formation underneath the Sterksel Formation. Measurements on the Waalre clay
resulted in general iron concentrations of 25 g kg-1 which is higher than the iron content of the
Sterksel Formation (Vriens, 2011). The iron species dominant in the Waalre Formation is siderite.
Frost wedges were observed in the top layer of the Waalre formation. These wedges were filled with
sand instead of clay, and showed a clear orange color which suggests the presence of iron oxides.
Possibly these wedges have served as a transport medium for groundwater with reduced iron.
The high concentrations at the bottom of the Sterksel formation that were found are caused by the
formation of a crust between the sand layer and underlying clay layer. This phenomenon was found
and described by Barral & Guitian ( 1991). Uniform crusts lie at the boundaries between the sandy
stratum and the neighbouring clay strata. The iron crusts occurring at texture boundaries may have
been produced as the result of the neighboring layers having different aeration conditions, leading to
the deposition of iron oxides at the interface. The organic matter content was not measured, but it is
possible that this layer has a higher organic matter content, and therefore has accumulated high
concentration in iron oxides. Small concentrations of sulfur were found in the crust sediments,
therefore it is possible that iron sulfides are present here. Because the clay layer is rich in sulfur and
iron, and most of the sand layer do not contain sulfur at all, upward seepage is a likely explanation.
The spatial variability of the iron within the Sterksel Formation might be caused by variability of
hydraulic conductivity. Conductivity has been showed to control longitudinal macroscale dispersion
in sand and gravel aquifers (Garabedian.,1991 and Hess, 1992). The random, but spatially correlated
variations in hydraulic conductivity cause small-scale variation in fluid velocitiy within an aquifer. It is
commonly believed that the velocity variations cause the scale dependent observations of
macrodispersion (Davis 1993). The precipitation of iron might be caused by difference of water
content. Dissolved ferrous iron, which is usually present in reduced sediments, oxidizes when it
encounters oxygen and converts to virtually insoluble iron oxides (Cornell & Schwertmann, 1996).
Van Loef (1999) proposes that reduced iron in wet sediment will oxidize and precipitate as the
sediment dries. The finer sediment will take longer to dry and dissolved iron will accumulate at the
edges of the finer layers. If the groundwater level has a temporal fluctuation, this could result in an
accumulation of iron oxides at the location where groundwater fluctuates over the year. The fine
grained sediment has a higher surface area and therefore more sites at which iron oxides can
54
precipitate. Therefore, iron oxide enrichments are associated with the fine grained fraction of the
sediment, (e.g. Barber II, 1991).
It is unknown which iron minerals are predominantly present in the Sterksel Formation. Riezebos
(1971) observed that 90% of the opaque grains is goethite or lepidocrite in Old Pleistocene and
younger Pleistocene terrace deposits. These grains have diameters in the sand class range. Ilmenite
was found as a minor mineral. Nelson & Niggli (1950) also characterised a sample from high terrace
deposits in northern Limburg which comprised ilmenite and rutile as opaque minerals. A high detrital
goethite content could thus be characteristic of Quaternary Meuse sediments.
It is not yet known why there is a different location of precipitation of iron and manganese in the
Sterksel Formation. If the groundwater comes from the lower layers, then manganese precipitates
later than iron. In the paper of Postma (2000) it is explained how in aquifers, flowing water
containing Fe2+ may strip MnO2 from the sediment and lead to Mn2+-enriched groundwater which
subsequently could re-oxidize and precipitate as MnO2 (Stollenwerk 1994 and Lee and Bennett
1998). The reaction is as follows:
2 Fe2+ + MnO2 + 2 H2O  2 FeOOH + Mn2+ + 2 H+ (Postma 1985).
This reaction could proceed in a timescale of hours. Groundwater discharge could lead to
manganese-rich deposits. This means that the manganese oxides that were precipitated were
reduced and dissolved by the iron rich groundwater. The iron can precipitate in its place, and the
manganese precipitates at a location ‘downstream’ from the groundwater flow.
