Global Journal of Engineering Sciences (GJES)
Contribution
of Statistical Analysis Methods to Assessment of the Physicochemical Quality of
the Dam
Authored by Aissam Gaagai
Abstract
We
used statistical analysis to try to evaluate the temporal variations concerning
surface water located in Baber watershed. So, we collected twenty-one (21) samples,
for each city, seven (7) ones were collected. In these samples, we determined
eleven (11) physicochemical parameters. Concerning the eastern part (S1): the
salinity of samples proves high TDs values, while low TDs levels were found in
the west (S2). In the dam (S3) TDs values are considered as intermediate on
average. Regarding the water type: It wasSO4-Na in S1, HCO3-Ca-Mg in S2 and
finally SO4-Ca-Mgin the dam (S3) which characterizes mixed water. In R-mode,
Cluster analysis was used; we classified seven (7) variables into two (2)
clusters which are controlled by SO4.About Q-mode, 21 samples were grouped into
three (3) groups corresponding to our three (3) stations: S1, S2 and S3. Factor
analysis is indicating to us two factors: these factors explains 87% of the
total variance related with data set about water quality. We have two factors
(Salinization and sulfates) that explain respectively 73% and 14% of the total
variance. We noticed by relying on time series analysis that Cl, Na, Mg, Ca and
SO4 have very similar trend with TDs, and we noticed similar pattern between K
and NO3. It suggests that five (5) components are controlling the evolution of
TDs. Negative correlations between K & No3 with TDs because of human
activities, like domestic sewages (thrown into the tributaries of Wadi Arab)
and fertilizers for example. We could notice, by establishing this study, the
real benefit of the used technique (statistical analysis) to interpret complex
data sets related to the analysis of spatio-temporal variations in the water
surface’s quality.
Keywords: Statistical analysis; Salinization;
Surface water; Dam; Baber
Introduction
Most
people in the world do not have access to safe drinking water, which has led to
an increase in water-borne diseases [1]. water used for consumption must be
potable. Drinking water is water that is free of diseases that produce
micro-organisms and chemicals that are dangerous to health [2]. Water consists
of different soluble salts and the quality of drinking water is affected by the
presence of these different soluble salts [3]. Surface water quality is mainly
influenced by the nature and anthropogenic processes especially in urban areas
and agricultural activities around rural areas [4]. These anthropogenic
processes lead to the contamination of water. Contaminated water not only
affects the health of the public, but also the consumption of polluted water
can cause various waterborne diseases such as diarrhea, dysentery and
complaints of skin, teeth and other abdominal diseases [5]. Transmission of the
disease by drinking water is therefore one of the main concerns for drinking
water supply [6]. World demand for water remains mainly growing due to
population growth, economic growth, rapid urbanization and increasing demand
for food and energy [7]. Therefore, assessing the availability of water
resources at the relevant spatial and temporal scales is important [8], as well
as the ability to assess the availability of freshwater resources has been a
question of importance in most countries for several decades [9].
The
main objective of this present work is the characterization of the chemistry of
surface water, and the determination of the or-igin of chemical elements
founded by using two techniques: time series analysis and the multivariate
statistical techniques. Our investigation area is located in Algerian
South-East. The vast majority of inhabitants (more than 20,000) are concentered
in the town of Baber.
Materials and Methods
Study area
The
study area is located at the downstream of the Wadi (River) Arab. The area of
the watershed is 567 km2, Babar dam with a capacity of 54 Mm3 is built to
impound the Wadi waters (Figure 1).
Methodology
The
application of multi-variate environmental statistical techniques such as
factor analysis (FA), hierarchical ascending classification (CAH) and time
series analysis (EN) has increased considerably in recent years for data
analysis. and draw useful information [10-12]. Thus, the application of
different multivariate statistical techniques can facilitate the interpretation
of complex data matrices and the understanding of how to increase the water
quality and ecological status of a freshwater system. It also helps to identify
possible factors influencing water systems, while being a valuable tool for
reliable management of water resources in the area. The objective of this work
is to characterize the chemical composition of surface waters and to determine
the origin of the chemical elements present using multivariate statistical
techniques and the time series. All the statistical computations were made
using Excel 2010 (Microsoft Office ®) and STATISTICA 6 (StatSoft, Inc. ®).
Physicochemical analysis
Monthly
surface water was sampled from Wadi Arab and Babar dam, from October 2007 to
April 2008 at three stations. The station 1 (S1) and station 2 (S2) are located
downstream of urban and industrial discharges and station 3 (S3) is located at
the lake of the dam (Figure 1). Total 21 samples (7 from each station) were
collected. Samples were analyzed for the major ions. The physico-chemical data
that were analyzed are pH, temperature of water (T), Total dissolved solids
(TDS), calcium (Ca2+),
magnesium (Mg2+), sodium (Na+), potassium (K+), chloride (Cl-), sulfate (SO42-),
bicarbonates (HCO3-), nitrate (NO3/
During the study
period, the change in water temperature is similar in the three stations and is
largely influenced by the general climate of the region. The low temperatures
coincide with the cold period and high temperatures coincide with the warm periods.
High temperatures correspond to the warm periods while the low temperatures
correspond to the cold ones. pH value of water samples varied from 7.4 to 8.9
indicating that water is slightly alkaline in nature. In station S2, pH values
are higher than the pH values of the two other stations, this being because of
the presence of carbonate formations thatwe found in this part of the field.
