Content uploaded by Kurt Friese
Author content
All content in this area was uploaded by Kurt Friese on Mar 31, 2015
Content may be subject to copyright.
Article
J. Braz. Chem. Soc., Vol. 25, No. 2, 208-218, 2014.
Printed in Brazil - ©2014 Sociedade Brasileira de Química
0103 - 5053 $6.00+0.00
Ahttp://dx.doi.org/10.5935/0103-5053.20130285
*e-mail: sartori_jg@hotmail.com
Application of a Multivariate Exploratory Analysis Technique in the
Study of Dissolved Organic Matter and Metal Ions in Waters from the
Eastern Quadrilátero Ferrífero, Brazil
Erik S. J. Gontijo,*, a Francysmary S. D. Oliveira,a Mariana L. Fernandes,a
Gilmare A. da Silva,b Hubert M. P. Roesera and Kurt Friesec
aDepartment of Environmental Engineering and bDepartment of Chemistry, Federal University of
Ouro Preto (UFOP), Morro do Cruzeiro, S/N, 35400-000 Ouro Preto-MG, Brazil
cUFZ - Department Lake Research, Helmholtz-Centre for Environmental Research,
Brückstraße 3a, 39114 Magdeburg, Germany
Amostras de água foram coletadas em 10 pontos em trechos do leste do Quadrilátero Ferrífero
(QF), que é uma região mineira situada no sudeste do Brasil. Os objetivos deste estudo foram
encontrar possíveis relações entre carbono orgânico dissolvido (COD), metais e outros parâmetros
físico-químicos medidos utilizando a rede neural de Kohonen como ferramenta para analisar esses
dados geoquímicos multivariados na área estudada. As análises físico-químicas foram feitas in situ
e em laboratório, onde as concentrações de COD e vários íons metálicos foram determinadas.
A rede de Kohonen permitiu a visualização e interpretação mais amigáveis dos dados, além de
definir relações entre eles. Assim, para os dados analisados, foi verificada relação entre COD e Fe
e um possível efeito da sazonalidade na distribuição das amostras. Possíveis evidências litológicas
puderam ser detectadas pela análise exploratória, especialmente se considerados os elementos
Ca, Mg, Mn e Sr.
Water samples were collected at 10 points in parts of the eastern Quadrilátero Ferrífero (QF),
located in a mining region in the southeast of Brazil. The aims of this study were to find possible
relationships among dissolved organic carbon (DOC), metals and other parameters measured in
the region studied and evaluate the Kohonen neural network as a tool to analyse this geochemical
multivariate data set. Physico-chemical analyses were performed in situ and in the laboratory,
where concentrations of DOC and a suite of metal ions were determined. The Kohonen neural
network allowed an easier visualisation and interpretation of the results and helped to define the
relationships among them. In this way, a relationship between DOC and Fe and a possible effect
of seasonality on the distribution of the samples were indicated. Signs of lithology were detected
in the analyses, especially considering the elements Ca, Mg, Mn and Sr.
Keywords: Quadrilátero Ferrífero - Brazil, dissolved organic carbon, Kohonen neural network,
environmental geochemistry
Introduction
The Quadrilátero Ferrífero (QF) is a geological structure
in the southeast of Brazil that is worldwide known for
its mineral deposits.1 It covers an area of approximately
7000 km2 in the Brazilian state of Minas Gerais and
constitutes a southern extension of the Espinhaço Mountain
Range, in the south-eastern part of the São Francisco
Craton.1,2 In this region, iron and gold are the dominant
products in the mining area, along with aluminium and topaz.
It is remarkable that the QF has become the most important
gold producer in the late seventeenth century, with a total
production that probably exceeded 1300 t in history.3,4
The basal unit and surrounding areas of the QF
are composed primarily of granitic gneisses. Above
this basement, there are three units of supracrustal
metasedimentary rocks called Rio das Velhas Supergroup,
Minas Supergroup and Itacolomi Group. The Rio das
Velhas Supergroup, considered as an Archean greenstone
belt, is composed of phyllites, schists and volcanic
Gontijo et al. 209Vol. 25, No. 2, 2014
metasediments, which tends to release major elements
such as Na, K, Ca, Mg, Mn and Fe, and trace elements
such as Ni, Cr, Co and V. The Minas Supergroup comprises
Proterozoic metasediments, which are source of Fe, Mn,
Ca and Mg. The Itacolomi Group consists predominantly
of quartzitic rocks.1,2
One of the consequences of mining and associated
activities is the alteration of the elements cycle in the
environment, which influences the availability of metals
to organisms. This is exactly what occurs along the
Quadrilátero Ferrífero, where the exploitation of iron ore,
for instance, acts as an important source of major elements
such as Fe and Mn, and trace metals. In addition, it is
important to consider that gold mining can release As and
Cu, which are present in minerals such as iron sulfides
(pyrite, FeS2), copper sulfides (chalcopyrite, CuFeS2) and
arsenic sulfides (arsenopyrite, FeAsS), often in paragenesis
with gold.2,5
In the aquatic environment, the dissolved organic
carbon (DOC) is considered a regulator of biotic and
abiotic processes. It is operationally defined as the fraction
of organic material that passes through a 0.45 µm filter.6
The dissolved organic matter is a vital resource that will
affect food webs either directly, by its use via organisms,
or indirectly via mechanisms such as turbidity, pH and
contaminant transportation.6,7 The DOC has the ability to
decrease the toxicity of many metals while it reduces the
availability of these elements to organisms by means of
chemical bonds. This has particular importance given that
the QF is a region rich in minerals and suffers from the
impacts caused by mining. The variety of minerals is able to
release a range of elements in the water bodies where they
interact with dissolved organic matter, especially humic
substances (HS), which constitute about 80% of the DOC
in natural waters.8 Considering that the concentration of
dissolved metal ions and organic material can influence
the formation of metal-HS complexes, it is important to
identify possible interactions between these chemicals and
the organic material.9
Due to the large number of variables generally analysed
in environmental geochemistry studies, techniques of
exploratory data analysis have been shown to be effective
in identifying patterns in a group of data, facilitating the
interpretation of results.10 An example of a tool developed
for multivariate exploratory analysis is the Kohonen
neural network (Self-Organising Maps, SOM), which
is an artificial intelligence technique that has the ability
to project high dimensional data in a space of lower
dimension, without loss of the original information. It was
developed by Teuvo Kohonen (Finland) and has a close
relationship with the organisation of the cerebral cortex.11
An important advantage of this tool is the ease of viewing
and interpreting the data.12 To summarise, the Kohonen
neural network comprises self-organising maps, which are
formed by neurons arranged in a two-dimensional array.
