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Groundwater Quality Assessment Using Chemometric Analysis in the Adyar

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Article  in  Archives of Environmental Contamination and Toxicology · September 2008

DOI: 10.1007/s00244-007-9117-y · Source: PubMed

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Arch Environ Contam Toxicol (2009) 56:654–669
DOI 10.1007/s00244-009-9310-2

Assessment of Water Quality Using Chemometric Tools:

A Case Study of River Cooum, South India
L. Giridharan Æ T. Venugopal Æ M. Jayaprakash

Received: 23 June 2008 / Accepted: 1 March 2009 / Published online: 20 March 2009
Ó Springer Science+Business Media, LLC 2009

Abstract Multivariate statistical techniques were applied The chemical composition of river water and its properties
to identify and assess the quality of river water. Thirty depends on several factors, such as geochemical nature of
samples were collected from the River Cooum, and basic the soil, precipitation, anthropogenic activities, etc. Spa-
chemical parameters—such as pH, effect concentration, tiotemporal variation of hydrochemical parameters and
total dissolved solids, major cations, anions, nutrients, and river water quality largely depends on these factors. The
trace metals—were evaluated. To evaluate chemical vari- quality of surface water in a particular region is basically
ation and seasonal effect on the variables, analysis of governed by natural processes, e.g., precipitation rate,
variance and box-and-whisker plots were performed. weathering processes, soil erosion, and anthropogenic
Cluster analysis was applied, and pre-monsoon and post- effects, such as urban, industrial, and agricultural activities
monsoon major and minor clusters were classified. The (Jarvie et al. 1998). Smith (2001) presented a detailed
relations among the stations were highlighted by cluster analysis of pollution loads caused by storm water events in
analysis, which were represented by dendograms to cate- an urban watershed. Ferrier et al. (2001) emphasized that
gorize different levels of contamination. Cluster analysis quantitative and qualitative characteristics of a hydrologic
clearly grouped stations into polluted and unpolluted system reflect the geomorphologic attributes of a water-
regions. The analysis classified the upper part of the river shed, modified by the influences of variations in climate
course into one unpolluted cluster; the middle and lower and anthropogenic activities. Stow et al. (2001) presented a
parts of the river clustered together, reflecting the presence long-term study of water quality in a watershed with mixed
of pollution. Factor analysis revealed that water quality is land use by deriving regressions for time-series analysis. In
strongly affected by anthropogenic activities, rock–water many surface aquifers, domestic sewage and industrial
interaction, and saline water intrusion. Seasonal variations effluents are the chief polluting sources, and surface runoff
in water chemistry were clearly highlighted by both cluster is a seasonal phenomenon that is largely influenced by the
and factor analysis. Factor-score diagrams were used suc- climate prevailing in the basin (Liao et al. 2006).
cessfully to delineate the stations under study by the Characterization and interpretation of various physico-
contributing factors, and seasonal effects on the sample chemical parameters of river water requires handling a
stations were identified and evaluated. These statistical large data set. Complexity is mainly associated with the
approaches and results yielded useful information about interpretation of a large number of measured variables,
water quality and can lead to better water resource with high variability arising from various factors, e.g.,
management. natural and anthropogenic (Simeonov et al. 2002). Multi-
variate statistical analysis offers a powerful means of
identifying similarities among the variables present in the
chemical composition of water (Vega et al. 1998; Ben-
graıne and Marhaba 2003). To identify the likely factors
L. Giridharan (&)
T. Venugopal
M. Jayaprakash
causing variations in hydrochemical composition, multi-
Department of Applied Geology, University of Madras, Chennai,
India variate statistical methods, such as principal component
e-mail: girilogu@yahoo.com factor analysis, can be useful tools. Such analysis is

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Arch Environ Contam Toxicol (2009) 56:654–669 655

