Geochemistry of Fluoride and Major Ion in the Groundwater Samples... Aquifer (South Eastern Tunisia), Through Multivariate and Hydrochemical Techniques.

Geochemistry of Fluoride and Major Ion in the Groundwater Samples... Aquifer (South Eastern Tunisia), Through Multivariate and Hydrochemical Techniques.

Journal of Applied Sciences Research, 5(11): 1941-1951, 2009
© 2009, INSInet Publication
Geochemistry of Fluoride and Major Ion in the Groundwater Samples of Triassic
Aquifer (South Eastern Tunisia), Through Multivariate and Hydrochemical Techniques.
1-2
Fadoua Hamzaoui-Azaza, 2 Rachida Bouhlila, 1Moncef Gueddari
1
Laboratory of Geochemistry and Environmental Geology, Department of Geology, Faculty of
Mathematical, Physical and Natural Sciences, University Campus.Tunis. Tunisia
2
Modeling in Hydraulic and Environment Laboratory; National Engineers School of Tunis. Tunisia.
Abstract: Groundwater quality in South-East of Tunisia has special significance and needs great attention
of all concerned since it is the major alternate source of domestic, industrial and drinking water supply
Major elements and Fluorine concentrations as well as temperature, pH and salinity, were monitored from
2005 in 14 wells capturing the Triassic aquifer. W ater of the Triassic aquifer is used unevenly by different
economic sectors. However, drinking water supply remains the primary use. Exploratory analyses of
experimental data have been carried out by statistical analysis (Principal Component Analysis PCA and
Cluster Analysis CA), and geographic information system (GIS) in an attempt to discriminate sources of
variation of water quality. The application of PCA and CA has achieved a meaningful classification of
wells waters samples based on spatial criteria. Results reveal that salinity and the major elements
concentrations, with the exception bicarbonates, increase towards groundwater flow. The saline load of
these waters is in first place controlled by sulfphate, chloride, sodium and calcium concentrations. The
chemical composition interpretation clearly evidences two main geochemical facies Na-Ca-Mg-ClSO4 and
Na-Ca-Mg-Cl-SO4. All analysed samples have Fluoride concentration above the upper limit proposed by
the W HO for human consumption The geochemical behaviour of Fluorine is controlled by the chemistry
of the groundwater.
Key words: Groundwater, geochemistry, major elements, Fluorine, multivariate data analysis, Triassic
aquifer.
INTRODUCTION
The Mediterranean region still only disposes of 3%
of the world’s water resources though it gathers 7.3%
of the world’s population [1 8 ]. In arid zones, water is a
rare and precious resource. The exploitation of water
resources is a complex problem in the framework of
sustainable agricultural development in these regions.
W hile the demography is expanding in the South of
Tunisia, the water needs of the three main sectors
(irrigation, drinking water supply and industry) are
expected to increase in order to provide the population
with employment and life conditions enabling the
population to settle in its homeland. One of the main
water resources in Tunisia is groundwater resources. In
South-eastern Tunisia, groundwater is known to contain
Xuoride concentrations that exceed the drinking water
standard of 1.5mg/l set by the W orld Health
Organization W HO [2 2 ].
Fluorine occurs mainly as free fluoride ions in
natural waters, although fluoride complexes of Al, Be,
B, and Si are also encountered under specific
conditions. The maximum tolerance limit of fluoride in
Corresponding Author:
drinking water specified by the W HO is 1.5 mg/l.
Ingestion of water with fluoride concentrations above
1.5 mg/l results in dental fluorosis characterized
initially by opaque white patches, staining, mottling
and pitting of teeth [1 3 -1 5 ]. These pathological signs were
observed dramatically among populations occupying the
study area which may have been related to the long
term intake of high fluoride groundwater. Since
drinking high fluoride groundwater is the major reason
for endemic fluorosis and has considerable impact on
human health, many efforts have been made in recent
years to study the hydrochemistry and genesis of high
fluoride groundwater as well as alternative technologies
of defluoridation [2 -1 1 -1 9 ].
It is well known that multivariate statistical
analyses significantly help to classify groundwater and
identify major mechanisms influencing groundwater
chemistry. W hen the hydrogeochemical interpretation is
combined with the knowledge of the geological and
hydrogeological setting, multivariate statistical methods
can also help understand groundwater flow in complex
aquifer systems [7 -2 3 ]
Fadoua Hamzaoui-Azaza, Adress: National Engineers School of Tunis; Modeling in Hydraulic
and Environment Laboratory Bp 37, Le Belvédère, 1002 Tunis Tunisia.
E-mail: [email protected].