55
Conclusions

How are iron and manganese oxides spatially distributed in the subsurface environment of
the Sterksel Formation at the Maalbeek pit?
In the lower part of the Sterksel Formation, the iron and manganese concentrations are the
highest, but the trend in concentration with depth is different per location. It is assumed that
the distribution has a strong correlation with the small grain size fraction, due to the pore
size and surface area. At the lower border of the Sterksel Formation a crust has formed
where iron has accumulated. Depth profiles show an irregular pattern of iron concentrations.
Most peaks are considered to be iron oxides, the background iron is assumed relatively
constant. Manganese oxide enrichments are not situated in the same locations as iron oxide
enrichments, but mostly just above them. The layers of enriched manganese are much
thinner than the iron oxide enrichments.

What is the required sampling density in order to characterize the spatial distribution of
iron and manganese oxides?
Most variograms showed high nuggets, which indicates that the sampling is not dense
enough. This means that the heterogeneities occur at very small scale and 50 cm distance is
not a dense enough sampling distance to accurately characterize the distribution of iron in
this formation. Instead, it might be a better approach to take many samples at a very short
distance, for instance every 10 cm horizontally and every 5 cm vertically for a location of 1x1
meter. The composition of the sediment of the different sampling locations is similar and
belong to the same population.

How do the different analytical methods compare to each other?
The CBD method extracts the reactive species of the iron in the sediment, but there are
reasons to believe that the results are an underestimation of the actual reactive iron content.
The aqua regia extraction is a widely accepted and reliable method, but has the limitation
that the silicate minerals of the sediment are not completely dissolved. Detection limits are
sufficiently low to get a complete view on the composition of the sediments. For
heterogeneous samples the reproducibility of the results is low because a small amount is
used for analysis. The XRF measurements have high detection limits for some elements, and
may overestimate the heavier elements. The reproducibility of the samples is good.
56

Is XRF a suitable method of measuring iron and manganese oxides in the field?
The hand held XRF is an effective method and could potentially be usable in field to measure
the sedimentary composition. However, the water content of the sediment should be low
and the device should be held still for a long time to get an accurate measurement. Because
helium flushing cannot be used in the field, the lighter elements cannot be analyzed in the
field. The reproducibility of the measurements is dependent on the heterogeneity of
sediment. It would be advisable to compare the results of the XRF measurements with an
extraction method, for example with hydrogen fluoride destruction.

Which diagenetic process led to the iron and manganese distribution at this location?
It is proposed that the iron and manganese oxides have been transported to this formation
after deposition. Due to coarse sediment within frost wedges, a pathway could have been
created through the clay of the Waalre Formation. This formation is anoxic and therefore
contains reduced iron and manganese. Upwelling of the groundwater could have brought
the reduced forms of iron and manganese to this area. Heterogeneity of the distribution of
iron and manganese is most likely caused by the grain size heterogeneity of this formation.
The iron and manganese have precipitated, mainly to the finer grain size fraction of the
sediment. Iron may have reduced the manganese oxides which moved them further upward.
57
Recommendations
Based on the results found in this thesis, it is recommended to get a detailed view on the grain size
distribution of this formation, and compare the grain size and rapid changes in grain size to the
concentrations of iron and manganese. Heterogeneous grain size distribution can be linked to
geochemical heterogeneity because Iron content of the sediment depends strongly on the surface
area of the sediment and thus on the grain size. Poulton (1999) estimated that concentrations of iron
oxides on surfaces are approximately 0.26 mg m-2.
Since the redox state of manganese and iron oxides is strongly dependent on the organic matter
content and the pH, it might be useful to measure the amount of organic and inorganic carbon and
the pH in the sediment and porewater.
Acknowledgements
I would like to thank my supervisors, Jasper Griffioen and Thilo Behrends for their guidance in this
thesis ; Dieneke van Meent and Helen de Waard for their assistance and ideas in the lab ; Ton Zalm
for his help with the setup of the aqua regia extractions and for performing the ICP-measurements;
Jan Gunnink for his input in the geostatistical analysis; Alejandra Morera for her help during the
fieldwork and her suggestions for writing; Wim Westerhoff for his views on the geology of the
studied area and the workers at the Maalbeek quarry for their assistance during the sampling
campaign.