In fact, this is one of the main advantages of the Kohonen
neural network, explicitly, the possibility of getting all data
information (relationship between samples, variables and
the influence of variables in samples) in a two-dimensional
array. In addition, this characteristic is also one of the
main advantages of this method compared with other
exploratory approaches, such as Principal Component
Analysis (PCA), where in most cases it is necessary to
work with multidimensional spaces (more than two-
dimensional arrays) provided by the principal components
(PC). Kohonen structures with higher dimensions are also
possible, but are less common.11-13
In the Kohonen neural network, it is assumed that
all the samples placed at the same neuron are similar
to each other according to the aspect analysed. Another
important attribute of this technique is the formation of
clusters of samples that are considered to possess the same
characteristics, because of its location in nearby neurons
(neighbouring neurons).12
A picture representing the typical architecture of the
Kohonen neural network is shown in Figure 1, where the
neurons are represented by columns, or tubes arranged
inside a box. In this type of representation, if a specific input
data has n samples, n input vectors x will be obtained. These
vectors x may be absorbance values of a spectrum, peaks
of a chromatogram or intensity values of different physico-
chemical parameters of the water quality. It is important to
note that the dimensionality of the vector is dependent upon
the number of variables in the data, which means that the
amount of weights of each neuron (w) will correspond to
the number of vector elements of the input data.12
Figure 1. Representation of the typical architecture of the Kohonen
neural network.
Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter J. Braz. Chem. Soc.
210
The Kohonen network was not performed before in
the study of metal ions and DOC in surface waters of
Quadrilátero Ferrífero. Furthermore, this kind of study
in the specific evaluated area was not found in scientific
literature yet. Considering similar studies worldwide, it
is possible to find successful applications of the SOM
technique in multivariate data analysis.13,14 Notably, the
Kohonen network has also been successfully used in
fields ranging from ecology until the evaluation of the
performance of a wastewater treatment plan.15-17
The present work was an initial study that aimed to
investigate the levels of dissolved organic matter in some
brown-coloured water bodies and its probable relationships
with metal ions and other parameters measured in the
eastern part of the QF. The application of a multivariate
exploratory analysis for this kind of study in the region is
an innovative to visualise the results and the relationships
among samples and its variables in an easy and effective
way
Experimental
Sampling and preliminary analyses
Ten sampling points were selected in upper Rio Doce
River Basin based on the accessibility and on visual
inspection of some water bodies. Areas with brown-
coloured waters were preferable in this process because they
could indicate higher levels of dissolved organic matter. The
study area comprised parts of the eastern QF and is shown
on the map in Figure S1 (Supplementary Information).
The physico-chemical parameters pH, temperature (T),
total dissolved solids (TDS), redox potential (ORP),
resistivity (Resis), conductivity (Cond) and turbidity (Turb)
were evaluated in situ using a multiparameter equipment
(Ultrameter II, Myron L Company) and a turbidimeter
(DM-TU Digimed) previously calibrated.
It is important to note that in the studied area, Cwa and
Cwb climates occur.18,19 Both climates are characterised by
a dry winter. In the area where the Cwa climate occurs, the
dry period is between April and September and the rainiest
months are November and December. The areas where the
Cwb climate occurs, the dry season is between May and
August and the rainfalls are concentrated mainly between
November and February. For this reason, more than one
sampling at different times was performed for some points
in order to evaluate possible influences of seasonality.
However, there was a problem in one sampling of point
1 because the swamp was totally dry in one winter field
trip. Consequently, it was not possible to collect water
for analyses at that time. From the data available in the
literature,18,19 the dry season in this work was considered
between April and September and the rainy season between
October and March.
About 1 L of water was collected in accessible areas
close to the water bodies (banks or on bridges) for the
determination of sulfate, chloride (Cl) and alkalinity
(Alc). The samples were kept refrigerated until laboratory
analyses. The methodology used was based on standard
methods proposed by the American Public Health
Association (APHA).20 About 40 mL of water was collected
for analyses of metals and about 20 mL for analyses of
DOC. These samples were filtered through membranes
of 0.45 µm and kept refrigerated at 4 ºC until analysis in
the laboratory. As described by Grasshoff, Kremling and
Ehrhardt,21 the storage of samples in plastic containers
can cause interference in the results of carbon analyses.
Therefore, it was chosen to keep the waters collected in
amber glass bottles to avoid any changes by means of
light in the humic material. After filtering the samples for
analysis of metals, they were acidified by adding 3-4 drops
of concentrated HNO3 to keep the metals in solution. All
reagents used in this work were of analytical grade.