especially useful because it highlights the relative signifi- from its neighborhood after it reaches Vanagaram near
cance of the combinations of chemical variables that can be Chennai. It flows through the Kancheepuram, Tiruvallur,
evaluated. Subsequent interpretation is simplified because and Chennai districts for a distance of approximately 68 km
these statistical tools reduce and categorize complex sets of and, after flowing through the heart of Chennai, it enters
data into groups with similar characteristics. Factor anal- into the Bay of Bengal. This river is almost stagnant and do
ysis attempts to explain correlations between observations not carry enough water except during the rainy season. This
in terms of underlying factors, which are not directly period runs from October to December and is referred to as
observable (Yu et al. 2003). the ‘‘northeast monsoon season’’ in Tamil Nadu. Chennai
In this study, data were further characterized and eval- receives the bulk of its rainfall from this monsoon.
uated using cluster analysis. Hierarchical cluster analysis is It has been observed that in the upper part of the river,
an objective technique, employed to identify natural there is no settlement along the bank of the river; hence this
groupings in a set of data, that classifies entities having part is not polluted by domestic effluents. However,
similar properties (Yeung 1999). This unsupervised pat- because of intense agricultural activities, the possibility of
tern-recognition technique uncovers the intrinsic structure pollution caused by fertilizers and pesticides is highly
or underlying behavior of a data set without making a priori expected. Currently fertilizers play a vital role in crop
assumption about the data. It classifies objects of the sys- growth. The bulk use of these fertilizers leaves behind
tem into categories or clusters based on their nearness or unused wastes, which are driven off by rain and enter into
similarity to one another (Vega et al. 1998). In this study, aquifers. Several investigators have reported on the release
classification based on the sampling site was performed of nitrates from agricultural activities, which contaminat-
with cluster analysis using Ward’s method (linkage ing river waters (Prasad 1998; Pacheco 2001). In the
between groups); Euclidian distance was used as a simi- middle and lower stretches of the River Cooum, domestic
larity measure; and the clusters were synthesized into sewage water is directed into the river and appears as a
dendograms. sewage water stream.
Statistical analysis has been successfully applied by
many investigators to sort out hydrogeochemical processes
from commonly collected hydrochemical data (Hitchon Analytical Methodology
et al. 1971; Seyhan et al. 1985; Ruiz et al. 1990; Grande
et al. 2003; Kamman et al. 2005; Causape et al. 2006; Ryu The presented data include the results from two sampling
et al. 2006). Several investigators have successfully dem- periods (September 2005 [pre-monsoon period] and Feb-
onstrated the utility of multivariate statistical analysis in ruary 2006 [post-monsoon period]) at 30 locations along
identifying and characterizing pollution sources and the Cooum River basin performed to evaluate seasonal
apportioning natural versus anthropogenic contributions variations in chemical compositions. For collection, pres-
(Cave and Reeder 1995; Villaescusa-Celaya et al. 2000; ervation, and analysis of the samples, standard methods
Facchinelli et al. 2001; Yu et al. 2003; Liao et al. 2006). In (Rainwater and Thatcher 1960; Brown et al. 1970; Amer-
this study, the effect of certain factors on river water—such ican Water Works Association 1971; Hem 1985; American
as agricultural, industrial, domestic, rock–water interaction, Public Health Association 1995) were followed. The effect
and saline water intrusion—were studied by applying the concentration and pH of water samples were measured in
previously mentioned multivariate statistical techniques. the field immediately after collection of the samples using
pH and conductivity meters. Before each measurement, the
pH meter was calibrated with reference buffer solution (pH
Study Area levels 4 and 7). Na? and K? were measured using a flame
photometer (Systronics Flame Photometer 128). Silica
In this study, water samples from the River Cooum were content was determined with the molybdate blue method
collected at 30 stations to evaluate the nature and quality of using an ultraviolet (UV) light–visible spectrophotometer.
the water (Fig. 1). The River Cooum originates from the Total dissolved solids (TDS) were measured using the
Kesevaram Dam, in the village of Kesavaram, which lies evaporation and calculation methods (Hem 1991). Ca2?
approximately 48 km west of Chennai. Although the River and Mg2? were determined titrimetrically using standard
Cooum originates from this dam, excess water from the ethylenediaminetetraacetic acid. Chloride was estimated by
Cooum tank (79.82° latitude and 13.02° longitude) joins AgNO3 titration. Sulphate was analysed using the turbidi-
this course at approximately 8 km, and this point is con- metric method (Clesceri et al. 1998). Nitrate, nitrite,
sidered the head of the River Cooum. In the upper part of phosphate, and fluoride were analysed using a UV light–
the river stretch, many agricultural activities are being visible spectrophotometer (Rowell 1994). Standard solu-
carried out. The river receives a sizeable quantity of sewage tions for the previously mentioned analysis were prepared

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656 Arch Environ Contam Toxicol (2009) 56:654–669

Fig. 1 Base map of Cooum

River depicting sample
locations and pollution sources

from the respective analytic reagent-grade salts. Trace skewness compares the manner in which variables are
metals were determined by a graphite furnace atomic distributed in a particular series with variables having a
absorption spectrophotometer (AAnalyst 700; Perkin– symmetrical distribution. Analysis of kurtosis and skew-
Elmer). Multielement Perkin–Elmer standard solutions ness are vital because most statistical methods require
were used for the estimation of trace metals. variables to conform to normal distribution (Papatheodorou
et al. 2006). Computation of the correlation coefficient
Statistical Methodology matrix is the first step in factor analysis between stan-
dardized variables. Eigenvalues quantify the contribution
Factor analysis was applied to the data matrix to reduce the of a factor to total variance. The contribution of a factor is
data to an easily interpretable form. Before applying factor significant when the eigenvalue is greater than unity
analysis, the data were standardized according to the cri- (Kaiser 1960). Initial factors are extracted and subjected to
teria presented by Davis (2002). Standardization of mathematical rotation. Varimax rotation procedure is used
variables is performed to remove the influence of different to maximize differences between the variables, thus facil-
units of measurement on the data by making them itating easy interpretation of the data. The first factor
dimensionless. Normalization of data is essential in factor accounts for as much variance as possible in the data set.
analysis because it involves the computation of a correla- The second factor accounts for as much residual variance
tion coefficient matrix, which requires equal distribution in as possible, and so forth. Factor loading indicates the
all variables. Factors are extracted by varimax rotation, degree of closeness between the variables and the factor.
which gives values closest to –1, 0, and ?1, suggesting the The largest loading, either positive or negative, suggests
negative, zero, and positive contribution, respectively, of a the meaning of the dimension: Positive loading indicates
variable toward a factor (Briz-Kishore and Murali 1992). that the contribution of variables increases with increased
Commonality attached to each row of the matrix gives an loading in a dimension, and negative loading indicates that
appreciation of how well each variable is explained by the contribution of variables decreases with decreased
‘‘m’’ factors. If many commonalities B0.8, more factors loading (Lawrence and Upchurch 1983). The study of
are required (Klovan 1975). By examining the factor factor scores reveals the extent of influence of each factor
loadings and their eigenvalues, those variables belonging to on overall water chemistry at all locations of sampling
a specific process can be identified. In certain cases, some stations. Extreme negative scores reflect areas essentially
variables may load high in [1 factor. Finally, factor scores unaffected by that particular factor, and positive scores
are evaluated to illustrate the station-wise variation of the reflect the areas most affected. Near-zero scores indicates
factors (Klovan 1975). areas affected to an average degree. In the present article,
Standardization of the values was performed after we the station-wise variation of factors is indicated by line
measured skewness in the variables. The measure of diagrams.

