ecological investigation of the ganges river using principal component analysis

Environmental Quality Management / DOI 10.1002/tqem / Autumn 2009 / 61

© 2009 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/tqem.20236

The study discussed

here used multi-

variate statistical

approaches to in-

terpret a large and

complex data ma-

trix obtained dur-

ing monitoring of

the Ganges River in

Varanasi, India. The study analyzed 18 physi-

cochemical and bacteriological variables in water

samples collected every three months for two

years from six sampling sites at locations where

the river has been affected by human-induced

and seasonal influences.

Principal component analysis (PCA) was ap-

plied to the data set to extract the parameters

that are most important for assessing variation in

water quality. Four principal factors were identi-

fied as being responsible for the data structure,

collectively explaining over 80 percent of the

total variance in the water-quality data. These

factors were nutrients (39.3 percent), sewage

and fecal contamination (29.4 percent), physico-

chemical sources of variability (6.3 percent), and

wastewater pollution from industrial and organic

loads (5.8 percent).

The study discussed here suggests that PCA

techniques offer a useful tool for identifying

important surface-

water-quality pa-

rameters.

Background: Understanding Surface-Water Quality

Impairment of Surface-Water QualityThe quality of surface water is an issue of sig-

nificant environmental concern worldwide. Nat-

ural processes (such as changes in precipitation

inputs, soil erosion, and weathering of crustal

materials) as well as anthropogenic influences

(including urban, industrial, and agricultural

activities and increasing consumption of water

resources) degrade surface waters and impair their

suitability for drinking.

Industrial wastewater discharges and runoff

from agricultural land contribute significantly to

water pollution (Singh, Malik, & Sinha, 2005).

Urban and industrial effluents are major sources

of the chemicals and nutrients found in aquatic

Archana Mishra and

Brahma Dutt Tripathi

Ecological Investigation of the Ganges River Using Principal Component Analysis

Identifying the principal factors

responsible for water-quality

variance

Archana Mishra and Brahma Dutt Tripathi62 / Autumn 2009 / Environmental Quality Management / DOI 10.1002/tqem

Kaufman, 1988; Wenning & Erickson, 1994), such

as PCA. Papers published in Analytical Chemistry

indicate the importance of multivariate statistical

tools in the treatment of analytical and environ-

mental data (Brown, Blank, Sum, & Weyer, 1994;

Brown, Sum, Despagne, & Lavine, 1996).

Principal component analysis can be used for

dimensionality reduction in a data set by retain-

ing those characteristics of the data that con-

tribute most to its variance, keeping lower-order

principal components and ignoring higher-order

ones. It is very useful in the analysis of data cor-

responding to a large number of variables.

PCA has been widely used because it offers

an unbiased method that can indicate associa-

tions between samples and variables (Wenning

& Erickson, 1994). It reduces the dimensionality

of data by explaining correlations among a large

group of parameters in terms of a smaller number

of underlying factors or “principal components”

(Jackson, 1991; Meglen, 1992).

Using PCA to Interpret Water-Quality Parameters

In recent years, many studies have been done

using principal component analysis to interpret

water-quality parameters (Lohani, 1984).

A number of researchers have applied PCA

techniques to the study of groundwater quality

(Gangopadhyay, Das Gupta, & Nachabe, 2001;

Winter, Mallory, Allen, & Rosenberry, 2000). PCA

has been successfully applied to sort out hydro-

geological and hydrogeochemical processes from

commonly collected groundwater-quality data

(Jayakumar & Siraz, 1997; Praus, 2005; Salman &

Abu Ruka’h, 1999).

Iyer, Sindhu, Kulkarni, Tambe, and Kulkarni

(2003) have constructed a PCA-based statistical

model for coastal water-quality data using infor-

mation from the Cochin coast in southwest India.

The model explains the relationship among the

various physicochemical variables that have been

ecosystems. High concentrations of toxic chemi-

cals and excessive levels of biologically available

nutrients can create a range of problems, includ-

ing toxic algal blooms and fish kills from lack

of oxygen, loss of aquatic plant beds and coral

reefs, and an overall diminishment of biodiver-

sity (Voutsa, Manoli, Samara, Sofoniou, & Stratis,

2001).