58
References
Aguilera R.V. and G. Jackson (1953) Iron oxide removal from soils and clays Soil Science Society of America
Proceedings, volume 17, pp.359-364
Armony M. (2001) Field Parametric Geostatistics. International Journal of Surface Mining, Reclamation and
Environment, volume 15, issue 2, pp. 100-122
Armstrong M. (1984) Common problems seen in variograms Mathematical geology, volume 13, issue 3, pp.
305-313
Argyraki A., M.H. Ramsey, P.J. Potts (1997) Evaluation of portable X-ray fluorescence instrumentation for in
situ measurements of lead on contaminated land The Analyst, volume 122, pp. 743–749
Anderson P.R., M.M. Benjamin (1990) Surface and bulk characteristics of binary oxide suspension
Environmental Science and Technology, volume 24, pp. 692–698
Bakker, I.J.I., P. Kiden, J. Schokker and J. Griffioen (2007). De geotop van de ondergrond: Een reactievat.
Deelrapport 2. Eerste statistische karakterisering van de geochemische reactiecapaciteit van
geotopgebieden in Noord-Brabant en het noorden van Limburg. TNO-Alterra, rapport 2007-UR0324/A.
Barber II L .B, E.M. Thurman and D.D. Runnells (1992) Geochemical heterogeneity in a sand and gravel
aquifer: effect of sediment mineralogy and particle size on the sorption of chlorobenzenes. Journal
of Contaminant Hydrology , volume 9, pp. 35–54
Barral M.T. (1991) Iron oxide accumulations in tertiary sediments of the Roupar Basin, Galicia, NW Spain
volume 18, issue 1 , pp. 31-43
Berendsen, H.J.A. (2004) De vorming van het land. Inleiding in de geologie en de geomorfologie Assen: van
Gorcum, 410 p.
Birnie A.C. and E. Paterson (1991) The mineralogy and morphology of iron and manganese oxides in an
imperfectly-drained Scottish soil Geoderma volume 55, issue3 pp. 219-237
Boenigk, W., (1978). Gliederung der altquartären Ablagerungen in der Niederrheinischen Bucht. Fortschritte
Geologie Rheinland und Westfalen, volume 28, pp. 135-212.
Boenigk, W and M. Frechen (2006) The Pliocene and Quaternary fluvial archives of the Rhine system.
Quaternary Science Reviews, volume 25 issue 5-6, pp. 550-574
Breeuwsma A., (1987) De mineralogische samenstelling van zand-en kleigronden. In: Locher, W.P.,
De bodemkartering (Stiboka), Malmberg, Den Bosch, pp. 95-99
59
Brooks S.C. and J.S.Herman, (1998) Rate and extent of cobalt sorption to representative aquifer minerals in
the presence of a moderately strong organic ligand Applied Geochemistry, issue 13 pp. 77–88
Balistrieri, L.S. and J.W. Murray(1982) The surface chemistry of δMnO2 in major ion seawater Geochimica et
Cosmochimica Acta, issue 46, pp. 1041–1052
Catts, J.G. and D. Langmuir(1986), Adsorption of Cu, Pb and Zn by δMnO2applicability of the site binding
-surface complexation model Applied Geochemistry, volume 1 pp. 255–264
Chen, C.T. and S. Kandasamy (2008). Evaluation of elemental enrichments in surface sediments off
southwestern Taiwan. Environmental Geology, issue 54, pp. 1333-1346
Claff S.R, L.A. Sullivan, E.D. Burtion and R.T. Bush(2010) A sequential extraction procedure for acid sulfate
soils: Partitioning of iron Geoderma volume 155, issues 3–4, pp. 224–230
Cornell, R. M.; Schwertmann, U. (1996) The Iron Oxides: Structure, Properties, Reactions, Occurrence and
Uses, 2nd edition.; Wiley-VCH: New York, ; p 664.