Metal analyses
The analyses of metals were performed in the
Laboratory of Environmental Geochemistry (LGqA) at
Federal University of Ouro Preto (UFOP). The metals were
analysed by inductively coupled plasma optical emission
spectrometry (ICP-OES Spectro / Ciros model CCD) in
radial mode. Output power of the generator was 1250 W,
the pumping rate was 2 mL min−1, the gas flow of the
plasma was 12 L min−1 and the gas flow of the nebulizer
was 0.90 L min−1. In all cases, argon was used as gas.
The calibration was performed in all cases using
standard stock solutions with analytical purity grade and
was evaluated by means of international reference material
NIST 1643c. The elements determined were the major
metals Al, Ca, Fe, K, Mg, Mn, Na and Ti; and the trace
metals As, Ba, Be, Cd, Co, Cr, Cu, Li, Mo, Ni, P, Pb, S,
Sc, Sr, V, Y and Zn.
DOC analyses
The analyses of DOC were performed in Niterói City
at Federal Fluminense University (UFF, Brazil) with a
TOC-Analyser V-CPH (Shimadzu, Japan). The method
involved the determination of the total dissolved carbon
(TDC) and the dissolved inorganic carbon (DIC) of the
samples whereas the DOC was obtained by calculating the
difference between the two values (TDC - DIC). For the
Gontijo et al. 211Vol. 25, No. 2, 2014
determination of the TDC, the samples were introduced
into a combustion tube, which was filled with an oxidation
catalyst and heated to 680 ºC. Thereby, all components of
the TDC are converted into CO2, which is detected by a
cell of non-dispersive infrared (NDIR) in the end of the
procedure.
The DIC was measured by the same equipment after
acidification of the samples using HCl to a pH less than 3.
At this point, all the carbonates were converted to CO2. At
the end of the process, all CO2 was volatilised by bubbling
air or nitrogen gas and detected by the NDIR cell.
Multivariate analyses
The technique used for exploratory analysis in this work
was the Kohonen neural network. The aim of this method
was to reduce the number of dimensions to be analysed
and preserve the relevant original information in order to
facilitate the observation and interpretation of the results.
Some of the metal contents determined had to be excluded
from Kohonen analysis because their values were below
the limit of quantification (LOQ). Therefore, the data set
was organised into a matrix of 16 samples (16 lines) and 19
variables (19 columns). The samples represent the sampling
points and the variables represent pH, DOC, Cond, Alc,
ORP, T, Turb, Resis, TDS, Cl, Ba, Ca, Fe, K, Mg, Mn, Na,
S and Sr values.
Before processing the data by the SOM algorithm,
the entire data set was autoscaled for all variables,
which means that the variance of the variables were
normalised and the averages calculated to zero. The
scaling of the variables is of vital importance in the
application of Kohonen network, because its algorithm
uses the Euclidean metric to measure distances between
vectors. If a variable has values ranging between 0 and
1000 and another variable has values ranging between 0
and 1, for instance, the first will virtually dominate the
organisation of the map because of the large impact on
the measurement of distances. Hence, in most cases it is
recommended that the variables are equally important.
The pre-processing of data ensures that all variables have
the same level of importance, allowing users to assess
the significance of all variables in the samples. Since
the variables investigated in this work refer to different
physical and chemical measurements, the scaling of the
data becomes obligatory.12
The Kohonen maps were created and initialised linearly.
In this process, the eigenvalues and eigenvectors of the
data were calculated. Then, the weight vectors of the map
have been initialised over the largest eigenvectors of the
covariance matrix in agreement with the size of the map,
which is generally 2. The Kohonen neural network was
trained with the data using the batch training algorithm,
where the entire data set is presented to the map before
any adjustment of weights is done. The neighbourhood
function used in training was the Gaussian, the structure
was hexagonal and shape of the map was planar.12
At the end of the process, a map was obtained that
shows the grouping of the samples and the influence of
the variables. The lighter colours in the neurons indicate
higher values for that variable. The darker colours represent
the lowest values for the same variable. It is important to
mention that the neurons of the map of the variables were
compared with the neurons of the map of groups of samples
to evaluate which parameters are influencing a given
sample. During the data training, architectures with several
orders were tested (from 2 × 2 to 6 × 6) for evaluation of
the groups of samples and it was chosen the architecture
that had the best sample distribution in groups (which was
more informative).
The software used to perform the multivariate analysis
of Kohonen neural network was freely available on the
internet.22
For comparison, a PCA analysis was also performed
using the same set of data through the computing
environment GNU Octave 3.6.4, freely available on the
internet at page http://www.gnu.org/software/octave/;
before data processing, the entire data set was autoscaled
for all variables.
Results and Discussion
The location of the points, season and types of water
bodies where the samplings were performed are shown
in Table 1. From some lithological data and maps of the
region, another table (Table 2) was created. This table shows
the stratigraphic units, rocks in the region of the sampling
points and some elements likely present in the waters
sampled. It is important to consider that the points 9 and
10 were collected in an area of environmental protection
(Private Reserve of Natural Heritage of Caraça). The results
of the parameters measured in the field and in the laboratory
are shown in Table 3.
A Kohonen neural network with hexagonal grids was
obtained after performing the multivariate analysis from
the data set in Table 3. Architectures of several orders
were evaluated (from 2 × 2 to 6 × 6) and the arrangement
5 × 5 with 25 neurons had the best sample distribution in
the map. In addition, after the Kohonen analysis, it was
possible to notice the formation of 4 different groups that
were circled as showed in the Figure 2. Furthermore, it
is important to mention that samples located at the same
Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter J. Braz. Chem. Soc.
