49

CHAPTER 5

MATERIALS AND METHODS

5.1 GENERAL

Geochemical processes within the groundwater and reactions with

aquifer minerals have a profound effect on water quality. These geochemical

processes are responsible for the seasonal and spatial variations in

groundwater chemistry. The geochemical properties of groundwater depend

on the chemical properties of water in the recharge area as well as on different

geochemical processes that are taking place in the subsurface. The quality of

water along the course of its underground movement is therefore dependent

on the chemical and physical properties of surrounding rocks, the quantitative

and qualitative properties of through-flowing water bodies, and the products

of human activity (Mathess 1982). Water quality analysis is one of the most

important issues in groundwater studies. The hydrochemical study reveals the

zones and quality of water that are suitable for drinking, agricultural and

industrial purposes. Chemical reactions such as weathering, dissolution,

precipitation, ion exchange and various biological processes commonly take place.

Hydro chemical study is a useful tool to identify these processes

that are responsible for groundwater chemistry (Jeevanandam et al 2007).

When water gets mingled with the garbage and effluents through industries, it

loses it originality. The two fundamental causes for groundwater's active

role in nature are its ability to interact with the ambient environment and the

50

systematized spatial distribution of its flow. Interaction and flow occur

simultaneously at all scales of space and time, although at correspondingly

varying rates and intensities. Flow chart 5.1 depicts the broad methodology

adopted in the present study.

Figure 5.1 Flow chart showing the methodology adopted

Collection of Data

Field DataLaboratory Data

Drainage

Map & Contour Map

Rain fall / Water

Level Data

Digital ImageProcessing

NRSA Land use

Land CoverClassification

Geology

Soil

GeomorphologyLineament

Scanning / Digitization

IRS 1D Satellite

DataTopo Sheet

Groundwater

Fluctuation

Water QualityAnalysis

Suitability analysis for

Drinking

Suitability analysis for

Irrigation

Preparation of various thematic

maps of the study area

Final Result and Conclusion

GSOI Map

51

5.2 GROUNDWATER QUALITY

Water quality refers to the chemical, physical and organic

compounds of water. For this study water quality parameters are determined

for (1) Turbidity, (2) Electrical conductivity, (3) Total dissolved solids, (4)

Hydrogen ion concentration, (5) Calcium, (6) Magnesium, (7) Sodium, (8)

Potassium, (9) Total alkalinity, (10) Bicarbonate, (11) Carbonate, (12)

Chloride, (13) Sulphate, (14) Nitrate, (15) Total hardness, (16) Fluoride, (17)

Iron, (18) Copper, (19) Lead, (20) Zinc and (21) Manganese. For assessing

the accuracy of results, the groundwater quality data are plotted on an anion-

cation balance control chart.

5.3 SAMPLING LOCATIONS

Groundwater samples from sixty two bore wells were collected

during the pre-monsoon (June-July 2006) and post-monsoon (November-

December 2006) and pre-monsoon (2011) seasons. The sampling locations

were selected to cover the entire study area and attention had been given to

the areas where pollution was expected. Hence, about one third of the

sampling locations are within the Tirupur and the rest of the sampling

locations are in parts of Avinashi, Palladam, Uthukuli, Kangayam, Uthukuli

and Pongallur unions. Surface water samples are collected from seven

locations only - due to pre-monsoon season - for assessing the quality of

water during the pre-monsoon (2011) within the study area. The details of

sample locations for groundwater and surface water are shown in Tables 5.1

and 5.2 respectively. They are illustrated in Figure 5.2.