Coston J.A, C.C. Fuller and J.A. Davis (1995) Pb2+ and Zn2+ adsorption by a natural aluminum- and iron-bearing
surface coating on an aquifer sand Geochimica et. Cosmochimica. Acta, volume 59 issue 17 pp. 3535
–3547
Davis, J.A., 1980. Adsorption of natural organic matter from freshwater environments by aluminum oxide.
Contaminants and Sediments, Volume. 2. Ann Arbor Science Publishers, Ann Arbor, MI, pp. 279-304.
Davis, J.A., 1982. Adsorption of natural dissolved organic matter at the oxide/water interface. Geochimica.
Cosmochimica Acta, issue 46, pp. 2381-2393.
Deb BC (1950) The estimation of free iron oxides in soils and clays and their removal. Journal of Soil Science,
issue 1,pp. 212–220
Fuller C.C., J.A. Davis, J.A. Coston and E. Dixon (1996) Characterization of metal adsorption variability in a
sand and gravel aquifer, Cape Cod, Massachusetts, U.S.A. Journal of Contaminant Hydrology,
volume 22, issue 3-4 pp. 165–187
van Gaans, P.F.M., J Griffioen, G. Mol and G. Klaver (2011) Geochemical reactivity of subsurface sediments
as potential buffer to anthropogenic inputs: A strategy for regional characterization in the
Netherlands Journal of soils and sediments, volume11 issue 2, pp. 336
Garabedian, S.P., LeBlanc, D.R., Gelhar, L.W. and Celia, M.A. (1991). Large-scale natural gradient tracer test in
sand and gravel, Cape Cod, Massachusetts, 2, Analysis of spatial moments for a nonreactive tracer.
Water Resources Research, volume 27, Issue 5, pp. 911
Granina L., (2004) Origin and dynamics of Fe and Mn sedimentary layers in Lake Baikal Chemical geology
volume 306 issue 1-2 pp. 55-72
60
Geluk, M.C., Duin, E.J.T., Dusar, M., Rijkers, R.H.B., Van den Berg, M.W., Van Rooijen, P., (1994) Stratigraphy
and tectonics of the Roer Valley Graben. Geologie en Mijnbouw, issue 73, pp. 129-141
Goovaerts P.( 1997) Geostatistics for Natural Resources Evaluation. Oxford University Press: New York; 512.
Haining R.P., R.Kerry and M.A. Oliver (2010) Geography, Spatial data analysis and geostatistics: an overview
Geographical Analysis, volume 42, issue 1, pp. 7-31
Heerdink R. and J.Griffioen(2008) Methodeontwikkeling voor het berekenen van het gehalte reactief ijzer uit
totaalgehaltes ijzer en aluminium in sediment Directie TNO Geological Survey of the Netherlands /
Deltares TNO-rapport | 2008-U-R1278/A
Van Helvoort P-J, J.Griffioen, N. Hartog (2007) Characterization of the reactivity of riverine heterogeneous
sediments using a facies-based approach; the Rhine-Meuse delta (The Netherlands) Applied
Geochemistry volume22, issue12, pp.2735 -2757
Hoselmann C.,( 1994) Stratigraphie des Hauptterrassenbereichs am unteren Mittelrhein. Geologisches
Institut der Universität zu Köln, Sonderveröffentlichungen pp. 96- 235
Hubner, R Rob Haslam (2011) Investigation of the effect of mesh size on the effectiveness of hotplate Aqua
regia extractions Communications in soil science and plant analysis. volume 42 issue2 pp. 159 -166
Hunt C.P, Singer M.J.,Kletetschka G., TenPas J. and Verosub K.L.(1995) Effect of citrate-bicarbonate- dithionite
treatment on fine-grained magnetite and maghemite Earth and Planetary Science Letters, issue 130,
pp. 87-94
Huisman, D.J., 1998. Geochemical characterization of subsurface sediments in The Netherlands.
PhD- thesis Wageningen Agricultural University. 175 pp.