Tel.: 0021670860396;
Fax: 0021670860396.
1941
J. App. Sci. Res., 5(11): 1941-1951, 2009
Our objective in this study was to better identify
the processes controlling the geochemical evolution of
groundwater quality in the study area with a special
emphasis in Fluorine behaviour. This by using
multivariate statistical methods to analyze the
geochemical data, principal components analysis (PCA),
Cluster analysis and geographical information systems
(GIS).
Finally, as an aid to management and future
development of groundwater resources in the region,
these approaches were also applied to divide the
territory in areas with distinct groundwater quality. This
may help, certainly, in the long run to propose new
and more efficient remedial measures to combat the
deterioration of water quality.
2. Study Area: The Triassic aquifer is located in
South-East Tunisia (Fig.1).
In this region, the average temperature is 20 °C
and the average precipitation is 200 mm per year,
which is distributed in an uneven form, with most
precipitation falling in a rainy period from December
to march. Dry periods occur from April to November,
when potential evapotranspiration is much higher than
precipitation.
The geology has been described by [5 ]. The
stratigraphic layers, in the study area, range from the
Permian to the Quaternary. The region is bordered by
three main structures that define South-East Tunisia:
the Dahar monocline, W est and North-W est; the
Medenine Tebaga monocline, South, and the plain of
the Jeffara, East and N orth. The hydrographical
network is quite dense and the main rivers in the
region are Smar, Zeuss, Om Ezzassar and M orra. The
general flow runs from the South-W est to the NorthEast (Fig.2). The water of this aquifer is used unevenly
by different economic sectors. However, drinking water
supply remains the primary use. Anthropogenic
activities in this region rely mainly on agricultural.
Potential resources are estimated to 120 l/s. The
withdrawal rate increased from 80 l/s in 1994 to 150
l/s in 2005 [1 0 ] . T he piezometric data (1989-2005) show
an average decline of 0.21 m/year. This decrease is due
to the decline of rainfall and is linked to the intensive
demand for water supply for domestic and agriculture
activities. The Triassic aquifer is characterized by
permeable formation. However, the existence of good
permeability alluvial deposits cause local preferential
paths that greatly influences drainage axes. The
recharge area of the Triassic aquifer is situated in the
upstream and is mostly due to indirect infiltration of
precipitation through rivers “wadis”. The analysis and
interpretation of hydro-geological borehole logs [1 0 ] show
that the aquifer is constituted by Triassic deposits
( s a n d s to n e , u n c o n s o l i d a t e d s a n d , l i m e s to n e ,
conglomerate and clay). The area is overlain by highly
permeable sandstone layers.
3. M ethodology:
3.1. Sampling and Laboratory Analysis Techniques:
In order to evaluate the seasonal variations in chemical
compositions, groundwater samples were collected
during July and December 2005 representing summer
and winter seasons.
W ater samples for physico-chemical analysis were
collected from 14 wells in the Triassic aquifer. W ater
samples were collected from active pumping wells.
Fieldwork included measurements of temperature and
pH. Electrodes were calibrated in the laboratory and in
the field.
The chemical analyses were performed in certified
laboratories (ISO 17025) of SONEDE of Ministry of
agriculture and hydraulic resources, using standard
methods. All of the water samples were pumped from
wells continuously used. W ater was only taken from
boreholes that were pumping for a significant amount
of time (more than 10 min) to get a representative
sample. W hen sampling, all water samples were filtered
in the field using 0.45 µm pore-size membrane filters.
Each sample was collected in two new 500 ml
polyethylene bottles. All sampling bottles were washed
with de-ionised water and again with filtered sample
water before filling it to capacity and then labelled
accordingly. For each sample, one bottle is acidified
(until pH of samples reached 1) with 35% nitric acid
for cation analysis (Na + , K + , Ca 2 + and M g 2 + ), whereas
the other is used for the determination of dissolved
anions (Cl -, SO 4 -, HCO 3 - and F -). Prior to analysis in
the laboratory, the samples were stored at a
temperature below 4 °C
In situ field measurements were made on water
samples for temperature (T°) and pH. Before each
measurement, the pH meter was calibrated with a
reference buffer solution of pH 4.
Chloride was determined by the standard titration
method or the Mohr method. Bicarbonates were
determined by the potentiometric method. Sulphate
concentration was measured by the gravimeter method
using BaCl2 . Sodium and Potassium concentrations
were determined with a flame photometer. Calcium and
magnesium ions are determined by the complexometric
method using ethylenediaminete tracetic acid bisodium
salt. Fluorine concentrations were measured by the
calorimetric method. Total dissolved solids (TDS) were
measured by evaporating a pre-filtered sample to
dryness.
