Evaluating groundwater pollution using statistical ... · PDF filehydrochemical data: ... but such judgments are subjective and open to ... Hence if the data is used for regulatory

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 4, No 3, 2014

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on October 2013 published on March 2014 456

Evaluating groundwater pollution using statistical analysis of

hydrochemical data: A case study from southeastern part of Pune

metropolitan city (India) Wagh G.S1, Sayyed M.R.G2, Sayadi M. H3

1- Department of Chemistry, S. P. College, Lonand, Tal. Khandala, Dist. Satara 41552

2- Geology Department, Poona College, Camp, Pune 411 001. India

3- Environmental and Civil Engineering Department, University of Birjand, (Iran)

[email protected]

ABSTRACT

The evaluation of groundwater pollution in the area affected by the effluents carrying Mula-

Mutha River and solid waste disposal practice in the SE part of Pune city has been attempted

by the statistical analysis of hydrochemical data. This study involved analysis of groundwater

samples from 51 sampling stations in three consecutive seasons from June 2007 to May 2008

and the results are based on the statistical analysis of groundwater chemistry data from the

five locations. The use of simple statistical analysis, frequency histograms, Q-Q plots,

hierarchical cluster dendograms and Pearson correlation coefficients has revealed that

different areas have different pollution levels and also different sources of contaminations.

The Manjari area is affected by effluents carried by river as well as the fertilizers used for

agriculture while the solid waste disposal practice has strong influence on groundwaters from

Mantarwadi followed by Fursungi and Uruli Devachi. The Hadapsar area has minimum

groundwater contamination.

Key words: Groundwater pollution; Statistical analysis; Solid waste disposal; Pune; India.

1. Introduction

Groundwater is one of the earth’s most important resources for human life and its quality

depends upon the geological environment, human activity, natural movement, recovery and

utilization (Reghunath et al 2002; Senthikumar et al, 2008). Although the water required for

the domestic consumption should possess a high degree of purity in the municipal cities,

improper way of solid waste disposal is largely responsible for the groundwater pollution. It

is beyond doubt that groundwaters are highly polluted, especially in the outskirts of the

metropolitan cities and large villages (Laitinen and Harris, 1975) chiefly because of the

sources like domestic, industrial and agricultural wastes; run-off from the urban areas and

soluble effluents (Lawson and Hileman, 1982; Wagh et al., 2009; Sayadi et al, 2010; Sayyed

and Sayadi, 2011; Sayadi and Sayyed, 2011; Sayadi et al., 2012; Sayyed et al, 2013). Thus

the polluted groundwater is a major cause of the spread of epidemic and chronic diseases in

human beings (Trivedi and Goel, 1984) such as typhoid, dysentery, jaundice, diarrhoea,

hepatitis etc. Rapid industrial development and urbanization of Pune city in the recent years

has resulted in the exponential increase in the generation of waste-water and municipal solid

wastes. Manjari village is on the right bank of Mula-Mutha River and most of the dug-well

water and bore-well water is seen to be appreciably contaminated. At Uruli-Devachi solid

waste disposal site the dumping and disposal of the solid wastes has polluted the soil, water

and air with an alarming rate. Despite the water for drinking and various domestic purposes is

supplied by the Pune Municipal Corporation (PMC, 2006) in Pune city area some population

from Pune city and nearby villages still make use of groundwater as a major source. But

Evaluating groundwater pollution using statistical analysis of hydrochemical data: A case study from

southeastern part of Pune metropolitan city (India)

Wagh G.S, Sayyed M.R.G, Sayadi M. H

International Journal of Geomatics and Geosciences

Volume 4 Issue 3, 2014 457

because of waste disposal and other human activities groundwater has been severely

contaminated and therefore becomes less suitable for drinking, domestic and agricultural

purposes. The discharge of surface run-off and the municipal sewage into the rivers and

streams; and dumping of the solid wastes by unscientific methods brings about the

deterioration of the ecosystem and the environment and for this reason over the past several

years the quality of groundwater in the villages situated at the eastern part of the Pune city

namely Manjari, Hadapsar, Fursungi, Mantarwadi and Uruli-Devachi has severely

deteriorated. The Manjari area is affected by the polluted water of Mula-Mutha River while

Fursungi, Mantarwadi and Uruli-Devachi areas are influenced by the solid waste disposal site.

The groundwater from Hadapsar area; however has lesser influence of the above two

pollution sources. Hence it was proposed to undertake studies on groundwater quality due to

effluents from Mula-Mutha River and also due to the solid waste disposal at Uruli-Devachi in

the eastern part of the Pune city. The present study involved the physico-chemical and

biological analysis of the groundwaters in understanding the level of pollution and detecting

their sources. It is well known that dissolved oxygen is essential for the survival of aquatic

life (Russo and Hanania, 1989) but due to decomposition of biomass and also due to the

presence of oxidizable substances the oxygen supply is depleted in the water (Raja et al,

2008). It also aimed at to know the present status and to understand the implication of

urbanization, not only in terms of the water quality, but also in terms of likely impact on the

health of the local community.