Huysmans M. & A. Dassargues (2009) Application of multiple-point geostatistics on modeling groundwater
flow and transport in a cross-bedded aquifer (Belgium) Hydrogeological journal, issue 17 pp. 19091911
Journel A.G., Huijbregts CJ. 1978. Mining Geostatistics. Academic Press, New York
Journel A.G. and T. Zhang (2006) The necessity of a multople point prior model Mathematical Geology,
volume 38 issue 5 pp. 591-610
Jordan and Rippey 2003 Lake sedimentary evidence of phosphorus, iron and manganese mobilisation from
intensively fertilised soils Water research, volume 37 issue6 pp. 1426-1432
Kaiser and Wright (2008) Draft Bruker XRF spectroscopy user guide: spectral interpretation and
sources of interference. (not published)
Kilbride C., J. Poole, T.R. Hutchings (2006) A comparison of Cu, Pb, As, Cd, Zn, Fe, Ni and Mn determined by
acid extraction/ICP–OES and ex situ field portable X-ray fluorescence analyses Environmental
pollution volume 143 issue1 pp. 16-23
61
Kumar A., R.K.Singhal., J. Preetha., K. Rupali., V.M.Joshi , A.G. Hegde and H.S. Kushwaha (2007) A non
-parametric statistical analysis in the measurement of outdoor gamma exposure to the residents
around Trombay Radiation Protection Dosimetry Volume 124, issue. 4, pp. 378–384
Lapperre, R.E. en H.M.C. Smit, (1995). Hydrogeologisch onderzoek naar de Formaties van Sterksel en Tegelen
in de groeve Maalbeek te Belfeld; Verslag van een 5-maands afstudeervak bij de Vakgroep
Waterhuishouding sectie hydrogeologie, pp. 123
Larsen F., D. Postma (1997) Nickel mobilization in a groundwater well field; release by pyrite oxidation and
desorption by Mn-oxides Environmental Science and Technology, issue 31, pp. 2589–2595
Lee, R.W., P.C. Bennett (1998),
Reductive dissolution and reactive solute transport in a
sewage-
contaminated glacial outwash aquifer Groundwater, issue 36 pp. 583–595
Lehman A., (2005) Jmp For Basic Univariate And Multivariate Statistics: A Step-by-step Guide., pp.123
Matheron G. (1971) The Theory of Regionalised variables and its Applications, Les Cahiers du Centre de
Morphologie Mathematique de Fontainebleau, n. 5.
Matheron , G. , 1963. Principles of geostatistics. Economic Geology, volume 58 pp. 1246- 1266.
McDonald, J.H.(2009), Handbook of Biological statistics, chapter Spearman rank correlation
Mehra O.P. and M.L. Jackson, (1960) Iron Oxide removal from soils and clays by a dithionite-citrate
system buffered with sodium bicarbonate Clay Mineralogy., volume7, pp. 317–327
McKenzie R.M. (1980) The adsorption of lead and other heavy metals on oxides of manganese and iron
Australian Journal of Soil Research.,issue 18, pp. 61–73
Murray J.W., (1975) The interaction of metal ions at the manganese dioxide-solution interface
Geochimica
et Cosmochimica Acta, issue 39, pp. 505–519
Nelson, H.W. and E. Niggli, (1950). Röntgenologisch onderzoek van de ondoorzichtige zware fractie van
enkele Nederlandse zanden. Proc. Koninklijke Nederlandse Akademie Van Wetenschappen, volume
53, issue 8, pp. 1240-1246
Nieuwenhuize, J., C.H. Poley-Vos, A.H. van den Akker, and W. van Delft. (1991) Comparison of microwave
and conventional extraction techniques for the determination of metals in soils, sediment and
sludge samples by atomic spectrometry. Analyst issue 116, pp. 347–351.
Niskavaara, H., C. Reimann, V. Chekushin, and G. Kashulina. (1997) Seasonal variability of total and easily
leachable element contents in topsoils (0–5 cm) from eight catchments in the European arctic
(Finland, Norway, and Russia). Environmental Pollution issue 96 pp. 261–274.