Effective Monitoring of Surface-Water Quality Prevention of river pollution requires effective

monitoring of physicochemical and microbiolog-

ical parameters (Bonde, 1977; Ramteke, Pathak,

Bhattacherjee, Gopal, & Mathur, 1994). Levels of

dissolved oxygen (DO)

and biological oxygen

demand (BOD) typi-

cally are used to indi-

cate the pollution sta-

tus of aquatic systems.

These parameters are

not always sufficient

(Voznaya, 1981). The

overall quality of water

can only be indicated by its physical, chemical,

and biological properties, along with analysis of a

large number of measured variables (Boyacioglu,

2006).

Long-term surveys and monitoring programs

for water quality provide better knowledge re-

garding river hydrochemistry and pollution, but

they produce large sets of data that are often dif-

ficult to interpret (Dixon & Chiswell, 1996).

Principal Component Analysis

Making Data More Manageable The challenges of data reduction and inter-

preting multiconstituent chemical and physical

measurements can be addressed through the ap-

plication of multivariate statistical analysis meth-

ods (Massart, Vandeginste, Deming, Michotte, &

The challenges of data reduction and interpreting multiconstituent chemical and physical measurements can be addressed through the application of multivariate statistical analysis methods, such as PCA.

Environmental Quality Management / DOI 10.1002/tqem / Autumn 2009 / 63Ecological Investigation of the Ganges River

liters per day (MLD) of domestic sewage and 80

MLD of untreated sewage and industrial effluents

(along with excreta from humans and various

warm-blooded animals) are directly or indirectly

discharged into the Ganges River, adversely af-

fecting the physicochemical and biological qual-

ity of the river’s water.

Monitoring SitesThe following six sites were selected for river-

water-quality monitoring:

Samne Ghat (Site 1), •

Assi Ghat (Site 2), •

Shiwala Ghat (Site 3), •

Harischandra Ghat (Site 4), •

Dashwamedh Ghat (Site 5), and •

Raj Ghat (Site 6). •

Each site was con-

sidered to be reason-

ably representative

of the river system’s

water quality.

The first site, which

receives much of the

sewage from the town,

is the most polluted.

Sites 2, 3, 4, and 5 fall within the midstream re-

gion. Site 6 is located upstream of Varanasi city,

in an area of relatively low river-water pollution.

Sampling and AnalysisSamples were taken at the selected sites every

three months for a period of two years (2005–

2007). Samples were collected across the width of

the river at all six sites with a view toward moni-

toring changes caused by anthropogenic sources.

Water sampling, sample preservation, and trans-

portation of samples to the laboratory were all

carried out in accordance with standard methods

(Eaton, Clesceri, & Greenberg, 1998).

monitored and the environmental conditions

that affect coastal water quality.

PCA techniques have been used to estimate

spatial and temporal patterns of heavy-metal

contamination (Shine et al., 1995) and to inves-

tigate nutrient gradients within a eutrophic res-

ervoir (Perkins & Underwood, 2000). Tauler, Bar-

celo, and Thurman (2000) used PCA to identify

the composition of the major herbicides causing

observed data variations in a water body.

Study Materials and MethodsThe research discussed here studied water

quality in the Ganges River using multivariate

principal component analysis to interpret and

extract the parameters that are most important

to assessing variations in the quality of the river’s

water.

Study AreaThe particular area studied in this research

covered the urban fringe of Varanasi, a city

situated in the eastern Gangetic plain of northern

India (from 82°15’E to 84°30’E and from 24°35’N

to 25°30’N).

The Ganges and its tributaries drain a large

(about one-million square kilometers) and fertile

basin that supports one of the world’s highest-

density human populations. Almost half the

population of India proper lives on one-third of

the land within 500 kilometers of the Himalayan

range along the Gangetic plains.

The Ganges, one of the most sacred rivers in

India, is being polluted by many sources. There

are 29 cities, 70 towns, and thousands of villages

along the banks of the river. All the sewage from

these municipalities (over 1.3 billion liters per

day) goes directly into the Ganges River.

The main sources of river pollution at Vara-

nasi are industrial effluents, domestic sewage,

and cremation of dead bodies (Tripathi, Sikan-

dar, & Shukla, 1991). At Varanasi, 190 million

The Ganges and its tributaries drain a large (about one-million square kilometers) and fertile basin that

supports one of the world’s highest-density human populations.