2. Study area

The study area (Figure 1) is situated towards the SE of Pune Metropolis which is

demographically sub-urban to rural. The northern part is drained by Mula-Mutha River on the

right bank of which Manjari village is situated. The groundwaters in this area are affected by

the effluents carried by the river as well as the chemical fertilizers used for the agricultural

crops. Towards the southwest part of the study area, 20km away from the Pune Head

Quarters, is situated the Municipal Solid Waste Disposal Site (MSWDS) on the eastern slope

of small hillock. June to September is the period of rainy season and occasional heavy rainfall

events lead to the dispersion of leachate in the surrounding low-lying areas. Two small

natural streams namely Kala Odha and Farshicha Odha further carry the leachate

downstream while a substantial part of leachate gets naturally collected in the adjoining small

abandoned quarry which acts as another point source. The total area available for MSW

dumping site is about 43 ha which has been receiving the domestic solid waste garbage from

the Pune city since 1993 and at present it amounts to be 1000 to 1200mT per day. The present

practice of solid waste disposal in the study area consists of biological decomposition of

waste and land filling (Dhere et al., 2008) where extra molecular (EM) culture is applied over

solid waste for decomposing the organic matter. However due to the un-segregated waste the

complete decomposition is not possible and only 150mT decomposed organic matter is

segregated per day and collected from local farmers (to be used as manure) while the

remaining solid waste is left as it is for land filling. The immediate effect of solid waste

decomposition are a foul and stinking smell felt from a distance and breeding of houseflies,

vermin and pathogens besides a very unpleasant sprawl (Kale et al. 2010). On the contrary,

the pollutants are released slowly toward the areas of the system with higher permeability,

thereby creating constant level of contamination and maintaining their toxicities (Kale et al.,

2010). In general the groundwater from the eastern area, which is being drained by the

streams namely Kala Oadha (on the north of the MSWDS) and Farshicha Oadha (on the

south of the MSWDS) both of which originating from the MSWDS (Figure 1) and carrying






the leachate, are seen to be highly polluted when compared with the western area. The

groundwater from Hadapsar area; however has lesser influence of the above two pollution

sources.

Figure 1: Sampling location map

3. Materials and methods

To assess the groundwater quality as many as 51 sampling stations, scattered in an area of

approximately 20km2, were selected and the water samples were collected in three

consecutive seasons i.e. rainy, winter and summer (June 2007 to May 2008). During the

course of present study 37 physico-chemical and biological parameters were determined for

each sample using the procedures given in ‘Standard Methods for the Examination of Water

and Waste Water (APHA, 1998). The measurements of temperature, colour, odour, pH,

electrical conductivity, total dissolved solids, salinity and turbidity were undertaken in the

field (immediately after the collection of samples) using a portable water quality analyzer.

Dissolved oxygen was determined in laboratory while parameter likes free carbon dioxide,

alkalinity, hardness, chemical oxygen demand, biological oxygen demand; chloride, calcium

and magnesium were analyzed by titrimetry in laboratory. The values obtained for these

parameters were statistically analyzed by using the SPSS (version 15) software.

4. Results and discussion

4.1 Statistical analysis of the groundwater data

Most of the studies of groundwater have placed a heavy emphasis on the variations in

chemical characteristics of groundwater in time and space (Kennedy et al., 1999) and hence

in a conventional groundwater studies researchers generally perform multiple groundwater

sampling and their subsequent chemical analyses (Lee et al., 2001). The gathering of

groundwater monitoring data around solid waste disposal is used to make a judgment on

whether a landfill is fulfilling its design purpose. For such judgment various methods are






available i.e. the data may be tabulated or displayed on a graph and then visual judgments

made, but such judgments are subjective and open to disagreement, which is not a desirable

situation. Hence if the data is used for regulatory purposes there is a need for objective,

defensible, robust methods by which data may be assessed and these can be provided by

statistics. Multivariate analyses of the hydrochemical data is an effective tool to reduce and

organize large data sets into groups with similar characteristics and then relating them to

specific changes in hydrogeochemical processes. Therefore multivariate statistical techniques

have been widely accepted and used in groundwater quality assessment over the last few

decades (Ako et al, 2010; Reghunath et al 2002; Elueze et al. 2004). However the application

of statistics to a particular situation can not be performed without thought. The choice of

statistical data analysis method will depend on the objective of the monitoring program. In

landfill monitoring, statistics may commonly be used to

1. Review data from a single well and/or a group of wells over time;

2. Compare data from separate wells or groups of wells without regard for time or actual

spatial distribution; and

3. Compare data from separate wells or groups of wells with regard for time and/or

actual spatial distribution.