212
Table 1. Location, type of water body and period of sampling in the studied area
Point Sample Coordinates Description (location) Type of water
body Season / Month / Year
Latitude Longitude
1S1A S 20o09’43.7’’ W 43o25’54.8’’ South plateau
(near Caraça Moutain Range) Swamp Rainy / October / 2010
S1B Rainy / March / 2011
2
S2A
S 20o15’55.7’’ W 43o28’32.7’’ East of peak of Frazão Lake
Rainy / October / 2010
S2B Rainy / March / 2011
S2C Dry / August / 2011
3S3A S 20o27’51.8’’ W 43o35’26.0’’ Itatiaia Mountain Range Swamp Rainy / November / 2010
S3B Dry / June / 2011
4S4A S 20o29’49.5’’ W 43o36’55.6’’ Garcia River River Rainy / November / 2010
S4B Dry / June / 2011
5 S5 S 20o13’49.7’’ W 43o25’06.5’’ Ouro Fino Stream (near the village of
Bento Rodrigues) Stream Rainy / December / 2010
6 S6 S 20o16’33.9’’ W 43o25’51.3’’ Gualaxo do Norte River River Rainy / December / 2010
7 S7 S 20o16’50.0’’ W 43o26’25.5’’ Stream that flows into the Gualaxo do
Norte River Stream Rainy / December / 2010
8 S8 S 20o09’43.5’’ W 43o25’11.5’’ Brumado Stream Stream Rainy / March / 2011
9S9A S 20o06’22.1’’ W 43o28’27.2’’ Cascatinha (Caraça Mountain Range) Stream Dry / May / 2011
S9B Dry / August / 2011
10 S10 S 20o05’52.9’’ W 43o29’23.4’’ Caraça Stream (Caraça Mountain Range) Stream Dry / May / 2011
Table 2. Stratigraphic units and lithology of the studied area; geological data from Dorr, Alkmim and Marshak23,24
Point Unit Some rocks present in the region Elements probably present in water
1
Caraça metaconglomerate, phyllite and quartzite Na, K, Ca, Si and Al
Quebra Osso banded iron formation and metachert Fe, Al, Ca, Mg and Ti
Piracicaba phyllite, dolomite and schist Ca, Mg, Na, K, Al and Fe
Itabira schist, phyllite, itabirite and dolomite K, Si, Ca, Mg, Fe and Al
2Itacolomi metaconglomerate, phyllite and quartzite Si, Al, Na, K and Ca
Piracicaba phyllite, dolomite and schist Ca, Mg, Na, K, Al and Fe
3 Itacolomi metaconglomerate, phyllite and quartzite Si, Al, Na, K and Ca
4Nova Lima schist, metamafic rocks and metagraywacke K, Si, Fe and Mg
Itacolomi metaconglomerate, phyllite and quartzite Si, Al, Na, K and Ca
5
Maquiné schist, conglomerate and phyllite Si, Al, Na, K and Ca
Dom Silvério Mn formation Mn
Caraça phyllite and metaconglomerate Si, Al, Na, K and Ca
6
Itacolomi metaconglomerate, phyllite and quartzite Na, K, Ca, Si and Al
Piracicaba schist, phyllite and dolomite Ca, Mg, Na, K, Al and Fe
Itabira schist, phyllite, itabirite and dolomite Fe, Si, Al, Na, Ca and Mg
7
Itacolomi metaconglomerate, phyllite and quartzite Si, Al, Na, K and Ca
Piracicaba phyllite, dolomite and schist Ca, Mg, Na, K, Al and Fe
Itabira schist, phyllite, itabirite and dolomite Fe, Si, Al, Na, Ca and Mg
8 Piracicaba phyllite, dolomite and schist Ca, Mg, Na, K, Al and Fe
9 Caraça metaconglomerate, phyllite and quartzite Si, Al, Na, K and Ca
10 Caraça metaconglomerate, phyllite and quartzite Na, K, Ca, Si and Al
Gontijo et al. 213Vol. 25, No. 2, 2014
neuron or at neighbouring neurons form groups with
similar characteristics. The map of the variables is shown
in Figure 3, where the grayscale bars beside the maps
indicate the intensity of each parameter evaluated. The
lighter colours in these bars mean higher values and a higher
importance in the formation of the groups for each variable.
It can be noted from Figure 2 and Figure 3 that K,
temperature and Cl were the parameters responsible for
making the samples S1B, S3A and S5 get closer and,
therefore, form group I. These parameters were important
because they had higher values for the samples of group
I considering the data obtained in this exploratory study.
Although Cl was not measured for the sample S3A,
the SOM algorithm estimates missing values during its
training process. In this way, it is possible to infer about
the behaviour of missing values.25
Potassium is a lithophile element that participates in
the formation of silicates, feldspars and micas (biotite and
muscovite), which are mineral constituents of rocks as
gneisses and schist’s. Muscovite and biotite (that have K in
their structure) are still part of the composition of quartzite
rocks. Consequently, evaluating the lithotype of the region
studied (Table 2) it can be noticed that the presence of K in
the analyses is an indication of lithological contribution of
this element for the waters in the studied area. The higher
values detected for this element in group I may be explained
by the fact that sampling of the samples S1B, S3A and S5
were performed in the rainy season (Table 1), where K is
more leached by high precipitation.