Conventionally, the process of
buffering calcite and dolomite, are dominant for the pH range 6.5 to 7.5 [16].
For the salinity, the S1 with high TDS (2837.1±285.3 mg/l), S2 with low TDS
(917±201.5 mg/l) and S3 with intermediate and average TDS (1557.1±295mg/l)
therefore, the salinity can be classified in this order S1>S3>S2. Higher
concentration of TDS observed in S1 and S3, due to the impact of solubility of
evaporate element as gypsum, anhydrite and halite [17,18]. High concentrations
of evaporite elements (Na, K, Cl and SO4) are recorded at S1 with 908, 7.7,
293.2 and 966.1 respectively. This is mostly due because of the presence of
evaporate formations in the concerned area. Another reason for the increase of
Na, Cl and SO4 concentration is related with the effluents input from the
industrial and urban sector [19]. It would also be wise to note that the S3
waters are strongly influenced by those of S1. The Ca and Mg content are
greater in S1; this is in connection with the dissolution of gypsum and epsomite.
As against the HCO3 concentration which is higher in S2 due to dissolution of
carbonates formations. The variation of the concentration of NO3 appears to be
similar in the three stations. However, we note that the high values are
recorded in periods of drought while low concentrations recorded during floods
[20].
Statistical analysis
In this case study,
environmental techniques and time series analysis were used to evaluate
temporal variations in surface water in the Baber watershed. The sources of
water pollution in the Baber watershed could more probably be derived from
industrial and urban wastewater, irrigation activity and mineral weathering.
The application of Piper diagram for water surface in the region can show three
chemical facies. The water type in S1 is SO4-Na for S1, in S2 is HCO3-Ca-Mg and
is SO4-Ca-Mg water in the dam. These water types are, in fact, a reflection of
the predominant influence of gypsum, anhydrite and halite in the Eastern part
of study area. The predominant influence of carbonate material is in the
western part of study area and mixed water in the dam. In the cluster analysis
(R-mode) seven variables were classified into two clusters controlled by SO4.
These groups are cluster 1 with evaporate elements: Na, Cl and K, cluster 2
with carbonate elements: Ca, Mg and HCO3. In Q-mode, 21 samples were classified
into three groups. The first group is made up by the samples belonging to the
station S1. Second group 2 is represented by the samples belonging to the
station S2. The third group is made up by the samples belonging to the station
S3. In PCA, the two main principal components explain 87.2% of the total
variance. Time series analysis also provided the same results. The
autocorrelation shows that TDS, Ca, Mg, Na, K, Cl and HCO3 have a strong linear
interrelationship, and they are subjected to periodically changing sources. In
fact, NO3 and SO4 are affected by anthropogenic sources, urban action and
geological condition. Spectral density functions of Ca, Mg, Na, Cl and HCO3 has
almost similar trend with TDS. It suggests that TDS was affected by these
elements and that TDS plays a more vital role on the surface water quality. But
the multiple peaks of the spectral density functions of pH, K, SO4 and NO3
resulted from the human activity, fertilizer and domestic sewage. The
cross-correlations of Ca, Mg, Na, Cl and SO4 have very similar trend with TDS
and K have similar pattern as NO3. It suggests that five components are the
controlling factors of the TDS. K and NO3 represent a negative correlation with
TDS, it resulted from the human activity, fertilizer and domestic sewage inputs
from the tributaries of Wadi Arab. The results of this study clearly
demonstrate the usefulness of the multivariate statistical analysis in
hydrochemistry.
Conclusion
To quantify pollution,
environmental techniques and time series analyzes were used to assess temporal
variations in surface water in the Babar watershed. Sources of Water Pollution
in the Babar Watershed Could Be Further Derived from Industrial and Urban Wastewater,
Irrigation Activities and Mineral Storms. In the group analysis (R mode), seven
variables were classified into two groups controlled by SO4.
These groups are group 1 with evaporated elements: Na, Cl and K, group 2 with
carbonate elements: Ca, Mg and HCO3. In Q mode, 21 samples were classified into
three groups. The first group consists of the samples belonging to the S1
station. The second group 2 is represented by the samples belonging to the
station S2. The third group consists of the samples belonging to the S3
station. In the ACP, the two main components explain 87.2% of the total
variance. Time series analysis also yielded the same results. The
autocorrelation shows that TDS, Ca, Mg, Na, K, Cl and HCO3 have a strong linear
interrelation, and they are subjected to periodically changing sources. Indeed,
NO3 and
SO4 are
affected by anthropogenic sources, urban action and geological status. The
spectral density functions of Ca, Mg, Na, Cl and HCO3 have
an almost similar trend with TDS. He suggests that TDS has been affected by
these elements and that the TDS plays a more vital role in the quality of
surface water. But the multiple peaks of the spectral density functions of pH,
K, SO4 and NO3 result from human activity, fertilizers and domestic wastewater.
The cross-correlations of Ca, Mg, Na, Cl and SO4 have a very similar tendency with TDS. By cons K has a
model similar to NO3. He suggests that five components are the controlling
factors of TDS. K and NO3 are
negatively correlated with TDS, resulting from human activity, fertilizers and
domestic sewage from tributaries of Oued El Arab. The results of this study
clearly demonstrate the utility of multivariate statistical analysis in
hydrochemistry.
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