The objectives of monitoring for which statistics can be applied include

1. Making comparisons of observations with regulatory standards; and

2. Early warning and alerting authorities before the problem becomes critical.

In order to better understand the hydrogeochemistry of the groundwater system, multivariate

analyses can be performed using not only concentrations of chemical species (major ions,

redox-related species), but also other physicochemical data such as temperature, redox

potential, dissolved oxygen, electrical conductivity, pH and alkalinity (Lee et al. 2001). The

underlying governing processes in the groundwater system are not directly revealed by spatial

or temporal measurements of chemical or physical properties (Suk and Lee, 1999). Many

workers (e.g. Usunoff and Guzman-Guzman, 1989; Ritzi et al, 1993; Ochsenkuhn et al, 1997,

Suk and Lee, 1999, Lee et al, 2001 etc.) have applied factor and/or cluster analyses data in

order to understand spatial distribution and temporal evolution patterns of many chemical and

physical parameters, without significant loss of measurement and chemical analysis data, and

without obscuring the geochemical meaning of the data. The use of Q-mode hierarchical

cluster analysis in statistically classifying geochemical data has been proven to provide

suitable basis for objective classification of water composition into different hydrochemical

facies (Meng and Maynard, 2001, Guler et al, 2002, Guler and Thyne, 2003, Kebede et al,

2005).

4.1 Simple statistical parameters

For simple statistical analysis the chemical data of the groundwater samples (for all three

seasons together) from five areas were analyzed using SPSS software (version 15) for

selected eight parameters (Table 1). The mean and standard deviations for the parameters

(Figure 2) indicate that except for the alkalinity and hardness other six parameters show

considerably high values in the Mantarwadi area while alkalinity is higher in Manjari area

and hardness in Uruli-Devachi area. This indicates clearly that the Mantarwadi area is

severely affected by the solid waste disposal practice followed by Fursungi and Uruli-






Devachi. While the Manjari area is affected by the effluents carried by the Mula-Mutha River,

Hadapsar area seems to have been least affected by both the solid waste disposal and/or the

effluents carried by the river.

4.2 Frequency distribution (histogram) analysis

A frequency histogram is a useful device for exploring the shape of the distribution of values

of a variable and can be employed in screening of outliers, checking normality or suggesting

another parametric shape for the distribution. In the present study (Figure 3) it has been found

that for EC and TDS, (except Mantarwadi) the samples from all areas show approximately

normal distribution. The presence of outliers along with high value of mean suggest higher

pollution in the Mantarwadi area. For alkalinity, chloride and sulphate the frequency

distribution patterns suggest that the groundwater from Hadapsar area is less polluted (as the

values are mostly less than mean) while from Mantarwadi area the groundwater is highly

polluted (as the mean value is quite high although most of the values are less than mean). In

case of hardness it is seen that there is quite a lot variation in its values from Mantarwadi and

Uruli Devachi area owing to the presence of outliers (Uruli-Devachi) and almost uneven

distribution (Mantarwadi).

4.3 Quantile-Quantile (Q-Q) Plots

A normal distribution is often a reasonable model for the data but without inspecting the data

it is risky to assume a normal distribution. The most useful tool for assessing normality is a

uantile-Quantile (Q - Q) plot which is a scatter plot with the quantiles of the values on the

horizontal axes and expected normal values on the vertical axes.

Table 1: Descriptive statistical analyses of ground water samples

Manjari EC TDS Alkalinity BOD COD Cl SO4 Hardness

Mean 1214.24 785.88 294.61 6.80 21.33 200.86 75.20 305.55

Median 1200.00 780.00 300.00 4.50 16.00 190.00 72.00 310.00

Standard

Deviation 334.00 223.19 51.81 4.93 13.81 84.77 29.40 81.12

Minimum 500.00 330.00 170.00 1.20 8.00 70.00 30.00 161.00

Maximum 1800.00 1200.0

0 390.00 18.00 50.00 347.00 135.00 450.00

Std. Error -3.54 -3.37 -5.05 5.31 0.84 -1.98 -1.44 -3.36

Hadapsar EC TDS Alkalinity BOD COD Cl SO4 Hardness

Mean 618.89 391.85 253.19 4.10 14.30 61.53 37.92 155.70

Median 600.00 410.00 200.00 4.00 10.00 56.80 22.00 160.00

Std.

Deviation 279.18 165.18 140.18 3.02 5.91 27.40 39.65 57.21

Minimum 100.00 60.00 120.00 0.60 8.00 28.40 2.30 65.00

Maximum 1200.00 685.00 650.00 11.50 30.00 140.00 117.00 257.00

Std. Error -2.12 -2.21 -1.61 7.59 2.15 -1.26 -0.28 -2.25

Fursungi EC TDS Alkalinity BOD COD Cl SO4 Hardness

Mean 2114.07 1356.6

7 260.41 13.51 27.81 456.07 148.93 444.78






Median 1850.00 1190.0

0 260.00 9.00 24.00 246.00 140.00 360.00

Std.