In the environment, Cl can originate both from the
weathering of rocks and by man’s influence via sewage
discharges.26 In all samples, the concentrations of Cl were
very low, ranging from 0.50 mg L−1 to 4.66 mg L−1. As the
rocks of the region do not have Cl in their composition,
probably the source of Cl can be atmospheric and/or from
plants and animals (biogenic origin). It is important to note
that the samples S1B and S8 may have some anthropogenic
influence due to the proximity of a village (Santa Rita
Durão). The breeding could influence the sample S2B
because this type of activity is common in the region and
Table 3. Results of the physico-chemical parameters and metals analysed in natural waters of the upper Rio Doce River basin (Quadrilátero Ferrífero)
Sample pH DOCa,e /
(mg L−1)
Tempera-
ture / oCAlcb,f Condg /
µS
ORPh /
mV
Turbi /
NTU
Resisj /
kΩ
TDSk /
(mg L−1)
Cla /
(mg L−1)
Baa /
(µg L−1)
Caa /
(mg L−1)
Fea /
(µg L−1)
Ka /
(mg L−1)
Mga /
(mg L−1)
Mna /
(µg L−1)
Naa /
(mg L−1)
Sa /
(mg L−1)
Sra /
(µg L−1)
S1A 5.92 2.79 24.0 NDc10.8 155 6.57 90.4 6.9 0.98 8.7 1.22 4342.0 0.98 0.16 112.6 1.3 0.34 4.8
S1B 5.30 1.40 24.1 4.3 26.8 102 4.10 39.5 15.6 4.49 1.3 0.76 253.1 0.11 0.03 32.4 0.3 0.11 2.1
S2A 6.64 2.62 25.0 ND 28.6 108 7.29 35.0 17.9 2.41 14.7 1.35 11.5 0.32 0.65 31.6 0.3 0.09 6.4
S2B 6.16 1.17 27.4 11.9 19.5 58 32.60 48.6 12.5 4.49 54.3 2.05 36.9 0.32 0.79 4.4 0.5 0.13 7.7
S2C 7.70 1.51 22.4 5.0 11.2 268 64.80 87.6 7.2 0.50 6.9 1.54 77.6 0.06 0.45 35.7 0.3 < LOQd3.1
S3A 6.33 ND 22.7 7.6 19.8 109 2.81 50.3 12.4 ND 9.9 0.83 82.2 2.50 0.25 78.7 0.6 0.17 8.8
S3B 6.29 3.88 18.0 5.0 7.8 211 3.74 98.2 6.6 ND 8.1 2.00 10.6 0.45 0.33 72.6 1.0 0.45 12.7
S4A 7.45 2.71 19.1 17.5 41.2 59 36.80 24.1 26.1 ND 7.7 2.42 68.8 0.60 1.47 9.8 1.0 0.14 11.8
S4B 6.73 0.72 16.0 18.4 39.8 207 0.99 24.9 26.4 ND 9.4 4.06 65.2 0.36 1.99 6.4 1.5 0.27 16.7
S5 7.09 1.40 24.4 6.0 7.5 79 34.30 132.1 4.7 2.66 6.7 0.52 70.5 1.90 0.15 21.7 0.4 0.07 2.1
S6 7.25 1.48 25.7 36.4 127.3 45 267.00 0.0 80.7 2.33 27.3 6.27 680.0 0.95 2.40 105.3 12.3 3.15 11.0
S7 6.89 1.38 22.2 8.9 35.5 62 279.00 28.2 22.5 0.66 20.8 2.48 356.3 1.86 0.88 234.6 2.5 0.22 4.3
S8 7.30 < 0.50 21.4 8.9 20.5 61 9.67 48.0 13.1 4.66 10.4 1.37 71.7 0.18 0.62 2.0 0.4 0.07 3.9
S9A 5.40 3.83 15.6 2.0 8.0 247 1.24 119.7 5.2 0.50 5.9 1.65 469.4 0.48 0.14 8.3 1.5 0.19 2.1
S9B 6.22 2.20 16.8 3.0 4.6 183 ND 189.9 3.0 0.75 1.3 0.30 104.0 0.12 0.14 3.9 0.3 < LOQ 0.9
S10 5.75 3.63 13.9 2.5 5.0 218 0.79 172.7 4.0 ND 6.0 0.92 119.1 0.26 0.09 5.6 1.0 0.09 2.0
aStandard deviation calculated by replicate analyses was less than 10%; bGiven in mg CaCO3 L−1; cND: Not determined; dLOQ: Limit of quantification; eDOC: dissolved organic
carbon; fAlc: alkalinity; gCond: conductivity; hORP: redox potential; iTurb: turbidity; jResis: resistivity; kTDS: total dissolved solids.
Figure 2. Map of groups of samples (natural waters) obtained by Kohonen
neural network.
Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter J. Braz. Chem. Soc.
214
it could be seen during the field trips. These three points
were the ones with the highest concentrations of Cl, with
values above 4 mg L−1.
The summer in the southern hemisphere (December to
March) corresponds to the period where higher volumes of
precipitation are recorded considering the studied area.19
Consequently, Cl and K can be more leached by water from
rainfalls and showed higher concentrations. This explains
higher values in group I along with temperature. The second
sampling of point 3 (S3B) plots in group II because the
sampling was done in the dry season (June 2011, Table 1).