1942
J. App. Sci. Res., 5(11): 1941-1951, 2009
Fig. 1: Location of study area
The analytical precision for the measurements of
cations (Ca 2 + , Mg 2 + , Na + , and K + ) and anions (HCO 3 -,
Cl- , SO 4 2- and F -), indicated by the ionic balance error
(IBE) was computed on the basis of ions expressed in
mq/l. The value of IBE was observed to be within a
limit of ±7%.
3. 2. M ultivariate Statistical Analysis: The
multivariate statistical analysis is a quantitative and
independent approach of groundwater classification
allowing the grouping of groundwater samples and the
making of correlations between chemical parameters
and groundwater.
The application of various multivariate approaches
principal component analysis (PCA), Correspondence
analysis (CA) offers a better understanding of water
quality and allows comparison of different samples of
waters [1 6 -2 3 ]. The analytical data can be used for the
classification of water and for ascertaining various
factors on which the chemical characteristics of water
depend.
3. 2. 1. Correlation M atrix: The importance of linear
correlations between variables are determined by
coefficients in the (-1, 1) interval. The relationship
between two parameters is more significant when the
coefficient approaches the extreme values of -1 and 1.
A positive coefficient suggests a commonality between
the correlated elements, such as similar evolutionary
patterns. A negative coefficient indicates that the
variables in question are evolving in opposite
directions.
3. 2. 2. Principal Component Analysis (PCA): PCA
is a statistical method that provides a global picture of
the data, by reducing it to a small set of principal
components. The data are relative to quantitative,
continuous, hom ogeneous or non-homogeneous
variables, which are a priori correlated. Through PCA,
the relationships between variables can be equally
summarized, represented, ordered, visualized and
defined [1 6 -2 1 ].
Several inertia axes can be defined, factorial axis,
F1, also called the principal inertia axis, produces the
maximum explanation or the best variance, axis F2,
which is perpendicular to F1, expresses the maximum
inertia. The resulting space of individuals allows
identifying the various individuals existing as groups [2 3 ].
In this study, PCA was applied to chemical data
from the Triassic aquifer to extract the principal factors
corresponding to the different sources of variation in
the data. Here, PCA was selected for the reasons stated
above.
3. 2. 3. Cluster Analysis (CA): Cluster analysis is a
powerful tool for identifying and selecting the
homogeneous groups from the hydrochemical data
within a particular data set. The term cluster analysis
encompasses a number of different algorithms and
methods for grouping objects of similar kind into
respective categories. This tool sorts different objects
into groups such that the degree of association between
the objects is maximal if they belong to the same
group and minimal otherwise. This method groups
samples into distinct populations that may be
significant in the hydrogeological context, as well as
1943
J. App. Sci. Res., 5(11): 1941-1951, 2009
from the statistical point of view. T here are two types
of cluster analysis: R and Q-modes. In the present
study Q-mode cluster analysis was performed on the
water chemistry data to group the samples in terms of
water quality [9 ].
The results of hierarchical clustering methods
depend on the specific measure of similarity and the
linking method [1 2 ]. In our study, and in order to
perform CA, an agglomerative hierarchical clustering
was developed using a combination of the W ard’s
linkage method as a clustering algorithm and Euclidean
distances as a measure of similarity. The Euclidean
distance is the geometric distance in multidimensional
space. W ard’s method is known to be distinct, as it
uses an analysis of variance approach to evaluate the
distances between clusters. The result of such analyses
is a graph, called dendrogram [9 ].
3. 2. 4. Data Standardization: Standardization tends
to minimize the effect of the difference of variance in
variables, eliminates the influence of different units of
measurement on the data by making them
dimensionless [2 1 ]. Thus, To eliminate the impact of
different measurement units, the data were Wrst
standardized before analyzing through multicomponent
techniques as follows: Z = (x-µ)/ó (where the Z is
standardized value, x indicates the original value of the
measured parameter, the µ is mean of the variable and
the ó is standard deviation) [1 2 ].
3. 2. 5. Software: The computer program ANDAD
6.00, developed by the Geo-Systems Center of Instituto
Superior Tecnico, Portugal [8 ] was performed to conduct
several multivariate statistical analyses.
3. 3. Geochemical M odeling: The state of saturation
of the groundwater with respect to the main mineral
phases present in the aquifer was determined using the
hydro-geochemical model Phreeqc for W indows [1 7 ]. This
determination was done in order to investigate the
thermodynamic controls on the composition of the
water and also to calculate approximately the level to
which the groundwater has equilibrated with these
minerals.
