CHAPTER 5 MATERIALS AND METHODS -...
Transcript of CHAPTER 5 MATERIALS AND METHODS -...
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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
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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
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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.
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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
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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
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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.
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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
- :
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(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
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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
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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
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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
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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
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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
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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)
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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
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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.
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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
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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
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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
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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).
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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.
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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).
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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
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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.