From Figure 3, it can be noticed that the variables DOC
and Fe indicated higher concentrations at neurons in the
same location. These higher values influenced the positions
of the samples in group II and suggest that Fe has positive
relationship with DOC considering the samples analysed in
this paper. This observation could indicate a complexation
between these variables, since Fe can effectively bind
organic matter, especially humic substances.27,28 Although
a mining region, some elements that were expected to be
present in larger quantities were found below the LOQ
(e.g., As and Cu, with LOQ values of 57.7 µg L−1 and
4 µg L−1, respectively). An explanation for this fact could
be that the waters were collected in areas of environmental
protection (as in the case of points 9 and 10) without any
evident anthropogenic influence. Consequently, it was not
possible to indicate if there are relationships among these
elements and dissolved organic matter in the samples using
the Kohonen neural network.
The values for DOC in the samples ranged from
0.72 mg L−1 to 3.88 mg L−1. The highest values for DOC
were detected in a swamp (sample S3B) and in a stream
(sample S9A), located in Itatiaia Mountain Range and
Caraça Mountain Range, respectively. The lowest value
was detected in Brumado Stream (sample S8).
During the second sampling in Caraça (sample S9B)
the concentration of DOC obtained was only 2.2 mg L−1,
much lower compared to the first sampling, where it was
3.83 mg L−1. A possible explanation for this observation
could be the sampling at the end of the dry season after more
than 4 months with low average volumes of precipitation,
around 36 and 42 mm month−1.19 As a result, the water level
in aquifers and the amount of water in soils probably were
low at that time (base-flow situation). Therefore, a smaller
amount of humic material was transported by the direct
reaction of rainfalls into the water bodies of the region
(surface and interflow). Therefore, the DOC concentration
was lower, especially if considering that the main source
of the DOC at that time was probably the groundwater,
which has lower concentrations of dissolved organic matter
compared to the top-most soils.29 On the other hand, the first
sampling was performed at the end of the rainiest months
Figure 3. Maps of the distribution of individual variables obtained by Kohonen neural network. The colour bars indicate the intensity of the measured
variable: the lighter the colour, the more intense the variable value.
Gontijo et al. 215Vol. 25, No. 2, 2014
with volumes of precipitation exceeding 210 mm month−1,
mainly between November and February.19 Consequently,
the amount of water available was higher and a greater
concentration of humic material was leached into streams
due to a higher volume of water at that time. It is important
to consider that in base-flow situations, the concentration of
DOC decreases and other factors may also have influenced
the results.29
Looking at point 1 (samples S1A and S1B) it can be
seen (Table 3) that the levels of dissolved organic matter
were higher (sample S1A) in the beginning of the rainy
season compared to the values at the end of the rainy season
(sample S1B). A hypothesis to explain this behaviour could
be the effect of dilution after the water body dries out in
the dry season, as seen in the third field trip to this point.
In this way, a large amount of DOC probably is carried
into the swamp during first rainfalls (in the beginning of
the rainy season). This organic matter could be originated
from the degradation of living organisms in the dry winter
period. After the rainiest months between November and
February, the DOC concentration decreases by dilution
effect. In addition, the organic matter from the dry season
was already almost completely decomposed at that time.
This would explain the decrease of DOC at point 1 in the
end of the rainy season. At this point it is important to
consider that Steinberg29 describes that in the onset of the
rainy season the concentration of DOC rises rapidly with
the discharge.25
Group II was formed by having higher values of DOC,
Fe, Resis and ORP. At point 1 (especially for sample S1A),
the presence of banded iron formations (BIF), which are
covered frequently by canga (the Brazilian name for
a ferruginous breccia surface formation, consisting of
fragments of hematite, cemented by goethite), phyllites
and schist’s explain the higher Fe contents. However, it is
important to note that the content of Fe was lower in the
sample S1B, what could be explained by the dilution effect
as previously explained. The sample S2C was in this cluster
due to a high redox value, which was the dominant factor
for the composition of group II.
All samples from group III had waters more alkaline
than other groups. Therefore, it was possible to affirm that
the pH was the predominant variable for its formation (Sr
was also important for the formation of this cluster). In
addition, for being a large group there is some heterogeneity
among some of its samples. The sample S6, for instance,
was highlighted by a water more alkaline and containing
higher concentrations of S. The highest value for this
element is probably due to the presence of sulfide rocks
upstream of this point. The highest alkalinity may be
explained by increased concentrations of carbonate, which
is evidenced by the presence of Ca and Mg. These ions
may originate from dolomite rocks, which are part of the
lithology units of Piracicaba and Itabira.
Considering only the samples S6 and S7, it can be
observed that they had higher concentrations of Ca, Mg and
Mn (lighter colours in the bottom right neurons of these
three variables in Figure 3). These values may indicate a
common lithological origin, especially because Ca, Mg, and
Mn are lithophile elements that participate in the formation
of dolomites and schists. These kind of rocks are present
upstream of these two sampling sites (S6 and S7). It is
remarkable that these samples had higher values of Na,
which along with Ca, Mg and Mn were responsible for
increased levels of TDS. All of these five variables were
responsible for the fact that samples S8 and S2A were far
away from samples S6 and S7 in a same group.
The higher levels of Sr were observed in the cluster
formed by the samples S4A, S4B, S6 and S7. Sr has
similar chemical properties like Ca and Mg and can replace
both elements within their minerals. Consequently, the
presence of dolomite in the lithological groups of Itabira
and Piracicaba and of phyllites and schists in the Nova
Lima and Itacolomi lithological groups could explain the
presence of Sr in the collected samples. For this reason,
the relationship observed among Ca, Mg and Sr may be an
indicative of their lithological origin.