62
Ridings M., A.J. Shorter, J. Bawden-Smith (1999), Strategies for the investigation of contaminated sites using
field portable X-ray fluorescence (FPXRF) techniques Contaminated Site Remediation: Challenges
Posed by Urban and Industrial Contaminants, CSIRO, Wembley, Australia pp. 213–217
Potts P.J., P.C. Webb, O. Williams-Thorpe, R. Kilworth (1995), Analysis of silicate rocks using field-portable Xray fluorescence instrumentation incorporating a mercury (II) iodide detector: a preliminary
assessment of analytical performance The Analyst, issue120 pp. 1273–1278
Pansu M. and Gautheyrou J. (2006) Handbook of Soil Analysis Mineralogical, Organic and Inorganic
Methods , XIX, 993 pp. 183
Postma,D. and C.A.J (2000) Reduction of Mn-oxides by ferrous iron in a flow system: Column experiment
and reactive transport modeling Geochimica et Cosmochimica Acta, volume 64,issue 7, pp.1237
-1247
Potts, P.J., Webb, P.C., Williams-Thorpe, O., Kilworth, R., (1995). Analysis of silicate rocks using fieldportable X-ray fluorescence instrumentation incorporating a mercury (II) iodide detector: a
preliminary assessment of analytical performance. The Analyst volume 120, pp. 1273-1278.
Poulton, S.W., and R Raiswell (2005) Chemical and physical characteristics of iron oxides in riverine and
glacial meltwater sediments Chemical geology volume 218 issue 3-4 pp. 203 -221
Poulton S.W (1999) Surface area, iron oxide and organic carbon relationships in sediments,in: H. Armannsson
Geochemistry of the earth's surface, 5th international symposium, Reykjavik, Iceland, pp. 279–282
Riezebos, P.A. (1971). A contribution to the sedimentary-petrological description of the Maas deposits in
Southern
Limburg
(the
Netherlands).
Geologie.
&
Mijnbouw,
volume
50,
pp.505-514.
Rutten A., and G. de Lange (2003) Sequential extraction of iron, manganese and related elements
in S1
sapropel sediments, eastern Mediterranean Palaeogeography, palaeoclimatology, palaeoecology,
volume 190 pp. 79-101.
Sastre, J A Sahuquillo, M Vidal, G Rauret (2002) Determination of Cd, Cu, Pb and Zn in environmental
samples: Microwave-assisted total digestion versus aqua regia and nitric acid extraction Analytica
Chimica Acta volume 462 issue 1, pp. 59 -72
Schmid, F and R. Schmidt (2007) Multivariate extensions of Spearman's rho and related statistics.
Statistics and probability letters, volume 77 issue 4, pp. 407-416
Schwertmann, U and R. M. Taylor, Iron oxide. In Minerals in Soil Environments 2nd Edn,. Soil Science Society
of American journal Madison, Wisconsin, pp. 379-428
Siegel, S. (1956) Nonparametric Statistics. New York: Mc Graw- Hill Book Company, Inc.
63
Simanova A.A., J.S. Loring and P. Persson (2011) Formation of ternary metal-oxalate surface complexes on
particles. Journal of Physical Chemistry, volume 115, issue 43, pp. 21191-21198
Smeth, J.B.de (2011) Field portable X-ray Fluorescence, possibilities and limitations. Presentation at ESA
research seminar, Faculty of geo-information science and earth observation.
Spijker J., (2005) Geochemical patterns in the soils of Zeeland : Natural variability versus anthropogenic
impact Nederlandse, Geografische Studies issue 330, pp.18 -19
Stumm W., (1992) Chemistry of the Solid-Water Interface John Wiley, New York
Stumm, W. and J.J Morgan, (1996): Aquatic Chemistry, Chemical Equilibria and Rates in Natural Waters, 3rd
ed. John Wiley & Sons, Inc., New York, 1022p.