52

Table 5.1 Sample locations of groundwater

Sample

NoUnion/Block Location of sampling Source

01 Tirupur Sathya Colony Bore well

02 Tirupur K.P.N. Colony Bore well

03 Tirupur V.O.C. Nagar Bore well

04 Tirupur Lakshminagar Bore well

05 Tirupur Ashoknagar Bore well

06 Tirupur Padmavathipuram Bore well

07 Tirupur Narayan asamynagar Bore well

08 Tirupur M.G.R.Nagar Bore well

09 Tirupur T.V.K. Nagar Bore well

10 Tirupur Kallampalayam Bore well

11 Tirupur Pethichettypuram Bore well

12 Tirupur Karuvam Palayam Bore well

13 Tirupur Kathiravan School Bore well

14 Tirupur Kalaimahal School Bore well

15 Tirupur Raja Street Bore well

16 Tirupur Boompuhar Siva Engineering Bore well

17 Palladam Priya Hotel Bore well

18 Tirupur Al-Ameen School Bore well

19 Tirupur Chairman Kandaswamy Nagar Bore well

20 Tirupur Kathir Nagar Bore well

21 Pongallur Pudupalayam Bore well

22 Tirupur Amukkiam Bore well

23 Uthukuli Kittangani-Reddipalayam Bore well

24 Tirupur Sarkar Periyapalyam Bore well

25 Tirupur Koolipalayam Bore well

26 Tirupur Boyampalyam Bore well

27 Tirupur Nerupperuchal Bore well

28 Tirupur Kutthampalayam Bore well

29 Tirupur Anuparpalayam Bore well

30 Avinashi Rakiyapalayam Bore well

31 Avinashi Devampalayam Bore well

32 Avinashi Ayekoundampalayam Bore well

33 Avinashi Puliaghadu Bore well

34 Avinashi Punthottam Bore well

35 Avinashi Kandampalayam Bore well

36 Avinashi Karatankadu Bore well

37 Avinashi Varathakadu Bore well

38 Tirupur Solipalayam Bore well

53

39 Tirupur Murugampalayam Bore well

40 Palladam Sedapalayam Pudur Bore well

41 Palladam Agraharampudur Bore well

42 Tirupur Sultanpet Bore well

43 Palladam Attayampalyam Bore well

44 Palladam Perumampalayam Bore well

45 Palladam Valaiyapalayam Bore well

46 Palladam Unjapalayam Bore well

47 Palladam Kalivelampatty Bore well

48 Palladam Sedapalayam Bore well

49 Palladam Eduvampalayam Bore well

50 Palladam 63,Velampalayam Bore well

51 Palladam Arumudhampalayam Bore well

52 Palladam Arulpuram Bore well

53 Palladam Kuppandampalayam Bore well

54 Palladam Nochiyapalayam Bore well

55 Palladam Malaiyampalayam Bore well

56 Pongallur Nalakalipalayam Bore well

57 Palladam Perumanai Bore well

58 Palladam Chettipalayam Bore well

59 Kangayam Manur Bore well

60 Tirupur Chennimalaipalayam Bore well

61 Palladam Nallur Bore well

62 Palladam M.Pudupalayam Bore well

Table 5.2 Sample locations of surface water

Sl.No. Union/Block Location of sampling Source

1. Uthukuli Kittangani Kulam Lake

2. Tirupur Noyyal river - Sift polytechnic

College

River

3. Tirupur Koolipalayam Pond

4. Tirupur Anna Weavers Nagar Pond

5. Tirupur Noyyal river - K.P.N. Colony River

6. Tirupur Kolakkaraipudur Kulam Pond

7. Palladam Noyyal river - Agraharampudur River

54

Figure 5.2 Sample locations

5.4 METHODOLOGY ADOPTED FOR PHYSICOCHEMICAL

PARAMETER ANALYSIS

Sampling and water analysis have been carried out, following the

standard procedure of American Public Health Association (APHA 1995). For

the analysis all the instruments were calibrated appropriately according to the

commercial grade calibration standard prior to the measurements. The various

methods adopted for the analysis of the ion chemistry are listed in Table 5.3.

55

Table 5.3 Methods adopted for physicochemical parameters analysis

Chemical parameters Units Methods used

Turbidity NTU Nephelometric Method

Hydrogen ion concentration (pH) - pH meter

Electrical Conductivity (EC) (µS/cm) EC meter

Total Dissolved Solids (TDS)(mg/l)

SEC x Conversion factor (0.55

to 0.75)

Calcium (Ca2+

) (mg/l) Titration with EDTA

Magnesium (Mg2+

) (mg/l) Calculation (TH- Ca+)

Sodium (Na+) (mg/l) Flame photometer

Potassium (K+) (mg/l) Flame photometer

Carbonate (CO3-) (mg/l) Titration with HCl

Bicarbonate (HCO32-

) (mg/l) Titration with HCl

Chloride (Cl-) (mg/l) Titration with Ag NO3

Sulphate (SO42-

) (mg/l) Spectrophotometer

Nitrate (NO3-) (mg/l) Colorimeter

Total Alkalinity (mg/l) Titration Method

Fluoride (F-) (mg/l) Spectrophotometer

Iron (Fe3+

) (mg/l) Phenanthroline Method

5.5 MECHANISM CONTROLLING GROUNDWATER

CHEMISTRY

According to Gibbs plot, evaporation and precipitation dominance

are the two important processes of determining the composition of water.