Finally, group IV was formed due to higher
concentrations of Ba found at point S2B. The highest
values of this element can be explained due to the increased
leaching at the rainy season, which was the period of
sampling (March). The concentrations of Ba decreased
considerably in the samplings performed in the dry season
or during the beginning of the rainy season, as explained
for other parameters before. The temperature also showed
a higher value at this point, which is explained by the
sampling done in the summer time.
Considering the seasons of the year, it may be noted that
all samples of group I were collected in the rainy season
(summer), which presented higher temperatures and higher
levels of Cl and K (probably as a result of leaching), as shown
by the lighter colours in Figure 3 and discussed before. Most
samples in group II were collected in the dry period (winter).
Hence, lower temperatures (darker colours in Figure 3) were
observed in this group if it is compared to others groups.
Group III is heterogeneous and samples were collected
during the rainy and dry seasons. Group IV refers to the rainy
period, where the sample S2B had higher temperatures and
higher concentrations of Ba, as mentioned before.
No relationship was observed between the formation of
clusters and the type of water body (swamp, lake, stream
or river). This can be shown by groups II and III, where
Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter J. Braz. Chem. Soc.
216
samples were taken from rivers, streams and lakes and
plotted within the same group.
To evaluate a possible influence of sulfate concentration
on the DOC content in this exploratory study, sulfate was
analysed from seven selected samples, since it is known
that S is a compositional part of the humic substances
(Figure 4).30 However, this study was unable to demonstrate
an influence due to the low number of analyses. As sulfate
was not measured for most samples, it was not included
in the SOM analysis.
Part of the sulfate in the waters of the QF comes from
the oxidation of sulfides such as pyrite (FeS2), which are
abundant in rocks such as schist’s and amphibolites in the
region. Among the anthropogenic sources of sulfate in
surface waters, the discharge of domestic and industrial
effluents and the use of coagulants in treated waters are
known.26
For comparison and to validate and show the important
applicability of Kohonen neural network in this study, a
PCA analysis of the same data set (Table 3) was performed.
The results of the PCA analysis is shown in Figure 5 and
Figure 6, which represent the scores and loadings plots of
PC1×PC2.
It was necessary to use 11 PC to explain 99.31% of
the data set variability with the PCA approach and the
first and second components explained only just 56.5% of
variance (Figure 5 and Figure 6). To analyse all information
in the data, it would be necessary to observe the principal
components in all possible combinations, that could be
in a two or three-dimensional way. This certainly would
be a hard task, and would make data evaluation and,
consequently, data interpretation very difficult. In this way,
the Kohonen neural network exhibited the great advantage
of projecting all data in a two-dimensional space without
loss of information.
Figure 5 shows the samples separated in seven groups.
It can be seen that the samples of the groups IV, V and
VI were located in the same quadrant (left bottom). The
same samples formed the group II in the Kohonen analysis
(Figure 2) showing a similarity between the results of the
two methods. The samples S9A, S9B and S10 are quite
near in both methods indicating similarities among them.
The group I in the PCA scores plot (Figure 5) is composed
by the same sample (S2B) of the group IV in the Kohonen
map (Figure 2).
These similarities between both methods show the
ability of Kohonen network to explore the data with
robustness and reliability. Figures 2 and 5 are not able to
present exactly the same relationships because only little
more than half of the data variability is presented with the
PCA approach (Figure 5).
In the loadings plot (Figure 6), it can be observed
that Fe and DOC are near indicating some similarity
between them, although they are presented in different
quadrants. The argument for the scores plot is valid here,
since all variance in the data set is not represented. The
Figure 4. Concentration of sulfate in selected water samples from the
Quadrilátero Ferrífero.
Figure 5. Scores plot on PC1 and PC2 in the study of dissolved organic
matter and metal ions in waters from the eastern Quadrilátero Ferrífero,
Brazil.
Figure 6. Loadings plot on PC1 and PC2 in the study of dissolved
organic matter and metal ions in waters from the eastern Quadrilátero
Ferrífero, Brazil.
Gontijo et al. 217Vol. 25, No. 2, 2014
relationships among DOC, Fe, ORP and Resis in Kohonen
map (Figure 3) are also demonstrated in the loadings plot
(Figure 6).
In general, the main tendencies (greater similarities or
differences among samples or variables, and the influences
of variables in samples) of the data set were shown by
both the Kohonen neural network and the PCA methods,
although the PCA was not able to present all data variance
while Kohonen neural network expressed it very efficiently
in a two-dimensional space.
Conclusions
The multivariate exploratory data analysis by the
application of Kohonen neural network was effective in
this study, especially considering an easy and friendly
data interpretation, with complex nonlinear relationships.
Furthermore, this technique allowed a separation of all
samples into groups with similar characteristics while
reducing a high dimensional space to a two-dimensional
space. It was one of the main advantages of this technique
when compared with PCA, which was applied to the same
data set. In the latter method, it was necessary to work in
a multidimensional space (11 PC) making the analysis of
the results difficult. Nevertheless, both methods showed
similar relationships of the data set.
A positive relationship between DOC and Fe was
noted in the Kohonen neural network. This observation
possibly indicates a complexation between both variables as
described in the literature. A certain influence of seasonality
on the distribution of samples could be noticed considering
that some groups in the Kohonen map were formed as
a result of their sampling date (rainy or dry period). In
addition, some samples that were collected at the same point
in different seasons stayed in different groups due to the
effects of leaching or dilution. Some relationships among
some elements in the Kohonen neural network indicated a
contribution from the lithology of the studied area as it can
be found by the elements Ca, Mg, Mn and Sr in the maps
of the distribution of variables. Chloride may be partly also
from biogenic origin since the rocks in the region studied
do not have Cl in their structure.