Deviation 1212.99 785.98 72.05 12.80 20.43 393.75 69.75 263.38

Minimum 200.00 110.00 72.00 0.80 6.00 35.00 50.00 100.00

Maximum 4500.00 2980.0

0 380.00 64.00 104.00 1205.00 350.00 1080.00

Std. Error -1.72 -1.69 -3.24 1.05 -0.04 -1.09 -1.75 -1.59

Mantarwadi EC TDS Alkalinity BOD COD Cl SO4 Hardness

Mean 2729.2

6 1753.63 231.70

16.1

9 35.48 684.93 167.04 2124.81

Median 2470.0

0 1620.00 226.00 6.00 22.00 532.00 150.00 2200.00

Std. Deviation 1732.0

3 1100.09 52.25

20.3

6 34.47 594.88 69.01 1268.85

Minimum 900.00 600.00 160.00 2.00 10.00 133.00 102.00 350.00

Maximum 8510.0

0 5500.00 440.00

73.0

0

135.0

0 2570.00 450.00 3910.00

Std. Error -1.56 -1.57 -3.92 0.53 -0.25 -1.11 -2.03 -1.65

Uruli-Devachi EC TDS Alkalinity BOD COD Cl SO4 Hardness

Mean 1312.12 852.24 245.91 0.78 15.06 171.67 107.79 2265.76

Median 1320.00 850.00 250.00 0.50 14.00 172.00 106.00 2000.00

Std. Deviation 288.87 171.94 27.41 0.64 4.89 44.70 36.37 879.31

Minimum 670.00 480.00 198.00 0.40 10.00 78.00 40.00 1240.00

Maximum 1950.00 1260.00 292.00 2.50 30.00 266.00 204.00 5800.00

Std. Error -4.43 -4.76 -7.77 50.4

9 3.67 -3.10 -2.06 -2.54

0

1000

2000

3000

Manjari Hadapsar Fursungi Mantarwadi Uruli-Devachi

EC

(µ

S/c

m)

Mean Std. Deviation

0

1000

2000


TD

S (

mg

/l)

Mean Std Dev






0

100

200

300


Alk

ali

nit

y (

mg/l

)

Mean Std Dev

0

10

20

30


BO

D

(mg

/l)

Mean Std Dev

0

10

20

30

40


CO

D (

mg/l

)

Mean Std Dev

0

200

400

600

800


Ch

lorid

e (

mg/l

)

Mean Std Dev

0

100

200


Su

lph

ate

(m

g/l

)

Mean Std Dev

0

1000

2000

3000


Hard

nes

s (m

g/l

)

Mean Std Dev

Figure 3: Frequency histograms for average EC, TDS and alkalinity in the groundwaters

from the study area.

Approximately on the diagonal line and if the data falls near the line it is reasonable to

assume that the samples come from the same distribution. In the present case (Figure 4) it can

be stated that with respect to EC and TDS (except Hadapsar) the areas have differential

effects of pollution which is more pronounced in Mantarwadi area and less pronounced in

Manjari area. However in case of alkalinity, chloride and sulphate it is found that the

Hadapsar and Mantarwadi areas show wide variations in the content of these pollutants even

though the values are quite high for Mantarwadi area. Hardness shows normal distribution in

Manjari and Hadapsar areas but in other areas the distribution is not normal indicating more

of anthropogenic input than the natural.






Manjari Electrical Conductivity Hadapsar Electrical Conductivity

Fursungi Electrical Conductivity Mantarwadi Electrical Conductivity

Uruli-Devachi Electrical Conductivity Total Dissolved Solids

Total Dissolved Solids Total Dissolved Solids







Alkalinity Alkalinity Alkalinity

Alkalinity Alkalinity

Figure 3 (Cont.): Frequency histograms for average chloride, sulphate and hardness in the

groundwaters from the study area

Majari Chloride Hadapsar Chloride Fursungi Chloride






Mantarwadi Chloride Uruli Devachi

Sulphate Sulphate Sulphate

Sulphate Sulphate

Hardness Hardness Hardness

Hardness Hardness

Figure 3 (Cont.): Frequency histograms for average chloride, sulphate and hardness in the

groundwaters from the study area






4.4 Hierarchical cluster analysis

Hierarchical cluster analysis is a multivariate statistical technique intended to classify

hydrochemical observations so that the members of the resulting groups or subgroups are

similar to each other and distinct from the other groups (Ayenew et al, 2009). Thus cluster

analysis is a technique designed to perform classification by assigning observation to groups

so that each group is more or less homogeneous and distinct from other groups. Hierarchical

technique is therefore the most widely applied clustering technique in the environmental

science which joins the most similar observations and then successively connects the next

most similar observations. This cluster groups the data over variety of scales by creating a

cluster tree or dendogram. The tree is not a single set of clusters but rather a multi level

hierarchy where clusters are at one level or joined as clusters at the next level. The

dendograms were constructed by using the parameters EC, TDS, Alkalinity, BOD, COD,