Evaporation of surface water and moisture in the unsaturated zone are the

main processes in the evolvement of groundwater chemical composition.

Evaporation concentrates the remaining water and leached to precipitation and

deposition of evaporates that are eventually leached into the saturated zone.

This is expected, as evaporation greatly increases the concentration of ions

formed by chemical weathering, leading to high salinity TDS (Wen et al

2005). Gibb’s diagram representing the ratio of Na+: (Na

+ + Ca

2+) and Cl

- :

56

(Cl- + HCO3

-) as a function of total dissolved solids, is widely used to assess

the functional sources of dissolved chemical constituents, such as

precipitation-dominance, rock-dominance and evaporation-dominance (Gibbs

1970).

5.6 GROUNDWATER QUALITY ANALYSIS USING GIS

All naturally occurring water contains some impurities. Water is

considered polluted when the presence of impurities is sufficient to limit its

use for a given domestic and/or industrial purpose. Geographic Information

System is an information system which is generally designed especially for

handling spatial data particularly. Unlike manual cartographic analysis, GIS

had advantage of handling attributes of data in conjunction with spatial

features. Spatial variation and zonation maps of various water quality

parameters have been developed by means of software, namely Arc View GIS

3.2a.

5.7 ASSESSMENT OF GROUNDWATER QUALTIY

Nowadays the quality of groundwater is deteriorating day by day

due to over exploitation of groundwater and improper disposal of solid waste

and dumping of untreated effluents into the water bodies. The available

groundwater cannot be used directly. The quality of groundwater depends

upon its physical and chemical characteristics which play a major role vis-a-

vis the health of the people. Hence the suitability of groundwater for drinking

and irrigation has been assessed and compared for the seasons.

5.7.1 Groundwater quality assessment based on salinity hazard

The first groundwater quality assessment is based on salinity

hazard. Electrical conductivity is a good measure of salinity hazard to plants

as it reflects the total dissolved solids in groundwater. Excess salinity reduces

57

the osmotic activity of plants and thus interferes with the absorption of water

and nutrients from the soil. The primary effect of high EC water on crop

productivity is the inability of the plant to compete with ions in the soil

solution of water. The higher the EC, the less the quantity of water available

to plants even though the soil appears wet. Since plants usually transpire

‘pure’ water, plant water in the soil solution decreases dramatically as EC

increases. The amount of water transpired through a crop is directly related to the

yield. Therefore, irrigation water with a high EC reduces yield potential quality of

groundwater based on salinity hazard. Table 5.4 represents this phenomenon.

Table 5.4 Groundwater quality based on salinity hazard

Sl.

NoSymbol

EC

(µS/cm)Water Class Remarks

1 C1 < 250 Low

Can be used for irrigation on

most crops in most soils with

little likelihood that soil

salinity will develop

2 C2 251 - 750 MediumCan be used if a moderate

amount of leaching occurs

3 C3 751 - 2250Medium-

High

Cannot be used on soils with

restricted drainage

4 C4 2250 - 3000 High

Unsuitable for irrigation

under ordinary conditions,

but it may be used

occasionally under very

special circumstances.

5 C5 > 3000 Very High Unsuitable for irrigation

5.7.2 Groundwater quality assessment based on total dissolved solids

The second quality of groundwater analysis lies on the basis of

Total Dissolved Solids (TDS). To ascertain the suitability of groundwater for

any purpose, it is essential to classify the groundwater depending upon its

58

hydrochemical properties, based on the TDS values (Caroll 1962; Davis and

De Wiest 1966; Freeze and Cherry 1979). Table 5.5 represents this report.

Table 5.5 Groundwater quality based on total dissolved solids

Sl.No TDS (mg/l) Classification

1 < 1000 Fresh water type

2 1,000 - 10,000 Brackish water type

3 10,000 - 100,000 Saline water type

4 > 100,000 Brine water type

5.7.3 Groundwater quality assessment based on total hardness

The third quality of groundwater assessment rests on the basis of its

hardness. The hardness of water varies considerably from place to place. In

general, surface water is softer than groundwater. The hardness of water

reflects the nature of the geological formation with which it has been in

contact. The TH of the groundwater is calculated using the formula given

below (Sawyer and McCartly 1967):

2 2

3 50TH asCaCO mg / l Ca Mg meq / l x (5.1)

The classification of water based on TH is shown in Table 5.6.