Further studies will be necessary to measure and
characterise the dissolved organic matter in the area and
to fully understand its role on the cycle of elements in the
QF, especially considering the impacts of mining. However,
in this study it was possible to perform a screening of the
evaluated area, particularly considering the concentrations
of DOC and of some metal ions. In addition, the use of the
Kohonen neural network for the first time with chemical
data of this studied area showed that it certainly is a
promising technique that may help to analyse a variety of
environmental results with complex interdependencies in
an easy way.
Supplementary Information
Supplementary information (Figure S1) is available free
of charge at http://jbcs.sbq.org.br as PDF file.
Acknowledgements
The authors of this paper gratefully acknowledge
CAPES, CNPq, FAPEMIG, Fundação Gorceix and UFOP
for financial support for this research.
References
1. Deschamps, E.; Ciminelli, V.; Lange, F.; Matschullat, J.;
Raue, B.; Schmidt, H.; J. Soils Sediments 2002, 2, 216.
2. Mikutta, C.; Kretzschmar, R.; Environ. Sci. Technol. 2011, 45,
9550.
3. Borba, R. P.; Figueiredo, B. R.; Matschullat, J.; Environ. Geol.
2003, 44, 39.
4. Varcjao, E. V. V.; Bellato, C. R.; Fontes, M. P. F.; Mello, J. W. V.;
Environ. Monit. Assess. 2011, 172, 631.
5. Pereira, A.; van Hattum, B.; Brouwer, A.; van Bodegom, P.;
Rezende, C.; Salomons, W.; J. Soils Sediments 2008, 8, 239.
6. McDonald, S.; Bishop, A. G.; Prenzler, P. D.; Robards, K.; Anal.
Chim. Acta 2004, 527, 105.
7. Wood, C. M.; Al-Reasi, H. A.; Smith, D. S.; Aquat. Toxicol.
2011, 105, 3.
8. Ravichandran, M.; Chemosphere 2004, 55, 319.
9. Castro, G. R.; Padilha, C. C. F.; Rocha, J. C.; Valente, J. P. S.;
Florentino, A. O.; Padilha, P. M.; Ecletica Quim. 2005,
30, 45.
10. de Oliveira, L. C.; Sargentini Jr, E.; Rosa, A. H.; Rocha, J. C.;
Simões, M. L.; Martin-Neto, L.; Silva, W. T. L.; Serudo, R. L.;
J. Braz. Chem. Soc. 2007, 18, 860.
11 . Rogers, J.; Object-oriented Neural Networks in C++; Academic
Press: San Diego, CA, USA, 1997.
12. Garcia, J. S.; da Silva, G. A.; Arruda, M. A. Z.; Poppi, R. J.;
X-Ray Spectrom. 2007, 36, 122.
13. Çinar, O.; Merdun, H.; Ecol. Res. 2009, 24, 163.
14. Gamble, A.; Babbar-Sebens, M.; Environ. Monit. Assess. 2012,
184, 845.
15. Çinar, O.; Process Biochem. 2005, 40, 2980.
16. Lee, W. S.; Kwon, Y. S.; Yoo, J. C.; Song, M. Y.; Chon, T. S.;
Ecol. Modell. 2006, 193, 602.
17. Chon, T. S.; Ecol. Info. 2011, 6, 50.
18. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F.;
Meteorol. Z. 2006, 15, 259.
Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter J. Braz. Chem. Soc.
218
19. de Sá Júnior, A.; de Carvalho, L. G.; da Silva, F. F.; Alves,
M. de C.; Theor. Appl. Climatol. 2012, 108, 1.
20. APHA; AWWA; WEF; Standard methods for the examination
of water and wastewater, 21st ed.; American Public Health
Association, American Water Works Association and Water
Environment Federation: Washington, D.C., 2005.
21 . Grasshoff, K.; Kremling, K.; Ehrhardt, M.; Methods of Seawater
Analysis, 3rd ed.; Wiley-VCH: New York, 1999.
22. Alhoniemi, E.; Himberg, J.; Parhankangas, J.; Vesanto, J.;
Laboratory of Computer and Information Science, Finland,
2000. Available at http://www.cis.hut.fi/projects/ somtoolbox/,
accessed in November, 2013.
23. Dorr, J. V. N.; Physiographic, Stratigraphic and Structural
Development of the Quadrilátero Ferrífero, Minas Gerais,
Brazil, U.S.G.S. Professional Paper 641-A: Washington, 1969.
24. Alkmim, F. F.; Marshak, S.; Precambrian Res. 1998, 90, 29.
25. Kohonen, T.; Self-Organizing Maps, 3rd ed.; Springer: Berlin/
Heidelberg/New York, 2001.
26. Environmental Control Agency of São Paulo State (CETESB);
Significado ambiental e sanitário das variáveis de qualidade
das águas e dos sedimentos e metodologias analíticas e de
amostragem; CETESB: São Paulo, SP, Brasil, 2009.
27. Laglera, L. M.; Battaglia, G.; van den Berg, C. M. G.; Mar.
Chem. 2011, 127, 134.
28. Laglera, L. M.; Battaglia, G.; van den Berg, C. M. G.; Anal.
Chim. Acta 2007, 599, 58.
29. Steinberg, C.; Ecology of Humic Substances in Freshwaters,
Springer-Verlag: Berlin, 2003.
30. Stevenson, F. J.; Humus Chemistry: Genesis, Composition and
Reactions, 2nd ed.; John Wiley & Sons: New York, 1994.
Submitted: August 12, 2013
Published online: December 3, 2013