HCO3, Cl, SO4, Ca, Mg and Hardness. These dendograms (Figure 5) indicate that the

parameters like BOD, COD, Mg, Ca and SO4 show a close association. The parameters like

alkalinity and HCO3 (except for Manjari and Hadapsar) are again closely associated with the

above mentioned parameters indicating that the source of alkalinity and HCO3 to be different

for Hadapsar and Manjari areas. EC and TDS show close relationships in all the areas (except

Hadapsar) while hardness shows various associations in different areas indicating different

sources to the waters causing hardness. In Manjari area the hardness is more due to chloride

and HCO3 while in Hadapsar it is due to Ca, Mg, chloride and SO4. In Fursungi hardness is

more related to chloride while in Mantarwadi and Uruli-Devachi area due to all hardeners

causing parameters. Hierarchical cluster analysis suggests that the hydrogeochemical

compositions are quite different in all these five areas, although some similarities can be

drawn in the areas of Manjari and Hadapsar on one hand and Mantarwadi and Uruli-Devachi

on other hand.

4.5 Pearson correlation coefficient analysis

Correlation analysis is primarily descriptive technique to estimate the degree of association

amongst the variables involved. Most popularly Pearson correlation coefficient method is

used to determine the correlations between the variations in the different parameters studied

(Le Maitre, 1982) in which the negative and positive association between the two different

parameters can be determined. It is also called the linear correlation coefficient because it

measures the linear association between two variables (Helsel and Hirsch, 2002). The

Pearson correlation coefficient values were obtained for the parameters like EC, TDS,

Alkalinity, BOD, COD, HCO3, Cl, SO4, Ca, Mg and Hardness which indicate different levels

of significance (Table 2). According to the values of correlation coefficient obtained an

obvious and very strong positive correlation has been found between EC and TDS for the

water samples from all the five areas. However a different hydrogeochemistry can be

deduced for the groundwaters from these five areas based upon the correlations amongst

different parameters. Based upon the correlation of EC and TDS with alkalinity in Manjari

area it can be said that the sources of pollution for Manjari are different (it is highly irrigated

area and is also influenced by the effluents from the river). A minimum contamination can be

seen in Hadapsar area as suggested from the correlation of TDS with HCO3, chloride and SO4.

A strong correlation between Ca and Cl in Fursungi and Mantarwadi is indicative of a strong

influence of the waste disposal while in Uruli-Devachi and Mantarwadi accelerated

evaporation can be envisaged due to the strong correlation between Ca and SO4.






Manjari Electrical Conductivity Hadapsar Electrical Conductivity

Fursungi Electrical Conductivity Mantarwadi Electrical Conductivity

Uruli Devachi Electrical Conductivity








Total Dissolved Solids

Alkalinity Alkalinity Alkalinity

Alkalinity Alkalinity

Figure 4: Quantile-Quantile (Q-Q) Plot for chloride, sulphate and hardness

Manjari Chloride Hadapsar Chloride






Fursungi Chloride Mantarwadi Chloride

Uruli Devachi Chloride

Sulphate Sulphate Sulphate

Sulphate Sulphate

Hardness Hardness Hardness






Hardness Hardness

Figure 4 (Cont.): Quantile-Quantile (Q-Q) Plot for chloride, sulphate and hardness

Figure 5: Dendogram showing the hierarchical clusters for selected water quality parameters.