Table 5.6 Groundwater quality based on total hardness

Sl.No. Total Hardness as CaCO3 (mg/l) Type of Water

1 < 75 Soft

2 750 – 150 Moderately Hard

3 150 – 300 Hard

4 > 300 Very Hard

5.7.4 Groundwater quality assessment based on non-carbonate

hardness

The fourth groundwater quality assessment is measured on the non-

carbonate hardness. It is usually caused by the presence of calcium and

59

magnesium sulfates in the water and these sulphates are more soluble as the

temperature rises. It is expressed in the following equation (Raghunath

1987):

NCH = (Ca2+

and Mg2+

) – (CO32-

+ HCO3-

50 (5.2)

where the concentrations are reported in meq/l. In the above equation, when

the difference is negative, NCH = 0.

5.7.5 Groundwater quality assessment according sodium

percentage

The fifth groundwater quality assessment is based on the

percentage of sodium. Wilcox (1955) recommended a classification for rating

irrigation water on the basis of the percentage of soluble sodium.. It defined

as follows:

2 2

(Na K ) x 100Na%

(Ca Mg Na K ) (5.3)

where all the ionic concentrations are expressed in meq/l. This kind of

classification based on sodium percentage appears Table 5.7

Table 5.7 Groundwater quality based on the percentage of sodium

Sl.No Sodium percentage Water Class

1 < 20 Excellent

2 21 – 40 Good

3 41- 60 Permissible

4 61 – 80 Doubtful

5 > 81 Unsuitable

60

5.7.6 Groundwater quality assessment based on sodium

adsorption ratio

The sixth groundwater quality assessment is based on sodium /

alkalinity hazard. Sodium Adsorption Ratio (SAR) is an important parameter

for determining the suitability of groundwater for irrigation because it is a

measure of alkali/sodium hazard to crops. The SAR is calculated as follows:

/2)Mg(Ca

NaSAR

1/222 (5.4)

where all the concentrations are expressed in meq/l. Classifications of

irrigation water, based on SAR values, are indicated in Table 5.8 (Raghunath

1987).

Table 5.8 Groundwater quality based on sodium / alkalinity hazard

Sl.

NoSymbol SAR Water Class Remarks

1 S1 < 10 Excellent

Can be used for irrigation on almost

all soils with little danger of

developing harmful levels of

sodium

2 S2 11-18 Good

May cause on alkalinity problem in

fine-textured soils under low

leaching conditions. It can be used

on coarse textured soils with good

permeability

3 S3 19 - 26 Doubtful

May produce on alkalinity problem.

This water requires special soil

management such as good drainage,

heavy leaching, and possibly the use

of chemical amendments such as

gypsum.

4 S4 > 27 UnsuitableUnsatisfactory for irrigation

purposes

61

5.7.7 Groundwater quality assessment according to USSL

classification

The seventh groundwater quality is assessed according to USSL

classification. In order to assess the suitability of groundwater for irrigation

purposes, the values of EC and SAR are compared and plotted on U. S

Salinity Laboratory diagram. It directs indication of salinity and alkali

hazards. The classification of irrigation water based in USSL is presented in

Table 5.9 (U. S Salinity Laboratory Staff 1954).

Table 5.9 Groundwater quality according to USSL classification

Sl.No USSL Classification Water Class

1

C1 – S1

C2 – S1

C3 – S1

C4 – S1

Good

2

C1 – S2

C2 – S2

C3 – S2

C4 – S2

Moderate

3

C1 – S3

C2 – S3

C3 – S3

C4 – S3

C1 – S4

C2 – S4

C3 – S4

Bad

62

5.7.8 Groundwater quality assessment based on permeability index

The eighth quality of groundwater in this regard is rated according

to Permeability Index (PI). The permeability of soil is affected by long-term

use of irrigation water and is influenced by sodium, calcium, magnesium and

bicarbonate contents of the soil. Permeability Index is calculated as follows:

100)NaMg(Ca

/2)HCO(N

22

1/2-

3

a

xPI (5.5)

where all the concentrations are expressed in meq/l. On the basis of PI, the

water quality is reported in Table 5.10.