The saturation indices (SI) describe quantitatively
the deviation of water from equilibrium with respect to
dissolved minerals and are expressed as S.I. = Log
(IAP/Kt), where IAP is the ion activity product and Kt
is the equilibrium solubility constant. If the water is
exactly saturated with the dissolved mineral, SI equals
to zero. Positive values of SI indicate supersaturation
and the mineral would tend to precipitate, and negative
one indicates undersaturation and the mineral would
tend to dissolve [6 -2 0 ].
RESULTS AND DISCUSSIONS
4.1. Hydrochemical Data: The chemical composition
of groundwater in Triassic aquifer is controlled by
many factors that include geological structure and
mineralogy of the watersheds and aquifers, and
geochemical processes within the aquifer. The
interaction of all factors leads to various water facies.
The statistical parameters such as minimum, maximum,
mean, median and standard deviation measured in the
groundwater of the Triassic aquifer are presented in
Table 1 for both summer and winter.
4.1.1. Temperature and pH: The samples of the study
area varied in the temperature value from 22 to 32.1
during summer and 15 to 29.8 during winter. This
parameter varied with sampling location, time of
collection; and season of the year.
The pH value is related to the amount of
hydrogen/hydroxide concentration in water. The pH in
all water samples ranged from 7 to 7.99 in the winter
and summer. The pH trend shows minor variations
with season.
4.1. 2. Salinity: W ater salinity ranged from 796 and
3028 mg/l in winter and from 790 mg/l to 3055 mg/l
in summer. During the both seasons, water samples
taken from the same sampling site, showed minimal
variation in salinity.
The existence of recharge processes from upstream
zone is confirmed by low mineralization (Fig. 3) in
most samples (F3, F5, F7, F8, F9 and F10 wells).
Another feature of the Triassic aquifer is the presence
of river (W adi) deposits at the surface. The water
salinity in the aquifer system increases with depth due
to the slow water movement and higher mineralization
rate.
Besides, the variation of the mineralization
conforms partially to the main groundwater flow
directions, indicating that the groundwater salinity is
someway controlled by the residence time in the
aquifer system. In fact, relatively low TDS values,
characterizing the southern and western bands of the
study area, reveal the dilution of the groundwater by
the recharge coming from the massifs, which border
the region.
4.1.3. Chlorides and Sodium: Sodium is the dominant
cation in Triassic aquifer. During the period of this
study, the sodium concentrations are ranging between
76 and 552 mg/l in winter and between 89 and 552
mg/l in summer.
Those of the chloride vary between 109 and 552
mg/l in winter and between 107 and 555 mg/l in
summer.
1944
J. App. Sci. Res., 5(11): 1941-1951, 2009
Fig. 2: Piezometric map surface
Table 1: Summary statistic of chemical parameters in groundwater samples from the Triassic aquifer
Summer
Winter
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Parameter
Mean
Median
Minimum Maximum
Variance
Deviation
Mean
Median
Minimum
Maximum
Variance
Deviation
T
25.89
26.00
22.00
32.10
5.50
2.35
20.64
22.90
14.00
29.80
23.16
4.81
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------pH
7.73
7.90
7.30
8.00
0.06
0.25
7.72
7.90
7.15
7.99
0.09
0.30
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Na
240.64
206.00
89.00
639.00
28598.25
169.11
215.79
160.00
76.00
552.00
22961.41
151.53
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------K
9.07
9.00
7.00
12.00
2.99
1.73
9.43
9.00
7.00
13.00
3.03
1.74
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Ca
111.71
100.00
70.00
201.00
1880.99
43.37
107.00
97.00
66.00
199.00
1836.46
42.85
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Mg
64.29
63.00
38.00
113.00
468.84
21.65
67.14
65.00
40.00
125.00
468.75
21.65
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------SO4
571.21
526.00
182.00
1263.00
123119.10
350.88
575.71
516.00
221.00
1283.00
119289.60
345.38
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------HCO3
208.00
212.00
162.00
260.00
638.46
25.27
157.86
171.00
101.00
230.00
2589.05
50.88
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Cl
211.71
174.00
107.00
555.00
16563.14
128.70
219.43
163.00
109.00
552.00
17607.19
132.69
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TDS
1430.00 1320.00 790.00
3055.00
522154.15
722.60
1409.79
1316.00
720.00
3028.00
553756.03
744.15
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------F
2.21
2.24
1.71
3.10
0.12
0.34
2.21
2.27
1.80
2.80
0.07
0.27
Fig. 3: Salinity iso-concentration map in Triassic aquifer
Spatial distribution maps show the same pattern of
salinity ones as well as temporal evolution trends.