Table 2: Pearson correlation coefficients between the water quality parameters

Parameters EC TDS Alkalinity BO

D

CO

D HCO3 Cl SO4 Ca Mg Hardness

EC 1

TDS 0.9

9 1

Manjari Hadapsar

Fursungi Mantarwadi

Uruli Devachi






Alkalinity 0.7

8 0.76 1

BOD 0.0

3 0.05 -0.15 1

COD

-

0.0

8

-

0.05 -0.25 0.94 1

HCO3 0.4

2 0.41 0.23 0.14 0.17 1

Cl 0.8

9 0.88 0.71 0.19 0.11 0.44 1

SO4 0.8

8 0.89 0.60 0.24 0.19 0.48

0.9

5 1

Ca 0.5

1 0.52 0.68 -0.18 -0.24 0.40

0.3

5

0.2

6 1

Mg 0.7

6 0.79 0.61 0.13 0.07 0.09

0.7

2

0.6

9

0.3

7 1

Hardness 0.3

8 0.39 0.10 0.54 0.58 0.50

0.5

5

0.6

8

-

0.1

5

0.2

0 1

Table 2(a): Hadapsar

Parameter

s EC TDS

Alkalinit

y BOD COD HCO3 Cl SO4 Ca Mg

Hardnes

s

EC 1

TDS 0.82 1

Alkalinity 0.69 0.63 1

BOD 0.40 0.41 0.30 1

COD 0.29 0.38 0.33 0.87 1

HCO3 0.92 0.74 0.54 0.28 0.18 1

Cl 0.80 0.72 0.92 0.33 0.29 0.63 1

SO4 0.83 0.75 0.77 0.71 0.70 0.69 0.79 1

Ca 0.55 0.45 0.40 -0.23 -0.32 0.56 0.58 0.15 1

Mg 0.79 0.65 0.59 0.09 0.04 0.78 0.71 0.57 0.74 1

Hardness 0.67 0.68 0.60 0.76 0.78 0.47 0.68 0.85 0.17 0.44 1

Table 2(b): Fursungi

Parameter

s EC TDS

Alkalinit


Hardnes

s

EC 1

TDS 1 1


BOD 0.61 0.63 0.18 1

COD 0.65 0.66 0.28 0.96 1

HCO3 0.37 0.40 0.86 0.21 0.24 1

Cl 0.97 0.96 0.24 0.64 0.65 0.21 1

SO4 0.55 0.56 0.50 0.58 0.62 0.32 0.53 1

Ca 0.80 0.82 0.24 0.53 0.56 0.41 0.76 0.28 1

Mg 0.86 0.88 0.38 0.69 0.71 0.48 0.82 0.46 0.88 1

Hardness 0.95 0.96 0.35 0.69 0.70 0.41 0.94 0.55 0.89 0.91 1






Table 2(c) : Pearson correlation coefficients between the water quality parameters

Table2 (d): Mantarwadi

Parameter

s EC TDS

Alkalinit


Hardnes

s

EC 1

TDS 1 1


BOD 0.47 0.47 0.02 1

COD 0.51 0.51 -0.02 0.98 1

HCO3 0.53 0.54 0.70 -0.13 -0.12 1

Cl 0.99 0.99 0.64 0.51 0.55 0.51 1

SO4 0.93 0.92 0.81 0.30 0.32 0.64 0.92 1

Ca 0.93 0.93 0.77 0.17 0.22 0.69 0.92 0.95 1

Mg 0.72 0.75 0.47 0.11 0.15 0.56 0.68 0.64 0.76 1

Hardness -

0.32

-

0.32 -0.09 -0.54 -0.49 0.20

-

0.39

-

0.22

-

0.21

-

0.12 1

Table 2(e): Uruli Devachi

Parameter

s EC

TD

S Alkalinity BOD

CO

D

HCO

3 Cl SO4 Ca Mg

Hardnes

s

EC 1

TDS 0.9

8 1

Alkalinity 0.3

5

0.3

4 1

BOD 0.4

7

0.4

8 0.06 1

COD 0.6

4

0.6

1 0.39 0.71 1

HCO3 0.0

6

0.0

3 0.59 -0.56 0.00 1

Cl 0.6

4

0.6

6 -0.21 0.53 0.28 -0.54 1

SO4 0.7

4

0.7

5 0.49 0.74 0.80 0.00

0.4

3 1

Ca 0.7

7

0.7

5 0.31 0.77 0.84 -0.17

0.4

6 0.84 1

Mg 0.5

2

0.5

2 0.53 0.21 0.48 0.27

0.1

6 0.62

0.5

3 1

Hardness 0.7

0

0.7

1 0.40 0.35 0.50 0.07

0.4

4 0.62

0.6

5 0.55 1

5. Conclusions

The statistical analysis of hydrochemical data clearly indicates that there are different levels

of groundwater pollution in five localities which can be attributed to different sources. The

result of statistical and frequency distribution analysis suggest highest pollution level in

Mantarwadi while Hadapsar area is least polluted. The Q-Q plots indicate differential effect

of pollution which is more pronounced in Mantarwadi and less in Manjari. On the basis of

hierarchical cluster analysis quite different hydrogeochemical compositions in all five

localities have been envisaged. However some similarities can be drawn in Manjari and

Hadapsar on one hand and Mantarwadi and Uruli Devachi on the other hand. The Pearson






correlation coefficient analysis indicates different sources of contaminations i.e. effluents in

Manjari area and leachate in Fursungi, Mantarwadi and Uruli Devachi area.

Following are some remedial measures suggested in order to arrest further ground water

contamination.

1. To prevent the mixing of leachate in to the streams during rainy season.

2. To produce the biogas from biodegradable solid waste.

3. To give proper treatment to the leachate before leaving it into the open space.

4. To produce the fertilizer/manure from solid waste.

Acknowledgements

This study is an out come of the project being funded by the University of Pune, which is

thankfully acknowledged.

6. References

1. Ako B. D, Adeniyi F. I, Adepoju J. F., (1990), Statistical Test and Chemical Quality

of Shallow Groundwater from a Metamorphic Terrain, Ile‐Ife/Modakeke, SW Nigeria

Jour. African earth sciences, 10(4), pp 603‐613.