Coliform were detected by presumptive inocula-

tion into tubes of MacConkey broth, and their

incubation at 37±2°C for 48-hour gram charac-

teristics was also observed by gram staining. The

most probable number (MPN) of coliform per 100

milliliters (mL) of water was determined using

standards tubes.

For confirmation of indicator bacterial species,

other tests—such as IMViC, fermentation, Voges-

Proskauer, nitrate reductase, oxidase, catalase,

citrate, and hydrogen sulfide (H2S) testing—were

performed using specific media and indicators

(Eaton et al., 1998; Sirockin & Cullimore, 1969).

Data TreatmentExhibit 1 shows the parameters used in the

present study, along with their abbreviations and

measurement units.

Because water-quality parameters involve dif-

ferent magnitudes and scales of measurement,

the data must be standardized to produce a nor-

mal distribution of all variables (Davis & Samp-

son, 1973). In the present study, we reduced the

All samples were transported to the labora-

tory in cold packs and were analyzed within

seven hours of collection. The pH of the water

samples was determined by a portable pH meter

at the collection site immediately after sampling

since biological and chemical reactions between

the atmosphere and samples can readily alter pH

(Hutton, 1983).

Eighteen physicochemical and bacteriologi-

cal parameters were determined using prescribed

standard methods. A total of 480 analyses were

carried out (18 variables in 16 samples).

Enumerating the Bacterial PopulationFor bacterial analysis, samples were collected

in sterile bottles at each site and were kept packed

in cold ice in cooler boxes in the field. The sam-

ples were returned to the laboratory for analysis

as soon as possible. We used Himedia Laborato-

ries for bacterial analysis.

For members of the coliform group, qualita-

tive analysis was carried out with multiple-tube

fermentation techniques (Eaton et al., 1998).

Exhibit 1. Water-Quality Parameters Used in This Study, with Associated Abbreviations and Units

Parameter Abbreviation UnitpH pHTemperature Temp °CElectricity Conductivity EC µmho/cmDissolved Oxygen DO mg/LTransparency Trans CmChloride Cl mg/LAcidity Aci mg/LAlkalinity Alk mg/LNitrate NO3 mg/LPhosphate PO4 mg/LBiological Oxygen Demand BOD mg/LChemical Oxygen Demand COD mg/LTotal Bacterial Density TBD ×103 LTotal Coliform TC ×103/100 mLFecal Coliform FC ×103/100 mLFecal streptococci FS 100 mLEscherichia coli EC ×103/100 mLClostridium perfringens CP 100 mL


components and their initial eigenvalues, along

with the percentage of variance contributed by

each component.

Exhibit 5 shows the Scree plot of the eigen-

values for each component. Four principal com-

ponents cumulatively account for over 80 percent

of the total variance in the water-quality data set.

The Scree plot shows a pronounced change of

slope after the third eigenvalue (Cattell & Jaspers,

1967).

The first four components clearly are the most

significant, representing most of the water-qual-

ity variance in the Ganges River at Varanasi. The

variance percentages contributed by these four

principal components

are 39.3 percent (PC1),

29.4 percent (PC2), 6.3

percent (PC3), and 5.8

percent (PC4).

Component load-

ings (correlation coef-

ficients) measure the

degree of closeness

between the variables

and the PC. The largest loading, either positive

or negative, “suggests the meaning of the dimen-

sion: positive loading indicates that the contribu-

tion of the variables increases with the increasing

loading in a dimension; and negative loading

indicates a decrease” (Jayakumar & Siraz, 1997).

In general, component loadings larger than

0.45 may be taken into consideration. In this

case, the most significant variables (represented

by high loadings) have been considered in evalu-

ating the components (Mazlum, Ozer, & Mazlum,

1999).

Component loading and communalities for

each variable included in the four selected com-

ponents are shown in Exhibit 6 (before varimax

rotation) and Exhibit 7 (after varimax rotation).

Communalities provide an index of the efficiency

of the reduced set of components and show each

dimensionality of the data set while minimizing

the loss of information.

We converted the raw data collected into a

unitless form with a zero mean and a variance

of one. This was accomplished by subtracting

the mean of the data set from each variable and

dividing by the standard deviation. We extracted

the initial factor solution from the standardized

covariance or correlation matrix of the data with

multivariate principal components extraction.