4.1. 4. Calcium: The calcium oscillates between 66
and 199 mg/l in winter and between 70 and 201 mg/l
in summer.
1945
J. App. Sci. Res., 5(11): 1941-1951, 2009
Spatial distribution map depict a slight difference
between each season and an increase towards
groundwater flow.
4.1.5. M agnesium: Magnesium concentrations show
small seasonal variability with concentrations range
between 40 and 125 mg/l in winter and between 38
and 113 mg/l in summer. Spatial distribution of
concentrations shows a similar pattern of calcium.
4.1. 6. Sulphates: Sulphate is the dominant anion in
Triassic aquifer. Sulphate concentrations varied from
221 to 1283 mg/l in winter and from 210 to 1263 mg/l
in summer. The spatial distribution maps of sulphate
concentration do not show any significant variation
between seasons. High concentrations are located in
North-Est, in Hajjem well and in Hezma 4 well at the
South-East of the study area.
4.1.7. Potassium: Potassium concentrations in the
Triassic aquifer were nearly homogeneous within each
sample site.
The values range from 7 to 13 mg/l in winter and
from 7 to 12 mg/l in summer.
4.1. 8. Alkalinity: W ater alkalinity of the Triassic
aquifer is the only product of bicarbonate ions, given
that pH values are almost near to neutral. The
bicarbonate ion concentrations ranged from 101 to 230
mg/l in winter and from 162 to 260 mg/l in summer.
The spatial distribution of alkalinity is rather different
from the maps of the main ions. In fact, wells where
water shows the lowest concentration major ion (F3,
F7, F8 and F9 wells) are characterized by the highest
bicarbonate concentration levels.
Na + , Ca 2 + , Mg 2 + , SO 4 2 - and K + and Cl- generally
follow similar trends to TDS, with values increasing
from the south to the north and the highest values
occur in the east of the study area.
4.1. 9. Fluorine: The Fluorine concentration in the
waters from Triassic aquifer varied between 1.84 and
2.8 mg/lin winter and between 1.71 and 3.1 mg/l in
summer.
The most frequently observed concentrations
ranged between 1.96 and 2.53 mg/lin winter and
between 2.15 and 2.56 mg/l in summer. The Fluorine
concentrations in the waters of all wells are under the
recommended concentration in the drinking water, with
respect to dental fluorosis (1.5 mg/l). The variations in
the concentrations, during both seasons, are limited
during the studied period.
Fluorine iso-concentrations map depicts two zones
where waters are relatively rich in Fluorine: the first
zone is located in the extreme south-west of the
T riassic aquifer, where the highest Fluo rine
concentrations recorded at well F12, and the second
one is located at the north, which corresponds to well
F4. Lowest concentrations levels are found in the area
of wells F10, F5 and F11.
The comparison of the hydrochemical data with the
Tunisian drinking water standards (NT) and W HO
standards shows that the majority of the samples
exceeded the guide value for water fluoride content
(1.5 mg/l), which explains the existence of many cases
of dental fluorosis in the south of Tunisia. In fact,
research on the relationship between Fluorine
concentration in drinking water and endemic fluorosis
has been conducted in many parts of the world [1 4 ].
4. 2. Hydrochemical Facies: Chemical data of
representative samples from the study area presented by
plotting them on a Piper trilinear diagram for summer
and winter season (Fig. 4). This diagram reveals the
analogies of water types in the study area. Based on
hydro-chemical facies, two types of water that
predominates in the study area during both seasons of
the year 2005: Na-Ca-Mg- SO4 and Na-Ca-Mg-ClSO4 type waters
4. 3. Statistical Analyses:
4.3.1. Correlation M atrix: Compositional relations
among dissolved species can reveal the origin of
solutes and the process that generated the observed
water compositions.
Correlations between major ions were carried out
using Spearman’s correlation analysis. The results are
shown in Table 2.
The correlation coefficients values of TDS with
Na + , Cl - , Ca 2 + , Mg 2 + and SO 4 2 - are 0.97, 0.79, 0.88,
0.89 and 0.97 respectively. These values indicate the
interdependence of the Total dissolved salt and major
ion in Triassic aquifer.
These positive correlations indicate that the
referred elements contribute to the groundwater
salinization. The mineralization would be expected to
result from the increasing ionic concentrations due to
both evaporation of recharge water and to the
interactions effects between the groundwater and the
geological formations.
Indeed, a strong positive correlation was found
between Na + -Cl-, Ca 2 + -Mg 2 + and Ca 2 + -SO 4 2 - it can also
be deduced that for most of the groundwater samples
these parameters originate from a common source.