Stollenwerk, K.G., (1994), Geochemical Interactions between constituents in acidic
groundwater and
alluvium in an aquifer near Globe, Arizona Applied Geochemistry, issue 9 pp. 353–369
Stookey L.L. (1970), Ferrozine-A New Spectrophotometric Reagent for Iron Analytial Chemistry, volume 42,
issue 7, pp. 779-781
Tighe M., P. Lockwood, S. Wilson & L. Lisle (2004): Comparison of Digestion Methods for ICP-OES Analysis of a
Wide Range of Analytes in Heavy Metal Contaminated Soil Samples with Specific Reference to
Arsenic and Antimony, Communications in Soil Science and Plant Analysis, volume 35 issue 9-10,
pp. 1369-1385
Van der Veer, G.(2006) Geochemical soil survey of the Netherlands. Netherlands Geographical Studies
347 pp.1 -245
issue
Vriens B. (2011) The diagenetic origin of siderite in Early Pleistocene fluviodeltaic deposits in Maalbeek, The
Netherlands. MSc thesis. Department of geochemistry, Faculty of Geosciences, Utrecht University and
TNO Geological Surfvey of the Netherlands
Westerhoff W., (2001)Stratigraphy and sedimentary evolution, the lower Rhine-Meuse system during the
Late Pliocene and Early Pleistocene (southern North Sea Basin). Geology of the Netherlands,
Westerhoff, W.E., H.A. Kemna, and W. Boenigk, (2008). The confluence area of Rhine, Meuse, and Belgian
rivers: Late Pliocene and Early Pleistocene fluvial history of the northern Lower Rhine Embayment.
Netherlands Journal of Geosciences / Geologie en Mijnbouw, volume 87 issue 1 pp. 107-126.
Yu K.C, L.I Tsai ., S.H. Chen, D.J. Chang and S.T.Ho (2001) Multivariate correlations of geochemical binding
phases of heavy metals in contaminated river sediment, Journal of environmental science and
health, part A: toxic/hazardous substances and environmental engineering, volume 36, issue1, pp.116
Zagwijn, W.H. and J.W.Chr. Doppert (1978) Upper Genozoic of the Southern North Sea Basin:
Palaeoclimatic and Palaeogeographic evolution Geologie en Mijnbouw issue 57 pp. 577-588
64
Zonneveld, J.I.S. (1958), Litho-stratigrafische eenheden in het Nederlandse Pleistoceen. Mededelingen van de
Geologische Stichting, Nieuwe Serie issue12: pp. 31-64.
Figures:
Figure 3: Distribution of the Netherlands lithostratigraphic Sterksel Formation. Meijer T., (2007), Wikipedia
website: http://nl.wikipedia.org/wiki/Formatie_van_Sterksel
Figure 31: Heavy mineral composition, grain size and thickness of the Sterksel Formation Westerhoff, 2009
Figure 5: Google maps, satellite view, zoomed in on north Limburg
Figure 32 An example of a variogram Clark, I. (1979), Practical Geostatistics, Applied Science, Essex, England.
Figure 12: Mechanism of X-ray fluorescence. Amptek Inc. USA http://www.amptek.com
Figure 33 Detectable elements at the mining mode of a portable XRF. De Smeth 2011 Presentation at ESA
research seminar, Faculty of geo-information science and earth observation.