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Arch Environ Contam Toxicol (2009) 56:654–669 657

Cluster analysis is a powerful tool for identifying and Results and Discussion
evaluating similar groups in hydrochemical data. The
object of this analysis is to look for similar groups of items The basic statistical parameters of the data matrix for both
or for variables that group together in clusters. Cluster the pre-monsoon and post-monsoon periods are listed in
analysis organizes sampling entities into discrete classes or Table 1. During the pre-monsoon period, skewness ranges
groups such that within-group similarity is maximized and from –0.68 to 1.53; during the post-monsoon period,
among-group similarity is minimized (McGarial et al. skewness ranges from –4.34 to 4.79. Skewness results for
2000; Zeng and Rasmussen 2005). An unsupervised pat- most variables show only narrow variability, and all pre-
tern-recognition technique uncovers intrinsic structure or monsoon variables are found to be well within the skew-
underlying behavior of a data set, without making a priori ness index. During the post-monsoon period, few variables
assumption about those data, such that the objects of the are found to have high skewness values. Logarithmic
system can be classified into categories or clusters based on transformation of the data was performed to decrease
their nearness or similarity to one another (Vega et al. skewness in the variables. Kurtosis was also applied on the
1998). In this study, classification based on sampling site data matrix to measure peakedness of the probability dis-
was performed with cluster analysis using Ward’s method tribution. A distribution with positive kurtosis has a higher
(Ward 1963); Euclidian distance was used as a similarity probability of variables than the normal distribution around
measure; and data were synthesized into dendograms. the mean and also indicates a higher probability of dis-
Euclidean distance is the geometric distance in a multidi- tributed variables with extreme values. A distribution with
mensional space. Ward’s method is known to be distinct negative kurtosis indicates a lower probability of normally
because it uses an analysis-of-variance approach to evalu- distributed variables of values near the mean as well as
ate the distances between clusters. This method minimizes extreme values. During the pre-monsoon period, kurtosis
the sum of squares of any two (hypothetical) clusters ranges from –1.31 to 2.5; during the post-monsoon period,
derived at each step of analysis. kurtosis ranges from –1.62 to 24.89. During the pre-

Table 1 Summary statistics of geochemical data

Geochemical Pre-monsoon Post-monsoon
data
Mean r Kurtosis Skewness Confidence CV Mean r Kurtosis Skewness Confidence CV
(mg/l) level (mg/l) level

pH 6.70 0.15 -0.35 -0.42 0.05 2.17 8.15 0.53 -1.62 -0.03 0.19 6.47
EC 2978.49 1059.93 0.42 0.01 379.28 35.59 1506.88 593.35 -0.70 0.39 212.32 39.38
TDS 1906.23 678.35 -0.42 0.01 242.74 35.59 964.40 379.74 -0.70 0.39 135.89 39.38
Ca 152.00 48.28 0.92 -0.47 17.28 31.76 118.73 41.93 0.47 0.87 15.01 35.32
Mg 38.98 19.68 -1.02 0.56 7.04 50.49 44.80 36.07 -0.76 0.69 12.91 80.52
Na 377.09 228.83 2.50 1.38 81.88 60.68 168.71 77.44 -0.46 0.58 27.71 45.90
K 31.26 17.62 0.15 0.81 6.30 56.36 12.25 8.36 0.37 0.97 2.99 68.29
HCO3 321.35 114.01 2.34 1.08 40.80 35.48 18.80 9.06 1.50 0.76 3.24 48.17
SO4 268.73 36.24 -0.49 0.29 12.97 13.49 111.93 45.23 -1.54 -0.23 16.18 40.41
Cl 565.53 320.14 0.31 0.66 114.56 56.61 440.73 190.81 -0.14 0.60 68.28 43.29
F 1.34 0.56 1.03 0.47 0.20 41.67 0.60 0.79 15.47 3.65 0.28 132.63
NO3 62.15 42.67 -0.94 0.58 15.27 68.66 8.36 3.80 3.01 1.27 1.36 45.45
NO2 0.57 0.34 -0.13 0.44 0.12 60.22 1.87 2.22 -0.56 0.94 0.79 118.35
PO4 8.33 5.87 -0.22 0.91 2.10 70.46 1.12 1.42 -0.10 1.23 0.51 127.11
SiO2 24.94 4.20 -0.93 -0.68 1.50 16.82 26.97 2.53 21.85 -4.34 0.90 9.38
Cu 0.07 0.01 -0.81 0.28 0.01 21.10 0.09 0.09 24.89 4.79 0.03 104.79
Co 0.06 0.03 -0.53 -0.56 0.01 40.60 0.05 0.06 21.86 4.37 0.02 114.66
Zn 0.03 0.02 1.74 1.53 0.01 78.56 0.02 0.01 2.01 1.51 0.00 65.27
Fe 0.44 0.34 -1.31 0.31 0.12 77.03 0.45 0.30 -0.10 0.80 0.11 64.95
Pb 0.44 0.25 -0.67 0.57 0.09 56.83 0.27 0.19 -0.16 0.80 0.07 70.31
Cr 0.51 0.24 -1.14 -0.05 0.08 46.25 0.25 0.14 -0.66 0.43 0.05 53.52