The High correlation between SO 4 2 - and Mg 2 + (r =
0.92) suggests that a part of the SO 4 2 - and Mg may
also be derived by the weathering of a magnesium
sulphate mineral.
1946
J. App. Sci. Res., 5(11): 1941-1951, 2009
Fig. 4: Piper diagram showing the hydrochemical facies of the groundwater samples from Triassic aquifer
Table. 2: Spearm an correlation coeU cients of physico-chem ical param eters in the groundwater of the Triassic aquifer.
Param eter
T
PH
Na
K
Ca
Mg
SO 4
H CO 3
Cl
TD S
F
T
1.00
-0.25
-0.10
0.12
0.12
0.02
-0.15
0.11
0.13
-0.05
-0.45
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------pH
1.00
-0.22
-0.38
-0.43
-0.44
-0.27
0.31
-0.50
-0.27
0.02
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Na
1.00
0.70
0.84
0.85
0.96
-0.01
0.76
0.97
0.75
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------K
1.00
0.81
0.82
0.73
-0.32
0.82
0.76
0.27
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Ca
1.00
0.88
0.86
-0.30
0.92
0.88
0.52
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Mg
1.00
0.92
-0.39
0.91
0.87
0.52
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------SO 4
1.00
-0.22
0.80
0.95
0.72
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------H CO 3
1.00
-0.38
-0.08
0.01
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Cl
1.00
0.79
0.41
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------RS
1.00
0.67
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------F
1.00
Magnesium and chloride are highly interrelated
among themselves (r=0.91). This interrelationship
indicates that the hardness of the water is permanent in
nature.
The relationships of F - with TDS, Na + , Ca 2 + and
2SO 4 are observed from the correlation coefficient
values of 0.67, 0.75, 0.52 and 0.72 respectively. These
correlation coefficients indicate a signiWcant relationship
among them and they also deduce the existence of
minerals rich in Fluorine in Triassic aquifer and some
genetic relationship. Besides, the positive correlation of
F - with Na + helps to stabilize F - ions in the
groundwater of the Triassic aquifer [4 ]. In contrast to
HCO -3 and K + shows poor linkage.
1947
J. App. Sci. Res., 5(11): 1941-1951, 2009
In general, the mineralogy of the bedrock is the
primary source of fluoride in groundwater and is
responsible for the difference of fluoride concentration
of groundwater between different bedrock types.
Dissolution of Fluorite (CaF 2 ) is a plausible source of
fluoride ion in groundwater [1 ].
4. 3. 2. Principal Component Analysis: PCA was
performed on a data set of 14 samples and 10
chemico-physical elements (T, pH, TDS, Na + , Cl -, Ca 2 + ,
Mg 2 + , SO 4 2 - , K + , HCO 3 - and F -). Two principal
components (PC) or factors (eigen value) explaining 76
% of the variance or information contained in the
original data set was retained, which is sufficient to
give a good idea of the data structure. The eigen
values and the percentage of the variance explained by
each eigenvector are listed in Table 3. The scores of
variables onto the two principal component axes are
plotted in Figure 5, which enables the identification of
several processes of water mineralization.
Factor 1 (F1) explains more than 62 % of total
variance and contains large loadings on Na + , Cl -, Ca 2 + ,
Mg 2 + , SO 4 2 - , K + ,TDS and F-. It represents the
weathering of halite and evaporates minerals from the
underlying geology. Factor 2 contributes to 13.90 % of
the total variance and is strongly associated with,
temperature and pH.
4. 3. 3. Cluster Analysis: The dendrogram of the Qmode cluster analysis built on the wells shows three
main groups of waters (Fig. 6) with an imaginary
horizontal line (phenon line) on the cluster [7 ]. These
groups are:
Cluster 1, which correspond to wells F6, F11, F12,
F13 and F14, is characterized by relatively high TDS.
These stations are nearer to the downstream region.
Based on the overall chemical composition, these
waters are characterized by Na + -Ca 2 + - Mg 2 + -Cl - -SO 4 2 facies.
Cluster 2 which correspond to wells F1, F2, F3,
F4, F5, F7, F8, F9 and F10), is located in the upstream
zone of the study area where the aquifer system seems
to receive most of its meteoric recharge. This group is
characterized by Na + -Ca 2 + - Mg 2 + -SO 4 2 - water types and
by medium and low salinity.