Diagonalization of the correlation matrix

transforms the original p-correlated variables

into p-uncorrelated (orthogonal) variables called

principal components (PCs), which are weighted

linear combinations of the original variables

(Meglen, 1992; Mellinger, 1987; Wenning & Er-

ickson, 1994). The characteristic roots (eigenval-

ues) of the PCs are a measure of their associated

variances, and the sum of eigenvalues coincides

with the total number of variables (Vega, Pardo,

Barrado, & Deban, 1998).

Study Results and Discussion Exhibit 2 shows the correlation matrix for

the water-quality parameters obtained with PCA.

Only a few parameters had significant correlation

relationships. High and positive correlations (r =

0.55 to 0.942) can be observed between pH, BOD,

chemical oxygen demand, temperature, and vari-

ous bacterial populations that are responsible for

fecal contamination in the river. BOD is strongly

correlated with nitrate (0.751) and phosphate

(0.552), which indicates contamination with or-

ganic matter.

DO shows a negative correlation with tem-

perature and pH because the solubility of oxygen

decreases as the water temperature rises and

organic matter is partially oxidized. A seasonal

fluctuation seems to be responsible for this type

of correlation.

Exhibit 3 summarizes the descriptive statis-

tics of the study’s data set. Exhibit 4 shows the

Component loadings (correlation coefficients) measure the degree

of closeness between the variables and the PC.


Exhi

bit 2

. Cor

rela

tion

Mat

rix

pHTe

mp

ECDO

Tran

sCl

Aci

Alk

NO3

PO4

BOD

COD

TBD

TCFC

FSEC

CP

pH1.

000

Tem

p.4

271.

000

EC

.154

–.20

91.

000

DO

–.54

7–.

261

–.30

01.

000

Tra

ns–.

441

–.67

4–.

170

.508

1.00

0

Cl

.308

–.07

4.6

68–.

605

–.16

71.

000

Aci

–.44

8.2

61.6

35–.

603

–.60

5.6

541.

000

Alk

.544

.192

.647

–.49

0–.

472

.730

–.83

81.

000

NO

3.4

54.2

62.6

25–.

717

–.70

3.7

11.8

02.7

871.

000

PO

4.3

85.1

86.6

03–.

580

–.40

9.8

46.7

39.8

07.7

521.

000

BO

D–.

581

–.24

7–.

425

–.79

4–.

480

–.57

5–.

724

–.64

1–.

751

–.55

21.

000

CO

D–.

014

–.03

4–.

060

–.09

7–.

133

–.12

5–.

061

–.15

1–.

025

–.06

1–.

101

1.00

0

TB

D.3

27.7

57–.

239

–.38

6–.

831

–.09

6.2

73.1

34.4

18.1

21.3

50.1

431.

000

TC

.053

.194

.028

–.15

4–.

306

–.12

2.0

19–.

017

.123

–.04

4.1

07.0

15.2

701.

000

FC

.037

.596

–.47

9–.

261

–.43

3–.

094

–.02

9–.

168

.108

–.03

5.1

00.1

93.7

18.1

661.

000

FS

.221

.707

–.36

9–.

333

–.60

9–.

096

.107

–.01

7.2

45.0

58.2

44.2

50.8

35.2

20.8

891.

000

EC

.186

.701

–.41

2–.

354

–.52

7–.

060

.071

–.04

9.1

76.0

67.2

08.2

23.7

78.2

12.9

22.9

421.

000

CP

.070

.631

–.45

0–.

257

–.57

0–.

142

.042

–.09

5.1

93.0

10.1

17.2

54.7

95.1

69.9

41.9

22.8

841.

000


counted for 39.3 percent of the total variance.

It was positively correlated with electrical con-

ductivity (EC), chloride (Cl), acidity, alkalinity,

nitrate, phosphate, and BOD.

By contrast, DO showed a negative contribu-

tion to this variance. This can be explained by the

fact that a large amount of dissolved organic mat-

ter consumes significant amounts of oxygen. Or-

ganic matter in urban wastewater consists mainly

of carbohydrates, proteins, and lipids. As the

amount of available dissolved oxygen decreases,

these constituents undergo anaerobic fermenta-

tion processes, leading to creation of ammonia

and organic acids.

Principal Component 2The second PC was highly loaded with total

bacterial density (TBD), fecal coliform (FC),

fecal streptococci (FS), Escherichia coli (EC), and

Clostridium perfringens (CP), which show the sew-

age discharge and fecal contamination.