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658 Arch Environ Contam Toxicol (2009) 56:654–669

monsoon period, almost of all variables lie near zero value, into river water is high at station 30. In the case of station
reflecting that the distribution is normal ‘‘mesokurtic.’’ 17, both industrial as well as domestic effluents influence
During the post-monsoon period, high positive values are the chemical composition of the water, and all of these
obtained for silicates, fluoride, and heavy metals Cu and stations are characterized by high TDS. Cluster 3
Co, indicating peaked distribution. Peaked distribution of (anthropogenic), comprising the remaining 18 stations, lies
the previously mentioned variables indicates surface runoff in the middle and lower parts of the river stretch. In this
of sporadic high emissions from certain point sources. region, it has been observed that the industrial and
Coefficient of variation (CV) was used to compare the domestic effluents are directed into river water. Industries
variability of C 2 series. The series of data for which the are concentrated in the suburban (middle part of the river)
CV is large indicates that the group is more variable. and intense settlements near the river course, and the for-
Among the variables, pH shows the minimum CV, mation of slums on the river bank influences the quality of
reflecting that there is not much variation in pH throughout river water.
the river course during both seasons. The seasonal effect is During the post-monsoon season, the data are also
apparent with respect to the CVs of many variables. In classified into three major groups (Fig. 3b). Station 30
general, heavy metals show much higher CVs among the alone comes under one category and is mainly influenced
variables during both seasons. by saline water intrusion. Cluster 1 (stations 1 through 5, 7
Analysis of variance (ANOVA) is a statistical tool that through 9, and 13) is located in the upper part of the river
permits the testing of significant differences between sev- course and is apparently unpolluted. Because there is no
eral means by comparing variances. ANOVA tests the point source of pollution and because it receives heavy
variation between the mean values of the given variable. In precipitation, this water behaves almost like freshwater.
the present study, it was observed that among the major Cluster 2 (stations 11-15 and 6-10-12-14) also lies in the
ions, Mg shows p C 0.05, and all other major ions and upper part of the river. The TDS of the water is slightly
nutrients show p \ 0.05, indicating that seasonal effect is higher than the other grouping in the same region but lower
significant with respect to all major ions and nutrients than the limit set by the World Health Organization. Sta-
except Mg. In the case of heavy metals, Cu, Co, and Fe tions 28 and 29 form cluster 3, which is located near the
show p [ 0.05, whereas the other metals show p \ 0.05, eastern end of the River Cooum and the ocean, where
reflecting that seasonal effect is significant for Cu, Co, and population density is considerably high. The quality of
Fe. Box-and-whisker plots (Fig. 2) of individual variables river water at these stations is influenced by domestic
were constructed to evaluate chemical variation and sea- effluents as well as saline water intrusion. Cluster 4 (sta-
sonal effect on the variables. These plots were constructed tions 16 through 26) is comprises suburban and urban
to evaluate different patterns associated with spatial vari- areas, where domestic and industrial effluents are directed
ations in river water quality. Most monsoon variables into the river, thus degrading water quality. Station 30
showed deviations from normal distribution and included forms a separate cluster that is directly influenced by saline
outliers and extremes. water because this station lies at the river’s confluence
point. A distinct variation is caused by the seasonal effect,
Cluster Analysis which is reflected by the grouping of stations falling into
different clusters.
Cluster analysis was applied to reveal the relation among
the stations and to elucidate water chemistry. It is a useful Correlation Studies
tool in organizing a particular set of data from various
points into clusters or groups and determining relations Analytic results of the geochemical data were analysed
between the various points (McGarial et al. 2000). Cluster using Statistical Package for Social Sciences (SPSS version
analysis is also helpful in determining the seasonal effect 11.5; Chicago, IL) for factor analysis as described by Nie
on each station. During the pre-monsoon period (Fig. 3a), et al. (1985). Before the data were investigated, the raw
cluster 1 (stations 1 through 9) is characterized as an data were standardized. Standardization of the variables is
unpolluted region that lies in the upper part of the river. In performed to eliminate the influence of different units of
this region, there are no settlements and no point sources of measurement on the data by making them dimensionless.
pollution near the river stretch. Cluster 2 (anthropogenic Close inspection of correlation matrix is useful because it
and saline water intrusion) (stations 17, 25, 26, and 30) is can reveal associations between variables that can show
classified as a highly polluted region. This region lies in the overall coherence of the data set and indicate participation
eastern part of the river where settlements along the river of the individual chemical parameters that influence fac-
course are dense, and domestic effluents are directed into tors, a phenomenon that commonly occurs in
the river. Moreover, the influence of saline water intrusion hydrochemistry (Helena et al. 2000). The correlation

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Arch Environ Contam Toxicol (2009) 56:654–669 659

Fig. 2 Box-and-Whisker plot representing chemical and seasonal variation of variables

coefficient values exhibiting ?1 or –1 between the vari- the correlation results are as follows: During the pre-
ables show that a strong correlation exists, and a value of monsoon period, Ca2? and Na? have a positive correlation
zero indicates that there is no relation between them. In with HCO3, and the correlation matrix of the post-monsoon
general, geochemical parameters showing a correlation period shows that Mg2? and Na? have a significant cor-
coefficient [0.7 are considered to be strongly correlated, relation with HCO3, suggesting that rock–water interaction
whereas values between 0.5 and 0.7 shows moderate cor- as well as precipitation and leaching contribute to water
relation. In this study, the relation between various chemistry.
elements was studied. During the pre-monsoon period, Ca2? has a low corre-
The correlation matrix (Tables 2, 3) shows a distributive lation with SO42–; however, it does not have any
pattern of positive and negative correlations among the correlation at all with NO3, suggesting that agricultural
variables. During both the pre- and post-monsoon periods, activities may not contribute to the source of these ions.