4.4. Geochemical M odeling: The saturation indices
(SI) of Fuorite (CaF 2 ) and Calcite (CaCO 3 ) in the
groundwater samples are plotted in (Fig. 7) which
show that the most of the samples is oversaturated with
respect to calcite whereas, majority of samples have
been found undersaturated with respect to Xuorite. This
situation of solubility control on the higher
concentration of Xuoride can be explained by the fact
that fluoride ions in groundwater can be increased as
a result of precipitation of CaCO 3 on the other hand,
calcite and Xuorite are the main minerals controlling
the aqueous geochemistry of elevated Xuoride ion
contamination occurring in the groundwater of Triassic
aquifer.
All water samples undersaturated with respect to
gypsum, anhydrite and halite. So, carbonate mineral
phases may affect the chemical composition of the
study area. As saturation state indicates the direction of
the process, thus, precipitation of calcite and dolomite
and dissolution of gypsum are expected [3 ]
Conclusion: The aim of this paper is to address the
integrated role of geochemical processes and
mineralogy of aquifers in evolution of groundwater
composition and its impact on groundwater quality to
help in management and protection of groundwater
resources in southern Tunisia using geochemical
modeling techniques and statistical analyses.
The chemical composition of groundwater in
Triassic aquifer is strongly influenced by its interaction
with hydrologic parameters such as the flow path and
residence time.
The hydrochemical data from the present study
indicate that concentrations of cations and anions are
relatively low in most samples from Triassic aquifer
and a slight seasonal variation was detected. Besides,
the present study conWrms that Fluoride concentrations
above drinking water standards have been detected in
the groundwater of the study area. In fact, it was found
that all analysed samples have Fluoride concentration
above the upper limit proposed by the W HO for human
consumption.
Based on hydrochemical facies during summer and
winter seasons of the year 2005, the main type of
waters that predominate in the study area are: Na-CaMg- SO4 and Na-Ca-Mg-Cl-SO4 types. The results of
the statistical analyses corroborate the geochemical
methods and results and provide further information
about the water quality of the various water samples.
Results obtained from PCA indicate that variables
responsible for water quality are mainly related to the
soluble salt variables (Na + , Cl-, Ca 2 + , Mg 2 + , SO 4 2 - and
K + ). The results of the cluster analysis showed two
clusters of water quality. Samples from cluster 1
characterize waters from the aquifer systems under
confined conditions. The majority of these samples
have Na-Ca-Mg-Cl-SO4 facies. Samples from cluster 2
are mostly located in preferential recharge areas and
have Na-Ca-Mg- SO 4 water type.
1948
J. App. Sci. Res., 5(11): 1941-1951, 2009
Fig. 5: Principal Component Analysis -1 st factorial plans
Table 3: Vectors, eigen values and cum ulative variance
Factors
Eigen value
% total variance
% cum ul. variance
1.00
6.88
62.54
62.54
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------2.00
1.53
13.90
76.44
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------3.00
1.09
9.92
86.36
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------4.00
0.70
6.37
92.72
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------5.00
0.42
3.85
96.58
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------6.00
0.19
1.75
98.33
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------7.00
0.11
1.02
99.35
Fig. 6: Dendrogram of Q-mode cluster analysis (W ard's linkage method and squared Euclidean distances).
1949
J. App. Sci. Res., 5(11): 1941-1951, 2009
Fig. 7: Plot of calcite saturation index versus fluorite saturation index
8.
ACKNOW LEDGM ENTS
This research has been funded in cooperation with
the National Society of Drinking W ater in Tunisia
(SONEDE) and the Resources W ater Direction of
Medenine.
9.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
Abu Rukaha, Y.,
K. Alsokhny, 2004.
G e o c h e m i c a l a s s e s s m e n t o f g ro u n d w a te r
contamination with special emphasis on fluoride
concentration, North Jordan. Chemie der Erde, 64:
171-181.
Al-Salamah, I.S., I.N. Nassar, 2009. Trends in
drinking water Quality for some wells in Qassim
Saudi Arabia, 1997-2009. Journal of applied
sciences :1-7
Appelo, C.A.J., D. Postma, 1993. Geochemistry,
Groundwater and Pollution, Balkema, Rotterdam.
Armienta M.A., N. Segovia, 2008. Arsenic and
Fluoride in the groundwater of Mexic. Environ
Geochem Health, 30: 345-353.
Bouaziz, S., Y. Jedoui, E.Barrier, J. Angelier,
2003. Neotectonics in the Tyrrhenian marine
deposits of the southeastern Tunisian coast:
imp lications for sea level changes. C.R.
Geoscience, 335: 247-254.