Table 5.10 Groundwater quality based on Doneen chart

Sl. No Classification of water based on PI Usage Quality

1 Class I Good for irrigation

2 Class II Good for irrigation

3 Class III Unsuitable for irrigation

5.7.9 Groundwater quality assessment based on residual sodium

carbonate

The ninth groundwater quality assessment relies on the basis of

Residual Sodium Carbonate (RSC). The bicarbonate hazard may be

expressed as RSC. The excess sum of carbonate and bicarbonate in

groundwater over the sum of calcium and magnesium influences the

unsuitability for irrigation. This is denoted as residual sodium carbonate

index (Raghunath 1987). It is calculated as follows:

2 2 2

3 3RSC (HCO CO )-(Ca Mg ) (5.6)

63

where all the concentrations are reported in meq/l. The classification of

groundwater based on RSC is presented in Table 5.11.

Table 5.11 Groundwater quality based on residual sodium carbonate

Sl. No RSC Water Class

1 < 1.25 Good

2 1.25 - 2.5 Doubtful

3 > 2.5 Unsuitable

5.7.10 Groundwater quality assessment based on corrosivity ratio

The tenth groundwater quality assessment is Corrosivity Ratio

(CR). Badrinath et al (1994) used an index to evolve the corrosive tendency

of groundwater pipes. It is expressed in the following equation:

2

4

2

3 3

Cl SO CR

HCO CO (5.7)

where the concentrations are expressed in meq/l. The classification of

groundwater based on CR is reported in Table 5.12.

Table 5.12 Groundwater quality based on corrosivity ratio

Sl. NoStatus of Corrosivity

Ratio

Water transported in

metallic pipes

1 CR < 1 Safe

2 CR > 1 Unsafe

64

5.8 GROUNDWATER QUALITY ANALYSIS USING

MULTIVARIATE STATISTICAL METHODS

Multivariate analysis is a very useful due to its relative importance

in evaluating the combination of large chemical variable data set. They are

used as analytical tools to reduce and organize large hydro-geochemical

datasets into groups with similar characteristics. The rotation mode factor

analysis is widely used as a statistical technique in hyro-geochemistry. This

analysis is useful for interpreting the groundwater quality data and relating

them to specific changes in hydro geological processes. The factor has been

successfully applied to sort out hydro-geochemical processes from commonly

collected groundwater quality data (Senthil Kumar et al 2008). The basic

purpose of such analysis in the study of hydro-geochemistry of an aquifer is

to find a set of factors, few in number, which can explain a large amount of

the variance of the analytical data. In the present study, large data sets, which

are obtained during the pre-monsoon (June-July 2006), post-monsoon

(November-December 2006) and pre-monsoon (June-July 2011) seasons, are

subjected to Factor Analysis (FA) and Correlation Matrix studies to identify

water quality responsible for seasonal variations in groundwater quality.

The objectives of the study are to extract information about:

Source identification for estimation of possible sources on the

determined water quality parameters of the study area. The

final results can provide a valuable tool in developing

assessment strategies for effective water quality management

as well as in finding rapid solutions on pollution problems

(Simeonov et al 2003)

The influence of the possible sources (natural and

anthropogenic) on the groundwater quality.

65

5.8.1 Factor analysis

Mapping of groundwater contamination is often complicated by

infrequent and uneven distribution of monitoring locations, analytical errors

in sample analyses, and large spatial variation in observed contaminants

over short distances due to complex hydrogeologic conditions. While

numerical simulation modeling is commonly used to delineate

groundwater contamination, this approach may be limited by insufficient

knowledge of local hydrostratigraphic conditions. Principal Component

Analysis (PCA) is a multivariate statistical procedure designed to classify

variables based on their correlations with each other. The goal of PCA,

and other factor analysis procedures, is to consolidate a large number of

observed variables into a smaller number of factors that can be more

readily interpreted.