65
Appendixes A to D
Appendix A: Depth profiles reactive iron
14.00
14
Aa
14
Ab
13.00
13
13
12.00
12
12
11.00
11
11
10.00
10
10
9.00
9
9
8.00
8
8
7.00
7
0
13
1000
2000
3000
7
0
16
Ad
Ac
1000
2000
3000
0
1000
13.00
Ae
2000
3000
Af
12.00
12
14
11.00
11
12
10
10
10.00
9.00
8.00
9
8
8
6
7.00
6.00
5.00
7
66
4.00
4
0
1000
2000
3000
0
1000
2000
3000
0
1000
2000
3000
11
11
Ba
11
Bb
10
10
10
9
9
9
8
8
8
7
7
7
6
6
6
5
5
5
4
4
4
0
11
1000
2000
3000
0
13
Bd
1000
2000
3000
2000
3000
Bc
0
1000
2000
3000
Be
12
10
11
9
10
8
9
7
8
7
6
6
5
5
4
4
0
1000
2000
3000
0
1000
67
6
6
Ca
9
Cb
Cc
8
5
5
7
4
4
3
3
6
5
4
2
2
1
1
3
2
1
0
0
0
9
1000
2000
3000
0
0
9
Cd
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
1000
2000
3000
0
1000
12
Ce
2000
3000
2000
3000
Cf
10
8
6
4
2
0
0
0
68
1000
2000
3000
0
0
1000
2000
3000
0
1000
Da
Db
Dc
12
12
12
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
1000
2000
3000
0
0
Dd
1000
2000
3000
0
De
12
12
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
1000
2000
3000
0
1000
2000
3000
2000
3000
Df
12
0
1000
2000
0
1000
69
Ea
7
Eb
7
6.5
6.5
6.5
6
6
6
5.5
5.5
5.5
5
5
5
4.5
4.5
4.5
4
4
4
3.5
3.5
3.5
3
3
3
0
500
1000
Ed
7
0
6.5
6
6
5.5
5.5
5
5
4.5
4.5
4
4
3.5
3.5
3
3
0
70
2000
Ee
7
6.5
1000
500
1000
0
1000
Ec
7
2000
0
500
1000
Appendix B Depth profiles of manganese
The x-axis is the concentration in ppm, measured by ICP-OES. The second half of section D and
section E did not give results above detection limit. It is assumed that this is an error of the ICP-OES.
14
14
Aa
14
Ab
13
13
13
12
12
12
11
11
11
10
10
10
9
9
9
8
8
0
100
13
8
0
Ad
Ac
100
16
0
Ae
12
100
13
Af
12
14
11
11
10
12
10
9
9
10
8
8
7
8
7
6
6
6
5
5
4
4
0
100
4
0
100
0
100
71
11
Ba
11
Bb
11
10
10
10
9
9
9
8
8
8
7
7
7
6
6
6
5
5
5
4
4
4
0
50
11
100
Bd
10
0
50
11
100
Be
10
9
9
8
7
8
6
7
5
6
4
5
3
2
4
0
72
50
100
0
50
100
Bc
0
50
100
6
Ca
6
Cb
9
Cc
8
5
5
7
4
4
3
3
6
5
4
2
2
1
1
3
2
1
0
0
0
100
200
9
0
0
Cd
50
100
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
150
Ce
0
50
100
12
150
Cf
10
8
6
4
2
0
0
0
50
100
150
0
0
200
400
600
0
50
100
150
73
9
Da
12
Db
8
14
Dc
12
10
7
10
6
8
8
5
6
4
6
3
4
4
2
2
2
1
0
0
0
74
50
100
150
0
0
100
200
0
100
200
Appendix C Frequency histograms
Figure 34 Histograms of total iron, measured by ICP-OES. Iron concentration in ppm (left) and natural logarithm of concentrations
(right)
Figure 35 Frequency histogram of manganese, measured by ICP-OES. Concentrations in ppm (left) and natural logarithm of
concentrations (right)
75
Appendix D Variograms
Figure 36 Horizontal correlation of total iron (left) and reactive iron (right) of section A
Figure 37 Vertical correlation of total iron (left) and reactive iron (right) of section A
76
Figure 38 Horizontal correlation of total iron (left) and reactive iron (right) of section B
Figure 39 Horizontal (left) and vertical (right) correlation of XRF measured total iron in section B
Figure 40 Vertical correlation of total iron (left) and reactive iron (right) of section B
77
Figure 41 Horizontal correlation of total iron (left) and reactive iron (right) of section C
Figure 42 Vertical correlation of total iron (left) and reactive iron (right) of section C
Figure 43 Horizontal correlation of total iron (left) and reactive iron (right) of section D
78
Figure 44 Vertical correlation of total iron (left) and reactive iron (right) of section D
Figure 45 Horizontal correlation of total iron (left) and reactive iron (right) of section E
Figure 46 Vertical correlation of total iron (left) and reactive iron (right) of section E
79