2. Allegrini I., Santis F. D., Palo V. D, Liberti A., (1984), Measurement of particulate

and gaseous ammonia at a suburban area by means of diffusion tube (denuders).

Journal of aerosol science, 15, pp 465–471.

3. APHA, (1998), Standard method for examination of water and wastewater. APHA,

AWWA, WPCF, Washington DC 20th edition.

4. Ayenew T, Fikre S, Demlie M, Wohnlich S., (2009), Hierarchical cluster analysis of

hydrochemical data as a tool for assessing the evolution of dynamics of groundwater

across the Ethiopian rift. International journal of physical sciences, 4(2), pp 76-90

5. Burkhardt J, Sutton M A, Milford C, Storeton-west R L, Fowler D., (1998), Ammonia

concentrations at a site in southern Scotland form 2 yr of continuous measurements.

Atmospheric environment, 32, pp 325–331.

6. Carrillo-Rivera J. J, Cardona B. A, Edmunds W. M., (2002), Use of abstraction

regime and knowledge of hydrogeological conditions to control high-fluoride

concentration in abstracted groundwater: San Luis Potosí basin, Mexico. Journal of

hydrology, 261, pp 24 47.

7. Chaudhuri S, Furlan S, Clauer N., (1992), The signature of water–rock interactions in

formation waters of sedimentary basins: Some new evidence. Paper presented at the

7th international symposium on water–rock interaction, Park City, Utah, USA.

8. Danalatos D, Glavas S., (1999), Gas phase nitric acid, ammonia and related

particulate matter at a Mediterranean coastal site, Patras, Greece. Atmospheric

environment, 33 pp 3417–3425.






9. Dhere A. M, Pawar C. B., Pardeshi P. B, Patil D.A., (2008), Municipal solid waste

disposal in Pune city – An analysis of air and groundwater pollution, Current science,

95(6), pp 773-777.

10. Elueze A A, Omidiran J O, Nton M E., (2004), Hydrogeochemical Investigation of

Surface Water and Groundwater around Ibokun, Ilesha Areas, South Western. Nigeria

– journal of mining and geology, 40(1), pp 57 – 64.

11. Guler C, Thyne G. D., (2003), Hydrological and geological factors controlling surface

and groundwater chemistry in Indian Wells-Owens valley area, southeastern

California, USA. Jour. Hydrology, 285(1-4), pp 177-198.

12. Guler C, Thyne G. D, McCray J. E, Turner A. K., (2002), Evolution of graphical and

multivariate statistical methods for classification of water chemistry data. Hydrogeol.

Jour, 10(4), pp 455-474.

13. Helsel D R, Hirsch R M., (2002), Statistical Methods in Water Resources Chapter A3,

USGS, (http://water.usgs.gov/pubs/twri/twri4a3/).

14. Kale S.S, Kadam. A.K, Suyash Kumar, Pawar N.J., (2010), Evaluating pollution

potential of leachate from landfill site, from the Pune metropolitan city and its impact

on shallow basaltic aquifers. Environ Monit Assess, 162, pp 327–346. DOI

10.1007/s10661-009-0799-7.

15. Kebede S, Travi Y, Alemayehu T, Ayenew T., (2005), Groundwater recharge,

circulation and geochemical evolution in the source region of the Blue Nile River,

Ethiopia. Appl. Geochem: 20(9), pp 1658-1676.

16. Kennedy L.G, Everett J.W, Dewers T, Pickins W, Edward D., (1999), Application of

mineral iron and sulphide analysis to evaluate natural attenuation at fuel contaminated

site. J. Environ. Eng, 125, pp 47-56.

17. Kerminen V M, Teinila K, Hillamo R., (2000), Chemistry of sea-salt particles in the

summer Antarctic atmosphere. Atmospheric environment, 34, pp 2817–2825.

18. Kharaka Y.K, Carothers W.W., (1986), Oxygen and hydrogen isotope geochemistry

of deep basin brines. In P. Fritz and J.C. Fontes (Eds.), Handbook of environmental

geochemistry, The terrestrial environment, B, Amsterdam: Elsevier, 2, pp 305–361

19. Laitinen H. A, Harris W. E., (1975), Chemical Analysis, , McGraw-Hill Book Co.,

New York, pp 361- 363

20. Lambert S. J., (1991), Fossil meteoric groundwaters in the Delaware Basin of

southeastern New Mexico. In H. P. Taylor, J. R. Jr. O’Neil, and I. R. Kaplan (Eds.),

Stable isotope geochemistry: A tribute to Samuel Epstein. The geochemical society

special publication, 3, pp 135–156






21. Lawson E.R, Hileman L.H., (1982), Nutrient distributions in runoff from Ouachita

Mountain watersheds. In Proc. 2nd Biennial Southern Silvicultural Res. Conf. USDA

forest service southern forest and range Exp. Stn., Ashville, NC. pp 477 – 482

22. Le Maitre R.W., (1982), Numerical petrology. Elsevier scientific publishing

company, Amsterdam

23. Lee J.Y, Cheon J. Y, Lee K. K, Lee S. Y, Lee M. H., (2001), Statistical evaluation of

geochemical parameter distribution in a ground water system contaminated with

petroleum hydrocarbons. J. Environ. Qual, 30, pp 1548- 1563.