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660 Arch Environ Contam Toxicol (2009) 56:654–669

Fig. 2 continued

During the post-monsoon period, although Ca2? has some during the post-monsoon period. Although Na and Cl have
correlation with SO42–, as in the case of the pre-monsoon a strong correlation, the variation of Na with Cl is only
period, it does not have any correlation with NO3. If pol- moderately correlated with NO3. This correlation result
lution is related to the influence of domestic sewage, then reveals that although sewage water is polluting river water,
there will be some association of NO3, Na, and Cl ions precipitation dilutes the effect of sewage water on the
because all of these constituents are usually enriched in chemical composition of the water during the post-mon-
sewage. The correlation matrix for the pre-monsoon period soon period. During both periods, some pairs of
shows that Na? is strongly correlated with Cl– and also that constituents show moderate to strong correlation (r [ 0.6),
the variation of Na? with Cl– is significantly correlated to e.g., the major exchangeable ions Na–Ca and Na–Mg
nitrate, reflecting that the mixing of domestic sewage water correlate significantly in both the pre- and post-monsoon
into river water strongly contributes to water chemistry periods, respectively. Moreover, the pairs of constituents

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Arch Environ Contam Toxicol (2009) 56:654–669 661

Fig. 3 a Dendogram showing the relation among pre-monsoon river water samples.b Dendogram showing the relation among post-monsoon
river water samples

—i.e., Cl–SO4, Mg–SO4 Ca-SO4, and Na–SO4—show little chemical variables available for evaluation. Subsequent
or no correlation, suggesting that overall water chemistry is interpretation is simplified because these statistical tools
not predominated by a dissolution/precipitation reaction; reduce and categorize complex sets of data into smaller
rather, extraneous sources, such as pollution caused by groups with similar characteristics. Factor-score diagrams
anthropogenic activities, dominate the water’s chemical of the pre- and post-monsoon periods are presented in
make-up. Figs. 4 and 5, respectively.
The first six factors, which account for approximately
Factor Analysis 80% of variance during the pre-monsoon period and 83%
of variance during the post-monsoon period (all of which
Characterization and interpretation of various parameters is have eigenvalues [ 1), were extracted from the principal
often a complex problem. However, factor analysis offers a factor matrix after varimax rotation (Tables 4, 5).
powerful means of identifying the similarities among
variables that represent water chemistry. To identify the Pre-Monson Factors
likely factors causing variations in hydrochemical compo-
sitions, multivariate statistical methods of analyzing Factor 1 during the pre-monsoon period, which explains
hydrochemical data, such as factor analysis, can be useful 33% of total variance, has high loadings of the ions Na, Cl,
tools. Such an analysis is especially useful because it SO4, Mg, F, and NO3. The loading pattern of the previously
reveals the relative significance of the combinations of mentioned variables indicates that their source of origin

123
 662

123
Table 2 Pre-monsoon correlation coefficient matrix
pH EC TDS Ca Mg Na K HCO3 SO4 Cl F NO3 NO2 PO4 SiO2 Cu Co Zn Fe Pb Cr

pH 1.00
EC -0.02 1.00
TDS -0.02 1.00 1.00
Ca 0.30 0.39 0.39 1.00
Mg 0.07 0.55 0.55 0.02 1.00
Na -0.04 0.93 0.93 0.16 0.59 1.00
K 0.09 0.41 0.41 0.30 -0.04 0.27 1.00
HCO3 -0.01 0.60 0.60 0.47 0.06 0.40 0.59 1.00
SO4 0.13 0.71 0.71 0.48 0.52 0.60 0.17 0.21 1.00
Cl -0.10 0.97 0.97 0.26 0.57 0.95 0.27 0.48 0.66 1.00
F -0.16 0.59 0.59 0.39 0.32 0.48 0.20 0.34 0.53 0.57 1.00
NO3 0.02 0.58 0.58 0.04 0.37 0.52 0.22 -0.01 0.61 0.58 0.49 1.00
NO2 0.27 0.30 0.30 0.32 0.24 0.16 0.42 0.25 0.44 0.25 -0.05 0.15 1.00
PO4 0.11 0.26 0.26 0.36 -0.01 0.08 0.58 0.70 0.02 0.13 -0.14 -0.20 0.54 1.00
SiO2 0.01 0.61 0.61 0.14 0.30 0.47 0.55 0.50 0.34 0.51 0.32 0.65 0.25 0.37 1.00
Cu -0.07 -0.08 -0.08 0.10 -0.20 -0.07 0.01 0.13 0.01 -0.15 0.08 -0.14 -0.16 0.07 -0.13 1.00
Co 0.15 0.22 0.22 -0.03 0.13 0.14 0.14 0.30 0.11 0.18 -0.20 0.11 0.42 0.53 0.44 -0.23 1.00
Zn 0.20 0.24 0.24 0.02 0.18 0.23 0.05 -0.08 0.39 0.17 0.19 0.58 0.00 -0.18 0.27 0.08 0.19 1.00
Fe -0.09 -0.15 -0.15 0.25 -0.20 -0.27 0.02 0.30 -0.01 -0.22 -0.12 -0.45 0.35 0.50 -0.12 0.32 0.33 -0.26 1.00
Pb 0.10 -0.12 -0.12 0.02 -0.11 -0.25 0.21 0.09 0.06 -0.19 -0.03 -0.03 0.35 0.36 0.09 0.39 0.34 0.03 0.45 1.00
Cr 0.13 -0.19 -0.19 0.07 -0.18 -0.11 -0.27 -0.27 0.02 -0.22 0.02 -0.11 -0.40 -0.39 -0.31 0.22 -0.35 0.21 -0.23 -0.17 1.00
Arch Environ Contam Toxicol (2009) 56:654–669
 Table 3 Post-monsoon correlation coefficient matrix
pH EC TDS Ca Mg Na K HCO3 SO4 Cl F NO3 NO2 PO4 SiO2 Cu Co Zn Fe Pb Cr