In the case of groundwater, concentrations of different

constituents may be correlated based on underlying physical and

chemical processes such as dissociation, ionic substitution or carbonate

equilibrium reactions. Principal component analysis helps to classify

correlated variables into groups that are easier to interpret for the underlying

processes. The data obtained from the laboratory analysis are used as

variables for factor analysis. Factor analysis is performed using the ‘SPSS

14.0 for Windows’. The data are standardized according to the criteria

presented by Davis (1978). The main objective of the method tells about

determining:

the number of common factors influencing a set of

observations and

the strength of the relationship between each factor and each

observation

66

There are three stages in FA (Gupta et al 2005):

For all the variable, a correlation matrix is generated

Initial set of factors are extracted. The factors are extracted

based on the fundamental theorem of FA, which says, that

every observed value can be written as a linear combination of

hypothetical factors. There are a number of different

extraction methods, including centroid, maximum likelihood,

principal component and principal axis extraction.

The factors are rotated to maximize the relationship between

some of the factors and variable. By rotating, it is easy to find

a factor solution, equal to that obtained in the initial

extraction. Anyhow, it has the simplest interpretation.

5.8.1.1 Temporal variations in water quality using factor analysis

The multivariate statistical method is executed to analyze the water

quality dataset including fourteen important parameters at 62 sample locations

from the study area, which consist of parts of different unions viz. Avinashi,

Tirupur, Palladam, Utukuli, Pongallur and Kangayam in the district of

Tirupur. For temporal variations, three seasons are taken into consideration:

Pre-monsoon - June-July, 2006

Post-monsoon -November-December, 2006

Pre-monsoon – June-July, 2011.

5.8.2 Correlation matrix and their relationships

Commonly, correlation coefficient is used as a measure to establish

the relationship between two variables. It is simply a measure that exhibits

67

how well one variable predicts the other (Kurumbein and Graybill 1965).

Correlation analysis is widely used in statistical or numerical concepts for

parametric classification. Statistical data generally provide a better

representation than graphical data because (1) there is a finite number of

variables that can be considered (b) variables are generally limited by

convention to major ions and (c) a superior relationship may be deduced by

using certain procedures.

5.8.3 Cluster analysis

The assumptions of cluster analysis techniques include

homoscedasticity (equal variance) and normal distribution of the variables

(Alther 1979). Equal weighing of all variables requires the long-

transformation and standardization (z-scores) of the data. Comparisons based

on multiple parameters from different samples are made and the samples are

grouped according to their ‘similarity’ to each other. The classification of

samples according to their parameters is termed Q-mode classification. This

approach is commonly applied to water-chemistry investigations in order to

define groups of samples that have similar chemical and physical

characteristics. This is because a single parameter is rarely sufficient to

distinguish between different waster types. Individual samples are compared

with the specified similarity/dissimilarity and linkage methods and then

grouped into clusters. The linkage rule used here is Ward’s method (Ward

1963). Linkage rules iteratively link nearby points (samples) by using the

similarity matrix. The initial cluster is formed by linkage of the two samples

with the greatest similarity. Ward’s method is distinct from all other methods

because it uses an analysis of variance (ANOVA) approach to evaluate the

distances between clusters. Ward’s method calculated the error sum of

squares, which is the sum of the distances from each individual to the center

68

of its parent group (Judd 1980). It helps to form smaller distinct clusters than

those formed by other methods (StatSoft.Inc.1995).

5.9 WATER QUALITY INDEX

Water Quality Index (WQI) is a reflection of composite influence

of individual quality characteristics on the overall quality of water (Horton

1965). Water quality indices aims at giving a single value to the water quality

of a source on the basis of one or the other system, which translates the list of

constituents and their concentrations present in a sample into a single value.

One can compare different samples for quality on the basis of the index value

of each sample. Water quality indices can be formulated in two ways: (i)

Index numbers increase with the degree of pollution (increasing scale indices)

and (ii) Index numbers decrease with the degree of pollution (decreasing scale

indices). One may classify the former as ‘water pollution indices’ and the

latter as ‘water quality indices’. But this difference appears as an essential

cosmetic: water quality is a general term; of which ‘water pollution’ that

indicates ‘undesirable water quality’ is a special case. In this study, water

quality indices with increasing scale indices are considered. Figure 5.3

illustrates how index values are calculated.