24. McCurdy T, Zelenka M P, Lawrence P M, Houston R M, Burton R., (1999), Acid

aerosols in the Pittsburgh Metropolitan area. Atmospheric environment, 33, pp 5133–

5145.

25. Meng S X, Maynard J B., (2001), Use of statistical analysis to formulate conceptual

models of geochemical behaviour: ater chemical data from the Botucatu aquifer in

Sao Paulo state, Brazil. Jour. Hydrol., 250 pp 78-97.

26. Ochsenkuhn K M, Kontoyannakos J, Ochsenkuhn-Petropulu M., (1997), A new

approach to a hydrochemical study of groundwater flow. J. Hydrol, 194 pp 64–75.

27. PMC, (2007), Pune Municipal Corporation, Environmental statues report 2006-2007.

28. Possanzini M, Santis F. D, Palo V. D., (1999), Measurements of nitric acid and

ammonium salts in lower Babaria. Atmospheric environment, 33, pp 3597–3602.

29. Raja P, Muhindhar Amarnath A, Elangovan R. Palanivel, M., (2008), Evaluation of

physical and chemical parameters of river Kaveri, Tiruchirappalli, Tamil Nadu, India.

Journal of environmental biology, 29(5), pp 765-768.

30. Reghunath R, Sreedhara Murthy T R, Ragharan B R., (2002), The Utility of

Multivariate Statistical Techniques in Hydrogeochemical Studies: An example from

Karnataka, India. Water research. 36, pp 2437 – 2442

31. Ritzi R W, Wright S L, Mann B, Chen M., (1993), Analysis of temporal variability in

hydrogeochemical data used for multivariate analyses. Ground water, 31, pp 221–229.

32. Russo S. O, Hanania G.I. H., (1989), Ion Association, Solubilities, and reduction

potentials in aqueous solution. J. Chem. Educ. 66(2), pp 148-153.

33. Sayadi, M.H., Sayyed, M.R.G., Suyash Kumar., (2010), Short-term accumulative

signatures of heavy metals in river bed sediments in the industrial area, Tehran, Iran."

Environmental monitoring and assessment, 162.1-4, pp 465-473.

34. Sayadi, M.H., Sayyed, M.R.G., (2011), Comparative assessment of baseline

concentration of the heavy metals in the soils of Tehran (Iran) with the comprisable

reference data. Environmental earth sciences 63, pp 1179-1188.






35. Sayadi, M.H., Kargar, R., Doosti, M.R., Salehi, H., (2012), Hybrid constructed

wetlands for wastewater treatment: a worldwide review. Proceedings of the

International academy of ecology and environmental sciences. 2(4), pp 204-222.

36. Sayyed, M. R. G., Sayadi, M.H., (2011), Variations in the heavy metal accumulations

within the surface soils from the Chitgar industrial area of Tehran. Proceedings of the

International academy of ecology and environmental sciences, 1, pp 36-46.

37. Sayyed M. R. G., Wagh G. S., Supekar, A., (2013) Assessment of impact on the

groundwater quality due to urbanization by hydrogeochemical facies analysis in SE

part of Pune city, India, Proceedings of the International academy of ecology and

environmental sciences, 3(2), pp 148-159.

38. Senthikumar G, Ramanathan A. L, Nainwal H. C, Chidambaram S., (2008),

Evaluation of the hydrogeochemistry of Groundwater using factor analysis in the

Cuddalore coastal region, Tamilnadu, India. Indian journal of marine sciences, 37(2),

pp 181-185.

39. Suk H, Lee K K., (1999), Characterization of a ground waterhydrochemical system

through multivariate analysis: Clustering into ground water zones. Ground water 37,

pp 358–366.

40. Trivedi R.K, Geol P.K., (1984), Chemical and biological methods for water pollution

Studies, Environmental publications, Karad, pp 215

41. Usunoff E.J, Guzman-Guzman A., (1989), Multivariate analysis in hydrochemistry:

An example of the use of factor and correspondence analyses. Ground water, 27, pp

27–34.

42. Varsanyi I, Matray J. M, Kovács O. L, (1999), Hydrogeochemistry in two adjacent

areas in the Pannonian Basin (Southeast-Hungary). Chemical geology, 156, pp 25–39.

43. Wagh, G.S., Sayyed, M.R.G., Sayadi, M.H., (2009), Application of the statistical

methods to the groundwater pollution data from the solid waste disposal site at Pune

(India). Geochimica et Cosmochimica Acta Supplement. 73, p 1399.

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