pH 1.00
EC -0.72 1.00
TDS -0.72 1.00 1.00
Ca -0.59 0.67 0.67 1.00
Arch Environ Contam Toxicol (2009) 56:654–669

Mg -0.45 0.76 0.76 0.12 1.00

Na -0.67 0.96 0.96 0.60 0.68 1.00
K -0.71 0.81 0.81 0.54 0.65 0.68 1.00
HCO3 -0.58 0.62 0.62 0.31 0.52 0.59 0.62 1.00
SO4 -0.58 0.79 0.79 0.72 0.44 0.78 0.52 0.45 1.00
Cl -0.69 0.98 0.98 0.60 0.79 0.93 0.83 0.59 0.67 1.00
F 0.11 0.13 0.13 -0.13 0.27 0.15 -0.08 0.02 0.33 0.06 1.00
NO3 0.01 -0.01 -0.01 -0.14 0.14 -0.05 0.03 0.09 -0.22 0.03 -0.27 1.00
NO2 -0.42 0.44 0.44 0.08 0.70 0.31 0.51 0.29 0.10 0.50 -0.10 0.44 1.00
PO4 -0.64 0.79 0.79 0.38 0.83 0.65 0.83 0.51 0.46 0.82 0.00 0.15 0.77 1.00
SiO2 -0.29 0.33 0.33 0.34 0.19 0.31 0.21 -0.10 0.25 0.33 0.08 0.24 0.20 0.22 1.00
Cu -0.24 0.15 0.15 0.07 0.25 0.03 0.20 0.01 0.16 0.13 0.04 0.09 0.36 0.32 0.05 1.00
Co -0.08 0.05 0.05 -0.11 0.25 -0.10 0.14 0.03 0.04 0.03 0.15 0.22 0.34 0.29 0.02 0.89 1.00
Zn 0.31 -0.45 -0.45 -0.29 -0.39 -0.44 -0.17 -0.19 -0.43 -0.42 -0.18 0.27 -0.18 -0.24 -0.22 -0.17 -0.06 1.00
Fe -0.35 0.43 0.43 0.40 0.15 0.51 0.13 0.03 0.44 0.39 0.14 -0.27 0.06 0.13 0.27 0.17 -0.02 -0.44 1.00
Pb 0.41 -0.40 -0.40 -0.19 -0.39 -0.29 -0.53 -0.40 -0.26 -0.38 -0.08 -0.01 -0.42 -0.55 -0.07 -0.15 -0.23 0.01 -0.05 1.00
Cr -0.21 0.34 0.34 0.03 0.27 0.38 0.37 0.52 0.14 0.36 0.01 0.30 0.20 0.36 -0.09 -0.06 0.03 0.26 0.02 -0.52 1.00
663

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664 Arch Environ Contam Toxicol (2009) 56:654–669

Fig 4 Pre-monsoon factor-

score line diagrams

arises anthropogenically, such as agriculture, industrial, the river, suggesting that the composition of river water in
and domestic effluents. Moreover, the percentage abun- this region is highly influenced by these constituents.
dance of the variables suggests saline water intrusion in the Riverine nitrite and phosphate originate mainly from
lower part of the river course. The high loadings of Na, Cl, agricultural or domestic effluents. In the region near the
and NO3 indicate that pollution caused by mixing sewage middle part of the river, no agricultural activity is being
water into river water affects the chemical composition of carried out; hence the high loadings of nitrite and phos-
the water. The factor-score diagram shows that factor phate must be attributed to domestic effluents. The
scores gain significance from station no. 14 and that high concentration of NO2–N indicates fresh input of organic
positive values are observed at the eastern part of the river, pollution load into the water system. In this region,
indicating that the lower part of the river is more affected domestic effluents containing water softeners contribute to
than the central region by this factor. The intrusion of the higher concentration of phosphates in the water (Ra-
saline water into the river course at the eastern part of the jmohan and Elango 2005). The increased abundance of Co
river is reflected in the factor scores; ion loadings also in the middle part of the river, along with the high con-
support this point. The middle part of the river runs through centration of nutrients in a region where organic matter
the urban area and receives the bulk of domestic sewage pollution is high, suggests that this may be caused by
and industrial effluents (Ramesh et al. 1995). The factor- sediment fluxes or anthropogenic sources (Windom et al.
score diagram and the correlation of the ions Na, Cl, and 1988). The concentration of many metallurgical and
NO3 clearly indicate that sewage water is being mixed into chemical industries near the course of the river adjacent to
the middle part of the river and that the chemical compo- the urban area, as well as the uncontrolled direct mixing of
sition of river water is highly influenced by this factor. the effluents into river water, accounts for the higher
Factor 2 during the pre-monsoon period, which accounts concentration of this ion in the water.
for 17% of total variance, has high loadings of Co, NO2, Factor 3 during the pre-monsoon period, accounting for
and PO4. The factor-score diagram shows that the eastern approximately 9% of total variance, has high loadings of K,
and western parts of the river are not affected by this factor. HCO3, PO4, and SiO2 and may be considered as being
Significant scores are observed only in the central part of affected by silicate-weathering and anthropogenic factors.