Principal Component 3The third PC was weighted with pH and DO,

representing the physicochemical source of the

variability in water quality.

variable’s level of contribution to the four se-

lected components.

Principal Component 1The first PC (nutrients) represents influences

from nonpoint sources, such as agricultural run-

off and atmospheric deposition. This factor ac-

Exhibit 3. Descriptive Statistics

Mean Std. Deviation Analysis NpH 7.6366 .2109 216Temperature 27.8556 3.3657 216Electric Conductivity 449.1574 155.2798 216Dissolved Oxygen 3.9083 2.0174 216Transparency 23.8963 7.6336 216Chloride 33.8541 10.1813 216Acidity 16.3036 6.4029 216Alkalinity 117.9032 59.9250 216Nitrate .273505 .170281 216Phosphate .5850 .3666 216Biological Oxygen Demand 19.6032 11.1427 216Chemical Oxygen Demand 297.9977 1712.0381 216Total Bacterial Density 11492.3148 6724.4868 216Total Coliform 120890.8796 156003.7307 216Fecal Coliform 36368.7963 50166.8307 216Fecal Streptococci 1085.0620 1132.6995 216Escherichia coli 9116.2269 9144.6253 216Clostridium perfringens 8012.1343 10096.9555 216

Exhibit 4. Total Variance Explained

Component Initial Eigenvalues Total % of Variance Cumulative %1 7.074 39.298 39.2982 5.285 29.362 68.6593 1.127 6.260 74.9204 1.047 5.817 80.7365 .905 5.028 85.7656 .796 4.423 90.1877 .460 2.556 92.7438 .275 1.528 94.2719 .243 1.349 95.62010 .223 1.241 96.86111 .151 .837 97.69812 .110 .609 98.30713 8.431E-02 .468 98.77514 8.105E-02 .450 99.22515 5.080E-02 .282 99.50716 4.036E-02 .224 99.73217 3.487E-02 .194 99.92518 1.343E-02 7.462E-02 100.000Extraction Method: Principal Component Analysis. Note: When components are correlated, sums of squared loadings cannot be added to obtain a total variance.


Correlation Components Matrix Exhibit 8 shows the correlation components

matrix (component score covariance matrix) of the

four varimax-rotated PCs. It indicates that there are

no correlations among the four principal compo-

Principal Component 4The fourth PC was loaded with BOD, which

represents the organic load in wastewater from

domestic and industrial sources disposed in the

river at Varanasi.

Exhibit 5. Scree Plot

Exhibit 6. Component Matrix

Component 1 2 3 4

pH .592 –.188 .657 –.156Temperature .647 .473 –.277 –.216Electric Conductivity .270 –.818 –.025 .212Dissolved Oxygen –.774 .218 .873 –.097Transparency –.848 –.160 .256 –.123Chloride .515 –.679 .301 –.161Acidity .742 –.511 .016 .065Alkalinity .652 –.633 –.099 –.152Nitrate .833 –.421 .015 .087Phosphate .658 –.559 .159 –.160Biological Oxygen Demand .754 –.351 .053 .681Chemical Oxygen Demand .127 .224 .517 .133Total Bacterial Density .735 .557 –.169 .005Total Coliform .212 .188 –.591 .564Fecal Coliform .506 .763 .228 –.105Fecal Streptococci .650 .706 .101 –.030Escherichia coli .615 .711 .147 –.081Clostridium perfringens .565 .752 .180 –.045

Extraction Method: Principal Component Analysis. Note: Four components extracted.


extracted in this case represent four different

processes or sources of contamination affecting

the study area:

nutrients,•

sewage and fecal contamination,•

physicochemical variability, and •

wastewater pollution from domestic and in-•

dustrial sources, and its organic load.

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Exhibit 7. Rotated Component Matrix

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Extraction Method: Principal Component Analysis. Rotation Method: Equamax with Kaiser Normalization.


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Archana Mishra pursues environmental research at the Department of Botany, Center of Advanced Study, Banaras Hindu University, in Varanasi, India. She can be reached by e-mail at [email protected].

Brahma Dutt Tripathi, PhD, is a professor in the Department of Botany and coordinator of the Center of Environmental Science Technology at the Center of Advanced Study, Banaras Hindu University, Varanasi, India. He can be reached by e-mail at [email protected].

ecological investigation of the ganges river using principal component analysis

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