5.9.1 Water quality values and water quality

The different ranges of WQI and their status of water quality on the

basis of increasing scale indices are given in Table 5.13. For calculation of

WQI, selections of parameters are of great importance. The importance of the

parameters depends on the intended use, fourteen physico-chemical

parameters : hydrogen ion chemistry (pH), total dissolved solids (TDS),

calcium (Ca+), magnesium (Mg

2+), sodium (Na

+), potassium (K

+), carbonate

(CO3-), bicarbonate (HCO3

-) chloride (Cl

-), sulphate (SO4

2-), nitrate (NO3

-)

fluoride (F-), total hardness (TH) and total alkalinity (T.Alk).

69

Groundwater Samples Collected

Results of Groundwater Quality Parameters

Selected Index Water Quality

Parameters: TDS, EC, pH, Ca, etc.

Unit Weight Calculation

Quality Rating Calculation

WQI Calculation

Qualitative Ranking

Excellent (< 25)

Good (26 – 50)

Fair (51 – 75)

Poor (76 – 100)

Very Poor (101- 150)

Worst (> 151)

Figure 5.3 Process of water quality index calculation

Table 5.13 Water quality index values and water quality

Sl.No WQIStatus of water

qualityUse of water

1 < 25 ExcellentAll purposes like potable,

industrial and agricultural

2 26 - 50 Good Domestic and agricultural

3 51 - 75 Fair Agricultural and industrial

4 76 - 100 Poor Agricultural

5 101 - 150 Very PoorNot much, possibly for

agriculture

6 > 151 WorstCan be used only after proper

treatment.

70

5.9.2 Water quality index calculation

By adoption of Horton’s method and application of modifications

proposed by Tiwari and Mishra, Water Quality Index (WQI) is carried out.

For computing WQI three steps are followed. In the first step, each of the

parameters has been assigned a weight (wi) according to its relative

importance in the overall quality of water for drinking purposes. In the

second step, the relative weight (Wi) is computed from the following

equations (Ramakrishnaiah et al 2009). It is illustrated in Table 5.14.

1

ii n

ii

wW

w (5.8)

where, Wi is the relative weight, wi is the weight of each parameter and n is

the number of parameters. In the third step, a quality rating scale (qi) for each

parameter is assigned by dividing its concentration in each water sample by

its respective standard. This is done according to the guideline laid down in

the BIS and the result multiplied is by 100.

i

i

i

Cq x 100

S (5.9)

where, qi is the quality rating, Ci is the concentration of each chemical

parameter in each water sample in mg/l, and Si is the Indian drinking water

standard for each chemical parameter in mg/l, according to the guideline of

the BIS 10500 (1991).

71

Table 5.14 Relative weight of chemical parameters

Chemical

Parameters

Indian

Standards

Weight

(wi) Relative weight (Wi)

Turbidity 5 2 0.04348

TDS 500 4 0.08696

pH 7 4 0.08696

TH 300 2 0.04348

Ca 75 2 0.04348

Mg 30 4 0.08696

Cl 25 3 0.06522

F 1.5 4 0.08696

SO4 250 4 0.08696

Na 200 3 0.06522

K 12 2 0.04348

HCO3 300 3 0.06522

Fe 0.3 4 0.08696

NO3 45 5 0.10870

wi = 46 Wi = 1.0

For computing the WQI, the SI is first determined for each

chemical parameter, which is then used to determine the WQI as per the

following equation:

i iSI W . q (5.10)

WQI SI (5.11)

where, SI is the sub-index of ith

parameter, qi is the rating based on

concentration of ith

parameter and it is the number of parameters. The

computed WQI values are then classified into six types.

5.10 GROUND WATER FLOW AND QUALITY MODELING

The methodology adopted in this study consists of two phases. The

first phase helps to develop a regional groundwater flow model using field

observed datasets of aquifer properties, observation of well measurement for

72

the period, January 2004 to December 2010, and water quality measurement

data for the same period. These data also included land use pattern, pumping

locations and recharge to the groundwater system of the study area. The

second phase assesses the sand mining impact by the developed regional

groundwater flow model. The methodology for the prediction of ground water

quality is shown in Figure 5.4.

Figure 5.4 Methodology for prediction of ground water quality

The various data collected from the PWD like observation well head

measurement, quality measurement, meteorological data, the amount of

pumping and its location, different land use pattern, aquifer property,

lithology of the aquifer system for the present study area, are useful in

determining the recharge to the groundwater system, the pattern of the model

grid structure and boundary condition for the proposed regional groundwater

flow model. Based on these collective observations the boundary conditions,

amount of recharge and grid structure for simulation are obtained for the

study area.