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Arch Environ Contam Toxicol (2009) 56:654–669 665

Fig 5 Post-monsoon factor-

score line diagrams

The weathering reaction of microcline yields K, HCO3, and this river water have also indicated higher nitrate concen-
SiO2. The factor-score diagram shows that the middle part trations in the middle and lower stretches of the river
of the river is affected with respect to this factor. It has (Ramesh et al. 1995). Many industrial activities (chemical,
been observed that SiO2 is positively correlated with K and paint, chrome plating, tanning industries) are being carried
HCO3, suggesting that the sources of these ions have the out near the river course, and effluents are discharged into
same origin. The fact that fertilizer surface runoff remains the river. These industrial effluents must be the cause of the
near the upper part of the river explains the highly signif- increased abundance of Zn in this region of the river.
icant factor scores in this region. In the case of the middle Factor 5 during the pre-monsoon period, which accounts
and lower parts of the river, domestic effluents containing for 6.5% of total variance, is explicitly a heavy-metal
water softeners contribute to the higher concentration of factor, with high loadings of Cu, Pb, and Fe. The factor-
phosphate ions in river water (Rajmohan and Elango 2005). score diagram shows a distributive pattern of this factor,
Factor 4 during the pre-monsoon period, which account with many significant scores in the middle and lower parts
for 8% of total variance, has high loadings of NO3 and Zn. of the river. In addition to industrial activities, the atmo-
The factor-score diagram shows high positive scores at the spheric depositions resulting from automobile pollution
middle and lower parts of the river course. It is known that (Varrica 2003; Sharma 2003) as well as urban runoff
nitrate is stable in aquifers because dissolved oxygen is caused by precipitation may lead to the increased con-
present (Hamilton and Helsel 1995). It has been observed centration of Pb in aquifers.
that no agricultural activities are being carried out in the Factor 6 during the pre-monsoon period, accounting for
middle and lower parts of the river; however, at many points 6% of total variance, has high loadings of Ca, with some
in this region, domestic sewage water is discharged into the positive values of NO2. The factor-score diagram shows a
river. Domestic sewage loads are the main source of organic distributive pattern, with significant scores at many sta-
matter in this region. The oxidation products of many tions, near the middle part of the river. Anthropogenic
organic loads in the river system lead to the increased activities and rock–water interaction in this region increase
concentration of NO3-N in river water. Previous studies of the concentration of these ions in the water.

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666 Arch Environ Contam Toxicol (2009) 56:654–669

Table 4 Pre-monsoon rotated

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
component matrix
pH -0.135 0.107 0.024 0.204 -0.149 0.814
EC 0.911 0.099 0.339 0.116 -0.073 0.044
TDS 0.911 0.099 0.339 0.116 -0.073 0.044
Ca 0.353 -0.143 0.359 -0.231 0.225 0.655
Mg 0.688 0.280 -0.295 0.076 -0.180 0.033
Na 0.881 0.026 0.157 0.122 -0.179 -0.062
K 0.131 0.156 0.826 0.144 0.020 0.104
HCO3 0.383 0.138 0.781 -0.243 0.154 0.045
SO4 0.783 0.094 -0.089 0.245 0.201 0.368
Cl 0.932 0.094 0.192 0.073 -0.156 -0.066
F 0.691 -0.327 0.157 0.127 0.206 -0.054
NO3 0.541 0.052 0.045 0.747 -0.102 -0.059
NO2 0.226 0.686 0.130 -0.057 0.105 0.438
PO4 0.012 0.546 0.659 -0.279 0.186 0.171
SIO2 0.391 0.323 0.565 0.460 -0.092 -0.128
Cu -0.045 -0.295 0.078 0.018 0.805 -0.074
Co 0.032 0.795 0.169 0.170 0.017 0.048
Zn 0.172 -0.037 -0.066 0.803 0.107 0.183
Fe -0.120 0.408 0.072 -0.476 0.635 0.079
Pb -0.190 0.446 0.076 0.177 0.700 0.044
Cr -0.130 -0.661 -0.202 0.162 0.097 0.310
Extraction method was principal Eigen value 7.01 3.56 1.89 1.74 1.36 1.27
component analysis. Rotation % Variance 33.37 16.96 8.99 8.29 6.48 6.02
method was varimax with Cumulative variance 33.37 50.33 59.33 67.62 74.09 80.11
Kaiser normalization