Cidu, R., R. Biddau, L. Fanfani, 2009. Impact of
past mining activity on the quality of groundwater
in SW Sardinia (Italy). Journal of Geochemical
Exploration, 100: 125-132.
Cloutier, V., R.R. Lefebvre, M . Therrien, M .
Savard, 2008. Multivariate statistical analysis of
geochemical data as indicative of the hydrogeochemical evolution of groundwater in a
sedimentary rock aquifer system. Journal of
Hydrology, 353: 294-313.
10.
11.
12.
13.
14.
1950
CVRM , 2000. Programa ANDAD. Manual do
utilizador. CVRM-Centro de Geosistemas, Instituto
Superior Técnico, Lisboa.
De Andrade, A.E., H. Araujo, Q. Palacio, I.H.
Souza, R. Alipio de Oliveira, L.M.J. Guerreiro,
2008. Land use effects in groundwater composition
of an alluvial aquifer (Trussu River, Brazil) by
multivariate techniques. Environmental Research,
106: 170-177.
DGRE, 2004. Annuaires de l’exploitation des
nappes profondes en Tunisie, pp: 200.
Essadki, A.H., B . Gouricha, Ch. Vial, H. Delmasc,
M. Bennajaha, 2009. DeXuoridation of drinking
water by electrocoagulation/electroXotation in a
s tirre d ta nk re ac tor with a c o m p ar ative
performance to an external-loop airlift reactor.
Journal of Hazardous Materials, 168: 1325-1333.
El Yaouti, F., A. El M andour, D. Khattach, J.
Benavente, O. Kaufmann, 2009. Salinization
processes in the unconfined aquifer of Bou-Areg
(NE Morocco): A geostatistical, geochemical, and
tomographic study. Applied Geochemistry, 24: 1631.
Fantong, W .Y., H. Satake, S.N. Ayonghe, E.C.
Suh, S.M.A. Adelana, E. Bi, S. Fantong, H.S.
Banseka., C.D. Gwafogbe, L.N. W oincham, U.
Yoshitoshi, J. Zhang, in press. Geochemical
provenance and spatial distribution of Xuoride in
groundwater of Mayo T sanaga River Basin, Far
N orth Region, Cameroon: implications for
incidence of Xuorosis and optimal consumption
dose. E nviron G eo chem H ealth, D O I
10.1007/s10653-009-9271-4.
Guo, O., Y. W ang, T. Ma, R. Ma, 2007.
Geochemical processes controlling the elevated
fluoride concentrations in groundwaters of the
Taiyuan Basin, Northern China. Journal of
Geochemical Exploration, 93: 1-12.
J. App. Sci. Res., 5(11): 1941-1951, 2009
15. Maatouk, F., B. Jmour, H. Ghedira, K. Argoubi,
A. Abid, 1998.
Dental fluorosis at Kairouan
(Tunisia) Actualités odonto-stomatologiques . 203:
315-320.
16. Marengo, E., M.C. Gennaro, E. Robotti, A.
Maiocchi, G. Pavese, A.I. Alberto Rainero, 2008.
Statistical analysis of ground water distribution in
A lessand ria P rovince (P ied m ont-Italy).
Microchemical Journal, 88: 167-177.
17. Parkhurst, D.L., C.A.J. Appelo, 1999. User’s guide
to PHREEQC (version 2): a computer program for
speciation, batch reaction, one dimensional
transport, and inverse geochemical calculations. US
Geol Surv W ater Resour Invest Rep., 99-4259.
18. PNUE Programme des Nations Unies pour
l'Environnement, 2004. Plan d’Action pour la
Méditerranée: MAP Technical Report Series N o
158.
19. RaWque, T., S. Naseem, M.I. Bhanger, T.H.
Usmani, 2008. Fluoride ion contamination in the
groundwater of Mithi sub-district, the Thar Desert,
Pakistan. Environ Geol, 56: 317-326.
20. Subyani, A.M., 2005. Hydrochemical identification
and salinity problem of ground-water in W adi
Yalamlam basin, W estern Saudi Arabia. Journal of
Arid Environments, 60: 53-66.
21. Venugopal, T., L. Giridharan, M. Jayaprakash,
2008. Groundwater Quality Assessment Using
Chemometric Analysis in the Adyar River, South
India Arch Environ Contam Toxicol., 55: 180-190.
22. W HO (W orld Health Organization), 1993.
Guidelines for drinking water quality, 2nd ed. pp:
188.
23. Yidana, S.M., D. Ophori, B. Banoeng-Yakubob,
2008. A multivariate statistical analysis of surface
water chemistry data -The Ankobra Basin, Ghana.
Journal of Environmental Management, 86: 80-87.
1951