Post-Monsoon Factors highly significant for this factor. The factor-score diagram
shows significant scores in the middle part of the river; in
Factor 1 during the post-monsoon period, which explains this region, the flow of river water is almost stagnant. This
43% of total variance, has high loading of the ions Mg, K, stagnancy of river water facilitates more rock–water
Na, Cl, PO4, HCO3, and NO2. The factor-score diagram interaction, thus leading to augmentation of these ions in
shows that the western part of the river is unaffected by this river water. It is observed from the correlation matrix that
factor. In this region, there is no point source of pollution, pH is negatively correlated with all of these ions, sug-
and the intense rainfall during this period dilutes the water; gesting that an increase in hydrogen ion concentration in
hence river water in this region behaves almost like the water increases the previously mentioned ions in the
freshwater. The factor-score diagram shows that there is a chemical make-up of the water. The factor-score diagram
gradual increase from the western (upstream) to the eastern also depicts low positive scores at the downstream area,
part (downstream) of the river. The eastern part of the showing that the water in this part is only moderately
study area is adjacent to the ocean, and saline water affected by this factor. In the downstream region, saline
intrusion contributes to the chemical composition of river water intrusion increases the concentrations of these ions in
water. The factor-score diagram shows less significant the water. The higher loading of Fe is attributed to rock–
values from stations 12–22, demonstrating that the middle water interaction as well as anthropogenic activities. The
part of the river is only moderately affected. The seasonal high concentration of Fe observed in the middle stretch of
effect on the chemical composition of water in this region the river is caused by the strong association of dissolved Fe
is highly significant because the point sources of pollution with finer particles and colloids (Sholkovitz 1976). More-
in this urban area are highly diluted. over, effluents from the industries near the river course also
Factor 2 during the post-monsoon period, accounting for contribute Fe to river water.
13% of total variance, has high loadings of Ca, Na, SO4, Factor 3 during the post-monsoon period, which
Cl, and Fe, as well as a positive loading of silicate, and thus accounts for 10% of total variance, has high loadings of Cu
may be described as having been influenced by a silicate- and Co. The factor-score diagram illustrates that except at
weathering factor. It is apparent that the seasonal effect is one station (station 26), all other stations are only

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Arch Environ Contam Toxicol (2009) 56:654–669 667

Table 5 Post-monsoon
Factor1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
rotated component matrix
pH -0.541 -0.581 -0.135 -0.130 0.271 0.010
EC 0.753 0.621 -0.019 0.117 0.082 0.057
TDS 0.753 0.621 -0.019 0.117 0.082 0.057
Ca 0.193 0.852 -0.044 0.031 -0.288 0.014
Mg 0.919 0.047 0.137 0.000 0.286 0.102
Na 0.664 0.644 -0.167 0.119 0.157 0.054
K 0.737 0.389 0.102 0.281 -0.211 -0.028
HCO3 0.644 0.191 -0.089 0.406 -0.062 -0.270
SO4 0.370 0.775 0.030 0.072 0.234 -0.109
Cl 0.803 0.528 -0.043 0.102 0.034 0.097
F 0.023 0.079 0.071 0.027 0.927 -0.066
NO3 0.172 -0.338 0.088 0.261 -0.234 0.711
NO2 0.749 -0.147 0.332 0.002 -0.143 0.340
PO4 0.865 0.200 0.241 0.168 -0.073 0.105
SIO2 0.075 0.432 0.005 -0.144 0.089 0.775
Cu 0.117 0.119 0.951 -0.068 -0.020 0.005
Co 0.101 -0.084 0.945 0.080 0.108 0.066
Zn -0.409 -0.360 -0.061 0.594 -0.227 0.125
Fe 0.034 0.684 0.080 -0.180 0.216 0.065
Pb -0.391 -0.138 -0.244 -0.586 -0.044 0.063
Cr 0.292 0.016 -0.084 0.849 0.085 0.056
Extraction method was principal Eigen value 9.04 2.69 2.05 1.47 1.21 1.00
component analysis. Rotation % Variance 43.04 12.84 9.76 6.99 5.76 4.76
method was varimax with Cumulative variance 43.04 55.89 65.65 72.63 78.39 83.16
Kaiser normalization

moderately affected by this factor. It is apparent that the Agricultural activities lead to fertilizer remaining in the
seasonal effect is highly significant for this factor. Both of soil, and the use of NPK fertilizers is high in this region.
these heavy metals have no significant lithologic origin; Fertilizer surface runoff contributes to the abundance of
hence it must be attributed to anthropogenic activities. nitrate in river water. Heavy precipitation and subsequent
Factor 4 during the post-monsoon period, accounting for soil–water interaction leads to increased loadings of sili-
7% of total variance, has high loadings of Cr and Zn. In the cates in the water.
suburban area and near the entry point of the urban area, a
number of industries and small tanneries are situated along
the river course. Effluents from these industries increase Conclusion
the concentration of these metals in the water.
Factor 5 during the post-monsoon period, accounting for The application of chemometric techniques for character-
6% of total variance, is explicitly a fluoride factor. The ization and evaluation of hydrochemical data has been
factor-score diagram illustrates that water is moderately successfully demonstrated. Variation within and between
affected by this factor throughout the stretch of the river variables are highlighted using parametric and nonpara-
except at a few stations in the middle part of the river. The metric statistical techniques. Box-and-whisker plots
lithology of the study area has no significant fluoride- demonstrate seasonal and chemical variations of various
bearing minerals; hence it is logical to assign the high chemical parameters of river water. The significance of the
fluoride level to anthropogenic activity and, to a lesser seasonal effect on variables is evaluated by ANOVA. The
extent, to chemical weathering (Saxena et al. 2003; Sub- results show that among the major ions, Mg is nonsignificant
barao 2003). regarding seasonal effect, whereas Cu, Co, and Fe are found
Factor 6 during the post-monsoon period, accounting for to be nonsignificant. Cluster analysis also highlights sea-
approximately 5% of total variance, has high loadings of sonal variation among the stations, and results show that the
silicate and nitrate. The factor-score diagram shows that upper part of the river is grouped into one unpolluted major
the upper part of the river has significant factor scores, cluster. The middle and lower parts of the river are grouped
reflecting that this region is affected by this factor. into another major cluster for which industrial and domestic

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668 Arch Environ Contam Toxicol (2009) 56:654–669

sewage and saline water intrusion influence river water alluvial aquifer (Pisuerga River, Spain) by principal component
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